The rise of empirical econ, in one chart


Goddammit I need more memory

I apologize for the click-baiting title, but this is pretty cool. John C. McCallum has assembled a (rough) estimate of the price of computer memory (mainly RAM) over time. I’ve adjusted the prices for inflation and graphed it over time. The results are pretty amazing (keep in mind the y-axis is log-scale).


Without cheap memory, you can say goodbye to big data sets and complex calculations which really enabled empirical econ to take off. Sure, CPU speeds matters as well and the RCT folks were always a little less reliant on large data sets, but can you imagine having to bootstrap those standard errors with 2mb of RAM? File this under “things are getting better.” Hat tip to Data is Plural, a newsletter you really should subscribe to if you like random data sets.

I’m upset that my football team lost, so I’m going to have to ask you to leave the country


I AM THE LAW! Except when the Broncos lose. Then I just turn to jelly.

In the NYT, immigration judges contemplate how biases might creep into the decisions they make:

In all, 336 people from 13 countries and even more ethnic backgrounds appeared in San Francisco’s immigration court recently over three days. All of them were facing possible deportation, because they either were in the United States illegally or had committed crimes serious enough to jeopardize their legal presence as noncitizens. One challenge facing Judge Marks was deciding whether to deport some of them immediately after they had testified. Another challenge was her own biases.

“You have to go through some hypotheticals in your brain,” said Judge Marks, wrestling with the weighty decisions she must make, the little time she has to make them and all the impressions she and her judicial colleagues form from the bench about the immigrants before them.

“Would I treat a young person the same way I’m treating this old person?” she said. “Would I treat a black person the same way I’m treating this white person? This situation of rush, rush, rush as fast as we can go, it’s not conducive to doing that.”

The solution? Anti-bias training:

Now, as the country struggles with how these instinctive judgments shape our lives, the Justice Department is trying to minimize the role of bias in law enforcement and the courts. More than 250 federal immigration judges attended a mandatory anti-bias training session in August, and this summer the Justice Department announced that 28,000 more employees would go through a similar exercise.

This seems reasonable, but what about factors that influence decisions that go beyond the characteristics of the immigrant? Enter a recent (unpublished) paper by Daniel Chen:

I detect intra-judge variation in judicial decisions driven by factors completely unrelated to the merits of the case, or to any case characteristic for that matter. Concretely, I show that asylum grant rates in U.S. immigration courts differ by the success of the court city’s NFL team on the night before, and by the city’s weather on the day of, the decision. My data including half a million decisions spanning two decades allows me to exclude confounding factors, such as scheduling and seasonal effects. Most importantly, my design holds the identity of the judge constant. On average, U.S. immigration judges grant an additional 1.5% of asylum petitions on the day after their city’s NFL team won, relative to days after the team lost. Bad weather on the day of the decision has approximately the opposite effect. By way of comparison, the average grant rate is 39%. In contrast, I do not find comparable effects in sentencing decisions of U.S. District Courts, and speculate that this may be due to higher quality of the federal judges, more time for deliberation, or the constraining effect of the federal sentencing guidelines.

Yikes. If it’s true, then there are all sorts of external factors which affect the fates of thousands of asylum seekers, some of whom are turned away because the judge is just having a bad day. This wouldn’t be the first paper to find that irrelevant, external factors influence judicial decisions. A recent paper by Ozkan Eren and Naci Mocan find similar effects (this time via college football – go figure) on decisions in juvenile courts. Others have found that judges are less likely to rule in the defendant’s favour when they are hangry.

Maybe judges should take some sort of mood test before they are allowed to review cases. Or maybe, despite what the folks at ProPublica think, it’s time to let the machines do the work for us.

Hat tip to Charles Kenny for the Chen paper.

The European Union wants to use aid to kill people. Seriously.


Here we go. Deep breaths. From the Guardian:

When international donors and the Afghan government convene in Brussels next week, the EU secretly plans to threaten Afghanistan with a reduction in aid if the war-torn country does not accept at least 80,000 deported asylum seekers.

According to a leaked restricted memo (pdf), the EU will make some of its aid “migration sensitive”, even while acknowledging that security in Afghanistan is worsening.

Let’s be absolutely clear here: deporting people from Europe to Afghanistan harms them. The evidence of the enormous individual benefits of migration is – at this point – pretty irrefutable. At the very least, sending people to a poor conflict ridden country condemns them to a lower lifetime income, fewer opportunities, worse health outcomes and shorter lives (the average life expectancy – unweighted – in Europe is 78, in Afghanistan it is 60). Even worse, deported people face persecution and violent deaths. If we send 80,000 people back to Afghanistan, some of them will die unnecessarily and many more will  suffer.

