Recently in Economics Category

Back in April, in an article about partisan perceptions of the economy, John Sides and I wrote:

A colleague and I were talking the other day about how much we pay our research assistants. It turns out that she pays much more. In fact, sometimes I don't get around to paying my research assistants at all, but she pays hers a decent amount.

My colleague, who's an untentured professor, said that was understandable because she makes less money than I do, so she can better relate to the students' lifestyles. That's a pretty scary thought--it should really go the other way, right? I get paid more so I should be able to afford to be more generous. But maybe she's right; if so, it's a sobering insight.

Robin Hanson writes,

In academia, one often finds folks who are much more (or less) smart and insightful than their colleagues, where most who know them agree with this assessment. Since academia is primarily an institution for credentialling folks as intellectually impressive, so that others can affiliate with them, one might wonder how such mis-rankings can persist.

I added the bold font myself for emphasis. Granted, Robin is far from a typical economist. Nonetheless, that he would write such an extreme statement without even feeling the need to justify it (and, no, I don't think it's true, at least not in the "academia" that I know about) . . . that I see as a product of being in an economics department.

P.S. Robin definitely is correct about the "more (or less) smart and insightful" bit. But here I think there are two things going on. First, in any group of people you'll see some variation, especially given that there are other factors going on than "smart and insightful" when it comes to selecting people in an academic environment. Second, there's more to life--even to academic life--than being smart and insightful. Even setting aside teaching, advising, administration, etc., some other crucial qualities for academic research include working hard, having the "taste" to work on important problems, intellectual honesty, and caring enough about getting the right answer. I know some very smart and insightful people who have not made the contributions that they are capable of, because (I think) of gaps in some of these other important traits.

Greg Mankiw writes:

The next time you hear someone cavalierly point to international comparisons in life expectancy as evidence against the U.S. healthcare system, you should be ready to explain how schlocky that argument really is.

He points to the following claim by Gary Becker:

National differences in life expectancies are a highly imperfect indicator of the effectiveness of health delivery systems.for example, life styles are important contributors to health, and the US fares poorly on many life style indicators, such as incidence of overweight and obese men, women, and teenagers. To get around such problems, some analysts compare not life expectancies but survival rates from different diseases. The US health system tends to look pretty good on these comparisons.

Becker cites a study that finds that the U.S. does better than Europe in cancer survival rates and in the availability of hip and knee replacements and cataract surgery.

It makes a lot of sense to think of health as multidimensional, so that some countries can do better in life expectancy while others do better in hip replacements and cancer survival.

But I disagree with Mankiw's claim that it's "schlocky" to compare life expectancy. If the U.S. really is spending lots more per person on health care and really getting less in life expectancy compared to other countries . . . that seems like relevant information.

"A fondness for collecting a salary and getting away with as little intellectual intercourse as possible is endemic to the academic world." Not just the academic world, I think. Working is hard work. That's why they call it work. On the other hand, I'm doing this for free.

This issue reminds me of a discussion that's sometimes come up about a well-known listserv participant who is (a) very helpful, and (b) very rude. Or maybe I'm exaggerating a bit: this person is (a) often helpful, and (b) often rude. Anyway, I've always maintained that, rudeness aside, this person is altruistic, providing free statistical help to strangers. But it's true that answering listserv questions isn't intellectually taxing. Sort of like writing this blog, it's work-like without usually quite being work.

P.S. I think the point is best made by keeping the listserv and its well-known participant anonymous.

The other day while waiting for a bus, I was thinking about how city buses should be smaller and run more frequently. Instead of a 40-seater every 15 minutes, they could run a 10-seater every 5 minutes. (More precisely, they could run as frequently as necessary during rush hour to handle all the passengers--a bus a minute if necessary--but more spaced out at other times. For example, on weekend mornings the bus is never crowded, so they could run the much smaller buses with just slightly higher frequencies than they currently run big buses now.)

The advantages of my proposal are clear: the bus comes more frequently, also since the lag time is smaller, loading and unloading won't take so much time, and as an extra bonus, you'll probably skip a lot more stops because there are fewer people on the bus who might want to get off at any particular point. Also, I don't know about fuel efficiency, but I wouldn't be surprised if the fuel cost per passenger is lower because you're not having to run these huge empty buses in off-peak hours. Finally, van-sized buses could maneuver better in traffic.

The only additional cost that I see is having to hire more bus drivers, but with unemployment at 9%, I don't think it would be hard to find people to do this. What really irritates me are those huge, huge buses that take forever to fill up and take about a half hour just to go a few crosstown blocks. If they were broken up into vans, the wait would be less and the ride much more pleasant.

P.S. Yes, I know this isn't one of the world's most important problems. But it is a big expenditure, so why not try to do it right?

P.P.S. I'm sure there's lots of research on this topic but it's not something I'm at all informed on. The above are just my personal impressions.

P.P.P.S. To those of you who discuss the cost: Sure, it would cost money. But there's a real economic benefit: people would be able to get around the city faster! A good use of stimulus funds, etc.

Greg Mankiw looked up the Consumer Reports of ratings of car companies and found:

Dead last was Chrysler. CU recommended zero percent of the Chrysler vehicles they tested. That's right--zero. Second to last was General Motors. CU recommended 17 percent of GM models. By contrast, most other companies had half or more of their models get the thumbs up. Honda was the top ranked brand; CU recommended 95 percent of its models.

Mankiw writes:

Is it any surprise that Chrysler and GM are now in the process of going out of business? From the perspective of the Consumer Reports advice, it looks like their business model was to count on the ignorance of the buying public about the quality of their products. Their bankruptcy should perhaps be viewed as a success of the market system.

This makes sense to me, but I wonder if it explains too much. Presumably these companies have been making crappy cars for awhile. How did the companies stay alive so long? In all seriousness, perhaps the market system would've been more successful had it shut down those companies 10 or 15 years ago.

Beyond this is the principal-agent problem, or moral hazard, or whatever it's called, by which the people who make the decisions to make crappy cars are probably not actually going broke themselves: the companies might fall apart, but they'll do OK, I assume. So I can see how the companies could stay alive for awhile, living off their assets and their ability to borrow money. I just don't completely see it as a "success of the market" that they've been hanging on so long when the low quality of their products has been public knowledge.

Robert Frank defends carbon offsets at the sister blog. I'm sympathetic to much of Frank's argument; in particular, the fact that Al Gore has a big house isn't much of an argument against carbon offsets. (If the crops are failing and the flood waters are rising, it won't be much help to stand on a street corner shouting: But Al Gore had a big house!)

But I'm not happy with the example that Frank chooses to illustrate his point. He writes:

A few days ago I read Atul Gawande's article on health care costs and thought his story was interesting enough that I wanted to know the statistics on what factors predict high or low costs.

Commenter Marc pointed me to this recent article by Elliott Fisher, Julie Bynum, and Jonathan Skinner on regional variation in heath costs across the United States.

Commenter Ao pointed to this Congressional Budget Office report which, to me, was a bit disappointing. It had some nice maps and charts but did not seem nearly as serious as Gawande's article in trying to understand what was going on.

Finally, Alan Zaslavsky, a statistician who specializes in healh-care economics (and who uses multilevel models) wrote:

Atul Gawande's article in the New Yorker is an excellent review of some of the issues we have been struggling with in health policy research. While a lot of energy has gone into looking at the impact of various incentive schemes (public reporting of quality measures, "pay for performance") on quality of healthcare, it has been difficult to address the kinds of issues of organizational culture described in the article. The differences in provider structure and culture that are key to success or failure in providing high-quality, efficient care are not that readily brought into analysis -- the variables are just not measured and available. So instead we have very convincing case studies -- the McAllen market at one extreme and the integrated Kaiser, Geisinger etc systems at the other.

One problem is that many of the analyses take place at the level of the health (insurance) plan, but health plans in most cases are not in the business of providing care, they are in the business of buying it. (Even some of the original staff-model HMOs went through the transition to being insurance companies, like HIP in New York.) The variables that are routinely available for analysis at the health plan level are very crude proxies for the underlying organizational structures and cultures. For example, some economists have told me that they are perplexed by findings that not-for-profit plans provide better-quality care than for-profits, since both types of firms should be subject to similar incentives. What this leaves out is the distinct histories of some of the leading not-for-profits, and consequent differences in organizational cultures.

I do highly recommend the work of the Dartmouth group (including Jon Skinner and Elliott Fisher, mentioned in the article) on area variations to interested readers. However, it does suffer from some of the same limitations -- the variations can be found and clearly show that more is not always better, but it is hard to say what actually drives the differences or what can be done to implant the cultural and organizational features that would make more areas look like the best areas. [emphasis added]

While I [Zaslavsky] am a proponent of a universal system of health care, I don't think that will be enough to solve our problems without some fundamental changes in the incentives and structures under which care providers operate. Paradoxically, "rationalization" has meant squeezing out not the most wasteful aspects of care but some of the unprofitable but essential services that could make care more effective.

Perhaps more could be done to take the quantitative analyses of Fisher et al. and Zaslavsky and his colleagues, and see what is needed to move toward useful recommendations. This is all on top of the difficult political issues; for example, doctors in the U.S. get paid a lot and I don't think they'd be happy about getting less money.

Amid an article about the GM bankruptcy, Mark Ambinder (political correspondent for the Atlantic Magazine) has the following offhand comment:

Purists -- and virtually every academic economist one happens to encounter -- wonder what happened to the once inviolate principle of rewarding risk-takers.

On a literal level, I don't think that's correct: the idea is that risk-takers can win big if they win, but if they lose, they lose: that's what "risk" is all about. I don't think anybody (except the risk-takers themselves, along with their friends and families) think that risk-takers should be rewarded when their bets lose.

But that's all obvious and fits in with all the moral-hazard, perverse-incentives things we've been hearing about for awhile.

Why I'm going on about this

What interests me is the centrality of "risk" in the world of economics now. Until being pointed to the article linked to above, I had never heard of the "once inviolate principle of rewarding risk-takers." Then again, it's been almost 30 years since I've taken an economics class. In that class, the idea of "risk" wasn't mentioned at all, I think. We learned about about supply and
demand, inflation and unemployment, money, investment, the stock market, etc. But the whole "risk" thing didn't come up.

Since then I've read enough to know that academic economists have been talking about
risk for awhile, but I don't think it was in the forefront of discussion. For example, I don't think a magazine columnist 30 years ago would've written about the inviolate principle of rewarding risk-takers, or anything of the sort. Things have changed--a lot.

Atul Gawande wrote an interesting article about health-care costs, focusing on McAllen, Texas, which he describes as "one of the most expensive health-care markets in the country. . . . In 2006, Medicare spent fifteen thousand dollars per enrollee here, almost twice the national average." In some ways, Gawande's article is like a case-control regression analysis without the numbers: he compares McAllen to the national average and to various other places in the United States, and looks the similarities and differences to find systematic patterns. He concludes that the key problem is "untenably fragmented, quantity-driven systems of health care," in which doctors are motivated to do more and more, with no apparent beneficial effects on the patients.

What do the experts say?

I imagine this is an area where health-economics statisticians have done some research. I'd be interested to hear the comments of Sharon-Lise Normand, Alan Zaslavsky, or some other expert in this field. They very well may have run some regression analyses to try to understand the factors that explain variation in health care costs at the regional, state, and local levels.

Smoking

As a minor point, I was puzzled by an offhand comment that Gawande made:

An unhealthy population couldn't possibly be the reason that McAllen's health-care costs are so high. (Or the reason that America's are. We may be more obese than any other industrialized nation, but we have among the lowest rates of smoking and alcoholism, and we are in the middle of the range for cardiovascular disease and diabetes.)

I don't know how things go with alcoholism, but my impression of smoking was that it caused a net decrease in health care costs: smokers tend to die younger, and to die quickly once they get seriously ill, thus sparing the health care system some of the big-ticket end-of-life costs. For example, from this 1997 article by Barendregt et al. on the health care costs of smoking:

Health care costs for smokers at a given age are as much as 40 percent higher than those for nonsmokers, but in a population in which no one smoked the costs would be 7 percent higher among men and 4 percent higher among women than the costs in the current mixed population of smokers and nonsmokers. . . . If people stopped smoking, there would be a savings in health care costs, but only in the short term. Eventually, smoking cessation would lead to increased health care costs.

Yuck.

Daniel Carlat posts a link to this news article by John Fauber about a medical researcher, James Stein, who took big bucks in lecturing and consulting fees from drug companies over a 12-year period, before stopping a few months ago. Stein said:

I was sure I could avoid bias because I controlled the content and I had these strong personal convictions. Well, unfortunately, over the past several months, I've learned that I was wrong. I've learned that I could not stay unbiased, that I could not control all the content of my talks, and that my personal convictions were not good enough.

Regarding disclosure as a potential solution, Stein said:

I really felt that if I stood up in front of a crowd and said that these are my disclosures, look how honest I am, that I was really managing conflict of interest. But actually the medical literature and the social science literature tells me that it is actually the opposite effect. Although it is laudable to disclose your relationships, actually thinking that disclosure manages relationships is harmful. It has the perverse effect that when you disclose your relationship, the recipient of your information becomes more trusting, and the social scientists also have shown us that professionals who disclose actually become more biased.... I would argue ... that the solution is not disclosure, because if you are doing something that is wrong or unethical, don't disclose, just don't do it!

There was also this amazing bit:

Huge fines or convictions for gross ethical conduct were being issued against every drug company that he worked with. Doctors were being investigated on allegations of taking kickbacks.

Catherine Rampell posted some attractive county-level Human Development Index maps and also discussed my criticisms of the index: I wrote, "if you go by the maps that everybody's linking to...you're pretty much just mapping state income and giving it a fancy transformation and a fancy new name." In its defense, she wrote:

Which is, I [Rampell] suppose, why the American Human Development Index, an adapted version of the U.N.'s original H.D.I., was created: because the U.N.'s index was not designed to capture the levels of variation that would occur within a single country. It was designed to make international comparisons.

This, to me, indicates the problem with the index. It was advertised as putting U.S. states on an international scale (Louisiana vs. Croatia and all that) but, if it needs to be redefined for the U.S., it seems to me that you're losing the universal interpretation, which is a big justification for the index in the first place. At this point, I'd rather map each of the components of the index separately (as Rampell actually does illustrate on her blog).

Greg Mankiw reports on an article by Betsey Stevenson and Justin Wolfers that finds:

By many objective measures the lives of women in the United States have improved over the past 35 years, yet we show that measures of subjective well-being indicate that women's happiness has declined both absolutely and relative to men. . . . Relative declines in female happiness have eroded a gender gap in happiness in which women in the 1970s typically reported higher subjective well-being than did men. . . .

Mankiw concludes: "It sounds like either the women's movement was a mistake or subjective happiness is not the right objective." The bit about the women's movement doesn't make sense to me--this reasoning seems to contradict the point Mankiw made a few days ago about the difficulty of making inferences based on n=1.

If I had to make a quick guess, I would've gone with the hypothesis of economic stress combined with the difficulty of having a job and taking care of the kids, but Stevenson and Wolfers discuss this issue (see pages numbered 15 and 17 and Table 3 of the linked article) and show that the data don't particularly support this hypothesis.

Getting back to Mankiw's comment: Setting aside the line about the women's movement--who knows, maybe the women's movement was a mistake, it's hard to say with n=1 what might have happened in its absence--I think he's right that subjective happiness is not an "objective." People have written about this: you don't become happy by aiming for happiness as an objective, you become happy by doing things that make you happy (or, just by being the kind of person who's happy in any case). It's an interesting issue, but I'm not sure how this is relevant to the Stevenson and Wolfers study.

