Poverty Incidence, Infrastructure Development and Human Capital: an Empirical Study Of Provinces in the Philippines Edgardo Manuel Miguel M. Jopson De La Salle University – Manila Email: manuel.jopson@gmail.com / edgardo_jopson@dlsu.ph Cellular number: (+63) 916 464 4412 Abstract: Current literature in economic development emphasizes the impact of investing in infrastructure and human capital. In the Philippines with its provinces’ different economic situations, a generalized policy recommendation can yield problematic results. Using data gathered from the National Statistics Office (NSO), the Department of Public Works and Highways (DPWH), as well as the National Statistical Coordination Board (NSCB) from 2009, and employing Ordinary Least Squares estimation procedure, the study aims to present the possible relationships between poverty and specific factors of economic development, such as the valuation of buildings, roads, educational attainment and population growth. This study may be of contribution by providing a clearer picture on the rural economic situation of the Philippines and aid policymakers in decision making. JEL Classification: O15, O18, O40 Keywords: Poverty, infrastructure, human capital, rural economic development, Philippines 1. Introduction poverty, the Philippines must address this problem in the most efficient way that it can. 1.1 Background of the Study The problem of poverty is far from being eradicated in most developing countries. In the Philippines alone in 2012, there is an estimated 22.9% of the population of the country is considered poor, with the Autonomous Region of Muslim Mindanao having the highest level of poverty incidence at 46.9% (National Statistical Coordination Board, 2013). To meet the Millennium Development Goal of the United Nations of eradicating extreme In a study made by the Asian Development Bank in 2007 the critical constraints to poverty reduction are access to economic opportunities (lack and slow growth of productive employment and opportunities), human development (access to primary and secondary education and health service), access to basic social services and productive assets (basic infrastructure, poor’s limited access to financing and land) and the lack on the coverage of social safety nets (ADB, 2007: 41-48). These problems are indeed faced by mostly the Filipino lower class, and until countries in terms of its Human Development Index today still are so. By studying the economic situation or HDI at 0.627 (UNDP, 2011). Functional literacy of the Philippines in a broader perspective that rate is still at 84.1%, with 81.9% for males and encompasses not just physical and material outputs 86.3% for females in 2003. In the year 2006 life but includes health, wellness, education, political expectancy for females and males were 72.5% and situations, the 67.8%. GDP per capita for both current and 1985 and prices are at 68,989 and 14,653 respectively. What do investments - it would be possible to find appropriate these numbers represent? Functional literacy rate, life solutions to the problem of poverty. expectancy, primary and secondary enrolment rate among infrastructure others in development, relation to employment, and GDP per capita are the components for the According to the Asian Development Bank, the main causes of poverty in the country are the following: a. b. Low to moderate economic growth for possible to infer that the Philippines is in need of the past 40 years; increasing its HDI. Low growth elasticity of poverty sector; regard to the initial construction, but provides the community an overall positive benefit from it; e. High inflation during crisis periods; f. High levels of population growth; g. High and persistent levels of inequality (income and assets) which dampen the impacts of economic expansion; and h. indicators for growth in an economy. Building infrastructure not only provides employment with Failure to fully develop the agricultural positive Infrastructure development is an integral part of economic development, as it is one of the key Weakness in employment generated and the quality of jobs generated; d. measuring the quality of living of a group of people, and from merely inspecting these numbers, it is reduction; c. computation of HDI, which is used as a yardstick for Recurrent shocks and exposure to risks as economic crisis, conflicts, natural disasters, and “environmental poverty” (ADB, 2009: 2) creating roads and bridges make transportation of goods and services easier, more efficient and less costly. Buildings create space for commerce, government and housing, water pipelines and sewerage systems provide households, businesses and government buildings clean water and hygienic disposal of human waste and dirt, parks and other recreational areas provide additional income for the economy from both foreign and local tourism leading to an increase of commercial establishments, an The Philippines is currently a developing increase of employment and an overall increase of economy that is beginning to make its presence felt in the economy’s GDP. Infrastructures give way to a the international market, with a quarterly gross multitude of human activities just waiting to be domestic product growth for the year 2012 rate as established. follows: 6.3%, 6.0%, 7.2% and 6.8%. However, in 2011 the Philippines still ranks 112 out of 187 2 Capital deepening is a vital concept in capital Although Catanduanes is an internationally known theory. Given a steady state Economy with one kind surfing spot, it still draws significantly less tourists of capital good, capital deepening is defined as the than Camarines Sur, due to it being a kept secret case wherein the per worker capital good stock is a among pro surfers (Puraran Surf Beach Resort, decreasing function of its own rate of interest . In 2013). According to the Provincial Framework and Neo-classical macroeconomics which focuses on Physical Development Plan (PDPF) of Catanduanes, capital accumulation and its links to saving decisions, although the growth rate of the travellers to ′ (π) = πππππ and the rate of Catanduanes has shot up to 198% from 2008 to 2009, where r is the principal rate Camarines Sur has still hauled in 38,385 foreign of return and πΏ is the rate of