One of my students decided to commemorate the end of his final exam with study memes:
Travel and sickness, and attempts to finally submit a paper, have kept me off blogging for a week, but here are some links I liked in the interim:
- The most beautiful, least accessible memorial on the planet?
- An excellent and interesting “Ask me anything” from a Peace Corps volunteer in Ghana, with his fellow teachers and student
- If you live outside the US and want to sign up and livestream Netflix, etc, there is apparently MediaHint for your browser
- Google Street View protects Bambi too
- See the ridiculously good papers at the ridiculously good conference I attended last wee
On April 17, a paper arrived in the inbox of Annals of Mathematics, one of the discipline’s preeminent journals. Written by a mathematician virtually unknown to the experts in his field — a 50-something lecturer at the University of New Hampshire named Yitang Zhang — the paper claimed to have taken a huge step forward in understanding one of mathematics’ oldest problems, the twin primes conjecture.
Editors of prominent mathematics journals are used to fielding grandiose claims from obscure authors, but this paper was different. Written with crystalline clarity and a total command of the topic’s current state of the art, it was evidently a serious piece of work, and the Annals editors decided to put it on the fast track.
Just three weeks later — a blink of an eye compared to the usual pace of mathematics journals — Zhang received the referee report on his paper.
“The main results are of the first rank,” one of the referees wrote. The author had proved “a landmark theorem in the distribution of prime numbers.”
A new paper from Liran Einav and Jonathan Levin:
Many believe that “big data” will transform business, government and other aspects of the economy. In this article we discuss how new data may impact economic policy and economic research. Large-scale administrative datasets and proprietary private sector data can greatly improve the way we measure, track and describe economic activity. They also can enable novel research designs that allow researchers to trace the consequences of different events or policies. We discuss some of the challenges in accessing and making use of these data. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in economics.
Tuesday’s post on Bill Gates’ plea for better GDP numbers generated an interesting debate about how to “do development” more generally.
I came away with three general lessons I think we can apply, even if you could care less about income statistics. But first the insightful criticisms that brought me to these conclusions.
One day we’ll have the Star Trek machine that freely generates both GDP data and Earl Grey tea, hot. In the meantime, the world involves trade-offs. When policymakers say “Do more X” they are implicitly saying “Do less Y”. Some people simply don’t think in terms of the opportunity cost. Savvier ones know that more people will agree with you if you are vague about Y.
Most statistics agencies can calculate a GDP figure. Fewer can capably run panel surveys of labor markets, a firm census, or indices of manufacturing output. The tax agencies have outdated rolls, and little ability to monitor compliance. Information on bank and credit regulation can be pretty poor. My point: You probably only think GDP is more useful data than these things if you don’t actually live in the country.
First, there are still trade-offs. Most leaders (say, of a statistics agency) will have the time and political capital to change one or two big things a year, at best. Should the ingredients of GDP be that one big thinig?
We’re right to think in terms of cost-benefit terms when evaluating different options. Maybe GDP would pass this test. My sense is that most of the things I just mentioned would do better in cost-benefit terms. I may be wrong.
You might say “well these things improve GDP measurement anyways,” and you might be right. But the head of a statistics agency would choose to invest in different things if his outside financial incentives tilted towards getting external donors and academics the GDP and MDG measures they want for their big reports. That’s not necessarily the information governments need to govern well.
My general takeaway lessons:
First, academics, aid agencies and big foundations are biased towards funding the institutions and information that help them make the world more legible and manipulable. These are seldom the policies best at making people better off. We ought to be more self aware.
Second, to the extent academics, governments or foundations are going to mess around to try to improve other people’s welfare (and they will), I think they’re less likely to do damage if they think about their job in terms of reducing frictions or constraints. (Other people call this solving market and government failures.) Figure out the binding constraints and how to get rid of them.
Third, policy involves trade-offs, whether you say them out loud or not. Tale less seriously the people who ignore or obscure those trade-offs.
A reminder this discussion was spurred by Morten Jerven’s terrific book, Poor Numbers, which you should check out.
P.S. A reminder: I have never actually been a policymaker, and I wrote this post in 15 minutes, so you should probably not take me very seriously.
There are powerful forces having to do with the sociology of the profession and the socialization process that tend to push economists to think alike. Most economists start graduate school not having spent much time thinking about social problems or having studied much else besides math and economics. The incentive and hierarchy systems tend to reward those with the technical skills rather than interesting questions or research agendas. An in-group versus out-group mentality develops rather early on that pits economists against other social scientists.
That’s one of the more interesting quotes, but it makes Dani sound much more critical of mainstream economics than the full discussion implies. He also extolls all that is right in economics. Read it in full.
Dani’s actually one of the four or five scholars that, at a young and impressionable age, influenced me to do what I’m doing today. As an MPA/ID student at Harvard, I was attracted to both political science and economics PhD programs, and asked him what I should do. He won’t remember this, but his answer stuck with me: “Look at the scholars you admire, and do what they did.”
This wasn’t a suggestion to do economics, but to follow your heart. As it happens, I looked around, and my heart was with the scholars who brought economic models and methods to new and under-explored political and social questions, with real-world implications. 13 years later, here I am.
This is a long way of saying: read all of Dani’s advice.
Morten Jerven has a terrific book, Poor Numbers, chronicling the vagaries and inaccuracies of our main measure of poverty and development: Gross Domestic Product. Essential reading for anyone studying development.
In what I can only assume made Morten’s publisher faint in ecstasy, Bill Gates gives it a rousing review.
…it is clear to me that we need to devote greater resources to getting basic GDP numbers right. As Jerven argues, national statistics offices across Africa need more support so that they can obtain and report timelier and more accurate data. Donor governments and international organizations such as the World Bank need to do more to help African authorities produce a clearer picture of their economies. And African policymakers need to be more consistent about demanding better statistics and using them to inform decisions.
I would like to see better GDP numbers–who wouldn’t?–but it’s hard for me to see the constraint on development this revelation would relieve, and why it’s anywhere close to the top ten constraints poor countries face.
The problem with those of us in the development complex, be we academics or Presidents or foundations or NGOs, is we want the world nicely ordered with levers to pull and a dashboard to monitor. And so we put a lot of energies into levers and dashboards and monitors.
I think of poverty and political powerlessness in terms of constraints and frictions–the limitless host of things, little and big, that made it more difficult to run a business profitably or turn a profit or invent a new product or get your kid educated or select the leader who serves your interests. States and institutions and norms and technology and organizations reduce these frictions and relieve these constraints. That is the fundamental driver of development. This is the basic logic behind almost every theory of development in your textbooks, from growth models to poverty traps to everything in between.
Reducing frictions and eliminating constraints is maybe the best thing outsiders can try to help with, freeing entrepreneurs and citizens to do their thing. (Well, I guess we can also help by giving them a big freaking market to sell things to, but that’s another story).
To the extent that missing information and measurement constrains development, or creates frictions, there’s a long list of more likely candidates than GDP. A sample:
- small banks who don’t know the creditworthiness of the mass of potential borrowers,
- village leaders who don’t know what funds the local bureaucrats get from the center
- citizens who don’t know their MP’s meteoric rise in wealth
- farmers who don’t know prices a district to the west
I kind of wish Gates would say “we need credit bureaus” or “we need freedom of information acts” instead.
I’m not even sure information of these sorts are even the most important frictions to address. To the extent we pay them attention or design programs, I think it’s because they seem cheaper and easier to tackle than the harder ones. But they are all a far sight better than better GDP data.
The litmus test: If we went back in a time machine, and Gates wanted to expand sales or product development or factories in Asia or Africa, would he have called for these things or better GDP data?
Postscript: The discussion continues here.
A career in academia or law or business or any other face-paced, race-to-the-top profession exhausts and exhilarates at the same time. Keguro Macharia is a Kenyan-born academic who has decided it’s time to get out of the fast lane.
