The unit of analysis

Bill Easterly put a quote on his non-blog yesterday from a Jane Jacobs book, Cities and the Wealth of Nations, (now almost 30 years old) on the unit of analysis in development questions. It makes a case for considering other units of analysis than the nation.

Nations are political and military entities… But it doesn’t necessarily follow from this that they are also the basic, salient entities of economic life or that they are particularly useful for probing the mysteries of economic structure, the reasons for rise and decline of wealth.

As a labor economist, I’m kind of surprised that it’s still an issue, but it seems necessary to reiterate even 30 years after Jacobs brought it up in her book. Though Easterly and Jacobs were talking about wealth and economics in particular, I think the insight is relevant for all kinds of decision making, and especially important when we’re talking about social norms (yes, I’m on a social norms kick–it doesn’t help that a friend told me last night that all my research was boring except for the social norms stuff. I’m here all night, folks).

At the risk of sounding like an echo, I was a bit taken aback last week how many of the people at the conference wanted to talk about scaling up to national level, how to effect change at a national level, and how to measure national-level social norms (some confusion around the term, here), even while admitting how watered down programs get at that level and how difficult it is to generalize across countries. Research suggests that reform and program implementation at that level are not very compatible with leveraging social norms for behavioral change due to lack of identification with the relevant social group (the nation).

Child obesity, Latin America and a good reminder

A few weeks ago, Adam Ozimek and I of Modeled Behavior had a discussion in the comments section here about the soda ban in New York City and the debate around paternalism. When I was slow to respond, we continued over email, just proof that you’re never really going to end a debate with an economist.

Adam was kind enough to send me a link to a piece in the Atlantic, which I thought did a much better job of summing up the arguments against the soda ban and paternalism in general, which I had, up to that point, not seen as convincingly articulated. What I liked about the argument is that it alluded to culture and how creating laws that are both nonsensical and devoid of cultural understanding and social norms makes for really bad law. And this I can totally get behind.

With that in mind, I spent much of last week searching for recent programs in the developing world for adolescent girls. The scope of this new project is rather wide and includes programs aimed at increasing political and community participation by girls, delaying marriage and sexual debut, improving education, health status, and bargaining power, decreasing HIV and violence against women, and so much more. I was thumbing through websites on health and violence and found the program Agente F, partially sponsored by Telefonica, one of the major cell carriers in Latin America. It’s intended to teach kids about healthy eating habits and avoid obesity, which, apparently, is fast becoming a problem in Latin America. I didn’t know. I thought we were still dealing with hunger and poverty, but apparently I’m behind the times. I have been unable to ascertain how widely this program is used, or whether anyone has actually played the game, but it’s interesting in that it has a lot of institutional support, at any rate.

I consider myself somewhat adept at Latin American cultures, and some more than others, having lived and spent time in many Latin American countries. I tell people “buen provecho” when they’re eating and can sing happy birthday in Spanish, Portuguese and Venezuelan (it’s a different song). I know where it’s appropriate to wait in line and where you’ll never get your coffee if you don’t hustle your way to the counter. I can talk to you a little bit about Catholics and saints and am sure to take a shower immediately if I get wet in the rain (RIP, Tomas.). I’m not a native, by any means, and I surely make mistakes, but it’s not a completely foreign world to me.

So I was struck by how many of the questions on the Agente F game I was unable to answer. Not just the ones about how many bones are in the body or how many muscles. Those, I guessed on and mostly did fine. One question in particular asked what should you do to ensure a good night’s sleep? I said exercise, but the answer was take a cold shower before going to bed. I see the logic. Your body needs to cool down before going to bed, and it’s often hot in many Latin American countries, which can make it difficult to sleep, but I thought it was a very odd answer.

A few questions were in this vein. The answers seemed totally foreign to me and reminded me how important cultural context is in creating programs and legislation with the policy goals of influencing behavior and actions. Despite my experience living in Latin America, I’m not a native. I have no idea whether taking a cold shower before bed would sound like a reasonable thing to a Mexican or a Colombian; maybe it’s totally within the realm of reason. Heck, maybe it’s within the realm of reason for natives of the United States and I’ve totally missed the boat. Regardless, culture is an important element to take into consideration when designing programs and laws.

Replication, or the lack thereof, in Economics

My scientist friends have always been puzzled by my responses to questions about replicating studies in Economics. It’s just not done very often. In fields like astrophysics and biology, replication is almost as important, if not more important in some cases, as the novel finding itself, but not so in Economics. I’ve seen evidence that other social sciences are similar and there was some recent debate about the replication of psychology experiments and the failure to come to the same conclusions using similar methodologies. (There were other pieces on this, but this is one that I found today). In short, journals favor novel and interesting outcomes, so obvious or unsurprising results are far less likely to be published. The publication of the novel results leads to a power imbalance (she already published this, so she’s the expert and gets the soapbox). No one wants to fund or highlight research that’s already been done. Replications that confirm are boring and replications that challenge established findings have to be 110% on everything.

