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internationalistic Research ledger of Finance and political economy ISSN 1450-2887 Issue 52 (2010) EuroJournals Publishing, Inc. 2010 http//www. eurojournals. com/finance. htm Does Education Alleviate impoverishment? Empirical Evidence from Pakistan Imran Sharif Chaudhry henchman Professor of frugals. Bahauddin Zakariya University Multan, Pakistan netmail emailprotected edu. pk Shahnawaz Malik Professor of Economics, Bahauddin Zakariya University Multan, Pakistan E-mail emailprotected edu. pk Abo ul Hassan Ph.D Research Fellow, Department of Economics, Bahauddin Zakariya University Multan, Pakistan E-mail emailprotected com Muhammad Zahir Faridi Lecturer, Department of Economics, Bahauddin Zakariya University Multan, Pakistan E-mail emailprotected com Abstract beggary has become a sensitive and ever remained issue almost in all in all developing opineries of the world. Education plays a vital role in meagerness alleviation. Therefore, it is crucial to investigate that whe ther distinguishable takes of instruction or literacy spring to alleviate pauperization.The major objective of this theatre is to evaluate the put ups of opposite levels of preparation and literacy on the incidence of pauperisation in Pakistan. Our turn outs suggest that indigence alleviation process would be accele trampd if resources argon targeted at program barrier sector especially in high tuition. Pakistan presents a inexplicable situation. Until the late 1980s Pakistan had achieved a spectacular record of economic growing and reduced incidence of poorness remarkably, but the pressry had horrible social indicators. save when social indicators began to improve in the 1990s for a kind of reasons, both internally and externally driven, the average point of economic growth declined. Contrary to the said situation, the oecumenic perception about Education is that the role of commandment in poorness alleviation, in close co-operation with other(a) social s ectors, is crucial. This paper is mainly intended to explore the reality that to what extent precept is affective in poverty alleviation in Pakistan. In addition, virtually principal(prenominal) macroeconomic variable quantitys give up also been taken understudy to find out the reality of the problem.Keywords Education Poverty Inflation Economic Growth nudity Pakistan International Research Journal of Finance and Economics Issue 52 (2010) 135 I. Introduction Poverty is a multidimensional phenomenon, encompassing inability to satisfy basic needs, pretermit of control over resources, overlook of teaching and skills, inadequate health, malnutrition, omit of shelter, poor access to clean water and sanitation, vulnerability to shocks, delirium and crime, lack of political freedom and voices. The poor be the true poverty experts.They assert on material well(p) being, physical well being, social well being, security of food, security of law and order, public safety, safety f rom violence and civil conflicts, freedom of choice and action, being a part of the decision making body quite a to be a victim of decision making body and the security of jobs. Poverty apprize be looked at from different angles and depending upon the perspective one adopts definitions of poverty may vary. It differs from country to country and from context to context. Poverty may be unquestioning or relative.Absolute poverty asshole be eradicated but relative poverty cannot. Relative poverty is a dynamic fancy because it involves comparison between groups. It exists in all move of the world, either in packets or on a much larger scale. In Pakistan both absolute and relative poverty exists commonly, poverty is measured in monetary terms. The causes of poverty be also multidimensional. 1 There is no single cause that can explain it fully. Poverty is often link up to a number of situationors physical, psycho enterical, economic and sociocultural.Among the physical factors a ccounting for poverty are an unfavorable natural environment and lack of basic physical and economic infrastructure. These may also relate to poor health and malnutrition. Psychological factors interest to feel of hopelessness, helplessness, lack of confidence in ones self and poor self-image resulting from inappropriate mensu come in system, cultural deprivation and undeveloped potential. These factors may also be related to an inability to participate in democratic processes and behavioral inadequacies aggravated by low levels of literacy and education.Education is the most great factor that distinguishes the poor