Archive for the ‘economics’ Category
Over at Econ Talk, Russ Roberts interviews James Heckman about censored data and other statistical issues. At one point, Roberts asks Heckman what he thinks of the current identification fad in economics (my phrasing). Heckman has a few insightful responses. One is that a lot of the “new methods” – experiments, instrumental variables, etc. are not new at all. Also, experiments need to be done with care and the results need to be properly contextualized. A lot of economists and identification obsessed folks think that “the facts speak for themselves.” Not true. Supposedly clean experiments can be understand in the wrong way.
For me, the most interesting section of the interview is when Heckman makes a distinction between statistics and econometrics. Here’s his example:
- Identification – statistics, not economics. The point of identification is to ensure that your correlation is not attributable to an unobserved variable. This is either a mathematical point (IV) or a feature of research design (RCT). There is nothing economic about identification in the sense that you need to understand human decision making in order to carry out identification.
In contrast, he thought that “real” econometrics was about using economics to guide statistical modelling or using statistical modelling to plausibly tell us how economic principles play out in real world situations. This, I think, is the spirit of structural econometrics, which demands the researcher define the economic relation between variables and use that as a constraint in statistical estimation. Heckman and Roberts discuss minimum wage studies, where the statistical point is clear (raising wages do not always decrease unemployment) but the economic point still needs to be teased out (moderate wage increases can be offset by firms in others ways) using theory and knowledge of labor markets.
The deeper point I took away from the exchange is that long term progress in knowledge is not generated by a single method, but rather through careful data collection and knowledge of social context. The academic profession may reward clever identification strategies and they are useful, but that can lead to bizarre papers when the authors shift from economic thinking to an obsession with unobserved variables.
On Twitter, Michigan higher ed prof Julie Posselt compares the quantitative GRE scores for various social science disciplines. Take home message: social sciences are comparable in terms of recruits, but economics has stronger math skills. Take home point #2: there is still a lot of overlap. The bottom third of econ overlaps with other social sciences. This probably reflects that the extraordinarily mathematical approach to econ is a phenomenon of the strong programs that attract those with degrees in physical science and they have pushed the more traditional economics student to the bottom of the distribution.
Remember acid rain? For me, it’s one of those vague menaces of childhood, slightly scarier than the gypsy moths that were eating their way across western Pennsylvania but not as bad as the nuclear bombs I expected to fall from the sky at any moment. The 1980s were a great time to be a kid.
The gypsy moths are under control now, and I don’t think my own kids have ever given two thoughts to the possibility of imminent nuclear holocaust. And you don’t hear much about acid rain these days, either.
In the case of acid rain, that’s because we actually fixed it. That’s right, a complex and challenging environmental problem that we got together and came up with a way to solve. And the Acid Rain Program, passed as part of the Clean Air Act Amendments of 1990, has long been the shining example of how to use emissions trading to successfully and efficiently reduce pollution, and served as an international model for how such programs might be structured.
The idea behind emissions trading is that some regulatory body decides the total emissions level that is acceptable, finds a way to allocate polluters rights to emit some fraction of that total acceptable level, and then allows them to trade those rights with one another. Polluters for whom it is costly to reduce emissions will buy permits from those who can reduce emissions more cheaply. This meets the required emissions level more efficiently than if everyone were simply required to cut emissions to some specified level.
While there have clearly been highly successful examples of such cap-and-trade systems, they have also had their critics. Some of these focus on political viability. The European Emissions Trading System, meant to limit CO2 emissions, issued too many permits—always politically tempting—which has made the system fairly worthless for forcing reductions in emissions.
Others emphasize distributional effects. The whole point of trading is to reduce emissions in places where it is cheap to do so rather than in those where it’s more expensive. But given similar technological costs, a firm may prefer to clean up pollutants in a well-off area with significant political voice rather than a poor, disenfranchised minority neighborhood. Geography has the potential to make the efficient solution particularly inequitable.
These distributional critiques frequently come from outside economics, particularly (though not only) from the environmental justice movement. But in the case of the Acid Rain program, until now no one has shown strong distributional effects. This study found that SO2 was not being concentrated in poor or minority neighborhoods, and this one (h/t Neal Caren) actually found less emissions in Black and Hispanic neighborhoods, though more in poorly educated ones.
