1. (9 pts) Consider the following economic model describing workers' earnings: In(wage) = Bo + Beduc...
? ANSWER FROM LETTER "E" AND DOWNWARDS III- (15pts) You are given the following economic model 0.013 -$26 Rsquare- 0.4177 0.0012ten (0.00024 log(wage) 0.478 + 0.085edu + 0.059ten-0.058/emale-0.01 ledu.female-0.02 1/emale./en- Std errors (0.113) (0.008) (0.007) (0.174) (0.006 With all the variables described as follows: log(wage) -log of average hourly wage; female is a dummy variable equal to 1 if the observed person is a female, and O if make; edu female is an interaction variable equal to education'female; edu is...
0.024 .00 d5 002 0.009 0.0033(.005) Constant 100 .324) a What is the estimated ctr to eacandeely o setively on education? Explain. (2ps) b. Based on the estimated models (1)nd (2) "d wth.5%-a" hdiol by mother's sth" married worker. (Aps) II. (15pts) You are given the following three estimated models, with all the variables described as in question II: Dependent variable: og(wage) (3) 2) educ 0.118 0.062 0.116 C0.024) (0.006) (0.024) 0.095s C0.029) meduc 0.094* C0.029) married 0.252 0.247* C0.071)...
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....
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...
II. (15pts) You are given the following three estimated models, with all the variables described as in question II Dependent variable: educ 0.118***0.062***0.116** (0.024) (0.006) (0.024) meduc 0.094* C0.029) 0.095*** (0.029) married 0.252*0.247** (0.071) (0.068) 0.025 0.026* 0.020** (0.011) (0.011) (0.009) meduc.educ -0.006 C0.002) 0.006* C0.002) marrted. ten 0.009 (0.000 C0.009) (0.009) 0.0002 -0.0003 0.0003 C0.001) (0.0005) (0.001) Constant 4.694*** 5.597*** 4.932 (0.325) (0.100) (0.324) observations 857 R2 857 857 0.152 0.175 *p<0.1; p<0.05; p<0.01 Note: a. Wha education? Explain....
asap i beg u IL. (15pts) You are given the following three estimated models, with all the variables described as in question II Dependent variable: log (wage) educ 0.056** 0.062* 0.049* 0.022) (0.006) (0.022) feduc 0.027 C0.027) 0.021 0.028) married 1.000 0.606 (0.518) (0.466) age 0.032 C0.114) (0.103) (0.116) 0.034 feduc. educ -0.001 (0.002) -0.0003 (0.002) married.age 0.037 0.025 C0.016) (0.014) 0.001 0.001 -0.00000 C0.002) C0.002) C0.002) agesq Constant 5.306**5.218* 5.090*** (1.923) C1.723) C1.932) observations 741 R2 741 0.151 741...
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...
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....