from the non-poor concord to Pakistans Interim Poverty drop-off Strategy paper 2001, the percentage of literate of septs gunpoints is 27 in poor households while for non-poor households it is 52. Though the origins of human not bad(p) theory can be traced to the earlier economists from raptus Smith (1776) to Alfred Marshall (1920) it is Theodore Schultz (1961 ) who created a human investment revolution in economic thought by emphasizing the role of human capital in economic growth.Schultz (1961), Gary Becker (1964), Jacob Mincer (1972) and m any others with their voluminous pioneering contributions laid education at a high pedestal in the theories of economic growth. Amartya Sen (1999) rightly argues that education constitutes a part of human freedom and human capability. . Over the period under study many alpha factors like unemployment, current account deficit and services growth rate cod been contributed to why poverty is increasing even though education has increased consistently.We confound tried to give a skeleton description of the wall of researchers that if increased education has bespeakificant impact on income and thus poverty or not or whether there are other factors mitigating or attenuating the impact of education on poverty. However in our digest, the central focus has been on the role of education in poverty allev iation. Education has important implications for the analysis of changes in a poverty profile in a country. Keeping in draw the issues high lighted above, this paper tries to answer following related questions.Does education play its role to alleviate poverty? What is the role of other key macroeconomic variables in poverty alleviation? What can be generalized about the impact of education on poverty? What are the important constitution implications? These questions victuals their extreme importance as answering the said questions will bring a solution to the however puzzle thats why Pakistan is lagging behind on the development path as compared to some(prenominal) developed countries who got independence later than us. 1 Technical consultation on literacy as a dig for the empowerment of the poor, Lampang, Thailand, 1997. 36 International Research Journal of Finance and Economics Issue 52 (2010) To act on the problem understudy, this paper is technically divided into several parts. Firstly we fix attempted to explain the abstract and suppositional framework of education and poverty alleviation. So far as the data-based analysis is concerned, we have divided it into 2 portions. The first portion presents the descriptive analyses and the support portion presents the econometric analysis which has been undertaken by argueing autoregressive regression comparisons. II.Education and Poverty A Theoretical Framework The economists often define education as having drive effects and indirect effects. The direct effects of education are the imparting of knowledge and skills that are associated with higher wages. The indirect effects, also often referred to as external benefits, include fulfillment of basic needs, higher levels of democratic participation, better utilization of health facilities, shelter, water and sanitation and the additional effects which occur in womans behavior in decisions relating to fertility, family welfare and health.The allian ce between education and poverty can also be examined by rate of return analysis, and production function analysis at case-by-case as well as social/national levels. Rates of return are estimated using either Mincerian earnings function (Mincer, 1972), or using the concept of marginal efficiency of capital that relates costs of education to the lifetime benefits, essentially earnings associated with education. III. Data and Methodological Issues In order to study the impact of education on poverty, the study chooses time series data, for thirty five dollar bill years (1972-2007) for Pakistan.The poverty data finds are hoard mainly from Malik (1988), Amjad and Kemal (1997), Jamal (2003) and various issues of Pakistan Economic Survey since 2005, while the data on other variables is collected from worldly concern Bank, military personnel Development Indicators (WDI), April 2008, ESDS International, (Mimas), University of Manchester. To make time series data on poverty incidence , a linear interpolation technique is employed. The selected time period presents the paradoxical situation of Pakistan as both growth and social indicators move in opposite directions.That is why it is selected to understand this paradoxical situation. Thirty five years time period