Does Inflation Rate and Year-over-Year GDP% acts as significant predictors of the Unemployment Rate?

Unemployment is a concern in various countries and there are various econometric determinants that influence the rate of unemployment. The present study was conducted to evaluate the question that “Does Inflation Rate and Year-over-Year GDP% act as significant predictors of Unemployment Rate”? The common variables considered for the prediction of the unemployment rate are Inflation, GDP, Fiscal Deficit, Rate of Interest provided by the financial instruments, and Debt/GDP. The study was conducted to identify the impact of Inflation Rate and Year-over-Year GDP% on the Unemployment Rate. Such a study may provide a roadmap for policymakers and Governance to identify the factors which may reduce the rate of unemployment. Data from 10 countries were randomly selected based on the economic and demographic status of different countries. The unemployment rate, inflation rate, and the Year over Year (YoY) GDP % were considered as the variables for the study. The results indicated that the regression equation of the unemployment rate with the inflation rate and the Year over Year (YoY) GDP % (p>0.05 for the constructed regression) was not significant. Moreover, the individual variables like inflation rate and the Year over Year (YoY) GDP % were also not correlated to the unemployment rate (since the p values for both the correlation coefficients were >0.5). The null hypothesis was retained and the alternate hypothesis was rejected. Hence, it was concluded that the unemployment rate could not be significantly predicted from the inflation rate and the Year over Year GDP%.

The validity of the regression equation was further justified by the coefficient of determination (R-squared was only 50%). Hence, the variables considered for framing the regression equation could be considered to be non-significant. However, the p-value for the constant was less than 0.05 in the constructed regression. This indicated that the unemployment rate can be better predicted from other variables apart from the inflation rate and the Year over Year (YoY) GDP %.
Keywords: Inflation Rate, Unemployment Rate, and YoY GDP%.
Does Inflation Rate and Year-over-Year GDP% acts as significant predictors of the Unemployment Rate?
Background
Unemployment is a concern in various countries and there are various econometric determinants that influence the rate of unemployment. The field of Econometrics tries to address or evaluate such parameters by bridging the concepts of statistics, mathematics and economics. Such analysis provides a robust inference in developing theory and ability to forecast such phenomenon. Ordinary least squares regression equation has been frequently deployed to predict the rates of unemployment from various economic variables. Various researchers have carried out econometric analyses to predict the rates of unemployment from different economic variables, either alone or in a combination of different variables. The present study was conducted to evaluate the question that “Does Inflation Rate and Year-over-Year GDP% acts as significant predictors of Unemployment Rate”? The study would help to identify the impact of Inflation Rate and Year-over-Year GDP% on the Unemployment Rate. This will help to provide a roadmap for the policymakers and Governance to identify the factors which may reduce the rate of unemployment.
Review of Literature
Unemployment is a concern in various countries and there are various econometric determinants that influence the rate of unemployment (Farmer, 1999). The common variables considered for the prediction of the unemployment rate are Inflation, GDP, Fiscal Deficits, Rate of Interest provided by the financial instruments and Debt/GDP. Gross Domestic Product (GDP) signifies the market value of the accepted, recognized final products and services produced within a given time frame in a country. It measures the economical development of a country. GDP is a sum of investments provided by the private and public sectors, the spending of the government to maintain the staff salaries and developments in a country, the Net exports occurring in a country and the real household expenditure and also the borrowing of funds as loan from the International monetary funds and other financial instruments. Since, one of the components of GDP includes borrowing of funds as loan from the International monetary fund and other financial instruments, the estimation of GDP as an indicator for the economical development of a country has been a debatable issue. Therefore, a parameter called Debt/GDP is often considered to measure the economical development of a country in comparison to GDP. The lower is the ratio, the better is the economic development of a country (Vienneau, 2005).
