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. 1092** (0.0009) (0.0009) (0.0011) (0.0012) Female - 0.263** - 0.432** - 0.451** (0.004) (0.024) (0.024) Female x Years of education 0.0121** 0.0134** (0.0017) (0.0017) Potential experience 0.0137** (0.0012) Potential experience - 0.000182** (0.000022) Midwest - 0.095** (0.006) South - 0.092** (0.006) West - 0.029** (0.007) Intercept 1.533** 1.629** 1.697** 1.362** (0.012) (0.012) (0.016) (0.023) R2 0.208 0.258 0.258 0.267The sample size is 52,970 observations for each regression. Female is an indicator variable that equals 1 for women and O 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 signicant at the *5% or "1% signicance level. Scenario A Consider a man with 14 years of education and 3 years of experience who is from a western state. Use the results from column (4) of the table and the method in @y 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.) Key Concept 8.1 The Expected Effect on Y of a Change in X, in the Nonlinear Regression Model The expected change in Y, A Y, associated with the change in X1, AX,, holding X2,..., Xx constant, is the difference between the value of the population regression function before and after changing X1 , holding X2,..., Xx constant. That is, the expected change in Y is the difference: AY = f( X1 + AX1, X2 . ..., XK) - f(X 1, X2. ..., XK). The estimator of this unknown population difference is the difference between the predicted values for these two cases. Let f(X1, X2..., XK) be the predicted value of Y based on the estimator f of the population regression function. Then the predicted change in Y is A Y = ( ( X , + AX , , x 2 . . . X K ) - f ( X 1 , X 2 . ... , XK )