Does Education Raise National Income - Coursework Example

Does education raise national income? Word count: 2180 words Introduction Education impacts competencies and skills that are vital to human development and raise the quality of life, bringing wide-ranging benefits to both societies and individuals. Education has long been realized and acknowledged as a fundamental right with far-reaching consequences for societal growth and human development (Chevalier, 2004). The effect of quality education that any child attains, in the course of their life and its relationship to the growth of national income, is a field that has raised concern, discussion and often heated debate. There are two basic arguments that one can expect to find some connection between education and growth. First and foremost, at the general level it is clear that standards of living have risen so much over the last years and to be specific since 1800 because of education. Progress of the sort enjoyed in some countries like Europe was not observed in the illiterate societies. People with only limited knowledge usually find hard to function at all in advanced economies. According to (Lopez-Bazo, 2008), education is necessary for people to benefit from technological progress as well as to add and contribute to it. Secondly, various econometric studies suggest that the incomes people can apply for depends on their education level. If learned people earn more than those without education, then the same should be true of countries? If government spending on education bears returns of some sort, in the same way as investing and spending on fixed capital, then it is reasonable to talk about spending in human capital, as an equivalent to spending and investing in fixed capital. The process of education can be analyzed as an investment decision. In this paper, panel data analysis will be applied to give a more comprehensive assessment of the effect of education on national income in both developed and developing economies. Data on GDP, primary and secondary school enrollment as well as government expenditure on education will be gathered. This data will then be regressed to see whether there is any evidence that education impacts on economic growth. Section 2 sets out the theoretical consideration that underlie the study and which guide the search for appropriate data and estimation strategies. Section 3 outlines the sources of data that has been employed in this paper, while section 4 outlines the econometric techniques used to estimate the model. Section 5 sets out the main results of the study whereas section 6 concludes. THEORETICAL FRAMEWORK There are various macro-economic theories that have been advanced relating to the growth of a country. In Neoclassical growth models such as the Solow model, Cass model and Koopmans model, a country’s growth rate are negatively correlated to its starting level of income per person. The Solow model to be specific, proposed that the analysis of economic growth begins by making an assumption of the standard neo-classical production function. This function demonstrates decreasing returns to capital. That is. For ease of explanation and exposition, it is assumed that there are only two primary inputs, labour (L) and capital (K) and one aggregate output (Y). The model also covers a case where there are multiple outputs and inputs provided the production function formed is homothetic. (Glewwe, 2004) stated that, if in a country, there is an increase in the average number of years of education of its labor force by one and assumes that uneducated and educated labour are perfect substitutes so that it does not matter whether an individual’s education has gone up by the same amount, or whether some people have expanded their education by more years, and others less than one year then labour supply will rise by the same amount. The increase in economic output resulting from this is the increase in effective labour capital multiplied by the share of labour in the overall product. It is quite likely that countries with high levels of education will also have more capital per worker; indeed if the amount of capital per effective worker is the same before and after the increase in educational attainment, then they will have to increase their education level. Taking the rate of saving and population growth as exogenous, Solow suggested that those two variables determine the steady-state level of income per capita. In that, the higher the rate of saving, the richer the country and the higher the population growth the poorer the country. A graphical representation of the Solow model is illustrated in figure 1. The Solow model makes the following predictions; In case economic growth occurs due to capital accumulation, then the following is expected. a) Economic growth will be strong when the country starts to accumulate capital but will decline with time. b) Countries will have a tendency of converging in standard of living and GDP per capita. However, there is limited theory relating to how education or investment in human capital affects economic growth of any country. In this regard, this paper will tend to fill this gap. The models and analysis in this study tend to look at growth in a cross-section of countries and explain it in terms of primary, secondary school enrollment and government expenditure on education. The regression model equation below will be employed in determining the effect of education on economic growth. Where, PE = Primary Education, gross primary school enrollment SE = Secondary Education,gross secondary school enrollment The Government= proportion of government expenditure spent on education as a percentage of gross domestic product. Growth= economic growth rate of the country which will be obtained by the formula Therefore, the growth rate of year 1950 will not be available sine there is no base year. The resulting hypothesis to be tested is that primary education; secondary education and government expenditure on education have a positive correlation on economic growth of both developed and developing countries. All the variables included in the model are expected to have positive coefficients. There is an expectation that government expenditure will have the greatest magnitude of economic growth. However, there are various factors that affect economic growth which include inflation levels, unemployment, and investment in capital goods among many others (Glewwe, 2004). Therefore, the economic growth rates obtained in this paper will not only reflect changes in education, hence, the results can be biased. DATA To measure the effect of education on economic growth rate, the annual growth rate of 3 developed countries and 3 developing countries is employed in this paper. Development of this paper involved an integrated approach involving analysis of secondary data. Secondary information was collected through a review of relevant literature, government policy documents and publications, and publications and reports produced by international agencies such as World Development Indicators | Data – The World Bank, United Nations Data and UNESCO. The study will attempt to determine the effect of schooling on national income by collecting data of 3 developing countries and 3 developed countries from year 1950 to 2010, those developing countries are China, Malaysia and Thailand. On the other side, those developed countries will be France, United Kingdom and United States. The table 1 indicates the sample means and standard deviations of each variable included in the model. The sample means shown in the table above indicate the relevant characteristics of developed and developing countries mentioned earlier. In addition to creating a hypothetical benchmark, the means also enable one to determine any characteristic difference between countries and their contribution to education. It seems that developed countries have a higher secondary school enrollment than developing countries whereas in developing countries more students are enrolled in primary school than in developed countries. Furthermore, developed countries spend a high percentage of their GDP on education. The coefficient of FITTEDSQ in both the developed and developing countries is not statistically significant. This indicates that when an additional variable of the square of S is introduced, the specifications of the model will not improve. The figure 2 and 3 indicate the graphical growth trends for both the developed countries and developing countries. Growth in developed countries is relatively constant over time with very minor variations as indicated in the graphs. On the other hand, in developing countries, growth keeps changing from time to time. ECONOMETRIC METHOD Since the data to be collected is time series and involving various exogenous variables, the study will use Stata in conducting regression. To be more specific, panel data analysis will be employed. Panel data are a type of longitudinal data, or data collected at different points in time. (Baum, 2006) stated that the longitudinal data may include; a) Time series data where there are so many observations. b) Pooled cross sections where two or more independent samples of many units (N) are drawn from the same population at different time periods. c) Panel data where there are two or more observations on many units (N). RESULTS From the results in table 2 and 3, in both developed and developing countries, primary school enrollment is negatively related to growth. This is demonstrated by the negative coefficient of the primary education. This rejects the hypothesis stated earlier. Primary education as a predictor of changes in economic growth is statistically significant at 5 percent significance level in model 1 and 3 for the developed countries and in model 2 for the developing countries. Secondary school enrollment has a positive correlation with economic growth in both the developed and developing countries. This proves the hypothesis stated in the previous section to be true. However, in model 2 of the developing countries, an increase in secondary school enrollment leads to a decline in economic growth. Hence, if an assumption is made that the variations across the variables is random and uncorrelated with economic growth included in the model, then, secondary education becomes inversely related to economic growth. In all the models, secondary education is not statistically significant. On the other hand, government expenditure on education has an inverse relationship with economic growth. This nullifies the hypothesis stated in the previous chapters. However, just like in the case of secondary education, increase in government expenditure on education in model 2 of the developing countries causes an increase in economic growth. Government expenditure on education is not statistically significant in this model. The r-squared in all models is relatively low hence the exogenous variables included in the model