A recent NBER paper, however, challenges the distributional neutrality of the Acid Raid Program (h/t Dan Hirschman)—but here, it is residents of the Northeast who bear the brunt, rather than poor or minority neighborhoods. It is cheaper, it turns out, to reduce SO2 emissions in the sparsely populated western United States than the densely populated east. So, as intended, more reductions were made in the West, and less in the East.
The problem is that the population is a lot denser in the Northeastern U.S. So while national emissions decreased, more people were exposed to relatively high levels of SO2 and therefore more people died prematurely than would have been the case with the inefficient solution of just mandating an equivalent across-the-board reduction in SO2 levels.
To state it more sharply, while the trading built into the Acid Rain Program saved money, it also killed people, because improvements were mostly made in low-population areas.
This is fairly disappointing news. It also points to what I see as the biggest issue in the cap-and-trade vs. pollution tax debate—that so much depends on precisely how such markets are structured, and if you don’t get the details exactly right (and really, when are the details ever exactly right?), you may either fail to solve the problem you intended to, or create a new one worse than the one you fixed.
Of course pollution taxes are not exempt from political difficulties or unintended consequences either. And as Carl Gershenson pointed out on Twitter, a global, not local, pollutant like CO2 wouldn’t have quite the same set of issues as SO2. And the need to reduce carbon emissions is so serious that honestly I’d get behind any politically viable effort to cut them. But this does seem like one more thumb on the “carbon tax, not cap-and-trade” side of the scale.
Ever since the publication of Piketty’s Capital in the 21st Century, there’s been a lot of debate about the theory and empirical work. One strand of the discussion focuses on how Piketty handles the data. A number of critics have argued that the main results are sensitive to choices made in the data analysis (e.g., see this working paper). The trends in inequality reported by Piketty are amplified by how he handles the data.
Perhaps the strongest criticism in this vein is made by UC Riverside’s Richard Sutch, who has a working paper claiming that some of Piketty’s major empirical points are simply unreliable. The abstract:
Here I examine only Piketty’s U.S. data for the period 1810 to 2010 for the top ten percent and the top one percent of the wealth distribution. I conclude that Piketty’s data for the wealth share of the top ten percent for the period 1870-1970 are unreliable. The values he reported are manufactured from the observations for the top one percent inflated by a constant 36 percentage points. Piketty’s data for the top one percent of the distribution for the nineteenth century (1810-1910) are also unreliable. They are based on a single mid-century observation that provides no guidance about the antebellum trend and only very tenuous information about trends in inequality during the Gilded Age. The values Piketty reported for the twentieth-century (1910-2010) are based on more solid ground, but have the disadvantage of muting the marked rise of inequality during the Roaring Twenties and the decline associated with the Great Depression. The reversal of the decline in inequality during the 1960s and 1970s and subsequent sharp rise in the 1980s is hidden by a fifteen-year straight-line interpolation. This neglect of the shorter-run changes is unfortunate because it makes it difficult to discern the impact of policy changes (income and estate tax rates) and shifts in the structure and performance of the economy (depression, inflation, executive compensation) on changes in wealth inequality.
From inside the working paper, an attempt to replicate Piketty’s estimate of intergenerational wealth transfer among the wealthy:
The first available data point based on an SCF survey is for 1962. As reported by Wolff the top one percent of the wealth distribution held 33.4 percent of total wealth that year [Wolff 1994: Table 4, 153; and Wolff 2014: Table 2, 50]. Without explanation Piketty adjusted this downward to 31.4 by subtracting 2 percentage points. Piketty’s adjusted number is represented by the cross plotted for 1962 in Figure 1. Chris Giles, a reporter for the Financial Times, described this procedure as “seemingly arbitrary” [Giles 2014].9 In a follow-up response to Giles, Piketty failed to explain this adjustment [Piketty 2014c “Addendum”].
