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string(92) ‘ of Fund and Economics ISSN 1450-2887 Issue 52 \(2010\) © EuroJournals Posting, Inc\. ‘

Intercontinental Research Log of Financing and Economics ISSN 1450-2887 Issue 52 (2010) © EuroJournals Creating, Inc.

com/finance. htm Really does Education Relieve Poverty? Empirical Evidence via Pakistan Imran Sharif Chaudhry Associate Mentor of Economics. Bahauddin Zakariya University Multan, Pakistan Email: [email, protected] edu. pk Shahnawaz Malik Professor of Economics, Bahauddin Zakariya College or university Multan, Pakistan E-mail: [email, protected] edu. pk Abo ul Hassan Ph.

G Research Guy, Department of Economics, Bahauddin Zakariya College or university Multan, Pakistan E-mail: [email, protected] com Muhammad Zahir Faridi Lecturer, Department of Economics, Bahauddin Zakariya School Multan, Pakistan E-mail: [email, protected] com Abstract Lower income has become a hypersensitive and ever remained issue almost in every developing countries of the world. Education plays a vital role in low income alleviation. Consequently , it is important to review that if different degrees of education or literacy trigger to alleviate lower income.

The major target of this study is to evaluate the effects of distinct levels of education and literacy on the chance of lower income in Pakistan. Our results suggest that poverty alleviation process would be quicker if assets are geared towards education sector especially in higher education. Pakistan shows a paradoxical situation. Until the late 1980s Pakistan acquired achieved an amazing record of economic development and decreased incidence of poverty extremely, but the region had horrible social signals.

However when cultural indicators began to improve in the 1990s for a variety of causes, both internally and externally driven, the typical rate of economic development declined. Contrary to the said scenario, the general notion about Education is that the role of education in low income alleviation, in close co-operation with other sociable sectors, is crucial. This daily news is mainly designed to explore the reality that to what level education is usually affective in poverty reduction in Pakistan. In addition , several important macroeconomic variables have also been taken understudy to find out the truth of the problem.

Keywords: Education, Poverty, Inflation, Economic Progress, Openness, Pakistan International Research Journal of Finance and Economics , Issue 52 (2010) 135 I. Launch Poverty is known as a multidimensional sensation, encompassing incapability to satisfy fundamental needs, lack of control over solutions, lack of education and abilities, poor health, weakness, lack of refuge, poor usage of clean drinking water and sanitation, vulnerability to shocks, physical violence and criminal offense, lack of politics freedom and voices. The poor are the true low income experts.

That they assert about material wellness, physical well being, social wellness, security of food, reliability of rules and purchase, public security, safety coming from violence and civil clashes, freedom of choice and actions, being a section of the decision making body rather to be a victim of decision making physique and the protection of jobs. Poverty can be looked at by different perspectives and depending upon the perspective one particular adopts meanings of lower income may vary. It differs by country to country and from circumstance to framework. Poverty may be absolute or perhaps relative.

Total poverty may be eradicated nevertheless relative poverty cannot. Comparative poverty is a dynamic principle because it involves comparison between groups. It exists in all parts of the world, either in packets or on a much bigger scale. In Pakistan both equally absolute and relative low income exists normally, poverty is usually measured in monetary terms. The causes of low income are also multidimensional. 1 You cannot find any single trigger that can explain it completely. Poverty can often be related to several factors: physical, psychological, economic and sociocultural.

Among the physical factors accounting for poverty are an bad natural environment and lack of standard physical and economic infrastructure. These may also relate to poor health and malnutrition. Psychological factors refer to truly feel of hopelessness, helplessness, not enough confidence in one’s home and poor self-image as a result of inappropriate value system, social deprivation and undeveloped potential. These elements may also be associated with an inability to be involved in democratic operations and behavioral inadequacies irritated by lower levels of literacy and education.

Education is the central factor that distinguishes the indegent from the non-poor, according to Pakistan’s Interim Poverty Decrease Strategy Newspaper 2001, the percentage of well written of people heads is definitely 27 in poor homeowners while for non-poor households it really is 52. Though the origins of human capital theory may be traced towards the earlier economic analysts – via Adam Jones (1776) to Alfred Marshall (1920) – it is Theodore Schultz (1961) who a new ‘human expenditure revolution in economic thought’ by emphasizing the role of human being capital in economic growth.