depreciation, lead to a tourists and 147,758 domestic tourists- significantly per capital return that is higher than before (Hirota, less than Catanduanes’ 8,984 foreign tourists and 1979), which can be done by providing more 36,722 domestic tourists; hence indicated in the employment in the economy as well as increasing its PDPF are policies to increase their revenues in capital (McEachern, 2012). Take for example the tourism by investing in eco-tourism. In comparison to province of Catanduanes, barely featured in mass Camarines Sur’s performance, Catanduanes has media, literature and politics, it is the easternmost shown improvement as a rural province which can be province in the Bicol region. However according to seen from its academic performance as well as its the NSCB Catanduanes is the top Bicol province in significant spike in tourism. the marginal condition π return ( π + πΏ = π ′ (π)) HDI, ranking 21st among the provinces of the country (National Statistical Coordination Board, 2013), and 1.2 Statement of the Problem when we consider its human capital we can find some In the Philippines where the population of the interesting data. In 2011’s Civil Engineering Board poor and oppressed greatly outnumber the elite and Exam, the top 1, 2 and 3 are from the Catanduanes powerful, it has become more difficult to determine State Colleges, and obtained a passing rate of key indicators in terms of the quality of life of every 69.84%- well above the national mean of 34.28% and individual, even more difficult to make sound has board decisions when it comes to finding solutions to examination top passers since (GSRubio/PR and alleviate poverty by maximizing the limited resources Information Services, 2013); for the Board Exam for the Nurses has had an 85% passing rate in 2009, and in significance of capital deepening and infrastructure 2007 ranked 45 of all provinces in the Philippines; development with respect to poverty incidence, and in the Licensure Examination for Teachers in the which pertains to policies that may be made in terms elementary level in 2007 has ranked 40 of all of allocation of resources to particular sectors of the provinces with a passing rate of 42% (National economy that will be at most opportunity cost- Statistical Coordination Board, 2007). However the minimizing and maximizing its effect to benefit province’s society. consistently had income civil engineering generating activities in country has. This study determines the comparison to other provinces in the same region such as Camarines Sur, such as tourism, is less. 1.3 Objectives 3 attributed to the United Nation’s Millennium This research paper intends to: Development 1. Present an econometric model that would allow the proponent to determine the relationship of poverty via the poverty Goals in developing a global partnership for development and for eradicating extreme poverty, which is for the improvement of the quality of living of the people of the economy. incidence with infrastructure development and capital deepening and make relevant and 1.5 Scope and Limitations statistically sound conclusions; 2. policy via an increase in government spending, with regards to creation of new roads, developing providing human scholarships (additional school capital and years), e.g. training and its significance to the well-being of society that will be determined by the significance of the relationship of poverty with infrastructure development and human capital Least Squares estimation method1 and is limited to a cross-section analysis, which might not perfectly capture reality, however does not mean that it should be considered insignificant altogether. The data used in this research will be drawn from the databases of the National Statistical Coordination Board, NSO, and DPWH. This study will also include infrastructure development and capital Deepening determinants such as the value of new constructions, population and number of households with access to development; 3. The method used in this study uses the Ordinary Describe the effect of an expansionary fiscal Provide a supplementary aide to policy makers and make sound recommendations water, as well as the average years of schooling. 2. Review of Related Literature from the regression analysis generated from 2.1 Poverty Incidence the econometric method. The World Bank uses three key factors to 1.4 Significance of the Study measure poverty: The study attempts to determine whether or not there is a significant link on a. infrastructure One has to define the relevant welfare measure. development and capital deepening to the poverty b. incidence of the country. It can serve as a One has to select a poverty line – that is a contribution to the field of rural development in the threshold below which a given household or Philippines in the continuous efforts of the county to individual will be classified as poor. c. attain its macroeconomic goals to sustainable One has to select a poverty indicator– which economic growth and development. It may also aide is used for reporting for the population as a policymakers in the rural areas in the country in whole or for a population sub-group only. creating sound economic decisions and policies as well as future projects that may benefit their respective communities and how it may affect the well-being of every individual. The paper can also be 1 According to the Gauss-Markov Theorem, holding all assumptions true, OLS is BLUE (Gujarati & Porter, 2009). 