Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We use data on sisters to jointly address heterogeneity bias and endogeneity bias in estimates of wage equations for women. This analysis yields evidence of biases in OLS estimates of wage equations for white and black women, some of which are detected only when the two sources of bias are addressed simultaneously. For both white and black women there is evidence of upward bias in the estimated returns to schooling. Bias-corrected estimates of the effect of marriage on wages, for white women, suggest a positive marriage premium. We also use the sibling data to identify our models, and test a number of other commonly-used identifying assumptions as overidentifying restrictions.