Schultz (1961), Gary Becker (1964), John Mincer (1972) and many others using their voluminous groundbreaking contributions placed education for a high base in the ideas of economical growth. Amartya Sen (1999) rightly states that education constitutes a element of human liberty and human capability.. In the period beneath study many important factors like unemployment, current account deficit and services expansion rate had been contributed to for what reason poverty is increasing despite the fact that education has grown consistently.

We have tried to offer a brief explanation of the argument of experts that in the event that increased education has significant impact on profits and thus poverty or certainly not or whether there are other factors mitigating or attenuating the impact of education on poverty. However in the analysis, the central concentrate has been around the role of education in poverty alleviation. Education offers important significance for the analysis of changes in a poverty account in a region. Keeping in view the issues high lighted previously mentioned, this newspaper tries to response following related questions.

Will education enjoy its role to alleviate poverty? What is the role of other important macroeconomic variables in low income alleviation? What can be general about the effect of education on poverty? What are quite policy implications? These inquiries keep all their extreme importance as addressing the stated questions will bring a solution to the hitherto puzzle that’s why Pakistan is lagging behind for the development course as compared to some developed countries who acquired independence afterwards than us. 1 Specialized consultation about literacy as a tool pertaining to the personal strength of the poor, Lampang, Thailand, 1997. thirty-six International Analysis Journal of Finance and Economics , Issue 52 (2010) To pursue the situation understudy, this kind of paper is usually technically divided into several parts. Firstly we certainly have attempted to describe the conceptual and assumptive framework of education and poverty relief. So far as the empirical examination is concerned, we have divided that into two portions. The first portion presents the descriptive studies and the second portion gives the econometric analysis that can be undertaken by simply considering autoregressive regression equations. II.

Education and Low income: A Assumptive Framework The economists typically define education as having ‘direct effects’ and ‘indirect effects’. The direct associated with education will be the imparting expertise and abilities that are linked to higher pay. The roundabout effects, also often referred to as external benefits, incorporate fulfillment of basic needs, higher numbers of democratic engagement, better usage of health features, shelter, water and sterilization and the added effects which occur in women’s behavior in decisions in relation to fertility, relatives welfare and health.

The relationship between education and low income can also be analyzed by level of returning analysis, and production function analysis – at specific as well as social/national levels. Costs of returning are believed using either Mincerian revenue function (Mincer, 1972), or perhaps using the concept of marginal efficiency of capital that pertains costs of education for the lifetime rewards, essentially revenue associated with education. III. Info and Methodological Issues To be able to study the impact of education on lower income, the study selects time series data, to get thirty five years (1972-2007) for Pakistan.

The poverty info sets will be collected primarily from Malik (1988), Amjad and Kemal (1997), Jamal (2003) and various problems of Pakistan Economic Survey since 2006, while the data on various other variables is definitely collected coming from World Financial institution, World Expansion Indicators (WDI), April 2008, ESDS Worldwide, (Mimas), University of Manchester. To make period series info on lower income incidence, a linear interpolation technique is utilized. The selected time frame presents the paradoxical circumstance of Pakistan as both equally growth and social symptoms move in reverse directions.

That is why it is chosen to understand this kind of paradoxical circumstance. Thirty five years time period is long enough for capturing long run effect of most of the varying constructed from this study. We certainly have tried to retain in view the trouble of endogeniety while selecting the informative variables intended for our analysis. The study chooses the absolute poverty (poverty headcount index), education literacy charge, primary university level registration rate, central school level and the university or college level enrollment widely used proxies for education) as the main element variables.

Additionally , some beneficial variables (Growth rate, pumpiing rate, and Trade openness) have also been contained in our version. In this examine, autoregressive designs are employed pertaining to econometric scientific investigation. In our first low income autoregressive regression model, expansion, literacy rate, CPI, and hcr(-1) are accustomed to analyze whilst in the second version, some registration rates at various levels are considered. In order to achieve the objectives of the study, trade openness is also considered to look into the robustness of globalization. Journal values with the variables are used in the research.

