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“Impact of Education and Health on Poverty in Pakistan A Critical Study” Sabeen Khurram Khan*, Mohamed Nasr** and Natasha Hamidani*** INTRODUCTION Poverty, headcounts and also known as income gap is when the proportion of populations income falls below a specified poverty-line. The income required bringing the poor class beyond the poverty level, and comprehending the income disparity amongst the poor is termed as the FGT Index. Regardless of the measure through which poverty is gauged; the poverty-line, consumption levels vital to meet the food and other fundamental needs of the general community play an important role in the poverty estimation. (Amjad and A. R. Kemal 1997). Poverty is a multi-dimensional concept among others things it includes lack of access to sufficient health services and sanitation, a high degree of illiteracy, insufficient income, and scarcity of basic rights and security. This multi faceted concept of human deprivation interacts in many significant ways; e.g. good health leads to higher productivity and improves the performance and results in increased incomes. In Pakistan, significant research have been carried out that measures poverty. In these studies incidence of poverty has been measured for one, two or at the most for three different survey years. These surveys used different definitions and methodologies that yielded results which were not strictly comparable. Analysis of poverty provides insight of the nature and extent of poverty, not just as measure of consumption, but also as a human development index. Raise in human development contributes positively to educational attainment and health improvement. Eventually this investment in human capital will also help alleviate poverty. Collectively, the educational attainment and health of population is, therefore, vital to the country’s ability to lessen poverty level and develop economically. Policy-makers and economist are always interested to understand and evaluate the linkages of poverty alleviation, education and health. ____________________________ *Sabeen Khurram Khan, COMSATS Institute of Information Technology, Park Road, Chak Shahzad Islamabad, Pakistan. [email protected] **Dr. Mohamed Nasr, COMSATS Institute of Information Technology, Park Road, Chak Shahzad Islamabad, Pakistan ***Natasha Hamidani, COMSATS Institute of Information Technology, Park Road, Chak Shahzad Islamabad, Pakistan. [email protected] SIGNIFICANCE The study aims to identify the relationship between two factors of human resources on poverty alleviation. It will be of considerable importance: for the improvement of the poor regions of the country, but will also provide invaluable insight in to this important matter for the government. It will also help to identify the weak areas of socio-economic setup. Moreover, this research will suggest certain strategies and techniques on practical basis. With the help of those strategies, poverty can be reduced and the factors which amplify poverty could also be relegated. Furthermore, this research will cast light upon need to develop its relation to the reduction of poverty with reference to the improvements in the quality of Education and Health. Thus, this research will be an authentic fact file to alleviate poverty and to improve the quality of life. More specifically the purpose of the study is: To find out whether poverty is decreasing in Pakistan To determine the nature and strength of relationship of Education and Health with poverty alleviation. To find out the extent of reduction in poverty due to improvement in Education and Health. To analyze techniques and remedies to reduce poverty. PROBLEM STATEMENT: This study is entitled as: “Do Education and Health Help Alleviate Poverty? With Reference to Pakistan” The focus of the study will be to investigate the relation and dependency between Education, Health and poverty alleviation (with special reference to Pakistan). LITERATURE REVIEW Improvement in human resources and increase in investment by human capital contributes to poverty reduction (Li Wei 1994). Angell and Graham found that there is a significant link between poverty alleviation and social sector reforms (SSR). Further, they also cited that long-term investments in human capital and short-term safety net measures are given more attention now than in the past. A study done by Shenggen Fan, peter hazel, and Sukhaded Thorat’s (2000) showed that government expenditure on education and health results in poverty reduction in rural India. Gupta and Mitra (2004) study assessed the likely link among poverty, health and economic growth; by using panel data for Indian States. They concluded despite the fact that economic growth reduces poverty but health improvement is also essential for poverty alleviation. Explanatory variable such as literacy and