Naked Statistics: Stripping the Dread from the Data

by Charles Wheelan

As an example, let’s look at a paper by three economists examining the wage trajectories of a sample of roughly 2,500 men and women who graduated with MBAs from the Booth School of Business at the University of Chicago.1 Upon graduation, male and female graduates have very similar average starting salaries: $130,000 for men and $115,000 for women. After ten years in the workforce, however, a huge gap has opened up; women on average are earning a striking 45 percent less than their male classmates: $243,000 versus $442,000. In a broader sample of more than 18,000 MBA graduates who entered the workforce between 1990 and 2006, being female is associated with 29 percent lower earnings. What is happening to women once they enter the labor force? According to the authors of the study (Marianne Bertrand of the Booth School of Business and Claudia Goldin and Lawrence Katz of Harvard), discrimination is not a likely explanation for most of the gap. The gender wage gap fades away as the authors add more explanatory variables to the analysis. For example, men take more finance classes in the MBA program and graduate with higher grade point averages. When these data are included as control variables in the regression equation, the unexplained portion of the gap in male-female earnings drops to 19 percent. When variables are added to the equation to account for post-MBA work experience, particularly spells out of the labor force, the unexplained portion of the male-female wage gap drop...