What do economists do all day?

Economics is a pretty varied field. And yet, external impressions of the field still paint it as quite homogenous, often focused primarily on macroeconomics (in the case of the general public), or on a narrow, dogmatic neoclassical microeconomics (in the case of some sociologists). So, in the spirit of trying to explore the heterogeneity of modern economics, here are short descriptions of three papers from the most recent issue of the American Economic Review that caught my eye as showing off the diversity of interesting things you might not expect to see in economics. Note, even more than usual, reference here is not endorsement.

First up, we have a paper based on the Sapir-Whorf hypothesis! Rather than posting the abstract, I’ll post the first paragraph because it much more clearly articulates the argument of the paper:

The Effect of Language on Economic Behavior: Evidence from Savings Rates, Health Behaviors, and Retirement Assets
Languages differ in whether or not they require speakers to grammatically mark future events. For example, a German speaker predicting rain can naturally do so in the present tense, saying: Morgen regnet es which translates to “It rains tomorrow.” In contrast, English would require the use of a future marker like “will” or “is going to,” as in: “It will rain tomorrow.” In this way, English requires speakers to encode a distinction between present and future events, while German does not. Could this characteristic of language influence speakers’ intertemporal choices?

The paper uses survey data on cultural traits (the World Values Survey!), cross-country regressions, cites linguists (including explicitly invoking the Sapir-Whorf hypothesis), and so on. Cool right? And in a top econ journal!

Next up, a paper on the meeting point of post-WWII US economic and political imperialism:

Commercial Imperialism? Political Influence and Trade during the Cold War
We provide evidence that increased political influence, arising from CIA interventions during the Cold War, was used to create a larger foreign market for American products. Following CIA interventions, imports from the US increased dramatically, while total exports to the US were unaffected. The surge in imports was concentrated in industries in which the US had a comparative disadvantage, not a comparative advantage. Our analysis is able to rule out decreased trade costs, changing political ideology, and an increase in US loans and grants as alternative explanations. We provide evidence that the increased imports arose through direct purchases of American products by foreign governments.

The paper relies on recently released CIA documents to identify exactly when CIA influence was exercised. According to the authors’ analysis, following a CIA intervention, the foreign government would increase its purchases of US goods. Even better, the goods bought by the new government tended to be things which the US was comparatively bad at producing, the opposite of what is predicted by purely economic theories of trade (that is, even if you expected an increase in trade following the installation of a new regime that was pro-trade, in the absence of some political machinations, that trade would be in the things the US has a comparative advantage in and vice versa). So, score one for political power and imperialism driving economic behavior!

Finally, a shorter paper by the ever-controversial Steven Levitt (and co-author Fryer) on race and intelligence:

Testing for Racial Differences in the Mental Ability of Young Children
Using a new nationally representative dataset, we find minor differences in test outcomes between black and white infants that disappear with a limited set of controls. However, relative to whites, all other races lose substantial ground by age two. Combining our estimates with results in prior literature, we show that a simple model with assortative mating fits our data well, implying that differences in children’s environments between racial groups can fully explain gaps in intelligence. If parental ability influences a child’s test scores both genetically and through environment, then our findings are less informative and can be reconciled with a wide range of racial differences in inherited intelligence.

Rather than the large, approximately one standard deviation, differences found in intelligence tests among older children, Fryer and Levitt find a minuscule .0055 standard deviation difference among children between 8 and 12 months old. That is, all of the difference in intelligence pops up after the first year. The results for different models after that are somewhat inconclusive, but the data do suggest that you don’t need any genetic difference between racial groups to explain racial gaps in intelligence tests at older ages.

So, three papers, about the role of language in shaping economic behavior, about the role of political and military imperialism in shaping trade, and on the (non)role of genetics in shaping racial disparities. Welcome to economics, 2013?*

*Ok, to be fair, there’s plenty of stuff that looks exactly like what you’d expect (asset pricing, job search over the business cycle, fiscal and monetary policy models, etc.). My point is just that there’s a lot more than that too!

