Archive for the ‘prose’Category

A misuse of life expectancy

Jared Diamond is going back and forth with Acemoglu and Robinson over his review of their new book, Why Nations Fail. The exchange is interesting in and of itself, but I wanted to highlight one passage from Diamond’s response:

The first point of their four-point letter is that tropical medicine and agricultural science aren’t major factors shaping national differences in prosperity. But the reasons why those are indeed major factors are obvious and well known. Tropical diseases cause a skilled worker, who completes professional training by age thirty, to look forward to, on the average, just ten years of economic productivity in Zambia before dying at an average life span of around forty, but to be economically productive for thirty-five years until retiring at age sixty-five in the US, Europe, and Japan (average life span around eighty). Even while they are still alive, workers in the tropics are often sick and unable to work. Women in the tropics face big obstacles in entering the workforce, because of having to care for their sick babies, or being pregnant with or nursing babies to replace previous babies likely to die or already dead. That’s why economists other than Acemoglu and Robinson do find a significant effect of geographic factors on prosperity today, after properly controlling for the effect of institutions.

I’ve added the bolding to highlight an interpretation of what life expectancy means that is wrong, but all too common.

It’s analagous to something you may have heard about ancient Rome: since life expectancy was somewhere in the 30s, the Romans who lived to be 40 or 50 or 60 were incredibly rare and extraordinary. The problem is that life expectancy — by which we typically mean life expectancy at birth — is heavily skewed by infant mortality, or deaths under one year of age. Once you get to age five you’re generally out of the woods — compared to the super-high mortality rates common for infants (less than one year old) and children (less than five years old). While it’s true that there were fewer old folks in ancient Roman society, or — to use Diamond’s example — modern Zambian society, the difference isn’t nearly as pronounced as you might think given the differences in life expectancy.

Does this matter? And if so, why? One area where it’s clearly important is Diamond’s usage in the passage above: examining the impact of changes in life expectancy on economic productivity. Despite the life expectancy at birth of 38 years, a Zambian male who reaches the age of thirty does not just have eight years of life expectancy left — it’s actually 23 years!

Here it’s helpful to look at life tables, which show mortality and life expectancy at different intervals throughout the lifespan. This WHO paper by Alan Lopez et al. (PDF) examining mortality between 1990-9 in 191 countries provides some nice data: page 253 is a life table for Zambia in 1999. We see that males have a life expectancy at birth of just 38.01 years, versus 38.96 for females (this was one of the lowest in the world at that time). If you look at that single number you might conclude, like Diamond, that a 30-year old worker only has ~10 years of life left. But the life expectancy for those males remaining alive at age 30 (64.2% of the original birth cohort remains alive at this age) is actually 22.65 years. Similarly, the 18% of Zambians who reach age 65, retirement age in the US, can expect to live an additional 11.8 years, despite already having lived 27 years past the life expectancy at birth.

These numbers are still, of course, dreadful — there’s room for decreasing mortality at all stages of the lifespan. Diamond’s correct in the sense that low life expectancy results in a much smaller economically active population. But he’s incorrect when he estimates much more drastic reductions in the economically productive years that workers can expect once they reach their economically productive 20s, 30s, and 40s.


[Some notes: 1. The figures might be different if you limit it to “skilled workers” who aren’t fully trained until age 30, as Diamond does; 2. I’m also assumed that Diamond is working from general life expectancy, which was similar to 40 years total, rather than a particular study that showed 10 years of life expectancy at age 30 for some subset of skilled workers, possibly due to high HIV prevalence — that seems possible but unlikely; 3. In these Zambia estimates, about 10% of males die before reaching one year of age, or over 17% before reaching five years of age. By contrast, between the ages of 15-20 only 0.6% of surviving males die, and you don’t see mortality rates higher than the under-5 ones until above age 85!; and 4. Zambia is an unusual case because much of the poor life expectancy there is due to very high HIV/AIDS prevalence and mortality — which actually does affect adult mortality rates and not just infant and child mortality rates. Despite this caveat, it’s still true that Diamond’s interpretation is off. ]

The great quant race

My Monday link round-up included this Big Think piece asking eight young economists about the future of their field. But, I wanted to highlight the response from Justin Wolfers:

Economics is in the midst of a massive and radical change.  It used to be that we had little data, and no computing power, so the role of economic theory was to “fill in” for where facts were missing.  Today, every interaction we have in our lives leaves behind a trail of data.  Whatever question you are interested in answering, the data to analyze it exists on someone’s hard drive, somewhere.  This background informs how I think about the future of economics.

