Archive for August, 2012

When randomization is strategic

Here’s a quote from Tom Yates on his blog Sick Populations about a speech he heard by Rachel Glennerster of J-PAL:

Glennerster pointed out that the evaluation of PROGRESA, a conditional cash transfer programme in Mexico and perhaps the most famous example of randomised evaluation in social policy, was instigated by a Government who knew they were going to lose the next election. It was a way to safeguard their programme. They knew the next Government would find it hard to stop the trial once it was started and were confident the evaluation would show benefit, again making it hard for the next Government to drop the programme. Randomisation can be politically advantageous.

I think I read this about Progresa / Oportunidades before but had forgotten it, and thus it’s worth re-sharing. The way in which Progresa was randomized (different areas were stepped into the program, so there was a cohort of folks who got it later than others, but all the high need areas got it within a few years) made this more politically feasible as well. I think this situation, in which a government institutes a study of a program to keep it alive through subsequent changes of government, will probably be a less common tactic than its opposite, in which a government designs an evaluation of a popular program that a) it thinks doesn’t work, b) it wants to cut, and c) the public otherwise likes, just to prove that it should be cut — but only time will tell.

16

08 2012

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.

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[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. ]

Monday miscellany

  • This week’s must-read article is Atul Gawande on “Big Medicine,” comparing it to, of all things, the Cheesecake Factory. The sections on quality control and efficiency at the restaurant chain are incredible, and the description of the ICU command center will make you think it’s science fiction (though of course it’s not). For some follow-up, here are responses by Karen GrepinAustin Frakt, and more Austin Frakt. (Update: here’s an interesting, critical take on the subject.)
  • Practice Fusion has taken a a big (HIPAA-friendly) dataset from their work with health insurance records and claims in the US and made it available for a data analysis contest: Analyze This! Also, here’s an interesting post from them on syndromic surveillance.
  • Mainly Macro on how a memo by Greg Mankiw and John Taylor for the Romney campaign is giving economics a bad name.
  • Indolaysia on “Policy analysis without causal identification: gun ownership and state terror” in two parts: part 1 and part 2. Includes .do file to replicate his analysis.
  • Worth re-reading given recent events: Ryan Lizza’s profile of Paul Ryan.
  • Simply Statistics wonders what your CV would look like if you included all the things you failed to do.
  • And a bit off topic but still fun: the history of rock & roll, the world’s biggest wave, and Where the Hell is Matt (2012).

13

08 2012

Friday photos

Friday photos may be a new recurring feature on this blog — while I won’t post reviews of every place I go on weekends (or during the week for work), it’s hard to resist sharing some highlights of Ethiopia. A beautiful and fascinating country:

Medhane Alem, the largest monolithic church in the world, is just one of a dozen churches at Lalibela carved from solid rock in the 14th century AD:

Medhane Alem at Lalibela

Swimming at the “Queen of Sheba’s Bath”, in Aksum, northern Ethiopia:

Queen of Sheba's Bath, Aksum

More photos of travel around Ethiopia can be found here.

10

08 2012

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

08 2012

Sentimental narratives

In his contribution to the book Humanitarianism and Suffering, historian Thomas Laqueur charts the birth of “the sentimental narrative” and its role in changing hearts and inspiring action. “In the late eighteenth century,” he writes, “the ethical subject was democratized; more and more people came to believe it was their obligation to ameliorate and prevent wrongdoing to others.”

The sentimental narrative Lacquer identifies is a sneaky one. Superficially, it seems humane, a good-hearted response to the impoverished and their plight. But it also objectifies the sufferers it nominally empowers—people with pain to ameliorate, against whom wrongdoings are to be prevented, on whose behalf this compassion is to be invested. However many noble or real or useful things that investment may bring, it also flatters us, by affirming our own righteousness.

That’s from Jina Moore’s essay in the Boston Review on telling stories about Africa as a foreigner. It’s definitely worth a read, as is her follow-up blog post, “Good News from Africa,” (in the sense that the news is well-done, not that the news is always “good”) which highlights several examples of the extraordinary writing she’d like to see more of. And follow @itsjina on Twitter.

07

08 2012

Monday miscellany

  • Three-Toed Sloth discusses what makes a statistician vs. a data scientist, a debate you’re likely to encounter if your work intersects with quantitative work in any way.
  • Eight (relatively) young economists on the future of their field, via pretty much everyone. I’d love to see what similarly junior economists in the 1970s would have guessed would befall economics over the last 40 years; I could see at least some of them getting the broad sweep right, but I’d also bet they would overestimate how much progress a couple academic generations can make. Call me a pessimist.
  • This post by Lee Crawfurd points to Esther Duflo’s Tanner lecture on paternalism in development economics, which restates some of the themes from Poor Economics. I highly recommend the lecture based on what I’ve read; though my read was quick I found it both fascinating and dense (in the sense that it is idea- and information-rich, not dull) and realized I will need to revisit it, maybe more than once. I also liked this quote from Lee’s post:

Back in England, I can’t imagine anything worse than having to meet all of my neighbours after work to figure out how we are going to run the rubbish collection or fix the potholes in the road. That stuff just gets done. Services get delivered without me having to think about it at all. All I need is a mechanism to complain if things don’t work, but don’t ask me to help you plan how to fix it.

  • A dose of pop culture remix: what would 2001 have looked like had it been made in 2012? This trailer answers that question (which I’m sure you’ve wondered, right?).
  • This looks interesting: the “Public Health Twitter Journal Club” is currently picking its next article for discussion from a selection of smoking-related papers.
  • Finally, a non-link observation: watching the Olympics abroad (specifically, on ArabSat) makes me realize how much the coverage I’ve watched during previous Olympiads is shaped by being in the US. Not only has (for better or worse) the coverage focused on the actual events, rather than endless interviews and inspirational backstories, I’ve been impressed by the difference in the quantity of Americans on display. While the US does have one of the largest Olympic teams, many events I’ve watched haven’t had American competitors at all, or only had one who didn’t medal, whereas coverage in the US is always biased towards events in which Americans are traditionally strong. I guess it’s blindingly obvious in a sense, but a good reminder: often you only see what you’re looking for.

06

08 2012