Dependent variable: average hourly earnings (AHE). Regressor (1) (2) (3) College( X,) 5.46 5.48 5.44 Female( X2) -2.64 -2.62 -2.62 Age( X3 0.29 0.29 Northeast( X4) 0.69 Midwest( Xs) 0.60 South( Xe) 0.27 Intercept 12.69 4.40 3.75 Summary Statistics SER 6.27 6.22 6.21 0.176 0.190 0.194 4000 4000 4000 Table 1: Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 1988 Data from the Current Populations Survey Lsize denote the lot size (in square feet), Age denote the age of the house (in yeas), and Poor denote a binary variable that is equal to 1 if the condition of the house is reported as "poor". An estimated regression yields Price = 119.2 + 0.485BDR + 23.4Bath + 0.156/size + 0.002/.size + 0.090Age - 48.8Poor, R2 = 0.72, SER = 41.5. (a) Suppose that a homeowner converts part of an existing family room in her house into a new bathroom. What is the expected increase in the value of the house? (b) Suppose that a homeowner adds a new bathroom to her house, which increases the size of the house by 100 square feet. What is the expected increase in the value of the house? (c) What is the loss in value if a homeowner lets his house run down so that its condition becomes "poor"? (d) Compute the #2 for the regression. 3. (SW 6.6) A researcher plans to study the causal effects of police on crime using data from a random sample of U.S. counties. He plans to regress the county's crime rate on the (per capita) size of the county's police force. (a) Explain why this regression is likely to suffer from omitted variable bias. Which variables would you add to the regression to control for important omitted variables? (b) Use your answer to (a) and the expression for omitted variable bias with a single re- grossor to determine whether the regression will likely over- or underestimate the effect of police on the crime rate. (That is, do you think that E(8,) > 8, or E(8, )