IRB QOTD: Schrag, “How Talking Became Human Subjects Research”

For better or for worse, social science research is now governed by an institutional review board system that seems to have the problems and promises of medical research, and not social science, as its priority. Zachary Schrag has an excellent article on the history of social sciences and the IRB, “How Talking Became Human Subjects Research.” Schrag summarizes the argument quite vividly:

“This article draws on previously untapped manuscript materials in the National Archives that show that regulators did indeed think about the social sciences, just not very hard. … Compared to medical experimentation, the social sciences were the Rosencrantz and Guildenstern of human subjects regulation. Peripheral to the main action, they stumbled onstage and off, neglected or despised by the main characters, and arrived at a bad end.”

Recommended.

“Poor Numbers”: A Q&A with Morten Jerven on Economic Statistics in Africa

One of the most interesting parts of the history of national income statistics is how rapidly and widely they diffused across the globe. Before World War II, hardly any country had official national income data, and none routinely relied on them to make policy. In the 1950s, the United Nations codified a global standard, the System of National Accounts, and official national income data became a requirement of modern nationhood – something every country had to produce, knowledge seen as indispensable for planning purposes, development aid, even assessing UN dues. And yet, while the UNSNA was a global standard, not all of the globe was equally suited to produce numbers according to its rules. In particular, the economies of Africa did not look like the economies of Western Europe where national income statistics were pioneered. Essential data that powered national income statistics, including censuses, income and payroll tax data, and more, simply did not exist in many poorer countries. African economies were just not as calculable, as those of Western Europe (at least not in the framework of the UNSNA). Thus, producers and users of African national income statistics have long known that such data were not perfect. But how bad were these numbers? Where are the problems? Are the uncertainties uniform (like the systematic undercounting of the informal sector) or more idiosyncratic? And at worst, users of such data hoped that even if the absolute GDP levels were off, the trends were still meaningful – that we might not know exactly how much poorer one nation was than another, but we could tell which countries were growing the fastest and thus assess the impact of economic development policies.

For the past five years, economic historian Morten Jerven has been arguing that African economic statistics are truly poor; so poor that we have misled ourselves into believing that we know much more about African economies than we really do. His new book, Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It, summarizes this research and presents a coherent case for skepticism, and especially for criticism of the seemingly standardized world economic databases (such as the World Bank’s Development Indicators). These supposedly comparable data leave very little trace of the messy process that produced them, and thus leave end users incapable of assessing just how bad (or, less often, good) the statistics they are working with really are. As a consequence, we are collectively capable of being surprised when countries manage to update and improve their statistical output and produce seemingly fantastic outcomes, as when Ghana’s GDP shot up by 60% after a recent revision. The blog Democracy in Africa has a more detailed, chapter by chapter review of the book here. Below is a short Q&A with Professor Jerven about his new book, including his experience interviewing government statisticians across Africa, interacting with World Bank officials, and trying to convince the development community to better understand the available data.

Read the full post »

Ranking Programs in Sociology

If you like quantitative rankings of sociology departments, today is like some sort of holiday.*

Over at Scatterplot, Neal Caren has an analysis of top 20 placements within Sociology. As he’s careful to note, but as ought to be stated many times over, this is only an analysis how well departments do at placing their students in top 20 sociology programs, which is not an especially great measure of the success of a program.. although it’s better than nothing, especially if you want such a job. For a program like, Michigan, for example, this would miss how well (or poorly) we do at placing students in professional schools (Business, Social Work, Policy, etc.), which might be more relevant for an individual student. Neal’s results are very much in line with Burris 2004: 88% of top 20 assistant profs come from top 20 programs, as ranked by the current USNWR. Neal goes on to do some back of the envelop calculations to show:

So when you start graduate school in one of these [top 20] departments, the odds of getting an early career job in a similar department is about one in twenty. In March Madness speak, think of yourself as a 4 seed trying to win the national championship.

[A]ssuming schools ranked 20 to 50 average 10 incoming students a year, that is about 300 folks a year competing for one slot [in a top 20 program]. Those are roughly the odds that an eight seed has of winning the tournament, which has happened once so far (Villanova in 1985).

Over at OrgTheory, Kieran Healy has released the results of the All Our Ideas survey of “best” sociology department. Head over there for methodological details and results. Kieran also presents some nice graphs of vote-similarity, which interestingly places Michigan most closely with Stanford (maybe Woody Powell and Jason Owen-Smith were on OrgTheory voters’ minds?).

Neal, Kieran – there’s a (potentially) interesting merging of data that could be done here. Specifically, do top 20 placements match better with the AOI rankings or the USNWR rankings? If we assume that such rankings are somehow measuring within-discipline prestige, then it seems like this would be one way to test which measure is “better.”

*Maybe Passover? Smarch Christmas? I’m not sure.

On the Origins of the Kuznets Curve

Today, I’m blogging from the Harvard University Archives. I’m five boxes deep into the Simon Kuznets collection, for my second visit related to my dissertation research. I just came across a letter that, while not especially important to my dissertation, seemed like it might be of wider use to anyone interested in the history of the “Kuznets Curve.” Simon Kuznets, in addition to spearheading the first official US national income statistics in 1932-1934, is probably most famous for this curve/hypothesis.* In his presidential address to the American Economics Association in December of 1954, later published as Economic Growth and Income Inequality, Kuznets argued that income inequality and economic growth might have an inverted-U shaped relationship. That is, as economic growth/development increases, inequality first goes up and then comes back down.

Kuznets based this empirical story on a few decades worth of data from the US and UK, and asserted in the conclusion that: “The paper is perhaps 5 per cent empirical information and 95 per cent speculation, some of it possibly tainted by wishful thinking.” (1955: 26) Since 1955, the paper has generated an entire literature asking whether or not the curve is real, whether it’s reversed in the past 30 years to create a new pattern, and so on. Much of the early research supporting the Kuznets curve came from cross-national studies which mapped inequality against current GDP/capita and showed the expected inverted-U shaped relationship. These studies, like Kuznets’, lacked longitudinal data. The recent consensus seems to be that the Kuznets curve does not hold within countries (e.g. Gallup 2012). That is, within a particular country, the relationship between economic growth and income inequality does not follow the expected path. Here are two graphs from Gallup 2012 that help sell the argument. The first is the relationship between log income and inequality, which looks like a Kuznets curve (though as Gallup notes, the fit is not quadratic for unlogged data):
Gallup 2012 Figure 3

The second is the author’s estimates of within-country income inequality trajectories (each line is one country). Note the chaos, and the absence of any coherent story, with some tendency towards a U-shape (not a upside-down U!):
Gallup 2012 Figure 8

Gallup further notes that Kuznets’ original data only had half of the original story. Yes, Kuznets shows a decrease in inequality in the US and UK in the late 19th century to post-WWII period. But Kuznets has no data on the initial upsurge in inequality! That is, Kuznets only has the falling half of the curve. Gallup graphs Kuznets’ income inequality data against historical gdp data now available from the work of Angus Madison to give some sense of what Kuznets was seeing:
Gallup 2012 Figure 1
Gallup (2012: 6) summarizes:

Kuznets showed that inequality fell in two high-income countries as they grew richer after World War I, but he had no evidence of rising inequality at low income levels. Ironically, Kuznets’ prediction that inequality will rise during the early stages of development, for which he had no evidence, is better remembered than his prediction that inequality will fall at higher incomes.

What did Kuznets have, if not data? He had a model. Specifically, Kuznets motivated his proposed empirical story (rising then falling inequality) with a two sector model of income inequality. Kuznets argued that agricultural incomes were likely as or more equal than industrial/urban incomes, but industrial incomes were much higher on average. Thus, as workers moved from agricultural to industrial labor, inequality would increase (low agricultural wages to higher industrial wages), until so few workers were in agriculture that a tipping point was reached and inequality came back down. This story falls apart, however, if agricultural incomes in the pre-development period are actually more unequal than industrial ones, because “if agricultural incomes are more unequal than industrial wages, perhaps due to unequal land ownership, movement out of agriculture into industry could reduce inequality right away.” (Gallup 2012: 7)

