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Past estimates of the effect of family income on child development have often been plagued by endogeneity and measurement error. In this paper, we use an instrumental variables strategy to estimate the causal effect of income on children's math and reading achievement. Our identification derives from the large, non-linear changes in the Earned Income Tax Credit (EITC) over the last two decades. The largest of these changes increased family income by as much as 20%, or approximately $2,100, between 1993 and 1997. Using a panel of roughly 4,500 children matched to their mothers from National Longitudinal Survey of Youth datasets allows us to address problems associated with unobserved heterogeneity, endogenous transitory income shocks, and measurement error in income. Our baseline estimates imply that a $1,000 increase in income raises combined math and reading test scores by 6% of a standard deviation in the short-run. Test gains are larger for children from disadvantaged families and are robust to a variety of alternative specifications.

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This paper studies the nature and impact of credit constraints in the market for human capital. We derive endogenous constraints from the design of government student loan programs and from the limited repayment incentives in private lending markets. These constraints imply cross-sectional patterns for schooling, ability, and family income that are consistent with U.S. data. This contrasts with the standard exogenous constraint model, which predicts a counterfactual negative ability -- schooling relationship for low-income youth. We show that the rising empirical importance of familial wealth and income in determining college attendance (Belley and Lochner 2007) is consistent with increasingly binding credit constraints in the face of rising tuition costs and returns to schooling. Our framework also explains the recent increase in private credit for college as a market response to the rising returns to school.