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Assessing the evidence on neighborhood effects from Moving to Opportunity

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.

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Notes

  1. 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.

  2. 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).

  3. 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.

  4. 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).

  5. 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.

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

  7. 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.

  8. 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 (\(^\star \)).

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

  10. 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.

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

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

  13. 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 \(U_{j+1, i}-U_{j, i}= U_i - U_i = 0\).

  14. 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.

  15. 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.

  16. 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).

  17. 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.

  18. 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.

  19. 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.

  20. 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).

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Acknowledgements

I thank Francisca G.-C. Richter, Jeffrey Kling, my Math Corps students, and several seminar participants and anonymous referees for contributing to this paper. I am also grateful to Mary Zenker for research assistance and Paul Joice at HUD for help accessing the data. The research reported here was supported in part by the Institute of Education Sciences, US Department of Education, through Grant R305C050041-05 to the University of Pennsylvania. The views stated herein are those of the author and are not necessarily those of the Federal Reserve Bank of Cleveland, the Board of Governors of the Federal Reserve System, or the US Department of Education.

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Appendices

Appendix 1: Full contingency table for states of world

See Table 7.

Table 7 States of the world possible in unrestricted neighborhood effects model with binary variables

Appendix 2: The neighborhood effects identified by MTO

Effects from moving to high-quality neighborhoods are not identified by MTO. Given the evidence in Sect. 5.2.2, any definition of treatment of the form D2 would have to restrict measures of quality to the lower half of the national distribution of neighborhood quality to satisfy assumption A5.

Once the focus on quality is restricted to accommodate A5, we can see that A5 appears more reasonable than A5\(^*\), as it is likely that some households will move to a relatively high-quality neighborhood regardless of whether they receive a voucher through MTO or not. Under assumptions (A1–A6, EH, D2-NQB) the Wald estimator identifies the LATE:

$$\begin{aligned} \frac{E[ Y |x, Z=1] - E[Y | x, Z=0 ]}{E[ D |x, Z=1] - E[D | x, Z=0 ]} = \triangle ^{\mathrm{LATE}}\left( x, \pi ^0(x), \pi ^1(x)\right) \end{aligned}$$
(14)

If we believe assumption A2 will fail to hold when treatment is defined under D2-NQB for the reasons discussed in Sect. 5.2.1, we could alternatively define treatment under D2-NQJ to generate a transition-specific analogue to 14:

$$\begin{aligned}&\triangle ^{\mathrm{LATE}}_{j,j+1}\left( x, \pi ^0_j(x), \pi ^1_j(x)\right) \\&\quad \equiv E\left[ Y(D=j+1) - Y(D=j) |x, D(Z=1)=j+1, D(Z=0)=j\right] . \end{aligned}$$

Versions of the model have been estimated in Kling et al. (2007a) and Ludwig et al. (2008) under (A1–A6, SI, and D2-NQJ-NQP). A dose–response analysis is used in Kling et al. (2007a) to determine whether parameters are constant across all j to \(j+1\) transitions in \(\{1, \ldots , J \}\). Aliprantis and Richter (2016) estimate the model under (A1–A6, EH, D2-NQJ-NQK). That analysis makes A2 more plausible by relaxing D2-NQJ-NQP–D2-NQJ-NQK and allows for the identification and estimation of LATEs that are heterogeneous over unobservables by relaxing SI to EH.Footnote 18

Appendix 3: Assumptions about the distribution of unobservables

The interpretation of the treatment effect parameters will be very different depending on the assumptions we make about the relationship between the unobservables in the model. Ignorability is a standard assumption made in the statistics and econometrics literature about the relationship between the unobservable component determining selection into treatment and those determining potential outcomes. Ignorability is fundamentally an assumption about what the econometrician is able to observe; it is that the econometrician can observe all characteristics connecting selection into treatment with treatment effect heterogeneity. Although this assumption may be unrealistic in many applications, it is adopted frequently because it is helpful for identification for reasons that will be discussed shortly.

An implication of Ignorability is that conditional on observables, selection into treatment is not related to treatment effect heterogeneity. Formally, Ignorability can be written in our model as

  • Ig .