Consumption is the largest component of the Gross Domestic Product and refers to the private or real household expenditure on the consumption of different essential and non-essential commodities. Since consumption involves expenditure and cash flow in the market, an increase in the expenditure on consumption may attribute to a reduction in the rate of unemployment. This is because increased expenditure and cash flow in the market would necessitate services and manpower for production, marketing and retailing, which will lead to a decrease in the unemployment rate. Therefore, if the component of Consumption in GDP increases, there will also be a reduction in the rate of unemployment. Investment refers to the business investments done by private companies or public sector companies and even government spending for industrialization and setting up production units. Since such a phenomenon requires the need for equipment and manpower, an increase in business investments done by private companies, public sector companies or governments would promote employment and decrease the rate of unemployment (David, 2011).
Government spending refers to the expenditure of governments in paying staff salaries and various developments in a country. Such a phenomenon also increases the GDP of a country. Moreover, increased expenditure by the Government in paying staff salaries and various developments in a country also reduces the rate of unemployment. This is because increase in staff salaries will motivate increased consumption and on the other hand increased developments in a country will engage manpower. The net exports refers to the production of goods and services which are indigenous to a country and are extended to other countries. Purchase of products and services by other countries helps a country to earn foreign revenue, which will increase the GDP of a country and also reduce the rate of unemployment (Vassilis, 2008).
Currently it is contended that the long term relation with respect to changes in rates of Gross Domestic Product growth and unemployment rate, is determined by potential output. Potential output is defined as the capability of an economy of any country to produce products and services when the labour and capital in a country is utilized to its maximum. Such a variable could not be measured, but may be definitely felt with a decrease in unemployment. Thus the rate of such growth is an estimate of competent labour force and labour supply as per the needs of the industry and developmental activities of a government (Blinder, 1997).
Inflation refers to the rise of general price of goods, commodities and services over a period of time. Inflation is best reflected by the consumer price index prevailing in a country. This means the commodity which was available at particular price may become costly or cheap in relation to a period of time. When the general consumer price index prevailing in a country, increases over a period of time, inflation also increases. This means that the currency could afford fewer goods and services. Hence, inflation indicates a reduction in the purchasing power per unit of a currency. Inflation has been debated to increase or decrease the economic development of a country (Vassilis, 2008).
A rise in inflation has been viewed as a positive or negative process. Negative effects of inflation include a tendency of common household, or investors to hold back money or reduce spending under different heads. Such measures are observed due to the apprehension of future risk of inflation. This would lead to a reduction in investments. The positive effects of inflation cause financial instruments (for example centralized banks) to adjust the real rates of interests (Pesaran, 1990). Hence, inflation rate may or may not influence unemployment rate. A relation of unemployment rate with inflation rate has been reflected and evidenced by the well known Philips curve. The curve reflected an inverse relationship of unemployment rate with inflation rate. This indicated that lower is the rate of inflation, higher would be the rate of unemployment. However, when increased inflation rate leads to increased unemployment rate the process is called stagflation. In such a situation the relationship between the unemployment rate and inflation rate is violated, in context to the Philips curve (Vassilis, 2008).
Fiscal deficits denote the total expenditure of a Government over and above the revenue generated, excluding the money acquired from borrowings or as a debt. Fiscal deficits are sometimes considered to be a positive factor in driving the economic development in a country. Fiscal deficits help a country to negate the impacts of recession. Some Fiscal conservatives are of the opinion that governments must avoid deficits and should favour a balanced budgetary policy. Fiscal deficits have been estimated to reduce the rate of unemployment, but it may also increase the burden of debt indirectly (Vassilis, 2008).
