Here is a paper I wrote for a class on development management. Though I wrote it, the views expressed here are not necessarily my own.
“There is but one social science,” economist George Stigler is reported to have once said, “and we are its practitioners.” Though Stigler meant his statement as a slight at the qualitative nature of economics’ “softer” alternatives, the “we” in his pithy remark can also double to mean “we men,” a distinction that has been borne out by a long history of female exclusion from the social sciences in general and from economics in particular.
These three themes embedded in Stigler’s remark – (1) the promotion of economics as the pinnacle of social science research employing (2) rigorous measurement and mathematical analysis thereby unconsciously resulting in (3) women having lower status within the profession – though not first identified by Robert Chambers, were perhaps best articulated by him. These ideas were included as part of his bigger project on professionalization within the social sciences and the need for participatory practices in development, which were collectively formalized in his late ‘90s work Whose Reality Counts?
It would be pleasant to believe that in the fifteen years since Chambers’ book was first released the social sciences have “learned their lesson,” lead by an infusion of humility and inclusion within the economics profession. Unfortunately, Chambers’ critiques can be levied just as easily today as they were when he initially wrote his book.
Economists as Kings and Scientists
The economics profession is still very much living in the shadow cast by Paul Samulson who first formalized its study in the 1940s by applying advanced mathematical models to what was previously a largely qualitative field – there is no math in Keynes’ General Theory, for instance. All of that has changed. Led by economists, mathematics has now spread to virtually every social science; it has leak into such non-mathematical fields as history, sometimes going by the name “cliometrics,” several sub-branches of modern philosophy, and, according to Chambers, even cultural anthropology. Only the post-positivists have remained immune from the influence of mathematics, but then that’s how they make a living.
As Chambers argues forcefully in his book, economists are at the “head of the class” according to traditional status hierarchies within the social sciences. This fact is easy enough to observe. The President has a Council of Economic Advisors; there is no Council of Sociologists. When hundreds of billions of dollars in government bailout funds are at stake it is the economists, not the historians or philosophers or feminists, that are brought to the table for discussion. Demographers are not appointed to the position of Treasury Secretary. The Federal Reserve controls the money supply and sets interest rates, power that ultimately radiates to affect the lives of billions of people worldwide; yet, an ethnomusicologist has never been considered for the job. Only those in security studies enjoy equal power in the U.S. government, with their appointments as heads of the various U.S. intelligence agency posts, presidential advisors, and, often, the powerful position of Secretary of State. Similarly prominent positions reserved for economists exist in most Western-style governments worldwide.
That economists have come to hold positions that deal with economic matters seems logical enough; however, the hubris they display through the use of formal mathematics and sophisticated statistical techniques is anything but logical. The term “social science” was meant to denote a type of analytical rigor directed toward the social world, not the conversion of “people to things,” as Chambers himself chides, or the notion that complex social systems can be studied and controlled in the same manner that a laboratory physicist can careful control and monitor her experimental setup. If there is an analogy between the social and physical sciences, economists are more like geologists – they know something, but are unable to precisely predict the location and veracity of the next earthquake, tidal wave, or volcanic eruption, and do not posses sufficient technology to easily and swiftly deal with such catastrophes when they do occur.
Of course, economists do not phrase their doctrine of mathematical rigor this way, but rather as a sanguine belief that mathematics, applied properly, can tease out casual relationships with precision as well as offer prescriptive policies. For example, economist Ed Glaeser, in his recent op-ed “The Role of Economics in an Imperfect World,” phrased things this way:
Hubris has been part of the human condition, with or without math, long before the Black-Scholes asset-pricing formula. Mathematical models create discipline. They ensure that we specify our assumption and that our conclusions then follow from our assumptions. Statistics then provide us with indispensable tests of our theories.
But if this were true we would expect – or at least hope – that testing of various theories would generate consensus within the profession, anchoring a public policy question with normative prescriptions so that that the next question could be addressed and so on until a socially efficient outcome was reached. But surveys show that economists are still widely divided on most important public policy issues.
And what is true about economic consensus in general is true for development economics in particular. Late last year, for instance, Twitter exploded after Daron Acemoglu and James Robinson brazenly responded to Jeffery Sachs’ Foreign Affairs review of their popular book Why Nations Fail? Sachs also famously disagrees (on nearly everything) with NYU economist William Easterly, but so too does Easterly disagree with the various “poverty trap” theories put forth by Paul Collier in his best-selling The Bottom Billion. If data has done anything to resolve these controversies it doesn’t show.
As Chambers puts it, “Figures so selected are then accepted, repeated, cumulatively misquoted, and used, consciously or unconsciously, to reinforce predisposition and prejudice.” Economist Tyler Cowen calls this phenomenon “mood affiliation,” the notion that one first unconsciously chooses a “mood” and then selects or interprets results to comport with this pre-selected world view. In the universe that Chambers and Cowen envision, data does little to resolve fundamental disputes over theory as Glaeser suggests.
