Human Capital, Population and Economic Growth: A Cointegration Approach

Human Capital, Population and Economic Growth: A Cointegration Approach Fozia Sarwar 1, Amjad Fakher 2, * Sajid Ali 3, & Khalil Mudassar 4 1 Visiting Lecturer, Islamia University Bahawalpur, Pakistan. 2 Evaluation Officer, Social Welfare and Bait-ul- Mal Department, Lahore, Pakistan. 3 Lecturer in Economics, Government College of Science, Multan, Pakistan. 4 Assistant Professor, Government Sadiq Egerton College, Bahawalpur, Pakistan. * sajidali1136@gmail.com Abstract This study examines the role of human capital formation in economic growth in Pakistan by using the secondary data for the period of 1973 to 2012. Time series properties of data are analyzed by ADF unit root test. To check the cointegration among series, the Johansen Cointegration test is applied. The results of test showed that there are long run relationship between economic growth, employed labor force, population, education expenditures, health expenditures and fixed capital. Education expenditures, employed labor force and population growth have negative relation with economic growth while health expenditures and gross domestic fixed capital formation have positive and significant effect on economic growth. Keywords: Human Capital; Economic Growth; Population; Pakistan 1. Introduction With the start of charming era of capitalist economy almost for half a century, the most important concern of the world s nations has remained to increase the economic growth. Synonymously, it meant to accelerate the productivity and to raise the per capita and aggregate income. But with the time the achievement of high income growth does not remain such a problem but the sustainability of high economic growth rate became the point of debate. The modern growth theory, along with the fixed capital formation views growth as endogenous phenomenon and human capital formation is much necessary for sustainable economic growth rates (Ashton 2002). Human capital formation means the investment in human beings for proper utilization of human resources. Such an investment resulted in increase in productivity. Due to increase in capability, productivity and skills of labor increase the employment and contributes positively to national income (Rodrik 2003). Human capital formation is the only process which reversed the negative effects of increasing population through proper utilization of labor force. It is now a largely accepted fact that the given diminishing marginal productivities capital accumulation alone is not enough for sustained economic growth (Barro and Martin 1995). Economy of Pakistan is the 27 th world s largest economy based on its purchasing power and due to 37% cultivated area, warm climate and world s 2 nd largest canal system. Pakistan is considered as agri-based economy. The journey of Pakistan on the path of economic growth started from the miserable condition at the time of its independence in 1947 and reached to 11% per year rise in economic growth in 60 s. This economic boom ended with great political turnover in country which resulted in partition of country into two parts. The remaining Pakistan grew at the rate of almost 5% in 70 s. The economic decade of 70 s is considered as u-turn of economic policies of Pakistan from the policies that were practiced in 60 s (State Bank Annual Report). The economic growth of Pakistan has remained more or less five percent on average. Despite of all, Pakistan is far behind in social indicator statistics in comparison with other nations. Even Pakistan has enjoyed a good economic growth rate in its economic history, but these growth rates remained 20

unable to fulfill the requirements of increasing population and failed to absorb the growing labor force which resulted in massive concentration of wealth and high inequality among the people (Nasir 2002). The growth rate in various years of Pakistan is represented in below Figure. Figure Trends in Growth Rate Growth Rate 9 8 7 6 Growth Rate 5 4 3 2 1 0 1990-91 1992-93 1994-95 1996-97 1998-99 2000-01 2002-03 2004-05 2006-07 2008-09 2010-11 Years Source: various issues Economic survey of Pakistan (2010-11), Ministry of finance Pakistan is the 6 th populous denomination of the world with 2 percent population growth rate. During 1950-2008, urban population of Pakistan has increased by seven times while total population has increased by four times. In beginning years of Pakistan, the growth rate of population was very high but later on it became moderate due to declining fertility and birth rates. Our increasing labor force is not supporting our economic growth. The reasons are poor quality of labor force regarding skills, education level and health. Due to poor health conditions the life expectancy of Pakistan is 64.13 years and according to estimation, 48.1 million labor force is unemployed (Economic Survey of Pakistan). Accumulation of human capital is an important contribution to economic growth. The role of education and health cannot be ignored for the development of human capital. They both are inversely related with poverty and have direct and significant role in sustainable economic growth. An educated and skilled workforce is able to generate ideas and better to implement new technologies for improving efficiency. The relationship between human capital formation and economic growth matters for an additional reason (Colman 1990). A large proportion of Pakistan s population is living below the poverty line. In Pakistan, just economic growth is not enough to generate the well being of dwellers of the country. Thus the improvement in economic growth indicators can be enhanced by improvement in health and education expenditures (Akram and Khan 2008). Pakistan s growing population can be used to enhance economic development of the country. Education is the major investment in human capital due to its higher rate of return. Along with education, experience, quality of schooling, numbers of schooling years and technical training have significant positive impact on earnings (Ahmad 2005). 21

