On Sunday I blogged the new Stata program I wrote for applying extreme value theory. It includes a novel computation to reduce bias for the generalized extreme value distribution (GEV). To document the efficacy of that correction and the package as a whole, I set my computer to testing it on simulated data. Since Sunday the little Lenovo has run half a billion regressions.
One topic I’m studying for my main client, the Open Philanthropy Project, is the risk of geomagnetic storms. I hadn’t heard of them either. Actually, they originate as solar storms, which hurl magnetically charged matter toward earth, jostling its magnetic field. Routine-sized storms cause the Auroras Borealis and Australis. Big ones happen roughly once a decade (1972, 1982, 1989, 2003, a near-miss in 2012…) and also mostly hit high latitudes. The worry: a really big one could send currents surging through long-distance power lines, frying hundreds of major transformers, knocking out power to continent-scale regions for months or even years, and causing an economic or humanitarian catastrophe.
My best assessment at this point is that if one extrapolates properly from the available modern data, the risk is much lower than the 12%-chance-per-decade cited by the Washington Post last summer. But that’s a preliminary judgment, and I’m not a seasoned expert. And even if the risk is only 1%, it almost certainly deserves more attention. More from me on that in time. (For a mathematician’s doubts about the 12% figure see Stephen Parrott.)
As they committed to do, the members of the Development Assistance Committee have struck a deal to revise how loans are counted as Official Development Assistance (ODA). They announced it today at the end of their two-day High-Level Meeting in Paris.
The specifics validate the rumors I passed on last month. The benchmark discount rate will be changed from the fixed 10% to the IMF’s new “unified” rate. Now, that rate is 5%. Based on a 10-year moving average of the U.S. 10-year treasury rate, it will plunge to 4% in a little over two years, I estimate, if its formula is followed faithfully. However, I believe that the IMF has not yet committed to updating the rate so mechanically, perhaps because a lower rate would make borrowers’ current debt loads look less sustainable, leaving less room for new lending, to the discomfort of some donor governments. So the DAC has hitched itself to a benchmark whose future is a bit fuzzy.
To the IMF rate will be added a simple risk premium, so that loans will count more as aid when going to countries deemed more likely to default: a 1% premium for upper-middle-income countries (UMICs), 2% for lower-middle-income countries (LMICs), and 4% for low-income countries (LICs). As it happens, the IMF’s sibling, the World Bank, curates this three-way classification of developing countries, which is effectively annexed to the statistical definition of aid.
After two years of communiqués, consultations, and commentary, the Development Assistance Committee has gotten down to brass tacks in fixing the definition of Official Development Assistance (ODA). I hear that a negotiating group composed of key donor representatives named for its chair, the UK’s Mark Lowcock, has reached a tentative deal. The Lowcock group’s role being unofficial, DAC chair Erik Solheim has turned its consensus into a proposal for discussion with the entire DAC membership.
I have not seen the document, so I cannot report definitively on it. It appears purely focussed on the issue that forced the discussions in the first place, which is when and how much to count loans as aid. I gather that the principle elements of the compromise are:
This morning, one of my key secret intelligence sources (code name: MOM) alerted me to the appearance of a big New York Times exposé of foreign-government funding for U.S. think tanks seeking to influence domestic policy.
The article starts this way:
The agreement signed last year by the Norway Ministry of Foreign Affairs was explicit: For $5 million, Norway’s partner in Washington would push top officials at the White House, at the Treasury Department and in Congress to double spending on a United States foreign aid program.
But the recipient of the cash was not one of the many Beltway lobbying firms that work every year on behalf of foreign governments.
It was the Center for Global Development, a nonprofit research organization, or think tank, one of many such groups in Washington that lawmakers, government officials and the news media have long relied on to provide independent policy analysis and scholarship.
My client GiveWell, working closely with the foundation Good Ventures through the Open Philanthropy Project, is seriously considering labor mobility as a cause to which Good Ventures should commit resources:
It appears to us that moving from a lower-income country to a higher-income country can bring about enormous increases in a person’s income (e.g., multiplying it several-fold), dwarfing the effect of any direct-aid intervention we’re aware of.
But there are worries: Does letting more workers into a wealthy country take jobs from people already working there? Or does the competition for jobs reduce wages all around? These possibilities are a particular concern as they apply to low-skill workers, who are poorer.
For due diligence, GiveWell hired me to review the evidence on the potential side effects of immigration. Here is my full report, in draft, ready for comments from anyone. To read my conclusion, skim down a few paragraphs to the bullet points.
In March I recounted how former colleagues Michael Clemens, Steven Radelet, and Rikhil Bhavnani wrote an excellent paper in 2004 on the impact of foreign aid on economic growth, “Counting Chickens When They Hatch.” The idea captured in that title is that it is important to think about the likely timing of the impacts of aid. Don’t design your analysis as if you expect that funding for teaching 6-year-olds will raise economic growth in four years. Match the follow-up period to the type of aid. Count your chickens only when they hatch.
