Most field experiments have the hallmarks of a bad field research project. There are four:
Takes a long time. Anything requiring a panel survey a year or two apart, or a year of setup time, suffers from this problem.
Risky. There are a hundred reasons why any ambitious project may fail, and many do.
Expensive. This is driven by any kind of primary data collection, but especially panel or tracking surveys, and especially any Africa or conflict/post-conflict research.
High exit costs. This is where experiments excel. If your historical data collection, formal theory, or secondary dataset isn’t working for you, you can put it aside. If your field experiment goes poorly, not only are you stuck with it to the bitter end, but it will take more not less time.
These are all important considerations for any research project, but I was more struck by his aside that he is “suspicious whenever someone puts the method before their question.” Do people running field experiments put the method first? I would argue that they do so less than folks who write (credible) non-experimental social science papers.
The procedure for writing a paper based on a field experiment is 1) think of something you’d like to study and 2) try to come up with an experiment that lets you study it. What about non-experimental papers? Academic lore holds that the current process for grad students writing in economics is 1) sit in a room for four years trying to think of a natural experiment* that happened somewhere and 2) write a paper about whatever that natural experiment is. This is why, for example, we know a lot about the financial returns to education for students who would drop out of school if not for rules that force them to stay until age 17, or the benefits of getting a GED for someone who barely passes the necessary exam.
Let’s take a concrete example: the price elasticity of labor supply. Should we care about the labor supply of cab drivers? Trick question – it doesn’t matter whether taxi driver labor supply is interesting! What’s important is that variations in weather mean that their effective wage changes at random, so we can study their labor supply. That’s where the light is.
In contrast, experiments let us pick our topic and then study it. For example, Jessica Goldberg ran an experiment studying the exact same issue (how labor supply responds to changes in wages) but with a representative sample of Malawians, doing the most common kind of paid work in the country (informal agricultural labor). This kind of work is also common in much of sub-Saharan Africa. Her method – a field experiment – let her pick the topic of her research, and as a result what she studied is the most important category of labor across a wide region.
I’m not saying that Camerer et al.’s cab driver paper isn’t good research, or even that Goldberg’s paper is better. My claim is much simpler, and very hard to dispute: the former paper’s topic was much more driven by its method (finding a useful instrument) than was the latter’s by its method of setting up a targeted experiment.
There are exceptions to this pattern – sometimes a government agency or an NGO has an experiment they want to run that falls into your lap, for example, and some IV-driven research is based on a targeted search. In general, however, it’s misleading to claim that the experimental method often comes before the topic. That’s a key advantage of running experiments: an RCT lets us choose where to shine the light instead of constantly standing under streetlamps.
I suspect this isn’t what Blattman was driving at – there are topics where observational research is more appropriate (or even the only option, e.g. almost anything in international trade) and we shouldn’t stop studying them just because we can’t do RCTs on them. Nevertheless, the knee-jerk assumption that RCTs are methods-driven rather than topic-driven is pretty common, and, I think, wholly misguided.
I listen to the Freakonomics Radio podcast every week, out of a vague sense of wanting to be on top of how applied microeconomics is seen in the media, and, more important, because I need something to distract my brain as I do all the menial tasks to wrap up my fieldwork here in Malawi; eventually I just run out of other podcasts that interest me. The show is usually a slightly-less-interesting version of This American Life, rather than a real show about microeconomics, which is too bad; I think the world really needs a microeconomics podcast and I wish this one were it but it isn’t, quite. What really bothers me is their pattern of failing to talk about the interesting economics of an issue – there was one short on whether selling beer at sports venues could reduce public drunkenness, which it appears to have done in some cases, but no real explanation of why that may have happened.*
I’ve never been more frustrated with how shallow their coverage is than this week, when they spent the whole episode plugging Freakonomics Experiments, a website that helps you make decisions. The idea is that if you can’t choose whether to, say, change jobs, then you go to the site, take a survey, and then it flips a coin (presumably using a computerized pseudorandom number generator) for you to tell you which option to pick. The heart of the episode is focused on the claimed psychological benefits of flipping a coin to make decisions, and in particular on how it may be preferable to have someone else do the coin flip. I’m not convinced that the Freakonomics team actually believes that claim, but even if they do it’s not at all why they are running this website. They talk very little about the real reason, and that’s unfortunate: unlike hearing people talk about how flipping coins to make choices has improved their lives, the real reason for Freakonomics Experiments is actually interesting, and actually has something to do with economics.