2. Literature Review Many studies have been carried out to observe the relationship between human capital and economic growth in different countries. These studies suggest that human capital formation deals with such capabilities like literacy, skill development, health facilities and experience. Furthermore, it is said that the share of human capital in economic growth is greater than the amount of physical capital. Some important studies from developed and developing countries are included in this section. Sabot (1989) estimated a human capital wage function with the help of OLS method. He included measures of reasoning ability and cognitive skills and the usual variables measuring the employment experience and the education level of employees. The results have shown that the experience of workers had significant and positive while its square was significant and negative. The production function also included a gender variable. Males performed significantly better on the math exam than females with the same level of ability and qualification. Females performed significantly better in the reading exam. The education of father has a significant and positive impact on the achievement of cognitive skills by sons. It has no clear influence on the cognitive skills and acquisition process of daughters. Greiner (1990) analyzed that the main cause of human capital formation was public education. He examined the structural growth model and traced out implications of public debt. He concluded that the primary surplus GDP ratio was positively and linearly related to debt GDP ratio which led to sustainable public debt. In his analysis it was revealed that a loose fiscal policy cannot allow sustainable growth in the long run unless the government was a creditor. We can fulfilled intertemporal budget constraint of the government if the ratio of primary surplus to GDP was positively and linearly related to debt ratio. Barkley (1991) studied the determinants of inter-district labor migration in Pakistan using data of population censuses of 1972 and 1981. He computed district in-migration rates and also estimated regression analysis to identify the major determinants of inter-district migration during 1971-1980. He found a significant positive correlation between district in-migration rates and urbanization, higher levels of years of education, developed infrastructure and roads, and the percentage of previous migrants in a district s population. He estimated the result that inter-district migration appeared to be the outcome of a rational decision making process for better economic prospects. Birdsall et al. (1993) estimated the costs of Pakistan in terms of income growth foregone over the last three decades in Pakistan of relatively low investments in education. They used an econometric analysis of the relationship between years of schooling and economic growth in different countries to compare Pakistan s actual rate of growth. They said that much of the difference between Pakistan and East Asia countries in enrollment ratio is due to the gender gaps in enrollments in Pakistan. The result was estimated by equating primary enrollment ratios for girls to the enrollment ratios for boys. This simulation raised primary enrollments from 30 to 44 percent and secondary enrollments from 11 to 18 percent. When these changes in enrollment rates applied to the coefficients they indicate an increase in the annual growth rate of 0.61 percentage points in analysis. Khilji (2005) examined the relationship of human capital formation and economic growth with reference to population growth in Pakistan. It was argued that Pakistan s growing population can be used to enhance economic development of the country. Education was the major investment in human capital due to its higher rate of return. Along with education experience, quality of schooling, numbers of schooling years and technical training have significant positive impact on earnings. However, there was a gender gap in earning as males earn more than females. High women fertility 22

was negatively related with growth. He also concluded that gender and regional imbalance, mortality and fertility rates, high dependency ratio and overpopulation were the other major obstacles in the way of development. Mustafa et al. (2005) explored the role of vocational education and skills development programs in human capital formation. They argued that training and skills development were fundamental part of human capital formation. Individuals with better skills can become more productive. They reviewed the status of vocational training and its impact on human capital formation in Pakistan. The effect of the rate and changeability of increase in institutions, education enrolment and teachers on output growth was estimated. The labor market survey revealed that 58 percent enterprises affirm that the pass-outs of vocational education after employment needed 3-6 months on-the-job training specific to the plant for improving general skill proficiency. Koyin et al. (2006) examined the relation among economic growth, investment in human capital and health expenditure for OECD countries. The model was constructed by using production function as health capital was one component of production function. The main sources of determining health expenditures have three categories as income, demographic factors and other exogenous factors such as the nature of the health care system, the rate of inflation, health care structure and nonmedical health determinant of population. For demographic perspective, urban population ratio, crude mortality rate, population growth and population density were used. The results indicated that less expenditure on health will lead to higher mortality rate. There was a negative relationship between unemployment and health expenditures. With higher unemployment people will spend less to health care. The higher population growth in countries with higher population density will lead to lower health expenditures. Chaudhary et al. (2010) explored the causality relationship between human capital, economic growth and trade liberalization in Pakistan. A time series data on trade liberalization and economic growth for the period 1972-2007 were used. Real gross domestic product, gross fixed capital formation, employed labor force and human capital index were used. Linear modal was determined and augmented dickey- fuller test was performed to determine the stationary of variable. All the variables were found positively correlated with each other and found statistically significant. Trade openness was positively related with economic growth. Labor force participation had positive but human and physical capital had inverse relation with growth. Using error correction model short run analysis showed that the trade liberalization had positive but insignificant impact on growth. Moreover, labor force participation with two years lag was positively and significantly related with growth. Faridi et al. (2010) estimated the effect of education level on level of employment. They collected primary data from Bahawalpur district of Pakistan. They made a comprehensive statistical analysis of the workers and applied econometrical technique by using logit model. Their operational model consists of different variables i.e. years of education, enrolment, health status, household assets and marital status. Logit estimates found that experience of worker has significant and positive impact on level of education. Opportunities of employment and level of education both were positively correlated. Furthermore, the health of worker had also positive and significant impact on employment. 23