Specifically, the tools of economics will continue to evolve and become more empirical.  Economic theory will become a tool we use to structure our investigation of the data.  Equally, economics is not the only social science engaged in this race: our friends in political science and sociology use similar tools; computer scientists are grappling with “big data” and machine learning; and statisticians are developing new tools.  Whichever field adapts best will win.  I think it will be economics.  And so economists will continue to broaden the substantive areas we study.  Since Gary Becker, we have been comfortable looking beyond the purely pecuniary domain, and I expect this trend towards cross-disciplinary work to continue.

I think it’s broadly true that economics will become more empirical, and that this is a good thing, but I’m not convinced economics will “win” the race. This tracks somewhat with the thoughts from Marc Bellemare that I’ve linked to before: his post on “Methodological convergence in the social sciences” is about the rise of mathematical formalism in social sciences other than economics. This complements the rise of empirical methods, in the sense that while they are different developments, both are only possible because of the increasing mathematical, statistical, and coding competency of researchers in many fields. And I think the language of convergence is more likely to represent what will happen (and what is already happening), rather than the language of a “race.”

We’ve already seen an increase in RCTs (developed in medicine and epidemiology) in economics and political science, and the decades ahead will (hopefully) see more routine serious analysis of observational data in epidemiology and other fields (in the sense that the analysis is more careful about causal inference), and  advanced statistical techniques and machine learning methodologies will become commonplace across all fields as researchers deal with massive, complex longitudinal datasets gleaned not just from surveys but increasingly from everyday collection.

Economists have a head start in that their starting pool of talent is generally more mathematically competent than other social sciences’ incoming PhD classes. But, switching back to the “race” terminology, economics will only “win” if — as Wolfers speculates will happen — it can leverage theory as a tool for structuring investigation. My rough impression is that economic theory does play this role, sometimes, but it has also held empirical investigation in economics back at times, perhaps through publication bias (see on minimum wage) against empirical results that don’t fit the theory, and possibly more broadly through a general closure of routes of investigation that would not occur to someone already trained in economic theory.

Regardless, I get the impression that if you want to be a cutting-edge researcher in any social science you should be beefing up not only your mathematical and statistical training, but also your coding practice.

Update: Stevenson and Wolfers expand their thoughts in this excellent Bloomberg piece. And more at Freakonomics here.


08 2012

Aid, paternalism, and skepticism

Bill Easterly, the ex-blogger who just can’t stop, writes about a conversation he had with GiveWell, a charity reviewer/giving guide that relies heavily on rigorous evidence to pick programs to invest in. I’ve been meaning to write about GiveWell’s approach — which I generally think is excellent. Easterly, of course, is an aid skeptic in general and a critic of planned, technocratic solutions in particular. Here’s an excerpt from his notes on his conversation with GiveWell:

…a lot of things that people think will benefit poor people (such as improved cookstoves to reduce indoor smoke, deworming drugs, bed nets and water purification tablets) {are things} that poor people are unwilling to buy for even a few pennies. The philanthropy community’s answer to this is “we have to give them away for free because otherwise the take-up rates will drop.” The philosophy behind this is that poor people are irrational. That could be the right answer, but I think that we should do more research on the topic. Another explanation is that the people do know what they’re doing and that they rationally do not want what aid givers are offering. This is a message that people in the aid world are not getting.

Later, in the full transcript, he adds this:

We should try harder to figure out why people don’t buy health goods, instead of jumping to the conclusion that they are irrational.


It’s easy to catch people doing irrational things. But it’s remarkable how fast and unconsciously people get things right, solving really complex problems at lightning speed.

I’m with Easterly, up to a point: aid and development institutions need much better feedback loops, but are unlikely to develop them for reasons rooted in their nature and funding. The examples of bad aid he cites are often horrendous. But I think this critique is limited, especially on health, where the RCTs and all other sorts of evidence really do show that we can have massive impact — reducing suffering and death on an epic scale — with known interventions. [Also, a caution: the notes above are just notes and may have been worded differently if they were a polished, final product — but I think they’re still revealing.]

Elsewhere Easterly has been more positive about the likelihood of benefits from health aid/programs in particular, so I find it quite curious that his examples above of things that poor people don’t always price rationally are all health-related. Instead, in the excerpts above he falls back on that great foundational argument of economists: if people are rational, why have all this top-down institutional interference? Well, I couldn’t help contrasting that argument with this quote highlighted by another economist, Tyler Cowen, at Marginal Revolution:

Just half of those given a prescription to prevent heart disease actually adhere to refilling their medications, researchers find in the Journal of American Medicine. That lack of compliance, they estimate, results in 113,00 deaths annually.

