JAD. Decentralization, Local Government Capacity and Efficiency of Health Service Delivery in Uganda

JAD Journal of African Development Spring 2013 | Volume 15 #1 Decentralization, Local Government Capacity and Efficiency of Health Service Delivery i...
Author: Tabitha Sherman
34 downloads 3 Views 225KB Size
JAD Journal of African Development Spring 2013 | Volume 15 #1

Decentralization, Local Government Capacity and Efficiency of Health Service Delivery in Uganda JUSTINE NANNYONJO

and

NICHOLAS OKOT1

ABSTRACT

The study investigates the impact of decentralization and local government capacity on efficiency of health service delivery in Uganda applying qualitative analysis, and two stage Data Envelopment Analysis (DEA) on quantitative data covering a sample of 44 districts over the period 2008/09 and 2009/10. The results show that health institutions in about 31 districts out of 44 were technically efficient, while those in about 13 districts were technically inefficient under variable returns to scale (VRS), implying that health resources were not efficiently used in these institutions. Health institutions in 56.8 percent and 45.5 percent of the districts were operating at optimal scale in 2008/09 and 2009/10, respectively. Those in the remaining districts were scale inefficient, with a majority of them operating under decreasing returns to scale (DRS). Effective and accountable decentralized governance in the health sector in Uganda is typically constrained by weak capacity, particularly under-staffing at the districts levels. Policy recommendations include strengthening professional staffing in some districts to improve their capacity to deliver efficient health services; transfers of officials from districts with excess capacity to those with inadequate resources; and districts whose operations are characterized by increasing returns to scale should be considered for future expansion to consolidate their efficiency. 1 Nannyonjo: Statistics Department, Bank of Uganda; email: [email protected]; Okot: Statistics

Department, Bank of Uganda; email: [email protected]. We wish to thank the coordinators, African Economic Research Consortium (AREC) officials, and participants of the Institutions and Service Delivery in Africa project for their insightful comments. The views in this paper are our own and do not represent the views or opinions of our employer; the Bank of Uganda or its management. 

125

JOURNAL OF AFRICAN DEVELOPMENT

INTRODUCTION Uganda began its health sector decentralization process in 1997 following the enactment of the Local Government Act, 1997. The rationale was to increase both allocative and productive efficiency in health service provision. Decentralization of health services delivery facilitates decision making and monitoring at districts and lower levels local governments involving community participation. In the process, the District Local Governments (DLGs) become accountable for resources allocated and monitoring the quality of services provided. It is believed that decentralized systems offer opportunities for increased beneficiaries’ involvement in the direct decision making process in health services prioritization, quality, cost and preferences. This is attributed to the fact that, DLGs are more acquainted to the beneficiaries’ requirements, responsive to new developments and is in contact with communities. Administratively, this proved attractive to the central government because part of the burden of financing health services could be shifted to sub-national units and private providers. The medium-term policies to improve health service delivery are clearly documented in Uganda’s Poverty Reduction Strategic Plan (PRSP)3 in which the DLG system has been mandated with the implementation of the national health policy. The National Health Sector Strategic Plan (HSSP) is the major policy framework which documents all the strategies for the provision of public health services within a decentralized system in Uganda. This is in line with observation in the Poverty Eradication Action Plan (International Monetary Fund, 2003), which states in part that, “ill-health affects productivity and economic activities”. Thus, to achieve and maintain sustainable development, Ugandan identified health and economic growth as mutually reinforcing. This means the efficient provision of health services through the decentralized system was identified as an essential prerequisite for sustained development because without good health, the entire productive population (namely; individuals, families, communities and the nation) cannot effectively achieve identified social and economic goals. It is therefore clear

3

The medium term planning framework has since been transferred and revised within the newly established National Development Plan (NDP).



 126

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

that the health sector plays a critical role in poverty eradication and promotion of development. To facilitate health service decentralization initiatives there has been increased annual budgetary allocation for the provision of Primary Health Care at the DLG and sub-county levels. Whereas policy has given the DLGs and lower level local governments central roles in the management of health service delivery, the performance of the decentralized system has run short of expectations in some DLGs, which has widened regional disparities in equity to access quality health services. The poor performance has largely been attributed to local government capacity constraints. The Republic of Uganda (2007)4 for example, notes that although there was an improvement in the national performance against the Health Sector Strategic Plan (HSSP) indicators, there were marked variations in performance between DLGs, which have largely been attributed to inadequacy of management capacity in some districts. Obwona et al (2000) indeed pointed-out that ‘financial and institutional constraints have adversely affected the ability of the sub-national governments to adequately deliver services of sufficient quality.” The constraints identified include weaknesses in the institutional arrangements for monitoring service delivery, local capacity to manage service delivery and poor framework for accountability. This has led to instances where the intended beneficiaries do not get access to the services or if they do, they will be inefficient and of low quality. The central government’s capacity to monitor such services is often undermined by human resource and financial constraints. Despite these challenges to decentralized health service delivery, there has been no rigorous empirical analysis and documentation of the institutional framework and relationship between DLG capacity and efficiency of health service delivery in Uganda. This study aims to fill this gap in the literature by empirically analyzing the impact of DLG capacity on efficiency in health service delivery in Uganda. The objective is to increase understanding of the process of health service delivery under a decentralized system and how capacity and management at DLGs impact on efficiency of health services delivery. The specific objectives are: to evaluate the structure and roles of DLG in the management of the health system across districts; to investigate and document the political economy and institutional framework for health service delivery; to identify factors that determines the quality of management; to quantify the relationship between local 4

Annual Health Sector Performance Report - 2006/07

 127

JOURNAL OF AFRICAN DEVELOPMENT

governments capacity and efficiency in health service delivery and to draw policy recommendations to improve health service delivery. The rest of the paper is organized as follows: section 2 presents the political economy and framework for health service delivery while in section 3, we described the methodology employed and data used. The empirical results and a discussion are provided in section 4. Section 6 concludes with some policy implications. BACKGROUND POLITICAL ECONOMY

The concept of decentralization is broad and encompasses the transfer of responsibility for planning, resource generation allocation and management away from the central government and its agencies to sub-national governments. According to Rondineli et. al., (1983) and Alam et. al., (1994), the concept of decentralization could take the form of: (i) devolution of administrative responsibilities from the central government (de-concentration); allocation of decision making and management authority to semi-autonomous units (delegation); transfer of supervisory while retaining the supervisory role with the central government (devolution); transfer of function from central government to non-governmental institution (Privatization). The shift to some form of decentralization has been a preferred policy model for many countries because of its historical impact on efficiency of public service delivery (see, Mills et. al., 1990, Rondinelli et al., 1983). Developments in democratic governance in Africa in recent years and increased attention to the quality of public service delivery has facilitated the process of deepening decentralization of service delivery. Uganda embraced the decentralization process in 1992 following the success of the National Resistance Councils (NRC) between 1986 and 1990. The legal frameworks for the decentralized system of governance in Uganda are contained in the Constitution of Uganda 1995, Articles 176 - 207 and the Local Government Act, 19975. These

5

The Local Governments Act 1997, state that, there shall be administrative units based on the district as a unit under which there shall be lower local governments and administrative units. The lower units may take the form of a local government at District, City status or Municipality levels.