4 For welfare measure, the World Bank does not solely depend on monetary measures on welfare, than in monetary units, which is more abstract (World Bank, 2011). rather it considers the level of consumption in a higher regard, since consumption is a better outcome indicator, better measured, and better reflects a household’s ability to meet its basic needs. Why is it a better indicator? Actual consumption is more closely related to a person’s well-being in the sense of having enough to meet current basic needs. Income is only one of the components which will allow consumption of goods (others include In terms of the non-monetary part, certain facets of a human being’s wellbeing is being analysed, namely health and nutrition, education, composite indices of wealth and other subjective perceptions. It is based on the judgement in terms of each of the component’s “poverty line”, for example, in education; the poverty line is at some level of illiteracy (World Bank, 2011). questions of access, availability, etc.). In terms of its In terms of the problem of choosing a poverty ability to be measured, in poor agrarian and urban line, there are two main ways: by absolute poverty economies with many informal settlers, income flows lines, or relative poverty lines. may change in an unpredictable way during the year. For farmers, one added difficulty in estimating 1) Relative poverty lines: These are defined with income includes excluding the inputs purchased for respect to the overall distribution of income or agricultural production from the farmer’s revenues. consumption in a given country; an example Finally, large shares of income are not monetized if would be to set the poverty line at 50 percent of households consume their own production or the country’s mean income or consumption. exchange it for some other goods, and it might be 2) Absolute poverty lines: These are set in some difficult to price these. Estimating consumption may absolute standard of what households should be be difficult for the institutions that measure able to have in order to meet their basic needs. consumption of these individuals, but it may be more For monetary measures, these absolute poverty substantial if the consumption module in the lines are often based on estimates of the cost of household survey has been better designed. And basic food needs, to which a provision is added finally, why does it better reflect the household’s for non-food needs. There are two methods: ability a) to meet basic needs? Consumption The food-energy intake method: defines expenditures reflect not only the goods and services the poverty line by looking for the that a household can command based on its current consumption expenditures or income level at income, but also whether that household can access which a person’s typical food energy intake credit markets or household savings at times when is enough to meet a predetermined food current income is low or even negative, due perhaps energy requirement. If applied to different to seasonal variation or harvest failure. Basically regions or provinces within the same consumption for the people that these institutions will country, the essential food consumption conduct studies upon can grasp the idea of pattern consumption in a much more concrete way rather consuming the needed nutrient amounts will of the population group just 5 vary. This technique can result to variances does not reach the defined threshold (e.g. percentage in poverty lines in excess of the cost-of- of the population with less than 3 years of education) living differential facing the poor. (World Bank, 2011). b) The cost of basic needs method: values an explicit bundle of foods typically consumed by the poor at domestic prices. To this, a specific allowance for non-food goods, consistent with the expenditures of the poor, is added. However defined, poverty lines will always have a high arbitrary element; an example would be the calorie threshold underlying both methods might be assumed to vary with age. (World Bank, 2011) In choosing a poverty indicator, one must take into account that the poverty measure itself is a 2.2 Human Development Index (HDI) Conceptualized by the UNDP in 1990, the Human Development Index (HDI) attempts to quantify human development. As it recognizes the complications of human development, the HDI may not be that comprehensive to be able to capture all the facets of the development of the human being. However the UNDP points out that this simple composite method can already draw attention to the issues of human development quite effectively (National Statistical Coodrination Board, 2013). statistical function which interprets the comparison of The computation for HDI is done in 7 steps. The the indicator of well-being and the poverty line which first step is to identify the indicators to be used for is made for each household into one aggregate HDI, namely Health, which is measured by life number for the population as a whole or a population expectancy; sub-group. Many alternative measures exist but the literacy rate as well as combined primary, secondary following three measures are most commonly used: and tertiary enrolment rate; and income, measured by the incidence of poverty, which is also known as the real income per capita. Next is to set the appropriate headcount index, the depth of poverty, known as well maximum and minimum value of each of the as the poverty gap, and poverty severity, or the indicators above. Then we compute for the index for square of the poverty gap. However this research will each indicator as follows: education measured by functional only be using the headcount index. The headcount index is the portion of the π΄ππ‘π’ππ ππππ’ππ − πππ ππππ’ππ πππ₯ ππππ’ππ − πππ ππππ’ππ population whose income or consumption is below After which we can compute for the average the poverty line, i.e. the share of the population that functional literacy rate and enrolment indices to cannot afford to buy a basic basket of goods. An generate the education index by getting: analyst using several poverty lines, which we can say one for poverty and one for extreme poverty, can estimate the incidence of both poverty and extreme poverty, due to the nature of the measurement. πΈππ’πππ‘πππ πππππ₯ = 1/2(πΉπ’πππ‘πππππ πππ‘ππππ¦ πππ‘π + πΈππππππππ‘ πΌππππππ ) Then we calculate for the income index: Similarly for non-monetary indicators, poverty incidence measures the share of the population which ππππ£ππππ ′ π ππππ πππ πππππ‘π ππππππ − min ππππππ πππ£ππ max ππππππ πππ£ππ − min ππππππ πππ£ππ 6 After which we obtain the second income index, amount of money invested in constructing a building income index II by converting a province’s price per is a better and more meaningful determinant for capita income into purchasing power parity then determining the quality of infrastructure that is being compute for income index as follows constructed, rather than just counting the frequency that a building is being made in the area. In a general log π¦ − log 100 ππππππ πππππ₯ πΌπΌ = log 40 000 − log 100 sense, the more one invests in a certain province there would be a greater incentive for getting a return And finally we assign the weights to the various on investment. Since the infrastructure is created for components to compute for HDI of the given the benefit of the individuals interested in using it, economy. (Human Development Network, 2008). For the value of the building would be a better indicator. the purpose of this research, the proponent has As for the proponent’s reason for choosing number of chosen to estimate the effect of an increase in HDI to good condition roads as another measure for poverty through average school years in order to infrastructure better pinpoint its effect since average school years is hypothesizes that having better roads means that a function of the index, and better captures the actual there is a more efficient transportation in the area, conditions of human development. and when there is a more efficient way of moving development, the proponent from one place to another within the province, it 2.3 Infrastructure Development would be easier to make transactions and will be Infrastructure, by definition, is the system of beneficial to the community with regards to public works of a country, state, or region as well providing general access to their communities. Hence as the good quality roads are considered by the proponent resources (as personnel, buildings, or equipment) required for an activity (Merriam- as an ideal measure for infrastructure development. Webster, 2013). Infrastructure development is the economy’s investment in terms of its infrastructure, 2.4 Human Capital may it be of the construction of roads, highways, Human capital formation is truly an integral part buildings, bridges and any relatively permanent and of measuring the development of a certain economy. fixed structure development that will benefit the It is possible to have great infrastructure development economy in terms of its efficiency to transport goods but without the optimal capital depth, one cannot and services, its ability to house the people, business sustain its economic existence. Increase in the quality and government offices, for an extended duration of of labour, investment in capital, increase in current time. capital πΎπ‘ are but examples of capital deepening. For this research the proponent has chosen value Given a steady state Economy with one kind of of buildings and number of good condition roads (in capital good, capital deepening is defined as the case kilometres) infrastructure wherein the per worker capital good stock is a development. The proponent has chosen the value of decreasing function of its own rate of interest . In buildings infrastructure Neo-classical macroeconomics which focuses on development because the proponent believes that the capital accumulation and its links to saving decisions, as as an an indicator indicator for of 7 the marginal condition π ′ (π) = πππππ and the rate of return ( π + πΏ = π ′ (π)) iv. The marginal propensity to consume + the marginal propensity to save =1; where r is the principal rate of return and πΏ is the rate of depreciation, lead to a v. Law of motion of population π = πΆπ π ππ‘ ; per capital return that is higher than before (Hirota, ππ vi. Law of motion of capital πΎΜ = = 1979), ππ‘ π ππ‘ − πΏπΎπ‘ ; The basis of capital deepening is rooted in the vii. Technology is free; Harrod-Neutral production function, which is in its basic form π = πΉ(π΄πΏ, πΎ), where Y is income, A viii. Continuous t in e; defined as the technological shifter, L defined as ix. All L are fully employed; labour and K is defined as capital (McEachern, 2012). x. minimal government role. For the purpose of this research, the proponent is limiting the components of capital deepening into The Solow Growth model can be expressed as follows; three components: access to clean water, growth rate π π¦ = (πΏ + π + π)π of population, and the human development index, in which the proponent will use the mean years in school as a proxy to the human development index since literacy rate is an integral part of this index. Where sy is the proportion of income saved, πΏ as depreciation rate of capital, n as the growth rate of population, and g as the growth rate of technology. 3. Theoretical Framework This research used The above equation is also known as the breakeven a neoclassical macroeconomic growth theory, and will be creating a model that fits the assumptions of a Solow-Swan Growth model. In a macro economy, there are three indicators of growth and development: increase in infrastructure; technological development and capital deepening. According to the Solow-Swan Growth model, holding its assumptions constant; investment; the balanced growth path; the steady state in the macro economy (McEachern, 2012). Clearly in this model we can see that capital is an integral part of 3 variables: Depreciation of capital, e.g. infrastructure, population, and technology. Therefore capital can improve by affecting one of these variables. Now, since income is inversely related with poverty, whereas an increase in income per capita in an economy decreases the number of people living i. Constant returns to scale; below the poverty line, with of course assuming that ii. Inada Condition; the increase in income is distributed among the iii. Population grows at a constant rate π, people of the economy. Since income is not our capital depreciates at a constant rate πΏ, immediate concern in this study and our dependent and technology grows at a constant variable that captures the effects of economic growth rate π; 8 is a poverty incidence, then the Solow growth model initial semi log model specification, shown by the is an essential primary tool to capture development. following: As for our capturing variables within the Solow ππΌπ = π½1 π + π½2 ln π΅ππππ + π½3 π ππππ + π½4 π»2 ππ growth, we use value of building constructions as + π½5 ππ + π½6 πΈππ + ππ well as road development to capture infrastructure development, and its contribution to the model is on the depreciation rate of capital. As there is an increase in infrastructure expenditure, then capital 4.2 A Priori Expectations The following variables with their A Priori expectations are presented