Suppose a researcher interested in evaluating the extent of gender-based discrim

Question

Suppose a researcher interested in evaluating the extent of gender-based discrimination in the labor market, estimates the relationship between female workers’ daily wages and their age and education, ignoring the fact that the average women has less work experience than the average man. Based on her findings reported in the column “Regression 1” of the table below, and assuming that the average wage for men is $86.50, what would be her estimate of wage discrimination? After consulting with you, a policy expert on labor market discrimination, she adds worker experience to her analysis. Her new results are reported in the column “Regression 2.” What is the effect of including experience on her implied measure of wage discrimination? Which of the two analyses better captures the extent of wage discrimination against women? Please explain.

Determinants of Worker (Daily) Wages
Attribute Prices
(Point estimates in first 2 columns from worker-level regressions, women only)

Attribute

Regression 1

Regression 2

Mean of Attribute for Men

Constant
Interpret as the average wage for

someone with Age= Education=Experience=0

7.27

5.75

1

Age (Number of years)

1.10

.74

38.2

Education (Number of years)

2.36

1.48

14.3

Experience

0

2.31

13.8

Determinants of Worker (Daily) Wages
Attribute Prices
(Point estimates in first 2 columns from worker-level regressions, women only)

Attribute

Regression 1

Regression 2

Mean of Attribute for Men

Constant
Interpret as the average wage for

someone with Age= Education=Experience=0

7.27

5.75

1

Age (Number of years)

1.10

.74

38.2

Education (Number of years)

2.36

1.48

14.3

Experience

0

2.31

13.8

Explanation / Answer

Solution:

The wage of female from regression 1 using mean vaules of men = 7.27+1.1*38.2+2.36*14.3 = 83.04 so the wage discrimination = 86.5-83.04 = 3.46

The wage of female from regression 2 using mean vaules of men = 7.27+0.74*38.2+1.48*14.3+2.31*13.8 = 87.06 so the wage discrimination = 86.5-87.06 = -0.56

If experience is included in the regression then females wage is more than men so we see that if experience is included there is no wage discrimination seen against females..

If we talk of wage discrimination against females i.e. females getting lesser wage than men then we see that regression 1 captures the better extent of wage discrimination which can be seen above

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