Relationship between Per Capita Gross Domestic Product and Secondary School Enrolment Rate - Assignment Example

?RESEARCH METHODS 27th, January, RESEARCH METHOD 0 Definitions 2.0 3.0 Introduction 4.0 Methods 5.0 Results and discussion 6.0 Conclusion 7.0 Reference list 8.0 Appendix Definitions ypc05 = per capita GDP (constant prices, chain series) in 2005 ypc90 = per capita GDP (constant prices: chain series) in 1990 dlypc = ln(ypc05) – ln(ypc90) lypc90 = ln(ypc90) seced = proportion of secondary school age population enrolled at Secondary school in 1990 lseced =ln(seced) govgdp = government share of real GDP per capita in 1990 open = openness = ratio of (exports+imports) to GDP in 1990 cpi90 = consumer price index value in 1990 cpi85 = consumer price index value in 1985 infl = five-year inflation rate = ln(cpi90) – ln(cpi85) credit = ratio of private credit by deposit money banks and other financial institutions to GDP in 1990 RESEARCH METHODS Abstract Introduction: This report is based on a research that was carried out with the aim of determining the relationship between per capita gross domestic product and two economic determinants; secondary school enrolment and banks’ credit rates. This is to aid the finance minister’s decision on whether to fund secondary education or banks. Motivation: The need to determine the relationship between the above variables with the aim of advising the minister for finance is the key drive into this work. Problem statement: This work seeks to determine the existence and degree of relationship between per capita gross domestic product and both secondary school enrolment rate and bank rates. Approach: The research adopted a secondary approach of data collection upon which data was collected from recognized institutions. This was followed by an in depth analysis of the results using Stata software. Results: Analysis identified significant relationship between per capita gross domestic product and secondary school enrolment rate. Though relationship with bank rates was not significant, the general model indicated higher effects of secondary school enrolment on per capita gross domestic product than effects of bank rates. Conclusion: Financing should be directed to secondary schools and not banks. Introduction Gross domestic product is the measure of a country’s total productivity level. It refers to the total cost of output in commodities. Elements of gross domestic product include ‘consumption, investment, government purchase, and net export’ (Mankiw, 2008, p. 496). Both consumption and net export of an economy are factors of the territory’s available economic resources and its level of disposable income. With high levels of disposable income, people are able to purchase into consumptions as well as invest into export dealings. Investments, on the other hand, refer to monetary value of resources that are used for production processes. Whether through private or public sector, investment rates and levels depend on the availability of resources and the capacity to acquire such resources through savings or borrowings. The last component of gross domestic product is government expenditure through central government, local governments, and governmental institutions in public utilities such as education (Mankiw, 2011, p. 198). Per capita gross domestic product measures the net output per person. It therefore depends on a country’s population size and may have a different trend from the real gross domestic product (Boyes and Melvin, 2007, p. 389, 390). One of the fundamental contributors to economic growth is the availability of resources for injection into the economy. Since financial institutions are a source of monetary resource through provision of loans, they are of prime importance to economic growth. Provision of loans to investors and private consumers for instance has direct effects on consumption, investments, and net export (Brooks, 2008, p. 502; Yartey et al, 2008, p. 22). Credit rates of banks, which is a factor to their lending capacity determines availability of loans to investors and consumers. Similarly, lower banks credit rates leads to reduced availability of loans relative to demand. This leads to higher interest rates on borrowers, reduced borrowings with a consequently reduced consumption and investment capacity. The overall impact is then a reduced gross domestic product and a