Please answer all parts for upvote. Suppose the following wage regression is run

Question

Please answer all parts for upvote.

Suppose the following wage regression is run where the results are presented in a table below: Wage-p0+ ?? *Male-B2*Age + ?3"Grade-B4"Married + ?5*Experience where the variables are as defined in class. Refer to the empirical results when answering the questions Coefficients Standard Error Intercept Male Age Grade Married Experience -7.856 1.845 0.123 0.677 0.713 0.0096 0.404 0.093 0.015 0.021 0.099 0.0003 t Stat 19.444 19.793 7.902 31.920 7.133 26.003 a)According to these regression results, do married individuals earn more than those non-married? b)How much less would a female expect to earn on average than a male (when controlling for age, grade, marital status, and experience)? c) Which variables are statistically significant at the 95 percent confidence level? Calculate the average wage of a 20-year old married male with 16 years of education and 104 weeks of experience. Do the same for a 20-year old married female with 12 years of education and 0 work experience d) e) By how much will wages change with an additional year of education? Suppose male are systematically more likely to be in a union than females and that union wages are higher than non-union wages. What effect will re-running this regression with a union dummy variable included have on the male coefficient? f)

Explanation / Answer

a) The coefficient of married is 0.713 which says that if a person is married he earns 0.713 units more than non married people.

b) the coefficient of male is 1.845 which says that males on average earn 1.845 per dollar greater than females. This means females earn 1.845 per dollar less than males.

c) z score at large sample data is t stat only. t stat at 95% is 1.96.

All the t stats given in the last column of the table is greater than 1.96 at absolute level. This means all the variables are significant at 95% level of confidence.

d) average wage = -7.856 + 1.845(1) + 0.123(20) + 0.677(16) + 0.713(1) + 0.0096(104) = 8.9924

Average wage= -7.856 + 1.845(0) + 0.123(20) + 0.677(12) + 0.713(1) + 0.0096(0)

= 3.441

e) the coefficient on grade is 0.677 which gives the change in average wage due one year change in education years.

Wages change by 0.677 units with additional years of education.

f) when we will rerun the regression with union dummy variable included, the coefficient on males will get lower. This happens because earlier males variable was showing effects of both gender and union. Now when union variable is included separately, part of the difference between wage earning between males and females is explained by union and other part is explained by males variable.

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