3. Data and Methodology This study is based on the secondary sources of data on Pakistan for the period of 1973 to 2012. Gross domestic product at market prices is taken as a dependent variable. The data for this study are obtained from Pakistan Economic Survey (of various years), State Bank of Pakistan and World Development Indicators. 3.1 Econometric Model To examine the relationship between human capital formation, population and economic growth, the specified model has been analyzed by employing the method of Johansen s Cointegration Technique. The gross domestic product is dependent variable while employed labor force, education expenditures, health expenditures, gross domestic fixed capital formation and population are independent variables. An econometric model of the selected variables used in this study is given as: LGDP= β 1 + β 2 LEDEXP + β 3 LEMP_LF + β 4 LGDFCF + β 5 LHE + β 6 LPOP + µ Here LGDP = Log of Gross Domestic Product LEDXP=Log of Education Expenditures LEMP_LF= Log of Employed Labor Force LGDFCF= Log of Gross Domestic Fixed Capital Formation LHE= Log of Health Expenditures LPOP= Log of Population µ = Error Term We discussed the unit root test to check the stationarity which is prerequisite for cointegeration. We also applied the Johansen s Cointegeration Test, after that we discussed the Vector Error Correction Model (VECM) which is designed to deal non-stationary series that are known to be co integrated and at the end Granger causality test is described. 4. Empirical Methodology The first step of empirical methodology is to check stationarity of variables by using unit root test. The purpose of testing the stationarity is to escape from the results of spurious regression. When a pair of independent variables is found to be apparently related according to an OLS regression, it is called spurious regression. 4.1 Unit Root Test The co integration test can only be applied to those variables which are non stationary in level i.e having trending pattern in data. So it is our first task to determine the form of trends in data and remove them. Augmented Dicky Fuller (ADF) test is applied with trend and intercept to determine the non stationarity of each variable. 24

The equation for ADF test is as follows: Without Intercept and Trend: ΔY t = δy t-1 + α iσδy t-1 + µ t With Intercept and no Trend: ΔY t = β 1 + δy t-1 + α iσδy t-1 + µ t With Intercept and Trend: ΔY t = β 1 + β 2t + δy t-1 + α iσδy t-1 + µ t The null and alternative hypotheses are as follows: H 0: δ = 1 (non-stationary) H 1: δ < 1 (Stationary) 4.2 Cointegration Test Cointegration test is used for the reason that a linear combination of two or more time series cannot be stationary even all the time series are individually stationary. The co integration will be performed if all the variables have same order of integration. Cointegration involves level of the variables in a straight forward regression to observe the long run equilibrium relationship between variables. So we apply Johansen s Cointegration Test in this study. 5. Descriptive Analysis Descriptive statistics is the discipline of describing the main quantitative features of a collection of data. They give uncomplicated and simple summaries about the data and the measures. It is used for quantitative analysis of data. Descriptive statistics of the variables of our model is given in following table. Table 1 Descriptive Statistics of the Variables GDP EDEXP EMP_LF GDFCF HE POP Mean 2148162 47635.27 30.77 374345.5 13234.45 110.05 Median 855943 25520.80 29.90 148076 7321 110.36 Maximum 10242799 253700 48.10 2094743 59898 161 Minimum 54673 796.80 18.55 6812 198.72 63.34 Std. Dev. 2669239 60229.68 7.93 514760.5 14992.78 29.84 Skewness 1.52 1.83 0.39 1.97 1.45 0.08 Kurtosis 4.46 6.05 2.34 6.32 4.49 1.77 Jarque-Bera 17.67 35.01 1.63 41.07 16.38 2.37 Probability 0.00014 0.0000 0.44287 0.0000 0.00027 0.30590 Observations 40.00 40.00 40.00 40.00 40.00 40.00 All the estimations are carried out by E-views 3.1 (Quantitative Micro Software) 25