P.S. If I were Betsey Stevenson, I might be a little unhappy that Mankiw referred to the authors unalphabetically as Wolfers and Stevenson!

P.P.S. Mankiw has fixed this and put the authors in the correct order.

See the end of this entry for explanation.

Alex Hoffman pointed me to this widely-circulated map comparing the fifty states in something called the Human Development Index:

As Alex points out, the coding of the map is kind of goofy: the states with the three lowest values are Louisiana at .801, West Virginia at .800, and Mississippi at .799, but their color scheme makes Mississippi stand out as the only yellow state in a sea of green.

But I'm concerned about more than that. Is Alaska really so developed as all that? And whassup with D.C., which, according to the table, is #4, behind only Connecticut, Massachusetts, and New Jersey? I know about gentrification and all that, but can D.C. really be #4 on any Human Development Index worth its name?

Time to look behind the numbers.

Greg Mankiw has a nice little discussion of the difficulty of evaluating the effects of interventions in the n=1 setting:

As Mankiw points out, the bad news about the unemployment rate is bad news with or without the recovery plan and thus--although it certainly seems to knock down the predictions shown in that graph--it does not provide much information on the causal effect of the fiscal stimulus. Especially given that the graph comes from a report released in early January, before anyone knew what would end up being included in the final version of the stimulus plan.

Groves rules out use of sampling in 2010 census:

President Barack Obama's pick to lead the Census Bureau on Friday ruled out the use of statistical sampling in the 2010 head count, seeking to allay GOP concerns that he might be swayed to put politics over science. Robert M. Groves, a veteran survey researcher from the University of Michigan, also testified during his confirmation hearing that he remains worried about fixing a persistent undercount of hard-to-reach populations . . . Census officials have already acknowledged that tens of millions of residents in dense urban areas -- about 14 percent of the U.S. population -- are at high risk of being missed because of language problems and an economic crisis that has displaced homeowners.

My comments:

I have a great respect for Bob Groves, and I would trust his decisions on what to do with the Census more than I would trust my own.

Bob's statement that "there is simply no time to prepare for it" seems eminently reasonable to me, especially given the cost constraints under which the census operates. On the statistical merits of the issue, I'm pretty sure that adjusted numbers would be better than unadjusted numbers. The census people know what they're doing, and there are known problems of nonresponse, and, for anything where I care about the damn answer, I'd use their adjusted estimates over the raw numbers.

As a social scientist, I hope the census bureau could release two sets of numbers, one unadjusted for political reasons and one adjusted for those of us who want the most accurate inferences possible.

That said, I'm ignoring a possible indirect effect of adjusting the numbers: If people know that the census will do adjustment, maybe they'll be less likely to participate in the enumeration in the first place. It's hard to measure such an effect and, hey, it might be important. I don't know.

I'm not thinking so much of individuals deciding whether to respond to the census, but rather of the decisions of local jurisdictions, where various spending formulas depend on population. For example, if it's known that the census won't be adjusted, then I'd expect the government of New York City to put a lot of effort into convincing people to participate. If it is known that the census will be adjusted, then there'd be a lot less motivation for localities to do what it takes to boost participation.

Conditional on the data already being collected, you'd definitely want to make statistical adjustments; it's a tougher call to decide on this ahead of time. Also, if you know for sure you won't be adjusting, this will affect the effort you put into collecting the data in different places. So if you're not going to adjust, you might as well make that decision right away.

P.S. To expand on this slightly, I think any debates over census adjustments are fundamentally political debates, not statistical disagreements. The scientific consensus on adjustment is pretty easy (although people can argue about the details of implementation, as noted by Lawrence in comments below). It's the political consensus that's difficult, as there are clear winners and losers. With a lack of political consensus, all you need is a little bit of dust and confusion in the air to give a sense of a lack of scientific consensus, which then gets piped back in to justify inaction in the political process.

My former Columbia colleague Matt Kahn sent me this article by Michael Cragg and himself on the political economy of congressional support for legislation intended to mitigate greenhouse gas production:

Stringent regulation for mitigating greenhouse gas emissions will impose different costs across geographical regions. Low-carbon, environmentalist states, such as California, would bear less of the incidence of such regulation than high-carbon Midwestern states. Such anticipated costs are likely to influence Congressional voting patterns. This paper uses several geographical data sets to document that conservative, poor areas have higher per-capita carbon emissions than liberal, richer areas. Representatives from such areas are shown to have much lower probabilities of voting in favor of anti-carbon legislation. In the 111th Congress, the Energy and Commerce Committee consists of members who represent high carbon districts. These geographical facts suggest that the Obama Administration and the Waxman Committee will face distributional challenges in building a majority voting coalition in favor of internalizing the carbon externality.

They make some interesting points, somewhat related to the much-remarked issue that the Democratic-leaning northern and midwestern states tend to pay more in taxes than they get back in government spending, while Republican-leaning sunbelt states are generally net beneficiaries of federal funds. When looked at from this perspective, you can see it's not so simple as Democrats vs. Republicans. Also, is straight carbon emissions the only story? I see from the map that Michigan has low carbon emissions per capita, but, at least traditionally, the politicians there support heavy industry. I suppose that, nowadays, carbon emissions is much more about extraction than about industrial production.

Cragg and Kahn do an analysis at the congressional district level, which makes a lot of sense. I haven't looked at income and voting by congressional district, but when you look at it by county, the patterns vary a lot by state. In California, Washington, and Oregon, the richer counties are nowadays the most Democratic. But in Texas and Oklahoma, the pattern goes the other way, with richer counties being more Republican. For example, suburbs of Dallas. So I think you have to be careful about using phrases such as "conservative, poor areas" and "liberal, richer areas." This pattern fits some parts of the country but not others (a point we made ad nauseum in Red State, Blue State). I think I know what Cragg and Kahn mean by this--they mean that, when they run a regression, both the average liberalness and the average income in the congressional district predicts lower carbon emissions--but you're just asking for trouble if you blur these concepts.

The other thing I wonder is if Cragg and Kahn have fully accounted for the partisan nature of Congressional voting. To put it bluntly: the Democrats have a majority in both houses of Congress, and so their votes count more than the Republicans'. This should affect their analysis and conclusions. On pages 17-18, they do discuss differences between the parties, but unless I'm missing something (and maybe I am), they're downplaying the relevant fact that the Democrats are in the driver's seat.

I also have a few comments about the data display (of course):

Justin Wolfers writes:

Dick [Easterlin] was the first economist to start taking subjective well-being data seriously. While this sort of research is now pretty mainstream, I have to imagine that it took a fair bit of courage back in the early 1970's.

This was interesting to me: the idea that it would take courage to study a particular research topic. Especially something such as subjective well-being, which doesn't have any direct political connections. I mean, it's not like we're talking about the economic benefits of torture, or whatever. "Subjective well-being" seems pretty innocuous to me: whatever objections made it courageous to study this topic must have been intellectual and stylistic rather than political.

P.S. Back when I taught at Berkeley, I did get some flak for doing research on Bayesian statistics--some students told me that other faculty had told them not to take my course--but I wouldn't describe my decision to do work on that topic as "courageous." I think the atmosphere in economics in the 1970s must have been much different than anything I've ever experienced.

This was pretty yucky:

Adderall, a stimulant composed of mixed amphetamine salts, is commonly prescribed for children and adults who have been given a diagnosis of attention-deficit hyperactivity disorder. But in recent years Adderall and Ritalin, another stimulant, have been adopted as cognitive enhancers: drugs that high-functioning, overcommitted people take to become higher-functioning and more overcommitted. . . . In 2005, a team led by Sean Esteban McCabe, a professor at the University of Michigan's Substance Abuse Research Center, reported that in the previous year 4.1 per cent of American undergraduates had taken prescription stimulants for off-label use; at one school, the figure was twenty-five per cent. . . . white male undergraduates at highly competitive schools--especially in the Northeast--are the most frequent collegiate users of neuroenhancers.

Lots of creepy stories if you follow the link. Or maybe I have the wrong attitude: I don't happen to need these sorts of drugs, so who am I to say that others shouldn't be able to attain similar levels of productivity through chemical means? Maybe I'm like somebody with two good legs, complaining about the development of a new super-efficient prosthetic limb.

Anyway, without passing judgment on any of this, I'd just have to say that I feel fortunate to have grown up in a noncompetitive environment, in which nobody was telling us that we had to work twice as hard to compete in the global marketplace, etc. I also consider myself fortunate to have grown up before success was defined as becoming super-rich. There really does seem to be more pressure now on students--more opportunities, sure, but more pressure, a tradeoff that I wouldn't like, I think.

Aleks sent me this. I have nothing to say on the substance here, but the grumpy-old-man quotes are amusing:

Quantitative Models And Methods: A Tour of the Social Sciences has just come out. The book is edited by Jeronimo Cortina and myself, and it is intended to give the reader a sense of how research is done in different areas of social science. It is not a book of statistical methods, nor is it that sort of academic book that has a zillion little chapters of things that people submitted because they couldn't get them accepted into journals. Rather, it is a set of in-depth examples and discussions of social science research from a variety of perspectives.

I think the book should be especially useful for courses for graduate students or advanced undergraduates in social science, who typically aren't familiar with the way people think in neighboring fields. For example, a political science student might know a little bit about economics but nothing about psychology. Or a sociology student might not know much about historical data collection. And so forth.

Here's the table of contents:

I. Models and Methods in the Social Sciences (Andrew Gelman)
1. Introduction and overview
2. What's in a number? Definitions of fairness and political representation
3. The allure and limitations of mathematical modeling: Game theory and trench warfare

II. History (Herbert Klein and Charles Stockley)
1. Historical background of quantitative social science
2. Sources of historical data
3. Historical perspectives on international exchange rates
4. Historical data and demography in Europe and the Americas

III. Economics (Richard Clarida and Marta Noguer)
1. Learning from economic data
2. Econometric forecasting and the flow of information
3. Two studies of interest rates and monetary policy

IV. Sociology (Seymour Spilerman and Emanuele Gerratana)
1. Models and theories in sociology
2. Demographic explanations of social disturbances in the 1960s
3. Studying the time series of lynchings in the South
4. Attainment processes in a large organization

V. Political Science (Charles Cameron)
1. What is political science?
2. The politics of Supreme Court nominations: the critical role of the media environment
3. Modeling strategy in congressional hearings

VI. Psychology (E. Tory Higgins, Elke Weber, and Heidi Grant)
1. Formulating and testing theories in psychology
2. Some theories in cognitive and social psychology
3. Signal detection theory and models for tradeoffs in decision making

VII. To Treat or Not to Treat: Causal Inference in the Social Sciences (Jeronimo Cortina)
1. The potential-outcomes model of causation; propensity scores
2. Some statistical tools for causal inference with observational data
3. Migration and Solidarity

The cover is an adaptation of this image that was sent to us from Chris Albon last year after we asked for cover ideas on the blog. Thanks, Chris. You're getting a free copy!

Another talk in NYU's Statistics in Society series. It looks interesting:

I recently read Nicholas Chistakis and James Fowler's Connected, and now everything I see makes me think of social networks.

For example, Richard Florida links to a research article by Bart Bronnenberg, Sanjay Dhar, and Jean‐Pierre Dubé, who write:

We [Bronnenberg et al.] document evidence of a persistent "early entry" advantage for brands in 34 consumer packaged goods industries across the 50 largest U.S. cities. Current market shares are higher in markets closest to a brand's historic city of origin than in those farthest. For six industries, we know the order of entry among the top brands in each of the markets. We find an early entry effect on a brand's current market share and perceived quality across U.S. cities. The magnitude of this effect typically drives the rank order of market shares and perceived quality levels across cities.

I haven't read the article, but assuming it's findings are correct, could some of this be the effect of employees and investors in the company, as well as local pride? I doubt Heinz Ketchup currently employs a lot of people in the Pittsburgh area, but over the years it must add up to a lot of people. Then add in their friends and relatives, along with people who get business from Heinz (suppliers and the like), and that's a whole bunch of Pittsburghers with some connection to Heinz.

The social network bit is the idea that the employees and the like are multiplied by their friends. Beyond this, of course, people are creatures of habits, tastes can get established young, and so forth.

Also, Heinz ketchup is something that anyone can buy. The very fact that it's (a) substitutable with other items and (b) just different enough to be distinguishable (it doesn't taste _exactly_ like other ketchups, it's not a pure commodity), might make it particularly susceptible to this sort of effect. It may be no coincidence that Bronnenberg et al. found this effect in the area of low-cost packaged foods.

James Heckman recently posted this article, which is based on a paper from 1980. (This sort of thing happens; for example, I just published an article based on work from 1986.) Heckman's tongue-in-cheek article begins:

This paper uses data available from the National Opinion Research Center's (NORC) survey on religious attitudes and powerful statistical methods to evaluate the effect of prayer on the attitude of God toward human beings.

He sets up a model for the intensity of prayer, given its effectiveness. The key assumption is as follows:

Accept on faith that the conditional density of x [the intensity of prayer in the population] given y [God's attitude arrayed on a scale ranging from 0 to 1] is of the form g(x|y) = a(y) exp(xy).

That is, the higher y is, the more prayer we'd see, which makes sense. (Heckman labels the function a(y) as "unknown," but, unless I'm missing something, a(y) is a normalizing constant that can be calculated in closed form by integrating exp(xy) over x. Perhaps this mistake, if it is one, can be caught before the article appears in press.)

Given the reasonable enough model above, Heckman points out that you can differentiate the density of x and learn something about the distribution of y, the effectiveness of prayer.

What does it all mean?

Of course Heckman is joking, but it appears he might be making a more serious point when he comments:

Provided conditional density (1) is assumed, we do not need to observe a variable in order to compute its conditional expectation with respect to another variable whose density can be estimated. For example, one can extend current empirical work in a variety of areas of economics to estimate the effect of income on happiness or the effect of income inequality on democracy.

I don't think this is literally an issue. True, all four of the variables Heckman mentions--income, happiness, income inequality, and democracy--can only be measured with error, but certainly they can be (and are) measured when they are studied empirically.

But I got a little worried that maybe there's something more going on here, some reason I should be giving a little less credence to studies linking economics to psychology and political science. Is Heckman implying that those cross-disciplinary studies have, at bottom, no more foundation than his argument on the effectiveness of prayer?

So I went back to Heckman's article to try to find the flaw in the reasoning. (By "flaw," I don't mean that Heckman was making a mistake; rather, I'm speaking of the hidden logical flaw that makes the reasoning flow, just as in those mathematical arguments where you "prove" 1=0 by means of a series of algebraic expressions that include a division-by-zero.)

Rereading carefully, I found the flaw. I actually think this article would be a good one for a take-home exam in a theoretical statistics class. I'll give the answer below.

Slate has a beautiful animated rendering of the job gains/losses over the past 2 years. It would be very difficult to show the trends without animation.

Two other things I like: The quantity circles are so much more informative than using color to paint states: we all know that most job losses are in NY and CA, because they're the biggest! Those circles help control for state population density.

The animation helps control for job gains in the previous period: it hides the cities that are relatively stable, but it nicely shows boom-bust cities (NYC) and stagnation-bust cities (Detroit).

(Via Peter's Twitter.)

From "the fundamentals are still strong" to "the worst economic crisis since the Great Depression" . . . from "the economy doesn't really needs saving" to "the crash of 2009" . . . from crisis to "glimmers of hope" . . .

