Today’s xkcd is a work of subtle comic genius. The strip’s regular readers will all understand it as a reference to a debate about validity of FiveThirtyEight’s election-forecasting technique. For anyone else, read on.

For stat-heads like me, the most exciting part of Tuesday’s vote was not the outcome itself, because anyone who looked at the data seriously realized that President Obama was an overwhelming favorite for re-election. Rather, it was the resolution of a silly debate between the innumerate majority of the blogosphere* and media and a small number of data-based forecasters, most prominently Nate Silver of FiveThirtyEight. Tons of people called the election a “tossup”, and critiqued Silver for calling Obama the favorite. A lot of this exhibited a misunderstanding of statistics. A 90% chance of victory does not mean that Obama will win for sure. It means that in 9 out of 10 runs of the election we’d expect him to win, with the remaining 1 in 10 being due to last-second shifts in the electorate toward Romney compared with the polls.

So how did Silver do? Unfortunately I can’t find a way to permalink to the results, but as of 9:10 AM Central African Time on November 7th, MSNBC’s results have Silver correctly predicting every single state that has been called, and correctly forecasting the leader in the states yet to be decided. That includes Florida, where a naive state-level polling average predicted a Romney win – but Silver adjusted those polls to account for recent movement toward Obama nationally.

This is an absolutely dominating performance. It is a triumph for the use of data over hand-waving, and for statistics as a field. If anything, Silver’s track record is such that his predictions were too modest. For many swing states, he only predicted the victor had a barely-better-than-50% chance of winning. If he was correctly judging his margin of error, he’d have made a couple of mistakes tonight. He was hedging against the chance that the polls were biased but in fact these days polls are pretty damned accurate.

Tomorrow morning, I want to see virtually every political commentator and all those crazy dudes running websites forecasting Romney landslides to admit that they were wrong, and that analyzing the data and using it to form the best possible forecasts not only works, but works spectacularly well.

*There were also critiques by fairly smart people, pointing out Silver’s lack of transparency and arguing that using 3 significant digits for the probability of an Obama win is unreasonable. This post is not about them.

The official Chinese Communist Party term for the catastrophic Great Leap Forward, which led to economic collapse and a famine that killed as many as 45 million people, is “the three years of economic difficulty.”

Most followers of this blog are probably aware of Section 377 of the Indian Penal Code, a law written by the British government of colonial India and then exported to their other colonies. It bans “sodomy”, which back in the day meant any sex other than vaginal, and even banned oral sex between straight couples in the past. In Malawi, it is most famous as the legal pretext for the sentencing of a homosexual* couple to hard labor in 2010; the two were later pardoned.

This awful law has evidently been overturned, according to a Times of India article from this morning. If true this is excellent news and a big advance for human rights in the country.

I have been very impressed with the apparent change in attitudes towards homosexuality here this year. There are regular columns in the newspapers exploring the challenges facing gays and lesbians, and rarely written in a judging or admonishing tone. Most of the people I have met this year also have also expressed fairly tolerant attitudes towards homosexuals, although I suspect that to some extent they are bullshitting me because I am always the mzungu and frequently in charge as well. I’m looking forward to the results of research by Tara McKay and kim dionne , in the field this summer, that measures attitudes toward homosexuality in a systematic and representative way – and I’m optimistic that the situation is better than people watching Malawi from the outside think.

Hat tip on the Times of India story: Nancy Marker

*The description of the couple as homosexual is sometimes contested because Tiwonge Chimbalanga was a biological male transsexual who was living as a woman.

Jed Friedman at Development Impact summarizes a new paper by De Vreyer, Guilbert, and Mesple-Somps that measures the effect of locust swarms on human capital formation in Mali. I very much like the idea behind this paper – early childhood health and nutrition shocks strike me as an increasingly important sub-field of development economics, and the literature on them motivates my own predominant focus on health. The authors find big effects on educational attainment: young boys who experience a locust swarm and the attendant local agricultural catastrophe end up finishing half a year less school than they would have otherwise, and for girls the effect is a full year. Their approach is a difference-in-difference method, which means they compare the changes in educational attainment in locust-affected regions with changes in other areas.