I understand that countries have to deport people sometimes. But when these nasty, cynical policies are tied to development aid, those aid policies are forever tainted. It is another worrying sign that EU aid is being used to harm and endanger the very people it should be helping.

When I saw Alfonso Cuarón’s adaptation of PD James’s “Children of Men” six years ago, I worried it might some day come true. I worry more now. Shame on these people. Shame on all of them.

The sum of parts

You’re doing it wrong

The IMF has a new paper out on gender budgeting efforts in sub-Saharan African countries:

Gender budgeting is an initiative to use fiscal policy and administration to address gender inequality and women’s advancement. A large number of sub-Saharan African countries have adopted gender budgeting. Two countries that have achieved notable success in their efforts are Uganda and Rwanda, both of which have integrated gender-oriented goals into budget policies, programs, and processes in fundamental ways. Other countries have made more limited progress in introducing gender budgeting into their budget-making. Leadership by the ministry of finance is critical for enduring effects, although nongovernmental organizations and parliamentary bodies in sub-Saharan Africa play an essential role in advocating for gender budgeting.

These sorts of efforts have certainly improved in both scope and sophistication. Back when I worked in the budget division of the Malawian Ministry of Finance, I was only asked once to perform any sort analysis of the gender focus of the budget. The request that landed on my desk had come from the Commonwealth, who wanted to know how many times the word “gender” had been used in any of the previous presentations of the national budget to parliament. After fishing out the transcripts from the Ministry’s library, I eventually discovered the answer was “zero.”

Universal Basic Income: The Next Generation

"Wait, you're saying that in the Federation you don't have to worry about money? You can just schlep around the galaxy seducing alien women?"

“Wait, you’re saying that in the Federation you don’t have to worry about money? You can just schlep around the galaxy seducing alien women?”

Over at Five Thirty Eight, there’s a nice piece by Daniel Flowers on the idea of a universal basic income (UBI). Proponents say it will allow people to choose their careers and live their lives without having to worry about ever being poor. A common criticism is that it will create a massive disincentive to work at all. Several experiments have already been run which have found small, but non-negligible effects on the willingness to work. It is one of the outcomes that a host of new experiments of giving people a long term guaranteed basic income will test.

I am a little worried that these new experiments won’t capture the long term, generational impact of a universal basic income. Let’s imagine I really wanted to be a filmmaker (*cough*), but decided to become an economist because filmmaking is more likely to leave me in poverty. If I’m half way through my career as an economist and I start receiving a basic income, it might be too late for me to really break into filmmaking. Even if I pull it off, adjustment costs will be high, and it’s likely I’d end up embarking on a career which would be less successful than if I had started at a much earlier age. It is these decisions that will largely be picked up when the targets of a UBI experiment are largely adult workers.

What is more interesting is the impact on the next generation. Let’s imagine the UBI is introduced and governments can credibility commit to providing it for one’s entire lifetime. Now, all those aspiring filmmakers can select into the job of their choice with lower adjustment costs and a higher likelihood of actually being accomplished at what they’d most like to do. Who knows if this would have a net positive or negative impact on the creation of value, but it certainly would lead to better sorting. But even the most ambitious UBI experiments which are being proposed are unlikely to pick up these effects. Instead what we need to do is find a group of high schoolers and offer some of them (randomly) a credible lifetime UBI, then sit back and see how it affects career decisions and labour market participation in the long run.

As a side note – how much does relative poverty in developing countries lead to sub-optimal career decisions?

The limitations of the Absolute Palma Index, in two graphs


Last year, the ODI’s Chris Hoy released a really useful and thoughtful paper pointing out that the basic maths of inequality are often not on the side of the poor. Even if economic growth is evenly spread, the absolute difference between the incomes of the poor and the richest must increase. That is, if you are 10 times as rich as I am and our incomes both grow by 10%, you’ll be taking home more money than I will at the end of the day. If we wanted to see a decrease in absolute differences of income around the world, it would require that the income of the poorest grow a great, great deal faster than that of the richest, something we are unlikely to see any time soon.

