1. Estimate the following regression model that explains wages.

wage = α + β1 education + β2 experience + β3 tenure + e

Have Excel place the output in cell G1 on the worksheet titled Regression 1. Job tenure, by the way, measures how many years a worker has been with their current employer.

Answer questions 1-4 in the other Word document.

2. Now run the same regression but generate the variable Female dummy. Remember that the variable will equal 1 if the person is female, 0 otherwise. To create the dummy variable in column E, you will need to use Excel’s =if( ) function to convert the text variable gender to a numeric female dummy variable.

The function below checks the text in cell K2 and returns the value 1 if the text equals female and 0 otherwise.

=if(K2=”Female”,1,0)

Copy the contents of cell E2 all the way down to the bottom of the data.

Now estimate the regression model:

wage = α + β1 education + β2 experience + β3 tenure + β4 female dummy + e

Be sure the female dummy variable data is in column E and adjust the Input X Range appropriately. Have Excel place the output in cell G1 on the worksheet titled Regression 2.

Answer questions 5-7.

3. In columns F, G, H, and I, generate dummy variables indicating the region a given worker resides in (East, South, Midwest, or West). Have Excel place the output in cell G1 on the worksheet titled Regression 3.

wage = α + β1 education + β2 experience + β3 tenure + β4 female dummy +

+ β5 East dummy + β5 South dummy + β5 Midwest dummy + e

Answer questions 8-10.

4. A person’s hourly wage is not likely to increase by a constant amount with additional years of education, experience, or job tenure. But the regression model in part 1 implies that, on average, a person’s wage should increase by $1.79 per hour for each additional year of education they obtain. This finding is probably not realistic, since it suggests that an additional year of graduate school would increase a person’s wage by the same amount as gaining an additional year of high school education.

It would be more realistic to assume that a person’s wage increases by a fixed percentage with each additional year of education. If you transform the dependent variable in a regression model and measure it in logarithmic form, the estimated coefficients should be interpreted as semi-elasticities. In other words, the coefficient estimates tell us the expected percentage change in a person’s wage if they obtain an additional year of education.

Compute the values for the natural logarithm of the wage variable in column L on the Data worksheet. The formula for the natural logarithm operation in Excel is simply:

=ln(A2)