Imbens and Angrist (1994) showed it is possible to identify an interpretable parameter, the LATE, even if Ignorability fails. Recent work in Heckman and Vytlacil (2005), Heckman et al. (2006), and Carneiro et al. (2011) has further defined and estimated treatment effect parameters when relaxing the assumption of Ignorability by assuming that unobservable treatment effect heterogeneity is related to the unobservable determinants of selection into treatment. Formally, the assumption of Essential Heterogeneity is that

  • EH \(\hbox {COV}(U_1-U_0, V) |X \ne 0\).

Figure 5 helps to illustrate the implications of Ig and EH. The top panel in the figure shows that average treatment effects are allowed to vary across observable characteristics. Ig and EH characterize different scenarios once we select a particular value of observable characteristics, \(x^*\). In the middle panel of the figure we see a scenario of Ig. The distributions of the potential outcomes must be independent of V given \(x^*\), so the levels of the potential outcomes must be constant across V given \(x^*\). The differences between these levels given \(x^*\) and \(U_D = F_V(V)\), the marginal treatment effects (MTEs), are thus constant for all \(U_D\) given \(x^*\).

The bottom panel of Fig. 5 shows a contrasting scenario of EH. In this scenario the difference \(U_1-U_0\) is correlated with V, resulting in MTEs that vary across \(U_D\). In the example displayed the effect of treatment is large for low levels of V, while for large values of V the effect of treatment decreases. Given our latent index model, this implies that for the given observable characteristics \(x^*\), treatment effects are large for those who would be most likely to select into treatment in the absence of the program. Finally, Fig. 6 shows that while Ig and EH are mutually exclusive, they are not exhaustive since individuals might select on the level while not selecting on the gain.

The contrast in the role of instrumental variables under Ig versus EH is shown clearly in Fig. 5. Under Ig it does not matter who is induced into treatment by the instrument since all variation from Z identifies the same homogeneous parameter. Unlike EH, one might assume Ig and estimate parameters without the existence of an instrument, perhaps implemented with propensity score matching. In fact, it may appear to be superfluous to use an instrument in conjunction with the Ig assumption. This is not necessarily the case, though, as adding a valid instrument Z to the latent index in Eq. 5 can make Ig a more plausible assumption.

In contrast to Ig, under EH the selection into treatment induced by the instrument is of central interest for interpreting parameters. Since MTEs vary over the support of \(U_D\), the subinterval induced into treatment by the instrument will determine the parameter(s) identified by the instrument. Different instruments that induce different intervals of \(U_D\) into treatment will identify different parameters.

Fig. 5
figure 5

Examples of Ignorability and Essential Heterogeneity

Fig. 6
figure 6

Example violating both Ignorability and Essential Heterogeneity

Appendix 4: External validity

Although external validity is the motivation for studying causal effects, and there is no clear reason for prioritizing internal validity over external validity (Manski 2013b), the literature has focused most formal attention on internal validity (Aliprantis 2015a). The text has adopted these priorities for the sake of publication, but here we also consider why estimated parameters will not be experiment invariant unless an assumption also holds that restricts the permissible types of peer effects (Sobel 2006). Interested readers are also directed to the careful discussions of these issues in Sobel (2006) and Ludwig et al. (2008).

Assumptions across and within individuals

The parameters in Sect. 4.1 are all defined conditional on the joint distribution (UV) where we define \(U \equiv (U_0, U_1)\). Assumptions about how these random variables interact across individuals have implications for the joint distribution (UV) and will change the interpretation of the parameters we have defined.

One possibility satisfying A6 is for X to be a bundle of individual-level characteristics including baseline neighborhood characteristics, with one element captured in the unobservables V being peer effects on the selection decision.Footnote 19 We now take some terminology from Sobel (2006) to consider the implications of changes to the distribution of V. We suppose the MTO experiment involves N individuals, that there are \(k_1\) people assigned to \(Z=1\), and that \(k_0=N-k_1\) are assigned to \(Z=0\), here again abstracting from the Section 8 group for the sake of exposition. Let \(R(k_0,k_1)\) denote the set of possible realizations of such a randomization, with \(r \in R(k_0,k_1)\) denoting one possible realization. If peer effects determining selection into treatment are a part of V, then different realizations r may result in different distributions of V, which we write as \(F_{V | r}\). Returning to the fact that all of the parameters defined in Sect. 4.1 are defined assuming some distribution of (UV), this implies that these parameters might be very different for some realization r compared to another realization \(r^{\prime }\) (Sobel 2006).

