Homework Answers
A. The estimated returns to education = B1 = 0.12.
B. The estimated coefficient of the dummy variable FEMALE is B2 = -0.08 which is negative indicating that higher educated women actually earn comparatively less than higher educated men or vice-versa.
C. B2 is the incremental change in the earnings provided the individual is male/female. For example, the estimated earnings of female, ceteris paribus, is approximately 0.08 units less than that of men.
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2. In the earnings regression below, B, is estimated to be 0.12 and B2 is estimated...
SECTION 9.3 Measures of Regression and Prediction Intervals 505 12.)Earnings of Men and Women The table...
SECTION 9.3 Measures of Regression and Prediction Intervals 505 12.)Earnings of Men and Women The table shows the median annual earnings (in dollars) of male and female workers from 10states in a recent year. The equation of the regression line is 1.005x 10,770.313. Source .S. Census Burean) Median annual earnings so.976 46,763 46,934 of male workers, r Median annual earg 40.214 36.834 36,841 31 110 40.173 of female workers, y Median annual earnings of male workers, x 47.092 51.628 46.123...
Earnings functions attempt to find the determinants of earnings, using both continuous and binary variables. One...
Earnings functions attempt to find the determinants of earnings, using both continuous and binary variables. One of the central questions analyzed in this relationship is the returns to education. Your estimated regression looks like the following: In(Earn) = (0.16) + (0.012)Educ + (0.006)Exper – (0.0001)Exper2 te -0.01 0.101 0.033 0.0005 where Earn is average hourly earnings, Educ is years of education, Exper is years of experience, and e is the (estimated) error. a. [10 points) What is the effect of...
2. Using data from 2013 on 64 black females, the estimated linear regression between WAGE (earnings...
2. Using data from 2013 on 64 black females, the estimated linear regression between WAGE (earnings per hour, in S) and years of education, EDUC is WAGE8.451.99EDUC The standard error of the estimated slope coefficient is 0.52. Construct and interpret a 95% interval estimate for the effect of an additional year of education on a black female's expected hourly wage rate a. b. The standard error of the estimated intercept is 7.39. Test the null hypothesis that the intercept A-0...
3. Consider you are interested in estimating the determinants of earnings. For this purpose you estimate...
3. Consider you are interested in estimating the determinants of earnings. For this purpose you estimate a regression of annual earnings (EARN , in $000's) as a function of the years of education EDUC, with 12 being high school, 16 a bachelor degree, etc.) and the years of experience EXP number of years working). The estimated regression you obtain from a sample of one thousand individuals is (standard errors in parenthesis): EARN;= -50.3 +3.0*EDUC +1.5*EXP (0.42) (0.44) 7.1 3.4 RP=0.45...
2. Exercises: 5.2, part (a), (b), (c), (d) and (e) 5.2 Suppose a researcher, using wage...
2. Exercises: 5.2, part (a), (b), (c), (d) and (e) 5.2 Suppose a researcher, using wage data on 250 randomly selected male workers and 280 female workers, estimates the OLS regression www Wage - 12.52 + 2.12 Male, R? = 0.06, SER = 4.2, (0.23) (0.36) www. where Wage is measured in dollars per hour and Male is a binary variable that is equal to 1 if the person is a male and if the person is a female. Define...
Suppose the following model of wage determination: log(w) 5.40 0.0654edu + 0.0140exp + 0.0117tenu + 0.199marr...
Suppose the following model of wage determination: log(w) 5.40 0.0654edu + 0.0140exp + 0.0117tenu + 0.199marr - 0.188f - 0.091south + 0.184urban (0.0025) (0.0032) (0.039) (0.027) (0.11) (0.0063) (0.038) (0.026) n 935, R2 .253. where w monthly earnings, edu = years of education, exp tenu-years with current employer, marr 1 if married, f 1 if female, south 1 if live in south, years of work experience, urban 1 if live in urban area Interpret the coefficient of the variable f....
1. (9 pts) Consider the following economic model describing workers' earnings: In(wage) = Bo + Beduc...
1. (9 pts) Consider the following economic model describing workers' earnings: In(wage) = Bo + Beduc + Bztenure + Bzblack + Batenure? + Bseduc. black + Botenure black With all the variables described as follows: In(wage) = natural log of monthly earnings; educ = years of education; tenure = years with current employer; black = 1 if black and 0 if not; tenure? = tenure*tenure educ.black is an interaction variable equal to educ*black; tenure,black is an interaction variable equal to...
please show all the work Are years of experience a significant predictor of earnings? Yes, because...
please show all the work
Are years of experience a significant predictor of
earnings?
Yes, because it is statistically significant in all
specifications.
Yes, because it has a positive magnitude in all
specifications.
Yes, because the t-stats in all specifications are lower than
1.96
Yes, because the t-stats in all specifications are higher than
100.
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%
In specification (3),...
