CONVENORS

DENR-PAWB

CIP

NEDA

Mapping Population-Biodiversity Connections in the Philippines Final Report

DONOR AND PARTNERS

USAID

DOH

POPCOM

NAMRIA

NSO

DILG

NAPC

NCIP

DEPARTMENT OF ENVIRONMENT AND NATURAL RESOURCES– PROTECTED AREAS AND WILDLIFE BUREAU (DENR–PAWB) Ninoy Aquino Parks and Wildlife Nature Center Quezon Avenue, Diliman 1101 Quezon City, Philippines Tel. Nos. (632) 9246031-35, Fax No. (632) 9240109 [email protected] www.pawb.gov.ph CONSERVATION INTERNATIONAL FOUNDATION–PHILIPPINES (CIP) 5 South Lawin Avenue, Philam Homes 1104 Quezon City, Philippines Tel. Nos. (632) 4128194, Fax No. (632) 4128195 [email protected] www.conservation.org NATIONAL ECONOMIC AND DEVELOPMENT AUTHORITY (NEDA) NEDA sa Pasig Building, 12 Escriva Drive, Ortigas Center, Pasig City (632) 6313745, 6336015, 6365517 www.neda.gov.ph

OFFICER-IN-CHARGE DIRECTOR of DENR–PAWB: Theresa Mundita Lim COUNTRY EXECUTIVE DIRECTOR of CIP: Romeo Trono DIRECTOR III of NEDA: Joselito Bernardo

COVER PHOTOGRAPH: Children in cheerful mood by M. de Guia; Background forest by Sierra Madre Biodiversity Corridor Program; and Eagle flying by G. Villoria

ISBN 971-926-40-1-2 Philippine Copyright © 2004 by Conservation International Foundation–Philippines. ALL RIGHTS RESERVED. DISCLAIMER: The designation of geographical entities in this report and the presentation of the material do not imply the expression of any opinion whatsoever on the part of the Department of Environment and Natural Resources, Conservation International Foundation–Philippines and the National Economic and Development Authority or their supporting organizations considering the legal status of any country, territory, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. The “Mapping Population-Biodiversity Connections in the Philippines” project (MPBCPP) was completed through the assistance of the United States Agency for International Development under its extension project grant #492-G-00-00-0008-00. The findings, interpretations, conclusions and opinions expressed in the report are those of the authors and do not necessarily reflect the opinions of the institutions to which the authors are affiliated, the United States Agency for International Development, Department of Environment and Natural Resources, National Economic and Development Authority and other collaborating institutions.

SUGGESTED CITATION: Lasmarias, N., O. Coroza, M. Silverio, F. Lansigan, D. Lagunzad and C. Morales. 2004. Mapping Population-Biodiversity Connections in the Philippines. Department of Environment and Natural Resources– Protected Areas and Wildlife Bureau, Conservation International Foundation–Philippines and National Economic and Development Authority. Quezon City, Philippines.

To the Reader This report presents an empirical study that sought to determine the link of population variables with the environment in the Philippine setting. After carefully examining a compilation of socioeconomic and demographic variables, 36 sets of indicators comprising 75 parameters or variables were initially selected through a final consensus of 38 workshop participants. After further analysis on the hierarchy by which these parameters affect biodiversity, the number was scaled down to 3 sets of indicators comprising of 24 variables. The percentage provincial forest cover was likewise chosen as a biodiversity indicator representing wildlife habitat status. Using statistical and spatial approaches, the association between the population and biodiversity was analyzed. Although population-environment (P-E) linkages have been established in studies in some countries in Asia, Latin America and Africa, very few researches have actually dealt with measurements of the magnitude of influence exerted by population parameters on environmental changes in the Philippines. In this report, the potential use of strategic tools and methods to measure this magnitude is demonstrated. Although empiricalstatistical and cartographic models were used in the study, the project recognizes that systems and agent-based modeling approaches that are currently being developed and empirically tested internationally can be used to carry out similar research work. The project’s over-riding goal is to convey these core messages: 1. People do affect the environment; 2. The state of the environment determines the quality of human life; and 3. Everyone has a stake in the environment and therefore must share the burden of safeguarding the quality of that environment. Given these, the project hopes that this work can help: 1. Convince policymakers to seriously consider and incorporate socioeconomic and demographic factors in the formulation of policies for the development, rehabilitation and protection of our environment; 2. Provide a framework for empirically assessing the impact of human activities on the state of the environment; and 3. Encourage our people to pursue development goals and meet economic needs without unnecessarily jeopardizing the quality of the Earth’s natural heritage, our biodiversity, and the future of the next generations. The project provided a rare opportunity for government agencies, non-governmental organizations and academic institutions to bring together a pool of specialists in the field of demography, statistics, economics, geography, biology and biodiversity to study and address environmental issues and concerns from a collective vantage point. The invaluable information presented here in the form of digital images, sounds, files and maps, as well as this textual report, may prove useful for planning and management purposes. The scale, resolution and formatting in which the information is made available may not always meet the level of accuracy potential users may require. Users are also cautioned that seeming data inaccuracies may be largely due to different methods used by various agencies and institutions in generating them and the loss of some important properties when converted to digital form. The report is divided into seven major chapters. Chapter 1 presents the environment problem (specifically the biodiversity problem), the rationale, and the objectives of the project. Chapter 2 provides an overview of Philippine biodiversity, the country’s socioeconomic and demographic profile, and the government’s policies on population and the environment. Chapter 3 explains the population-environment theories and debates, as well as the models used and some of the empirical evidences of the population-environment link worldwide. Chapter 4 describes the framework and methodology applied by the project in examining the population-environment link in the Philippines. Chapter 5 presents the discussion on the results and major findings of the project. Chapter 6 provides a summary and conclusions of this report. The last chapter outlines the policy implications of this project and the recommendations for policies, programs and further research on the subject of populationenvironment linkage. Finally, the annexes contain supplementary information to support the main contents of the report, including the socioeconomic, demographic and pressure index maps generated. To provide more information support for this report, the project team employed digital technology and stored much of the information in DVD. The reader is encouraged to use the DVD for further information. The Project Team

iii

L. Co

iv

Vaccinium oscarlopezianum, species new to science, found in Isabela province

Acknowledgements The “Mapping Population-Biodiversity Connections in the Philippines” Project (MPBCPP) was convened by the Department of Environment and Natural Resources–Protected Areas and Wildlife Bureau (DENR– PAWB), the Conservation International–Philippines (CIP) and the National Economic and Development Authority (NEDA). For all their support and data sharing, we thank: the staff of the National Mapping and Resource Information Authority (NAMRIA), especially the Information and Database Department headed by Dir. Linda Papa; Ana Urmeneta of the Department of the Interior and Local Government; Administrator Carmencita Ericta of the National Statistics Office; Emil Francis de Quiros and Philip Tuaño of the National Anti-Poverty Commission; Atty. Reuben Lingating and Dir. Grace Pascua of the National Commission on Indigenous Peoples; Dr. Crispinita Valdez and Charity Tan of the Department of Health; Mia Ventura, Monette Sangalang and Dir. Thomas Osias of the Commission on Population; and PSupt. Armando Ramolete, PCSupt. Arturo Lomibao, Supt. Antonio Mendoza, PO2 Joel Adajar and PO2 Rolando Zalsos of the Philippine National Police. The core project team of tireless members contributed to ensure that the collaborative efforts resulted in quality products: Dr. Oliver Coroza (CI–Philippines GIS Manager and P-E Team Leader), who took over the management of the project in July 2003 and saw through its successful completion, prepared the biodiversity profile in Chapter 2 excerpted from CI’s annual report and portions of Chapter 6, sections of the methodology chapter, contributed to the project’s conceptual framework, and led the analytical and technical aspects pertaining to spatial analyses; Noela Lasmarias (CI–Philippines Palawan-Program Resource Economist) assisted in thinking through the project’s framework, project’s hypotheses and final choice of variables, and prepared sections of Chapter 1, the socioeconomic profile in Chapter 2, Chapter 3, sections of Chapter 4, Chapter 5 and portions of Chapter 6; and Connie Morales (CI–Philippines P-E Project Coordinator) who untiringly and effectively coordinated all activities of the project and prepared most of Chapter 1. Dr. Mely Silverio (Consultant, Demography and Statistics) provided valuable advice on demography and statistical concepts, and contributed portions of Chapter 6. Dr. Felino Lansigan (Consultant, Environmental Statistics) and Dr. Daniel Lagunzad (Consultant, Biology and Ecology), respectively, guided the statistical analyses, contributed to Chapter 6, and provided important scientific advice on biodiversity indicators; and together prepared the project’s conceptual and analytical frameworks. Other members of the CI–Philippines National P-E team: Kirk Riutta, former Population-Environment Fellow of the University of Michigan and P-E Project Manager, who successfully provided technical and management guidance during the first nine months of the project, Rosheila Rodriguez (CI–Philippines GIS Associate), who coordinated the preparation of the maps and managed the database together with the team of tireless research assistants: Ezekias Foncardas Jr., Luvie Paglinawan, Michelle Encomienda, Eric Briones and Reena Delas Armas. The energetic Country Executive Director of CI–Philippines: Romeo Trono, for his continuous support and unfailing guidance. Protected Areas and Wildlife Bureau: Dr. Theresa Mundita Lim, Marlyn Mendoza and Tess Agayatin for their feedback and co-convenorship; National Economic and Development Authority: Joselito Bernardo and Fay Mancebo for their comments and co-convenorship; Layout Artist: Eric John Azucena, with the indefatigable support of Rosheila Rodriguez and Michelle Encomienda; Content and Copy Editors: Dr. Victoria Espaldon (Population Geography), Maria Anicia Sta. Ana and Kathleen Hughes (CI–DC); To the staff members of CI–Philippines, we thank them for their encouragement and support throughout v

the implementation of the project, especially, Liza Valenzuela-Duya, who prepared and furnished us the red list of threatened species for the CPAs, Allan Espiritu, who took care of logistics and administrative concerns, Antonio Lacanlalay and his supportive Finance staff, who made certain we had enough resources to keep us going and Roberto Evangelista Jr. for IT services. For their unceasing help on the map and database preparations, we thank the staff of CIM Technologies, namely: Richard Jude Limgenco, Ronald Abay, Jess Bucu, Angie Galolo, Evelyn Varias, Jose Esico, Ronald Garcia, Segundo Escoto, Cresencio Cruz Jr. and Joey Villegas; the staff of the Information and Database Division of NAMRIA, namely Bobby Crisostomo, Gemma Asis, Jeff Hunt and Ma. Paz Lagaday; and our CIP volunteers, namely: Elson Aca, Leonard Soriano, Michael Calunsod and Lou Angeli Ocampo. The project convenors and team gratefully acknowledge the financial support provided by the United States Agency for International Development, and the valuable comments and ideas provided by its staff, namely, Oliver Agoncillo from the Office of Energy and Environment (OEE), and Pinky Serafica from the Office of Population, Health and Nutrition (OPHN)—with a few helpful comments from Rene Acosta of OEE. Likewise, the project convenors and team are very thankful for: the data shared by the Philippine Biodiversity Conservation Priority-setting Program (PBCPP) and by other government agencies; and for the insights, comments and suggestions of the participants of the workshop (see Annex 7), and of the local and international peer reviews, all without whom this project would not have been possible. Photo Credits Convenors of the “Mapping Population-Biodiversity Connections in the Philippines” project would like to thank the individuals and organizations for permission to use their photographs in this report. Their names appear beside each photograph in the pages of this report.

vi

Table of Contents To the Reader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x List of Annexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxii 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 The Population-Biodiversity Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Rationale and Importance of the Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 Project Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Philippine Biodiversity, Socioeconomic and Demographic Profile . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Philippine Biodiversity Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Socioeconomic Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1. State of the Economy and the Natural Resources Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.2. Labor Trends and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.3. Trends in the Poverty Incidence and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.4. Family Income and Expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Demographic Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.1. Population Growth Trends and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.2. Population Growth and Agricultural Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.3. Migration and Urbanization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.4. Health and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4 Government Population and Environment Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3. Review of Population-Environment Theories, Frameworks and Evidences . . . . . . . . . . . . 19 3.1 Population and Environment Debate Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.1. The Malthusian and Neo-Malthusian Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.2. Esther Boserup: Population Growth and Technological Change . . . . . . . . . . . . . . . . . . . . . 19 3.1.3. Population Revisionism in the 1980s . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.4. Multiphasic Response Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2 Models and Approaches on the Study of Population-Environment . . . . . . . . . . . . . . . . . . . . . 21 3.2.1. Multiplicative Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2.2. Mediating Factors Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 Population and Environment Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.1. Population Growth and Agricultural Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.2. Relationship between Fertility and Natural Resource Use . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.3. Migration and the Rural Upland Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.4. Poverty and Environmental Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.5. Health and Environmental Degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.6. Gender Roles in Biodiversity Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.7. Education and Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.8. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4. Population-Environment Framework and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.1 Conceptual and Analytical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 vii

4.2.1. 4.2.2. 4.2.3. 4.2.4. 4.2.5.

Scope of the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Identification and Definition of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Hypothesized Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Data Sources and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Analytical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5. Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.1 Descriptive Profile of the Socioeconomic and Demographic Indicators . . . . . . . . . . . . . . . . . 42 5.2 Bivariate Correlation Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.2.1. Correlation between Forest Cover and Basic Demographic Indicators . . . . . . . . . . . . . . . . 44 5.2.2. Relationships between Demographic Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2.3. Relationship between Education, Poverty and Urban Growth . . . . . . . . . . . . . . . . . . . . . . . 47 5.2.4. Relationship between Forest Cover and Health Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.2.5. Summary of the Correlation Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.3 Multiple Regression Model and Summary Index of Socioeconomic and Demographic Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.1. Analysis of the impact of socioeconomic and demographic variables on forest cover using multivariate analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.2. Index of Socioeconomic-demographic Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.3. Index of CPA Vulnerability to Human-Induced Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4 Special Case: Mindanao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.1. Socioeconomic and Demographic Pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.2. Role of Armed Conflict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.1 Summary of Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7. Policy Implications and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 7.1 Integration of population and socioeconomic dimensions in conservation strategies and programs at the national and local levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 7.2 Population and environment concerns must be integrated into development policies, programs and plans of government . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 7.3 Unequivocal population policies, strategies and programs must be complemented with well-targeted human capital investments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 7.4 Integration of population and biodiversity conservation interventions either in a single program or as collaborative programs among organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 7.5 Develop, refine and test P-E approaches to better understand the relationship, enable effective communication of results, and design more appropriate interventions. . . . . . 74 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Annexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

viii

List of Tables Table 1.

Philippine national income account, 1998 to 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Table 2.

Peso-US Dollar nominal exchange rates, 1980 to 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Table 3.

Industry sector share to Philippine Gross Domestic Product. . . . . . . . . . . . . . . . . . . . . . . 7

Table 4.

Key labor indicators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Table 5.

Labor force distribution by industry group and by gender. . . . . . . . . . . . . . . . . . . . . . . . . . 9

Table 6.

Average weekly hours by major industry group and by gender. . . . . . . . . . . . . . . . . . . . . . 9

Table 7.

Poverty incidence by family size and urban–rural divide . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Table 8.

Poverty incidence by employment sector, 1997 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Table 9.

Distribution of families (percent) by main source of income and income class, 1997 . . 11

Table 10.

Median age, dependency ratio and distribution of population by broad age-group. . . . . 13

Table 11.

Projected agricultural land requirement from 2010 to 2030 . . . . . . . . . . . . . . . . . . . . . . . 13

Table 12.

Net migration rates by region, 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Table 13.

Net migration rate and percent forest cover in Region IV, 2000 . . . . . . . . . . . . . . . . . . . . 14

Table 14. Table 15.

Functional literacy rates of population 10–64 years old, by region and urban–rural divide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Infant mortality rate, by region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Table 16.

Comparison of contraceptive prevalence rates in Southeast Asia . . . . . . . . . . . . . . . . . . . 18

Table 17.

Socioeconomic and demographic predictor or independent variables . . . . . . . . . . . . . . . 33

Table 18.

Descriptive statistics for basic socioeconomic-demographic indicators. . . . . . . . . . . . . . . 43

Table 19.

Provinces with population growth rates above the national average. . . . . . . . . . . . . . . . . 43

Table 20.

Pearson correlation between percent forest cover and some demographic indicators . . . 46

Table 21.

Correlates of contraceptive use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Table 22.

Correlates of crude death rate and infant mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Table 23.

Correlation between access to health services, sanitation and morbidity . . . . . . . . . . . . . 51

Table 24.

Correlates of access to family planning and contraceptive use . . . . . . . . . . . . . . . . . . . . . 52

Table 25.

Result of the stepwise multiple linear regression model for provincial data (Dependent variable: natural log of arcsine percent forest cover) . . . . . . . . . . . . . . . . . . 54

Table 26.

Provinces with highest and lowest projected deforestation rate in ten years (2000 to 2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 List of provinces with high to very high socioeconomic-demographic pressure indices . . 58 List of CPAs with “extremely high urgent” index of priority or vulnerability to human-induced pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Table 27. Table 28. Table 29.

Results of the Pearson correlation analysis for selected socioeconomic and demographic variables in Mindanao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Table 30.

Ranking of conflict areas, year 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Table 31.

Relationship between conflict, socioeconomic and demographic variables based on Pearson correlation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

ix

List of Figures

x

Figure 1.

Unemployment rate, 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Figure 2.

Poverty incidence of the Philippines, 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Figure 3.

Philippine population and growth rate, 1800 to 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 4.

Secondary schools cohort survival rate by region, 1999 . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Figure 5.

Poverty incidence in high and low deforestation regions in the Philippines . . . . . . . . . . 25

Figure 6.

Causes and underlying processes that bring environmental change . . . . . . . . . . . . . . . . . 29

Figure 7.

Conceptual framework of interconnections between human pressure, state of the environment and the human response to this pressure-state link . . . . . . . . . . . . . 30

Figure 8.

Framework of analysis of the population-biodiversity link . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 9.

Hierarchy of Socioeconomic and demographic variables affecting biodiversity . . . . . . . 32

Figure 10.

Map indicating the degree of socioeconomic and demographic pressures in Philippine provinces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Figure 11.

Map indicating the vulnerability of conservation priority areas to socioeconomic and demographic pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Figure 12.

Indicative number of threatened animals by CPAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

List of Annexes Annex 1. Annex 2. 2.1. 2.2. 2.3. Annex 3. 3.1. 3.2. 3.3. Annex 4. 4.1. 4.2. Annex 5. 5.1. 5.2. 5.3. 5.4. 5.5. 5.6.

Matrix of Socioeconomic and Demographic Variables Data on Biological Indicators, and Socioeconomic and Demographic Variables Forest Cover by Province Threatened Animals by CPA (DVD) Socioeconomic and Demographic Variables by Province (DVD) Statistics Descriptive Statistics for Data at the Provincial Level Pearson Correlation for Data at the Provincial Level (DVD) Kendall and Spearman Rank Correlation for Data at the Provincial Level (DVD) Indices of Pressure Index of Socioeconomic-demographic Pressure on Forest Cover per Province Index of CPA Vulnerability to Anthropogenic Pressure Maps Population Density Percent Male Labor Force in Agriculture, Hunting and Forestry Net Migration Rate of Males Percent Forest Cover Index of Socioeconomic-demographic Pressure on Forest Cover Terrestrial and Inland Water Areas of Biodiversity Importance

5.7. Index of Biological Priority or Vulnerability by CPAs 5.8. Integrated Terrestrial and Inland Water Biodiversity Conservation Priorities 5.9. Socioeconomic Pressures from the PBCPP 5.10. Percent Urban Population by Province Annex 6. Algorithms for Areal Weighting Method Annex 7. List of Participants and Contributors

xi

List of Acronyms

xii

APIS

Annual Poverty Indicators Survey

ARMM

Autonomous Region of Muslim Mindanao

CAR

Cordillera Administrative Region

CI–DC

Conservation International–Washington, D. C.

CIDS

Center for Integrative and Development Studies

CIP

Conservation International–Philippines

CPA

Conservation Priority Area

CPP

Communist Party of the Philippines

DENR

Department of Environment and Natural Resources

DILG

Department of the Interior and Local Government

DOH

Department of Health

DOLE

Department of Labor and Employment

DVD

Digital Video Disc

ESSC

Environmental Science for Social Change

FAO

Food and Agriculture Organization

FMB

Forest Management Bureau

FNRI

Food and Nutrition Research Institute

GA

Government Agency

GDP

Gross Domestic Product

GIS

Geographic Information System

GNP

Gross National Product

GPS

Global Positioning System

GVA

Gross Value Added

HDI

Human Development Index

HIV/AIDS

Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome

IUCN

International Union for the Conservation of Nature

JAFTA

Japan Forest Technology Association

LGU

Local Government Unit

LRD

Land Registration Division

MPBCPP

“Mapping Population-Biodiversity Connections in the Philippines” Project

NAMRIA

National Mapping and Resource Information Authority

NAPC

National Anti-Poverty Commission

NCIP

National Commission on Indigenous Peoples

NCR

National Capital Region

NDHS

National Demographic and Health Survey

NDS

National Demographic Survey

NEDA

National Economic and Development Authority

NGO

Non-government Organization

NIPAS

National Integrated Protected Areas System

NSCB

National Statistical Coordination Board

NSO

National Statistics Office

OECD

Organisation for Economic Co-operation and Development

PAWB

Protected Areas and Wildlife Bureau

PCSD

Philippine Council for Sustainable Development

P-E

Population and Environment

PBCPP

Philippine Biodiversity Conservation Priority-setting Program

PIDS

Philippine Institute for Development Studies

PPMP

Philippine Population Management Program

PNP

Philippine National Police

POPCOM

Commission on Population

PRB

Population Reference Bureau

PRE

Population-Resources-Environment

PSR

Pressure-State-Response

RSDAD

Remote Sensing and Resource Data Analysis Department

UN

United Nations

UP

University of the Philippines

UNCED

United Nations Conference on Environment and Development

UNDESA

United Nations Department of Economic and Social Affairs

UNFPA

United Nations Population Fund

UNICEF

United Nations Children’s Fund

USAID

United States Agency for International Development

xiii

L. Co

xiv

Dinapigue ultramafic forest, Isabela province

Foreword

The Philippines has a unique biological heritage. More than half of the Philippines’ biodiversity is found nowhere else in the world. Unfortunately, the Philippines also faces an escalating crisis that threatens not only to destroy the terrestrial, freshwater and marine biodiversity but also endangers the economic, agricultural, public health, scientific, cultural and spiritual benefits we derive from biodiversity. The loss of biodiversity weakens the world and humankind. It reduces the quality of life for all people and may in fact be a survival issue for communities who depend directly upon healthy and productive ecosystems to meet their daily needs. The primary threat to biological diversity in the Philippines is habitat degradation and eventual loss, caused by destructive resource use, development-related activities, and human population pressures. This threat is linked to key root causes: (1) conflicting policies/jurisdiction and weak implementation; (2) poverty; (3) a national development paradigm that is biased towards short-term profits instead of long-term sustainability; (4) inefficient judicial mechanism and weak enforcement; and (5) lack of awareness on the value of the environment at all levels. Although we believe that we must protect the diversity of life for its own natural value, we also believe that the future of human welfare hinges on our success to protect this diversity. A few years ago CI, together with the DENR and UP, convened the Philippine Biodiversity Conservation Priority-setting Program (PBCPP) to identify priority areas for conservation. This initiative made qualitative assessments of areas based on biological and socio-economic criteria. Although it recognized the pressures that population and socioeconomic conditions exert on the environment, there was still a need to examine how, to what degree and where do population variables influence biodiversity and the environment. Hence the “Mapping Population Biodiversity Connections in the Philippines” Project (MPBCPP) was conceived. The results of the project imply that policies and interventions, which concentrate on biodiversity conservation alone, are insufficient in stopping biodiversity losses and forest destruction unless population and development concerns are adequately addressed. In order to avert the threats, we must also recognize and attend to the key root causes of these threats. Integration of population-environment concerns in development policies and programs requires cooperative efforts between and among key institutions and stakeholders. We hope that the MPBCPP output will provide an eye-opener and will become a valuable and useful tool for researchers, development workers, administrators and policy-makers in understanding and tackling the concerns regarding population and environment. We trust that the policy recommendations of this project will be transformed into catalytic actions, which will resolve the biodiversity and population crisis in our country. Partners and alliances in various sectors are important in achieving our dream of meeting conservation targets at a scale consistent with the needs of the Filipino people.

ROMEO TRONO Country Executive Director Conservation International–Philippines

xv

M. R. Duya

Agta father and child

xvi

Republic of the Philippines

DEPARTMENT OF ENVIRONMENT AND NATURAL RESOURCES PROTECTED AREAS AND WILDLIFE BUREAU

Message Daily, we experience our collective power to change the Philippine environment. Our mere movement to and from the places of our birth, homes or work has tremendous impact on our limited resources. Each of the 82 million-strong Filipinos demands his or her share of clean air, water, sustenance and livelihood from the land and the seas around us. Sadly, changes in our population size and composition have been linked to many negative effects such as pollution and biodiversity loss. On the bright side, we, as a species, have proven capable of developing decisive action needed to reduce the loss of our endemic plants and animals. With the work on sustainable development we began last 1987, the Philippines has been a consistent world leader in developing National Biodiversity Action Plans and its iterations. This volume is a logical continuation of that effort. Building on more than 15 years of iterative consultation, we have identified our critical protected areas and tremendously increased conservation initiatives. Through our richly textured experience, we have a better grasp of the links between our population and the Philippine environment. With this level of competence, we and our partners have embarked on creating systematic and analytical tools that examine this effect. Hopefully, the analytical tools you find in this volume will significantly increase your options to make sustainable development a reality in the Philippines. We are confident that by using the information provided here, concerned government and non-government organizations will significantly widen the information base from which decisions and policies affecting population-biodiversity connections are made and make our sustainable future reachable.

THERESA MUNDITA S. LIM, DVM OIC Director

xvii

CONSERVATION INTERNATIONAL

Message As human populations continue to increase, the changes encountered by the Earth and its inhabitants are rapidly intensifying. The world population has now reached 6.3 billion people, and although the actual population growth rate is slowing slightly, the Population Reference Bureau still predicts a population of slightly more than 9 billion people in 2050. The inevitable result of this growth, if it goes unchecked, is that the health and well being of people and the environment will be diminished—and that there will be reduced capacity for everything else on our planet. For the past 15 years, Conservation International has focused its conservation efforts on biodiversity Hotspots—1.6 percent of the Earth’s surface that contains more than 60 percent of its biodiversity. These areas are also the most threatened by human activities. Our efforts to conserve these areas are complicated by the fact that they also usually have rapidly increasing human populations. In terms of species-per-unit area, the Philippines is the “hottest” of the Hotspots, with the highest concentration of Critically Endangered and Endangered species on Earth and more than 93 percent of its natural vegetation already gone. The population of the Philippines is now roughly 82 million, and is projected to increase more than 60 percent to almost 133 million by 2050, unless there is effective intervention. Conservation International has been committed to working in the Philippines since 1990; however, it is only recently that we have seriously begun to tackle, with partners, the essential linkage between population and biodiversity. It is clear that if we are to make headway in tackling the multi-faceted problems in Philippine biodiversity conservation, our interdisciplinary, scientific approach must include human population issues. We must also succeed at using inter-organizational approach—successfully bring together a variety of organizations with a wide range of expertise to address these issues. This investigation on the linkage between population and biodiversity endeavors to do just that. This project grew out of the 2002 Philippine Biodiversity Conservation Priority-setting Program, an innovative initiative that broke down traditional boundaries among individuals and institutions, and developed a consensus-based strategy toward a common goal of saving Philippine biodiversity. I would like to extend my sincerest congratulations to our co-convenors, partners and the Conservation International–Philippines’ Population and Environment team for this groundbreaking project. We deeply value our ongoing partnership with PAWB–DENR, NEDA, DOH, POPCOM and NAMRIA, as well as with other agencies that have contributed data and expertise to make this publication possible. Conservation International is firm in its conviction that we all must work together to save the rich biodiversity of the Earth’s “hottest Hotspot.” Recognizing that no single person or organization can successfully tackle these complex issues alone, we are heartened by the great progress made through the partnership and collaboration demonstrated by this project. This document lays a solid foundation from which key policy makers, donors, and partners can move forward to address the complex problems ahead. We are hopeful that future work will continue in the spirit of true collaboration that has been essential to this project’s success. Mabuhay ang Pilipinas!

SUSIE ELLIS, Ph. D. Vice-President, Indonesia and Philippines Programs xviii

Republic of the Philippines

NATIONAL ECONOMIC AND DEVELOPMENT AUTHORITY

Message On behalf of the National Economic and Development Authority (NEDA) and the Philippine Council for Sustainable Development (PCSD), I would like to congratulate the convenors of the “Mapping PopulationBiodiversity Connections in the Philippines” Project (MPBCPP). My congratulations also go to the United States Agency for International Development (USAID) for sharing its resources to the project. The Philippine government recognizes the threats to the environment and has sought the support of all sectors of society to minimize such threats through sustainable means. It acknowledges the need for the present generation to track development within the country’s ecological carrying capacity. The results of the MPBCPP provide insights indispensable in carrying out the principles of sustainable development. The empirical results of the study shall serve as basis for evolving appropriate policies and programs to ensure that economic activities are being pursued without seriously decimating and degrading our biodiversity and ecological balance. This report will also be helpful in enabling all our development partners at various levels of governance to better appreciate the socioeconomic and demographic forces which impact on biodiversity and the environment and to use this understanding in better planning of their activities in their localities. We, at NEDA and the PCSD, are hopeful that MPBCPP can be a working force in sustainable development. Again, congratulations to you and more power.

ROMULO L. NERI Socioeconomic Planning Secretary and PCSD Chair

xix

Republic of the Philippines

DEPARTMENT OF ENVIRONMENT AND NATURAL RESOURCES NATIONAL MAPPING AND RESOURCE INFORMATION AUTHORITY

Message I extend my warmest congratulations to all the planners and implementers of the “Mapping PopulationBiodiversity Connections in the Philippines” Project (MPBCPP). The eventual publication of the report is a monumental feat in Philippine biodiversity literature in terms of new research, data collection and better use of existing data. One of the most pressing problems faced by humankind today is the loss of biological diversity. This trend will never be reversed unless a shift is made towards conservation and sustainable resource use. Increasing awareness on this issue over the past few decades has emphasized the need to improve our understanding of the interactions between human society and biodiversity. Humans are part of nature and act as integral influences on ecosystems. Mapping does not only produce maps. It is fundamental to the process of bringing order into the world. Mapping helps decision-makers better understand the problems of conservation and sustainable development. I am confident that this report will be an effective tool for coordinated and participatory activities to mitigate the biodiversity crisis. Empowering the human resource system in its quest to effectively handle scarcity and diversity in the midst of ecosystems deterioration is a massive task. Real progress will require a genuine partnership among a wide range of stakeholder institutions and individuals. By committing to this task, the National Mapping and Resource Information Authority will continuously help bolster conservation activities with a view to achieving more comprehensive and integrated results through the use of modern science. Being a close partner institution in undertaking the MPBCPP was a step in the right direction. I wish you continued success for the development of more linkages between population and use of biodiversity. More power and Mabuhay!

USEC. DIONY A. VENTURA, MNSA Administrator

xx

D. Blackburn

Conserving environmental resources for future generations

xxi

Executive Summary

xxii

“Mapping Population-Biodiversity Connections in the Philippines” Project (MPBCPP) takes off from the previous project, Philippine Biodiversity Conservation Priority-setting Program (PBCPP). While PBCPP focused on designating priority areas for conservation, it recognized the pressures that population and socioeconomic conditions exert on the environment. The PBCPP did not tackle such factors extensively, although the qualitative assessments of participants from the academe, government institutions and non-government organizations based on field experiences revealed that certain conservation priority areas were facing very high socioeconomic pressures. The need to examine how and to what degree population variables influence biodiversity and the environment therefore became imperative. The MPBCPP was conceived and implemented to address this major gap in the PBCPP analysis. MPBCPP is a collaborative effort of three organizations: the Protected Areas and Wildlife Bureau of the Department of Environment and Natural Resources (PAWB–DENR), Conservation International–Philippines (CIP), and the National Economic and Development Authority (NEDA). With the funding support from the United States Agency for International Development (USAID), the objectives of the MPBCPP were attained through the data provided by various agencies, namely: the Department of Health (DOH), the Department of the Interior and Local Government (DILG), the National Statistics Office (NSO), the Philippine National Police (PNP), the Commission on Population (POPCOM), the National Commission on Indigenous Peoples (NCIP), the National Anti-Poverty Commission (NAPC), and the National Mapping and Resource Information Authority (NAMRIA). The project initially identified 36 sets of socioeconomic and demographic indicators comprising of 75 variables from a series of meetings, consultations and workshops during the early phase of the project. These variables were identified and finalized based on their relevance in the population-environment interactions, and the availability of data at the provincial level for a particular point in time. A clustering framework for the 36 sets of socioeconomic indicators was also performed to analyze the level at which these variables affect biodiversity. As a result, the final three sets of socioeconomic and demographic indicators comprising of 25 variables were identified, namely: basic population variables (population density and population growth); net migration rate (disaggregated into gender); poverty rate; education (as percentage of population aged 15 years and over with high school education); unemployment rate; and the percent distribution of the labor force disaggregated by nine industry sectors and by gender. Forest cover, as habitat for wildlife species, was used as a biodiversity indicator. Lower percentage of forest cover was assumed to be indicative of a deteriorated wildlife habitat; hence, maybe associated with a larger number of threatened animal species in the wild. To determine the impact of the indicators on threatened species, an indicative number of categorized threatened animal species was determined to the level of Conservation Priority Areas (CPAs) identified from the PBCPP. This needed to be associated with socioeconomic and demographic indicators. However, data for the demographic and socioeconomic variables were not available for CPAs, but most of these were available for provinces only. To address the lack of information at the biodiversity conservation priority areas level, a weighting procedure was tested to disaggregate provincial data of the variables to correspond to the parameters needed for the CPAs using the assumption of homogeneous spatial distribution of these parameters within a province. Some obvious overestimation from the weighting method was detected. In the absence of site verification of how tight the weighted estimates of the variables were or how large the variations were from the true value, cartographic modeling was deemed a better option. The cartographic model used overlays of a quantitatively determined provincial map of socioeconomic-demographic pressure produced by the project and the biodiversity importance map of CPAs, based on the PBCPP participants’ knowledge and field experiences, as well as on the compiled information on threatened animal species. There were two types of analyses done: bivariate correlation analysis and multiple regression analysis. The correlation analysis was performed using all 75 variables while multiple regression analysis used the 25 variables

identified using a clustering method. The correlation analysis between socioeconomic and demographic variables show: (1) Poverty rate tends to be high when the proportion of the population (15 years old and over) attaining high school education is low; high unemployment rate is positively associated with low percentage of population 15 years old and over with high school education. The results emphasize the importance of education and human resource development in alleviating poverty. (2) High biodiversity areas are found in rural and upland areas. Since poverty rates are relatively higher in upland and rural areas, poverty reduction measures are important components that need to be carefully considered in conservation strategies. (3) High fertility rates are positively associated with smaller proportion of the population (15 years old and over) who attained high school education. It is also positively associated with high unemployment rate and high poverty rate. Since high unemployment and poverty rates are associated with lower forest cover, (hence, higher risk of biodiversity losses), there is no gainsaying the importance of population-development-environment interventions. (4) High percentage of forest cover is associated with high fertility rate and population growth, emphasizing the need for coordinated fertility reduction and development strategies to provide appropriate and adequate incentives for conservation. (5) High proportion of the labor force in non-natural resource-based industries, specifically the proportion of women in manufacturing, is associated with high percentage forest cover, low fertility, and high prevalence of modern contraceptive method. The results support the argument that women tend to have less children and use contraception more when given better education and the opportunity to work offfarm. (6) The greater contraceptive prevalence rate is associated with low proportion of barangay health stations and workers to population, as well as with low infant and maternal mortality rates. This tends to indicate that improved health services lead to a better spread of contraceptive use and its added benefit of health improvements of mothers and infants. (7) High ratios of population-to-barangay stations and to-barangay health workers are associated with low incidence of diseases indicating that increased access to efficient health facilities and services have a significant positive impact on health status. (8) Two migration patterns are evident, i.e., rural–urban and lowland–upland. Without rural development that provides alternative jobs and income—which lessen the dependence on natural resources or the push of rural poor into cities—these two patterns will likely to continue to exacerbate problems in the cities and biodiversity losses in the uplands. (9) Given current technology and population growth, agricultural expansion will likely to continue in the next 10 to 30 years. This implies that a three-pronged approach that tackles population management, conservation and environmental protection, and agricultural modernization is needed to increase the productivity and provide adequate income and food security to rural and upland farmers. If productivity continues to be low and population continues to rise, the projections made in this study show that more land will likely be opened for agricultural production, which will exacerbate deforestation and biodiversity losses. For Mindanao, the correlation analysis found the following relationships. First, urban areas tend to have higher population densities, smaller household sizes and lower child-to-woman ratios than rural areas. Second, high fertility rate is associated with high poverty rate. Third, high female net migration rate is highly associated with high total fertility rates and high child-to-woman ratio. However, female net migration rate is lower in areas with high region-based armed conflict implying the unattractiveness of these areas to women. Fourth, high male net migration in Mindanao is also associated with high female net migration rate. This result implies that when male members of family migrate, the wives and the rest of the family follow. Moreover, net male migration is low in areas with high unemployment rate. xxiii

Lastly, ideology-based armed conflict is present in Mindanao as well. However, the correlation analysis showed where ideology-based armed conflict is high, the religion-based armed conflict is low. Moreover, religionbased armed conflict appears to be more predominant in rural areas than in urban areas. The multivariate analysis using provincial data showed that population density, percent of males in agriculture, and net male migration rate were statistically significant in determining forest cover using a log-linear regression model. The regression analysis showed that the a priori expectations on the relationship between population density and agriculture were met, i.e., that the increase in these two variables is associated with lower percent forest cover. The model indicated that a 10 percent increase in population density would tend to be associated with a 5 percent decrease in the percent forest cover; a 10 percent increase in the number of males engaged in agriculture, hunting and forestry would tend to decrease forest cover by 5.03 percent. Net migration rate, however, appeared to be positively associated with forest cover contrary to the hypothesized relationship. The result indicated that net migration rate is higher in areas with more forest cover, implying that migration to natural resource rich areas continues to be a problem and can compromise the integrity of the remaining forest and upland resources. Further examination of the data showed two migration patterns that may be occurring at present, namely: rural–urban and lowland–upland, with the latter still dominant. Controlling for net migration rate and percentage of males engaged in agriculture (hunting and forestry), a ten year projection on the impact of population density on forest cover revealed the top ten provinces which will likely experience very high deforestation given current population growth rate. In the order of magnitude of deforestation, these provinces are Palawan, Isabela, Cagayan, Oriental Mindoro, Quezon, Bulacan, Maguindanao, Lanao del Sur, Agusan del Sur and Occidental Mindoro. These provinces are also considered of extremely high to very high biodiversity importance based on the Philippine Biodiversity Conservation Priority-setting Program (PBCPP) result. The cartographic model, using overlays of the PBCPP map of biodiversity importance and the socioeconomic-demographic pressure map on forest cover, generated by the project substantiated and refined the ranking of conservation priority areas according to their biodiversity importance. The results of the analyses imply that policies and interventions that focus on biodiversity conservation alone are insufficient in abating biodiversity losses and destruction of forest resources unless population and development concerns are adequately addressed. The results also clearly show that there are intervening factors affecting population-biodiversity links that need to be addressed as well. There is a need for firm and unequivocal government population policy, which is clearly integrated in the development framework. A review of the population policies from the mid-1980s to the present revealed that population policies have continuously shifted from clear and firm programs to ambiguous pronouncements. The population policy and programs of the Macapagal administration, for instance, views family planning as mainly a health intervention, with modest fertility targets that reversed the “accelerated program” of the Estrada administration. As result of the shifting policies, the contraceptive prevalence rate remains at 46 percent in 2000—the lowest among the Southeast Asian countries like Indonesia (57 percent), Malaysia (55 percent), Thailand (72 percent), and Vietnam (75 percent). The effect of the shift in the population policy during the Aquino administration (1986 to 1992) resulted in the decline in contraceptive prevalence rate from 44 percent in 1985 to 40 percent in 1990–1995. The relatively more aggressive programs of the Ramos and Estrada administrations resulted in the increase in the prevalence rate to 46 percent. The figures clearly indicate that government support and firm commitment to reducing fertility rates do matter in making family planning programs successful. Integration of population-environment concerns in development policies and programs does not necessarily mean that all conservation and development projects must have population components. Rather, cooperative efforts between and among conservation-oriented, development-oriented and population-oriented institutions are warranted so that these three concerns are addressed. Obviously, firm government support matters in moving population-environment initiatives forward. In addition to firm and unequivocal population policies, the project recommends that future initiatives on the population-environment initiatives should be uniquely tailored to specific provinces or CPAs and to the xxiv

specific biodiversity resources being protected. A clear, national population policy should guide, support, and expand the local governments’ role in shaping site-specific population-environment interventions. Site-specific strategies and programs are more targeted to the peculiar characteristics of and to vulnerability of the people living in the area. Future researches may focus on the use of investigative tools such as ethnographic and systems modeling. Furthermore, future spatial analyses of population-land use changes need to use geo-referenced, high-resolution socioeconomic and demographic data at the barangay level for more reliable estimates of variables for the conservation priority areas and gather historical time series data. On top of the project’s findings, some value-added outputs came about. The project has also created a reference template for a national coastline base map, which NAMRIA will endorse for nationwide use for digital data compatibility. The project was able to digitize socioeconomic thematic maps, which can be used for national planning. New partnerships had been created with government agencies that do not normally tackle population-biodiversity connections. Population-oriented agencies were introduced to the fascinating though complex field of biodiversity, and vice versa. Hopefully, these new partnerships will become a platform for encouraging participation and holistic programming by diverse institutions from the environment, demographic and socioeconomic sectors.

xxv

M. R. Duya

Luzon Scops Owl, Otus longicornis

xxvi

Introduction 1.1

The Population-Biodiversity Problem extinction (Hilton-Taylor, 2000). These can no longer be considered as signs of an impending biodiversity crisis, because, apparently, the Philippines is already in the middle of one. The country stands to lose its rich biological heritage irreversibly and prove many experts’ prediction of it becoming “a global biodiversity disaster area” (Ong et. al., 2002; Terborgh, 1999; Linden, 1998). With population growing at a rate considered to be one of the fastest in Southeast Asia at 2.36 percent annually, a poverty incidence of almost 47 percent in the rural areas and 34 percent nationwide, and rising unemployment, the country is indeed faced with the dilemma and difficult challenge of seeking a balance and softening the trade-offs between development and environmental protection. It is apparent that actions and project undertakings of the kind that will help reverse, if not stem, the tide of the current population and biodiversity problems, are imperative and urgent. This document reports on a project, which led to methods and procedures that can contribute to resolving the population-biodiversity problem by providing a guide to decision-making and policy-making in the area dealing with people and their environment.