The truth, which everyone has already guessed from the title, is that they are running an experiment. The interesting part is why this is necessary. Take the example of changing jobs. Suppose we want to know what the effect of changing jobs is on your income ten years down the road. We’re trying to estimate b in the equation Y = bC + e, where Y is your eventual income, C is an indicator for whether you changed jobs, and e is an error term. b tells us how much your income goes up if you change jobs versus the case where you stay in the same one. We can’t just compare people who did change jobs to people who did not, because C is not assigned at random. Indeed, headhunters are much more likely to swoop in and hire away people who are going to be worth more in the future, and hence earn more irrespective of where they work. The “coin flip” solves this problem. People who use the site aren’t sure what they want to do, and the random number generator tells them what to do. Now C is assigned at random and we can really measure what happens when you change jobs.
This is all very clever. It’s even more clever than it appears: even if people don’t always obey the random number generator, it is still a valid instrumental variable for the choice of whether to change jobs. That is, we can focus on the variation in C that is driven by the Freakonomics Experiments website – to speak very imprecisely, we can look just at those who do what the “coin” says to do – and look at the effects on that group. What disappoints me is the huge missed opportunity here to talk about the difficulties of doing research on economics (and decisionmaking more broadly) and to help the show’s listeners learn about what kinds of before-and-after comparisons are untrustworthy and why. Going with the cute story, instead of talking about the real reasons for the project, does their audience a disservice.
*I can think of several – common practice at Michigan football games, for example, is to get as drunk as possible before the game so you can stay buzzed for the whole game (And by “you” I of course mean undergrads, since we responsible adults would never do such things). This is an interesting example of the substitution effect: beer at home is a very close substitute for beer at the game. Interestingly the timing and volume of consumption have a big effect on how close those two goods are, which is something that I’ve not seen elsewhere.
This fascinating piece from the Smithsonian Magazine discusses the re-discovery after 40 years of a family that had lived in total isolation in Siberia, having fled the anti-religious Bolsheviks. There’s much to love about this crazy story, which has shades of Hiroo Onoda, the Japanese soldier who refused to surrender until World War II had been over for nearly three decades.
As a development economist, I love how it skewers the still-disturbingly-popular narrative of how subsistence farming is a happy, care-free existence. Deprived of the risk-sharing that even very rural modern economies now have, the Lykovs lost their mother in a famine. Without specialization and trade, all of their possessions fall apart and they end up hunting in the Siberian winter without shoes. Going off the grid and living off the land, as it turns out, is pretty awful.
Even more delightful is something the article doesn’t address directly. The Lykovs fled because of religious persecution, but also because of a broader rejection of modernity and change. They went to the woods to adhere to their timeless traditions of Old Believer Christianity and simple potato farming. Potatoes, indeed, are the lifeblood of their simple, autarkic economy. By the time they were rediscovered, potatoes were virtually all they ate and they even used the peels to make the floor of their hut.
Potatoes are certainly an important aspect of Russian culture – Russia production comes in third in the world – but they are far from timeless. They are a New World crop, and hence their consumption outside the Americas dates back only a few hundred years. This is a pattern I’ve happened across over and over as I’ve interacted with different cultures around the world: many valued traditions have much shorter histories than the people practicing them imagine. This is exemplified by food. Maize (corn), for example, is absolutely central to Malawian culture – it’s far and away the dominant component of people’s diets, and people grow it in literally any patch of open dirt they can find. Here’s a small corn-growing operation that I spotted between two clinics and a church in downtown Zomba:
This is hard to square with the fact that people only corn in this region in the late 1800s. Other cases abound: capsicum peppers in Indian food and tomatoes in Italian food are two items from the list of New World produce that plays a key role in the traditional foods of Old World societies.
A lot of development work comes down to changing the local culture, and some people object to that in principle. The big shifts in what people have traditionally eaten highlight the fact the culture changes all the time, and more rapidly than we might imagine. They make me a lot more comfortable with the idea of targeted attempts to change aspects of a local culture to improve society as a whole.