What do John McCain, Casey Mulligan, and Barack Obama have in common? Ask Joe Bafumi, Larry Bartels, Alan Gerber, and Greg Huber.

Maybe because I spend so much time working with numbers, I'm as interested in the process of statistics as much as in its outcomes.

A couple months ag I told you about my struggles with the GDP of Russia and how I had inadvertently become entangled with the question.

More recently, I heard about Dick Morris's claim that, "In the last five months, according to the Federal Reserve Board, the money supply in the United States has increased by 271 percent."

271%??? Where did that implausible-looking number come from? Bill Peterson traced this to a 27.1% (note the decimal point) annualized rate of growth in M1 reported on a Federal Reserve website. So it sounded like a simple case of innumeracy (compounded by some partisan foolishness on Morris's part that, I argued here, doesn't do the Republican Party any favors).

But then an anonymous commenter wrote, "Dick Morris was referring to the Federal Reserve Adjusted Monetary Base which did, in fact, grow by a multiple between 2.5x-3x in the five months spanning October, 2008 through March, 2009." The commenter provided a couple of links and concluded,

In short, Dick Morris is right and you are wrong. I believe it is called a cruel irony when you publicly mock someone's intelligence only to find out subsequently that they are correct and you, well, you stepped in it.

I've made mistakes before and so it hardly shocked me that I got something wrong again! Apparently I'd been too quick to believe the Chance News entry that had gotten me started on this. In retrospect, it seemed pretty silly that I was so quick to trust the zero-budget Chance News while disparaging the well respected newspaper, The Hill (where Morris's column had originally appeared).

At this point, I really wanted to see the "271%" so I could issue a full-throated retraction. Unfortunately (or, maybe, fortunately, in the sense that it led to this story), when I followed the links supplied by the commenter, I could not find a 271% growth in the money supply anywhere! Which led back to the original puzzle of where the number came from. Was it simply a mis-transcribing of the 27.1%, or was there something else going on?

I was reminded of a legal consulting project I once worked on, where the statistician on the other side had done an analysis which I had then replicated, getting completely different results. But I didn't feel confident about my own claims until I tracked down how the other guy had done it wrong. It took me 2 hours to get the correct answer myself and to check it to my satisfaction [amusingly, I first typed "statisfaction" there], and 6 hours to get into the problem in enough depth to figure out what the other statistician had done wrong. (I bill by the hour so I remember these time totals. And, believe me, the other guy billed lots lots more than 8 hours to get his wrong answer!)

OK, back to Dick Morris's 271%. The latest insight came from Robert Waldmann, who commented as follows:

I [Waldmann] think I understand how he missed the damned dot, overlooked the concept of "annualised" and decided to call a 271% increase "tripling" not "almost quadrupling".

He mixed up H and M1. The monetary base has roughly tripled I think (and if I'm wrong well Morris is ignorant too).

If he didn't know about money multipliers, the money supply process, fractional reserve banking and my mother's maiden name (all equally certain) he might think this meant the money supply tripled. So he sends his long suffering research assistant to find the proof that the money supply tripled. The poor unfortunate guy came back with the number which Morris miss read due to the fact that "He puts ideology first and the [data] a distant second."

This story has the ring of truth to it: the research assistant was sent to do an impossible task, and Morris's ideology blocked him from realizing the mistake. (And, presumably, nobody edits his column at The Hill.) Interesting.

I remain ignorant regarding the money supply. One of the few things I remember from economics class in 11th grade is that "the money supply" is not well defined because of the presence of nonmonetary assets such as stocks, bonds, real estate, etc., as well as checking accounts and the like.

P.S. I'm still waiting for the anonymous commenter to come back to me with more data. I still think it's possible that there's a 271% in there (or, at least, "a multiple between 2.5x-3x," as the commenter claimed) that makes sense and that I just didn't know where to look.

Last month I reported on a statistical analysis by Josh Millet at Criteria Corp., suggesting that the economic climate for small business is improving. Millet now has an update (posted on 1 Apr but I assume it's serious):

With the final March numbers now in, the Hiring Activity Index nudged upwards very slightly again this month, to 62.3% from 61.4% in February. To me [Millet] this is an encouraging sign that the February jump in hiring activity by small businesses was not just a blip. If the data we're seeing means anything, the hiring situation for small and medium-sized businesses has begun to rebound.

Here's the graph I made of his numbers:

Millet also answers a bunch of potential criticisms of his measure:

There were some interesting comments and questions about the HAI and its potential utility as a leading economic indicator. We [Criteria Corp.] do sell our software on a subscription basis, and someone pointed out that if non-active subscribers didn't renew because of the downturn, this could artifically inflate the HAI because it is based on the percentage of our customer base that is actively doing pre-employment testing in a given month. This is a legitimate point, but I [Millet] will say that while low levels of use are a reason that customers sometimes do not renew, we haven't see non-renewal rates climb much since November, when the HAI dropped by 10 points. It was also suggested that higher numbers of job-seekers may result in applicants for positions that may not have been desirable previously--this is theoretically possible, but I don't see much evidence for it. What is most certainly true is that companies are getting far more applicants per open positon, as I previously blogged about here. However, since the HAI is based on the percentage of companies testing in a month, not the overall volume of tests, this shouldn't influence the HAI unduly, and wouldn't in any case explain the plunge in November and (partial) rebound in February.

Richard Posner defended the rationality of people who bought stocks during the bubble, writing:

People buy common stock when stock prices are rising. They (notoriously) bought houses during the early 2000s when house prices were rising. Since almost no one can predict the ups and downs of the stock market or the housing market, these purchases must have been motivated, Akerlof and Shiller argue, by something other than a rational investment strategy. But this is not at all obvious . . . Stocks have generally been a good investment, at least when held for a considerable period. . . .

I agree with Nate, who disagrees with Richard Posner by pointing out that, in fact, there was evidence that stocks were overpriced during the early 2000s, even at the time.

I'd like to add one comment. During all these bubble years, the experts were telling us over and over again how we should be buying stocks, how stocks were the best investment over the long term, and how we were all irrational for not putting more of our money into the stock market.

What's the logic here? People were being irrational by hesitating to buy stocks when they were going up, then they were finally being rational by buying stocks when they had very high prices?

I think all this discussion is hindered by the overloading of the term "rational." I imagine that just about everybody takes his or her money management seriously, and I'm sure people are trying to behave rationally with their investments. The trouble is that there are lots of rules out there to follow, so there's more than one way to be rational. I agree with Nate that Posner's implicit assumption--that people were following expert advice, and so they must have been applying (prospectively) good judgment--is misguided.

Jeff pointed me to this graph from congressmember Paul Ryan:

Ryan is actually being generous to the Democrats here. You can't imagine how things are going to look around 2150 or so!

This is pretty funny.

Image via Wikipedia

While one could argue a lot about the formula, the author Zeeshan-ul-hassan Usmani has made a good example of how to properly publish a working paper in this age: not just that he has the paper, he has an interactive demonstration, graphs, data, and a 30-second "executive" summary of the methodology for all of us with attention deficit disorders. He could have a comment section, but that's the way to go!

Why I don't (usually) publish with Wiley. I want to get it, though.

Steven Levitt writes of Time Magazine's list of the 100 people who "shape our world," that one year they included him but that, in his opinion, "Economists have not figured very prominently on the previous lists; there has been roughly one economist in the top 100 per year."

One per hundred seems pretty good to me, considering that economists represent only 0.1% of the employed population in the United States!

I guess the real moral of the story is that, whatever people have, they will consider it as a baseline and then want more.

P.S. Of course I'm happy that Nate is ranked in the top 200, but, no, he's not an economist. He's a sabermetrician, or, if you want to use a more general term, "statistician." If you call someone an economist just because he majored in economics in college, then I'm a physicist.

At the airport they have different terminals for different airlines, with flights leaving from all over the place. Why not have a simpler system, where all the flights to Chicago leave from one section of the airport, all the flights to L.A. leave from another section, and so forth? Then you could buy a "ticket to Chicago"--no airline specified--and then just go to the gate and get on the next flight to the Windy City.

The analogy is the supermarket, where products are organized by what they are, not who manufactures them. If the supermarket were like the airport, they'd have all the Proctor & Gamble products in the same place, and so forth. Or imagine a bookstore where the books were arranged by publisher and you had to look at the Random House books, then the Knopf books. etc. That's what it's like going to the airport, with the extra thrill of having occasional flight delays.

One could argue that flying waste so much fuel that anything that makes air travel more of a hassle is a good idea, and maybe that's true. If so, it's the only argument I know in favor of the current system.

Some statistical analysis says yes:

The HAI [Hiring Activity Index] is essentially a measure of how actively our [Criteria Corp's] customers (made up mostly of SMBs of between 10 and 500 employees) are administering pre-employment tests through our system (and presumably, therefore, hiring) . . . the HAI is the percentage of our customers who are actively hiring (administering tests) in a given month. From January 2008 (when we began tracking the HAI) to October 2008 the HAI remained very steady, within a few points of 65%. (If this seems low, consider that even in the best of times many 30 or 40 person companies will not be hiring every month.)

But as the financial markets plummeted and the unemployment rate surged in November, the HAI sunk about ten points, and by January reached its lowest level since we started tracking it, 53.28%. . . . So I [Josh Millet] was very pleasantly surprised to see a fairly strong uptick in the HAI in February, to 61.41%. It is only one data point, to be sure, but it suggests that for SMBs the hiring picture improved somewhat in February. Could it be an upwards blip in a downward trend? Of course, but the eight point jump in the HAI is the biggest we've seen since we started tracking the index. For those, like me [Millet], inclined to think that the current recession, although brutal and severe, will not be as long-lasting as some suppose, the February HAI reading is cause for hope. . . . Small and medium-sized businesses did not lead us into this recession, but they may just lead us out of it--and don't look now, but it may have already started.

I couldn't resist taking the horrible table that was posted and making a simple graph:

I assume they've done some simple checks with the data and made sure that this isn't some computer glitch, for example a problem with the software causing a bunch of these things to be counted twice, or some change in the calculation or the population of users so that the denominator suddenly changed?

I won't even try attempt to evaluate this--as I never tire of reminding people, my last econ class was in 11th grade--I'm just throwing this out there, first as an interesting example of a Freakonomics-style index and second as potentially important economic news. Again, I'll leave it to others to judge this.

It could be an interesting and important project (an econ M.A. thesis?) for someone to put together a whole bunch of this sort of measure to get some sort of aggregate that could be useful in monitoring aspects of the economy not captured by traditional statistics.

$7,600 (World Bank 2007)

$9,100 (World Bank 2007)

$14,700 (PPP adjusted, World Bank 2007)

$4,500 (World Bank 2006)

$7600 or $14,400 (gross national income: "Atlas method" or "purchasing power parity," World Bank 2007)

$12,600 (IMF 2008), $9,100 (World Bank 2007), or $12,500 (CIA 2008)

$2,637 in 2000 US dollars (World Bank 2007); that's $3,200 in 2007 dollars

$2,621 (World Bank 2006) or $8,600 (IMF)

Sure, I realize these statistics cannot be calculated exactly, and, sure, I realize there are definitional issues within a country and choices to be made when converting to other currencies. Still, there's a lot of variation here!

At the very least, this is a good example for a statistics, economics, or political science class to illustrate the difficulties of measurement.

P.S. See here (scroll down to item 3) for why we've been looking this up.

Ian Ayres suggests a gas tax that would start off with a rebate:

The government would offer a $500 advance tax rebate each year for every car you choose to sign up for the tax. In return, you would commit to pay an extra $1 for each gallon of gas you buy.

For obvious reasons, I like this idea--I'd like to get that extra $500. And since the government is giving out stimulus money anyway, now's the time to try it!

But I'm puzzled by their suggested implementation:

The actual tax paid would be based on miles driven and fuel economy. Thus a Chevy Impala rated at 19 m.p.g. would be charged $5.26 each 100 miles, while a Prius rated at 46 m.p.g. would be charged $2.17 per 100 miles.

Wouldn't it be simpler to just charge $1 per gallon of gas (with people who didn't get the rebate getting some sort of sticker exempting them from the tax)? Why have a complicated system based on miles per gallon when you can simply tax the gas itself?

In any case, I get Ayres's main point which is that this rebate system is more of a way to make things psychologically palatable to people than to be a realistic policy suggestion.

Perhaps another way to go on this would be to follow the "you polluted, you clean it up" policy, by which the tax is more directly tied to the cost of keeping the roads going, securing the supply of oil, cleaning the air, retrofitting coal plants to pollute less, etc. Maybe people would be less unhappy paying a higher gas tax if it were clearly going to maintaining the transport system and cleaning up the pollution it creates?

I thought that economists might be interested in my thoughts on the new book by Angrist and Pischke and, more generally, on the different perspectives that statisticians and economists have on causal inference. So I wrote them up as a short document and asked an econometrician friend where to send it. He said that the Journal of Economic Literature does book reviews so I sent it there. They returned it to me with kind words on my review but the note: "The JEL has avoided reviewing textbooks, focusing instead on research monographs. The review makes fine points about the coverage in this textbook, but neither the book nor the review are attempting to advance the state of the art."

Fair enough. So where to send the review. I asked some colleagues and they all agreed that JEL is the only economics journal that reviews books. So I guess econ textbooks just don't get reviewed!

This surprised me, given that book reviews appear in several top statistical journals, including the Journal of the American Statistical Association, the American Statistician, Biometrics, the Journal of the Royal Statistical Society, Statistics in Medicine, and Technometrics. There are also lots of places that review books in political science.

I'm surprised that there's only one place for book reviews for economists.

See here for my thoughts on the surprising stability of the economics curriculum.

Someone sent me an email asking if I would consider any form of advertising or sponsorship for the blog. I replied, "I wasn't planning to have any advertising or sponsorship on the blog, but I guess it's possible (if unlikely)." And he offered $1000 to sponsor us over two years (for a link in the "Research supported by..." section, where we currently list NSF, NIH, and Yahoo Research).

For $1000 it certainly wasn't worth the hassle. At this level, at least, blogs aren't big business quite yet.

Image via Wikipedia

Here is a chart by Johannes Ruf (using a bibliography prepared by Bernhard Ganglmair), listing a number of papers that list the value of life as estimated by such trade-offs:

So, allowing some tolerance for inflation and deflation, and taking the average of all of the above, we arrive to about 4 million dollars. If we assume that the average life expectancy is 78 years, and that half of the day is waking time, the value comes down to about $12 for a waking hour of life - arbitrary, but the right order of magnitude. Additional complexity can be entered into this model to improve it.

I will now address what we can do with these numbers.

Bilmes and Stiglitz estimate cost of the Iraq war to 3 trillion, while the US military casualties currently number 4250. So, the cost of lives lost is 17 billion, but the economic cost borne by the United States is 3 trillion. What is the true cost of the Iraq war in human lives? 3 trillion divided by 4.2 million comes down to over 720,000 lives. This is the true casualty count, which accounts for people having to work on stuff that explodes instead of spending time with their families. On the Iraqi side, there were about 100,000 civilian lives lost, but it's hard to estimate the full cost of war to Iraq - the Lancet study claims numbers considerably larger than this.

Andrew Gelman has also written about this - when is one's risk of radon to health sufficient to justify the cost of measurement or remediation. I'd again like to acknowledge Johannes and Bernhard's help with the research, but all flaws are solely my own. The difficulties of estimation shouldn't stop us from studying the problem - maybe better awareness of this will help save a million lives in the future.