Rationale and Importance of the Project

In a previous endeavor in 2000, a project called the “Philippine Biodiversity Conservation Priority-setting Program” (PBCPP) was implemented by Conservation International–Philippines (CIP), the Protected Areas and Wildlife Bureau of the Department of Environment and Natural Resources (PAWB–DENR), the Center for Integrative and Development Studies of the University of the Philippines (UP–CIDS), and other CI partners to identify, assess and prioritize for biodiversity conservation specific geographic areas that contain the biodiversity representativeness of the different centers of endemism in the Philippines. The PBCPP also formulated biodiversity conservation

strategies and actions for the identified conservation priority areas (CPAs). Using existing data, researches and field experiences of participants from various fields of expertise, it was determined that population pressures presented a real and growing threat to biodiversity, attributed mainly to the encroachment into and over-exploitation of biodiversity-rich areas by subsistence folks, opportunists and extraction industries, as well as, by the expansion of agriculture, human settlements, and infrastructure into forestlands. While the DENR has been actively pursuing the establishment of protected areas in the remaining key biodiversity areas with the help of NGOs, communities

Mapping Population-Biodiversity Connections in the Philippines

The Philippines is one of the 17 megadiversity countries, which together contain between 70 to 89 percent of global biodiversity, and thus form an integral part of the global heritage for conserving the diversity of life on earth (Ong et. al., 2002; Mittermeier et. al., 1997). The country has more than 52,177 described species, more than half of which are found nowhere else in the world, and is ranked in the top 17 megadiversity countries in terms of biological richness on a per hectare basis (Ong et. al., 2002). Unfortunately, the Philippines is counted among the 25 biodiversity hotspots because of the tremendous population or socioeconomic threats besetting its resources (Myers et. al., 2000; Heaney and Regalado, 1998). As a consequence, today, we see the following: • only about less than seven (7) percent of the original forest cover remains (Heaney, 1998); • only five percent of the coral reef cover is in excellent condition (Gomez, 1991); • only 120,000 hectares of the original 400,000 to 500,000 hectares of mangrove forests are in pristine state (Primavera, 2000); and • 418 wildlife species are threatened with

1.2

1

1

INTRODUCTION

and development partners, to date, there is no concerted and sustained effort among concerned government agencies to integrate population and environmental or biodiversity concerns into government policies, programs and activities. Government population policy is continually shifting and, currently, is equivocal about reproductive health due to strong religious and political influences. A weak government policy, poor planning, and the continuing increase in population aggravate the biodiversity problem. CI has thus identified population factors as one of the drivers of biodiversity loss. Hence, CI designed its population-environment (P-E) program in July 1998 to demonstrate the need to integrate demographic analysis into conservation actions and planning. While the population-environment and population-development-environment debates have continually surfaced and resurfaced since the time of Thomas Robert Malthus1, an 18th century English

1

Mapping Population-Biodiversity Connections in the Philippines

2

economist and population theorist, there are very few efforts in the Philippines to develop the analytical and methodological tools that integrate spatial techniques into both qualitative and quantitative procedures to demonstrate the population-environment link. CI–Philippines with its partners embarked on a project entitled “Mapping Population-Biodiversity Connections in the Philippines,” which started in October 2002, and can be considered a relatively new initiative for the country and for the CI–Philippines organization2. The results and findings of the PBCPP laid the foundation for establishing the connection of Philippine demography to biodiversity. The “Mapping Population-Biodiversity Connections in the Philippines” Project (MPBCPP) builds on these findings by identifying key demographic trends and spatially demonstrating the P-E interaction throughout the country and in the PBCPP-

The Malthusian population theory on the population limits of subsistence produced a profound impact on attitudes and ideas concerning population growth. Malthus first published an essay in 1788, challenging the Utopian ideas during the 18th century that viewed a growing labor force as a way to prosperity since wealth and progress were dependent on manual labor. His essay, entitled, An Essay on the Principle of Population as it affects the future improvement of society; with remarks on the speculations of Mr. Godwin, M. Condorcet, and other writers, was written neither as a text in demography nor as an exposition of some new law of population growth, but to refute the Utopian ideas gaining currency during his time (Peters and Larkin, 1997). Note that this report will use interchangeably the compound terms “population-environment” and “population-biodiversity”. As the biodiversity problem falls under the scope of the wide array of concerns about the environment, the report primarily focuses on that latest area of the “population-environment” link involving population and biodiversity. Hence, it borrows concepts from “populationenvironment” theories and experiences that may help contribute to resolving the biodiversity problem.

BOX 1. An Orientation to Biodiversity Biodiversity refers to the great variety and variability of organisms found on earth and the environment in which they live. It includes the variety among different individuals, species and ecosystems. The word biodiversity is the result of a merger of and contraction of two words: biological and diversity (Conservation International–Philippines, 2002). As the foundation of healthy and functioning ecosystems, it is essential for the survival of humans to provide rich soils, clean air and water, abundant forests and numerous animal species. These services are important because it provides basic human needs—food, clothing, shelter and valuable medicines. The Philippines is one of the tropical countries where levels of biodiversity and endemism are remarkably high. The Philippine ecosystem is a dynamic one. It seeks to attain a balance between the “input and output” factors. This balance is called homeostasis, or popularly known as ecological balance. The ecosystem’s main function is to maintain its stable existence. In the case of humans, every person has to maintain this balance in order to prevent havoc in the environment. Any excessive input may result in an imbalance or may imperil the system, possibly resulting in the degradation of natural resources or ultimately, the extinction of species. Most of these are threats brought about by anthropogenic causes. Megadiversity (Mittermeier et. al., 1997) is a term used to characterize a set of 17 countries that holds the greatest numbers of species of living organisms, especially among the best-known groups—plants, birds, mammals, reptiles and amphibians.

Continued on next page... 2

1.3

at a greater risk of losing their remaining biodiversity resources? (c) Are the current government policies and strategic plans for both environment and population management adequate to address the potential risk of biodiversity loss due to socioeconomic and demographic pressures?

INTRODUCTION

identified conservation priority areas. The project expects to answer the following questions: (a) What are the key socioeconomic and demographic variables associated with biodiversity indicators? (b) Which provinces and PBCPP-identified conservation priority areas in the country are more vulnerable to socioeconomic and demographic pressures and may be

Project Objectives

The main objective of the project is to help build the capacity of the United States Agency for International Development (USAID), the Protected Areas and Wildlife Bureau of the Department of Environment and Natural Resources (PAWB–DENR), the National Mapping and Resource Information Authority (NAMRIA), the Commission on Population (POPCOM) of the Department of Health (DOH), and the National Economic and Development Authority (NEDA) to use the strategic tools developed in this project for considering demographic and environmental interactions in their planning activities. To meet the main objective and to answer the questions posed in section 1.2, the project, through the use of statistical and spatial methodologies, attempted to:

(1) Identify and analyze the demographic determinants of biodiversity losses, specifically, forest cover of provinces and the biologically important status of species in PBCPP-identified conservation priority areas (CPAs); (2) Spatially examine how key socioeconomic and demographic trends threaten the remaining forest cover in the provinces and CPAs, and which of these provinces and CPAs are most vulnerable to these pressures; (3) Produce digitized maps of socioeconomic and demographic indicators and pressure indices packaged in a DVD database for easy access and use by government planners and development agencies; and

Mapping Population-Biodiversity Connections in the Philippines

The megadiversity country concept is based on four premises: • The biodiversity of every nation is critically important to that nation’s survival, and must be a fundamental component of any national or regional development strategy; • Biodiversity is, by no means, evenly distributed on our planet, and some countries, especially in the tropics, harbor far more concentrations of biodiversity than others; • Some of the richest and most diverse nations also have ecosystems that are under the most severe threat; and • To achieve maximum impact with limited resources, concentration must be placed heavily (but not exclusively) on those countries richest in diversity and endemism and most severely threatened; investment in them should be roughly proportional to their overall contribution to global biodiversity. Hotspots (Myers et. al., 2000) refer to areas that have a high diversity of plants and animals, which are endemic—found in that region and nowhere else on earth—and at the same time, face serious pressures or threats of species loss and habitat destruction. The main criteria, therefore, to assess if a place is a biodiversity hotspot are its species endemism and the imminent threat of habitat destruction it faces. On a per hectare basis, the Philippine islands harbor more diversity of life than any other place on earth (Heaney, 2002). It has a high degree of unique life forms found nowhere else and has lost 75% or more of its original vegetation (Ong et. al., 2002).

Continued on next page... 3

convince, influence and facilitate the integration of population into conservation and, conversely, of environment into development and population policies, programs, strategic plans and implementation activities of government, non-government organizations, and development agencies. The project team also encourages researchers and academics to use the information and refine the analytical tools and methods to examine and demonstrate population-biodiversity links at the CPA, protected area and community levels.

Megadiversity videoclip

Pressures to Biodiversity • Unsustainable utilization of resources leads to destruction of old-growth forests, freshwater and marine ecosystems that lead to an extinction crisis (Ong et. al., 2002). • Extractive industries such as logging and mining have destroyed most of the forests (Mallari et. al., 2001). • High human population density and growth rates have further aggravated the situation as rainforests were converted to agricultural areas and plantation to meet the demands of a growing population (Cincotta et. al., 2000). • Many economic systems and policies failed to put value on the environment and its resources (Romero, 1995). • Deficiencies in knowledge of biodiversity and its importance.

H. Castro

Mapping Population-Biodiversity Connections in the Philippines

Hotspots videoclip

CI and Cemex

CI and Cemex

INTRODUCTION

(4) Recommend potential directions in future activities that examine population-biodiversity or population-environment linkages and policies that may minimize or abate any negative implications of the population-biodiversity interaction based on the results of statistical and spatial analyses. The project team hopes that the compiled demographic statistics and spatial database, findings and tools developed in the course of the project will

Calamian deer, Axis calamianensis, endemic to Palawan

4

Philippine Biodiversity, Socioeconomic and Demographic Profile 2.1

2

Philippine Biodiversity Status

M.R. Duya

The Philippines is considered a center of species 1995) and more than 2,000 species of reef fish diversity and endemism (or originating or naturally (Nañola et. al., 2000), 800 species of marine algae, 16 occurring in the country), with more than 1,100 species of seagrass, 23 species of cetaceans, and five terrestrial vertebrates species, including: species of sea turtles, making the Philippines one of • more than 576 bird species, 34 percent of the world’s richest countries in terms of coastal and which are endemic; marine species. The country is also considered as the • more than 204 mammal species, 54 percent top marine biodiversity hotspot in the world (Roberts of which are endemic; et. al., 2002). • more than 101 amphibian species, 78 The loss of habitat and the degradation of percent of which are endemic; and other ecosystems due to environmental factors brought • more than 258 reptile species, 66 percent about by socioeconomic and demographic pressures of which are are pushing the status endemic. of Philippine wildlife Invertebrate species to the brink of diversity is also high, with extinction. The 2003 895 species of butterflies, International Union for 39 percent of which are the Conservation of endemic. The number of Nature (IUCN) Red List plant species is estimated of Threatened Species to be in the range of (Hilton-Taylor, 2003), 10,000 to 13,000, more under the critically than half of which are endangered (next to Luzon bleeding-heart pigeon, Gallicolumba luzonica endemic. Philippine extinction), endangered rainforests have the highest level of endemism in and vulnerable categories, includes 416 Philippine the Indo-Malayan realm on a per-unit-area basis wildlife species, of which: (Conservation International, 2003). • 68 are threatened birds (59 endemic) Other forest types also provide the habitat species; for Philippine species and form part of the remaining • 197 are threatened plants (167 endemic) forest cover, which is estimated to be already about 18 species; percent of the country’s total land area (ESSC, 1999). • 51 are threatened mammals (41 endemic) Other indicators of habitat loss are: only 5 percent species including the dugong (Dugong of remaining coral reefs remain in pristine condition; dugon) and humpback whale (Megaptera mangrove cover was reduced by 80 percent during the novaeangliae); last 75 years; and the seagrass beds declined by 30 to 50 • 23 are threatened amphibians (all endemic) percent during the last 50 years (Ong et. al., 2002). species; The Philippines’ vast 36,289-km coastline holds • 9 are threatened reptiles, including 4 species an estimated 27,000 km2 of coral reefs containing 500 of sea turtles (3 endemic species); and of the world’s 800 known coral species (Veron, 2000). • 16 are threatened invertebrates, which In the Calamianes region alone, 20 undescribed coral include 13 species of insects, 3 molluscs (2 species were discovered recently (Werner and Allen, giant clams and 1 endemic gastropod). 2000). There are 40 species of mangrove (Zamora, While reliable data on rates of population

Mapping Population-Biodiversity Connections in the Philippines

5

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE Mapping Population-Biodiversity Connections in the Philippines

decline for threatened species are scarce, estimates for some notable species are available. The population of Philippine tamaraw (Bubalus mindorensis), which was estimated to be approximately 10,000 individuals during the early 1900’s (Harrison, 1969), has declined to about 253 individuals as of 2002 (PAWB–DENR, 2003). During the mid-1960s, the Philippine eagle (Pithecophaga jefferyi) population was estimated to be around 600 pairs, although a more recent estimate placed the number

2.2

of breeding pairs at 30 (Rabor, 1968; Heaney and Regalado, 1998). The nesting green sea turtle (Chelonia mydas) population in the Philippine Turtle Islands has declined by about 80 percent from 1960 to the present. Among all threatened marine species in the Philippines, the long-term prognosis for populations of the dugong is probably the bleakest. Local or site-specific extinctions are already being recorded.

Socioeconomic Profile

2.2.1. State of the Economy and the Natural Resources Sector The Philippines is a middle-income country with a medium-sized economy. From 1980–2000, the annual growth rate of the gross national product (GNP) and gross domestic product (GDP) fluctuated, dropping to negative percentage growth in 1981–1985 (Table 1) brought about by extreme political turmoil that led to a capital outflow, large peso devaluation, debt crisis, and, eventually, to a near economic collapse (Lamberte et. al., 1992; Lasmarias, 2003). The economy started to recover after 1986 as international donors and financial institutions, buoyed by the ouster of former President Marcos and the restoration of democratic institutions, infused new capital and allowed the restructuring of the country’s foreign debt. The series of attempts to topple the newly installed government in the late 1980s, the El Niño phenomenon, and the energy crisis in 1991–1992 again slowed economic recovery (Tabunda

et. al., 2002). The East Asian financial crisis in 1997– 1998 also affected the economy, although to a lesser extent compared to neighboring countries. In general, the fluctuation in national income followed the periods of economic recession and recovery from internal and external shocks. Table 1 also shows that per capita GDP, in real terms, is eroded by the large population, which grew by 2.36 percent from 1995–2000. To date, per capita GDP is still below the 1980 level both in peso and dollars terms, as the peso continued to depreciate from PhP7.50 per US dollar in 1980 to over PhP44 per dollar in 2000 (Table 2). From 1980 to 2000, the share of agriculture, fishery and forestry decreased slightly from 23 percent to 20 percent. The forestry sector, however, showed a far worse situation. The unfavorable situation of Philippine forests meant that its income-generating potential for the macro-economy and dependent communities has been greatly reduced. Forestry gross

Table 1. Philippine national income account, 1998 to 2000 Gross Domestic Product In Pesos (millions; 1985=100) Per capita GDP (PhP) Average annual growth rate (in percent; 5-year moving average) In US Dollars (millions) Per capita GDP (US $) Gross National Product In Pesos (millions; 1985=100) Net factor income from abroad (million Pesos) Per capita GNP (PhP) Average annual growth rate (in percent; 5-year moving average) In US Dollars (millions) Per Capita GNP (US $)

1980

1985

1990

1995

2000

609,768 12,677

571,883 10,584

720,690 11,872

802,866 11,701

954,962 12,483

6.1

-1.1

4.7

2.2

3.6

81,302 1,690

30,746 569

10,414 172

9,524 139

21,605 282

608,096 -1,672 12,643

551,952 -19,931 10,215

716,929 -3,761 11,810

825,164 22,301 12,026

1,012,614 57,652 13,237

6.0

-1.8

5.4

2.9

4.2

81,079 1,686

29,675 549

10,360 171

9,789 143

22,910 299

Sources: National Statistical Coordination Board and National Statistics Office

6

Table 2. Peso-US dollar nominal exchange rates, 1980 to 2000 Year

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

Nominal exchange rate PhP/$ Percent change 7.50 7.50 0.00 8.50 13.33 11.10 30.59 16.70 50.45 18.60 11.38 20.40 9.68 20.60 0.98 21.10 2.43 21.70 2.84 24.30 11.98

Year

Nominal exchange rate PhP/$ Percent change 27.50 13.17 25.50 -7.27 27.10 6.27 26.40 -2.58 25.70 -2.65 26.20 1.95 29.50 12.60 40.90 38.64 39.10 -4.40 44.20 13.04

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Source: www.pids.gov.ph

Table 3. Industry sector share to Philippine Gross Domestic Product Share to real GDP (%)

2. INDUSTRY SECTOR a. b. c. d.

Mining and quarrying Manufacturing Construction Electricity, gas and water

3. SERVICE SECTOR a. Transportation, communication and storage b. Trade c. Finance d. Ownership dwellings and real estate e. Private services f. Government services GROSS DOMESTIC PRODUCT

The population of working age (15–64 years old) grew by an average of 3 percent per year from 1985–2000, and the labor force participation rate did not change much in the 1990s (Table 4). While the number of new jobs created is declining, the unemployment rate has not drastically increased or decreased. This indicates that a large proportion of the labor force seeks employment elsewhere or overseas and the dismal performance of the labor sector. The increase in the number of new jobs created in 1986–90 was mainly attributed to the generally favorable economic climate after the fall of the Marcos regime. Some 6 million new jobs were estimated to be created as new investors came in and the economy started to recover. Jurado and Sanchez (1998), however, questioned the accuracy of the data because of possible underestimation of the base population figures prior to 1986 or overestimation Table 4. Key labor indicators Indicators Labor force (‘000) Labor force participation rate (%) New jobs (thousands) Average employment rate (%) Average unemployment rate (%)

1981–85

1986–90

1991–95

1996–2000

no data

22,774

26,336

29,870

63

65

66

66

457

1,799

727

519

90

91

92

92

10.2

8.7

8.2

8.1

1985

1990

1995

2000

24.6

22.3

21.5

20.0

23.0

21.3

21.3

19.8

Source: DOLE, Labor Statistics, 1991 and 2000.

1.6

1.0

0.2

0.1

35.1

35.5

35.4

34.5

2.1 25.2 5.1

1.5 25.5 5.8

1.3 25.3 5.5

1.1 24.8 5.1

2.8

2.6

3.2

3.4

40.4

42.2

43.0

45.6

5.5

5.7

5.9

7.1

14.5 3.0

14.9 4.2

15.4 4.2

16.0 4.9

5.6

5.6

5.5

5.1

6.8 4.9

6.8 5.1

6.9 5.2

7.4 5.1

100.0

100.0

100.0

100.0

of the population figures for one of the two years following the recorded extremely high new job creation figures. Indeed, the unemployment rate dropped by 1.5 percentage points from 10.2 percent in 1981–85 to 8.7 percent in 1986–90. In 2000, three regions appear to have the highest unemployment rate: Regions III (10.1 percent), IV-A (12 percent) and VII (10.4 percent); two have the lowest unemployment rate—Region II (5.4 percent) and ARMM (5.5 percent) (Figure 1). The rest of the regions are in the range of having 7 percent to 10.1 percent unemployment rate. Historically, male employment is much higher than female employment, although there are industry sectors that are male-dominated and female-dominated. Wholesale trade and community service sectors are usually female-dominated, while construction,

Source: National Statistical Coordination Board.

Notes: Labor force data for 1990, 1995 and 2000

Mapping Population-Biodiversity Connections in the Philippines

1. AGRIGULTURE, FISHERY, FORESTRY a. Agriculture and fishery b. Forestry

2.2.2. Labor Trends and Implications

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE

value added (GVA) has been sharply declining, so that in 1995–2000, forestry contributed a meager 0.1 percent to GDP (Table 3). All extractive natural resource-based sectors (agriculture, forestry, fishery and mining) show declining contribution to GDP—perhaps a reflection of the dismal state of these resources. GDP data further show the growing importance of the services sector, although the manufacturing sector contributes up to about 25 percent to GDP.

7

electricity and gas, and transportation sectors are markedly male-dominated. Table 5 shows that the proportion of females in the wholesale and retail trade sectors exceed the proportion of males by almost 50 percentage points, and by a little over 10 percent in the community, social and personal services. Furthermore, the average hours worked by males exceed that of females in male-dominated industries and vice-versa (Table 6). However, in the construction industry, both sexes spend as much time at work, although only a very small percentage (2%) of those employed in construction are female. The data further show that there is really very little change in the average hours worked by both sexes since the 1990s. Since females, historically, are the housekeepers and child tenders, any increase in the average hours worked in industry, in addition to household chores and child rearing, implies additional burden to women. It would then be a rational choice of women, who spend more hours working, to have fewer children.

PCSDS, Palawan

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE Mapping Population-Biodiversity Connections in the Philippines

8

Figure 1. Unemployment rate, 2000 (Source: www.pids.gov.ph)

Women participating in livelihood training programs

1995

Major industry group TOTAL Agriculture, fishery and forestry Mining and quarrying Manufacturing Electricity, gas and water Construction Wholesale and retail trade Transportation, storage and communication Financing, insurance, real estate and business services Community, social and personal services Industry not adequately defined or reported

2000

Male

Female

Male

Female

64 75 92 54 82 98 34 95 60 45 58

36 25 7 46 19 2 66 5 40 55 42

63 76 92 53 81 98 36 95 57 44 83

37 24 8 47 19 2 64 5 43 56 17

Source: Philippine Labor Statistics, various years.

Table 6. Average weekly hours by major industry group and by gender Major industry group and gender ALL INDUSTRIES Agriculture, fishery and forestry Mining and quarrying Manufacturing Electricity, gas and water Construction Wholesale and retail trade Transportation, storage and communication Financing, insurance, real estate and business services Community, social and personal services Industry not elsewhere classified

1990

1995

2000

Total

Male

Female

Total

Male

Female

Total

Male

Female

43 37 43 45 46 45 52

44 39 45 47 46 45 51

43 28 33 43 46 45 53

42 35 42 44 45 45 51

42 38 43 46 45 45 49

42 28 25 42 43 45 52

42.7 35 45 45 45 44 51

42.5 37 45 46 45 44 49

43 28 43 43 44 46 53

52

52

46

50

51

45

50

51

44

47

48

45

46

48

44

45

47

43

48

47

48

46

45

46

45

45

46

45

45

48

45

44

47

42

41

41

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE

Table 5. Labor force distribution (%) by industry group and by gender

Source: Philippine Labor Statistics, various years.

Data on poverty incidence shows that the incidence is higher among families with more members (Table 7)—an empirical regularity observed not only in the Philippines but also in other parts of the world. For instance, Table 7 shows that in 2000, families with six members (the average for the Philippines) had an incidence of poverty of about 40.5 percent. This increases by almost 17 percentage points when the family size increases to 9 or more members. From 1985 to 2000, the same trend was recorded, showing that the poverty incidence becomes progressively higher as family size becomes larger. The Bicol Region (Region V) and three regions in Mindanao (ARMM, CARAGA and Central Mindanao or Region XII) had the highest incidence of

poverty (Figure 2). Of these, ARMM had the highest at 54.7 percent. The National Capital Region (or Metro Manila) and Region IV (Southern Tagalog) had the lowest poverty incidence in the range of 5.7 to 17.1 percent in 2000. In 1997, Balisacan (1999), an economist and professor at the University of the Philippines’ School of Economics, calculated the poverty incidence in the major employment sectors and found that agriculture, which had a population share of 40 percent, had the highest incidence, depth and severity of poverty (Table 8). Depth of poverty indicates how far family income is below the poverty line. Mining and construction are ranked second and third. The data thus indicate that families dependent on the natural resource-based sector for employment are among the poorest in the country. The poverty situation worsened at the height of and after the Asian financial crisis in 1997–1998 and

Mapping Population-Biodiversity Connections in the Philippines

2.2.3. Trends in the Poverty Incidence and Implications

9

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE Mapping Population-Biodiversity Connections in the Philippines

10

Table 7. Poverty incidence by family size and urban-rural divide Family size National Urban Rural Family size 1 2 3 4 5 6 7 8 9 or more

1985 44.2 33.6 50.7

1988 40.2 30.1 46.3

19 20 26.6 36.4 42.9 48.8 55.3 59.8 59.9

12.8 18.4 23.2 31.6 38.9 45.9 54 57.2 59

Poverty incidence (%) 1991 1994 39.9 31.8 31.1 24 48.6 47 14.9 19 20.7 25.3 31.8 40.8 47.1 55.3 56.6

9.8 14.3 17.8 23.7 30.4 38.2 45.3 50 52.6

Source: NSO, Family Income and Expenditure Survey, various years.

Figure 2. Poverty incidence of the Philippines, 2000 (Source: www.pids.gov.ph; Family Income and Expenditure Survey, NSO)

1997 31.8 17.9 44.4

2000 33.7 19.9 46.9

9.8 14.3 17.8 23.7 30.4 38.2 45.3 50 52.6

9.8 15.7 18.6 23.8 31.1 40.5 48.7 54.9 57.3

is not surprising, therefore, that the agricultural sector has the highest poverty incidence.

2.2.4. Family Income and Expenditures Approximately 48 percent of the 14.2 million families Table 8. Poverty incidence by employment sector, 1997 in 1997 derived their income from wages, 35 percent from entrepreneurial activities, and 18 percent from Poverty Population Sector share (%) Incidence Depth Severity other sources (Table 9). Wages from non-agricultural employment comprised the highest number (40 percent Agriculture 40.1 42.3 11.5 4.3 out of the 14.2 million families). A high proportion of Mining 0.6 30.0 10.0 4.5 7.0 13.5 2.7 0.9 families that derived income from agricultural wages, Manufacturing 0.7 9.5 2.4 0.9 crop farming and gardening, and livestock and poultry Utility 7.7 23.1 5.0 1.6 Construction raising earned between PhP20,000 to PhP49,999 per Trade 8.8 13.5 2.9 0.9 year. This was equivalent to US$678 to $1,695 using Transport 8.0 13.7 2.8 0.9 1.9 3.0 0.5 0.1 the nominal exchange rate of P29.50 per US dollar in Finance 12.5 9.9 2.2 0.7 1997. This was equivalent to $452–$1,132 in year 2000 Services at the peso-dollar exchange rate of P44.20. What this Notes: Poverty incidence - proportionate number of families whose income is below the poverty threshold. means is that many families (about 26 percent for the Depth of poverty - mean distance below the poverty line as a proportion of that line. three main sources of income mentioned above) in the Severity of poverty - the mean of the squared proportionate poverty agricultural sector earned below the poverty threshold gap. of PhP82,938 or $1,876 per year for a family of six. It Source: Balisacan, 1999.

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE

2000, respectively, reaching up to 55 percent in 2000 as indicated in Figure 2 for certain regions.

Table 9. Distribution of families (percent) by main source of income and income class, 1997 Main source of income

Income class Under 20,000

20,000– 29,999

30,000– 39,999

40,000– 49,999

50,000– 59,999

60,000– 79,999

80,000– 99,999

100,000– 249,999

250,000 and over

3.87 0.82 0.54 0.28 1.70 1.47 1.32 0.05 0.09 0.01 0.22 0.12 0.05

7.98 2.24 1.30 0.94 4.29 3.75 3.16 0.12 0.39 0.07 0.55 0.32 0.10

10.38 3.52 1.60 1.92 5.22 4.37 3.60 0.13 0.59 0.05 0.85 0.48 0.14

10.14 3.94 1.42 2.52 4.73 3.67 2.87 0.11 0.64 0.04 1.06 0.54 0.14

8.20 3.54 0.86 2.68 3.47 2.55 2.05 0.07 0.40 0.03 0.92 0.50 0.10

12.88 6.44 1.11 5.33 4.71 2.87 2.25 0.14 0.46 0.03 1.83 1.06 0.17

9.05 5.08 0.56 4.53 2.69 1.32 1.01 0.05 0.25 0.01 1.37 0.74 0.11

27.66 16.91 0.45 16.45 5.71 1.74 1.36 0.11 0.26 0.00 3.97 2.19 0.39

9.83 5.38 0.05 5.33 2.16 0.27 0.18 0.04 0.04 0.00 1.89 0.98 0.24

1.43

0.04

0.05

0.08

0.10

0.09

0.19

0.13

0.44

0.32

2.27

0.01

0.05

0.12

0.24

0.18

0.35

0.31

0.80

0.21

0.09 0.23

0.00 0.00

0.01 0.01

0.01 0.01

0.01 0.02

0.02 0.02

0.01 0.02

0.01 0.03

0.01 0.04

0.01 0.06

0.29

0.00

0.01

0.01

0.01

0.02

0.03

0.03

0.11

0.08

17.46

1.36

1.45

1.63

1.47

1.19

1.74

1.28

5.05

2.30

Note: Total number of families in 1997 = 14,192,462 Source: Family Income and Expenditures Survey, NSO.

Mapping Population-Biodiversity Connections in the Philippines

PHILIPPINES Wages and salaries Agricultural Non-agricultural Entrepreneurial activities Agricultural Crop farming and gardening Livestock and poultry raising Fishing Forestry and hunting Non-agricultural Wholesale and retail Manufacturing Community, social, recreational and personal services Transportation, storage and communication services Mining and quarrying Construction Entrepreneurial activities N.E.C. Other sources of income

Total number of families 100.00 47.87 7.89 39.98 34.67 22.00 17.80 0.83 3.12 0.24 12.67 6.92 1.45

11

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE Mapping Population-Biodiversity Connections in the Philippines

12

2.3

Demographic Profile

2.3.1. Population Growth Trends and Implications

The 1998 National Demographic and Health Survey revealed the following important findings: (1) the total fertility rate (TFR) of 3.7 births per woman was higher than the total wanted fertility of 2.7, implying that women were not able to attain their desired fertility; (2) more than half of married women (51.4%) said that they do not want any more children; (3) of births five years prior to the survey, 27 percent of women said that they would have preferred to delay childbearing and another 18 percent had not planned the pregnancies; and (4) there is an unmet family planning need—among women not currently practicing contraception, about nine percent would like to space births by two to more years and 11 percent want no more children (Herrin and Pernia, 2003). In 2003, the country’s total fertility rate declined by 0.2 percentage points or from 3.7 to 3.5 children per woman. However, the figure was still one of the highest in Southeast Asia. For instance, it is estimated that from 2000–2005, total fertility rate will average 1.93 children per woman for Thailand, 2.35 for Indonesia, 2.90 for Malaysia, and 2.30 for Vietnam (United Nations, 2003). The 2000 Family Planning Survey shows that contraceptive prevalence is 47 percent, and only about 32 percent of the current contraceptive use is accounted for by modern methods. For the same period the contraceptive prevalence rates of Indonesia and Thailand reached 55 percent and 74 percent, respectively, with modern methods accounting for most of the contraceptive use (Herrin and Pernia, 2003). The 2003 National Demographic and Health

Population change can be examined in terms of changes in population size, structure and distribution. These changes come from changes in births, deaths and net migration. Demographic transition, an empirical regularity observed by demographers, is characterized by a fall in child mortality rate followed by a fall in fertility rate. Accordingly, at the first stage of the transition, population grows because of a decline in mortality, and youth-dependency ratio rises. At the second stage of the transition, fertility falls with mortality settling at a low level. The youth-dependency ratio also starts to decline, but the old-age dependency ratio rises. When this happens, the economically active population also increases, providing a window for the so-called “demographic bonus” that can spur economic growth. Most developing countries, including the Philippines, fall within stage two of the transition. The Philippine population increased by almost four times from about 19.2 million in 1948 to 76.5 million in 2000 (Figure 3). It is currently growing at 2.36 percent. Fertility rate is estimated at 3.6, a figure way above the population replacement rate of 2.1. The population is expected to double in 30 years if the current population growth rate of 2.36 percent continues. Both growth and fertility rates mentioned are considered one of the highest in Southeast Asia. This observation is based on the fact that Thailand and Indonesia, which used to have higher or almost the same population growth rates and fertility rates as the Philippines in the 1960s, have lowered their fertility rates to 1.9 and 2.9 births per woman, respectively, by successfully implementing family planning programs. Figure 3. Philippine population and growth rate, 1800 to 2000 Ambiguous government population policy and commitment to population reduction, especially from the 1980s onwards, are seen as the main hindrance to attaining a more aggressive population program (Orbeta, 2002). Without a clear policy and target for population reduction, there are fears that fertility rate will again rise, while child mortality rate will either continue to decline or level off to a certain rate. If this happens, population will grow at an even faster rate and may bring the country back to stage one of the demographic transition.

Age group Population agedistribution: Less than 15 15–64 65+ Total Median age Youthdependency ratio Youth and elderly dependency ratio

1970

Census

1980

Census

1990

Census

1993 NDS

1995

Census

1998

NDHS

45.7 51.4 2.9 100 16

42 54.6 3.4 100 18

39.5 57.1 3.4 100 19

39.3 56.8 3.9 100 20.1

38.4 58.1 3.5 100 20

38.5 57.3 4.2 100 20.6

88.9

76.9

69.2

69.2

66.1

67.2

94.6

83.2

75.1

76.1

72.2

74.5

2.3.2. Population Growth and Agricultural Expansion Given current population growth and technology, agricultural expansion will likely continue. Table 11 shows that the agricultural land per capita has decreased from the 1960s to present. In 2000, only about 0.156 hectares of agricultural land was available per person. This can be an indication that agricultural technology has increased the productivity of each unit of land. It can also imply that the country can no longer afford additional areas to be converted to agriculture so that the available land per person is getting smaller. What is not apparent in the data though is the intrusion of small-scale agriculture in forest areas, which is not usually accounted for in official statistics. This means that, for the rural poor without much opportunity for employment in the lowland and urban areas, intrusion into open access forests is the only option for survival. Assuming that the 0.156 hectares per person is maintained, without agricultural modernization that will substantially increase production per unit area of land to feed the growing population, there will be an increasing need to expand the area for agriculture by 300 to over 400 hectares per year or an increase in area for agricultural production to about 2.63 percent per year. Table 11. Projected agricultural land requirement from 2010 to 2030 Year

Agricultural land (‘000 has.)

1961 1970 1975 1980 1985 1990 1995 2000 2010 2020 2030

7,713 8,310 9,192 10,625 10,910 11,140 11,180 11,930 15,069 19,028 24,026

Annual change in agricultural land areas % (has./yr) 66 176 287 57 46 8 150 314 396 448

0.86 2.12 3.12 0.54 0.42 0.07 1.34 2.63 2.63 2.63

Population per capita agricultural land (‘000) (ha.) 27,872 0.277 36,684 0.227 42,071 0.218 48,098 0.221 48,098 0.227 60,703 0.184 68,617 0.163 76,499 0.156 96,596 0.156 121,972 0.156 154,015 0.156

Notes: NDS = National Demographic Survey NDHS = National Demographic and Health Survey.

Note: FAO classifies agricultural land to consist of arable land, land planted with permanent crops, and permanent pasture.

Source: National Demographic and Health Survey, 1998.

Sources: FAO for agricultural land and NSO for population data.

Mapping Population-Biodiversity Connections in the Philippines

Table 10. Median age, dependency ratio and distribution of population by broad age-group

effective and more aggressive fertility rate reduction, therefore, the youth-dependency ratio will continue to be high—increasing the burden of the working population to provide for a large population of young people, as well as limiting the capacity of the government to provide basic services.

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE

Survey showed a slight increase in the contraceptive prevalence rate to 48.9 percent, with 33 percent using modern contraceptive methods. Notwithstanding, with the increase in contraceptive use, the figures are still way below those of Thailand and Indonesia. The reason for the country’s low contraceptive use is the often cited negative “side effects” and “health concerns” regarding modern contraceptive methods. It thus appears that lack of information and access to family planning services are holding back contraceptive use. This situation appears to be a supply-side problem—a failure of public policy (Herrin and Pernia, 2003). In terms of structure, the continued high fertility rate resulted in a population composed largely of young people. As expected, therefore, the youthdependency ratio was 60.4 percent in 2000—meaning that 604 persons below 15 years old were being supported by 1000 persons of working age (15–64 years old) (Table 10). In 1970, at 94.5 percent or 945 per 1000, the dependency ratio (youth and elderly) was almost one child or elderly to every person of working age. This has been declining, except for an upsurge in 1998. In 2000, the dependency ratio, although still considered high, has gone down to about 66.9 percent or 669 young and elderly persons for every 1000 persons of working age—a decline of about 7.6 percentage points in two years from 1998 and about 27.7 percentage points in three decades. Note also that in a few years, the young will reach reproductive age, thus, increasing the population momentum. Without

13

Migration in the 1950s and early 1960s was characterized by population movement toward the frontiers and associated with agriculturally-based motivations. From the 1970s onward, the trend was replaced by more complex migration streams, e.g., toward the metropolitan areas, principally to Metro Manila and neighboring Central Luzon (Region III) and Southern Tagalog (Region IV), which was dominated by young single women (Orbeta and Pernia, 1999). Current data on net migration rates reveal large population movements toward Central Luzon and Southern Tagalog, while that toward Metro Manila has declined (Table 12). This is a phenomenon of reversed movements from crowded Metro Manila to suburban and peripheral areas of the metropolis—a pattern of temporary circular migration between the metropolitan core and its periphery, and is expected to continue in the future. Among the 16 regions in the Philippines, the Bicol Region (Region V), Samar-Leyte provinces (Region VIII), and Central Visayas (Region VII) have the highest net out-migration rates of 3.5 percent to 7 percent (Table 12). The Bicol Region, incidentally, also has one of the highest poverty incidence of 47 percent in 1997 and 49 percent in 2000 (refer back to Figure 2). Regions VII and VIII also have high poverty

rates in 2000 (32 percent and 38 percent, respectively). Interestingly, the Autonomous Region of Muslim Mindanao (ARMM) and the Cordillera Administrative Region (CAR), which are areas of more intense armed conflicts, have negative net migration rates (or net outmigration) for females, but positive rates for males. This implies that women, and perhaps also children, generally move out of these regions to avoid being caught in the armed conflict. Note also that many provinces in Region IV still have a relatively large forest cover and are experiencing heavy influx of migrants like Aurora, Palawan, Quirino and Rizal (Table 13). Unless these provinces are able to provide for the large number of migrants with jobs and other basic services, their forest resources may be in danger of being lost to exploitation.

PCSDS, Palawan

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE

2.3.3. Migration and Urbanization

Mapping Population-Biodiversity Connections in the Philippines

Women and children migrating into upland areas

14

Table 12. Net migration rates by region, 2000 Region Philippines Region I Region II Region III Region IV Region V Region VI Region VII Region VIII Region IX Region X Region XI Region XII ARMM CAR CARAGA Metro Manila

Total 0.0045 -0.0133 0.0160 0.0199 0.0627 -0.0701 -0.0038 -0.0355 -0.0533 -0.0091 -0.0021 0.0335 0.0198 -0.0040 -0.0010 0.0497 0.0291

Net migration rate Male 0.0061 -0.0056 0.0053 0.0091 0.0464 -0.0323 -0.0005 -0.0140 -0.0219 -0.0028 0.0013 0.0166 0.0092 0.0074 0.0008 0.0241 0.0069

Source: Department of the Interior and Local Government, Local Government Profiles, 2000.

Female -0.0016 -0.0077 0.0107 0.0107 0.0162 -0.0379 -0.0033 -0.0215 -0.0314 -0.0063 -0.0034 0.0169 0.0107 -0.0114 -0.0018 0.0256 0.0222

Table 13. Net migration rate and percent forest cover in Region IV, 2000 Province Region IV: Average Aurora Batangas Cavite Laguna Marinduque Occidental Mindoro Oriental Mindoro Palawan Quezon Rizal Romblon

Percent forest cover

All

Male

Female

47.47

0.0645

0.0485

0.0160

79.82 22.03 29.95 28.77 36.42

0.3637 -0.0043 0.1077 0.1408 -0.0807

0.3418 -0.0012 0.0514 0.0680 -0.0340

0.0219 -0.0031 0.0563 0.0729 -0.0468

59.98

-0.0100

-0.0016

-0.0084

65.98

-0.0202

-0.0067

-0.0135

63.04 43.20 50.47 42.47

0.0804 -0.0252 0.2043 -0.0464

0.0451 -0.0100 0.0991 -0.0182

0.0353 -0.0152 0.1052 -0.0282

Net migration rate

Sources: National Statistics Office and Department of Environment and Natural Resources

Investment in human capital is critical in increasing the country’s productive capacity and ability to provide an improved standard of living. Human capital investments are in the form of education and health. Indicators on the performance of basic education include literacy (simple and functional) and school participation outcomes (cohort survival). Functional literacy—the ability to read, write and compute—rose from 75 percent in 1989 to 84 percent in 1994 (Table 14). There is no significant gap in the literacy rates between men and women, with women’s literacy rate tending to be slightly higher than men’s except in the Autonomous Region of Muslim Mindanao (ARMM). As expected, the literacy rates in urban areas are higher than in rural areas. Primary and secondary school participation stood at 96 percent and 72 percent respectively in school year 2000/01. However, the cohort survival rate remained low at 67 percent for primary school and 73 percent for secondary schools, which had barely risen in the last 20 years (NSCB 2001). Regional data in 1999 shows (Figure 4) that Region VIII (Eastern Visayas), Region VI (Western Visayas), Region IX (Western Mindanao), and ARMM have the lowest secondary school cohort survival rates.

1989 Total Male Female Philippines 75.4 74.5 76.2 Urban Rural NCR 90.6 91.5 89.9 CAR 73.7 73 74.3 75.1 74.8 75.3 Region 1 Region 2 72.0 70.9 73.2 84.1 84.4 83.8 Region 3 79.8 79.3 80.4 Region 4 Region 5 68.8 66.2 71.3 71.3 68.7 73.8 Region 6 70.6 69.4 71.9 Region 7 65.5 63.4 67.8 Region 8 57.7 57.3 58.1 Region 9 76.5 74.1 78.8 Region 10 74.1 73 75.2 Region 11 63.1 65.1 61.1 Region 12 ARMM Region

Total 83.8 88.4 79.1 92.4 78.6 86.4 86.6 87.3 88.0 82.6 80.9 80.9 79.7 75.4 83.4 79.4 77.4 61.2

1994 Male 81.7 86.9 76.8 91.8 76.8 85.6 85.6 86.1 86.3 81.3 77.3 78.5 75.7 72.6 79.5 75.6 74.2 63.2

Table 15. Infant mortality rate, by region Female 85.9 89.8 81.7 93.0 80.5 87.3 86.6 88.5 89.8 84.5 84.8 83.2 84.2 78.1 87.4 83.2 80.7 59.1

Sources: National Statistics Office and Department of Education, Culture and Sports.

Region Philippines

NCR CAR Region 1 Region 2 Region 3 Region 4 Region 5 Region 6 Region 7 Region 8 Region 9 Region 10 Region 11 Region 12 ARMM

1980 59 48 58 66 43 60 67 60 51 77 56 67 51 80

1990 57 46 63 56 62 45 53 64 61 55 76 64 57 56 56 74

1994 50 35 55 48 55 41 47 59 56 49 67 60 54 53 54

1998 36 41.5 37.1 28.7 35.3 31.4 26 23.6 60.8 44.6 41 40.9 48.4 55.1 53.2 55.1

Notes: 1. Infant mortality rate refers to infant deaths per 1000 live births. 2. Years with no entry have no available data. Source: National Statistical Coordination Board

Mapping Population-Biodiversity Connections in the Philippines

Table 14. Functional literacy rates (%) of population 10–64 years old, by region and urban–rural divide

In terms of health, infant mortality in the Philippines is declining but the rate of decline has slowed down since the 1980s (Table 15). As a consequence, the current rate remains relatively higher than neighboring countries in Southeast Asia (Herrin and Pernia, 2003). In 1980, the country’s infant mortality rate was at 59 deaths per 1,000 live births. This slightly decreased to 57 in 1990, and finally to 36 in 1998. According to the United Nations Children’s Fund or UNICEF (1998), the infant mortality rate in Thailand was 26 deaths per 1,000 live births—a figure which was much lower than in the Philippines. Based on the National Nutrition Surveys conducted by the Food and Nutrition Research Institute (FNRI), child malnutrition in the country remains high. The prevalence of underweight children aged 0–6 years old was 8.4 percent in 1993 while the prevalence of stunted growth was 5.5 percent. The 6th National Nutrition Survey, however, found marked improvement in the overall nutrition situation, specifically in the reduction in the percentage of underweight and stunted children aged 0–5 and 6–10 years. The 2003 survey found that 26.7 percent of children aged 0–5 years were underweight, while 30.4 percent had stunted growth (Barba, 2003). Micronutrient deficiencies, particularly of iron and iodine, also remain high among children, and among pregnant and lactating mothers. In fact, from 1993 to 1998, the rate of micronutrient deficiency rose

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE

2.3.4. Health and Education

15

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE Mapping Population-Biodiversity Connections in the Philippines

16

from 49 percent to 57 percent among infants 6–12 months old, from 44 percent to 51 percent among pregnant women, and from 43 percent to 48 percent among lactating women (Madriaga et. al., 1999). In 2003, among pregnant and lactating women, anemia remained a public problem with 43.9 percent and 42.2 percent prevalence, respectively (Barba, 2003). Studies highlighting the impact of nutrition and health on productivity conclude that improved nutrition and health of children positively affect early abilities, subsequent school performance, and future economic productivity. Furthermore, adequate nutrition at early ages influences the productivity of investments in education. Micro-level studies in the Philippines reveal that high fertility has adverse effects on child survival, nutrition and educational outcomes. At the household

level, assuming that the economic resources and social characteristics of parents are the same, the number of siblings in a family is negatively related to the educational attainment of children aged 7–12 years— implying that the more children in a household, the lower is the educational attainment of these children (Paqueo, 1985). At the aggregate level, high fertility and rapid population growth adversely affect the capacity of the economy to mobilize resources for human capital investment, particularly in education, since high fertility translates into rapid growth in the number of school-age children (Orbeta and Pernia, 1999). The rapid growth in school-age children puts a strain on the capacity of the basic education sector to accommodate the additional students, let alone improve the quality of the education provided.