industrialization contributed to growth, better health conditions and poverty reduction. The major proposition is that better health results in increased economic growth, at the same time economic growth causes in improved health status of people. For high living standards and accelerated economic growth increased investment in growth promoting areas like industry, education and health is required. Human development is critical in nurturing growth. Growth and human development reciprocate and strengthen each other: human development promotes growth, and growth promotes human development. A program that is effective and promotes growth and human development creates a "virtuous circle" of swift progress in poverty reduction‖. Hence, efficient strategies for human development are imperative factor of the growth strategy. By adopting a pro-poor growth strategy and promoting human development, poverty can be reduced (Kemal 2000). Of the many characteristics of the poor, the major are: lack of education and skills, large family size, and other human resources. According to O. S. Verma, the only asset of the poor is human capital, and in reducing poverty, its development is of fundamental importance. Protection of poor against health hazards and risks, building marketable skills and eradication of destructive practices like child labor, play a pivotal role in human capital development. In order to increase the productivity and contribution by the society, the relevance, quantity and quality of social services need to be ensured. Studies conducted by Human Resource Development in Asia and the Pacific in the 21st Century show that primary schooling improves the productivity of small farmers. Evidence from 13 low income countries shows, that 4 years of schooling is accompanied by some 8% increase in farm output. It is important to mention that there were complementary investments in better roads, access to marketing facilities, fertilizers and improved crop varieties facilitating to increase the productivity. This all lead to the fact that the positive impact of 4 years of primary schooling is higher. There is positive correlation between government spending on education and productivity of people. According to human capital’s conventional theory developed by Becker (1962) and Mincer (1974) education and training are the major sources of human capital accrual that, in turn, have direct and positive effect on individuals’ life time earnings. Streeten (1983) noted that improvements in the nutrition, health and skills of people result in the development of the human resources and poverty alleviation. ECO-UNDP is assigned the task to intensify poverty reduction efforts through human resources development in compliance with the UN Millennium Development Goals. Special programs at national and regional levels are planned to accelerate development of areas lagging behind in economic growth and appropriate income redistribution policies. (ECO-UNDP) THEORETICAL FRAMEWORK This research will be conducted to investigate the relation and dependency between different factors of human resources (Education and Health) development ultimately resulting in poverty reduction. The main indicators of human resources being considered as explanatory variables are Education and Health. The dependent/response variable in the model is the poverty status of individuals which will be categorized into two categories: poor and non- poor. All explanatory variables and response variable in the model are qualitative. Since the response variable is dichotomous, a binomial logistic regression model will be set-up. One of the most significant factors to lessen the poverty rate within the country is education. The better the education of the people within the country, higher will be their earning rate. Education facilitates a society in accumulation of technical, managerial and entrepreneurial skills that are needed to overcome natural, environmental and physical, and resource constraints for development; consequently it increases output and boosts living standards (Farooq & Ofosu, 1992). The better is the education that people attain, the higher will be their productivity, and will enable them to earn more income. The World Health Organization (WHO) defines health: ―as a state of complete physical, mental, and social well-being—not merely the absence of disease or infirmity.‖ One of the principal non-income characteristic of poverty is Low health status. The poor are most vulnerable to sickness and face untimely death caused due to dietary reasons. Furthermore, their children tend to have less birth weight and usually are unable to have approach to medical care. Hence poor people suffer more from ill health as compared to the poor. According to the one of the reports by World Health Organization, ―the poorest 20% of the global population are 14 times more likely to die in childhood than the richest 20% of the world’s population.