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Recommendations For Strategy Of Raising The Country’s Per Capita Gross Domestic Product Report


Abstract

This report is based on the research that has been conducted to investigate the effects of the secondary education and banks credit rates per capital gross domestic products. Motivated by an assignment to advice the minister for finance on policymaking, the research used regression analysis and test of hypothesis to analyze sets of data.

The research established that secondary education has higher positive impacts on per capital gross domestic product as compared to bank credit rates. It, therefore, concludes that the available finances are allocated to the secondary education.

Introduction

Gross domestic product is defined as a measure of a country’s total productivity level. It refers to “the market value of all finished goods and services” within a territorial boundary and in a specified period (Mankiw, 2008, p. 496). Elements of gross domestic product include “consumption, investment, government purchase, and net export” (Mankiw, 2008, p. 496).

Investments are monetary value of resources that are used for production of goods and services. Realized through individual citizens, corporate bodies and governments, investment rates and levels are the factors of availability of resources and the capacity to acquire such resources through savings or loans.

Another element of the gross domestic product is government expenditure through such avenues as central and local governments and their agencies. This category of expenditure includes payments to civil servants and payments for public service utilities such as education and healthcare among other services that are provided by either state or federal government.

Consumption as an element of the gross domestic product as well as net export also depends on availability of resources and disposable income in a country. High levels of disposable income correspond to relatively high consumption and trade levels (Mankiw, 2008, p. 496).

While real gross domestic product measures a country’s productivity level, per capital gross domestic product measures the value that is attributable to an individual citizen. The per capital gross domestic product is a factor of population growth and size and has an impact on living standards and utility levels in societies (Boyes and Melvin, 2007, p. 389, 390).

Brooks (2008, p. 502) explains that one of the fundamental pillars of the economic growth is the availability of resources for consumption, investment or government expenditure. Financial institutions which are sources of loans the expenditures are, therefore, instrumental factors in economic growth.

The ability to provide loans to investors and private consumers, for instance, has directs impacts on consumption, investments, and net export (Brooks, 2008, p. 502).

Particularly, credit growth rates among financial institutions determining the lending rates to entities and, consequently, affect consumption and investment levels.

A crisis in the financial sector strains the banks’ lending capacity leading to reduced lending with high interest rates. The consequence will be the reduced consumption and the investment capacity to negative economic growth (Brooks, 2008, p. 502).

Economic growth through expansion of gross domestic product has also been associated with the education levels among countries. One of the relationships between education and economic growth is the derived empowerment to career developments. Individuals who proceed to high schools and tertiary institutions have high propensity to good employment opportunities and income.

Education also leads to development of rational decision making that prompts savings and investments. As a result, investment in education is an effective initiative to stimulating economic growth. Though economic stability through education appears to be realized only at individual level, there is a derived macroeconomic benefit to both governments and societies.

Employment opportunities, for example, generate taxes to the government besides the investments and increased consumption levels. Higher education levels, attained through secondary education, are also associated with the technological advancement in economic processes. This leads to lower operational costs resulting in more savings and higher production capacity (Bloom et al, 2005, p. 16).

Evaluation and analysis of relationships among variables, such as per capita gross domestic product, education rates, and bank credit rates are possible through the linear regression models.

This statistical tool identifies the existence of a relationship between a dependent variable and a set of explanatory variables and establishes significance of such relationships besides comparing the effects of independent variables on the dependent variable (Gujarati and Porter 2009, p. 13-20). Linear regression makes assumptions of linearity, ‘homoscedasticity’, and normality of variables (Newbold et al, 2010, p. 428).

This paper seeks to investigate the relationship between per capital gross domestic product and the rate of enrolment in secondary schools and credit rates of financial institutions.

The paper seeks to answer two research questions, ‘Is there a significant relationship between per capital gross domestic product and both secondary schools enrolment rates and financial institutions’ credit rates?’ and

‘Is the relationship between per capita gross domestic product and high school enrolment rate stronger than the relationship between per capita gross domestic product and 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: There is no significant relationship between per capita gross domestic product and the considered explanatory variables;

H 1: There is a significant relationship between per capita gross domestic product and the considered explanatory variables.

Using a comparative 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 linearity, ‘homoscedasticity and normality.

Methods

Participants and design

The set of data used in the research relates to statistics of different countries across the globe. The countries, therefore, formed the participants of this work. The research organised the data for inferential analysis.

Materials

The research used existing secondary sources for data collection. The resources were identified to be reliable since they were obtained from established institutions such as the United Nations and the University of Pennsylvania.

Procedure

The research procedure involved acquisition of sets of data from the sources. This was followed by organization and subsequent transformation of data into derived variables. The research then used ‘Stata’ software for analysis into making inference and conclusions.

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

The research used ‘Stata’ to test the following model

Where βi is a constant and ui represents noise. The symbols in the model are as defined above.