 128

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

laws created decentralized administrative units referred to as DLG, county, subcounty, parish and village local councils. At the inception of the process in 1997, there were a total of 39 DLGs; this was expanded to 56 DLGs by the year 2000, 87 DLGs by 2008 and 114 DLGs as at 1st July 2010. One of the main arguments for disaggregation of DLGs has been the desire to take services nearer to the communities to improve planning, access and accountability. In principle, devolution and delegation of power to lower local governments was expected to encourage more community participation in decision making and to hold policy makers accountable for the quality of service provided. This involved delegation of authorities to: improve access to public services; increase participation in decision-making; develop local capacity and enhance transparency and accountability (Mugabi, 2004). Decentralization was therefore envisaged to contribute to poverty reduction and development through the bottom-up approach to planning and monitoring service delivery. In the literature, Uganda is considered as one of the success stories in the implementation of a decentralized system of public service delivery. In 1997, the Uganda government devolved all the responsibilities of public service provision under, agriculture, health and education to lower level local governments. An essential aspect of improving public service delivery is the capacity of the DLGs to manage the process. This explains the weak relationship between expenditure and the outcomes of service delivery in many developing countries. Public health service delivery in Uganda is often marred by cases in which expenditure does not reflect the quality and outcomes of the services delivered. This has in part been attributed to weak institutional processes and governance among some DLGs. Poor delivery of services implies that most of the intended beneficiaries do not have access to the service or if they do the quality is not commensurate to the resources invested. It is not uncommon to visit a health facility with no doctor at the duty station to serve clients or the personnel are available, there are no drugs, equipment or even electricity. This suggests that there could be weakness in the institutional design and framework for health service delivery. Thus the next sub-section contributes directly to policy evaluation of the institutional arrangements for decentralized health service delivery in Uganda.

129

JOURNAL OF AFRICAN DEVELOPMENT

INSTITUTIONAL FRAMEWORK FOR HEALTH SERVICE DELIVERY

The theoretical framework for accountability for measuring service delivery was well described by the World Development Report 2004, which indicated that there are two routes for accountability between beneficiaries/clients and front line providers for public service provision. The approaches involve accountability through the long-route (citizens’ voice and compact between policymakers and providers) and the short-route (client power) between citizens and providers (frontline managers) as illustrated in Figure 1. Under the long-route approach, the citizens influence policymakers through their voice (voting powers), who then exerts compact (through delegation, financing and enforcement) pressure on frontline providers of health services. The shortroute on the other hand involves direct clients’ power onto the frontline providers. This framework may not be appropriate where a country’s democratic system is weak. In weak-democratic societies the citizen’s power to influence the actions of policymakers and implementers are limited by their in ability to monitor, reward or penalize the frontline service providers (Samia, Jishnu and Markus, 2008). In Uganda, the Health Sector Strategic Plan (HSSP) I & II provide the framework for reforms of health services delivery under the decentralized system in line with the government’s aim to provide health services for its citizens to enhance their quality of life and productivity. The national health system in Uganda comprises of all the institutions, structures and actors whose actions have the primary purpose of achieving and sustaining good health. The boundaries of the national health system include the public health centers (including the army, police and prison health units), private not-for-profits Non Government Organizations (NGOs and religious institutions) and private health practitioners as illustrated in Figure 2.



 130

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

Figure .1 Theoretical Frameworks for Accountability Relationships

Source: World Bank adopted from Samia and Nazanul (2008)

Decentralization provides a critical step towards attaining systematic health care service provision objectives contained in the HSSP through devolution of functions which used to be performed by the central government to DLGs. This was designed to allow stakeholder participation in the planning and budgetary decision making process thus, allowing clients to hold policy makers and providers accountable for the quality of services provided. The framework in Figure 2 provides a basis for voice compact and client power.

131

JOURNAL OF AFRICAN DEVELOPMENT

Figure 2. Institutional Framework for Health Service Delivery in Uganda

Ministry of Finance, Planning and Economic Development

Donors and NGOS

Referral Hospitals

Ministry of Health (Headquarters)

Districts Local Governments

District Health Management Committee Not-for-Profits (NGOs & Religious)

Private Provides for profits Public Health Service Providers (Health Centres)

Beneficiaries/ Clients /Communities

Source: Author’s presentation based on the health service provision in Uganda



 132

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

The voice is expressed through the elected representatives (politicians) at parliament and elected councilors at DLG6. The compact relationship is handled at two levels. At the central level, the parliamentarians receive previous financial year performance reports and approve sector budgets from the line ministries. The DLG councillors redistribute the resources according to priorities and demands on each unit. Services are directly provided to the citizens or clients who in addition to influencing decision through their voice possess (client) powers to directly confront frontline service providers. The accountability framework for health service delivery fits well within the decentralized local government framework. The three distinct levels of the organization of the health service delivery system share different responsibilities as highlighted below. (a) The Ministry of Heath (MoH): MoH is the national coordinator of health service delivery. The core functions of the MoH are: (i) Policy formulation; (ii) Resource mobilization; (iii) Capacity development and technical support; (iv) Provision of nationally coordinated services e.g. epidemic control; (v) Coordination of health research; and (vi) Monitoring and evaluation of the overall sector performance. To accomplish its coordination roles in resource mobilization, MoH provides Ministry of Finance Planning and Economic Development (MoFPED) with the health sector budget proposal as MoH budget and provide guidelines for project (mostly funded by donors) prioritization as well as district expenditure ceiling. (b) The District Health Service Committee: The districts health service committee prioritize projects within the districts and are responsible for: (i) Implementation of the National Health Policy; (ii) Planning and management of district health services; (iii) Provision of disease prevention, health promotion, curative and rehabilitative services; (iv) Control of other communicable diseases of public health importance to the district; (v) Vector control; (vi) Health education; (vii) Ensuring provision of safe water and environmental sanitation; and (viii) Health data collection, management, interpretation, dissemination and utilization. As discussed in the previous sections, Uganda had 81 district local 6

In Uganda the elected politicians (i.e. the President, Members of Parliament, District local government and lower level local governments) hold elective positions for five years and may seek re-election thereafter. 133

JOURNAL OF AFRICAN DEVELOPMENT

governments as at 1st July 2008, which were further subdivided into; Counties, Sub-counties, Parishes and Villages. The health service infrastructure follows this pattern, with health centers (HC) of increasing capacity (designated HCI, HCII, HCIII and HCIV). In addition, most districts have at least one hospital. The Minimum Health Care Package is delivered through this network. At the districts are district executive councils which are responsible for implementing decisions and its members are in full time service. The Chairperson of the district council (LCV) is the overall coordinator of district programs and directs any business of the council, and is answerable to it. The council elects a speaker and deputy speaker from among its members. The Chief Administrative Officer (CAO) is the chief of the administrative arm of the district responsible to council. The CAO is also the district accounting officer, coordinator of various departments (including health service), and chief monitor of the implementation of district projects. Under health service is a team headed by the district health officer that is responsible for planning and management of district health services. THE HEALTH SUB-DISTRICT (HSD)