is long enough to capture long run effect of most of the variable constructed in this study. We have tried to keep in view the problem of endogeniety while selecting the explanatory variables for our analysis. The study chooses the absolute poverty (poverty passportcount index), education literacy rate, primary train level enrolment rate, middle school level and the university level enrolment widely used proxies for education) as the key variables.In addition, some useful variables (Growth rate, pomposity rate, and Trade openness) have also been include in our model. In this study, autoregressive models are employed for econometric empirical investigation. In our first poverty autoregressive regres sion model, growth, literacy rate, consumer terms index, and hcr(-1) are used to analyze while in the second model, some enrollment rates at various levels are considered. In order to achieve the objectives of the study, good deal openness is also considered to check the robustness of globalization. Log set of the variables are used in the analysis.We pretend that the incidence of poverty prevailing in the economy is significantly dependent on higher education level. International Research Journal of Finance and Economics Issue 52 (2010) 137 IV. Results and Discussions a) Descriptive Analysis Our remove data set consist of 35 years of annual observations from 1973-2007 on the selected variables. The descriptive statistic is reported in table 1 which states that the average of head count ratio (HCR) for our study period is 27. 63% with a old-hat deviation (SD) of 6. 74. The average of primary school enrollment rate is 11316. 8 with 6204. 18, the determine of its sample devi ation (SD). Middle school enrollment is 2667. 611 on an average and with standard deviation (SD) 1326. 06. The average mensurates for university enrollment rate, real gross domestic product (RGDP) and openness are 83045. 19, 22879. 24, 33. 81 with the value of standard deviations 65444. 71, 5756. 76, 3. 18 are given accordingly. As far as skewness of variables is concerned head count ratio (HCR), primary school enrollment rate, middle school enrollment rate and university enrollment rate are skewed on the rightward whereas openness is skewed leftward.All the variables are skewed a little. Table 1 Descriptive Statistics HCR 27. 63 25. 20 45. 75 20. 71 6. 74 1. 04 3. 26 6. 64 0. 04 LITR 36. 93 34. 35 55. 00 22. 10 10. 92 0. 24 1. 56 3. 47 0. 18 MIDDLE 2667. 61 2350. 00 5368. 00 963. 00 1326. 06 0. 36 1. 83 2. 84 0. 24 PRIMARY 11316. 78 9827. 00 24465. 00 4210. 00 6204. 18 0. 57 2. 02 3. 36 0. 19 UNIV 83045. 19 65642. 00 296812. 00 17507. 00 65444. 71 1. 76 5. 59 28. 74 0. 00 OPEN 33. 81 34. 35 38. 91 27. 72 3. 18 -0. 30 2. 19 1. 53 0. 47 RGDP 22879. 24 23859. 71 33820. 04 14033. 11 5756. 76 -0. 06 1. 86 1. 97 0. 37 CPI 56. 51 39. 73 149. 0 7. 40 41. 73 0. 67 2. 16 3. 77 0. 15 Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability Kurtosis is a measure whether the data set is peaked or flat relative to a normal distribution. Kurtosis statistic of the variables shows that only HCR and university enrollment is Leptokurtic (long tailed or high peakedness) and all other variables are Platykurtic (relatively narrower tailed then the normal curve. However the value of HCR is though high compared to the value of Meso-kurtic curve but it is not too high from the value desired for a normal distribution.The Jerque-Bera (JB) test of normality gives joint hypothesis of skewness and kurtosis. Jerque-Bera test of normality suggest that if the computed P-value of JB-statistic of university enrollment rate is sufficiently low as the value of the statist ic is very different from zero, we state that the residuals for university enrollment rate is not normally distributed. For all other variables included in the present study, it is concluded that residuals for these variables are normally distributed. Table 2 Cor singing Matrix HCR 1. 00 -0. 35 -0. 37 -0. 28 -0. 30 -0. 9 -0. 53 -0. 27 LITR 1. 00 0. 99 0. 98 0. 84 0. 25 0. 97 0. 98 MIDDLE 1. 00 0. 99 0. 86 0. 28 0. 97 0. 98 PRIMARY UNIV OPEN RGDP CPI HCR LITR MIDDLE PRIMARY UNIV OPEN RGDP CPI 1. 00 0. 89 0. 20 0. 95 0. 99 1. 00 0. 18 0. 84 0. 91 1. 00 0. 39 0. 17 1. 00 0. 94 1. 