Archive for the ‘economics’ Category
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!
Last month, Howard Aldrich made—as he often does—a good point in the comments:
There’s been an interesting subtle shift in the rhetoric regarding whose responsibility it is to pay for an individual’s post-secondary education. My impression is that there was a strong consensus across the nation 50 years ago, and certainly into the late 1960s, that governments had a responsibility to educate their students that extended up through college. However, I perceive that consensus has been under attack from both the left and the right….Liberals argue that much of the public subsidy goes to the wealthier high income students whose parents don’t really deserve the subsidy. Conservatives argue that as students benefit substantially from their college education, they should pay most of the cost.
This month, I’ve been writing about the history of cost-benefit analysis. (Why yes, I do know how to have a good time.) On the surface, it has nothing to do with universities. But there are important links to be made.
One of the arguments I’m playing with is that economic thinking—here just meaning a rational, cost-benefit, systematic-weighing-of-alternative-choices sort of thinking—has been particularly constraining for the political left. On the right, when people’s values disagree with economic reasoning, they ignore the economics and forge ahead. On the left, while some will do the same, the “reasonable” position tends to be much more technocratic. Think Brookings versus Heritage. Over time, one thing that has pulled “the left” to the right has been the influence of a technocratic, cost-benefit strain of thought.
Yes, I know these are sweeping generalizations. But stay with me for a minute.
There are a couple of big economic arguments for asking individuals, not the public, to pay for higher education. Howard’s comment gets at both of them.
One is that while there is some public benefit in educating people, individuals capture most of the returns to higher education. If that is the case, it makes sense that they should pay for it, with the state perhaps making financing available for those who lack the means. Milton Friedman made this argument sixty years ago, and since then, it has become ever more popular.
The other is that providing free higher education is basically regressive. The wealthier you are, the more likely you are to attend college (check out this NYT interactive chart), and relatively few who are poor benefit. Milton Friedman made this argument, too, but it is particularly associated with a 1969 paper by Lee Hansen and Burton Weisbrod, and continues to be made by commentators across the political spectrum.
Both of these arguments have become economic common sense (even though support for the latter is actually pretty weak). Of course it’s fair for individuals to have to pay for the education that they benefit so much from. And of course it doesn’t make sense to pay for the education of the upper-middle class while the working poor who never make it to college get nothing.
Indeed, these arguments have been potent enough that it has become hard to argue for free higher education without sounding extreme and maybe economically illiterate. Really, it kind of amazes me that free college is even being talked about seriously these days by President Obama and Bernie Sanders.
But even the argument for free college now depends heavily on claims about economic payoff. The Obama proposal headlines “Return on Investment,” arguing that “every dollar invested in community college by federal, state and local governments means more than $25 [ed: !] in return.” The Sanders statement starts, “In a highly competitive global economy, we need the best-educated workforce in the world.” The candidate who is a self-described socialist relies on a utilitarian, economic argument to justify free higher education.
So what’s the problem with thinking about college in terms of economic costs and benefits? After all, it’s an expensive enterprise, and getting more so. Surely it doesn’t make sense to just wantonly spend without giving any thought to what you’re getting in return.
The problem is, if the argument you really want to make is that college is a government responsibility—that is, a right—starting with cost-benefit framing leads you down a slippery slope. Benefits are harder to measure than costs, and some benefits can’t be measured at all. All sorts of public spending becomes much harder to justify.
Now, this might be fine if you generally think that small government is good, or that the economic benefits of college are pretty much the ones that matter. But if you think it’s worth promoting college because it might help people become better citizens, or increases their quality of life in some difficult-to-measure way, or you just want to live in a society that provides broad access to education, well, too bad. You’ve already written that out of the equation.
If you really believe there are social benefits to making public higher education freely available, then cost-benefit arguments will always betray you. But rights, on the other hand, aren’t subject to cost-benefit tests. Only a moral argument that defends higher education as a right—as something to value because it improves the social fabric in literally immeasurable ways—can really work to defend real public higher education.
Seem too unrealistic? Think about high school. There’s no real reason that free college should be subject to a cost-benefit test when free high school is not. Individuals reap economic benefits—lots of them—from attending high school, too. And high school is at least as regressive as college: the well-off kids who attend the good public schools reap many more benefits than the low-income kids who attend the crummy ones. It only makes sense, then, that families should pay for high school themselves, right? Perhaps with government loans, if you’re too poor to afford it.
And yet no one is making this argument. Because we all still agree—at least for now—that children have the right to a free primary and secondary education. We may argue about how much to spend on it, or how to make it better, but the basic premise—governments have a responsibility to educate students, in Howard’s words—still holds.
So I support the free college movement. But I’d like to see its champions stop saying it’s because we need to be globally competitive, or because it’s got a huge ROI.
Instead, say it’s because our society will be stronger when more of us are better educated. Say that knowing higher education is an option, and an option you don’t have to mortgage your future for, will improve our quality of life. Say that colleges themselves will be better when they return to seeing students as students, and not as revenue streams.
Say it’s because it’s the right thing to do.
Every October when the Nobel prize in economics is announced, you hear the same trite and hackneyed things. Already, the Guardian has one of those tedious “economics is not a science” articles just to prepare for tomorrow. To help you save time, I’ve collected the following cliches so you can just clip and paste them into your tweets, Facebook messages, and blog posts:
- Economics is not a science.
- Actually, there is no Nobel Prize in economics.
- The so-called Economics Nobel prize.
- This prize refutes the policies of [insert politician you hate].
- This prize supports the policies of [insert politician you love].
- This prize is long overdue.
- This prize rewards [my favorite field].
- This prize rewards free-market fundamentalists.
- This prize proves that free-market fundamentalists are wrong.
- This person did not deserve the prize.
- This person deserved the prize.
- This is a rather mathematical/statistical prize for a technical point that I can’t summarize here.
- This prize is for proving the obvious.
- I predicted this all along.
- I am completely surprised by this.
- I can’t believe they gave this to a non-economist.
- I can’t believe they gave this to a person not from [circle one: Harvard/MIT].
- Harvard is slipping, straight to toilet.
- Steve Levitt does/does not know the work of these prize winners.
Actually, I have a Granovetter post ready to go if he ever wins, since he is the sociologist whose work is most known in economics. Add your own cliches in the comments.