Within each district (at county level) are health service zones called Health Sub-Districts (HSDs). The HSDs are intended to be functional subdivisions of the district health system aimed at: (i) further decentralization of the management of routine health service delivery from the District Health Office to lower levels; (ii) improving planning and management of district health services ; (iii) increasing equity of access to essential health services; (iv) achieving optimum balance between curative care, disease prevention and health promotion; and (v) fostering community involvement in the planning, management and delivery of health care. The operational responsibility for health service delivery was devolved to the HSDs. Within the Health Sub-district is built a team of health workers to ensure delivery of Minimum Health Care Package. The leadership of the HSD is based at an existing hospital or an up-graded health centre (government, NGO or private) located within the HSD. The HSD is an almost self-contained sub-system within the District Health System, in which the planning, implementation, monitoring and supervision of all basic health services will be undertaken.



 134

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

LOCAL PARTICIPATION IN DECISION MAKING/ COMMUNITY EMPOWERMENT

Decentralization can alleviate technical problems in design and implementation given the greater relevance to local needs, conditions and available resources. Where decentralization does facilitate and encourage local participation, then the benefits of community involvement – improved project design and implementation due to better match with beneficiary needs and better appreciation of local constraints etc. - can follow, and result in greater efficiency in government activities, especially in the long term. To ensure that communities are empowered to take responsibility for their own health and well-being, and to participate actively in the management of their local health services, the Government of Uganda has initiated a number of measures: (i) developed guidelines for community capacity building for effective participation in resource mobilization and in the monitoring of health activities; (ii) promoted the establishment of health committees with an appropriate gender balance at each of the different levels of the local government system; (iii) established management boards for all publicly owned tertiary hospitals with extensive delegated authority for their efficient operation; (iv) developed guidelines for the establishment and operation of facilities; (v) promoted and supported community-based health services; (vi) established the national health assembly with adequate representation from the district, civil society, donors and other key partners. In the decentralized system, the planning process is at sub-county level where the sub-county development committees collect and prioritize all subcounties plans. The priorities of all the sub-counties are thereafter presented to the county development committee, which in turn prioritize the draft plans for onward transmission to the district development committee for deliberations. In the case of Kampala city councils the sub-country development committees draw up priorities for the division and finally present them to the district development committee. The district development committee sets priorities of the district and approves the respective projects, representing a strong mandate of the communities. All the approved proposed plans are then compiled into the draft district development plan for onward transmission to the MoFPED for preparation of the district development plan. Each of the planning stages described in this paragraph is consistent with the health sector planning framework - the channels of transmission from bottom-up is shown by the dotted lines in Figure 2.

135

JOURNAL OF AFRICAN DEVELOPMENT

The government of Uganda’s policy shift towards decentralization of service delivery is a good drive if some of the institutional weaknesses and implementation gaps are addressed. The current framework accurately addresses the planning, budgeting and financial management. However, the questions of capacity of the local government to deliver efficient services to the communities still merit empirical evaluation. It is argued that there exist several constraints arising from the local government capacity and the availability of infrastructure at the decentralized units. This study will pay more attention to the efficiency of delivery of health services under the decentralized local government system. METHODOLOGY AND DATA The methodology involved literature review, primary and secondary data collection and two stage econometric (DEA and Probit) estimation of the relationship between DLG capacity and efficiency in health service delivery over the period 2008/09 and 2009/10. DATA

Health input and output data and also institutional dimensions including broad management measures important in evaluating service delivery outcomes were taken into account in the analysis of health service delivery. Primary data from health service providers (frontline organizations at districts) and beneficiaries (citizens or clients) were collected from a sample of 44 districts and some health facilities. Secondary quantitative and additional qualitative data were collected from the MoH. The secondary qualitative data on health input and output (performance) indicators were obtained from the Health Information Management System supplemented by information from National Service Delivery Surveys and Statistical Abstract for 2009 and 20107. The primary data collection (see, Appendix 1 for sampling methodology) involved in-depth interviews and focus group discussions with a sample of individuals drawn from among: stakeholders at different levels of policy (central and local government) and development partners, implementers (health service providers), community facilitators, and other opinion leaders. For the sampled districts, the capacities of management at local government level were assessed by 7

The Statistics Abstract is an annual Publication of the Uganda Bureau of Statistics (UBOS).



 136

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

analyzing information on staff categories and their qualifications, and management systems at the DLGs and sub-counties. Efficiency in health service delivery was assessed using DEA on a two period panel of indicators including expenditure on key inputs, and management of funds and health data, and quality of service delivery available in the health facilities. The variables used in the assessment of the quality of health service delivery were drawn from observations on infrastructure, available services and their level of implementation (including Diptheria, Pertussis, Tetanus vaccine coverage, new Out Patient Department attendance per capita and proportion of expectant mothers delivering in government or private not-for-profit units), and skills of health care providers while delivering health services, along with interviews with the clients from the sampled Health Centre IV (HC-IV). ECONOMETRIC METHODOLOGY The impact of Local Governments’ capacity8 on efficiency in health service delivery in Uganda was assessed using a 2-stage DEA on annual panel data, over the periods 2008-09 and 2009-10. In choosing this methodology, it is first noted that there were a total of 819 districts whose performance may depend on local government management capacity among other factors. These districts are treated as operating under conditions that were heterogeneous, thus we assumed that they may have district specific characteristics which may have influence on efficiency of health service delivery, for example, management quality. The panel data for the DEA was based on information from a sample of hospitals and health centers in 44 districts10. The two stage method11 involves solving a DEA problem in the first stage analysis, involving only the traditional inputs and outputs. In the second stage, the 8

 Management indicators used included inputs (quantified in financial terms) utilized by the local governments to deliver health services (or outputs) to the target beneficiaries at sub-counties.

9

This was the total number of district local governments in Uganda at the beginning of the financial year 2008/2009. The number has since increased to 114 districts as at 1st July 2010.

