The study lends credence to the mantra of demographic Population-Growthtransition as a Poverty Nexus significant determinant of Evidence from the Philippines economic growth.

The previous study and this project give a comprehensive picture of the effects of population in the Philippines. Both studies offer important implications for policy alternatives for population programs and companion projects that would help trigger the positive effects that a slower population growth rate

The PopulationGrowth-Poverty Nexus sought to establish that there are critical links among population,, economic growth, and poverty reduction.

Final Report September 2006

Asia Pacific Policy Center

A ASSIIAA--P PAACCIIFFIICC P POOLLIICCYY CCEENNTTEERR ______________________________________________________________________________

Population—Growth—Poverty Nexus Evidence from the Philippines

FINAL REPORT

13 September 2006

______________________________________________________________________________

The study was funded by the Philippine Center for Population Studies. The study team is composed of Dennis S. Mapa, Rosemarie G. Edillon and Carlos Abad-Santos with Arsenio M. Balisacan as Technical Adviser. The research and technical assistance of Sharon Faye A. Piza, Kristine Joy S. Briones and Sharon L. Fangonon are gratefully acknowledged. The team is also grateful to the participants of the various round table discussions where initial drafts of the report was presented All errors and omissions are sole responsibilities of the authors and the Asia Pacific Policy Center.

CONTENTS _____________________________________________________________ Page Overview An Overview of the Intra-country Study on the Impact of Population Dynamics on Economic Growth and Poverty Reduction in the Philippines

1

Part I Young Population Matters – More is not Necessarily Merrier: A Study on the Determinants of Income Growth in the Philippines

19

Part II Population Growth and Income: Implications on Revenues and Expenditures of LGUs

49

References

76

Annex A Share of Young Dependents as Proxy for Demographic Transition

A-1

Annex B Bayesian Averaging for the Classical Estimates

B-1

Annex C Annex Tables

C-1

I. An OVERVIEW of the INTRA-COUNTRY STUDY on the IMPACT of POPULATION DYNAMICS on ECONOMIC GROWTH and POVERTY REDUCTION in the PHILIPPINES Rosemarie G. Edillon 1

_____________________________________________________________ “The more, the merrier”—an oft quoted phrase; reasonably true when applied to parties, picnics or other fun gatherings, but when the analogy is extended to population and development, it is an overly simplified, if not careless, generalization. Two studies conducted by the Asia-Pacific Policy Center, with support from the Philippine Center for Population and Development, substantiate this judgment. Elsewhere in the literature, debates about the impact of population on development have been very controversial. Mapa provides a quick run-through of these developments. Going back more than two centuries, the Reverend Malthus claimed the impact to be negative. This was picked up much later by growth theorists like Barro and Sala-i-Martin (2004) using more recent data. On the other hand, there are researchers that claim almost the opposite. Simon (1981) and Boserup (1998) argued that population growth promotes competition, thus “inducing technological change and stimulating innovation” and therefore, its impact on economic growth may even be positive. A popular trend in the literature lately is the emphasis on other factors, notably rule of law and the quality of institutions, as being primarily responsible for the growth or lack of it in a given country. Such is the thesis, e.g., of Norton (2003), Easterly and Levine (2002), and Acemoglu, Johnson and Robinson (2001). A growing strand in the literature beginning in the late 90s is the focus on demographic transition, rather than population growth rate, as a crucial determinant of economic growth. Bloom, Canning and Sevilla (2001) describe demographic transition as “a change from a situation of high fertility and high mortality to one of low fertility and low mortality.” The transition is reflected in sizable changes in the age distribution of the population. The situation of low mortality and fertility creates a bulge in the age pyramid that will move, over time, from young people (infants and children) to prime age (workers), and eventually to old age (elderly). Depending on the position of this bulge on the age pyramid, the value of output per capita—the most widely used measure of economic performance—will change correspondingly. The change from high to low mortality and fertility can create the so-called “demographic dividend”. This roughly corresponds to Phase 2 where you see the bulge in the age-sex pyramid among the productive age group. The three phases are illustrated below. 1

Ms. Edillon is presently the Vice President and Executive Director of the Asia-Pacific Policy Center (APPC). 1

Figure 1. Phase One of the Demographic Transition: Philippines, 2000

Philippines 2000 70 and Over 60 - 64 50 - 54 40 - 44 30 - 34 20 - 24 10 - 14 0-4

-10

-5

0

5

Male male

10

Female female

Figure 2. Phase Two of the Demographic Transition: Thailand, 2000

Thailand 2000 70 and Over 60 - 64 50 - 54 40 - 44 30 - 34 20 - 24 10 - 14 0-4

-10

-5

0 Male Male

5

10

Female Female

Figure 3. Phase Three of the Demographic Transition: Japan, 2000

Japan 2000 70 and Over 60 - 64 50 - 54 40 - 44 30 - 34 20 - 24 10 - 14 0-4

-10

-5

0 Male male

2

5 Female female

10

The first study, conducted by Mapa and Balisacan in 2004, examined the effects of population dynamics and economic growth using data from 80 different countries, spanning the period 1975 to 2000. The study lends credence to the mantra of demographic transition as a significant determinant of economic growth. Observe that the age pyramids shown above to represent the three phases of the demographic transition are the actual age pyramids of three different countries as they were in 2000: the Philippines for Phase 1, Thailand for Phase 2 and Japan for Phase 3. It should be obvious that the Philippines has yet to experience the so-called demographic dividend. The study concludes that we have been paying a high price for unchecked population growth. When pitted against Thailand, the study showed that had the Philippines followed Thailand’s population growth path during the period 1975 to 2000, the average income per person in the Philippines would have been 0.77 percentage point higher every year or a cumulative increase of about 22 percent in the average income per person in the year 2000 (an additional US$253 increase in the average income per person in the Philippines, to US$1404 from US$1151). Moreover, the reduction in the number of the poor due to the estimated increase in income is about 4.03 million in the year 2000. This is equivalent to an average of 161,200 Filipinos taken out of poverty per year during the period 1975 to 2000 (equivalent to 678,000 households during the period, or 56,500 households per year for 25 years). While the first study provides strong evidence of the negative impact of the Philippines unchecked population growth on economic growth, and subsequently on poverty reduction, it is vulnerable to criticisms that some other variables may be confounding the results. To be fair, the model is quite comprehensive, covering variables such as quality of governance, openness to trade, quality of human capital, etc. Still, one can argue that there could be the more significant unobservables such as history, religion, culture and the like. This latter criticism can only be addressed by considering data from countries of peoples who went through the same history and are characterized by the same culture and traditions; in other words, units belonging to the same country. This second study attempts to estimate the impact of population dynamics on income (economic) growth and poverty reduction, this time using the Philippines’ provincial data from 1985 to 2003. It comes in two parts: the first estimates the intra-country model, and the second applies the result of the first part to simulate its likely impact on the balance sheet of local government units. Part I. Young Population Matters—More is not necessarily Merrier: A Study on the Determinants of Income Growth in the Philippines (1985 to 2003) Using intra-country data to demonstrate the effects of population dynamics on economic growth is quite a challenge, especially for the Philippines. First, some information is not uniformly available across all provinces like governance ratings and openness to trade. Second, and more importantly, there is no province in the Philippines that has undergone demographic transition, even up to 2003 and therefore, we need to first identify a variable that adequately indicates that such a transition will likely occur. The variable that can best proxy for demographic transition is the proportion of young dependents. We only need to refer to the age pyramids above to see that this variable attains its highest value during Phase 1 and its lowest during Phase 3. In Annex A, we show the results of the statistical test which essentially proves this hypothesis.

3

Recall that we expect the proportion of young dependents to exert a negative effect on economic growth, meaning that we expect provinces with higher proportion of young dependents to grow at a slower rate than provinces with lower proportion of young dependents, ceteris paribus. During Phase 3, the impact may either be positive or neutral. Concerning the Philippines, however, we need not be concerned of this latter relationship. Determinants of Per Capita Income Growth The result of the intra-country regression model confirms that, indeed, the proportion of young dependents has a negative and significant effect on income growth. The estimated coefficient of -0.09 implies that a one-percentage point increase in the proportion of young dependents in 1985 results in an estimated 9 basis points decrease on the average growth rate of income per person from 1985 to 2003, all things being the same. Note that the percentage of young dependents in the Philippines in 1985 is quite high at 42 percent, about 7 percentage points higher than Thailand’s. We could have increased average per capita income growth by 0.63 percentage point per year, if we had Thailand’s 35% dependency share in 1985. The results support the earlier study of Mapa and Balisacan (2004), using cross-country data. The only way to achieve the “demographic bonus” of positive growth in the medium term is to enter into the second phase of the demographic transition. Other variables also affect per capita income growth. 1. The natural logarithm of initial income is negatively and significantly correlated with income growth. On the average, provinces with higher income per capita at the start of the sample period (1985) experienced a lower average growth rate from 1985 to 2003 relative to provinces with lower initial income per capita, all other things being equal. This follows the general results of growth models about conditional convergence. In particular, the model estimates that it would take about 23 years before half the initial gap between the average income per person (in 1985) and the steady state income per person will be eliminated (half life of convergence). 2. The measures of initial inequality are both significant but of opposite signs, with initial inequality having positive sign, while its square has a negative sign, all things being the same. The opposite signs of the coefficients imply that the relationship between inequality and income growth is quadratic (parabolic) or that the relationship of income growth and inequality follows an inverted U shape. Below a certain threshold, inequality and growth are positively related but above the threshold, inequality negatively affects income growth. 3. The location variable for the provinces in the ARMM has a negative and significant impact on the average provincial income growth suggesting that these provinces have been experiencing “growth discount” over the years, relative to the other provinces. Provinces in the ARMM region have lower average per capita income growth of about 2.29 percentage points compared to that of the average of the other provinces, all things being equal. 4. Net migration has a negative and significant effect on average provincial growth rate. The estimated coefficient implies that for every 10,000 net migrants entering the province during the period 1985 to 1990, the estimated average growth rate per person decreases by 0.08 percentage point (or 8 basis points) all things being the same. 4

5. The model also captures potential spillover effects where the average growth rate of per capita income in the province is affected by its neighboring provinces. In the model, this variable exerts a negative and significant influence. This means that as the average growth rate of per capita income of the neighbors increases, the average growth rate of per capita income in the home province decreases. This phenomenon is labeled “beggar thy neighbor” and most probably occurs when neighboring provinces compete with each other to attract investments and “clients”. 6. The education variable, measured by the number of years of schooling of the household head, is included in the model to measure human capital. However, the education coefficient, while positive, is not significant in explaining variations in the average provincial income growth in the Philippines. 7. Two time-varying policy variables, infrastructure index and change in electricity, are included in the model and as expected, the two variables are positively related with income growth. However, of the two, only the infrastructure index is a significant determinant of income growth, while improvement in the access to electricity is not. Robustness Procedures: Bayesian Averaging of the Classical Estimates (BACE) A major criticism against empirical growth econometrics is the modeler’s choice of control variables—which explanatory variables are to be included or excluded in the regression models. Some skeptics may argue that variables, such as population growth, significantly affect income growth depending on which other variables are held constant. This paper uses the BACE approach, suggested by Sala-i-Martin, Doppelhofer and Miller (SDM; 2003), in testing for the robustness of the variables to determine the variables that are strongly or robustly related to income growth. This procedure entails running regression analysis for 792 times, each time with 7 variables, two are always included: initial income and education of the household head, and the remaining 5 all the different combinations of the remaining 10 variables. All these variables show up in previous growth regression models. (a) Variables Robustly Related to Growth The robustness procedure shows that the proportion of young dependents is robustly related to growth. The other variables found to be strongly related to growth are the mean initial income, ARMM, and the inequality measures. (b) Variables Marginally Related to Growth Variables identified as marginally related to growth are the net migration and neighborhood effects. (c) Variables Not Robustly Related to Growth The rest of the variables show little evidence of robust partial correlation with income growth using the empirical test. These variables that are considered as weak determinants are education, change in CARP, change in the proportion of households with electricity, change in the quality of roads, infrastructure index, the indicator variable landlock, mortality rate, and the number of typhoons.

5

From Population Dynamics to Income Growth Suppose we consider the ten provinces with the lowest proportion of young dependents in 1985. The average among these ten is 35.89. It would be interesting to find out what could have been the income growth profile if all provinces started out in 1985 with a lower proportion of young dependents, at most 35.89. Figure 4 illustrates the result where the solid line graph plots the simulated income per capita. The estimated national average per capita income in year 2003 (18 years later) would have been higher by 1,620 pesos (from 27,443 pesos to 29,063; all in 1997 prices), or 7.12% higher than the actual. Adjusting for inflation, this amount corresponds to an additional 2,227 pesos on the average income per person in 2003.