The set of hypothesis is

The set of hypothesis is

Against the alternative hypothesis,

Against the alternative hypothesis

Since the probability value is high, higher than 0.05, the null hypothesis is accepted. This implies that there is no significant relationship between the variables as expressed in the above model. Further, the model explains only 19.77% of the analyzed data making it unreliable.

The following set of hypothesis on relationships between change in per capital gross domestic product and the individual explanatory variables tests on existence of significant singe relationships.

H 0: βi=0, no significant relationship between the dependent and the explanatory variable,

significant relationship The probability values Application of student-t distribution tables yields the same conclusions as follows

The table value is 2.04 leading to rejection of the null hypothesis, at 95% confidence interval.

Application of student-t distribution

This leads to acceptance of the null hypothesis, at 95% confidence interval, since the computed value falls within the acceptance region.

The table value is 2.04 leading to rejection of the null hypothesis

The relatively smaller computed value leads to acceptance of the null hypothesis, at 95% confidence interval.

While the general model suggests the absence of relationship between per capital gross domestic product and all the explanatory variables, single inferential tests show the existence of a significant positive relationship between the dependent variable and the percentage of secondary school enrolment.

The contradiction is attributable to 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 a positive impact 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 adoption of the null hypothesis of ‘homoscedasticity’.

The ‘Bera’ and ‘Jarque’s skewness- kurtosis’ test, however, leads to the rejection of the null hypothesis of normality. Normality assumption was, therefore, not correct.

Remedy for lack of normality

Remedying lack of normality involves exclusion of extreme values, values corresponding to Zimbabwe. 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 impacts,

Effect of ‘lseced’ on GDP per capital =0.2599967*In (65) %- In (55%) = 4.34%

Effect of credit on GDP per capital= 0.1564118* (52%-38%) =2.2%

Revaluation of specification and estimation of the model does not affect the advice because secondary school education still holds higher effects on per capital gross domestic product.

Conclusion

Gross domestic product and per capital gross domestic products are subject to government expenditure, consumption and investments among other factors. This research evaluated the impacts of government expenditure in secondary education and commercial banks on per capital gross domestic product in order to advice the finance minister on a suitable policy decision.

The research concludes that secondary education funding should be preferred over banks. This is because of its higher and more significant contribution to per capital gross domestic product as compared to banks credit rates.

Reference list

Bloom, D., Canning, D., & Chan, K., 2005. Higher education and economic development in Africa. Available at: http://siteresources.worldbank.org/EDUCATION/Resources/278200-1099079877269/547664-1099079956815/HigherEd_Econ_Growth_Africa.pdf, p. 16.

Boyes, W & Melvin, M., 2007. Economics. Boston, MA: Cengage Learning

Brooks, C. 2008., Introductory Econometrics for Finance. London, UK: Cambridge University Press

Gujarati, D. & Porter, D., 2009. Basic econometrics. New York, NY: McGraw-Hill

Mankiw, G. 2011., Principles of Economics. Mason, OH: Cengage Learning

Newbold, P, Carlson, W & Thorne, B., 2010. Statistics for business and economics. London, UK: Pearson.

Appendix

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 dIvoire 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

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Swarm. (2019, May 25). Recommendations For Strategy Of Raising The Country’s Per Capita Gross Domestic Product [Blog post]. Retrieved from https://ivypanda.com/essays/recommendations-for-strategy-of-raising-the-countrys-per-capita-gross-domestic-product-report/

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Swarm. "Recommendations For Strategy Of Raising The Country’s Per Capita Gross Domestic Product." IvyPanda, 25 May 2019, ivypanda.com/essays/recommendations-for-strategy-of-raising-the-countrys-per-capita-gross-domestic-product-report/.

1. Swarm. "Recommendations For Strategy Of Raising The Country’s Per Capita Gross Domestic Product." IvyPanda (blog), May 25, 2019. https://ivypanda.com/essays/recommendations-for-strategy-of-raising-the-countrys-per-capita-gross-domestic-product-report/.


Bibliography


Swarm. "Recommendations For Strategy Of Raising The Country’s Per Capita Gross Domestic Product." IvyPanda (blog), May 25, 2019. https://ivypanda.com/essays/recommendations-for-strategy-of-raising-the-countrys-per-capita-gross-domestic-product-report/.

References

Swarm. 2019. "Recommendations For Strategy Of Raising The Country’s Per Capita Gross Domestic Product." IvyPanda (blog), May 25, 2019. https://ivypanda.com/essays/recommendations-for-strategy-of-raising-the-countrys-per-capita-gross-domestic-product-report/.

References

Swarm. (2019) 'Recommendations For Strategy Of Raising The Country’s Per Capita Gross Domestic Product'. IvyPanda, 25 May.

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