A study was carried out to determine the Influence of Inflation and Corporate Taxation on Employment in the United States of America. The study found that a correlation does not exist between corporate taxation, inflation, and unemployment rates in the United States of America. Therefore, the study has nullified our hypothesis that increasing inflation and increasing corporate taxation leads to increasing unemployment rates.
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|Adjusted R Square||0.037715|
|Coefficients||Standard Error||t Stat||P-value||Lower 95%||Upper 95%||Lower 95.0%||Upper 95.0%|
|CORPORATE TAX RATE||5.381133||5.214047||1.032045||0.307566||-5.1205||15.88276||-5.1205||15.88276|
A regression tool is highly effective in establishing the relation of variables from a data set. After the regression test was carried to determine the relationship between inflation and corporate tax towards unemployment, the results indicate that there lies no correlation between the two independent variable and the dependent variable. Indeed, this is indicated by a low R square at 0.07866. This implies that only 7.8% of the unemployment rates in the United States of America are explained by the independent variables; inflation and corporate tax. Since the closer R nears value 1, the more the regression line fits data, this data is widely dispersed from the regression line (Long and Freese 162). A highly dispersed data nullifies the existence of any statistically significant correlation.
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The results of this study are statistically insignificant. The regression results indicate a significance F value of 0.158276. This value is way far above 0.05, therefore, rendering the study statistically insignificant. Additionally, we can see that the p-value for the intercept is at 0.028, the inflation P-value is at 0.663 while the corporate tax P-value is at 0.307. Therefore, the intercept value is the only one that falls below 0.05 hence rendering the study statistically insignificant since a majority of the P-values should be below 0.05 (Park 33).
In this study, the regression line is;
Therefore, from this regression line, we can see that for every unit increase in inflation, the unemployment rate increases by 0.051 units. Conversely, for every unit increase in corporate tax, the unemployment rate increases by 5.381 units.
The expectations of the study were not met. Therefore, the results nullify our hypothesis that increased corporate tax and increased inflation leads to increasing unemployment rates. Therefore, this study confirms that our hypothesis lacked the basis for generalizability since by regressing United States of America data for 40 years has proved the inexistent of this relationship between the parameters studied. Overall, this study has informed us on the lack of a relationship between any of the two independent variables with the independent variables as evidenced by high P-values. Thus the conventional reasoning that an increase in corporate tax can lead to an increasing unemployment rate is nullified. Conversely, the study proves that increasing inflation rate does not influence the unemployment rate. A regression study must prove the existence of correlation to be validly acceptable.
Despite the fact that our study failed to reaffirm our hypothesis, we are confident that it constitutes a research study worth influencing decision making within a nation. Indeed, the study is a confirmation that the government cannot control inflation to control unemployment rate reliably. Still, the government cannot control corporate taxation to influence unemployment rates since all these variables are uncorrelated.
Hungerford, Thomas L. “Corporate Tax Rates and Economic Growth Since 1947.” Economic Policy Institute, 2013, www.epi.org/publication/ib364-corporate-tax-rates-and-economic-growth/. Accessed 24 Oct. 2018.
US.gov. “Bureau of Labor Statistics Data.” Databases, Tables & Calculators by Subject, 2018, data.bls.gov/timeseries/CUUR0000SA0L1E?output_view=pct_12mths. Accessed 24 Oct. 2018.
US.gov. “Bureau of Labor Statistics Data.” Databases, Tables & Calculators by Subject, 2018, data.bls.gov/timeseries/LNS14000000. Accessed 24 Oct. 2018.
Long, J. Scott, and J. Freese. qRegression Models for Categorical Dependent Variables Using Stata, r Third Edition. Stata press, 2014.
Park, Hun Myoung. “Linear regression models for panel data using SAS, Stata, LIMDEP, and SPSS.” (2015).