10

We assumed that efficiency in health service delivery in each DLG is affected by non-traditional inputs factors, which were outside the control of the local authorities. Such factors may include location characteristics such as urban or rural that may affect the quality of infrastructure; civil wars or conflicts which may affect the availability and quality of service offered by local governments; local participation in decision making may result in greater efficiency in health service delivery; and special government programs in a district may affect the health outcomes. 11

The two stage approach with the consideration of impact environmental variables has a number of advantages. As highlighted by Coelli et al., (1998), namely: (i) It can accommodate more than one

137

JOURNAL OF AFRICAN DEVELOPMENT

efficiency scores from the first stage are regressed upon the environmental variables. The sign of the coefficients of the environmental variables indicates the direction of the influence, and standard hypothesis tests were used to assess the strength of the relationship. The second stage regression was used to correct the efficiency measures scores for environmental factors by using the estimated regression coefficients to adjust all efficiency scores to correspond to a common level of environment (e.g. the sample means). In the second stage of the model, a probit regression model was used as it accounts for censored data. The two stage approach can also be used to assess the influence of various management factors upon efficiency. For example, the effects training of the managers of local governments can be estimated by including these factors in the second-stage regression. DATA ENVELOPMENT ANALYSIS (DEA)

Data envelopment analysis or DEA is a nonparametric technique developed in the work of Charnes, Cooper and Rhodes (1978). Their study extended the single intput-output technical efficiency measure, pioneered by Farrell (1957), to a multiple output- oriented relative efficiency measure. Since then, a number of variations and extensions of the original model as well as related software have been developed; see for instance Cooper et al., (2000) and Thanassoulis (2001). In the health care research, the DEA has gained much popularity in the 1990s. Using mathematical programming, DEA estimates the maximum potential output (or the efficiency frontier) for a given set of inputs, and has primarily been used in the estimation of efficiency. It allows a researcher to reveal the technical efficiency of each production unit (also known as a decision making unit (DMU); which in this case a local government unit). DEA gives an estimate of the extent of inefficiency of a given DMU, relative to the best performing DMU. A DMU is considered inefficient if it lies below the efficient frontier. DMUs on the frontier are of course, the efficient units.

variable; (ii) It can accommodate both continuous and categorical variables; (iii) It does not make prior assumptions regarding the direction of the influence of the categorical variable; (iv) One can conduct hypothesis tests to see if the variables have a significant influence upon efficiencies; (v) It is easy to calculate; and (vi) The method is transparent.



 138

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

MATHEMATICAL SPECIFICATION OF THE DEA MODEL

Assume that there is data on K inputs and M outputs related to the health sector, on each of the N Decision Making Units (DMUs) - district local governments. For the ith DMU these are represented by vectors xi and yi, respectively. The K x N input matrix, X, and the M x N output matrix, Y represent the data of all N DMUs. The purpose of the DEA is to construct a non-parametric envelopment frontier over the data points such that all observed points lie on or below the production frontier. An intuitive way to introduce the DEA is via the ratio form. For each firm we would like to obtain measure of the ratio of all outputs over all inputs, such that:

u’yi/v’xi,

(1)

where u is an M x 1 vector of output weights and v is a vector of input weights. To select optimal weights we specify the mathematical programming problem: Maxu, v (u’yi/v’xi), Subject to u’yj/v’xj ≤ 1, j = 1, 2, N u, v ≥ 0.

(2)

This involves finding the values for u and v, such that the efficiency measure of the ith DMU is maximized, subject to the constraint that all efficiency measures must be less than or equal to one. One problem with this particular ratio is that it has an infinite number of solutions. To avoid this one can impose the constraint v’xi = 1, which provides: Maxμ, v (μ’yi), Subject to v’xi = 1, μ’yj – v’xj ≤ 0, j = 1, 2, N (3) μ, v ≥ 0. where the notation from u to μ reflects a transformation. This form is known as the multiplier form of the linear programming problem.

139

JOURNAL OF AFRICAN DEVELOPMENT

Using the duality in linear programming, and assuming variable returns to scale as suggested by Banker, Charnes and Cooper, (1984)12, one can derive an equivalent envelopment form of this problem: Minθ, ξ θ, subject to

- yi + Yξ ≥ 0, θxi – Xξ ≥ 0

(4)

N1’ ξ = 1 and ξ ≥ 0, where θ is scalar and ξ is a N x 1 vector of ones. The restriction N1’ ξ = 1 allows for variable returns to scale. This envelopment form involves fewer constraints than the multiplier form (K + M < N + 1), and hence is generally the preferred form to solve. The value of θ obtained will be the efficiency score for the ith DMU in period t. It will satisfy θ ≤ 1, with a value of 1 indicating a point on the frontier and, hence, a technically efficient DMU, according to the Farrell (1957) definition. Note that the linear programming problem must be solved N times for each time period for each firm in the sample. A value of θ is then obtained for each firm in each time period (year). Hollingsworth, et al., (1999) provides a thorough review of the various applications at the micro level. More recent applications of DEA to measure hospital efficiency can be found in Bhat (2001), Giokas (2002), and Hofmarcher, et al., (2002). These studies elaborate DEA’s applicability, strength and limitations. Methodological comparisons between DEA and regression analysis have been discussed by Nyhan and Cruise (2000), who examined the efficiency of managed care organizations; and by Giuffrida and Gravelle (2001), who investigated the delivery of primary care. Seiford (1996) Berger et al. (1997), and Cooper, et al., (2000) point out that DEA has strong empirical advantages13. Despite these

12

Variable returns to scale is realistic compared to constant returns to scale given that local government health units may not operate at optimal scale due to imperfect competition, constraints on financial and human resources etc. 13 The advantages of the DEA according to Cooper, 2000 are; (i) unlike linear or non-linear regression, DEA does not require a specific functional form for the production process, and there is no need to specify a distributional form for the inefficiency term. (ii) DEA more easily accommodates both multiple inputs and multiple outputs. As a result, it is particularly useful for analysis of efficiency in health service delivery, which is evaluated using several output indicators. (iii) when multiple outputs and inputs are



 140

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

mythological advantages, no statistical inference can be associated with the DEA efficient frontier because it is not derived from statistical analysis but rather mathematical programming (Mirmirani et al., 2008). ENVIRONMENTAL FACTORS

In the second stage, the efficiency scores from the first stage are regressed upon the environmental variables using a panel probit model, whereby values of the technical efficiency scores in a certain range from the DEA regression are transformed to a single value. The panel probit model can briefly be defined as follows: Let

w *it = z’itβ + αi + cit while w it = θ *it if w *it > 0 w it = 0 if w *it ≤ 0 where wit is the efficiency score obtained for a DMU in period t, during the first stage of the estimation (using DEA); β is a K x 1 vector of unknown parameters; Z’i is a K x 1 vector of known constants relating to environmental factors; αi is the DMU specific effect and cit is the random error term of the panel probit model. It is assumed that αi and cit are independently and normally distributed, independent of z’i1 ……… z’iT , with zero means and variances σ2α and σ2c, respectively. INPUTS AND OUTPUTS VARIABLES In deciding which inputs and outputs to be included, we draw on information from the framework for health service delivery in Uganda presented in Figure 2. The input and output variables used in the analysis were derived from the DLGs’ health sector monitoring indicators used by the MoH. In addition, to capture the impact of institutional capacity on local health service delivery, we include institutional variables that could influence the service delivery outcomes such as; accountability and local participation. The input, output and environmental variables used and their definition are detailed in Appendix 2

used in a study, contrary to regress techniques, DEA does not require the construction of any subjective efficiency index to identify the most efficient DMU production units. (iv) while a regress function reveals the average efficiency of the units under investigation, DEA shows the efficiency frontier. (v) Smith (1997) further suggests that DEA’s ability to provide meaningful results with a small sample also contributes to its growing popularity.

