Femalex Years of education Potential experience Potential experience Midwest South West Intercept R 1.533" (0.012) 0.208 (0.004) (0.012) 0.258 -0.432" 0.024) 00121" (0.0017) 1.697" (0.016) 0.258 -0.451" (0.024) 0.0134 (0.0017) 0.0136 (0.0012) -0.000184" (0.000021) -0.005 (0.006) -0.092 (0.006) -0.028" (0.007) 1.320 (0.023) 0.267 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for wwomen 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 equils 0 otherwise (the omitted region is Northeast). Standard emors 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 4 years of experience who is from a westem state. Use the results from column (4) of the table and the method in Key Concept 8.1 to estimate xpected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience he expected change in the logarithm of average hourly earnings (AME) associated with an additional year of experience is (Round your response to two decimal places)
Femalex Years of education Potential experience Potential experience Midwest South West Intercept R 1.533" (0.012) 0.208 (0.004) (0.012) 0.258 -0.432" 0.024) 00121" (0.0017) 1.697" (0.016) 0.258 -0.451" (0.024) 0.0134 (0.0017) 0.0136 (0.0012) -0.000184" (0.000021) -0.005 (0.006) -0.092 (0.006) -0.028" (0.007) 1.320 (0.023) 0.267 The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for wwomen 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 equils 0 otherwise (the omitted region is Northeast). Standard emors 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 4 years of experience who is from a westem state. Use the results from column (4) of the table and the method in Key Concept 8.1 to estimate xpected change in the logarithm of average hourly earnings (AHE) associated with an additional year of experience he expected change in the logarithm of average hourly earnings (AME) associated with an additional year of experience is (Round your response to two decimal places)
Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...
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Transcribed Image Text:Read the box "The Return to Education and the Gender Gap."
The Return to Education and the Gender Gap
Dependent variable: logarithm of Hourly Earnings.
*
Regressor
Years of education
Female
Female x Years of education
Potential experience
Potential experience²
Midwest
South
West
Intercept
(1)
0.1035**
(0.0009)
1.533"
(0.012)
0.208
(2)
0.1050**
(0.0009)
-0.263**
(0.004)
1.629"
(0.012)
0.1001"
(0.0011)
0.258
-0.432**
(0.024)
0.0121"
(0.0017)
1.697**
(0.016)
(4)
0.1013**
(0.0012)
0.258
-0.451"
(0.024)
0.0134"
(0.0017)
0.0136**
(0.0012)
-0.000184**
(0.000021)
-0.095"
(0.006)
-0.092**
(0.006)
-0.028"*
(0.007)
1.328"
R²
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
(0.023)
0.267
Transcribed Image Text:Female
Female x Years of education
Potential experience
Potential experience
Midwest
South
West
Intercept
1.533"
(0.012)
0.208
-0.263"
(0.004)
1.629"
(0.012)
-0.432**
0,024)
0.258
00121"*
(0.0017)
1.697**
(0.016)
-0.451**
(0.024)
0.258
0.0134"
(0.0017)
0.0136**
(0.0012)
-0.000184"
(0.000021)
-0.095"
(0.006)
-0.092
(0.006)
-0.028"*
(0.007)
1.328"
R²
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.
(0.023)
0.267
Scenario A
Consider a man with 18 years of education and 4 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 t
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 (AME) associated with an additional year of experience is % (Round your response to two decimal places.)
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