Ok, so why did Kuznets think that agricultural incomes were more equal than industrial ones? An archival document I just perused before starting this post sheds some light on the answer: Kuznets had some intuitions backed by absolutely no data. Data on income distribution are still poor now, and were even worse back in the 1950s, with historical data being extremely limited to a handful of studies (some conducted by Kuznets himself). These studies did not readily break incomes down by urban/rural or industrial/agricultural divides. Kuznets wrote to one of his collaborators, Selma Goldsmith (then at the Office of Business Economics, what is now the Bureau of Economic Analysis) to ask about the data she had been working on to produce official income distribution data for the United States (see, e.g., Goldsmith et al. 1954). In a letter dated August 15, 1954, Kuznets writes about his current work on economic growth and income inequality:

In my model, I somehow began with the assumption that inequality within the ag. sector is lesser than within the non-ag—although I had no evidence whatsoever. … the distributions that are available (in your D. of C. publication and elsewhere) show inequality among the ag. group (i.e. farm operator families) to be greater than among the non-ag—certainly in recent years. … Yet one would expect that with the much wider range of income opportunities in the non-ag sector, the inequality in size distribution would be appreciably wider in the latter. … Do you have any explanation for it?**

Let’s just stress that again: Kuznets had absolutely no evidence that inequality within the agricultural sector was less than the industrial one. Kuznets guesses that this discrepancy between his theoretical intuition and the data has something to do with the data not quite matching what they want – there’s higher year-to-year variability in farm incomes, but in the long-run the inequality within families might be less than appears in the data, or something like that. He’s not sure. And that uncertainty, among many others, is reflected in his strong caveats at the end of presidential address.

Years alter, Kuznets himself would abandon the curve and caution against looking for historical laws of development, especially in cross-sectional data (Moran 2005). And yet the debate over it lives on, as does the search for laws of economic growth and development.

*In 1971, Kuznets won the Nobel Prize in economics, “for his empirically founded interpretation of economic growth which has led to new and deepened insight into the economic and social structure and process of development.”
**Papers of Simon Kuznets, Harvard University Archives, HUGFP88.10 Misc. Correspondence, Box 4.

VotD: The Changing Methodology of Economics

Sociologists often look at Economics and see a field dominated by rigid formal theorizing – utility-maximizing rational choice etc. Our image of economics often looks like an intermediate undergraduate textbook. But what’s actually going on at the forefront of the economics research frontier? This table from Hammermesh (2013)’s Six Decades of Top Economics Publishing: Who and How? shows some revealing trends in the methodology of economics from 1963 to present:

Hammermesh 2013

The big findings, as I see it, are the rapid decline in pure theory articles and the big increase in the use of proprietary data. Mid-20th century economics involved a lot of formal theorizing in both macro and micro (Samuelson’s program), and a lot of empirical research using newly available kinds of standardized data (e.g. the National Income and Product Accounts and their non-US equivalents). Now, empirical work is more likely to come from proprietary or experimental data than secondary sources. Finally, though there is an uptick in experimental studies, they are a pretty small fraction of papers still.

For more fun facts about econ journal publishing, see also Card and DellaVigna (2013) Nine Facts about Top Journals in Economics. Also, kudos to the AEA for releasing the full-text of JEL articles for free!

VotD: Economists in Positions of Authority

Posting has lagged a bit as I’ve been back and forth between collecting and processing archival data. This slower rate of posting will likely continue for a few weeks, at least, though I have a special post planned (an author Q&A) for sometime this month. In the meantime, I might post some shorter snippets that catch my eye… like this chart from Hallerberg and Wehner’s The Technical Competence of Economic Policy-Makers in Developed Democracies. Hallerberg and Wehner are interested in the conditions under which governments are more likely to have technically competent economic policy-makers – understood here as graduate training in economics, though the authors have more measures than just this one – as opposed to policy-makers with stronger political backgrounds (such as previously elected officials). One straightforward finding, to give an example, is that presidential systems are more likely to appoint technically competent finance ministers than parliamentary systems (where ministers are usually themselves elected MPs). This chart does a nice job of showing the relative proportions of advanced economics degree holders among prime ministers/presidents, finance ministers, and central bank heads:

Comparison of the Economic Training of Economic Policy-Makers from Hallerberg and Wehner 2013

Other findings include the sensible one that finance ministers and central bank heads appointed during financial crises are more likely to have technical competence and that left-wing governments are especially more likely to appoint economists during crises. Check out the full paper for details.