It’s an intuitively plausible approach, but it’s tricky to get right: in a simple “diff-in-diff” you need to assume “parallel trends”, which is that the rate of increase in educational attainment is the same across the locust-hit areas and the unaffected areas. When you can control for other factors, then you need to have parallel trends conditional on those observed, controlled-for factors. The authors have some nice data on school infrastructure that allows them to control for one potential concern: what if more schools were built in unaffected areas, maybe because the government invested more heavily in the places with the best payoff? This turns out not to affect their results.

Another check people usually do when they run a diff-in-diff is a “placebo test”. For example, if you have lots of years of data, then you can look for effects on affected areas the year before the actual event occurs. This is what Jensen and Oster do in their paper on Cable TV and women’s empowerment in India. De Vreyer, Guilbert, and Mesple-Somps do something different: they want to confirm that this is an effect caused by the locusts, and that it is a direct effect and not a general equilibrium or macroeconomic impact. To do this they look at impacts on kids in affected cities. I am hesitant about two aspects of this approach. First, if there are macroeconomic effects I’d expect them to impact the entire country, and not to be particularly concentrated in locust-affected regions. So it’s not clear that we learn anything about macroeconomic effects (or spillovers, as I’d call them) from this.

Second, the more compelling aspect of this is to use the urban areas as a validity check: if this is an effect of the locusts, and not some other correlated phenomenon, then it should impact rural areas alone (barring spillovers). And indeed that’s what they find – there are no effects on children from urban locations. But I’m a bit dubious of this distinction for a typical heavily-agrarian developing country. First, the authors identify 340 “cities” in their data, and the use of scare quotes here is their choice but I agree with it. Mali has an average personal income of $1,127 in purchasing-power-parity dollars, which puts it just ahead of Malawi in terms of economic development. I don’t know a ton about the Malian economy, but Wikipedia also tells me that agriculture is the dominant sector, which stands to reason given its level of development. I would venture that most of those 340 cities are fairly small trading posts (some are evidently neighborhoods of the geographically-larger urban locales, although Bamako, the capital, is excluded from the paper). In that case, I would bet a decent sum of money that these, too, are fairly rural areas in the sense of the importance of agriculture. Just go to a trading center in sub-Saharan Africa and walk around, looking for crops.

Even if these are “true” cities, one thing I’ve noticed during my time in Malawi is that under the surface, almost everyone is still a farmer. In informal conversations, I often try to get at where people’s livelihoods come from, and many Malawians who live and work in cities still maintain farms, either on the outskirts or farther afield. So I’m not sure I’d expect a zero impact from locusts in urban areas, and I’d definitely expect some spillovers. This speaks to one of the big advantages of modern development economics’ focus on fieldwork: there is a lot you can learn just by going to the place you are studying and talking to people. Perhaps this is obvious, but as best I can tell it’s something that economists still rarely do. Could Mali be different, such that the assumption that urban areas aren’t engaged in agriculture is valid? Indeed it could – and one of the best ways to find out is to go there.

The BNL Times has a story about a former professional “sex cleanser” who was hired to have sex with widows:

‘I started performing the duties of cleansing widows way back in 1985. I slept with widow after widow and each time I was hired to do the activity I pocketed around K4,000 in addition to sharing with the widow the property left behind by her deceased husband,; said Aniva.

He claims he does not know his age although he believes he should be in the 50s.

Widow cleansing (kupita kufa), commonly practiced in Nsanje, requires a widow to sleep with her in-laws before her dead spouse is buried, in the belief that this pleases the departed soul. In some cases, men, like Aniva, are hired at a fee to perform this ritual on behalf of the in-laws.

My understanding is that widow cleansing (which I’ve also heard called “widow inheritance”) is required because it is believed that otherwise the widow will die as well. I didn’t know that it could be done by proxy, but I’m not surprised that some people are taking advantage of that option – it’s reasonable to worry about HIV in a situation like that. However, the proxy thing challenges my initial read on the practice, which is that it was really just an institutionalized way of letting in-laws take sexual advantage of women. Even if that’s the source of the practice, people take the mystical aspects seriously enough to pay other men to have the actual sex.

Aniva himself has come clean and is campaigning against the practice. That’s an unmitigated good thing, but it makes me a little more dubious of the factual accuracy of his claims; he now has an incentive to play up the practice and make it seem more extreme.

A new paper by David G. Blanchflower, Andrew J. Oswald, and Sarah Stewart-Brown argues that consuming more fruit and vegetables increases psychological well-being. This is the kind of paper that usually sends me on a rant about over-interpreting results and not recognizing that ceteris non paribus. But these guys get it exactly right – here are the last two sentences of their abstract:

Reverse causality and problems of confounding remain possible.  We discuss the strengths and weaknesses of our analysis, how government policy-makers might wish to react to it, and what kinds of further research — especially randomized trials — would be valuable.

This is indeed an interesting, suggestive result – it’s not something I would have thought to look for – and I think the topic merits more study. Kudos to the authors for realizing the limitations of their work.

I want to point out one particular kind of reverse causality that doesn’t appear to be mentioned in the paper: reporting biases. Other studies have found that when you have people self-report behaviors, people who are happy/healthy/otherwise doing tend to over-report their “good behavior”. This is particularly true for retrospective reporting. Reporting biases of this kind helped create the famous result that fiber intake reduces the risk of colorectal cancer. More careful research has found no link whatsoever.

Another thing that comes to mind is that I recall hearing about a couple of programs in the US that try to encourage people to consume more fruits and vegetables, including at least one which was randomized. These would be great sources of variation for further research on the healthy eating-well being connection.

Hat tip: Chris Blattman

My old friend Jamie Lumsden recently posted the following on my facebook wall:

This is a great question. On the surface it’s just an idle curiosity – why are the electronics I buy made it this poor, far-away place over here and not this other poor, far-away place over there? But really, this is about why some countries are growing richer and others are not, and about the associated differences in income across places. As I never tire of pointing out, average income is the single strongest determinant of all kinds of important stuff, from health to happiness to the probability of a civil war. And the cross-country differences in income are staggering: in Malawi, >the typical person’s income isn’t a whole lot different from what it was in 1800. In America, for all our hand-wringing about inequality within our own country, even people near the bottom of the income distribution are better-off than monarchs were a few hundred years ago. Fixing this problem – achieving growth in Africa to rival what has happened in America and is happening in China – is of enormous importance. As Nobel laureate economist Robert Lucas put it in a 1988 paper,

The consequences for human welfare involved in questions like these are simply staggering: Once one starts to think about them, it is hard to think about anything else.

What can be done about this? In fact, “making iPhones” (and other such high-value-added activity) is the only possible solution to Africa’s long-standing poverty. While we don’t know a whole lot about how to advance economic development, one thing that we do know for sure is that the answer is never going to come from continued small-holder farming. In Malawi around nine of every ten people is a farmer, and anecdotally even people with other jobs often still have farms. The comparable figure for the United States is less than 1%. This is no accident – for farming to become more efficient, we need to use labor-saving technologies. These necessarily push people into other fields. Think about it this way: if Americans were all farmers at current levels of productivity we would produce over 100 times as much food as we consume. Instead, all that spare labor has to work on other things, and it is those things that make us rich. The path of economic development is one of moving the labor force into manufacturing and services, where people can earn good incomes producing stuff that we actually need, instead of continuing to scrape an existence out of the soil. Worrying about the well-being of factory workers in developing countries is a noble ideal, but it’s important to bear in mind that if you shut down such a factory, the outside option is subsistence farming, where a bad drought can literally kill you.

Identifying that the basic problem is that people need better jobs is a long way off from solving it. The field of development economics has historically been focused on this very issue – why is that China makes iPhones and Africa does not – but that history is a long series of sadly over-hyped failures.* What follows is a list of explanations that I take seriously. I’m leaving out reasons that (in my view) have been discredited, like the fad in the early 2000s of blaming everything on government debts**:

