“People think it’s easy to contract HIV. That’s a good thing, right? Maybe not.”

That’s the title of my guest post on the World Bank’s Development Impact blog, describing my job market paper. Here’s a bit of the introduction:

People are afraid of HIV. Moreover, people around the world are convinced that the virus is easier to get than it actually is. The median person thinks that if you have unprotected sex with an HIV-positive person a single time, you will get HIV for sure. The truth is that it’s not nearly that easy to get HIV – the medical literature estimates that the transmission rate is actually about 0.1% per sex act, or 10% per year.

One way of interpreting these big overestimates of risks is that HIV education is working. […] The classic risk compensation model says this should be causing reductions in unprotected sex.

Unfortunately, the risk compensation story doesn’t seem to be reflected in actual behavior – at least not in sub-Saharan Africa, where the HIV epidemic is at its worst. […] If people are so scared, why don’t they seem to be compensating away from the risk of HIV infection? I tackle that question in my job market paper, “The Effect of HIV Risk Beliefs on Risky Sexual Behavior: Scared Straight or Scared to Death?” My answer is surprising.

You can read the whole thing on their site by following this link. My post is part of their annual Blog Your Job Market Paper series, which features summaries of research from development economics Ph.D. students on the job market. People who follow this blog should check out that series, which has featured some really awesome research this year. More generally, Development Impact is by popular acclamation the best development-focused blog out there; I read every post.

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Devastating fact of the day

Liberia had only 50 physicians for the whole country before the [Ebola] epidemic

I learned this awful fact (which perhaps I would have known already, had I been following the response to Ebola more closely) from Mead Over. He suggests that the best way to fend off the next Ebola epidemic may be to shift the monitoring of disease outbreaks from passive detection clinics to active monitoring by teams who go out and test everyone.

Other sources disagree on exactly how many doctors Liberia had pre-Ebola. In this piece from back in August, Dr. Frank Glover states that there were actually 200 doctors in Liberia before the epidemic – and that 150 left after the initial outbreak.

To put this figure in context, Liberia is a country of 4 million people. According to healthgrades.com, it has about the same number of doctors as Battle Creek, Michigan, a smallish town (pop. 52,347) best solely known for making breakfast cereal. The standard way of counting how many doctors a country has, relative to its overall population, is the number of physicians per 1,000 people. On the World Bank’s page showing this number for different countries by year, the only entry for Liberia, from 2010, is “0.0”. The rate is less than the rounding error in the table.

This awful fact prompts three thoughts:

1. Over’s argument that we should look for alternative ways to address Ebola (and other similar disease outbreaks in the future), without relying on strengthening overall healthcare systems, is very compelling. Yes, it would be nice to achieve solid gains in general healthcare on the back of international concern about Ebola. But that doesn’t seem like a realistic solution to this problem. We are talking about a place with nearly no health system to strengthen. Moreover, this outbreak has horribly weakened what system there was. Doing better, cheaper monitoring could help stave off the next such disaster for Liberia’s healthcare system – or that of another African country.

2. The elasticity of physician labor supply with respect to disease risk is enormous, if we take Glover’s comments at face value. Such a large response might be totally rational – while Ebola is not easy to catch from casual contact, doctors could have reasonably feared they’d be pressed into service treating Ebola patients with totally inadequate supplies and training, which is exceedingly risky. If developing countries want to retain their physicians, they should focus on supporting them rather than trying to make it illegal to leave.

3. Liberia is not the only country that has such a massive healthcare deficit. That World Bank table lists 10 countries whose latest rate of doctors per 1,000 is too low to register. All are in sub-Saharan Africa. It is not surprising that countries with essentially no doctors experience high rates of transmission for diseases that are basically only spread from patients to those taking care of them. In the long term, it is incredibly important to address the physician deficit, not just in West Africa but in worldwide.

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A few highlights from NEUDC 2014

My posting to this blog has gone into a semi-involuntary hiatus this fall because I am on the academic job market right now, and am dedicating nearly 100% of my time to that process.

I’m breaking that trend to talk about some awesome new papers I saw last weekend. I was lucky enough to go to NEUDC for the first time, to I present my job market paper. In the other sessions I attended, I came across some fascinating stuff. A few highlights that I can’t resist sharing:

“Alcohol and Self Control – A Field Experiment with Cycle Rickshaw Pullers in India”, Frank Schilbach (only an abstract is currently available online)

In some parts of the developing world people drink very heavily, and this could contribute to getting trapped in poverty, since alcohol can exacerbate self-control problems. Schilbach uses a randomized incentive to stay sober to show that cutting down on drinking during the work day is as effective as providing people with access to commitment savings accounts, among his high-alcohol use population. This is a fascinating result, and I wonder how much it will generalize to other populations where alcoholism is a less-severe problem (his subjects drink, on average, 6 days a week, at a rate of something like 5 drinks per day.

“The Cost of Keeping Track”, Johannes Haushofer

Haushofer augments a standard rational model of intertemporal choices in a very simple, intuitive way: if you decide to undertake a transaction in the future, you must pay a fixed cost in each period to keep track of that decision. He motivated this brilliantly in the session by asking the audience if they had ever paid a bill before it was due, and pointing out that that is technically irrational – you’re giving up interest on the money in question. But it can be rationalized by the fact that it’s not worth the effort to remember to take care of the bill in the future. This augmentation generates a bunch of well-known “predictable irrationalities”: for example, people tend to discount future gains “too much” compared with future losses, but actually do the opposite for future losses, discounting them too little. It also predicts loss aversion and status quo bias. I’m looking forward to more research on the empirical implications of this model, which I think has the potential to bring a lot of clarity to how we think about behavioral anomalies in decisionmaking.

“The Role of Road Quality Investments on Economic Activity and Welfare: Evidence from Indonesia’s Highways”, Paul J. Gertler, Marco Gonzalez-Navarro, Tadeja Gracner, and Alexander D. Rothenberg

Road maintenance funding in Indonesia is set according to arbitrary guidelines by the central government. The authors exploit this fact to measure the effect of higher-quality roads on household income. Higher-quality roads help the economy substantially, and they can show that this is due to better roads leading to a shift from agriculture into manufacturing. Some of the elasticities they find are massive: a 1% improvement in average road quality leads to a 6% increase in hours worked in manufacturing. This type of work is exciting and important because transportation infrastructure is vital for economic development, but the empirical evidence for exactly how big its benefits are is still pretty thin. These results can be plugged into cost-effectiveness calculations to help justify desperately-needed increases in funding for road maintenance in the developing world.

This is just a small sample of the cool research on display – I wasn’t able to go all the presentations I wanted to. I was really impressed with the quality of the work I saw across the board.

Now, back to is the joy of filling out webforms and tracking jobs in huge spreadsheets.

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Sometimes rational behavior means nobody has any idea why they are doing something

The always-excellent Planet Money podcast recently did an episode on why milk is always at the back of the grocery store. It’s a fantastic piece, and well worth the 16-minute listen, but can be summarized pretty briefly. It turns out that there are two theories for the milk-in-the-back phenomenon: exploiting behavioral economics and cost minimization. The behavioral economics story is the one I was more familiar with: milk is an extremely common purchase, and it is placed in the back in order to force people to walk past a range of tempting items. Since consumers are prone to impulse purchases, this induces them to spend more.

The cost-minimization theory is one I wasn’t familiar with, but it rings true. Milk is extremely prone to spoilage and must be kept consistently cold to keep it drinkable. Keeping it in the back is the cheapest way to maintain the “cold chain” needed for this; moving it to the front would cause more losses due to spoilage and/or require.