Chris Masse pointed me to this blog by Panos Ipeirotis, who argues that some online prediction markets give probabilities that are too good to be true:

On the front page of Suday's New York Times, the primest of prime real estate, Hiroko Tabuchi writes:

As recession-wary Americans adapt to a new frugality, Japan offers a peek at how thrift can take lasting hold of a consumer society, to disastrous effect. . . . Today, years after the recovery, even well-off Japanese households use old bath water to do laundry, a popular way to save on utility bills. Sales of whiskey, the favorite drink among moneyed Tokyoites in the booming '80s, have fallen to a fifth of their peak. And the nation is losing interest in cars; sales have fallen by half since 1990.

How is this "disastrous"? Using bath water to do laundry makes sense to me. Unfortunately our apartment is not set up to do this, but why not? Cars are much better made than they used to be, probably most people in Japan who want a car badly enough have one already, so it makes sense that car sales would fall--people can continue driving their dependable old cars. Finally, I have nothing against whiskey, but is it really "disastrous" that sales have fallen to a fifth of their peak? Fads and all that.

Sure, I can see that this is all evidence that Japan's economy is far from booming, but I'm a bit disturbed to see frugality treated as a "disaster" in itself.

What really bothers me, though, is that the assumptions in the article are completely unstated. I'd be happier if the reporter had written something like this:

You might think that it's a good thing that the Japanese have become more energy-efficient and less into trendy conspicuous consumption: even well-off Japanese households use old bath water to do laundry, a popular way to save on utility bills, and sales of whiskey, the favorite drink among moneyed Tokyoites in the booming '80s, have fallen to a fifth of their peak.

Even the notorious Japanese tendency to buy new cars and appliances every two years, whether they need it or not, has abated. The nation is losing interest in cars--sales have fallen by half since 1990--and people are sticking with old-fashioned television sets rather than snapping up expensive flat-screen TVs.

But this frugal behavior is having a disastrous effect [or, is symptomatic of an underlying economic disaster]. . . .

This puts the assumptions front and center, at which point they could quote experts on both sides of the issue or whatever.

P.S. Just to be clear: my point here is not that a newspaper reporter wrote something I might disagree with, but rather that sometimes people seem trapped within their unstated assumptions. (Yes, I'm sure that happens to me too.)

From Jessica, I saw a review by "Econjeff" of my review of Joshua Angrist and Jorn-Steffen Pischke's new book, "Mostly Harmless Econometrics: An Empiricist's Companion."

Econjeff pretty much agrees with what I wrote, but with one comment:

I [Econjeff] am a bit surprised by Gelman's call for more on hierarchical models; I think economists are right to treat these as a combination of useful pedagogical tool for education research design and an unnecessarily functional-form dependent way to get the standard errors right when then the unit of treatment differs from the units available in the data.

I think this is a common perception of multilevel (hierarchical) models among economists. Regular readers of this blog will not be surprised to hear that I disagree completely! The purpose of a multilevel model is not to "get the standard errors right" but rather to model structure in the data.

An analogy that might help here for economists is time series analysis. If you have data with time series structure and you ignore it, you can get over-optimistic standard errors. But that's not the main reason people do time series modeling. The main reason is that the time series structure is interesting and important in its own right. We are interested in individual and contextual effects and unexplained variation at the individual and group levels, just as we are interested in autocorrelation, periodicity, long-range dependence, and so forth.

See chapters 1 and 11 of ARM for more discussion of motivations for multilevel modeling.

Chris Masse writes:

The reality check is that the social utility of the prediction markets is marginal. The added accuracy is minute, and, anyway, doesn't fill up the gap betweeen expectations and omniscience (which is how people judge forecasters). In our view, the social utility of the prediction markets lays in efficiency, not in accuracy. In complicated situations, the prediction markets integrate facts and expertise much faster than the mass media do. It is their velocity that we should put to work.

Interesting. This relates to other technology-based ways of aggregating information, such as using cell phone traffic to track epidemics.

Here's Lindley. I suspect I'd agree with Lindley on just about any issue of statistical theory and practice. I've read some of Lindley's old articles and contributions to discussions and, even when he seemed like something of an extremist at the time, in retrospect he always seems to be correct. That said, I disagree with him on Taleb. I think the difference is that Lindley was evaluating The Black Swan based on its statistical content, whereas I liked the book because it was full of ideas and stories that sparked thoughts in my mind (and, I think, in the minds of many readers).

Also, I disagree with Lindley 100% about Karl Popper. Even though, again, I think Lindley and I are extremely close on issues of statistical practice and theory.

And here's Robert. I like his connection of "black swans" to "model shift." This fits in well to my three stages of Bayesian Data Analysis (model building, model fitting, model checking), with model checking being the all-important but often neglected ugly sister. (As I've discussed many times, you rarely see graphical model checks in a published paper, because either (a) the model didn't fit, in which case, at worst you'd be too embarrassed to admit it, or at best you'd fix the model and there'd be nothing to report, of (b) the model fits ok, in which case the model check is probably only worth a sentence or two.)

From a philosophical point of view, I think the most important point of confusion about Bayesian inference is the idea that it's about computing the probability that a model is true. In all the areas I've ever worked on, the model is never true. But what you can do is find out that certain important aspects of the data are highly unlikely to be captured by the fitted model, which can facilitate a "model shift" moment. This sort of falsification is why I believe Popper's philosophy of science to be a good fit to Bayesian data analysis.

Also, I agree with Christian's characterization of Black-Scholes etc. as not "n accurate representation of reality, but rather a gentleman's agreement between traders that served to agree on prices." The way I put it was that these graduate programs in "financial mathematics / financial engineering" served a useful function by screening for students who were mathematically able and willing to work hard. It's too bad they couldn't have been learning statistics instead, but, for better or worse, competence in statistics is easier to fake than competence in math.

Christian also has an interesting conclusion:

Encouraging a total mistrust of anything scientific or academic is not helping in solving issues, but most surely pushes people in the arms of charlatans with ready answers.

I wonder what Taleb would say about this. Possibly he'd reply that it's better to have citizens to think critically than to be awed by their financial advisors.

In the spirit of Bullwinkle, I think that all blog entries should be required to have two titles. . . .

Anyway, Seth linked to this amusing note by Preston McAfee.

P.S. In a comment to my earlier entry, somebody linked to McAfee's free introductory economics textbook. I started reading it, and it seems great so far. Maybe if I'd read a book like this thirty years ago I would've become an economist. Or maybe not, I dunno. It's not like my statistics textbooks were so delightful; I just liked the subject. And I've never read a poli sci textbook in my life.

I received a free copy in the mail of an introductory statistics textbook; I guess the publisher wants me to adopt it for my courses. The book isn't bad, actually it's pretty good: it follows the "Moore and McCabe" format, starting with descriptive statistics (up to correlation and regression), then a bit on data collection, then probability, then statistical inference, and at the end chapters on various more advanced topics.

I showed the book to Yu-Sung and he said: Wow, it's pretty fancy. I bet it costs $150. I didn't believe him, but we checked on Amazon and lo! it really does retail for that much. What the . . . ? I asked around and, indeed, it's commonplace for students to pay well over $100 for introductory textbooks.

Well. I'm planning to write an introductory textbook of my own and I'd like to charge $10 for it. Maybe this isn't possible, but I think $40 should be doable. And why would anybody require their students to pay $150 for a statistics book when something better is available at less than 1/3 the price?

This won't be easy, because I'm planning to write an entirely new kind of intro book, starting from scratch. But why hasn't someone written a more conventional book at a cut-rate price? Or maybe they have, and I just haven't heard about it?

It just mystifies me that, in all these different fields, it's considered acceptable to charge $150 for a textbook. I'd think that all you need is one cartel-breaker in each field and all the prices would come tumbling down. But apparently not. I just don't understand.

P.S. More thoughts here.

We were discussing the Angrist and Pischke book with Paul Rosenbaum and I mentioned my struggle with instrumental variables: where do they come from, and doesn't it seem awkward when you see someone studying a causal question and looking around for an instrument?

And Paul said: No, it goes the other way. What Angrist and his colleagues do is to find the instrument first, and then they go from there. They might see something in the newspaper or hear something on the radio and think: Hey--there's a natural experiment--it could make a good instrument! And then they go from there.

This sounded fun at first, but I actually prefer this to the usual presentation of instrumental variables. The "find the IV first" approach is cleaner: in this story, all causation flows from the IV, which has various consequences. So if you have a few key researchers such Angrist keeping their ears open, hearing of IV's, then you'll learn some things. This approach also fits in with my fail-safe method of understanding IV's when I get stuck with the usual interpretation.

Sometimes the "lead with the natural experiment" approach can lead to missteps, as illustrated by Angrist and Pischke's overinterpretation of David Lee's work on incumbency in elections. (See here for my summary of Lee's research along with a discussion of why he's estimating the "incumbent party advantage" rather than the advantage of individual incumbency.) But generally it seems like the way to go, much better than the standard approach of starting with a causal goal of interest and then looking around for an IV.

In this spirit, let me again mention my own pet idea for a natural experiment:

The Flynn effect, and the related occasional re-norming of IQ scores, causes jumps in the number of people classified as mentally retarded (conventionally, an IQ of 70, which is two standard deviations below the mean if the mean is scaled at 100). When they rescale the tests, the proportion of people labeled "retarded" jumps up. Seems like a natural experiment that might be a good opportunity to study effects of classifying people in this way on the margin. If the renorming is done differently in different states or countries, this would provide more opportunity for identifying treatment effects.

I think it would be so cool if someone could take this idea and run with it.

Now that we're on the topic of econometrics . . . somebody recommended to me a book by Deirdre McCloskey. I can't remember who gave me this recommendation, but the name did ring a bell, and then I remembered I wrote some other things about her work a couple years ago. See here.

And, because not everyone likes to click through, here it all is again:

I just read the new book, "Mostly Harmless Econometrics: An Empiricist's Companion," by Joshua Angrist and Jorn-Steffen Pischke. It's an excellent book and, I think, well worth your $35. I recommend that all of you buy it.

I also have a few comments.

On the often-interesting judgment and decision making listserv, George Christopoulos wrote:

It seems that in situations similar to the present economic situation economic agents are less willing to take risks and instead they prefer safer options.

Could somebody point to studies that show this negative relationship between depression /recession (or when generally when wealth resources are low) and increased (relative?) risk aversion?

There were a couple of responses on the list, but they seemed to me to miss the point slightly. The respondents referred to econ literature on stock market trading and on wealth and economic decision making, but my impression was that Christopoulos was looking for something more psychological: something like a meta-analysis of studies of uncertainty aversion (I prefer to avoid the term "risk aversion" or even "loss aversion," for reasons I've discussed at length on this blog) over time, to see if subjects in an identical experiment show more uncertainty aversion in bad times than good.

The next step would be to analyze such data to separate out, to the extent possible, effects of individual economic status and national trends. The hypothesis might be that both have effects: that people suffering personal reversals might show more uncertainty aversion, and that, on top of this, everyone might tend to show more uncertainty aversion during economic downturns.

Could be an interesting study, although I doubt that such data are available.

This one just came from Life Science Journals :

Based on your research profile, we would like to offer you the following free subscription to Nature Biotechnology.

Click here to sign-up for your free subscription which is available without any obligation to qualified scientists.

What part of my "research profile" are they talking about??

Mark Thoma has an interesting discussion of the challenge that the economics profession, and individual economists, have when they give policy recommendations.

Mark's basic point goes as follows. Consider the following four stages of a model:

(a) assumptions about fundamental principles of how the world works,
(b) normative principles (that is, fundamental goals, views about how the world should be),
(c) conclusions about the likely effects on policy,
(d) recommendations about policies.

In any rigorous economic model, there should be a mapping leading from (a) to (c). Further reasoning (possibly mathematical modeling, as in cost-benefit analysis) will take you from (b) and (c) to (d).

That's all fine. But Mark's point is that the reasoning can go the other way too: start with (b) and (d), and then you can figure out what (c) needs to be, and then you can go back one more step and figure out what model (a) you need to get started! Even if economists are not doing this reasoning-from-conclusions-to-assumptions explicitly, you could well believe it's going on implicitly as well as being induced by various pressures such as the selection of what research results to report and even what problems to work on.

This is inevitable, and I discuss it in the decision analysis chapter (22, I think it is) of Bayesian Data Analysis. We call it the garbage-in-garbage-out problem: If you can come with any decision you'd like by just altering the inputs of your analysis, then what's the point of decision analysis (or, by extension to the above-linked example, economic modeling) at all?

My answer is something that I call "institutional decision analysis," which has two principles:

1. It can be a good idea to provide reasoning to justify your decisions. As an individual person, you might not have to justify your personal decisions to anyone (except to your spouse), but an institution--whether it be a business, a government agency, a nonprofit organization, or some other grouping--often needs some path of bread crumbs connecting assumptions to recommendations. (Here, I carefully say "connecting" rather than "leading from" to be agnostic about the direction of the reasoning.)

2. As Mark noted, an overall decision recommendation on anything important is likely to be so dependent on assumptions to such an extent that it's probably fair to say that the analyst is reasoning from conclusions to assumptions (from (d) to (c) and then to (a), in my above notation). But, even then, formal decision analysis can be useful in making relative recommendations. This is the point that we made in our article about decision making for home radon [link fixed]. In the economics context, this might suggest that economists of different political persuasions could still give useful recommendations about how to spend money or cut taxes, or where in the economy such policies would make more or less sense.

Strange Maps has this cool picture of Polish election results compared to the pre-1914 partition border:

I can't tell what the colors represent, but it's striking nonetheless. In linking to it, Matt Yglesias writes,

History's impact can often be surprisingly long-lasting. It's been a long time since taking midwestern agricultural products via train to Chicago and then by boat across the Great Lakes, across the Eerie Canal, down the Hudson, and to the port at New York was a major element in the American economy. But we still have two giant cities in Chicago and New York . . . I wouldn't be surprised if the German-run bit of Poland was richer in 1918 than the rest of it, and that the differential has persisted since then. By the same token, we can expect the East Germany part of Germany to remain poorer than the West Germany part for a long time.

Here are some graphs that I posted a couple of years ago and that found their way into chapter 5:

More pictures here (for those of you who haven't bought the book yet). For the book, we cleaned up the graphs a bit, but the general point remains, that the states that are rich and poor now are the ones that were rich and poor 80 years ago.

Chris Blattman writes,

Several aspiring graduate students have written me [Blattman] about becoming an impact evaluator. . . . I think the best advice is: don't get a PhD to do evaluations. The randomized evaluation is just one tool in the knowledge toolbox. . . . Yes, the randomized evaluation remains the "gold standard" for important (albeit narrow) questions. Social science, however, has a much bigger toolbox for a much broader (and often more interesting) realm of inquiry. . . .

I pretty much agree with Chris on the substance of his remarks, but I think he's missing something when he merges "impact evaluation" and "randomize evaluation" into a single concept. Policy analysis is a big area, and it certainly includes observational studies. We care about the impacts of all sorts of policies that can't be directly studied using experimentation.

P.S. In a different direction, it's interesting to me that policy evaluation is considered part of economics (a little bit) but not really part of political science--but maybe things are changing.

Nate Silver and Greg Mankiw have an interesting exchange about the use of exogenous instruments to estimate causal effects. Unfortunately, the subject is macroeconomics, a topic on which I know next to nothing beyond what I learned in Mr. Cutlip's econ class in 11th grade. But I think it is, in Greg's phrase, "a teachable moment" on the subject of causal inference.

Greg summarizes the exchange pretty well, although I think he's missing a key point.

