The data set “names” contains CV, call-back and employer information for 4,870 fictitious CVs sent in response to employment advertisements in Chicago in 2001. The CVs contained information concerning the race of the applicant. Because race is not typically included on a CV, CVs were differentiated on the basis of so-called “white sounding names” and “African American sounding names”. A large collection of fictitious CVs were created and the presupposed “race” (based on the “sound” of the name) was randomly assigned to each CV. These CVs were sent to prospective employers to see which CVs generated a phone call (a “call back”) from the prospective employer. You can find variable descriptions overleaf.
1) What is the call back rate in the sample? What is the call back rate for whites and for African Americans?
2) Use a regression model to test the effect of black on call back rate. Make sure to use a regression model that appropriately accounts for the binary nature of the dependent variable (this applies for the entire assignment and only one model is needed for each question). Do call back probabilities differ significantly by race (black)? If yes, by how much? Please include a screenshot of your model that includes the stata command.
3) Using call back probability as dependent variable, does an African American/white name on the CV (black) have a different effect for men and women? Please explain what model you are estimating and why. Please also make sure to include your regression output, and steps to get there.
4) Estimate a regression model that tests for racial bias in call back rates and controls for all relevant variables. What is the effect of race (black) for a sample average applicant, i.e. use mean values for continuous variables and median values for categorical variables. Please make sure to include all steps of how you calculate this effect. You can use screenshots if you calculate this in stata
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