Figure 4. Simulated Average per capita Income 35,000

30,000

25,000

20,000

15,000

10,000 1985

1988

1991

1994

Actual

1997

2000

2003

Simulated

Simulation Results – Selected Provinces In some provinces, the potential increase in average per capita income is much higher. These are provinces where the proportion of young dependents is somewhat large in 1985, so that the improvement in bringing it down to 35.89 is considerable. These are the cases of Camarines Norte where the initial proportion of young dependents is 47.03%, Camarines Sur (45.86%) and Davao Oriental (44.37%), to name a few. The figures show that Camarines Norte’s income per person in 2003 would have been 3,297 pesos higher (in 1997 prices), an increase of 16.18% in the province’s per capita income. In Camarines Sur, average income per person would have been higher by 2,764 pesos (an increase of 14.37%) and in Davao Oriental, higher by 2,152 pesos (12.11%). 6

Figure 5a. Simulated per capita Income: Camarines Norte 25,000 22,000 19,000 16,000 13,000 10,000 1985

1988

1991

1994

1997

2000

2003

Figure 5b. Simulated per capita Income: Camarines Sur 25,000 22,000 19,000 16,000 13,000 10,000 1985

1988

1991

1994

1997

2000

2003

Figure 5c. Simulated per capita Income: Davao Oriental 30,000 26,000 22,000 18,000 14,000 10,000 1985

1988

1991

1994

Actual per capita income

7

1997

2000

2003

Simulated per capita income

Growth Accounting: Population dynamics explains large component of provincial growth differentials Following the simulation method in Phase 1, we select pairs of provinces and try to explain why one province performed much poorly than another comparable province. We refer to this exercise as growth accounting and we are particularly interested in the role of population dynamics in explaining what we call this “growth differential”. As an example, we compare Camarines Norte and Misamis Occidental, where the latter enjoyed a higher growth rate in per capita income of 3.3%. Of the growth differential of 1.2 percentage points between Camarines Norte and Misamis Occidental, 0.58 ppt is explained by the difference in the proportion of young dependents in 1985. This amounts to 48% of the growth differential, the highest that can be explained by the variables included in the model. The growth accounting exercise shows that indeed the proportion of young population matters to the provincial per capita income growth and having more young population creates constricting effect on income growth. From Income Growth to Poverty Reduction The final step estimates the effect of the population dynamics to reduction in poverty, via the growth channel (or the “expansion of the pie”). Previous empirical studies (notably Balisacan [2005] and Balisacan and Pernia [2003] and Balisacan and Fuwa [2002]) have shown that slow to modest but unsustained growth of the country is primarily to blame for the very slow reduction in poverty. The scatter plot of the average growth rate of per capita income and rate in reduction of headcount poverty, from 1985 to 2003, for the provinces in the data set is given in Figure 6. The graph illustrates a positive relationship between average per capita income growth rate and the rate of headcount poverty reduction. The strength of this relationship is reflected in the growth elasticity of poverty reduction, estimated to be 1.45. This means that a one percent increase in the rate of average income growth increases the rate of poverty reduction by roughly 1.45%. Figure 6. Scatter plot of Average Growth Rate of Per capita Income and Rate of Reduction of Headcount Poverty 15

Rate of Poverty Reduction

10 5 0 -5 -10 -15 -20 -2

-1

0

1

2

3

Rate of Income Grow th

8

4

5

6

7

Extending the previous result, we estimate that, under the scenario of lower proportion of young dependents, the poverty headcount in 2003 is 17.65 million, lower by about 2.82 million individuals. The reduction in poverty headcount (from 26.12% to 22.52%) is due to the estimated increase in the mean income per person of about 2,227 pesos in 2003. This is a sizable reduction in the number of the poor and should be enough to get serious about the relationship between population and development. Table 1. Reduction in Poverty Poverty Headcount (Individuals)

Scenarios

Number

%

Status quo

20,465,409

26.12

With low proportion of young dependents

17,646,631 *

22.52

2,818,778

3.60

Difference * assuming the same population in 2003

Part II. Population Growth and Income: Implications on Revenues and Expenditures of LGUs The best rationale for population management program is still the improvement in welfare. Previously, we have seen that this improvement in welfare can come by way of per capita income growth. Given the potential benefits we can derive from demographic transition, government intervention to facilitate the transition can be justified. However, we need to consider the redistributive impact as well. Income redistribution is, by and large, the responsibility of government. By their very characteristics, the poor are marginalized from the sectors that experience high growth. The task of government is to “link” the poor to these sectors either as a source of raw materials or source of labor. By this, we mean the roads, access to communication and technology, health and education services, etc. To a large extent, however, the coverage, and ultimately, the effectiveness of these strategies, depends on the fiscal resources that are available to government. In the literature, studies have found an inverse relationship between the size of government (measured as a proportion of government consumption expenditure to GDP) and per capita GDP of the country. This is clearly demonstrated in Korea and Thailand, where the correlation coefficients are estimated to be -0.95 and -0.82, respectively. In the case of the Philippines, the coefficient is still negative, albeit a low -0.40. The chart is drawn below (Figure 7). The chart drawn in Figure 8 shows that despite the negative relationship between government spending as % of GDP and GDP per capita, government spending per capita need not decrease. Especially if the spending has been properly targeted, more fiscal resources will lead to better quality provision of basic services. It is easy to think how this can be related to population, especially concerning the expenditure side. Given the same aggregate amount of fiscal resources, if these were to be distributed to fewer 9

people, then the per capita amount will be higher. We need only to show that a managed population will result in the same, if not higher, amount of fiscal resources. In fact, the net impact may even be positive and the savings can be plowed back to increase the resources of LGUs to effect redistribution. Figure 7. Government Spending as % of GDP and Per Capita GDP 18 16 14 12 10 8 6 4 2 0 1976

1978

1980

1982

1984

1986

1988

GGCE (% of GDP) KOR GGCE (% of GDP) THA GDP PC (Phil, 1975=100) PHL

1990

1992

1994

1996

1998

2000

GGCE (% of GDP) PHL GDP PC (Phil, 1975=100) KOR GDP PC (Phil, 1975=100) THA

Note: Person’s r: Korea -0.95, Thailand -0.82, Philippines -0.40. Source: Author’s estimates based on World Development Indicators, World Bank.

Figure 8. General Government Consumption Expenditure Per Capita (constant $) 1,200 South Korea 1,000

800

600

400

Thailand

200

Philippines

0 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20 Source: Author’s estimates based on World Development Indicators, World Bank.

10

That a managed population will result in the same, if not higher, amount of fiscal resources is not obvious in the case of LGUs in the Philippines. For one, almost all LGUs depend on the IRA for their revenues and a key determinant of the IRA is related with the constituent population. Moreover, we observe high standards of living in provinces and cities with high population density. This second part of the study will link the issue of population management and growth (first part results) to the balance sheet of LGUs in terms of the effect on: (a) local government taxes and fees derived from higher per capita incomes, (b) IRA changes resulting from changes in aggregate tax revenues and population distribution across provinces and (c) lower expenditures on social services and government overhead due to a lower population base. To do this, we adopt an accounting framework for two reasons. One is that this is a limitation imposed by the data. The other is that an accounting framework imposes a quality of governance where expenditure is not endogenous, consistent with the intent of the Local Government Code and the IRA formula. What is the likely effect on LGU revenue? The first part of the study showed that population management, reflected in a lower young dependency share, can result in higher per capita incomes. Higher per capita incomes can lead to higher per capita tax payment. With progressive taxation, the percentage increase in tax payment will even be higher than the percentage increase in income, where both are expressed in per capita terms. However, going into the future, lower dependency share will result in a lower population, or in other words, a smaller tax base. The net result, therefore, is something that has to be empirically determined. There are two main components of LGU revenues: locally generated revenues and external source revenues. Locally generated revenues are composed of four main components: (a) real property taxes, (b) business taxes, (c) fees and charges, and (d) receipts from economic enterprises. External sources come from shares from the IRA, shares from national wealth, borrowings, and grants. Previous studies have suggested that per capita income and other variables (that proxy for standard of living) affect the local revenues generated by LGUs. This is to be expected, given that these taxes are imposed on consumption goods that vary with standard of living—real property, profits from business, fees and charges. The effect on IRA is not as straightforward. The IRA allocation is not based on population, per se, but on the proportion of the population of the LGU unit to the total population of all LGU units. Hence if population growth decreases at the same rate across all LGUs, the proportional IRA shares of all LGUs remain the same. However, demonstrating this will require simulating a total of 3.02231E+23 2 scenarios. This was not done anymore in this study, especially considering that the next census will likely occur in 2010 and the results will be used to modify the IRA share computation only in 2012. The other effect on the IRA will come by way of the impact of per capita income on internal tax revenues. Given the same tax collection efficiency, higher per capita income will result in proportionately higher per capita tax revenues because of progressive tax rates on income and higher consumption on income elastic goods and services. In the study, we compute the IRA following the formula stated in the law, i.e., as 40% of internal tax revenues. 3 Tax revenues, meanwhile, is modeled using as 2 3

This is the sum of {78 taken 1} + {78 taken 2} + … + {78 taken 77} + {78 taken 78}. Strictly speaking, the law states that the IRA is 40% of internal tax revenues of 3 years before. 11

factors the tax due per capita, population and tax collection efficiency. In the simulation, we assume the same tax collection efficiency. What is the likely effect on LGU expenditures? The expenditure components of interest to the study are social sector-based expenditures, namely, on health, education, social security, labor and housing. These sorts of expenditures variables are supposedly constituent-based and are assumed to increase with the increase in population. This imposition of “unitary elasticity” is far from original, and is most likely the very intent behind the IRA formula. Instead of specifying a behavioral model showing the relationship between population and LGU expenditures, we simply multiply the simulated population with the most recent values of per capita expenditures for health, education, social security, housing and labor in each LGU. For other expenditure components such as general administration expenditures, which represents overhead cost of the LGU administration and economic services expenditures, we shall assume that these expenditures proportionately follow the changes in social sector expenditures. Therefore, with the imposition that social sector expenditures should be constituent-based, lower population will mean lower aggregate expenditures. Theoretically, the LGU will realize “savings” or a higher budget surplus from lower population. What does the data say about the effect on LGU revenue? The results show that per capita local revenue for all revenue components is elastic with respect to per capita income of the constituents. The highest is with respect to per capita real property tax, where a one percent increase in per capita income is estimated to result in a 2.24 percent increase in per capita real property tax revenue. This can easily be explained by the fact that the these taxes are levied on consumption goods and services that are income elastic—real property, business income, fees and charges. We also expect per capita internal tax revenues to be elastic with respect to per capita income even when we separately consider income and consumption taxes. Income tax follows a progressive tax schedule and taxable consumption is also income elastic. The resulting model conforms to this design. The net effect of population management, meaning the simultaneous effect of higher per capita income and lower constituency base, has to be empirically determined. In the following, we present two different simulations. Simulation 1: What if the young dependency share in 1985 across provinces were at most 35.89%? Effect on LGU Revenue Extending the simulation done in Part I of this study, we simulate the scenario where, in 1985, the proportion of young dependents in all provinces is at most 35.89. This means pegging the proportion of young dependents in 1985 to 35.89 in the provinces that previously had a higher proportion than this. We project the 2003

12

population using the same growth rate of the province, but starting from a lower base (due to the reduction in proportion of young dependents). With a simulated 2003 population, we now compare the aggregate actual provincial revenues with the aggregate simulated provincial revenues by using the estimated income elasticities above. The results show that 70% of provinces are net gainers, exhibiting increases in their aggregate local revenues. Next to Metro Manila, Davao del Sur stands to gain the most in additional revenues from lower population growth and higher per capita incomes. The simulated increase in total local revenues for Davao del Sur is more than 230 million pesos. Among the net gainers, the average increase is almost P43 million or about 2 percent over their actual revenues from all sources. There are twenty two net losers in our simulation, mainly because of the reduction in the IRA. Thus, it may seem that indeed there is a tradeoff between population management and IRA. However, this is only half the picture. We need to find out what happens to expenditure in order to complete it. Effect on LGU Expenditures Our computations show that the average decrease in LGU expenditures across all provinces is 10.2 percent or P108.11 million per province. Among all provinces, Negros Occidental has the largest decrease in LGU expenditures in absolute terms, with “savings” of over P369 million in real terms (1997 prices). Maguindanao has the largest percentage decrease in LGU expenditure of 21.47 percent. While these figures do not necessarily represent actual savings to the LGU, they represent the opportunity cost of high population growth. Net Effect on Provincial Revenue and Expenditures Putting together the effect on both revenues ands expenditures will provide us a picture of the total net monetary gains accruing first to the LGUs, then to their constituency. Our estimates show that all 4 provinces stand to gain from population management, or in particular, if the proportion of young dependents in 1985 were at most 35.89%. Next to Metro Manila, Negros Occidental stands to gain the most, more than 1 billion pesos (in 1997 prices). Overall (excluding Tawi-tawi), the average net monetary benefit is 331 million pesos. This amount, or even some of it, could have been used to expand or upgrade the quality of services. The results of our simulations reinforce the conclusions of the first phase of the study (national level) and the first part of the second phase study that lower population growth, among others, contributes to economic growth. We have further extended the results to confirm that the economic impact translates to greater revenues and lower expenditures for LGUs, and possibly, better provision of public goods and services. Simulation 2: What if only one province successfully managed its population profile? The earlier simulations were done on all provinces, regardless of their differences in key attributes and characteristics. However, provinces are heterogeneous and exhibit large differences in their socio-economic and geo-physical attributes. It is also a bit 4

Tawi-tawi exhibits negative balance but this is because the actual balance was already negative in the first place. 13

unrealistic to peg the proportion of young dependents in 1985 at the same level in all the provinces. We now simulate the case where only one province successfully managed its population profile. We present two examples that we will label as “managed” provinces. For each of these “managed” provinces, we identify a comparator province. Each pair exhibited similar characteristics in 1985 (our base year) except for their respective proportion of young dependents and compared their revenue and expenditure attributes in 2003. In the simulation, we peg the young dependency share of the managed provinces, namely Camarines Norte and Camarines Sur, as equal that of the comparator provinces, respectively, Misamis Occidental and Nueva Ecija. The next table present the comparative statistics of these two pairs of provinces. Table 2. Provincial Statistics PAIR 1 Characteristics

Misamis Occidental

Prop’n of young dependents, 1985

PAIR 2

Camarines Norte

Nueva Ecija

Camarines Sur

39.34

47.03

37.98

45.86

Population, 2003

484,029

491,734

1,785,046

1,592,941

Total Revenues*

1,276,315,629

835,432,878

3,031,047,142

2,414,413,372

Per Capita Revenues

2,637

1,699

1,698

1,516

Total Local Revenues

161,363,126

99,005,169

412,836,618

317,663,867

333

201

231

199

1,196,653,283

809,160,177

3,055,483,137

2,152,850,771

2,472

1,646

1,712

1,351

215,815,540

177,054,629

652,168,402

390,231,655

446

360

365

245

Per Capita Local Revenues Total Expenditure Per CapitaTotal Exp. Social Services Exp.** Per Capita Social Ser. Exp.

* All amounts are in nominal (2003) prices. Total Revenue excludes loans and borrowings of the LGU **These include expenditures for health, education, housing, social security and labor. Source: BLGUF, FIES, and CPH

Looking at the fiscal profile of these LGUs, we observe that the comparator provinces indeed have higher revenues and expenditures, both in absolute amounts and per capita values. These provinces likewise spend higher amounts for social sector services like health, education, and housing in absolute and per capita values. We are not able to determine if managed population growth has an impact on the quality of governance, given data constraints, but this can be an interesting hypothesis to test when sufficient data permits.