Ordinary least squares regression equation”, are frequently deployed to predict the rates of unemployment from various economic variables (Fair, 1996). Regression equation predicts the most likely value of a criterion (dependent variable) from the magnitudes’ of independent variables. The dependent variable is called the criterion while the independent variables are referred to as predictors (Fair, 1996). Regression equations are assessed by the p value or the level of significance. If the p value is less than 0.05 the regression equation is considered significant, which means that the independent variables may be successfully deployed in predicting the most likely value of a dependent variable. If the p value is greater than 0.05, the regression equation is considered to be non-significant, which means that the independent variables cannot successfully in predict the most likely value of a dependent variable. Regression equation also provides the correlation coefficients for the independent variables independently with the dependent variable. The correlation coefficient may have a range of values between -1 to +1, including 0. A positive correlation coefficient indicates that increasing the value of one variable will also increase the value of other variable. On the other hand, a negative correlation coefficient indicates that increasing the value of one variable will decrease the value of other variable (Dodge, 2003).

Methodology
Data Collection and Sampling
Data from 10 countries were randomly collected from tradingeconomics.com. Random selection was based on the economic and demographic status of different countries as reflected in Table 1. The economic status was designated as “Developed” and “Developing” countries. The demographic status was designated as “Americas”, “European Union” and “Asia”. The unemployment rate, inflation rate and the Year over Year (YoY) GDP % were considered as the variables for the study. A multiple regression equation was performed on the observed data set. Regression equation was based on “Ordinary least squares regression equation”, as such methods frequently deployed to predict the rates of unemployment from various economic variables. The dependent variable was unemployment rate and the independent variables were inflation rate and the Year over Year (YoY) GDP %.
Hypothesis Testing & Statistical Inference
The null hypothesis contended that unemployment rate could not be significantly predicted from inflation rate and the Year over Year (YoY) GDP %. Any observed prediction must have occurred due to chance factors of random sampling and in reality there is no such relation and the predictions are considered to be non-significant (p >0.05 for the regression equation). The null hypothesis further contends that the individual variables like inflation rate and the Year over Year (YoY) GDP % are not at all correlated to unemployment rate and any such correlations must have occurred due to chance factors of random sampling. The null hypothesis contends that the p value for the correlation coefficients of unemployment rate with inflation rate and unemployment rate with Year over Year (YoY) GDP % is > 0.05.
The alternate hypothesis contended that unemployment rate could be significantly predicted from inflation rate and the Year over Year (YoY) GDP %. Any observed prediction has not occurred due to chance factors of random sampling and in reality there is a significant relation and the predictions are considered to be significant (p< 0.05 for the regression equation). The alternate hypothesis further contends that the individual variables like inflation rate and the Year over Year (YoY) GDP % are significantly correlated to unemployment rate and any such correlations have not occurred due to chance factors of random sampling. The alternate hypothesis contends that the p value for the correlation coefficients of unemployment rate with inflation rate and unemployment rate with Year over Year (YoY) GDP % is > 0.05.
Statistical Software
The statistical analysis was carried out through R programming software (McCullough, 1999).
Results
Table 1: Descriptive Data of the Different Countries (www.tradingeconomics.com)
  GDP YoY Unemployment Rate Inflation Rate
United States 2.1 5 0.5
European Union 1.6 10.7 0.2
China 6.9 4.05 1.5
Japan 1.6 3.3 0.3
Brazil -4.5 7.5 14.25
India 7.4 4.9 5.41
Australia 2.5 5.8 1.5
Venezuela -4 7.9 68.5
Iran 0.6 10.9 10.8
UAE 4.6 4.2 3.5
Table 2: Econometric Regression Equation Analysis
Multiple Linear Regression – Estimated Regression Equation