negative economic growth (Brooks, 2008, p. 502: Schadler, 2005, 1996). Credit rates also affects prices of capital goods which consequently contributes to production processes (Segoviano et al, 2006, 10). According to Chami (2008, p. 61), education levels have also been associated with economic growth through expansion of gross domestic product. Education for instance empowers people through career developments into well paying jobs. Individuals who proceed to high schools and tertiary institutions have high propensity to good employment opportunities and income (Brewer and McEwan, 2010, p. 63). Education also contributes to development of rational thinking that prompts savings and investments. It is therefore a direct stimulus to per capita gross domestic product. Employment opportunities with higher income for instance lead to higher taxes to the government as well as greater potential for investments and increased consumption levels. Higher education levels, attained through secondary education, also facilitate the use of advanced technology in economic ventures. As a result, lower operational costs and high levels of savings and production capacity are initiated (Bloom et al, 2005, p. 16). Since elements per capita gross domestic products are variables, statistical analysis such as regression analysis and test of hypothesis can be used to determine and ascertain existence and degree of the relationships. Regression analysis for example determines existence of a relationship between a dependent variable and a set of explanatory variables and establishes significance of such relationships. The analysis tool also determines the rate of contribution of each explanatory variable to a dependent variable (Freund et al, 2006, p. 35- 43; Gujarati, 2009, p. 13-20). Linear regression however makes assumptions of linearity, homoscedasticity, and normality of variables (Allen, 2004, p. 182- 185; Newbold, Carlson & Thorne, 2010, p. 428). This paper seeks to investigate the relationship between per capita gross domestic product and two dependent variables, rate of enrolment in secondary schools and credit rates of financial institutions. The paper will answer two research questions, ‘Is there a significant relationship between per capital gross domestic product and the two dependent variables?’ and ‘Which of the explanatory variables has higher effects on per capita gross domestic product, high school enrolment rate or credit rates of banks and other financial institutions?’ In order to answer the research questions, the paper will test the following sets of hypothesis, H 0: ?i=0; There is no significant relationship between per capita gross domestic product and the considered explanatory variables. H 1: not all are zero; there is a significant relationship between per capita gross domestic product and the considered explanatory variables. Using analytical approach, the effects of the two independent variables on per capita gross domestic product will be analysed. The paper will also test on the validity of statistical assumptions of regression analysis. Methods Participants and design The set of data used in the research relates to statistics of economic indicators of a variety of countries. The countries therefore formed the participants of this work. Data was collected, transformed before analysis. Materials Secondary resources were used for data collection. These were reliable resources as they were obtained from established institutions. Procedure The research procedure involved acquisition of sets of data from the sources, organization, and subsequent transformation of the data into derived variables. Analysis was then done by use of ‘Stata’ software. Result and discussion Developed spreadsheet The attached spreadsheet in appendix 1 shows the compiled data for the fifty countries that were considered in the project. Testing hypothesis The research used ‘Stata’ to test the following model dlypci = ?1 + ?2lypc90i + ?3lsecedi + ?4govgdpi + ?5openi + ?6infli + ?7crediti + ui Where ?s are constants and u represent noise. The symbols in the model are as defined above. The set of hypothesis is H 0:?2=?3=?4=?5=?6=?7=0, there is no significant relationship Against the alternative hypothesis, H 1: At least one of ?2, ?3, ?4, ?5, ?6?7, ?0, there is a significant relationship. The null hypothesis is accepted because of the high probability value of