1. Poor institutions. “Institutions” here is political economy jargon for stuff like corruption and the rule of law. Anecdotally these seem bad in Africa, and Acemoglu, Johnson and Robinson have argued that you can account for the entire income gap between Africa and other places based on institutions. The legal and social environment seem like they could be important, but I don’t think I’m alone in doubting that everything can be explained by those things. That’s equivalent to saying that stuff like malaria and AIDS have no negative effect on income at all. Indeed, the statistical strategy of the linked paper assumes that malaria isn’t responsible for current poverty in Africa, even though other research has argued that it’s very important. One reason is that childhood malaria, if survived, can cause stunting and brain damage, severely limiting adult earnings. Moreover, there is obvious two-way causality between institutions and economic development; poorer people have a stronger reason to engage in corruption, for example. Their empirical strategy accounts for that reverse causality issue, but Michigan Econ’s own David Albouy has raised some doubts about its robustness. I would say this matters but it’s unclear how much.
2. The legacy of colonialism and slavery. This is very closely related to the above. I don’t want to diminish the magnitude of the harm done by colonization and especially by slavery, but I don’t like this line of reasoning very much. First off, it’s very hard to prove – we’re basically relying on cross-country comparisons using possibly-untrustworthy colonial data. Second, there seem to be cases, like Ethiopia, that aren’t doing very well despite not having been colonies, and ones like Botswana that are fairly rich despite a colonial past, so at the least we can say that other factors must be critical as well.
3. Geography. Africa is hot and dry with highly-variable rainfall. This makes agricultural success harder than in other places. I don’t buy this as a complete explanation because there are economically-successful places with very little agriculture. More important is that Africa has a fairly high average elevation but no continental mountain ranges and few low-lying areas as well. That means a lot of the continent can be thought of as a high plateau. One effect of this is on agriculture, because it means that it’s harder to find rivers that can be used for irrigation. Stephen Carr has made this point for Malawi in particular but it’s likely to be true in general – you need water flowing downhill to do gravity-fed irrigation, and hence more mountains and hills than you tend to find in Africa. But of course I argued earlier that the key is to get away from agriculture, and it is in moving into manufacturing in particular that Africa’s geography hurts. It has virtually no navigable rivers, so it’s very hard to produce stuff in the African heartland and ship it to the world. The US economy’s engine used to be the automotive industry of the Midwest, taking advantange of our rivers and digging a number of canals to allow cars to be shipped around the world. Malawi lies in Africa’s own Great Lakes region, but it’s not particularly plausible to ship vast amounts of goods to the ocean .* That means that for almost any area not right on the coast, manufacturing is pretty much out of the question until the roads get much, much better. The current dirt road system that covers most of the continent is hamstrung by the intense wet season that makes many of them impassable (a concern for my own project). That means much of Africa may need to follow the “India model” of a move directly into services: a viable plan, but it’s unfortunate to have the options limited in that way.
4. Education. The impact of educational attainment on income is arguably the single most-studied topic in economics, with hundreds or even thousands of papers on the financial returns to education. Africa has historically had lower quality education and lower attendance, but big gains have happened, especially recently. Ted Miguel has said that Africa’s recent increases in GDP growth may be attributable in large part to the spread of education to more of the population since decolonization in the 1960s. Proving a link between national education and GDP growth is much harder than showing the relationship between individual education and income, but it’s plausible. You need to be pretty smart and well-educated to work in an electronics factory or at a call center. China has more and better education, and hence its people have moved into higher-skill jobs.
5. Health and the burden of disease. Africa is fairly infamous for the wide range of diseases here, many of them very nasty. It is the continent where people have lived the longest and where we are in the closest contact with our primate relatives, who are veritable fountains of horrible illnesses. The tropical environment is also a factor. (Although I did stay at a Holiday Inn Express last night read a bunch of books on this stuff, I’m not a real epidemiologist, so I may be missing some explanatory factors here). A large body of evidence suggests that disease, especially early in life, causes descreases in both physical and mental human capital. The most compelling research is on intestinal helminths (hookworm, etc.) by Hoyt Bleakley, looking at US history, and a team of researchers led by Ted Miguel and Michael Kremer, in present-day Kenya. They find that eliminating these parasites, and the associated drop in anemia, causes huge gains in educational attainment and wages. Bleakley argues that these account for much of the reduction in the former North-South income gap in the US, and followups on Kremer and Miguel’s work have found similar results in Kenya. There is also feedback between poverty and disease: richer people stay healthier, so it’s possible that societies may get stuck in a low-income, high-disease equilibrium.
6. Export-led growth. Many of Asia’s growth successes appear to lean heavily on exports, often with heavy government encouragement. I’m not a trade economist, so I don’t know why this approach works, and even the trade economists I’ve asked about this don’t seem very sure. One bit of evidence on this (which I can’t think of a citation for now, but which I’ve seen raised in several talks) is that firms that begin exporting appear to become more efficient and profitable; international competition may spur more effort, and learning from one’s new, international peers probably brings big benefits. Inasmuch as this really works, Africa doesn’t have a whole lot of it going on. I suspect that truly broad and deep economic growth in Africa will come only when regions of the continent find their export niche (think of textiles in Bangladesh, for example) and start investing heavily in it.***
7. Random chance and path-dependence. There’s suggestive evidence that once economic success in a particular industry gets rolling in a region, it has a lot of momentum and even picks up speed. The reason we make iPhones in China now is not that they have skilled labor at a cheap price (wages there are actually getting pretty high) but that they have a supply network there, with skilled workers, easy access to parts, institutional knowledge of good production techniques, relationships with other companies, and reputations for quality and efficiency. If fortune blesses an area with a head start in a given field, it tends to keep that advantage. This is probably the most important reason why you see some areas specializing heavily in certain things.