The unanswered question, and one that Hoy even posits himself end of the paper, is whether or not focusing on absolute differences in income makes more sense than doubling down on the relative differences in income that are captured by traditional inequality measures such as the Gini, Thiel or Palma indices. We know that income is correlated with lots of good outcomes for the beholder – better health, education, happiness and political power. However, if we are being truly honest with ourselves, we would have to admit that we don’t quite fully understand whether relationships are absolute or relative in nature (although we suspect both matter for happiness). Do the richest 1% of Americans have more political power in the US than the richest 1% of Nigerians have in Nigeria? These are the questions we must ask ourselves if we are to make a strong case for caring about absolute income differences.

In the meantime, I woke up this morning to find that Nick Galasso from Oxfam has made a pitch for using the “Absolute Palma Index” as the next big measure of inequality. The Absolute Palma is a variation of the Palma Index of inequality, which itself is the ratio of the share of income earned by the top 10% of the distribution and that of the bottom 40% of the distribution. The Absolute Palma, by contrast, is the absolute difference between the average income of the top 10% and the average income of the bottom 40%.

As the title suggests, I think there are limitations to the Absolute Palma Index, so consider the post a word of caution. I can think of one strong case against absolute measures: while they might be reasonable at describing immediate gains across a country’s income distribution after a year of growth, they aren’t very useful at describing differences between countries across the globe.

I happened to be playing around with data from Christoph Lakner and Branco Milanovic’s paper on the global income distribution, so I decided to see how the Absolute Palma Index varied across countries. Check out the graph below, which looks at how the Absolute Palma Index varies with mean income across countries. I’ve also highlighted countries which are either very unequal, very equal or somewhere in the middle as measured by the traditional Palma Index.



The first thing to note is that there is almost a one-to-one relationship between the log of GDP and the log of the absolute Palma. This is hardly surprising – take any income distribution and raise all incomes by a set percentage and by definition you will see an increase in the Absolute Palma. What this means is that on this index, poor countries do really, really well and rich countries do terribly. And that is most of the story. Log per capita income explains about 93% of the variance in the log of the Absolute Palma. The relative Palma explains most of the remaining unexplained variance, but on the whole has very, very little explanatory power.

The result is that we get some pretty counter-intuitive results. Even though Denmark, Sweden and Norway  are considered by pretty much every person I’ve ever ever spoken to be the most equal places on the planet, they come out as being more unequal than countries that are at the top of the relative Palma Rankings, places like South Africa, Honduras and Brazil.

Which of these countries would you rather be poor in? Presumably the one with the highest average income for the poorest 10%. If we graph the same relationship, instead using the average income of the bottom decile, we find the relationship is less strong, especially so for the poorest countries of the world. But if I had to choose whether I wanted to be born poor in a country with a high or low Absolute Palma index, sign me up for more inequality!



Now for the caveats: the data here is as good as 2008, so the basic cross-sectional relationship may have changed (although it hasn’t appeared to have done so ipapen the years leading up to 2008). There is also a difference between moving between countries of different average/median/poorest decile levels and observing individual countries as they grow richer or poorer. This means that there might be use in keeping track in how growth is `allocated’ across the income distribution, something which is already done (and was done carefully in Chris Hoy’s paper).

Absolute measures might tell us something interesting in the world, and I welcome more work on them. But there is a world of difference between adding a tool to the (now overflowing) box of inequality measures and pushing for headline measure that automatically penalizes rich, developed countries for being rich and developed. In addition, before we begin agonizing about absolute differences within countries, someone needs to make a pretty compelling case that they matter more than both absolute levels or relative differences, because these are things we already go through great pains to measure. If we are worried that the incomes of the poor aren’t growing fast enough, then why isn’t it enough to measure that?

Stata code and underlying data available here.

Update: good comments from Chris Hoy below.

So how do you feel about not winning the lottery?

"Here's to exogenous shocks to our neighbour's wealth"

“Here’s to exogenous shocks to our neighbour’s wealth”

Happy New Year. So I’ve been thinking a lot about the charity GiveDirectly recently. They were my charity of choice a year ago and I am planning to make another donation soon. For those of you who are not in the know, GiveDirectly makes unconditional cash transfers to poor people in Kenya and Uganda. For every dollar I donate, roughly 91 cents of that ends up with a household, which is then free to do whatever they want with it.

The other day GiveDirectly sent me an e-mail which linked to a series of interviews with residents of a single village that had been on the receiving end of these unconditional transfers. What is particularly astonishing is that the charity not only asked recipients how they were faring (pretty good, thank you very much), but roughly half of the interviews are with households which were not deemed eligible.