Let that sink in for a moment. Residents of a wealthy country, the United States, do something very, very stupid. All of the RCTs show that taking these medicines will make them live longer, but people fail to overcome the barriers at hand to take something that is proven to make them live longer. As a consequence they die by the hundreds of thousands every single year. Humans may make remarkably fast unconscious decisions correctly in some spheres, sure, but it’s hard to look at this result and see any way in which it makes much sense.

Now think about inserting Easterly’s argument against paternalism (he doesn’t specifically call it that here, but has done so elsewhere) in philanthropy here: if people in the US really want to live, why don’t they take these medicines? Who are we to say they’re irrational? That’s one answer, but maybe we don’t understand their preferences and should avoid top-down solutions until we have more research.

reductio ad absurdum? Maybe. On the one hand, we do need more research on many things, including medication up-take in high- and low-income countries. On the other hand, aid skepticism that goes far enough to be against proven health interventions just because people don’t always value those interventions rationally seems to line up a good deal with the sort of anti-paternalism-above-all streak in conservatism that opposes government intervention in pretty much every area. Maybe it’s a good policy to try out some nudge-y (libertarian paternalism, if you will) policies to encourage people to take their medicine, or require people to have health insurance they would not choose to buy on their own.

Do you want to live longer? I bet you do, and it’s safe to assume that people in low-income countries do as well. Do you always do exactly what will help you do so? Of course not: observe the obesity pandemic. Do poor people really want to suffer from worms or have their children die from diarrhea? Again, of course not. While poor people in low-income countries aren’t always willing to invest a lot of time or pay a lot of money for things that would clearly help them stay alive for longer, that shouldn’t be surprising to us. Why? Because the exact same thing is true of rich people in wealthy countries.

People everywhere — rich and poor — make dumb decisions all the time, often because those decisions are easier in the moment due to our many irrational cognitive and behavioral tics. Those seemingly dumb decisions usually reveal the non-optimal decision-making environments in which we live, but you still think we could overcome those things to choose interventions that are very clearly beneficial. But we don’t always. The result is that sometimes people in low-income countries might not pay out of pocket for deworming medicine or bednets, and sometimes people in high-income countries don’t take their medicine — these are different sides of the same coin.

Now, to a more general discussion of aid skepticism: I agree with Easterly (in the same post) that aid skeptics are a “feature of the system” that ultimately make it more robust. But it’s an iterative process that is often frustrating in the moment for those who are implementing or advocating for specific programs (in my case, health) because we see the skeptics as going too far. I’m probably one of the more skeptical implementers out there — I think the majority of aid programs probably do more harm than good, and chose to work in health in part because I think that is less true in this sector than in others. I like to think that I apply just the right dose of skepticism to aid skepticism itself, wringing out a bit of cynicism to leave the practical core.

I also think that there are clear wins, supported by the evidence, especially in health, and thus that Easterly goes too far here. Why does he? Because his aid skepticism isn’t simply pragmatic, but also rooted in an ideological opposition to all top-down programs. That’s a nice way to put it, one that I think he might even agree with. But ultimately that leads to a place where you end up lumping things together that are not the same, and I’ll argue that that does some harm. Here are two examples of aid, both more or less from Easterly’s post:

  • Giving away medicines or bednets free, because otherwise people don’t choose to invest in them; and,
  • A World Bank project in Uganda that “ended up burning down farmers’ homes and crops and driving the farmers off the land.”

These are a both, in one sense, paternalistic, top-down programs, because they are based on the assumption that sometimes people don’t choose to do what is best for themselves. But are they the same otherwise? I’d argue no. One might argue that they come from the same place, and an institution that funds the first will inevitably mess up and do the latter — but I don’t buy that strong form of aid skepticism. And being able to lump the apparently good program and the obviously bad together is what makes Easterly’s rhetorical stance powerful.

If you so desire, you could label these two approaches as weak coercion and strong coercion. They are both coercive in the sense that they reshape the situations in which people live to help achieve an outcome that someone — a planner, if you will — has decided is better. All philanthropy and much public policy is coercive in this sense, and those who are ideologically opposed to it have a hard time seeing the difference. But to many of us, it’s really only the latter, obvious harm that we dislike, whereas free medicines don’t seem all that bad. I think that’s why aid skeptics like Easterly group these two together, because they know we’ll be repulsed by the strong form. But when they argue that all these policies are ultimately the same because they ignore people’s preferences (as demonstrated by their willingness to pay for health goods, for example), the argument doesn’t sit right with a broader audience. And then ultimately it gets ignored, because these things only really look the same if you look at them through certain ideological lenses.

