Consider the example in class, where we have data on workers Earning, Schooling years and Gender. And the model is specified as: Earning = β0 + β1Schooling + β2G + u where the model satisfies the 4 assumptions. And G=1 if the worker is female. (1) Interpret the effect of schooling years on male worker’s earning. (Hint: E(Earning|G = 0)) (2) Interpret the effect of schooling years on female worker’s earning.
Earning = β0 + β1Schooling + β2G + u
(1)
For male worker’s G = 0,
then,
Earning = β0 + β1Schooling + u
With the increase in 1 year of schooling, the male worker’s earning will increase by β1.
Without schooling (schooling = 0), the male worker’s earning is β0.
(2)
For male worker’s G = 1,
then,
Earning = β0 + β2 + β1Schooling + u
With the increase in 1 year of schooling, the female worker’s earning will increase by β1.
Without schooling (schooling = 0), the female worker’s earning is β0 + β2.
Consider the example in class, where we have data on workers Earning, Schooling years and Gender....
Consider the example in class, where we have data on workers Earning, College diploma and Gender. And the model is specified as: Earning=β0 +β1C+β2G+β3(C·G)+u where the model satisfies the 4 assumptions. And C=1 if the worker has college education, G=1 if the worker is female. (1) What is the average earning difference between male workers who has a college diploma and who has not a college diploma.(Hint: E(Earning|C = 1, G = 0) − E(Earning|C = 0,G = 0)) (2)...
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...
Consider the following least squares specification between test scores (m and the student-teacher ratio: Ý 607.8 + 5.32 in gncome). According to this equation, a 1% noease income is assocated with an increase in test scores of 0 607.8 points O B. 5.32 points ° C. 0.0532 points O D. 53.2 points Reset Selection Mark for Review What's This? Part 12 of 18-0 Question 12 of 18 1.0 Poir Consider the population regression of log earnings Y where Y- In(Eamings...
a. (5) From the multiple regression model we want to test the following hypothesis: Ho: β1-0 and β2-β3 and β5-1 Rewrite the null hypothesis Ho in the form of RB-r using the matrix R and two vectors B and r b. (5) Consider the following wage regression result: log(wage) 3.240.06educ 0.51Female 0.01educ Female, where educ denotes years of education and Female is a dummy variable for females. What is the return to schooling for male workers? What is the return...
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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...
3. (20 pts) Suppose that we have 4 observations for 3 variables y,I, 2 and consider a problem of regressing y on two (qualitative) variables r, 2. Data: 22 obs no. y (Income) 2 (Management Status) I (Gender) 1 None Female 2 None Male Yes Female Yes Male 4 To handle the qualitative variables r, 12, we define dummy variables 1, 22 as for 1, 22= Yes Male for 1, 219 22 -1. for 22= None for 1= Female -1,...
10. You have a data set that contains information about individuals gender, the number of children they have, their family income, and whether they are in the labor force You estimate the following linear probability model: Plaborforcel po+B1 children +B2 female +B3 (children x semale)+u a.) In terms of the model's parameters, what is the marginal effect of having an additional child on a woman's probability of being in the labor force? What is the marginal effect of having an...
1.13 Consider a multiple regression model 1.15 Consider a multiple regression model: with a dummy variable: h(wage)-A, + β.educ + β white + β,NonWhite + u where wage and educ denote the annual income and the number of years of education, respectively. White indicates the dummy variable taking 1 if white and zero otherwisc. Non White indicates the dummy variable taking 1 if non-white (African, Hispanic, Asian, Pacific Islander, Native American, etc.) and zero otherwise. Which of the following is...
An expert witness statistician was analyzing data from a workers compensation discrimination lawsuit filed by female workers at a bank. The data provided to the expert contain the following information: SALARY in dollars), EDUCAT (number of years of schooling), EXPER (# of months of work experience prior to joining the bank), MONTHS (# of months since joining the bank), MALES (an indicator for a worker's gender: 0 for a female, 1 for a male). As part of the investigation, the...
Consider the Solow growth model that we developed in class. Output at time t is given by the production function Y AK Lt, where A is total factor productivity, Kt is total capital at timet and L is the labour force. Total factor productivity A and labour force L are constant over time. There is no government or foreign trade and Y, + 1, where Ct is consumption and I is investment at tim. Every agent saves s share of...