Nate noticed a newspaper article where Greg related research by Christina and David Romer on the effects of "exogenous" tax cuts on the economy. Nate writes:

The type of tax cut that Romer and Romer think falls into this category is what they call an "exogenous" tax cut -- one designed not to counter business cycles, but rather a "spontaneous" tax cut under relatively healthy economic circumstances.

This is very much not the type of tax cut that we are contemplating right now. Instead, what is being contemplated is a countercyclical action in an unhealthy economy designed to return the economy to normal growth. Romer and Romer are not all that keen on this type of tax cut; in fact, they argue that such "countercyclical fiscal policy is not achieving its intended purpose" . . .

Greg repiies:

Why did the Romers focus on exogenous policy changes? The reason is that these are the only changes that can be used to reliability identify the effects of tax policy. . . . The Romers focus on exogenous tax changes for the same reason doctors conduct randomized drugs trials--not because they are interested in randomization as a prescriptive tool, but because randomization solves a statistical identification problem.

And now here are my thoughts, again with full recognition that I can really only comment on the statistical issues here, not the economics.

First, Greg is right that it is generally considered desirable or even optimal to estimate treatment effects using randomized experiments or exogenous implementations (but see here for an opposite view from James Heckman), even when the ultimate goal is to understand how the intervention works in the wild, so to speak.

But there is the potential for treatment interactions--that is, a treatment might be more effective in some conditions than in others. There's lots of evidence for treatment interactions in various settings, ranging from education to job training. And this is what Nate is talking about. Again, without attempting to comment on the economics, the treatment effect could vary enough that Nate could be right about the direct relevance of the Romers' study of exogenous tax changes.

To put it another way, Greg is talking about identifiability and Nate is talking about generalizability.

Greg writes, "I usually don't respond to blogosphere commentary on my work because, after all, time is scarce." But since he's had time to respond once, perhaps he'll be able to respond again and clarify this issue. (I think my time is particularly non-scarce since I'm responding to blogosphere commentary on somebody else's work!) In any case, I like the idea of shifting the debate to a discussion of treatment interactions since then it might be more possible to resolve this on a technical level. Perhaps a teachable moment for me as well as for others.

John Quiggin sent me this article of his from 1987 that made the same argument as my paper with Edlin and Kaplan on why and how it's rational to vote. In his article, Quiggin wrote:

There is strong evidence that voting behaviour is both ends-directed and rational. That is, electors choose to vote because of the effects their vote will have, and do not vote if these effects are insufficient to outweigh the costs of voting. However, as Downs' paradox shows, rationality and egoism together imply non-voting. The evidence suggests that egoism is the postulate which must be abandoned. . . . voters' interest in political information increases with the importance of political choices. Once again, this is consistent with rationality but not with egoism.

Our article had more math and more focus on U.S. politics but the basic point is the same.

Also let me use this as yet another excuse to plug a wonderful article, The Norm of Self-Interest, by psychologist Dale Miller, in which he argues the following:

A norm exists in Western cultures that specifies self-interest both is and ought to be a powerful determinant of behavior. This norm influences people's actions and opinions as well as the accounts they give for their actions and opinions. In particular, it leads people to act and speak as though they care more about their material self-interest than they do.

NY Times ran an article Risk Mismanagement:

VaR uses this normal distribution curve to plot the riskiness of a portfolio. But it makes certain assumptions. VaR is often measured daily and rarely extends beyond a few weeks, and because it is a very short-term measure, it assumes that tomorrow will be more or less like today. Even what's called "historical VaR" -- a variation of standard VaR that measures potential portfolio risk a year or two out, only uses the previous few years as its benchmark.

Nonsense. VaR is an innocent and useful mathematical construct, completely independent of the distribution or the model used. It can be as simple as subtracting variance from the mean to penalize for the risk of a distribution. Don't throw the baby (VaR) out with the bathwater (dubious VaR practices).

The real failure of risk management was in the bad short-tailed models (that underestimated the probability of a default) that they fit in a bad way (overfitting to a small amount of one-sided historic data, without using priors that would include the possibility of a disaster).

But even once these problems are fixed, economy will still be a feedback system, not something pretty, simple and linear. Let's hope for a marriage of statistical modeling with systems and complexity theory. In the meantime, I'll hope for using more common sense based on substance and less mathematics based on arbitrary metrics. This would help prevent disasters in the first place.

Ted Dunning sent me this graph:

So, how do the polling data compare to the contract prices from Intrade on the day before the election? Below is a graph with a data point for each state, with the horizontal axis representing the polling data and the vertical axis representing the Intrade contract price.

The quick message that I get from here is that Intrade prices are way biased toward 50/50. For example, the price for DC is something like .04, which is ridiculous. (To two decimal places, it should certainly be .00).

John Quiggin writes:

The strong version [of the efficient markets hypothesis], which gained some credence during the financial bubble era says that asset prices represent the best possible estimate taking account of all information, both public and private. It was this claim that lay behind the proposal for 'terrorism futures' put forward, and quickly abandoned a couple of years ago. It seems unlikely that strong-form EMH is going to be taken seriously in the foreseeable future, given the magnitude of asset pricing failures revealed by the crisis.

I have two comments:

1. It was my impression that under classical economic theory, the economy is always at a phase transition (by analogy to ice water): there's said to be enough "water" (i.e., trading) that prices reflect a consensus, but there's enough "ice" (i.e., new information entering the system) that it is rational for prices to move. I don't know any of the theory beyond this, but I imagine that much of the debate must center on how large the fluctuations are in this phase transition. In may mathematical systems (although not ice water, I think), these fluctuations can be large.

2. A point that I always thought was under-emphasized is that the proposed terrorism futures markets were to be run by by convicted criminal John Poindexter--a guy who was actually involved in what were arguably terrorist activities (the project of secretly sending weapons to Iran in the 1980s). The defenders of the terrorism futures markets never seemed interested in this point; see, for example, the comments here. But to me this was a pretty serious issue: really we're talking about an unrepentant criminal here. I'm not saying that terrorism futures are necessarily a bad idea, but I was highly skeptical of that particular implementation.

Seth writes:

A friend of mine who works for one of these newspapers said that the end has been coming for a long time. In the early 1990s, if I remember correctly, the audience started to shrink. At the time, and for a long time thereafter, this was ignored. Had the problem been recognized back then it might have been possible, given a lot of time to experiment, to find a solution, a way to survive much longer. But now it is too late.

I don't know about this. I've been reading for at least 10 years about the decline of newspapers. Newspapers and magazines have had a lot of stories on this topic for awhile. In the 1990s, the story was how daily newspapers were reducing the number of reporters because investors were demanding huge profit margins. Everyone understood that there were long-term problems; if nothing else, old newspapers were disappearing faster than new ones were being created.

So, I think lots of people did know about the problem--it was not being ignored. But it wasn't clear what to do about it. "Time to experiment" sounds like a good idea in theory, but in practice there was no solution available.

Sergey Aksenov pointed me to this article by Deepak Hegde and David Mowery:

Dan Goldstein pointed me to this op-ed referring to the work of Arthur Brooks on charitable contributions of Democrats and Republicans. For convenience, I'll repeat my comments from two years ago:

I was thinking more about a framework for understanding these findings by Arthur Brooks on the rates at which different groups give to charity. Some explanations are "conservatives are nicer than liberals" or "conservatives have more spare cash than liberals" or "conservatives believe in charity as an institution more than liberals." (My favorite quote on this is "I'd give to charity, but they'd spend it all on drugs.")

But . . . although I think there's truth to all of the above explanations, I think some insight can be gained by looking at this another way. Lots of research shows that people are likely to take the default option (see here and here for some thoughts on the topic). The clearest examples are pension plans and organ donations, both of which show lots of variation and also show people's decisions strongly tracking the default options.

For example, consider organ donation: over 99% of Austrians and only 12% of Gernans consent to donate their organs after death. Are Austrians so much nicer than Germans? Maybe so, but a clue is that Austria has a "presumed consent" rule (the default is to donate) and Germany has an "explicit consent" rule (the default is to not donate). Johnson and Goldstein find huge effects of the default in organ donations, and others have found such default effects elsewhere.

Implicit defaults?

My hypothesis, then, is that the groups that give more to charity, and that give more blood, have defaults that more strongly favor this giving. Such defaults are generally implicit (excepting situations such as religions that require tithing), but to the extent that the U.S. has different "subcultures," they could be real. We actually might be able to learn more about this with our new GSS questions, where we ask people how many Democrats and Republicans they know (in addition to asking their own political preferences).

Does this explanation add anything, or am I just pushing things back from "why to people vary in how much they give" to "why is there variation in defaults"? I think something is gained, actually, partly because, to the extent the default story is true, one could perhaps increase giving by working on the defaults, rather than trying directly to make people nicer. Just as, for organ donation, it would probably be more effective to change the default rather than to try to convince people individually, based on current defaults.

P.S. More Arthur Brooks links are at item 5 here.

Steve Buyske points me to this:

Boy do I hate this. A straight time series would do so much better. They should also follow the general principle of extending the series, going back before 1929 and after 1933. But my main feeling is that this spiderweb action is just horrible.

John Side posts this graph from Lee Drutman of lobbying expenditures and bailout funds for several financial firms:

This is fun, and I recognize that this graph, in John's words, "a back of the envelope analysis" and "only subjective," but . . . isn't there a problem with selection bias here--you're only considering cases that had bailouts. I'd like to see a lot more data points here: firms with many different levels of lobbying but zero bailout.

But the graph raises an interesting theoretical point: larger firms will be giving more total lobbying dollars and thus may be more likely to be bailed out? Even beyond any "too big to fail" argument based on economics.

This discussion from Keynes (from Robert Skidelsky, linked from Steve Hsu) reminds me of a frustrating conversation I've sometimes had with economists regarding the concept of "risk aversion."

Lane Kenworthy writes (link from here and here):

The notion that political parties are a key determinant of income inequality has been around for a long time. I suspect many non-academics take its truth for granted. Among American scholars, the notion is perhaps most closely associated with Douglas Hibbs . . .

[In his recent book, Unequal Democracy], Larry Bartels suggests that a key part of the story is different policies pursued by Democratic and Republican presidents. . . . Bartels' argument, while by no means novel, is very much a fresh one. It is based on extensive empirical analysis of the post-World War II period. Is he correct? I think Bartels probably has it right for part of this period, but I'm not convinced that his hypothesis holds up for the other part. . . .

This relates to some ideas I had after seeing Bartels speak on his work at Columbia a couple of years ago; see here and here. In particular, in that last link, I wrote the following:

After seeing Larry Bartels present his findings on how the economy has done better, for the poor and middle class, under Democratic presidents than Republican presidents, I was puzzled. Not that it couldn't be true, but it seemed a little mysterious, given the general sense that presidents don't have much control over the econony--business cycles just seem to happen sometime.

But the general perceptions about Presidents and the economy have changed over time.

I might be wrong here, not having lived through the entire postwar period, but my perception is that, during most of this time, "competence" was not an issue; rather, there was a general belief that the president could do some things, most notably help labor (for the Democrats) or business (for the Republicans).

The exception here was the 1976-1996 period, during which there was a real sense of economic incompetence or powerlessness of some presidents (Ford with his Whip Inflation Now, Carter with stagflation, the residual view of Democrats being incompetent for the economy, George H.W. Bush with the deficit and the regression, perhaps extending to Dole in 1996). Then, since 2000, we've returned to the general attitude that both parties have essential competence but have different goals. (Not that everyone agrees on the "competence" issue, but it seems to me that the battle is more being fought on priorities than competence--in contrast to 1992, for example.)

So, the conventional wisdom based on the 1976-1996 period is that presidents can't do much, they're at the mercy of the business cycle, etc., which makes Bartels's results seem like some sort of fluke, or a perhaps meaningless juxtaposition of one-off results. But taking the 1948-1972 and 2000-2004 perspectives, Bartels's graph makes a lot of sense. From this perspective, the Democrats did their thing, and the Republicans did theirs, and you'd expect to see a big difference at the low end of the income scale. (Again, this is inherently short-term reasoning, not long-term, but as Larry pointed out in his talk, the evidence is that voters are susceptible to short-term inferences.)

In summary: we're used to thinking of presidents as fairly powerless surfers on the global economy, able to tinker with tax rates but not much more--but thinking about the entire postwar period, there's certainly been at least the perception that presidents can deliver the economic goods to their constituencies. So from that perspective, Larry's curves should not be much of a surprise--at least in that the slope for Democrats goes down (i.e., poor people do better under Democratic presidents) and the slope for Republicans goes up (i.e., rich people do better under Republican presidents). The relative positions of the lines is another story, which perhaps corresponds to random alignments of the business cycle.

Perhaps Kenworthy can connect this thinking more directly to his arguments. My time frames don't quite align with his, but it's a similar idea of breaking the period into smaller segments.

And, to comment on my comments . . . when posting the above in 2006, I wrote, "since 2000, we've returned to the general attitude that both parties have essential competence but have different goals. . . . we're used to thinking of presidents as fairly powerless surfers on the global economy, able to tinker with tax rates but not much more. . ." Things sure have changed in 2 1/2 years!

Not to pick on Greg Mankiw, but is he saying that it was a good thing that the stock market rose 50% during the two years he was in the Council of Economic Advisors? I'm not at all trying to blame him for what happened, but in retrospect wouldn't one want to regret the big climb in the stock market that preceded the fall? This is completely out of my area of expertise so I defer to others on this. It looks like Mankiw is making a joke of some kind but I don't know enough about the background to really understand the point. (Probably this is the same reaction that many readers to this blog get when I make a statistics joke.)

Mankiw calculates that McCain's tax plan would tax him at a marginal rate of 83%, while Obama's would tax his marginal dollar at 93%. He concludes:

The bottom line: If you are one of those people out there trying to induce me [Mankiw] to do some work for you, there is a good chance I will turn you down. And the likelihood will go up after President Obama puts his tax plan in place. I expect to spend more time playing with my kids. They will be poorer when they grow up, but perhaps they will have a few more happy memories.

I don't quite follow Mankiw's reasoning on the marginal tax rates, except I do get his point that his marginal dollars are all ultimately going to his kids--none of it will be spent in his lifetime, so in that sense he's talking about different varieties of an estate tax.

I'm more interested in the decision implications.

To start with, it does sound like Mankiw's kids are already well provided for, and, although I'm sure they'd disagree with me on this, it's not clear that they would benefit from having more money in the bank when their parents are gone.

So, from that point of view, the question is why Mankiw isn't already spending more time playing with his kids? I can't speak for him, but for me, I have to say that it can be fun to work (or even to write blog entries). But, more than that, I feel a sense of obligation to get things done. At some level, getting paid is part of the motivation, but in any particular example I'm not quite sure how it fits in. I do lots of work things that pay me $0; I think they're important, so I do them.

On the other hand, if I really, really didn't need the money, I could set my salary to $0 and spend the money on extra postdocs. That would be pretty cool but I can't really live on $0 and keep my current lifestyle.

For Mankiw, I'm not sure; maybe he makes enough from his textbooks that he doesn't need much of his academic salary and could possibly do more by converting it into postdocs and research assistants. Or maybe he already has more research assistants than he knows what to do with; I don't know. But his division of waking hours into "working" or "playing with kids" is, I would guess, not very sensitive to the marginal tax rate.

