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 (1 ) (2) (3) (4) Years of education 0.1035" 0.1050" 0.1001" 0.1 129" (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" 0-01 34" (0.0017) (0.0017) Potential experience 0.01 39\" (0.0012) Potential experience2 ' 0000225" (0.000029) Midwest - 0.095\" (0.006) South - 0.092\" (0.006) West - 0.029\" (0.007) Intercept 1 .533" 1.629" 1.697" 1 .554" (0.012) (0.012) (0.016) (0.023) [32 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 17 years of education and 2 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.)