14

The simulations are done separately for Camarines Norte and Camarines Sur. The population counts of the other provinces are pegged at the actual levels. What this amounts to is that the population shares of these “managed” provinces will decrease relative to the actual. The results are given in the table below: Table 3. Simulation Results 1 Province / Parameter

Actual

Simulated

Difference

% Difference

Camarines Norte Total Revenue

635,000,642

613,190,991

(21,809,651)

(3.4)

Total Expenditure

599,154,007

521,866,347

(77,287,659)

(12.9)

35,846,636

91,324,643 55,478,008

154.8

Revenue – Expenditure Net Impact Camarines Sur Total Revenue

1,831,204,705

1,753,915,245

(77,289,450)

(4.2)

Total Expenditure

1,579,220,926

1,370,096,654

(1,209,124,272)

(13.2)

251,983,780

383,818,592 131,834,812

52.3

Revenue – Expenditure Net Impact

In the two provinces, the net impact is positive. The drop in the population shares of Camarines Norte (from 0.6212% to 0.6195%) and Camarines Sur (from 2.0085% to 2.0069%), though almost nil, still results in reduction in revenues, mostly due to the fact that the increase in local revenues was overcome by the decrease in population base. On the other hand, we still expect benefits, mostly from savings in expenditures that will more than offset the possible decrease in revenues. For Camarines Norte, the net benefit is 55 million pesos and for Camarines Sur, this amounts to about 132 million pesos. What the previous simulation proves is that provincial LGUs, by themselves, can manage their own population programs, independent of the rest and still be assured of positive net impacts. Of course, there are positive benefits to be gained if all provinces adopted an aggressive population program, but there really is no need for individual provinces to adopt a “wait and see” attitude. In fact, the very first simulation also shows that the “early managers” still emerge as winners. How will municipal LGUs be affected? Invoking certain assumptions, the analysis of the relationship between lower population and higher per capita income on LGU revenues and expenditures may be extended to the municipal level. Mainly, we assume that the aggregate impact is simply the sum total of the impact at the individual local LGUs.

15

We extend our previous simulation to include the municipalities and cities comprising the provinces of Camarines Norte and Camarines Sur. All forty-nine municipalities and cities of the two provinces were included in our simulation. The simulated population is assumed to be distributed across cities and municipalities in the same manner as in the 2000 Census of Population and Housing. The changes in revenues are assumed to be distributed across cities and municipalities according to their share in the total, according to revenue source. Finally, following our previous methodology, we assume the same level of per capita LGU spending. Table 4. Simulation Results 2 Camarines Norte

Camarines Sur

Population Population, 2003

491,734

1,592,941

Simulated 2003 Population

428,303

1,382,000

63,431

210,941

73,864,232

236,997,702

540,102,880

1,536,942,369

Actual Other Revenues

21,033,530

57,264,634

Actual Total Revenues

635,000,642

1,831,204,705

83,143,910

261,449,825.68

509,949,356

1,438,333,459

Simulated Other Revenues

20,097,724

54,131,960

Simulated Total Revenues

613,190,991

1,753,915,245

132,094,156

291,137,946

Actual Expenditures on General Administration, Economic Services and Others (GenEcoOthers)

467,059,851

1,288,082,980

Actual Total Expenditures

599,154,007

1,579,220,926

Simulated HESHL Expenditures

115,054,717

252,584,751

Simulated GenECo Others Expenditures

406,811,631

1,117,511,902

Simulated Total Expenditures

521,866,347

1,370,096,654

Actual Surplus

35,846,636

251,983,780

Simulated Surplus

91,324,643

383,818,592

Net Impact

55,478,008

131,834,812

Difference in Population Revenues Actual Local Revenues Actual IRA

Simulated Local Revenue Simulated IRA

Expenditures Actual Expenditures on Health, Education, Social Services, Housing and Labor (HESHL)

Impact

16

The table above summarizes the results. On the revenue side, we expect the reduction in individual LGU revenues, following the reduction in the aggregate revenues. The percentage changes, however, vary depending on the extent to which the LGU depends on the IRA. In the local LGUs of Camarines Norte, the percentage changes vary from almost 0 to less than -5%. In Camarines Sur, the range is much wider, from 0.2% to -6%. Since locally sourced revenues are expected to increase, the reduction is mainly due to the IRA and the percentage changes vary according to the extent of dependence of the LGU on the IRA. Putting it another way, we can safely conclude that the supposed negative effects on the IRA can be easily overcome by increasing locally source revenues. With consideration for the expenditure side, we first recall that the simulated population is about 13% lower than the actual. Clearly, the reduction in revenues of at most (-) 6% is easily offset by the savings in expenditure, assuming the LGUs spend the same amount per capita. These results are summarized in Table 4. We then note that the benefits of population management can be realized even at low levels of disaggregation. Concluding Remarks The link between population and development is real. The two studies suggest at least two channels through which population management can improve welfare. First is through the increase in per capita income, what we used to call the “growth channel.” The second is through the increase in fiscal resources that can be used to finance the “redistribution channel.” Before we conclude, we should take note of the assumptions of governance quality on which the second part of the study is based. Simply put, we assumed that governance quality will be as they were. This is reflected in the efficiency with which the state and LGUs are able to collect taxes and in the manner of spending where we assumed the same amount per capita. Perhaps if the quality of governance were improved, we will come up with even better results, notwithstanding the already good results demonstrated. The comparison between the “managed” and comparator province also suggests that “good governance” seems to go with economic growth. Recall that the comparator provinces fared better in terms of per capita revenue collection, and allocated higher per capita spending in social services. These pairs of provinces were similar in terms of the initial conditions that matter to economic growth—infrastructure, inequality, income per capita, etc., and only significantly differed with respect to the share of the young dependents. The introduction used in this paper provides the RED light that signifies the urgency of the problem. The Philippine experience shows that demographic transition does not happen as a matter of course, at least not in 100 years (or 50 years if we consider only the post-World War II era). The first phase study showed that we have forgone the benefits that our Asian neighbors managed to obtain from the demographic dividend. This second phase study proved that this is a very big loss, in fact. We lose out via the growth channel and the redistribution channel. We can extend the argument further and say that this leads us to a vicious cycle of slow growth, less fiscal resources leading to even slower growth, etc.

17

Once the second phase of the demographic transition is started, the experience of other countries shows that the benefits can last at least a century, BUT, we have to begin the transition. The best way to start is to mobilize a constituency for a strong population policy. We do this by first recognizing the problem that links (unmanaged) population and (slow) development. The next step is to advocate for deliberate steps to reduce fertility rate. Herrin and Costelo (1998) analyzed the population trend of the country going into the future. They have identified what they call the sources of future population growth— unwanted fertility (16%), wanted fertility (19%), and population momentum (65%). Clearly, the problem calls for varied solutions. These solutions can also vary with respect to moral convictions, NOT with respect to whether or not unchecked population is a problem, BUT with respect to the means of addressing the problem. Parents can help a lot by providing the moral suasion to discourage youngsters from engaging in early sex. This should be reinforced by the Church. Schools can help educate these same youngsters about the ill effects of early marriages, early pregnancies, large families, etc. As this can be responsible for at most 65% of the problem, such concerted efforts will greatly reduce the likelihood of the (unmanaged) population leading to (slow) development problem. There can be other strategies: •

Policies and programs that encourage couples to have fewer kids

•

IEC campaigns to encourage couples to “desire fewer kids”

•

Policies and programs that encourage women and/or couples to delay age of having their first child

•

IEC campaigns to encourage women and couples to delay age at first birth

•

Policies and programs that encourage (if not require) household investments on human capital

•

Policies that require national government and LGUs to maintain a certain quality of service provision, possibly proxied by per capita spending in real terms

•

Policies and programs that encourage LGUs and other service providers to implement population management

•

Reduce cost of “effort to meet desired fewer number of kids”

Others would say that with economic growth, population management follows. We do not even dispute this claim. However, we need to first exert that big push to get out of the vicious nexus that we find ourselves in: the nexus that is unchecked population → slow development → slow poverty reduction.

18

ANNEX A – Share of Young Dependents as Proxy for Demographic Transition _____________________________________________________________ In the multi-country study of Mapa and Balisacan (2004), the variable that represented demographic transition was the difference between total population growth rate and workers’ population growth rate. This did not work out well when applied to the Philippine setting, because there was not much variability observed among the provinces. The first problem that needs to be resolved, therefore, is to find another indicator that exhibits sufficient variability when computed for the Philippine provinces, and relates closely with the phenomenon of demographic transition. The principle of demographic transition and how it relates to development is based on the life-cycle hypothesis. The interest usually is finding the “bulge” in the agesex pyramid of a country – the young age group, the productive and the elderly. In the following, we examined the age distribution of 224 countries as of 2000 based on data from the US Bureau of Census. We operationally define the “bulge” as the age group with the highest share in the population. The Annex Table A below lists the countries according to this bulge. We next compute for the proportion of young dependents in the population, those ages 0 to 14 years. Overall, the (unweighted) average share of young dependents in the population is 31.62%. In comparison, in the Philippines, the figure is 37.2%. We next group the countries according to the position of the “bulge” and compute for the mean proportion of young dependents. We are interested in knowing if there is a significant difference across the groups. We do this using the technique called Least Significant Difference. The result is shown below. The numbers pertain to the position of the bulge: 1, if 0-4; 2 if 5-9; 3 if 10-14, and so on. A line is drawn encompassing the groups for which there is no significant difference in the values, at the 0.05 level: 1

2

3

4

5

6

7

8

9

10

11

What the results show is that the proportion of young dependents of countries in group 1 is significant higher than in all the other groups; the proportion in group 2 countries is also significantly higher than in groups 3 – 11 and significantly higher than in group 1; the value for groups 3 and 4 are not significantly different from each other but are significantly different from all the rest. Meanwhile, we take the countries in groups 5 to 11 as belonging to one group. The proportions of young dependents in these countries do not significantly differ from each other but are significantly different from those in groups 1, 2, 3 and 4.

A-1

Annex Table A Age group w/ highest share Group 1

Group 2

0 to 4 years old

5 to 9 years old

List of Countries Afghanistan

French Guiana

Mozambique

American Samoa

Gabon

Namibia

Angola

Gambia

Nepal

Bahrain

Gaza Strip

Niger

Belize

Ghana

Nigeria

Benin

Guam

Oman

Bhutan

Guatemala

Pakistan

Bolivia

Guinea

Panama

Brunei

Guinea-Bissau

Papua New Guinea

Burkina Faso

Haiti

Paraguay

Burundi

Honduras

Philippines

Cameroon

India

Rwanda

Central African Republic

Indonesia

Sao Tome &Principe

Chad

Iraq

Saudi Arabia

Colombia

Israel

Senegal

Comoros

Jordan

Sierra Leone

Congo (Brazzaville)

Kenya

Solomon Islands

Congo (Kinshasa)

Kiribati

Somalia

Cote d'Ivoire

Kuwait

Sudan

Djibouti

Laos

Swaziland

Dominican Republic

Liberia

Syria

East Timor

Libya

Tajikistan

Ecuador

Madagascar

Tanzania

Egypt

Malawi

Togo

El Salvador

Malaysia

Turkmenistan

Equatorial Guinea

Maldives

Turks and Caicos Islands

Eritrea

Mali

Uganda

Ethiopia

Marshall Islands

Uruguay

Fiji

Mauritania

West Bank

French Guiana

Mayotte

Yemen

Gabon

Micronesia, Federated States of

Zambia

Gambia

Morocco

Argentina

Jamaica

Reunion

Bahamas

Lesotho

Saint Pierre and Miquelon

Botswana

Mexico

Suriname

Cambodia

Nauru

Vanuatu

Cape Verde

Netherlands Antilles

Venezuela

Chile

New Caledonia

Virgin Islands

Dominica

Nicaragua

Iceland

Peru

A-2

Annex Table A, cont’d. Age group w/ highest share Group 3

Group 4

Group 5

Group 6 Group 7

Group 8

Group 9 Group 10 Group 11

10 to 14 years old

15 to 19 years old

List of Countries Albania

Faroe Islands

Saint Kitts and Nevis

Anguilla

French Polynesia

Samoa

Armenia

Georgia

South Africa

Azerbaijan

Grenada

Tonga

Bangladesh

Kazakhstan

Tuvalu

China

Kyrgyzstan

United Arab Emirates

Costa Rica

Latvia

Uzbekistan

Cyprus

Moldova

Vietnam

Estonia

Mongolia

Zimbabwe

Algeria

Macedonia

Sri Lanka

Brazil

Montserrat

Trinidad and Tobago

Burma

Poland

Tunisia

Guyana

Puerto Rico

Turkey

Iran

Saint Lucia

Ireland

Saint Vincent and the Grenadines

Czech Republic

Mauritius

Taiwan

Hungary

Romania

Thailand

Lebanon

Slovakia

Serbia and Montenegro

25 to 29 years old

New Zealand

Portugal

Spain

Northern Mariana Islands

South Korea

30 to 34 years old

Antigua and Barbuda

Guernsey

North Korea

Barbados

Man, Isle of

Norway

Cuba

Italy

Saint Helena

Greece

Jersey

Seychelles

Guadeloupe

Martinique

20 to 24 years old

35 to 39 years old

Andorra

Denmark

Netherlands

Aruba

France

Palau

Australia

Germany

San Marino

Austria

Greenland

Singapore

Belgium

Hong Kong S.A.R.

Sweden

Bermuda

Liechtenstein

Switzerland

Bosnia and Herzegovina

Lithuania

United Kingdom

Canada

Luxembourg

United States

Cayman Islands

Macau S.A.R.