I went to the webpage of physicist / computer scientist David MacKay and found that he had written a book on energy policy for general audiences. It's basically a physics book where he computes the energy costs of different aspects of our lifestyles and then estimates the potential for getting power from various non-carbon-emitting sources. It's a fun read and I recommend taking a look. I don't know enough to offer any serious endorsement or criticism of his claims, but he presents his reasoning very clearly, which I like. He has lots of graphs, and I view his book as being somewhat in the spirit of Red State, Blue State, as organizing a bunch of information so that the reader is in a better position to make his or her own judgments. (Again, I'm in no position to endorse or criticize MacKay's specific recommendations.)

My main suggestion is that MacKay follow up on one of his suggestions and connect his work to that of advocates on different sides of the issue. He begins his book as follows:

I [MacKay] recently read two books, one by a physicist, and one by an economist. In Out of Gas, Caltech physicist David Goodstein describes an impending energy crisis brought on by The End of the Age of Oil. . . .

In The Skeptical Environmentalist, Bjørn Lomborg paints a completely different picture. "Everything is fine." Indeed, "everything is getting better." Furthermore, "we are not headed for a major energy crisis," and "there is plenty of energy." How could two smart people come to such different conclusions? I had to get to the bottom of this.

This sounded good, and I was looking forward to the resolution. But in all the rest of the book, MacKay never mentioned Goodstein or Lomborg again (except once in a brief aside to say that their books are "full of interesting numbers and back-of-envelope calculations," and once to cite Lomborg's estimate of bird deaths caused by wind turbines)!

This was a letdown. I think MacKay's argument would be stronger if he could loop back and address the arguments of Goodstein, Lomborg, and others.

It's an old, old story but always worth hearing again, this time from Kevin Carey:

Tyler Cowen's recent remark against team players reminded me of my paper a few years ago, Forming Voting Blocs and Coalitions as a Prisoner's Dilemma: A Possible Theoretical Explanation for Political Instability:

Individuals in a committee or election can increase their voting power by forming coalitions. This behavior is shown here to yield a prisoner's dilemma, in which a subset of voters can increase their power, while reducing average voting power for the electorate as a whole. This is an unusual form of the prisoner's dilemma in that cooperation is the sefilsh act that hurts the larger group. Under a simple model, the privately optimal coalition size is approximately 1.4 times the square root of the number of voters. When voters' preferences are allowed to differ, coalitions form only if voters are approximately politically balanced. We propose a dynamic view of coalitions, in which groups of voters choose of their own free will to form and disband coalitions, in a continuing struggle to maintain their voting power. This is potentially an endogenous mechanism for political instability, even in a world where individuals' (probabilistic) preferences are fixed and known.

Cool jargon, huh? Here's a pretty picture from the article:

And here's a schematic of the reasoning:

Dear Mr. Leonard,

A colleague pointed me to your article about our paper on why it is rational to vote. I'm glad you think our article is "pretty funny." We try to be entertaining even in our most serious writings. I agree with your comment that "we don't need a rational choice framework to provide a reason for participating in the process." And, in a world where nobody was making rational choice arguments, our article might not be necessary. But with prominent economic writers such as Steven Levitt telling people that it's irrational to vote, we think our article offers a useful corrective.

Beyond this, we are making a point which I believe you overlooked, which is that if you _are_ voting for rational reasons, than what is rational is to be voting for (perceived) social benefits, not for your own pocketbook. It is indeed irrational to vote if the gain that you're expecting is a potential $300 tax cut or better health insurance for yourself or whatever. But it is _not_ necessarily irrational to vote if your goal is to help the country as a whole.

Yours,
Andrew Gelman

P.S. If you're interested, our longer research article on rational voting is here.

See here.

Boris passed this along. We've struggled with cost of living indexes (see here, here and here), so maybe this will be helpful.

Serena Ng sends along this paper by Emanuel Moench, Simon Potter, and herself. Here's the abstract:

Igor Carron forwards this (see the second item in the linked page). I don't know anything about this but it might be of interest to some of you.

Alex Tabarrok has an interesting discussion of saving strategies. Alex writes:

There are people who don't save much because they have very low incomes, their behavior does not seem to be in error, especially when we take into consideration the various welfare programs that will cover people in their old age. . . . So let's focus on people with moderate to high incomes. . . . Over confidence and in particular the idea that we are special and will live a long life suggests the error is saving too much. . . . Availability bias probably also suggests we save too much - we see people who saved too little in the street but the ones who saved too much are dead and gone. . . . I do not know which error is more prevalent but if we are to be neither spendthrift nor miser we need to recognize both types of error.

My guess is that Alex is a little too optimistic about people's savings strategies, given all the credit card debt out there. Also, as some of his commenters note, it's easy for people to get used to a particular spending pattern, and it's easier to ramp it up than to scale it down. So, for psychological purposes, it might be better to plan for a gradually increasing standard of living than something completely flat over time.

But I'm sympathetic with Alex's general point that both kinds of errors are relevant. It reminds me of when I asked the students in my decision analysis class to raise their hands if they'd never missed a flight. I then said to them: You go to the airport too early! A retrospective rather than a prospective analysis but still essentially correct, I think.

Our publisher is putting together our new book (no, not Red State, Blue State, I'm talking about our next book, A Quantitative Tour of the Social Sciences), and we need a cover design. Now. Any ideas? Free book to the person with the best idea. And anybody with a particularly good idea, I'll take to lunch. (Or maybe Jeronimo, my coeditor, will take you to lunch if you're in Houston...)

Some background: The book has sections on history, economics, sociology, political science, and psychology, and each section has a different author (or set of authors). It's not a statistics book; rather, it's a set of discussions and case studies, giving the reader (most likely a student of one of the social sciences) a sense of how to think like a historian, economict, sociologist, etc. It's based on a course I created for our Quantitative Methods in Social Science program at Columbia. Anyway, there will be plenty of time for book promotion later; now, I'm just trying to give you enough information to come up with a good cover design for us.

Here's the table of contents:

Ubs writes:

A TV journalist's career success is strongly correlated to how well-known he is to the audience, which in turn is strongly correlated to how much face time he gets. When you watch an interview on TV, if most of what you see are is person being interviewed, you won't remember the journalist so much. If more of your time is devoted to watching and hearing the interviewer talk, he'll be more recognizable next time. The latter probably does not make for a better interview, but it does make for a better chance of the journalist getting more gigs.

Dan Lakeland posts this graph:

When I took science in 9th grade, I remember being disturbed by a gap in the story. From one direction, we were told about atoms and subatomic particles and how they clustered into molecules. From the other, we were told about cells--single-celled animals and single human cells, then multicelled animals, then larger things such as jellyfish, etc., building up to people. We even talked about the parts of a cell--nucleus, axons, cilia, etc.

But we never were given the link between molecules and cells. And what really bothered me was that there was never even any recognition of the gap. This was really too bad, because long molecules are cool--there are proteins shaped like hooks that grab onto other molecules, etc. But it was either atoms or cells, nothing in between.

I was thinking about this recently after reading two blog entries by Steven Levitt. Here he writes that rich people aren't really so much richer than poor people because rich people pay more for "fancy cars, expensive wine, etc." This confuses me because I thought that, under the usual principles of economics, we should assume that fancy cars, etc., are worth their price--otherwise competitors would come into the market and sell them for less. Levitt's related point is that the narrowing of the gap between rich and poor can be credited to Wal-Mart. I can see how this could be true, but once again I'm confused, because I thought standard economic theory said that if Wal-Mart didn't exist, someone would invent it. I have an uncomfortable feeling here that economics is sometimes telling us that things are inevitable (the law of supply and demand) and other times is celebrating unique organizations such as Wal-Mart.

I'm not saying that economists are wrong on this--clearly, supply and demand are powerful forces, and it's also clear that organizations such as Toyota or Bell Labs or, for that matter, City Harvest, can make a difference. Marketing is an art, and just as, if Picasso had never been born, there would still be abstract art but there would be no Picassos, I can well imagine that in a different world, there would be no Wal-Mart, and maybe Americans would all be paying fifteen cents more each for peanut butter, or whatever.

But . . . I'm still disturbed by the lack of connection that is made between the fundamental principles of economics (under which $5,000 worth of expensive wine has the same value as $5,000 worth of Cheetos) and the sort of technocratic reasoning (the kind of thing that makes me, as a statistician, happy) where you try to assign a cost to each thing.

Really this applies to economics, or "freakanomics," in general: For example, you can do some data analysis to see if sumo wrestlers are cheating, or you can just say that sumo wrestling supplies an entertainment niche and leave it to the wrestlers to figure out how to optimally collude. Either sort of analysis is ok, but I rarely see them juxtaposed--it's typically one or the other, and the conclusions seem to depend a lot on which mode of analysis is chosen.

I don't think there are any easy answers here--to borrow a physics analogy, a stable economy is necessarily at a phase transition, entrepreneurs can't repeal the law of supply and demand, and conversely "supply and demand" don't mean squat if nobody's there to take advantage of opportunities, etc. But I think there can be trouble if you can pull out a macro or a micro argument and not always see the connection between them.

P.S. This problem is not at all unique to economics. For example, some political scientists (such as myself) study public opinion and others study strategic bargaining among political actors. And we tend to work in parallel, even though of course these concepts interact. I study voters' attitudes on issues and where they stand compared to the Democrats and Republicans, whereas Thomas Ferguson studies campaign contributions by major industries. It's all part of the same big picture but it's hard to put it all together in one place.

And I'm not saying this to criticize Levitt: he has interesting things to say both in the "big picture" sense and in detailed technical analyses. I just think there's a big gap there that's not often acknowledged.

This article by Tim Harford reminds me of an example I used to give in my decision analysis class:

When I was younger, people used to complain about candy bars getting smaller and smaller. (For example, Stephen Jay Gould has a graph in one of his books showing the size of the standard Hershey bar declining from 2 ounces in 1965 gradually down to 1.2 ounces in 1980, and for that matter I can recall tunafish cans gradually declining from 8 ounces to 6 ounces.) And I remember going to the candy machine with my quarter and picking out the candy bar that was heaviest--I don't remember which one--even if it wasn't my favorite flavor, to get the most value for the money.

But now I realize that, rationally, candymakers should charge more for smaller candy bars. The joy from eating the candy is basically discrete--I'll get essentially no more joy from a 1.7-ounce bar than from a 1.4-ounce bar. But the larger bar will be worse for my health (no big deal if I eat just one, but with some cumulative effect if I eat one every day, similarly with the sodas and so forth). And, given the well-known fact that nobody can eat just part of a candy bar, I get more net utility from the small bar, thus they should charge more.

See here for a link to a research study on this.

Aleks pointed me to this article by Stan Liebowitz on the recent financial crisis:

In the course of commenting on our article on religion, income, and voting, David Beckworth links to this interesting paper on religiosity and the business cycle:

Mainline Protestant denominations--which tend to have higher income earners--do well in terms of growth during economic booms while evangelical Protestants denominations--which tend to have lower income earners--actually struggle. (During economic downturns the outcomes are reversed--evangelicals Protestant denominations thrive.) In general, I [Beckworth] find mainline Protestants to have a strong procyclical component to their religiosity while evangelicals have a strong countercyclical component. These findings can be explained by again appealing to the labor-leisure choice explained by economic theory.

I haven't had a chance to really look this over, but it seems important, and it reminds me of Robert Putnam's comment that, although we think of religious attendance and denomination as fixed demographic descriptors of people, it's pretty common for people to change denominations--even to switch between Protestant and Catholic. Putnam also said there was evidence that people switch religions to match their political beliefs.

Regarding Beckworth's paper itself, what it really needs (from my perspective) are some graphs that directly map the data to the findings. Regressions are great, but I need some scatterplots to really be convinced. And then the challenge is to map the graphs to the regression estimates. This takes work--sometimes a lot of work--but the payoff is a new level of confidence building, a step beyond mere statistical significance.

Bob Erikson pointed me to this paper by Edward Glaeser:

Economists are quick to assume opportunistic behavior in almost every walk of life other than our own. Our empirical methods are based on assumptions of human behavior that would not pass muster in any of our models. The solution to this problem is not to expect a mass renunciation of data mining, selective data cleaning or opportunistic methodology selection, but rather to follow Leamer's lead in designing and using techniques that anticipate the behavior of optimizing researchers. In this essay, I [Glaeser] make ten points about a more economic approach to empirical methods and suggest paths for methodological progress.

This is a great point. The paper itself has an unusual format: the ten key points are made in pages 3-5, and then they are expanded upon in the rest of the paper. I think Glaeser's specific analyses are limited by his focus on classical statistical methods (least-squares regression, p-values, and so forth), but his main points are important, and I'll repeat them here:

I hate to publicize this sort of thing, but two different people forwarded it to me, so I thought I should comment. It's a paper by Peter Klimek, Rudolf Hanel, and Stefan Thurner:

The quality of governance of institutions, corporations and countries depends on the ability of efficient decision making within the respective boards or cabinets. Opinion formation processes within groups are size dependent. It is often argued - as now e.g. in the discussion of the future size of the European Commission - that decision making bodies of a size beyond 20 become strongly inefficient. We report empirical evidence that the performance of national governments declines with increasing membership and undergoes a qualitative change in behavior at a particular group size.

I admire the goal of doing empirical analysis, and the graphs are great, but I agree with the Arxiv blogger that their mathematical model of "a critical value of around 19-20 members" is "somewhat unconvincing" (except that I'd remove the "somewhat"). Do people really believe this sort of thing? It seems like numerology to me.

The problem with counting countries

Another problem, to my mind, is the reference to the number of countries in the European Union. I understand that these are sovereign states, but I don't think it makes sense to count them equally. Applying a model in which all voters are equal doesn't make sense to me.

P.S.

I am unhappy with the authors' attempts to imply that their work is relevant to actual politics. That said, I like the rest of the paper--it's a fun model, and you have to start somewhere. After all, I wrote a paper on coalitions myself that had no empirical relevance. So I can hardly object to this sort of academic exercise.

The comments on this entry--yes, I prefer a "sewer" to an "airport," at least when it comes to train stations--prompt me to elaborate on my comments on the Chicago public library, which was widely praised when it was built, enough so that I visited it one day when I was living there. It was stunningly difficult to get to the books--they were hidden on the fourth floor, I believe, and in some small section of low, widely-separated shelves. They didn't understand the concept of book density--the goal of minimizing the travel time between book A and book B.

I had a similar experience yesterday when visiting my friend at MIT. He had a huge office, which at first impressed me, but then I realized that these huge offices and impressive spaces lead to one of MIT's notorious problems: the need to take long walks through featureless corridors. I think it would be a better place with all the spaces sized down by half. That's the Bell Labs style. Bell Labs did it right, and their choice was particularly impressive given that they had tons of extra space and tons of extra money, so they could've built huge offices if they'd wanted to.

James Annan writes,

I wonder if you would consider commenting on Marty Weitzman's "Dismal Theorem", which purports to show that all estimates of what he calls a "scaling parameter" (climate sensitivity is one example) must be long-tailed, in the sense of having a pdf that decays as an inverse polynomial and not faster. The conclusion he draws is that using a standard risk-averse loss function gives an infinite expected loss, and always will for any amount of observational evidence.

I looked up Weitzman and found this paper, "On Modeling and Interpreting the Economics of Catastrophic Climate Change," which discusses his "dismal theorem." I couldn't bring myself to put in the effort to understand exactly what he was saying, but I caught something about posterior distributions having fat tails. That's true--this is a point made in many Bayesian statistics texts, including ours (chapter 3) and many that came before us (for example, Box and Tiao). With any finite sample, it's hard to rule out the hypothesis of a huge underlying variance. (Fundamentally, the reason is that, if the underlying distribution truly does have fat tails, it's possible for them to be hidden in any reasonable sample. It's that Black Swan thing all over again.) I think that Weitzman is making some deeper technical point, and I'm sure I'm disappointing Annan by not having more to say on this . . .