141

JOURNAL OF AFRICAN DEVELOPMENT

EMPIRICAL FINDINGS AND DISCUSSIONS This section presents the qualitative and empirical findings of the study. The qualitative findings are based on data collected through interviewing stakeholders of the health system, including policy makers, frontline service providers and beneficiaries. The empirical findings are based on estimating the DEA and probit models to assess the impact of local government capacity on efficiency of the health service delivery, using both secondary and primary data over the periods 2008/09 and 2009/10. QUALITATIVE FINDINGS DISTRICT LOCAL GOVERNMENT - HEALTH SERVICE ADMINISTRATORS

The district administrators are considered policy coordinators providing links between the councillors (politicians), local government technocrats, frontline health service providers and the beneficiaries. The district questionnaire was designed to capture holistic information on local government capacity for health, constraints to service delivery, resource mobilization and the governance system. From the institutional framework, they are considered to be at the center of health service delivery. The result of the micro-level survey reveal that in all cases decisions to recruit health and other district local government staffs are approved through the District Service Commission (DSC). The main source of health inputs are from the local government and direct donor support through the MoH. The communities are fully represented through their leaders in the decision making process however, there are been some weakness in consultation and providing feedback to the clients. Inadequacies of health professionals in some districts were attributed to push factors which inter alia include, remuneration and other incentives, career growth opportunities, work environment and other related factors that include inadequate equipment and supplies, and work load on few health workers (Ministry of Health, 2009)14. The other constraints identified to affect service delivery were; health infrastructure, equipment, electricity and communication infrastructure to access some communities. 14 See, Ministry of Health, (2009), “Human Resource for Health”, Bi-Annual Report, September.





 142

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

MANAGEMENT AND STAFF OF THE HEALTH FACILITIES

The frontline providers of health services were required to provide information on the input, processes and outcomes of health service delivery under a decentralized local government. The findings reveal that 100% of respondents perceive health units were under staffed and lack the required equipment to meet customer’s demand. Yet in all cases the administrative decision are taken by the doctors who are often difficult to retain in rural health facilities. The planning approach is bottom-up therefore local priorities are taken into consideration in seeking input resources. The resources from central government, donors, MoH and National Drug Authority are provided through the local government reallocation system. These frontline providers engage beneficiaries of health services in decision making through community meetings and by sharing monthly reports. CLIENTS, INFORMATION AND PARTICIPATION

The clients include all health service users who include individuals, community based organizations and associations. The majority of clients who indicated that they often access public health services are in the low income category. In the perceptions of the clients interviewed, while the personnel available are skilled, they are inadequate and are considered slow in attending to patients. Inadequate qualified personnel, lack of facilities, drugs and distances to health centers were considered the main constraints to health service provision. Respondents also noted that absenteeism by qualified staff at the health centers makes the quality of services delivered poor. However, this can only be investigated through further research on the level of participation in service delivery decision under the local government. The beneficiary communities further indicated that whereas the decentralized system gives them the voice to hold policymakers and providers accountable for the quality of services provided, they are not often consulted during the planning process. Participation in planning, budgeting, management of resources and monitoring the quality of services delivered has remained the prerogative of the technocrats at the districts or sub-county. Apart from sensitization programs organized by some CBOs, the district health committees often approve the annual plans without consultation with the beneficiaries. In many instances the communities are not made aware in advance about such capacity building programs. Mobilization initiatives through mass media (such as 143

JOURNAL OF AFRICAN DEVELOPMENT

local radios, newspaper and posters) was considered to be the best avenue to increase awareness and participation15. However, access to such information is limited among rural communities who do not have radios, purchase newspaper or travel to the district headquarters where the notices are posted. Accountability was also found to depend on information being available to citizens, in a sufficiently comprehensible form, about how resources are being used (Goetz et al., 2001). It also requires a dynamic civil society, able to engage effectively with local government on these issues. The field findings show that this is still a relatively weak combination in Uganda. QUANTITATIVE FINDINGS

The averages for most of the variables, both input and output variables were quite low as shown in Table 1. However, there are wide gaps between the maximum and minimum values. This could indicate that health facilities could be operating under optimal scale and that there were wide variations between districts both in terms of health inputs and outputs (performance). INPUT ORIENTED VARIABLE RETURNS TO SCALE TECHNICAL EFFICIENCY (VRSTE)

Under the assumption of variable returns to scale we estimate technical and scale efficiency for public health facilities for 44 DLGs, using an input oriented variable returns to scale DEA. The estimated technical efficiency (TE) results are decomposed into pure technical efficiency and scale efficiency. This has been accomplished through conducting a constant returns to scale (CRS) and a Variable returns to scale (VRS) on the same data, for the periods 2008/09 and 2009/10. Table 2 presents input oriented technical and scale efficiency score for the district health facilities during the two years and the details are indicated in Appendix 3.

15  To this end, many attribute the success of the national immunization program against the six killer diseases to the use of the mass media for community mobilization.



 144

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

Table 1: Summary statistics of variables used in the analysis Variables

Std. Dev.

Mean

Median

Max

Min

75.4 0.9

75 0.9

135 1.9

27 0.4

18.6 0.3

66.9

72

99

1

22.8

31.4

29

111

4

16.3

54.8

49

199

19

32.8

45.6

44

96

14

14.7

No of staff

337.4

323

681

87

136.2

No of health facilities

53.7

45

200

15

33.2

Funds spent on drugs ,%

72.4

78.5

127

16

24.4

No of beds

2,868.3

538.5

23,066

118

4,561.7

Output Variables DPT Immunization (%) OPD attendance Pit latrine coverage (PLC, %) Delivery in government facilities (%) TB cases attended (%) Intermittent Presumptive Treatment (IPT, %)

Input Variables

The results reveal that the mean variable returns to scale (VRS) technical efficiency (TE) of the Ugandan health system was 0.92 over the period 2008/092009/10. The maximum VRS TE was 1, indicating full efficiency, while the minimum was 0.4. The mean VRS stood at 91.7 percent and 92.8 percent for 2008/09 and 2009/10, respectively. These findings suggest that if these district health facilities were run efficiently, the health centers could have produced 8.3 percent and 7.2 percent more output for the same volume of inputs. Thus, there is scope to increase output of health services at the districts by 8.3 percent and 7.2 percent in the two years, respectively. The level of efficiency is in the range found by Yawe and Kavuma (2008). The maximum VRS TE for the health units was 1 during both periods while the minimum VRS TE was 0.5 and 0.4 during 2008/09 and 2009/10, respectively. Thirty-two (or 72.7% in 2008/09) and 31 (70.5% in 2009/10) of the health facilities at the district level registered a VRST TE score of 100 percent.