What I might have expected was a degree of unhappiness or animosity over not being selected to receive a $1000 USD transfer. GiveDirectly uses its own methods of determining whether or not a household is classified as “poor” (in the village in question it was households without a metal roof on their primarily residence). Even though (I presume) the charity goes through great pains to make the selection criteria transparent, to people on the ground the whole endeavour might seem a bit, well, random. A bit like a manna from heaven.

Recently, three academics who have previously studied GiveDirectly released a paper suggesting that these transfers do have some sort of negative spillovers on households that didn’t receive the transfer. Johannes Haushofer, Jeremy Shapiro and James Reisinger found that non-recipients in villages which received GiveDirectly transfers reported substantially lower levels of life satisfaction. So if this negative spillover, which I will go ahead and call the Haushofer Effect (there – I just branded it – coming to a book store near you), really exists, then I would expect a substantial amount of lamentation in the GiveDirectly interviews of non-recipients.

To the contrary, most non-recipients said that, overall, they were happy that their village had received the transfers. I found this hard to believe, but after going through 50 interviews of non-recipients, most replied positively to the question “Are you, overall, happy that GiveDirectly came to your village?,” a handful replied neutrally, and only one was vocally unhappy about it. There was another question aimed more at the negative effects of not being selected, and even then only about 25% responded with identifiably-negative comments.

So what is going on here? Why is the Haushofer Effect not appearing in these qualitative interviews? As much as I would like to believe that people do feel happy about seeing their neighbours get a shitload of money, I think I am more likely to believe one of the following:

(1) People don’t want to appear selfish, especially in front of a charity which might might might might give them a ton of cash some day. One respondent actually spelled it out: “”I am happy with your coming with the hope that one day I will also benefit.”

(2) The more complicated answer is that there is something about the conditionality of the question that changes its meaning. These families might be honestly happy about the fact that their neighbours (who are poorer) got transfers. But all the negative externalities associated with that (envy, local prices, etc) still make them unhappy in aggregate. A great example of this appeared in a recent episode of This American Life, where Neil Drummond tried to reconcile the fact that he really was happy his old friend Ta-Nehisi Coates had found fame and fortune with the reality that their friendship was slowly dissolving as a result of it.

(3) This village is different than the average village in the study above in some unobservable way.


I have no sense as to which answer is the most likely. And none of it will stop me from donating to GiveDirectly again. That said, while the charity should be praised for putting these interviews up on their website, they could take a step further and link to the paper on negative spillovers.


Update: GiveDirectly’s Max Chapnick has a helpful reply/explanation in the comments below, rightly pointing out that the academic paper I cited relies on within-village randomization (rather than GD’s method of targeting poor households), so the Haushofer effect might be primarily driven by the unfairness inherent in that lottery mechanism. This is a pretty plausible reason for the differences between the empirical study and the informal interviews.

The IMF, inequality and the trickle-down of empirical research

"It took so many assumptions to put you together!"

“It took so many assumptions to put you together!”

By Nicolas Van de Sijpe

recent IMF staff discussion note has received a lot of attention for claiming that a smaller income share of the poor lowers economic growth (see also here and here). This piece in the FT is fairly typical, arguing that the paper “establishes a direct link between how income is distributed and national growth.”

It quotes Nicolas Mombrial, head of Oxfam International’s office in Washington DC, saying that (my emphasis): “the IMF proves that making the rich richer does not work for growth, while focusing on the poor and the middle class does” and that “the IMF has shown that `trickle down’ economics is dead; you cannot rely on the spoils of the extremely wealthy to benefit the rest of us.”

The aim of this blog post is to clarify that the results in Table 1 of  the paper, which are based on system GMM estimation, rely on assumptions that are not spelled out explicitly and whose validity is therefore very difficult to assess. In not reporting this and other relevant information, the paper’s application of system GMM falls short of current best practices. As a result, without this additional information, I would be wary to update my prior on the effect of inequality on growth based on the new results reported in this paper.

The paper attempts to establish the causal effect of various income quintiles (the share of income accruing to the bottom 20%, the next 20% etc.) on economic growth. It finds that a country will grow faster if the share of income held by the bottom three quintiles increases. In contrast, a higher income share for the richest 20% reduces growth. As you can imagine, establishing such a causal effect is difficult: growth might affect how income is distributed, and numerous other variables (openness to trade, institutions, policy choices…) might affect both growth and the distribution of income. Clearly, this implies that any association found between the income distribution and growth might reflect things other than just the causal effect of the former on the latter.