More

Seth is skeptical of skepticism in evaluating scientific research. He starts by pointing out that it can be foolish to ignore data, just because they don't come from a randomized experiment. The "gold standard" of double-blind experimentation has become an official currency, and Seth is arguing for some bimetallism. To continue with this ridiculous analogy, a little bit of inflation is a good thing: some liquidity in scientific research is needed in order to keep the entire enterprise moving smoothly.

As Gresham has taught us, if observational studies are outlawed, then only outlaws will do observational studies.

I think Seth goes too far, though, and that brings up an interesting question.

I was sorry to see Steven Levitt repeating the claim about driving a car being good for the environment. I wrote about this last week when it appeared in the other New York Times column of John Tierney, but perhaps it's worth repeating:

When preparing our GSS survey questions on social and political polarization, one of our questions was, "How many people do you know who have a second home?" This was supposed to help us measure social stratification by wealth--we figured people might know if their friends had a second home, even if they didn't know the values of their friends' assets. But we had a problem--a lot of the positive responses seemed to be coming from people who knew immigrants who had a home back in their original countries. Interesting, but not what we were trying to measure.

I like John Tierney's New York Times column (for example, here), but sometimes he goes over the top in counterintuitiveness.

Here, for example, Tierney writes about someone who says, "in some circumstances it’s better to drive than to walk. . . . If you walk 1.5 miles, Mr. Goodall calculates, and replace those calories by drinking about a cup of milk, the greenhouse emissions connected with that milk (like methane from the dairy farm and carbon dioxide from the delivery truck) are just about equal to the emissions from a typical car making the same trip. . . . Michael Bluejay, who’s done some number-crunching at BicycleUniverse.info, says that walking is actually worse than driving if you replace the calories with food in the standard American diet and if the car gets more than 24 miles per gallon. . . ."

This is interseting to me because these guys are making a classic statistical error, I think, which is to assume that all else is held constant. This is the error that also leads people to misinterpret regression coefficients causally. (See chapters 9 and 10 of our book for discussion of this point.) In this case, the error is to assume that the walker and the driver will be making the same trip. In general, the driver will take longer trips--that's one of the reasons for having a car, that you can easily take longer trips. Anyway, my point is not to get into a long discussion of transportation pricing, just to point out that this seemingly natural calculation is inappropriate because of its mistaken assumption that you can realistically change one predictor, leaving all the others constant.

As we like to say, it's a great classroom example.

P.S. More here (also see discussion in the comments below).

Thomas Ferguson and Hans-Joachim Voth write:

From Indonesia and Malaysia to Italy, politically connected firms are more valuable than their less fortunate competitors. Yet a key event in the history of the twentieth century has not been examined in terms of the value of political connections—the Nazi rise to power. We systematically assess the value of prior ties with the new regime in 1933. To do so, we [Ferguson and Voth] combine two new data series: A new series of monthly stock prices, collected from official publications of the Berlin stock exchange, and a second series that uses hitherto unused contemporary data sources, in combination with previous scholarship, to pin down ties between big business and the Nazis. . . .

Drawing on previously unused contemporary sources about management and supervisory board composition and stock returns, we find that one out of seven firms, and a large proportion of the biggest companies, had substantive links with the National Socialist German Workers’ Party. Firms supporting the Nazi movement experienced unusually high returns, outperforming unconnected ones by 5% to 8% between January and March 1933. . . .

By international standards, the value of connections with the Nazi party was unusually high. Comparison with the results of Faccio (2006) suggests that in her sample of 47 countries from around the globe, only Third World countries with poor governance showed similarly high returns. Also, associations with the NSDAP were formed voluntarily, not through family links; also, they were not in place decades before their political value became apparent, as in many Third World countries. One question for future research is how many of these connections turned out to be valuable in the end and through which channels the party rewarded its supporters. Though some businessmen felt that the donations were large, their value was small compared to the rise in stock market value of connected firms. Interestingly, even recently formed affiliations such as those resulting from the fundraising party in Berlin on February 20, 1933, appear to have boosted firms’ fortunes on the stock market. Returns were not arbitraged away by many other firms entering the fray. This suggests that Hitler’s rise to power may have come as a genuine surprise to many, that an ideological distaste for his party kept numerous businessmen from contributing, or that NSDAP representatives deliberately focused their attention on a subgroup of sympathetic business contacts.

Interesting stuff. Certainly not what you usually see in the history books.

I just ran into this article by W. Michael Cox and Richard Alm on the comparison of incomes and spending of rich and poor:

The share of national income going to the richest 20 percent of households rose from 43.6 percent in 1975 to 49.6 percent in 2006 . . . families in the lowest fifth saw their piece of the pie fall from 4.3 percent to 3.3 percent.

Income statistics, however, don’t tell the whole story of Americans’ living standards. Looking at a far more direct measure of American families’ economic status — household consumption — indicates that the gap between rich and poor is far less than most assume.

The top fifth of American households earned an average of $149,963 a year in 2006. As shown in the first accompanying chart, they spent $69,863 on food, clothing, shelter, utilities, transportation, health care and other categories of consumption. The rest of their income went largely to taxes and savings.

The bottom fifth earned just $9,974, but spent nearly twice that — an average of $18,153 a year. How is that possible? . . . lower-income families have access to various sources of spending money that doesn’t fall under taxable income. These sources include portions of sales of property like homes and cars and securities that are not subject to capital gains taxes, insurance policies redeemed, or the drawing down of bank accounts. While some of these families are mired in poverty, many (the exact proportion is unclear) are headed by retirees and those temporarily between jobs, and thus their low income total doesn’t accurately reflect their long-term financial status.

So, bearing this in mind, if we compare the incomes of the top and bottom fifths, we see a ratio of 15 to 1. If we turn to consumption, the gap declines to around 4 to 1. . . .

Let’s take the adjustments one step further. Richer households are larger — an average of 3.1 people in the top fifth, compared with 2.5 people in the middle fifth and 1.7 in the bottom fifth. If we look at consumption per person, the difference between the richest and poorest households falls to just 2.1 to 1.

This would be a good example for an intro statistics class when the topic of measurement comes up. The challenge for a stat class is to focus on measurement issues--how to design a survey to estimate people's income, assets, and spending patterns, or how to design an experiment or observational study to estimate the effects of changes in income on spending.

From the economics perspective, the example confuses me--on one hand, it makes sense to use consumption, not income, as a measure of well-being. On the other hand, if I were given the choice between two options:

(a) Earning $100,000 next year and spending $50,000, or
(b) Earning $40,000 next year and spending $60,000,

I'd prefer option (a). So I don't really know how to think about this. This sort of thing always confuses me in discussions of the utility of money (which I teach in my decision analysis class): it's good to have more money, but, usually, it's not money that brings joy, it's the things that money buys that do it.

In the example above, it would certainly make sense to adjust income for taxes and transfer payments and probably for household size (even if not by simply dividing by the number of people). It's harder for me to think how whether to adjust for savings or for non-cash benefits such as health-insurance.

I'll try to clarify my recent entry on unintended consequences by focusing on a less politically-loaded example.

Millions of people in south Asia are exposed to high levels of arsenic in their drinking water. It's a natural contaminant (something to do with the soil chemistry) but it's become an increasingly important problem in the past decades because people have been digging millions of deep (~ 100 feet) tubewells. The background is that the surface water is often contaminated, and international organizations have been encouraging the locals to dig these tubewells which draw clean water from hundreds of feet below ground. Unfortunately, some of that water is contaminated with arsenic. A true unintended consequence. But what to do next?

There are various solutions out there, including a low-cost device for purifying surface water. My connection to this is that I've been involved in a project to give information to people in Bangladesh about where and how deep to dig to find arsenic-free deep water. In some places you have to drill hundreds of feet deep, and this can be expensive (relative to Bangladeshis' incomes). So we're setting up an insurance system for people there, so they can pay a little bit more but be assured of eventually getting a safe well, or their money back. The idea is to provide incentives for well-drillers also, to set up an ongoing system where there is trust and so that safe wells can be installed.

More unintended consequences?

Two concerns about unintended consequences arise. First, on the physical level, there is a concern that, if people build wells taking clean water from deep aquifers, they'll start using that water more and more (just as we in the developed world flush our toilets with fresh water, etc), leading to changes in the water flow that might bring arsenic down there or have other bad consequences. I don't know enough to evaluate this concern so I'm just trusting my colleagues on this.

The second concern is something I mentioned to my collaborators the other day: should we really be offering this insurance scheme at all? The goal of the program is to get people to dig deeper wells than they otherwise would've done, by setting up incentives for customers and well-drillers to get together. (I should explain that this is intended to be a revenue-neutral, "at cost," system: not a subsidy for Bangladeshis to dig wells, but not a moneymaker for us, either. The money would be made by the drillers, and this would provide an incentive for the program to continue.)

Anyway, I asked my collaborators whether maybe we shouldn't be doing this program at all, since we're trying to get people to do something they wouldn't do themselves.

One of my colleagues replied that, no, it was a good idea, and for us not to do it would be "paternalistic" in that we're saying that we know what's best for the locals. We can offer the insurance and they can decide. But, wait! I said. If we really want to be non-paternalistic, we wouldn't get involved at all, right?

Defaults

It seems that these debates come down to the choice of the default. If the default is to do our insurance program, then it's paternalistic to consider not doing it. But if the default is for us to stop messing around in Bangladesh, then it's paternalistic to try to motivate them to dig deep wells. (The unintended consequence of the mid-1990s intervention--encouraging moderately deep tube wells--is cautionary, but it's not clear that this should be a message that we shouldn't get involved.)

Melissa Lafsky writes in Freakanomics discusses how biofuels, which have been proposed as an environmentally-friendly alternative energy source, have been estimated to create more pollution than drilling for more oil. And then, of course, climate change is itself a huge unintended consequence of industrialization. I just have a couple of comments.

1. Alex Tabarrok wrote:

The law of unintended consequences is what happens when a simple system tries to regulate a complex system. The political system is simple, it operates with limited information (rational ignorance), short time horizons, low feedback, and poor and misaligned incentives. Society in contrast is a complex, evolving, high-feedback, incentive-driven system. When a simple system tries to regulate a complex system you often get unintended consequences.

I like this description but it doesn't quite fit either of the examples here. To start with, climate change was an unanticipated consequence of industrialization. But industrialization was not designed to regulate the climate (schemes such as cloud-seeding aside). So maybe Alex's paragraph is more of a description of perverse unintended consequences.

To take the other example: Yes, biofuels were proposed to regulate climate change, so the first half of Alex's description works. But the second part isn't quite appropriate, because the unintended consequences were discovered in advance. According to the quoted report, "Prior analyses made an accounting error." So in this case it doesn't sound like a problem in anticipating feedback.

2. This brings me to my second point, which is that the problem seems to have been discovered before the massive shift to biofuels actually happened, so the problem "for the next 93 years" won't really happen. According to the article, "scientists [are] already calling for government reform on biofuel policies." So this is more of an anticipated than an actual unintended consequence.

I showed Jamie Galbraith these graphs on changes in income inequality by state:

and he wrote:

I think it's consistent with my two-part take on the U.S. income distribution. The very rich are geographically very concentrated, not just in the rich states but in the rich counties within the rich states, and their income-share experience is governed by the rise of the stock market since the early 1970s.

The income experience of the poor in poor states is governed mainly by federal social welfare policies -- and especially by the rising real value of the Social Security benefit, since 1972. Assuming SS is counted as income, that could take care of it, though food stamps may also play a role, and in recent years the EITC has also been very significant for the bottom decile. There may also have been a significant rise in the minimum wage in the 1960s under Johnson; in real terms the peak year would have been 1970.

In richer states, the bottom 10 percent may have had some income support from the state, which would mean that the expansion of SS and other federal benefits in the early 70s were a smaller share of their income. Also the federal minimum wage is much less important in rich states than in poor ones.I have not done any quantitative work on this, but New York and other rich states did have "Home Relief" before the Feds came in. And other things, such as public housing. I'd look first at the differential effect of the minimum wage in rich and poor states. I read somewhere that the latest increase had zero effect on New York, but will reach about a million women here in Texas.

This is interesting. I'd noticed the patterns of increasing income among the poor in the poor states and the rich in the rich states, but I really had no thought of where this could've been coming from.

Jamie also pointed me to this paper by himself and Travis Hale on developments in income inequality in the past forty years:

In this note we report on the evolution of between-sector wage inequality in the United States from 1969 – 2006. Our calculations take advantage of new NAICS sectoral classification, merging these with the earlier SIC scheme to achieve a single unified series. We compare this measure to the standard CPS-based Gini coefficient of household income inequality, showing that the evolution of the two series is very close. We show that between-sector variations dominate between-state variations in determining the evolution of inequality. The high importance of between-sector variations in driving overall U.S. pay inequality raises important questions about the standard invocation of education and training as a remedy for inequality, since the choice of specialization has become a speculative decision, whose income prospects depend heavily on the ebb and flow of sectoral economic fortunes.

This seems really important. I'm curious how it relates to the trends in income inequality in other countries, compared to the U.S.

Robin Hanson writes,

To make sense of social complexity we would ideally want to add lots of randomization to people's real choices, and then collect lots of data on what happens to them. But this seems a lot to ask of people. For example, people who eat at a restaurant might be willing to tell you how they felt later after eating there, but they'd be reluctant to eat a random item from the menu even one percent of the time.

Would people be more willing to have a few of their options randomly excluded? For example, would people mind much if on a menu of one hundred items one of the items was randomly excluded each time - "sorry we are out of that today"? Data about choices under such reduced menus would still have a key randomization component.

This idea occurred to me while talking to a cancer doctor who thought he could get thousands of cancer patients to agree to release data on their progress, but who would be more reluctant to accept a random treatment. Once standard drugs have failed, there are about twenty alternative drugs a patient could try, which they usually pick based on the side effects etc. Patients probably wouldn't mind much having one of these options taken off the menu.

My thoughts:

I think I'd eat a random item 1% of the time as part of an experiment--after all, 1% of the time would correspond to three lunches per year.

To get to your main proposal: I think if you exclude one item, you'll get a study that is a mix of experiment and observational study, which could probably be analyzed in a way more robustly than purely observational data could be analyzed, but requiring more information than the analysis of a pure experiment.

This sounds like something that marketing researchers might have studied too.

P.S. See here for much more from the marketing researchers.

Stephen Dubner and Steven Levitt wrote this Freakanomics column, which concludes, "if there is any law more powerful than the ones constructed in a place like Washington, it is the law of unintended consequences." What I'm wondering is, what sort of law is this? Obviously it's not a real "law" like the law of gravity or even one of those social-science laws like Gresham's law or the statement that democracies usually don't fight each other. But it's supposed to be more than just a joke in the manner of Murphy's law, right?

I've remarked previously that unintended consequences often were actually intended but Dubner and Levitt's examples seem actually unintended. So these seem like real examples, but I don't know what it takes for this to be a "law." Surely there must be dozens of other examples of intended consequences that actually happened? Or unintended consequences which, although unfortunate, were minor compared to the intended consequences? The Freakanomics article was interesting; now I want to hear a statement of the law itself...