There is a bit of a mystery as to where the 1.2 and 1.25 multipliers used to adjust the Kopczuk-Saez estimates upward came from. The spreadsheet that generated the data (TS10.1DetailsUS) suggests that Piketty was influenced in this choice by the inflation factor that would be required to bring the solid line up to reach his adjusted SCF estimate for 1962. Piketty did not explain why the adjustment multiplier jumps from 1.2 to 1.25 in 1930.
This comes up quite a bit, according to Sutch. There is reasonable data and then Piketty makes adjustments that are odd or simply unexplained. It is also important to note that Sutch is not trying to make inequality in the data go away. He notes that Piketty is likely under-reporting early 20th century inequality while over-reporting the more recent increase in inequality.
A lot of Piketty’s argument comes from international comparisons and longitudinal studies with historical data. I have a lot of sympathy for Piketty. Data is imperfect, collected irregularly, and prone to error. So I am slow to criticize. Still, given that Piketty’s theory is now one of the major contenders in the study of global inequality, we want the answer to be robust.
[Ha — I wrote this last night and set it to post for this morning — when I woke up saw that Fabio had beat me to it. Posting anyway for the limited additional thoughts it contains.]
Last week Fabio launched a heated discussion about whether economics is less “racially balanced” than other social sciences. Then on Friday Justin Wolfers (who has been a vocal advocate for women in economics) published an Upshot piece arguing that female economists get less credit when they collaborate with men.
The Wolfers piece covers research by Harvard economics PhD candidate Heather Sarsons, who used data on tenure decisions at top-30 economics programs in the last forty years to estimate the effects of collaboration (with men or women) on whether women get tenure, controlling for publication quantity and quality and so on. (Full paper here.) Only 52% of the women in this population received tenure, compared to 77% of the men.
The takeaway is that women got no marginal benefit (in terms of tenure decision) from coauthoring with men, while they received some benefit (but less than men did) if they coauthored with at least one other women. Their tenure chances did, however, benefit as much as men’s from solo-authored papers. Sarsons’ interpretation (after ruling out several alternative possibilities) is that while women are given full credit when there is no question about their role in a study, their contributions are discounted when they coauthor with men.
Interesting from a sociologist’s perspective is that Sarsons uses a more limited data set from sociology as a comparison. Looking at a sample of 250 sociology faculty at top-20 programs, she finds no difference in tenure rates by gender, and no similar disadvantage from coauthorship.
While it would be nice to interpret this as evidence of the great enlightenment of sociology around gender issues, that is probably premature. Nevertheless, Sarsons points to one key difference between sociology and economics (other than differing assumptions about women’s contributions) that could potentially explain the divergence.
Sociology, as most of you probably know, has a convention of putting the author who made the largest contribution first in the authorship list. Economics uses alphabetical order. Other disciplines have their own conventions — lab sciences, for example, put the senior author last. This means that sociologists can infer a little bit more than economists about who played the biggest role in a paper from authorship order — information Sarsons suggests might contribute to women receiving more credit for their collaborative work.
This sounds plausible to me, although I also wouldn’t be surprised if the two disciplines made different assumptions, ceteris paribus, about women’s contributions. It might be worth looking at sociology articles with the relatively common footnote “Authors contributed equally; names are listed in alphabetical order” (or reverse alphabetical order, or by coin toss, or whatever). Of course such a note still provides information about relative contribution — 50-50, at least in theory — so it’s not an ideal comparison. But I would bet that readers mentally give one author more credit than the other for these papers.
That may just be the first author, due to the disciplinary convention. But one could imagine that a male contributor (or a senior contributor) would reap greater rewards for these kinds of collaborations. It wouldn’t say much about the hypothesis if that were not the case, but if men received more advantage from papers with explicitly equal coauthors, that would certainly be consistent with the hypothesis that first-author naming conventions help women get credit.
Okay, maybe that’s a stretch. Sarsons closes by noting that she plans to expand the sociology sample and add disciplines with different authorship conventions. It will be challenging to tease out whether authorship conventions really help women get due credit for their work, and I’m skeptical that that’s 100% of the story. But even if it could fix part of the problem, what a simple solution to ensure credit where credit is due.
A few days ago, economist Noah Smith posted this tweet:
This raises an interesting question: what is the racial balance of the economics profession and how does that compare with similar fields?
