The trouble with estimating neighbourhood effects, part 2

When we think of the causal effect of living in one neighbourhood compared to another we think of how the social interactions and lifestyle of that area produce better outcomes. Does living in an area with more obese people cause me to become fatter? (Quite possibly). Or, if a family moves to an area where people earn more will they earn more? (Read on).

In a previous post, we discussed such effects in the context of slums, where the synergy of poor water and sanitation, low quality housing, small incomes, and high population density likely has a negative effect on residents’ health. However, we also discussed how difficult it is to estimate neighbourhood effects empirically for a number of reasons. On top of this, are the different ways neighbourhood effects can manifest. Social interactions may mean behaviours that lead to better health or incomes rub off on one another. But also there may be some underlying cause of the group’s, and hence each individual’s, outcomes. In the slum, low education may mean poor hygiene habits spread, or the shared environment may contain pathogens, for example. Both of these pathways may constitute a neighbourhood effect, but both imply very different explanations and potential policy remedies.

What should we make then of, not one, but two new articles by Raj Chetty and Nathaniel Henderen in the recent issue of Quarterly Journal of Economics? Both of which use observational data to estimate neighbourhood effects.

Paper 1: The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects.

The authors have an impressive data set. They use federal tax records from the US between 1996 and 2012 and identify all children born between 1980 and 1988 and their parents (or parent). For each of these family units they determine household income and then the income of the children when they are older. To summarise a rather long exegesis of the methods used, I’ll try to describe the principle finding in one sentence:

Among families moving between commuting zones in the US, the average income percentile of children at age 26 is 0.04 percentile points higher per year spent and per additional percentile point increase in the average income percentile of the children of permanent residents at age 26 in the destination where the family move to. (Phew!)

They interpret this as the outcomes of in-migrating children ‘converging’ to the outcomes of permanently resident children at a rate of 4% per year. That should provide an idea of how the outcomes and treatments were defined, and who constituted the sample. The paper makes the assumption that the effect is the same regardless of the age of the child. Or to perhaps make it a bit clearer, the claim can be interpreted as that human capital, H, does something like this (ignoring growth over childhood due to schooling etc.):

humancap1

where ‘good’ and ‘bad’ mean ‘good neighbourhood’ and ‘bad neighbourhood’. This could be called the better neighbourhoods cause you to do better hypothesis.

The analyses also take account of parental income at the time of the move and looks at families who moved due to a natural disaster or other ‘exogenous’ shock. The different analyses generally support the original estimate putting the result in the region of 0.03 to 0.05 percentile points.

But are these neighbourhood effects?

A different way of interpreting these results is that there is an underlying effect driving incomes in each area. Areas with higher incomes for their children in the future are those that have a higher market price for labour in the future. So we could imagine that this is what is going on with human capital instead:

humancap2

This is the those moving to areas where people will earn more in the future, also earn more in the future because of differences in the labour market hypothesis. The Bureau of Labour Statistics, for example, cites the wage rate for a registered nurse as $22.61 in Iowa and $36.13 in California. But we can’t say from the data whether the children are sorting into different occupations or are getting paid different amounts for the same occupations.

The reflection problem

Manksi (1993) called the issue the ‘reflection problem’, which he described as arising when

a researcher observes the distribution of a behaviour in a population and wishes to infer whether the average behaviour in some group influences the behaviour of the individuals that compose the group.

What we have here is a linear-in-means model estimating the effect of average incomes on individual incomes. But what we cannot distinguish between is the competing explanations of, what Manski called, endogenous effects that result from the interaction  with families with higher incomes, and correlated effects that lead to similar outcomes due to exposure to the same underlying latent forces, i.e. the market. We could also add contextual effects that manifest due to shared group characteristics (e.g. levels of schooling or experience). When we think of a ‘neighbourhood effect’ I tend to think of them as of the endogenous variety, i.e. the direct effects of living in a certain neighbourhood. For example, under different labour market conditions, both my income and the average income of the permanent residents of the neighbourhood I move to might be lower, but not because of the neighbourhood.

The third hypothesis

There’s also the third hypothesis, families that are better off move to better areas (i.e. effects are accounted for by unobserved family differences):

humancap3

The paper presents lots of modifications to the baseline model, but none of them can provide an exogenous choice of destination. They look at an exogenous cause of moving – natural disasters – and also instrument with the expected difference in income percentiles for parents from the same zip code, but I can’t see how this instrument is valid. Selection bias is acknowledged in the paper but without some exogenous variation in where a family moves to it’ll be difficult to really claim to have identified a causal effect. The choice to move is in the vast majority of family’s cases based on preferences over welfare and well-being, especially income. Indeed, why would a family move to a worse off area unless their circumstances demanded it of them? So in reality, I would imagine the truth would lie somewhere in between these three explanations.