We �vidence that the incidence of low income prevailing in the economy is significantly dependent on higher education level. Worldwide Research Journal of Finance and Economics , Concern 52 (2010) 137 4. Results and Discussions a) Descriptive Analysis Our full data arranged consist of 35 years of total annual observations by 1973-2007 around the selected factors. The descriptive statistic is definitely reported in table you which says that the typical of brain count proportion (HCR) intended for our study period is usually 27. 63% with a common deviation (SD) of 6th. 74. The regular of main school enrollment rate is definitely 11316. almost 8 with 6204. 18, the value of its standard deviation (SD). Middle college enrollment is definitely 2667. 611 on an typical and with standard deviation (SD) 1326. 06. The typical values for university enrollment rate, real gross home product (RGDP) and visibility are 83045. 19, 22879. 24, 33. 81 while using value of standard deviations 65444. 71, 5756. seventy six, 3. 18 are given accordingly. As far as skewness of parameters is concerned brain count proportion (HCR), major school registration rate, midsection school registration rate and university enrollment rate happen to be skewed within the rightward while openness can be skewed leftward.

All the variables are skewed a little. Stand 1: Descriptive Statistics HCR 27. 63 25. 20 45. seventy five 20. 71 6. 74 1 . apr 3. 26 6. sixty four 0. ’04 LITR thirty-six. 93 thirty four. 35 55. 00 twenty-two. 10 twelve. 92 0. 24 1 . 56 several. 47 0. 18 MIDSECTION 2667. sixty one 2350. 00 5368. 00 963. 00 1326. 06 0. thirty-six 1 . 83 2 . 84 0. twenty-four PRIMARY 11316. 78 9827. 00 24465. 00 4210. 00 6204. 18 0. 57 2 . 02 several. 36 0. 19 UNIV 83045. 19 65642. 00 296812. 00 17507. 00 65444. 71 1 . seventy six 5. fifty nine 28. seventy four 0. 00 OPEN 33. 81 34. 35 37. 91 twenty seven. 72 several. 18 -0. 30 installment payments on your 19 1 . 53 0. 47 RGDP 22879. twenty four 23859. 71 33820. ’04 14033. 10 5756. seventy six -0. summer 1 . 86 1 . 97 0. 37 CPI 56. 51 39. 73 149. 0 six. 40 41. 73 0. 67 installment payments on your 16 three or more. 77 0. 15 Imply Median Maximum Minimum An std. Dev. Skewness Kurtosis Jarque-Bera Probability Kurtosis is a measure whether the info set is peaked or perhaps flat relative to a normal circulation. Kurtosis figure of the variables shows that only HCR and university enrollment is Leptokurtic (long tailed or substantial peakedness) and all other variables are Platykurtic (relatively narrow tailed then a normal contour. However the benefit of HCR is even though high when compared to value of Meso-kurtic curve but it is usually not too high from the value desired for any normal circulation.

The Jerque-Bera (JB) evaluation of normality gives joint hypothesis of skewness and kurtosis. Jerque-Bera test of normality suggest that if the calculated P-value of JB-statistic of university enrollment rate can be sufficiently low as the significance of the figure is very unlike zero, we state that the residuals to get university registration rate is usually not normally distributed. For all those other factors included in the present study, it is concluded that residuals for these variables are normally given away. Table a couple of: Correlation 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. twenty-five 0. 97 0. 98 MIDDLE 1 ) 00 0. 99 0. 86 0. 28 0. 97 zero. 98 PRIMARY UNIV WIDE OPEN RGDP CPI HCR LITR MIDDLE MAIN UNIV AVAILABLE RGDP CPI 1 . 00 0. fifth 89 0. twenty 0. 96 0. 99 1 . 00 0. 18 0. 84 0. 91 1 . 00 0. 39 0. seventeen 1 . 00 0. 94 1 . 00 The degree of the relationship of the variables is also approximated and reported in table 2 . All the variables are negatively linked to each other. The results claim that openness is highly correlated and primary, middle, university enrollment costs and RGDP are somewhat correlated with HCR. 138

