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Local average neighborhood effects from moving to opportunity

  • Dionissi Aliprantis
  • Francisca G.-C. Richter

This paper estimates Local Average Treatment Effects (LATEs) of neighborhood quality from the Moving to Opportunity (MTO) housing mobility experiment in a generalized model with multiple treatment levels. We propose a new approach to identifying parameters that exploits the identification of unobservables in the multi-level model. The variation in neighborhood quality induced by MTO only allows us to identify LATEs of moving from the first to the second decile of the national distribution of quality, but in other applications the approach may allow for the estimation of Marginal Treatment Effects. Estimated LATEs on employment, labor force participation rates, earnings, income, welfare receipt, and body mass index are consistent with standard theories of neighborhood externalities.

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Paper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 1208.

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Date of creation: 2012
Date of revision:
Handle: RePEc:fip:fedcwp:1208
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  1. Dionissi Aliprantis, 2012. "Assessing the evidence on neighborhood effects from moving to opportunity," Working Paper 1233, Federal Reserve Bank of Cleveland.
  2. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
  3. Pedro Carneiro & James Heckman & Edward Vytlacil, 2010. "Estimating marginal returns to education," CeMMAP working papers CWP29/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Lisa Sanbonmatsu & Jeffrey R. Kling & Greg J. Duncan & Jeanne Brooks-Gunn, 2006. "Neighborhoods and Academic Achievement: Results from the Moving to Opportunity Experiment," Journal of Human Resources, University of Wisconsin Press, vol. 41(4).
  5. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
  6. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
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