Nigeria is going to be the most important country in the world

I recently came across this article from the Washington Post that presents graphs of population projections through 2100. The writing seems overly pessimistic to me; it has an attitude toward African governance and economic progress that rang true in 1995, but is outdated and incorrect in 2015.

That said, the graphs are great, and fairly surprising. Especially this one:

Nigeria

They are projecting Nigeria’s population to grow to 914 million people by 2100. Even if the truth is just half that figure, Nigeria will draw just about even with the US as the third-most-populous country in the world. Moreover, Nigeria is forecast to be the only country in the top 5 that will be unambiguously growing in population over the current century, making it a source of dynamism and labor supply for the world economy.

Based on my rigorous, in-depth investigation strategy of listening to African pop music and occasionally catching an episode of Big Brother Africa, Nigeria already plays an outsized role in an increasingly salient pan-African culture. The growth of Africa, and the rising importance of Nigeria within Africa (currently one in every seven Africans is Nigerian, a figure that will rise to one in four), mean that its importance is only going to rise.

There will be challenges: for the sake of the environment and the good of its people, the continent needs to urbanize and move away from subsistence agriculture. But the 21st century is shaping up to be an exciting one, and a positive one for Africans in general and Nigerians in particular.

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Why do we ever believe in scientific evidence?

Late last night the internet exploded with the revelation that the most influential social science study of 2014 was apparently invented from whole cloth. LaCour and Green (2014) showed that a brief interaction with an openly-gay canvasser was enough to change people’s minds – turning them from opposing gay rights to supporting them. The effects were persistent, and massive in magnitude. This study was a huge deal. It was published in Science. It was featured on This American Life. I told my non-academic friends about it. And, according to an investigation by Broockman, Kalla, and Aronow, as well as a formal retraction by one of the study’s authors, it was all made up. The report by Broockman, Kalla, and Aronow is a compelling and easy read – I strongly recommend it.

The allegation is that Michael LaCour fabricated all of the data in the paper by drawing a random sample from an existing dataset and adding normally-distributed errors to it to generate followup data. I have nothing to add to the question of whether the allegation is true, other than to note that many people are persuaded by the evidence, including Andrew Gelman, Science, and LaCour’s own coauthor, Donald Green.

What I do have to add is some thoughts on why I trust scientists. Laypeople often think that “peer review” means the kind of analysis that Broockman, Kalla, and Aronow did – poring over the data, looking for mistakes and fraud. That isn’t how it works. Referees are unpaid, uncredited volunteers who don’t have time  to look at the raw data themselves. (I have also never been given the raw data to look at when reviewing an article). The scientific method is fundamentally based on trust – we trust that other scientists aren’t outright frauds.* Nothing would work otherwise.

Why do we trust each other? After all, incidents like this one are not unheard of. Speaking from my own experience running field experiments, one important reason is that faking an entire study would be really hard. You’d have got to write grants, or pretend you’re writing grants. Clear your study with the IRB, or sometimes a couple of IRBs. You’d have to spend a significant portion of your life managing data collection that isn’t really happening, and build a huge web of lies around that. And then people are going to want to see what’s up. This American Life reports that LaCour was showing his results to the canvassers he worked with, while the data was coming in (or supposedly coming in). To convincingly pull all of this off, you would basically have to run the entire study, only not collect any actual data.

It is hard to imagine anyone who is realistic with themselves about the incentives they face choosing to go through with all of this. Most scientists don’t get hired by Princeton, don’t make tons of money, don’t get their results blasted across all media for weeks. Most of them work really hard for small payoffs that seem inscrutable to outsiders. The only way to get big payoffs is with huge, sexy results – but huge results invite scrutiny. You might get away with faking small effects that are relatively expected, but if your study gets attention, people are going to start digging into your data. They will try to replicate your findings, and when they can’t they will ask questions. If you do manage to walk the tightrope of faking results and not getting caught, you did a ton of work for nothing.

I can barely conceive of going through all the effort and stress of running a field experiment only to throw all that away and make up the results. I trust scientists to be honest because the only good reason to go into science is because you love doing science, and I think that trust is well-placed.

*Incidentally, this is why I don’t particularly blame Green for not realizing what was up. When I coauthor papers with people, the possibility that they are just making stuff up never even crosses my mind. I am looking for mistakes, sharing ideas, and testing my own ideas and results out – not probing for lies. News accounts show that Green did see the data used for the analysis, just not the underlying dataset or surveys.

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Garment workers are people, not props for your viral video

I recently came across this post about a video that raises the question “Who made my clothes?”

The video, started by an organization called Fashion Revolution, suggests an answer: young women like Manisha, who are miserable, and whom you can help by refusing to by the t-shirts they make and instead donate.*

But wait a second – who did make my clothes? Specifically, who are the people in the video who (it is suggested) made the t-shirts being sold? The only people with any agency in the video are the westerners who are choosing not to buy the shirts. The garment workers appear only in still photos in which they appear harrowed and fearful. They don’t do or say anything.