in the following table: infrastructure will depreciate less and less since allocation of resources to infrastructure will decrease ππΌ Poverty incidence- the percentage of the population wear-and-tear and will be more updated and efficient that is under the poverty line (Estache, 2003) (Calderón & Servén, 2003). per of the Philippines. Source: NSCB As for human development, population growth rate is affected by many factors, which include the province ln π΅πππ Value of building health and wellbeing of the people. As we have constructions for 2011- the reviewed in the literature, a human development amount (in Php) used for the improvement will reduce poverty by increasing development of buildings in income per capita, as individuals who are more the efficient tend to work better and provide better Philippines. The proponent opportunities for the person to grow, which in turn opted to set of in the natural logarithmic form to observe improves the economy. We use years of schooling as percentage changes. Expected a capturing variable of human development, as to have a negative effect on education is one of the most appalling reasons in the poverty incidence. Source: literature that promote growth and development. 4. Empirical Analysis provinces NSO Quick Stats π πππ Distance of good roads (in km)- distance of road in 4.1 Model Specification kilometres considered in good condition by the Department For this research the regression model to be of formed is based on economic theories, research Public Works Highways materials gathered as well as the proponent’s and (DPWH). Expected to have a negative intuition. Using the classical linear regression model effect on poverty incidence. through the ordinary least squares estimation, this Source: DPWH will establish the empirical portion of the theoretical framework which will determine the empirical validity of the research. This cross-section study π»2 π Percentage of households with access to clean water. Expected to have a negative across the 78 provinces of the Philippines with the 9 effect on Poverty Incidence. 5.2 Regression of the Original Model Source: NSCB ππΌπ = π½1 π + π½2 ln π΅ππππ + π½3 π ππππ + π½4 π»2 ππ π Population growth rate. + π½5 ππ + π½6 πΈππ + ππ Expected to have a negative effect on poverty incidence, to be interpreted as additional human capital. Source: NSO Quick Stats πΈπ Presented above is the original model constructed. It consists of poverty incidence as the dependent variable to the value of buildings constructed, distance of DPWH certified good roads, Mean years of schooling (set percentage of households with access to potable as 3 water, population growth rate and the average years HDI). in school which serves as a proxy to the HDI which is Expected to have a negative a function of literacy index, life expectancy index and a proxy components for of the effect on poverty incidence. the income index (see review of related literature). Source: NSCB 4.3 Data Gathered The data that the proponent will use is collected from the National Statistical Coordination Board The proponent used an Ordinary Least Square (OLS) estimation method having all the Classical Regression National Statistics Office, as well as the Department of Public Works and Highways (DPWH) for the data dates are not exactly the same, this study is more met, Zero Mean Assumption i.e. πΈ(π’π ) = π = 0; b. Homoscedasticity i.e. π£ππ(π’π ) = π 2 ; c. No perfect Multicollinearity among all independent variables; d. Non- autocorrelation; e. Zero covariance between independent variables and the stochastic disturbance interested in averages through time, and since time is term; not of the essence of this study, a simple cross section is used. assumptions a. regarding the distance of DPWH at par with the current standard of the department. Although the (CLM) enumerated as follows: (NSCB), the National Statistics Office (NSO), and from the provinces Quick Stats, also taken from the Model f. Number of observations should be greater than number of parameters to be estimated; 5. Estimation and Inference g. Sufficient variation in the values of the independent variables (Gujarati & Porter, 5.1 Summary of the Data From the data gathered, 78 (with the exception for the information gathered on DPWH which seems to lack information on five provinces, namely Basilan, Lanao del Sur, Sulu, Tawi-Tawi, and Maguindanao) of the provinces have provided varied 2009). With the CLM assumptions taken into account and met, then it according to Gauss and Markov, the OLS estimate is the best linear unbiased estimator (Carter Hill, Griffiths, & Lim, 2011). However for this research, the proponent will only test for the statistics to the information needed in this research. 10 three critical assumptions, namely Multicollinearity, else we accept the null hypothesis that it does not Autocorrelation, and Homoscedasticity. affect the dependent variable. In this case population growth rate and Education prove to be well in the Running the regression analysis2, the estimated coefficient values of the model are presented in the generated model: ππΌπ range of the acceptance of the alternative- which is to say that these variables do have some correlation regarding the poverty incidence of the Philippines. Synthesizing these results, we can infer that three = 107.3815 − 0.0697046 πππ΅ππππ out of the five variables that have been tested with − .0361233 Roadπ −1.378105 π»2 ππ − 3.702187ππ respect to the poverty incidence of the Philippines − 7.765788πΈππ + ππ has some significant impact, namely population, 5.3 Significant Statistical Findings on the Original distance of good roads and education. A 1 unit Model increase in the population growth rate of the province corresponds to a 3.702187 % decrease in poverty The interpretation of the results generated has incidence – it means that as we increase population provided some interesting and meaningful results. In poverty incidence decreases. Probably because more determining the validity of the model, one has to look population corresponds to a larger work force that at the R-squared and the probability values of the increases the production in a certain province hence independent variables. First of all, we have to increasing the income per individual