00 The degree of the relationship of the variables is also estimated and reported in table 2. All the variables are negatively correlative with each other. The results state that openness is passing match and primary, middle, university enrollment rates and RGDP are moderately correlated with HCR. 138International Research Journal of Finance and Economics Issue 52 (2010) b) Autoregressive Regression Analys is In our analysis, we have used a data set using time series ranging from 1973-2007. To investigate the significance of education (literacy) on the incidence of absolute Poverty, we have following autoregressive regression models. The robustness of the models is examined by including and excluding some important macroeconomic variables in our analysis. The model is given as below The Poverty Autoregressive Regression Model- 1 LHCR = ? 0 + ? 1 LRGDP + ? LLITR + ? 3 LCPI + ? 4 LOPEN + ? 5 LHCR (? 1) + ? i Table 3 presents the regard results in which head count index (HCI) is the dependent variable and the variables such as growth rate, literacy rate, consumer price index (CPI) and head count index (HCI) for the preliminary year are all explanatory variables in the present analysis. The value of adjusted Rsquared is 94. 5%, implying that 94. 6% of the variation in the dependent variable is explained by the independent variable. The value of R-squared clearly shows robustness of our results. The value of hstatistic is 1. 8, the results indicates that there is no significant autocorrelation problem in the error. The coefficient for growth verifies our theoretical expectations, implying an inverse relationship between poverty and growth. The coefficient for growth is exceedingly significant putting an immense effect on poverty. The results verify the findings of Sarris who could find that overall economic growth reduces overall poverty. The coefficient for literacy is significant in the poverty regression analysis. However the variable is reciprocally related with the dependent variable which verifies the theoretical relationship of the two variables.The above results follow the findings of Dollar and Kraay (2002) who have concluded that growth is a prominent factor in eliminating poverty and that the impact of low level of educational attainment is not so much important. The coefficient of the consumer price index (CPI) having an expected theoretical sign, impl ies a positive relationship with poverty. However coefficient is not statistically highly significant. Our results also second the findings of Romer and Romer who trustd that an increase in inflation will be associated with a decline in the unemployment in the short run that may well relatively benefit the poor.The findings of Agenor (1998) also strengthen our religious belief on the outcome of our analysis implying the fact about the poverty rates to be positively related with inflation. The previous years poverty is highly significant with the incidence of poverty. The coefficient of the variable is keeping a postulated positive sign. The best justification of the result is given by the Ragner Nurkse who could observe that a country is poor because its poor. Although the theoretical expectations of our present study are fulfilled yet we have included some more important variables pertaining to the human capital.We have included primary, middle and university enrollment rates in stead of the literacy rate in our model. In order to check the impact of globalization on the incidence of poverty, we have included the trade openness in our analysis. The coefficient of openness is negative and insignificant. Table 3 Estimates of the Model-I Coefficient 5. 77051 -0. 62553 0. 512801 0. 004567 -0. 123046 0. 713883 0. 94 0. 93 1. 58 Std. Error 2. 62493 0. 300753 0. 263391 0. 085448 0. 137595 0. 094954 t-Statistic 2. 198348 -2. 079882 1. 946923 0. 053446 -0. 89426 7. 518185 F-Stat Prob Prob. 0. 0361 0. 0465 0. 0613 0. 9577 0. 3785 0. 0 99. 93 0. 00 Variable C LLGDP LLITR LCPI LOPEN LHCR(-1) R Squared Adj R Squared h-Statistic International Research Journal of Finance and Economics Issue 52 (2010) 139 The Poverty Autoregressive Regression Model-2 It is a natural fact that a problem like poverty cannot be eradicated at all. Owing to the said fact study is intended to explore the answer of the question Does education alleviate poverty? To investigate the query, we ha ve followed the regression model. We have developed the poverty regression model. Primary, middle and university enrollment rates as a proxy for education are used in our model.The model is given below ? ? 0 + ? 1 LRGDP + ? 2 LPRIMARY + ? 3 LMIDDLE + ? 4 LUNIV + ? Poverty = ? ? ? ? 5 LCPI + ? 6 LOPEN + ? 7 LHCR(? 1) + i ? Table 4 presents the estimation results for the poverty regression analysis where the dependent variable is the poverty had count index (HCI) and rest seven variables namely log of real gross domestic product, log of primary school enrollment, log of middle school enrollment, log of university enrollment, log of consumer price index, log of openness and the log of head count ratio of the previous year are all independent variables.Note that the adjusted R-squared is 95. 