To try to get around this problem, the authors use a system GMM estimator. This estimator consists of (i) differenced equations where the changes in the variables are instrumented by their lagged levels and (ii) equations in levels where the levels of variables are instrumented by their lagged differences (Bond, 2002, is an excellent introduction). Roughly speaking, the hope is that these lagged levels and differences isolate bits of variation in income share quintiles that are not affected by growth or any of the omitted variables. These bits of variation can then be used to identify the causal effect of the income distribution on growth. The problem with the IMF paper is that it does not tell you exactly which lagged levels and differences it uses as instruments, making it hard for readers to assess how plausible it is that the paper has identified a causal effects.

Continue reading

I drink your milkshake


The Ethiopians appear to be close to finalizing construction of a large hydroelectric dam on the Omo river, primarily to generate power but also to support local irrigation efforts.  Over the past five years the project has received substantial foreign financing and investment by China and indirectly by the World Bank. However, there appears to have been little consideration of the potential downstream impacts: the Omo river feeds Lake Turkana, which is a source of livelihood for a large number of communities in northern Kenya. The possibility that the lake may be partially drained is obviously upsetting a lot of people, although it does not seem that the Kenyan government is making a big fuss over the project.

This is a typical problem of negative externalities: the Ethiopians aren’t factoring in the welfare of Kenyan Turkana residents in the decision to build the dam. There’s actually some research showing that this is a common problem. From a recent World Bank paper by Sheila Olmstead and Hilary Sigman:

This paper examines whether countries consider the welfare of other nations when they make water development decisions. The paper estimates econometric models of the location of major dams around the world as a function of the degree of international sharing of rivers. The analysis finds that dams are more prevalent in areas of river basins upstream of foreign countries, supporting the view that countries free ride in exploiting water resources. There is weak evidence that international water management institutions reduce the extent of such free-riding.

By their very nature dams generate inequality in the flow of water between upstream and downstream areas. It is easier to pay the cost of hurting downstream communities when they are are in a different country (hey, they don’t vote for you). Ergo, countries are more likely to build dams when the costs are external.

It would be interesting to see what mitigates these effects – it is possible that Kenya’s relative indifference is due to lack of political power on the part of the northern tribes. Are dams with substantial cross-border costs less likely in areas where the proximate ethnic group is quite powerful?


You just don’t get me

Timothy Taylor has an excellent write up on the behavioural economics results coming out of the recently-released 2015 World Development Report. One of the most striking findings is that World Bank staff tend to overestimate the tendency for poor people to be fatalistic. From Taylor’s post:

What do development experts think that the poor believe, and how does it compare to what the poor actually believe? For example, development experts were asked if they thought individuals in low-income countries would agree with the statement: “What happens to me in the future mostly depends on me.”  The development experts thought that maybe 20% of tthe poorest third would agree with this statment, but about 80% actually did. In fact, the share of those agreeing with the statement in the bottom third of the income distribution was much the same as for the upper two-thirds–and higher than the answer the devleopment experts gave for themselves!

A number of other bloggers have picked up on this result, albeit without too much discussion about what this implies. I think the implicit assumption here are that development professionals are out of touch with the poor. I think there’s a number of ways we can interpret these results. Here’s the graph in question:


So the first possibility is the implicit one, that Bank staff don’t know what the poor believe, and possibly even that they assume the poor are fatalistic, possibly to a fault. Development economics is only starting to turn its head towards the convergence of fatalism, aspirations and economic outcomes (see, for example, the recent paper by Kate Orkin and her co-authors on aspirations in Ethiopia). The story that development experts buy into this belief is an easy one to believe, but not necessarily the right one. Note that it doesn’t at all take into account what the truth is, only perceptions.

Imagine your life’s outcomes are determined by (A) your own actions and (B) everything else, including randomness. How much weight would you put on (A) vs (B)? There’s no easy answer to this, but it is perfectly possible that the world’s poor ARE poor because (B) is actually much larger than (A). When you live in a country with terrible institutions, no social safety net, frequent economic or environmental shocks, it becomes very clear that (B) dominates (A).

So the second possibility is that Bank staff aren’t assuming the poor are being fatalistic, but that they are being realistic. That they (correctly?) judge that they have little control over their own lives. If they did, then they probably wouldn’t be poor. In this case, if the responses from the above sample are genuine (we might worry that respondents would be unwilling to admit that they have little control), then it’s the poor who have it the wrong way around: they are too optimistic about how much control they have over their own lives.

The second possibility isn’t necessarily any more likely than the first, but we should be cautious about what stories eventually emerge out of the above figure – there are a number of potentially overlapping biases at play, to the extent that it is not just a straightforward story of development professionals not `getting’ the poor.