Question

Using a model from a study on wage and education data, among a sample of students...

Using a model from a study on wage and education data, among a sample of students without college degrees, where wage is an hourly dollar figure and education refers to additional years of schooling, suppose the following results for a regression of earnings on prior earnings:

waget = -.48 +.54education

A) Predict the hourly wage for someone with 14 years of schooling.

B) What is the average level of education if the average hourly wage is $6.00?

C) Do you think this intercept makes sense?

D) What do you think would happen to the parameters if we included college graduates?

Business   Economics   
0 0
Add a commentTranscribed image text
Next >

Homework Answers

Answer #1

(A) When Education = 14,

Wage = - 0.48 + (0.54 x 14) = - 0.48 + 7.56 = 7.08

(B) When Wage = $6,

6 = - 0.48 + 0.54 x Education

0.54 x Education = 6.48

Education = 12 (years)

(C) The intercept is -0.48 which means that a person with 0 years of schooling will earn a wage of -$0.48, which has no logical sense since wage cannot be negative.

(D) Inclusion of college graduates will most likely increase the coefficient of Education. It means that as years of education rises, wage rate rises at a faster rate than before.

0 0
Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Log In

Sign Up

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 6.69 4.23 1.58 0.1206 Education 1.13 0.34 3.32 0.0018 Experience 0.37 0.10 3.70 0.0006 Age −0.09...
Using data from 50 workers, a labour economist runs a multiple regression of Y (hourly wage...
Using data from 50 workers, a labour economist runs a multiple regression of Y (hourly wage in $) on X1 (years of education), X2 (gender) and X3 (an interaction between X1 and X2), and obtains the following regression results. Coefficients Standard error Intercept -16.78 2.14 Years of education 2.77 0.16 Gender (coded 1 for female and 0 for male) 8.84 3.01 Interaction between years of education and gender -1.02 0.22 Based on the above information, the estimated effect of X1...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 6.03 3.70 1.63 0.1100 Education 1.25 0.34 3.68 0.0006 Experience 0.51 0.15 3.40 0.0014 Age −0.03...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 8.36 4.05 2.06 0.0447 Education 1.96 0.35 5.60 0.0000 Experience 0.43 0.17 2.53 0.0149 Age −0.02...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 7.56 4.35 1.74 0.0889 Education 1.53 0.38 4.03 0.0002 Experience 0.42 0.12 3.50 0.0010 Age −0.04...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 8.23 4.40 1.87 0.0678 Education 1.23 0.38 3.24 0.0022 Experience 0.53 0.18 2.94 0.0051 Age −0.08...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 6.69 4.23 1.58 0.1206 Education 1.13 0.34 3.32 0.0018 Experience 0.37 0.10 3.70 0.0006 Age −0.09...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience +...
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 7.17 4.26 1.68 0.0991 Education 1.81 0.35 5.17 0.0000 Experience 0.45 0.10 4.50 0.0000 Age −0.01...
15. Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience...
15. Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 6.89 3.61 1.91 0.0626 Education 1.29 0.34 3.79 0.0004 Experience 0.44 0.16 2.75 0.0085 Age...
The following model has been estimated using a large company's human resource date: log(wage) = 1.439...
The following model has been estimated using a large company's human resource date: log(wage) = 1.439 + 0.0834 x edu + 0.0512 x exper + 0.1932 x male where wage is hourly wage, edu is years of schooling, exper is years of relevant work experience, and male is a dummy variable for male (i) How much more do men at the firm earn than women, on average? (ii) How much per hour will a fresh male college graduate (16 years...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT