(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Dionissi Aliprantis writes:

I have just published a paper (online here) on what we can learn about neighborhood effects from the results of the Moving to Opportunity housing mobility experiment. I wanted to suggest the paper (and/or the experiment more broadly) as a topic for your blog, as I am hoping the paper can start some constructive conversations.

The article is called “Assessing the evidence on neighborhood effects from Moving to Opportunity,” and here’s the abstract:

The Moving to Opportunity (MTO) experiment randomly assigned housing vouchers that could be used in low-poverty neighborhoods. Consistent with the literature, I [Aliprantis] 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.

I haven’t had a chance to read the paper, but I can share this horrible graph:

And Figure 4 is even worse!

But don’t judge a paper by its graphs. There could well be interesting stuff here, so feel free to discuss.

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