Homework Help is Here – Start Your Trial Now!
Math
Statistics
The Return to Education and the Gender Gap Dependent variable: logarithm of Hourly Earnings. Regressor (1) (2) (3) (4) Years of education 0.1035** 0.1050** 0.1001** 0.1113** (0.0009) (0.0009) (0.0011) (0.0012) - 0.432** (0.024) Female - 0.263** - 0.451** (0.004) (0.024) Femalex Years of education 0.0121* 0.0134** (0.0017) (0.0017) Potential experience 0.0132** (0.0012) Potential experience? -0.000181** (0.000027) - 0.095** (0.006) Midwest - 0.092** (0.006) South - 0.023** (0.007) West Intercept 1.533** 1.629** 1.697** 1.661** (0.012) (0.012) (0.016) (0.023) 0.208 0.258 0.258 0.267 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for women and 0 for men. Midwest, South, and West are indicator variables denoting the region of the United States in which the worker lives: For example, Midwest equals 1 if the worker lives in the Midwest and equals 0 otherwise (the omitted region is Northeasf). Standard errors are reported in parentheses below the estimated coefficients. Individual coefficients are statistically significant at the *5% or **1% significance level. Scenario A Consider a man with 18 years of education and 5 years of experience who is from a western state. Use the results from column (4) of the table and the method i Key Concept 8.1 to estimate the expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience. The expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience is %. (Round your response to two

The Return to Education and the Gender Gap Dependent variable: logarithm of Hourly Earnings. Regressor (1) (2) (3) (4) Years of education 0.1035** 0.1050** 0.1001** 0.1113** (0.0009) (0.0009) (0.0011) (0.0012) - 0.432** (0.024) Female - 0.263** - 0.451** (0.004) (0.024) Femalex Years of education 0.0121* 0.0134** (0.0017) (0.0017) Potential experience 0.0132** (0.0012) Potential experience? -0.000181** (0.000027) - 0.095** (0.006) Midwest - 0.092** (0.006) South - 0.023** (0.007) West Intercept 1.533** 1.629** 1.697** 1.661** (0.012) (0.012) (0.016) (0.023) 0.208 0.258 0.258 0.267 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for women and 0 for men. Midwest, South, and West are indicator variables denoting the region of the United States in which the worker lives: For example, Midwest equals 1 if the worker lives in the Midwest and equals 0 otherwise (the omitted region is Northeasf). Standard errors are reported in parentheses below the estimated coefficients. Individual coefficients are statistically significant at the *5% or **1% significance level. Scenario A Consider a man with 18 years of education and 5 years of experience who is from a western state. Use the results from column (4) of the table and the method i Key Concept 8.1 to estimate the expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience. The expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience is %. (Round your response to two

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
See similar textbooks
icon
Related questions
Question
The Return to Education and the Gender Gap Dependent variable: logarithm of Hourly Earnings. Regressor (1) (2) (3) (4) Years of education 0.1035** 0.1050** 0.1001** 0.1113** (0.0009) (0.0009) (0.0011) (0.0012) - 0.263** (0.004) - 0.432** (0.024) - 0.451** (0.024) Female Female x Years of education 0.0121** 0.0134** (0.0017) (0.0017) Potential experience 0.0132** (0.0012) Potential experience? - 0.000181** (0.000027) - 0.095** (0.006) Midwest South - 0.092** (0.006) West - 0.023** (0.007) 1.697** (0.016) Intercept 1.533** 1.629** 1.661** (0.012) (0.012) (0.023) 0.208 0.258 0.258 0.267 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for women and 0 for men. Midwest, South, and West are indicator variables denoting the region of the United States in which the worker lives: For example, Midwest equals 1 if the worker lives in the Midwest and equals 0 otherwise (the omitted region is Northeast). Standard errors are reported in parentheses below the estimated coefficients. Individual coefficients are statistically significant at the *5% or **1% significance level. Scenario A Consider a man with 18 years of education and 5 years of experience who is from a western state. Use the results from column (4) of the table and the method in Key Concept 8.1 to estimate the expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience. The expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience is %. (Round your response to two decimal places.)
Transcribed Image Text:The Return to Education and the Gender Gap Dependent variable: logarithm of Hourly Earnings. Regressor (1) (2) (3) (4) Years of education 0.1035** 0.1050** 0.1001** 0.1113** (0.0009) (0.0009) (0.0011) (0.0012) - 0.263** (0.004) - 0.432** (0.024) - 0.451** (0.024) Female Female x Years of education 0.0121** 0.0134** (0.0017) (0.0017) Potential experience 0.0132** (0.0012) Potential experience? - 0.000181** (0.000027) - 0.095** (0.006) Midwest South - 0.092** (0.006) West - 0.023** (0.007) 1.697** (0.016) Intercept 1.533** 1.629** 1.661** (0.012) (0.012) (0.023) 0.208 0.258 0.258 0.267 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for women and 0 for men. Midwest, South, and West are indicator variables denoting the region of the United States in which the worker lives: For example, Midwest equals 1 if the worker lives in the Midwest and equals 0 otherwise (the omitted region is Northeast). Standard errors are reported in parentheses below the estimated coefficients. Individual coefficients are statistically significant at the *5% or **1% significance level. Scenario A Consider a man with 18 years of education and 5 years of experience who is from a western state. Use the results from column (4) of the table and the method in Key Concept 8.1 to estimate the expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience. The expected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience is %. (Round your response to two decimal places.)
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 3 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Similar questions
  • SEE MORE QUESTIONS
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman
SEE MORE TEXTBOOKS