This list is not comprehensive, but it contains are the main factors that came to mind when I was writing this. My view on these (which is perhaps obvious from my research agenda) is that we should focus mainly on reasons 4 and 5. Items 1-3 are very good reasons for Africa’s relatively slow development but are terrible solutions. It’s unclear what we can do about the continent’s poor institutions, history of slavery or geographic misfortune other than to say “that’s really terrible,” to apologize for the past sins of our culture in the case of slavery. There’s no level we can pull to change that stuff. Number 6 is still a little ambiguous – it’s not entirely clear what works, and I still suspect that exports may be a result of growth and not a cause. Number 7 isn’t something we can take advantage of directly, but it means that if we can get some momentum going in any industry in Africa, it make develop very quickly. 4 and 5 tend to be very cheap and cost-effective, and moreover they are valuable in their own right. Indeed, things like health and education are some of the most important goods that we want to buy with the income gains from economic growth. One way that I get to sleep at night is by remembering that even if whatever I’m working on doesn’t make an appreciable dent in poverty in Malawi, at least some of what I do is worthwhile in and of itself.

So why aren’t iPhones made in Africa? I’ve tried to offer the best answers I can, but the truth is that we don’t know – and if we did know, and the reason was something tractable, we might be able to make huge improvements in the lives of lots of people.

*Development economics has recently branched out into studying other social and public policy problems in poor countries, partly because we have failed to directly solve the fundamental problem of raising incomes.

**No one else seems to remember this, by the way. It was huge! Bono was all over odious debts, which were the cause of all the world’s problems. And now we don’t even talk about them.

***This is different from the “import substitute industrialization” approach that several African countries have tried, where they attempt to improve their trade balance by investing in the manufacture of things they import a lot of. That approach is foolish in two ways. First, if you import a lot of something, you probably have a comparative disadvantage in making it; the best possible world is one where regions specialize in making what they’re good at and import the rest, rather than one in which imports are low. Second, did China start making iPhones because it was buying lots of iPhones? Of course not, that’s obviously ridiculous.

I don’t normally have any contact with the respondents in my studies, and that is by design. My being present during interviews could change the results, particularly given the sensitive topic. We work very hard to ensure that the surveys are done in private, with gender-matched interviewers, and that a rapport can be developed, so people feel comfortable telling the truth. There’s also a potential confidentiality concern: we take great pains to keep people’s identifying information private and separate from other data, and having me interact with the respondents seems like it could be an issue. The whole process is designed to ensure confidentiality and data quality, since those are the be-all and end-all of human subjects research, at the cost of reducing the extent to which the people in my study seem like actual people, with thoughts and feelings and emotions. I don’t see them, which pushes them somewhat disturbingly toward not really existing as individuals. In chats with other researchers, I’ve found this to be a common experience.

