What You See and What She Gets: Isolating the Effect of Inconsistent Racial Classification on Women's Earnings and Income

Aliya Saperstein, University of California, Berkeley
Bryan L. Sykes, University of Washington, Seattle

Recent studies have shown that using different measures of race results in different estimates of everything from vital rates to racial disparities in income and medical treatment. Here, instead of comparing conclusions based on different measures, we combine measures of interviewer-classified and self-reported race to examine whether differences exist in the earnings and income of people who are consistently classified compared to those who are not. Because inconsistent racial classification is not randomly distributed across the population, we use propensity-score matching techniques to ensure comparison between individuals who are similar on all other characteristics, except their racial identity and classification. Drawing on data from the 1988 National Survey of Family Growth, we find that women who self-report a race that differs from the interviewer’s report have significantly lower earnings and income than their consistently classified counterparts. This finding has implications for understanding how racial inequalities are perpetuated.