It’s really hard to challenge established findings. Look at how long (three years after publication) and how many papers it took for Emily Oster to admit her paper on missing women and Hepatitis B was wrong. Regardless, she still has a job and now tenure at Chicago. Or how many papers have been written challenging Donohue and Levitt’s abortion paper and they still stand by it.

I got a bit far afield, though. Economists are not generally in favor of duplication of effort. If someone’s doing it already, unless you can do it a lot better, you shouldn’t really do it. Hence persistent ideas of comparative advantage and gains from trade.

However, the recent spate of randomized control trials, particularly in development settings, has prompted more and more debate about the validity of these experiments and appears to have resulted in at least one group that’s eager to test and replicate in order to confirm (or deny?) the validity of certain projects.

Clearly, there are limits to what can be replicated using existing data, and limited funding to collect new data using similar methods.It’s unclear to me how they will choose appropriate experiments to reproduce or test, and as much faith as economists tend to put in a sample size of one, I’d bet we won’t be too happy with a sample size of two, but I think it’s a good start. The Development Impact Blog by the World Bank will keep up with the process of replication, so worth following if you’re interested. I know I’ll be watching.

h/t @JustinWolfers

Though kind of dated now, Daniel Hamermesh’s paper on replication in economics is here.

RCTs and placebo effects

A few weeks ago, a paper was posted on the CSAE 2012 Conference website that seemed to fly in the face of much of the current research that is happening in development economics. The advent of RCTs (randomized control trials) brought about a significant change in the way we do policy analysis, but also in the costs of it. This paper suggested that RCTs were capturing placebo effects. Just like when people believe they are taking curative medicines, they feel better, so do those benefiting from RCTs experience placebo effects from knowing they are part of an experiment.

The answer, according to the researchers, is to conduct a double-blind experiment, where neither the researchers nor the participants whether they were part of the treatment or control.

The paper garnered a lot of attention early on. I noticed many colleagues and others had the immediate and short reaction of “wow” and “yikes”, and I wasn’t the only one. Berk Ozler, at the Development Impact Blog, has a good review of the paper up with a great, punny title. Among other problems:

First, it turns out that the modern seeds are treated with a purple powder in the market in Morogoro (to prevent cheating and protect the seed from insect damage during storage), so the experimenters sprayed the traditional seeds with the same purple powder. As you can immediately tell, this is less than ideal. First, as this is a not a new product, farmers in the blind RCT are likely to infer that the seeds they were given are modern seeds. Given that beliefs are a major part of the story the authors seem to want to tell, this is not a minor detail. Second, if the purple powder really does protect the seeds from insect damage, the difference between the MS and TS is now reduced.

Berk’s analysis is well worth a read. Kim Yi Dionne also addresses placebo effects, though a different paper.

Update: the original post said that this paper was forthcoming in Social Science and Medicine. This is not the case. Sorry for the confusion and thanks to Marc Bellemare for catching it.

Update #2: The Economist has a nice review of this paper up as well on the Free Exchange blog. It doesn’t touch most of the analysis issues, but it does explain well why double-blind experiments might not be useful in Economics. h/t @cdsamii

Why we educate women

The World Bank’s Development Impact Blog has recently been hosting guest posts from job market candidates in economics and a few days ago, Berk Ozler, a regular contributor, decided to synthesize some of the lessons from their papers and one by Rob Jensen (forthcoming in the QJE). With a brief mention of the fact that some are working papers, and certainly subject to change, Ozler concludes that we’ve been going about increasing women’s educational attainment in the developing world in the wrong way. Backward, he calls it. Instead of making it easier for women to go to school by providing school uniforms or scholarships or meals, we should be concentrating on changing women’s opportunities to work. If women see the possibility of work or higher wages or more openings, then they will likely demand more education for themselves or for their female children.

From a purely incentive-based approach, it makes perfect sense. If female children are likely to bring in earnings, particularly if they might be comparable to or even higher than their brothers, then parents have an incentive to educate female children. Higher earnings perhaps mean better marriage matches, but most certainly mean better insurance for parents as they age. Women with their own incomes can choose to take care of their parents.

From a feminist perspective, however, it’s a bit problematic. Such analysis implicitly values waged work over non-waged work, a problem inherent in many economics questions, most apparent in how we measure GDP. We know that increasing women’s education levels is valuable in and of itself, regardless of whether those women go on to work. More education for women means later marriage, lower fertility, reduced HIV/AIDS transmission, reduced FGM, and more.

It’s reasonable to think that regardless of how we set up the incentives–either by showcasing opportunity or reducing the immediate costs of schooling–all of these things will happen. And certainly job creation and the encouragement of seeking new opportunities to work is desirable. But if we choose to focus all of our resources on showcasing opportunity (particularly when it may set up unrealistic or very difficult to achieve expectations. note I haven’t read the Jensen paper yet), then we reinforce the idea that “women’s work”, or work in the home, is worth less than waged work.