‖ Likewise, more Indian women die through pregnancy in a week as compared to European women in a year‖ (DFID, 2000). For poor people illness is cause of suffering and pain which for them is key characteristic of being poor. For them, illness is probable largest cost they bear when a sole household bread winner is not able to earn his or her income. So, good health is an important element in reducing poverty. Better healthcare, besides good education, is anticipated to improve work output of existing and prospective generations. Presently the healthcare status of Pakistanis, in particular, females, is not up to the mark. According to the Human Development report of UNDP (2001), ―female life expectancy in Pakistan is 65.1 years, higher than the male life expectancy of 62.9 years; but it is lower than the female life expectancy in most developing countries‖. The reasons for poor health (physical disabilities) include malnutrition, bad sanitary conditions, and backward medical facilities which are the outcomes of financial constraints. In the past few decades, it has been researched that the rate of people bearing poor health are usually illiterate or semi-literate. Hence, education has a direct link with health. VARIABLES: Following are the variables: Independent Growth Domestic Product (GDP), Education Expenditure (EXPE), Health Expenditure (EXPH), Life Expectancy (LIFE), Literacy (LIT) Dependent Poverty (HCR) DATA & METHODOLOGY The survey data for this research has been taken from Economic Survey of Pakistan – 2008, Pakistan Social and Living Standards Measurement Survey (PSLM – 2006),World Bank data sets, International Financial Statistics (IFS) etc. Following model has been estimated to investigate the relationship of Education and Health on poverty in Pakistan. Poverty, Health and Expenditure Equation HCR = f (GDP, EXPE, EXPH, LIFE, LITI) The Multivariate co integration methodology could be defined as: (S)t = (GDP, EXPE, EXPH, LIFE, LIT) The null hypothesis of non-cointegration among variables is rejected when the p ^ estimated likelihood test statistic i { n ln(1 i} exceeds its critical value. t r 1 Error Correction Method (ECM) has also used to capture the short-run disequilibrium situations as well as the long-run equilibrium adjustments between variables. Following model has been estimated. Poverty & Health and Education equation D( HCR) O D(GDP) 1 D( EXPH ) 2 D( EXPE ) 3 D( LIFE) 4 D( LIT ) The coefficients 1 to show short run elasticities of the independent variables on dependent variable. Whereas D stands for first difference and (-1) indicates lag value. is error correction term, adjustment coefficient.. Estimation and Results First of all, we checked the stationarity of different variables that we used in our study. For this purpose we applied Augmented Dickey-Fuller (ADF) test. Table 1 gives the results of ADF tests. Results showed that these variables are integrated of order one i.e. I(1). Table 1. See Appendix Table 2. See Appendix Now we observed the association between the dependent (HCR) and independent variables (Esucation and health) using the Multivariate Co integration Methodology. The study finds that there is existence of statistically significant relationship between dependent and independent variables. So improvement in education and health status influence positively to alleviate poverty. Table 3: See Appendix Table 3.1: See Appendix Table 3.2: See Appendix Study finds a link between Poverty and other growth terms when allowing this relationship to vary with other controlled variables. This seems to be good for poverty alleviation when they allowing other well being indicators with GDP growth. In order to check stability of long-run relationship between GDP and independent variables, we estimate VAR Model. Table 4: See Appendix Table 4 indicates that education, life expectancy and literacy has a significant effect on poverty at 5% confidence interval in the long run. Thus empirical results show that there exists a long run relationship between them. SUMMARY & CONCLUSION From the above mentioned discussion, one reaches to the conclusion that there exists a strong and effective relationship between poverty, education and health. The two main factors that really play a pivotal role in poverty alleviation are better health and sanitary conditions and productive quality education. Keeping in the mind these factors, the current economic condition of Pakistan can be greatly improved by improving the health and educational conditions. REFERENCES Anand, Sudhir., and Martin Ravallion (1993). ―Human Development in Poor Countries: On the Role of Private Incomes and Public Services‖ in The Journal of Economic Perspectives, Vol.7, No.1. (Winter, 1993) Becker, G. (1964). Evaluating the impacts of human capital stocks and accumulation on economic growth: some new evidence. Oxford Bulletin of Economics and Statistics, 58(1), 9-28. Fan, Shenggen., Peter Hazell, and Sukhadeo Thorat (2000) ―Government Spending, Growth and Poverty in Rural India‖ Gupta, Indrani., and Arup Mitra (2004). ―Economic Growth, Health and Poverty: An Exploratory Study of India‖ in Developed Policy Review, Vol.22 (2004) ―Equity and Development‖ World Development Report 2006 (New York: Oxford University Press) Kemal,Amjad (1997) ―Structural Adjustment, Macroeconomic Policies, and Poverty Trends in Pakistan.