and eventually consider the fact that all the a priori expectations for reducing the number of the individuals succumb to every explanatory variable in the model have been poverty, however intuitively speaking this can only met- which proves that intuitively speaking the model be possible when this increase in population is is correct. utilized in the economy i.e. provision of primary and Regarding the coefficient of the R-squared of the model, we can see that it is at .4606; meaning to say that 46.06% of the model explains the real world. We can see that this coefficient is adequate- lower than 50%- however cannot be discounted as insignificant. Considering that the data used is cross-section which usually has a low R-squared, the Goodness-of-Fit of the data indicated by the R-squared proves that it is a relatively good model. secondary education, jobs, etc. that reduces poverty. Evident in the analysis, in the generated model there is a significant finding where there is a 7.765788% decrease in poverty for every 1% increase in the average years in school (Education)- this may imply that as the education does have a very significant impact on the poverty incidence of the Philippines. For Roads, there is a .0361233% decrease in poverty incidence for every 1 kilometre increase in the distance of roads deemed by DPWH of good Now giving a thought on the validity of the independent variables by looking at the probability condition, which may signify that there is a potential decrease in poverty when roads are constructed. values, we set the critical region at p-value < 0.05 5.4 Corrective Measures and Corrected Model 2 Results in Appendix A 11 Since there is no problem in the model regarding the two determinants of growth and development, multicollinearity and heteroscedasticity, it is safe to thus verifying the macroeconomic theories behind the say that the model generated is indeed a good model. In terms of the a priori expectations, it is safe approximate of what occurs in the real world3. to say that the model fits these expectations, since in However it is in the best interest of the researcher to the empirical test the relationship of poverty find a better alternative model that has more incidence to value of buildings, good roads, access to significant safe water, population growth, and education is variables to better explain the phenomenon of poverty. negative. Since the value of buildings and access to safe We can observe that there is a . 697046 % water are clearly insignificant due to the results decrease in the poverty incidence level when the generated, the best action to take is to find a way to value of building construction increases by 1%. This improve the model in such a way that more of these corresponds to a significant change in poverty intuitively sound components of poverty can result to incidence and has indeed met with the a priori which significant figures, statistically speaking. However indicates that infrastructure development has a since there is no reason to do a corrected model, then significant impact on alleviating poverty. However the proponent has no choice but to accept the model due to the insignificance of the p-value, it must be as it is. considered insignificant in this study, however further research may be conducted to prove ππΌπ = π½1 π + π½2 ln π΅ππππ + π½3 π ππππ + π½4 π»2 ππ + π½5 ππ + π½6 πΈππ + ππ otherwise.4 With regards to access to safe water, which also has a p-value greater than the 5% or even 10% confidence interval, this research considers the impact of a percentage increase in the households With the following estimates: with access to safe water as an insignificant factor with respect to poverty. Similar to the result from ππΌπ = 107.3815 − 0.0697046 πππ΅ππππ − .0361233 Roadπ −1.378105 π»2 ππ − 3.702187ππ value of building, further research may be conducted to prove otherwise. − 7.765788πΈππ + ππ At the 90% confidence interval, since the p-value is at 0.0595, there is enough ground to deem good 6. Conclusion quality roads as a significant factor in reducing I have presented a model that illustrates the poverty, and due to the a priori expectations to the possible effects of infrastructure development and effect of infrastructure development on poverty the capital deepening with respect to the poverty proponent will use it as a gauge to measure the incidence of the Philippines. In both the original and strength of it as a determinant of growth and corrected models, they have shown that there is development. The generated model suggests that for indeed a negative relationship between poverty and 3 See Appendix B. 4 In the first run of the regression that the proponent has conducted, it has given a significant result for the increase in the value of buildings (See Appendix C) 12 every 1 kilometre increase in the length of road thus aides in the alleviation of poverty (McEachern, considered by the DPWH at par with their standards, 2012). then there will be a . 0361233% decrease in poverty incidence, a slight but possibly present change as additional good roads not only provide employment in the construction of it, but also provide accessibility in the province, gaining the confidence of investors due to the accessibility, providing more employment Regarding the coefficient of the R-squared of the generated model, we can see that it is at .4606; meaning to say that 46.06% of the model explains the real world. We can see that this coefficient is adequate- lower than 50%- however cannot be discounted as insignificant. Considering that the data and reducing poverty. used is cross-section which usually has a low RAside from the value of building constructions, squared, the Goodness-of-Fit of the data indicated by the model suggests that there is also a 3.702187% the R-squared proves that it is a relatively good decrease in poverty incidence for every 1 unit model. increase in the growth rate of population. Considering that this value is significant, there is a corresponding decrease in poverty for an increase in The model’s results, which its findings can be summarized as follows: population can be interpreted in many ways- that a. All a-priori expectations are met; population should not be considered a problem given b. there may be a 