I’ve spent the past few weeks engaged in the delightful task of data cleaning, which means poring over the thousands of data points my staff collected to spot potential problems. Obsessively browsing my data for errors has reminded me of one of my favorite aspects of Chichewa, which is shared by the other Bantu languages I’ve encountered: Chichewa has neither pronouns nor gendered nouns. This is reflected in how native Chichewa speakers translate phrases to English – unless they are very experienced with our pronoun system, or have a bunch of time to think about it, they tend to pick “she” or “he” as the pronoun to refer to a person roughly at random. So I have notes on my data like “The children say their mother has gone to the market, he sells tomatoes there.”
While this is a bit amusing, I’m not bringing it up to mock these kinds of mistakes. First off, I don’t speak any language as well as my enumerators speak English (maybe Spanish a while back, but I’m way out of practice). Second, I never corrected these issues even out in the field, because I could always tell what they meant, and more importantly because I don’t think the generally-accepted English system of attempting to put force a gender onto all pronouns referring to people is a good one. I’m a big advocate of the singular (or indeterminate-number) they, which was used by Shakespeare but was stolen from us regular folk by the plague of grammatical prescriptivists who have seized control of our language.
The question of gender equality and women’s rights in Africa is a pretty contentious one. I’ve previously highlihted one specific aspect of Malawian culture that is probably more pro-women than the American equivalent, and much more knowledgable people than myself have made a broader case that the importance of gender inequality in commonly-highlighted areas is overblown. Sociologist Susan Watkins makes this case with regard to AIDS in an excellent book chapter called “Back to Basics” which I cannot find online. In joint work with Michelle Poulin, Watkins argues that an over-focus on women is actually hurting them by failing to target the key drivers of the HIV epidemic. Conversely, certain aspects of women’s disempowerment in Africa are undeniable, such as their relatively rarity in the upper levels of government (although America is still not much better).
This, though, is a very straightforward advantage of the language that most people are raised speaking in Malawi. In English, many sentences are subtle power struggles. Do we default to “he” to refer to people of unspecified gender? Do we force a switch to “she”, or try for a hybrid “he/she” or “she/he”? Switching the default to “she” implicitly acknowledges that the default person is male, and over the long term it’s just as unfair as “he”. “They”, my own preference, still leaves me subconsciously assuming a male. Indeed, even when I’m given no pronouns or references to gender at all, I still automatically assume people are male. Not only are there no girls on the internet, but when I was frustrated by the high error rate of one of my data enterers, I found myself growing to hate “him” and “his” laziness (as it turns out the person was indeed male). There are probably many reasons for this, but I attribute it largely to the fact that I am forced, even mentally, to use a gendered pronoun. Chichewa places no such strictures on its speakers. The fact that a generic person does not have to have a gender (which would most likly end up being male) is a small advantage to women in Malawi, and one America would do well to emulate.
*Lest you think this latter aspect makes the language easier to learn, instead of genders Chichewa nouns fall into one of 11 different categories that govern the prefixes on associated adjectives and verbs. This means that even when I can make myself understood, I’m constantly embarassing myself with grammatical mistakes as egregious as saying “Me like a apples”.
The solution to America’s burgeoning entitlement crisis is not benefit cuts, nor abolishing social welfare programs, but immigration. It is time for politicians to be bold and take a stand on this.
Like many fiscally-conservative outlets, the Economist has not slowed down in its warnings about America’s long-run fiscal challenges. We have a bunch of unfunded entitlements, the claim goes, and what we need to do is cut benefits in order to solve the problem. And all the haranguing over the Fiscal Cliff and the Debt Ceiling is doing nothing to address the underlying fiscal imbalances. Medicare and Social Security benefits (that is, total outlays on these programs) must be cut!, they claim.
There’s something to this line of reasoning, but we all need to be a lot more honest about why these fiscal imbalances exist. The gap between inflows into these systems comes from two sources. One is the rising per-unit cost of healthcare. The steady upward march of healthcare prices has slowed somewhat, but still drastically outpaces inflation. The other is the declining “worker-to-beneficiary ratio”: the number of people paying into Medicare and Social Security is declining over time, while the number of people getting benefits is rising.
Conservatives like to carp about Obamacare not doing enough about healthcare costs, but neither party has made any serious proposals to do anything about these cost rises. Healthcare is an overly-complicated muddle in which no one knows how much anything really costs and there is little competition on either side of the market. I could write a whole book about the subject, and only know about small parts of it. Before I started my Ph.D. my job at Tom MaCurdy’s research firm was focused on one particular issue, which is that doctors and health care organizations are a lot like mechanics: they both tell you what services you need and sell you those services. This is one factor leading to excessive treatment and testing, which drives up costs, but there are many more.
Fixing the problem of healthcare prices will take a long time if it is possible at all, and has little to do with Medicare per se. Prices are rocketing upward in the private sector as well, and most private-sector payment policies are equally as stupid as what government programs do. Clearly we cannot focus on health care if we want to have a chance of doing anything about entitlement reform in the near term.
What about the beneficiary ratio? Can’t we fix that, by raising the age at which people are benefit-eligible? I definitely think raising the retirement age is a good idea: Sam King and I laid out the case for this a few years ago. Raising the eligibilty age for these programs will do little, however, and following the George W. Bush individual accounts plan would probably not do anything at all.
The reason is that as long as people retire at age 65, they will need to fund that retirement somehow. That funding can only come from claims on the income of current workers. If people have individual retirement accounts, they will hold investments that they can cash in through various means, but they would always need to grab some of current workers’ incomes, e.g. by selling stocks to them. No matter what mechanism we use to fund retirements, we will still have a worker-beneficiary ratio problem if people continue to retire at 65. This isn’t my idea – Ron Lee of UC Berkeley and Andrew Mason of the University of Hawaii have written an excellent book on the subject, available for free here. The graphs are awesome.
If we raise the Medicare and Social Security eligibility age, we will do a little to help abate this issue, especially if this induces a norm of a higher retirement age. Given how healthy many 65-year-olds are nowadays, this is sensible, as are programs to help older workers of any age who cannot work. Changing norms takes a long time, however, and we have a burgeoning problem already.
We have a solution to this problem, and it’s already being debated elsewhere in the halls of Congress: immigration reform. In a heartening turn of events, a number of Republicans are advocating letting America’s millions of illegal migrants stay in the country legally, and making legal immigration more streamlined. Where people are still failing, however, is in linking immigration reform to our fiscal challenges. Letting in more immigrants is the one thing rich countries can do to increase the number of workers per retireee. Not only that, but naturalized citizens have more children than native-born Americans, further alleviating our long-term problem. Loosening immigration restrictions would buy is decades of much-needed breathing space to start edging up the retirement age.
Far from being a selfish move, this would have massive benefits for both migrants and their home countries. Michael Clemens has made a passionate moral case for more immigration – here he is on Development Drums. Immigrants to the US see a huge spike in earnings, much of which they send back home to help their families. Many eventually return home, bringing wealth and skills to their communities. In attempting to gain skills that will be valuable abroad, people who want to migrate get more education and training, so even the ones who don’t make the cut end up richer (and bring benefits to their communities). For example, a large number of Filipinos become nurses in order to work abroad; many fail to find positions overseas, contributing to the country’s relatively high-quality healthcare.
Finally, this is simply the right thing to do. The world’s current national borders were largely determined through imperial conquest and deal-making by current rich-world countries. They have no special status, and it’s hard to see a moral case for caring more about people who lived on one side of an arbitrary line on a map than another.
America is on the verge of something wonderful here. I am the great-grandson of European emigrants who settled in America. My Irish and Italian forebears were the wrong kind of immigrants. They were different from the good kind, who had come earlier. They didn’t assimilate. They were genetically inferior, and not as smart as people from England or Germany. They stole our jobs and destroyed our way of life. Until they didn’t – until we realized that like all naturalized citizens, they were the proudest and most loyal Americans of all. Our culture has always cycled between the open borders that characterized the country originally, and periods of xenophobia and racism in which we have tried to keep foreigners out. It is time to get back to our roots. In so doing, we will also address the largest long-run economic problem we face.
My parents came to visit me in Malawi back in December, and this did wonders for my mom’s level of concern about my welfare. She was able to see that Malawi at least looks relatively safe. We got to discussing safety and violence after the horrific murders of 20 kindergarteners that month. I made the off-hand claim that I am physically safer here than in the US. I’ve heard about awful crimes in both places, but I’m convinced in particular that my chances of being murdered are much lower here.
A couple of weeks ago I got around to looking that up to see whether the data confirmed my guess. I quickly found this Wikipedia page listing the intentional homicide rate for every country, which reports murder statistics from the UN Office on Drugs and Crime (UNODC). The UNODC figures assert that Malawi has an intentional homicide rate of 36.0 per 100,000 people, which is the twelfth-highest murder rate in the world. That’s a truly horrific figure, if true. It’s more than any US city save Detroit and New Orleans, but just 20% of Malawians live in urban areas.