That’s why I wish Easterly would take a more pragmatic approach to aid skepticism; such a form might harp on the truly coercive aspects without lumping them in with the mildly paternalistic. Condemning the truly bad things is very necessary, and folks “on the inside’ of the aid-industrial complex aren’t generally well-positioned to make those arguments publicly. However, I think people sometimes need a bit of the latter policies, the mildly paternalistic ones like giving away medicines and nudging people’s behavior — in high- and low-income countries alike. Why? Because we’re generally the same everywhere, doing what’s easiest in a given situation rather than what we might choose were the circumstances different. Having skeptics on the outside where they can rail against wrongs is incredibly important, but they must also be careful to yell at the right things lest they be ignored altogether by those who don’t share their ideological priors.

Mimicking success

If you don’t know what works, there can be an understandable temptation to try to create a picture that more closely resembles things that work. In some of his presentations on the dire state of student learning around the world, Lant Pritchett invokes the zoological concept of isomorphic mimicry: the adoption of the camouflage of organizational forms that are successful elsewhere to hide their actual dysfunction. (Think, for example, of a harmless snake that has the same size and coloring as a very venomous snake — potential predators might not be able to tell the difference, and so they assume both have the same deadly qualities.)

For our illustrative purposes here, this could mean in practice that some leaders believe that, since good schools in advanced countries have lots of computers, it will follow that, if computers are put into poor schools, they will look more like the good schools. The hope is that, in the process, the poor schools will somehow (magically?) become good, or at least better than they previously were. Such inclinations can nicely complement the “edifice complex” of certain political leaders who wish to leave a lasting, tangible, physical legacy of their benevolent rule. Where this once meant a gleaming monument soaring towards the heavens, in the 21st century this can mean rows of shiny new computers in shiny new computer classrooms.

That’s from this EduTech post by Michael Trucano. It’s about the recent evaluations showing no impact from the One Laptop per Child (OLPC) program, but I think the broader idea can be applied to health programs as well. For a moment let’s apply it to interventions designed to prevent maternal mortality. Maternal mortality is notoriously hard to measure because it is — in the statistical sense — quite rare. While many ‘rates’ (which are often not actual rates, but that’s another story) in public health are expressed with denominators of 1,000 (live births, for example), maternal mortality uses a denominator of 100,000 to make the numerators a similar order of magnitude.

That means that you can rarely measure maternal mortality directly — even with huge sample sizes you get massive confidence intervals that make it difficult to say whether things are getting worse, staying the same, or improving. Instead we typically measure indirect things, like the coverage of interventions that have been shown (in more rigorous studies) to reduce maternal morbidity or mortality. And sometimes we measure health systems things that have been shown to affect coverage of interventions… and so forth. The worry is that at some point you’re measuring the sort of things that can be improved — at least superficially — without having any real impact.

All that to say: 1) it’s important to measure the right thing, 2) determining what that ‘right thing’ is will always be difficult, and 3) it’s good to step back every now and then and think about whether the thing you’re funding or promoting or evaluating is really the thing you care about or if you’re just measuring “organizational forms” that camouflage the thing you care about.

(Recent blog coverage of the OLPC evaluations here and here.)


07 2012

Addis taxi economics

A is in his early 20s, and he’s my go-to taxi driver. He speaks good conversational English, which he picked up in part through being befriended by a Canadian couple who lived in Ethiopia for a while. Addis traffic is crazy but a bit more forgiving than some cities I’ve seen — there don’t seem to be many real traffic rules, but there’s more deference to other drivers. “A, you drive like a pro,” my friend says. “How long have you been driving?” “Oh, just six months!” (We gulp.)

In Addis “taxi” is used to refer to both ancient minibuses that drive set routes throughout the city and to traditional blue-and-white cars — often ancient-er — that will take you wherever you want to go. (Google Images of Addis taxis here.) A‘s car is the latter type, an old model that breaks down often and has one window handle you have to pass around to roll down each window.

Minibuses charge a flat rate on pre-specified routes, usually just a few Birr (ie, less than $0.20 US), but the personal taxis can charge much more. So having a few reliable drivers’ cell numbers is helpful because the prospect of your continued business helps ensure that you’ll get a better price for each ride.

Regarding taxis more generally: always negotiate a fare before you get in. Depending on the mood of the driver, current traffic and road construction, and the evident wealth, race, or nationality of the prospective passenger, the prices quoted will vary widely. I was once quoted 60 Birr and 150 Birr as starting prices ($3.50 and $8.80 US) by two drivers standing right next to each other!

