Relationship Between GDP per capita and Income Inequality

Running Head: INEQUALITY AND FISCAL POLICY 2

INEQUALITY AND FISCAL POLICY 2

Inequality and Fiscal Policy

Student’s Name

Course Title and Code

Instructor’s Name

Date Submitted

Inequality and Fiscal Policy

Introduction

The relationship between fiscal policy and inequality is a subject that has attracted the attention of researchers as well as policymakers. This can be attributed to the rising income inequality in developed economies, a phenomenon that has been observed over the recent three decades. Consequently, most research papers address this issue by examining the factors that might impact the rate of poverty and inequality in a country. Some of the factors that have been identified to have an effect on inequality include: Gross domestic product (GDP), debt/GDP ratio, net national income, annual deficit. Consequently, one of the questions that might arise is the association between each of the aforementioned variables and inequality, when these relationships are assessed from an isolated perspective (IMF, 2014; Hayes & Vidal, 2015).

The objective of this report is to explore the association and the significance of the relationship between inequality, as measured by the Gini coefficient and the GDP of Canada using simple linear regression. In this context, inequality and GDP are regarded as the dependent and independent variables respectively. The report relies on a sample of 37 historical Gini coefficients (%) and GDP ($ per capita) from the year 1980 up to 2017. This report presents evidence-based recommendations that can be used by policymakers to reduce the levels of inequality in the country by implementing policies that address the GDP.

The results of the analysis indicate that there is a weak positive association between the GDP and the Gini coefficient. In particular, an increase in the GPD ($ per capita) by $ 1 results into a rise in the Gini coefficient by 0.00009%. Consequently, it implies that an increase in the GDP by $ 1 has a positive impact on inequality. The increase in one dollar would lead to a negligible increase in the concentration of wealth by 0.00009%, hence would benefit individuals that are currently wealthier more as compared to their poor counterparts (Lustig, 2017).

Results

Descriptive Statistics

From the analysis, the descriptive statistics of the Gini coefficient and the GDP were as summarized in Table 1

Table 1

Descriptive Statistics of the Gini Coefficient and the GDP

Descriptive Statistics
	Gini		GDP
Mean	0.304324	Mean	28090.546
Standard Error	0.001977	Standard Error	1768.9291
Median	0.307	Median	26220.276
Mode	0.315	Mode	#N/A
Standard Deviation	0.012023	Standard Deviation	10759.976
Sample Variance	0.000145	Sample Variance	115777078
Kurtosis	-1.623869	Kurtosis	-1.3549564
Skewness	-0.184407	Skewness	0.1859918
Range	0.037	Range	33863.524
Minimum	0.284	Minimum	11656.348
Maximum	0.321	Maximum	45519.872
Sum	11.26	Sum	1039350.2
Count	37	Count	37
Confidence Level (95.0%)	0.004009	Confidence Level (95.0%)	3587.5545

Data Source: OECD, 2019.

In addition, Figure 1 illustrates the relationship between the GDP and Gini coefficient of Canada as from 1980 up to 2016 using a scatter diagram.

Figure 1: Scatter Plot of the Gini Coefficient Versus the GDP of Canada Data Source: OECD, 2019.

Inferential Statistics

Table 2 provides a summary of the regression statistics when a simple linear regression model was fitted to the dataset with the Gini coefficient as the dependent variable and the GDP as the independent variable. From Table 2, it can be deduced that in this case, approximately 72% of the variation in the Gini index can be attributed to the variation in GDP as indicated by the R Square value.

Table 2

Regression Summary Statistics

Multiple R	0.849781
R Square	0.722128
Adjusted R Square	0.714189
Standard Error	0.006428
Observations	37

Data Source: OECD, 2019.

In order to determine if the results of the regression were random, an analysis of variance (ANOVA) was conducted and the results were as summarized in Table 3. From these results, it is evident that at the 5% level, there was insufficient evidence to prove that the results of the regression analysis were random.

Table 3

ANOVA Statistics

	df	SS	MS	F	Significance F
Regression	1	0.0038	0.0038	90.9573	0.0000
Residual	35	0.0014	0.0000
Total	36	0.0052

Data Source: OECD, 2019.

Finally, in order to assess the significance of the GDP as a predictor of the Gini coefficient, the Wald test was adopted and the results were as summarized in Table 4. From Table 4, the relationship between the Gini index and the GDP of Canada between 1980 and 2016 can be modelled by a regression equation of the following form:

Gini = 0.2776510 + 0.0000009*GDP.

From the p-value column, it is evident that the null hypothesis that the association between the GPD and the Gini index is insignificant can be rejected at the 5% level. Consequently, a conclusion can be made that in this case, the GDP had a significant positive impact on the level of inequality in Canada during the period under study.

Table 4

Results of Hypothesis Test

	Coefficients	Standard Error	t Stat	P-value
Intercept	0.2776510	0.0029898	92.8673005	0.0000000
GDP	0.0000009	0.0000001	9.5371559	0.0000000

Data Source: OECD, 2019.

