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Assessing the evidence on neighborhood effects from Moving to Opportunity

Dionissi Aliprantis

Empirical Economics, 2017, vol. 52, issue 3, No 3, 925-954

Abstract: Abstract The Moving to Opportunity (MTO) experiment randomly assigned housing vouchers that could be used in low-poverty neighborhoods. Consistent with the literature, I find that receiving an MTO voucher had no effect on outcomes like earnings, employment, and test scores. However, after studying the assumptions identifying neighborhood effects with MTO data, this paper reaches a very different interpretation of these results than found in the literature. I first specify a model in which the absence of effects from the MTO program implies an absence of neighborhood effects. I present theory and evidence against two key assumptions of this model: that poverty is the only determinant of neighborhood quality and that outcomes only change across one threshold of neighborhood quality. I then show that in a more realistic model of neighborhood effects that relaxes these assumptions, the absence of effects from the MTO program is perfectly compatible with the presence of neighborhood effects. This analysis illustrates why the implicit identification strategies used in the literature on MTO can be misleading.

Keywords: Moving to Opportunity; Neighborhood effect; Program effect (search for similar items in EconPapers)
JEL-codes: C30 H50 I38 J10 R00 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (8)

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Working Paper: Assessing the Evidence on Neighborhood Effects from Moving to Opportunity (2015) Downloads
Working Paper: Assessing the evidence on neighborhood effects from moving to opportunity (2014) Downloads
Working Paper: Assessing the evidence on neighborhood effects from Moving to Opportunity (2012) Downloads
Working Paper: Assessing the evidence on neighborhood effects from moving to opportunity (2011) Downloads
Working Paper: Assessing the evidence on neighborhood effects from moving to opportunity (2011) Downloads
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DOI: 10.1007/s00181-016-1186-1

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