‖ Lawrence, John. E. S. (1992). ―Literacy and Resources Development: An Integrated Approach‖ in Annals of the American Academy of Political and Social Sciences, Vol.520, World Literacy in the Year 2000 Maddala, G.S. (2001).―Introduction to Econometrics‖, 3rd Edition. (England: John Wiley & Sons Ltd.) Mincer, J., 1974. Schooling, Experience and Earning. National Bureau of Economic Research, New York, U.S.A. Morrisson, Christian (2002). ―Health, Education and Poverty Reduction‖ in OECD Development Centre, Policy Brief No.19 Nurual Islam. Growth, Poverty, and Human Development: Pakistan ―Pakistan Poverty Assessment (Poverty in Pakistan: Vulnerability, Social Gaps, and Rural Dynamics)‖ October 28, 2002 Sahibzada, Mohibul Haq (1997). ―Poverty Alleviation in Pakistan, Present Scenario and Future Strategy‖ (Islamabad: Institute of policy Studies) Sekaran, Uma. ―Research Methods for Business (A Skill Building Approach)‖, 4th Edition. (Carbondale: South Illinois University) Wei, Li (1994). ―Human Resources Development and Poverty Alleviation: A Study of 23 Poor Countries in China‖ in Asia-Pacific Population Journal Vol.9, No.3, September 1994 APPENDIX Table 1. Augmented Dickey-Fuller (ADF) Test on the levels and on the First Difference of the Variables (1980-2007) Mackinnon Critical Values for Rejection of Hypothesis of a Unit Root Variables Level GDP 1.455 EXPE stationary 0.0924 EXPH stationary -0.084 LIFE 1.7101 LIT stationary 2.0821 POV -2.645 First Differences -4.596 1% -2.656 -3.088 -5.477 5% 10 % -1.954 Decision Order -2.656 -1.609 Non-stationary at level but I(1) stationary at first difference -1.954 -1.609 Non- -2.656 at level but I(1) stationary at first difference -1.954 -1.609 Non- at level but I(1) stationary at first difference -3.163 -2.656 -1.954 -1.609 Non-stationary at level but I(1) stationary at first difference -3.243 -2.656 -1.954 -1.609 Non- -2.964 non-stationary at first difference -2.656 -1.954 at level but I(1) stationary at first difference -1.609 Stationary at level but I(0) Table 2: Poverty has a Unit root Null Hypothesis: HCR has a unit root Exogenous: Constant, First level Constant First level t-Statistic t-statistic Augmented Dickey-Fuller test statistic -2.470130 -2.828 Test critical values: 1% level -3.724070 -3.724070 5% level -2.986225 -2.986225 10% level -2.632604 -2.632604 *MacKinnon (1996) one-sided p-values. Table 3: Johansen’s Test for Multiple Cointegration Vectors Cointegration Test among HCR, GDP, EXPE, EXPH, LIFE, LIT Unrestricted Cointegration Rank Test (Trace) Hypothesized No. of CE(s) None * At most 1 * At most 2 * At most 3 * At most 4 At most 5 Eigenvalue Trace Statistic 0.05 Critical Value Prob.** 0.957054 0.873010 0.770990 0.721412 0.483170 0.042355 182.0023 115.8985 72.56197 41.60820 14.76972 0.908844 95.75366 69.81889 47.85613 29.79707 15.49471 3.841466 0.0000 0.0000 0.0001 0.0014 0.0641 0.3404 Trace test indicates 4 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Table 3.1 : Johansen’s Test for Multiple Cointegration Vectors Cointegration Test among HCR, GDP, EXPE, EXPH, LIFE, LIT Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized No. of CE(s) None * At most 1 * At most 2 * At most 3 * At most 4 At most 5 Eigenvalue Max-Eigen Statistic 0.05 Critical Value Prob.** 0.957054 0.873010 0.770990 0.721412 0.483170 0.042355 66.10381 43.33655 30.95376 26.83848 13.86088 0.908844 40.07757 33.87687 27.58434 21.13162 14.26460 3.841466 0.0000 0.0028 0.0177 0.0070 0.0578 0.3404 Max-eigenvalue test indicates 4 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Table 3.2 : Johansen’s Test for Multiple Cointegration Vectors Cointegration Test among HCR, GDP, EXPE, EXPH, LIFE, LIT Normalized cointegrating coefficients (standard error in parentheses) HCR GDP EXPE EXPH LIFE -1.000000 2.48E-05 - 9.595014 4.650295 -15.67096 (2.2E-06) (9.63607) (7.06311) (2.28041) LITERACY -0.403285 (0.69521) Table 4: Vector Auto-Regression Estimates Variables Coefficients t-Statistics C 0.0045 10.40 DPOV(-2) -1.688 -5.138* DEXPE(-2) 4.406 2.364** DEXPH(-2) 0.645 0.2991 DLIT(-2) 5.5726 3.859* DLIFE(-2) 2.541 3.201* CE(-1) -0.0176 -5.80* R-squared = 0.903 Adjusted- R squared = 0.795 ARCH LM Test F = 8.395* Normality: Skewness and Kurtosis Note: *(**) represents the coefficients are statistically significant at 1% (and 10%) levels.