| Summary: | This dissertation is comprised of three essays. The first essay tests the empirical validity of a statistical discrimination model that incorporates employer's race. I show that if an employer statistically discriminates less against an employee that shares the same race (match) than an employee who does not share the same race (mismatch), then a match employee's wage correlates with measures of skill (AFQT) more than a mismatch employee's wage. Using data from the NLSY97, which includes information about the racial background of employees and their supervisors, I find support for this prediction for young black and white male employees after controlling for sample selection.
The second essay tests whether the theoretic model that explains the racial wage gap can also explain the gender wage gap. Specifically, I test whether the correlation between AFQT and wage is stronger for a employer-employee couple that shares the same gender than for a couple with opposite genders. I find that the data does not support this hypothesis.
In the third essay we use a unique dataset of 597 economics faculty from research universities across the US to rate 36 economics journals based on authors' salaries. We estimate the expected salary conditioning on a faculty member's publications and other individual characteristics. This method determines the average marginal effect of each paper published in a particular journal on the conditional expected salary. We then rank the journals according to their average marginal effect. To account for co-authorship, we use two different weight methods, giving us two rank lists.
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