3.70% decrease in that this human resource is utilized by provision of poverty incidence for every 1% education and employment, or that population is not a increase in the population (1 unit problem at all and in fact we must promote an increase increase in population, or that this research is merely population); stating out the fact that the overpopulation issue does c. in the growth of there could be a 7.77% decrease in not hold as much importance when it comes to poverty incidence for every 1 unit alleviating poverty- with this the research is limited increase education; to. d. poverty incidence for every 1 The impact of education to poverty is indeed kilometre econometrically significant. Having a probability e. the macroeconomic theory in education, that by Psacharopolous the more time road The model may explains 46.06% of the real world; increase in the average years of schooling. The demonstrates in their standards; 0.000) it is indeed possible to infer that there could be results generated by the econometric model clearly increase considered by DPWH at par with value to a point that it is negligible (at rounded off a 7.765788 % decrease in poverty for every unit there could be a . 04% reduction in f. No problems with the critical assumptions of the classical linear regression model. invested in education, the higher the rate of return, 13 The proponent has generated estimates of the business perspective, the goods produced will be more efficiently transported from the farm, to the econometric model: local and urban marketplace and eventually to the ππΌπ = π½1 π + π½2 ln π΅ππππ + π½3 π ππππ + π½4 π»2 ππ + π½5 ππ + π½6 πΈππ + ππ consumers. An increase in the value of building constructions, though deemed insignificant in this study, also proves to yield a negative impact on Presented as follows: poverty, which gives enough evidence, though ππΌπ statistically insignificant for this research, to claim = 107.3815 − 0.0697046 πππ΅ππππ that − .0361233 Roadπ −1.378105 π»2 ππ − 3.702187ππ constructions may lessen the population of the − 7.765788πΈππ + ππ impoverished by providing employment in the indeed a higher spending on building construction of the buildings plus additional space for This study suggests that the best way to alleviate poverty for the Philippine economy is for the government to increase its allocation of budget on educational programs, such as increasing the number of public schools up to the secondary level, increase the generation of scholarship programs in order to further increase the number of graduates inclusive of but not limited to academic scholarships, merit scholarships and pay-it-forward programs that will increase the literacy rate of the county. An increase in businesses and local government units to provide additional employment in the community. In order to fully maximize these conditions, the government must fully utilize its resources in order to maximize the results of its projects to address the problem of poverty in the country; that is to say that good governance and clean auditing of government funds must be integral in order for this model’s generated result to be of any use, since in this study we assume that good governance is a constant. population will actually lessen poverty if and only if the additional increase in population will correspond The model generated could not immediately be to an increase in education and job opportunities in judged as a failure due to the insignificance of order to fully utilize the additional human resource. building construction and access to safe water, Given these information, the proponent recommends although it is in the best interest of the proponent to that the government should focus in projects generate a greater amount of significant results rather regarding the deepening of the pool of capital than the indicated beforehand. Throughout the available in the country that includes investment in research, the proponent has not drifted away from the human capital in the form of education, as well as the sound economic theories that this study is based full utilization of the population. upon, and the a priori expectations have always been It is also suggested that the government should also increase its budget allocation on road met. Nevertheless, this study can serve as a improvement to provide access to rural communities supplementary study, an addition to the studies made in order to efficiently transport goods and services with regard to the topic of poverty. Poverty is one of from one point to another i.e. in an agricultural the most pressing problems that humanity faces as a 14 race. In the world that we live in today, with all the technological advancements and scientific breakthroughs, it is integral to find ways in order to reduce poverty in order to aide in the attainment of United Nation’s Millennium Development Goals where alleviation of poverty is one of them. 15 Appendix A. Summary of the Data From the data gathered, 78 (with the exception for the information gathered on DPWH which seems to lack information on five provinces, namely Basilan, Lanao del Sur, Sulu, Tawi-Tawi, and Maguindanao) of the provinces have provided varied statistics to the information needed in this research. Presented hence is a summary of the information gathered: Table 1. Data Summary Variable Observations Mean S.D. Min Max Poverty incidence 78 33.46154 14.55056 0 61.6 Value of building constructions 78 1604619 2650193 4712 1.50E+07 Distance of good road 73 65.77438 57.60592 0 258.1 Households w/ access to safe water 78 0.7822965 0.368596 0.009443 2.560873 Population growth rate 78 1.620769 0.632611 0.08 4.12 Average years in school (up to secondary) 78 8.455128 1.383732 4.6 12.6 Presented below is the summary of the results yielded using the data gathered from NSCB, NSO and DPWH. The results will be then examined for possible problems in heteroscedasticity, multicollinearity and autocorrelation. This table displays the variables involved in the econometric model, with their corresponding estimated coefficients, probability values, standard deviations, and the coefficient of determination represented by the value of the R-squared. Table 2. Dependent Variable: Poverty Incidence (OLS Estimation: Across 78 Provinces of the Philippines) Value of Estimate Significance5 Constant *** (11.02191) -0.0697046 (s.e.) (.8792123) Distance of good roads * (0.229012) Access to safe water -1.378105 (s.e.) (3.605055) ** (s.e.) Average years in school -3.702187 (2.09804) *** (s.e.) -7.765788 (1.414965) Root MSE 11.028 R-squared 0.4606 Adjusted R-squared F-Test Ramsey RESET 6 -0.0361233 (s.e.) Population growth rate 5 107.3815 (s.e.) Value of building constructions (ln) *** 0.4204 11.44 (5,67) 0.06556 Legend: * -significant at the 10% level; ** -significant at the 5% level; *** -significant at the 1% level Ho: no omitted variable bias; H1: omitted variable bias present. Accept Ho at 95% level of significance. 