He writes from the perspective of an outsider and a scholar, but the existential question will be familiar to many more:
I am leaving the United States, resigning from my job, and moving back to Kenya. As I have been trying to narrate this move to those who have known about it—over the past year—I have wondered about the partiality of the stories I was telling. They were not untrue; they were simply not what I really wanted to say, not what I permitted myself to say. In the most benign version, I have said that I cannot build a life here.
…Most often when I talk about building a life, I have meant something closer to saying that I cannot imagine—or desire—a life here. And this, it strikes me, is a much harder thing to confess. After all, the academy tells me what I should desire: tenure, full professorship. Indeed, the academy provides at least a 20-year plan: undergrad, grad (10 years); tenure track through tenure (5-7 years); and if one is on a fast train to somewhere, one can achieve full professor within 5-7 years of achieving tenure. All of these come with immense benefits, and because of immense luck, I have been in a position to benefit from what these might mean. Being located in a research institution provides privilege and access: from here, the gaze is always upwards. Were I more conceited, I would say that my momentum is steadily propelling me upwards in what might be very rewarding ways. Given this scenario, why quit?
I’m not sure this is “the life” I want to imagine. I worry about any life that can so readily be “imagined.” Where is the space for fantasy, for play, for the unexpected, for the surprising?
…We are trained to hang in, hang on, hang together. This, after all, is the lesson of graduate training. “It will get better,” we assure students who struggle to learn. We are so definite. Were we more honest, we would say, “it might get better,” “perhaps,” “maybe,” or, simply, “we don’t know.” Instead, we say, “there are no guarantees, but.” And that “but,” that barely uttered, barely hearable “but” carries so much weight. Everyone wants to hear the “but.” Everyone invested in the academy is always hearing the “but.” We are a community organized around “but.” Lauren Berlant calls this “cruel optimism.”
His words bite. I remember a summer after my first year in grad school, working in rural Kenya alongside a cluster of ridiculously hardworking, intense academics whose names you would all recognize, thinking, “I do not want my life to be like this”.
Fast forward 10 years: I’m not sure if I was simply socialized by the PhD and my peers to change my preferences, or if I simply grew to love my work like a vocation, not a job. I think a little of both.
Either way, I am now the living caricature of what I once maligned. More days than not I love it. But the temptation of quitting–or at least crossing to the slow lane–never goes away. All I will say for now: not this year.
Paul Miller chronicles his journey.
And now I’m supposed to tell you how it solved all my problems. I’m supposed to be enlightened. I’m supposed to be more “real,” now. More perfect.
But instead it’s 8PM and I just woke up. I slept all day, woke with eight voicemails on my phone from friends and coworkers. I went to my coffee shop to consume dinner, the Knicks game, my two newspapers, and a copy of The New Yorker. And now I’m watching Toy Story while I glance occasionally at the blinking cursor in this text document, willing it to write itself, willing it to generate the epiphanies my life has failed to produce.
I didn’t want to meet this Paul at the tail end of my yearlong journey.
Actually, he begins his year more active, productive, focused, and happy. But eventually feels cut off.
The economist’s interpretation: the partial equilibrium (one person cutting out) is negative. The general equilibrium (all cutting out) could be positive. Although I doubt it.
I’m influenced by the sense that Americans had a back patio culture rather than a front porch culture well before the Internet, so offline life is not nearly so interactive even without the Internet.
There is a lot of talk about “investing in poor women”. They will grow businesses, get richer, be empowered, and invest in children. So give them a cow or a grant or a microloan.
It’s been a very effective marketing message for aid agencies, but it’s not clear it’s true. Especially the part about women entrepreneurs and women empowered. A lot of the experimental evidence has been pretty pessimistic on this front–to the surprise of many.
This article talks about how established female entrepreneurs don’t have the same returns to cash as men. This one discusses the weak effects of microloans on business growth. This book on how conditional cash transfers do many good things, but starting female enterprises and empowering women is not among them. There’s some evidence giving poor women cows helps, but a host of similar trials (not quite out) are suggesting more tepid results.