145

JOURNAL OF AFRICAN DEVELOPMENT

Therefore, twelve (27.3%) and thirteen (29.5%) of the district health facilities can be said to have been managed inefficiently over the same time period. INPUT ORIENTED SCALE EFFICIENCY

To provide insight into the impact of the institutional framework into efficiency of health service delivery, scale efficiency and types of returns to scale were calculated for each district, as the difference between the VRS TE and Constant Return to Scale (CRS) TE. A difference in the two technical efficiency scores for a particular health unit indicates that the unit has scale inefficiency. The results indicate that 25 (56.8%) and 20 (45.5%) of the districts health facilities were operating under optimal scale with efficiency score of 100 percent in 2008/09 and 2009/10, respectively. This means that they had the most productive size for the particular input output mix. Among these health facilities, doubling of the inputs leads to doubling of the service output. The health units in the remaining districts had efficiency scores of less than 1 and as such they were scale inefficient. The inefficiency of a health unit may arise because it was operating under increasing returns to scale (IRS) or decreasing returns to scale (DRS). Health units in17 (38.6%) and 19 (43.2%) districts were operating under decreasing returns (IRS) to scale, in 2008/09 and 2009/10 respectively, implying that an increase in the inputs (medical staff, hospitals, beds, funds, and health centers) to any of these health units would result in less than proportionate increase in its outputs. The health units in the remaining districts i.e. two (4.5%) and five (11.4%) were operating at increasing IRS in the two periods, respectively; meaning that an increase in the inputs to any of these health units would result in more than proportionate increase in the outputs. In order to operate at the most productive size, a health unit exhibiting DRS should scale down its inputs. Similarly, if any health unit is displaying IRS, it should expand both its inputs and outputs. This analysis could be used to reallocate resources from those districts which were operating under DRS to those operating under IRS.



 146

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

Table 2: Input oriented Efficiency scores of Districts health Units in 2008-09 and 2009-10 2008/9 and 2009/10

2008/09

2009/10

CRS

VRS

SCLE

CRS

VRS

SCLE

CRS

VRS

SCLE

Mean

0.85

0.92

0.92

0.844

0.917

0.915

0.856

0.928

0.921

Maximum

1.00

1.00

1.00

1.000

1.000

1.000

1.000

1.000

1.000

Min

0.36

0.40

0.59

0.362

0.503

0.613

0.382

0.398

0.593

STDDEV

0.19

0.15

0.12

0.200

0.150

0.130

0.170

0.140

0.120

100%

32 (72.7%)

31 (70.5%)

< 100%

12 (27.3%)

11 (25%)

CRS

25 (56.8%)

20 (45.5%)

IRS

2 (4.5%)

5 (11.4%)

DRS

17 (38.6%)

19 (43.2%)

DOES LOCAL GOVERNMENT CAPACITY EXPLAIN EFFICIENCY IN THE HEALTH SECTOR?

To capture the impact of institutional capacity on local health service delivery, we estimated a probit model that uses efficiency scores from the results of DEA estimation as a binary16 dependent variable that is affected by environmental variables. The probit model estimation result in Table 3 indicates that the DLG capacity is significant at 1% level of significance using proportion of professional staff to the population of a district (PSP). The coefficient on PSP is 3.9, which shows that as the proportion of professional staff per 1,000 people in a district increases by one unit, the efficiency of health service delivery of that district is likely to increase by 3.9 units. This result indicates that local government technical capacity is an important factor in providing efficient services in the Ugandan health system. However, the other institutional variables in the model are not significant in explaining efficiency in health service delivery. This could be explained by low access to information and participation in the planning and management of resources by the community. 16

The binary variables were generated from the DEA results where; technical efficiency (VRSE) score of 85% - 100% were assigned 1 otherwise 0 on the lower efficiency score.

147

JOURNAL OF AFRICAN DEVELOPMENT

CONCLUSIONS AND POLICY IMPLICATIONS In this paper, we set out to investigate the impact of decentralization and local government capacity on health services delivery. The results show that health institutions in about 31 districts out of 44 were technically efficient. Those in about 13 districts were technically inefficient under variable returns to scale (VRS). This implies that the health resources were not efficiently used in health institutions of the 13 districts. Health institutions in 25 (56.8%) districts and 20 (45.5%) out of 44 districts were operating at optimal scale in 2008/09 and 2009/10, respectively. Those in the remaining districts were scale inefficient of which those in 17 (38.6%) and 19(43.2%) districts operated in 2008/09 and 2009/10, respectively, and the remaining were operating under increasing returns to scale (IRS). The study has established that local governments in Uganda suffer from weak institutional capacity particularly through a lack of adequate staffing levels which indeed poses a big challenge to effective and efficient implementation of health care delivery services. An implication of the results is that effective and accountable decentralized governance in the health sector in Uganda is typically constrained by weak capacity particularly staffing levels at the district health offices and within health units. In view of the foregoing, we propose the following policy recommendation to improve efficiency in health service delivery: (i) (ii)

(iii)

public policy should strengthen the DLGs capacity to manage health services by increasing the professional staffing levels; excess health resources in one district (in particular human resources) could be transferred to other districts with inadequate resources and DLGs in health services that are operating at IRS may be considered for future expansion.



 148

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

Table 3: Panel Probit Estimates Variables

Coefficient

Std. Error

z-Statistic

Prob.

PSP – professional staff per ‘000 people

3.892897

1.050532

3.705643

0.0002

FAC – number of health facilities (health units)

-0.005269

0.006175

-0.853203

0.3935

1.31E-05

5.46E-05

0.240594

0.8099

INF - level of access to information about the district budgets.

-0.267038

0.362315

-0.737032

0.4611

PAR – level of local participation in decision making.

-0.119334

0.413246

-0.288771

0.7728

DEV - level of development of the district.

0.507648

0.341810

1.485179

0.1375

HARD – hard to reach area or not.

-0.319268

0.389417

-0.819863

0.4123

0.772727

S.D. dependent var

0.421472

0.414098

Akaike info criterion

1.126534

13.88967

Schwarz criterion

1.323594

-42.56748 -0.483721

Hannan-Quinn criter.

1.205924

BED - number of beds.