Virgin Islands British

40 to 44 years old

Belarus

Russia

Ukraine

45 to 49 years old

Croatia

50 to 54 years old

Finland

Japan

Monaco

Gibraltar

Malta

Qatar Slovenia

A-3

Our conclusion, therefore, is that the variable “proportion of young dependents” can effectively discriminate between countries in the Phase 1 and those beginning to enter Phase 2 of the demographic transition. 1 However, it fails as a discriminator among countries in the Phase 2 and those possibly entering Phase 3 of the demographic transition. With respect to the Philippine population profile, however, we need not be concerned about this latter result because none of the provinces appear to be nearing this phase, let alone the transition from Phase 2 to Phase 3.

1

We assume that Phase 2 means that the bulge is found in the age groups that belong to any of the productive age group 20-64 years, or using our representation, groups 5 through 11. A country may be considered as being in the more advanced stage of Phase 2, or beginning to enter Phase 3 as the bulge gets higher.

A-4

ANNEX C – Annex Tables _____________________________________________________________ Table #

Table title

Page

1

Change in per capita income using simulated proportion of young dependents

C-2 to 3

2

Change in population using simulated proportion of young dependents

C-4 to 5

3

Change in per capita local revenues

C-6 to 9

4

Change in the internal revenue allotment of provinces

C-10 to 11

5

Change in total revenues of provinces

C-12 to 13

6

Change in Expenditures of LGUs

C-14 to 15

7

Net effect on provincial revenue and expenditures (in million pesos)

C-16 to 17

8

Change in population and LGU revenue at the municipal level

C-18 to 19

9

Change in expenditure of LGUs at the Municipal Level

C-20 to 21

Net eefect on municipal revenue and expenditures

C-22 to 23

10

C-1

Annex Table 1. Change in per capita income using simulated proportion of young dependents

Province

Actual Simulated dependency Actual per capita dependency income, 2003 share, 1985 share, 1985

Simulated per capita income, 2003 Actual change

% change

Abra

44.35

29,631

35.89

33,209

3,579

12.08

Agusan del Norte

45.33

23,150

35.89

26,290

3,140

13.56

Agusan del Sur

47.51

21,977

35.89

25,699

3,722

16.94

Aklan

37.87

19,227

35.89

19,747

520

2.71

Albay

44.01

20,236

35.89

22,576

2,341

11.57

Antique

41.94

25,672

35.89

27,854

2,183

8.50

Aurora

42.36

21,949

35.89

23,950

2,001

9.12

Basilan

41.32

13,115

35.89

14,112

997

7.60

Bataan

39.09

31,184

35.89

32,560

1,376

4.41

Batanes

42.08

33,322

35.89

36,223

2,901

8.71

Batangas

42.24

25,677

35.89

27,972

2,296

8.94

Benguet

39.59

35,230

35.89

37,033

1,803

5.12

Bohol

38.32

22,708

35.89

23,465

757

3.33

Bukidnon

45.87

25,694

35.89

29,391

3,697

14.39

Bulacan

36.62

29,361

35.89

29,650

290

0.99

Cagayan

40.23

22,855

35.89

24,233

1,378

6.03

Camarines Norte

47.03

20,372

35.89

23,669

3,297

16.18

Camarines Sur

45.86

19,228

35.89

21,992

2,764

14.37

Camiguin

36.83

25,698

35.89

26,025

327

1.27

Capiz

40.72

24,687

35.89

26,349

1,662

6.73

Catanduanes

40.53

37,925

35.89

40,374

2,450

6.46

Cavite

34.39

32,523

-

-

-

-

Cebu

38.40

25,864

35.89

26,754

891

3.44

Cotabato

43.82

21,674

35.89

24,119

2,445

11.28

Davao

43.41

28,699

35.89

31,761

3,062

10.67

Davao del Sur

42.52

29,340

35.89

32,084

2,744

9.35

Davao Oriental

44.37

17,771

35.89

19,922

2,152

12.11

Eastern Samar

41.73

18,502

35.89

20,018

1,516

8.20

Ifugao

39.22

29,630

35.89

30,991

1,362

4.60

Ilocos Norte

35.76

30,782

-

-

-

-

Ilocos Sur

40.02

25,705

35.89

27,178

1,473

5.73

Iloilo

38.91

26,009

35.89

27,091

1,082

4.16

Isabela

43.33

23,940

35.89

26,466

2,526

10.55

Kalinga Apayao

43.49

24,138

35.89

26,742

2,604

10.79

La Union

40.96

30,791

35.89

32,971

2,180

7.08

Laguna

38.90

35,668

35.89

37,146

1,478

4.14

Lanao del Norte

47.57

25,817

35.89

30,214

4,397

17.03

C-2

Annex Table 1. Change in per capita income using simulated proportion of young dependents, cont’d.

Province

Actual share of Simulated share Simulated per of young young Per capita capita income, dependents dependents income, 2003 2003 Actual change Percent change

Lanao del Sur

41.31

20,273

35.89

21,810

1,538

7.59

Leyte

42.76

21,265

35.89

23,329

2,064

9.71

Maguindanao

48.92

14,926

35.89

17,787

2,861

19.17

Marinduque

45.37

17,521

35.89

19,908

2,387

13.62

Masbate

45.09

16,202

35.89

18,341

2,138

13.20

Metro Manila

33.15

40,867

-

-

-

-

Mindoro Occidental

44.37

30,307

35.89

33,977

3,670

12.11

Mindoro Oriental

45.23

20,162

35.89

22,866

2,704

13.41

Misamis Occidental

39.34

21,376

35.89

22,394

1,019

4.77

Misamis Oriental

42.28

30,046

35.89

32,750

2,704

9.00

Mt. Province

42.33

23,640

35.89

25,784

2,145

9.07

Negros Occidental

42.18

25,263

35.89

27,499

2,237

8.85

Negros Oriental

38.16

20,892

35.89

21,542

650

3.11

Northern Samar

42.84

20,621

35.89

22,647

2,026

9.82

Nueva Ecija

37.98

19,041

35.89

19,585

544

2.86

Nueva Vizcaya

36.34

43,241

35.89

43,502

261

0.60

Palawan

44.09

20,120

35.89

22,471

2,351

11.69

Pampanga

37.28

31,637

35.89

32,236

598

1.89

Pangasinan

41.93

25,776

35.89

27,963

2,188

8.49

Quezon

40.69

19,590

35.89

20,901

1,311

6.69

Quirino

36.38

36,910

35.89

37,153

243

0.66

Rizal

40.01

31,633

35.89

33,442

1,808

5.72

Romblon

39.79

16,908

35.89

17,822

914

5.40

Samar (western)

44.52

22,004

35.89

24,718

2,714

12.33

Siquijor

35.96

16,715

35.89

16,730

15

0.09

Sorsogon

42.78

17,346

35.89

19,035

1,689

9.74

South Cotabato

45.23

31,531

35.89

35,760

4,229

13.41

Southern Leyte

37.08

21,820

35.89

22,173

353

1.62

Sultan Kudarat

44.66

17,952

35.89

20,204

2,252

12.55

Sulu

48.23

8,340

35.89

9,848

1,507

18.07

Surigao del Norte

43.59

19,936

35.89

22,117

2,181

10.94

Surigao del Sur

40.96

18,797

35.89

20,128

1,331

7.08

Tarlac

41.29

30,943

35.89

33,281

2,338

7.56

Tawi-Tawi

45.11

10,728

35.89

12,147

1,419

13.23

Zambales

36.44

26,304

35.89

26,499

195

0.74

Zamboanga del Norte

39.43

14,859

35.89

15,586

727

4.89

Zamboanga del Sur

45.72

23,709

35.89

27,066

3,357

14.16

C-3

Annex Table 2. Change in population using simulated proportion of young dependents

Province

Actual Simulated Simulated share of young share of young Actual dependents dependents population, 2003 population, 2003

Difference

Percent Change (-)

Abra

44.35

35.89

199,174

173,225

25,949

(13.03)

Agusan del Norte

45.33

35.89

550,514

468,725

81,789

(14.86)

Agusan del Sur

47.51

35.89

560,795

451,110

109,686

(19.56)

Aklan

37.87

35.89

414,204

397,497

16,707

(4.03)

Albay

44.01

35.89

1,092,604

960,684

131,920

(12.07)

Antique

41.94

35.89

476,847

427,139

49,708

(10.42)

Aurora

42.36

35.89

170,512

158,491

12,022

(7.05)

Basilan

41.32

35.89

318,013

289,181

28,832

(9.07)

Bataan

39.09

35.89

574,558

536,235

38,323

(6.67)

Batanes

42.08

35.89

16,164

14,538

1,626

(10.06)

Batangas

42.24

35.89

1,982,248

1,772,672

209,576

(10.57)

Benguet

39.59

35.89

598,580

559,315

39,265

(6.56)

Bohol

38.32

35.89

1,080,776

1,029,217

51,559

(4.77)

Bukidnon

45.87

35.89

1,079,598

900,895

178,703

(16.55)

Bulacan

36.62

35.89

2,505,933

2,419,015

86,918

(3.47)

Cagayan

40.23

35.89

919,543

847,523

72,021

(7.83)

Camarines Norte

47.03

35.89

491,734

405,258

86,476

(17.59)

Camarines Sur

45.86

35.89

1,592,941

1,336,991

255,950

(16.07)

Camiguin

36.83

35.89

72,299

71,244

1,056

(1.46)

Capiz

40.72

35.89

664,578

611,499

53,079

(7.99)

Catanduanes

40.53

35.89

201,336

184,026

17,310

(8.60)

Cavite

34.39

-

2,413,200

2,413,200

-

-

Cebu

38.40

35.89

3,527,075

3,348,164

178,911

(5.07)

Cotabato

43.82

35.89

1,002,163

872,033

130,130

(12.98)

Davao

43.41

35.89

1,391,098

1,217,730

173,368

(12.46)

Davao del Sur

42.52

35.89

2,054,386

2,005,114

(49,272)

(2.40)

Davao Oriental

44.37

35.89

445,062

390,027

55,035

(12.37)

Eastern Samar

41.73

35.89

389,760

353,413

36,346

(9.33)

Ifugao

39.22

35.89

169,499

159,656

9,844

(5.81)

Ilocos Norte

35.76

-

490,551

490,551

-

-

Ilocos Sur

40.02

35.89

544,654

508,298

36,355

(6.67)

Iloilo

38.91

35.89

1,965,029

1,865,214

99,815

(5.08)

Isabela

43.33

35.89

1,241,087

1,095,877

145,210

(11.70)

Kalinga Apayao

43.49

35.89

279,488

249,562

29,926

(10.71)

La Union

40.96

35.89

655,896

607,667

48,229

(7.35)

Laguna

38.90

35.89

2,227,951

2,065,182

162,769

(7.31)

Lanao del Norte

47.57

35.89

748,617

602,922

145,695

(19.46)

C-4

Annex Table 2. Change in population using simulated proportion of young dependents, cont’d.

Province

Actual Simulated Simulated share of young share of young Actual dependents dependents population, 2003 population, 2003

Difference

Percent Change (-)

Lanao del Sur

41.31

35.89

831,020

749,378

81,642

(9.82)

Leyte

42.76

35.89

1,784,002

1,586,644

197,358

(11.06)

Maguindanao

48.92

35.89

776,522

612,780

163,743

(21.09)

Marinduque

45.37

35.89

213,642

179,413

34,229

(16.02)

Masbate

45.09

35.89

746,116

639,961

106,155

(14.23)

Metro Manila

33.15

-

10,686,357

10,686,357

-

-

Mindoro Occidental

44.37

35.89

405,938

350,498

55,440

(13.66)

Mindoro Oriental

45.23

35.89

725,958

623,014

102,944

(14.18)

Misamis Occidental

39.34

35.89

484,029

458,228

25,801

(5.33)

Misamis Oriental

42.28

35.89

1,180,744

1,061,594

119,150

(10.09)

Mt. Province

42.33

35.89

139,033

123,259

15,774

(11.35)

Negros Occidental

42.18

35.89

2,520,921

2,264,463

256,458

(10.17)

Negros Oriental

38.16

35.89

1,159,913

1,116,915

42,998

(3.71)

Northern Samar

42.84

35.89

527,223

467,272

59,951

(11.37)

Nueva Ecija

37.98

35.89

1,785,046

1,719,016

66,030

(3.70)

Nueva Vizcaya

36.34

35.89

370,350

366,723

3,627

(0.98)

Palawan

44.09

35.89

798,388

681,332

117,056

(14.66)

Pampanga

37.28

35.89

1,962,113

1,911,898

50,215

(2.56)

Pangasinan

41.93

35.89

2,476,846

2,240,229

236,617

(9.55)

Quezon

40.69

35.89

1,656,483

1,524,356

132,127

(7.98)

Quirino

36.38

35.89

154,780

151,001

3,779

(2.44)

Rizal

40.01

35.89

2,053,867

2,053,867

Romblon

39.79

35.89

270,539

Samar

44.52

35.89

706,797

-

-

255,179

15,360

(5.68)

605,887

100,910

(14.28)

Siquijor

35.96

35.89

75,079

75,079

-

-

Sorsogon

42.78

35.89

688,585

613,983

74,603

(10.83)

South Cotabato

45.23

35.89

1,671,818

1,405,837

265,981

(15.91)

Southern Leyte

37.08

35.89

357,677

348,167

9,510

(2.66)

Sultan Kudarat

44.66

35.89

600,196

512,849

87,347

(14.55)

Sulu

48.23

35.89

589,588

468,622

120,966

(20.52)

Surigao del Norte

43.59

35.89

462,812

400,892

61,920

(13.38)

Surigao del Sur

40.96

35.89

481,016

481,016

-

-

Tarlac

41.29

35.89

1,134,280

1,029,435

104,845

(9.24)

Tawi-Tawi

45.11

35.89

318,680

266,907

51,773

(16.25)

Zambales

36.44

35.89

647,532

644,906

2,626

(0.41)

Zamboanga del Norte

39.43

35.89

836,994

785,984

51,010

(6.09)

Zamboanga del Sur

45.72

35.89

1,984,015

1,684,093

299,922

(15.12)

C-5

Annex Table 3. Change in per capita local revenues PER CAPITA LOCAL REVENUES Change in per capita income