International Research Log of Fund and Economics , Concern 52 (2010) b) Autoregressive Regression Evaluation In our research, we have applied a data set using period series including 1973-2007. To investigate the significance of education (literacy) on the occurrence of complete Poverty, we have following autoregressive regression types. The sturdiness of the types is examined by which include and excluding some crucial macroeconomic parameters in our evaluation. The unit is given because below: The Poverty Autoregressive Regression Model- 1 LHCR =? zero +? you LRGDP +? LLITR +? 3 LCPI +? four LOPEN +? 5 LHCR (? 1) +? i actually Table a few presents the estimation ends in which brain count index (HCI) is definitely the dependent variable and the parameters such as development rate, literacy rate, customer price index (CPI) and head count index (HCI) for the previous year are all explanatory factors in the present research. The value of tweaked Rsquared is definitely 94. 5%, implying that 94. 6% of the variation in the based mostly variable is definitely explained by the independent varying. The value of R-squared clearly reveals robustness of the results. The cost of hstatistic is 1 . almost 8, the effects indicates there is no significant autocorrelation issue in the error. The coefficient pertaining to growth confirms our theoretical expectations, implying an inverse relationship between poverty and growth. The coefficient to get growth is extremely significant putting an huge effect on lower income. The outcomes verify the findings of Sarris who also could find that overall economic growth reduces overall lower income. The pourcentage for literacy is significant in the poverty regression examination. However the adjustable is inversely related with the dependent varying which verifies the theoretical relationship of the two variables.

The above effects follow the results of Buck and Kraay (2002) that have concluded that growth is a dominant factor in removing poverty and that the impact of low level of educational attainment is not so much important. The coefficient from the consumer cost index (CPI) having a great expected assumptive sign, implies a positive romance with lower income. However agent is not really statistically very significant. Our results likewise second the findings of Romer and Romer whom believed that the increase in pumpiing will be connected with a decline in the unemployment in the short run that may well relatively benefit the poor.

The findings of Agenor (1998) also reinforce our hope on the outcome of our analysis implying the truth about the poverty prices to be efficiently related with pumpiing. The previous year’s poverty is extremely significant with the incidence of poverty. The coefficient with the variable can be keeping a postulated positive sign. The very best justification from the result has by the Ragner Nurkse who have could notice that a “country is poor because really poor. ” Although the assumptive expectations of the present study are satisfied yet we now have included more important factors pertaining to your capital.

We now have included principal, middle and university registration rates rather than the literacy price in our model. In order to examine the impact of globalization on the incidence of poverty, we have included the trade visibility in our analysis. The coefficient of openness is adverse and minor. Table several: Estimates with the Model-I Coefficient 5. 77051 -0. 62553 0. 512801 0. 004567 -0. 123046 0. 713883 0. 94 0. 93 1 . 49 Std. Problem 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) Ur Squared Adj R Squared h-Statistic Worldwide Research Record of Finance and Economics , Concern 52 (2010) 139 The Poverty Autoregressive Regression Model-2 It is a brilliant fact that problems like lower income cannot be exterminated at all. Due to the explained fact examine is intended to explore the answer with the question “Does education alleviate poverty? ” To investigate the query, we now have followed the regression style. We have created the lower income regression model. Primary, middle and school enrollment costs as a proxy for education are used inside our model.

The model has below:? 0 +? 1 LRGDP &? 2 LPRIMARY +? several LMIDDLE +? 4 LUNIV +? Low income =?? 5 LCPI &? 6 LOPEN +? 7 LHCR(? 1) + µ i? Table 4 gives the evaluation results intended for the lower income regression research where the based mostly variable may be the poverty had count index (HCI) and remaining several variables namely log of real low domestic product, log of primary institution enrollment, journal of middle section school enrollment, log of university enrollment, log of consumer value index, log of visibility and the journal of head count percentage of the earlier year are independent factors.

Note that the adjusted R-squared is 96. 9% suggesting that the approximately 95. 9% variation in the dependent varying is the result of the 3rd party variables. The coefficient pertaining to LRGDP is definitely keeping a bad sign implying the inverse relationship of LRGDP while using incidence of poverty. The theoretical romantic relationship of LRGDP and LHCR also supports the negative relationship of such two factors. But the agent for LRGDP is statistically insignificant pervading a little influence on the prevalence of poverty.

