Question

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The Return to Education and the Gender Gap Dependent variable: logarithm of Hourly Earnings. (3) Regressor Years of education (1) 0.1035** (0.0009) (2) 0.1050** (0.0009) -0.263** (0.004) 0.1001** (0.0011) -0.432** (0.024) 0.0121** (0.0017) 0.1104** (0.0012) -0.451** (0.024) Female Female x Years of education 0.0134** (0.0017) Potential experience 0.0139** (0.0012) Potential experience Midwest South -0.000214** (0.000029) -0.095** (0.006) -0.092** (0.006) -0.028** (0.007) 1.486** (0.023) 0.267 West Intercept 153750 1.533** 1.629** (0.012) 0.258 1.62921 1.697** (0.016) 0.258 16.016) 0.208 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for women and 0 for men. Midwest, South, and West are indicator variables denoting the region of the United States in which the worker lives: For example, Midwest equals 1 if the worker lives in the Midwest and equals 0 otherwise (the omitted region is Northeast). Standard errors are reported in parentheses below the estimated coefficients. Individual coefficients are statistically significant at the 25% or **1% significance level.

1. Are years of experience a significant predictor of earnings?

1. Yes, because it is statistically significant in all specifications.
2. Yes, because it has a positive magnitude in all specifications.
3. Yes, because the t-stats in all specifications are lower than 1.96
4. Yes, because the t-stats in all specifications are higher than 100.

2. When controlling only for years of education by how much less do females earn?

A. 26.3%

B. 0.263%

C. 2.63%

D. 43.2%

3. In specification (3), when using the dummy variable:

1. Only the intercept changes
2. Both the intercept and slope change
3. Only the slope changes
4. Neither the intercept or the slope change

4. In specification (2), when using the dummy variable:

1. Only the intercept changes
2. Both the intercept and slope change
3. Only the slope changes
4. Neither the intercept or the slope change

5. A man with 17 years of education and 4 years of experience is from a western state. What is the expected increase in earnings with one additional year of experience? (Hint: Use exact approach by comparing fitted values)

A. -1.90%

B. 1.90%

C. 1.20%

D. -1.20%

6. Testing the joint hypothesis of regional effects, experience and experience², what is the numerator in the F-stat (use adjusted R²instead of R²):

A. 0.0025

B. 0.0022

C. 0.0018

D. 0.0030

Answer 1

Q1) option A)

Significant in all specifications

2) option A)

As dependent variable is in log

So, female earn (.263)*100 = 26.3% less than male

3) option B)

Both Intercept & Slope changes, when compared (3) with (2)

4) option B)

Both Intercept (& Slope changes , when (2) compared with (1)

5) option C)

Using specification (4)

Exp = 4, edu = 17, west = 1

Then Differentiating wrt exp

= .0139 - 2*.000214*exp

= .0139 - .000428*4

= .012188

= .012188*100

= 1.2%

6) option C)

F statistic numerator = (Rsquare of unrestricted model - Rsquare of restricted model)/number of restrictions

Regional effects & experience is introduced in (4)

Then as compared with (3), we have 5 restrictions

So , F numerator = (.267-.258)/5

= .0018