Introductory Econometrics 4th Edition by Jeffrey Wooldridge

Exercise 15

Use the data in LOANAPP.RAW for this exercise; see also Computer Exercise
(i) Estimate a probit model of approve on white. Find the estimated probability of loan approval for both whites and nonwhites. How do these compare with the linear probability estimates
(ii) Now, add the variables hrat, obrat, loanprc, unem, male, married, dep, sch, cosign, chist, pubrec, mortlatl, mortlat2, and vr to the probit model. Is there statistically significant evidence of discrimination against nonwhites
(iii) Estimate the model from part (ii) by logit. Compare the coefficient on white to the probit estimate.
(iv) Use equation to estimate the sizes of the discrimination effects for probit and logit.
Use the data in LOANAPP.RAW for this exercise. The binary variable to be explained is approve, which is equal to one if a mortgage loan to an individual was approved. The key explanatory variable is white, a dummy variable equal to one if the applicant was white. The other applicants in the data set are black and Hispanic.
To test for discrimination in the mortgage loan market, a linear probability model can be used: approve 0 + 1white + other factors.
(i) If there is discrimination against minorities, and the appropriate factors have been controlled for, what is the sign of 1
(ii) Regress approve on white and report the results in the usual form. Interpret the coefficient on white. Is it statistically significant Is it practically large
(iii) As controls, add the variables hrat, obrat, loanprc, unem, male, married, dep, sch, cosign, chist, pubrec, mortlat1, mortlat2, and vr. What happens to the coefficient on white Is there still evidence of discrimination against nonwhites
(iv) Now, allow the effect of race to interact with the variable measuring other obligations as a percentage of income (obrat). Is the interaction term significant
(v) Using the model from part (iv), what is the effect of being white on the probability of approval when obrat = 32, which is roughly the mean value in the sample Obtain a 95% confidence interval for this effect.