Factors Affecting Gross Domestic Product - Assignment Example

Factors Affecting Gross Domestic Product Table of content Table of Contents Introduction Purpose Importance 2 Literature review 2 Study one 2 Study two 3 Methodology 3 Research hypothesis 4 Results and data analysis 5 Conclusion and findings 8 Reference 8 Introduction Purpose This paper was designed to study the correlation between Gross Domestic Product and variables that have been realized to influence its rate. The factors include happy index, crime rate, corruption, income and if the country is developed or developing. Given this study, its results will verify that the dominance of some factors is likely to boost GD while others’ also end up having a negative impacts it. This will help in pinpointing significant strategies regulating the above factors in the hope of heading a country to healing. Importance The calculation of Gross Domestic Product GDP of countries around the world is far much important in the process of determining how a country is faring. In this case, the GDP acts as a clue in matters pertinent to financial wealth of the country through measuring standard of living in a given country. Consequently, every country develops interest in knowing about the GDP correlations to ensure a boost in the wellbeing. Some of these GDP correlations realized in every country are happy index, crime rate, corruption, income and if the country is developed or developing. it is intrinsic to gain the knowledge about how these factors affect the GDP in order to tailor projects that can restore a nation. Literature review Many studies have been carried out both having the aim of testing the hypothesis presented in the paper. Through carrying out a study on the GDP of different countries, the researchers give their prediction on whether the factors are likely to increase on reduce the GDP. Study one Andrew E Clark and Claudia Senik presents study which shows that happiness index and income have impact on the GDP. The variables in play were income, happiness index and GDP. They confirm the availability of correlation between the factors and the former. In order to test their hypothesis, the authors verified the significant relationship between happiness index and income of a country. Evidently, they found that increase in happiness index insinuates that income is also likely to increase on the other hand. The authors assert that happiness of psychological importance to the citizens, especially in their endeavors of increasing their income (Clark and Claudia, 20). The study also showed that income has a significant relationship with GDP as can be shown by the correlation presented. The study concluded that the income affects the well-being in an indomitable way regardless of other factors. Clarifying the results, the study indicates that income improves a country’s ability to adapt. Study two In a study carried out by the J. Shao, he finds out the correlation between corruption and Gross Domestic Product. They carried out a quantitative study while using corruption as an independent variable while GDP was the dependent variable. The results from the study indicate that the observed correlation between the two was negative. In the intuition of corruption, both economic and social status is at risks of deteriorating. Conclusively, the study asserts that effect of corruption on administration and leadership is well accepted to have adequate correlation (Shao and et al, 27). Consequently, the presence of corruption reduces the GDP prospect of a country. The study shows that there is significance dependence between the two factors. Methodology In the event of studying the correlation between the three factors and GDP, the research encompassed visiting two databases to obtain data for 50 countries. The databases visited were World Bank website and bureau labor statistics. The data were for the GDP, happiness index, income and developing or not developing. The data were filled in excel in order to give chance for running a regression analysis. The first data analysis was done on determining the correlation between corruption and income. The second regression analysis was run between income and Gross Domestic product. Further, another analysis was carried to realize correlation between GDP happiness indexes. The analysis was concluded for the developing or not developing and the GDP. Research hypothesis The study encompassed four hypothesis to help in verifying the relationship between the subjective independent variable and dependent variable. Consequently, null testing was used to give the most precise and definitive relationship between the variable. The null hypothesis was expressed in terms of no {significant} differences or no {significant} difference between the two groups. The alternative hypothesis was also defined. The following were the hypotheses: Hypothesis 1 H0 There is no significant relationship between crime rate and GDP H1 There is a significant relationship between crime rate and GDP Hypothesis two H0 There is no significant relationship between happiness index and GDP H1 There is a significant relationship between happiness index and GDP Hypothesis three H0There is no significant