INCREASE IN PER CAPITA REVENUE SIMULATED PER CAPITA LOCAL REVENUES Receipts Receipts from Real from Business economic Fees and property Real Business economic Fees and enterprise3 charges4 property tax tax2 tax1 tax enterprise charges

Real property tax

Business tax

Receipts from economic enterprise

Abra

22.83

51.57

79.65

18.98

0.13

6.47

11.64

13.45

3.77

29.30

63.21

93.10

22.76

Agusan del Norte

81.59

88.02

71.01

47.91

0.14

25.98

22.33

13.46

10.70

107.57

110.34

84.47

58.61

Agusan del Sur

35.54

27.10

69.31

43.64

0.18

14.14

8.59

16.42

12.18

49.68

35.69

85.73

55.82

Aklan

54.07

53.72

44.79

37.12

0.03

3.43

2.71

1.69

1.65

57.50

56.43

46.48

38.77

Albay

51.86

61.50

45.65

22.21

0.12

14.08

13.30

7.38

4.23

65.94

74.79

53.03

26.44

Antique

57.16

22.34

42.30

13.14

0.09

11.40

3.55

5.02

1.84

68.55

25.88

47.32

14.98

Aurora

80.73

29.27

24.56

26.06

0.10

17.26

4.99

3.13

3.91

97.99

34.26

27.69

29.96

Basilan

8.44

9.94

9.38

6.78

0.08

1.50

1.41

1.00

0.85

9.94

11.35

10.37

7.63

Bataan

410.77

90.35

69.38

29.53

0.05

42.47

7.44

4.27

2.14

453.25

97.79

73.65

31.67

Batanes

178.86

108.71

320.31

80.98

0.09

36.53

17.68

38.95

11.60

215.39

126.39

359.26

92.59

Batangas

321.74

119.59

104.14

53.03

0.09

67.47

19.98

13.01

7.80

389.21

139.57

117.15

60.83

Benguet

182.12

228.02

109.04

64.91

0.05

21.85

21.79

7.79

5.46

203.97

249.82

116.83

70.37

Bohol

39.45

51.44

87.17

24.70

0.03

3.08

3.20

4.05

1.35

42.53

54.64

91.22

26.05

Bukidnon

57.54

24.64

34.84

48.59

0.15

19.44

6.63

7.01

11.52

76.98

31.27

41.85

60.11

Bulacan

170.59

103.07

51.71

33.94

0.01

3.94

1.90

0.71

0.55

174.53

104.97

52.42

34.49

Cagayan

52.21

59.94

60.85

33.52

0.06

7.38

6.75

5.12

3.32

59.59

66.69

65.97

36.84

Camarines Norte

40.45

34.98

49.65

25.13

0.17

15.38

10.59

11.24

6.70

55.83

45.58

60.89

31.83

Camarines Sur

47.89

48.68

33.87

18.34

0.15

16.17

13.09

6.81

4.34

64.06

61.76

40.68

22.68

Camiguin

40.01

51.42

71.64

62.69

0.01

1.19

1.22

1.27

1.31

41.20

52.64

72.91

64.00

Province

Fees and charges

1 change

in per capita income x 2.24 x per capita real property tax change in per capita income x 1.79 x per capita business tax 3 change in per capita income x 1.36 x per capita receipt from economic enterprise 4 change in per capita income x 1.57 x per capita receipt from fees and charges 2

C-6

Annex Table 3. Change in per capita local revenues, cont’d. PER CAPITA LOCAL REVENUES Change in per capita income

INCREASE IN PER CAPITA REVENUE SIMULATED PER CAPITA LOCAL REVENUES Receipts Receipts from Real from Business economic Fees and property Real Business economic Fees and enterprise3 charges4 property tax tax2 tax1 tax enterprise charges

Real property tax

Business tax

Receipts from economic enterprise

Capiz

59.78

44.85

38.00

15.77

0.07

9.44

5.64

3.57

1.75

69.21

50.49

41.58

17.52

Catanduanes

41.72

24.21

20.72

32.70

0.07

6.32

2.92

1.87

3.47

48.04

27.13

22.59

36.17

Cavite

258.01

142.91

50.27

47.14

0.00

0.00

0.00

0.00

0.00

258.01

142.91

50.27

47.14

Cebu

174.14

198.18

45.63

38.99

0.04

14.05

12.74

2.19

2.21

188.19

210.92

47.82

41.20

Davao

56.48

45.65

48.23

22.77

0.11

14.14

9.10

7.19

4.00

70.62

54.75

55.42

26.77

Davao del Sur

99.10

152.12

34.37

32.64

0.10

21.74

26.58

4.49

5.02

120.84

178.70

38.86

37.66

Davao Oriental

45.33

27.11

33.91

13.01

0.13

12.88

6.14

5.74

2.59

58.21

33.25

39.64

15.60

Eastern Samar

18.00

19.20

46.75

20.51

0.09

3.46

2.94

5.35

2.76

21.45

22.14

52.10

23.27

Ifugao

20.41

18.03

33.05

30.05

0.05

2.20

1.55

2.12

2.27

22.61

19.57

35.17

32.32

107.51

99.10

135.01

54.45

0.00

0.00

0.00

0.00

0.00

107.51

99.10

135.01

54.45

Ilocos Sur

50.69

53.66

92.68

38.71

0.06

6.81

5.74

7.41

3.65

57.50

59.40

100.10

42.36

Iloilo

96.26

93.02

36.01

22.26

0.04

9.38

7.22

2.09

1.52

105.64

100.24

38.10

23.79

Isabela

57.62

63.59

54.28

24.46

0.11

14.26

12.54

8.00

4.25

71.89

76.13

62.28

28.71

Kalinga Apayao

10.11

23.34

9.58

5.94

0.11

2.56

4.71

1.44

1.06

12.67

28.04

11.02

7.00

La Union

68.93

102.92

102.23

38.55

0.07

11.44

13.61

10.10

4.49

80.37

116.52

112.34

43.04

Laguna

388.47

226.48

60.06

71.90

0.04

37.73

17.52

3.47

4.90

426.20

244.01

63.53

76.80

Lanao del Norte

109.56

67.16

79.02

26.67

0.18

43.83

21.40

18.83

7.48

153.39

88.56

97.85

34.15

Lanao del Sur

21.50

6.29

2.72

1.65

0.08

3.82

0.89

0.29

0.21

25.32

7.18

3.01

1.86

Leyte

47.92

57.37

37.44

20.48

0.10

10.91

10.41

5.08

3.27

58.83

67.78

42.51

23.75

Province

Ilocos Norte

Fees and charges

1 change

in per capita income x 2.24 x per capita real property tax change in per capita income x 1.79 x per capita business tax 3 change in per capita income x 1.36 x per capita receipt from economic enterprise 4 change in per capita income x 1.57 x per capita receipt from fees and charges 2

C-7

Annex Table 3. Change in per capita local revenues, cont’d. PER CAPITA LOCAL REVENUES Change in per capita income

INCREASE IN PER CAPITA REVENUE SIMULATED PER CAPITA LOCAL REVENUES Receipts Receipts from Real from Business economic Fees and property Real Business economic Fees and enterprise3 charges4 property tax tax2 tax1 tax enterprise charges

Real property tax

Business tax

Receipts from economic enterprise

Maguindanao

17.83

28.24

16.30

6.68

0.20

8.03

10.13

4.37

2.11

25.86

38.37

20.68

8.78

Marinduque

50.80

36.79

84.28

32.22

0.14

16.25

9.37

16.05

7.23

67.05

46.16

100.33

39.44

Province

Masbate

Fees and charges

26.07

15.98

10.93

11.49

0.14

8.08

3.94

2.02

2.50

34.15

19.93

12.95

13.99

687.97

740.05

37.39

130.15

0.00

0.00

0.00

0.00

0.00

687.97

740.05

37.39

130.15

62.79

66.43

76.90

29.66

0.05

7.01

5.91

5.11

2.32

69.80

72.34

82.01

31.99

229.83

154.53

37.05

40.56

0.09

48.52

25.99

4.66

6.01

278.34

180.52

41.71

46.57

13.17

23.36

20.53

22.31

0.09

2.80

3.96

2.60

3.33

15.97

27.32

23.13

25.65

153.34

62.92

46.52

26.29

0.09

31.84

10.41

5.75

3.83

185.19

73.32

52.27

30.12

Negros Oriental

50.79

41.66

31.74

40.57

0.03

3.70

2.42

1.38

2.07

54.49

44.08

33.12

42.64

Cotabato

65.25

34.17

59.79

10.21

0.12

17.28

7.21

9.43

1.90

82.53

41.38

69.22

12.10

Northern Samar

15.65

18.93

42.20

12.43

0.10

3.61

3.48

5.79

2.01

19.26

22.40

47.99

14.44

Nueva Ecija

44.19

50.71

40.94

16.87

0.03

2.96

2.71

1.63

0.79

47.15

53.41

42.57

17.66

Nueva Vizcaya

40.94

77.69

81.17

26.12

0.01

0.58

0.88

0.68

0.26

41.52

78.57

81.86

26.38

Mindoro Occidental

57.46

31.02

37.13

20.66

0.13

16.33

7.02

6.28

4.12

73.79

38.05

43.41

24.78

Mindoro Oriental

50.10

44.83

40.86

23.57

0.14

15.77

11.24

7.66

5.21

65.87

56.07

48.52

28.78

Palawan

64.12

56.58

28.45

26.77

0.12

17.59

12.36

4.65

5.15

81.71

68.94

33.10

31.92

Pampanga

71.11

87.42

22.65

20.25

0.02

3.15

3.08

0.60

0.63

74.26

90.51

23.25

20.87

Pangasinan

66.36

84.46

60.63

30.34

0.09

13.21

13.39

7.19

4.24

79.57

97.85

67.81

34.58

166.20

85.83

53.24

28.11

0.07

26.07

10.72

4.97

3.09

192.26

96.55

58.22

31.21

Metro Manila Misamis Occidental Misamis Oriental Mt. Province Negros Occidental

Quezon 1 change

in per capita income x 2.24 x per capita real property tax change in per capita income x 1.79 x per capita business tax 3 change in per capita income x 1.36 x per capita receipt from economic enterprise 4 change in per capita income x 1.57 x per capita receipt from fees and charges 2

C-8

Annex Table 3. Change in per capita local revenues, cont’d. PER CAPITA LOCAL REVENUES

Rizal

INCREASE IN PER CAPITA REVENUE SIMULATED PER CAPITA LOCAL REVENUES Receipts Receipts from Real from Business economic Fees and property Real Business economic Fees and enterprise3 charges4 property tax tax2 tax1 tax enterprise charges

Real property tax

Business tax

Receipts from economic enterprise

40.48

28.96

76.33

20.69

0.01

0.62

0.36

0.70

0.22

41.10

29.32

77.04

20.91

208.24

116.42

25.15

34.84

0.06

27.90

12.43

2.01

3.27

236.14

128.85

27.16

38.11

Province

Quirino

Change in per capita income

Fees and charges

Romblon

32.30

26.38

33.99

21.31

0.06

4.09

2.66

2.56

1.89

36.39

29.04

36.55

23.20

Samar

26.44

20.22

11.86

13.48

0.13

7.66

4.66

2.04

2.74

34.10

24.88

13.90

16.22

Siquijor

49.94

37.92

48.94

49.83

0.00

0.10

0.06

0.06

0.07

50.04

37.99

49.00

49.91

Sorsogon

29.62

25.91

24.05

18.62

0.10

6.76

4.71

3.27

2.98

36.38

30.62

27.32

21.61

South Cotabato

67.82

67.59

20.40

27.58

0.14

21.35

16.95

3.82

6.09

89.17

84.54

24.22

33.67

Southern Leyte

27.91

32.60

51.79

25.94

0.02

1.06

0.98

1.17

0.69

28.96

33.58

52.95

26.63

Sultan Kudarat

42.14

30.42

34.48

10.35

0.13

12.41

7.13

6.05

2.14

54.55

37.55

40.52

12.49

1.62

5.26

2.95

4.81

0.19

0.69

1.78

0.75

1.43

2.31

7.03

3.70

6.24

Surigao del Norte

53.62

39.33

42.15

19.55

0.11

13.76

8.04

6.44

3.52

67.38

47.37

48.60

23.07

Surigao del Sur

45.24

43.64

50.17

30.68

0.07

7.51

5.77

4.96

3.57

52.75

49.40

55.13

34.25

Tarlac

57.90

60.32

27.28

25.44

0.08

10.26

8.51

2.88

3.16

68.15

68.83

30.16

28.61

Tawi-Tawi

1.16

9.16

3.19

2.47

0.14

0.36

2.27

0.59

0.54

1.52

11.43

3.79

3.01

Zambales

94.19

74.78

590.39

56.84

0.01

1.63

1.03

6.10

0.69

95.82

75.82

596.48

57.53

Zamboanga del Norte

28.68

29.16

146.21

13.62

0.05

3.29

2.66

9.98

1.10

31.97

31.82

156.20

14.72

7.59

5.82

14.77

5.46

0.15

2.52

1.54

2.93

1.27

10.12

7.36

17.70

6.74

Sulu

Zamboanga del Sur 1 change

in per capita income x 2.24 x per capita real property tax change in per capita income x 1.79 x per capita business tax 3 change in per capita income x 1.36 x per capita receipt from economic enterprise 4 change in per capita income x 1.57 x per capita receipt from fees and charges 2

C-9

Annex Table 4. Change in the internal revenue allotment of provinces Province Abra

Actual IRA, 2003 (nominal)

Actual IRA, 2003 (in 1997 prices)

Simulated IRA, 2003 (in 1997 prices)

Difference

777,838,370

556,460,358

557,985,459

1,525,101

Agusan del Norte

1,046,998,523

754,902,373

743,454,157

(11,448,216)

Agusan del Sur

1,248,421,181

900,131,272

876,328,760

(23,802,511)

Aklan

732,648,510

523,539,196

537,928,766

14,389,571

Albay

1,477,393,349

1,102,230,582

1,090,268,448

(11,962,134)

