The previous study and this project give a comprehensive picture of the effects of population in the Philippines.

The Population-Growth-Poverty

Nexus sought to establish that

there are critical links

among population,,

economic growth, and

poverty reduction.

The study lends

credence to the mantra

of demographic

transition as a

significant

determinant of

economic growth.

Population-Growth-Poverty Nexus

Evidence from the Philippines

Final ReportSeptember 2006

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

Asia Pacific Policy Center

AAASSSIIIAAA---PPPAAACCCIIIFFFIIICCC PPPOOOLLLIIICCCYYY CCCEEENNNTTTEEERRR ______________________________________________________________________________

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 Part I Young Population Matters – More is not Necessarily Merrier: A Study on the Determinants of Income Growth in the Philippines Part II Population Growth and Income: Implications on Revenues and Expenditures of LGUs References Annex A Share of Young Dependents as Proxy for Demographic Transition Annex B Bayesian Averaging for the Classical Estimates Annex C Annex Tables

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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. Edillon1

_____________________________________________________________

“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).

2

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

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

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

Philippines 2000

-10 -5 0 5 10

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40 - 44

50 - 54

60 - 6470 and Over

male femaleMale Female

Thailand 2000

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30 - 3440 - 44

50 - 54

60 - 6470 and Over

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Japan 2000

-10 -5 0 5 10

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male female

Male Female

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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.

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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.

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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.

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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

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%).

10,000

15,000

20,000

25,000

30,000

35,000

1985 1988 1991 1994 1997 2000 2003

Actual Simulated

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Figure 5a. Simulated per capita Income: Camarines Norte

Figure 5b. Simulated per capita Income: Camarines Sur

Figure 5c. Simulated per capita Income: Davao Oriental

10,000

13,000

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25,000

1985 1988 1991 1994 1997 2000 2003

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Actual per capita income Simulated per capita income

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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

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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

Difference 2,818,778 3.60

* 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

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

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

Figure 8. General Government Consumption Expenditure Per Capita (constant $)

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

0

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19601962

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South Korea

Thailand

Philippines

Source: Author’s estimates based on World Development Indicators, World Bank.

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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+232 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 This is the sum of {78 taken 1} + {78 taken 2} + … + {78 taken 77} + {78 taken 78}. 3 Strictly speaking, the law states that the IRA is 40% of internal tax revenues of 3 years before.

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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

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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 all4 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.

14

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 PAIR 2 Characteristics

Misamis Occidental

Camarines Norte Nueva Ecija Camarines Sur

Prop’n of young dependents, 1985 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

Per Capita Local Revenues 333 201 231 199

Total Expenditure 1,196,653,283 809,160,177 3,055,483,137 2,152,850,771

Per CapitaTotal Exp. 2,472 1,646 1,712 1,351

Social Services Exp.** 215,815,540 177,054,629 652,168,402 390,231,655

Per Capita Social Ser. Exp. 446 360 365 245

* 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.

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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)

Revenue – Expenditure 35,846,636 91,324,643

Net Impact 55,478,008 154.8

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)

Revenue – Expenditure 251,983,780 383,818,592

Net Impact 131,834,812 52.3

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.

16

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

Difference in Population 63,431 210,941

Revenues

Actual Local Revenues 73,864,232 236,997,702

Actual IRA 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

Simulated Local Revenue 83,143,910 261,449,825.68

Simulated IRA 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

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

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

Impact

Actual Surplus 35,846,636 251,983,780

Simulated Surplus 91,324,643 383,818,592

Net Impact 55,478,008 131,834,812

17

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.

18

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.

A-1

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 age-sex 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-2

Annex Table A

Age group w/ highest share 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

Group 1

0 to 4 years old

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

Group 2

5 to 9 years old

Iceland Peru

A-3

Annex Table A, cont’d.

Age group w/ highest share 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

Group 3

10 to 14 years old

Estonia Mongolia Zimbabwe Algeria Macedonia Sri Lanka Brazil Montserrat Trinidad and Tobago Burma Poland Tunisia Guyana Puerto Rico Turkey Iran Saint Lucia

Group 4

15 to 19 years old

Ireland Saint Vincent and the Grenadines Czech Republic Mauritius Taiwan Hungary Romania Thailand

Group 5

20 to 24 years old

Lebanon Slovakia Serbia and Montenegro New Zealand Portugal Spain

Group 6

25 to 29 years old Northern Mariana Islands South Korea

Antigua and Barbuda Guernsey North Korea Barbados Man, Isle of Norway Cuba Italy Saint Helena Greece Jersey Seychelles

Group 7

30 to 34 years old

Guadeloupe Martinique 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

Group 8

35 to 39 years old

Cayman Islands Macau S.A.R. Virgin Islands British Belarus Russia Ukraine

Group 9

40 to 44 years old Qatar

Croatia

Group 10

45 to 49 years old Slovenia

Finland Japan Monaco

Group 11

50 to 54 years old Gibraltar Malta

A-4

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.