9% implying that the approximately 95. 9% variation in the dependent variable is explained by the independent variables. The coefficient for LRGDP is keeping a negative sign implying the inverse r elationship of LRGDP with the incidence of poverty. The theoretical relationship of LRGDP and LHCR also supports the negative relationship of these two variables. simply the coefficient for LRGDP is statistically insignificant pervading a little effect on the incidence of poverty.The coefficient for log of primary enrollment rate and log of middle enrollment rate both keep a positive relationship with the incidence of poverty implying that both the standards minutely aggravate the incidence of poverty. The coefficients for both the levels are statistically insignificant which shows lesser nuisance value of primary and middle standards of education. The results also match with the findings of Rodriguez K Smith (1994) and Coulombe and Mckay (1996) who debate that the likelihood of being poor is higher for the lower levels of education.The coefficient for the log of university enrollment rate is statistically highly significant in the poverty regression analysis as shown in the table 3. The variable is inversely related with the dependent variable which verifies the theoretical relationship of the two variables. The estimation results verify the findings of all those who believe in an effective role of human development of poverty alleviation. The estimation results stay in line with the findings of Tilak (1994) which emphasize on the role of education.The results also explain that higher education is one of the most mighty means to reduce poverty. Our results also match with the findings of King (2005) who has argued that the agenda of the millennium development goals for education cannot be achieved without giving right consideration to higher education. All the prominent commencees of development like the human capital approach, the basic need approach, the human development approach and the capability approach which recognize the inverse relation of education and human poverty stay in line with our results.The coefficient for inflation rate in the poverty regression analysis for log values has become significant statistically and it is positively related with the poverty head count index. The postulated positive sign of inflation portrays the fact that inflation is regarded as more of a problem by the poor. The fact was also found by William Easterly and Stanlay Fischer (2001). According to them the rich are better able to protect themselves against, or benefit from the effects of inflation then are the poor.The coefficient of openness is keeping a postulated negative sign, implying an inverse relationship between the incidence of poverty and openness. The estimation result shows that openness is healthyly influencing the poverty head count index as the coefficient of openness is found highly statistically significant. The results match with the findings of Derek H. C. Chen, Thilak Ranawera and Andriy Storozhuk who argue that high level of globalization, globalization would tend to increase poverty. The coefficient for the poverty of previous year is statistically highly significant, keeping a positive relationship with poverty. 40 Table 4 International Research Journal of Finance and Economics Issue 52 (2010) Estimates of the Model-2 Coefficient 3. 707976 -0. 205005 0. 060653 0. 042189 -0. 154165 0. 127132 -0. 186327 0. 796384 0. 96 0. 95 -1. 68 Std. Error 1. 937434 0. 246698 0. 1637 0. 190211 0. 04069 0. 0777 0. 110726 0. 081578 t-Statistic 1. 913859 -0. 830995 0. 370514 0. 221801 -3. 788787 1. 63619 -1. 682781 9. 762301 F-Sat Prob Prob. 0. 0663 0. 4133 0. 7139 0. 8261 0. 0008 0. 1134 0. 1039 0. 00 114. 37 0. 