Yesterday, though, things went a little differently. A man approached me while I was working on my laptop, editing my followup survey in a parked minibus and asked if I was very busy. Yes, I told him, but I still met his hand with my own. He asked if I was Jason Kerwin; I don’t know how he knew my name, but I guess there aren’t too many azungu busdrivers in Mwambo. He claimed to have been one of our respondents. I can’t say for sure whether he was, and didn’t try to verify his story. He said that he still had more questions about the purpose of our research, and that the enumerator hadn’t explained it to his satisfaction. This wasn’t a complaint – he was incredibly kind and polite, a big smile lighting up his thin face. I did my best to reiterate what was on the consent form, stressing that we’re there to learn about the HIV epidemic rather than to push some purported solution.

Seemingly satisfied, he then asked a question that caught me off-guard. If a cure for HIV is found in January 2013, he said, could we bring it to the respondents who are HIV-positive? We don’t do testing, I said, so we don’t know who is positive. Right – but maybe some of them told us they were positive, so then we’d know – could we bring it to them? I told him the truth, which is that the rules about identifying information in the study would make that hard. If a cure was found, we would need permission from the ethics boards, an exception to use people’s names for that purpose. That’s the same, I said, as it is at testing centers – they can’t use people’s test results for whatever they want, because it could turn out badly. But I told him that we’d definitely try to do that if a cure arose.

Then he asked his real question. “You’re getting a Ph.D., right? So you’re an expert? Do I think,” he asked, “that a cure will be found soon?” This was a punch in the guts. I didn’t know what to say. Thankfully, I was saved from having to figure that out by my study’s protocol, which forbids us to discuss HIV with respondents other than in very specific contexts. “I’m not a doctor or an expert on HIV, although I have studied it for many years. But I’m not allowed to talk about that issue with you because of the rules of the study. You should really ask a doctor or an HSA [health surveillance assistant] about that.”

In that situation, I honestly didn’t care about the rules of the study. This guy was a human being, and he deserved a candid response from me. At that moment, I would have been willing to ignore what I’m told I’m supposed to do and do the right thing. But I needed that rule, as a crutch. An excuse. A way to avoid telling him my honest opinion. Because the truth is that I don’t think a cure will be found soon. It’s optimistic to think that we will ever be able to cure this goddamned nightmare of a virus. How could I say that, though? How could I possibly tell this guy, one of the nicest people I’ve had the privilege of talking to, that HIV is an incurable death sentence? There is just no way. The right thing to say was not the harsh truth, it would have been to lie. And I’m an awful liar. So I leaned on the rules, shunted his question elsewhere. He didn’t argue, or even seem dejected. He thanked me with another big smile, shook my hand again, and went on his way.

So I started the car, pulled it slowly past him on the path, and took it around the corner to a point where he couldn’t see me anymore, so I could let my emotions out. I drove back toward the rest of my survey team. Bouncing over dusty roads, eyes wet, biting my upper lip.

The two questions in the post’s title come up almost every time I tell people what I do. The former is voiced more frequently, but “what the hell is the point” is often just under the surface. In trying to justify my research agenda and field of choice, I frequently cite the contributions of the winners of this year’s Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel.* Al Roth and Lloyd Shapley developed the theory of matching markets, and in Roth’s case, for extensive work in applying matching theory to real-world markets.

In the simplest case of a matching market, there are two sides to the market, and every person on one side must match to exactly one person on the other. The classic example, which actually prompted a lot of the development of the field, is the “stable marriage problem”. There are N men and N women, each of a different “quality”. Each man must marry exactly one woman and vice versa. It turns out that under these basic rules, it is possible to find a stable set of marriages – an outcome where there is no pair of men and women who would prefer to dump their current spouses and marry each other instead.

Marriage doesn’t seem much like economics to most people: where is the money and the prices? How is this going to help me pick stocks? The stable marriage problem . First, a more general version of the same problem turns out to describe the matching process between freshly-minted MDs and residency positions. One of Al Roth’s most famous applications of matching was re-designing the National Residency Matching Program, a.k.a. “the match”. The system as currently designed is un-gameable and works amazingly well, a sharp contrast to the near-collapse of the old residency matching process prior to the redesign.

Second, money can indeed be involved (in the matching markets parlance, this is called the transferrable-utility case). The marriage problem, and matching models more generally, can be extended to a case where spouses pay each other off to get more desirable matches. Cynical as this seems, there’s a pretty compelling body of evidence that financial considerations play a strong role in matters of the heart, from marriage to divorce to childbearing. The study of marriage, led also by Gary Becker, was one of the first ways that economists started exploring how market decisions like labor supply are related to non-market activities like getting married.