16 Appendix B. Testing for the Critical Assumptions Multicollinearity According to Gujarati & Porter (2009), multicollinearity is a fact of life; it cannot be removed or isolated. However it is possible for us to test whether or not the level of multicollinearity is tolerable, dangerous or perfect. In the instance wherein there is perfect multicollinearity it is safe to assume that it would be impossible for any researcher to find any estimates for the X values, since their standard errors will be infinite (determinant will be zero). If multicollinearity is less than perfect but at a dangerous level, this may result to bloated standard errors, insignificant p-values of the t statistics though the R-squared is deemed a fitting model; this results to a wholesale acceptance of the null hypothesis, which increases the probability of committing a type II error. This may cause the researcher to omit good regressors for the model since these X estimates will be deemed insignificant. Presented below is the result of the multicollinearity test via analysis of the VarianceInflating Factor, commonly known as the VIF: Table 3. VIF Test Variable VIF Value of building constructions (ln) 1.51 0.662259 Average years in school (up to secondary) 1.45 0.687475 1.1 0.909241 Households w/ access to safe water 1.08 0.924616 Distance of good roads (DPWH) 1.03 0.97049 Mean VIF 1.24 Population growth rate To determine the severity of multicollinearity in the model, we must look at the generated values for the VIF whether or not they are greater than or equal to 10, otherwise the level of multicollinearity is tolerable. As we can see, all the VIF values generated are less than 10. Moreover the individual VIF’s tolerance levels, taken into account by 1/VIF are all greater than 10%. Therefore it is safe to conclude that the model has a tolerable level of multicollinearity. Autocorrelation The term autocorrelation may be defined as “correlation between members of series of observations ordered in time or space, simply put: πΈ(π’π π’π ) = 0 This phenomenon causes an overestimation of the R-squared, as well as incorrect t-statistics as well as p-values. The root cause of this is from the underestimation of the standard errors, leading to wrong policy recommendations and counterintuitive signs in the econometric model. 1/VIF Since this research deals with a cross-section data, then there is no need to test for problems in autocorrelation since it only appears in time-series data. (Gujarati & Porter, 2009) Homoscedasticity Homoscedasticity is the equal spread of variances, symbolically speaking it is written as πΈ(π’π2 ) = π 2 , ∀π = 1,2, … , π. If plotted in a graph, the points should not follow a pattern. The problem of heteroscedasticity (or heteroskedasticity) is most common in cross-section data. When Heteroscedasticity is not properly treated, it will cause the OLS to no longer be the Best Linear Unbiased Estimate (BLUE), since it causes the values of R-squared, t-stats, standard errors to be all wrong. Using the Breusch-Pagan-Godfrey test for Heteroscedasticity, we obtain the following results: 17 Table 4. Breusch-Pagan test for heteroscedasticity Ho: Constant variance Variables: fitted values of poverty incidence Chi-squared (1) 0.11 Prob > Chi-squared 0.7379*** Decision: Accept Ho at 95% confidence interval To interpret this result we must consider that given that the null hypothesis indicates homoscedasticity while the alternative indicates heteroscedasticity, if the Prob > chi2 presented in the test is greater than 0.05, the null hypothesis is accepted; which implies that the model exhibits homoscedasticity. Since the probability is at 0.7379- significantly greater than the acceptance level at 0.05, then it is safe to accept the null hypothesis and say that there is homoscedasticity; which implies that there is no problem of heteroscedasticity in the model. A similar result has come up with a generation of the White’s Test: Table 5. White's Test for heteroscedasticity Ho: homoscedasticity Ha: unrestricted heteroscedasticity chi-squared (20) Prob > chi-squared 14.1 0.8256*** Decision: Accept Ho at 95% confidence interval 18 Appendix C. Previous Test Run with corresponding VIF, BreuschPagan-Godfrey Test and White’s Test Table 3. Dependent Variable: Poverty Incidence (natural log) (OLS Estimation: Across 78 Provinces of the Philippines) Constant Significance7 Value of Estimate *** 4.97415 (s.e.) Value of building constructions (ln) (.44817) ** -0.0816 (s.e.) Distance of good roads (.343216) * -0.0011684 (s.e.) (0.0010007) Access to safe water -0.1459578 (s.e.) (0.1546506) Population growth rate *** -0.2838653 ** -0.2526639 (s.e.) HDI (ln) (0.9415) (s.e.) (0.0972426) Root MSE .4765 R-squared 0.3657 Adjusted R-squared 0.3176 F-Test *** 7.61 (5,66) 1.108 VIF Breusch Pagan test Heteroscedasticity *** 0.0049 7 Legend: * -significant at the 10% level; ** -significant at the 5% level; *** -significant at the 1% level Since VIF < 10, then tolerable level of multicollinearity 9 Presence of heteroscedasticity 8 19 References Calderón, C., & Servén, L. (2003). The Effects of Infrastructure Development on Growth and Income Distribution. Washington, D.C.: World Bank Policy Research Working Paper Number 3400. Carter Hill, R., Griffiths, W. E., & Lim, G. (2011). Principles of Econometrics. New Jersey: John Wiley & Sons, Inc. Estache, A. (2003). On Latin America’s Infrastructure Privatization and its Distributional Effects. Washington DC.: The World Bank, Mimeo. GSRubio/PR and Information Services. (2013). 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