I cannot possibly square that high of a murder rate with my experience here. I collected the data for my survey in Traditional Authority Mwambo, a rural area that conveniently has about 100,000 people in it. I was there for about 4 months, and during that time I befriended all of the local authorities, especially the police. In managing my research team, I was very cognizant of crime and our personal security, and pursued any and all rumors with my friends at the Jali and Kachulu police stations and at the local road traffic police as well. For their part, they were very open about the cases they were dealing with, and at one point the Jali police actually helped us find a different, more-secure place to stay out there. If Mwambo matched the national average, you’d expect 12 murders there over the course of four months. Even if the cities in Malawi had murder rates of 150 per 100,000, nearly triple the rate of the US city with the most murders per person, we would expect to see 7.5 murders a year and at least 1 or 2 over the course of 4 months. I heard about zero. I discussed a wide range of crimes, including some shootings, with local authorities there, but no homicides whatsoever.
Why am I writing about statistics from Wikipedia in the middle of the night? Because the Internet is serious business.
Data nerds such as myself like to talk about using the “smell test” on their results, and frankly this number just stinks every way I sniff it. Another way it smells is that nationwide, 36 murders per 100,000 people is about 100 murders per week. There are definitely murders reported in the Malawian press, but I would venture that I see about 1 or 2 per week, not 100. Alternatively, we can look at the distribution of all causes of death. Malawi has a death rate of 1350 per 100,000 people, so according to the UNODC murders cause 2.7% of all deaths in the country. That would mean that murder would rank above tuberculosis and ischemic heart disease in this ranking of the top ten causes of death in Malawi. Incidentally, it would also mean murder should itself be on that list, knocking off malnutrition.
The Wikipedia article has numerous caveats and hedges, including the suggestion that the data may include attempted murders as well as successful ones. However, it also has a link to the underlying table from the UN Office on Drugs and Crime. Annoyed by my inability to square the reported murder rate with other facts about Malawi, I decided to see where they were getting it from. In the footnotes, they attribute it to the World Health Organization Global Burden of Disease Mortality Estimates. After digging through the WHO website, I came to this page where one can download the datasets used for the Global Burden of Disease calculations. These are files that contain observations by year, country, gender, and disease, where disease is represented by an ICD code (there are different files for the ICD-7, ICD-8, ICD-9, and ICD-10 codes). If you know the ICD code you want you can look up total deaths as well as deaths by age bracket.
I didn’t get that far, though: none of the files have any entries for Malawi, and the data availability index doesn’t list Malawi data for any year. There is a country code for Malawi (1270) but it doesn’t actually appear to get used. I can’t say for certain where the claim of 36 murders per 100,000 people comes from, but I can tell you it’s definitely not from the WHO Mortality Database.
Now, any number of things could have gone wrong here. Maybe I took a wrong turn as I hunted for the WHO data the UNODC rely on, or overlooked something else obvious. It’s also possible that entries got miscoded, either in the UNODC or the WHO files, leading me astray. Or maybe there was private communication between those two UN offices, and the underlying data actually isn’t public.
Fortunately, there are tricks I can use even when I can’t get my hands on the actual data. Back in 1938, Frank Benford observed that many datasets have the property that the leading digits of numbers (the “7” in “743”, for example) are logarithmically distributed, and death rates were actually one of the examples he leaned on in demonstrating what we now call “Benford’s Law”. If the law holds exactly, we’d expect 30.1% of leading digits to be “1”s, 17.6% to be “2”s, and so on, with a known, predictable percentage for each digit. And we can run a statistical test to see if deviations from the expected pattern are large enough to be meaningful, or are just random fluctuations. Using the firstdigit package in Stata, I ran this test on the UNODC spreadsheet’s mortality rates from 2008, which is the most-populated year in the table. As you can see, there are more leading “1”s than we’d expect under Benford’s Law, and across all digits the deviation from Benford is statistically significant at the 5% level – the p-value is 0.011, so we’re just barely above the cutoff to get 3 stars in a journal article.