In Table 1, the detailed descriptive analysis is carried out. It exhibits that the average of gross domestic product is 214861 with standard deviation of 2669239. The average of education expenditures is 47635.27 with standard deviation of 60229.68. The average for employed labour force is 30.77 with the standard deviation of 7.93. The average for gross domestic fixed capital formation is 374345 with standard deviation of 514760.5. The average for infant mortality rate is 4.53 with standard deviation of 0.15. The average for health expenditures is 13234.45 with standard deviation of 14992.78. The Average for population is 110.05 with standard deviation of 29.84. The skewness is a measure of the lack of symmetry in the data. Education employed labor force and population are little skewed as compared to the rest of the variables. Kurtosis statistic of the variables shows that only employed labor force and population are platykurtic (flat or short tailed) and all other variables are relatively leptokurtic (longtailed or higher peak). Correlation coefficient shows the degree of linear relationship between two variables. A Correlation Matrix is a table which shows all possible correlation coefficients between a set of variables. Correlation matrix of the variables of our model is given in following table. Table 2 Correlation Matrix of the Variables GDP EDEXP EMP_LF GDFCF HE POP GDP 1.00 EDEXP 0.98 1.00 EMP_LF 0.92 0.91 1.00 GDFCF 0.98 0.98 0.88 1.00 HE 0.99 0.99 0.94 0.98 1.00 POP 0.87 0.85 0.98 0.82 0.89 1.00 All the estimations are carried out by E-views 3.1 (Quantitative Micro Software) Correlation matrix shows the strength of the relationship of variables. Table 2 shows that all variables are positively and highly correlated with each other. The results of our regression equation showed that it is a spurious regression. So we have to apply the time series analysis. The objective of this analysis is to examine the short run and long run relationship of dependent and independent variables. 6. Unit Root Test After finding the spurious regression the first step is to test the stationarity of variables. The Augmented Dickey Fuller (ADF) test developed by Dickey and Fuller (1979) for unit root has been used at level, at first difference and at the second difference of each series. 26

Table 3: Results of Augmented Dickey-Fuller Test (ADF) for Unit Root. Results of unit root test with trend and intercept variables level 1 st difference 2 nd difference conclusion LEDEXP 2.48 5.39 7.22 I(1) LEMP LF 2.66 6.41 7.67 I(1) LGDFCF 5.37 9.31 6.74 I(1) LGDP 4.50 4.77 7.27 I(1) LHE 3.54 4.07 6.81 I(1) LPOP 0.97 6.00 9.69 I(1) The null hypothesis is that the series is non-stationary, or contains a unit root. The rejection of null hypothesis for ADF test is based on the MacKinnon critical values 5%. The ADF results show that all the variables are not stationary at level but stationary at first difference, so the null hypothesis for stationary is rejected at 5 % level of significance for all variables and the order of integration is one for all the variables. 7. Johansen Cointegration Test Now we check the cointegration among variables using Johansen cointegration test. The first step is to select optimal lag length by computing unrestricted VAR model based on various optimal lag length criteria. Table 4 presents the results of optimal lag length selection criteria of the unrestricted VAR model. Table 4 : Optimal Lag Length lag LogL LR FPE AIC SC HQ 0 133.5496 NA 2.22e-11-7.502919-7.233561-7.411060 1 330.7619 313.2196* 1.75e-15* -16.98600* -15.10049* -16.34299* 2 358.2020 33.89648 3.58e-15-16.48247-12.98082-15.28830 3 396.9837 34.21922 5.66e-15-16.64610-11.52830-14.90079 * Indicates lag order selected by the criterion. By LR, FPE, AIC, SC and HQ criteria the optimal lag length is one. After the selection of optimal lag length, the next step is to apply Johansen Cointegration Test. Johansen Cointegration test with linear trend and intercept is shown in the table: 27

Table 5: Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.744949 141.3000 117.7082 0.0007 At most 1 * 0.616916 93.47976 88.80380 0.0220 At most 2 0.544275 59.89723 63.87610 0.1033 At most 3 0.349807 32.39197 42.91525 0.3676 At most 4 0.246327 17.32498 25.87211 0.3911 At most 5 0.191199 7.427094 12.51798 0.3021 Trace test indicates 2 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 6: Unrestricted Cointegration Rank Test (Maximum Eigen value) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.744949 47.82026 44.49720 0.0210 At most 1 0.616916 33.58254 38.33101 0.1589 At most 2 0.544275 27.50526 32.11832 0.1651 At most 3 0.349807 15.06699 25.82321 0.6284 At most 4 0.246327 9.897883 19.38704 0.6298 At most 5 0.191199 7.427094 12.51798 0.3021 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values 28