Note: VotD stands for “Visualization of the Day,” and may become a recurring (though certainly not daily) feature.

Boston! + ESS Talk Promo

Dear Readers,

I’m going to be in the Boston/Cambridge area from March 18 to March 28. I’ll be visiting Harvard University’s archives and presenting a brand new paper at the Eastern Sociological Society Meetings. Leave a comment or send me an email if you’re going to be in the area and want to meet up!

Here’s the abstract and title for the ESS talk, based on a paper co-authored with Isaac Reed:

On the Formation of Social Kinds:
Expanding the Causal Repertoire of Sociological Research

Current understandings of causality in the social sciences suffer from an unnecessarily monolithic definition of “cause.” Building on Reed (2011)*, we offer a new typology of causation. This typology distinguishes forcing causes from forming causes. Forcing causes describe the relations between existing social objects that produce, determine or explain why a process has a certain well-defined outcome rather than others. Forcing causes coincide with existing definitions of causes in the extensive literature on causal inference, and some of the literature on social mechanisms. Forming causes describe how objects and outcomes in the social world are shaped or reshaped, or in extreme cases, brought into being. We argue that some of the literature in historical and cultural sociology has been misidentified as ‘merely’ interpretive or descriptive rather than causal because it deals with forming causes. Additionally, we argue that confusion results when scholars conflate questions about forcing and forming causes. We illustrate these claims with two short examples from very distinct literatures: the historiography of the French revolution and the debates over the performativity of the Black-Scholes-Merton options pricing model. We close with a brief discussion of how adopting this distinction can help initiate a conversation about making rigorous claims about formal causes.

The talk is part of what rates to be an excellent collection of six panels on Comparative Cultural Sociology. Stop by and check it out!

*If you haven’t read Isaac’s book, and you want to learn more about the role of meaning and interpretation in the production of social knowledge, you really should.

Specific Generalities: Historical vs. Sociological Generalization

What counts as a “general” story? What determines what findings are bigger or more important than others?

I think historical research and sociological research tend to answer this question in two very different ways. For sociologists, a general story, claim, finding, whatnot is generalizable to different cases and contexts. Structural holes shape competition in inter-firm networks, but they also shape competition in interpersonal networks. And so on. Because of this, any case can be interesting if it serves as a model for other similar cases might work.

In history, or at least my outsider impression of it, an important story is one that is empirically “big.” If a claim characterizes a long period of time or covers an event that touches a lot of people, then it’s a big, important, general claim. This doesn’t mean that you can’t study a small event – a single protest, a single court case, whatever – but you make claims about its importance by arguing that the small event characterizes a big system or process. What you don’t claim, or at least don’t always claim, is that your small event is a case of a whole class of phenomena.

So, for example, I think of my own work on the history of national income statistics as being a “big story” because national income statistics are a worldwide phenomenon and they shape our understanding of the economy as a whole, and thus they are a small part of a massive story. But what can be harder for me is to treat the history of national income statistics as a case of something else – for example, pitching my story as a case of how ideas and knowledge practices shape politics, comparable to Somers and Block’s work on Malthus and so on. In sum, two different approaches, two different kinds of generality.

Designing Social Research

This post’s title has nothing to do with research design. Instead, I want to talk about graphic design. Specifically, I want to propose a fun game/topic of debate.*

Which field’s top journal has the best design, layout, and formatting?

Because I’m asking, I’ll let each field pick two journals as the “top” (Sociology has 2, so this makes my job easy). Neither AJS nor ASR are amazing, though I kind like ASR’s title pages. The motivation for this game is the new issue of APSR (including several very interesting articles for sociologists, especially this one on a political theory of the corporation). I have to say, for a two column format, I think APSR has ASR beat.

So, academic design snobs, which journal will reign supreme?

*And by fun I mean incredibly nerdy.

Follow

Get every new post delivered to your Inbox.

Join 90 other followers