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The second paper uses propensity score matching to estimate the impact that living in a multigenerational household (including the child’s mother and at least one maternal grandparent) has on test scores for children of the NLSY79. If the addition of a grandparent adds more resources (for example: income and care) to the household than s/he uses, child test scores will increase. If grandparents use more resources than they add, household resources will be diluted and child test scores will decrease. After using propensity score matching and child-level fixed effects, I find evidence that children living in these households score lower on tests than those who do not.

I control for the nonrandom decision of household type using a propensity score matching. I use kernel weighting to match children from multigenerational households with similar non-multigenerational households and fixed effects to control for unobserved heterogeneity. For robustness, I use cross sectional data to estimate the importance of matching method and time invariant independent variables that cannot be included in a fixed effects model. Even after propensity score matching, I find evidence that children living in these households score lower on tests than those who do not.