Consider the following model for wage: log(wage) = ?0 + ?1female + ?2married + ?3educ +...
Consider the following model for wage: log(wage) = ?0 + ?1female + ?2married + ?3educ + ?4exper + u
where educ is years of education, exper is years of experience, female = 1 if individual is a woman, = 0 otherwise, and married = 1 if individual is married, = 0 otherwise.
(a) What is the benchmark group in this model? 1
(b) Modify this model (using interaction terms) so that the return to education can vary by marital status.
(c) Modify this model (using interaction terms) so that the return to experience can vary by gender and explain how you would test the null hypothesis that there are no gender differences in returns to experience.
Homework Answers
a) The bench mark group is unmarried male.
b) Now model will be
log(wage) = ?0 + ?1married + ?2educ + ?3exper + u
c) Now it will be
log(wage) = ?0 + ?1female + ?2educ + ?3exper +?4exper*female+ u
To check for no gender differences in return to experience we will check the null hypothesis
Ho : b4 =0
Ha: b4 != 0
Add Answer to:
Consider the following model for wage: log(wage) = ?0 + ?1female
+ ?2married + ?3educ +...
3. [40 pts Log of wage rate was regressed on the following explanatory variables: log(wage)0.389 ...
3. [40 pts Log of wage rate was regressed on the following explanatory variables: log(wage)0.389 0.227 female0.082educ0.0056 female educ (0.119) (0.168) (0.008) (0.0131) 0,00n() (0.005) (0.00024) where the female dummy takes on one if the worker is a woman. a) Explain carefully why the interaction term female educ is included in the above regression. (b) Consider two workers: a female worker and a male one with the same amount of experience and tenure. Both of them have no formal education....
What is the predicted difference in hourly wage between a married and a single female? Is this di...
what is the predicted difference in hourly wage
between a married and a single female? Is this difference
significant? Please provide evidence.
A regression of hourly wage for females on years of schooling (educ), experience (exper), hours worked (hrswk), and marital status (married-1), based on the dataset cps4 yielded the following equation: 8. wage18.3+2.15 educ+ 0.105 exper+ 0.0827 hrswk+ 0.815 married (s.e.) (1.6) (0.106) (0.0151) (0.0242) (0.403) n 2443, R 0.242332
A regression of hourly wage for females on years...
Could I please get an answer to this problem? Thank you. 4. Consider the following regression...
Could I please get an answer to this problem? Thank
you.
4. Consider the following regression model of log(rage) log(wage)-As + &female + βί educ + u, where wage is hourly wage, female is a dummy variable indicating gender (1 for women and 0 for men), and educ is the number of years of education. Augment the model to allow the return to education to differ by gender
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
9. A regression of log(wage) is run on a set of following variables: educ (years of education), e...
9. A regression of log(wage) is run on a set of following variables: educ (years of education), exper (years of experience) and numdep (number of dependents). The regression results are listed as follows. > a-1m(1wage-educ+exper+numdep,data-wage1) > summary(a) Call: LmCformula lwage educ exper numdep, data wage1) Residuals: -2.04105-0.30678-0.05124 0.30711 1.41812 Coefficients: Min 10 Median Max Estimate Std. Error t value Preltl (Intercept) 0.180983 0.117485 1.540 0.1 educ exper numdep 0.099472 0.007862 12.652 < 2e-16 0.010510 0.001569 0.013218 0.016486 0.802 0.423 Signif....
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0....
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0.132) (0.009) (0.005) (0.196) n-526 R-square With all the variables described as follows: logiwage)-log of average hourly wage: female is a dummy variable equal to 1 if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education 'female; edu is the number of years of schooling exp is the number...
can I please get an answer to this problem? 3. Consider the following simple model for...
can I please get an answer to this problem?