It’s possible to delve deeper: what I’m really curious about is not all the statistics – it would be hard to get the ones for big countries like the US wrong – but specifically the figures attributed to the WHO Global Burden of Disease. If I break the data down into observations that list “WHO” as the source and everything else, only the WHO data looks suspicious (p=0.040), while everything else conforms reasonably well to Benford’s Law (p=0.214).* Or I can use the slightly-broader “PH” category for all public health-derived rates. Those look iffy (p=0.025) whereas the non-PH murder rates look alright (p=0.154). What’s more these aren’t just cases of large samples helping me to find spurious “statistically significant” effects: there are just 61 values coded PH in the data, and 187 overall.
The takeaway from that is that not just the Malawi murder but all the UNODC data supposedly derived from public health sources is questionable. I’m not trying to claim that these statistics were necessarily faked intentionally. I can imagine a number of ways they could have been screwed up by mistake. There might even be some reason why Benford’s Law would hold for some of these murder rates and not for others. Even if there was intent I have no idea who might have been responsible. What I am trying to claim is that they shouldn’t be taken seriously, or relied on for anything of importance, until someone can verify their source. And I do think this matters. People rely on these numbers, and draw judgments based on them. A glance at the top-ranking countries on Wikipedia’s list, would, for example, neatly confirm someone’s preconceived notions about Africa being a violent place. The top three African countries on that list are Zambia, Uganda, and Malawi – all have their statistics attributed to the WHO, and none actually appear in the WHO mortality data.
EDIT: I changed the Wikipedia article to remove the entries that I tried to trace down but could not find, until the source of the UNODC numbers is located or they are replaced with something better (Nameless has a suggestion in this post’s comments).
* I looked at all this a while ago but was just sitting on it until a recent Andrew Gelman post that cites the UNODC statistics prompted me to do something with it. I know Gelman wouldn’t like the fact that I’m leaning on p-values for the Benford’s law analysis, but I just don’t have any intuitive grasp of chi-square values.
My knee-jerk response is that this is great: subsidies are distortionary, and fairly paternalistic. The premise is that elites or policymakers can better decide what the poor need than the poor themselves, which is a bit grating if you really think about it. While some might be more tolerant of India’s subsidies because they are designed by other Indians rather than by white people/foreigners/etc., I am definitely not.
The other perspective is that when people are given cash instead of an in-kind handout or a subsidy, they will spend it poorly – wasting it on stuff that’s useless or bad for them, like alcohol or tobacco. I’m pretty sympathetic to this view, too, and I don’t see it as contradictory to my dislike of paternalism. People may have reasonable goals that they can’t stick to, and subsidies can be a useful tool for committing their spending. There’s increasing interest in the possibility that these kinds of commitment problems are important in driving persistent poverty. One of my favorite papers, by Banerjee and Mullainathan, develops a very intuitive model that could explain this behavior. A counter-intuitive prediction of that model is that if the poor face the temptation to misspend, large lump sums of money may be preferable to getting the same amount of cash in small installemtns: rather than “burning a hole in your pocket, the large sum lets you buy something worthwhile instead of frittering away your cash on trivialties. On the empirical side, Kathleen Beegle, Emanuela Galasso, Jessica Goldberg, Charles Mandala and Tavneet Suri are working on a project that will randomly vary whether workers receive payments in lump sums or small installments. I’m also in the planning phases of a project with Lasse Brune that will look at this issue.
But understanding the fundamental determinants of consumption behavior is a pretty hefty task; even an optimist like myself must admit that it will take economists a long while to sort it out. Looking directly at cash transfer programs, however, we already know quite a bit. Evidence from variousAfricancontexts shows that unconditional cash handouts can have big benefits. One potential drawback is that they may reduce labor supply; since people tend to work for money, that’s easy to predict from even very simple economic models.
It’s hard to say how this would play out in India. In some ways, gender inequities are far worse there than in Africa – while Africa does have “missing women”, for example, there is no evidence of the female infanticide nor of the deep favoritism toward young male children that is famously prevalent in much of India. On the other hand, my impression from visiting India is that transactional sex is not nearly as common there, which may be related to a culture that is prudish enough that some people still riot over public kissing. But the former issue is still a major concern – if men control this money moreso than the subsidies it replaces, and if women show less son preference than men, this program could do harm to girls that are already some of the most disadvantaged children on earth.