Mean dependent var S.E. of regression Sum squared resid Log likelihood Avg. log likelihood

149

JOURNAL OF AFRICAN DEVELOPMENT

REFERENCES Ahmad J., Shantayanan D., Stuti K., Shekhar S., (2005), “Decentralization and Service Delivery,” World Bank Policy Research Working Papers, No. 3606: pp.1-29. Alam, M.M., Huque, A.S., and Watergaard (1994), “Development through Decentralization in Bangladesh: Evidence and Perspectives”, University Press Limited – Dhaka. Alesina A. and Rodrik D., (1994), “Distributive Politics and Economic Growth”, The Quarterly Journal of Economics, Volume 109, No. 2 (May), pp. 465-490. Berger, A.N., Brockett P.L., Cooper W.W., and Pastor J.T., (1997), New Approaches for Analysing and Evaluating the Performance of Financial Institutions, European Journal of Operational Research, (April), Volume 98, No 2 , pp.169-174. Bhat, R., (2001), Methodology note: Data Envelopment Analysis (DEA), Journal of Health Management, (July-December), Volume 3, No. 2, pp.309-328. Charnes, A., Cooper W.W., and Rhodes E., (1978), “Measuring the Efficiency of Decision Making Units,” European Journal of Operational Research, 2, pp. 429-444. Coelli (1996). “A Guide to DEAP Version 2.1. A Data Envelopment Analysis (Computer) Program,” CEPA Working Paper 96/08, Department of Econometrics, University of New England, Armidale. Coelli, T., Prasada Rao D.S. and Battese G.E., (1998). An introduction to Efficiency and Productivity Measurement. Kluwer Academic Publishers: Boston/Dordrecht/London. Cooper, W.W., Seiford L.M., and Tone K., (2000), Data Envelopment Analysis: A

Comprehensive Text with Models, Applications, References and DEA solver software, Kluwer Academic Publishers: Boston. Farell, M.J. (1957), “The Measurement of Productive Efficiency,” Journal of the Royal Statistics Society, Series A, CXX, Part 3, pp.253-290. Giokas, D. (2002), “The use of Goal Programming, Regression Analysis and Data Envelopment Analysis for Estimating Efficient Marginal Costs of Hospitals”, Journal of Multicriteria Decision Analysis, July-October, Volume 11, No. 4-5, pp.261-268.



 150

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

Girishankar Navin (1998), “Reforming Institutions for Service Delivery: A framework for Development Assistance with an Application to the HNP Portifolio.” World Bank, Policy Research Working Papers, Volume 2039. Giuffrida, A., and H. Gravelle (2001), Measuring Performance of Primary Health Care: Econometric Analysis and DEA, Applied Economics, Volume 33, No. 2, pp.163-175. Government of Uganda (2005), “Ministry of Health National Health Policy”,

Ministry of Health, Kampala. Hofmarcher, M.M., I. Paterson, and M. Riedel (2002), Measuring Hospital Efficiency in Austria –a DEA Approach, Health Care Management Science, (February), Volume 5, No.1, pp.7-14. Hollingsworth, B., P.J. Dawson, and N. Maniakadis (1999), Efficiency Measurement of Health Care: a Review of Non-parametric Methods and Applications, Health Care Management Science, Volume 2. No. 3, 161-172. International Monetary Fund (2005), “Uganda: Poverty Reduction Strategy Paper”, IMF Country Report No. 05/307. Kahkonen S. and Lanyi A., (2001) “Decentralization and governance: Does decentralization improve public service delivery?” PREM Notes, World

Bank, Number 55, available on the PREM website; (http://prem). Mugabi, J. and Njiru, C. (2006), “Managing Water Services in Small Towns: Challenges and Reform Issues for Low-Income Countries”, J. Urban Plann. Dev., 132(4), 187-192. Mills, A., Maganu, Vaughal, J. P., Smith, D.L., and Tabibzadeth (1990), “Health System Decentralization: Concepts, Issues and Country Experiences”, World Health Organization: Geneva. Mirmirani S., Li H.C., and Ilacqua J. A., (2008), “Health Care Efficiency in Transition Economies: An Application of Data Envelopment Analysis”, International Business & Economics Research Journal, (February). Volume 7, Number 2. Nyhan, R.C., Cruise P., (2000), “Comparative Performance Assessment I Managed Care: Data Envelopment Analysis for Health Care”, Managed Care Quarterly, Volume 8, No.1, pp.18-27. Obwona M, Steffensen J, Trollegaad S, Mwanga Y, Luwangwa F, Twodo B, Ojoo A and Seguya F (2000), “Fiscal Decentralization and Sub-National Government Finance in Relation to Infrastructure and Service Provision in Uganda”, Economic Research Policy Centre (EPRC), Uganda, March 2000.

151

JOURNAL OF AFRICAN DEVELOPMENT

Rondinelli, D. A., Nellis, J.R., and Cheema, G.S., (1983), “Decentralization in Developing Countries: A Review of Recent Experience”, World Bank Staff Working Papers No. 581. The World Bank: Washington D.C. Samia A, Jishnu D, and Markus G (ed.) (2008), “Are You Being Served?: New Tools The International Bank for for Measuring Service Delivery.” Reconstuction and Development / The World Bank – Washington DC. Seiford, L.M., (1996), “Data Envelopment Analysis: The Evaluation of the State of the Art (1978-1985)”, The Journal of Productivity Analysis, Volume 7, pp.99-137. Thanassoulis, E. (2001). Introduction to the Theory and Application of Data Envelopment Analysis: a Foundation Text with Integrated Software, Kluwer Academic Publishers: Boston. The Republic of Uganda, (2007) “Annual Health Sector Performance Report (2006/07)”, Minstry of Health, Kampala – (October). World Bank (2001), World Bank Development Report 2001. The World Bank, Washington, D.C. Yawe B. L. and Kavuma S. N., (2008), “Technical Efficiency in the presence of Desirable and Undesirable outputs: a case study of selected district referral hospitals in Uganda”, Health policy and Development, Vol. 6 (1), pp 37-5.



 152

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

APPENDIX APPENDIX 1: SAMPLING METHODOLOGY

The ownership of health services providers in Uganda is classified under government, private not-for-profit providers, and private health centers. Our focus is on assessment of efficiency and capacity of local government to manage public health service delivery under the decentralization system. A total of 2,30117 health units are owned by government of which 60 are hospitals, 147 are HC-IV, 762 are HC-III and 1,332 HC-II. These are the four main medium of public health services delivery in addition to the six regional referral hospitals directly managed by the MoH. This study focused on analysis of the capacity and efficiency of local governments to deliver health services at HC IV facilities and hospitals in a sample of 44 districts in Uganda. We employ the two-stage purposive stratified random sampling method. In which regions and districts constitute the strata and the health units are randomly drawn from within each stratum. The 44 districts were drawn from the 81 district local government present in 2008. From this strata a total of 140 HC IV were randomly sampled from each district. The interviews were carried out for each group of stakeholders within the unit sample area.

17

This number of health units is based on the information reported in the health performance report for FY 2008/09. 153

JOURNAL OF AFRICAN DEVELOPMENT

APPENDIX 2: VARIABLES AND DEFINITIONS

(i) Inputs Variables Input Indicators 1.

Number of staff at local governments (districts).

2.

Financial resources available.

3.

Management system (manual/computerized).

4.

Number of staff at health facilities (doctors, nurses, clinical officer, nurses, anaesthetic assistant, laboratory assistant, community health worker etc).

5.

Number of beds in the health facilities.

6.

No of equipment in the health facilities.



 154

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

(ii) Outputs Variables Management indicators

Activity

1.

Proportion of received funds that has been spent.

Management of Primary Health Care Conditional Grant (PHC-CG)

2.

Proportion of indicative PHC-CG budgets spent on medicines at National and Joint Medical stores as by agreed guidelines.

Expenditure on key inputs.

3.

Timeliness of Health Management Information System reporting

Management of health data

Service delivery indicators 4

New Outpatient Department (OPD) attendances per capita.

5.

Proportion of expectant mothers delivering in Government of Uganda and Private Not For Profit units.

6.

Proportion of expected TB cases that are notified.

7.

Proportion of pregnant women receiving Intermittent Presumptive Treatment 2 (IPT 2) (second dose of pyrimethazine in pregnancy).

8

The proportion of children < 1 year receiving 3 doses of Diphtheria, Pertussis and Tetanus (DPT) preventive vaccine.

9.

Pit latrine coverage as a measure of sanitation coverage.

10.

Availability of HIV/AIDS control activities by level e.g. Voluntary Counselling and Testing (VCT).

155

JOURNAL OF AFRICAN DEVELOPMENT

iii. Environmental Variables 1. 2. 3. 4. 5.

6.

7.

8.

9.

10.

Proportion of skilled staff (economist, statistician, administrative) to total staff at local governments and health facilities. Level of development of the district. Region (urban or rural). Conflict area or not. Client power: The number of service users in the sample that report on the quality and quantity of services using either; citizen report cards, suggestion boxes at health centers and/or Uganda Participatory Poverty assessment surveys is included to measure client power. The higher the client power over frontline providers of health services, the higher the expected efficiency in the provision of services.

Structure of incentives: Two indicators will be included to measure the structure of incentives to staff in the health service delivery chain: (i) The average monetary remuneration in a district (including staff benefits such as housing). Districts which offer better remuneration to staff are expected to perform better in terms delivering health services. (ii) The quality of work environment (including condition of health buildings and information systems). A higher quality work environment would attract skilled staff and lead to better quality health services. Citizen Voice: The numbers of people in the sample that attend to local radio talk shows on ‘issues at hand in health’ in a district are included to measure citizen voice. Higher citizen voice enables citizens to hold politicians and policy makers accountable for public services. It is therefore expected to lead to better services for the poor. Accountability: The number of health committees/ Management Boards in each district in included as a measure of the level of accountability. Accountability mechanisms such as health committees are said to support more responsive policies and effective services. Local participation: Level of local participation on the health committees/ Management Boards at the different levels of districts. Higher local participation at HSDs is expected to lead to greater efficiency in health service delivery. Local elite capture: The percentage of civil society in a district sample that has access to the local newspapers and/or bi-annual budget performance report, are included to measure the level of local elite capture. The government currently publishes information about budgetary disbursements to local governments in the local newspapers. The budget performance report gives actual budget release performance for both development and recurrent budgets and by sector. It clearly specifies which sectors and districts spent above or below their pro rata budgets. It is one of the budget transparency actions initiated by government to counter the problem of leakage of funds. It is expected that empowering the civil society with information on budget performance will lead to less temptation by local elites to misappropriate resources that were budgeted for health service delivery, and vice versa. Thus the higher the percentages of civil society with access to information on the budgets and budget performance, the better the expected health delivery.



 156

NANNYONJO AND OKOT: HEALTHCARE DELIVERY IN UGANDA

APPENDIX 3: ESTIMATE OF INPUT ORIENTED EFFICIENCY SCORES OF DISTRICT HEALTH UNITS 2008 AND 2009

Districts Region

Efficiency 2008/9 CRSTE

VRSTE

SCALE

Efficiency 2009/10 Return

CRSTE

VRSTE

SCALE

to Scale

Kampala

CENTRAL

EAST

NORTH

1.000

1.000

1.000

CRS

Return to Scale

1.000

1.000

1.000

CRS

Kayunga

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Masaka

0.683

1.000

0.683

DRS

0.642

1.000

0.642

DRS

Mpigi

0.785

1.000

0.785

DRS

0.725

0.812

0.893

DRS

Mubende

1.000

1.000

1.000

CRS

0.941

0.973

0.967

DRS

Mukono

0.362

0.557

0.650

DRS

0.570

0.754

0.755

DRS

Nakasong l Rakai

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

0.676

0.856

0.790

DRS

0.570

0.701

0.813

DRS

Ssembab l Wakiso

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

0.522

0.762

0.685

DRS

0.602

0.767

0.785

DRS

Bugiri

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Busia

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Iganga

0.578

0.701

0.825

DRS

0.382

0.398

0.961

IRS

Jinja

0.846

1.000

0.846

DRS

0.781

1.000

0.781

DRS

Kamuli

0.585

0.646

0.905

DRS

1.000

1.000

1.000

CRS

Kapchorw

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Katakwi

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Mayuge

1.000

1.000

1.000

CRS

0.826

0.830

0.995

IRS

Mbale

1.000

1.000

1.000

CRS

0.972

1.000

0.972

DRS

Pallisa

0.820

1.000

0.820

DRS

0.723

0.734

0.985

DRS

Soroti

0.542

0.566

0.959

IRS

0.967

1.000

0.967

DRS

Tororo

0.665

1.000

0.665

DRS

0.758

1.000

0.758

DRS

Adjumani

0.771

0.781

0.986

DRS

0.871

0.885

0.985

IRS

Apac

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Gulu

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Kitgum

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Kotido

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Moroto

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Moyo

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

157

JOURNAL OF AFRICAN DEVELOPMENT

APPENDIX 3: ESTIMATE OF INPUT ORIENTED EFFICIENCY SCORES OF DISTRICT HEALTH UNITS 2008 AND 2009

Districts Region

Efficiency 2008/9 CRSTE

VRSTE

SCALE

Efficiency 2009/10 Return

CRSTE

VRSTE

SCALE

to Scale

Nakapirip iPader i

Return to Scale

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

0.800

1.000

0.800

DRS

Bundibug

0.906

1.000

0.906

DRS

0.796

0.842

0.945

IRS

Bushenyi

0.441

0.719

0.613

DRS

0.593

1.000

0.593

DRS

Hoima

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Kabale

0.703

1.000

0.703

DRS

0.816

1.000

0.816

DRS

Kabarole

1.000

1.000

1.000

CRS

0.846

1.000

0.846

DRS

Kanungu

0.712

0.994

0.716

DRS

0.928

1.000

0.928

DRS

Kasese

0.473

0.503

0.940

DRS

0.579

0.940

0.616

DRS

Kibaale

0.697

0.697

0.999

CRS

0.791

1.000

0.791

DRS

Kisoro

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Kyenjojo

0.569

0.573

0.993

IRS

0.617

0.651

0.948

IRS

Masindi

0.797

1.000

0.797

DRS

0.558

0.565

0.987

DRS

Mbarara

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

Rukungiri

1.000

1.000

1.000

CRS

1.000

1.000

1.000

CRS

WEST

MEAN

0.844

0.917

0.915

0.856

0.928

0.921

STDEV

0.198

0.154

0.126

0.174

0.139

0.117



 158

Suggest Documents