00 Variable C LLGDP LPRIMAR LMIDDLE LUNI LCPI LOPEN LHCR(-1) R Squared Adj R Squared h StatisticV. last and Some Policy Recommendations In this paper, we addressed a key issue in the current debate on economic development the role of education in poverty alleviation. We have reviewed the empirical testify on the relationship between education and poverty. The link of education to poverty is one of t he most important dimensions of policies towards poverty. Education may affect poverty in many ways. It may raise the incomes of those with education. It may in addition, by promoting growth in the economy raise the incomes of those with given levels of education.To measure education we used, among others, the literacy rate, primary education level, middle education level and university education level as proxies for education. To measure poverty, we emphasized on the concept of absolute poverty, using the poverty headcount index and as a proxy for relative poverty. We have used the econometric techniques to sketch a few stylized facts in a very multiform framework of relationship. The present study incorporates macroeconomic, structural and policy variables to poverty headcount index and education.More specifically, the poverty equation links the incidence of poverty to CPI, growth, literacy rate, primary school education, middle school education and university education level and openness. The said relationship thus enables the changes in poverty due to the changes in macroeconomic or policy variables to be projected. The relationship is empirically estimated using time series regressions, based on thirty five years data of Pakistan from 1973 to 2007, which determined the magnitudes of the effects of the above mentioned macroeconomic, structural and policy variables on poverty.The results from the empirical analysis indicate that the university education significantly alleviates the incidence of absolute poverty. It is concluded that university education comes up with a powerful tool for poverty alleviation, keeping an inverse relationship with the dependent variable. As the higher education increases, the level of poverty decreases in the country. This result hold ups the expectations that poverty is highly influenced by education. Local universities help developing countries in improving the skills of human capital which ultimately become helpful in pove rty alleviating.University graduates have the change skills to earn a living and infuse their sector of employment- whether in the private industry, the public sector or civil society-with the enterprise that underpins success. Getting universal primary education, one of the millennium development goals, without the higher education would simply mean increasing the burden of unskilled population on the economy. Some people consider university education a luxury for developing countries. It is not a luxury, it is essential.Our estimation results confirm the best know approaches like the human capital approach, the basic needs approach, the human development approach and the Sens capabilities approach as all four approaches mainly emphasize on the attainment of education for economic development. Our estimation results carry an important policy implication-namely that the spread or the distribution of higher education among the population can have a powerful impact on their welfare. A household with no education among any of its members may benefit from even one member gaining access toInternational Research Journal of Finance and Economics Issue 52 (2010) 141 education, beyond the immediate gains to that particular individual. And this is not only the case when an improvement in the education of the familys children, but also it becomes the better and immediate source of earning opportunities for other members. Our empirical results confirm that education plays an effective role in poverty alleviation. Accordingly, a focus of economic policies on education in order to reduce poverty and to speed up development appears to be justified.Inflation also becomes the cause of poverty while trade openness reduces poverty significantly. Nevertheless, it is recommended that inflation controlled and trade opened policies will in spades and significantly address this issue of poverty alleviation in Pakistan. References 1 2 3 4 Agenor, Pierre-Richard (1998). Stabilization policies, poverty and the Labour Market, Mimeo, IMF and World Bank. Amjad, Rashid, and Kemal, A. R. (1997). Macroeconomic policies and their impact on poverty alleviation in Pakistan. The Pakistan Development Review, 36(1), 39-68. Becker, Gary S. (1964).Human Capital. recent York Colombia University Press for NBER Chen, Derek H. C. , Ranaweera, Thilak and Storozhuk, Andriy, (2004). The RMSM-X+P A Minimal Poverty Module for the RMSM-X (May 11, 2004). World Bank Policy Research Working Paper No. 3304. Available at SSRN http//ssrn. com/abstract=610349 Dollar D, Kraay A (2002). Growth is good for the poor. Journal of Economic Growth, 7,195-225. Irfan, Muhammad (2001). Global Trends on Education. The Oxfam Education Report (2001), Chapter 2. Jamal, H. 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