Using Sibling Data to Estimate the Impact of Neighborhoods on Children's Educational Outcomes
利用兄弟姐妹数据，通过家庭固定效应模型控制家庭层面不可观测因素，估计社区对儿童教育成果的影响，发现即使控制家庭异质性，社区效应仍可能存在。
Studies that attempt to measure the impact of neighborhoods on children's outcomes are susceptible to bias because families choose where to live. As a result, the effect of family unobservables, such as the importance parents place on their children's welfare, and other unobservables that are common to geographically clustered households, may be mistakenly attributed to neighborhood influences. Previous studies that attempt to correct for this selection bias have used questionable instrumental variables. This paper introduces an approach based on the observation that the latent factors associated with neighborhood choice do not vary across siblings. Therefore, family residential changes provide a source of neighborhood background variation that is free of the family-specific heterogeneity biases associated with neighborhood selection. Using a sample of multichild families whose children are separated in age by at least three years, I estimate family fixed effect equations of children's educational outcomes. The fixed effect results suggest that the impact of neighborhoods may exist even when family-specific unobservables are controlled. This finding is robust to many changes to estimation techniques, outcome measures, variable definitions, and samples but is sensitive to the exact formulation of the neighborhood measure. Daniel Aaronson is a researcher at the Federal Reserve Bank of Chicago. He thanks Joe Altonji, Becky Blank, Greg Duncan, Judy Hellerstein, Sandy Jencks, Bruce Meyer, John Karl Scholz, Lauren Sinai, and the reviewers for helpful suggestions. He takes responsibility for all errors and omissions. The views expressed in this paper are those of the author and are not necessarily those of the Federal Re