The pourcentage for sign of primary enrollment rate and record of middle section enrollment level both maintain a positive romance with the occurrence of low income implying that both the criteria minutely worsen the prevalence of poverty. The coefficients for both the levels are statistically insignificant which will shows reduced nuisance worth of primary and middle section standards of education. The results likewise match with the findings of Rodriguez K Smith (1994) and Coulombe and Mckay (1996) who have believe that the probability of being poor is higher for the low levels of education.

The pourcentage for the log of university registration rate is usually statistically extremely significant inside the poverty regression analysis as shown inside the table 3. The changing is inversely related with the dependent variable which verifies the theoretical relationship of the two variables. The evaluation results verify the results of all individuals who believe in a powerful role of human advancement poverty pain relief. The evaluation results be in line while using findings of Tilak (1994) which stress on the position of education.

The results also explain that higher education is one of the most effective means to reduce poverty. The results likewise match with the findings of King (2005) who has argued that the plan of the millennium development desired goals for education cannot be accomplished without supplying right thought to higher education. All the visible approaches of development just like the human capital approach, the fundamental need procedure, the human creation approach as well as the capability procedure which recognize the inverse relation of education and human poverty stay in line with our outcomes.

The pourcentage for pumpiing rate inside the poverty regression analysis intended for log beliefs 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 difficulty by the poor. The fact was also found simply by William Easterly and Stanlay Fischer (2001). According to them the rich happen to be better able to safeguard themselves against, or gain from, the effects of inflation then would be the poor.

The coefficient of openness is keeping a postulated negative sign, implying an inverse relationship between incidence of poverty and openness. The estimation result shows that visibility is powerfully influencing the poverty head count index as the coefficient of openness is found highly statistically significant. The results match with the conclusions of Derek H. C. Chen, Thilak Ranawera and Andriy Storozhuk who argue that high level of globalization, the positive effect would tend to increase poverty. The coefficient for the poverty of previous yr is statistically highly significant, keeping an optimistic relationship with poverty. 45 Table some: International Research Journal of Finance and Economics , Issue 52 (2010) Estimates of the Model-2 Coefficient three or more. 707976 -0. 205005 zero. 060653 0. 042189 -0. 154165 0. 127132 -0. 186327 0. 796384 zero. 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. zero. 0663 zero. 4133 0. 7139 zero. 8261 0. 0008 zero. 1134 zero. 1039 0. 00 114. 37 0. 00 Changing C LLGDP LPRIMAR LMIDDLE LUNI LCPI LOPEN LHCR(-1) R Squared Adj L Squared l Statistic

Versus. Conclusion and several Policy Suggestions In this daily news, we tackled a key a significant the current issue on economical development: the role of education in poverty reduction. We have evaluated the empirical evidence within the relationship between education and poverty. The hyperlink of education to low income is one of the most critical dimensions of policies toward poverty. Education may influence poverty in many ways. It may raise the incomes of those with education. It may in addition , by endorsing growth throughout the economy raise the incomes of those with given numbers of education.

To measure education we utilized, among others, the literacy price, primary education level, central education level and university education level as unblock proxies for education. To measure poverty, we emphasized within the concept of total poverty, making use of the poverty headcount index and as a proxy server for comparative poverty. We certainly have used the econometric techniques to sketch some stylized specifics in a very sophisticated framework of relationship. This current study incorporates macroeconomic, strength and coverage variables to poverty headcount index and education.

Specifically, the poverty equation links the prevalence of poverty to CPI, growth, literacy rate, primary school education, middle school education and university education level and openness. The said romantic relationship thus allows the changes in poverty because of the changes in macroeconomic or plan variables to get projected. The partnership is empirically estimated employing time series regressions, based upon thirty five years data of Pakistan by 1973 to 2007, which determined the magnitudes with the effects of all these macroeconomic, strength and plan variables in poverty.