That’s the debate, but the best part of the episode is that no one knows what the right answer is. They talk to a range of experts who voice various opinions on the subject, and get support for both theories. The first interview is with a guy who is the dairy buyer for a major grocery store chain. He voices support for the tempting-consumers theory, and literally says he “believes” that that’s why they do it. He is the guy that is doing this! And he believes that the reason why he puts the milk in the back is that it tempts the customer to spend more. The cold chain theory doesn’t fare a ton better either, getting barely more than half of the votes from people who are in the business of selling milk.

None of this implies that the people who run grocery stores are not behaving optimally, however. You don’t need to understand your own strategy to maximize profits, or even your own well-being. Imagine you run a big grocery store, or even just the dairy section of one: you make tons of decisions and face hundreds of constraints. And you observe what happens to costs, volumes sold, and profits, whenever you change something. So your process has ended up a certain way, and you can legitimately not know the exact reason why. You know that if you do different stuff with the milk, profits go down – from your own experience, from watching other stores, etc. You keep doing what you’re doing, because you are sitting at an optimum, but you don’t actually know why.*

My gut instinct is that this kind of rational behavior – where people are at an optimum but don’t know exactly why – is exceedingly common. I am reminded of the complex traditions around using one’s signals on unlit roads in rural Africa. If you approach traffic headed in the opposite direction, you have to lower your high-beams and turn on your right turn signal (the one closest the the approaching car). I’ve heard various of explanations for this: that it reminds people to dim their headlights, that it informs approaching cars about the location of the edge of your vehicle, etc. No one knows why they do it, they just do. Realizing that there has to be some good reason (or reasons!), when I drive on dark roads in Malawi, I do the same thing.

*And of course there might be multiple reasons – a reasonable model of dairy section location would involve the firm minimizing the costs of all its items and balancing that against the value of all its sales.
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How to reduce the incidence of rape using public policy

Rape is a big problem in the United States. Reliable figures are very hard to get for such a stigmatized and personal crime, but regardless of the exact prevalence, it happens much too often for us to be comfortable with. Well-publicized cases out of India suggest that in developing countries the problems are even worse (but reporting is probably worse as well).

Although most people agree that rape is an important issue, there is a dearth of actual practical policy suggestions for dealing with it. Encouraging women to be more careful and aware of their surroundings smacks of victim-blaming and is unlikely to do much to reduce all-too-common acquaintance rapes and date rapes. Pointing the finger at “rape culture” could be part of the right answer, but it’s not obvious what to do about that culture, and the people who make major contributions to rape culture probably aren’t reading think pieces about it. More important, neither of these proposed solutions is backed by actual evidence.

However, an excellent new paper by Scott Cunningham and Manisha Shah does provide evidence about something that works to decrease rape: decriminalized prostitution, specifically “indoor” prostitution (massage parlors, escort services, and online sex work). Rhode Island unintentionally decriminalized the practice in 2003 (they have an excellent discussion of how this happened), and Cunningham and Shah show that this increased the size of the sex market and also reduced cases of gonorrhea (which is mainly spread by sex workers) by 39% across the whole population. Even more striking, they demonstrate that the overall incidence of rape declined by 31%.

One of the best features of the paper is that they use what I once heard Steve Levitt refer to as a “mosaic of evidence” to support their claim. They draw on a range of different datasets to illustrate their findings both visually and numerically, and to buttress their assumptions. Their main estimates are based on a difference-in-differences between Rhode Island and the rest of the US, but these are buttressed by a “synthetic control” approach. The synthetic control method, from Abadie and Gardeazabal (2003), finds other states that look similar in terms of previous trends for the outcome, and uses a weighted average of them to form a comparison group. The authors provide tons of graphs to show exactly what they are doing, and they all look about as compelling as this one does:

Rapes over time

What’s especially striking about this graph is that Rhode Island re-criminalized prostitution in 2009, exactly when rape cases go back up.