Almost all of the taxi business seems to come from internationals and upper-class Ethiopians. Thus, taxis often congregate around the neighborhoods, hotels, and restaurants frequented by these groups. You’ll also get quoted a higher starting price if you’re seen coming out of a nice hotel than if you pick a cab just around the corner.

Starting prices definitely differ by race as well. (Here I cite conversations with Chinese-American and Bengali-American friends living in Addis.) Drivers will generally assume you’re from America (if you’re Caucasian), China (if you’re East Asian), and India (if you’re South Asian) and charge accordingly. White people get the highest starting prices, whereas if they assume you’re Chinese or Indian the starting price will be about 70% of the white price. This is, of course, entirely anecdotal, so econ PhD students take note: there’s some fascinating research to be done on differential pricing of initial and final fares for internationals living in Addis. In economics this differential pricing is called price discrimination (which can actually be good for consumers as it allows producers to provide services to a broader range of people, who often have different preferences and ability to pay).

A doesn’t own his taxi, and says that most drivers don’t either. Instead, he rents/leases his from a man who owns many taxis. That guy made enough money (“he is rich now!”) that he now goes to Dubai to buy other cars to import into Ethiopia. (Dubai is the go-to place for importing many things here.) A pays the owner a flat rate to have the taxi for a 10-day period, with more or less automatic renewals as long as he’s doing well enough to keep paying the fee. If he gets sick or wants to take a day off he has to pay that day’s rental fee out of earnings from another day, so A gets up at 6 am and drives until after midnight. Seven days a week.

A is only six months into the job, but he’s already looking for the next gig. He aspires to work as a tour guide — better pay and better hours, he says. And, I think, less risk of injury: almost all the taxis in Addis are from an era before airbags and seatbelts became commonplace. I think A would be a great tour guide — I hope it works out.


06 2012

Ethiopia bleg

Bleg: n. An entry in a blog requesting information or contributions. (via Wiktionary)

Finals are over, and I just have a few things to finish up before moving to Addis Ababa, Ethiopia on June 1. I’ll be there for almost eight months, working as a monitoring and evaluation intern on a large health project; this work will fulfill internship requirements for my MPA and MSPH degrees, and then I’ll have just one semester left at Princeton before graduating. After two years of “book-learning” I’m quite excited to apply what I’ve been learning a bit.

One thing I learned from doing (too many?) short stints abroad is that it’s easy to show up with good intentions and get in the way; I’m hopeful that eight months is long enough that I can be a net benefit to the team I’ll be working with, rather than a drain as I get up to speed. I plan to get an Amharic tutor after I arrive — unfortunately I figured out my internship recently enough that I wasn’t able to plan ahead and study the language before going.

I’m especially excited to live in Ethiopia. I have not been before — this will be my first visit to East Africa / the Horn of Africa at all. I’ll mostly be in Addis, but should also spend some time in rural areas where the project is being implemented. I’ve already talked with several friends who briefly lived in Addis to get tips on what to read, what to do, who to meet, and what to pack. That said I’m always open for more suggestions.

So, I’ll share what I’ve already, or definitely plan to read, and let you help fill in the gaps. Do you have book recommendations? Web or blog links? RSS suggestions? What-to-eat (or not eat) tips? Here’s what I’ve dug up so far:

  • Owen Barder has several informative pages on living and working in Ethiopia here.
  • Chris Blattman’s post on What to Read About Ethiopia has lots of tips, some of which I draw on below. His advice for working in a developing country is also helpful, along with lists of what to pack (parts one and two), though they’re obviously not tailored to life in Addis. Blattman also links to Stefan Dercon’s page with extensive readings on Ethiopian agriculture, and helpfully organizes relevant posts under tags, including posts tagged Ethiopia.
  • As for a general history, I’ve started Harold Marcus’ academic History of Ethiopia, and it’s good so far.
  • Books that have gotten multiple recommendations from friends — and thus got bumped to the top of my list — include The EmperorCutting for StoneChains of Heaven, and The Sign and the Seal. Other books I’ve seen mentioned here and there include Sweetness in the BellyWaugh in AbyssiniaNotes from the Hyena’s BellyScoop, and A Year in the Death of Africa. If you rave about one of these enough it might move higher up the priority list. But I’m sure there are others worth reading too.
  • For regular information flow I have a Google Alert for Ethiopia, the RSS feed for’s Ethiopia page, and two blogs found so far:  Addis Journal and Expat in Addis. (Blog recommendations welcome, especially more by Ethiopians.) There’s also a Google group called Addis Diplo List.
  • One of my favorite novels is The Beautiful Things That Heaven Bears — the story of an Ethiopian immigrant in Washington, DC’s Logan Circle neighborhood in the 1980s. It’s as much about gentrification as it is about the immigrant experience, and I first read it as a new arrival in DC’s Petworth neighborhood — which is in some ways at a similar ‘stage’ of gentrification to Logan Circle in the 80s.
  • I’ve started How to Work in Someone Else’s Country, which is aimed more at short-term consultants but has been helpful so far.
  • Also not specific to Ethiopia, but I’m finally getting around to reading the much-recommended Anti-Politics Machine, on the development industry in Lesotho, and it seems relevant.

