Studying how much you are affected by living in a certain place isn’t easy. After all, people are not first observed, then randomly distributed across places, then observed again. In more concrete terms, those who end up living in a place with high-priced real estate and those who end up living in a place with low-priced real estate may well differ in a number of ways in their backgrounds, education levels, and job history, and it would be peculiar to say that the places in which people ended up live “caused” these differences. Instead, it often seems more plausible that the place where people end up living is caused by earlier mechanisms of social and economic sorting.
Nonetheless, here are two examples of how knowing something about place effects seems important. Imagine that there is a program to provide support for people from low-income neighborhoods to move to higher-income neighborhoods. Will the shift in the place they are living affect their job prospects or the outcomes for their children? Or imagine that a retiree moves from one state to another. Will the shift in place affect their health or the health care they receive? A couple of papers in the Fall 2021 issue of the Journal of Economic Perspectives tackle such questions of place effects head-on.
(Full disclosure: I’ve been Managing Editor of the JEP since the first issue in 1987, and so may be predisposed to believe that the articles are of interest. Fuller disclosure: The JEP and all its articles back to the first issue have ben freely available online for a decade now, courtesy of the American Economic Association, so there is no financial benefit for me or anyone else from recommending the articles.)
It turns out that there are certain situations where one can look at those who changed places, compare them with a “control group” that did not change places, and draw some plausible conclusions. Eric Chyn and Lawrence F. Katz provide an overview and contextual interpretation of this research in “Neighborhoods Matter: Assessing the Evidence for Place Effects” (Journal of Economic Perspectives, 35:4, pp. 197-222). Here’s how they describe perhaps the most prominent study in this area:
Beginning in 1994, the Moving to Opportunity housing mobility demonstration randomized access to housing vouchers and assistance in moving to less-distressed communities to about 4,600 families living in public housing projects located in deeply impoverished neighborhoods in five cities: Baltimore, Boston, Chicago, Los Angeles, and New York. The program randomized families into three groups: 1) a low-poverty voucher group (also called the “experimental group”) that was offered housing-mobility counseling and restricted housing vouchers that could only be used to move to low-poverty areas (Census tracts with 1990 poverty rates below 10 percent); 2) a traditional voucher group that was offered regular Section 8 housing vouchers that had no additional locational constraints (also called the Section 8 group); and 3) a control group that received no assistance through the program.
As researchers began to think seriously about place effects, they sought out other situations in which people ended up moving out of low-income areas. For example, studies looked at situations where people had to move because public housing was demolished, or where Hurricane Katrina destroyed existing housing, in comparison to outcomes for a similar group that was not forced to move. There are also studies where one group of low-income households is offered information and counseling to help them match up with rental options in areas with higher average incomes, while a control group is not offered such assistance and is thus much less likely to move to those other areas. Notice that in one way or another, all of these approaches, in different ways, have an element of randomness which allow the researcher to make a plausible estimate of the causal effects of living in a different place.
The overall finding from this line of research is that when lower-income household move to a higher-income neighborhood, it has substantial effects for younger children who grow up in the new neighborhood, smaller or even nil effects for those who are older teenagers at the time of relocation, and not much effect on the job or income outcomes for the adults in those households. Interestingly, the positive effects for younger children don’t seem to primarily be reflected in school test scores, but instead show up as a result of gains in noncognitive skills as reflected in measures like numbers of school absences or suspensions, and chances of repeating a grade. The authors write: “Studies of the Moving to Opportunity demonstration and Chicago public housing demolitions found no evidence that relocating
to less distressed areas had impacts on the economic outcomes of adults, but both
settings revealed large long-run gains for younger children …”
In the same issue, Tatyana Deryugina and David Molitor discuss “The Causal Effects of Place on Health and Longevity” (Journal of Economic Perspectives, 35: 4, pp. 47-70). They point out that the size of regional differences in health across places are actually fairly similar across the European Union and the United States. They write:
Three main results emerge from comparing the regional variation in life expectancy in the United States and Europe. First, average life expectancy is 2.8 years higher in Europe than in the United States. Second, the overall variation in life expectancy, as captured by the standard deviation or interdecile range of the life expectancy distribution, is similar in both contexts. Third, most of the regional variation in life expectancy in Europe is explained by country of residence, whereas in the United States, most of the variation is within-state.
A primary issue is the problem of figuring out whether it’s the place that matters, or whether it’s the prior sorting of people by income and occupation–which is often correlated with place. For a given place, health could also be affected by issues like local public health policies, or peer effects (like whether there is a culture of substance abuse or outdoor exercise), or by local environmental contamination. In addition, these factors often overlap: for example, a location with lower incomes may also receive less health care, be more affected by environmental pollution, and have peer effects that do not reinforce good health. It’s not obvious that “place” is always the most useful way to think about these kinds of factors, rather than focusing on the underlying determinants. The authors mention a classic example from the research in this area:
As a vivid example of geographic differences in mortality across the United States, Fuchs (1974) compared mortality rates in Nevada and Utah, which are neighboring states with similar climates and, at the time, similar income levels and physicians per capita. Fuchs noted that, nonetheless, adult mortality rates were substantially higher in Nevada than in Utah, which he attributed to Nevada’s high rates of cigarette and alcohol consumption as well as “marital and geographical instability.” Even today, the average person born in Utah has a life expectancy 1.9 years higher than the average person born in Nevada.
But broadly speaking, the approach is to look at movers between areas, and then to think carefully about what what can learn from such comparisons. For example, one source of data is to look at the elderly, who are covered by Medicare insurance, who move between states. Patterns of medical practice vary across the US, so you can observe Medicare patients with similar earlier patterns of health care use who move to a place where Medicare recipients on average get more care, or where they get less care, or those who don’t move at all. One can also look at doctors who move between areas and, looking at doctors who had similar patterns of Medicare charges before their move, see if their pattern of Medicare charges tend to stay the same when they go to a different place. As the authors write:
Other indirect evidence that local conditions matter for health comes from papers that use movers to study how local conditions affect health care provision and other non-health outcomes that could ultimately affect health. For example, Song et al. (2010) show that when Medicare recipients move between regions, rates of medical diagnoses change. Finkelstein, Gentzkow, and Williams (2016) study Medicare recipients who move between areas and show that place of residence affects movers’ medical spending. Molitor (2018) looks at cardiologists who move and finds that, on average, their own practice patterns change by 60–80 percent of the difference in local norms between their new and original practice regions.
There are lots of complexities when looking a place effects of health. So far, the research based on movers from one place to another (that is, not on correlations looking at health effects in different places) suggests that place effects on health are real, but does not yet give strong answers as to why they are real. The authors write:
The observed geographic dispersion in life expectancy and evidence from movers between areas strongly suggest that where one lives matters for when one dies. Determining whether place health effects are large or trivially small, however, has not been accomplished until very recently. New evidence comparing movers to other movers to estimate place health effects make it reasonable to conclude that, at least for some groups, place of residence has a sizable effect on health. However, more research is needed to build on these findings and, in particular, to understand the effect of place at younger ages on long-term longevity. Although there are many plausible mechanisms through which these place effects may materialize, the question of what it is exactly that causes some places to be better for health than others has so far not been answered directly by any existing study.