Cunningham and Shah are very careful to say that they cannot conclude exactly why decriminalizing prostitution reduces cases of rape. They consider a number of potential mechanisms, and conclude that the most likely one is that, for some men, rape and prostitution are substitutes. That is, men commit rape in part due to sexual desire, which can be satisfied in other ways. While Cunningham and Shah’s paper cannot demonstrate this for sure, their finding is consistent with other research by Todd Kendall that finds that the rollout of the internet, and the attendant increase in the accessibility or pornography, appears to have driven a decrease in cases of rape.

One potential issue with decriminalized prostitution is that it may just be substituting one form of oppression of women for another: decriminalized indoor prostitution may attract abusive pimps or madams, or encourage the illegal sex trade. The experiences of Nevada and several countries in Europe suggest a solution, however – full legalization and regulation of sex work would allow for law enforcement to protect women who engage in sex work, and undermine the market for illicit paid sex. A similar issue potentially affects pornography, where some actresses are exploited (but some emphatically state that they are doing what they love). Here, too, the answer is to have a legal but well-regulated market: exploitation may happen where pornography is legal, but if it were forced underground and run by criminal organizations, it would probably be much worse.

Cunningham and Shah’s paper, along with the earlier Kendall paper, suggest two ways to reduce rapes through public policy: legalized access to pornography, and legalizing sex work. These policies would both operate through the same mechanism – which is that some men commit rape due to sexual urges. That’s not an idea that we are particularly comfortable with as a society – a common claim is that rape is an act motivated by power, or anger, and not by sexual gratification. But if we want to reduce the incidence of this awful crime, we need to move out of our comfort zone, and rely on the evidence rather than what we’d like to believe.

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Odds ratios are a catastrophe

Adam Larson sent me the following question about a study of obesity and a press release about it from NPR. The claim, made in both the press release and the underlying article, is that weight discrimination makes the already obese 3 times as likely to remain obese, and the non-obese 2.5 times as likely to become obese. Adam writes:

They interpret odds ratios of 2.5 and 3 as “2.5 times as likely” and “3 times as likely”.

Balderdash, yes? I assume what they’re getting at is that in one group something like 85% remained obese; in the other 75%. This gives an odds ratio of (.85/.15)/(.75/.25)=1.89

So common sense would call it a 10 percentage point decrease or a 12% decrease, right?

Adam is spot-on. An odds ratio is the odds of an event happening for one group divided by the odds of a thing happening in another. Odds are summaries of probabilities that get used by sports books and nearly no one else, because they are counter-intuitive non-linear approximations to probabilities. If an event has an X% chance of happening, the odds that it happens are (X%)/(100-X%). The basic problem with odds ratios is that long ago someone (we should figure out who and curse their name) realized that for rare outcomes, an OR is approximately a relative risk, or (% chance thing occurs in treatment group)/(% chance thing occurs in control group). That is:

(0.01/0.99)/(0.02/0.98) ≈ 0.5 = 0.01/0.02

That has ever since been taught to applied statisticians working in certain fields (public health is one example) who use odds ratios for the scientifically important reason that they are the default output of many regression packages when you run a logistic regression.*

And so people misinterpret them constantly, presently odds ratios as relative risks even when they are not small, and the approximation does not hold. This is even before we get into the fact that calling a change from P=0.01 to P=0.02 a “100% increase in risk” is itself fairly absurd and misleading. It’s a one percentage-point increase. There is no intrinsic sense in which “the risk tripled” actually means anything. Did you know that if you go in the ocean you are infinity times more likely to get eaten by a shark than if you stay on land? (You probably did, but it’s a stupid number to think about. What is actually relevant is that the absolute risk went up by some fraction of a percentage point.)