QUESTION 5 Suppose that a researcher, using wage data on 250 randomly selected male workers and 2...
QUESTION 5 Suppose that a researcher, using wage data on 250 randomly selected male workers and 280 female workers, estimates the OLS regression: Wage 12.52 + 2.12 x Male, R2 0.06, SER 4.2 (0.23) (0.36) where Wage is measured in dollars per hour and Male is a binary variable that is equal to 1 if the person is a male and O if the person is a female. Define the wage gender gap as the difference in the mean earnings...
Please help with 4. a) b) c)
1. Use the LNU dataset that allows you to estimate a wage equation. Estimate a wage equation including a dummy variable for female (FEMALE). Interpret the estimated coefficient for the variable Female. 2. Estimate the same model adding a dummy variable for public sector (PUBLIC). Interpret the estimated coefficient for the variable PUBLIC. Compare the estimate for FEMALE in this model and the model above where the variable PUBLIC was not included.3. Estimate the wage equations above separately for Men and...
SECTION 9.3 Measures of Regression and Prediction Intervals 505 12.)Earnings of Men and Women The table shows the median annual earnings (in dollars) of male and female workers from 10states in a recent year. The equation of the regression line is 1.005x 10,770.313. Source .S. Census Burean) Median annual earnings so.976 46,763 46,934 of male workers, r Median annual earg 40.214 36.834 36,841 31 110 40.173 of female workers, y Median annual earnings of male workers, x 47.092 51.628 46.123...
Earnings functions attempt to find the determinants of earnings, using both continuous and binary variables. One of the central questions analyzed in this relationship is the returns to education. Your estimated regression looks like the following: In(Earn) = (0.16) + (0.012)Educ + (0.006)Exper – (0.0001)Exper2 te -0.01 0.101 0.033 0.0005 where Earn is average hourly earnings, Educ is years of education, Exper is years of experience, and e is the (estimated) error. a. [10 points) What is the effect of...
2. Using data from 2013 on 64 black females, the estimated linear regression between WAGE (earnings per hour, in S) and years of education, EDUC is WAGE8.451.99EDUC The standard error of the estimated slope coefficient is 0.52. Construct and interpret a 95% interval estimate for the effect of an additional year of education on a black female's expected hourly wage rate a. b. The standard error of the estimated intercept is 7.39. Test the null hypothesis that the intercept A-0...
3. Consider you are interested in estimating the determinants of earnings. For this purpose you estimate a regression of annual earnings (EARN , in $000's) as a function of the years of education EDUC, with 12 being high school, 16 a bachelor degree, etc.) and the years of experience EXP number of years working). The estimated regression you obtain from a sample of one thousand individuals is (standard errors in parenthesis): EARN;= -50.3 +3.0*EDUC +1.5*EXP (0.42) (0.44) 7.1 3.4 RP=0.45...
2. Exercises: 5.2, part (a), (b), (c), (d) and (e) 5.2 Suppose a researcher, using wage data on 250 randomly selected male workers and 280 female workers, estimates the OLS regression www Wage - 12.52 + 2.12 Male, R? = 0.06, SER = 4.2, (0.23) (0.36) www. where Wage is measured in dollars per hour and Male is a binary variable that is equal to 1 if the person is a male and if the person is a female. Define...
Suppose the following model of wage determination: log(w) 5.40 0.0654edu + 0.0140exp + 0.0117tenu + 0.199marr - 0.188f - 0.091south + 0.184urban (0.0025) (0.0032) (0.039) (0.027) (0.11) (0.0063) (0.038) (0.026) n 935, R2 .253. where w monthly earnings, edu = years of education, exp tenu-years with current employer, marr 1 if married, f 1 if female, south 1 if live in south, years of work experience, urban 1 if live in urban area Interpret the coefficient of the variable f....
1. (9 pts) Consider the following economic model describing workers' earnings: In(wage) = Bo + Beduc + Bztenure + Bzblack + Batenure? + Bseduc. black + Botenure black With all the variables described as follows: In(wage) = natural log of monthly earnings; educ = years of education; tenure = years with current employer; black = 1 if black and 0 if not; tenure? = tenure*tenure educ.black is an interaction variable equal to educ*black; tenure,black is an interaction variable equal to...
please show all the work
Are years of experience a significant predictor of
earnings?
Yes, because it is statistically significant in all
specifications.
Yes, because it has a positive magnitude in all
specifications.
Yes, because the t-stats in all specifications are lower than
1.96
Yes, because the t-stats in all specifications are higher than
100.
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%
In specification (3),...
QUESTION 5 Suppose that a researcher, using wage data on 250 randomly selected male workers and 280 female workers, estimates the OLS regression: Wage 12.52 + 2.12 x Male, R2 0.06, SER 4.2 (0.23) (0.36) where Wage is measured in dollars per hour and Male is a binary variable that is equal to 1 if the person is a male and O if the person is a female. Define the wage gender gap as the difference in the mean earnings...
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