Let me know what I’ve missed in the comments. And happy 200th blog post to me.

(Note: links to books are Amazon Affiliates links, which means I get a tiny cut of the sales value if you buy something after clicking a link.)


05 2012

Stats lingo in econometrics and epidemiology

Last week I came across an article I wish I’d found a year or two ago: “Glossary for econometrics and epidemiology” (PDF from JSTOR, ungated version here) by Gunasekara, Carter, and Blakely.

Statistics is to some extent a common language for the social sciences, but there are also big variations in language that can cause problems when students and scholars try to read literature from outside their fields. I first learned epidemiology and biostatistics at a school of public health, and now this year I’m taking econometrics from an economist, as well as other classes that draw heavily on the economics literature.

Friends in my economics-centered program have asked me “what’s biostatistics?” Likewise, public health friends have asked “what’s econometrics?” (or just commented that it’s a silly name). In reality both fields use many of the same techniques with different language and emphases. The Gunasekara, Carter, and Blakely glossary linked above covers the following terms, amongst others:

  • confounding
  • endogeneity and endogenous variables
  • exogenous variables
  • simultaneity, social drift, social selection, and reverse causality
  • instrumental variables
  • intermediate or mediating variables
  • multicollinearity
  • omitted variable bias
  • unobserved heterogeneity

If you’ve only studied econometrics or biostatistics, chances are at least some of these terms will be new to you, even though most have roughly equivalent forms in the other field.

Outside of differing language, another difference is in the frequency with which techniques are used. For instance, instrumental variables seem (to me) to be under-used in public health / epidemiology applications. I took four terms of biostatistics at Johns Hopkins and don’t recall instrumental variables being mentioned even once! On the other hand, economists just recently discovered randomized trials. (Now they’re more widely used) .

But even within a given statistical technique there are important differences. You might think that all social scientists doing, say, multiple linear regression to analyze observational data or critiquing the results of randomized controlled trials would use the same language. In my experience they not only use different vocabulary for the same things, they also emphasize different things. About a third to half of my epidemiology coursework involved establishing causal models (often with directed acyclic graphs)  in order to understand which confounding variables to control for in a regression, whereas in econometrics we (very!) briefly discussed how to decide which covariates might cause omitted variable bias. These discussions were basically about the same thing, but they differed in terms of language and in terms of emphasis.

I think an understanding of how and why researchers from different fields talk about things differently helps you to understand the sociology and motivations of each field.  This is all related to what Marc Bellemare calls the ongoing “methodological convergence in the social sciences.” As research becomes more interdisciplinary — and as any applications of research are much more likely to require interdisciplinary knowledge — understanding how researchers trained in different academic schools think and talk will become increasingly important.


05 2012

Facebook's brilliantly self-interested organ donation move

How can social media have a big impact on public health? Here’s one example: Facebook just introduced a feature that allows users to announce their status as organ donors, and to tell the story of when they decided to sign up as a donor. They’re — rightly, I think — getting tons of good press from it. Here’s NPR for example:

Starting today, the social media giant is letting you add your organ-donation status to your timeline. And, if you’d like to become an organ donor, Facebook will direct you to a registry.

Patients and transplant surgeons are eager for you to try it out.

Nearly 114,000 people in this country are waiting for organs, according to the United Network for Organ Sharing. But there simply aren’t enough organs to go around.

It’s an awesome idea. Far too few Americans are organ donors, so anything that boosts sign-up rates is welcome. As Ezra Klein notes, organ donation rates would be much higher if we simply had people opt out of donating, rather than opt in, but that’s another story. (And another aside: I hope they alerted some smart people beforehand to help them rigorously measure the impact of this shift!)

Call me a cynic, but I think the story of why Facebook chose to do this — and in the way they did it — is more interesting.Yes, there’s altruism, but Facebook is a business above all. Maybe they’re just trying to cultivate that Google ethos of “we sometimes spend lots of money on far-sighted things just to make the world a better place.” Facebook will certainly garner lots of public good will from this.