A standard assumption on the nature of peer effects resolves this problem by ensuring the effects defined in Sect. 4.1 are the same for all realized random assignments r. This assumption simply assumes there are no peer effects at all. In the context of our model, Angrist and Imbens (1995) state the stable unit treatment value assumption (SUTVA) from Rubin (1978) as

  • SUTVA (a) for all \(j \ne i\)

  • SUTVA (b) and for all \(j \ne i\)

Note that SUTVA is an assumption across different individuals. Under SUTVA, Ig and EH are primarily assumptions within individuals. In this case, unobservables are primarily thought to represent individual-level causal variables. Although (UV) can represent social interactions under SUTVA, these social interactions cannot be related to treatment or assigned treatment.Footnote 20 When SUTVA is relaxed, however, Ig and EH become assumptions not only about individual-level causal variables, but also about social interactions.

A less restrictive assumption on peer effects that still keeps the effects in Sect. 4.1 identical across realizations of the randomization is that the distribution of peer effects will be identical under all realizations r. I label this as the stable peer effects assumption (SPEA):

  • SPEA

Note that neither SUTVA nor SPEA is necessary to define and estimate the parameters in Sect. 4.1. However, the model illustrates how the lack of such an assumption dramatically changes their interpretation. Since the distribution of peer effects included in V might change in different contexts, this could have very important consequences, both in terms of whether the parameters in the model are invariant to the realization of randomized voucher assignment (Sobel 2006) and in terms of parameter invariance to classes of policy interventions. Importantly, this discussion illustrates that, just like Ig or EH, parameter invariance is an assumption about the unobserved variables in the model.

Appendix 5: List of assumptions

Given the joint model of potential and outcomes and selection into treatment:

$$\begin{aligned} Y(D)&= \mu _D(X_D) +U_D,\\ D^*&= \mu _X(X_0) + \gamma Z - V, \end{aligned}$$

with

$$\begin{aligned} D = j\quad \text{ if }\,D^* \in ( C_{j-1}, C_j ], \end{aligned}$$

there are several assumptions about the model considered throughout the paper. I list them here for the reader’s reference:

  • A1 \(\gamma _i = \gamma \) for all i and \(\gamma \ne 0\)

  • A2

  • A3 The distribution of V is absolutely continuous

  • A4 \(E[ |Y(j)| |X ] <\infty \) for all j

  • A5 \(0< Pr(D=j | X) < 1\) for all X, j

  • A6 \(X = X_j = X_k\) almost everywhere for all \(j \ne k\)

  • D1 Treatment is moving with the aid of the program (i.e., using an MTO voucher).

  • D2 Treatment is moving to a high-quality neighborhood.

  • M1 \(D_i \equiv \mathbf {1}\{\text {individual}\,i\,\text {lives in a high-quality neighborhood}\}\)

  • M2 \(Z_i \equiv \mathbf {1}\{\text {individual}\,i\,\text {received an MTO voucher}\}\)

  • M3 \(Y_i \equiv \mathbf {1}\{\text {individual}\,i\,\text {is employed}\}\)

  • NQB Neighborhood quality D is a binary function of a latent index of neighborhood quality q: \(D \equiv \mathbf {1}\{ q \ge q^* \}\)

  • NQJ Neighborhood quality D is a multi-valued function of a latent index of neighborhood quality q: \(D \equiv j \times \mathbf {1}\{C_{j-1} < q \le C_{j} \}\)

  • NQP Neighborhood quality q is a one-dimensional vector that is a scalar function of neighborhood poverty p: \(q = \alpha p\)

  • NQK Neighborhood quality q is a one-dimensional vector that is a linear combination of K observable neighborhood characteristics: \(q = \alpha _1 X_1 + \cdots + \alpha _K X_K\)

  • SUTVA (a) for all \(j \ne i\)

  • SUTVA (b) and for all \(j \ne i\)

  • SPEA for randomization R

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Aliprantis, D. Assessing the evidence on neighborhood effects from Moving to Opportunity. Empir Econ 52, 925–954 (2017). https://doi.org/10.1007/s00181-016-1186-1

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Keywords

  • Moving to Opportunity
  • Neighborhood effect
  • Program effect

JEL Classification

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  • H50
  • I38
  • J10
  • R00