3. Consider the following simple model for wages log(wage-Ao + β,educ + u, where wage is the hourly wage and educ is the number of years of education. Define a. gender dummy female equaling 1 for women and 0 for men. Write out an enhanced model to account for possible wage differences between women and men due to the basic wage level and due to amount of education.
hi can someone answer part e) f) g) h) with workings olease thanks 2) A cross-sectional study suggests the following wage equation: In(wage,)-α + βι EDUC' + β:FEMALE + β3EXPER, + β4FEMALE EXP...
hi can someone answer part e) f) g) h) with workings olease
thanks
2) A cross-sectional study suggests the following wage equation: In(wage,)-α + βι EDUC' + β:FEMALE + β3EXPER, + β4FEMALE EXPERi + ei Where: In(wage): Natural logarithm of f hourly wage; EDUC: Years of education; EXPER: Years of work experience; FEMALE: Dummy which equals 1 if the respondent is female and 0 otherwise; FEMALE EXPER:Interaction between FEMALE, and EXPER a) What is meant by the population level regression...
3. [40 pts Log of wage rate was regressed on the following explanatory variables: log(wage)0.389 0.227 female0.082educ0.0056 female educ (0.119) (0.168) (0.008) (0.0131) 0,00n() (0.005) (0.00024) where the female dummy takes on one if the worker is a woman. a) Explain carefully why the interaction term female educ is included in the above regression. (b) Consider two workers: a female worker and a male one with the same amount of experience and tenure. Both of them have no formal education....
what is the predicted difference in hourly wage
between a married and a single female? Is this difference
significant? Please provide evidence.
A regression of hourly wage for females on years of schooling (educ), experience (exper), hours worked (hrswk), and marital status (married-1), based on the dataset cps4 yielded the following equation: 8. wage18.3+2.15 educ+ 0.105 exper+ 0.0827 hrswk+ 0.815 married (s.e.) (1.6) (0.106) (0.0151) (0.0242) (0.403) n 2443, R 0.242332
A regression of hourly wage for females on years...
Could I please get an answer to this problem? Thank
you.
4. Consider the following regression model of log(rage) log(wage)-As + &female + βί educ + u, where wage is hourly wage, female is a dummy variable indicating gender (1 for women and 0 for men), and educ is the number of years of education. Augment the model to allow the return to education to differ by gender
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
9. A regression of log(wage) is run on a set of following variables: educ (years of education), exper (years of experience) and numdep (number of dependents). The regression results are listed as follows. > a-1m(1wage-educ+exper+numdep,data-wage1) > summary(a) Call: LmCformula lwage educ exper numdep, data wage1) Residuals: -2.04105-0.30678-0.05124 0.30711 1.41812 Coefficients: Min 10 Median Max Estimate Std. Error t value Preltl (Intercept) 0.180983 0.117485 1.540 0.1 educ exper numdep 0.099472 0.007862 12.652 < 2e-16 0.010510 0.001569 0.013218 0.016486 0.802 0.423 Signif....
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0.132) (0.009) (0.005) (0.196) n-526 R-square With all the variables described as follows: logiwage)-log of average hourly wage: female is a dummy variable equal to 1 if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education 'female; edu is the number of years of schooling exp is the number...
can I please get an answer to this problem?
3. Consider the following simple model for wages log(wage-Ao + β,educ + u, where wage is the hourly wage and educ is the number of years of education. Define a. gender dummy female equaling 1 for women and 0 for men. Write out an enhanced model to account for possible wage differences between women and men due to the basic wage level and due to amount of education.
hi can someone answer part e) f) g) h) with workings olease
thanks
2) A cross-sectional study suggests the following wage equation: In(wage,)-α + βι EDUC' + β:FEMALE + β3EXPER, + β4FEMALE EXPERi + ei Where: In(wage): Natural logarithm of f hourly wage; EDUC: Years of education; EXPER: Years of work experience; FEMALE: Dummy which equals 1 if the respondent is female and 0 otherwise; FEMALE EXPER:Interaction between FEMALE, and EXPER a) What is meant by the population level regression...
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