Antique

811,141,251

579,628,884

579,852,366

223,482

Aurora

484,791,023

320,102,228

324,703,156

4,600,928

Basilan

664,040,771

476,705,705

480,320,658

3,614,953

Bataan

835,893,084

551,931,092

561,612,785

9,681,693

Batanes

176,328,000

136,015,346

137,594,020

1,578,674

2,415,357,607

1,606,617,140

1,595,371,348

(11,245,792)

896,491,697

641,344,154

652,656,256

11,312,102

Bohol

1,768,069,783

1,181,546,049

1,212,457,836

30,911,787

Bukidnon

1,549,630,665

1,095,942,661

1,058,767,913

(37,174,747)

Bulacan

2,412,325,352

1,592,832,136

1,666,497,423

73,665,287

Cagayan

1,796,960,680

1,386,133,959

1,402,660,389

16,526,430

723,936,003

540,102,880

521,038,811

(19,064,069)

2,060,066,623

1,536,942,369

1,485,096,197

(51,846,172)

221,228,904

156,459,342

160,811,530

4,352,188

1,081,942,257

773,139,059

781,515,741

8,376,683

500,834,000

373,654,418

377,390,513

3,736,096

Cavite

2,110,068,662

1,403,548,886

1,516,809,395

113,260,510

Cebu

3,898,054,810

2,604,948,800

2,683,666,214

78,717,414

Cotabato (North)

1,687,673,635

1,265,165,047

1,252,753,047

(12,412,000)

Davao (norte)

1,341,517,020

962,664,565

938,755,288

(23,909,276)

Davao del Sur

2,618,796,437

1,879,232,610

1,984,711,399

105,478,788

Davao Oriental

906,269,285

650,333,401

647,442,253

(2,891,148)

Eastern Samar

921,724,340

658,497,289

663,379,230

4,881,941

Ifugao

524,950,352

375,545,965

381,987,195

6,441,230

1,066,056,257

770,591,303

800,038,142

29,446,839

Ilocos Sur

1,234,384,749

892,266,375

906,315,148

14,048,773

Iloilo

1,870,394,421

1,336,554,675

1,378,818,302

42,263,627

Isabela

Batangas Benguet

Camarines Norte Camarines Sur Camiguin Capiz Catanduanes

Ilocos Norte

2,643,134,741

2,038,853,083

2,038,120,800

(732,283)

Kalinga Apayao

529,791,647

379,009,395

379,243,657

234,262

La Union

989,439,967

715,209,754

725,031,176

9,821,422

Laguna

2,172,978,110

1,445,394,200

1,466,286,075

20,891,875

Lanao del Norte

1,314,452,973

929,618,341

894,625,803

(34,992,538)

C-10

Annex Table 4. Change in the internal revenue allotment of provinces, cont’d. Actual IRA, 2003 (nominal)

Actual IRA, 2003 (in 1997 prices)

Simulated IRA, 2003 (in 1997 prices)

Lanao del Sur

1,730,635,223

1,256,262,917

1,262,534,261

6,271,344

Leyte

1,841,759,012

1,315,787,447

1,299,343,437

(16,444,010)

Maguindanao

1,337,468,731

970,864,539

928,015,808

(42,848,732)

393,054,000

326,641,945

321,335,413

(5,306,532)

Masbate

1,179,224,269

879,777,247

864,834,228

(14,943,019)

Metro Manila

7,736,642,788

5,037,129,288

5,523,084,761

485,955,474

857,294,857

712,442,717

708,625,554

(3,817,163)

Mindoro Oriental

1,140,103,000

947,466,409

934,318,094

(13,148,315)

Misamis Occidental

1,097,915,123

776,476,646

792,168,158

15,691,512

Misamis Oriental

1,683,628,074

1,190,709,420

1,190,096,377

(613,043)

461,573,005

330,206,235

331,731,371

1,525,135

Negros Occidental

4,593,853,050

3,282,695,725

3,290,099,455

7,403,729

Negros Oriental

2,326,345,702

1,554,624,483

1,598,084,749

43,460,266

Northern Samar

850,986,604

607,960,914

605,145,994

(2,814,920)

2,555,308,431

1,687,242,304

1,744,864,874

57,622,570

790,556,699

609,817,176

630,510,915

20,693,740

Palawan

2,484,674,322

2,064,853,313

2,061,911,265

(2,942,048)

Pampanga

1,922,931,815

1,269,690,917

1,337,056,300

67,365,382

Pangasinan

3,131,542,297

2,263,613,429

2,266,240,147

2,626,719

Quezon

2,341,337,934

1,557,381,666

1,573,459,227

16,077,561

Quirino

492,712,256

380,066,853

389,182,015

9,115,162

1,731,681,636

1,151,858,172

1,247,688,665

95,830,494

487,228,504

404,904,330

412,795,390

7,891,060

1,484,710,292

1,060,705,093

1,049,374,641

(11,330,453)

Siquijor

245,445,364

164,023,503

169,140,356

5,116,853

Sorsogon

997,500,676

744,199,743

741,808,826

(2,390,917)

South Cotabato

1,550,889,806

1,162,625,008

1,103,640,284

(58,984,724)

Southern Leyte

870,849,109

622,151,063

639,415,465

17,264,402

Sultan Kudarat

1,087,354,020

815,135,268

803,508,905

(11,626,363)

830,425,106

602,803,093

570,258,718

(32,544,375)

Surigao del Norte

1,127,989,149

813,297,886

809,637,224

(3,660,663)

Surigao del Sur

1,047,115,409

754,986,650

783,852,830

28,866,180

Tarlac

Province

Marinduque

Mindoro Occidental

Mt. Province

Nueva Ecija Nueva Vizcaya

Rizal Romblon Samar (western)

Sulu

Difference

1,408,867,000

930,259,523

931,903,840

1,644,316

Tawi-Tawi

310,948,204

225,716,368

213,962,844

(11,753,524)

Zambales

1,040,647,386

687,128,126

720,247,387

33,119,262

Zamboanga del Norte

1,671,757,965

1,200,131,973

1,222,014,277

21,882,304

Zamboanga del Sur

1,175,410,838

843,811,220

774,994,916

(68,816,304)

C-11

Annex Table 5. Change in total revenues of provinces Actual revenues, 2003 (in million pesos) Province

IRA

Local

Simulated revenues, 2003 (in million pesos)

Other

IRA

Local

Other

Difference

Percent change

Abra

556.5

34.5

39.8

558.0

36.1

40.1

3.4

0.54

Agusan del Norte

754.9

158.8

129.3

743.5

169.2

129.1

(1.3)

(0.12)

Agusan del Sur

900.1

98.5

17.6

876.3

102.4

17.2

(20.4)

(2.00)

Aklan

523.5

78.6

17.2

537.9

79.2

17.8

15.6

2.51

Albay

1,102.2

198.0

64.3

1,090.3

211.5

64.4

1.7

0.12

Antique

579.6

64.3

5.9

579.9

66.9

6.0

2.9

0.44

Aurora

320.1

27.4

8.8

324.7

30.1

9.1

7.6

2.12

Basilan

476.7

11.0

11.4

480.3

11.4

11.5

4.1

0.82

Bataan

551.9

344.8

68.7

561.6

352.0

70.4

18.6

1.93

Batanes

136.0

11.1

3.9

137.6

11.5

4.0

2.0

1.36

1,606.6

1,186.4

96.9

1,595.4

1,252.9

99.4

57.7

2.00

641.3

349.6

97.7

652.7

358.5

100.3

22.8

2.09

Bohol

1,181.5

219.1

30.1

1,212.5

220.7

31.0

33.4

2.33

Bukidnon

1,095.9

178.8

41.1

1,058.8

189.4

40.0

(27.7)

(2.11)

Bulacan

1,592.8

900.4

118.3

1,666.5

886.4

122.0

63.3

2.42

Cagayan

1,386.1

189.9

168.1

1,402.7

194.2

171.0

23.7

1.36

540.1

73.9

21.0

521.0

78.7

20.4

(14.9)

(2.35)

1,536.9

237.0

57.3

1,485.1

252.9

55.7

(37.4)

(2.04)

Camiguin

156.5

16.3

4.7

160.8

16.4

4.9

4.6

2.61

Capiz

773.1

105.3

12.7

781.5

109.3

13.0

12.7

1.42

Catanduanes

373.7

24.0

5.8

377.4

24.6

5.8

4.4

1.10

Cavite

1,403.5

1,202.6

271.8

1,516.8

1,202.6

287.3

128.7

4.47

Cebu

2,604.9

1,611.7

297.1

2,683.7

1,634.3

306.5

110.8

2.45

Cotabato (North)

1,265.2

169.8

65.3

1,252.8

179.0

65.1

(3.4)

(0.23)

Batangas Benguet

Camarines Norte Camarines Sur

Davao (norte)

962.7

240.8

23.7

938.8

252.8

23.4

(12.3)

(1.00)

Davao del Sur

1,879.2

653.8

54.5

1,984.7

772.6

60.8

230.6

8.91

Davao Oriental

650.3

53.1

49.1

647.4

57.2

49.3

1.3

0.18

Eastern Samar

658.5

40.7

3.9

663.4

42.0

4.0

6.3

0.89

Ifugao

375.5

17.2

5.2

382.0

17.5

5.3

6.9

1.72

Ilocos Norte

770.6

194.3

154.0

800.0

194.3

160.2

35.6

3.18

Ilocos Sur

892.3

128.4

472.1

906.3

131.8

482.7

28.1

1.88

Iloilo

1,336.6

486.5

30.8

1,378.8

499.4

32.0

56.5

3.05

Isabela

2,038.9

248.2

137.4

2,038.1

261.9

138.5

14.1

0.58

Kalinga Apayao

379.0

13.7

1.0

379.2

14.7

1.0

1.2

0.31

La Union

715.2

205.1

257.7

725.0

214.1

264.6

25.7

2.18

1,445.4

1,664.1

352.7

1,466.3

1,673.9

357.2

35.3

1.02

Laguna

C-12

Annex Table 5. Change in total revenues of provinces, cont’d. Actual revenues, 2003 (in million pesos) Province Lanao del Norte

IRA

Local

Simulated revenues, 2003 (in million pesos)

Other

IRA

Local

Other

Difference

Percent change

929.6

211.4

22.1

894.6

225.5

21.5

(21.5)

(1.85)

Lanao del Sur

1,256.3

26.7

12.0

1,262.5

28.0

12.1

7.6

0.59

Leyte

1,315.8

291.2

123.2

1,299.3

306.0

123.0

(1.7)

(0.10)

Maguindanao

970.9

53.6

32.7

928.0

57.4

31.0

(40.7)

(3.85)

Marinduque

326.6

43.6

5.7

321.3

45.4

5.6

(3.6)

(0.95)

Masbate Metro Manila

879.8

48.1

22.4

864.8

51.8

22.0

(11.6)

(1.22)

5,037.1

17,050.8

855.7

5,523.1

17,050.8

880.3

510.6

2.23

Mindoro Occidental

712.4

59.4

24.1

708.6

63.1

24.1

(0.1)

(0.01)

Mindoro Oriental

947.5

115.7

59.9

934.3

124.1

59.6

(5.1)

(0.45)

Misamis Occidental Misamis Oriental Mt. Province

776.5

114.1

12.3

792.2

117.4

12.7

19.3

2.14

1,190.7

545.5

35.6

1,190.1

580.8

36.5

35.7

2.01

330.2

11.0

3.7

331.7

11.3

3.7

1.9

0.54

Negros Occidental

3,282.7

728.7

264.1

3,290.1

772.0

268.4

55.0

1.29

Negros Oriental

1,554.6

191.1

91.1

1,598.1

194.7

94.3

50.3

2.74

Northern Samar Nueva Ecija Nueva Vizcaya

608.0

47.0

14.8

605.1

48.6

14.7

(1.2)

(0.19)

1,687.2

272.6

104.8

1,744.9

276.4

109.1

65.7

3.18

609.8

83.7

24.7

630.5

83.7

25.7

21.7

3.02

Palawan

2,064.9

140.5

34.1

2,061.9

146.9

34.2

3.6

0.16

Pampanga

1,269.7

395.2

44.6

1,337.1

399.4

47.1

74.0

4.33

Pangasinan

2,263.6

598.9

120.6

2,266.2

626.8

122.3

32.3

1.08

Quezon

1,557.4

552.2

85.8

1,573.5

576.6

88.0

42.6

1.94

380.1

25.8

22.5

389.2

25.4

23.1

9.4

2.20

1,151.9

790.0

33.1

1,247.7

883.7

37.3

193.7

9.81

Quirino Rizal Romblon

404.9

30.8

3.9

412.8

31.9

4.0

9.1

2.07

1,060.7

50.9

19.7

1,049.4

54.0

19.5

(8.4)

(0.74)

Siquijor

164.0

14.0

3.4

169.1

14.0

3.6

5.3

2.90

Sorsogon

744.2

67.6

54.9

741.8

71.2

55.0

1.3

0.15

South Cotabato

1,162.6

306.6

40.7

1,103.6

325.6

39.2

(41.4)

(2.74)

Southern Leyte

622.2

49.4

13.6

639.4

49.5

14.0

17.8

2.59

Sultan Kudarat

815.1

70.5

15.3

803.5

74.4

15.1

(7.8)

(0.87)

Samar (western)

Sulu

602.8

8.6

21.2

570.3

9.0

19.7

(33.6)

(5.31)

Surigao del Norte

813.3

71.6

49.0

809.6

74.7

48.9

(0.5)

(0.06)

Surigao del Sur

755.0

81.6

26.9

783.9

92.1

28.6

41.0

4.75

Tarlac

930.3

193.9

27.9

931.9

201.5

28.2

9.6

0.83

Tawi-Tawi

225.7

5.1

9.1

214.0

5.3

8.5

(12.2)

(5.08)

Zambales Zamboanga del Norte Zamboanga del Sur

687.1

528.5

168.5

720.2

532.5

175.2

43.8

3.16

1,200.1

182.2

64.0

1,222.0

184.5

65.4

25.6

1.77

843.8

66.8

39.8

775.0

70.6

36.1

(68.7)

(7.23)