Before you get upset or pessimistic, read on.
There’s a big hole in this evidence. These programs don’t necessarily do the simplest thing for the most needy people: find very poor women who don’t already have a business, give them cash, and let them decide what they want to do themselves. Get rid of the conditions and the cows and the ridiculous microloan interest rates and let them do their thing.
Now things start looking up. That’s what I and several coauthors did with AVSI Uganda, working with some of the poorest women in the world. Here is the policy report and brief.
The short story: in 18 months, they become petty traders, incomes double, with a big boost to savings and poverty reduction. (Did I mention that income doubles?)
We also randomly evaluated the paternalistic part–whether social workers held the women accountable for investing the money in business, and provided follow-up visits and advice. This actually boosted incomes (by some measures at least), but not so much that it was worth the expensive follow-up. Better, we argue, to just give more women more cash. Or find a way to deliver the support and accountability cheaply.
Why did this intervention build new female businesses where others have seen tepid results? Hard to say. It’s a different country than the others, and women in northern Uganda may be further behind, and more constrained. So perhaps we should expect more potent results. The more coiled the spring, the bigger the bounce on release.
My hunch, though, is that it mattered that these women weren’t already entrepreneurs, that they were given cash, and that they weren’t confined to cows or tailoring training or other things we think are good for them.
Two other big findings. One is that the cash transfers were large enough that they shifted the whole economy, transforming prices, agricultural wages, and the distribution of income. There were winners and losers. More on this in a future paper and post, but the immediate lesson is this: big transfer programs usually ignore the distributional effects, and (more importantly) the distributional conflicts.
The other big finding: for all the increase in business and incomes, we don’t see any evidence these women feel more “empowered”. No more decision-making power in the household, no more independence, and no less domestic violence (among other measures). And they are no less depressed or stressed. This is a common-enough finding in the psych literature that there’s a name for it–”the impact paradox”. But you don’t hear that very often in the program designs or sales pitches. Aid may have to make the case for investing women on the economic case alone. Forthunately that may be a strong case to make.
The story, of course, is more nuanced. Read the full report or see the policy brief here.
In a panel at the Calgary Comic & Entertainment Expo on April 27th, “Star Trek” and “Big Bang Theory” actor Wil Wheaton fielded a question from an audience member, who asked him to explain to her newborn daughter “why it’s awesome to be a nerd.”
The result is worth watching:
Related, @clairemelamed points me to this tweet:
@Sweet_Me_73: Dear Teen Girls, Go for the nerdy ones. Trust us. Sincerely, Grown Women Everywhere”
Which confirms my own highly scientific observation, which is that, at age 25, women make a sudden, unexpected, unannounced change to their partner preferences. No one warns you of this. So, to the under-25 males in the audience, there is hope.
…is this Friday. The Working Group in African Political Economy, known by its unfortunate acronym, WGAPE. Schedule here, with papers.
- Science says I’m sexier with a beard
- Is it possible to end poverty in a generation?
- I am usually distressed by the state of formal theory on why we vote. I like this new paper by Ali and Lin.
- 11 buzzfeed lists that explain the world, plus a bonus: 14 hairless cats that look like Vladimir Putin
- 66 behind the scenes photos of the best one of the Star Wars movies
Eleven percent of the Malawian population is HIV infected. Eighteen percent of sexual encounters are casual. A condom is used one quarter of the time.
A choice-theoretic general equilibrium search model is constructed to analyze the Malawian epidemic. In the developed framework, people select between different sexual practices while knowing the inherent risk.
The analysis suggests that the efficacy of public policy depends upon the induced behavioral changes and general equilibrium effects that are typically absent in epidemiological studies and small-scale field experiments.
For some interventions (some forms of promoting condoms or marriage), the quantitative exercise suggests that these effects may increase HIV prevalence, while for others (such as male circumcision or increased incomes) they strengthen the effectiveness of the intervention.
A new paper by Greenwood, Kircher, Santos and Tertilt.
