Unemployment and its affects on Corporate Profits

By Michael Sparico

Introduction:

The topic of my project is the relationship between the unemployment rate and corporate profits. More specifically, what affect does the unemployment rate have on the level of corporate profits? This topic is interesting especially given the current economic conditions. The unemployment rate has been rising throughout that last year while corporations have been suffering as a whole. Therefore, analyzing the affect of the unemployment rate on corporate profits will be useful in predicting the future of the economy. It is believed that as the unemployment rate decreases than corporate profits should increase due to an increase in workers, non-farm payroll and productivity. After running the initial series of regressions this hypothesis has been proved correct. The unemployment rate has an inverse affect on corporate profits.

Model:

The economic principle that is the foundation for this project is rather simple. Corporate profits are an indicator of the relative health of a firm. This statistic is important because it permits a quick and basic value that can be used to determine successful firms from non-successful firms. The level of corporate profits is therefore dependant upon the amount of labor that is available to them. As more people become unemployed there will, in result, be less people employed by a specific firm; and therefore it will be difficult for any one firm to produce at the profit maximizing level. This entails that the more people that are employed the higher the level of production and eventually higher profits. Fundamentally speaking, the higher the unemployment rate then the lower corporate profits will inevitably become, and therefore, the lower the unemployment level the higher corporate profits.

Data:

The data set that was generated for this project is annual data that dates back to the year 1950 and includes all years through 2002. The data that was used was all macro economic data gathered from the DRI database in eviews. The dependant variable in this regression is corporate profits which is measured in billions of dollars. The key independent variable is unemployment which is measure in thousands of people. Originally the key independent variable was the unemployment rate, but that was a percentage and percentages don’t grow over time like all other macro economic data.

There were other independent variables that were included in the regression because they were believed to be important to the regression and therefore they were necessary to incorporate. The first is non-farm payroll, which is the total amount of money available for workers for all industries that are non agricultural, this variable is measured in thousands of dollars. The second was productivity, which is measured as the total output per hour that firms produced. And lastly wages were included because the wage offered by a firm has a direct effect on the firm’s total profits and the personnel that is employed. The wages variable was measured as dollars per hour.

Results:

The results of the estimation produced some interesting output. The key independent variable the number of people unemployed had a coefficient that was estimated at -.0374 with a standard deviation of .00446 and a T statistic of -8.38. Similarly the only other independent variable that was left in the regression beside the key independent variable was wages. The wages coefficient was estimated at 68.926 with a standard deviation of 2.361 and a T statistic of 29.193. The results of this regression are accurate in terms of variable coefficients, however due top serial correlation the standard errors for each variable has been skewed and therefore the regression must be re-run adjusted for the effects of serial correlation.

Corporate Profits = b0+b1* Unemployed +b2* Wages+E

Dependent Variable: CPBEFTAXCAPCONIVAL
Method: Least Squares
Date: 10/30/03 Time: 21:50
Sample: 1950 2002
Included observations: 53
Variable	Coefficient	Std. Error	t-Statistic	Prob.
NUMUNEMPLOYED	-0.037356	0.004457	-8.380879	0.0000
WAGES	68.92570	2.361043	29.19290	0.0000
C	22.10646	18.51605	1.193908	0.2381
R-squared	0.962037	Mean dependent var		266.3266
Adjusted R-squared	0.960519	S.D. dependent var		254.2255
S.E. of regression	50.51439	Akaike info criterion		10.73733
Sum squared resid	127585.2	Schwarz criterion		10.84886
Log likelihood	-281.5393	F-statistic		633.5385
Durbin-Watson stat	0.539676	Prob(F-statistic)		0.000000

Conclusion:

In conclusion the results of the regression are satisfactory in terms of their estimated coefficients, however the regression most likely has been restricted too far because serial correlation has skewed the standard errors of all of the independent variables. Regardless of extraneous errors, the key independent variable of unemployed people still has a negative sign and therefore as the number of people unemployed rises by one percent, corporate profits will as a result decrease. Eviews cannot adjust for data that has a general pattern to it. All or most macro economic data will come a pattern that allows economists to forecast the future of the economy buy utilizing the data from prior periods. Therefore the results of the regression have been conducted under non-efficient terms, and therefore all tests and confidence intervals are no longer valid.