In fact, such disagreement even extends to the data-heavy Randomized Controlled Trial (RTC) craze. For instance, in a 2008 blog post – again involving Jeffery Sachs – Harvard development economist Dani Rodrik cited an RTC to support Sachs’ view that insecticide-treated bed nets should be given away for free rather than sold at a nominal price. However, Mead Over from the Center for Global Development quickly responded by arguing that the RTC did not at all support Sachs.
Intellectual disagreement, of course, is to be encouraged. Indeed, it can be a powerful force in creating new knowledge and moving research programs forward. However, when paired with the hubris of those such as Stigler or Glaeser it can be dangerous. This is doubly true when economists are hoisted into powerful government positions and then expected by both government officials and the general public to carefully predict and steer the economy as a physicist would a laser beam. The latter two parties would do well to realize the limitations of what is possible in matters of economics, but so too should economists chisel away their patina of scientism and refrain from promoting themselves as surgeons who simply choose to operate on matters of public policy instead of on human patients.
Holy Measurement Error Batman
Part of the idolatry of the economics profession is the belief that “data” can erase all sins. To the extent that reliable data can serve as evidence to support an argument or claim there is no harm done. But the fabrication of unreliable data where data simply doesn’t exist can indeed be harmful. In Chambers’ book he references Gerry Gill’s paper Ok, The Data’s Lousy, But It’s All We’ve Got, summarizing Gill’s piece with his own colorful observation:
At worst, they [economists and consultants] grub around and grab what numbers they can, feed them into their computers, and print-out not just numbers but more and more elegant graphs, bar-charts, pie diagrams and three-dimensional wonders of graphic myth with which to adorn their reports and to justify their plans and proposals.
In The Black Swan Nassim Taleb offers an elegant parallel for much of the data hunger in economics. In his analogy you have just boarded a plane in Atlanta that is destined for New York City. Suddenly a flight attendant’s voice can be heard over the intercom, “The pilot has misplaced the map for the NYC airport,” he says, “but don’t worry he’s going to use the map for Chicago’s airport.” But Chicago is not New York, the two airports are in different parts of their respective cities, the runways are facing different directions and require different approach patterns, JFK is positioned next to Jamaica bay while O’Hare is fifteen kilometers inland from Lake Michigan. The dissimilarities are nearly endless. If placed in such a scenario Taleb argues any rational person would disembark the plane. In economics, however, researchers proudly pat themselves on the back, remarking the good fortune that they found data to apply to their cause.
Perhaps the single best example in all of economics comes from the recent developments in African GDP revisions. Economists have long known that African nations had severe obstacles in collecting the sort of data needed to construct accurate GDP figures: poor infrastructure, widespread corruption and institutional failures, low-skilled government workers, technology shortages, and so on. Yet, GDP figures were constructed anyway and indiscriminately imported into the databases of prominent international organizations such as the OECD, IMF, and World Bank. Economists have used the data for thousands, perhaps tens of thousands, of regressions to determine both the causes of poverty and to assess the multitude of macro-level poverty alleviation interventions that have been tried over the decades. And now it turns out that unreliable GDP data may invalidate a large portion of that work.
How bad is it? Alwyn Young from Britain’s London School of Economics reconstructed GDP for 29 Sub-Saharan countries using household-level income gathered in DHS health surveys. He estimates that for the past two decades the region has been growing between 3.4 and 3.7 percent per year, or roughly four times the figures reported in international data sources. Compounded over time the difference between his findings and official statistics are enormous.
To give some sense of the magnitudes, in 2010 Ghana’s government revised its GDP figure upwards by 60%, meaning that roughly $13 billion worth of economic activity had been overlooked. This is all the more alarming because “[o]ver the past thirty years Ghana has been one of the most scrutinized, measured, studied, pick-over economies in Africa.” If the pending investigation in Nigeria yields a similar GDP revision it would be the equivalent of “40 economies roughly the size of Malawi’s” hiding in the African country. Similar news is certain to appear Africa-wide as more governments undertake revision investigations at the behest of international aid organizations and researchers.
With such enormous misinformation about GDP figures – much of it dating back decades – what is really “known” about development economics becomes quite murky. This should serve as a tale of caution to economists, and indeed all social scientists, about the fallacy that some data is better than none at all.
The Second Sex
One key concept for Chambers is what he terms “Normal Professional Status” – a status hierarchy within and between practicing professions composed of five primary factors: education and training, competence and specialization, gender, influence and wealth, and location. Regarding gender Chambers writes, “The high-status professionals are mainly men, while those of lower status are mainly women, or have a higher proportion of women.” Cambers continues by specifying several examples of women being positioned at the bottom of the academic ladder. Again, little has changed in the time since he wrote. Female exclusion is still prevalent both in economics and in the social sciences more broadly.