Robust analysis?

As a slight detour, we might want to consider if these are causal effects, even if the underlying assumptions hold. The paper presents a range of analyses to show that the results are robust. But these analyses represent just a handful of those possible. Given that the key finding is relatively small in magnitude, one wonders what would have happened under different scenarios and choices – the so-called garden of forking paths problem. To illustrate, consider some of the choices that were made about the data and models, and all the possible alternative choices. The sample included only those with a mean positive income between 1996 to 2004 and those living in commuter zones with populations of over 250,000 in the 2000 census. Those whose income was missing were assigned a value of zero. Average income over 1996 to 2000 is a proxy for lifetime income. If the marital status of the parents changed then the child was assigned to the mother’s location. Non-filers were coded as single. Income is measured in percentile ranks and not dollar terms. The authors justify each of the choices, but an equally valid analysis would have resulted from different choices and possibly produced very different results.

-o-

Paper 2The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates

The strategy of this paper is much like the first one, except that rather than trying to estimate the average effect of moving to higher or lower income areas, they try to estimate the effect of moving to each of 3,000 counties in the US. To do this they assume that the number of years exposure to the county is as good as random after taking account of i) origin fixed effects, ii) parental income percentile, and iii) a quadratic function of birth cohort year and parental income percentile to try and control for some differences in labour market conditions. An even stronger assumption than before! The hierarchical model is estimated using some complex two-step method for ‘computational tractability’ (I’d have just used a Bayesian estimator). There’s some further strange calculations, like conversion from percentile ranks into dollar terms by regressing the dollar amounts on average income ranks and multiplying everything by the coefficient, rather than just estimating the model with dollars as the outcome (I suspect it’s to do with their complicated estimation strategy). Nevertheless, we are presented with some (noisy) county-level estimates of the effect of an additional year spent there in childhood. There is a weak correlation with the income ranks of permanent residents. Again, though, we have the issue of many competing explanations for the observed effects.

The differences in predicted causal effect by county don’t help distinguish between our hypotheses. Consider this figure:

usincomes1

Do children of poorer parents in the Southern states end up with lower human capital and lower-skilled jobs than in the Midwest? Or does the market mean that people get paid less for the same job in the South? Compare the map above to the maps below showing wage rates of two common lower-skilled professions, cashiers (right) or teaching assistants (left):

A similar pattern is seen. While this is obviously just a correlation, one suspects that such variation in wages is not being driven by large differences in human capital generated through personal interaction with higher earning individuals. This is also without taking into account any differences in purchasing power between geographic areas.

What can we conclude?

I’ve only discussed a fraction of the contents of these two enormous papers. The contents could fill many more blog posts to come. But it all hinges on whether we can interpret the results as the average causal effect of a person moving to a given place. Not nearly enough information is given to know whether families moving to areas with lower future incomes are comparable to those with higher future incomes. Also, we could easily imagine a world where the same people were all induced to move to different areas – this might produce completely different sets of neighbourhood effects since they themselves contribute to those effects. But I feel that the greatest issue is the reflection problem. Even random assignment won’t get around this. This is not to discount the value or interest these papers generate, but I can’t help but feel too much time is devoted to trying to convince the reader of a ‘causal effect’. A detailed exploration of the relationships in the data between parental incomes, average incomes, spatial variation, later life outcomes, and so forth, might have been more useful for generating understanding and future analyses. Perhaps sometimes in economics we spend too long obsessing over estimating unconvincing ‘causal effects’ and ‘quasi-experimental’ studies that really aren’t and forget the value of just a good exploration of data with some nice plots.

 

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Sam Watson’s journal round-up for 30th April 2018

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

The Millennium Villages Project: a retrospective, observational, endline evaluation. The Lancet Global Health [PubMedPublished May 2018

There are some clinical researchers who would have you believe observational studies are completely useless. The clinical trial is king, they might say, observation studies are just too biased. And while it’s true that observational studies are difficult to do well and convincingly, they can be a reliable and powerful source of evidence. Similarly, randomised trials are frequently flawed, for example there’s often missing data that hasn’t been dealt with, or a lack of allocation concealment, and many researchers forget that randomisation does not guarantee a balance of covariates, it merely increases the probability of it. I bring this up, as this study is a particularly carefully designed observational data study that I think serves as a good example to other researchers. The paper is an evaluation of the Millennium Villages Project, an integrated intervention program designed to help rural villages across sub-Saharan Africa meet the Millennium Development Goals over ten years between 2005 and 2015. Initial before-after evaluations of the project were criticised for inferring causal “impacts” from before and after data (for example, this Lancet paper had to be corrected after some criticism). To address these concerns, this new paper is incredibly careful about choosing appropriate control villages against which to evaluate the intervention. Their method is too long to summarise here, but in essence they match intervention villages to other villages on the basis of district, agroecological zone, and a range of variables from the DHS – matches were they reviewed for face validity and revised until a satisfactory matching was complete. The wide range of outcomes are all scaled to a standard normal and made to “point” in the same direction, i.e. so an increase indicated economic development. Then, to avoid multiple comparisons problems, a Bayesian hierarchical model is used to pool data across countries and outcomes. Costs data were also reported. Even better, “statistical significance” is barely mentioned at all! All in all, a neat and convincing evaluation.

Reconsidering the income‐health relationship using distributional regression. Health Economics [PubMed] [RePEcPublished 19th April 2018

The relationship between health and income has long been of interest to health economists. But it is a complex relationship. Increases in income may change consumption behaviours and a change in the use of time, promoting health, while improvements to health may lead to increases in income. Similarly, people who are more likely to make higher incomes may also be those who look after themselves, or maybe not. Disentangling these various factors has generated a pretty sizeable literature, but almost all of the empirical papers in this area (and indeed all empirical papers in general) use modelling techniques to estimate the effect of something on the expected value, i.e. mean, of some outcome. But the rest of the distribution is of interest – the mean effect of income may not be very large, but a small increase in income for poorer individuals may have a relatively large effect on the risk of very poor health. This article looks at the relationship between income and the conditional distribution of health using something called “structured additive distribution regression” (SADR). My interpretation of SADR is that, one would model the outcome y ~ g(a,b) as being distributed according to some distribution g(.) indexed by parameters a and b, for example, a normal or Gamma distribution has two parameters. One would then specify a generalised linear model for a and b, e.g. a = f(X’B). I’m not sure this is a completely novel method, as people use the approach to, for example, model heteroscedasticity. But that’s not to detract from the paper itself. The findings are very interesting – increases to income have a much greater effect on health at the lower end of the spectrum.

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions. Journal of the Royal Statistical Society: Series C Published April 2018.

When I used to take econometrics tutorials for undergraduates, one of the sessions involved going through coursework about the role of advertising. To set the scene, I would talk about the work of Alfred Marshall, the influential economist from the late 1800s/early 1900s. He described two roles for advertising: constructive and combative. The former is when advertising grows the market as a whole, increasing everyone’s revenues, and the latter is when ads just steal market share from rivals without changing the size of the market. Later economists would go on to thoroughly develop theories around advertising, exploring such things as the power of ads to distort preferences, the supply of ads and their complementarity with the product they’re selling, or seeing ads as a source of consumer information. Nevertheless, Marshall’s distinction is still a key consideration, although often phrased in different terms. This study examines a lot of things, but one of its key objectives is to explore the role of direct to consumer advertising on prescriptions of brands of drugs. The system is clearly complex: drug companies advertise both to consumers and physicians, consumers may request the drug from the physician, and the physician may or may not prescribe it. Further, there may be correlated unobservable differences between physicians and patients, and the choice to advertise to particular patients may not be exogenous. The paper does a pretty good job of dealing with each of these issues, but it is dense and took me a couple of reads to work out what was going on, especially with the mix of Bayesian and Frequentist terms. Examining the erectile dysfunction drug market, the authors reckon that direct to consumer advertising reduces drug requests across the category, while increasing the proportion of requests for the advertised drug – potentially suggesting a “combative” role. However, it’s more complex than that patient requests and doctor’s prescriptions seem to be influenced by a multitude of factors.

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Chris Sampson’s journal round-up for 24th April 2017

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

The association between socioeconomic status and adult fast-food consumption in the U.S. Economics & Human Biology Published 19th April 2017