the general regression model. This implies that there is no significant relationship between the variables as expressed in the above model. Further, the model explains only less than 20% of the analyzed data making it unreliable. An alternative test of hypothesis to test for significance in relationship between the dependent variable and each explanatory can also be undertaken through considerations of coefficients of the regression. The following set of hypothesis are considered, H 0: ?i=0, no significant relationship between the dependent and the explanatory variable, H 1: ?i?0, there is a significant relationship. The probability values for ?2, ?3 and ?7 are 0.013, 0.117 and 0.136 respectively. This means that the null hypothesis for ?2 should be rejected. The hypothesis is however accepted for ?3 and ?7. Application of student-t distribution tables yields the same conclusions as follows. For ?2, t= =2.59 The table value is 2.04 leading to rejection of the null hypothesis, at 95% confidence interval. For ?3, = 1.599 This leads to acceptance of the null hypothesis, at 95% confidence interval, since the computed value falls within the acceptance region. For ?7, =1.52 The relatively smaller computed value leads to acceptance of the null hypothesis, at 95% confidence interval. Even though the general model suggests absence of relationship between per capital gross domestic product and the explanatory variables, single inferential tests indicates existence of a significant relationship between the per capita gross domestic product and the percentage of secondary school enrolment. The contradiction can be explained by the existence of many other variables in the general model that do not contribute to the dependent variable. Advice to the finance minister From the model, unit percentage increase in secondary school enrolment leads to a corresponding increase in per capital gross domestic product by 0.2502821*In (65) %- In (55%) = 4.18% A unit percentage increase in bank credit has an effect of 0.2124701* (52%-38%) =3% on per capital gross domestic product, though this effect is not significant. The minister should therefore direct the funds to secondary education. Test for validity of statistical assumptions The statistical assumptions made over the considered set of data are linearity, homoscedasticity, and normality Using the RESET test for the null hypothesis of a linear mode against an alternative hypothesis of a nonlinear model leads to acceptance of the null hypothesis. The LM test for ‘homoscedasticity’ also leads to acceptance of the null hypothesis of homoscedasticity. A consideration of ‘Bera’ and ‘Jarque’s skewness- kurtosis’ test however leads to rejection of the null hypothesis of normality. Remedy for lack of normality Remedying lack of normality involves elimination of extreme values. Values for outliers, data corresponding to Zimbabwe are subsequently exluded. A subsequent test over the assumptions indicates linearity, homoscedasticity and normality. Effects of re specifying and re estimating the model After re specifying and re estimating the model, secondary education and bank credit rate had the following effects, Effect of ‘lseced’ on per capita gross domestic product =0.2599967*In (65) %- In (55%) = 4.34% Effect of credit on per capita gross domestic product = 0.1564118* (52%-38%) =2.2% Revaluation of specification and estimation of the model does not alter the previous advice to the minister. This is because secondary school education still holds higher effects on per capital gross domestic product. Conclusion Since per capita gross domestic product is a derivative of gross domestic product, the two economic indicators are determined by levels of investments, consumption, government expenditure, and net export of a country. This research, on the impact of government expenditure on per capita gross domestic product, evaluates the degree of contribution of both secondary education and bank credit rate on per capita gross domestic product with the aim of advising the minister of finance on the most effective option for financing. Statistical analysis to the revelation that secondary education has higher and effects on GDP leads to recommendation that available funding should be directed towards secondary education. Reference list Allen, M., 2004. Understanding Regression Analysis. New York, NY: Springer Science & Business Bloom, D., Canning, D., & Chan, K., 2005. Higher education and economic development in Africa. Available at: p. 16. [Accessed on 26 January 2012] Boyes, W & Melvin, M., 2007. Economics. Boston, MA: Cengage Learning Brewer, D. and McEwan, P., 2010. Economics of Education. San Diego, CA : Elsevie Brooks, C. 2008., Introductory Econometrics for Finance. London, UK: Cambridge University Press Chami, R., 2008. Macroeconomic consequences of remittances. Washington, DC: International Monetary Fund Freud, R., Wilson, W. And Sa P., 2006. Regression analysis: statistical modeling of a response variable. London, UK: Academic Press Gujarati, Damador & Porter, Dawn. (2009). Basic econometrics. New York, NY: McGraw-Hill Mankiw, G. 2011., Principles of Macroeconomics. Mason, OH: Cengage Learning Mankiw, G., (2011). Principles of Economics. Mason, OH: Cengage Learning Newbold, Paul, Carlson, William & Thorne, Betty. (2010). Statistics for business and economics. London, UK: Pearson. Schadler, S., 2005. Adopting the euro in central Europe: challenges of the next step in European integration. Washington, DC: International Monetary Fund Segoviano, M. and Basurto M., 2006. Default, credit growth, and asset prices, Issues 2006-2223. Washington, DC: International Monetary Fund Yartey, C., 2008. The Determinants of Stock Market Development in Emerging Economies: Is South Africa Different?, Issues 2008-2032. Washington, DC: International Monetary Fund Appendix 1   country ypc90 ypc05 open govgdp CPI90 CPI85 seced credit 1 Algeria 5314.63 6291.14 73.97 10.85 98.12 85.65 61 0.4 2 Australia 23209.99 34323.39 28.85 13.46 112.1 84.85 82 0.13 3 Bangladesh 1616.16 2166.01 17.81 8.18 20.8 23.39 19 0.21 4 Belgium 24558.91 31750.13 124.59 14.84 112.3 68.78 103 0.35 5 Brazil 7811.24 9000.3 13.39 21.34 50.76 34.79 38 0.24 6 Burkina Faso 926.09 1290.77 59.15 38.37 51.68 40.56 7 0.18 7 Cameroon 2710.21 2579.45 30.56 10.67 42.21 30.51 28 0.28 8 Canada 25534.32 34590.49 49.94 15.21 108.9 91.43 101 0.77 9 Chile 8639.98 16965.69 47.41 16.17 45.54 43.04 73 0.47 10 China 1929.15 6482.99 23.82 20.27 22.95 30 49 0.86 11 Cote d`Ivoire 2890.67 2315.96 63.45 12.74 47.54 32.77 22 0.4 12 Ecuador 4882.98 5755.93 41.55 21.28 32.49 59.65 55 0.12 13 Egypt 3595.06 5230.06 62.33 7.41 33.39 37.41 76 0.28 14 Ethiopia 859.95 963.19 27.95 18.38 32.49 38.94 14 0.23 15 France 23657.62 28779.31 32.71 16.86 120.7 75.72 99 0.92 16 Germany 24599.27 29547.74 40.66 12.02 113.3 69.59 98 0.93 17 Ghana 1258.5 1530.09 58.88 18.12 38.46 57.32 36 0.05 18 Greece 17022.2 25467.06 36.71 14.13 79.84 48.97 93 0.35 19 Hungary 11441.58 16216.88 36.52 27.65 38.39 30.4 79 0.45 20 India 2001.59 3365.34 17.05 28.29 29.55 33.71 44 0.26 21 Indonesia 3216.91 4883.97 46.59 18.32 26.45 33.55 44 0.37 22 Iran 5691.14 9498.28 75.76 13.88 260 83.44 55 0 23 Italy 23168.6 27794.86 42.58 13.32 114.2 64.35 83 0.48 24 Japan 26384.61 29780.3 16.86 10.71 131.2 89.34 97 1.92 25 Kenya 2061.24 2017.39 43.02 8.41 26.49 32.17 24 0.3 26 Korea 11908.21 22048.39 32.56 10.16 71.63 53.58 90 0.9 27 Madagascar 1071.44 862.79 57.23 12.09 31.15 36.51 18 0.15 28 Malawi 935.71 1179.62 55.7 6.72 29.12 25.23 8 0.13 29 Malaysia 8418.95 16481.49 139.83 13.87 43.53 50.1 56 0.67 30 Mali 880.52 1254.06 45.73 19.82 41.02 26.29 7 0.12 31 Morocco 4499.87 5096.45 44.93 10.7 28.23 21.05 35 0.13 32 Nepal 1453.76 1885.79 31.53 16.32 21.8 23.78 33 0.12 33 Netherlands 24618.6 32638.07 78.34 17.61 100.9 63.98 120 1.4 34 Nigeria 1339.46 1810.23 56.44 7.02 40.67 103.41 25 0.12 35 Pakistan 2425.93 3269.38 32.09 18.53 26.24 28.75 23 0.24 36 Peru 4024.44 5733.98 24.54 12.71 44.23 22.04 67 0.04 37 Philippines 3385.71 4063.08 74.32 13.53 25.53 27.8 73 0.2 38 Poland 7194.65 12666.11 27.72 20.19 27.51 41.98 81 0.02 39 Saudi Arabia 22516.86 20731.34 79.73 17.74 48.85 62 44 0.64 40 South Africa 7915.05 9609.77 38.4 22.27 45.73 29.33 74 0.84 41 Spain 19111.88 29150.46 27.62 11.87 98.98 51.88 104 0.75 42 Sri Lanka 3151.19 5328.64 54.8 23.42 21.91 22.73 74 0.18 43 Sudan 955.79 1959.82 29.33 6.41 163.5 52.41 24 0.06 44 Syria 1816.6 2595.87 71.3 23.84 129.5 140.14 52 0.07 45 Thailand 5405.67 8666.41 90.5 11.93 38.47 35.79 30 0.72 46 Turkey 5366.32 7132.83 24.63 15.27 69.38 45.98 47 0.13 47 Uganda 740.1 1167.26 27.08 32.61 39.99 62.88 13 0.02 48 U.K 21742.5 30275.79 36.97 16.48 102.7 68.53 85 1.13 49 Venezuela 10146.72 10972.88 46.47 21.96 38.18 60.39 35 0.23 Read More