Figure 4. Secondary schools cohort survival rate by region, 1999 (Source: www.pids.gov.ph)

Government Population and Environment Policies

Mapping Population-Biodiversity Connections in the Philippines

PCSDS, Palawan

Several international events in the 1990s triggered agency in the implementation of the family planning a rethinking of population and environment issues, program. The Commission on Population, which used namely: the UN Conference on Environment and to be under the Office of the President, was placed Development (UNCED) in 1992; the International under the DOH and shifted its focus on population Conference on Population and Development in 1994; and development activities. the Fourth Conference on Women in 1995; and the The 1989–1993 Five-year Directional Plan World Summit on Social Development in 1995. The focused on two major areas, namely: (1) integrated Philippines came out with the Philippine Agenda 21 population and development; and (2) family planning as a commitment to UNCED. International financial and responsible parenthood. The latter was designed support for environmental initiatives also became to reduce total fertility from 4.31 children per woman available. in 1989 to 3.74 in 1993. This was to be achieved Within the last decade, the Philippines passed by expanding the practice of family planning and several important laws to improve environmental responsible parenthood among married couples and natural resource management. For biodiversity from 48.6 percent in 1989 to 55.4 percent in 1993. conservation, the major accomplishment was the The program during the Aquino administration was passage of Republic Act 7586 in 1992 providing for essentially a health program with demographic effect the establishment and (World Bank, 1991). management of National Fertility reduction was Integrated Protected no longer a primary Areas System or NIPAS. goal. With respect to There were population management, also significant changes the Philippines was during the Ramos among the first country administration (1992– in Southeast Asia to 1998), namely: (1) the launch a population unequivocal support program in 1970. Hence, to fertility regulation; during the 1970s, there (2) passage of the Family planning programs lead to better maternal- and child-health care was a vigorous and active Local Government promotion of family planning methods through the Code, which devolved frontline services such as Commission on Population (POPCOM). There was a health and family planning to local government clinic-based program ran by the Department of Health units; and (3) the conduct of several international and a community-based program by POPCOM. conferences that provided venues for rethinking Modern family planning methods, thus, became more population and environment issues, e.g., the UN accessible even to the villages. Conference on Environment and Development in Since the mid-1980s, however, the population 1992, the International Conference on Population program received weak and equivocal support from the and Development in 1994, the Fourth Conference political leadership, which is largely attributed to a more on Women in 1995, and the World Summit on Social conservative stand of the government as influenced by Development in 1995. The Ramos administration’s the continued opposition of the Catholic Church to population program focused on the close relationship modern family planning methods. The family planning between population, resources and the environment, program suffered from much diluted commitments and, subsequently, adopted the “populationduring the Aquino administration (1986–1992) (Orbeta, development” framework. The family planning program 2002). The population policy statement of 1987 shifted again refocused itself in moderating population growth emphasis from fertility regulation to family welfare, and as well as in improving maternal and child health. the Department of Health (DOH) became the lead The plan sets the following demographic targets: (1)

PHILIPPINE BIODIVERSITY SOCIOECONOMIC AND DEMOGRAPHIC PROFILE

2.4

17

Thailand and Vietnam (Table 16). The figures in Table 16 clearly show that the effect of the shift in the population policy during the Aquino administration (1986–1992) resulted in the decline in contraceptive prevalence rate from 44 percent in 1985 to 40 percent in 1990–1995. The relatively more aggressive programs of the Ramos and Estrada administrations also resulted in the increase in the prevalence rate to 46 percent. The figures clearly indicate that government support and a firm commitment to reducing fertility rates makes family planning programs successful.

Table 16. Comparison of contraceptive prevalence rates in Southeast Asia Country Indonesia Malaysia Philippines Thailand Vietnam

1970

1977

1985

1990– 1995

1995– 2000

1990– 2000

na 7 8 na na

19 34 22 32 na

48 51 44 65 58

55 48 40 74 65

57 na 46 72 75

57 55 46 72 75

Source: Orbeta, 2002.

CI–DC

PHILIPPINE BIODIVERSITY, SOCIOECONOMIC AND DEMOGRAPHIC PROFILE Mapping Population-Biodiversity Connections in the Philippines

18

reduction in population growth rate from 2.46 percent in 1993 to 2.28 percent in 1998; (2) reduction in crude birth rate for 30.67 births per 1000 population in 1993 to 28.54 in 1998; (3) reduction in crude death rate from 6.86 deaths per 1000 population in 1993 to 6.32 in 1998; (4) reduction in infant mortality rate from 55.21 deaths per 1000 live births in 1993 to 49.39 in 1998; (5) reduction in total fertility rate from 3.85 children per woman in 1993 to 3.57 in 1998; and (6) increase in contraceptive prevalence from 42.5 percent in 1993 to 51.6 percent in 1998. The Estrada administration (1998–2000) inherited the Philippine Population Management Program (PPMP) Directional Plan for 1998–2001. After a period of ambivalent support for family planning during the early days of the administration, it later came out with a stronger stand and support for the program. The Philippine Commission on Population or POPCOM completed the 2000– 2004 Philippine Population Management Program Directional Plan, which expanded the populationresources-environment (PRE) concept to a Population and Sustainable Development Framework to explicitly emphasize human resources (or people) in addition to environment. One of the objectives of the Plan was to help couples achieve their desired family size within the context of responsible parenthood. Some of the Plan’s targets were: (1) reduction of fertility from 2.7 to 2.1 through a more aggressive family planning program; (2) increase in the contraceptive prevalence rate from 47 percent to 60 percent; (3) increase in the proportion of modern conceptive use from 28.2 percent to 32.5 percent; (4) reduction in teenage pregnancies; and (5) reduction of health care expenditures for reproductive health or family planning (direct government subsidy) from 70 percent to 30 percent. The Arroyo administration (2001–2004), however, shifted policy and views family planning primarily as a health intervention, as articulated in the Directional Plan (www.popcom.gov.ph). The target fertility rate was increased to 2.7 percent in 2004, reversing the “accelerated program” that the Department of Health adopted during the Estrada administration. As result of the shifting policies between administrations, the contraceptive prevalence rate remained at 46 percent in 2000—the lowest among the Southeast Asian countries like Indonesia, Malaysia,

Tagbanua boy with paddlers for fishing

Review of Population-Environment Theories, Frameworks and Evidences

3

“Although some species may be now increasing, more or less rapidly, in numbers, all cannot do so, for the world would not hold them.” Charles Darwin3, On the Origin of Species

Population and Environment Debate Revisited

3.1

Is there a population problem? Both traditionalists and revisionists accept that population growth is a challenge especially in the developing world. The debate, however, revolves on whether the gravity of the population growth is enough to be called a threat. The various perspectives on this debate can be categorized into the traditionalists’ (Malthusian and neo-Malthusian) and the revisionists’ (Boserup, Davis and other economic demographers) perspectives.

that affected death rate, including misery, disease, famine and war. Thus, Malthus felt that there was no way to escape the positive checks (Peters and Larkin, 1997). Similarly, neo-Malthusians, like Paul Ehrlich and Garrett Hardin (a noted ecologist and professor emeritus of biology at the University of California in Santa Barbara who wrote the best known essay, Tragedy of the Commons, in 1968), believed that either humans must voluntarily control their numbers beyond moral restraint (family planning, contraceptives of every sort,

3.1.1. The Malthusian and Neo-Malthusian Perspective

preventive. Preventive checks were those that affected the birth rate, and in Malthus’ view, moral restraint was The framing of the population-environment the only acceptable one. Positive checks were those relationships led to two opposing views. On one

3

Charles Darwin first published his book, On the Origin of the Species, in 1859. It is currently available in audio production (Eccles, 2003).

4

Carrying capacity is the amount of activity or population that can be supported in a sustainable manner in a given land. It is a term heard frequently in ecological circles and is central to the whole ecological debate. Its full significance, however, cannot be conveyed by a simplistic definition. To explain, on a physically finite planet, the reserves of materials and energy are also finite. The laws of thermodynamics show that for any process to occur, a natural driving force based on low entropy matter or energy is required. This applies to all forms of life and particularly to human economic activities. The limit to human activity is a function of the number of people, the amount of activity per person and the specific resources used or resource inefficiency of each activity (ECO, 1999).

Mapping Population-Biodiversity Connections in the Philippines

and even abortion) or nature will definitely control them through mechanisms that are very unpleasant for humans. War, famine and disease are considered minor In 1798, Thomas Malthus, a noted 18th century checks on the growth of human population and are economist, wrote An Essay on the Principle of Population usually indicators of overpopulation and going beyond and 170 years later Paul Ehrlich, a professor of the carrying capacity4 of the land. The consequences population studies at Stanford University and a of overpopulation can be gleaned from events in recent recognized modern day version of Thomas Malthus, decades: either one group kills off another, as events in wrote The Population Bomb. Malthus’ work challenged the Bosnia, Middle East and Uganda have shown; diseases, pro-natalist (more births means more workers, hence, like AIDS, cholera and malaria, kill off a portion of more aggregate wealth) sentiment during his time. the population; or massive human deaths occur due His theory is based on the assumption that population to insufficient food, such as what happened during would always tend to increase at a geometric rate, whereas food the famines in Central Africa and Afghanistan (Otten, production can only be increased at an arithmetic rate. Hence, undated). population would always press against the means of subsistence, unless prevented by some very powerful 3.1.2. Esther Boserup: Population Growth and Technological Change and obvious checks, which are either positive or

19

L. Co

REVIEW OF POPULATION-ENVIRONMENT THEORIES, FRAMEWORKS AND EVIDENCES

hand, the “limits to growth” perspective by the Club growth, placing it along several other factors of equal of Rome,5 a non-profit, non-governmental think or greater importance. Revisionism is distinguished tank, considers population growth as fundamentally by its moderate stand on the impact of population detrimental to the global system (Meadows et. al., growth, although it concludes that developing countries 1972, 1992; Brown et. al., 1999). On the other hand, can benefit from reduction in population growth, the opposite perspective is that population growth emphasizing the intermediate to long term, direct and provides a positive impetus for technological progress indirect impacts, and feedback mechanisms within the (Boserup, 1965, 1976, 1981; Simon, 1981, 1996). Esther social, economic and political systems (Kelley, 2001). Boserup, a Danish economist, argued that population growth and critical population densities can stimulate 3.1.4. Multiphasic Response Theory agricultural innovation and change. Rather than death rate as check, Boserup sees the intensification The multiphasic response theory of Kingsley Davis, of agricultural systems and technological innovation a senior research fellow at the Hoover Institution, to cope with population growth, e.g., working more Stanford University, hours and adopting who devoted his more intensive ways career to the study of growing crops of demography ranging from forestand sociological fallow to bush-fallow, phenomena, states through shortthat “…the process fallow and annual of demographic cropping, to multiple change and response cropping—the most is not only continuous intensive of the five but also reflexive and systems. Hence, behavioral—reflexive there is a primary in a sense that a change trade-off between Involving the community in decision-making and development planning in one component leisure and work. is eventually altered

Mapping Population-Biodiversity Connections in the Philippines

3.1.3. Population Revisionism in the 1980s

20

Since the time of Malthus, the “traditionalist” or “alarmist” view of the adverse consequences of rapid population growth dominated the popular, and to some extent, the scientific discourse, although swings in thought occurred from time to time as evidenced by the theories of Boserup and Davis. In the 1980s, however, a “population revisionist” view emerged as a notable retreat from the traditionalist view of the 1960s and 1970s that puts population growth as the greatest deterrent to economic growth. The revisionists downgraded the importance of population

5

by the change it has induced in other components; behavioral in a sense that the process involves human decisions in the pursuit of goals with varying means and conditions.” Davis cited the case of Japan’s responses to modernization—i.e., abortion, increase of contraception, sterilization, emigration and postponement of marriage. The stimulus to this multiphasic response was not population pressure and the prevalence or threat of dire poverty, but rather the rising prosperity: viewed from the standpoint of the individual’s desire to get ahead and appear respectable, forcing a modification of his or her reproductive behavior (Peters and Larkin, 1997).

The Club of Rome is a non-profit, non-governmental think tank composed of a group of scientists, economists, businessmen, international high civil servants, heads of State and former heads of State who pool their different experiences from a wide range of backgrounds to come to a deeper understanding of the world problematique (www.clubofrome.org).

Natural and social scientists introduced a variety of models to study the population-environment links, including some of the decomposition models. Understanding the way population and environment interact is important in formulating policies and designing strategies.

other factors such as education of women, availability and adoption of contraception, and child health (UNFPA, 2001). And just as population affects the environment, the environment affects the population as well. 3.2.2. Mediating Factors Framework

3.2.1. Multiplicative Models

demographer of the University of North Carolina, elaborated a mediating framework for understanding the impacts of population growth on land use and agricultural production in rural areas in Latin America. Bilsborrow’s framework considers how socioeconomic conditions such as poverty, government policies and market demands determine whether population growth leads to technological change in agriculture, soil erosion or out-migration. There are several transformations of the mediating factor framework, which emphasize mediating factors either individually or in combination, or collapse them into a larger concept of “development.” Schmink8 (1994), a professor at the University of Florida, for instance, emphasizes social organizations and culture as filters that help focus the population-environment link and considers environmental change as a social process as well. Social, cultural and institutional factors can also be collapsed into a “development” concept that stresses

6

John P. Holdren is Teresa and John Heinz Professor of Environmental Policy and Director of the Program on Science, Technology, and Public Policy at the Kennedy School of Government, as well as Professor of Environmental Science and Public Policy in the Department of Earth and Planetary Sciences at Harvard University (www.amacad.org/events/holdren_bio.htm).

7

Richard Bilsborrow is an economist-demographer and professor at the Carolina Population Center, University of North Carolina. His recent research has focused on the linkages among demographic processes, development, and environment (www.cpc.unc.edu/bios/index. php?person=bilsb).

8

Marianne Schmink is professor of Latin American studies and anthropology, and Director of the Tropical Conservation and Development program at the Center of Latin American Studies, University of Florida. Since 1980, she has worked to build interdisciplinary research and training programs in the Center for Latin Americans Studies, integrating social science and natural science perspectives to address conservation and development issues with a focus on the Amazon region (www.latam.ufl.edu/people/peopcore.html).

Mapping Population-Biodiversity Connections in the Philippines

While there is no consensus on an overarching theory of population-environment linkages, theorists agree that the overall human pressure on the environment is a product of three factors: population size, the level of affluence or per capita consumption or production, and the level of environmentally damaging technology. These three factors determine total resources used and amount of wastes generated per unit consumption or production. The well-known formula was introduced by Paul Ehrlich and John P. Holdren6: I = P x A x T or Impact = Population x Affluence x Technology (United Nations Population Fund or UNFPA, 2001). An alternative multiplicative scheme was later proposed in which the interactive effects between population, consumption and technology are further specified— i.e., distinguishing between ultimate or driving forces and aggravating factors behind environmental impacts (Shaw, 1989a, 1989b, 1989c and 1992). For environmental degradation, Shaw (1989c) notes that consumption and technology are ultimate causes, while population is an aggravating factor that increases the impact on the environment. Although the IPAT assumes independence of each of the PAT components, in reality they interact with each other and their relative importance vary from time to time. There are also other factors that affect each of the PAT components. For example, population change is determined by fertility, mortality and migration, which are in turn affected by a host of

Numerous studies focus on the context in which the population and environment relationship occur. The mediating perspective posits that social, cultural, institutional and political factors shape the association between the two. UNFPA, for instance, follows a simple framework wherein the interaction between population (size, distribution and composition) and environment (land, water and air) is influenced or mediated by the institutional and policy contexts, science and technology, and cultural contexts where they occur (UNFPA, 2001). Bilsborrow7 (1992), an economist-

REVIEW OF POPULATION-ENVIRONMENT THEORIES, FRAMEWORKS AND EVIDENCES

Models and Approaches on the Study of Population-Environment

3.2

21

REVIEW OF POPULATION-ENVIRONMENT THEORIES, FRAMEWORKS AND EVIDENCES Mapping Population-Biodiversity Connections in the Philippines

22

a North-South “dependency” (Jolly, 1991, as cited by Panda, 2004). The dependency perspective suggests that environmental outcomes are products of prevailing models of development. Common international political and economic forces shape both demographic 3.3

and environmental outcomes in developing countries because current development models promote the dependence of the South (developing countries) on the North (developed countries).

Population and Environment Dynamics

The PBCPP’s socioeconomic working group highlighted the role of population factors in the protection of the conservation priority areas (CPAs) (Ong et. al., 2002). In particular, the said working group identified population variables such as relative density and migration trends, and the local economy (specifically in terms of income and poverty) as major determinants of threat to the CPAs. Chu and Yu (2002), economists from the National Taiwan University’s Department of Economics, remarked on the adverse impact of direct population pressure on land use and, consequently, upon biodiversity, particularly in developing countries where fertility rates have not declined as much as mortality rates in the twentieth century: With population pressure mounting, people in these countries have had to make more intensive use of the land, exploiting more natural resources, and trading more extensively with people in other areas. Trees and vegetation have made way for farmland, ranches, roads and human settlement. The continuous expansion of these human activities over the years has brought about biodiversity decline in those areas. Cramer (2002), professor from the University of California at Berkeley, points to “population growth and socioeconomic development” as “general, ultimate causes” of air pollution. Barton and associates (1997), researchers from the IUCN Social Policy Group, further note how “problems of…environmental degradation constitute the ‘true Achilles’ heel’ of the ‘development’ enterprise” in large part due to the untenable assumption “that natural resources are practically unlimited and that population size will spontaneously stabilize with increased income and improved education and health.” Ness (1997), professor emeritus at the University of Michigan’s Department of Sociology, airs a similar concern on the far-reaching effects of population on the environment:

Population growth and migration in many places are associated with habitat destruction and loss of biodiversity; extensive deforestation produces soil erosion, loss of water storage leading to massive flooding, and increases the potential for destructive global climate changes. Population growth can overwhelm both natural and man-made water and sewage systems, leading to extensive illness and death. In turn, demographers attribute population growth to the three major forces: fertility, mortality and migration. As such alterations in the population are theoretically measured in the so-called “balancing equation” which summarizes the “inflow-outflow relationship” between levels in human births, deaths and territorial movements, on one hand, and the consequential population change, on the other (Shryock et. al., 1976). To adequately monitor the symbiotic linkage between population and environment, researchers measure patterns, levels and trends in human fertility, mortality and migration as well as other socioeconomic and demographic characteristics of the population. 3.3.1. Population Growth and Agricultural Production A high population growth rate implies higher pressure on natural resources to produce food and supply other basic needs. It is generally regarded as the single most important force that drives increases in agricultural demand. Ultimately, high population growths will be a burden to the capacity of the natural environment to support a given population’s achievement of a certain quality of life (Cohen, 1995). For instance, the number of people that can be supported in a given area is dependent on the food supply and the individual food requirements. With high population growth, the direction of food production will be to

PCSDS, Palawan

REVIEW OF POPULATION-ENVIRONMENT THEORIES, FRAMEWORKS AND EVIDENCES Mapping Population-Biodiversity Connections in the Philippines

intensify production on existing agricultural land or and small sizes of land holdings resulted in forest open forestlands for agricultural production or both. losses, land degradation, decreasing productivity of It is, therefore, expected that population growth will be these lands, and eventually in a deteriorating condition positively associated to the rate of deforestation. among rural people (Kansakar, 1989). Recent expert assessments are cautiously The same trend is observed in the dry tropical optimistic about the ability of global food production regions in India. High population growth increased the to keep up with demand in the foreseeable future, demand for land. Because of this, common property predicated on the assumption that population growth resources were significantly physically degraded by rates will continue to decline. However, the experts over-exploitation during the periods between 1950–52 predict that food insecurity, associated with poverty, and 1982–84. A number of plants and tree species is expected to persist among millions of people, and disappeared altogether, while in some areas, the ability environmental side effects of destructive farming to graze cattle was no longer feasible because of technologies are expected to become major threats to depleted forage and changed vegetation (Jodha, 1990). the sustainability of food production (United Nations, 2001). Moreover, the expansion of croplands and 3.3.2. Relationship between Fertility and Natural Resource Use harvesting of wood for fuel, in addition to commercial logging, are important contributory factors to deforestation in many areas. Cruz and Cruz (1990), Evidence for a relationship between natural resources an environmental economist and a senior social and fertility emerges in a number of studies and in development specialist at many settings around the the World Bank, concluded world. So far, however, that, while commercial this relationship is logging was expected largely unexamined. It to significantly decline is important not only to after the imposition of describe the effects of the total log ban in many fertility, migration and other areas, forest conversion demographic factors on to agriculture will likely natural resources, but also continue to be significant the feedback of affected Male and female labor force in agriculture as upland population resources on shaping continues to grow at demographic processes. 2.5 percent annually. They calculated, using 1980 Geist and Lambin (2001), professors of the University population census data, that a third of the total of Louvain, Belgium and the University of Düsseldorf, population resides in the uplands and almost half of the Germany, respectively, provide empirical evidence on upland population occupies forestlands. In Northeast the effects on natural resources, but scant empirical Thailand, Panayotou and Sungsuwan (1989), resource evidence on the feedback. Ultimately, according to economist and graduate student at Harvard University, theory, natural resources affect fertility by changing empirically demonstrated that population density is either the supply of or the demand for children, based the single most important cause of deforestation. on a relationship of resource dependence (Easterlin The analysis showed that in Northeast Thailand, a 10 and McCrimmins, 1985). percent increase in population density resulted in a 15 There are studies that attempt to correlate and percent decrease in forest cover. document farm size and family size. In 1984, Stokes In Nepal, an International Union for the and Schutjer (professors of agricultural economics at Conservation of Nature (IUCN) study in 1989 showed the Pennsylvania State University) postulated that a that large family sizes (hence, higher fertility rates) larger farm size creates a demand for children as labor are traditionally desired to provide more labor for to put and keep the land in production. Alternatively, cultivation, animal tending, and fodder and fuelwood land tenure can counteract the relationship between collection. However, the increasing population pressure farm size and family size. Under the land-security

23

income opportunities are low in the migrants’ places of origin. Since in-migration increases the population density of areas of destination, it may affect the environment. Fertility rates and family sizes may also be relatively higher in these areas because of the expectation that larger natural resource endowments and land holding can provide better food security (Boserup, 1990). The extent to which land is cleared for agriculture in destination areas depends on population density, as suggested by the theories of Malthus, Boserup and others. Is it true then that the poor are involved in causing deforestation and environmental degradation? Barbier (1997), professor of economics at the University of Wyoming, explains that indeed they are, to the extent that the poor tend to live on “low potential” or marginal lands, which have low productivity and are more prone to degradation. When that “low 3.3.3. Migration and the Rural Upland Environment potential” is exhausted or can no longer adequately provide for the family’s needs, the poor are forced to Population change, particularly via migration, has an migrate to other marginal areas. Thus, the process of “in-migration– important impact on deforestation–land the rural environment. degradation” begins. One should bear in In the Philippines, mind that migration, for example, with the as a process through lowland increasingly which population in control of large interacts with the landholdings devoted rural environment, is to cash crops like important, because sugar cane, cattle it is through outgrazing, industrial migration into either estates and human urban or rural areas settlements, the that a population rural poor can only may respond to a Settlement in the foothills of degraded mountain areas find new land on deteriorating rural environment, and via in-migration that the population steep adjoining slopes. After forests are cleared for exerts pressure on the fragile rural environment. subsistence agriculture, flooding and soil erosion Rural-to-urban migration is a major concern in urban increase (Cruz, 1997). The situation is aggravated by growth and in the deteriorating conditions of the the failure of the Philippine agrarian reform program urban environment. However, in countries like the to provide better tenurial rights among the poor. One Philippines where most of the population live in rural question, however, remains: how much does the poor areas, rural-to-rural and lowland-to-upland movements contribute to deforestation and land degradation visare more common. Many analyses on the possible à-vis large development activities like commercial impact of migration focused on deforestation, because logging, mining, pasture, large landholding and agrodeforestation is associated as well with significant loss industrial estates? Deforestation, however, is not only caused of biodiversity, soil loss and global warming. Large tracts of open forests attract migrants, by spontaneous migration. Government-sponsored especially when the incidence of poverty is high and migration in Indonesia, out of Java to the outer islands, PCSDS, Palawan

REVIEW OF POPULATION-ENVIRONMENT THEORIES, FRAMEWORKS AND EVIDENCES Mapping Population-Biodiversity Connections in the Philippines

24

hypothesis, greater land security creates economic security that lowers the need for large number of children. The study in Petén, Guatemala was not able to find a significant relationship between farm size or security of land tenure and family size (Sutherland et. al., 2004). The same study found that the frontier hypothesis, i.e., greater land availability is associated with higher fertility; seem to hold in Petén at the aggregate level. At the micro level, the perception of greater land availability did not correlate well with higher fertility rates because the actual land available for clearing is much less than the perceived availability of vast tracks of uncleared land, as most of these were either within national parks and protected areas, or consolidated by large absentee landowners.

The environment-poverty link is understood according to how it affects people’s opportunity and capacity, security and empowerment (World Bank, 2001). Opportunity declines when poor people, who depend on the natural resources for livelihood, can no longer support themselves due to degraded resources and at the same time, lack alternative livelihood opportunities. Capacity is impaired when contaminated water and soil, pollution and other environment-related diseases damage the poor people’s health. The security of the poor is threatened by the destruction and deterioration of ecosystems, making 3.3.4. Poverty and Environmental Stress them more vulnerable to natural disasters since their Majority of the rural poor are increasingly clustered capacity and ability to predict, prevent and respond to on low-potential lands located in fragile and highly adverse impacts are limited. Empowerment is related vulnerable areas—on steep slopes and in forests. Land to the ability of the poor to have access to and control conversion to industrial, agro-industrial and settlement over local natural resources. In many cases, the poor development, demographic pressures, intergeneration are at a disadvantage and have the weakest voice in the land fragmentation, privatization of common lands, management of local resources (World Bank, 2001). and the consolidation of commercial agriculture with reduced labor requirement are factors that squeezed the rural poor out of high-potential land, forcing them to exploit marginal resources. In the Philippines, poverty and deforestation are linked—the higher the poverty incidence, the higher is the deforestation rate. In 2001, using 1998 data, Pabuayon, an economist from the University of the Philippines at Los Baños, was able to show that regions in the country with the highest rates of deforestation were also regions with high poverty

3.3.5. Health and Environmental Degradation

Mapping Population-Biodiversity Connections in the Philippines

Health concerns underlie much of the discussion about the consequence of environmental degradation. The World Health Organization (WHO) categorizes environmental threats to health as “modern hazards,” which are associated with development that occurs without adequate environmental-health safeguards, and “traditional hazards,” which are generally associated with lack of development. Pollution, poor control Figure 5. Poverty incidence in high and low of hazardous wastes, chemical and radiation hazards, deforestation regions n the Philippines deforestation and other problems related to ecological and climate change and stratospheric depletion are some of the modern hazards (WHO, 1997). Emerging and re-emerging infectious diseases are also associated with modern hazards. Traditional environmental health hazards include poor disease vector control, poor sanitation, contamination of food and drinking water, indoor and outdoor pollution from fires (including use Source: NSCB, 2002 Philippine Statistical Yearbook; DENR, Forestry of fuel wood for cooking and heating), and poor waste disposal. incidence. Conversely, those regions with the lowest While improvements in sanitation, water supply deforestation rates have lower poverty incidence. Using and housing, combined with modern medicine, have 2000 data, Figure 5 shows the same trend. resulted in the declining significance of many traditional The relationship between environment health diseases, the level of mortality and morbidity is and poverty is complex and varies across local often much higher than what is directly attributed to socioeconomic and large macroeconomic contexts these diseases. Many food-borne diseases, for instance, (United Nations Population Fund or UNFPA, 2001). can lead to serious and chronic consequences affecting

REVIEW OF POPULATION-ENVIRONMENT THEORIES, FRAMEWORKS AND EVIDENCES

in addition to subsequent spontaneous migration two to three times more, accounts for 80 percent of deforestation in the destination islands. In Brazil, government-sponsored migration to frontier areas in the 1960s, intended to ease population pressure in the urban areas, also resulted in rapid deforestation. Deforestation was further aggravated by the government incentives given to large companies in the 1970s for the development of the frontier areas (Geores and Bilsborrow, 1991).

25

REVIEW OF POPULATION-ENVIRONMENT THEORIES, FRAMEWORKS AND EVIDENCES Mapping Population-Biodiversity Connections in the Philippines

26

cardiovascular, renal or immune system, e.g., rheumatic heart disease and, indirectly, respiratory tuberculosis (Bunning et. al., 1997). Rapid population growth and the associated incursions into natural land and water habitats have fostered the growth and spread of pathogens like HIV/AIDS, Ebola and other zoonotic diseases previously confined to certain hosts. There is also increasing prevalence of dengue, malaria and other mosquito-borne arboviruses previously confined to certain domains (UN Department of Economic and Social Affairs or UNDESA, 2001). High fertility and internal migration (rural-to-urban and rural-to-rural) stretch the resources of places of destination, e.g., cities and uplands, to adequately provide for the health and sanitation, as well as housing services. Crowded living conditions, poor sanitation and nutrition, and lack of access to safe drinking water among poor households facilitate the spread of diseases such as tuberculosis and other respiratory and gastrointestinal diseases. 3.3.6. Gender Roles in Biodiversity Management Gender refers to the different social roles that men and women play, and the power relations between them (Population Reference Bureau or PRB, 2001). Men and women play varying and different gender-based roles and responsibilities in their own lives, families, communities, and society as a whole. They also have different access to and control over natural resources, as well as opportunities to participate in decisions over their use of these resources. Ignoring gender distorts one’s understanding of the human impact on the environment. There are several stereotypes that typecast women in certain occupations and social status. For instance, women are typecast as having a “caring nature.” Therefore, they are usually believed to be better nurturers of the environment, caregivers, teachers and social workers. This “caring nature,” however, is not of biological origin, but rather a learned gender-based characteristic. In many upland farming communities, including the Ikalahans in Nueva Vizcaya, women farmers take on the role of planting crops for household consumption, while men most often plant commercial crops (Lasmarias, 1989). Aside from farming responsibilities, women have

additional domestic responsibilities such as childcare, food preparation, water and fuelwood collection, and maintaining family health. Men generally have limited domestic responsibilities. In the Democratic Republic of Congo, men usually plant permanent crops on land where they have secure tenure, while women plant food crops on rented, steep erosion-prone land. As a result, women do not have the incentive to invest in soil conservation efforts since future access to the land is not assured (PRB, 2001). Men and women are rich sources of knowledge on sustainable resource management practices and biodiversity species. In Brazil, a survey done in the Jau Botanical Park revealed that female midwives know of certain species of medicinal plants, while the traditional medicine men knew of others (PRB, 2001). Understanding the roles of men and women plays a crucial role in targeting conservation policies and interventions. Gender-responsive interventions and policies seek to achieve environmental outcomes while explicitly taking into account both men’s and women’s needs, opinions and interests. They are based on social, health and ecological researches that provide a more comprehensive picture of the impact of humans on the environment and, conversely, the impact of environmental change on men and women. 3.3.7. Education and Income Education is critical in shaping people’s perceptions, attitudes and behavior towards population and the environment. Affluence and education are correlated with pro-environment views that may translate into greater willingness to take responsibility for environmental protection (Orians and Skumanich, 1997). Affluence (high income) and income growth, however, raise expectations about what is “required” in a new home—larger and with more appliances and facilities, which, therefore, may result in higher consumption and demand for resources. In Philippine households, on the average, 4.2 percent of expenditures are for education. The higher income groups also tend to spend more in education than the lower income groups (Orbeta, 2002). Likewise, Orbeta (2002), an economist at the Philippine Institute for Development Studies, noted that school attendance rate is higher among high-income households and urban areas than among low-income households and rural areas.

3.3.8. Summary Researches in the Philippines and other countries point to the following important points.

Mapping Population-Biodiversity Connections in the Philippines

(1) High population growth, population density and high fertility rates are found to be significant factors that drive increases in the demand for agricultural land, eventually, resulting in deforestation, land degradation and biodiversity losses. This was demonstrated in studies made in Northeast Thailand, Nepal and India (Panayotou and Sungsuwan, 1989; Kansakar, 1989; and Jodha, 1990). (2) The state of the natural resources affects fertility by changing either the supply of or demand for children, based on a relationship of resource dependence. Children provide additional farm labor to put and keep land in production. Greater land availability, for instance, is found to be associated with higher fertility in Petén, Guatemala.

(3) Migration is a significant source of changes in population and in the deteriorating upland and urban environments. In Indonesia and Brazil, spontaneous and government-sponsored migration resulted in high rates of deforestation in the destination areas (Geores and Bilsborrow, 1991). In the Philippines, the lack of employment opportunities and increasing control of agricultural lands by large landholdings in the lowland areas resulted in massive migration to upland and forested areas. This initiated the process of “inmigration–deforestation–land degradation.” After forests and marginal upland areas are cleared for subsistence farming and logging, flooding and soil erosion followed (Cruz, 1997). (4) Poverty and deforestation are linked. Philippine deforestation and poverty data from 1994 to 2000 showed that deforestation is consistently higher when poverty is high (see Figure 5). (5) High fertility and in-migration stretch the ability of the destination areas to provide adequate resources for food and basic services like health and sanitation. Crowded living conditions, poor sanitation and nutrition, and lack of access to safe drinking water facilitate the spread of diseases such as tuberculosis and other respiratory and gastrointestinal diseases. (6) Men and women are rich sources of knowledge on sustainable resource management practices and on biodiversity species. Understanding the roles played by men and women provides more gender-responsive conservation, development and population policies and programs. (7) Education is critical in shaping people’s perceptions, attitudes and behavior towards population and environment. Higher educational attainment also enhances a person’s ability to participate in the formal labor sector, associated with rise in income, and decline in fertility rate. Rapid population growth, together with a large proportion of the younger age group, results in a rapidly increasing demand for educational facilities. Developing countries, like the Philippines, have difficulty in keeping up with this rise in demand. This difficulty can result in the deferment of the goal of providing universal education along with its potential for lowering fertility.

REVIEW OF POPULATION-ENVIRONMENT THEORIES, FRAMEWORKS AND EVIDENCES

Income and its distribution is a primary determinant of school attendance. On education and labor force participation, the hypothesis is that the higher the educational attainment, the higher is the labor participation rate. Moreover, labor force participation also rises with increasing need for income. For women, entry into the labor force can either be due to the reason that they want to complement their household income when it falls below a certain threshold or to be the primary breadwinner. Education, on the other hand, has an impact on fertility. In general, lower fertility is associated with higher educational levels. This is specially true for women who are at a considerable disadvantage throughout much of the developing world. Rapid population growth, together with large proportions of people in the younger age groups, means a rapidly increasing demand for educational facilities. In most cases, developing countries, like the Philippines, have difficulty in keeping up with this demand, resulting in the deferment of the goal of universal education along with its potential effect of lowering fertility (Peters and Larkin, 1997).

27

4

Mapping Population-Biodiversity Connections in the Philippines

4.1

28

Population-Environment Framework and Methodology

Conceptual and Analytical Framework

The study of population and environment linkages and interactions can follow a variety of approaches. Studies published in relevant literature used various methods to analyze the forces that influence historical and geographical land use, and land cover changes. The analyses have often focused on the outcomes of land cover change such as forest cover decline, and urban spatial expansion brought about by slow-acting processes and factors that trigger rapid change. These causes include not only biophysical factors (e.g., natural disasters, floods and droughts) but also institutional, socioeconomic and demographic elements (e.g., policies on resources use and management, population and demographics—size, structure and distribution, poverty, urbanization and industrialization, and property rights regime). In general, researchers use two approaches to understand how land cover changes occur. One approach is the comparative analysis of data at cross-sectional, national and regional levels from various areas throughout the world to determine what meta- and macro- level factors bring about the land change patterns. Another approach involves the analysis of several local case studies from which generalizations can be made regarding the factors and processes that cause changes in land cover. An alternative approach is to combine the two methods by selectively identifying the causes and underlying driving forces of land cover changes from a wide variety of case studies in relevant literature. The methodologies used in the case studies may also be classified into three groups, namely: (1) quantitative methods, (2) qualitative methods, and (3) combined quantitative-qualitative methods (Moran et. al., 2003; Turner et. al., 2003; Fox et. al. 2003; Rindfuss et. al., 2003). Meta-analysis has been used that considered several published local level case studies to understand the proximate causes and drivers of forest cover change (Geist and Lambin, 2002; McConnell and Keys, 2003). Human activities and related interventions and actions such as agricultural expansion put pressure

on forest resources. Underlying driving forces include socioeconomic processes and demographic factors such as human population dynamics that underpin the proximate causes. Proximate causes are human activities and land uses that directly affect the environment, thus constituting final or near-final or direct sources of change. The analysis has generally classified the proximate causes of forest cover decline into four general groups: (1) infrastructure extension; (2) agricultural expansion; (3) wood extraction; and (4) other sources, such as forest destruction from mining activities and natural causes (fires and typhoons). On the other hand, the underlying driving forces are grouped into five broad categories, namely: (1) demographic factors; (2) economic factors; (3) technological factors; (4) policy and institutional factors; and (5) cultural factors (Geist and Lambin, 2002). Figure 6 summarizes the causes and underlying processes that bring about environmental change. The diagram identifies what data to collect and the methodology to be used for the analysis. The figure also helps to explain the linkages and relationships among the environmental variables, and socioeconomic and demographic factors. Moreover, it reflects how society responds to the state of biodiversity by employing conservation interventions that can reduce the rate of biodiversity loss or minimize the effects of the proximate and ultimate causes of biodiversity loss. Clearly, the current state of biodiversity already reflects the outcome of past conservation interventions. And whether this will continue to deteriorate or improve will depend on society’s present and future actions. Alternatively, Figure 6 can be transposed into a form that corresponds to the “pressure-state-response” (PSR) framework (Organisation for Economic Cooperation and Development or OECD, 1993). This PSR framework simply describes that human activities apply pressures (e.g., logging, mining or land conversions) on the environment, such that they bring around changes in the state of the environment (e.g., biodiversity, ecosystem diversity, pollution and others).

BIODIVERSITY LOSS/STATE Special diversity Threatened flora and fauna Ecosystem diversity Decline in forest cover Genetic diversity Cultural diversity Lanscape

DIRECT FACTORS (PROXIMATE CAUSES) Habitat loss Infrastructure expansion Agricultural expansion, etc. Resource use/ Overexploitation Wood extraction, etc. Other factors Introduction of exotic species Pollution Climate change

SOCIOECONOMIC & POLITICAL DRIVERS (ULTIMATE CAUSES) Social factors Economic factors Demographic factors Technological factors/ Policy and institutional factors

of study that will enable analysis of the feedback loop. For instance, to determine how society responds to environmental changes requires a closer analysis of policies at the macro level or of adaptive responses at the community level. To determine the relationship between environmental degradation and health requires analysis of how much of the disease incidence is due to the specific environmental problem. Analytically, the project examined the socioeconomic and demographic factors (or ultimate causes) underpinning the more obvious causes (i.e., proximate causes) of biodiversity loss. With this purpose, the study on population-environment link was guided by an analytical framework, as shown in Figure 8. This framework defines the biodiversity status, in terms of forest cover, to represent the current state of the habitat, and the biological importance of conservation priority areas to represent the state of species diversity. The underlying factors that lead to the current state of forest cover and status of biological importance of CPAs are: (1) the demographic processes (fertility, mortality and migration) and the outcomes of these processes (population size, structure and distribution); and (2) socioeconomic factors such as poverty, unemployment and urbanization. It is emphasized that the current state of biodiversity reflects the outcome of past and present socioeconomic and demographic processes as well as society’s responses, i.e., the feedback mechanism (such as policies and actions), to lessen the negative effects or enhance the positive effects of these processes on biodiversity.

Mapping Population-Biodiversity Connections in the Philippines

Society then responds to the changes in this state or to the pressures with economic polices and programs, environmental actions and remedies to either prevent, diminish or mitigate the pressures and, for this project’s case in point, biodiversity loss or damage. Adopting the PSR framework, the chart in Figure 7 illustrates the conceptual framework of the population-environment/ biodiversity link as a guide for this project. On a conceptual level, population is seen as shaping the environment inasmuch as the environment influences population. The relationship portrayed in Figure 7 underscores the idea that any alteration in one force (e.g., population) has a propensity to induce changes in the other (e.g., environment). In effect, the population-environment link tends to be characterized as being more dynamic rather than static because each force varies according to the changes induced by alterations in the other. Despite the perceived two-way feedback relationship between population and environment, however, the project focused only on one path of association between these two forces of nature, that which specifically proceeds from population to the environment—particularly that part of the environment that concerned biodiversity. Hence, the project focused on the potent impact of demographics upon biodiversity. The choice is influenced by the availability of data and timeframe

SOCIETY’S RESONSE CONSERVATION INTERVENTIONS (Past, present and future)

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

Figure 6. Causes and underlying processes that bring environmental change (adapted from Ong, 2003)

29

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

Figure 7. Conceptual framework of interconnections between human pressure, state of the environment and the human response to this pressure-state link.

Figure 8. Framework of analysis of the population-biodiversity link

Mapping Population-Biodiversity Connections in the Philippines

Socioeconomic Drivers

30

Demographic changes Fertility Mortality Migration, etc. Socioeconomic characteristics

influential predictor variables

Direct Factors

Infrastructure expansion

Biodiversity Status or Loss

Forest cover

Agricultural extension Technological development Resource use and overexploitation, etc.

