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Empirical Economics

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16.01.2017

Assessing the evidence on neighborhood effects from Moving to Opportunity

verfasst von: Dionissi Aliprantis

Erschienen in: Empirical Economics | Ausgabe 3/2017

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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.

Vorheriger Artikel Local housing costs and basic household needs

Nächster Artikel Neighborhood price externalities of foreclosure rehabilitation: an examination of the Neighborhood Stabilization Program

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My measure of quality is a normalization of the first principal component of these variables, or the one-dimensional vector explaining the most variation in these variables.

It has also been found that suburban movers have much lower male youth mortality rates (Votruba and Kling 2009) and tend to stay in high-income suburban neighborhoods many years after their initial placement (DeLuca and Rosenbaum 2003; Keels et al. 2005).

Section 8 vouchers pay part of a tenant’s private market rent. Project-based assistance gives the option of a reduced-rent unit tied to a specific structure.

This is the author’s current interpretation of the literature, most prominently represented by Kling et al. (2007a) and Ludwig et al. (2008). However, the distinction between program and neighborhood effect parameters has not always been made clearly. Some studies do seem to equate program effects with neighborhood effects, even when using this indirect logic. Early examples where this distinction is unclear are Ludwig et al. (2001) and Kling et al. (2005), and more recent examples include Ludwig et al. (2013), Sanbonmatsu et al. (2012), and Gennetian et al. (2012).

This interpretation of the results from MTO can be found in Kling et al. (2007a), Ludwig et al. (2013, pp. 228–229), Angrist (2014, p. 106), Angrist and Pischke (2010, p. 4). Some preliminary instrumental variable analysis can be found in Ludwig et al. (2008), and recent papers like Aliprantis and Richter (2016) and Pinto (2014) that have estimated neighborhood effects models using the MTO data have found evidence of neighborhood effects on adult employment.

See the Appendix of Ludwig et al. (2008) or Ludwig et al. (2013) for examples.

State 18 describes a state of the world in which an individual will be employed regardless of the neighborhood in which they reside, yet receiving an MTO voucher will cause them to become employed. State 19 implies that an individual will be employed regardless of the neighborhood in which they reside, yet receiving an MTO voucher will cause them to become unemployed. Finally, State 20 describes a state of the world in which the individual is both always employed (columns 3 and 4) or else is never employed (columns 5 and 6), which simply cannot happen in our model as structured.

Aliprantis and Richter (2016) is one example of neighborhood effects estimated under weaker assumptions than NQB and NQP in which the estimated effects contradict conclusion (\(^\star \)).

See Aliprantis (2015a, b) or Heckman and Vytlacil (2005) for further discussion.

While using an MTO voucher did initially require moving to a neighborhood with particular poverty characteristics (<10%), this requirement only had to be met for 1 year. Since subsequent moves were frequent, often involuntary, and tended to be to low-quality neighborhoods (de Souza Briggs et al. 2010; Sampson 2008), the initial MTO move does not to capture the entire sequence of neighborhood characteristics, even when measured by poverty alone. Here I measure mobility using residence at the time of the interim evaluation, but other ways of dealing with dynamics, whether within the static models discussed here or within an expanded dynamic model, could also be appropriate.

A discussion related to Assumption NQB can also be found in Angrist and Imbens (1995).

An alternative and complementary approach is to use an unordered choice model as in Pinto (2014).

To be precise, the model in Kling et al. (2007a) is the limit of this model as \(J \rightarrow \infty \). Ludwig and Kling (2007) estimate a similar model with poverty replaced by beat crime rate. Effects in these analyses are constant in U under the specification in Eq. 3 since they assume \(U_j=U\) for all \(j \in \{1, \ldots , J\}\), so \(U_{j+1, i}-U_{j, i}= U_i - U_i = 0\).

Weights are used for two reasons. First, random assignment ratios varied both from site to site and over different time periods of sample recruitment. Randomization ratio weights are used to create samples representing the same number of people across groups within each site-period. This ensures neighborhood effects are not conflated with time trends. Second, sampling weights must be used to account for the subsampling procedures used during the interim evaluation data collection.

Nevertheless, race will be correlated with the neighborhood characteristics causally affecting outcomes due to the history of racial discrimination in the USA. Aliprantis and Kolliner (2015) study race and neighborhood characteristics in the context of MTO.

It is worth noting that the same general conclusion also holds in models assuming NQP. For example, Quigley and Raphael (2008) point out that “The effect of treatment under the MTO program was, on average, to move households in the five MTO metropolitan areas from neighborhoods at roughly the 96th percentile of the neighborhood poverty distribution to neighborhoods at the 88th percentile” (p. 3).

DeLuca and Rosenbaum (2003) find that 66% of the suburban group and 13% of the city group lived in the suburbs of Chicago 14 years after original placement through Gautreaux. DeLuca and Rosenbaum (2003) cite limited availability of housing, rather than the choice to not move through the program, as the reason only 20% of eligible applicants moved through Gautreaux. This claim is based on evidence that 95% of participating households accepted the first unit offered to them. Furthermore, it is likely that Gautreaux induced larger changes in school quality than MTO (Rubinowitz and Rosenbaum 2000, p. 162). Taken together, this evidence is suggestive that Gautreaux induced more households into high-quality neighborhoods than MTO.

Note that NQK need not be adopted only in conjunction with NQJ. A version of Assumption NQB-NQK is adopted in Sampson et al. (2008) using a similar index of neighborhood quality to that used in this analysis.

See p. 677 of Heckman and Vytlacil (2005) for a relevant discussion of A6, and see Brock and Durlauf (2007) for a related model of peer effects on the selection decision.

Although this model of neighborhood effects has additional mechanisms relative to those typically included in models of social interaction, such models are still useful to consider in this context. For example, Manski (1993) and Brock and Durlauf (2007) specify models relaxing SUTVA (a) and Manski (2013a) specifies a model relaxing SUTVA (b).

Aliprantis D (2015a) Covariates and causal effects: the problem of context. Federal Reserve Bank of Cleveland Working Paper 13-10R

Aliprantis D (2015b) A distinction between causal effects in structural and rubin causal models. Federal Reserve Bank of Cleveland Working Paper 15-05

Aliprantis D, Kolliner D (2015) Neighborhood poverty and neighborhood quality in the Moving to Opportunity experiment. Federal Reserve Bank of Cleveland Economic Commentary Number 2015-04. https://clevelandfed.org/~/media/content/newsroom%20and%20events/publications/economic%20commentary/2015/ec%20201504%20neighborhood%20poverty/ec%20201504%20neighborhood%20poverty%20and%20quality%20in%20the%20moving%20to%20opportunity%20experiment%20pdf.pdf?la=en

Aliprantis D, Richter FG-C (2016) Evidence of neighborhood effects from Moving to Opportunity: LATEs of neighborhood quality. Federal Reserve Bank of Cleveland Working Paper no. 12-08R. http://dionissialiprantis.com/pdfs/LATEs_of_nbd_quality_REStat1.pdf

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Titel: Assessing the evidence on neighborhood effects from Moving to Opportunity
verfasst von: Dionissi Aliprantis
Publikationsdatum: 16.01.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 3/2017
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-016-1186-1

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