Discussion

The association between the GDP of a country and its level of inequality is a subject of keen interest amongst researchers. From the results in Table 1, it is evident that the average Gini coefficient for Canada during the period under study was 0.304324 with a 95% confidence interval of (0.300315, 0.30833) which implies that on average, 70% of the country’s citizens shared all of its wealth between 1980 and 2016, whereas the remaining 30% received nothing. Moreover, the average GDP over the same period was 28090.546 with a 95% confidence interval of (24,508.992, 31,672.101) and a standard deviation of 10759.976, which implies that there was a high level of variability on the level of expenditure on locally produced goods and services amongst the country’s citizens (Shi & Sicular, 2014).

From an inferential perspective, it is evident that a significant percentage of variation in the level of inequality (72%) could be explained by the variation in the GDP as shown in Table 2. Table 3 further validates that the results were not by chance but could be explained by the relationship between the variables in the study (Hayes & Vidal, 2015).

Finally, this report arrives at a conclusion that the following model can be used to explain the relationship between GDP and the Gini coefficient:

Gini = 0.2776510 + 0.0000009*GDP

By implication, an increase in the GDP (per capita) by $ 1 results to a rise in the Gini index by 0.0000009. Even though this value seems negligible, it is essential to note that the GDP of a country might rise to the tunes of billions. For instance, if the GDP of Canada rose by $ 1 million over a five-year period, for instance, then the Gini coefficient would rise by 0.9, all other factors held constant.

In summary, the results that were obtained in this study add a new dimension to the debate on inequality and economic growth. Even though it would be anticipated that a rise in the GDP of a country leads to a decline in the level of inequality, the results obtained are of contradicting nature (Brown & Hood, 2016). It could be argued that in a country, there are prospects of individuals who are already wealthy being able to acquire more wealth as their poor counterparts remain in a stagnant state of poverty (Hayes & Vidal, 2015).

Conclusion

In conclusion, this report establishes that the GDP of a country has a significant positive impact on inequality; hence a rise in the GDP of a country increases its rate of inequality. However, it is essential to note that this study was limited to one country and explored the relationships from a bivariate perspective. Therefore, the results might not reflect the impact of other variables such as the population level, net national income, debt/GDP ratio and annual deficit on the level of inequality as established by other researchers (IMF, 2014; Hayes & Vidal, 2015; Brown & Hood, 2016).

References

Browne, J. &. Hood A. (2016). Living Standards, Poverty and Inequality in the UK: 2015-16 to 2020-21. The Institute for Fiscal Studies. Retrieved from https://www.ifs.org.uk/uploads/publications/comms/R114.pdf

Hayes T. J. &. Vidal. X. D. (2015). Fiscal Policy and Economic Inequality in the U.S States: Taxing and Spending from 1976 to 2006. Political Research Quarterly, 68(2), pp. 392-407. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.876.1563&rep=rep1&type=pdf

IMF. (2014). IMF Policy Paper: Fiscal Policy and Income Inequality. International Monetary Fund. Retrieved from https://www.imf.org/external/np/pp/eng/2014/012314.pdf

Lustig N. (2016). Fiscal Policy, Inequality, and the Poor in the Developing World. United Nations University World Institute for Development Economics Research. Retrieved from https://www.wider.unu.edu/sites/default/files/Publications/Working-paper/PDF/wp2016-164-revised-august.pdf

OECD. (2019). Gross domestic product (GDP). OECD. Retrieved from https://data.oecd.org/gdp/gross-domestic-product-gdp.htm

OECD. (2019).Income Inequality. OECD. Retrieved from https://data.oecd.org/inequality/income-inequality.htm

Shi, L. & Sicular T. (2014). The Distribution of Household Income in China: Inequality, Poverty and Policies. The China Quarterly, Volume 217, pp. 1-41. Retrieved from https://www.researchgate.net/publication/265334763_The_Distribution_of_Household_Income_in_China_Inequality_Poverty_and_Policies

Gini 11656.347645 13044.380139000001 13248.255289000001 13985.973910999999 15204.944549 16278.936602 16797.974048 17683.794344999998 18872.200038999999 19710.988518999999 20173.939793000001 20160.722120999999 20557.458181999998 21367.198009 22555.699667000001 23401.882116000001 23962.500064 25167.575106 26220.275740000001 27745.751306999999 29265.012234000002 30107.952161000001 30854.823032 32227.437055999999 33799.776589000001 36212.643556000003 37999.831042999998 39441.137676999999 40277.618235000002 38797.769183999997 40011.927775999997 41565.258932999997 42145.104092000001 44101.430586000002 45519.871933000002 44406.701910999996 44819.110868000003 0.28899999999999998 0.28799999999999998 0.29199999999999998 0.3 0.29699999999999999 0.29399999999999998 0.29299999999999998 0.29099999999999998 0.28599999999999998 0.28399999999999997 0.28899999999999998 0.29599999999999999 0.29599999999999999 0.28999999999999998 0.29099999999999998 0.29299999999999998 0.30099999999999999 0.30399999999999999 0.31 0.308 0.315 0.317 0.317 0.315 0.32100000000000001 0.315 0.316 0.317 0.315 0.316 0.316 0.313 0.317 0.32 0.313 0.318 0.307

GDP

Gini