But I think, even more importantly, Facebook gets magnificent cover for introducing new modules on health/wellness. Check out the screenshot from their newsroom post on the new features:

That’s right — in the new Health & Wellness section you can enter not only whether you’re an organ donor, but also these categories: “Overcame an Illness,” “Quit a Habit,” “New Eating Habits,”Weight Loss,” “Glasses, Contacts, Others,” and “Broken Bone.”

All life events some people may want to share, of course. But Facebook makes money off of advertising, and just think of how much money Americans spend on weight loss, or on trying to quit smoking (or more usually, continuing it), or on glasses and contacts. Then think how much more advertisers will pay to show ads to segments of the billions of Facebook users who have shared the fact that they’re actively trying to lose weight.

Maybe Facebook has seen this sort of health data as a major growth area for some time, but was wary of introducing such features in the wrong way. On any other news day the introduction of these features would have triggered a new outbreak of the “Facebook feature prompt privacy outcry” and “Why does Facebook need your health data?” stories. Sure, we’ll get some of those this time, but I think any backlash will pale in comparison to the initial PR bump.

I don’t think there’s necessarily anything wrong with the move, and I certainly welcome any boost in organ donor registration. It may just be that this is a case where Facebook’s business interests in inducing us to share more of our personal information with them just happens to happily coincide with a badly needed public good. Either way, the execution is brilliant, because so far I’ve mostly seen news stories talking about how great organ donation is. And I just updated my Facebook status.


05 2012

Obesity in the US

One of my classmates whose primary interest is not health policy posted this graph on Facebook, saying “This is stunning… so much so in fact that I’m a bit skeptical of its accuracy.”

The graph compares obesity rates by state in 1994 vs. 2008, and unfortunately it is both terrifying and accurate. (I can’t find the original source of this particular infographic, but the data is the same as on this CDC page.)

I think those of who study or work in public health have seen variations on these graphs so many times that they’ve lost some of their shock value. But this truly is an incredible shift in population health in a frighteningly short period of time. In 1994 every state had an adult population that was less than 20% obese, and many were less than 15% obese. A mere 14 years later, Colorado is the only state under 20%, and quite a few have rates over 30% — these were completely unheard of before.

I did a quick literature search, trying to understand what causal factors might be responsible for such a rapid shift. It’s a huge and challenging question, so maybe it should be unsurprising that I didn’t find an article that really stood out as the best. Still, here are three articles that I found helpful:

1. Specifically looking at childhood obesity in the US (which is different from the rates highlighted in the map above, but related): “Childhood Obesity: Trends and Potential Causes” by Anderson and Butcher (JStor PDF, ungated PDF). Their intro:

The increase in childhood obesity over the past several decades, together with the associated health problems and costs, is raising grave concern among health care professionals, policy experts, children’s advocates, and parents. Patricia Anderson and Kristin Butcher document trends in children’s obesity and examine the possible underlying causes of the obesity epidemic.

They begin by reviewing research on energy intake, energy expenditure, and “energy balance,” noting that children who eat more “empty calories” and expend fewer calories through physical activity are more likely to be obese than other children. Next they ask what has changed in children’s environment over the past three decades to upset this energy balance equation. In particular, they examine changes in the food market, in the built environment, in schools and child care settings, and in the role of parents-paying attention to the timing of these changes.

Among the changes that affect children’se nergy intake are the increasing availability of energy dense, high-calorie foods and drinkst hroughs chools. Changes in the family, particularly increasing dual-career or single-parent working families, may also have increased demand for food away from home or pre-prepared foods. A host of factors have also contributed to reductions in energy expenditure. In particular, children today seem less likely to walk to school and to be traveling more in cars than they were during the early 1970s, perhaps because of changes in the built environment. Finally, children spend more time viewing television and using computers.

Anderson and Butcher find no one factor that has led to increases in children’s obesity. Rather, many complementary changes have simultaneously increased children’s energy intake and decreased their energy expenditure. The challenge in formulating policies to address children’s obesity is to learn how best to change the environment that affects children’s energy balance.

2. On global trends: “The global obesity pandemic: shaped by global drivers and local environments” by Swinburn et al. (Here’s the PDF from Science Direct and an ungated PDF for those not at universities.) Summary:

The simultaneous increases in obesity in almost all countries seem to be driven mainly by changes in the global food system, which is producing more processed, affordable, and effectively marketed food than ever before. This passive overconsumption of energy leading to obesity is a predictable outcome of market economies predicated on consumption-based growth. The global food system drivers interact with local environmental factors to create a wide variation in obesity prevalence between populations.