While Chambers wrote in 1997 that no woman had every won the Nobel Prize in Economics, as of 2013 only Elinor Ostrom has achieved that honor, though even she was technically a political scientist. When the Chronicle of Higher Education created a visual representation of academic papers authored by women over the four centuries between 1665 and 2010 (using data from the University of Washington’s Eigenfactor Project), it found that only 9.7% of economics papers were female-authored. Since 1991 the percentage has increased, but is still paltry at just under fourteen percent.
Again, what is true in general is also true for development in particular. It was not until 2011 when the International Monetary Fund (IMF) was first headed by a woman, Christine Lagarde; meanwhile, The World Bank has never had a female president. There has been only one female USAID administrator, Henrietta Fore, who served two years from 2007 until 2009. Yet, since its founding in 1946 USAID has disbursed a third of a trillion dollars in development loans and grants, the World Bank nearly $450 billion since it began, while the IMF currently has over a trillion dollars in pledged or committed resources.
In this regard, the economics profession is not alone. The pattern of female underrepresentation is replicated throughout the social sciences. When women are represented they tend to be in those specialties associated with what feminists scholars have identified as feminine values such as community, connection, sharing, nature, life, and interdependence: household decision making and subjective well-being in economics, gender and family studies in sociology, women’s studies and concept of self in anthropology, women candidates in U.S. election studies, feminist history in history, early childhood and infant learning in cognitive science, pregnancy outcomes in occupational health, hospital queuing in operations research, society and fertility in demography, minority students in education, and, of course, Lie algebras in mathematics (meanwhile a measly 1.5% of papers on Riemannian manifolds have been authored by women).
But these feminine values are not appreciated in a system that treats “truth” and “measurement” eponymously. Instead, women are pushed either to the edges of their fields or must choose lower-status academic professions that place a greater value on concepts of qualitative research and intuition. This is not to say that only women express or appreciate these feminine values, but as Chambers points out, men who are empathetic to such ideas are driven to positions of lower status. Nor is it to say that all women are more interested in “community” than in “measurement.” However, it does seem true that these feminine values come more easily to women than to men and as such women are more often required to make career decisions that are at odds with personal sensibilities.
The problems identified by Chambers fifteen years ago are very much alive today. Economists still enjoy high status positions due, in part, to the way they are perceived by the general public and the important government positions they fill. They in turn sustain their status by using advanced mathematics to lend an air of science and control to the profession, in practice, however, rarely moving toward consensus. The emphasis on measurement and data depreciates feminine values and thus drives women to the fringes instead of incorporating their viewpoint as an important and necessary alternative. Here’s hoping that fifteen years from now Chambers’ concerns will be a distant memory.
Blattman, Chris. “Africans Are Richer (and Getting Richer) Than You Think.” Chris Blattman, October 30, 2012. http://chrisblattman.com/2012/10/30/africans-are-richer-and-getting-richer-than-you-think/.
Chambers, Robert. Whose Reality Counts?: Putting the First Last. First Edition. Intermediate Technology Publications, 1999.
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Over, Mead. “Sachs Not Vindicated.” Center for Global Development, January 18, 2008. http://blogs.cgdev.org/globalhealth/2008/01/sachs-not-vindicated.php.
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Taleb, Nassim. Taleb on Black Swans, Fragility, and Mistakes. Podcast, May 3, 2010. http://www.econtalk.org/archives/2010/05/taleb_on_black_1.html.
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Tong, Rosemarie. Feminist thought : a more comprehensive introduction. Boulder, Colo.: Westview Press, 2009.
 Glaeser, “The Role of Economics in an Imperfect World.”
 Klein, “An Amazing Consensus Among Economists: Not.”
 Murphy, “Thanksgiving Eve Twitter Debate: Sachs Vs Everyone.”
 Easterly, Easterly on Growth, Poverty, and Aid.
 Chambers, Whose Reality Counts?.
 Cowen, “The Fallacy of Mood Affiliation.”
 Rodrik, “Jeff Sachs Vindicated.”
 Over, “Sachs Not Vindicated.”
 Taleb, Taleb on Black Swans, Fragility, and Mistakes.
 Blattman, “Africans Are Richer (and Getting Richer) Than You Think.”
 Jerven, “Lies, Damn Lies and GDP.”
 Moss, “» Ghana Says, Hey, Guess What?”.
 Jerven, “Lies, Damn Lies and GDP.”
 Chambers, Whose Reality Counts?.
 The Chronicle of Higher Education, “Women as Academic Authors, 1665-2010.”
 Services, “Standard Program Report.”
 The World Bank, “Total Disbursements by Country.”
 International Monetary Fund, “Factsheet — The IMF at a Glance.”
 Tong, Feminist thought.
 The Chronicle of Higher Education, “Women as Academic Authors, 1665-2010.”