The results from the empirical analysis indicate the university education significantly alleviates the prevalence of complete poverty. It can be concluded that school education comes up with a powerful tool for poverty alleviation, keeping an inverse relationship together with the dependent variable. As the bigger education improves, the level of low income decreases near your vicinity. This effect confirms the expectations that poverty is extremely influenced by simply education. Neighborhood universities support developing countries in bettering the skills of human capital which ultimately become attractive poverty treating.

University teachers have the specialised skills to earn a living and infuse all their sector of employment- if in the personal industry, people sector or civil society-with the enterprise that underpins success. Obtaining universal principal education, one of many millennium expansion goals, without the higher education could simply imply increasing the responsibility of not skilled population within the economy. A lot of people consider college or university education extra for developing countries. Not necessarily a luxury, it is necessary.

Our appraisal results what is best known techniques like the individual capital way, the basic needs approach, your development way and the Sen’s capabilities procedure as all four approaches primarily emphasize within the attainment of education for economic creation. Our evaluation results take an important coverage implication-namely the spread or the distribution of higher education among the population may have a powerful influence on their well being. A household with no education amongst any of it is members might benefit from also one member gaining usage of

International Analysis Journal of Finance and Economics , Issue 52 (2010) 141 education, over and above the immediate profits to that particular person. And this isn’t just the case when an improvement inside the education of the family’s kids, but also it becomes the better and immediate method to obtain earning chances for different members. Each of our empirical effects confirm that education plays a powerful role in poverty reduction. Accordingly, a focus of economic policies upon education in order to reduce lower income and to increase development appears to be justified.

Pumpiing also turns into the cause of poverty while operate openness decreases poverty substantially. Nevertheless, we recommend that inflation handled and operate opened plans will definitely and significantly talk about this issue of poverty pain relief in Pakistan. References [1] [2] [3] [4] Agenor, Pierre-Richard (1998). Stabilization plans, poverty and the Labour Marketplace, Mimeo, IMF and Universe Bank. Amjad, Rashid, and Kemal, A. R. (1997). Macroeconomic plans and their influence on poverty relief in Pakistan. The Pakistan Development Review, 36(1), 39-68. Becker, Whilst gary S. (1964).

Human Capital. New York Colombia University Press for NBER Chen, Derek H. C., Ranaweera, Thilak and Storozhuk, Andriy, (2004). The RMSM-X+P: A Minimal Low income Module for the RMSM-X (May 10, 2004). World Bank Insurance plan Research Doing work Paper Number 3304. Sold at SSRN: http://ssrn. com/abstract=610349 Dollars D, Kraay A (2002). Growth is wonderful for the poor. Diary of Financial Growth, several, 195-225. Irfan, Muhammad (2001). Global Tendencies on Education. The Oxfam Education Statement (2001), Phase 2 . Jamal, H. (2003). Poverty and inequality through the adjustment ten years: Empirical conclusions from home surveys.

The Pakistan Development Review, 42(2), 125-136. Khan, Mosin (1990). Macroeconomic Procedures and the Equilibrium of Obligations in Pakistan. 197286, IMF Working Paper /90 /78, Washington D. C. Malik, Muhammad Hussain (1988). New Evidence for the Incidence of Poverty in Pakistan. The Pakistan Expansion Review, 27(4), 509-516. Marshall, Alfred. Guidelines of Economics. London: Macmillan 1890 (1st edition), (1920) (8th edition). Book VI, Ch. IV, 2, a few and four (pp. 560—566), Chapter XI, 1 (pp. 660—661) and Chapter XII, § 9 (pp. 681—684). Mincer, Jacob (1972). Schooling, Experience and Earnings.

New York: NBER. Mincer, Jacob (1958). Investment in Human Capital and Personal Profits Distribution. Record of Personal Economy, 66. Ministry of Finance Pakistan (2001). Dealing with Poverty in Pakistan, www. finance. gov. pk Pakistan Economic Review (various Issues), Ministry of finance, Federal government of Pakistan, Islamabad. Romer, Christina and David Romer (1998). Economic Policy and the Well-Being in the Poor. National Bureau of Economic Exploration Working Paper 6793, Nov Sarris, Alexender H. (2001). The Position of Financial Development and Poverty Decrease: An Scientific and Conceptual Foundation.

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