It helps to start with a baseline model. In higher education research, the common finding is that Blacks and Latinos are under represented among professors when compared to the population. Blacks and Latinos are each about 6% of the professoriate (e.g., see the National Center for Education Statistics summary here). Asians tend to be about 10% of the professoriate, which means they are over represented compared to the population. These numbers vary a little by rank, with lower ranks having more racial and ethnic minorities.
Finding the numbers for economics professors is tricky. You have to dig a little to find the data. In 2006, The Journal of Blacks in Higher Education counted 15 Black economists among 935 faculty in top 30 programs – a whopping 1.6%. There seem to be very few surveys of economists, but there is the 1995 Survey of Americans and Economists on the Economy conducted by the Washington Post and the Kaiser Family Foundation. That survey reports that .5% (<1%) of economics professors are Black, according to Bryan Caplan’s analysis of the data in the Journal of Law and Economics (Table 1, p. 398). The same article reports about 5% for Asian economists. This indicates that economics faculty are more likely to be White than the population as a whole and academia in general. If readers have access to more recent surveys of economists and their demographics, please use the comments.
Follow up question #1: Is economics similar to other related social science disciplines like political science or sociology? Answer: Political science has about 5% Black faculty and 3.4% Asian faculty according to this 2011 APSA report (Table 8, p. 40). Sociology has about 7% Black faculty and 5% Asian faculty according to this 2007 ASA report. So economics is more White than allied social science disciplines and about the same in terms of Asian faculty.
Follow up question #2: What about economics’ similarity to math intensive STEM fields like physics or math? According to a 2014 report from the American Institute for Physics, about 2% of physics faculty are Black and 14% are Asian (see Table 1). According to this 2006 study of the American mathematics faculty, 1% are Black and 12% Asian in the PhD programs (Table F5).
- Economics professors are less likely to be Black (~1%) than professors as a whole (~6%).
- Economics professors are less likely to be Black (~1%) than political scientists and sociologists (5%-7%).
- Black professors are equally common in econ, math, and physics (1-2% for each field).
- Asian economics professors are equally common as Asian professors in other social sciences (3.5% in political science, ~5% in economics and sociology).
- Economics professors are less likely to be Asian (5%) than in academia as a whole (10%) and even less than physics and mathematics (14% and 12%)
Bottom line: Economics has fewer Black faculty when compared to social sciences and fewer Asian compared to physical sciences. That’s something that makes you go “hmmmm….”
We often hear that democracy is under threat. But is that true? In 2005, Adam Przeworski wrote an article in Public Choice arguing that *wealthy* democracies are stable but poor ones are not. He starts with the following observation:
No democracy ever fell in a country with a per capita income higher than that of Argentina in 1975, $6055.1 This is a startling fact, given that throughout history about 70 democracies collapsed in poorer countries. In contrast, 35 democracies spent about 1000 years under more developed conditions and not one died.
Developed democracies survived wars, riots, scandals, economic and governmental crises, hell or high water. The probability that democracy survives increases monotonically in per capita income. Between 1951 and 1990, the probability that a democracy would die during any particular year in countries with per capita income under $1000 was 0.1636, which implies that their expected life was about 6 years. Between $1001 and 3000, this probability was 0.0561, for an expected duration of about 18 years. Between $3001 and 6055, the probability was 0.0216, which translates into about 46 years of expected life. And what happens above $6055 we already know: democracy lasts forever.
How does one explain this pattern? Przeworski describes a model where elites offer income redistribution plans, people vote, and the elites decide to keep or ditch democracy. The model has a simple feature when you write it out: the wealthier the society, the more pro-democracy equilibria you get.
If true, this model has profound implications of political theory and public policy:
- Economic growth is the bulwark of democracy. Thus, if we really want democracy, we should encourage economic growth.
- Armed conflict probably does not help democracy. Why? Wars tend to destroy economic value and make your country poorer and that increase anti-democracy movements (e.g., Syria and Iraq).
- A lot of people tell you that we should be afraid of outsiders because they will threaten democracy. Not true, at least for wealthy democracies.
This article should be a classic!