Who is Manisha? Why does she work in this factory? Does she support the idea of consumers refusing to buy the clothes she is paid to make? She doesn’t say anything in the video, and if Fashion Revolution gives her a voice or an identity at all, they don’t make it easy to find on their website:

I looked through the site’s blog, and didn’t see anything written by employees in the garment factories featured in the video. There are some quotes from people employed by garment makers that Fashion Revolution deems socially conscious, but nothing about the folks whose lives we are ostensibly trying to change. They appear only as a way to manipulate viewers of the videos. Fashion Revolution shows them to us – I want to know who they are and what they think about this campaign. I want to know why they work in these jobs, and what they would do instead if the jobs didn’t exist.

Fortunately, there is a way to learn about those questions. Planet Money conducted an epic 8-part investigation into how some t-shirts they bought were made (I previously mentioned this series in  this post). In the episode “Two Sisters, a Small Room and the World Behind a T-Shirt” the producers learn that two of the garment workers who produced their shirts are a pair of sisters from Bangladesh named Shumi and Minu – and then they travel to Bangladesh, meet Shumi and Minu, and talk to them about their lives and their careers. Bad work conditions do come up, but so do many other problems and concerns and joys and triumphs in their lives. The followup to “who made my clothes?” shouldn’t be “let’s stop buying them” but “what do the human beings who made my clothes see as their problems, and how can we help?”

*There is no evidence from Fashion Revolution’s donation page that the money is actually going to Manisha or any of the other garment workers in the video. Instead, it sounds like the donations are going to raising awareness about the issue. That’s probably fine, but do the donors in the video understand that? Or do they think they are directly helping Manisha?

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Is it impossible to prevent usury?

Usury laws are intended to prevent creditors from charging predatory interest rates. While morally pleasing, their practical effects are debatable, since borrowers whose credit profiles would call for ultra-high interest rates might not have access to credit at all in the presence of such laws.

But do they even work? A beautiful new paper by Brian Melzer and Aaron Schroeder says they do not, if sellers are allowed to offer loans directly:

We study the effects of usury limits on the market for auto loans and find little evidence of credit rationing. We show instead that loan contracting and the organization of the loan market adjust to facilitate loans to risky borrowers. When usury restrictions bind, auto dealers finance their customers’ purchases and raise the vehicle sales price (and loan amount) relative to the value of the underlying collateral. By doing so, they arrange loans with similar monthly payments and compensate for credit risk through the mark-up on the product sale rather than the loan interest rate.

Unless we are willing to ban auto dealers from offering loans – which I suspect would be difficult – then usury protection effectively does not exist in the auto loan market, even though usury is banned by law.

This is an example of why economics is such a compelling subject: even seemingly straightforward solutions to simple problems may completely fail to work. Human beings are extremely clever and complex animals, and it is quite tough to design systems to shape their behavior in the way we want to.

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Focal points and minibuses

It’s 1975. You’re on vacation in New York City, and you’ve made plans to meet an old friend, also on vacation there, tomorrow. The problem is that you don’t have their phone number or any other way to get in touch with them, and you didn’t agree on a place or time to meet. Where do you go to meet your friend – and at what time?

The above is one of my favorite game theory problems, because it is one that even non-experts can solve. It is interesting in that there are many “correct” answers – any place in the city, and any time, will work, as long as both you and your friend make the same choice. But what time and place will maximize the chances of that happening? I’ve thrown out the problem in a number of casual conversations, and the modal answer is 12:00 noon in front of the clock at Grand Central. Since the essence of the game is to guess what the other person will do, this is also the right answer in that it maximizes the chance that you and your friend will win – the “focal point” of the game. It’s also the answer that Tom Schelling got when he tried this problem out back when he invented it.

I was reminded of this problem by a recent post on what is easily my favorite new development blog, NPR’s Goats and Soda, that covered the reversal of the Kenyan government’s ban on decorating minibuses. A bit of background: minibuses, which go by various names in different countries, are the chief means of public transit in most of eastern and southern Africa. They work along fixed routes, and often for fixed fares (in Malawi it is technically illegal to raise prices unless a government board approves a fare hike). A minibus will typically sit at the origin point for its route until it is totally full, and then head to the end point.

Commuters are thus faced with a problem: there are lots of minibuses at the bus stop. I want to take the one that leaves soonest since it will arrive sooner. Which one do I board? NPR says it is the one with the most garish decorations, because “if young people prefer a painted bus that plays loud music, and they seem to, then it fills up faster”. That is the right answer – but the wrong reason! I would bet money that the painted bus will fill up faster even if there are no young people around or they don’t prefer it, because it is an obvious focal point for the game.

Minibus operators are very aware of this focal point problem, and employ multiple techniques to solve it. One approach is collective action: some bus stops have an enforced queue of buses*, so customers can only board the first one in the line. Another one that I particularly like involves faux customers called “timing boys” who board the bus and sit on it until it is nearly full, then get off (usually to a hail of jeers from the actual commuters). Since a fuller bus is more attractive (and anyway an obvious focal point even if customers are aware of the ruse), this attracts customers quicker and leads to faster departures. Policymakers should keep in mind that when a minibus owner gets “CNN BREAKING NEWS” spraypainted onto his bus, it’s more than just for fashion – it makes the transit network more efficient.