From table 6, we see that trace test indicates two co integrating vectors, while the maximum Eigen value test indicates one co integrating vector. On the basis of maximum Eigen value test, one cointegrating vector exists in our case. Results show that there exists long run relationship among variables. The cointegrating vectors have been normalized on LGDP to determine the signs and magnitude of the long run relationship among above equations. The results of cointegration tests are shown in Table 6 which indicates one cointegrating relationship. Table 7 represents normalized cointegrating coefficients. Table 7: 1 co integrating Equation(s): Log likelihood 335.1229 Normalized co integrating coefficients (standard errors in parentheses) LGDP LEMP_LF LEDEXP LGDFCF LHE LPOP C 1.000000 0.867419 0.846691-0.118119-1.129737 0.297015-0.11 (0.45554) (0.10250) (0.02952) (0.08910) (0.53646) (0.01380 There exists long run relationship among the variables. Health expenditures as proxy of human capital and gross domestic fixed capital formation has positive while population has negative relation with economic growth. More specifically 1% increase in health expenditures lead to 1.12% increase in economic growth. Since long run relationship is observed among variables. 8. Vector Error Correction Model After checking the cointegration among variables, the next step is to apply vector error correction model to analyses both long run relationship and short run dynamics. Table 8: Results of Error Correction Model Variables Coefficients Standard Errors speed adjustment of t-statistic -0.027593 (0.00510) [-5.40744] D(LGDP(-1)) -0.163347 (0.16698) [-0.97826] D(LHE(-1)) 0.210303 (0.06531) [ 3.21986] D(LPOP(-1)) -0.125160 (0.59426) [-0.21062] D(LGDFCF(-1)) 0.001576 (0.01193) [ 0.13215] D(LEMP_LF(-1)) -0.437449 (0.31284) [-1.39832] D(LEDEXP(-1)) -0.121164 (0.06361) [-1.90468] C 0.168010 (0.02700) [ 6.22174] R-squared 0.581500 Adj. R-squared 0.473000 F-statistic 5.359452 29

The results of error correction model showed that the sign of speed of adjustment is negative which indicates the adjustment of equilibrium towards long run equilibrium. 9. Conclusion and Suggestions The study is conducted to provide the empirical evidence on the relationship between human capital, population and economic growth. The analysis is based on the annual data for the period from 1973 to 2012. Time series properties of data are analyzed by ADF unit root test. Unit root test indicates that all variables are stationary at their first difference. So the variables have same order of integration I(1). To check the cointegration among series, the Johansen Cointegration Test is applied. The results of test indicate that there is long run relationship between economic growth, employed labor force, population, education expenditures, health expenditures and gross fixed capital formation. Education expenditures have negative relation with economic growth which is reverse to the theory, this is because of inadequate education expenditures or perhaps the major reason is poverty that in spite of free primary education people are less likely to willing to send their children to school, instead of this they send them for child labor. So the education expenditures do not affect the economic growth positively. Population growth has negative relationship with economic growth; the rapidly increasing population increases the dependency burden of households rather to increase the growth. Due to lack of job opportunities the increasing population does not positively contribute to economic growth of Pakistan. Against the theory the employed labor force has also the negative impact on growth but there is a strong reason behind it that there is low employment as compared to population and also there is a wide gap between the skills required and the skills available in labor force due to the lack of job training. Health expenditures and gross domestic fixed capital formation have positive and significant effect on economic growth. The error correction model showed the laged relationship among variables and the long run relationship is shown by Granger Causality Test. According to this study, some policy recommendations are stated as follows: i- Government should increase job opportunities so that the increasing population could enhance the economic growth of country and become a mean of growth rather than a hurdle in the way of economic development of the country. ii- Poverty is another major obstacle in the path of growth. A major share of government expenditures goes in the way of non development expenditures rather than the development activities. Government should make measures to alleviate poverty so that the fruits of government expenditures could be gained. iii- Government should improve the quality of labor and control the population growth of the country which has become the today s most challenging issue. iv- Technological advancements are also necessary to meet the necessities if the rapidly growing population and to compete the fast moving economies of the world. v- Inflation should be controlled. No doubt the subsidies given to education should be used on school buildings but training of teaching staff and improvement of teaching methods is also necessary 30

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