It’s an old stereotype, that people of lower socioeconomic status eat a lot of fast food, and that this contributes to poorer nutritional intake and therefore poorer health. As somebody with a deep affection for Gregg’s pasties and Pot Noodles, I’ve never really bought into the idea. Mainly because a lot of fast food isn’t particularly cheap. And anyway, what about all those cheesy paninis that the middle classes are chowing down on in Starbuck’s? Plus, wouldn’t the more well-off folk have a higher opportunity cost of time that would make fast food more attractive? Happily for me, this paper provides some evidence to support these notions. The study uses 3 recent waves of data from the National Longitudinal Survey of Youth, with 8136 participants born between 1957 and 1964. The authors test for an income gradient in adult fast food consumption, as well as any relationship to wealth. I think that makes it extra interesting because wealth is likely to be more indicative of social class (which is probably what people really think about when it comes to the stereotype). The investigation of wealth also sets it apart from previous studies, which report mixed findings for the income gradient. The number of times people consumed fast food in the preceding 7 days is modelled as a function of price, time requirement, preferences and monetary resources (income and wealth). The models included estimators for these predictors and a number of health behaviour indicators and demographic variables. Logistic models distinguish fast food eaters and OLS and negative binomial models estimate how often fast food is eaten. 79% ate fast food at least once, and 23% were frequent fast food eaters. In short, there isn’t much variation by income and wealth. What there is suggests an inverted U-shape pattern, which is more pronounced when looking at income than wealth. The regression results show that there isn’t much of a relationship between wealth and the number of times a respondent ate fast food. Income is positively related to the number of fast food meals eaten. But other variables were far more important. Living in a central city and being employed were associated with greater fast food consumption, while a tendency to check ingredients was associated with a lower probability of eating fast food. The study has some important policy implications, particularly as our preconceptions may mean that interventions are targeting the wrong groups of people.

Views of the UK general public on important aspects of health not captured by EQ-5D. The Patient [PubMed] Published 13th April 2017

The notion that the EQ-5D might not reflect important aspects of health-related quality of life is a familiar one for those of us working on trial-based analyses. Some of the claims we hear might just be special pleading, but it’s hard to deny at least some truth. What really matters – if we’re trying to elicit societal values – is what the public thinks. This study tries to find out. Face-to-face interviews were conducted in which people completed time trade-off and discrete choice experiment tasks for EQ-5D-5L states. These were followed by a set of questions about the value of alternative upper anchors (e.g. ‘full health’, ‘11111’) and whether respondents believed that relevant health or quality of life domains were missing from the EQ-5D questionnaire. This paper focuses on the aspects of health that people identified as being missing, using a content analysis framework. There were 436 respondents, about half of whom reported being in a 11111 EQ-5D state. 41% of participants considered the EQ-5D questionnaire to be missing some important aspect of health. The authors identified 22 (!) different themes and attached people’s responses to these themes. Sensory deprivation and mental health were the two biggies, with many more responses than other themes. 50 people referred to vision, hearing or other sensory loss. 29 referred to mental health generally while 28 referred to specific mental health problems. This study constitutes a guide for future research and for the development of the EQ-5D and other classification systems. Obviously, the objective of the EQ-5D is not to reflect all domains. And it may be that the public’s suggestions – verbatim, at least – aren’t sensible. 10 people stated ‘cancer’, for example. But the importance of mental health and sensory deprivation in describing the evaluative space does warrant further investigation.

Re-thinking ‘The different perspectives that can be used when eliciting preferences in health’. Health Economics [PubMed] Published 21st March 2017

Pedantry is a virtue when it comes to valuing health states, which is why you’ll often find me banging on about the need for clarity. And why I like this paper. The authors look at a 2003 article by Dolan and co that outlined the different perspectives that health preference researchers ought to be using (though notably aren’t) when presenting elicitation questions to respondents. Dolan and co defined 6 perspectives along two dimensions: preferences (personal, social and socially-inclusive personal) and context (ex ante and ex post). This paper presents the argument that Dolan and co’s framework is incomplete. The authors throw new questions into the mix regarding who the user of treatment is, who the payer is and who is assessing the value, as well as introducing consideration of the timing of illness and the nature of risk. This gives rise to a total of 23 different perspectives along the dimensions of preferences (personal, social, socially-inclusive personal, non-use and proxy) and context (4 ex ante and 1 ex post). This new classification makes important distinctions between different perspectives, and health preference researchers really ought to heed its advice. However, I still think it’s limited. As I described in a recent blog post and discussed at a recent HESG meeting, I think the way we talk about ex ante and ex post in this context is very confused. In fact, this paper demonstrates the problem nicely. The authors first discuss the ex post context, the focus being on the value of ‘treatment’ (an event). Then the paper moves on to the ex ante context, and the discussion relates to ‘illness’ (a state). The problem is that health state valuation exercises aren’t (explicitly) about valuing treatments – or illnesses – but about valuing health states in relation to other health states. ‘Ex ante’ means making judgements about something before an event, and ‘ex post’ means to do so after it. But we’re trying to conduct health state valuation, not health event valuation. May the pedantry continue.

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