Benjamin Nardella

Economics 043

Professor Schmidt

November 4, 2003

 

 

Does the Minimum Wage Increase Unemployment?

 

Section 1: Introduction

            My topic that I chose to present was comparing minimum wage and unemployment. The hypothesis that I chose was quite simple, that if the minimum wage would either increase or decrease unemployment and involved variables other than the minimum wage. After running the regression, which included nine variables (including unemployment rate as the dependent variable), the answer that the regression showed was that each and every variable was important to the unemployment rate. The conclusion that was made following the regression was that the unemployment rate either rises or falls not only depending on the minimum wage but on other variables as well.

 

Section 2: Model

            The two key variables that are focused on in the regression are the unemployment rate and the minimum wage. The regression showed the result that was predicted: If the minimum wage rises by a dollar ($1.11 approximately), then the minimum wage is likely to increase. Economically, the conclusion that can be made is that since the minimum wage is rising, it becomes expensive to keep more employers. In order for the company to be profitable, the company must decrease the amount of employees working in the company.  Through a simple supply and demand curve, we can see the comparison between price (wage) and quantity (workers).

 

Section 3: Data

            The equation shows a variety of different variables that may be measured in different units. Minimum wage is, as expected, measured in United States currency. Unemployment refers to the unemployment rate from the selected years. Population and the labor force are measured in thousands of people. The two variables that involve inflation are CPI (Consumer Price Index) and Inflation. Although the same, they are different in the way they measure inflation. Inflation measures the change in prices over a period of time, while CPI measures the change in a certain product’s goods and services over time. Govtsup stands for the amount of government surplus there is, either an increase or decrease each month. When creating the equation, all the variables besides the unemployment rate were important.

 

Section 4: Results

            The regression showed that each and every variable in the equation is important. One of the variable, govtsup, was taken out of the equation at first because of the t-statistic. The t-statistic was equal to approximately 1.363, which is too close to its critical value of 1.96. The equation that is used is as follows:

 

By performing an F-statistic to figure out if govtsup was necessary in the regression, the ending result showed that it was required in the equation. The F-statistic equaled to 1.86 approximately, which is lower than its critical value of 1.96. Although very close to dropping it, the govtsup variable could not be disposed of. From class earlier in the week, we learned about serial correlation. Serial correlation does influence my regression and does influence the final regression. However, serial correlation is not known of, therefore the conclusion to keep govtsup in the equation is still final. The regression is below:

 

 

Dependent Variable: UNEMP

Method: Least Squares

Date: 11/04/03   Time: 23:43

Sample(adjusted): 1959:01 2002:09

Included observations: 525 after adjusting endpoints

Variable

Coefficient

Std. Error

t-Statistic

Prob. 

MINIMUMWAGE

1.113130

0.180466

6.168085

0.0000

MONEYSUPPLY

0.001590

0.000205

7.741704

0.0000

POPULATION

0.000194

1.33E-05

14.62016

0.0000

REALGDP

-0.005619

0.000180

-31.22630

0.0000

LABORFORCE

0.000416

1.76E-05

23.66911

0.0000

INFLATION

-0.173792

0.016102

-10.79290

0.0000

GOVTSUP

0.000626

0.000459

1.362775

0.1735

CPI

-0.158707

0.009237

-17.18170

0.0000

C

-37.48853

2.088863

-17.94686

0.0000

R-squared

0.829552

    Mean dependent var

5.917714

Adjusted R-squared

0.826909

    S.D. dependent var

1.478307

S.E. of regression

0.615038

    Akaike info criterion

1.882727

Sum squared resid

195.1879

    Schwarz criterion

1.955814

Log likelihood

-485.2159

    F-statistic

313.9142

Durbin-Watson stat

0.334728

    Prob(F-statistic)

0.000000

 

Section 5: Conclusions

            From the regression, we can see that is the key variable in the equation because it fits the hypothesis made. Minimum wage, as stated beforehand, is $1.11, meaning that if the minimum wage increases by $1.11, then the unemployment rate will increase as well. By looking at the t-statistics, we can see that only one variable, govtsup, may not be essential to the regression. As of now, the conclusion that can be made from the regression is that each and every variable does influence the unemployment rate (especially looking at the minimum wage variable). A simple supply and demand curve can also demonstrate the regression that is presented above (see below).