Third, it highlights the way in which microeconomics has outgrown its origins in understanding prices and quantities of goods traded in a market. One more general framing of economics is that it is the study of how scarce resources are allocated, and how they should be allocated. Residency slots are certainly scarce resources and their allocation is fairly important. These days, economists go even further; I was once in a room full of Ph.D. candidates who said the core of the field was constrained optimization (a mathematical technique). I won’t go that far myself – frankly, I think in many cases people do not optimize – but I think “decisionmaking under constraints” is a reasonable description.

Most of my own research looks at risky sexual behaviors. Unlike most psychologists, or epidemiologists, I approach this as a decisionmaking problem: why do people choose to have unprotected sex, or multiple partners? This is certainly a constrained decision: there are time and monetary costs, and also the cost of a possible HIV infection. Matching is also a critical part of this process – to have sex, you need a partner, and to have risky sex, you need a partner who is willing to go along with that.

Economics was never about picking winners in the stock market, and thanks in no small part to the contributions of Shapley and Roth it’s no longer just about buying and selling goods. Moreover, it’s far from useless: I can’t immediately see how anything I’m working on will be as useful as The Match, but I’m still holding out hope.

Which is often called the “Nobel Prize in Economics”, but is not one of the five official Nobel Prizes.

Today UKaid was distributing sacks of maize to needy people at the Mpyupyu trading center on the northern edge of TA Mwambo. As I understand it, this is a 6-month program to alleviate hunger and maize shortages. Relative to inaction, this is a really great idea – by all reports the harvest was awful here last year and maize prices are at near-record highs for this time of year. The rains were poor last year and there’s no reason they’ll necessarily be much better this time. Even if they are, the next harvest is a long ways away.

My question is whether this is really the best option. Most of the research I’ve seen on food crises and famines points to giving cash as the superior option, for two reasons. First, with cash people can buy what they really need instead of taking whatever you hand them. Staple foods seem like an obvious choice, but who are we to decide that for people? Even if we had awesome, timely data on people’s needs, it’s hard to beat people’s own choices.

The other, more compelling reason is that nothing I’ve seen indicates there is any shortage of maize, either in Malawi as a whole or in this area. I can buy more nsima than I know what to do with at prices that aren’t much different from last year. Rather, what’s going on right now is limited purchasing power on the part of many farmers in Mwambo. The story I’ve heard is that they commonly sell a lot of their crop right after the harvest in order to meet pressing needs, and then need to buy maize later in order to eat. At that later date, the supply is lower and prices go up, making it even harder to afford food out of their limited remaining cash holdings. This pattern would be strongly exacerbated by a drought, when owners of small plots might start out with too little to make it through the year. On the flip side, that maize was being sold to someone, and in many cases constitutes a big chunk of people’s liquid wealth.

Imagine you’re one of the larger farmers in this region, and you have your own silo. You hold on to your maize, and maybe even buy more as an investment since prices tend to rise over the interim period between harvests. Even though the rains were bad, you’re doing okay, and might turn a profit this year once you pay back the loans you took out to buy extra fertilizer from ADMARC. Suddenly, however, UKaid shows up and starts just giving maize away. Now the market value of your maize is close to zero. You can still eat it – you’re not starving, not right away. But you can’t pay off your debts or invest in the next harvest. The next year, you’re going to be one of the people lining up for free food. This innocuous act of charity just made you poorer and caused a bunch of problems down the road. Not problems as big as the one they were trying to fight – the prospect of mass starvation – but problems nonetheless.*

Cash handouts do have their downsides. I mentioned that possibility to one of my enumerators and he said that you can’t do that because “there are too many drunkards.” People squandering the money on stuff like booze is a real danger. There’s a growing literature on the importance of who gets the money for household outcomes. The whole idea of targeting women for microloans hasn’t generated the amazing benefits we thought it would, but giving them the cash transfers may be a way to limit how much of the money gets blown on alcohol and transactional sex. We have the research and the knowledge; I think we can do better than handing out staple foods for free.

This argument is essentially just a paraphrase of Sen, for any development economics nerds playing along at home