C-13

Annex Table 6. Change in expenditures of LGUs Province

Actual Total Expenditures

Simulated Total Expenditures

Decrease

Percent decrease

Abra

631,793,414

549,481,692

82,311,721

13.03

Agusan del Norte

917,170,701

780,907,968

136,262,733

14.86

Agusan del Sur

923,944,663

743,231,038

180,713,625

19.56

Aklan

571,734,284

548,673,528

23,060,756

4.03

Albay

1,189,393,046

1,045,787,148

143,605,898

12.07

Antique

563,693,576

504,932,320

58,761,255

10.42

Aurora

299,221,507

278,125,368

21,096,139

7.05

Basilan

421,993,924

383,734,790

38,259,134

9.07

Bataan

881,828,711

823,010,563

58,818,148

6.67

Batanes

137,542,672

123,702,766

13,839,906

10.06

2,574,189,465

2,302,029,349

272,160,116

10.57

Batangas Benguet

915,777,962

855,705,816

60,072,147

6.56

Bohol

1,231,188,086

1,172,453,040

58,735,046

4.77

Bukidnon

1,294,039,006

1,079,839,668

214,199,338

16.55

Bulacan

2,307,621,378

2,227,581,745

80,039,633

3.47

Cagayan

1,465,094,389

1,350,344,742

114,749,647

7.83

599,154,007

493,787,205

105,366,801

17.59

1,579,220,926

1,325,475,045

253,745,880

16.07

Camiguin

137,149,616

135,147,245

2,002,371

1.46

Capiz

711,320,228

654,508,027

56,812,201

7.99

Catanduanes

352,488,571

322,182,532

30,306,039

8.60

Cavite

2,756,469,763

2,756,469,763

-

0.00

Cebu

4,065,025,784

3,858,827,518

206,198,266

5.07

Cotabato

1,220,273,194

1,061,822,035

158,451,158

12.98

Davao

1,058,857,002

926,895,388

131,961,614

12.46

Davao del Sur

2,343,863,924

2,343,863,924

-

0.00

Davao Oriental

651,025,138

570,520,742

80,504,395

12.37

Eastern Samar

633,596,620

574,511,776

59,084,844

9.33

Ifugao

336,191,219

316,666,975

19,524,245

5.81

Ilocos Norte

748,665,733

748,665,733

-

0.00

Ilocos Sur

1,159,069,812

1,081,702,523

77,367,288

6.67

Iloilo

1,666,241,142

1,581,603,002

84,638,140

5.08

Isabela

2,194,070,332

1,937,359,852

256,710,480

11.70

Kalinga Apayao

312,221,699

278,790,218

33,431,481

10.71

La Union

906,088,869

839,462,707

66,626,162

7.35

2,897,337,225

2,685,664,508

211,672,717

7.31

908,714,681

731,861,187

176,853,494

19.46

Camarines Norte Camarines Sur

Laguna Lanao del Norte

C-14

Annex Table 6. Change in expenditures of LGUs, cont’d. Actual Total Expenditures

Simulated Total Expenditures

Lanao del Sur

1,168,345,439

1,053,563,986

114,781,453

9.82

Leyte

1,493,226,525

1,328,035,973

165,190,552

11.06

Maguindanao

981,538,929

774,565,126

206,973,803

21.09

Marinduque

341,376,210

286,681,966

54,694,244

16.02

Masbate

876,895,941

752,133,825

124,762,116

14.23

19,217,344,659

19,217,344,659

-

0.00

Mindoro Occidental

756,995,430

653,609,932

103,385,498

13.66

Mindoro Oriental

995,040,715

853,939,203

141,101,512

14.18

Misamis Occidental

845,137,312

800,087,518

45,049,794

5.33

1,454,463,245

1,307,691,783

146,771,462

10.09

301,508,662

267,301,520

34,207,143

11.35

Province

Metro Manila

Misamis Oriental Mt. Province

Decrease

Percent decrease

Negros Occidental

3,630,340,321

3,261,018,448

369,321,873

10.17

Negros Oriental

1,459,479,629

1,405,376,495

54,103,135

3.71

Northern Samar

594,783,439

527,150,543

67,632,895

11.37

1,840,525,011

1,772,442,392

68,082,618

3.70

594,889,300

589,062,493

5,826,808

0.98

Palawan

1,998,800,524

1,705,746,095

293,054,429

14.66

Pampanga

1,652,891,101

1,610,589,681

42,301,420

2.56

Pangasinan

2,570,967,723

2,325,359,592

245,608,131

9.55

Quezon

2,060,924,010

1,896,537,942

164,386,067

7.98

Quirino

381,106,553

371,802,637

9,303,916

2.44

1,837,753,731

1,837,753,731

448,750,847

Nueva Ecija Nueva Vizcaya

Rizal

-

0.00

423,272,130

25,478,717

5.68

1,034,163,689

886,515,936

147,647,753

14.28

Siquijor

163,768,567

163,768,567

-

0.00

Sorsogon

852,835,589

760,437,713

92,397,876

10.83

South Cotabato

1,408,782,433

1,184,649,238

224,133,195

15.91

Southern Leyte

606,281,790

590,162,509

16,119,281

2.66

Sultan Kudarat

802,859,617

686,018,937

116,840,679

14.55

Sulu

611,811,005

486,285,874

125,525,132

20.52

Surigao del Norte

825,592,902

715,135,647

110,457,255

13.38

Surigao del Sur

726,386,592

726,386,592

-

0.00

1,025,745,911

930,933,067

94,812,844

9.24

Tawi-Tawi

397,711,399

333,098,735

64,612,664

16.25

Zambales

1,205,444,067

1,200,555,111

4,888,957

0.41

Zamboanga del Norte

1,163,584,988

1,092,671,606

70,913,382

6.09

829,174,600

703,828,882

125,345,718

15.12

Romblon Samar

Tarlac

Zamboanga del Sur

C-15

Annex Table 7. Net effect on provincial revenue and expenditures (in million pesos) Province Abra

Actual Revenue

Expenditure

Simulated Surplus

Revenue

Expenditure

Surplus

Net Impact

630.8

631.8

(1.0)

634.2

549.5

84.7

85.7

Agusan del Norte

1,043.0

917.2

125.8

1,041.7

780.9

260.8

135.0

Agusan del Sur

1,016.2

923.9

92.3

995.9

743.2

252.6

160.3

Aklan

619.3

571.7

47.6

634.9

548.7

86.2

38.6

Albay

1,364.5

1,189.4

175.1

1,366.2

1,045.8

320.4

145.3

Antique

649.9

563.7

86.2

652.8

504.9

147.8

61.6

Aurora

356.3

299.2

57.1

363.9

278.1

85.8

28.7

Basilan

499.1

422.0

77.1

503.2

383.7

119.5

42.4

Bataan

965.4

881.8

83.6

984.0

823.0

161.0

77.4

Batanes

151.0

137.5

13.5

153.1

123.7

29.4

15.9

Batangas

2,889.9

2,574.2

315.7

2,947.6

2,302.0

645.6

329.9

Benguet

1,088.7

915.8

172.9

1,111.5

855.7

255.8

82.9

Bohol

1,430.8

1,231.2

199.6

1,464.1

1,172.5

291.7

92.1

Bukidnon

1,315.9

1,294.0

21.8

1,288.2

1,079.8

208.3

186.5

Bulacan

2,611.5

2,307.6

303.9

2,674.8

2,227.6

447.2

143.3

Cagayan

1,744.1

1,465.1

279.0

1,767.8

1,350.3

417.5

138.4

635.0

599.2

35.8

620.1

493.8

126.3

90.5

1,831.2

1,579.2

252.0

1,793.8

1,325.5

468.3

216.3

Camiguin

177.5

137.1

40.4

182.1

135.1

47.0

6.6

Capiz

891.1

711.3

179.8

903.8

654.5

249.3

69.5

Catanduanes

403.4

352.5

51.0

407.9

322.2

85.7

34.7

Cavite

2,878.0

2,756.5

121.5

3,006.7

2,756.5

250.2

128.7

Cebu

4,513.7

4,065.0

448.7

4,624.5

3,858.8

765.6

317.0

Davao (norte)

1,227.2

1,058.9

168.3

1,214.9

926.9

288.0

119.7

Davao del Sur

2,587.5

2,343.9

243.6

2,818.1

2,343.9

474.2

230.6

Davao Oriental

752.6

651.0

101.6

753.9

570.5

183.4

81.8

Eastern Samar

703.2

633.6

69.6

709.4

574.5

134.9

65.3

Ifugao

398.0

336.2

61.8

404.8

316.7

88.2

26.4

Ilocos Norte

1,118.9

748.7

370.3

1,154.5

748.7

405.8

35.6

Ilocos Sur

1,492.8

1,159.1

333.7

1,520.8

1,081.7

439.1

105.4

Iloilo

1,853.8

1,666.2

187.5

1,910.3

1,581.6

328.7

141.1

Isabela

2,424.4

2,194.1

230.4

2,438.5

1,937.4

501.1

270.8

393.7

312.2

81.5

395.0

278.8

116.2

34.6

La Union

1,177.9

906.1

271.8

1,203.7

839.5

364.2

92.4

Laguna

3,462.1

2,897.3

564.8

3,497.4

2,685.7

811.8

246.9

Lanao del Norte

1,163.1

908.7

254.4

1,141.6

731.9

409.8

155.4

Lanao del Sur

1,295.0

1,168.3

126.6

1,302.6

1,053.6

249.1

122.4

Camarines Norte Camarines Sur

Kalinga Apayao

C-16

Annex Table 7. Net effect on provincial revenue and expenditures (in million pesos), cont’d Province

Actual Revenue

Expenditure

Simulated Surplus

Revenue

Expenditure

Surplus

Net Impact

Leyte

1,730.1

1,493.2

236.9

1,728.4

1,328.0

400.3

163.4

Maguindanao

1,057.1

981.5

75.6

1,016.5

774.6

241.9

166.3

376.0

341.4

34.6

372.4

286.7

85.7

51.1

Marinduque Masbate

950.3

876.9

73.4

938.7

752.1

186.6

113.2

22,943.6

19,217.3

3,726.2

23,454.2

19,217.3

4,236.8

510.6

902.9

845.1

57.8

922.2

800.1

122.1

64.3

1,771.7

1,454.5

317.3

1,807.4

1,307.7

499.7

182.5

344.9

301.5

43.4

346.8

267.3

79.5

36.1

Negros Occidental

4,275.5

3,630.3

645.2

4,330.5

3,261.0

1,069.5

424.3

Negros Oriental

1,836.8

1,459.5

377.4

1,887.1

1,405.4

481.7

104.4

Cotabato (North)

1,500.2

1,220.3

280.0

1,496.8

1,061.8

435.0

155.0

Northern Samar

669.8

594.8

75.0

668.5

527.2

141.4

66.4

2,064.6

1,840.5

224.1

2,130.4

1,772.4

357.9

133.8

Nueva Vizcaya

718.2

594.9

123.3

739.9

589.1

150.9

27.5

Mindoro Occidental

795.9

757.0

38.9

795.8

653.6

142.2

103.3

Mindoro Oriental

1,123.1

995.0

128.0

1,118.0

853.9

264.1

136.0

Palawan

2,239.4

1,998.8

240.6

2,243.0

1,705.7

537.3

296.7

Pampanga

1,709.5

1,652.9

56.6

1,783.5

1,610.6

172.9

116.3

Pangasinan

2,983.1

2,571.0

412.1

3,015.4

2,325.4

690.0

277.9

Quezon

2,195.5

2,060.9

134.5

2,238.0

1,896.5

341.5

206.9

Quirino

428.3

381.1

47.2

437.7

371.8

65.9

18.7

1,975.0

1,837.8

137.2

2,168.7

1,837.8

331.0

193.7

439.6

448.8

(9.1)

448.7

423.3

25.5

34.6

1,131.3

1,034.2

97.2

1,122.9

886.5

236.4

139.2

Siquijor

181.5

163.8

17.7

186.7

163.8

23.0

5.3

Sorsogon

866.7

852.8

13.9

868.0

760.4

107.6

93.7

South Cotabato

1,509.9

1,408.8

101.1

1,468.5

1,184.6

283.8

182.7

Southern Leyte

685.2

606.3

78.9

702.9

590.2

112.8

33.9

Sultan Kudarat

900.9

802.9

98.0

893.0

686.0

207.0

109.0

Metro Manila Misamis Occidental Misamis Oriental Mt. Province

Nueva Ecija

Rizal Romblon Samar (western)

Sulu

632.6

611.8

20.8

599.0

486.3

112.7

91.9

Surigao del Norte

933.9

825.6

108.3

933.3

715.1

218.2

109.9

Surigao del Sur

863.5

726.4

137.1

904.5

726.4

178.1

41.0

1,152.0

1,025.7

126.3

1,161.6

930.9

230.7

104.4

Tawi-Tawi

239.9

397.7

(157.8)

227.7

333.1

(105.4)

52.4

Zambales

1,384.1

1,205.4

178.7

1,427.9

1,200.6

227.4

48.7

Zamboanga del Norte

1,446.3

1,163.6

282.7

1,471.9

1,092.7

379.3

96.5

950.4

829.2

121.2

881.7

703.8

177.9

56.6

Tarlac

Zamboanga del Sur

C-17

Annex Table 8. Change in population and LGU revenue at the municipal level Actual Province/ Municipality Camarines Norte Basud

Simulated

Difference

Population

Revenues

Population

Revenues

Population

Revenues

491,734

376,302,812

428,303

363,186,089

(63,431)

(13,116,723)

35,523

28,359,400

30,941

26,999,897

(4,582)

(1,359,503)

Capalonga

27,186

25,675,060

23,679

24,423,439

(3,507)

(1,251,621)

Daet

83,894

59,062,188

73,072

59,457,172

(10,822)

394,984

San Lorenzo Ruiz (Imelda)

12,354

15,054,311

10,760

14,297,261

(1,594)

(757,051)

Jose Panganiban

45,955

34,436,841

40,027

33,396,625

(5,928)

(1,040,215)

Labo

83,856

58,848,068

73,039

56,553,972

(10,817)