are not good predictors of change in economic growth. Model 4 in both the developed and developing countries represents the ARCH results. The ARCH term’s t-ratio is statistically significant and hence a conclusion is made that the variance is autoregressive conditionally heteroskedastic (which for good measure should be repeated out loud three times). Model 5 gives the results of the Newey-West test in both the developed and developing countries. This is used to correct autocorrelation among the variables since large samples have been used. Conclusion The focus of this study was to find out the effects of schooling or education on national income for both developed countries and developing countries. This was done by collecting data from 3 developing countries which included China, Malaysia and Thailand and 3 developed countries which included France, United Kingdom and United States. This data was collected from the year 1950 to 2010 for different variables relating to education enrollment, government expenditure and life expectancy. These objectives were achieved by conducting panel data analysis in Stata. In both developed and developing countries increase in primary school enrollment decreases the national income of the countries. All the coefficient variables of primary education are negative in all the models depending on whether fixed or random effects are employed. An increase in secondary school enrolment leads to a increment in the overall GDP and economic growth of the respective countries in both the developing and developed countries. This is because improvement in secondary education addresses food insecurity and poverty in the long run by dealing with the root problems of numeracy in the society or community and of illiteracy. The importance of secondary education improvements to overall gains in economic growth suggests that this should be a high priority for future investigation. On the other hand, increase in government expenditure on education as a proportion of gross domestic product contributes to a decrease in the overall national income in both developed and developing countries. Developing countries are the most affected in terms of increase in government expenditure on education since most of the resources will be devoted to education, and little is devoted to other development projects. This will cause economic growth to decline. Among the various mechanisms employed in this study, there is the strongest support for the idea that education quality and education enrollment impacts economic output through changes in the rate of technical progress (Andrew Gelman, 2007). This is supported by the panel data models of the level of economic output and is also in agreement with the findings from the growth rate models. These findings of the education impact on economic growth of improved cognitive ability at a country-wide level complement, and extend similar findings at the individual or at the small farm level summarize other international studies and highlight the similarity of separate findings for the United States. References Andrew Gelman, J. H., 2007. Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press. Baum, C. F., 2006. An Introduction to Modern Econometrics Using Stata. Texas: Stata Press. Chevalier, A., 2004. Does Education Raise Productivity, or Just Reflect it?. The Economic Journal, Volume 114, pp. 499-517. Glewwe, P., 2004. Economic Growth and the Demand for Education: Is there a wealth effect. Journal of Development Economics, 74(1), pp. 33-51. Lopez-Bazo, E., 2008. Does Human Capital Stimulate Investment in Physical Capital? Evidence from a cost-system framework. Economic Modelling, 25(6), pp. 1295-1305. APPENDIX Table 1: summary statistics Developed Countries Variable Observations Mean Std. Dev. Min Max Growth 180 0.105 0.105 -0.12 9.46 PE 183 93.831 6.215 70 100 SE 183 80.432 13.551 33 100 Government 183 5.181 0.983 2.8 7.9 Ramsey Reset 1.121 (1.169) Developing countries Growth 180 0.116 0.11 -0.2 0.66 PE 183 98.077 13.984 73 130 SE 183 40.945 20.064 9 81 Government 183 3.337 1.739 0.1 7.7 Ramsey Resey 12.291 (28.001) Figure 1: the Solow model Figure 2: relationship between growth and time in years in the developed countries Figure 3: relationship between growth and time in years in the developing countries Table 2: regression output for developed countries (1) (2) (3) (4) (5) Growth Growth Growth Growth Growth PE -0.025 -0.028 -0.025 -0.023 -0.025 (0.01)* (0.015) (0.01)* (0.001)** (0.026) SE 0.006 0.005 0.006 0.000 0.006 (0.01) (0.01) (0.01) (0.001) (0.006) Government -0.067 -0.039 -0.067 -0.074 -0.067 (0.083) (0.095) (0.08) (0.001)** (0.043) Constant 2.313 2.593 2.313 2.691 2.313 (0.881)** (1.09)* (0.88)** (0.133)** (2.295) Observations 180 180 180 180 180 Number of country_1 3 3 3 R-squared 0.03 Standard error statistics in parentheses * significant at 5%; ** significant at 1% Table 3: regression output for developing countries (1) (2) (3) (4) (5) Growth Growth Growth Growth Growth PE -0.001 -0.003 -0.001 -0.002 -0.001 (0.001) (0.001)* (0.001) (0.001)** (0.001) SE 0.000 -0.000 0.000 -0.001 0.000 (0.001) (0.001) (0.001) (0.001) (0.000) Government -0.009 0.011 -0.009 -0.006 -0.009 (0.007) (0.011) (0.007) (0.004) (0.008) Constant 0.232 0.370 0.232 0.348 0.232 (0.085)** (0.123)** (0.085)** (0.057)** (0.108)* Observations 180 180 180 180 180 Number of Countries 3 3 3 R-squared 0.03 Absolute value of standard errors in parentheses * significant at 5%; ** significant at 1% Read More