Related, I am intrigued by a Telegraph article that “Scientists are on the brink of a cure for HIV“. I am hopeful but skeptical. Anyone with insight?
I have a tremendous sense of déjà vu at reading this “new” theoretical literature. The “poverty trap” view, which now has wide currency among the young, is just a resurrection of the “vicious circle of poverty” arguments of the 1950s—except now it is attired in sophisticated mathematical garb.
…The various arguments for industrial policy are just the old arguments for protection based on the “Big Push” depending on the irrelevant pecuniary externalities or Marshallian externalities of agglomeration and increasing returns. As with the old “development economics,” the empirical evidence particularly for Japan, Korea, and Taiwan does not support these theoretical curiosa.
That is UCLA’s Deepak Lal writing in the Cato Journal, with a broadside against the new development economics.
It’s an engaging read, but I was puzzled by the attack.
When I think of the young in development economics, I think of the micro-development crew (not just the experimenters) who are quite skeptical of the poverty trap. They’re finding plenty of frictions that slow or set back the poor. Sometimes they use the language of “traps”, usually sloppily. But overwhelmingly I see them taking a more “marginalist” view, which sees improvements on the margin as slow, but simply held back by market imperfections and other frictions.
At the macro level, it’s true the economics profession has revived industrial policy as a (barely) dignified object of study, but mainly as an empirical question–one that has yet to be resolved. There are a few boosters but most of the profession is cautiously skeptical. As usual, the truth will probably lie in between–so much industrial policy will be ineffective or bad, some will be good, but most of that will be in theory or by accident, since the academically optimal policy is almost always impossible to do in practice.
I will say this: The blind spot in development economics, at least until recently, is politics, and it’s here where we ought to be looking for traps. An awful lot of countries take two steps forward only to take several steps back into coups, wars, and other political chaos. Growth follows the fortunes of politics, and so countries’ paths of development, over decades, resemble mountains and valleys, going way up then down, rather than steady inclines.
If there are increasing returns to anything in this world, it is that je ne sais quoi that brings some kind of political equilibrium, most of all a means for steady and peaceful transitions of power. Some people call this “institutions”, which strikes me as correct, but is far too vague in its meaning and origins to be helpful. The fact that this is the direction in which so many great minds are turning is actually what makes me most hopeful about the future of development economics.
- “Why I let students cheat on my game theory exam”
- If NY apartments were big enough for a man cave, mine would contain the Han-Solo-in-carbonite coffee table
- Tyler Cowen’s favorite economics paper of the year comes from a Berkeley grad student
- From 1993 to 2009, U.S. universities added bureaucrats 10 times faster than they added tenured faculty?
This paper reports on the first randomized evaluation of the impact of introducing the standard microcredit group-based lending product in a new market. In 2005, half of 104 slums in Hyderabad, India were randomly selected for opening of a branch of a particular microfinance institution (Spandana) while the remainder were not, although other MFIs were free to enter those slums.
Fifteen to 18 months after Spandana began lending in treated areas, households were 8.8 percentage points more likely to have a microcredit loan. They were no more likely to start any new business, although they were more likely to start several at once, and they invested more in their existing businesses.
There was no effect on average monthly expenditure per capita. Expenditure on durable goods increased in treated areas, while expenditures on “temptation goods” declined. Three to four years after the initial expansion (after many of the control slums had started getting credit from Spandana and other MFIs ), the probability of borrowing from an MFI in treatment and comparison slums was the same, but on average households in treatment slums had been borrowing for longer and in larger amounts.
Consumption was still no different in treatment areas, and the average business was still no more profitable, although we find an increase in profits at the top end. We found no changes in any of the development outcomes that are often believed to be affected by microfinance, including health, education, and women’s empowerment.
The results of this study are largely consistent with those of four other evaluations of similar programs in different contexts.
Banerjee, Duflo, Glennerster and Kinnon have a new version of their paper.
A related aside: a couple of weeks ago I blogged an article that examined how microfinance organizations respond to “bad news”. The comments on the paper have been excellent, especially this one.