In a world where a woman becomes educated in hopes of finding work, but doesn’t, how does that affect her ability to make household decisions? To leave an abusive spouse? To educate her own children, male and female, equally? Jensen’s paper seems to imply the very promise of women’s wages is enough to change bargaining power, but I wonder if that will stick. Does failure to find work, for whatever reason when it is understood to be the sole goal of attaining more schooling, affect women’s status?

Duflo and Female Empowerment

When you volunteer with a left-leaning organization that requires forty-two hours of training on social justice and examining your own privilege and sensitivity, one of the first things you are taught is that empowerment is a silly word. Empowering, by definition, involves giving someone your power, which is, by this understanding of power, impossible. The idea is that we each have privilege and power that we didn’t necessarily earn, by way of our gender, skin color, or height, for example, and as we can’t give those things to another person; we can’t actually “empower” them.

The distinction seems like semantics, but it actually creates a very different outlook in social justice terms. There is a difference between trying to give someone your power–which is patriarchal in addition to futile–and creating an environment in which more people have access to power.

Hence, when I saw the title of Esther Duflo’s latest NBER working paper, I cringed a bit in anticipation of what might lie within. She and Abhijit Banerjee also sprinkle the term throughout their recent book Poor Economics, which I’ve recently finished, enjoyed, am excited to hear my students’ reactions. But I’m a proponent of presenting and discussing Duflo’s work, even if not always a proponent of the work itself, so I was willing to give it a try, hoping it was just a vocabulary issue.

Though I still think the term should be used more carefully, Duflo largely seems to be addressing issues of equality of treatment, investment, education, and salary in the developing world. It is a literature review, and a rather comprehensive one at that, covering the status of women all over the world and a number of experiments and papers that have sought to tease out the directionality of the relationship between gender equality and development.

For anyone interested in the state of women in the developing world and the relationship between equality and development, it’s a must-read.

Fads and RCTs and the job market

I spoke with a colleague last week whose university is hiring in Development this year. I was surprised, though perhaps I shouldn’t have been, that of six candidates they are flying out, five have job market papers using Randomized Control Trials. Maybe that’s an area that their department is trying to fill and thus that’s the kind of faculty they are interviewing, but it seemed odd along with a comment from another job market candidate.

A friend on the market, in development, told me that she felt she was having a hard time selling herself as a development economist. Without an RCT (and the requisite cash that accompanies these very-expensive projects), she didn’t feel like she was getting even enough attention to get a job. Her plan is to find a new line of research using US data in the next year to go on as a Labor economist.

I realize these are two very specific examples and might not be indicative of the market as a whole, but I do think that fads in economics are both fascinating and problematic. No single theoretical or empirical response to data issues is a panacea, and I wonder if we are putting too much stock in RCTs–and thus in those who were lucky enough or prescient enough–to get into them early. There’s still a lot of value in survey data, I think, and I hope we don’t lose those important results because of a love affair with RCTs.

Public Randomization

A significant issue in conducting randomized control trials in a community is the issue of fairness. The idea behind RCTs is to mimic the medical model by scientifically ascertaining just how useful a treatment might be. In the case of development economics, this could be a subsidy or an extra year of education, for example. In order to eliminate (or at least reduce) the effect of confounding factors, the researcher randomizes over the population, picking a representative sample to receive the treatment and compares their results to those who did not receive the treatment.

While in theory this should give us the best answer as to how to combat poverty, or get children to school, or determine the effect on whatever outcome we hope to affect, it’s also problematic. The process of randomization necessarily leaves some people out, essentially denying them help that could be life-saving or life-transforming. It might also provide benefits that researchers view as small, but that are capable of creating divisions in a community, or perhaps jealousy, suspicion, or bitterness.

Different RCTs deal with this in different ways. Some do nothing. Some hope the treatment group doesn’t notice. Some tell the control group that they will get the treatment after the analysis is done, some take this course but without informing the treatment or control groups. All of these solutions have their issues, which are dependent on the type of treatment. In some cases, control respondents might change their answers to certain questions to appear more sympathetic, or deserving of the treatment. Or they might anticipate how the treatment is going to affect them in the future and have their answers reflect their hopes rather than their actual state.

As in all survey data, the mere act of asking the question affects the answer.

Last week, Kim Yi Dionne, a professor at TAMU, posted on her blog about making the randomization process public. While I don’t think it solves the problem of people changing their answers to what they think they should be (either to make the treatment look better or worse), it does deal with the bitterness and competition that can often arise out of randomly selected treatment groups.

I especially love the education component of it.

[A Malawian research supervisor] posed a question to the audience: if he wanted to know how the papayas in the village tasted, would he have to eat every papaya from every tree (pointing to the nearby papaya trees)? Some villagers laughed, many said “ayi” (no) aloud. He said, instead he would eat one or two from one tree, then take from another tree, but probably not take one from every tree in the village so that he could know more about the papayas in this village.*

Every mentor I have had for research in the developing world has been adamant that we share findings with the community whose participation was requisite to our success. But rarely do we take the opportunity to educate about how we came to our conclusions, hoping the conclusions themselves will suffice.

I think it’s brilliant.