Biodiversity importance

dependent criterion

Methodology

Having explained the project’s conceptual and analytical framework and with cross-sectional data, the relationship between socioeconomic-demographic variables and biodiversity indicators was analyzed. Section 4.2.1 defines a clear scope of the relationship analysis. In section 4.2.2, the socioeconomicdemographic variables and the indicators representing biodiversity status are identified and defined. With a clear definition of the variables, in section 4.2.3 the hypotheses on the relationships between the socioeconomic-demographic variables and biodiversity indicators are posited. Section 4.2.4 describes the state of the data set used in the study. Finally, the analytical tools and the scope of the analyses are explained in section 4.2.5.

of single reforestation tree species or introduction of exotic tree species may increase forest cover but harm biodiversity by killing endemic or local species of plants and animals. 4.2.2. Identification and Definition of Variables

Mapping Population-Biodiversity Connections in the Philippines

The socioeconomic-demographic variables were identified in a participatory process where representatives from USAID, government, academe and non-government organizations involved in population and environmental studies arrived at a consensus on the list of possible relevant set of socioeconomicdemographic variables for the project. From a more than 100 socioeconomic and demographic variables, the choices were scaled down to 36 (see Annex 1) based 4.2.1. Scope of the Analysis on their relative importance to demographic studies and the availability of data at the provincial level. The project studied the population-biodiversity links The 36 variables were further analyzed using statistical and spatial analysis tools. This was a and clustered based on theoretical and empirical cross-section analysis that looked at the association information to determine their relevance in illustrating between a set of socioeconomic and demographic the population-biodiversity link and the level at which variables to biodiversity status—i.e., forest cover and they appear in the link. Using a hierarchy of influence, biodiversity importance, following the conceptual and the population-environment link is conceptualized as analytical framework explained in section 4.1. consisting of an interconnected series or parallels of In pursuing the analysis of the population- socioeconomic-demographic variables, which affect environment relationship, the variables pertaining biodiversity (i.e., forest cover as indicator of habitat to population or demography are referred as the integrity) at different levels (Figure 9). The clustering influential predictor variables, whereas those pertaining also illustrates the effect of a socioeconomicto biodiversity are referred to as the dependent criterion demographic variable on another. Primary variables can variables. Changes in the predictor variables influence be defined as socioeconomic-demographic indicators the changes in the dependent variable, but not the other that directly affect the proximate causes of biodiversity way around. It follows as a matter of association that changes. At the secondary level are sets of variables the time period for the collected data pertaining to that affect biodiversity status through the first-level or the gamut of predictor population variables should, primary variables. therefore, predate that of the dependent biodiversity For instance, Figure 9 shows that population variables. That is, the cause (population) predates size (number, density and growth), which directly the effect (biodiversity). While the study’s objectives affects biodiversity, is an outcome of fertility, mortality and methods are clear and simple—to establish a link and migration. Fertility, on the other hand, is affected between carefully selected variables—the fact must be by such factors as maternal and child health, the level recognized that a more complex web of relationships of education especially of women, and the use of actually exists in nature. Demographic characteristics contraception, which are in turn determined or affected singly or in combination can have different effects on by access to contraception and education services, an entire set of environmental factors not included in culture and religious beliefs. the research design, but which can have direct effects on Internal migration, especially lowland-to-upland the biodiversity indicators. For instance, introduction and rural-to-rural, exacerbates population pressure

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

4.2

31

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

in conservation priority areas either by increasing the number of people dependent on the resources or by changing the way people use the resource. Poverty, unemployment and social unrest (e.g., high crime rates and the presence of armed conflict) in the places of origin can trigger migration to areas with less conflict. Aside from migration, other factors that can affect resource use practices or the pattern of resource uses are technology, economic incentives (markets, prices and policies affecting both), culture and cultural practices, joblessness and employment opportunities for men and women, poverty and changing gender roles in resource management. The levels illustrated in Figure 9 further imply that the effect of a variable on biodiversity becomes less obvious as one goes up the chain of relationships. The choice of variables to examine the populationbiodiversity link is then based on the level at which they occur in the chain—the first choice being the set of variables that occur on the first level. Second level variables are included in the statistical analysis when data for the primary level are unavailable, difficult to measure, or when examining further links in cases where there seems to be no apparent statistical relationship between the primary variable and biodiversity. Hence,

the analysis includes migration, poverty, unemployment and gender (in terms of labor force distribution) as they affect resource use practices. Pre-disposing environmental factors (e.g., land characteristics, soil quality and topography), biophysical drivers (e.g., droughts, fires and floods) and social trigger events (e.g., war, social disorder and economic shocks) are not included in the analysis. Due to lack of data, economic incentives, culture and technology are also not included in the analysis. Using Figure 9, the 36 sets of predictor variables (composed of 75 individual parameters) were further rationalized and scaled down to three (3) manageable sets comprising of 25 individual variables. Table 17 presents the final set of socioeconomic and demographic predictor variables. The project qualifies that the term biodiversity criterion variables is interchangeably used with the term biodiversity indicators. Due to data limitations, the project chose to represent the biodiversity criterion variables with provincial forest cover and the biodiversity importance of conservation priority areas. The percentage forest cover of a province reflects the degree of threat to wildlife habitat and, therefore, to the species of plants and animals in the forest. Presumably,

Mapping Population-Biodiversity Connections in the Philippines

Figure 9. Hierarchy of socioeconomic and demographic variables affecting biodiversity

32

use forest cover as an indicator for the existence of a degree of habitat expanse. In conjunction with this, there is an underlying assumption of a progression in the degree of biological vulnerability to anthropogenic exploits as an area proceeds from being highly forested and highly biologically important to becoming less biologically important with less and less forest cover. 4.2.3. Hypothesized Relationships With a clear definition of the variables involved and of the scope of analysis, the following hypotheses are put forward to guide our series of analyses: (1) High population density, growth rate and fertility, as measured by the total fertility rate, exacerbate forest and biodiversity losses. Therefore, high population density, high growth rate and high total fertility rate are associated with lower percentage of forest cover. (2) Large numbers of migrants increase the demand for limited resources in the areas where they settle and compete with original community groups in resource use. Since migrants initially do not have

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

when the habitat is in good condition, there should be a smaller percentage of threatened species, and vice versa. The following theoretical considerations guided the selection of the criterion indicators of biodiversity: (1) The selection of the dependent criterion forest cover maximizes available data for the provinces. This rationale underlies the main decision to make operational the use of the predictor-criterion analysis framework. Most provinces would have information on this indicator for environmental biodiversity. For instance, an indicator for the provincial units, which was initially considered, was the rate of log production in the last decade, but only 20 provinces in the Philippines have this information. There is very low reporting on this indicator. However, a much larger sub-sample of 61 out of the 81 provincial units have information on forest cover for the period from the 1990s to 2000. (2) The biological indicators describe a unique and integral dimension of biodiversity degradation. This is a valid assumption considering that one can intuitively

Table 17. Socioeconomic and demographic predictor or independent variables Definition Population density Population growth rate Net migration rate of females Net migration rate of males Percent population aged 15 years and over who attained high school Poverty rate Unemployment rate Percent female labor force in agriculture, hunting and forestry Percent male labor force in agriculture, hunting and forestry Percent female labor force in construction Percent male labor force in construction Percent female labor force in electricity, gas and water Percent male labor force in electricity, gas and water Percent female labor force in fishing Percent male labor force in fishing Percent female labor force in manufacturing Percent male labor force in manufacturing Percent female labor force in mining and quarrying Percent male labor force in mining and quarrying Percent female labor force in a not-stated industry Percent male labor force in a not-stated industry Percent female labor force in service industry Percent male labor force in service industry Percent female labor force in trading Percent male labor force in trading

Mapping Population-Biodiversity Connections in the Philippines

Variable Name Demographic variables: DENSITY POPGROW MIGFEM MIGMALE Education variables: POPHS15 Socioeconomic variables: POVERTY UNEMP AGRIF AGRIM CONSF CONSM ELECF ELECM FISHF FISHM MANUF MANUM MINEF MINEM NSF NSM SERVEF SERVEM TRADEF TRADEM

33

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY Mapping Population-Biodiversity Connections in the Philippines

34

land rights, either for agriculture or settlement, they are forced to colonize open access forest areas, thus resulting in or exacerbating forest and biodiversity losses. It is, therefore, expected that high net migration rate is associated with lower forest cover. (3) Poverty and unemployment induce communities to look toward natural resources for incomeearning opportunities. Hence higher poverty incidence and unemployment are associated with lower forest cover. (4) Men and women have varying and crucial social roles. In many traditional Philippine upland communities, women farmers are in charge of crops for household use while men plant crops for cash (Lasmarias, 1989). In the formal labor sector, there are industries that are traditionally dominated by women, e.g., trading and services, while men dominate industries like mining, forestry and construction. It is hypothesized that the changing roles of men and women are associated with changing biodiversity and resource endowment. Specifically, a high rate of employment of men in the non-natural resource-based industry sectors is associated with higher forest cover, while a high rate of employment in agriculture, forestry and hunting is associated with lower forest cover, implying lower and higher pressure on biodiversity, respectively. (5) Employment opportunities for women outside of the traditionally women-dominated sectors imply additional income for the family, lessen the need for more children to augment family labor, and reduce resource extractive or exploitative activities. Hence, barring the effect of other factors, higher employment rate of women outside of the forest sector is associated with lower fertility and higher forest cover. (6) Higher educational attainment presents greater opportunity for employment. It may also open access to more appropriate technologies. Better education tends to lower fertility because, as studies have shown, higher educational attainment, especially among women, is associated with better knowledge of modern contraception. With the combined effect of higher prevalence of contraceptive use and opportunity for employment, the project expects that areas with greater percentage of population 15 years old and over with high school education have higher forest cover. Crime and armed conflict may affect the general economic and peace-and-order condition of an area

and the delivery of basic services. Crime rates in the lowland may encourage outflow of population, either to other lowland areas or to the uplands, resulting in increased population pressure in the uplands. The presence of armed conflicts in the upland areas may encourage the flow of population from the upland to the lowland to avoid the conflict, but increases the pressure on the lowland resources. However, the armed conflict occurring in fragile areas may, by itself, directly destroy already fragile habitats and vulnerable wildlife populations. It is also reasonable to expect that, to a large extent, the forests and wildlife sustain the food requirements of armed groups in the uplands and may have led to the opening up of additional areas for agriculture. It is, however, unclear what the direction of the net effect of armed conflict on biodiversity will be. If net population outflow from conflict areas occurs, population pressure will be eased and the remaining forest areas may be protected de facto by the presence of conflict. Technology, culture and economic incentives (prices and policies affecting prices and markets), albeit important, are not included in the set of predictor variables due to data limitations. Culture is often referred to in various literatures as a mediating factor in the population-environment link. Culture affects the roles played by men and women in resource management. It also affects the patterns of resource use, farming practices and technologies adopted. On the demographic side, culture and religion have an important impact on people’s views and practice of contraception. Many times, cultural and religious taboos prohibit the use of modern contraceptive methods. Modern technology can either be beneficial or detrimental to human society. It can present, for example, opportunities for increasing the productivity of a given land or can cause harm to humans and wildlife species (e.g., in agriculture, technology encourages dependence on chemicals and synthetic inputs). Markets, prices and government incentives (subsidies and taxes) can encourage certain technologies and the production of certain products, and may discourage conservation behavior. For instance, government subsidies on farm inputs, especially inorganic fertilizers, herbicides and pesticides, lower the prices and cost of synthetic inputs thereby encouraging their use while discouraging the practice of integrated

better explain the population-biodiversity relationship may be lost. For instance, often the population density figures do not reflect the true nature of the ecological stresses because per-person impact on biodiversity varies greatly by site. Site-specific micro-environmental

Mapping Population-Biodiversity Connections in the Philippines

Ideally, barangay or village-level data are appropriate in studying population-environment linkages especially when looking at conservation priority areas. While certain barangay-level information may exist in some provinces (e.g., Palawan), for most provinces, official government statistics offer only provincial-level data for socioeconomic and demographic indicators. With the exception of population size, neither barangay nor municipal data is available for all desired variables. As a consequence, the project team focused data collection on variables at the provincial level, which were readily available, or which offered alternative proxy variables. To enable the project to analyze the population- Female labor resource in rural barangays biodiversity interactions within conservation priority areas in the absence of barangay-level information, a spatial analysis technique using map overlays was used. By using provincial-level data, the project assumes a uniform distribution of socioeconomic and demographic characteristics either across the whole province or across the entire length and breadth of a CPA. With this assumption, peculiar socioeconomicdemographic and environmental characteristics peculiar to an upland area or conservation priority area that can

PCSDS, Palawan

4.2.4. Data Sources and Limitations

conditions, such as differences in ecological and geomorphological characteristics, make one site more vulnerable to population pressure than another. Therefore, one cannot assume that population density refers to a homogeneous land variable across sites. Macro-level (i.e., national-level) analysis, however, is important and can yield results that provide an immediate grasp of national and regional influences on population-environment interaction, which may have significant macro-level policy implications. Micro- or site-level analysis is needed for site-specific interventions inasmuch as it provides a more in-depth analysis of population-environment interactions (see also section 4.1). In the absence of physical variables for the population-biodiversity analysis, e.g., slope, soil types and erosion potential, climate, the project assumes homogeneity in land variable across space and across the socioeconomic-demographic variables collected along a census period. With respect to the biodiversity status represented by forest cover, provinciallevel data on forest cover (expressed as percentage of the land area covered by a province) were available to describe the characteristic of distribution of forest cover nationwide. Threatened plants and animals were initially considered by the project as another biodiversity indicator. However, this was not feasible given the possible bias in the species distribution data. There might be a probable overestimation or underestimation of the number of threatened plants and animals per CPA considering that data availability and number estimates are influenced by: (a) the incidence and extent of field biological studies conducted in the area; (b) existence of a well-documented species distribution data for a few taxonomic groups, in particular, bird species, which can be a source of bias; and (c) the assumption of homogeneity of species distribution over a province encompassing a CPA. Notwithstanding the data limitation in species

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

pest management and organic farming. A policy that provides tax holidays (a form of subsidy) to mining firms for exploration and mining operation encourages mining development, but places the remaining forests, biologically diverse areas, and areas identified as conservation priorities at risk of further degradation and hastens biodiversity losses.

35

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY Mapping Population-Biodiversity Connections in the Philippines

number estimates, the best alternative was to make use of the map of biologically important CPAs published from the work of the PBCPP in 2002. The map was an amalgamation of the areas of biological importance for plants, arthropods, amphibians and reptiles, birds and mammals. The importance of each area for a particular group of species and their eventual combination into a unified biological or biodiversity importance map were assessed and derived from the consensus of PBCPP participants who had the knowledge and field experiences on a specific species group. In the future, when the species data are updated on the maps, the spatial analysis technique described in the subsequent sections will provide flexibility of analysis and present quite easily a visual perspective of spatial trends to facilitate pinpointing of areas vulnerable to pressures and at greater risk of losing their biodiversity resources. Although CPAs included 137 terrestrial areas, 33 inland water areas, and 36 marine areas, the analysis had to exclude the marine areas and inland waters, because of the physical difficulty in projecting the effect of land-based population variables into open or inland waters. It is normally difficult to associate spatially demographic factors to a body of water where there is presumably no human settlement, except in the fringes of the coastline, such as with the Badjaos of Sulu. It may be possible, however, to spatially represent

BOX 2.

the degree of pressure to coastal areas and coral reefs if the direct users are identified, which provincial-level information cannot provide. Current biodiversity status and demographic characteristics are products of changes over time. The dynamics in the relationship between biodiversity and demographics may be better understood when looking at time-series information. However, good time-series information across provinces and CPAs are not available. For instance, provincial forest cover data, while officially published in 2000, is a product of analysis of satellite images from 1992–1998. Socioeconomic, health and demographic indicators are also not collected by the concerned government agency at the same periods, e.g., every year or every four years. Although the base year for the study is 2000, the socioeconomicdemographic variables used are dated from 1998–2000, depending on the latest available official statistics, but at least closest to the base year. In the absence of time series data, the project was left with the alternative of using cross-section data or information in explaining population-environment dynamics. Cross-section data provide information on the significance of the trend in relationship between variables across space or areas at specific time. For example, the relationship between poverty incidence and forest cover should be true to most of the provinces included in the study using 2000 data.

Map Overlay Analysis for Deriving the Priority or Vulnerability Index

In working with the cartographic modeling type of spatial analysis, it is important to conceptualize maps as a set of numeric data rather than a graphic portrayal of features found on the ground. In this regard, numeric data is referred to as representing levels of measurement of map overlays. These levels of measurement refer to the meaning of the numeric data assigned to classify groups of similar features reflected on a map. In the case of this project, an ordinal level of measurement was applied for the maps of concern. This means that numeric data were used to describe the rank of order values (i.e., ordinal data), e.g., 1 = very low threat, 2 = low threat, 3 = moderate threat, 4 = high threat, and 5 = very high threat. Because there was a need to compare the map of terrestrial conservation priorities from the PBCPP with the maps derived here using an objective empirical-statistical approach, the map combination procedure employed in the PBCPP was applied. Using information based on their own experiences and knowledge, socioeconomic and demographic experts, who participated in the PBCPP, derived a socioeconomic pressure map. This map was then combined with a map of terrestrial and inland water areas of biodiversity importance. In this project, an index map of socioeconomic-demographic pressure was derived for forest cover by establishing objectively which were the key socioeconomic-demographic variables associated with the forest cover status of the provinces through statistical analysis. Since it was recognized that forest cover was a surrogate indicator of habitat status, it was rationalized that the pressure on species habitat impinges on species finding refuge

Continued on next page... 36

Using provincial data, however, raised a new question, to wit, “How can the values of the socioeconomic and demographic variables for the CPAs be estimated?” Indeed, a major problem encountered by the project team focused on the virtual absence of such data aggregated at the CPA level and the non-coincidence of the CPA boundaries with the provincial boundaries. A process of weighting was tested and this allowed a systematic combination of available provincial data to obtain a single socioeconomic and demographic variable that corresponds to the CPAs. By using appropriate weights such as population size and land area, the procedure was able to account for the marginal contribution of each constituent province CI–Philippines, Palawan

This section describes the methods used in examining the population-biodiversity connection. First, the subsection 4.2.5 (A) discusses the feasibility of employing an estimation procedure for missing indicator data over CPAs using a spatial technique and the setback this will cause if no supporting validating data can be obtained. Nonetheless, an alternative procedure using another spatial technique was devised to circumvent the problem. Second, the subsection 4.2.5 (B) explains the statistical analyses applied to support the study of the populationbiodiversity connection. Third, subsection 4.2.5 Mangrove forest resource in CPAs (C) elucidates on the process to reflect and analyze the impact of socioeconomic and demographic factors on biodiversity indicators in terms of where the spatial trends are and their points of vulnerability.

A. Estimating the Socioeconomicdemographic Pressure for the Conservation Priority Areas

Map C = f (Map A, Map B)

(1)

which can be illustrated in Figure B1. where: Map B and Map A represent the numeric values of the biodiversity importance map and the pressure index map of forest cover, respectively; f is a function from a process of point operation in cartographic overlays; and Map C is a new map derived from the overlay. An example of an operation in this case is the determination of the priority level or vulnerability index for each CPA. For this case, Map C is calculated as a result of the combination of the degree of pressure on forest habitat, Map A (classified using the 20th percentile ranking of: 1 = very low pressure, 2 = low pressure, 3 = moderate, 4 = high pressure and 5 = very high pressure), and the

Mapping Population-Biodiversity Connections in the Philippines

there—and, thus, if an area is highly biologically important under a high socioeconomic-demographic pressure it then becomes highly vulnerable to exploits and becomes a typical candidate for being one of the top priorities for biodiversity conservation action. With this reasoning, the project team logically integrated the map index of forest cover pressure from this MPBCP project with the map of biodiversity importance from the PBCPP. First, the concept of combining the maps together is explained, as follows. The levels of measurement of the maps, which are being combined, are assumed to be all in ordinal form. The cartographic model of map combination or overlay is defined as:

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

4.2.5. Analytical Methods

Continued on next page... 37

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

to the aggregate socioeconomic and demographic variable for the CPA. The result was a value of the socioeconomic or demographic variable for each CPA. For purposes of reference and evaluation by peers, the details of the estimation procedure are described in Annex 6. After estimating the values, the next process was to assess the resulting values for each CPA. The estimated total population size from the CPAs accounted for a little less than 40 percent of the total population of the Philippines, which is a little over the estimated proportion of upland population in 1980 of about one third of the total population assuming the same annual growth rate. For some variables, however, there were obvious overestimations, such as net migration rates exceeding 100 percent. This raised an apparent problem since there were no actual CPA data to validate the estimates. Using more sophisticated techniques was not warranted because of the lack of supplementary data, besides population size. To remedy the problem, another spatial analysis technique was employed by the team as an alternative (see subsection C). This technique combines the features of cartographic modeling appropriate for map-based analysis of spatial trends and convergence of experts’

Mapping Population-Biodiversity Connections in the Philippines

9

advise. The concept behind using experts opinion is that they, being well informed in their fields of expertise, are better experienced to predict the future than theoretical or extrapolation approaches (Ritchie et. al., 1994). B. Statistical Analyses Bivariate Correlation Analysis. Two statistical analyses were used in the project: bivariate analysis and multivariate analysis. The bivariate statistical techniques, which employed both the parametric Pearson and nonparametric Spearman rank correlation coefficients, were used to measure the association or relationship among the initial 36 sets (comprising 75 parameters) of socioeconomic and demographic variables, and between a socioeconomic-demographic variable and forest cover. The distribution-free, nonparametric measure was also used to cross-check statistical results generated from the more stringent, parametric Pearson technique. The resulting bivariate correlations provided a preliminary insight as to which socioeconomic and demographic variable has a statistically significant association with the chosen biodiversity variable, at 9 least at 5 percent level of significance . The result

A finding (for example, the difference in the means between two random samples) is deemed statistically significant when it can be demonstrated that the probability of obtaining such a difference by mere chance is relatively low. In social science research, it is customary to describe one’s findings as statistically significant when the obtained result is among those that, theoretically, would occur no more than 5 out of 100 times (5 percent of the time) if the only factors operating are the chance variations that occur whenever random samples are drawn (see also the glossary).

degree of biodiversity importance (only two levels), Map B (classified using experts’ consensus ranking of: 4 = very high importance and 5 = extremely high importance). For the map set at hand, the suitable combination method is an ordinal overlay operation based on logical relationships defined by the Figure B1. Illustration of the map overlay process operator function f. The method provides a legitimate means of combining data of different levels of measurement (Tomlin, 1990). By using logical relationships, the interdependence of the attribute data in the maps is taken into account. In this case, the logical relationships established during the PBCPP were adopted. These provided the logical rules of combination of the map attributes. This is shown in a matrix in Table B1. The matrix bears similarity to the PBCPP matrix of combination, except that there is an additional fifth column or combination pitting a very low pressure against an extremely high and very high

Continued on next page... 38

incorrect estimate is 5 percent. In addition to the insight on the relationship between variables, the bivariate correlation analysis is a useful step in deciding whether to use linear regression models or other types of regression models. Note that the direction in the relationship between a socioeconomic-demographic variable and forest cover might change when all other predictor variables are analyzed simultaneously, justifying the use of the multivariate statistical approach, e.g, through a multiple regression technique. Multivariate Regression Analysis. By using a multiple regression technique, the project was able to identify which particular socioeconomic and demographic variable among a set of 24 predictor variables is significantly associated with the dependent criterion variable of biodiversity indicator in the provinces (i.e., forest cover), at least at the 5 percent level of significance. In other words, the use of multiple regression models allowed the project to pinpoint the simultaneous impact of the set of socioeconomic and demographic variables on biodiversity. Since predictor variables interact to influence the outcome of a population-biodiversity link, the multivariate level of analysis tends to draw a closer approximation of social reality inasmuch as, truly, no single influential factor of population operates in the absence of other population variables. The multiple regression models used in this study excluded observations from analyses using a

Table B1. Matrix of combinations of two maps Maps Biodiversity Importance

Levels Extremely High Very High

Very High EHu EHc

Pressure Index on Forest Cover High Moderate EHc VH VH VH

Low VH H

Very Low H H

Note: EHu is a score of extremely high urgent priority or, alternatively, denoting an extremely high vulnerability to human-induced pressure for an area of interest. EHc is extremely high critical priority or an extremely high vulnerability to human-induced pressure for an area of interest. VH is very high priority or a very high vulnerability to human-induced pressure and H is high priority or a high vulnerability to human-induced pressure. These scores or indices of priority or vulnerability can be interpreted very slightly different, as defined in the PBCPP report (Ong et. al., 2002). For the purpose of the above matrix, “extremely high urgent” areas need the most urgent attention because they are at immediate risk of losing a high percentage of biodiversity; “extremely high critical” areas are either a combination of extremely high biodiversity importance with high demographic-socioeconomic pressure or of very high biodiversity importance, but with very high demographic-socioeconomic pressure; VH and H are classified as such based on a combination of the levels of biodiversity importance and demographic-socioeconomic pressure, as shown above in the matrix. The indices merely indicate the level of progression from a high to an extremely high risk of losing biodiversity.

Mapping Population-Biodiversity Connections in the Philippines

biodiversity importance. To obtain the rule for such a combination, a panel of specialists and experts was pooled to provide that rule by scoring these specific combinations with a high priority. The experts and specialists came from fields of experience in biology, psychology, demography, environmental statistics and socioeconomics.

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

also provided insights on the association between two socioeconomic-demographic variables, e.g., between population size and contraceptive prevalence rate, or between total fertility rate and percent population aged 15 years and over with high school education. Moreover, the technique provided an insight into the degree of linear relationship between two variables and the direction of the relationship, i.e., whether direct (if the correlation coefficient is positive indicating that as one variable increases, the other increases as well) or inverse (if the correlation coefficient is negative indicating that as one variable increases, the other tends to decrease). The linearity in the relationship implies that the rates of corresponding changes between the correlated variables are constant, e.g., if one variable increases by, say, one percent (1%), the change in the other is one percent (1%) as well every time. Note that the value of the correlation coefficient, r, is restricted to the interval [-1, 1], i.e., the values are within the range of -1 to 1. The closer r is to -1 and 1, the stronger is the linear relationship between two variables. If the value of r is zero, then the two variables are linearly uncorrelated. A Pearson or Kendall and Spearman rank correlation coefficient is considered statistically significant at least at 5 percent level. This means that the researcher is confident that the probability of the method generating a correct estimate is 95 percent, or that the probability of that method generating an

Continued on next page... 39

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

list-wise deletion criterion. This deletion technique eliminated or disregarded provinces which do not have information on at least one of the variables specified in the regression equations. After the deletion, the resulting sub-sample consisted only of those provinces which have complete information on all the variables pertaining to socioeconomic and demographic predictors, as well as those referring to the criterion indicator of biodiversity status using forest cover. The statistical analyses used forest cover10 data for the period 1993–2000 as the dependent criterion indicator of wildlife habitat integrity for 79 provinces and 2 major cities (or a total of 81 observations or cases). Two major cities, namely, Marawi and Cotabato, were counted separately because there was no indication under which province or region they can be subsumed. Much as it has been desired to correlate values of socioeconomic and demographic predictors at the CPA level, the use of weighted spatial interpolation of values in the CPA was not pursued due to the limitations described earlier. Instead, the biodiversity importance of CPAs, used in lieu of threatened species, was analyzed using cartographic modeling to be described in subsection C.

Mapping Population-Biodiversity Connections in the Philippines

10

Both the bivariate and multivariate analyses established the population-biodiversity relationship. In addition, multivariate analysis provided a means of generating several summary indices that can be used to measure the impact of the various socioeconomic and demographic factors on biodiversity indicators. Regression coefficients derived from the multiple regression model were used as weights for the specific socioeconomic and demographic variables to generate the forest cover threat indices. These summary indices were transformed into map-based presentations to express their spatial trends. C.

Spatial Analyses using Modeling Technique

Cartographic

The previous section noted the use of multivariate analysis to generate summary indices for measuring the impact or pressure of the various socioeconomic and demographic factors on biodiversity indicators in the provinces. The pressure indices were translated into maps portraying their spatial distribution over provinces throughout the Philippines. From these maps, an ocular analysis was made to identify where

As a rule of thumb in statistics, the arcsine transformation for percent forest cover was applied before the correlation analysis was made. This is because of the wide range of percent values observed for percent forest cover ranging from 10 to 80 percent. If untransformed, the percent values would exhibit a beta distribution, which may not provide good results in contrast to a normally distributed data set (Nelson and Granger, 1979; Granger and Newbold, 1986).

As explained above, the scores of particular interest are the definitions of output values to a particular set of combination of the maps’ ordinal attributes. The levels of pressure on forest cover and the levels of biodiversity importance were combined according to the matrix in Table B1, which was then used as the point operator represented by the function f in the cartographic model of overlay expressed in equation (1) to arrive at Map C. This map can be called the biodiversity priority map or index map of vulnerability to human exploits in CPAs and was a product of the overlay model flowcharted below.

Degree of pressure on forest cover map

Degree of biodiversity importance map overlay through the combination matrix

Degree of priority for conservation or degree of vulnerability to exploits map

Figure B2. Flowchart of the map overlay process Continued on next page...

40

G. dela Torre-Foster

2000. The socioeconomic pressure map, which was based on low-resolution or gross socioeconomic and demographic data and experts’ assessment, showed the varying degrees of anthropogenic-based pressure exerted on each of the CPAs. The comparison provided an opportunity to validate the empiricalstatistical approach cum spatial modeling employed in this project against the approach used by the PBCPP. The biodiversity priority map or index map of vulnerability generated from this project (MPBCPP) was also compared with the integrated map of terrestrial conservation priorities from the PBCPP. The project made an additional analysis of vulnerability of CPAs to their risk of losing their important biodiversity by comparing a map of threatened fauna per CPAs with the index map of forest cover pressure.

Analyzing spatial trends

province. This was accounted for by proportioning the contributed pressure by the ratio r1 of the intersected area a1 of one province to the total intersected area (a1 + a2) of contributing provinces (e.g., see equation 2).

Province 1 High pressure

a1

CPA boundary

Province 2 Low pressure

a2

Not drawn to scale

Figure B3. Intersection of a CPA encompassing two provinces

a r1 = a +1 a 1

2

(2)

Mapping Population-Biodiversity Connections in the Philippines

Because the pressure index map of forest cover by provinces is being combined with the biodiversity importance map by CPAs, different levels of representation are obtained for areas by provincial and CPA polygons. A CPA polygon, when overlaid on provincial polygons, may be intersecting one or more of the provincial polygons (see Figure B3). In cases like this, there is a need to take account of the pressure contribution of each

Provincial boundary

POPULATION-ENVIRONMENT FRAMEWORK AND METHODOLOGY

and how socioeconomic and demographic spatial trends threaten forests in the provinces. The indices for forest cover threat were cross-checked with the forest cover distribution map by superimposing both of them to determine which provinces possess the greatest threat to its forest cover. Analysis for the CPAs followed a different track than the forest cover due to the limitation in socioeconomic-demographic data in CPAs. Using a spatial analysis technique, called cartographic modeling, coupled with experts’ experience for forecasting, a process of map overlay was performed. The process for this technique is discussed in Box 2. In summary, the technique combines the map of biodiversity importance by CPAs and forest pressure index map by provinces using a scoring method based on experts’ assessment. For instance, experts are made to independently score an area with a very high socioeconomic-demographic pressure index on forest and a low biodiversity importance on species. These scores were then summarized and arrived at a consensus in scoring each combination. A new map called the biodiversity priority map for conservation actions or index map of vulnerability to anthropogenic or human activities for CPAs was developed using the scores. Subsequently, the index map for CPAs was crosschecked with the socioeconomic pressure map and the integrated map of terrestrial conservation priorities that were generated from the PBCPP workshop in

41

5

Results and Discussion

This chapter presents and discusses the results of the statistical and spatial analyses performed, and the summary socioeconomic and demographic pressure indices generated from the analyses. It presents sections describing the descriptive statistics, bivariate correlation analyses, multiple regression models, and the summary impact indices. The correlation matrices and statistical model summaries can also be found in the DVD attached to the final report. The pressure index maps are also attached and can be accessed as well in the DVD. These results support the tasks and answer questions described in sections 1.2 and 1.3.

Mapping Population-Biodiversity Connections in the Philippines

5.1

42

Descriptive Profile of the Socioeconomic and Demographic Indicators

Each of the tables of descriptive statistics (Annex 3.1) has five columns showing the name of the variable, number of observations, and minimum, maximum and average values. The basic demographic statistics in Table 18 shows the very wide range in the minimum and maximum values for population density, i.e., from 24 persons per square kilometer to 15,617 persons per square kilometer. This can either be a reflection of the high state of urbanization and congestion of certain provinces or data error. A review of the data, however, reveals that there was no encoding error, but that the very high population density value belongs to Metro Manila, which is perhaps one of the most populated cities in the world. Table 18 also shows that 61 provinces have information on the forest area (for the period 1993– 2000) which ranges from a minimum of 5,178 (or 10 percent forest cover) to a maximum of 933,773 hectares (or 82 percent cover) with an estimated mean of slightly over 164,917 hectares (or 41 percent cover). The data indicates that there are provinces that have already very low percentage forest cover, i.e., as low as 10 percent of their total land area, while others have relatively high forest cover. The provincial population growth rates range from 0.79 percent to 5.79 percent for 1995–2000. The data show that 20 out of the 61 provinces have population growth rates above the national average of 2.36 percent (Table 19). The figures signal rapidly growing populations in these provinces and the possibility that, if unchecked, may comprise demographic transition. The country is now in a slow stage two process of the demographic transition

(declining birth rate and almost stable mortality rate). However, if fertility rate rises again, the country may regress to stage one (declining mortality, rising birth rate, and rapidly growing population). This scenario puts population concerns a high priority in the environment and economic development agenda. Net migration rate values range from negative, indicating net out-migration, to positive, indicating net in-migration. For example, the provincial data on net migration rates indicate that the range for male net migration rate is from -0.09 (or 9.0 percent net outmigration) to +0.34 or 34 percent net in-migration. For females, the maximum net migration rate is only about one third to that of the males at +11 percent net migration rate. This indicates that females have a lower propensity to migrate than males. The descriptive statistics on the distribution of the labor force by industry and gender show that a large proportion of the population is engaged in the natural resource-based sectors—fishery and agriculture, forestry and hunting. The labor distribution figures also show that there are, indeed, occupations that are either male-dominated or female-dominated. Based on the summary statistics in Table 18, agriculture, fishery, mining and construction are examples of industries that are clearly male-dominated, while there are almost as many males as there are females in the manufacturing and services sectors. What the figures do not show is the effect of gender differences in occupational preferences on resource use pattern, management and conservation behavior. Site-specific studies are the more appropriate sources of information to reveal the impact of the differences in gender roles.

Name of variable Percent forest cover Basic demographic indicators: Population density Population growth rate Net migration rate of males Net migration rate of females Education: Rate of population aged 15 years and over who attained high school Economic indicators: Poverty incidence Unemployment rate Percent male labor force in agriculture, hunting and forestry Percent male labor force in fishing Percent male labor force in mining and quarrying Percent male labor force in manufacturing Percent male labor force in electricity, gas and water Percent male labor force in construction Percent male labor force in trading Percent male labor force in service industry Percent male labor force in a not-stated industry Percent female labor force in agriculture, hunting and forestry Percent female labor force in fishing Percent female labor force in mining and quarrying Percent female labor force in manufacturing Percent female labor force in electricity, gas and water Percent female labor force in construction Percent female labor force in trading Percent female labor force in service industry Percent female labor force in a not-stated industry

Descriptive statistics Minimum Maximum 10.06 81.90

FORP

N 61

DENSITY POPGROW MIGMALE MIGFEM

81 81 75 75

24 0.79 -0.09 -0.09

15617 5.79 0.34 0.11

560 2.3333 0.0051 -0.0023

POP15HS

81

15.38

35.67

26.3963

POVERTY UNEMP

78 78

0.24 2.90

0.54 27.50

0.4166 12.3744

AGRIM

79

0.56

55.56

34.5163

FISHM MINEM MANUM ELECM CONSM TRADEM SERVEM NSM

79 79 79 79 79 79 79 79

0.03 0.01 0.34 0.03 0.87 0.25 6.19 0.00

32.83 6.65 11.52 1.58 13.96 22.80 32.85 0.47

5.3678 0.3889 2.5309 0.3482 3.9224 2.8218 13.738 0.1037

AGRIF

79

0.18

38.48

16.5006

FISHF MINEF MANUF ELECF CONSF TRADEF SERVEF NSF

79 79 79 79 79 79 79 79

0.00 0.00 0.13 0.00 0.01 0.51 4.42 0.00

7.26 0.43 10.90 0.28 0.36 9.52 24.88 0.54

0.4648 0.0452 2.1548 0.0459 0.0971 5.4377 11.3963 0.1189

RESULTS AND DISCUSSION

Table 18. Descriptive statistics for relevant socioeconomic-demographic indicators Mean 41.24

Source: National Statistics Office.

Population growth rate (%) 5.79 5.45 4.93 4.16 4.08 3.60 3.42 3.25 3.02 2.92 2.85 2.82 2.74 2.73 2.71 2.65 2.46 2.45 2.43 2.41

Livelihood for women

Mapping Population-Biodiversity Connections in the Philippines

Province Rizal Cavite Bulacan Maguindanao Laguna Palawan Lanao Del Sur Apayao Batangas Bohol Pampanga Cebu Bataan Southern Leyte Quirino Tarlac Oriental Mindoro Occidental Mindoro Guimaras Pangasinan

R. Rodriguez

Table 19. Provinces with population growth rates above the national average

43

RESULTS AND DISCUSSION Mapping Population-Biodiversity Connections in the Philippines

44

Bivariate Correlation Analyses

5.2

As explained in the methodology chapter, the parametric and forest cover is also weak (r = 0.061). Moreover, Pearson as well as the nonparametric Kendall or Spearman forest cover seems to be higher in areas where 11 rank correlations were used to initially establish population growth rate is also high, which does not the magnitude and strength of the linear association support the a priori hypothesis set by the project. between each of the biodiversity indicators, and The indication though is that rural and upland areas, socioeconomic and demographic variables. A positive where forest cover and poverty incidence are higher, have faster Pearson or Spearman rank correlation between two growing populations. An empirical regularity observed variables means that as the value of one variable rises, worldwide is that higher poverty incidence and the value of the other variable has a similar propensity larger family sizes go together (Orbeta, 2002). For to increase. On the other hand, a negative Pearson the poor, it would seem that the forest is still a or Spearman rank correlation implies that as the level destination area for land, food and to earn a living. of one variable increases; the level of its correlated (3) The relationship between forest cover and the variable tends to decrease. number of households is shown to be highly significant (r = -0.428). The correlation coefficient further indicates that there is a tendency for the forest cover 5.2.1. Correlation between Forest Cover and Basic Demographic Indicators to be lower when the number of households is higher. This also means that the larger the population size in an area, the lower is the forest cover. Taking a subset of Annex 3.212, the Pearson correlation coefficient, between forest cover (represented here (4) Population density and forest cover are significantly negatively correlated, indicating that as population by its transformation to the arcsine of percent forest 13 density increases, forest cover tends to decrease. This cover, ARCFORP ) and some basic demographic relationship is consistent with theory and indicators to examine how these variables are linearly empirically verified in Northeast Thailand that correlated. The first row of Table 20 shows the showed population density is a significant factor Pearson’s correlation coefficients, r, between percent driving deforestation (Panayotou and Sungsuwan, forest cover and selected demographic indicators—i.e., 1989; Cropper et. al., 1996). In fact the 1989 household size (HHSIZE), number of households study by Panayotou and Sungsuwan showed that (HH#), child-to-woman ratio (CWR), crude birth rate population density was the single most important (CBR), net migration rate of males (MIGMALE), net factor driving deforestation in Northeast Thailand. migration rate of females (MIGFEM), population growth rate (POPGROW), and total fertility rate (TFR). (5) The percent forest cover tends to be positively linearly correlated with household size, total fertility The correlation coefficient indicates the following: rate (TFR), and child-to-woman ratio (CWR). The (1) The linear relationship between percent provincial results of the correlation analysis do not support forest cover and crude birth rate is very weak the a priori hypothesis that forest cover will tend (r = 0.043). This implies that the provincial data to be lower when the household size, total fertility seem to indicate no significant relationship between forest rate, and child-to-woman ratio are high. There cover and the crude birth rate. are two possible explanations for this. First, for (2) The relationship between population growth rate

11

In social science research, both parametric and nonparametric correlations are used to provide support for each other’s findings. While parametric tools are powerful techniques of analysis, they do require stringent data assumptions (normality, homogeneous variance, etc.) compared to nonparametric tools, which do not impose as much restriction, but are only 96 percent efficient in establishing significant associations relative to their parametric counterparts. As such, using both parametric Pearson and nonparametric Spearman & Kendall rank correlations for the statistical analyses will allow us to make more solid conclusions on the bivariate associations. In the event that our assumptions of normality, linearity, homogeneity of variance, and others do not hold as required by parametric tools of analysis (like Pearson correlation) then the nonparametric Kendall and Spearman rank correlations can lend support to the results of the study. It is of course expected that the direction of bivariate relationships obtained will be the same for both parametric and nonparametric correlation techniques.

12

A complete set of bivariate correlation matrices (Annexes 3.2 to 3.3) is found in the DVD produced with this report.

13

The transformation of percent forest cover to arcsine, i.e., the inverse of a sine, reduces data variability.

5.2.2. Relationships between Demographic Variables Correlating population growth rate and net migration rates reveals that the two demographic variables are significantly positively correlated (r = 0.247 or 24.7

Mapping Population-Biodiversity Connections in the Philippines

percent for male net migration rate and r = 0.449 or 44.9 percent for female net migration rate)—implying that provinces with higher growth rates also have the tendency to have high in-migration rates. In the Philippines, the study of Cruz and Francisco (1993), resource economists from the University of the Philippines at Los Baños, shows that high net movement of population to forested

areas is due to the availability of land for subsistence farming and trees for logging. Note that the decision to migrate and the choice of destination, at least in the United States, but can also be true in the Philippines and elsewhere, is strongly influenced by age, gender, occupation, racial and ethnic identity, and socioeconomic status such as income and education (Orians and Skumanich, 1997). Indeed, the Philippine provincial data indicate that there is strong positive relationship between the percent of population aged 15 years and over with at least high school education and net migration rate. The implication is that this segment of the population with relatively higher educational attainment is relatively more mobile and appears to have relatively higher propensity to transfer to other places. The choice of destination areas may be determined by the employment opportunities and the level of education attained. From Table 20, two variables are also highly correlated when one is derived from the other or when both are computed from the same parameters. For example, total fertility rate (TFR) and child-to-woman ratio (CWR) are highly linearly related as expected since both are computed from the number of children of women of reproductive age (15 to 49 years old). The difference is that CWR represents the average number of children per 1000 women of reproductive age in a given year, while TFR represents the average children per woman throughout her reproductive years. Hence, in a multivariate analysis, either one may be used as a predictor variable, but not both at the same time to avoid a multicollinearity effect.

RESULTS AND DISCUSSION

communities in natural resource-rich areas, fertility rates and family sizes may be relatively higher because of the expectations that larger natural resource endowment and landholding can provide better food security (Boserup, 1990). The frontier hypothesis maintains that greater land availability is associated with greater levels of fertility (Easterlin and McCrimmins, 1985). Second, areas with high forest cover tend to attract migrants especially in the face of poverty, joblessness and displacement due to armed conflicts. The open access nature of most forest areas makes it an easier place for migrants to settle than lowland and urban lands. Third, when rural development and industrialization are unable to create enough jobs to absorb the rapidly growing labor force, it is observed that fertility tends to remain high and population moves toward resource rich areas (Peters and Larkin, 1997).

45

46

Child-to-woman ratio

CWR

CBR Crude birth rate (*) = significant at 5% level

Household size

1.000

arcsin of % forest cover for provinces

HHSIZE

Notes:

arcsin of % forest cover for provinces HHSIZE HH# CWR CBR MIGMALE MIGFEM DENSITY POPGROW TFR

Pearson correlation coefficient

-0.254(*) 1.000

1.000

0.116 -0.149 0.542(**) 1.000

0.043

CBR

Net migration rate of females

-0.100 0.017 -0.133 -0.048 1.000

0.417(**)

MIGMALE

Net migration rate of males

0.096 -0.249(*) 1.000

0.224

CWR

DENSITY Population density (**) = significant at 1% level

MIGFEM

MIGMALE

-0.428(**)

HH#

0.135

HHSIZE

TFR

POPGROW

-0.228(*) 0.205 -0.392(**) -0.234(*) 0.615(**) 1.000

0.203

MIGFEM

Total fertility rate

0.143 0.028 -0.260(*) -0.095 0.247(*) 0.449(**) -0.02 1.000

0.061

POPGROW

Population growth rate

-0.049 0.813(**) -0.212 0.026 0.012 0.144 1.000

-0.276(*)

DENSITY

Table 20. Pearson correlation between percent forest cover and some demographic indicators

Mapping Population-Biodiversity Connections in the Philippines

0.088 -.348(**) 0.716(**) 0.580(**) -0.256(*) -0.497(**) -0.250(*) -0.496(**) 1.000

0.133

TFR

RESULTS AND DISCUSSION

PCSDS, Palawan

RESULTS AND DISCUSSION

development, in general, and in poverty alleviation in particular (Orbeta, 2002). The country’s gross domestic product data shows that high population growth retards The correlation coefficients shown in Table 21 imply that the growth in per capita income. This is because high high forest cover in the provinces is positively correlated population growth lowers savings rate and investments or associated with high percentage of population aged in physical and human capital (Orbeta, 2002). At the 15 years and over who attained high school (POP15HS). household level, high fertility will have a depressing In the previous chapter, the study hypothesized that impact not only on family savings and human capital higher education expands a person’s opportunity investments (health, nutrition and education), but for employment, resulting in lower dependence on eventually on the attainment of well-being. The correlation analysis indicates that high the natural resources for income. Moreover, higher education results in higher usage of contraception and poverty rate (POVERTY) tends to be associated lower fertility rates, thus, further lowering the pressure with lower educational attainment (as expressed by on natural resources. The correlation analysis in Table the percentage of population aged 15 years and over 21 shows that a higher percentage of population with high school education) and urban growth rate aged 15 years and over who have attained high school (expressed as URBEXP or urban exponential growth education is, indeed, significantly associated with higher rate). Urban growth rate is percentage of families negatively correlated using contraception with education and and lower fertility, i.e., positively correlated lower total fertility with fertility, although rate, lower child-tothe correlation woman ratio, and coefficients do not smaller household appear statistically sizes. significant. The In terms major implication of employment, a here is that fertility percentage of 15may not be dependent year-olds and over on the spread of with at least high Women and children waiting their turn for health services industrialization or even school education on the rate of economic tend to be associated with higher percentage of employment in non-natural development. Fertility decline is more likely to precede resource-based industries, such as manufacturing, industrialization and to help bring it about rather than electricity, gas and water, and services (see Annex 3.2). to follow it (Caldwell, 1976). In China, even before Conversely, a high percentage of 15-year-olds and over the economy was opened to the rest of the world, a with at least high school education is associated with significant part of the recent declines in fertility rates lower percent employment in natural resource-based is a result of raising the legal marriage age, thus, industries, i.e., agriculture, forestry, hunting and fisheries. decreasing the number of years females are exposed This implies that higher educational attainment moves population to the possibility of conception (Peters and Larkin, away from dependence on natural resources and will have positive 1997). 5.2.3. Relationship between Education, Poverty and Urban Growth

5.2.4. Relationship between Forest Cover and Health Indicators Upland and rural areas, in general, have lower population densities than urban areas. The upland and rural areas are also less accessible than urban towns and cities, thus, may have relatively less access

Mapping Population-Biodiversity Connections in the Philippines

impact on conservation, in the long run, unless there are counter­ vailing factors like inappropriate government policies on resource use that discourages conservation behavior. While it would be erroneous to solely attribute the high incidence of poverty to high fertility in the country, researches present strong evidence that demographic changes play an important role in

47

48 FPACCESS

Percent families use of modern POVERTY contraceptive method (*) = significant at 5% level (**) = significant at 1% level

Percent families use of any contraceptive method

MODERN

ANYMET

-0.432(**) 0.017 0.625(**) 0.272(*) 1.000

-0.104

TRAD

0.645(**) 0.196 -1.000(**) -0.921(**) -0.625(**) 1.000

-0.058 -0.689(**) 0.059 0.595(**) 0.562(**) 0.344(**) -0.595(**) 1.000

-0.044

NOMET FPACCESS

Poverty rate

Couples with access to family planning

Percent of families did not use any contraceptive URBEXP

TFR

POP15HS

-0.332(**) 0.218 0.267(*) 0.138 0.377(**) -0.267(*) 0.507(**) 1.000

-0.010

POVERTY

Percent families use of traditional contraceptive method

-0.580(**) -0.251(*) 0.921(**) 1.000

0.125

MODERN

NOMET

TRAD

-0.645(**) -0.197 1.000

Child-to-woman ratio

0.096 1.000

1.000

0.058

ANYMET

Household size

0.224

0.135

CWR

CWR

1.000

arcsin of % forest HHSIZE cover for provinces

HHSIZE

Notes:

arcsin of % forest cover for provinces HHSIZE CWR ANYMET MODERN TRAD NOMET FPACCESS POVERTY POP15HS TFR URBEXP

Pearson correlation coefficient

Table 21. Correlates of contraceptive use

Mapping Population-Biodiversity Connections in the Philippines

0.088 0.716(**) -0.120 -0.161 0.022 0.120 0.253(*) 0.488(**) -0.503(**) 1.000

0.133

TFR

0.015 0.093 0.152 0.194 -0.012 -0.154 0.036 0.097 -0.016 0.032 1.000

0.118

URBEXP

Urban population exponential growth rate

Total fertility rate

who attained high school

Rate of population 15 years and over

-0.541(**) -0.443(**) 0.465(**) 0.425(**) 0.297(**) -0.464(**) 0.372(**) -0.004 1.000

-0.141

POP15HS

RESULTS AND DISCUSSION

USAID, Philippine Mission

RESULTS AND DISCUSSION

to basic government services, especially health services. to safe drinking water (WATER) and percentage of Access to health facilities and services is represented households with sanitary toilets (TOILET). This by the population-barangay health station (BHS) and implies that households without access to safe drinking population-barangay health worker (BHW) ratios. The water and sanitation facilities are more vulnerable to correlation analysis indicates that lower population- diseases. barangay station ratio and population-barangay health Since knowledge on the transmission of worker ratio are associated with high crude death diseases is an important factor in disease control and rates of males (CDRMALE) and females (CDRFEM) prevention, it is expected that education, as represented (Table 22). The relationship is especially significant in the study by the percentage of population 15 years between population-barangay health worker ratio and old and over with at least high school education, will female crude death rate. The correlation analysis play a significant factor. Indeed, a higher percentage of revealed the significant relationship between access to the 15-year-old and over population with high school health services and infant mortality rates. The higher education is associated with greater access to safe the ratios of the population-barangay health station drinking water and sanitary toilets, and, consequently, and population-barangay with lower incidence health worker, the of morbidity. Further lower are the male and implication of the female infant mortality result is the importance rates (IMRMALE and that should be put on IMRFEM) and the improving access not only mortality for children to health facilities but under 5 years old also to education (whether (U5MR). In other formal or non-formal) in words, the mortality disease prevention. rates are lower when there The correlation is increased availability coefficients further of health services in these Barangay health workers providing basic health services show that higher areas. forest cover is Likewise, lower ratios of barangay health stations associated with lower population-to-barangay health and workers are associated with higher incidence worker ratio (BHW), which tend to support our view of diseases like diarrhea, bronchitis, pneumonia, that far-flung areas, where forests usually still abound, tuberculosis and influenza (Table 23). These diseases have less access to government health services (Table are usually associated with high incidence of poverty 24). Since barangay health workers are the main and low-income households—i.e., those who can vehicles for family planning services, it follows too least afford expensive private medical services and that access to modern methods of family planning and are, therefore, more dependent on government health access to family planning services, in general, would services provided by barangay health stations and be low. Low forest cover, therefore, is associated with lower workers. The results, indeed, indicate that increase in access to access to family planning methods and services. The results, health facilities and services will significantly improve the health however, show that the correlation coefficients are status of people in the upland and rural areas, especially of not significant. It should be emphasized that the nonwomen, infants and children. Nevertheless, even with a lower significant correlation coefficients imply that either ratio of population to health facilities, improvement in the health the relationships can be significant but not linear, or status is possible for as long as more barangay health workers are that there are other factors, which are more significant present in these areas. in mediating the relationship between forest cover Sanitation and education are important factors and health. However, based on the direction in the in the incidence, spread and prevention of diseases. relationship (whether positive or negative signs in the Higher incidence of food-borne and water-borne coefficients), it can be concluded that improvement in the diseases, like diarrhea, is associated with low access delivery of barangay health services and provision of education in

Mapping Population-Biodiversity Connections in the Philippines

49

50

Female infant mortality rate

Population density

IMRFEM

DENSITY

URBEXP

U5MR

POP15HS

BHW

BHS

0.830(**) 0.763(**) 1.000

0.402(**)

IMRMALE

(**) = significant at 1% level

Male Infant mortality rate

IMRMALE

(*) = significant at 5% level

Crude death rate for females

0.943(**) 1.000

1.000

Crude death rate for males

0.318(*)

CDRFEM

0.371(**)

CDRMALE

CDRFEM

1.000

arcsin of % forest cover for provinces

CDRMALE

Notes:

URBEXP

U5MR

POP15HS

BHW

BHS

DENSITY

IMRFEM

IMRMALE

CDRFEM

arcsin of % forest cover for provinces CDRMALE

Pearson correlation coeficient

-0.245

BHS

-0.333(**)

BHW

-0.302(**) -0.195 -0.197 -0.295(**) -0.182 -0.296(**) -0.421(**) -0.310(**) -0.311(**) -0.359(**) -0.248(*) -0.232(*) 1.000 0.952(**) 0.648(**) 1.000 0.546(**) 1.000

-0.276(*)

DENSITY

-0.496(**) -0.426(**) -0.621(**) -0.658(**) 0.210 0.183 -0.029 1.000

-0.141

POP15HS

Urban population exponential growth rate

Under age-5 mortality rate

Rate of population 15 years and over who attained high school

Population-to-barangay health worker ratio

Population-to-barangay health station ratio

0.866(**) 0.817(**) 0.936(**) 1.000

0.373(**)

IMRFEM

Table 22. Correlates of crude death rate and infant mortality

Mapping Population-Biodiversity Connections in the Philippines

0.763(**) 0.738(**) 0.910(**) 0.874(**) -0.478(**) -0.352(**) -0.410(**) -0.690(**) 1.000

0.360(**)

U5MR

-0.064 -0.163 0.034 -0.001 -0.144 -0.159 -0.116 -0.016 0.017 1.000

0.118

URBEXP

RESULTS AND DISCUSSION

Morbidity rate due to influenza

Morbidity rate due to tuberculosis

Population-to-barangay health station ratio

FLU

TB

BHS

(**) = significant at 1% level

Morbidity rate due to pneumonia

PNEUMON

Percent of households with access to safe drinking water Percent of families with own sanitary toilets Urban population exponential growth rate

WATER TOILET URBEXP

0.044 0.174 0.231(*) 0.047 0.049 0.032 -0.375(**) 1.000

Rate of population 15 years and over who attained high school

-0.151 -0.327(**) -0.194 -0.155 -0.018 0.546(**) 1.000

-0.010

Poverty rate

-0.093 -0.076 -0.029 -0.107 0.025 1.000

-0.333(**)

POVERTY

POP15HS

0.093 0.050 0.136 0.200 1.000

-0.245

BHW

Population-to-barangay health worker ratio

0.332(**) 0.528(**) 0.042 1.000

0.352(**) 0.323(**) 1.000

-0.114

BHS

POVERTY

0.130

0.046

TB

BHW

FLU

PNEUMON

Mapping Population-Biodiversity Connections in the Philippines

(*) = significant at 5% level

Morbidity rate due to bronchitis

0.536(**) 1.000

1.000

Morbidity rate due to diarrhea

0.429(**)

BRONCHI

0.273(*)

DIARRHEA

BRONCHI

1.000

arcsin of % forest cover for provinces

DIARRHEA

Notes:

arcsin of % forest cover for provinces DIARRHEA BRONCHI PNEUMON FLU TB BHS BHW POVERTY POP15HS WATER TOILET URBEXP

Pearson correlation coeficient

Table 23. Correlation between access to health services, sanitation and morbidity

-0.237(*) -0.019 -0.296(**) -0.240(*) -0.182 0.183 -0.029 -0.004 1.000

-0.141

POP15HS

-0.016 0.211 0.022 -0.119 -0.108 0.092 -0.138 0.106 0.647(**) 1.000

0.071

WATER

-0.096 0.139 -0.153 -0.053 -0.149 -0.363(**) -0.301(**) 0.213 0.647(**) 0.635(**) 1.000

-0.039

TOILET

0.012 -0.150 -0.013 0.064 -0.020 -0.159 -0.116 0.097 -0.016 -0.171 0.137 1.000

0.118

URBEXP

RESULTS AND DISCUSSION

51

52

Population-to-barangay health station ratio

BHS

(**) = significant at 1% level

Population density

DENSITY

(*) = significant at 5% level

Couples with access to family planning

FPACCESS

0.562(**) 1.000

1.000

Percent families use of modern contraceptive method

-0.044

0.125

1.000

FPACCESS

MODERN

arcsin of % forest cover for provinces

MODERN

Notes:

URBEXP

POP15HS

POVERTY

BHW

BHS

DENSITY

FPACCESS

MODERN

Pearson correlation coeficient arcsin of % forest cover for provinces

URBEXP

POP15HS

POVERTY

BHW

0.043 0 1.000

0.011 -0.525(**) 0.952(**) 1.000

-0.245

BHS

-0.185 -0.531(**) 0.648(**) 0.546(**) 1.000

-0.333(**)

BHW

0.138 0.507(**) 0.042 0.032 -0.375(**) 1.000

-0.010

POVERTY

Urban population exponential growth rate

Rate of population 15 years and over who attained high school

Poverty rate

Population-to-barangay health worker ratio

-0.276(*)

DENSITY

Table 24. Correlates of access to family planning and contraceptive use

Mapping Population-Biodiversity Connections in the Philippines

0.425(**) 0.372(**) 0.210 0.183 -0.029 -0.004 1.000

-0.141

POP15HS

0.194 0.036 -0.144 -0.159 -0.116 0.097 -0.016 1.000

0.118

URBEXP

RESULTS AND DISCUSSION

5.2.5. Summary of the Correlation Analyses The correlation analyses provided some characterization of the upland rural households. The upland households are generally natural resource-dependent, specifically on agriculture and forests. They derive employment mainly from natural resource-based sectors, like agriculture, forestry and hunting. Most of these households have no social security, except perhaps for some access to government health services provided by barangay health workers. Upland households are generally poorer, have larger family sizes, and have higher fertility rates. The high fertility preferences in these households may be motivated by the need for children as additional labor. This preference stems from poor families reliance on children to provide additional labor for farm and household work, additional income for the family when they go to work, assistance to mothers in child rearing, and as old-age security for parents (Orbeta, 2002). The expectation of more children as greater security during

Agta fisherman in the Northern Sierra Madre coast ready for traditional fishing

Mapping Population-Biodiversity Connections in the Philippines

Plan International

old-age is often called “insurance birth.” Studies on the impact of additional children on household time allocation show that labor market

hours of mothers usually decline in the 14 months post-natal period, but return to the previous level after that. However, the older daughters’ labor market time increases and replaces that of the mother, while that of the father remains the same (Tiefenthaler, 1997). Some opposing results show that with more children, the time spent by mothers on household work increases and the labor market time decreases, while the labor market time of fathers increase (Garcia, 1990; King and Evenson, 1983; Quiazon-King, 1978). Both results, however, indicate that poor households with large family sizes are more constrained in allocating time between reproductive work and productive work, most especially the female members of the family. Notwithstanding, since these correlations take into account only one socioeconomic or demographic variable or correlate of biodiversity at a time, the methodology tends to be severely limited in describing the complex relationship between the population and environment. Bivariate correlation analysis does not take into account the simultaneous impact of all the socioeconomic and demographic variables on biodiversity—a limitation addressed by a multivariate statistical approach, the results of which are discussed in the next section.

RESULTS AND DISCUSSION

rural and upland areas will result in significant positive impact on reproductive health.

53

RESULTS AND DISCUSSION

Multiple Regression Model and Summary Index of Socioeconomic and Demographic Pressure

5.3

The multiple regression technique14 is used in the project to: (1) identify the socioeconomic and demographic variables that significantly influence biodiversity when these variables are simultaneously taken into account; and (2) obtain a summary index that measures the impact of these significant socioeconomic and demographic factors on the biodiversity indicators. The results are discussed for item (1) under section 5.3.1 for the multiple regression model and item (2) under section 5.3.2 for the summary indices. 5.3.1. Analysis of the impact of socioeconomic and demographic variables on forest cover using multivariate analysis

in Table 17, including the dependent criterion variable, were transformed to their natural logarithmic form so that a multiple linear regression model can be fitted. Among the 25 variables selected and hypothesized to affect the percentage forest cover of provinces, only three predictor variables came out to be statistically significant, namely: population density; percent male labor force in agriculture, hunting and forestry; and net male migration rate. Table 25 presents a summary of the results from the multiple linear regression analysis. Annexes 5.1 to 5.4 illustrate the spatial distribution trends of these significant predictor variables and forest cover using maps.

What are the socioeconomic and demographic variables The multiple regression model in Table 25 can that significantly influence the indicators of biodiversity, be rewritten as: i.e., percentage forest cover for the provinces? This ARCFOR=e4.229DENSITY-0.500AGRIM-0.503(MIGM+ 0.1) 0.331 question is answered using the results from the stepwise, multiple linear regression model. The where ARCFOR is the arcsine transformation of the predictor socioeconomic and demographic variables percent forest cover, DENSITY is the population

Table 25. Result of the stepwise multiple linear regression model for provincial data (Dependent variable: natural log of arcsine percent forest cover)

Mapping Population-Biodiversity Connections in the Philippines

Dependent variable: LNARCFOR (Constant) LNDENSITY LNAGRIM LNMIGM

Regression Residual Total R 0.780(c)

B

Standard error

Beta

4.229 -0.500 -0.503 0.331

0.829 0.083 0.146 0.101

-0.935 -0.562 0.347

Sum of squares 5.752 3.708 9.459 R square 0.608

ANOVA Mean square 1.917 0.086

df 3 43 46 MODEL SUMMARY Standard error Adjusted R square of the estimate 0.581 0.29364

t

Significance

5.099 -6.045 -3.438 3.260

0.000 0.000 0.001 0.002

F 22.235

Significance 0.000

Notes: LNARCFOR:

natural log of arcsine percent forest cover

LNDENSITY:

natural log of population density

LNAGRIM:

natural log of percent male labor force in agriculture

LNMIGM:

natural log of male net migration rate; a value of 0.1 is added to the net migration rates to avoid error in transforming to natural log negative values.

14

54

Unstandardized coefficients

COEFFICIENTS Standardized coefficients

For a detailed discussion of the multiple regression technique, refer to Retherford, R. D. and M. K. Choe. 1993. Statistical Models for Causal Analysis. New York, John Wiley and Sons, Inc.

PCSDS, Palawan

RESULTS AND DISCUSSION

density of provinces, AGRIM is the percentage of the Manila led to locating new industries and settlements male labor force in agriculture, hunting and forestry, in nearby provinces like Bulacan, Laguna and Cavite. and MIGM is the net migration rate of males. The For this reason, there is an increase in employment statistical model says that, all other variables constant, opportunities in these places. On the other hand, it can be expected that: provinces with high forest cover, like Palawan and • a ten percent (10%) increase in population Quirino, are migrant destinations too, implying a rural, density will result in a five percent (5%) decrease lowland–upland migration pattern. It appears from the in the arcsine percent forest cover; regression result that lowland–upland migration is still • a ten percent (10%) increase in the percentage quite strong. of the male labor force engaged in agriculture, Following the multiple regression model, the hunting and forestry results in a 5.03 percent impact of population density alone on forest cover in decrease in the arcsine percent forest cover; and 10 years is estimated with the assumption that provincial • a ten percent (10%) increase in male net migration population will continue to grow at the current rate. rate15 results in a 3.31percent increase in arcsine The estimates (Table 26) show that among the top 10 forest cover. provinces, which will likely experience the highest rate Population density of deforestation are Palawan, and the percentage of males Isabela, Cagayan, Oriental engaged in agriculture, hunting Mindoro, Quezon, Bulacan, and forestry met the a priori Maguindanao, Lanao del Sur, expectation that an increase Agusan del Sur and Occidental in density and percentage of Mindoro. The estimated forest males engaged in agriculture, loss for these 10 provinces hunting and forestry will likely ranges from 3,135 hectares result in low percentage forest to 17,308 hectares per year if cover. It was also hypothesized, their population grows at the a priori, that the higher the net current rate. Note that these migration (i.e., net in-migration), provinces still have relatively the lower will be the forest large percentage of forest cover or the higher will be the cover. The provinces that will deforestation. The regression experience the least forest loss result gave the opposite. It is are Capiz, Masbate, Camarines Male engaged in hunting and forestry plausible that the result reflects Norte, Sorsogon, Marinduque, the “pull” exerted by areas with high forest cover on Misamis Occidental, Guimaras, Siquijor, Biliran and migrants, i.e., these areas are relatively more attractive Metro Manila. to migrants. Eventually, though, it is expected that the For the 61 provinces with complete data on high positive net migration rate or in-migration will add all the variables (forest cover, net migration rate, to the pressure on the resources in the area resulting in population data and percent distribution of the labor the deterioration of the quality of the forests and other force by industry and gender), the total estimated forest resources. loss is about 11.54 percent or 1,128,476 hectares in ten The result may also imply that there are years if the current population growth continues. The two migration patterns simultaneously occurring at contribution of the top 10 provinces to deforestation present. First is rural–urban migration. Our data is approximately 48 percent. on Region IV previously cited show that provinces While other socioeconomic factors are not around Metro Manila, the most urbanized area in the statistically significant in the model, they may potentially country and the center of commerce and government, worsen the deforestation scenario at the community receive a lot of migrants. The congestion of Metro level. For instance, the bivariate correlation analyses

Rate was transformed by adding 0.1 to avoid a negative value that results in error in its natural log.

Mapping Population-Biodiversity Connections in the Philippines

15

55

RESULTS AND DISCUSSION

Table 26. Provinces with highest and lowest projected deforestation rate in ten years (2000 to 2010)

Province

Estimated decline in Projected Forest percent forest cover, 2000 forest cover, 2010 cover, 2010

Population growth rate, 2000

Projected population density, 2010

(%)

(pers/ sq.km)

(%)

(ha.)

4 2 2

3.60 2.25 2.26

74 159 139

15.09 7.05 7.80

4

2.46

205

4 3 ARMM ARMM

1.90 4.93 4.16 3.42

CARAGA

Forest cover change

Deforestation rate

(ha.)

(ha.)

(ha/yr)

931,586 552,983 507,008

758,507 490,275 450,470

-173,079 -62,708 -56,538

17,308 6,271 5,654

8.98

355,868

312,700

-43,168

4,317

237 1,318 438 264

4.87 15.82 7.54 11.02

387,915 126,982 143,711 184,191

350,312 89,793 108,268 150,134

-37,603 -37,190 -35,443 -34,057

3,760 3,719 3,544 3,406

1.79

83

4.34

355,374

322,884

-32,490

3,249

4

2.45

83

9.12

261,128

229,782

-31,346

3,135

6 5

1.00 1.71

282 210

2.01 1.08

93,688 45,732

89,012 41,523

-4,676 -4,209

-468 421

3

0.94

241

2.25

90,192

86,061

-4,131

413

3 4

2.04 1.81

391 280

1.92 3.80

34,506 34,506

30,677 31,263

-3,829 -3,243

383 324

10

1.27

291

1.58

44,024

41,113

-2,912

291

6 7 8 NCR

2.43 2.19 1.28 1.06

304 321 295 17,451

4.88 3.55 2.13 0.56

21,065 9,219 15,681 5,987

18,307 8,131 14,649 5,655

-2,758 -1,088 -1,032 -332

276 109 103 33

Region

Top 10 Palawan Isabela Cagayan Oriental Mindoro Quezon Bulacan Maguindanao Lanao del Sur Agusan del Sur Occidental Mindoro Least 10 Capiz Masbate Camarines Norte Sorsogon Marinduque Misamis Oriental Guimaras Siquijor Biliran Metro Manila Note: The p provincial popu

Mapping Population-Biodiversity Connections in the Philippines

Sources: Department of Environment and Natural Resources and the National Statistics Office

56

already provided indications on the potential direction of the relationship between poverty, population density, unemployment rate, percentage of males in agriculture and educational attainment. Poverty rate tends to be high in areas when either one of the following exists: a high population density; a high unemployment rate; a high percentage of males are engaged in agriculture, forestry and hunting; or a low percentage of the population aged 15 years and over who have at least high school education. The implication is that potential conservation interventions will have to address both population and development issues as well, otherwise the gains in conservation efforts will be overshadowed by the needs of a very fast growing population. The province of Palawan provides a good illustration of the point made above. Based on projections done in the project, the province is projected to experience one of the highest deforestation rates

within the next 10 years if current population growth rate continues. A business-as-usual analysis for south Palawan was done in 2003 using year 2000 barangaylevel data to spatially project the risk of habitat or forest losses within the Mt. Mantalingahan range using a logit regression model (Wong et. al., 2003). The study specifically looked at the impact of socioeconomic (income and quality of life or HDI), policy (tenure), demographic (population growth and population-toland ratio or population density), land use (richness and diversity of land uses in a given map area measured by a 30x30 pixel resolution, which is the unit for spatial and statistical analysis), and physical (slope and elevation) variables on deforestation. The results showed that the probability of deforestation is smaller when: (1) the elevation is higher and slope is steeper; (2) the pixel is farther from roads; and (3) there is more secure land tenure. On the other hand, the closer the pixel is to

0.331 for the transformed net migration rates for males (male net migration rate + 0.1), were used as weights in calculating the indices. The weight was calculated as the ratio of the regression coefficient to the total value of the three coefficients, i.e., 1.334. The resulting summary index value was expressed as: PINDEX = (-0.500/1.334)*DENSITY– (0.503/1.334)*AGRIM+(0.331/1.334)*MIGM

Mapping Population-Biodiversity Connections in the Philippines

The resulting index values derived from the above equation provide a composite measure of the potential impact of the three variables. These values were subsequently used to develop the index map (Figure 10), expressing the degree of socioeconomicdemographic pressure on forest cover by provinces. This map shows that provinces with very high socioeconomic-demographic pressure on forest cover are Abra, Mt. Province and Ifugao in the Cordillera Administrative Region (CAR), Nueva Vizcaya, Quirino and Aurora in Region 2, Occidental Mindoro and Palawan in Region 4, Samar and Eastern Samar in Region 8, and Surigao del Sur and Agusan del Sur in the CARAGA (Table 27). The three priority biodiversity corridors of the Critical Ecosystems Partnership Fund (i.e., Sierra Madre, Eastern Mindanao and Palawan corridors) appear to be facing high to very high socioeconomic-demographic pressure. The list compares well with the list of provinces earlier estimated to likely experience high deforestation rate within the next ten years given current population growth rate. The forests of Palawan, Occidental Mindoro and Agusan del Sur remain in the very high risk category of being lost due to socioeconomic and demographic pressures. The difference between the two is that the 10-year deforestation projection made earlier only considered changes in population density based on population growth rates, while the forest pressure indices consider all three of the 5.3.2. Index of Socioeconomic-demographic Pressure significant variables in the multiple regression equation. Nonetheless, the very high pressure indices in Figure The impact on percent forest cover by population 10 or Annex 5.5 verify the CPAs facing extremely high density, percentage of males in the labor force employed to very high socioeconomic pressures illustrated on the in agriculture, forestry and hunting, and net migration PBCPP map in Annex 5.9. The result can be used to rates for males is combined into an index that indicates prioritize the provinces and corresponding CPAs for the extent of socioeconomic-demographic pressure. appropriate intervention to relieve or minimize the The regression coefficients in the multiple regression socioeconomic and demographic pressures that hinder model, i.e., 0.500 for population density; 0.503 for success in conservation efforts. percent males in agriculture, forestry and hunting; and

RESULTS AND DISCUSSION

markets, the higher is the likelihood of deforestation. The higher the combined effect of population growth and population-land ratio implies higher demand for agricultural products and, consequently, the greater is the probability of deforestation. The index of richness in land uses surrounding a pixel is statistically significant as well, implying that the higher the index of richness in land uses, the higher is the probability of deforestation. What is interesting to note is the result on income and HDI, which indicated that as income and quality of life increase, so does the probability of deforestation. In economics, there is what is called a Kuznet hypothesis which states that growing incomes at the lowest levels will increase the probabilities of deforestation until the incomes exceed a “threshold” after which their continuing increase will result in less deforestation and better environmental quality. A research in Africa indicated that the lowest income per capita threshold for a Kuznet curve in deforestation is US$1,300 (Bhattarai and Hammig, 2001). The average income per capita in South Palawan for year 2000 was only approximately US$60, less than half of any Kuznet “threshold.” This result has a crucial implication for conservation strategies in the study area and, perhaps, in similar areas in the country as well: increasing incomes alone will be insufficient to prevent deforestation. Livelihood projects to increase income must be accompanied by strategies to: (1) resolve conflicting land uses and rising land demands by the fast-growing population; (2) improve wage opportunities outside of the forest and subsistence agriculture sectors to wean the upland poor from dependence on forest land and resources; and (3) reduce population growth and in-migration rates.

57

RESULTS AND DISCUSSION Mapping Population-Biodiversity Connections in the Philippines

58

Figure 10. Map indicating the degree of socioeconomic and demographic pressures in Philippine provinces (Note: A larger, full color map is attached as Annex 5.5)

Table 27. List of provinces with high to very high socioeconomic-demographic pressure indices Province Very High Abra Mt. Province Ifugao Nueva Vizcaya Quirino Aurora Occidental Mindoro Palawan Samar Eastern Samar Surigao del Sur Agusan del Sur

Region CAR CAR CAR 2 2 2 4 4 8 8 CARAGA CARAGA

Province High Ilocos Norte Cagayan Isabela Zambales Oriental Mindoro Catanduanes Masbate Northern Samar Samar Zamboanga del Norte Lanao del Sur Maguindanao

Region 1 2 2 3 4 5 5 8 8 9 ARMM ARMM

used to reflect an index for expressing the vulnerability of a CPA to socioeconomic and demographic pressures or over-exploitation. For illustrative purposes, in Table The relationship of socioeconomic and demographic 28, the CPAs with the highest index16 of priority for pressures in the provinces to the biodiversity in CPAs conservation or vulnerability to pressure are listed. was illustrated using a cartographic modeling technique The list shows that the island of Palawan has the that applied an experts-based rule for overlay analysis. most number of CPAs needing the topmost priority This helped circumvent anthropogenic-related data for conservation actions. Intuitively, these areas listed in the table are limitations in CPAs, where we can find as explained in the those sites with the “Spatial Analyses” greatest risk of losing section above. their important The systematic biodiversity when overlay of two placed under corresponding maps pressure from a on socioeconomic and business-as-usual demographic pressures human-induced in the provincial pressure scenario. forests (Annex 5.5) Hence, they are and on biodiversity Human-induced pressure to forest cover such as kaingin areas that can importance in the be regarded as CPAs (Annex 5.6) yielded another map (Annex 5.7) indicating geographic extremely vulnerable to pressure and would be the first areas that may be prioritized for appropriate to experience irrecoverable biodiversity loss when left interventions to alleviate or curtail pressures from unchecked. However, this does not mean that other human activities that may affect conservation efforts. This map shown in Annex 5.7, besides CPAs not classified as “extremely high urgent” will illustrating visually the trend where prioritization of be given less attention, especially when the biological conservation efforts should occur, can be alternatively information in these areas show that important wildlife

RESULTS AND DISCUSSION

Table 28. List of CPAs with “extremely high urgent” index of priority or vulnerability to humaninduced pressures

Mapping Population-Biodiversity Connections in the Philippines

G. Villoria

5.3.3. Index of CPA Vulnerability to Human-induced Pressure

CPA Extremely High Urgent Balbalasang-Balbalan National Park Central Sierra Madre Mountains Casecnan River Basin

Island

Luzon

Island

Palawan

Luzon

Victoria and Anapalan Ranges

Palawan

Luzon

Mt. Matalingajan Mt. Cabalantian-Mt. Capotoan Complex Mt. Hilong-hilong (Urdaneta) North Diwata (Bislig, Mt. Agtuuganon-Mt. Pasian

Palawan

Sablayan

Palawan

Iglit and Baco Mountains

Palawan

El Nido

Palawan

San Vicente-TaytayRoxas Forest

Palawan

16

CPA Extremely High Urgent Puerto Princesa Subterranean River National Park (Cleopatra’s Needle)

Samar Mindanao Mindanao

Based on a classification system of the PBCPP (Ong et. al., 2002), the following index of priority levels were defined: “extremely high urgent” areas need the most urgent attention because they are at immediate risk of losing a high percentage of biological diversity; and “extremely high critical” areas are either with extremely high biodiversity importance with very high socioeconomic pressure or with very high biodiversity importance, but with extremely high socioeconomic pressure. The latter term can take on a very slightly different interpretation if a different set of map combinations is used, as explained in Box 2.

59

RESULTS AND DISCUSSION Mapping Population-Biodiversity Connections in the Philippines

60

Figure 11. Map indicating the vulnerability of conservation priority areas to socioeconomic and demographic pressures (Note: A larger, full color map is attached as Annex 5.7)

species are at a greatest risk of being lost when placed under pressure. The conservation interventions may, therefore, differ depending on the area’s pressure index—for instance, biological studies may be required for areas without sufficient data. To illustrate the point, the map showing the biodiversity areas of extreme vulnerability (Figure 11) is compared with the map of threatened animals (Figure 12). The comparison shows that the conservation priority areas in Balbalasang-Balbalan of the Cordilleras, Central Sierra Madre Mountains of Eastern Luzon, and Mt. Hilong-hilong and North Diwata of Eastern Mindanao should be prioritized, as well, because they have the highest number of reported threatened fauna (i.e., the sum total of the number of critically-endangered, endangered and vulnerable animal species). However, this should be interpreted with caution because the number of biological surveys conducted influences the reported number of

threatened species. Hence, on the ground validation may be needed by visiting each area and assessing whether the number of threatened species has increased or decreased, and additionally the current threats and management activities. For example, BalbalasangBalbalan in the Cordillera, which is an ancestral domain area under the care and protection of indigenous communities, is predicted to have a vulnerability index of “extremely high urgent.” Site reassessment of the vulnerability index may reveal the need to downgrade the classification since the area is now under strict protection and management by the indigenous people (Balete, D., personal communication, June 2004). It is possible, however, that the classification remains “extremely highly vulnerable” because provinces currently facing high socioeconomic-demographic pressures surround the area. Encroachment into the CPA is possible unless the pressures in the surrounding provinces are not addressed.

CI–Philippines Population and environment policy consultations at the international level

Mapping Population-Biodiversity Connections in the Philippines

and broader experience of the field situation to guide the choice of interventions regardless of which map becomes the basis for prioritizing areas. In the absence of these initiatives, supplementary information such as the map of threatened animal species (Figure 12) can help provide a logical choice and can help support a decision made on priorities for conservation activities and vulnerabilities to biodiversity loss or extinction. To illustrate how the three maps can be used in prioritizing CPAs, Mt. Hilong-hilong and North Diwata in Eastern Mindanao are chosen as priority areas based, first, on the threatened fauna map in Figure 12. From the PBCPP map (Annex 5.9), the priority level of biodiversity conservation is noted as “extremely high critical,” but the new biodiversity priority map or vulnerability index map (Annex 5.7) raised this priority level to “extremely high urgent.” The priority level is further reassessed using two types of information, i.e., Figure 12 (number of threatened animals map) and Figure 10 (pressure index map). These two types of information verified that: (a) a very high number of threatened fauna is found in those areas; and (b) the socioeconomic-demographic pressure on habitat is very high as well, indeed, demonstrating that these areas’ vulnerability is very high and warrants that their priority level for conservation actions should be updated into the topmost priority, i.e., “extremely high urgent.” A similar analysis can be performed with the other areas. It may well be that the new biodiversity priority map of Figure 11 provides a new perspective, based on statistical data, into the status of biodiversity urgency in the Philippine islands.

RESULTS AND DISCUSSION

The index map of vulnerability showing areas where the risks of losing important biodiversity are located (Annex 5.7) is analogous to the map of integrated terrestrial and inland water biodiversity conservation priorities (Annex 5.8). The map on integrated biodiversity conservation priorities is derived from the combination of the biodiversity map (Annex 5.6) with the socioeconomic pressure map (Annex 5.9) from the PBCPP. Comparing the index map of vulnerability of this project and the integrated terrestrial and inland water conservation priority areas map of the PBCPP shows only one match for “extremely high urgent” priorities, i.e., San Vicente-Taytay-Roxas Forest in Palawan. The two maps differed in the locations of other CPAs where an “extremely high urgent” priority category was assigned. There are now three maps that may used as basis for prioritizing areas for biodiversity research and conservation initiatives, namely: Annex 5.8 or integrated conservation priorities processed through experts’ opinions based on personal knowledge and data of the CPAs; Annex 5.7 or the index of vulnerability; and Annex 5.5 or the pressure index map for forest cover based on statistical data. Each provides different levels of information and conservation intervention. Macro or national policies addressing populationdevelopment-environment interactions, however, can already use the pressure index map as it provides information on the extent of land areas under very high socioeconomic and demographic pressure given recent data. Conservation initiatives and researches at the CPA-level may require site-specific information

61

Caveat: The number of threatened animals per CPA was determined from records of a data set that was prepared for the PBCPP (Valenzuela-Duya, M., personal communication, 2004). The data do not represent a comprehensive list of species recorded or found in each CPA and, thus, do not completely reflect all of the threatened animals that may be found in the CPAs. Some extrapolations were made to estimate the number based on provincial level records. The number of threatened species may also be biased in favor of one taxonomic group, whose species records are very well updated by extensive biological surveys, e.g., the birds. Some areas, which have a low number of threatened animals, might be due to limited biological surveys and further field studies would be necessary to establish a better picture of their trend in threatened fauna. Hence, the map above reflects the status of the data obtained for threatened species and we use this to show the indicative state of threatened fauna distribution, as per currently collected records. Note that the number of threatened fauna is a sum of the number of animal species falling under the IUCN categories of critically endangered, endangered and vulnerable. M. de Guia

RESULTS AND DISCUSSION Mapping Population-Biodiversity Connections in the Philippines

Figure 12. Indicative number of threatened animals by CPAs

Blue-naped Parrot (Tanygnathus lucionensis)

62

Special Case: Mindanao

The following sections discuss Mindanao as a special case because of a combination of socioeconomic and demographic pressures on biodiversity and the presence of two major sources of armed conflict in the area, which may have some implications on the populationenvironment links not only in Mindanao but for the whole country as well. 5.4.1. Socioeconomic and Demographic Pressures

•

i.e., religion-based conflict operations are minimal in areas with high ideology-based conflict and vice-versa; and (2) the problem with religion-based conflict is predominant in rural areas rather than in urban areas (r = -0.571). High male net migration rate is significantly associated with high female net migration rate. Previous studies show that male members of the family usually migrate to an area first to assess the opportunities after which other member of the family follow. The correlation with unemployment rate,

Mapping Population-Biodiversity Connections in the Philippines

Eight (8) out of 20 provinces in Mindanao have recent data on forest cover (Japan Forest Technology Association or JAFTA, 1993–2000) (Annex 5.4). Of these, Agusan del Sur and Surigao del Sur in eastern Mindanao, and Lanao del Norte, Lanao del Sur, Maguindanao and Zamboanga del Sur in western Mindanao were found to have very high socioeconomic-demographic pressure in their CPAs based on the pressure map (Annex 5.9) established from the PBCPP. Provinces with CPAs of extremely high pressures can be found in Zamboanga del Norte of western Mindanao, and Agusan del Sur, Bukidnon and Davao of central Mindanao. Experts’ opinions on the socioeconomic pressure (Annex 5.9) of CPAs in these provinces appear to coincide with the project’s findings (Annex 5.5). There are more than 30 conservation priority areas in Mindanao, and all, except one (Pantukan Mabini-Maco Area within the Compostela Valley and Davao Oriental provinces), are considered under extremely high to high socioeconomic pressure (Annex 5.9). When this was combined with the biodiversity importance map (Annex 5.6) in the previous PBCPP project, the combination yielded the integrated priority map of Annex 5.8, as explained previously. It is interesting to note that the vulnerability index map (Annex 5.7) provides a new spatial trend of integrated biodiversity priorities of CPAs in Mindanao. It appears that there is a trend of progression from very high priority or vulnerability on the western side of Mindanao to extremely high critical in central Mindanao and to extremely high urgent on the eastern side of Mindanao. The trend seems to imply that a closer look should be directed at eastern Mindanao when designing conservation, population and development

interventions. Performing a multiple regression analysis separately for Mindanao provinces was not possible because of the lack of forest cover data. Hence, the project followed the results from the regression analysis done for the whole country to express the pressure in Mindanao. It is possible, however, to establish the relationships among the socioeconomic and demographic variables through a separate bivariate correlation analysis for Mindanao provinces only. The results of this analysis appeared to meet our a priori expectations on the relationships between the selected socioeconomic and demographic variables. The results of the correlation analysis presented in Table 29 show the following: • Urban areas (see also Annex 5.10) tend to have higher population densities, smaller household sizes, and lower child-to-woman ratios than rural areas. • High female net migration rate is highly associated with high total fertility rates and high child-to-woman ratio. It is significantly lower in areas with high religion-based armed conflict, indicating that areas of armed conflicts are less attractive destination places for female migrants. • Ideology-based armed conflict is also present in certain areas in Mindanao. The correlation analysis seems to show that: (1) religion-based armed conflict and ideology-based armed conflict differ in areas where they are prevalent (r = -0.679),

RESULTS AND DISCUSSION

5.4

63

64

Population density

Poverty rate

DENSITY

POVERTY

MIGMALE

TFR

POP15HS

0.809(**) -0.364 1.000

0.151 0.363 0.003 0.580(**) 1.000

0.058 0.452(*) -0.281 0.562(**) 0.304 1.000

TFR

Net migration rate of males

Total fertility rate

over who attained high school

Rate of population 15 years and

0.361 0.249 0.019 1.000

(**) = significant at 1% level

Net migration rate of females

(*) = significant at 5% level

Percent urban population

-0.164 1.000

MIGFEM

1.000

URBPCT MIGFEM DENSITY POVERTY POP15HS

URBPCT

Notes:

Correlation Coeficient URBPCT MIGFEM DENSITY POVERTY POP15HS TFR MIGMALE HHSIZE CWR UNEMP IDEOCON RELCON -0.247 0.490(*) -0.411(*) 0.318 0.160 0.238 0.189 -0.179 1.000

CWR

RESULTS AND DISCUSSION

Provinces with religion-based armed conflict

0.421 0.454 0.099 0.322 0.340 .(a) 0.097 -0.600(**) 0.115 -0.208 1.000

-0.571(*) -0.571(*) -0.175 -0.136 -0.416 .(a) -0.380 0.470(*) -0.008 -0.112 -0.679(**) 1

IDEOCON RELCON

Provinces with ideology-based armed conflict

0.340 -0.023 0.154 0.390 0.396 -0.502(*) -0.327 -0.404 0.288 1.000

UNEMP

RELCON

Unemployment rate

Child-to-woman ratio

Household size

0.062 -0.379 0.203 -0.770(**) -0.593(**) -0.220 -0.013 1.000

HHSIZE

IDEOCON

UNEMP

CWR

HHSIZE

-0.358 0.804(**) -0.427(*) -0.100 0.013 0.238 1.000

MIGMALE

Table 29. Results of the Pearson correlation analysis for selected socioeconomic and demographic variables in Mindanao

Mapping Population-Biodiversity Connections in the Philippines

1950s. In December 1968, Jose Maria Sison following his break with the old, pro-Soviet Partido Komunista

Pilipinas formed the Communist Party of the Philippines • or CPP (Sison, 2003). Since then, CPP has embraced the Maoist doctrine and grown in size with the aim of overthrowing the Philippine government through 5.4.2. Role of Armed Conflict guerilla warfare. Today, the CPP has an 11,500-strong There are two major sources of armed conflicts in guerrilla arm known as the New People’s Army (Center the Philippines. One is the secessionist movement for Defense and Information, 2002). Provinces with high percentage of barangays of certain Muslim groups mainly in Mindanao. The secessionist movement can be traced back to land with armed conflicts did not show any statistically conflict in the 1950s and 1960s due to the large number significant correlation with forest cover. However, it of Christian migrants encouraged by government and is important to address the intervening social issues proliferation in the number of large logging concessions that perpetuate armed conflicts so that development that forced indigenous communities out of their land. projects and the delivery of basic services can flow The conflict later became generally known as a religious efficiently and unhampered. Data on the combination conflict because of the marked divide in religious of ideology-based and historically religion-based beliefs between the conflicting parties—Muslims and conflict reveal that two regions in Mindanao (ARMM and Region 12) have more than 50 percent of their Christians. The other major source of armed conflict is the total number of barangays (the smallest political unit) communist ideology-based movement, found all over affected by armed conflicts. The correlation analyses the country. The Philippine communist movement for the conflict and socioeconomic-demographic data started in the 1930s and diminished in strength in the for Mindanao indicate that poverty is not a significant

RESULTS AND DISCUSSION

indeed, show that male net migration rate tend to be lower in areas with higher unemployment rate17. High total fertility rate is significantly associated with high poverty rates.

Table 30. Ranking of conflict areas, 2000 Poverty incidence

Number of provinces with reported conflict

ARMM 12 4 3 5 11 7 9 6 CAR 10 8 CARAGA 1 2 NCR

34.59 34.49 55.21 33.45 31.07 n.d. 25.56 22.69 34.82 83.23 23.14 47.14 39.53 29.48 53.90 8.14

57.0 48.4 20.8 17.0 49.0 31.5 32.2 38.3 27.8 31.1 32.9 37.8 42.9 29.6 24.8 5.7

2 4 9 6 5 5 3 2 4 4 3 4 3 2 2 0

Conflict areas (Total percentage of barangays affected) 90.00 76.06 70.65 32.74 28.96 25.90 20.80 16.04 15.75 12.10 9.96 8.21 6.59 2.72 1.18 0

Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Notes: Regions and provinces without or incomplete forest CAR (Apayao), CARAGA (Agusan del Norte and Surigao del Norte), ARMM (Basilan and Sulu), Region 2 (Batanes), Region 5 (Camiguin), Region 9 (Zamboanga del Sur), Region 10 (Bukidnon and Misamis Oriental), Region 11 (all provinces), and Region 12 (Cotabato, South Cotabato and Sultan Kudarat). Sources: Philipppine National Police, Department of Environment and Natural Resources, and National Statistics Office.

17

Note that even if the correlation coefficient is not significant (no asterisks) the negative sign show the trend of relationship, which is the inverse.

Mapping Population-Biodiversity Connections in the Philippines

Region

Percent forest cover

65

RESULTS AND DISCUSSION

factor that determines the extent of conflict in Mindanao provinces, except in the ARMM and Region 12—two Mindanao regions with the highest percentage of conflict-affected areas, high poverty rates and low forest cover (Table 30). The regional ranking can, however, change if all provinces have data. However, the five highest-ranking regions appear to support the common perception on the extent of armed conflict in these areas. Table 31 shows that there are several statistically significant relationships between conflict and some demographic variables. For areas affected by religionbased conflict, population growth rates are higher, contraceptive prevalence rates are lower, access to family planning services is lower, female infant mortality rates are higher, and percentage of the population with high school education is less. These relationships with reproductive health indicators, however, may not only be due to less efficient delivery of basic services because of conflict, but may also be due to cultural and religious beliefs on contraception. These areas are

also characterized by a population highly dependent on agriculture (i.e., more males are engaged in agriculture, hunting and forestry). There is also a tendency of the percentage of males in construction to be lower, which may indicate that infrastructure development is low; thus, the opportunity for jobs in this traditionally maledominated sector is low as well. The relationship between the ideologybased conflict affected areas and the socioeconomicdemographic variables are less apparent. It does appear though that the women in these areas are now employed in traditionally male-dominated sectors, specifically mining (Table 31). This may further explain the tendency for fertility and population growth rates to be lower. From the above data and analysis, it is important to examine the vulnerability of other natural resources, especially the coastal areas, to the impact of conflict. For instance, do site-specific data reveal the pattern and direction of migration due to conflict? How vulnerable are the biodiversity resources in these

Table 31. Relationship between conflict, socioeconomic and demographic variables based on Pearson correlation analysis

Mapping Population-Biodiversity Connections in the Philippines

Variables

66

ARCFOR DENSITY POPGROW TFR HHSIZE MIGMALE MIGFEM LIFEMALE LIFEFEM IMRFEM MMR ANYMET TRAD NOMET FPACCESS AGRIM CONSM MINEF TRADEF SERVEF NSF POP15HS WATER TOILET Notes: (*) = significant at 5% level

Arcsine of percent forest cover Population density Population growth Total fertility rate Household size Net migration rate of males Net migration rate of females Percent families use of any contraceptive method Percent families use of traditional contraceptive method Percent of families did not use any contraceptive Couples with access to family planning Life expectancy of males Life expectancy of females Female infant mortality rate Maternal mortality rate Percent male labor force in agriculture, hunting and forestry Percent male labor force in construction Percent female labor force in mining and quarrying Percent female labor force in trading Percent female labor force in service industry Percent female labor force in a not-stated industry Rate of population 15 years and over who attained high school Percent of households with access to safe drinking water Percent of families with own sanitary toilets (**) = significant at 1% level

Pearson Correlation Coefficient Ideological Religious Conflict Conflict 0.186 -0.068 -0.091 -0.138 -0.051 0.313(*) -0.04 0.107 -0.223 0.471(**) 0.314(*) 0.005 0.091 -0.04 0.134 -0.505(**) 0.132 -0.563(**) -0.133 0.276(*) -0.258(*) 0.143 0.193 -0.317(*) 0.233 -0.301(*) -0.192 0.319(*) 0.085 -0.483(**) -0.063

0.374(**)

0.047 0.320(*) 0.287(*) -0.017 -0.221

-0.269(*) -0.093 -0.204 -0.310(*) 0.259(*)

0.108

-0.277(*)

0.001 0.032

-0.407(**) -0.284(*)

I. Sarenas

men and religious leaders in the decision to use a type of contraception. It is important to learn from the experience of Indonesia, the largest Muslim country in Asia, in successfully curbing fertility rates and population growth. While there may be religious taboos on the use of modern contraceptive methods, reproductive health in the Muslim-dominated areas in Mindanao may be more successful when promoted as a health and education program and with the support of religious leaders.

RESULTS AND DISCUSSION

areas to the population pressure resulting from the influx of migrants or people caught by armed conflict seeking temporary refuge? In areas where religionbased conflict is predominant, poverty and population growth rates are high as well. Thus, development interventions may ease the contribution of poverty issues in exacerbating the armed conflict. Promoting education and reproductive health program should be carefully crafted to include cultural and religious factors, including the role of

Mapping Population-Biodiversity Connections in the Philippines

Maria Cristina Falls, Mindanao

67

6

Mapping Population-Biodiversity Connections in the Philippines

6.1

68

Summary and Conclusions

Summary of Findings

In summary, the preceding sections demonstrated that a unique configuration of socioeconomic and demographic variables significantly influence the level of threat to the biophysical environment, whether the latter manifests either as forest cover in the provinces or in terms of the biodiversity importance in the CPAs, both for the Philippines as a whole and those specifically located in Mindanao. In particular, the simplified accounting of the statistical results clearly showed that a specific set of socioeconomic and demographic factors significantly affect biodiversity in the provinces or conservation priority areas. Many of the statistical results amplified the expected relationships between fertility indicators, on one hand, and the biophysical environment, on the other. In particular, the current study labored under the fundamental theoretical assertion that an expansion in the human population generally tended to threaten the biological biodiversity (whether flora or fauna) in any given area. Hence, the present study viewed such socioeconomic and demographic conditions, other factors held constant, to hasten environmental degradation, defined herein either as a decline in forest cover or a decrease in the biodiversity importance of an area. The study found that these factors are high population density and high proportion of population engaged in agriculture, hunting and forestry. High population density also tended to occur in economically depressed areas. The a priori expectations on these two variables are supported by the result, but not the high net migration rate of males. The incentive provided by large tracts of forest to migrants and the landless poor is a plausible explanation of positive relationship between migration and forest cover. There are socioeconomic factors that seem to drive migration, namely: high rate of poverty and joblessness or unemployment. An upland migration study in 1997 showed that a third of the total Philippine population resides in the uplands and 50 percent of the upland population occupies forestlands (Cruz,

1997). The lowland-upland migration was driven by the expectation of higher income from logging and land availability for subsistence agriculture among the rural poor (Cruz and Francisco, 1993). In most cases, abandoned and closed-down logging concessions in the Philippines became open access as government failed to protect these areas from the influx of migrants, comprising of the lowland population displaced by large agro-industrial development, and the poor and landless who cannot find jobs in the lowland; thus, subsistence farming and small-scale logging often took over these areas. The connection between population and environment is not linear, and can be influenced by various mediating factors. The project did not identify and analyze the specific mediating variables, but saw some patterns of relationships among and between demographic and socioeconomic variables that may have helped shape the population-environment link, which were not apparent. These relationships are summarized below: (1) Poverty rate tends to be high when the proportion of the population (15 years old and over) attaining high school education is low; high unemployment rate is positively associated with low percentage of population 15 years old and over with high school education. These results emphasize the importance of education and human resource development in alleviating poverty. (2) High biodiversity areas are found in rural and upland areas. Since poverty rates are relatively higher in upland and rural areas, poverty reduction measures are important components that need to be carefully considered in conservation strategies. (3) High fertility rates are positively associated with smaller proportion of the population (15 years old and over) who have attained

(6)

A population center at the foot of the Sierra Madre Mountains

Mapping Population-Biodiversity Connections in the Philippines

(7)

G. Villoria

(5)

(8) Two migration patterns are evident, i.e., rural-to-urban and lowland-to-upland. Without rural development that provides alternative jobs and income—which lessen the dependence on natural resources or the push of rural poor into cities—these two patterns are likely to continue and exacerbate problems in the cities and biodiversity losses in the uplands. (9) Given current technology and population growth, agricultural expansion will likely continue in the next 10 to 30 years. This implies a three-pronged approach that tackles population management, conservation and environmental protection, and agricultural modernization to increase the productivity and provide adequate income and food security to rural and upland farmers. If productivity continues to be low and population continues to rise, the projections made in this study show that more land will likely be opened for agricultural production. Since lowland areas are more likely already privately owned, cultivated, or converted to other land uses, the direction of agricultural expansion would be the open access upland and marginal areas. This scenario can result in further forest and biodiversity losses.

SUMMARY AND CONCLUSIONS

(4)

high school education. It is also positively associated with high unemployment rate and high poverty rate. Since high unemployment and poverty rates are associated with lower forest cover, and therefore, higher risk of biodiversity losses, there is no gainsaying the importance of a population-development-environment intervention. High percentage of forest cover is associated with high fertility rate and population growth, which seems to validate the frontier hypothesis and emphasize the need for coordinated fertility reduction and development strategies that provide appropriate and adequate incentives for conservation. High proportion of the labor force in non-natural resource-based industries, specifically the proportion of women in manufacturing, is associated with high percentage of forest cover, low fertility, and high prevalence of modern contraceptive methods. The results seem to support the argument that women tend to have less children and use contraception more when given better education and the opportunity to work off-farm. The greater contraceptive prevalence rate is associated with low proportion of barangay health stations and workers to population, and with low infant and maternal mortality rates. This indicates that the use of contraception has an added benefit of health improvements of mothers and infants. High ratios of barangay stations and workers to population are also associated with low incidence of morbidity indicating that access to efficient health facilities and services have an impact on health status. The status of the population’s health and education affect its productivity and economic development in the long run.

69

Conclusions

The results clearly lead to the conclusion that policies benefits but are silent on how demographics affect and interventions that focus on biodiversity conservation alone are the integrity of the country’s natural resources and insufficient in abating biodiversity losses and destruction of forest environment. Yet the results show that demographics resources if population do affect the and development concerns environment and, are not considered. conversely, the Cooperative efforts environment affects between and among people and the conservation-, economy in various development-, and ways. The second population-oriented obvious conclusion institutions are drawn from the warranted so that study is that firm and these three concerns unequivocal population are addressed. policies, which include Families coming out of a rural-health facility While there are programs on fertility environmental policies in place to protect biodiversity reduction and reproductive health, as well as investments resources, e.g., through the protected areas system in human capital development (i.e., education and skills under the NIPAS Law, population policies have been improvement), are necessary to protect the remaining biodiversity ambiguous and not consciously and actively integrated resources so that the gains in economic development will not be into conservation efforts. On the other hand, current dissipated by population pressure. population policies and strategies emphasize only health PCSDS, Palawan

SUMMARY AND CONCLUSIONS Mapping Population-Biodiversity Connections in the Philippines

70

6.2

Policy Implications and Recommendations

7

In light of the general conclusions enumerated in the preceding section, this chapter presents policy implications and recommendations in the succeeding sections. These recommendations tackle policies, strategies and research gaps in understanding the population and environment interaction. 7.1

Integration of population and socioeconomic dimensions in conservation strategies and programs at the national and local levels

The formulation of more responsive and effective conservation programs and policies ought to take into account the specific set of socioeconomic and demographic factors that significantly affect the various indicators of biodiversity in the different provinces as well as in the conservation priority areas. Indeed, no general programs or policies can effectively address the conservation efforts aimed at protecting the biodiversity of the Philippines; rather, each conservation program and policy has to be uniquely tailored according to the nature of the biodiversity being protected as well as the scope of such program and policy, whether national or site-specific.

7.2

Population and environment concerns must be integrated into development policies, programs and plans of government

While the project framework examined the populationenvironment relationship, one cannot avoid bringing development concerns within the populationenvironment relationship. The project’s results clearly showed that socioeconomic factors do affect both biodiversity and demographics. Policymakers and program planners, therefore, need to address the multidimensional impact of economic development on the population-environment link inasmuch as population and environment affect economic development. Economic development, industrialization and

urbanization impact on population movements, job creation, and demand for goods and services (including natural resources like timber and forest products, and minerals); thus, account for some vital linkages between population and the environment. Rural development can be designed to reduce poverty, regulate the movement of the rural poor to the cities or to the natural resource rich areas (uplands and coastal areas), and relieve pressure on the remaining natural resources by providing jobs and incomes that meet basic needs and aspirations.

Mapping Population-Biodiversity Connections in the Philippines

makes them better advocates for integrated population and conservation initiatives. Programs and strategies must also account for the differences in the vulnerability of populations. Many factors contribute to vulnerability, such as poverty, poor health, low levels of education, gender inequality, lack of access to resources and services, and unfavorable geographic location. Deteriorating environmental conditions and extreme events do not have the same degree of impact on entire populations or on all households. Even within households, the effect may differ by age or gender. Hence, consideration of The expanded role of local government vulnerability must focus on the specific segment of the units in the population program is still a largely population, such as women and children. Vulnerable untapped resource. Local leaders are more attuned populations have less capacity to protect themselves to and knowledgeable of the peculiar socioeconomic, from current and future hazards, such as pollution, demographic and environmental conditions in catastrophes and large-scale environmental changes like their areas. The local leaders’ physical proximity to land degradation, biodiversity loss and climate change. deteriorating environmental and economic conditions

71

POLICY IMPLICATIONS AND RECOMMENDATIONS

Concerns for poverty in many environmentally degraded upland and rural areas have led many conservation- and development-oriented institutions to implement livelihood projects that augment the income of upland dwellers. These strategies and interventions need to be carefully analyzed, monitored and evaluated as studies show that the increase in incomes, unless large enough to wean the rural and upland communities from dependence on the natural resources, may result in further forest and biodiversity loss as additional incomes are invested in more efficient and destructive technologies. Upland development programs, mainly reforestation and awarding of tenurial instruments, despite modest achievements, do not appear to be effective in reducing upland poverty as shown by sector and rural-urban poverty figures (refer back to Table 7 of section 2.2.3). For instance, tree planting is seen as unattractive and frustrating among poor upland

Unequivocal population policies, strategies and programs must be complemented with well-targeted human capital investments

Mapping Population-Biodiversity Connections in the Philippines

7.3

72

communities because they need to wait 10 to 15 years before they can harvest the trees. In the meantime, government policies on harvesting trees planted on government lands can change. Markets and prices for the products can change as well. These insecurities on tenurial rights and markets play against current basic requirements for food, housing, education, health and others. On the other hand, those with legal tenurial rights awarded under the various programs of government (e.g., Integrated Social Forestry and Community-based Forest Management Agreements) often do not have the necessary capital and support services to be able to properly manage their forest and farm areas. It is important that upland development refocus its programs to correct the incentive structures and provide support services (e.g., credit, technologies and market linkages) to effectively reduce poverty and, consequently, address population and environmental concerns as well.

For population concerns to be addressed adequately and effectively, government policies, programs and plans must be clear and unambiguous. Researches in many parts of the world have shown that women in high-fertility areas have more children than they actually want. Effective reproductive health and family planning programs allow couples to have the number of children they desire, thus, reducing unwanted childbearing and lowering fertility rates. Lower fertility rates lead to slower population growth, allowing more time to cope with the adverse effects of that growth and easing stress on the environment. Moreover, reproductive health programs, including those implemented by institutions outside of government, are shown to be more successful when supported by government, e.g., China and Thailand. Population policies and programs must have clear fertility reduction targets complemented by investments in education and health to achieve the targets. This suggestion is based on the research findings 18

that future population growth in the Philippines will be influenced by three factors: unwanted pregnancies, high fertility preference, and population growth momentum (Herrin and Costelo, 1996). The same study showed that from 1995 to 2000, of the projected increase in population, 15.8 percent was due to unwanted fertility, 18.1 percent from high fertility preference, and 66.3 percent was due to population growth momentum. Effective reproductive health and family planning programs will obviously work to minimize unwanted fertility. But addressing high fertility preference and population growth momentum18 calls for approaches that tackle the perceived advantages of having many children, as discussed in the previous chapter. An example of perceived advantages is the expectation that childeren provide greater financial security for parents in their old age. Children provide additional labor in the farm and household or can provide additional income when they find work elsewhere. Investments in human capital are known to facilitate the decline in

Population momentum is the tendency for population to continue growing after reaching replacement fertility of approximately 2.1 children per woman as a result of past population growth that resulted in a high proportion of young people who are approaching reproductive age. These young people will produce generations larger than those of the older people moving out of the population through death (Engelman, 1998).

7.4

often contribute to greater environmental awareness. Better-educated people are more economically productive and invest more on technological advancement. Education also reduces the population’s vulnerability to environmental change by improving knowledge on and facilitating access to information and means to protect oneself. Furthermore, the fertilitydepressing effect of education contributes to reducing population pressure and the scale of human impact on the environment.

Integration of population and biodiversity conservation interventions either in a single program or as collaborative programs among organizations may be able to offer assistance in disseminating materials and information on reproductive health by linking critical aspects of conservation and natural resource management to health and economic wellbeing. Conversely, using the same concept, a health organization can assist in conservation and resource management by linking health and reproductive health to critical aspects of water supply, sanitation and health, and general well-being. The idea of linking environment and reproductive health/family planning services often arises in relation to protected areas management, e.g., parks, reserves and other areas given some level of government or private protection where human activities typically have limitations or are regulated to protect high ecosystem and environmental values. Many protected areas are also home to upland communities and indigenous peoples who derive income and sustenance from them. The complex interaction between demographics, economic activities, and environment in these areas are, therefore, more pronounced. Addressing the major aspects of population-development-environment needs the cooperation and collaboration of various organizations working in these areas. Since organizations may have different target outcomes, they need to identify a common framework, goal and indicators, by which collaborative activities can be assessed while, at the same time, achieving each organization’s objectives.

Mapping Population-Biodiversity Connections in the Philippines

While this aspect was not explicitly examined in this project, the experience of non-governmental organizations linking conservation and reproductive health activities suggests cost reductions associated with family planning delivery in remote areas by taking advantage of personnel and support networks already developed for conservation and development work (Engelman, 1998). Many organizations regard family planning as a separate and often controversial set of interventions linked to government and international donor initiatives. Environmental and natural resource conservation seem to be so distant from reproductive health. However, the natural resource base on which low-income or poor communities depend is deteriorating, thus, undermining their long-term health, wealth and well-being. Integration of population and environment initiatives can either be through a single project by a single organization or through collaborative programs by various organizations. For instance, local government units, health- and conservation-oriented organizations working in a community may coordinate activities to provide services related to natural resource or biodiversity conservation and family planning. The added advantage of a coordinated program between two or more organizations is that either one can take advantage of the presence of another in a community and save on implementation cost. For instance, using a common framework, strategies and monitoring indicators, environmental organizations

POLICY IMPLICATIONS AND RECOMMENDATIONS

the demand for more children. Decelerating the population growth momentum can be also achieved by delaying marriage and longer birth spacing. Empowering women by providing better education and greater economic opportunities is proven to be effective in delaying the age for marriage and increasing the use of modern contraception. Education also enhances individual choices and improves gender equality. Better-educated people are found to invest more on maintaining good health and

73

POLICY IMPLICATIONS AND RECOMMENDATIONS Mapping Population-Biodiversity Connections in the Philippines

74

7.5

Develop, refine and test P-E approaches to better understand the relationship, enable effective communication of results, and design more appropriate interventions

Quantitative methods, aside from the ordinary least squares multiple regression model used in the project, such as path analysis and factor analysis should also be considered as additional analytical tools. Path analysis may be used to draw the directions of relationships not only from population variables to those of the environment, but also between and among the population variables themselves. Note that this population-environment study limited itself to the analyses of the P→E link, i.e., how population shapes the environment. Future studies ought to consider the equally important feedback of E→P, i.e., how environment, in turn, influences the population dynamics. Factor analysis can help determine which of the various socioeconomic, demographic and health variables can be clustered around certain points of commonality. For instance, fertility measures such as crude birth rate, total fertility rate, and child-to-woman ratio may “band” together inasmuch as they all pertain to fertility. Moreover, female literacy and women labor in agriculture may also be clustered as an indicator of women’s status in a CPA. Notwithstanding the appeal of a more sophisticated statistical analysis, caution must be exercised as to how best to use the results since each demographic variable measures a unique dimension of a particular population dynamics. In the future, should a similar undertaking of linking socioeconomic and demographic factors to biodiversity and other related environmental variables be undertaken, the following strategies and activities pertaining to data collection and analysis are recommended when time and resources would permit.

interactions and feedbacks (Geist and Lambin, 2002). One approach to analysis of linkages and relationships between environmental variables, such as biodiversity, and socioeconomic and demographic factors is the use of empirical-statistical models, and process-based system models. This project used the empiricalstatistical model that determines the associations between the independent variables (e.g., socioeconomic and demographic factors) and the dependent variables (e.g., forest cover) based on the available historical data. Another approach is through systems modeling which considers the systems dynamics and processes, and also involves identifying the trajectories of change based on the knowledge of the land change processes (LUCC Report Series No. 6, 2001; Manson, 2000). While systems modeling approach is data-intensive and depends much on available knowledge of the underlying processes and interactions in the system, it is generic and does not require modification for each location. This allows for an objective comparative analysis of factors influencing forest cover change across different locations or CPAs since the same systems model is used. On the other hand, empiricalstatistical models, which simply highlight the temporal and spatial associations between variables, are dataspecific and require revision or updating of the models as new or additional data become available. System models that consider causal relationships offer a comprehensive approach to understanding landcover change, and also provide important information and inputs for policy formulation and planning. Research initiatives in the global environmental change programs (e.g., Lambin et. al. 1999; www. (1) Use of systems modeling approach to determine geo.ucl.ac.be/LUCC/lucc.html) have focused on the the minimum data sets required for the population development of different types of spatially explicit, and environment analysis that consider the causal integrative and explanatory land-cover change models. mechanisms in the system. This approach can best These system models encompass those that consider be tested at the CPA level considering the complex the behavioral theory linked to specific locations (e.g., relationships in a system and data required to agent-based models; LUCC 2001; Gimblett, 2002.); examine these relationships models developed from changes indicated in imageries from time series of remotely sensed data; and also Analysis of the multiple causal factors of dynamic spatial models that simulate land changes land or forest cover change requires understanding of under different scenarios (Veldkamp and Fresco, 1996; the systems dynamics, which should also include the Veldkamp and Lambin, 2001).

(5) Integration of a gender framework in data collection and analysis This is an important step in understanding the varying perspectives of men and women on natural resource use that will help planners design projects which break down assumptions about gender roles and remove barriers to women’s participation. Integrating gender analysis will result in a more accurate delineation of men’s and women’s roles and identify more appropriate interventions that consider different priorities and aspirations of men and women.

Mapping Population-Biodiversity Connections in the Philippines

(6) Develop a framework for examining population(2) Use of historical time series data on socioeconomic, environment security issues demographic and environmental variables (e.g., forest cover change) The project tried to find correlates of armed conflicts, but lacks the framework and data to examine As much as possible, periodicity in the collection in more detail the relationship between population, of biodiversity and socioeconomic data for the CPAs conflict and environmental security. The Project must be synchronized to be able to determine the time on Environmental Change and Acute Conflict at lag of effects on the environment. A random sample the University of Toronto, Canada, from 1993 to of primary data may also be collected for selected 1996 found evidences that scarcities of renewable CPAs to provide a basis for reliability analysis. resources—including cropland, forests, water and fish—contributed to violent conflicts in many parts (3) Use of geo-referenced, high resolution data of the developing world, even though these conflicts often appear to be caused solely by political, ethnic or The use of geo-referenced, high-resolution ideological factors (Homer-Dixo, 1994). population data (e.g., at the barangay level) as well as The armed conflict in Mindanao, although socioeconomic variables, provides for more reliable labeled as a religious conflict, stems from the estimates of values for the CPAs. For example, displacement of indigenous Muslim communities supplementary or auxiliary data such as slope and road from their land during the government-sponsored density may be considered. This will provide for the migration to Mindanao in the 1960s and the subsequent development and use of a more sound estimation spontaneous in-migration that followed. Migrations, procedure to impute CPA-level data by considering ethnic tensions, economic disparities and weak slope or other spatial information in the estimation of institutions appear to be the main causes of armed CPA-level data and linkage analysis. conflict. The framework should, therefore, explore the (4) Validation of data through groundtruthing linkages among environment, population, security and governance. Future studies should also be designed to Incorporate groundtruthing in a random sample explore the answers to the following questions: of sub-areas in the CPAs to determine the accuracy of estimation procedures employed. This may also be (1) What is known about and what more can be complemented with use of a high-resolution satellite learned from the links among population growth, imagery to check reliability of estimates (e.g., delineated renewable resource scarcities, migration and violent CPA boundaries and extent of forest cover). conflict in the Philippines?

POLICY IMPLICATIONS AND RECOMMENDATIONS

Given the current global trend in the use of process-based models, the shift from empiricalstatistical approach to systems analysis of the population-environment linkages should be pursued. The possibility of using system modeling (e.g., agentbased model) in the analysis may be explored to allow comparison of results, i.e., with the empirical-statistical model, that best represent the system being studied. Systems analysis helps define the particular data needed for the study including the temporal extent of the data to capture the temporal variability, as well as refine the linkages and relationships between the data or variables used in the analysis.

75

POLICY IMPLICATIONS AND RECOMMENDATIONS Mapping Population-Biodiversity Connections in the Philippines

76

(2) What and how can policies intervene to improve the social outcomes in areas affected by violent conflicts? (3) What are the critical methodological issues affecting research on these links?

particularly to utilize a digital template of a national base map derived from the 1:50,000-scale map sheets published by the National Mapping and Resource Information Authority (NAMRIA). This digital map was prepared in collaboration with the NAMRIA, as the national government agency mandated to do In addition to this document, as an added-value mapping for the country. Once all users in the country to this project study, several maps in digital form were patronize this map, they will have attained the goal of prepared to comprise a data set that potential users can unifying all digital map preparations unto a single base use in order that they may have a spatial perspective map, thus preserving compatibility and maintaining of the socioeconomic-demographic and biophysical common data reliability. profile of the Philippines. They are encouraged

Mapping Population-Biodiversity Connections in the Philippines

Platymantis sp.

77

M. R. Duya

Glossary 1. anthropogenic – of or relating to the study of the origins and development of human beings (e.g., anthropogenic pressure on forest resource which refers to that portion of pressures on forest resources attributed to human activities) 2. arbovirus – abbreviation of arthropod-borne virus; a virus that is primarily transmitted by arthropods (e.g., mosquitoes or ticks) 3. bivariate analysis – a simple correlation analysis between any two variables 4. correlation analysis – a statistical technique for measuring the degree of the linear relationship between variables 5. correlation coefficient – a number between -1 and 1, which measures the degree to which two variables are linearly related 6. digitized map – a map converted to digital or electronic form by using a mouse-like digitizing device to record the spatial coordinates of map features 7. endemism – the unique presence of a species to a particular geographic area 8. georeferencing – a process of assigning map coordinates to image data to conform to a map projection grid 9. groundtruthing – the use of a ground survey to confirm the findings of an aerial survey or to calibrate quantitative aerial observations 10. level of statistical significance – a value established by a researcher to indicate the risk of error when using statistical analysis to test a hypothesis (e.g. 0.05 level, which indicates the probability that an observed difference or relationship would be found by chance only 5 times out of every 100) 11. meta-analysis – an integrative analysis of the results of a collection of analyses from individual studies 12. multicollinearity – a condition in which the predictor variables in a regression model are themselves highly correlated, which thus makes it difficult to interpret the relative effects of the various predictor variables 13. multiple regression – a statistical model aimed to find a linear relationship between a single response variable and several possible predictor variables 14. multivariate analysis – a correlation analysis among many variables

Mapping Population-Biodiversity Connections in the Philippines

15. nonparametric tests – a term for a statistical test that depends on the estimation of parameters (e.g., the mean or the standard deviation), which characterize the distribution of the variable of concern in a population.

78

16. parametric tests – a term for a statistical test that depends on the estimation of parameters (e.g., the mean or the standard deviation), which describes the distribution of the variable of concern in a population. 17. pearson correlation coefficient – a correlation coefficient that measures the linear association between two variables that have been measured on interval or ratio scales 18. population dynamics – the aspect of population ecology that deals with the factors that cause changes in population densities or that affect population growth 19. predictor variable – the independent variable or the variable used to make predictions 20. rank correlation coefficient – a correlation coefficient between two random variables that is based on the ranks of the measurements and not their actual values 21. regression analysis – a statistical technique for measuring the type of causal relationship among variables 22. replacement fertility – refers to a total fertility rate of 2.1 children per woman, which is regarded as the average number of children each woman needs to have for a population to replace itself in the long-term, assuming no migration occurs. 23. spearman correlation coefficient – a correlation coefficient usually calculated on occasions when it is not convenient to give actual values to variables, but only to assign a rank order to instances of each variable 24. system dynamics – a methodology for the construction of simulation models of dynamic systems 25. system modeling – the process of describing a system in terms of mathematical equations 26. zoonotic diseases – diseases that can be passed on from animals, wild or domesticated, to humans, under natural conditions

References Alcala, A.C. and W.C. Brown. 1998. Philippine Amphibians: an Illustrated Field Guide. Bookmark, Inc., Makati City, Philippines. American Academy of Arts and Sciences. [Online]. Available from: [Accessed June 2004]. Balete, D. 2004. personal communication, June. Balisacan, A. R. 1999. “Reconstructing Poverty Profiles in the Philippines.” Discussion Paper No. 9915. University of the Philippines School of Economics, Quezon City, Philippines. Barba, C. VC. 2003. “The 6th National Nutrition Surveys: Initial Results.” Paper presented at the 2nd Conrado R. Pascual Memorial Lecture. Food and Nutrition Research Institute, Department of Science and Technology. Barbier, E. B. 1997. “The Economics of Tropical Forest Land Use Options” Land Economics, 73 (2): 174–195. Barton, T., G. Borrini-Feyerabend, A. de Sherbinin and P. Warren. 1997. Our People, Our Resources. International Union for the Conservation of Nature (IUCN), Gland, Switzerland and Cambridge, U.K Bhattarai, M. and Hammig, M. 2001. “Institutions and the Environmental Kuznets Curve for Deforestation: A Cross-country Analysis for Latin America, Africa and Asia.” World Development, 29 (6): 995–1010. Bilsborrow, R. 1992. ”Rural Poverty, Migration, and the Environment in Developing Countries.” Policy Research Working Papers, No. WPS 1017. The World Bank, Washington, D.C., U.S.A. Boserup, E. 1965. The Conditions of Agricultural Growth: the Economics of Agrarian Change Under Population Pressure. Aldine, Chicago, Illinois, U.S.A. ______. 1976. “Environment, Population, and Technology in Primitive Societies.” Population and Development Review, 2 (1): 21–36. ______. 1981. Population and Technological Change. University of Chicago Press, Chicago, Illinois, U.S.A. ______. 1990. “Economic Change and the Roles of Women,” in Persistent Inequalities: Women and World Development, (ed.) I. Tinker. Oxford University Press, New York, U.S.A. pp. 14–27. Brown, L. R., G. Gardner and B. Halweil. 1999. Beyond Malthus: Nineteen Dimensions of the Population Challenge. Worldwatch Environmental Alert Series. Norton, New York, U.S.A. and London, U.K. Bunning, V. K., J. A. Lindsay and D. L. Archer. 1997. “Chronic Health Effects of Microbial Food-borne Disease.” World Health Statistics Quarterly, 50 (1–2): 51–56.

Caldwell, J. C. 1976. “Toward a Restatement of Demographic Transition Theory.” Population and Development Review, 2: 321–366. The Campaign for Political Ecology (ECO). 1999. “Population and Carrying Capacity.” ECO Discussion Paper. [Online]. ECO, Available from: [Accessed May 2004]. Center for Defense and Information. 2002. In the Spotlight: The Communist Party of the Philippines. [Online]. Center for Defense and Information (CDI), Washington, D.C. Available from: [Accessed August 2004]. Chu, C. Y. C. and R. R. Yu. 2002 “Population Dynamics and the Decline in Biodiversity: a Survey of the Literature.” Population and Development Review (Supplement) 2002, (28): 126–143. Cincotta, R. P., J. Winewski and R. Engelman. 2000. “Human Population in the Biodiversity Hotspots.” Nature, 404: 990–992. The Club of Rome. [Online]. Available from: [Accessed June 2004]. Cohen, J. 1995. How Many People Can the Earth Support? W.W. Norton & Company, New York, U.S.A. Collar, N., N. A. D. Mallari and B. R. Tabaranza. 1999. Threatened Birds of the Philippines: the Haribon Foundation/ Birdlife International Red Data Book. Bookmark, Inc., Makati City, Philippines. Commission on Population (POPCOM). 2003. Demographic and Socio-economic Indicator System. [Online]. Available from: [Accessed November 2002].

Mapping Population-Biodiversity Connections in the Philippines

Cabigon, J. V. and W. Flieger, S. V. D. 1999. 1995 Gender-specific Life Tables for the Philippines, Its Regions and Provinces. National Statistics Office (NSO), Manila, Philippines.

79

REFERENCES

Conservation International Foundation–Philippines (CIP). 2002. Primer on Philippine Biodiversity and Basic Ecological Processes. Microbytes Desktop Publishing, Philippines. Cramer, J. C. 2002. “Population Growth and Local Air Pollution: Methods, Models, and Results,” in Population and Environment: Methods of Analysis. A supplement to Vol. 28, 2002, Population and Development Review, (eds.) W. Lutz, A. Prskawetz and W. C. Sanderson. The Population Council, New York, U.S.A. pp. 22–52 Cropper, M., C. Griffiths and M. Mani. 1996. Roads, Population Pressures and Deforestation in Thailand, 1976–1989. www.worldbank.org. Cruz, M. C. 1997. “Effect of Population Pressure and Poverty on Biodiversity Conservation in the Philippines,” in Population, Environment and Development, (eds.) R. K. Pauchari and L. Qureshy. Tata Energy Institute, New Delhi, India. Cruz, W. and M. C. Cruz. 1990. “Population Pressure and Deforestation in the Philippines.” ASEAN Economic Bulletin, 7 (2): 200–212. Cruz, W. and H. Francisco. 1993. “Poverty, Population Pressure, and Deforestation in the Philippines.” Paper presented at the Workshop on Economywide Policies and the Environment. World Bank, Washington, D.C., U.S.A. 14–15 December. Davies, J., P. M. Magsalay, R. Rigor, A. Mapalo and H. Gonzales. 1990. A Directory of Philippines Wetlands. Vol. 1–2. Asian Wetland Bureau Philippines Foundation and Haribon Foundation, Metro Manila, Philippines. Department of Environment and Natural Resources–Forest Management Bureau (DENR–FMB). 1990. 1990 Philippine Forestry Statistics. DENR–FMB, Quezon City, Philippines. ______. 2000. 2000 Philippine Forestry Statistics. DENR–FMB, Quezon City, Philippines. Department of Environment and Natural Resources–National Mapping and Resource Information Authority (DENR–NAMRIA). Land Cover Statistics. DENR–NAMRIA, Makati City, Philippines. Department of Health (DOH) and Department of Environment–National Mapping and Resource Information Authority (DENR–NAMRIA). 2000. 2001–2002 Women’s Health and Safe Motherhood Project. DOH, Manila and DENR–NAMRIA, Makati City, Philippines. Department of Health–National Epidemiology Center (DOH–NEC). 1999. Field Health Service Information System Annual Report 1999. DOH–NEC, Manila, Philippines. Department of the Interior and Local Government (DILG). 2000. Local Government Unit (LGU) Profile. DILG, Quezon City, Philippines.

Mapping Population-Biodiversity Connections in the Philippines

Department of Labor and Employment–Bureau of Labor and Employment Statistics (DOLE–BLES). Labor and Employment Statistics. [Online]. Available from: [Accessed April 2004].

80

Department of Science and Technology–Food and Nutrition Research Institute (DOST–FNRI). National Nutrition Surveys. [Online]. Available from: [Accessed April 2004]. Dickinson, E. C., R. S. Kennedy and K. C. Parkes. 1991. The Birds of the Philippines: an Annotated Checklist. British Ornithologists’ Union (Checklist No. 12), Tring, U.K Easterlin, R. A. and E. M. McCrimmins. 1985. The Fertility Revolution: A Supply-demand Analysis. University of Chicago Press, Chicago, Illinois, U.S.A. Eccles, S. 2003. The Project Gutenberg EBook of Audio: On the Origin of Species by Charles Darwin. [Online]. Available from: [Accessed April 2004]. Engelman, R. 1998. Plan and Conserve: a Source Book on Linking Population and Environmental Services in Communities. [Online]. Population Action International, Washington, D.C. Available from: [Accessed April 2004]. Environmental Science for Social Change (ESSC). 1999. Decline of the Philippine Forest. Bookmark Inc., Makati City and Environmental Science for Social Change, Manila, Philippines. Food and Agriculture Organization (FAO). 2001. “Global Forest Assessment 2000: Main Report.” Forestry Paper No. 40. Rome, Italy. Fox, J., R. R. Rindfuss, S. J. Walsh and V. Mishra (eds.) 2003. People and the Environment: Approaches for Linking Household and Community Surveys to Remote Sensing and GIS. Kluwer Academic Publishers, Boston, U.S.A. Gapud, V. P. 2003. personal communication. Garcia, M. 1990. “Resource Allocation and Household Welfare: Study of the Impact of Personal Sources of Income and Food Consumption, Nutrition and Health in the Philippines.” Thesis, Institute of Social Studies, The Hague, The Netherlands.

______. 2002. “Proximate Causes and Underlying Driving Forces of Tropical Deforestation.” BioScience, 52 (2): 143–150.

REFERENCES

Geist, H. J. and E. F. Lambin. 2001. “What Drives Tropical Deforestation? A Meta-analysis of Proximate and Underlying Causes of Deforestation Based on Subnational Case Study Evidence.” LUCC Report Series #4. LUCC International Project Office, Louvain-la-Neuve.

Geores, M. E. and R. E. Bilsborrow. 1991. “Deforestation and Internal Migration in Selected Developing Countries.” Paper presented at the Annual Meeting of the Southern Demographic Association, Jacksonville, Florida, U.S.A., 10–12 October. Gimblett, H. R. (ed.) 2002. Integrating Geographic Information Systems and Agent-based Modeling Techniques for Simulating Social and Ecological Processes. Oxford University Press, New York, U.S.A. Gomez, E. D. 1991. “Coral Reef Ecosystems and Resources of the Philippines.” Canopy International, 16 (5): 1, 6–7, 10–12. Granger, C. W. J. and P. Newbold. 1986. Forecasting Economic Time Series, 2nd edition. Academic Press, New York, U.S.A. Harrison, T. 1969. “The Tamaraw and Philippine Conservation.” Biol. Conservation, 1 (4): 317–18. Heaney, L. R. 1998. “The Causes and Effect of Deforestation,” in Vanishing Treasures of the Philippine Rainforest, L. R. Heaney and J. Regalado, The Field Museum, University of Chicago Press, Chicago, U.S.A. ______. 2002. “Biological Diversity in the Philippines: an Introduction to Megadiversity in a Nation of Islands,” in Philippine Biodiversity Conservation Priorities: A Second Iteration of the National Biodiversity Strategy and Action Plan, (eds.) P. S. Ong, L. E. Afuang and R. G. Rosell-Ambal. Department of Environment and Natural Resources–Protected Areas and Wildlife Bureau, Conservation International-Philippines, University of the Philippines Center for Integrative and Development Studies–Biodiversity Conservation Program, and Foundation for the Philippine Environment, Quezon City, Philippines. Heaney, L. R. and J. Regalado. 1998. Vanishing Treasures of the Philippine Rainforest. The Field Museum, University of Chicago Press, Chicago, U.S.A. Heaney, L. R., D. S. Balete, L. Dolar, A. C. Alcala, A. Dans, P. C. Gonzales, N. Ingle, M. Lepiten, W. Oliver, E. A. Rickart, B. R. Tabaranza, Jr. and R. C. B. Utzurrum. 1998. A Synopsis of the Mammalian Fauna of the Philippine Islands. Fieldiana Zoology New Series, Field Museum of Natural History. Herrin, A. and M. Costello. 1996. Sources of Future Population Growth in the Philippines and Implications for Public Policy. The Population Council, New York, U.S.A. Herrin, A. N. and E. M. Pernia. 2003. “Population, Human Resources and Employment,” in The Philippine Economy: Development, Policies and Challenges, (eds.) A. M. Balisacan and H. Hill. Oxford University Press, New York, U.S.A. pp. 283–310.

______. 2003. 2003 IUCN Red List of Threatened Species. International Union for the Conservation of Nature (IUCN), Gland, Switzerland and Cambridge, U.K. Homer-Dixo, T. F. 1994. “Environmental Scarcities and Violent Conflict: Evidemce from Cases.” International Security, 19 (1): 5–40. Japan Forest Technology Association (JAFTA). Land Cover Statistics (1993–2000). Jodha, N. S. 1990. Mountain Perspective and Sustainability: a Framework for Development Strategies. International Center for Integrated Mountain Development (ICIMOD). Kathmandu, Nepal. Jolly, C. L. 1991. “Four Theories of Population Change and the Environment.” Population and Environment, 16 (1): 61–90. Jurado, G. and M. T. Sanchez. 1998. “Philippine Employment and Industrial Relations Policies: an Assessment.” PIDS Disucussion Paper Series No. 98-10. Philippine Institute for Development Studies, Makati City, Philippines. Kansakar, K. 1989. Case Studies in Population and Natural Resources: Nepal. Population and Natural Resources Program, International Union for the Conservation of Nature (IUCN), Gland, Switzerland. Kelley, A.F. 2001. “The Population Debate in Historical Perspective: Revisionism Revised,” in Population Matters: Demographic Change, Economic Growth and Poverty in the Developing World, (eds.) N. Birdsall, A. C. Kelley and S. W. Sinding. Oxford University Press, Oxford.

Mapping Population-Biodiversity Connections in the Philippines

Hilton-Taylor, K. (ed.) 2000. 2000 IUCN Red List of Threatened Species. International Union for the Conservation of Nature (IUCN), Gland, Switzerland and Cambridge, U.K.

81

REFERENCES

King, E. and R. E. Evenson. 1983. “Time Allocation and Home Production in Philippine Rural Households,” in Women and Poverty in the Third World, (eds.) M. Buvinic, M. A. Lycette and W. P. McGreevey. Johns Hopkins University Press, Baltimore, Maryland, U.S.A. Lamberte, M. B., J. Y. Lim, R. Voz, J. T. Yap, E. S. Tan and M. S. V. Zingapan. 1992. Philippine External Finance, Domestic Resource Mobilization and Development in the 1970s and 1980s. Philippine Institute for Development Studies, Makati City, Philippines and Institute of Social Sciences, The Hague, Netherlands. Lambin, E.F., et. al. 1999. “Land-Use and Cover Change (LUCC): Implementation Strategy.” IGBP Report No. 48 and IHDP Report No. 10. International Geosphere-Biosphere Programme (IGBP) and the International Human Dimensions Programme on Global Environmental Change (IHDP). Stockholm, Sweden and Geneva, Switzerland. Land-Use and Land-Cover Change (LUCC) International Project Office. 2001. “Agent-based Models of Landuse and Land-cover Change: Report and Review of an International Workshop, October 4–7.” LUCC Report Series No.6. Belgium. Lasmarias, N. C. 1989. “The Economics of Soil Erosion: The Case of the Ikalahan.” M. S. Thesis, University of the Philippines at Los Baños, College, Laguna, Philippines. ______. 2003. “Economic Transition, Demographic Trends and Resource Use in the Philippines,” in Southeast Asia in Transition: Philippines. SEARCA, Los Baños, Laguna, Philippines. Linden, E. 1998. The Future in Plain Sight: Nine Clues to the Coming Instability. Simon & Schuster, New York, U.S.A. Madriaga, J. R., R. L. Cheong, J. A. Desnacido et. al. 1999. Prevalence of Iodine Deficiency in the Philippines. FNRI– DOST, Metro Manila, Philippines. Mallari, N. A. D., B. R. Tabaranza, Jr. and M. J. Crosby. 2001. Key Conservation Sites in the Philippines: a Haribon Foundation and Birdlife International Directory of Important Bird Areas. Bookmark, Inc., Makati City, Philippines. Manson, S. M. 2000. “Agent-Based Dynamic Spatial Simulation of Land-Use/Cover Change in the Yucatan Peninsula, Mexico.” Paper presented at the Fourth International Conference on Integrating GIS and Environmental Modeling (GIS/EM4). Banff, Canada. McConnell, W. and E. Keys. 2003. “Meta-analysis of Agricultural Change,” in Seeing the Forest and the Trees: Humanenvironment Interactions in Forest Ecosystems, (ed.) E. F. Moran. Center for the Study of Institutions, Population, and Environmental Change, Bloomington, Indiana, U.S.A. Meadows, D. H., D. L. Meadows and J. Randers. 1972. Limits to Growth. Universe Books, New York, U.S.A. ______. 1992. Beyond the Limits: Confronting Global Collapse, Envisioning a Sustainable Future. Chelsea Green Publishing Company, Post Mills, Vermont, U.S.A.

Mapping Population-Biodiversity Connections in the Philippines

Mittermeier, R. A., P. Robles Gil and C.G. Mittermeier (eds.) 1997. Megadiversity: Earth’s Biologically Wealthiest Nations. CEMEX, Monterrey, Mexico.

82

Moran, E. F., A. Siqueira and E. Brondizio. 2003. “Household Demographic Structure and its Relationship to Deforestation in the Amazon Basin” in People and the Environment: Approaches for Linking Household and Community Surveys to Remote Sensing and GIS, (eds.) J. Fox, R. R. Rindfuss, S. J. Walsh and V. Mishra. Kluwer Academic Publishers, Boston, U.S.A. pp. 61–89. Myers, N., R. A. Mittermeier, C. G. Mittermeier, G. A. B. da Fonesca and J. Kent. 2000. “Hotspots: Earth’s Biologically Richest and Most Endangered Terrestrial Ecoregions.” Nature, 403: 853–858. Nañola, C. L., A. L. Dantis, V. V. Hilomen, D. G. Ochavillo, M. C. G. Rañola and P. M. Aliño. 2000. “Evolutionary Insights from the Evaluation of Associated Reef Fish Distribution Patterns from Nationwide Fish Census Information.” Proceedings of the Ninth International Coral Reef Symposium. Bali, Indonesia, October 23–27. National Anti-Poverty Commission (NAPC). 2000. 2000 Poverty Updates. [CD-ROM]. NAPC, Manila, Philippines. National Commission on Indigenous Peoples (NCIP). 1998. 1996 Accomplishment Report of Both ONCC and OSCC. [CD-ROM]. NCIP, Manila, Philippines. National Statistical Coordination Board (NSCB). 1995. 1995 Census-based National, Regional and Provincial Population Projection. NSCB, Makati City, Philippines. ______. 1999. 1999 Philippine Statistical Yearbook. NSCB, Makati City, Philippines ______. 2001. The Countryside in Figures. NSCB, Makati City, Philippines.

National Statistics Office (NSO). 1995 Census of Population. NSO, Manila, Philippines. ______. 1997. 1997 Family Income and Expenditures Survey. NSO, Manila, Philippines. ______. 1998. Annual Poverty Indicators Survey 1998. NSO, Manila, Philippines.

REFERENCES

______. 2002. 2002 Philippine Statistical Yearbook. NSCB, Makati City, Philippines.

______. 1998. Census Facts and Figures. NSO, Manila, Philippines. ______. 1998. National Demographic and Health Survey. NSO, Manila, Philippines. ______. 2003. National Demographic and Health Survey. NSO, Manila, Philippines. ______. 1999. Annual Poverty Indicators Survey 1999. NSO, Manila, Philippines. ______. 2000. 2000 Maternal and Child Health Survey: Final Report. NSO, Manila, Philippines. ______. 2001. 2000 Census of Population and Housing. [Online]. Available from: [Accessed 25 November 2002]. ______. 2001. 2000 Family Planning Survey: Final Report. NSO, Manila, Philippines. National Statistics Office (NSO) and Department of Education, Culture, and Sports (DECS). 1994. 1994 Functional Literacy, Education and Mass Media Survey. NSO and DECS, Manila, Philippines. Nelson, H. L. and C. W. J. Granger. 1979. “Experience with Using the Box-Cox Transformation when Forecasting Time Series.” Journal of Econometrics, 10: 57–69. Ness, G. 1997. Population and Strategies for National Sustainable Development: a Guidebook to Assist Environmental Planners in Dealing with Population Issues, Earthscan Press, London, U.K. Ong, P. 2003. “The Philippines: Changing Landscapes, Humanscapes and Mindscapes in a Globalizing World.” Paper presented at the 2003 Third National Conference of the Philippine Studies Association. Philippine Social Studies Center, Diliman, Quezon City, Philippines. 5–6 December. Ong, P. S., L. E. Afuang and R. G. Rosell-Ambal (eds.) 2002. Philippine Biodiversity Conservation Priorities: a Second Iteration of the National Biodiversity Strategy and Action Plan. Department of Environment and Natural Resources–Protected Areas and Wildlife Bureau, Conservation International–Philippines, University of the Philippines Center for Integrative and Development Studies–Biodiversity Conservation Program, and Foundation for the Philippine Environment, Quezon City, Philippines. Orbeta, A. C. Jr. 2002. “Population and Poverty Links: a Review of the Links, Evidence and Implications for the Philippines.” PIDS Discussion Paper No. 2002-21. Philippine Institute for Development Studies, Makati City, Philippines.

Organisation for Economic Co-operation and Development (OECD). 1993. “OECD Core Set of Indicators for Environmental Performance Reviews.” OECD Environmental Monograph No. 83. OECD, Paris, France. Orians, C. E. and M. Skumanich. 1997. “The Population-Environment Connection: What Does it Mean for Environmental Policy?” Office of Policy Planning and Evaluation, US Environmental Protection Agency (USEPA). Otten, E. J. undated. “The Effect of Human Population on Biodiversity.” Biodiversity and Human Health. [Online]. Available from: [Accessed April 2004]. Pabuayon, I. M. 2001. “Development Strategies for Addressing Poverty in Philippine Forest Communities,” in Proceedings of the Conference on Natural Capital, Poverty, and Development. [Online]. University of Toronto, Toronto, Canada. Available from: [Accessed April 2004]. Panayotou, T. and S. Sungsuwan. 1989. “An Econometric Study on the Causes of Tropical Deforestation: the Case of Northeast Thailand.” Development Discussion Paper No. 284. Harvard Institute for International Development, Harvard University, Cambridge, Massachusetts, U.S.A. Panda, M. 2004. “Population and Environment Relationships in Developing Countries: A Select Review of Approaches and Methods.” Paper presented at the Population Association of America 2004 Annual Meeting. Boston, Massachusetts, April 1–3.

Mapping Population-Biodiversity Connections in the Philippines

Orbeta, A. C. Jr. and E. M. Pernia. 1999. “Population Growth and Economic Development in the Philippines: What Has Been the Experience and What Must Be Done?” PIDS Discussion Paper Series No. 99-22. Philippine Institute for Development Studies, Makati City, Philippines.

83

REFERENCES

Paqueo, V. 1985. “An Econometric Analysis of Educational Attainment Among Elementary School Age Children: Preliminary Analysis,” in Transactions of the National Academy of Science and Technology, Republic of the Philippines, Vol II. The Academy, Metro Manila, Philippines. pp. 359–380. Peters, G.L. and R. P. Larkin. 1997. Population Geography: Problems, Concepts, and Prospects. Kendall/Hunt Publishing Co., Iowa, U.S.A. Philippine Institute for Development Studies (PIDS). [Online]. Available from: [Accessed April 2004] Philippine National Police (PNP). 2000 Directorate for Intelligence. Population Reference Bureau (PRB). 2001. Women, Men, and Environmental Change: the Gender Dimensions of Environmental Policies and Programs. [Online]. Available from: [Accessed April 2004]. Primavera, J. H. 2000. “Development and Conservation of Philippine Mangroves: Institutional Issues.” Ecological Economics, 35: 91–106. Protected Areas and Wildlife Bureau (PAWB)–DENR. 2003. Support to the Implementation of the Tamaraw Conservation Program Proposal. DENR, Philippines. Quiazon-King, E. 1978. “Time Allocation and Home Production in Rural Philippine Households.” The Philippine Economic Journal, 17 (1&2): 185–202. Rabor, D. S. 1968. “The Present Status of the Monkey-eating Eagle, Pithecophaga jefferyii Ogilvie-Grant, of the Philippines.” IUCN, New Serv. 10: 312-314. Retherford, R. D. and M. K. Choe. 1993. Statistical Models for Causal Analysis. John Wiley and Sons, Inc., New York, U.S.A. Rindfuss, R. R., S. J. Walsh, V. Mishra, J. Fox and G. P. Dolcemascolo. 2003. “Linking Household and Remotesensed Data: Methodological and Practical Problems,” in People and the Environment: Approaches for Linking Household and Community Surveys to Remote Sensing and GIS, (eds.) J. Fox, R. R. Rindfuss, S. J. Walsh and V. Mishra. Kluwer Academic Publishers, Boston, U.S.A. pp. 1–29. Ritchie, J., C. R. Goeldner, R. E. Taylor and L. L. Judd. 1994. “Delphi Forecasting,” in Tourism Marketing and Management Handbook, S. F. Witt, and L. Moutinho. Prentice Hall, London, U.K. Roberts, C. M., C. J. McClean, J. E. N. Veron, J. P. Hawkins, G. R. Allen, D. E. McAllister, C. G. Mittermeier, F. W. Schueler, M. Spalding, F. Wells, C. Vynne and T. B. Werner. 2002. “Marine Biodiversity Hotspots and Conservation Priorities for Tropical Reefs.” Science, 295: 1280–1284.

Mapping Population-Biodiversity Connections in the Philippines

Romero, G. M. (ed.) 1995. Basic Environmental Course for Beginners: a Guided Training Manual. Haribon Foundation for the Conservation of Natural Resources in partnership with Friedrich Ebert Steftung, Manila, Philippines.

84

Schmink, M. 1994. “The Socioeconomic Matrix of Deforestation,” in Population and the Environment: Methods, Cases and Policies, (eds.) L. Arizpe, P. Stone, and D. Major. Social Science Research Council (SSRC), New York. U.S.A. Shaw, P. R. 1989a. “Paradox of Population and Environmental Degradation.” Paper presented at the annual meeting of the American Association for the Advancement of Science, San Francisco, California, U.S.A, 14–19 January. ______. 1989b. “Population, Environment, and Women: an Analytical Framework.” Prepared for the 1989 United Nations Population Fund (UNFPA) Interagency Consultative Meeting, New York, U.S.A., 6 March. ______. 1989c. “Rapid Population Growth and Environmental Degradation: Ultimate Versus Proximate Causes.” Environmental Conservation, 16 (3). ______. 1992. “The Impact of Population Growth on Environment: the Debate Heats Up.” Environmental Impact Assessment Review, 12. Shryock, H. S., J. S. Siegel and associates. 1976. The Methods and Materials of Demography. Condensed edition by Stockwell, Edward G. Academic Press, Inc., Orlando, Florida, U.S.A. Simon, J. 1981. The Ultimate Resource. Princeton University Press, Princeton, New Jersey, U.S.A. ______. 1996. The Ultimate Resource 2. Princeton University Press, Princeton, New Jersey, U.S.A. Sison, J. M. 2003. “Experience of the Communist Party of the Philippines in the Anti-imperialist and Anti-war Fronts.” Contribution to the 12th International Communist Seminar, The Marxist-Leninist Party and the Anti-Imperialist Front Facing the War, Brussels, 2–4 May.

Sutherland, E. G., D. L. Carr, and S. L. Curtis. 2004. “Fertility and the Environment in the Natural Resource Dependent Economy: Evidence from Petén, Guatemala.” Población y Salud en Mesoamérica. Revista Electronica, 2 (1), Art. 2.

REFERENCES

Stokes, C. and W. Schutjer. 1984. “Access to Land and Fertility in Developing Countries,” in Rural Development and Human Fertility, (eds.) W. Schutjer and C. Stokes. Macmillan, New York, U.S.A.

Tabunda, A. M. L. and J. R. G. Albert. 2002. “Philippine Poverty in the Wake of the Asian Financial Crisis and El Niño,” in Impact of the East Asian Financial Crisis, Revisited, (ed.) S. Khandker. The World Bank Institute and the Philippine Institute for Development Studies, Makati City, Philippines. Terborgh, J. 1999. Requiem for Nature. Island Press, Washington D.C., U.S.A. xii, 234 pp. Tiefenthaler, J. 1997. “Fertility and Family Time Allocation in the Philippines.” Population and Development Review, 23 (2): 377–397. Tomlin, C. D. 1990. Geographic Information Systems and Cartographic Modeling. Prentice-Hall, New Jersey, U.S.A. Turner II, B. L., P. A. Matson, J. J. McCarthy, R. W. Corell, L. Christensen, N. Eckley, G. K. Hovelsrud-Broda, J. X. Kasperson, R. E. Kasperson, A. Luers, M. L. Martello, S. Mathiesen, R. Naylor, C. Polsky, A. Pulsipher, A. Schiller, H. Selin and N. Tyler. 2003. “Science and Technology for Sustainable Development Special Feature: Illustrating the Coupled Human-Environment System for Vulnerability Analysis: Three Case Studies.” Proceedings of the National Academy of Sciences of the United States of America (PNAS), 100 (14): 8080–8085. United Nations. 2003. World Population Prospects: The 2002 Revision Population Database. [Online]. Available from: [Accessed May 2004]. United Nations Children’s Fund (UNICEF). 1998. [Online]. Available from: [Accessed August 2004] United Nations Department of Economic and Social Affairs (UNDESA). 2001. Population, Environment and Development. UNDESA, New York, U.S.A. United Nations Population Fund (UNFPA). 2001. “Population, Environment, and Poverty Linkages. Operational Challenges.” Population and Development Strategies No. 1. UNFPA, New York, U.S.A. University of Florida, Center for Latin American Studies. [Online]. Available from: [Accessed June 2004]. University of North Carolina, Carolina Population Center. [Online]. Available from: [Accessed June 2004]. Valenzuela-Duya, M. 2004. personal communication. .

Veldkamp, A. and E Lambin (eds.) 2001. “Predicting Land-Use Change.” Special issue of Agriculture, Ecosystems, and Environment, 85 (13). Veron, J. E. N. 2000. Corals of the World. Vol. 1–3. Australian Institute of Marine Science, Australia. Werner, T. B. and G. R. Allen (eds.) 2000. “A Rapid Marine Biodiversity Assessment of the Calamianes Islands, Palawan Province, Philippines.” RAP Bulletin of Biological Assessment 17. Conservation International, Washington, D.C., U.S.A. Wong, G. Y., M. Castrence and F. F. Maon. 2003. “Socializing the Pixel: an Integrated Approach to Modeling the Risk of Forest Loss in South Palawan.” A report prepared for the Palawan Conservation Strategy Development Project of the Conservation International and the Critical Ecosystems Partnership Fund. Unpublished. World Bank. 1991. New Directions in the Philippine Family Planning Program. ______. 2001. World Development Report 2000/2001. Attacking Poverty. Oxford University Press, New York, U.S.A. World Health Organization (WHO). 1997. Health and Environment in Sustainable Development. WHO, Geneva, Switzerland. Zamora, P. M. 1995. “Diversity of Flora in the Philippine Mangrove Ecosystem.” Biodiversity Conservation Reports, 1: 1-92. University of the Philippines Center for Integrative and Development Studies, Diliman, Quezon City, Philippines.

Mapping Population-Biodiversity Connections in the Philippines

Veldkamp, A. and L. O. Fresco. 1996. “CLUE-CR: an Integrated Multi-scale Model to Simulate Land-Use Change Scenarios in Costa Rica.” Ecological Modeling, 91: 23–248.

85

Annex 1

Matrix of Indicators

Various operational measures are presented below to gauge each of the three major demographic factors that shape the size, composition and growth of population; i.e. fertility, mortality and migration. The use of a wide gamut of indicators reflects a triangulated approach to the measurement of these multidimensional demographic forces such that the study assumes that no single measure can adequately describe the levels, patterns, and trends underlying fertility, mortality and migration. Indeed, each indicator has its inherent strengths and weaknesses and, as such, the study deems most appropriate to account for every possible operational perspective in the measurement of these demographic factors. Indicators

Conceptual Definition

I. Basic Demographic Indicators A. Fertility

1. Crude birth rate 2. Child-to-woman ratio

Mapping Population-Biodiversity Connections in the Philippines

3. Total fertility rate

86

4. Average household size 5. Number of households B. Mortality

6. Crude death rate by sex a. Crude death rate for females b. Crude death rate for males 7. Life expectancy at birth by sex a. Life expectancy of females b. Life expectancy of males

The number of births per 1,000 population. The number of children under 5 years of age per 1,000 women of child-bearing age (15–49) in a given year. The average number of children a woman will have within her reproductive years (15–49).

Rationale for Inclusion as PE Indicator These basic demographic indicators of fertility measure the levels, patterns, trends and rate of population growth, a potent force that impinges adversely upon environmental biodiversity. Other factors held constant, high levels of fertility tend to imply rapid population growth which, in turn, has a general proclivity to further environmental degradation. Rapidly growing populations clear forest reserves to make way for agriculture and housing. In addition, urbanization and industrialization pollute rivers, lakes, canals and other water sources as well as the air vital to life itself. The simplest and most basic measure of fertility. This measure is used as a rough fertility indicator, especially when detailed data on births are lacking. Reflects completed fertility history per woman.

The ratio of the total population by the Indicates mean number of members per household in population. total number of households. The total number of households in the Indicates total number of households in province. the province. Mortality indicators reflect the level of standard of living in a particular area during a specific period. More importantly, they imply population access to adequate health infrastructure and services. On the other hand, mortality indicators likewise show how environmental degradation adversely impacts upon people’s health through air pollution, lack of potable water, improper waste disposal, and other negative offshoots of urbanization and industrialization. The ratio of the total number of deaths to the total mid-year population (multiplied by 1,000) differentiated by sex. The average number of years newborn babies can be expected to live, based on current health conditions.

Simplest measure of overall level of mortality. This indicator reflects environmental conditions in a country, the health of its people, the quality of care they receive when they are sick, and their living conditions.

ANNEX 1

Indicators I. Basic Demographic Indicators A. Fertility

Computational Formula, Source of Computation

1. Crude birth rate 2. Child-to-woman ratio

3. Total fertility rate

5. Number of households B. Mortality

6. Crude death rate by sex a. Crude death rate for females b. Crude death rate for males 7. Life expectancy at birth by sex a. Life expectancy of females b. Life expectancy of males

Commission on Population (POPCOM); 1995 The ratio of the number of children aged 0–4 years to the total number of women aged 15–49. Sum of age specific fertility rates from ages 15–19 to 45–49.

National Statistics Office (NSO); 2000 Census of Population and Housing; 2000

National Statistical Coordination Board (NSCB); 1995 Census-Based National, Regional and Provincial Population Projection; 1995 Population size divided by number of National Statistics Office (NSO); www. households. census.gov.ph; 2000 Actual number of households in National Statistics Office (NSO); www. population. census.gov.ph; 2000

Total number of deaths divided by mid- National Statistics Office (NSO); Genderyear population. Specific Life Tables for the Philippines, Its Regions and Provinces; 1995 Life expectancy at age 0 taken from life National Statistical Coordination Board table for province. (NSCB); 1995 Census-Based National, Regional and Provincial Population Projection; 1995

Mapping Population-Biodiversity Connections in the Philippines

4. Average household size

Data Source

87

ANNEX 1

Indicators 8. Infant mortality rate by sex a. Female infant mortality rate b. Male Infant mortality rate 9. Under age-5 mortality rate

10. Maternal mortality rate

11. Morbidity rate a. Morbidity rate due to bronchitis b. Morbidity rate due to diarrhea c. Morbidity rate due to hypertension d. Morbidity rate due to influenza e. Morbidity rate rue to pneumonia f. Morbidity rate due to tuberculosis C. Migration

12. Net migration rate by sex a. Net migration rate of females b. Net migration rate of males D. Other Population Indicators 13. Population size

Mapping Population-Biodiversity Connections in the Philippines

14. Population density

88

15. Population growth rate

16. Urban population growth rate a. Urban population exponential growth rate b. Urban population geometric growth rate II. Family Planning and Reproductive Health Indicators

Conceptual Definition

Rationale for Inclusion as PE Indicator

The probability of infant deaths under one Indicative of available health infrastructure year old, per 1,000 live births. for infants. The probability that a newborn baby (including infants 0-1 years old) will die before reaching age five, if current living conditions stay the same. The number of maternal deaths due to pregnancy or childbirth per 1,000 live births. Number of cases of top six diseases (diarrhea, pneumonia, bronchitis, influenza, hypertension, tuberculosis), per 1,000 population.

Indicative of available health infrastructure for infants and children in province.

Reflects access to health infrastructure and services among mothers. Reflects most prevalent morbidity patterns and sources of pathological conditions in the environment.

Population movements, whether undertaken over short or long distances, or intended as permanent or transient relocations, significantly influence the environmental biodiversity, particularly in these geographic areas where these migratory streams occur. Human movements tend to destroy the delicate ecological balance of flora and fauna in these areas as lands are cleared for roads, houses, industries, agriculture and other institutions and infrastructure are established to support these settlements. The difference between in-migration and Indicates whether province is a net-sending out-migration within each province. or net-receiving area.

These indicators reflect the current size, growth and composition of human populations. The number of people who live in a given Measures absolute level of human area. presence. The average population size per square Measures degree of human congestion in a kilometer. particular geopolitical area. The estimated rate of population expansion Gauges rate of population expansion for between two census years. a particular area over a specific period of time.

The estimated (exponential and Measures logarithmic) rate of urban population growth. expansion between two census year.

rate

of

urban

population

These indicators measure the population’s actual levels of as well as the potential for control of fertility.

8. Infant mortality rate by sex a. Female infant mortality rate b. Male Infant mortality rate 9. Under age-5 mortality rate

10. Maternal mortality rate

11. Morbidity rate a. Morbidity rate due to bronchitis b. Morbidity rate due to diarrhea c. Morbidity rate due to hypertension d. Morbidity rate due to influenza e. Morbidity rate rue to pneumonia f. Morbidity rate due to tuberculosis C. Migration

12. Net migration rate by sex a. Net migration rate of females b. Net migration rate of males D. Other Population Indicators 13. Population size 14. Population density

16. Urban population growth rate a. Urban population exponential growth rate b. Urban population geometric growth rate II. Family Planning and Reproductive Health Indicators

Number of deaths to population aged less National Statistical Coordination Board than five, divided by mid-year population. (NSCB); 1995 Census-Based National, Regional and Provincial Population Projection; 1995 The ratio of the number of maternal Department of Health (DOH); Field deaths due to pregnancy or childbirth per Health Service Information System Annual 1000 live births. Report; 1999 The ratio of the number of cases with Department of Health (DOH); Field a particular disease relative to the total Health Service Information System Annual population. Report; 1999

The ratio of the difference between in- and Department of the Interior and Local out-migrants to the total population. Government, Planning Service (DILG); Local Government Unit (LGU) Profile; 2000

Taken from National Statistics Office (NSO) Taken from National Statistics Office (NSO) Pt=Po(1+r)t where Pt is the population size at time t, Po is the population size at time 0, r is the rate of population growth from time 0 to time t, and t is the length of time between time 0 and time t. Pt=Po(1+r)t where Pt is the population size at time t, Po is the population size at time 0, r is the rate of population growth from time 0 to time t, and t is the length of time between time 0 and time t.

National Statistics Office (NSO); www. census.gov.ph; 2000 National Statistics Office (NSO); www. census.gov.ph; 2000 National Statistics Office (NSO); www. census.gov.ph; 2000

National Statistics Office (NSO), National Statistical Coordination Board (NSCB); Census Facts and Figures, 2002 Philippine Statistical Yearbook; 1998, 2002

Mapping Population-Biodiversity Connections in the Philippines

15. Population growth rate

Computational Formula, Data Source Source of Computation Number of deaths to population aged National Statistics Office (NSO); Genderless than 1 year, divided by mid-year Specific Life Tables for the Philippines, Its population. Regions and Provinces; 1995

ANNEX 1

Indicators

89

ANNEX 1

Indicators 17. Contraceptive prevalence rate a. Percent families use of any contraceptive method b. Percent families use of modern contraceptive method c. Percent families use of traditional contraceptive method d. Percent families did not use any contraceptive method e. Bi-tubal ligation rates f. Percent prevalence of scalpel vasectomy g. Percent prevalence of no scalpel vasectomy 18. Couples with access to family planning

Conceptual Definition

The percentage of families with couples Measures level of contraceptive use of reproductive age (15–49) using any within segment of population at risk for method of contraception, relative to the pregnancy. total number of families with married women (15–49).

The number of families with married women 15–49 years old who know where to get family planning services, in relation to the total number of families with married women 15–49 years old.

Mapping Population-Biodiversity Connections in the Philippines

III. Health and Nutrition Indicators

90

Rationale for Inclusion as PE Indicator

Measures contraceptive and family planning supplies and services particularly among those at most risk for unwanted pregnancies. These indicators not only represent the level of health and nutritional fitness of a population but they likewise measure population access to infrastructure, institutions and services that promote and maintain health and nutrition. Gauges access of population to health facilities at one of the lowest level of political and territorial jurisdiction in the Philippines. Measures population access to potable water and, conversely, lack of access to the same gauges potential towards such waterborne diseases as diarrhea, cholera and the like. Measures population access to proper sewage and waste disposal.

19. Population-to-barangay health station ratio

The population relative to the number of barangay health stations.

20. Percent of households with access to safe drinking water

The percentage of households who get water from either a faucet, tubed/piped well or bottled water, to the total number of households.

21. Percent of families with own sanitary toilet

The percentage of families with watersealed or closed pit type of toilet facilities, to the total number of families. The number of pregnant or lactating Represents level of maternal health and married women who received iodine nutrition. supplements, iron supplements or a tetanus toxoid immunization.

22. Maternal nutrition rate a. Percent mothers receiving iodine supplement b. Percent mothers receiving iron supplement c. Percent mothers receiving tetanus toxoid supplement 23. Population-to-barangay health worker ratio 24. Rate of children fully immunized a. Immunization rate for children 12–23 months b. Immunization rate for children 24–35 months IV. Other Socioeconomic Indicators A. Education

The number of people per barangay health Gauges access of populace to health worker. service providers at the lowest judicial and territorial level of the barangay. The number of children aged 12 to 35 Measures level of child health specifically months who received immunization, in terms of protection against preventable relative to the total number of the same childhood diseases. ages. Other social and economic measures pertinent to the welfare of the populace. These indicators measure the level and quality of functional skills available in the population as well as imply some degree of awareness of the vital link between human survivorship and environmental biodiversity.

Computational Formula, Data Source Source of Computation Taken from National Statistics Office, National Statistics Office (NSO); 2000 Annual Poverty Indicators Survey (APIS), Family Planning Survey, Final Report; 1999 2000

17. Contraceptive prevalence rate a. Percent families use of any contraceptive method b. Percent families use of modern contraceptive method c. Percent families use of traditional contraceptive method d. Percent families did not use any contraceptive method e. Bi-tubal ligation rates f. Percent prevalence of scalpel vasectomy g. Percent prevalence of no scalpel vasectomy 18. Couples with access to family planning Taken from National Statistics Office National Statistics Office (NSO); Annual (NSO) Poverty Indicators Survey (APIS); 1999

ANNEX 1

Indicators

III. Health and Nutrition Indicators

19. Population-to-barangay health station ratio

Total population divided by number of Department of Health (DOH); Field barangay health stations. Health Service Information System Annual Report; 1999

20. Percent of households with access to safe drinking water

Number of households who get water National Statistics Office (NSO); 2000 from a faucet, tubed/piped well, or bottled Census of Population and Housing; 2000 water divided by the total number of households.

21. Percent of families with own sanitary toilet

Taken from National Statistics Office, National Statistics Office (NSO); Annual Annual Poverty Indicators Survey (APIS), Poverty Indicators Survey (APIS); 1999 1999 Taken from National Statistics Office National Statistics Office (NSO); Annual (NSO) Poverty Indicators Survey (APIS); 1999

24. Rate of children fully immunized a. Immunization rate for children 12–23 months b. Immunization rate for children 24–35 months IV. Other Socioeconomic Indicators A. Education

Total population divided by number of Department of Health (DOH); Field barangay health workers. Health Service Information System Annual Report; 1999 Taken from National Statistics Office National Statistics Office (NSO); 2000 (NSO) Maternal and Child Health Survey; 2000

Mapping Population-Biodiversity Connections in the Philippines

22. Maternal nutrition rate a. Percent mothers receiving iodine supplement b. Percent mothers receiving iron supplement c. Percent mothers receiving tetanus toxoid supplement 23. Population-to-barangay health worker ratio

91

ANNEX 1

Indicators 25. Functional literacy rate by sex a. Literacy rate of females b. Literacy rate of males

26. Rate of population 15 years and over who attained high school B. Economic Indicators

Mapping Population-Biodiversity Connections in the Philippines

27. Youth and elderly dependency ratio

92

28. Rate of employment by major industries by sex a. Percent female labor force in agriculture, hunting and forestry b. Percent male labor force in agriculture, hunting and forestry c. Percent female labor force in construction d. Percent male labor force in construction e. Percent female labor force in electricity, gas and water f. Percent male labor force in electricity, gas and water g. Percent female labor force in fishing h. Percent male labor force in fishing i. Percent female labor force in manufacturing j. Percent male labor force in manufacturing k. Percent female labor force in mining and quarrying l. Percent male labor force in mining and quarrying m. Percent female labor force in service industry n. Percent male labor force in service industry o. Percent female labor force in trading p. Percent male labor force in trading q. Percent female labor force in a not-stated industry r. Percent male labor force in a not-stated industry 29. Employment and unemployment rates a. Employment rate b. Unemployment rate 30. Percent families with strong material type of housing 31. Percent of families with housing by ownership (or amortization)

Conceptual Definition

Rationale for Inclusion as PE Indicator

The percentage of population who can read and write a simple message in any language or dialect and also have numerical skills which, in turn, must be sufficiently advanced to enable the individual to participate fully and effectively in the activities of daily life which require a fair capability of communicating by written language. The number of people 15 years and older who completed high school to the total population 15 years and older.

Both measure the actual as well as the potential for economic productivity in population.

Measures educational attainment.

These indicators pertain to levels and trends in economic participation as well as status in the population. The number of youth and elderly per 1000 Measures the number of youth and elderly population of working age (15 to 64). dependents per worker in the populace. The percentage of the total labor force Profiles pattern of economic participation working in eight major industries. by sex.

The number of people employed or Describes level of economic activity as unemployed, to the total labor force. well as inactivity in the populace. The percentage of number of families with These constitute important proxy measures houses made of strong materials. for socioeconomic status of the populace. The number of families who own or Indicates socioeconomic status of the amortize their housing unit, to the total population. number of families.

25. Functional literacy rate by sex a. Literacy rate of females b. Literacy rate of males

26. Rate of population 15 years and over who attained high school B. Economic Indicators 27. Youth and elderly dependency ratio

Data Source Commission on Population (POPCOM); 1994

Population aged 5 years and over who National Statistics Office (NSO); 2000 completed high school divided by total Census of Population and Housing; 2000 population aged 5 years and over.

Taken from National Statistics Office National Statistics Office (NSO); 2000 (NSO) Census of Population and Housing; 2000 Taken from National Statistics Office National Statistics Office (NSO); 1995 (NSO) Census of Population; 1995

Taken from National Coordination Board (NSCB)

Statistical National Statistical Coordination Board (NSCB); The Countryside in Figures, 2001 Edition; 2000

Taken from National Statistics Office National Statistics Office (NSO); Annual (NSO) Poverty Indicators Survey (APIS); 1999 Number of families who own or amortize National Statistics Office (NSO); 2000 housing unit divided by total number of Census of Population and Housing; 2000 families.

Mapping Population-Biodiversity Connections in the Philippines

28. Rate of employment by major industries by sex a. Percent female labor force in agriculture, hunting and forestry b. Percent male labor force in agriculture, hunting and forestry c. Percent female labor force in construction d. Percent male labor force in construction e. Percent female labor force in electricity, gas and water f. Percent male labor force in electricity, gas and water g. Percent female labor force in fishing h. Percent male labor force in fishing i. Percent female labor force in manufacturing j. Percent male labor force in manufacturing k. Percent female labor force in mining and quarrying l. Percent male labor force in mining and quarrying m. Percent female labor force in service industry n. Percent male labor force in service industry o. Percent female labor force in trading p. Percent male labor force in trading q. Percent female labor force in a not-stated industry r. Percent male labor force in a not-stated industry 29. Employment and unemployment rates a. Employment rate b. Unemployment rate 30. Percent families with strong material type of housing 31. Percent of families with housing by ownership (or amortization)

Computational Formula, Source of Computation

ANNEX 1

Indicators

93

ANNEX 1

Indicators 32. Poverty rate

Mapping Population-Biodiversity Connections in the Philippines

C. Political and cultural indicators

94

Conceptual Definition

The number of families with per capita Measures level of socioeconomic income below poverty threshold, to the deprivation in the population. total number of families. These indicators represent the political stability and social and cultural climate in the population. Socioeconomic and political disorder contribute to environmental degradation as human conflict tends to impinge adversely upon the delicate ecological balance especially in human settlement areas. Prevalence of top five languages in Indicates potential for integration to the Philippines as spoken at home per Philippine social and cultural mainstream. population of province.

33. Mother tongue a. First most prevalent mother tongue b. Second most prevalent mother tongue c. Third most prevalent mother tongue d. Fourth most prevalent mother tongue e. Fifth most prevalent mother tongue f. Number speaking first most prevalent mother tongue g. Number speaking second most prevalent mother tongue h. Number speaking third most prevalent mother tongue i. Number speaking fourth most prevalent mother tongue j. Number speaking fifth most prevalent mother tongue The top 5 tribal or indigenous group 34. Tribal or cultural indigenous groups population per population of province. a. First indigenous group b. Second indigenous group c. Third indigenous group d. Fourth indigenous group e. Fifth indigenous group f. Number in first indigenous group g. Number in second indigenous group h. Number in third indigenous group i. Number in fourth indigenous group j. Number in fifth indigenous group 35. Crime rates The number of crimes per 1,000 population. 36. Armed conflict a. Percent of barangays affected by ideology-based conflict b. Percent of barangays affected by religion-based conflict

Rationale for Inclusion as PE Indicator

Measures level of socio-ethnic diversity in the populace.

Gauges patterns and trends in economic and political disorder populace. Number of barangays affected by ideological Gauges patterns and trends in or religious conflict economic and political disorder populace.

social, in the social, in the

32. Poverty rate

C. Political and cultural indicators

Computational Formula, Data Source Source of Computation Number of families with per capita income National Anti-Poverty Commission below poverty threshold level divided by (NAPC); 2000 Poverty Updates; 2000 total number of families.

33. Mother tongue a. First most prevalent mother tongue b. Second most prevalent mother tongue c. Third most prevalent mother tongue d. Fourth most prevalent mother tongue e. Fifth most prevalent mother tongue f. Number speaking first most prevalent mother tongue g. Number speaking second most prevalent mother tongue h. Number speaking third most prevalent mother tongue i. Number speaking fourth most prevalent mother tongue j. Number speaking fifth most prevalent mother tongue 34. Tribal or cultural indigenous groups a. First indigenous group b. Second indigenous group c. Third indigenous group d. Fourth indigenous group e. Fifth indigenous group f. Number in first indigenous group g. Number in second indigenous group h. Number in third indigenous group i. Number in fourth indigenous group j. Number in fifth indigenous group 35. Crime rates

Taken from National Statistics Office National Statistics Office (NSO); 2000 (NSO) Census of Population and Housing; 2000

36. Armed conflict a. Percent of barangays affected by ideology-based conflict b. Percent of barangays affected by religion-based conflict

Number of affected barangays divided by Philippine National Police (PNP); the total number of barangays multiplied by Directorate for Intelligence; 2000 100.