C-1

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

10 Net eefect on municipal revenue and expenditures C-22 to 23

C-2

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

Province

Actual dependency share, 1985

Actual per capita income, 2003

Simulated dependency 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-3

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

Province

Actual share of young

dependents Per capita

income, 2003

Simulated share of young

dependents

Simulated per capita income,

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-4

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

Province

Actual share of young

dependents

Simulated share of young

dependents Actual

population, 2003 Simulated

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-5

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

Province

Actual share of young

dependents

Simulated share of young

dependents Actual

population, 2003 Simulated

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 35.89 270,539 255,179 15,360 (5.68)

Samar 44.52 35.89 706,797 605,887 100,910 (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-6

Annex Table 3. Change in per capita local revenues PER CAPITA LOCAL REVENUES INCREASE IN PER CAPITA REVENUE SIMULATED PER CAPITA LOCAL REVENUES

Province Real

property tax Business

tax

Receipts from

economic enterprise

Fees and charges

Change in per capita

income Real

property tax1

Business tax2

Receipts from

economic enterprise3

Fees and charges4

Real property tax

Business tax

Receipts from

economic enterprise

Fees and charges

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

1 change in per capita income x 2.24 x per capita real property tax 2 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

C-7

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

Province Real

property tax Business

tax

Receipts from

economic enterprise

Fees and charges

Change in per capita

income Real

property tax1

Business tax2

Receipts from

economic enterprise3

Fees and charges4

Real property tax

Business tax

Receipts from

economic enterprise

Fees and charges

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

Ilocos Norte 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

1 change in per capita income x 2.24 x per capita real property tax 2 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

C-8

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

Province Real

property tax Business

tax

Receipts from

economic enterprise

Fees and charges

Change in per capita

income Real

property tax1

Business tax2

Receipts from

economic enterprise3

Fees and charges4

Real property tax

Business tax

Receipts from

economic enterprise

Fees and charges

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

Masbate 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

Metro Manila 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

Misamis Occidental 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

Misamis Oriental 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

Mt. Province 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

Negros Occidental 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

Quezon 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

1 change in per capita income x 2.24 x per capita real property tax 2 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

C-9

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

Province Real

property tax Business

tax

Receipts from

economic enterprise

Fees and charges

Change in per capita

income Real

property tax1

Business tax2

Receipts from

economic enterprise3

Fees and charges4

Real property tax

Business tax

Receipts from

economic enterprise

Fees and charges

Quirino 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

Rizal 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

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

Sulu 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

Zamboanga del Sur 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

1 change in per capita income x 2.24 x per capita real property tax 2 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

C-10

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

Province Actual IRA, 2003

(nominal) Actual IRA, 2003 (in 1997 prices)

Simulated IRA, 2003 (in 1997 prices) Difference

Abra 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

Batangas 2,415,357,607 1,606,617,140 1,595,371,348 (11,245,792)

Benguet 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

Camarines Norte 723,936,003 540,102,880 521,038,811 (19,064,069)

Camarines Sur 2,060,066,623 1,536,942,369 1,485,096,197 (51,846,172)

Camiguin 221,228,904 156,459,342 160,811,530 4,352,188

Capiz 1,081,942,257 773,139,059 781,515,741 8,376,683

Catanduanes 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

Ilocos Norte 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 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-11

Annex Table 4. Change in the internal revenue allotment of provinces, cont’d.

Province Actual IRA, 2003

(nominal) Actual IRA, 2003 (in 1997 prices)

Simulated IRA, 2003 (in 1997 prices) Difference

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)

Marinduque 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

Mindoro Occidental 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)

Mt. Province 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)

Nueva Ecija 2,555,308,431 1,687,242,304 1,744,864,874 57,622,570

Nueva Vizcaya 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

Rizal 1,731,681,636 1,151,858,172 1,247,688,665 95,830,494

Romblon 487,228,504 404,904,330 412,795,390 7,891,060

Samar (western) 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)

Sulu 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 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-12

Annex Table 5. Change in total revenues of provinces

Actual revenues, 2003 (in million pesos)

Simulated revenues, 2003 (in million pesos)

Province IRA Local 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

Batangas 1,606.6 1,186.4 96.9 1,595.4 1,252.9 99.4 57.7 2.00

Benguet 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

Camarines Norte 540.1 73.9 21.0 521.0 78.7 20.4 (14.9) (2.35)

Camarines Sur 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)

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

Laguna 1,445.4 1,664.1 352.7 1,466.3 1,673.9 357.2 35.3 1.02

C-13

Annex Table 5. Change in total revenues of provinces, cont’d.