For this paper, under the assumption that their regression adjustment doesn’t change too much, we can actually back out what the percentages really are. First, the effect on the not-initially-obese:

Mean outcome = (% discriminated)*(mean for discriminated people) + (1 – % discriminated)*(mean for non-discriminated people)
0.058 = 0.08X + 0.92Y
Odds ratio = ((mean for discriminated people)/(1 – mean for discriminated people)) / ((mean for non-discriminated people)/(1 – mean for non-discriminated people))
(X/(1-X))/(Y/(1-Y)) = 2.54
Y = (50X)/(127-77X)
0.058 = 0.08*X + 0.92*(50X)/(127-77X)
X = 0.1230
Y= 0.0523

So the change is 7.2 percentage points. Put less clearly, P(became obese) has gone up by a factor of 2.35 for those who experienced weight discrimination, relative to those who did not. That is different from the OR of 2.54, but their figure isn’t too far from the relative risk.

Repeating the process for their other analysis, however, reveals how misleading ORs can be:
0.263= 0.08X + 0.92Y
(X/(1-X))/(Y/(1-Y)) = 3.20
Solving these equations for X and Y gives us:
X = 0.505
Y= 0.242

Here the risk ratio is 2.08, not 3.20. The percentage-point change of 26.3 remains completely comprehensible, as it always is. Misusing odds ratios here allowed them to overstate the size of their effect a factor of 50%! I suspect, but am not sure how to prove, that with regression adjustment these figures could look even more misleading.

As most people who read this already know, even if presented correctly the figures wouldn’t mean anything. There’s no reason to believe the relationship being studied is causal in nature. Indeed, it probably suffers from classic reverse causality: people who are gaining weight (or failing to lose weight) are likely to perceive a greater degree of weight discrimination. But presentation matters too. First, clear presentation can help us make use of studies, even when they are as limited as this one is. As the above derivation illustrates, figuring out what an odds ratio actually means involves 1) the annoying process of scrounging through the paper for all the variables you need and 2) solving a system of two equations for two unknowns, which most people can’t do in their head. This detracts very substantially from a paper’s clarity: in general, when I see odds ratios presented in a paper, I have no idea what they mean. An OR of 2 could mean that the risk went from 1% to 2% or (to use a variation on Adam’s example) from 75% to 86%, or a whole host of other things.

Second, poor presentation has consequences. Health risks are often reported using relative risks, or, worse yet, using ORs that are presented as relative risks. This is often extremely misleading, since a doubling of risk could mean that the chance went from 0.001% to 0.002%, or from 50% to 100%. Misleading and confusing people about risks undermines the basic goal of presenting health risks in the first place: to help people make better decisions about their health.

*I honestly believe that if we made mean marginal effects the default, and forced people to do ORs and AORs manually, they would disappear within 10 years. Being forced to construct ORs manually would also force people to understand what they are, which would stop people from using them.
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What empirical microeconomics tells us about reparations

Ta-Nehisi Coates argues that the United States government should pay reparations to African-Americans for slavery and institutionalized racism. The essay is long and full of supporting evidence, and generally makes a strong case that the US government bears responsibility for oppressing blacks for hundreds of years. While Coates digresses occasionally  – into claims of broader guilt by all Americans, or all whites, or into arguments that America’s current prosperity depends on its history of oppressing blacks – those claims are not necessary for his main point to hold water. That point is fairly straightforward: the US government was complicit in a moral evil, and it should take steps to make right for that evil, as it did, for example, for the internment of Japanese Americans during World War II.

Leaving aside the merits of the underlying idea, and the tasking of pinning down what the value of the reparations would be and how to allocate them, I wanted to discuss the practical aspect: what would providing reparations accomplish? Could transferring money to blacks help close the yawning gaps between them and whites that exist across a broad range of social indicators? Reparations need not be cash transfers – Coates cites Charles Ogletree’s idea of reparations in the form of job training programs – but usually the term is associated with the payment of cash to the afflicted group. This fixes a key economic question: what would happen if the US government made a massive financial transfer to every black person in America?

In some sense the right answer to this is “we don’t know”. We have never tried doing this, let alone in an experimental framework that would allow us to measure its effects. Coates does list one empirical example – the German payment of Holocaust reparations to the Israeli government, which is credited with funding the country through a tough spell and contributing to substantial economic growth. But most of those payments went to the government, not to individuals, so it is unclear how those effects would translate to the context of reparations to blacks in the US.