On balance, I think the shift to cash will do good on net. But this program is just crying out for a randomized phase-in process. There are legitimate questions about its impact, and the system lacks the capacity to roll it out to everyone at once. This is the textbook example of a case where a government should randomize the phase-in, say by locality, to see what the effects are. Unfortunately I don’t see any evidence that they’re planning to do that. And I’m sure this isn’t for lack of expertise – virtually any microeconomist would love to be able to access data on an experiment like that. I’ll even offer up myself: if Mr. Chidambaram happens to read this, I’ll gladly drop what I’m doing for a couple of days to set up a randomly-ordered list of the remaining districts where the scheme is to be rolled out. And I’ll do it for free!
A sprint toward the finish line for my dissertation fieldwork and a legally-mandated holiday in South Africa substantially cut down on my ability to post links and commentary here. So in the spirit of Boxing Day discounts, here are a eight great articles from the past month or so, for the price of one.
#1 via a comment thread on reddit, #2 via Melody Dye, #3 via Justin Schon, #s 4 & 8 also via reddit probably. #6 I stumbled across when doing research for my post on Malawi’s alleged foreign exchange shortage and couldn’t resist sharing.
After two years of planning, and four months in the field collecting data, I finally wrapped up data collection for my dissertation this past Friday. Also, thanks to outsourcing my data entry to the awesome people at IKI, I’ve already started looking through the baseline data from the project. The followup data – which will reveal our actual results – is being entered right now, and I should have my hands on it sometime after Christmas. At that point, I’ll be able to look into my main research question, which is how people change their risky sexual behavior in response to the perceived risk of HIV infection. It is typical to assume that the relationship is negative – that the riskier an act is, the less people will do of it. However, there is little empirical support for this in the case of HIV in Southern Africa. In previous work I’ve argued that the relationship may be heterogeneous, with certain people responding positively instead. But whether this happens, and what the impact is likely to be, remains an open question. I will get to answer it in just a handful of days.
Until then, I’m enjoying a much-needed vacation in South Africa with my parents. See you on the other side.
*”Basi” is Chichewa for “enough”, and is used colloquially when you’re done chatting with someone – “Basi, ndapita” (Enough, I’m going).
A few weeks after I described my endeavors to figure out why people were always saying “which” (“ati”) around me, I managed to find the stem of the verb “kuti” in my hardcopy of Paas’s English-Chichewa/Chinyanja dictionary. This is evidently not just an aspect of Nyanja slang – it’s a legitimate word with its own entry. I’m going pay attention to see how much I hear it used in the central (Chewa-speaking) region next time I am up there.
Why did I have to learn the word through my field staff the first time? First off, the verb stem, “-ti”, is just two characters long, below the limit needed to search for it online. Second, kuti/-ti means at least half a dozen different things, depending on the context. Some examples:
1) Which (ati/liti/etc. with the prefix changing according to the noun class)
2) Where (kuti, “in which area/direction”/pati, “on which spot”/muti, “in which room”)
3) That (kuti)
4) Isn’t that right?
5) To say
6) To think
I got lead astray by definitions 1 and 4, when I wanted #5. A book I have called Chichewa Intensive Course by Fr. N. Salaun actually has a whole section dedicted to the various meanings of kuti in its verb form alone:
Homonyms are pretty common in Chichewa. One of my employees likes to say thatit’s not a rich language, but that’s a narrow view. Even if the vocabulary is limited (I don’t know enough of the language to be able to say) the grammar certainly is not. The construction of verbs is rather elegant, and various verbs, adjectives and nouns are related in subtly elegant ways. To pick one example, “kugula” is “to buy” and “kugulitsa” is “to sell”.
However, there is one cluster of homonyms in Chichewa that is definitely an area where the language is limited, and is fairly confounding to me as a researcher. I am referring of course to “kwambiri”. It means both “a lot” and “very much”. But colloquially, either it or the adjective “-mbiri” seem to be the most common way to say “more”, “the most”, and “too much”. To pick one example of how this has tripped me up, it has meant that when I was writing survey questions for a project about vaginal drying practices, we couldn’t distinguish between a woman’s vagina being “very wet” and “too wet”, which is kind of central. On another survey, we had to totally rephrase questions about people’s most preferred time to receive income.
I’d imagine this is similar to Spanish speakers trying to translate survey questions that rely on “ser” and “estar” (which are two different senses of “to be”) for into English. Direct translation is often possible, but sometimes things are virtually untranslateable. It also speaks to a broader issue with surveys done in other countries: the fact that your survey questions have been copy-edited, field-tested and validated means almost nothing, because the actual questions your respondents will answer are the translated versions, and those will often be very different.
