Relationship between per capita Gross Domestic Product and Secondary School Enrolment Rate - Research Paper Example

RESEARCH METHODS 30th, January, RESEARCH METHOD 0 Definitions 2.0 3.0 Introduction 4.0 Methods 5.0 Resultsand 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 to form a basis for advising the finance minister on policymaking. It aimed at analyzing the relationship between per capita gross domestic product and two economic indicators, secondary school registration rates and banks credit rates, in order to determine the more effective one to per capita gross domestic product. Motivation: The work was motivated by the need to advise the minister for finance. Problem statement: This research sought to determine the existence and extent of relationship between per capita gross domestic product and secondary school enrolment rate and bank rates. Approach: Data was collected from secondary sources, transformed and analyzed using Stata software. Results: There exist a significant relationship between per capita gross domestic product and secondary school enrolment rate. Secondary school education also has higher impacts on per capita gross domestic product than bank credit rates. Conclusion: The minister should channel available funds to secondary schools instead of banks. Introduction Gross domestic product refers to a country overall output of goods and services. It relates to the total quantity of production and is determined through elements such as consumption, investments, government expenditure, and net export of a country. (Mankiw, 2011, p. 496). Availability of resources, either through the government or private sectors, loans or savings forms the backbone of the four elements of gross domestic product. Consumption for instance depends on people’s level of disposable income that can be obtained through savings or borrowings. The same applies to finances for resources into investments. Injections into the economy through government expenditure are on the other hand realized through central and local governments in the form of remuneration to civil servants as well as in public utilities. Net export, defined as the difference between exports and imports, is a factor of investments and therefore a derivative of disposable income (OECD, 2010, P. 32). Per capita gross domestic product is on the other hand the measure of output per person and a factor of both real gross domestic product and the country’s population (Boyes and Melvin, 2007, p. 389, 390). Banks and other financial institutions, through provision of loans to individual investors and entities, therefore play very important roles in ensuring availability of disposable income in an economy. Consequently, the institutions facilitate both consumptions and investments (Brooks, 2008, p. 502; Yartey et al, 2008, p. 22). Bank credit rates, a factor to their capacity to offer loans to the public, are therefore a tool to economic growth. Low credit rates for instance impose strains that can lead to scarcity of loans relative to demand. As a result, such situations would lead to increased lending rates that might not be favourable and might reduce consumptions and investment rates. A crisis in the sector is therefore expected to have a negative impact on per capita gross domestic product. (Raina and Bakker, 2003, p. 5: Schadler, 2005, 1996). High interest rates on loans for investments also lead to high production costs and an ultimate strain on disposable income (Brigham and Ehrhardt, 2010, p. 362). Another identified variable to per capita gross domestic product is individuals’ level of education. Essence of education in career development as well of academic qualifications as determinants of an employee’s salary explains the relationship (Brewer and McEwan, 2010, p. 63). High rational decision making together with technological advances that are associated with high education levels also illustrates a relationship between education levels and per capita gross domestic product. Rationale into savings facilitates accumulation of disposable income besides investments into exports. Education into technological innovation and application also has an impact of reduced costs for further savings and disposable income (Bloom et al, 2005, p. 16). Relationship between per capita gross domestic product and its determinants such as education and banks lending capacity can be investigated through statistical analysis techniques such as regression analysis and tests of hypothesis on significance. Regression analysis for example investigates existence of a relationship between a dependent variable and a set of independent variables and establishes significance of such relationships. It also helps to understand the degree to which each explanatory variable contributes to the dependent variable (Wang and Jain, 2003, p. 1-3; Gujarati, 2009, p. 13-20). Linear regression however makes assumptions of linearity, homoscedasticity, and normality of variables (Nisbet et al., 2009, p. 264; Newbold, Carlson & Thorne, 2010, p. 428). This paper seeks to study the relationship between per capita gross domestic product and two variables, high school attendance and credit rates of financial institutions. The paper will determine to answer the following research questions, ‘Is there a significant relationship between per capital gross domestic product and the two dependent variables, secondary school enrolment rates and bank credit rates?’ and ‘Which of the dependent variables induces higher effects on per capita gross domestic product?’ The following set of hypothesis is used to answer the questions, H 0: i=0; There is no significant relationship Against, H 1: not all are zero; there is a significant relationship The strength of relationship between per capita gross domestic product and the two variables is then investigated. Test on validity of regression analysis assumptions is also done. Methods Participants and design Participants in the research were selected countries across the globe. Economic data relating to the countries were then collected, transformed and subjected to analysis. Materials The research used secondary sources of information to generate the data that was used. The sources were however reliable and were obtained from well established international 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 developed spreadsheet from the transformed data is attached in appendix 1. The column headings are as defined above. Testing hypothesis The following is the general model to be tested, dlypci = 1 + 2lypc90i + 3lsecedi + 4govgdpi + 5openi + 6infli + 7crediti + ui s are constants and u represent mean deviations. The set of hypothesis is H 0:2=3=4=5=6=7=0, there is no significant relationship between the dependent variable and the independent variables Against the alternative hypothesis, H 1: At least one of 2, 3, 4, 5, 67, 0, there is a significant relationship in the model. Based on the stata output as shown in appendix 2, the null hypothesis is not rejected on the basis of high probability value, higher that the significance level of 0.05 per cent. The model is also not suitable for analysing the data because it only explains a less than a fifth of the data. Single tests on significance of individual explanatory variables can however be carried out to investigate individual relationships with per capita gross domestic product. 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 between the variables. The probability values for individual tests on 2, 3 and 7 are 0.013, 0.117 and 0.136 respectively. The null hypothesis is therefore rejected for 2 . The hypothesis is however accepted for 3 and 7. An alternative application of student t distribution could also be adopted for the individual tests as shown bellow. Application of student-t distribution tables yields the same conclusions as follows. For 2, Computed value= =2.59 The table value is 2.04, which leads to rejection of the null hypothesis, at 95% confidence interval. For 3, = 1.599 The null hypothesis is therefore not rejected at 95% confidence interval because the computed value falls within the acceptance region. Similarly, for 7, =1.52 The relatively smaller computed value forms the basis for acceptance of the null hypothesis, at 95% confidence interval. The general model indicates absence of a significant relationship between per capital gross domestic product and the explanatory variables, however, individual tests shows existence of a significant relationship between the per capita gross domestic product and the secondary school enrolment. The difference in the two inferential results can be attributed to the large number of explanatory variables in the general model. This is because the variables that are not related to per capita gross domestic product have a capacity to shift the model to biasness. Advice to the minister Based on the above model, a 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% On the other hand, a percentage increase in bank credit has an effect of 0.2124701* (52%-38%) =3% on per capital gross domestic product. This relationship is however not significant, based on the above tests. The minister should therefore consider financing secondary education instead of rescuing the banks. Test for validity of statistical assumptions The statistical assumptions made with respect to regression analysis are linearity, homoscedasticity, and normality Using the RESET test for linearity leads to adoption of the null hypothesis that the considered model is linear. The LM test for homoscedasticity also leads to acceptance of the null hypothesis of homoscedasticity. Bera’ and ‘Jarque’s skewness- kurtosis test on the other hand leads to rejection of the null hypothesis for normality, the model is not linear. Remedy for lack of normality In order to correct the lack of normality, data with extreme values are eliminated from the model. Exclusion of values for zimbambe leads to positive tests for normality, linearity, and homoscedascity. Effects of re specification and re estimation of the model After re specifying and re estimating the model, secondary education and bank credit rate had the following contribution to per capita gross domestic product, Effect of ‘lseced’ on per capita GDP=0.2599967*In (65) %- In (55%) = 4.34% Effect of credit on per capita GDP= 0.1564118* (52%-38%) =2.2% The advice to the finance minister is not affected by the alteration. This is because secondary school education still contributes grater margin to per capital gross domestic product as compared to the banks. Conclusion Per capita gross domestic product, as realized through consumption, investments, government expenditure, and net export is a factor of variables such as education levels and banks credit rates. This paper sought to investigate the relationship between changes in per capita gross domestic product with changes in secondary school enrolment rates as well as with changes in bank credit rates. The results indicate that secondary education has higher contribution to per capita gross domestic product than banks credit rates. It therefore concludes that the finance minister should dedicate the available resources to secondary education instead of financing the bank sector. Reference list 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 Brigham,E. and Ehrhardt, M., 2010, Financial Management Theory and Practice. Mason, OH: Cengage Learning Brooks, C. 2008., Introductory Econometrics for Finance. London, UK: Cambridge University Press Gujarati, Damador & Porter, Dawn. (2009). Basic econometrics. New York, NY: McGraw-Hill 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. Nisbet, R., Elder, J., Elder, J. and Miner, G., 2009. Handbook of statistical analysis and data mining applications. London, UK: Academic Press OECD, 2010, OECD Factbook 2010: Economic, Environmental and Social Statistics. New York, NY: OECD Publishing Raina, L. and Bakker, M., 2003, Non-bank financial institutions and capital markets in Turkey. Washington, DC: World Bank Publications Schadler, S., 2005. Adopting the euro in central Europe: challenges of the next step in European integration. Washington, DC: International Monetary Fund Wang, G. and Jain, C., 2003, Regression analysis: modeling & forecasting. New York, NY: Institute of Business Forec 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 Appendix 2 Read More