Even though no one has ever run this experiment, we do have evidence on what happens when people receive large cash transfers. The best evidence comes from a paper by Hoyt Bleakley (who is joining Michigan’s economics faculty in the fall) and Joseph Ferrie, about a lottery that distributed land at random to adult white males in Georgia.* The winners of this lottery received land worth approximately as much as the entire wealth holdings of the median person at the time. Given that the average black family has one sixth the wealth of the average white family, this is actually pretty close to the magnitude of the transfer we’d be talking about.

wealth-urban

This image (from Wonkblog) shows that the black-white wealth divide has widened rather than narrowing over time

Large cash transfers help: they make the recipients richer. But they don’t have the long-term social ramifications that you might hope for. The children and grandchildren of lottery winners end up no wealthier and no better-educated than non-winners. The big caveat with this comparison is that the Bleakley and Ferrie paper studies people from the 19th century, so the sample and context are quite different than they are today. However, I’d actually expect those differences to lead to larger effects than we’d see from targeting a poorer, more disadvantaged group. Overall, this suggests that wealth transfers – even massive ones – will not have transformational effects on socioeconomic status that last across generations.

On the other hand, a wide range of evidence suggests that, contrary to stereotypes, people (even poor people) do not “waste” cash transfers on alcohol, cigarettes, or other vices.** Those results are for transfers on a scale much smaller than reparations would operate on, and are for much poorer populations than the typical black American. But implicit in the the stereotype that money will go toward alcohol is notion that poorer people should have bigger problems with this.*** Since even very poor people seem to have no problem refraining from potentially-problematic spending, it is unlikely that this would be an issue for a reparations program.

Taken together, the evidence from empirical economics tells us that reparations, if done as pure financial transfers, would make blacks richer and with few downsides – but that they would not have transformative effects on the long-run gaps in outcomes between whites and blacks. While wealth is inherited, wealthy people also propagate success through their family lines by passing down other attributes – from education to behaviors to social connections to their race – that end up washing out the effects of wealth alone. To fix the black-white gap in a permanent way, we need to address all sorts of other differences as well; addressing wealth alone is not enough.

What about other ways of providing reparations? The literature on job training programs for marginalized groups is fairly discouraging, so I’m not convinced that Ogletree’s proposal would work well (although maybe we need to work on developing better job training). Another possibility is to work through the education system. Roland Fryer’s research has shown that improving middle-school educational outcomes for blacks helps them close gaps in other social outcomes. At the college level, there is robust, although not necessarily causal, evidence that high-quality colleges help blacks quite a bit (and matter much less for whites). One policy that might work is to replace affirmative action with an official reparations program, funded by the federal government, that creates additional slots at all universities to accommodate black students. This would reduce the racial tension that is stirred up by the current system, where people perceive that they are being denied admission based on their race, and where the moral and legal justification for the scheme is not made clear. It might reduce opposition to the program as well.

More broadly, we still need more evidence about what kinds of programs help generate permanent reductions in the black-white social divide. If reparations end up being taken seriously, then the government should fund and promote experimental and regression-discontinuity research into a wide range of possible programs in order to see which ones work. Financial transfers alone may not work – but we have the empirical tools needed to figure out what does.

*In a dark irony, this land came from one of the worst crimes against humanity the US government has ever committed.
**As I’ve discussed on this blog in the past, it is not obvious that these purchases are wasteful; we need to take seriously the idea that people have agency – that they can be trusted to make their own decisions.
***Basic economic theory actually suggests the opposite, since poorer people have a tighter budget constraint. But it also tells us that people’s unaltered choices maximize their own welfare, so this is a non-problem. Chris Blattman believes that the homeless in the US are fundamentally different from similar-looking populations in Africa, but that would only apply to the poorest black people who received reparations.
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