Bank Credit Rates Impact on Gross Domestic Product - Coursework Example

?RESEARCH METHODS 29th, January, Outline 0 2.0 Introduction 3.0 Methods 4.0 Discussion 5.0 Conclusion 6.0 Reference list RESEARCH METHODS Abstract This report is based on a research project commissioned to advise the minister for finance on economic policies. Motivated by the need to determine the most effective government expenditure towards improving per capita gross domestic product, the paper investigated the impacts of both secondary education and bank credit rates on per capital gross domestic product. Using regression analysis techniques, the paper identified more significant effects from secondary enrolment to the advice that the available finances be channelled to secondary school education. Introduction Gross domestic product measures a country’s total productivity level. It is defined as the total cost of economic outputs and consists of government expenditure, investments, net export, and consumption. Per capita gross domestic product, a derivative of real gross domestic product is on the other hand a product of population. Consumption in an economy is a factor of people’s disposable income. Similarly, available resources determine the level of a country’s export and hence its net export. Investments, which can be attained through public or private sectors also depends of capital through savings and loans while government expenditures includes spending from central and local governments (Mankiw, 2011, p. 198). Commercial banks and other financial institutions therefore play an important role in economic development through availing investment capital in the form of loans. Provision of financial support also boosts the level of disposable income at a time and as a result boosts consumption. (Brooks, 2008, p. 502). Banks lending capacity however depends on their credit rates that dictate availability of loans as well as loan interest rates. Financial crisis into low credit rates would therefore translate to lower circulation of money and a consequently strained economy through low consumption, investment and export levels (Brooks, 2008, p. 502). Education has also been identified as an integral factor of economic growth. Researchers and scholars argue that the level of income in jobs is significantly determined by a person’s academic qualifications. Similarly, educated individuals are relatively more informed and tend to budget their incomes into savings and investments. These observations qualify secondary education, which is a step into colleges and universities, as an important factor to improving per capital gross domestic product (Bloom et al, 2005, p. 16). Research into determining existence and significance of relationships between variables such as per capita gross domestic product and its factors can be undertaken through regression analysis. Linear regression also determines degree of impacts of each explanatory variable in a model and is based on assumptions of linearity, homoscedasticity, and normality of variables (Newbold, Carlson & Thorne, 2010, p. 428; Ryan, 2011, p. 407, 408). 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 two dependent variables, secondary education enrolment and bank rates?’ and ‘Which of the two variables has higher effects on per capita gross domestic product?’ The paper will test the following sets of hypothesis, H 0: ?i=0; There is no significant relationship H 1: not all are zero; there is a significant relationship 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 in the project were selected nations whose economic data were used. The research used secondary sources from reputable entities such the United Nations. Regression analysis was then adopted for data analysis and interpretation. Discussion Developed excel spreadsheet The developed spreadsheet of data for analysis is attached as appendix 1. Tests The following is the regression model to be tested. dlypci = ?1 + ?2lypc90i + ?3lsecedi + ?4govgdpi + ?5openi + ?6infli + ?7crediti + ui Where ?s are constants and u represent deviations. The symbols in the model are as defined above. The hypoyhesis to be tested is, H 0:?2=?3=?4=?5=?6=?7=0, there is no significant relationship Against the alternative hypothesis, H A: At least one of ?2, ?3, ?4, ?5, ?6?7 is not equal to zero, there exist a significant relationship. The high probability value, based on appendix 2, leads to acceptance of the null hypothesis that there is no significant relationship as stipulated by the model. The mode is also not appropriate because it explains only a small percentage of data, 20%. Significant relationship with each explanatory variable can however be tested using the following hypothesis, H 0: ?i=0, no significant relationship, 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 only secondary school enrolment has a significant relationship with per capita gross domestic product. While the general model indicates no relationship between the dependent and the explanatory variables, single tests identify significant relationship in secondary school enrolment. This difference can be attributed to impacts of the other explanatory variables that have no effects on per capita gross domestic product. Advice to the finance of minister Based on the model, a percentage increase in secondary school enrolment leads an increase in per capita gross domestic product by, 0.2502821*In (65) %- In (55%) = 4.18% A percentage increase in bank credit leads to 3 % increase in per capita gross domestic product as shown bellow, 0.2124701* (52%-38%) =3% The minister should therefore use the available funds on secondary education. Validity of the statistical assumptions Linear regression assumes linearity, homoscedasticity, and normality Using RESET test leads to acceptance of a null hypothesis that of a linear mode. LM test also leads to acceptance of a null hypothesis for homoscedasticity. Bera and Jarque’s skewness- kurtosis test however indicates absence normality. Remedying for lack of normality Ensuring normality of a set of data involves elimination of outliers. A test on the assumptions after eliminating extreme values for Zimbabwe leads to adoption of null hypotheses of linearity, normalcy, and homosceasticity. Impacts of re specifying and re estimating the model Elimination of values for Zimbabwe leads to new regression coefficients and the following effects, Effect of secondary enrolment =0.2599967*In (65) %- In (55%) = 4.34% Effect of credit rate = 0.1564118* (52%-38%) =2.2% The decision is not changed because secondary school enrolment maintains a higher impact on per capita gross domestic product. Conclusion Education and financial sector are identified with significant impacts on per capita gross domestic product. This paper has examined data from various countries to the conclusion that secondary education has higher contribution to per capita gross domestic product that banks credit rates. The paper therefore recommends that the finance minister allocate the available finances to secondary education. 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] Brooks, C. 2008., Introductory Econometrics for Finance. London, UK: Cambridge University Press Mankiw, G. 2011., Principles of Macroeconomics. Mason, OH: Cengage Learning Newbold, Paul, Carlson, William & Thorne, Betty. (2010). Statistics for business and economics. London, UK: Pearson. Ryan, T., 2011. Statistical Methods for Quality Improvement. New Jersey, NJ: John Wiley & Sons 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