Within populations, the interactions between environmental and individual factors, including genetic makeup, explain variability in body size between individuals. However, even with this individual variation, the epidemic has predictable patterns in subpopulations. In low-income countries, obesity mostly affects middle-aged adults (especially women) from wealthy, urban environments; whereas in high-income countries it affects both sexes and all ages, but is disproportionately greater in disadvantaged groups.

Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures. This absence increases the urgency for evidence-creating policy action, with a priority on reduction of the supply-side drivers.

3. Finally, on methodological differences and where the trends are heading: Obesity Prevalence in the United States — Up, Down, or Sideways?(NEJM, ungated PDF). Evidently there’s some debate over whether rates are going up or have stabilized in the last few years, because different data sources say different things. Generally the NHANES data (in which people are actually measured, rather than reporting their height and weight) is the best available (and that’s what the maps above are made from). An excerpt:

One key reason for discrepancies among the estimates is a simple difference in data-collection methods. The most frequently quoted data sources are the NHANES studies of adults and children, the BRFSS for adults, and the CDC’s Youth Risk Behavior Survey (YRBS)4 for high- school students. Although sampling strategies, response rates, age discrepancies, and the wording of survey questions may account for some variability, a major factor is that in calculating the BMI, the BRFSS and YRBS rely on respondents’ self-reported heights and weights, whereas the NHANES collects measured (i.e., actual) heights and weights each year, albeit from a considerably smaller sample of the population. Since people often claim to be taller than they are and to weigh less than they actually do, we should not be surprised that obesity prevalence figures based on self-reported heights and weights are considerably lower than those based on measured data.

I would greatly appreciate any suggestions for what to read in the comments, especially links to work that tries to rigorously assess (rather than just hypothesize on) the relative import of various drivers of the increase in adult obesity.


05 2012

On food deserts

Gina Kolata, writing for the New York Times, has sparked some debate with this article: “Studies Question the Pairing of Food Deserts and Obesity”. In general I often wish that science reporting focused more on how the new studies fit in with the old, rather than just the (exciting) new ones. On first reading I noticed that one study is described as having explored the association of “the type of food within a mile and a half of their homes” with what people eat.

This raised a little question mark in my mind, as I know that prior studies have often looked at distances much shorter than 1.5 miles, but it was mostly a vague hesitation. And if you didn’t know that before reading the article, then you’ve missed a major difference between the old and new results (and one that could have been easily explained). Also, describing something as “an article of faith when it’s arguably something more like “the broad conclusion draw from most most prior research“… that certainly established an editorial tone from the beginning.

Intrigued, I sent the piece to a friend (and former public health classmate) who has work on food deserts, to get a more informed reaction. I’m sharing her thoughts here (with permission) because this is an area of research that I don’t follow as closely, and her reactions helped me to situate this story in the broader literature:

1. This quote from the article is so good!

“It is always easy to advocate for more grocery stores,” said Kelly D. Brownell, director of Yale University’s Rudd Center for Food Policy and Obesity, who was not involved in the studies. “But if you are looking for what you hope will change obesity, healthy food access is probably just wishful thinking.”

The “unhealthy food environment” has a much bigger impact on diet than the “healthy food environment”, but it’s politically more viable to work from an advocacy standpoint than a regulatory standpoint. (On that point, you still have to worry about what food is available – you can’t just take out small businesses in impoverished neighborhoods and not replace it with anything.)

2. The article is too eager to dismiss the health-food access relationship. There’s good research out there, but there’s constant difficulty with tightening methods/definitions and deciding what to control for. The thing that I think is really powerful about the “food desert” discourse is that it opens doors to talk about race, poverty, community, culture, and more. At the end of the day, grocery stores are good for low-income areas because they bring in money and raise property values. If the literature isn’t perfect on health effects, I’m still willing to advocate for them.

3. I want to know more about the geography of the study that found that low-income areas had more grocery stores than high-income areas. Were they a mix of urban, peri-urban, and rural areas? Because that’s a whole other bear. (Non-shocker shocker: rural areas have food deserts… rural poverty is still a problem!)

4. The article does a good job of pointing to how difficult it is to study this. Hopkins (and the Baltimore Food Czar) are doing some work with healthy food access scores for neighborhoods. This would take into account how many healthy food options there are (supermarkets, farmers’ markets, arabers, tiendas) and how many unhealthy food options there are (fast food, carry out, corner stores).

5. The studies they cite are with kids, but the relationship between food insecurity (which is different, but related to food access) and obesity is only well-established among women. (This, itself, is not talked about enough.) The thinking is that kids are often “shielded” from the effects of food insecurity by their mothers, who eat a yo-yo diet depending on the amount of food in the house.

My friend also suggested the following articles for additional reading: