Table of Contents

Introduction. 3

Data Description. 4

Methods. 4

Results and Discussion. 6

Descriptive Statistics. 6

Inferential Statistics. 7

Conclusion. 10

References. 11

Analysis of US Inflation and GDP

Introduction

Gross domestic product (GDP) has been considered as the most complex proxy for an economy’s performance. Svigir and Milos (2017) report that economic growth is a product of actions taken by policymakers. These actions relate to fiscal, monetary, and other economic policies. The authors add that economic growth is influenced by many factors among them inflation. Inflation refers to a condition where prices are rising leading a decline in money’s purchasing power while GDP denotes a country’s national income and output over a given time period. Central banks and other policymakers are placing increased attention on price stability to achieve low and stable inflation rates (Barro, 2013).  They believe that high inflation is costly to both businesses and households due to the resulting poor performance.

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The association between price changes and economic growth has remained intricate. The influence of inflation on a country’s GDP has attracted debates and many studies for a longer period (Davcev, Hourvouliades, & Komic, 2017). Theoretical analyses and empirical studies have been explored, yet the evidence is not overwhelming. For instance, Svigir and Milos (2017) note that empirical studies have portrayed mixed results which include positive, negative and neutral relationships. Gillman and Harris (2009) report that inflation has been linked with negative effects on GDP of developed economies. Inflation remains a recurrent macroeconomic problem in many economies, and understanding its effects on their economic growth in this era of widespread goals of attaining high economic growth and development is paramount. Therefore, based on these mixed findings, the likely negative effects of price increases, unavailability of studies focusing on the 21^st century, and the literature gap on this sensitive matter, this document aims at assessing the effects of inflation rates on the US GDP. In addition, with the US GDP representing around 30% of the world economy (Trading Economics, 2018), it is important to analyze how inflation affects its performance.

Data Description

The analysis covers data for ten years (2008-2017). Data was obtained from reputable websites that collect, analyze, and store data on economic trends and performance of economies across the world. Specifically, inflation data was obtained from the US Inflation website where December values taken. On the other hand, the US GDP data was extracted from the Trading Economics website.

Table 1. US GDP and Inflation Data

	GDP ($billion)	Inflation
2008	14,718.58	0.10%
2009	14,418.74	2.70%
2010	14,964.37	1.50%
2011	15,517.93	3.00%
2012	16,155.26	1.70%
2013	16,691.52	1.50%
2014	17,427.61	0.80%
2015	18,120.71	0.70%
2016	18,624.48	2.10%
2017	19,390.60	2.10%

Methods

For a comprehensive analysis, descriptive and inferential statistics were necessary. The descriptive analysis involved the determination of standard deviation, mean, minimum and maximum values of observations. The minimum statistics was necessary in identifying the smallest data value while the maximum statistics helped in understanding the largest value. Mean and standard deviation were necessary in showing the average and dispersion respectively of GDP and inflation.

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A scatter diagram was also constructed following its usefulness in graphically illustrating the relationship of two numerical variables (Gerber & Finn, 2005). The authors add that the independent variable is plotted on the X-axis while the criterion variable is plotted on the Y-axis. In this paper, a scatter plot was used to show whether inflation and GDP had a connection. Therefore, the GDP data was plotted on the Y-axis while that of inflation was plotted on the X-axis.

Inferential statistics took the form of a simple regression analysis expressed as:

Y = α+β₁Inf + e

Regression model is useful while investigating bivariate as well as multivariate associations among variables (Troeger, n.d). The investigator hypothesizes that one variable depends on another or a combination of several variables. Therefore, the item of GDP is the dependent variable, which is represented by Y in the model. From the model, it is important to note that the GDP (Y) is a function of a constant or intercept term denoted by alpha (α), inflation, which is the explanatory variable and represented by (Inf) and its coefficient β₁, and e that denotes an error term. This error term represents other explanatory variables that have not been included in this function (Freedman, 2005).

This technique guided the student in making stronger causal conclusions from the observed data on the relationship between inflation rates and GDP. After analysis of data, the obtained R square, F-statistics, probability, constant, and coefficient values helped in understanding the connection between the study variables as well as the usefulness of the model in presenting this association. To perform all analyses and fit the model, the student used IBM SPSS for windows version 21 software. The results were presented in tables and figures.

Results and Discussion

Descriptive Statistics

Table 2. Descriptive Results

	N	Min	Max	Average	Std. Dev.
US GDP	10	14418.7400	19390.6000	16602.980000	1739.5191866
US inflation rate	10	.0010	.0300	.016200	.0090652

From Table 2, the minimum value for US GDP was $14,418.74 billion while the maximum was $19,390.60 billion. From these results, the range of GDP over the ten-year period was $4,971.86 billion ($19,390.60 billion – $14,418.74 billion). Additionally, the minimum value for inflation was 0.1% while the maximum value was 3% giving rise to a range of 2.9% (3%-0.1%). Results in Table 2 also indicate that the mean value and standard deviation of GDP was $16,602.98 and $1,739.52 respectively. It is also evident that the mean value and standard deviation for inflation rates was 1.6% and 0.91 respectively.

Figure 1 depicts the distribution of GDP against inflation in a pictorial manner. It is observed that the points of intersection (as represented by dots) are unevenly distributed across the entire diagram. These findings imply that there was no correlation between GDP and inflation from 2008 to 2017. They can also be interpreted to mean that there was a nonlinear connection, though a very weak one. These results concur with Munir and Mansur’s (2009) views, whose study covering 1970 to 2005, and analyzing the association between Malaysian inflation rates and GDP established a nonlinear relationship between these variables. The four points that seem to fall in a line represents GDP in 2012 to 2015 where inflation continued to decrease while GDP continued to rise.  

Figure 1. A scatterplot showing US GDP against inflation

Inferential Statistics

Table 3. Summary Output Results

Model	R	R2
1	.043a	.002

a. Predictors: (Constant), US inflation rate

From Table 3, the R value of 0.043 shows that inflation rates had a positive influence on GDP over the studied period. However, this influence was only 4.3% and could be due to instances when GDP rose while inflation dropped. These findings support the work of Svigir and Milos (2017) who established that a positiv connection between inflation and GDP existed, though it was not statistically significant. Further results indicate that R², which represents the coefficient of determination, was very low 0.002 (2%). This value implies that inflation rates influenced the variability of GDP by 2% only between 2008 and 2007, and 98% was influenced by other factors not considered by the current model.

Table 4. ANOVA Output

Model	SS	df	MS	F	Sig.
1	Regression	49653.326	1	49653.326	.015	.907b
Residual	27183689.680	8	3397961.210
Total	27233343.006	9
a. Criterion Variable: US GDP
b. Predictors: (Constant), US inflation rate

F-statistics results in Table 4 are important in evaluating the fattiness of the used model. F-statistic of 0.015 and its probability value of 0.907 imply that the model was not useful in predicting the variability of GDP following changes in inflation rates. This decision is based on the p-value, 0.907, which is larger than the 5% (0.05) significance level under which the analysis was done.

Table 5. Coefficient Results

	B	Std. Error	Beta	t	Sig.
1	(Constant)	16735.717	1243.194		13.462	.000
US inflation rate	-8193.618	67781.435	-.043	-.121	.907
a. Dependent Variable: US GDP

The coefficients results in Table 5 were useful in determining how much the explanatory variable contributed to the model as well as its significance. The p-value (denoted as sig. in Table 5) of t-test for inflation was analyzed relative to the predetermined significance level of 5%. These p-values assisted in testing the null hypothesis that: H_o: There is no effect of inflation over the GDP. As a general rule, explanatory variables whose p-values are equal to or lower than 5% imply that they are meaningful additions to the regression model and they significantly influence the changes in criterion variable.

From results in table 5, beta coefficient of inflation is – 8,193.618 while p-value is 0.907. This beta value means that for every unit rise in inflation rates led to $8,193.618 billion decline in GDP. Since inflation’s p-value of 0.907 > 0.05, the student failed to reject the null hypothesis. Using these results, a simple linear regression model can be written in this form: Y = 16,735.717 – 8,193.618Inf. + e.

These results are in agreement with Gillman and Harris’s (2009) findings that inflation negatively influenced the GDP growth. The findings further corroborate the studies by Munir and Mansur (2009), Barro (2013), and Anghel, Lilea, and Mirea (2017) who established that inflation rates had an adverse influence on GDP growth. However, contrary to Anghel, Lilea, and Mirea’s (2017) findings that the negative connection was statistically significant, this paper found the relation to be statistically insignificant.

Conclusion

 Through this paper, the student sought to analyze the connection between US inflation and GDP for the period 2008 through 2017 (10 years). Overall, the OLS and regression results indicated that inflation rates adversely affected the GDP. The results have supported the findings reported in a majority of the previous studies on this topic. These findings add knowledge to the extant literature on the connection between inflation and GDP. Further studies on this topic are recommended with a focus on more yearly or monthly data for the period 2008-2017. Additionally, the approach used in this paper can be modified to a threshold regression model. For policy purposes, central banks across the globe should formulate prudent policies to contain inflation at the lowest level possible.

References

Anghel, M.-G., Lilea, F. P., & Mirea, M. (2017). Analysis of the interdependence between GDP and inflation. Romanian Statistical Review, Vol. 3, 148-155.

Barro, R. J. (2013). Inflation and economic growth. Annals of Economics and Finance, Vol. 14 (1), 121-144.

Davcev, L., Hourvouliades, N., & Komic, J. (2017). Impact of interest rate and inflation on GDP in Bulgaria, Romania and FYROM. Journal of Balkan and Near Eastern Studies, Vol. 20 (2), 131-147.

Freedman, D. A. (2005). What is the error term in a regression equation? Retrieved December 2, 2018, from University of California, Berkeley: https://www.stat.berkeley.edu/~census/epsilon.pdf

Gerber, S. B., & Finn, K. V. (2005). Using SPSS for windows: Data analysis and graphics. Berlin, Germany: Springer Science & Business Media.

Gillman, M., & Harris, M. N. (2009). The effect of inflation on growth: Evidence from a panel of transition countries. Discussion Papers MT-DP – 2009/10, 1-21.

Munir, Q., & Mansur, K. (2009). Non-Linearity between Inflation Rate and GDP Growth in Malaysia. Economics Bulletin, Vol. 29 (3), 1555-1569.

Svigir, M., & Milos, J. (2017). Relationship between inflation and economic growth; comparative experience of Italy and Austria. Fip, Vol. 5 (2), 91-102.

Trading Economics. (2014). United States GDP. Retrieved from Trading Economics: https://tradingeconomics.com/united-states/gdp

Troeger, V. E. (n.d.). The simple linear regression model. Retrieved December 2, 2018, from University of Warwick: https://warwick.ac.uk/fac/soc/economics/staff/vetroeger/teaching/po906_week567.pdf