Some years later, and joined by another CGD recruit, Samuel Bazzi, those authors overhauled their paper and published it in the top-flight Economic Journal (ungated version). The final version is quite different but also excellent (it won the journal’s best-article prize). Instead of doing its own econometrics afresh, it modifies the three most-cited studies in the aid-growth literature in light of the “counting chickens” insight. Although those studies disagree on whether and when aid “works,” in the sense of boosting growth, Clemens, Radelet, Bhavnani, and Bazzi (CRBB) conclude that revising the studies to take timing into account causes all results to converge, to a ginger but positive appraisal. (Listen to Michael speak cautionarily about “Chickens” in this Library of Economics and Liberty podcast.)
I say “excellent” and I mean it. But, true to type, I actually doubt the econometric reasoning. I am not persuaded by these results that “aid inflows are systematically associated with modest, positive subsequent growth.” More…
The last of my three posts for CGD on redefining Official Development Assistance (ODA) tries to get away from the loan business (which has gotten most of my attention and is the reason the definition of ODA is up for discussion at all). In the new post I talk about what activities ought to dropped from or added to the definition in order to keep it credible and up to date.
The April issue of the Journal of Development Studies includes the final version of my article with Jonathan Morduch replicating the study of the impact of microcredit in Bangladesh by Mark Pitt and Shahidur Khandker. Properly, the journal also carries a reply from Mark Pitt. (Ungated versions of the dueling documents are here and here.) To my surprise, JDS did not solicit a rejoinder from us the way they did in a nearly identical situation involving a JDS editor as replicating author. Perhaps this is a sign of the strength the editors see in our paper…which is to say, maybe I should have just chilled.
But as usual, Pitt’s arguments are strongly worded even as their subject remains technical. So the average reader will absorb the style more than the substance, and wonder, I fear, who are these fools Roodman and Morduch? So for the public record, here is a rejoinder from yours truly. It’s a quote-and-response.
“RM [Roodman & Morduch] have backed off many of their prior claims and methods.”
No. The first version of our paper questions the exogeneity of the core intent-to-treat variables; highlights that an asserted discontinuity in treatment, central to Pitt & Khandker’s (PK’s) claim to quasi-experimental status, is absent from the data; observes that the magnitude of the impact estimates depends on an arbitrary censoring choice for the “log of 0″; and demonstrates that a more-robust linear estimator produces no evidence of impact. Those arguments stand.
I promise this is the last post on the incorporation of loans into the measurement of Official Development Assistance. Actually, it’s a cross-posting of something I just wrote for CGD. Leaving aside the question of whether to factor default risk into the determination of whether a loan is aid, I enunciate four principles for counting loans as aid.
Next week I’ll be in Paris, where I will participate in several meetings at the DAC on these very questions, and present my work to staff. I’m sure I’ll learn a lot about the substance and the process. I’ll report back to you after that.
The Crisis in Official Development Assistance (ODA) Statistics: Needed Revamp Would Lift Japan, Lower France
Sorry for the silence here.
I worked on two main projects over the last month. For GiveWell and Good Ventures, I began reviewing what is known about the economic impacts of immigration in receiving countries, particularly on low-wage workers. Good Ventures is considering labor mobility as an area in which it could actively support policy advocacy. But they want due diligence on the concern that allowing more immigrants in will depress earnings for those already here. I’ll share more of that work when it’s ready.
And for my old employer, the Center for Global Development, I wrote a paper that expands on my earlier work on how foreign aid should be defined for purposes of statistics. CGD posted the paper last Thursday and a blog post about it this morning. Go read that. (More blogging for CGD will follow.)
For many decades, researchers have been asking why some countries are so rich and some so poor. They have offered many answers, from corruption to banking to guns, germs, and steel. A widely if vaguely held view is that “institutions” are central. In seminal work on this theme, Nobelist Douglass North defined institutions as “the rules of the game in a society, or, more formally, … the humanly devised constraints that shape human interaction.” In their recent book, Why Nations Fail, Daron Acemoglu and James Robinson popularize earlier scholarship with Simon Johnson on the historical roots of key institutions that undergird private enterprise, such as rule of law and secure property rights.
My client GiveWell, which is working closely with the new foundation Good Ventures, commissioned me to review the scientific evidence on the impact of deaths on births. When a life is saved, especially a child’s life, do families go on to have fewer additional births than they otherwise would? Is the effect more than one-to-one or less? As I write just below, a criticism sometimes thrown at life-saving interventions is that they do as much harm as good by accelerating population growth.
Last summer the U.N. High-Level Panel of Eminent Persons on the Post-2015 Development Agenda (HLP) released a report that does a fine job of thinking through what international development goals should succeed the Millennium Development Goals, which expire in 2015. Perhaps to the surprise even of its authors, one idea in the report got lots of people talking: the call for a “data revolution”: More…
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Simulation results for different methods of fitting extreme value distributions. Pretty graphs. davidroodman.com/blog/2015/01/2…
Econometrics Beat: Dave Giles' Blog: Extreme Value Modelling in Stata davegiles.blogspot.com/2015/01/extrem…
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