ANNEX 1

Indicators

Taken from the National Commission on National Commission on Indigenous Indigenous Peoples Peoples (NCIP); 1996 Accomplishment Report of both ONCC and OSCC; 1995

Mapping Population-Biodiversity Connections in the Philippines

Total crime volume divided by population Philippine National Police (PNP); size, multiplied by 100 Directorate for Intelligence; 2000

95

Annex 2 Annex 2.1

Forest Cover by Province Total forest Cover (ha) 224,801 no data

Percent Forest 54.42 no data

Agusan del Sur

365,265

43.22

Aklan Albay Antique

81,655 81,117 120,217

46.46 32.20 46.39

Apayao

no data

no data

no data

no data

Aurora Basilan Bataan Batanes Batangas

256,900 no data 69,647 no data 69,714

78.80 no data 54.59 no data 23.16

1993–2000 no data 1993–2000 no data 1993–2000

Benguet

107,402

38.74

Biliran Bohol Bukidnon Bulacan Cagayan Camarines Norte Camarines Sur Camiguin Capiz Catanduanes Cavite Cebu Compostela Cotabato Cotabato City Davao Davao del Sur Davao Oriental Eastern Samar Guimaras Ifugao Ilocos Norte Ilocos Sur Iloilo Isabela Kalinga La Union Laguna

15,378 99,351 no data 142,954 468,477 86,446 179,543 no data 91,660 74,918 38,574 147,319 no data no data no data no data no data no data 267,204 20,112 152,277 155,673 99,872 119,151 552,985 531,058 55,453 50,638

30.91 24.36 no data 48.98 58.34 44.27 34.50 no data 37.51 50.24 30.74 32.91 no data no data no data no data no data no data 65.84 35.20 61.49 43.42 42.23 26.30 53.62 81.90 44.93 28.90

Lanao del Norte

126,309

39.26

Lanao del Sur

174,767

52.37

Leyte

220,937

36.96

JAFTA Land Cover Statistics no data JAFTA Land Cover Statistics no data JAFTA Land Cover Statistics Land Cover Mapping Project-LRD, RSRDAD JAFTA Land Cover Statistics JAFTA Land Cover Statistics no data JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics no data JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics no data no data no data no data no data no data JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics Land Cover Mapping Project-LRD, RSRDAD Land Cover Mapping Project-LRD, RSRDAD JAFTA Land Cover Statistics

Provincial name

Mapping Population-Biodiversity Connections in the Philippines

Abra Agusan del Norte

96

Indicators

Source JAFTA Land Cover Statistics no data Land Cover Mapping Project-LRD, RSRDAD JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics

Year of Data Used 1993–2000 no data

Remarks

1992–2002 1993–2000 1993–2000 1993–2000

1992–2002 1993–2000 1993–2000 no data 1993–2000 1993–2000 1993–2000 1993–2000 no data 1993–2000 1993–2000 1993–2000 1993–2000 no data no data no data no data no data no data 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1992–2002 1992–2002 1993–2000

Included in Kalinga

Total forest Cover (ha)

Percent Forest

128,254

29.51

Marawi City Marinduque Masbate Metro Manila

no data 34,881 59,281 5,178

no data 37.77 11.61 10.06

44,874

23.25

Misamis Oriental

no data

no data

Mountain Province

118,843

54.78

Negros Occidental Negros Oriental Northern Samar Nueva Ecija Nueva Vizcaya Occidental Mindoro Oriental Mindoro Palawan Pampanga Pangasinan Quezon

171,815 162,316 160,205 118,200 249,348 350,910 288,375 933,773 42,131 67,941 375,800

23.73 33.32 47.15 21.79 64.82 61.80 60.74 65.29 18.22 14.39 45.35

Quirino

227,295

76.30

Rizal Romblon Samar Sarangani Siquijor Sorsogon South Cotabato Southern Leyte Sultan Kudarat Sulu Surigao del Norte

66,072 57,577 264,157 no data 8,690 35,121 no data 80,353 no data no data no data

53.54 46.03 48.43 no data 28.97 17.11 no data 48.85 no data no data no data

Surigao del Sur

177,567

39.93

Tarlac Tawi-tawi Zambales Zamboanga del Norte

94,212 no data 125,210

27.07 no data 29.43

197,660

31.62

Zamboanga del Sur

166,131

21.56

Misamis Occidental

Land Cover Mapping Project-LRD, RSRDAD no data JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics Land Cover Mapping Project-LRD, RSRDAD no data Land Cover Mapping Project-LRD, RSRDAD JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics Land Cover Mapping Project-LRD, RSRDAD JAFTA Land Cover Statistics JAFTA Land Cover Statistics JAFTA Land Cover Statistics no data JAFTA Land Cover Statistics JAFTA Land Cover Statistics no data JAFTA Land Cover Statistics no data no data no data Land Cover Mapping Project-LRD, RSRDAD JAFTA Land Cover Statistics no data JAFTA Land Cover Statistics Land Cover Mapping Project-LRD, RSRDAD Land Cover Mapping Project-LRD, RSRDAD

Year of Data Used 1992–2002 no data 1993–2000 1993–2000 1993–2000 1992–2002 no data 1992–2002 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1993–2000 1992–2002 1993–2000 1993–2000 1993–2000 no data 1993–2000 1993–2000 no data 1993–2000 no data no data no data 1992–2002 1993–2000 no data 1993–2000 1992–2002 1992–2002

Source: Japan Forest Technology Association (JAFTA) Statistics 1993–2000 Land Cover Mapping Project – LRD, RSDAD 1992-2000 (NAMRIA)

Annex 2.2

Threatened Animals by CPA (Available in the DVD)

Annex 2.3

Socioeconomic and Demographic Variables by Province (Available in the DVD)

Remarks

Mapping Population-Biodiversity Connections in the Philippines

Maguindanao

Source

ANNEX 2

Provincial name

97

Annex 3 Annex 3.1

Statistics

Descriptive Statistics for Data at the Provincial Level

Mapping Population-Biodiversity Connections in the Philippines

Variables

98

FORPCT Percent forest cover for provinces ARCFORP Arcsin of percent forest cover for provinces HHSIZE Average household size HH# Number of households CWR Child-to-woman ratio ANYMET Percent families use of any contraceptive method MODERN Percent families use of modern contraceptive method TRAD Percent families use of traditional contraceptive method NOMET Percent families did not use any contraceptive method FPACCESS Couples with access to family planning CRIME Crime rate CBR Crude birth rate CDRMALE Crude death rate for males CDRFEM Crude death rate for females EMP Employment rate UNEMP Unemployment rate BTL Bi-tubal ligation rates VASEC Percent prevalence of scalpel vasectomy NSV Percent prevalence of no scalpel vasectomy LITMALE Literacy rate of males LITFEM Literacy rate of females IDEOCON Percent of barangays affected by ideology-based insurgency RELCON Percent of barangays affected by religion-based separatists IMRMALE Male infant mortality rate IMRFEM Female infant mortality rate LIFEMALE Life expectancy of males LIFEMALE Life expectancy of males

N

Minimum

Maximum

Mean

61

10.06

81.90

41.24

61

0.10

0.96

0.43

81

4.62

6.81

5.12

81

3,489.00

2,132,989.00

188,487.94

81

32.82

74.89

52.90

79

6.80

66.50

45.03

79

4.60

53.00

30.67

79

1.40

26.10

14.36

79

33.50

93.20

54.94

77

36.70

100.00

91.02

80

1.06

32.29

8.92

77

16.37

38.25

29.31

80

5.78

15.31

8.94

80

4.37

12.17

7.18

78

72.50

97.10

87.63

78

2.90

27.50

12.37

55

0

6.00

2.78

55

0

1.00

0.04

55

0

2.00

0.18

13

10.80

92.60

72.58

13

54.40

92.93

80.97

61

0

17.31

4.07

61

0

59.84

2.78

80

37.58

75.50

58.81

80

36.27

71.24

52.09

76

52.25

69.47

64.24

76

56.27

74.92

69.38

Minimum

Maximum

Mean

81

0

3.62

0.89

77

21.50

91.50

63.21

77

18.30

91.50

55.30

77

17.50

100.00

59.80

81

2.99

36.11

13.01

81

0.74

35.32

11.22

81

1.09

61.29

10.62

81

0

57.62

8.70

81

0

19.79

3.55

81

0

13.72

2.02

79

0.47

98.60

52.76

75

-0.09

0.34

0.01

75

-0.09

0.11

0

81

24.00

15617.00

559.79

81

0.79

5.79

2.33

81

1,610.17

212,381.29

7,186.00

81

0

2195.16

456.74

78

0.24

0.54

0.42

79

3.20

91.40

62.53

79

6.50

100.00

63.58

79

0.56

55.56

34.52

79

0.03

32.83

5.37

79

0.01

6.65

0.39

79

0.34

11.52

2.53

79

0.03

1.58

0.35

79

0.87

13.96

3.92

79

0.25

22.80

2.82

79

6.19

32.85

13.74

79

0

0.47

0.10

79

0.18

38.48

16.50

79

0

7.26

0.46

Mapping Population-Biodiversity Connections in the Philippines

MMR Maternal mortality rate IRON Percent mothers receiving iron supplement IODINE Percent mothers receiving iodine supplement TETANUS Percent mothers receiving tetanus toxoid supplement DIARRHEA Morbidity rate due to diarrhea BRONCHI Morbidity rate due to bronchitis PNEUMON Morbidity rate due to pneumonia FLU Morbidity rate due to influenza HYPER Morbidity rate due to hypertension TB Morbidity rate due to tuberculosis TONGUE Mother tongue MIGMALE Migration rate of males MIGFEM Migration rate of females DENSITY Population density POPGROW Population growth rate BHS Population-to-barangay health station ratio BHW Population-to-barangay health worker ratio POVERTY Poverty rate IMM1223 Immunization rate for children 12–23 months IMM2435 Immunization rate for children 24–35 months AGRIM Percent male labor force in agriculture, forestry and hunting FISHM Percent male labor force in fishing MINEM Percent male labor force in mining and quarrying MANUM Percent male labor force in manufacturing ELECM Percent male labor force in electricity, gas and water CONSM Percent male labor force in construction TRADEM Percent male labor force in trading SERVEM Percent male labor force in service industry NSM Percent male labor force in a not-stated industry AGRIF Percent female labor force in agriculture, forestry and hunting FISHF Percent female labor force in fishing

N

ANNEX 3

Variables

99

ANNEX 3 Mapping Population-Biodiversity Connections in the Philippines

100

Variables MINEF Percent female labor force in mining and quarrying MANUF Percent female labor force in manufacturing ELECF Percent female labor force in electricity, gas, and water CONSF Percent female labor force in construction TRADEF Percent female labor force in trading SERVEF Percent female labor force in service industry NSF Percent female labor force in a not-stated industry HOUSEOWN Percent of families with housing by ownership (or amortization) HOUSETYP Percent families with strong material type of housing POP15HS Rate of population 15 years and over who attained high school WATER Percent of households with access to safe drinking water TOILET Percent of families with own sanitary toilet TFR Total fertility rate INDIG Tribal or cultural indigenous groups U5MR Under age-5 mortality rate URBGEO Urban population geometric growth rate URBEXP Urban population exponential growth rate

Annex 3.2

N

Minimum

Maximum

Mean

79

0

0.43

0.05

79

0.13

10.90

2.15

79

0

0.28

0.05

79

0.01

0.36

0.10

79

0.51

9.52

5.44

79

4.42

24.88

11.40

79

0

0.54

0.12

69

61.71

90.01

76.80

77

7.40

96.20

55.61

81

15.38

35.67

26.40

81

16.74

97.17

75.18

77

11.60

100.00

81.97

77

2.56

5.58

4.09

66

0

100.00

32.90

74

32.67

101.77

73.89

80

1.19

12.66

6.14

80

1.18

11.92

5.93

Pearson Correlation for Data at the Provincial Level (Available in the DVD)

Annex 3.3

Kendall and Spearman Rank Correlation for Data at the Provincial Level (Available in the DVD)

Indices Annex 4.1

Annex 4

Index of Socioeconomic-demographic Pressure on Forest Cover per Province

Province

PINDEX

Pressure Level

54.42 9999 43.22 46.46 32.20 46.39 9999 78.80 9999 54.59 9999 23.16 38.74 30.91 24.36 9999 48.98 58.34 44.27 34.50 9999 37.51 50.24 30.74 32.91 9999 9999 9999 9999 9999 9999 65.84 35.20 61.49 43.42 42.23 26.30 53.62 81.90 44.93 28.90 39.26 52.37 36.96 29.51 9999 37.77

0.52 9999 0.57 0.41 0.23 0.52 9999 0.96 9999 0.54 9999 0.30 0.47 9999 0.50 9999 0.57 0.52 0.50 0.33 9999 0.27 0.43 0.21 0.31 9999 9999 9999 9999 9999 9999 0.40 9999 0.66 0.36 0.44 0.30 0.56 9999 0.35 0.32 0.32 0.57 0.38 0.41 9999 0.35

High Insufficient data Very high Moderate Very low High Insufficient data Very high Insufficient data Very high Insufficient data Very low High Insufficient data High Insufficient data Very high High High Low Insufficient data Very low Moderate Very low Low Insufficient data Insufficient data Insufficient data Insufficient data Insufficient data Insufficient data Moderate Insufficient data Very high Low Moderate Very low Very high Insufficient data Low Low Low Very high Moderate Moderate Insufficient data Low

Mapping Population-Biodiversity Connections in the Philippines

Abra Agusan Del Norte Agusan Del Sur Aklan Albay Antique Apayao Aurora Basilan Bataan Batanes Batangas Benguet Biliran Bohol Bukidnon Bulacan Cagayan Camarines Norte Camarines Sur Camiguin Capiz Catanduanes Cavite Cebu Compostela Cotabato Cotabato City Davao Davao Del Sur Davao Oriental Eastern Samar Guimaras Ifugao Ilocos Norte Ilocos Sur Iloilo Isabela Kalinga La Union Laguna Lanao Del Norte Lanao Del Sur Leyte Maguindanao Marawi City Marinduque

Percent Forest Cover

101

ANNEX 4

Province Masbate Metro Manila Misamis Occidental Misamis Oriental Mountain Province Negros Occidental Negros Oriental Northern Samar Nueva Ecija Nueva Vizcaya Occidental Mindoro Oriental Mindoro Palawan Pampanga Pangasinan Quezon Quirino Rizal Romblon Samar Sarangani Siquijor Sorsogon South Cotabato Southern Leyte Sultan Kudarat Sulu Surigao Del Norte Surigao Del Sur Tarlac Tawi-tawi Zambales Zamboanga Del Norte Zamboanga Del Sur

Percent Forest Cover

PINDEX

Pressure Level

11.61 10.06 23.25 9999 54.78 23.73 33.32 47.15 21.79 64.82 61.80 60.74 65.29 18.22 14.39 45.35 76.30 53.54 46.03 48.43 9999 28.97 17.11 9999 48.85 9999 9999 9999 39.93 27.07 9999 29.43 31.62 21.56

0.29 -5.60 0.30 9999 0.50 0.32 0.37 0.46 0.35 0.60 0.46 0.44 0.69 0.17 0.32 0.34 0.62 0.49 0.54 0.34 9999 0.38 0.21 9999 0.71 9999 9999 9999 0.48 0.41 9999 0.42 0.23 0.44

Very low Very low Very low Insufficient data High Low Moderate High Low Very high High Moderate Very high Very low Low Low Very high High High Low Insufficient data Moderate Very low Insufficient data Very high Insufficient data Insufficient data Insufficient data High Moderate Insufficient data Moderate Very low Moderate

Mapping Population-Biodiversity Connections in the Philippines

Note: PINDEX – summary index of socioeconomic-demographic pressure (see section 5.3.2)

102

Label

Index of CPA Vulnerability to Anthropogenic Pressure Conservation Priority Area (CPA) Batanes Islands Protected Landscape and Seascape Babuyanes Kalbario-Patapat National Park Apayao Lowland Forest Abulog River Buguey Wetlands Cagayan River Mt. Cagua Balbalasang-Balbalan National Park Mt. Cetaceo Abra River Peaks of Central Cordillera (above 1000 masl) Peñablanca Protected Landscape Northern Sierra Madre Natural Park Agno/Amburayan River Caraballo-Palali Mountain Range Central Sierra Madre Mountains Casecnan River Basin Aurora National Park Zambales Mountain Range (Mt. Tapulao and Mt. High Peak) Camp O’Donnel Mt. Arayat National Park Angat Watershed Forest Reserve Sierra Madre Portion along Bulacan, Nueva Ecija and Quezon border Umiray River Mt. Irid-Mt. Angelo Candaba Swamp Bataan Natural Park and Subic Bay Forest Reserve Mariveles Mountains Manila Bay Mt. Binuang and vicinity Kaliwa-Kanan River UP Land Grants (Pakil and Real) Polillo Island Pasig River Laguna de Bay Tadlak Lake Mt. Makiling Forest Reserve 7 Lakes of San Pablo City Mt. Banahaw-San Cristobal-Lucban Cone Complex Mt. Palay-Palay-Mt. Mataas na Gulod National Park Mt. Malarayat Range Taal Lake Pansipit River Quezon National Park Pagbilao and Tayabas Bay Lalaguna Marsh Ragay Gulf Bondoc Peninsula Mt. Labo Caramoan Peninsula Catanduanes Island Mt. Isarog National Park Lake Nabua

Vulnerability Level Insufficient Data Very High Very High Very High Insufficient Data Very High Insufficient Data Very High Extremely High urgent Extremely High critical Insufficient Data Extremely High critical Very High Extremely High critical Insufficient Data Extremely High critical Extremely High urgent Extremely High urgent Extremely High critical Very High High High High Very High Insufficient Data Very High Insufficient Data High High High Very High Insufficient Data Very High Very High Insufficient Data Insufficient Data Insufficient Data High Insufficient Data Very High High High Insufficient Data Insufficient Data Very High Very High Insufficient Data Very High Very High Very High High Very High Very High Insufficient Data

Mapping Population-Biodiversity Connections in the Philippines

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

ANNEX 4

Annex 4.2

103

ANNEX 4 Mapping Population-Biodiversity Connections in the Philippines

104

Label 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110

Conservation Priority Area (CPA) Lake Buhi/Lake Manapao/Lake Katugday Lake Bato Bacon-Manito Mt. Bulusan National Park Marinduque Lubang Island Mt. Calavite Puerto Galera Mt. Halcon Naujan Lake National Park Sablayan Iglit and Baco Mountains Malpalon Bogbog, Bongabong and Mt. Hitding Mt. Hinunduang South Mindoro Islands (Semirara Island Group) Coron Lakes Cuyo Island Group El Nido Lake Manguao San Vicente-Taytay-Roxas Forest Puerto Princesa Subterranean River National Park (Cleopatra’s Needle) Victoria and Anapalan Ranges Mt. Mantalingajan Ursula Island Balabac Group of Islands Burias Island Sibuyan Island Balogo Watershed Ticao Northwest Panay Peninsula Central Panay Mountains: Madjaas-Baloi Complex Jalaud River Northeastern Panay-Gigantes Mt. Villion-Mapili Mobo-Uson Malbug Daraga-Placer-Malatugon Mt. Silay-Mt. Mandalagan Mt. Canlaon National Park Ban-ban Ilog River Basay-Hinoba-an Mansangaban Cuernos de Negros (Mt. Talinis) Twin Lakes Catmon/Carmen Tabunan Forest Mactan, Kalawisan, Cansafa Bay Olango Island Argao Nug-as and Mt. Lantoy Mt. Kangbulagsing and Mt. Lanaya Mt. Cabalantian-Mt. Capotoan Complex Southern Samar Mountains Biliran and Maripipi Islands

Vulnerability Level Insufficient Data Insufficient Data High High Very High Extremely High critical Extremely High critical Extremely High critical Extremely High critical Very High Extremely High urgent Extremely High urgent Extremely High critical Extremely High critical Extremely High critical Very High Insufficient Data Extremely High critical Extremely High urgent Insufficient Data Extremely High urgent Extremely High urgent Extremely High urgent Extremely High urgent Very High Extremely High critical Very High Very High Very High Very High Very High Very High Insufficient Data High Very High Very High Very High Very High High Very High Very High Insufficient Data Very High Very High Very High Insufficient Data High High High High High High High Extremely High urgent Extremely High critical Insufficient Data

Jetafe Group of Islands (Calituban and Tahong-tahong Island) Rajah Sikatuna National Park Mt. Pangasugan (Northern Leyte Mountain Range); Lake Mahagnao Anonang-Lobi Range Mt. Nacolod-Cabalian Area Panaon Island Homonhon Island Dinagat (Mt. Kambinlio and Mt. Redondo) Siargao Island Lake Mainit Mimbilisan Protected Landscape Mt. Balatocan Mt. Hilong-hilong (Urdaneta), Agusan del Norte Agusan River North Diwata (Bislig, Mt. Agtuuganon-Mt. Pasian) Agusan Marsh Mt. Kaluayan-Kinabalian (Kimangkil Ridge), Bukidnon-Agusan del Norte border Mt. Tago Range Mt. Kitanglad Kalatungan Range Olangui River Munai Tambo Complex (Kolambugan uplands and associated mountains) Lake Lanao Lake Napalit Mt. Piagayungan (Ragang) Complex Mt. Butig/Lake Butig National Park Pulangi River Mt. Sinaka Marilog Forest Reserve, Bukidnon-Davao boundary South Diwata Mountain Ranges Pantukan Mabini-Maco Area Tumadgo Peak Mt. Apo Range Ligawasan Marsh South Cotabato/Sultan Kudarat (Mt. Daguma) Mt. Matutum Lake Sebu and Mt. Three Kings Mt. Busa-Kiamba Mt. Parker Lake Maughan Mt. Latian Complex (Sarangani Mountains) Lake Duminagat Mt. Malindang and Lake Duminagat Mt. Dapiak-Mt. Paraya Mt. Sugarloaf Mt. Timolan Lituban-Quipit Watershed Pasonanca Watershed Basilan Camotes Island Siquijor Camiguin Island Sulu Mt. Dajo National Park Tawi-tawi Island Manuk-manka Islands

Vulnerability Level High Very High Very High High Very High Very High Extremely High critical Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data Extremely High urgent Insufficient Data Extremely High urgent Insufficient Data Extremely High critical Insufficient Data Insufficient Data Extremely High critical Insufficient Data Very High Insufficient Data Insufficient Data Extremely High critical Very High Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data Very High Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data Very High Very High Very High Very High Very High Very High Insufficient Data High Very High Insufficient Data Insufficient Data Insufficient Data Insufficient Data Insufficient Data

Mapping Population-Biodiversity Connections in the Philippines

111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166

Conservation Priority Area (CPA)

ANNEX 4

Label

105

ANNEX 4 Mapping Population-Biodiversity Connections in the Philippines

106

Label 167 168 169 170

Conservation Priority Area (CPA) Sibutu and Tumindao Islands Cagayan Islands Tubbataha Reef National Marine Park Cagayan de Sulu

Vulnerability Level Insufficient Data Extremely High critical Extremely High critical Insufficient Data

Maps

Annex 5

Annex 5 consists of a set of 10 maps showing spatial distribution trends of: significantly correlated socioeconomicdemographic indicators with forest cover; pressure and vulnerability indices; conservation priority areas and their biodiversity and human-dimension attributes; and percent urban population. The maps in 1:4,500,000-scale are folded and enclosed in a side pocket of the back cover of this final report. These maps are the following: Population Density

Annex 5.2

Percent Male Labor Force in Agriculture, Hunting and Forestry

Annex 5.3

Net Migration Rate of Males

Annex 5.4

Percent Forest Cover

Annex 5.5

Index of Socioeconomic-Demographic Pressure on Forest Cover

Annex 5.6

Terrestrial and Inland Water Areas of Biodiversity Importance

Annex 5.7

Index of Biological Priority or Vulnerability by CPAs

Annex 5.8

Integrated Terrestrial and Inland Water Biodiversity Conservation Priorities

Annex 5.9

Socioeconomic Pressures from the PBCPP

Annex 5.10

Percent Urban Population by Province

Note that the tag number for each CPA (appearing in Annexes 5.6 to 5.9) corresponds to a label listed in a table in Annex 4.2.

Mapping Population-Biodiversity Connections in the Philippines

Annex 5.1

107

Annex 6

Algorithms for Areal Weighting Method

This annex explains the use of formulae that comprise the algorithms for solving the mathematical problem involved in the areal weighting method. The method pursues a two-stage process, stages 1 and 2, of applying the appropriate algorithm. In going through the stages, consider two cases of boundary overlap or intersection between the target zone (CPA) and the source zone (province). Section A6.1 explains the first case of intersection, while Section A6.2 explains the second case of intersection.

A6.1 Case 1: CPA in One Province The first case of stage 1 is illustrated using a typical example of a lone conservation priority area (CPA) inside a province (Figure A6.1). If, for instance, a demographic variable (V), e.g., population count P, whose value is available only at the provincial level and there is no other neighboring province, which can act as a source zone for the value of V, the lone CPA shall then take on the value of V of the province. This exhibits a case for employing the areal weighting method and also shows a situation far from reality. The CPA P cannot possibly be the same as the provincial P, because the CPA might be located in a probably forested and less populated zone. If the value of V is weighed with respect to the ratio of the area of the target zone to the source zone, a disproportionately small value is obtained. An alternative is to assume a proportionality relationship between population density and the variable V of interest. As such, the value of V is weighed proportionately to the

Mapping Population-Biodiversity Connections in the Philippines

ratio of the population density of the CPA to that of the province to obtain the unknown value of this variable V inside the target zone. Note that population data exist whose source zone is in the level of the municipality smaller in size than either the province or even the CPA. These data were acquired at low cost from the National Statistics Office and would help resolve the spatial incongruity problem of the first case. To illustrate this algorithm the interested reader is directed to Box 3, which leads to stage 2 of the first case.

108

However, using the ratio of the area of the target zone to the source zone will be more practical than using the ratio of the population density of the CPA to that of the province in a few exceptional cases such as variables not influenced by population characteristics. Examples of these are the number of rural health stations in a province and number of rural health workers in a province in which the trend in numbers does not appear to be determined by the population size in any province. For these few cases involving a lone province, the algorithm is applied that which uses the ratio of the area of the target CPA to the province in determining the unknown value of a variable in the target CPA. For the interested reader, the algorithm is described in Box 4.

A

Province

Provincial boundary

CPA

a

Not drawn to scale Figure A6.1. First case of intersection of a CPA falling completely within a province

A1

CPA boundary

Province 2

Province 1

a

ANNEX 6

Provincial boundary

a

1

A2

2

Not drawn to scale Figure A6.2. Second case of intersection of a CPA encompassing two provinces

A6.2 Case 2: CPA in Two or More Provinces The second case of stage 1 is now illustrated in which two or more provinces encompasses a CPA. Figure A6.2 is a typical example of the second case in which a CPA straddles two neighboring provinces. For this second case, three sets of algorithms are explained to estimate the value of an unknown data in a CPA. These three sets represent the stage 2 calculation methods to be employed to three categories of socioeconomic and demographic variables. These are count, density and ratio (or proportion or precentage) data. The algorithm for count data is shown in Box 4, for density data in Box 5, and for ratio data in Box 6.

Algorithm to Estimate Unknown Data for a Target CPA in a Single Province Scenario

To estimate data for a CPA denoted by Vc we express the relationship as:

Dc D c Vc = = Vp V •• V c p D Dpp

(1)

and is expanded to become

PPcc Ac A c Vc = = Vp V •• V c p PPpp Ap A p

(2)

where Vc is the indicator variable of interest in the target CPA zone c; Vp is the indicator variable in the

source zone (province) p; Dc and Dp are the population densities in the CPA and province, respectively; Pc and Pp

are the population sizes in the CPA and province, respectively; and Ac and Ap are the area sizes of the CPA and provincial zones, respectively.

Mapping Population-Biodiversity Connections in the Philippines

BOX 3.

109

ANNEX 6

So far, stages 1 and 2 were demonstrated without the added complexity of supplementing the estimates of the unknown values of the variables in the target CPAs with an auxiliary data coming from population size at the municipality level. This is one step higher in resolution than the province. A typical example of this is illustrated in Figure A6.3. Note that: A1,1, A1,2 and A1,3 are the areas of the municipalities within province 1; A2,1, and A2,2 are the areas of the municipalities within province 2; a1,1, a1,2, a1,3, a2,1 and a2,2 are the areas of intersection of the CPA and the municipalities within, respectively, provinces 1 and 2; P1,1, P1,2, P1,3, P2,1, and P2,2 are the population counts, respectively, within the municipalities of the two provinces. The interested reader is, however, directed to Box 7 for a description of the algorithms involved in estimating count, density and ratio data given the auxiliary population data at the municipality level. Provincial boundary CPA boundary

A2,1

A1,2

A1,1

a a

a

1,2

2,1

a

1,1

a

A2,2

2,2

1,3

A1,3

Mapping Population-Biodiversity Connections in the Philippines

Figure A6.3. Example of a CPA straddling two provinces, but intersecting municipalities with known population data

110

BOX 4.

Algorithms for a Variable that refers to Count Data

A1 and A2 be the areas of provinces 1 and 2, respectively; and a1 and a2 be the areas of intersection of provinces 1 and 2 with the CPA, respectively. Assign V1 and V2 as the values of demographic variables for, respectively, provinces 1 and 2.

Consider a CPA straddling two provinces (Figure A6.2). Let

To estimate the value of the demographic variable

Vc in the CPA we deploy the simple areal weighting

method as:

Vc = V1 •

a1

A1

+ V2 •

a2

A2

(3)

The above equation holds true only if we assume that the socioeconomic or demographic value within a province is uniformly distributed and if this variable refers to count data such as population size.

A1=100 km2, A2=150 km2, a1=50 km2, a2=90 km2, V1=10,000 people and V2=18,000 people, the calculated value of variable Vc is 15,800 people. Let

ANNEX 6

Algorithms for a Variable that refers to Density Data

BOX 5.

If our demographic variable is population density D and given that D1=100 people/km2 and D2=120 people/km2 are

the uniform population densities, respectively, of provinces 1 and 2, then it follows d1=100 people/km2 and d2=120 people/km2 are the population densities, respectively, of the CPA in intersection areas

a1 and a2. Using equation

Dc, is 122 people/km2. This calculated value can be verified by using the population size. Note that from equation (1) the population size inside the CPA, Vc, is 15,800 people and dividing this by the total area of the CPA, Ac=a1+a2=140 km2, the actual population density

(1) the calculated value of population density variable for the CPA,

inside the CPA is 112.86. Using the weighting equation (1) overestimates the estimated population density by 8.098%. If a weighting equation still has to be used for density variables, equation (1) has to be modified as:

Dc • Ac = D1 • A1 •

a1 A1

+ D2 • A2 •

a2 A2

(4)

Dc =

In equation (1), the demographic variable

D1 • a1 + D2 • a2 A2

(5)

V is substituted with the population density variable D whereby

V = D•A. Equation (4) shows a modification of equation (1) using the demographic variable, population density. Simplifying Equation (4) results to Equation (5), which now shows a weighting formula for estimating the density variable. Using the above given values,

D1=100 people/km2 and D2=120 people/km2, Ac=140 km2, a1=50 km2

and a2=90 km2, the population density estimate is obtained inside the CPA as 112.857. Note that this is the same value as the calculated above.

1 and 2 are not used, D1=100 people/km2 and D2=120 people/km2, but instead the population densities of the intersected parts of the CPA a1 and a2, respectively, as d1 and d2, then If population densities of provinces

equation (5) becomes;

Dc =

d1 • a1 + d2 • a2 a1 + a2

(6)

Mapping Population-Biodiversity Connections in the Philippines

Simplifying the equation, the following is obtained:

111

ANNEX 6

BOX 6.

Algorithms for a Variable that refers to Ratio, Proportion or Percentage Data

If the demographic variable is ratio, proportion or percentage, say, child-to-woman ratio R and given the following data in the Table A6.1 below, but assuming uniform distribution of data over the respective provinces so that R1=r1 and R2=r2, then equation (1) will appear as:

Rc = 10 •

a2 a1 90 50 +12 • = 10 • +12 • A2 A1 150 100

= 12.2

(7)

Table A6.1 Sample values for calculating child-to-woman ratio Zone Province 1

# children (0–4 yrs old) # woman (15–49 yrs old) C1=100 W1=1000

Child-to-woman Ratio R1=10

Province 2

C2=120

W2=1000

R2=12

CPA part 1

c1=?

w1=?

r1=10

CPA part 2

c2=?

w2=?

r2=12

CPA parts 1 & 2

Cc=?

Wc=?

Rc=?

Mapping Population-Biodiversity Connections in the Philippines

For the sake of argument, calculation is made for the number of children and number of women inside the CPA parts 1 and 2, and using the above data and equation (1) for count data.

112

c1 = C1 •

a1 A1

c2 = C2 •

a2 A2

a1 A1 a w2 = W2 • 2 A2 w1 = W1 •

50 100 90 = 120 • 150 = 100 •

= 50 = 72

= 1000 •

50 100

= 1000 •

90 = 600 150

The values of the child-to-woman ratio are

= 500

r1=10% and r2=12%, which tally with the above tabulated

values and consistent with the assumption of uniform distribution. Consequently, summing the component parts of the CPA rc = r1 + r2, we get 22%, which is incorrect. The way to do it correctly is to sum the number of children inside the CPA and the total number of women inside. Therefore, Cc = c1 + c2 and w c = w1 + w2 provides the actual child-to-woman ratio of Rc=(Cc/Wc)*100=11.09%. This is also not the same as the estimated R c value for the CPA in formula (7). The basic formula for the estimate of an unknown demographic ratio variable inside a CPA is:

100 • Cc / Wc = 100 • C 1 •

a1 a2 + C2 • A1 A2

where the variables are those defined in Table A6.1.

W1•

a2 a1 + W2 • A2 A1

(8)

Algorithms to Estimate Unknown Data for a Target CPA in a Multi-province Scenario with Auxiliary Data

ANNEX 6

BOX 7.

To explain the algorithm for estimating the unknown count data, e.g., number of children (0–4 yrs old), in the target CPA, the reader is referred to Figure A6.3 for the illustration of the area variables involved. There are three steps involved here: (1) estimate the population size P1c within that part of the CPA that falls within province 1 and the population size P2c within that part of the CPA that falls within province 2; (2) determine the population densities in each of CPA parts; and (3) approximate the unknown data using equation (1) or (2). For step 1, the following equations are derived from equation (3) to get the population size in each part of the CPA:

a1,1

P1c = P1,1 •

a1,2

+P1,2 •

A1,1

A1,2

a2,1

P2c = P2,1 •

+P1,3 •

(9)

A1,3

a2,2

+P2,2 •

A2,1

a1,3

(10)

A2,2

The second step involves the calculation of the population density inside the CPA. From equations (9) and (10), the value of the population density variable for each CPA part is, therefore:

D1c =

a1,1

P1,1 •

A1,1

a1,2

+P1,2 •

+P1,3 •

A1,2

a1,3 A1,3

(11)

A1c P2,1 • D2c =

a2,1

+P2,2 •

A2,1

a2,2 A2,2 (12)

A2c

The third and last step leads to the approximation of the unknown value of the variable of interest by using a combination of equations (1), (11) and (12);

A1,1

+P1,2 •

a1,2 A1,2

+P1,3 •

a1,3 A1,3

a1,1 + a1,2+ a1,3

V1c=

• V1

P1

(13)

A1

P2,1 • V2c=

a2,1 A2,1

+P2,2 • a2,1 + a2,2 P2

a2,2 A2,2 • V2

A2

(14)

Mapping Population-Biodiversity Connections in the Philippines

P1,1 •

a1,1

Continued on next page... 113

ANNEX 6

If the unknown variable of interest refers to count data, e.g., number of children, in a CPA, results of equations (13) and (14) are added, as follows: (15)

Vc = V 1c + V2c

If, however, the unknown variable of interest refers to density data, equations (6), (11) and (12) can be used for the weighting, as:

P1,1 •

a1,1

+P1,2 •

A1,1

a1,2

a1,3

+P1,3 •

A1,2

P2,1 •

A1,3

A2,1

•a1c +

A1c

Dc =

a2,1

+P2,2 •

a2,2 A2,2

A2c

•a2c 2c

(16)

Ac

Noting that equation.

A1c= a1c, A2c= a2c, Ac= a1c + a2c= a1,1 + a1,2 + a1,3 + a2,1 + a2,2 and summarizing the above m

n

∑∑

Pi,j •

i=1 j=1

Dc =

ai,j

m

Ai,j

n

∑∑

(17)

ai,j

i=1 j=1

Lastly, if the unknown variable of interest refers to ratio data, equations (8), (13), (14) and (15) are combined for the weighting to arrive at:

P1,1 •

a1,1 A1,1

+P1,2 •

a1,2

a1,3

+P1,3 •

A1,2

P2,1 •

A1,3

a1,1 + a1,2+ a1,3

Mapping Population-Biodiversity Connections in the Philippines

114

P1,1 •

+P2,2 •

A2,1

A1,1

+P1,2 •

A1,2

a1,3

+P1,3 •

P2,1 •

A1,3

a1,1 + a1,2+ a1,3

a2,1

+P2,2 •

A2,1

a2,1 + a2,2

•W1+

P1

P2

n

which can be summarized, as:

∑

m

Ci

i=1

100 • Cc/Wc =

Pi,j •

j=1

n

a2,2 A2,2 •W2

∑

ai,j •

∑

∑ Ci

n

∑ j=1

Ai,j n

Pi,j

j=1

n

m

ai,j

n

j=1

i=1

(18)

A2

A1

∑

•C2

A2

a1,2

∑

A2,2

P2

A1 a1,1

a2,2

a2,1 + a2,2

•C1+

P1 100 • Cc/Wc =

a2,1

Pi,j •

j=1

ai,j •

n

∑ j=1

∑

Ai,j

j=1

(19)

ai,j Ai,j n

Pi,j

∑ j=1

Ai,j

List of Participants and Contributors

#

Name

Address

Telephone Number 927-1212, 921-2333

Annex 7

E–mail

1

Abay, Ronald

CIM Technologies

2

Abejo, Socorro

National Statistics Office

3

Abuel, Marion

Conservation International–Philippines

4

Aca, Elson

Conservation International–Philippines, Volunteer

5

Acosta, Rene

United States Agency for International Development

552-9828, 552-9824

6

Adajar, Joel

Philippine National Police

725-5195

7

Agayatin, Tess

Protected Areas and Wildlife Bureau

925-8950, 924-6031 to 35

8

Agoncillo, Oliver

United States Agency for International Development

552-9828, 552-9824

[email protected]

9

Alarcon, Ma. Cecilia

Documentarist

10

Almocera, Ernesto Jr.

Philippine Legislators Committee on Population and Development Foundation

921-1044

[email protected]

11

Antolin, Artemio

Conservation International–Philippines, Sierra Madre Biodiversity Corridor

12

Asis, Gemma

National Mapping and Resource Information Authority

810-4831 loc. 420

[email protected]

13

Azucena, Eric John

International Rice Research Institute

0920-920-1089

[email protected]; [email protected]

14

Balais, Benjamin

National Mapping and Resource Information Authority

810-5466

[email protected]

15

Bede, Lucio

Conservation International–Brazil, Atlantic Forest

[email protected]

16

Bernard, Enrico

Conservation International–Brazil, Amazon

[email protected]

17

Bernardo, Joselito

National Economic and Development Authority

18

Blackburn, Daniel

19

Boltz, Fred

Conservation International–Washington, D.C.

20

Briones, Eric

Conservation International–Philippines

412-8194

[email protected]

21

Bucu, Jess

CIM Technologies

927-1212, 921-2333

[email protected]

22

Burris, Philip

Environmental Science for Social Change, Inc.

426-5921

[email protected]

23

Cabigon, Josefina

UP Population Institute–Demographic Research and Development Foundation

927-4166

josefi[email protected]

24

Calunsod, Michael

Conservation International–Philippines, Volunteer

25

Castro, Haroldo

Conservation International–Washington, D.C.

26

Co, Leonard

Conservation International–Philippines

412-8194

[email protected]

27

Coroza, Oliver

Conservation International–Philippines

412-8194

[email protected]

28

Corpuz, May

Philippine Center for Population and Development

844-6465

[email protected]

29

Crisostomo, Bobby

National Mapping and Resource Information Authority

810-5460

[email protected]

30

Cruz, Crescencio Jr.

CIM Technologies

927-1212, 921-2333

31

Curato, Ricky

National Research Council of the Philippines

837-0409

32

Dave, Radhika

Conservation International–Washington, D.C.

33

de Guia, Mike

34

de los Reyes, Pablo Jr.

[email protected] 412-8194

[email protected] [email protected]

[email protected]

631-3745, 633-6015

[email protected], [email protected] [email protected] [email protected]

[email protected]

Protected Areas and Wildlife Bureau

924-6031 loc. 233

[email protected]

373-3361 to 64 loc. 123, 124

[email protected]

de Quiros, Emil Francis

National Anti-Poverty Commission

36

de Souza, Roger-Mark

Population Reference Bureau

37

dela Torre-Foster, Genevieve

Conservation International–Philippines

412-8194

38

delas Armas, Reena

Conservation International–Philippines

412-8194

39

Despabiladeras, Ephraim

United States Agency for International Development

40

Dulawan, Wesley

United States Agency for International Development

552-9877

41

Duya, Mariano Roy

Conservation International–Philippines

412-8194

42

Edwards, Janet

Conservation International–Washington, D.C.

43

Encomienda, Michelle

Conservation International–Philippines

44

Ericta, Carmencita

National Statistics Office

45

Escalto, Josephine

Palawan Planning and Development Office

048-433-5501

46

Escoto, Segundo

CIM Technologies

927-1212, 921-2333

47

Esico, Jose

CIM Technologies

927-1212, 921-2333

Espaldon, Maria Victoria

University of the Philippines, Diliman– College of Social Sciences and Philosophy

981-8500 loc. 2375

[email protected] [email protected] [email protected] [email protected] [email protected] 412-8194

[email protected]

Mapping Population-Biodiversity Connections in the Philippines

[email protected] [email protected]

35

48

[email protected]

[email protected]

115

Mapping Population-Biodiversity Connections in the Philippines

ANNEX 7

116

#

Name

49

Espina, Raymund

Commission on Population

Address

Telephone Number

E–mail

50

Espiritu, Allan

Conservation International–Philippines

412-8194

51

Evangelista, Roberto Jr.

Conservation International–Philippines

412-8194

[email protected]

52

Fleras, Jomar

ReachOut Foundation

525-0655

jfl[email protected]

53

Foncardas, Ezekias Jr.

Conservation International–Philippines

412-8194

[email protected]

54

Francisco, Franklin John

Population Services Pilipinas, Inc.

831-2876

[email protected]

55

Galolo, Angie

CIM Technologies

927-1212, 921-2333

[email protected]

56

Garcia, Ronald

CIM Technologies

927-1212, 921-2333

57

Gelman, Nancy

Conservation International–Washington, D.C.

[email protected]

58

Hughes, Kathleen

Conservation International–Washington, D.C.

[email protected]

59

Hunt, Jeff

National Mapping and Resource Information Authority

60

Ibuna, Nancy

Conservation International–Philippines

61

Jimenez, Ruth

Conservation International–Mexico, Selva Maya

[email protected]

62

Kleinau, Eckhard

Environmental Health Project

[email protected]

63

Lacanlalay, Antonio

Conservation International–Philippines

64

Lagaday, Maria Paz

National Mapping and Resource Information Authority

65

Lagunzad, Daniel

66 67

[email protected]

412-8194

[email protected]

[email protected]

412-8194

[email protected]

University of the Philippines, Diliman– College of Science, Institute of Biology

920-5301

[email protected]

Lansigan, Felino

University of the Philippines, Los Banos– Institute of Statistics

049-536-2240

[email protected]

Lasmarias, Noela

Conservation International–Philippines

412-8194

[email protected] [email protected]

68

Lim, Theresa Mundita

Protected Areas and Wildlife Bureau

925-8945, 924-0109, 924-6031 to 35

69

Limgenco, Richard Jude

CIM Technologies

927-1212, 921-2333

70

Lingating, Reuben

National Commission on Indigenous Peoples

373-9636

71

Lomibao, Arturo

Philippine National Police

72

Lopez-Dee, Edward

National Statistical Coordination Board

73

Love, Greg

Conservation International–Washington, D.C.

74

Mallorca, Rachel

Department of the Interior and Local Government

631-3743

[email protected]

75

Mamuri, Madelyn

Commission on Population

0917-578-1802

[email protected]

76

Manansala, Annette

National Economic and Development Authority

929-6044

[email protected]

77

Mancebo, Fay

National Economic and Development Authority

631-2187

[email protected], [email protected]

78

Mendoza, Antonio

Philippine National Police

79

Mendoza, Marlyn

Protected Areas and Wildlife Bureau

925-8950, 924-6031 to 35

[email protected], [email protected]

80

Millerd, Eduard

Conservation International–Washington, D.C.

[email protected]

81

Milne, Sarah

Conservation International–Cambodia

[email protected]

82

Miraflores, Shirley

Documentarist

83

Morales, Connie

Conservation International–Philippines

84

Naguit, Shane

Conservation International–Philippines, Palawan Program

85

Ocampo, Lou Angeli

Conservation International–Philippines, Volunteer

86

Ogena, Nimfa

UP Population Institute–Demographic Research and Development Foundation

920-5402

[email protected]

87

Ortega, Arnisson Andre

UP Population Institute

920-5301

[email protected]

88

Osias, Thomas

Commission on Population

89

Pabon, Luis

Conservation International–Washington, D.C.

90

Pagcaliwagan, Dario

First Philippine Conservation, Inc.

631-8024

[email protected]

91

Paglinawan, Luvie

Conservation International–Philippines

412-8194

[email protected]

92

Palawan Council for Sustainable Development Staff

93

Papa, Linda

National Mapping and Resource Information Authority

810-4831 loc. 400

[email protected], [email protected]

94

Paraso, Glenn

Philippine Rural Reconstruction Movement

372-4991

[email protected]

95

Pascua, Grace

National Commission on Indigenous Peoples

373-9636

96

Plan International

97

Radoc, Benjamin

National Economic and Development Authority

631-3713

98

Ramolete, Armando

Philippine National Police

929-9429

99

Reolalas, Aurora

National Statistics Office

716-3921

[email protected]

100

Reynolds, William

United States Agency for International Development

552-9923, 552-9924

[email protected]

101

Riutta, Kirk

Conservation International–Washington, D.C.

102

Rodriguez, Rosheila

Conservation International–Philippines

412-8194

[email protected]

103

Rosell-Ambal, Ruth Grace

Wildlife Conservation Society of the Philippines

0918-926-3504

[email protected]

104

Saetae, Cathy

GMMS

105

Sangalang, Monnette

Commission on Population

899-3444

[email protected] [email protected]

412-8194

[email protected] [email protected]

[email protected]

[email protected]

[email protected]

[email protected]

Name

Address

Telephone Number

E–mail

Sarenas, Ivan

Freelance Photographer

107

Serafica, Rosalyn

United States Agency for International Development

108

Silverio, Mely

Conservation International–Philippines, Consultant

[email protected]

109

Soriano, Leonard

Conservation International–Philippines, Volunteer

[email protected]

110

Sta. Ana, Maria Anicia

UP Center for Integrative and Development Studies

111

Stephanson, Sheri

Conservation International–Washington, D.C.

112

Stover, Carina

United States Agency for International Development

552-9800, 553-9860

113

Tadingan, Leland

Protected Areas and Wildlife Bureau

924-6031 to 35

114

Tagtag, Anson

Protected Areas and Wildlife Bureau

924-6031 to 35

115

Tan, Charity

Department of Health

743-6744

116

Thaxton, Melissa

Population Reference Bureau

117

Trono, Romeo

Conservation International–Philippines

412-8194

[email protected] [email protected], [email protected]

552-9871

rserafi[email protected]

ANNEX 7

# 106

[email protected] [email protected]

[email protected], [email protected] [email protected]

118

Tuaño, Philip Arnold

National Anti-Poverty Commission

373-3361 to 64 loc. 132

119

Ulep, Emma

Housing and Land Use Regulatory Board

434-4168

[email protected]

120

Urmeneta, Ana

Department of the Interior and Local Government

920-4845

[email protected]

121

Valdez, Crispinita

Department of Health

743-0512, 711-6744, 743-1762

[email protected], [email protected]

122

Valenzuela-Duya, Liza

Conservation International–Philippines

412-8194

[email protected]

123

Varias, Evelyn

CIM Technologies

927-1212, 921-2333

124

Ventura, Mia

Commission on Population

531-6805, 531-6983

125

Villegas, Joey

CIM Technologies

927-1212, 921-2333

126

Villoria, Gil

Eco-Governance Project

127

Wesselman, Sebastian

Conservation International–Melanesia

128

Zalsos, Rolando Jr.

Philippine National Police

[email protected]

[email protected] 725-5195

Mapping Population-Biodiversity Connections in the Philippines

117