*In Malawi these queues are often enforced by cabals of stationary bandits known as “touts”, who impose an effective tax on the bus operators.

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Understanding heterogeneous treatment effect estimation using proof-by-Stata

Marc Bellemare asks whether splitting your sample by an observed covariate is a reasonable approach for estimating heterogeneity in treatment effects:

To get a treatment heterogeneity, wouldn’t it be better to maintain your sample as is, but to interact your treatment (i.e., land title, college degree, etc.) with groups (i.e., small and large plots, race, etc.), going so far as to omitting the constant in order to be able to retain each group

In general, selection on observables will not cause bias in OLS estimates. So this approach is okay. You can prove this formally by showing that your treatment variable of interest is uncorrelated with the error term in the selected sample – see page 7 these slides for a sketch of that proof. However, I don’t find that proof to be very useful for generating the intuition about why this is the case, so here is a brief proof-by-stata:

clear all
set seed 12345

*set up matrix of correlations between variables

matrix C = (1, .75, 0 \ .75, 1, 0 \ 0, 0, 1)

*simulate the data generating process – correlations between RHS variables
drawnorm T z u, n(1000) corr(C)

*generate y using our RHS variables
*T is the variable of interest
*z is an observed variable that changes how T affects y
gen y=1+2*T+0.3*z+u if z>0

reg y T z
reg y T z if z>0
reg y T z if z<0

So we get unbiased estimates of the average treatment effect and of the conditional treatment effects given z>0 and z.

You can also use this approach to see that for your point estimates, it doesn’t matter if you estimate the heterogeneous treatment effects by using a dummy variable interacted with the treatment instead. That is, it doesn’t matter provided you do a fully-saturated model – you have to interact the dummy with all your RHS variables, not just the treatment:

gen z_above_0 = z>0
reg y i.z_above_0##c.T i.z_above_0##c.z

*for comparison purposes, make T*below & T*above
gen T_z_above_0 = T*z_above_0
gen T_z_below_0 = T*(1-z_above_0)

reg y T_z_above_0 T_z_below_0 z_above_0 i.z_above_0##c.z

If you run the code yourself and mess with the seed value for the RNG, you can confirm that this method mechanically generates identical point estimates to the split-sample approach. However, the saturated approach assumes a common error term distribution across the whole sample, so this approach will not give you the same standard errors. Again, if you run it you can see they are the same.

One of the commenters on Marc’s blog pointed out that a case where this is definitely problematic is if we select our sample on a dependent variable. Suppose we have heterogeneity by unobserved characteristics u, and we try to get at this by splitting the sample using values of the outcome:

*now look at heterogeneity by unobserved variable, u

gen y2=1+2*T+0.3*z+u if u>0

reg y2 T z
sum y2, d

*try splitting the sample by y
local y2_high = r(p75)
reg y2 T z if y2>`y2_high’
reg y2 T z if y2<`y2_high’

The two separate regressions now each generate biased estimates of the mean treatment effect, and the CIs also don’t include the heterogeneous treatment effects by u. In other words, catastrophe. This is also something we can prove in general (page 15 of the slides linked above) – T is not independent of u. This just reinforces the maxim that selection on X is okay, whereas selection on y is a big problem.

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“Not-so-reluctant entrepreneurs” and other encouraging facts about economic growth in Africa

One of the best parts of Banerjee and Duflo’s Poor Economics is a chapter on “reluctant entrepreneurs” – people who own and manage their own businesses not because they want to, but because it’s the only source of income available to them. It’s a concept that ties together a lot of what we know about poverty alleviation and small enterprises in the developing world. Part of why microcredit and business training don’t achieve huge gains in lifting people out of poverty because the business-owners targeted by those policies aren’t passionate about growing their businesses. They’re just trying to get by.

This view of small businesses in Africa is challenged somewhat by a new NBER working paper by Diao and McMillan (“Toward an Understanding of Economic Growth in Africa: A Re-Interpretation of the Lewis Model”, ungated IFPRI working paper here). They cite a survey of small businesses in Tanzania that found that 54% of small business owners would not prefer a salaried job. That 54% is the exact opposite of a reluctant entrepreneur – they enjoy running their own business. A surprising 60% of all these businesses are growing, too, which is consistent with people enjoying running them.

The paper has many other interesting and encouraging datapoints – Angola, famous for being the leader of Africa’s growth boom, has faster growth in its agriculture sector than its mining sector.* Moreover, manufacturing is growing as a share of all exports. Both factors argue against the standard narrative that this is another export boom that won’t lead to broad-based economic development. The authors also argue that the current economic growth is led by domestic demand and accompanied by growth in the “in-between” sector – their word for the set of small businesses that includes “reluctant entrepreneurs”.

I was a lot less interested by their GE model of Rwanda’s economy – but the assemblage of data on Africa’s economic growth makes the paper worth reading in its own right. I have argued before that it is time for optimism about Africa, and now I feel even more confident about that.

*Bearing in mind the often-dubious quality of African GDP statistics.

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