Actual revenues, 2003 (in million pesos)

Simulated revenues, 2003 (in million pesos)

Province IRA Local Other IRA Local Other Difference Percent change

Lanao del Norte 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 879.8 48.1 22.4 864.8 51.8 22.0 (11.6) (1.22) Metro Manila 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 776.5 114.1 12.3 792.2 117.4 12.7 19.3 2.14 Misamis Oriental 1,190.7 545.5 35.6 1,190.1 580.8 36.5 35.7 2.01 Mt. Province 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 608.0 47.0 14.8 605.1 48.6 14.7 (1.2) (0.19) Nueva Ecija 1,687.2 272.6 104.8 1,744.9 276.4 109.1 65.7 3.18 Nueva Vizcaya 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 Quirino 380.1 25.8 22.5 389.2 25.4 23.1 9.4 2.20 Rizal 1,151.9 790.0 33.1 1,247.7 883.7 37.3 193.7 9.81 Romblon 404.9 30.8 3.9 412.8 31.9 4.0 9.1 2.07 Samar (western) 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) 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 687.1 528.5 168.5 720.2 532.5 175.2 43.8 3.16 Zamboanga del Norte 1,200.1 182.2 64.0 1,222.0 184.5 65.4 25.6 1.77 Zamboanga del Sur 843.8 66.8 39.8 775.0 70.6 36.1 (68.7) (7.23)

C-14

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

Batangas 2,574,189,465 2,302,029,349 272,160,116 10.57

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

Camarines Norte 599,154,007 493,787,205 105,366,801 17.59

Camarines Sur 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

Laguna 2,897,337,225 2,685,664,508 211,672,717 7.31

Lanao del Norte 908,714,681 731,861,187 176,853,494 19.46

C-15

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

Province Actual Total Expenditures

Simulated Total Expenditures Decrease Percent decrease

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

Metro Manila 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

Misamis Oriental 1,454,463,245 1,307,691,783 146,771,462 10.09

Mt. Province 301,508,662 267,301,520 34,207,143 11.35

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

Nueva Ecija 1,840,525,011 1,772,442,392 68,082,618 3.70

Nueva Vizcaya 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

Rizal 1,837,753,731 1,837,753,731 - 0.00

Romblon 448,750,847 423,272,130 25,478,717 5.68

Samar 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

Tarlac 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

Zamboanga del Sur 829,174,600 703,828,882 125,345,718 15.12

C-16

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

Actual Simulated Province

Revenue Expenditure Surplus Revenue Expenditure Surplus Net Impact

Abra 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

Camarines Norte 635.0 599.2 35.8 620.1 493.8 126.3 90.5

Camarines Sur 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

Kalinga Apayao 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

C-17

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

Actual Simulated Province

Revenue Expenditure 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

Marinduque 376.0 341.4 34.6 372.4 286.7 85.7 51.1

Masbate 950.3 876.9 73.4 938.7 752.1 186.6 113.2

Metro Manila 22,943.6 19,217.3 3,726.2 23,454.2 19,217.3 4,236.8 510.6

Misamis Occidental 902.9 845.1 57.8 922.2 800.1 122.1 64.3

Misamis Oriental 1,771.7 1,454.5 317.3 1,807.4 1,307.7 499.7 182.5

Mt. Province 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

Nueva Ecija 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

Rizal 1,975.0 1,837.8 137.2 2,168.7 1,837.8 331.0 193.7

Romblon 439.6 448.8 (9.1) 448.7 423.3 25.5 34.6

Samar (western) 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

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

Tarlac 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

Zamboanga del Sur 950.4 829.2 121.2 881.7 703.8 177.9 56.6

C-18

Annex Table 8. Change in population and LGU revenue at the municipal level

Actual Simulated Difference

Province/ Municipality Population Revenues Population Revenues Population Revenues Camarines Norte 491,734 376,302,812 428,303 363,186,089 (63,431) (13,116,723)

Basud 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 39,311 28,774,212 34,240 27,470,335 (5,071) (1,303,876)

Camarines Sur 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) Gainza 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) 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-19

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

Actual Simulated Difference

Province/ Municipality 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-20

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

Province/Municipality Actual total expenditures Simulated total expenditures Decrease

Camarines Norte 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

Camarines Sur 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

Bombon 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

Gainza 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

Naga City 189,441,419 164,355,125 25,086,293

C-21

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-22

Annex Table 10. Net effect on municipal revenue and expenditures

Actual Simulated Province/ Municipality

Revenue Expenditure Surplus Revenue Expenditure Surplus Net Impact

Camarines Norte 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

Camarines Sur 1,323,728,716 1,133,425,065 190,303,651 1,270,726,938 983,334,162 287,392,776 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

Gainza 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

Iriga City 144,611,901 129,166,876 15,445,025 138,218,763 112,062,284 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-23

Annex Table 10. Net effect on municipal revenue and expenditures

Actual Simulated Province/ Municipality

Revenue Expenditure 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