Using Panel Data to Examine Racial and Gender Differences in Debt Burdens

Debt burdens have risen for US households over the last several decades. As a result, several studies have investigated potential ethnic and gender differences in these debt burdens, along with the risks they pose. However, such estimations can be biased without correctly controlling for individual unobserved heterogeneity, and standard methods to deal with this, such as fixed effects, remove any time-invariant variables from the analysis. In this paper, I use the Hausman–Taylor (HT) estimator to estimate the relationship between these time-invariant demographics and debt burdens, allowing for potential correlation between some variables and the unobserved heterogeneity. I also consider some guidelines in determining the appropriateness of the HT estimation, both in terms of exogeneity assumptions as well as potential problems due to weak instruments. Using data from the National Longitudinal Survey of Youth 1979, the resulting estimates differ substantially from those of a typical random effects GLS estimator. In particular, the HT results find that after controlling for other variables, women are more likely to take on debt, especially nonhousing debt, but those who do take on debt tend to take on a lower amount than their male counterparts. No differences are found for black or Hispanic individuals with regard to the amount of debt, though black individuals are found to be slightly less likely to have debt.