(2,294,095)

Mercedes

43,608

29,757,525

37,983

28,506,233

(5,625)

(1,251,292)

Paracale

42,900

29,742,603

37,366

28,438,089

(5,534)

(1,304,515)

San Vicente

9,421

11,726,081

8,205

11,152,714

(1,215)

(573,367)

Santa Elena

44,937

37,828,501

39,140

36,238,154

(5,797)

(1,590,347)

Talisay

22,790

17,038,022

19,850

16,252,197

(2,940)

(785,825)

Vinzons Camarines Sur

39,311

28,774,212

34,240

27,470,335

(5,071)

(1,303,876)

1,592,941

1,323,728,716

1,382,000

1,270,726,938

249,513

(53,001,778)

Baao

48,057

29,331,522

41,693

27,888,767

(6,364)

(1,442,755)

Balatan

23,066

17,526,030

20,011

16,640,389

(3,054)

(885,641)

Bato

44,139

25,381,909

38,294

23,898,301

(5,845)

(1,483,608)

Bombon

13,173

12,649,611

11,429

11,949,054

(1,744)

(700,557)

Buhi

69,479

41,266,314

60,279

39,018,019

(9,201)

(2,248,295)

Bula

59,121

34,078,449

51,292

32,247,380

(7,829)

(1,831,069)

Cabusao

16,074

12,334,378

13,945

11,698,374

(2,129)

(636,005)

Calabanga

70,143

40,708,258

60,855

39,037,336

(9,289)

(1,670,922)

Camaligan

19,967

13,283,764

17,323

12,623,889

(2,644)

(659,875)

Canaman

29,675

12,472,187

25,745

11,851,479

(3,930)

(620,708)

Caramoan

39,785

29,064,431

34,517

27,324,792

(5,268)

(1,739,639)

Del Gallego

21,088

20,416,179

18,295

19,261,292

(2,793)

(1,154,887)

8,641

7,695,747

7,497

7,218,752

(1,144)

(476,995)

Garchitorena

23,639

21,304,424

20,509

20,020,048

(3,130)

(1,284,376)

Goa

50,179

17,836,268

43,534

17,218,226

(6,645)

(618,042)

Gainza

Iriga City

91,294

144,611,901

79,204

138,218,763

(12,089)

(6,393,138)

Lagonoy

43,024

34,586,053

37,327

32,522,341

(5,697)

(2,063,713)

Libmanan

89,806

53,218,235

77,914

50,336,333

(11,892)

(2,881,901)

Lupi

25,917

22,609,203

22,485

21,304,996

(3,432)

(1,304,207)

Magarao

21,924

21,575,673

19,021

20,372,989

(2,903)

(1,202,685)

Milaor

23,579

18,265,710

20,457

17,486,491

(3,122)

(779,219)

Minalabac

42,611

22,994,366

36,968

21,770,360

(5,643)

(1,224,006)

Nabua

72,455

40,168,107

62,860

38,463,518

(9,595)

(1,704,589)

Naga City

140,813

278,010,678

122,166

277,494,992

(18,647)

(515,686)

Ocampo

37,206

24,840,865

32,279

23,479,366

(4,927)

(1,361,499)

Pamplona

30,171

20,858,156

26,176

19,786,340

(3,995)

(1,071,816)

Pasacao

39,616

29,108,643

34,370

27,823,436

(5,246)

(1,285,207)

C-18

Annex Table 8. Change in population and LGU revenue at the municipal level, cont’d. Actual Province/ Municipality

Simulated

Difference

Population

Revenues

Population

Revenues

Population

Revenues

Pili

70,418

47,090,754

61,093

45,848,345

(9,325)

(1,242,409)

Presentacion (Parubcan)

16,524

15,959,182

14,336

14,980,234

(2,188)

(978,948)

Ragay

48,441

38,442,438

42,027

36,320,239

(6,415)

(2,122,200)

Sagnay

27,342

21,520,974

23,721

20,256,991

(3,621)

(1,263,983)

San Fernando

29,149

17,939,226

25,289

16,969,981

(3,860)

(969,246)

San Jose

33,340

19,755,039

28,925

18,653,593

(4,415)

(1,101,447)

Sipocot

57,482

39,153,460

49,870

37,259,290

(7,612)

(1,894,170)

Siruma

17,010

12,296,561

14,757

11,586,269

(2,253)

(710,293)

Tigaon

41,381

25,758,619

35,901

24,464,260

(5,480)

(1,294,360)

Tinambac

57,209

39,615,401

49,633

37,431,716

(7,576)

(2,183,685)

C-19

Annex Table 9. Change in expenditures of LGUs at the municipal level Province/Municipality

Actual total expenditures

Simulated total expenditures

Decrease

340,807,572

296,845,221

43,962,351

Basud

27,687,196

24,115,696

3,571,500

Capalonga

22,063,363

19,217,307

2,846,056

Daet

54,422,414

47,402,214

7,020,200

San Lorenzo Ruiz (Imelda)

13,087,829

11,399,569

1,688,260

Jose Panganiban

31,842,775

27,735,227

4,107,547

Labo

57,561,853

50,136,682

7,425,171

Mercedes

25,454,971

22,171,416

3,283,555

Paracale

28,945,061

25,211,303

3,733,758

San Vicente

11,536,718

10,048,543

1,488,175

Santa Elena

24,451,701

21,297,563

3,154,139

Talisay

16,612,616

14,469,678

2,142,938

Vinzons

27,141,077

23,640,023

3,501,054

1,133,425,065

983,334,162

150,090,903

Baao

28,615,300

24,825,992

3,789,308

Balatan

17,129,636

14,861,288

2,268,348

Bato

22,772,165

19,756,620

3,015,545

8,979,236

7,790,184

1,189,052

Buhi

36,397,498

31,577,653

4,819,845

Bula

29,859,459

25,905,396

3,954,062

Cabusao

11,909,233

10,332,183

1,577,050

Calabanga

41,017,129

35,585,541

5,431,588

Camaligan

15,764,752

13,677,146

2,087,607

Canaman

11,079,530

9,612,351

1,467,178

Caramoan

29,012,952

25,170,987

3,841,966

Del Gallego

16,315,596

14,155,045

2,160,551

6,957,920

6,036,536

921,385

Garchitorena

21,258,792

18,443,651

2,815,141

Goa

17,691,112

15,348,412

2,342,700

Iriga City

129,166,876

112,062,284

17,104,592

Lagonoy

33,509,483

29,072,075

4,437,407

Libmanan

49,081,690

42,582,173

6,499,517

Lupi

18,655,735

16,185,298

2,470,438

Magarao

19,464,576

16,887,029

2,577,546

Milaor

12,738,840

11,051,932

1,686,908

Minalabac

18,919,787

16,414,383

2,505,404

Nabua

33,808,654

29,331,630

4,477,024

189,441,419

164,355,125

25,086,293

Camarines Norte

Camarines Sur

Bombon

Gainza

Naga City

C-20

Annex Table 9. Change in expenditures of LGUs at the municipal level Province/Municipality

Actual total expenditures

Simulated total expenditures

Decrease

Ocampo

18,624,907

16,158,552

2,466,356

Pamplona

18,802,113

16,312,291

2,489,822

Pasacao

27,798,526

24,117,378

3,681,148

Pili

41,198,822

35,743,174

5,455,648

Presentacion (Parubcan)

15,594,313

13,529,276

2,065,037

Ragay

34,800,615

30,192,233

4,608,382

Sagnay

19,886,347

17,252,948

2,633,398

San Fernando

14,949,143

12,969,541

1,979,602

San Jose

19,215,635

16,671,054

2,544,581

Sipocot

37,112,228

32,197,736

4,914,491

Siruma

10,678,952

9,264,819

1,414,133

Tigaon

21,506,052

18,658,169

2,847,884

Tinambac

33,710,042

29,246,076

4,463,966

C-21

Annex Table 10. Net effect on municipal revenue and expenditures Province/ Municipality Camarines Norte

Actual Revenue

Expenditure

Simulated Surplus

Revenue

Expenditure

Surplus

Net Impact

376,302,812

340,807,572

35,495,239

363,186,089

296,845,221

66,340,868 30,845,628

Basud

28,359,400

27,687,196

672,204

26,999,897

24,115,696

2,884,201

2,211,997

Capalonga

25,675,060

22,063,363

3,611,698

24,423,439

19,217,307

5,206,132

1,594,435

Daet

59,062,188

54,422,414

4,639,775

59,457,172

47,402,214

12,054,958

7,415,184

San Lorenzo Ruiz (Imelda)

15,054,311

13,087,829

1,966,483

14,297,261

11,399,569

2,897,692

931,209

Jose Panganiban

34,436,841

31,842,775

2,594,066

33,396,625

27,735,227

5,661,398

3,067,332

Labo

58,848,068

57,561,853

1,286,215

56,553,972

50,136,682

6,417,290

5,131,075

Mercedes

29,757,525

25,454,971

4,302,553

28,506,233

22,171,416

6,334,816

2,032,263

Paracale

29,742,603

28,945,061

797,543

28,438,089

25,211,303

3,226,786

2,429,243

San Vicente

11,726,081

11,536,718

189,363

11,152,714

10,048,543

1,104,171

914,808

Santa Elena

37,828,501

24,451,701

13,376,799

36,238,154

21,297,563

14,940,591

1,563,792

Talisay

17,038,022

16,612,616

425,406

16,252,197

14,469,678

1,782,520

1,357,114

Vinzons

28,774,212

27,141,077

1,633,135

27,470,335

23,640,023

3,830,312

2,197,177

190,303,651 1,270,726,938

983,334,162

Camarines Sur

1,323,728,716 1,133,425,065

287,392,776 97,089,125

Baao

29,331,522

28,615,300

716,222

27,888,767

24,825,992

3,062,775

2,346,553

Balatan

17,526,030

17,129,636

396,394

16,640,389

14,861,288

1,779,101

1,382,707

Bato

25,381,909

22,772,165

2,609,744

23,898,301

19,756,620

4,141,682

1,531,937

Bombon

12,649,611

8,979,236

3,670,375

11,949,054

7,790,184

4,158,870

488,495

Buhi

41,266,314

36,397,498

4,868,816

39,018,019

31,577,653

7,440,367

2,571,550

Bula

34,078,449

29,859,459

4,218,990

32,247,380

25,905,396

6,341,983

2,122,993

Cabusao

12,334,378

11,909,233

425,146

11,698,374

10,332,183

1,366,191

941,045

Calabanga

40,708,258

41,017,129

-308,871

39,037,336

35,585,541

3,451,795

3,760,666

Camaligan

13,283,764

15,764,752

-2,480,988

12,623,889

13,677,146

-1,053,257

1,427,731

Canaman

12,472,187

11,079,530

1,392,657

11,851,479

9,612,351

2,239,128

846,470

Caramoan

29,064,431

29,012,952

51,478

27,324,792

25,170,987

2,153,805

2,102,326

Del Gallego

20,416,179

16,315,596

4,100,583

19,261,292

14,155,045

5,106,246

1,005,663

7,695,747

6,957,920

737,827

7,218,752

6,036,536

1,182,217

444,390

Garchitorena

21,304,424

21,258,792

45,632

20,020,048

18,443,651

1,576,397

1,530,765

Goa

17,836,268

17,691,112

145,156

17,218,226

15,348,412

1,869,814

1,724,658

144,611,901

129,166,876

15,445,025

138,218,763

112,062,284

Gainza

Iriga City

26,156,479 10,711,454

Lagonoy

34,586,053

33,509,483

1,076,571

32,522,341

29,072,075

3,450,266

2,373,695

Libmanan

53,218,235

49,081,690

4,136,545

50,336,333

42,582,173

7,754,160

3,617,615

Lupi

22,609,203

18,655,735

3,953,468

21,304,996

16,185,298

5,119,699

1,166,231

Magarao

21,575,673

19,464,576

2,111,097

20,372,989

16,887,029

3,485,959

1,374,862

Milaor

18,265,710

12,738,840

5,526,869

17,486,491

11,051,932

6,434,559

907,689

Minalabac

22,994,366

18,919,787

4,074,579

21,770,360

16,414,383

5,355,977

1,281,398

Nabua

40,168,107

33,808,654

6,359,453

38,463,518

29,331,630

9,131,888

2,772,435

C-22

Annex Table 10. Net effect on municipal revenue and expenditures Province/ Municipality

Actual Revenue

Expenditure

Simulated Surplus

Revenue

Expenditure

Surplus

Net Impact

Naga City

278,010,678

189,441,419

88,569,259

277,494,992

164,355,125

113,139,867 24,570,608

Ocampo

24,840,865

18,624,907

6,215,957

23,479,366

16,158,552

7,320,814

1,104,857

Pamplona

20,858,156

18,802,113

2,056,043

19,786,340

16,312,291

3,474,048

1,418,006

Pasacao

29,108,643

27,798,526

1,310,117

27,823,436

24,117,378

3,706,058

2,395,941

Pili

47,090,754

41,198,822

5,891,932

45,848,345

35,743,174

10,105,172

4,213,240

Presentacion (Parubcan)

15,959,182

15,594,313

364,869

14,980,234

13,529,276

1,450,958

1,086,089

Ragay

38,442,438

34,800,615

3,641,823

36,320,239

30,192,233

6,128,006

2,486,182

Sagnay

21,520,974

19,886,347

1,634,627

20,256,991

17,252,948

3,004,042

1,369,415

San Fernando

17,939,226

14,949,143

2,990,083

16,969,981

12,969,541

4,000,439

1,010,356

San Jose

19,755,039

19,215,635

539,405

18,653,593

16,671,054

1,982,539

1,443,135

Sipocot

39,153,460

37,112,228

2,041,232

37,259,290

32,197,736

5,061,554

3,020,322

Siruma

12,296,561

10,678,952

1,617,609

11,586,269

9,264,819

2,321,449

703,840

Tigaon

25,758,619

21,506,052

4,252,567

24,464,260

18,658,169

5,806,091

1,553,524

Tinambac

39,615,401

33,710,042

5,905,359

37,431,716

29,246,076

8,185,640

2,280,281

C-23