relationship between income and GDP H1 There is a significant relationship between income and GDP Hypothesis four H0 There is no significant relationship between corruption and GDP H1 There is a significant relationship between happiness index and GDP Results and data analysis The regression analysis for the variable “happy index” vs GDP SUMMARY OUTPUT Regression Statistics Multiple R 0.675034 R Square 0.455671 Adjusted R Square 0.444331 Standard Error 2009.989 Observations 50 ANOVA   df SS MS F Significance F Regression 1 1.62E+08 1.62E+08 40.18195 7.6E-08 Residual 48 1.94E+08 4040056 Total 49 3.56E+08         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 9301.703 1272.814 7.307981 2.48E-09 6742.538 11860.87 6742.538 11860.87 happy index -276.156 43.5652 -6.33892 7.6E-08 -363.75 -188.563 -363.75 -188.563 Analysis As can be shown from the regression analysis, the p-value is less than zero and far much smaller. This indicates that the beta happy index fits in the regression model. In this case, any change in the predictor will lead to change in the regression model. The p-value is less than the alpha level of 0.05. Further, from the table, the alpha is 9301.703 while the beta is -276.156. The corresponding test statistics are 7.30798 and -6.3389 insinuating that there is a big difference on both sides of the t-curve. Consequently, the above analysis shows that the null hypothesis of alpha = 0 and beta =0 should be rejected. In this case, the conclusion is that beta and alpha play a critical role and should be included in the regression model. The following graphs acts as validation of the assumptions: The first graph on top shows the normality assumption. Almost all the points are in cluster around the blue line insinuation that the error terms are approximately normal.The second graph, top right, shows the cyclic nature of the curve for the error terms. This indicates that the error term goes against provision of independence of error. The following is a multiple regression analysis run for the remaining three predictors SUMMARY OUTPUT Regression Statistics Multiple R 0.70809 R Square 0.501392 Adjusted R Square 0.468874 Standard Error 1965.099 Observations 50 ANOVA   df SS MS F Significance F Regression 3 1.79E+08 59541931 15.41893 4.44E-07 Residual 46 1.78E+08 3861613 Total 49 3.56E+08         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -25167.8 5093.615 -4.94104 1.07E-05 -35420.7 -14914.9 -35420.7 -14914.9 corruption 532.7306 180.3173 2.954407 0.004924 169.7707 895.6904 169.7707 895.6904 income -202.675 145.0285 1.397485 0.008972 -494.602 89.25202 -494.602 89.25202 developing or not developing 511.5085 585.9359 0.872977 0.38721 -667.919 1690.936 -667.919 1690.936 The r-squared is 50% meaning that the model is good for fitting the data for analysis For preditors, corruption and income The p-value is less than zero and far much smaller. This indicates that the beta fits in the regression model. In this case, any change in the predictor will lead to change in the regression model. The p-value is less than the alpha level of 0.05. Further, from the table, for corruption the alpha is -25167.8 while the beta is 532.7306 and for income the alpha is -25167.8 and beta 202.675. The corresponding test statistics are -4.94104 and 2.954407 insinuating that there is a big difference on both sides of the t-curve. Consequently, the above analysis shows that the null hypothesis of alpha = 0 and beta =0 should be rejected. In this case, the conclusion is that beta and alpha play a critical role and should be included in the regression model. However for the third predictor, the p-value is more than 5% alpha level indication that the beta does not fit the regression model. Any change in the predictor will not cause a change in the regression model. Further, the test insinuates a lower difference on both sides of the t-curve. This means that null hypothesis of alpha = 0 and beta = 0 should be accepted. Conclusion and findings At the end of carrying out the analysis of data found, the research was able to arrive to an adequate finding that verifies the correlation between the variables listed and the GDP. As for corruption, income, crime and happy index, they all have a positive correlation with the GDP. The results led to rejecting the null hypothesis and accepting that at 95% there is “There is a significant relationship between the variables and GDP except for the dummy variable”. This means that any change in the variables, crime rate, happy index and income will impact the GDP of any given country. Income and happy index have the strongest effect on the GDP because of the big difference between the T-statistics value. For the “developing or not developing” the null hypothesis, there is no significant difference between the variable and GDP, was accepted. This shows that the factor has no particular correlation with the GDP. Reference Clark, Andrew E, and Claudia Senik. "Will GDP growth increase happiness in developing countries?." (2010). Jia Shao,a, Plamen Ch. Ivanov , Boris Podobnik4, and H. Eugene Stanley. Quantitative relations between corruption and economic factors. Eur. Phys. J. B 56, 157–166 (2007) DOI: 10.1140/epjb/e2007-00098-2 Read More