Unemployment Rate = -0.36761889663355 GDP -0.0065332958887125 Inflation Rate +7.1856769937023
Variable Parameter S.E. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value
GDP[t] -0.367619 0.286339 -1.283861 0.240051 0.120025
Inf[t] -0.006533 0.05419 -0.120563 0.907424 0.453712
Constant 7.185677 1.310772 5.482017 0.000924 0.000462
Variable Partial Correlation
GDP[t] -0.436569
Inf[t] -0.045521
Constant 0.900599
Critical Values (alpha = 5%)
1-tail CV at 5% 1.9
2-tail CV at 5% 2.36
Multiple Linear Regression – Regression StatisticsMultiple R 0.504077
R-squared 0.254094
Adjusted R-squared 0.040978
F-TEST 1.19228
Observations 10
Degrees of Freedom 7
Multiple Linear Regression – Residual Statistics
Standard Error 2.666699
Sum Squared Errors 49.778971
Log Likelihood -22.214423
Durbin-Watson 2.294984
Von Neumann Ratio 2.549982
# e[t] > 0 3
# e[t] < 0 7
# Runs 7
Runs Statistic 1.472971
Multiple Linear Regression – Analysis of Variance
ANOVA DF Sum of Squares Mean Square
Regression 2 16.957279 8.478639
Residual 7 49.778971 7.111282
Total 9 66.73625 7.4151388888889
F-TEST 1.19228
p-value 0.358422
Discussion and Conclusion
From the above results it was evident that the regression equation of unemployment rate with inflation rate and the Year over Year (YoY) GDP % (p>0.05 for the multiple regression equation) was not significant. Hence, the null hypothesis has to be accepted and the alternate hypothesis was rejected. This meant that unemployment rate could not be significantly predicted from inflation rate and the Year over Year (YoY) GDP %. Any observed prediction must have occurred due to chance factors of random sampling and in reality there is no such relation and the predictions (regression equation framed) is considered to be non-significant. Moreover, the individual variables like inflation rate and the Year over Year (YoY) GDP % were also not correlated to unemployment rate ( since the p values for both the correlation coefficients were >0.5) and any such correlations must have occurred due to chance factors of random sampling. Thus once again the null hypothesis has to be accepted and the alternate hypothesis was rejected. However, the constant in the regression equation had a p value less than 0.05. This indicated that unemployment rate can be better predicted or may be forecasted from other variables apart from inflation rate and the Year over Year (YoY) GDP %. The scatter plot also indicated that the number of outliers were very high and a straight line based on the Goodness-of –fit could not be constructed, which supports the non-viability of the regression model constructed.
The validity of the regression equation was further justified by the co-efficient of determination (R-squared was only 50%). Hence, the variables considered for framing the regression equation could be considered to be non-significant. From an economic purview, the policy makers and Governance should act to identify the factors which may reduce the rate of unemployment. Although the regression equation was non-significant, but the trend reflected was that an increase in GDP will decrease the unemployment rate. Moreover, an increase in inflation rate will decrease the rate of unemployment. Such a phenomenon supports the Philips hypothesis as evidenced in review of literature. Hence, countries should endeavour to increase the GDP, either through motivating cash flow in the market, or by borrowing from monetary funds.
References
Blinder, A. (1997). “Is There A Core of Practical Macroeconomics That We Should All
Believe?,” American Economic Review, 87, 2.
Dodge, Y. (2003). The Oxford Dictionary of Statistical Terms. New York: Oxford University
Press
David, R. (2011). “Unemployment”. Advanced Macroeconomics (Fourth ed.). New York:
McGraw-Hill. pp. 456–512.
Farmer, Roger E. A. (1999). “Unemployment”. Macroeconomics(Second ed.). Cincinnati:
South-Western, 173–192
Fair,R (1996). “Computational Methods for Macroeconometric Models,” Handbook of
Computational Economics, v. 1, pp[1]-169.
H. P. Pesaran (1990), “Econometrics,” Econometrics: The New Palgrave, citing Ragnar
Frisch (1936), “A Note on the Term ‘Econometrics’,” Econometrica, 4(1), p. 95.
McCullough ,V. (1999). “The Numerical Reliability of Econometric Software,” Journal of
Economic Literature, 37(2), pp.633-655
Shumway, R. H. (1988). Applied statistical time series analysis. Englewood Cliffs, NJ:
Prentice Hall
Vassilis A. Hajivassiliou (2008). “computational methods in econometrics,” The New
Palgrav Dictionary of Economics, 2nd Edition.
 Vienneau, Robert L. (2005). “On Labour Demand and Equilibria of the Firm”. The Manchester School 73 (5), 612–619