P.S. Interesting comments below. Also, Alex Tabarrok has further elaboration:

The law of unintended consequences is what happens when a simple system tries to regulate a complex system. The political system is simple, it operates with limited information (rational ignorance), short time horizons, low feedback, and poor and misaligned incentives. Society in contrast is a complex, evolving, high-feedback, incentive-driven system. When a simple system tries to regulate a complex system you often get unintended consequences.

If a prediction market is not liquid enough, it's possible to manipulate it by throwing in small sums of money (thus, for example, a political candidate could boost his price by buying a bunch of shares). Presumably this could be useful, for example if you pump up your market share price, this might induce donors to contribute to the winning cause or could help attract endorsements.

At the other extreme, if the market is too liquid, there's a potential "moral hazard" or motivation to throw an election, to purposely hurt your side in order to make money on the pointspread if you've already placed a large bet in the other direction.

Now here's my question: there's clearly a sense in which a prediction market can be too small (too illiquid) to be trusted, and conversely if it is too large (too liquid) you get problems in the other direction. Is there an intermediate zone in which the market is liquid enough so it can't be easily manipulated, but not so liquid that it motivates point-shaving? Or do the zones of "too illiquid" and "too liquid" actually overlap, so there's no market size that does the job?

I imagine the answer would depend on some external parameters, such as the ease or difficulty of enforcing insider-trading restrictions. Possibly there's some theoretical work in this area. Justin? Robin?

P.S. I'm raising the questions above in all sincerity. This post is not intended to be a devastating argument that shoots down prediction markets; I'd just like to know if these issues have been considered and resolved in some way. A lot of the casual discussions of prediction markets have been of the "they're cool" or "they're silly" variety, but I imagine the researchers in this area have considered ways of assessing the problems arising from the issues noted above.

P.P.S. This paper by Robin Hanson (see comment below) discusses the first of these points, presenting theory and evidence that low-volume markets are hard to manipulate and thus implying that there is an intermediate zone where the markets can work well.

Ismail writes,

Can you explain a little bit about Fixed Effects in panel regressions and when is it appropriate to use it- for example does it make sense to use it on this data set.

My reply (beyond that it's funny to see a reference to an economics paper by someone named "Dollar") is that it's a good idea to model panel data using three error terms, at the unit, time, and unit*time levels. You can (and should) also have predictors at all three levels, as appropriate. Also I'm not a fan of the overloaded term "fixed effects," but that's another story. (Search this blog for more on the topic.)

Chris Masse sent these links: Using Prediction Markets to Track Information Flows: Evidence from Google, by Cowgill, Wolfers, and Zitzewitz, and a news article by Noam Cohen. Here's the abstract of the Cowgill et al. paper:

In the last 2.5 years, Google has conducted the largest corporate experiment with prediction markets we are aware of. In this paper, we illustrate how markets can be used to study how an organization processes information. We document a number of biases in Google’s markets, most notably an optimistic bias. Newly hired employees are on the optimistic side of these markets, and optimistic biases are significantly more pronounced on days when Google stock is appreciating. We find strong correlations in trading for those who sit within a few feet of one another; social networks and work relationships also play a secondary explanatory role. The results are interesting in light of recent research on the role of optimism in entrepreneurial firms, as well as recent work on the importance of geographical and social proximity in explaining information flows in firms and markets.

I love this sort of thing. In grad school I remember we talked about setting up a "betting board" where people could put up slips of papers with proposed bets, and then you could accept a bet by signing it with your name. We never did anything with it, and the technology is better now... The Cowgill et al. paper is interesting in how they go beyond the usual "prediction markets are cool" story to look into what information is really being used in the market.

P.S. I gotta say, though: Think harder about your tabular presentations! Do you really care that a certain coefficient is estimated at -0.188 with a standard error of 0.072??? It would be great if the younger economists, working on cool projects like this, could take the lead on graphical presentation--which, after all, is all about getting more information out of your analyses.

P.P.S. In his news article, Cohen writes:

A question never addressed in the report is what would seemingly be most interesting to an outsider: Do prediction markets work? Unlike surveys, the markets rely on something, I think the technical term is ... oh, yeah, greed, to get their results.

Ask me who I think will win a baseball game, an election and an Oscar, and I can try to be objective, but I can’t help being influenced by who I would like to see win. (The Yankees, Fred Thompson, Pee-wee Herman; or is it the Yankees, Pee-wee Herman, Fred Thompson?) Put $5 on it, however, and suddenly I am willing to use all the information I have at my disposal to come up with the best answer.

The attribution to "greed" seems naive to me. I'd be interested to hear the comments of Justin Wolfers or Robin Hanson or others who have thought more about these issues. I agree that a $5 bet can (for some people) induce some sincerity, but I wouldn't call that "greed"--unless they're paying New York Times reporters a lot less than I think, $5 seems below the "greed" threshold. Rather, I'd say that the $5 represents some signal that it's appropriate to take it seriously.

Also, not to keep going on about polls and forecasts, but (most) political polls are not set up to ask the question of "who will win" but rather the question of who would you like to see win. The point of the poll is to ask respondents something that they know about and is of general interest--in this case, their views on the issues, which candidate they support, etc. The voters--the general voting population--are the people who determine who wins the election, which is quite a bit different from the "Yankees" and "Pee-Wee Herman" examples given in the news article. (Yes, I know he's just being amusing, but I think there is a serious underlying point, which is that elections are not just something that people predict, they're also something that we jointly decide with our votes.)

I'll answer just about anybody's question for free. Then I'll post the question and answer on the blog (after checking that it's ok with you). Since I've done it for free, my answer is open-source, and I'd like to share it with as many people who might be interested. I also work on projects that are publicly funded, or help people who are working on such projects. Again, your tax dollars already paid for this, so I'll post on the blog as appropriate. Finally, sometimes I'll consult for money, in which case you can tell me if you don't want me to immediately spread our findings to the world in this way.

Aleks sent along this, which raises interesting statistical as well as legal, economic, ethical, political, etc. questions.

Below are 50 little graphs showing the 90th percentile and 10th percentile in income, within each state, for the past forty years. The patterns are pretty striking: the high end has increased pretty consistently in almost all the states, and the low end increased a lot in poor states, especially for the first half of the series. I don't really know what more to say about this--we made the graphs because we are trying to understand the differences between rich and poor states in the past 20 years, and what has made them into "blue" and "red" states--but the graphs are full of interesting patterns. Incomes are inflation-adjusted and presented on a logarithmic scale (with a common scale for all the graphs), and the states are ordered from poorest to richest.

OK, here's the picture:

Tall political scientist David Park and tall economist Robin Hansen point to this paper by Mankiw and Weinzierl on the idea of taxing tall people. I have no problem with the paper--it's wacky, but intentionally wacky, one might say, using height as an example of a variable that is correlated with income in the general population. Height isn't actually correlated very much with income (together, height and sex predict earnings with an R-squared of only 9% (see, for example, page 63 of our book), so in some way it's not a great example, but maybe that's part of their point too.

Anyway, David and Robin both quote a news article that states that Mankiw and Weinzierl "say that height is a 'justly acquired endowment': it is not unfairly wrested from anyone else, so the state has no right to seize its fruits."

Huh?

I think there's something I'm missing here, but . . . who ever said that you can only tax something that was "unjustly wrestled from someone else"? Haven't they ever heard of the sales tax? Even a society with no unjust wrestling at all would still need taxes. Setting redistribution aside entirely, there are reasons for higher taxes on the rich (they can more easily afford it) and reasons for not taxing the rich (depending on your mathematical model for the efficiency of the economy), but I don't really see how something being "justly endowed" means that it can't be taxed. The tax money needs to come from somewhere.

Why taxes?

The same article also says, "By the same logic, they imply ... the government has no right to force someone with the 'justly acquired endowment" of entrepreneurial genius to pay a higher tax rate." This also confuses me since I hadn't heard that anyone was proposing a tax on "entrepreneurial genius."

Maybe this is a difference between how economists and political scientists view the world. Mankiw and Weinzierl seem to view taxes as a way to punish people, whereas I see taxes as a way to raise money?

Lee Sigelman writes,

In a brief article (abstract here) in the current issue of Current Directions in Psychological Science, Dan Ariely and Michael Norton analyze the wide gap currently separating psychologically- and economically-based experimental research — a gap clearly perceptible in experimental work within political science, a heavy borrower from both psychology and economics.

“Psychologists have not traditionally been interested in the efficiencies and design of markets,” Ariely and Norton note, “while experimental economists have not customarily focused on emotion, memory, or implicit cognition.”

In addition to studying different topics, the two fields differ in their research methods as well:

It’s not just that psychologists enjoy lying to people while economists enjoy paying them. To find out what they want to find out, psychologists have to give their experimental subjects a “cover story” and transport them into a particular situation, for which purposes deception is often necessary. By contrast, economists want to know about experimental subjects’ ability to make informed decisions, and for that purpose deception would be counterproductive. At the same time, economists want to motivate their subjects to behave “normally,” so they explicitly define incentives to enable subjects to evaluate the costs and benefits of a particular course of action.

Finally, Sigelman quotes Ariely and Norton, who write,

Experimental economists might shift from asking whether deception is good or bad — a moral question — to exploring whether deception helps or harms social scientists’ ability to understand human behavior. Psychologists’ aversion to incentives, on the other hand, might be addressed by taking a broader view of what experimental economists are trying to accomplish with them: making people care about their behavior as much in the lab as they do in the real world.

Aversion to incentives?

I just have two things to add.

1. I applaud the call for researchers to become more aware of what is being done in other fields, but at some point, different people have different areas of expertise. (See my remarks here on why we shouldn't be disturbed that economists don't spend more time studying romance and here on different views of rationality.)

2. I question whether psychologists really have an "aversion to incentives." Giving incentives to research participants is one strategy out of many. It's natural for economists to privilege financial incentives--that's what they study--but maybe not so natural for others and maybe not always so relevant to the "real world." Many important real-world phenomena--such as political particpation--have little or no financial incentives at all!

"Instrumental variables" is an important technique in applied statistics and econometrics but it can get confusing. See here for our summary (in particular, you can take a look at chapter 10, but Chapter 9 would help too).

Now an example. Piero spoke in our seminar last Thursday on the effects of defamation laws on reporting of corruption in Mexico. In the basic analysis, he found that, in the states where defamation laws are more punitive, there is less reporting of corruption, which suggests a chilling effect of the laws. But there are the usual worries about correlation-is-not-causation, and so Piero did a more elaborate instrumental variables analysis using the severity of homicide penalties as an instrument.

We had a long discussion about this in the seminar. I originally felt that "severity of homicide penalties" was the wackiest instrument in the world, but Piero convinced me that it was reasonable as a proxy for some measure of general punitiveness of the justice system. I said that if it's viewed as a proxy in this way, I'd prefer to use a measurement-error model, but I can see the basic idea.

Still, though, there was something bothering me. So I decided to go back to basics and use my trick for understanding instrumental variables. It goes like this:

The trick: how to think about IV's without getting too confused

Suppose z is your instrument, T is your treatment, and y is your outcome. So the causal model is z -> T -> y. The trick is to think of (T,y) as a joint outcome and to think of the effect of z on each. For example, an increase of 1 in z is associated with an increase of 0.8 in T and an increase of 10 in y. The usual "instrumental variables" summary is to just say the estimated effect of T on y is 10/0.8=12.5, but I'd rather just keep it separate and report the effects on T and y separately.

In Piero's example, this translates into two statements: (a) States with higher penalties for murder had higher penalties for defamation, and (b) States with higher penalties for murder had less reporting of corruption.

Fine. But I don't see how this adds anything at all to my understanding of the defamation/corruption relationship, beyond what I learned from his simpler finding: States with higher penalties for defamation had less reporting of corruption.

In summary . . .

If there's any problem with the simple correlation, I see the same problems with the more elaborate analysis--the pair of correlations which is given the label "instrumental variables analysis." I'm not opposed to instrumental variables in general, but when I get stuck, I find it extremely helpful to go back and see what I've learned from separately thinking about the correlation of z with T, and the correlation of z with y. Since that's ultimately what instrumental variables analysis is doing.

Kaiser and I had the following discussion of rationality, following my earlier discussion of the rationality of voting. I wrote:

Any given behavior can be analyzed by economists either in a way as to show why it's really rational (even thought it doesn't look that way) or really irrational (even if it looks normal enough). I haven't quite figured out the rules for how they decide which way to lean in any given case.

Kaiser then wrote:

As for rational/irrational, I'm confused by the Kahneman work: he's saying irrationality is an anomaly which seems to indicate he thinks people should be rational but then if everyone is "irrational," could it be the theory is wrong in which case we shouldn't call that anomalous?

I replied:

Regarding rationality, my impression is that psychologists, unlike economists and political scientists, don't care so much about "rationality." Psychologists think of rationality as a process--as a way of thinking and making decisions--not as a particular algorithm. In that sense, Kahneman et al. are pointing out that much of our everyday rational thinking has systematic problems. It's no surprise that any particular form of rationality will be imperfect. What's interesting is the ways in which people make mistakes.

Dan Luu writes,

The short version of my question is: A lot of the papers I've been reading that sound really interesting don't seem to involve economics per se (e.g., http://home.uchicago.edu/~eoster/hivbehavior.pdf), but they usually seem to come out of econ (as opposed to statistics) departments. Why is that? Is it a matter of culture? Or just because there are more economists? Or something else?

If you don't mind getting random unsolicited email just because you write a blog, then there's the longer version of my question.

. . . could you please ask them to stop saying, "We know that voting doesn’t make good economic sense." I recognize that "Freakanomics" is intended to be entertainment, not scholarship, but I don't think these dudes are doing economics any favors by spreading this kind of misconception. Voting isn't a good way to make money, but that doesn't mean it "doesn't make good economic sense." The full story is here (based on an article by an economist and two political scientists). I'll repeat here for convenience, but I recommend going to the original entry to see some of the give-and-take in the comments:

Dan Schrage writes with a question about how to model group-level variation:

I [Dan] am trying to better understand the recommendation in your new book to always use random effects (pg. 246) in modeling. (I'm following your definition #5 here of fixed and random effects, as is standard in econometrics.) In econometrics, as I'm sure you know, the classical advice (dating from at least Mundlak (1978)) is this: If unobserved heterogeneity is correlated with regressors in your model, use fixed effects; otherwise, use random effects since they're more efficient. The idea is that random effects lumps this unobserved heterogeneity into the composite error term, and so if it the unobserved heterogeneity is correlated with the regressors, then the regressors are correlated with the error term, and this is bad news: estimators will be biased and inconsistent. So I'm trying to understand why your advice is essentially not to worry about this issue.

Is this just an argument about the bias/variance tradeoff, or is there something deeper here? Perhaps all of this just speaks to one of the common gaps between statistics and econometrics--econometricians tend to care a lot about asymptotic properties like consistency, and statisticians seem to care much less (correct me if I'm off here--I'm still trying to get a good sense of the divide). Econometricians are almost never willing to use an inconsistent estimator, no matter what the gain in efficiency.

My reply:

Nick Firoozye writes,

I [Firoozye] wanted to point your attention to the following podcast by Ian Ayres on Supercrunchers, where he shows himself an enthusiastic (if perhaps a bit naïve) proponent of the statistical method. Entertaining, definitely. One thing though that I thought you might be interested in is Russ Roberts’ (the interviewer's) own skepticism over the econometric method, which I think probably warrants a response. It may be that Roberts’ own view is due to his now-Austrian economics slant (i.e., somewhat anti-formallist approach) or perhaps to the fact that mainstream econometrics is a frequentist pursuit and one might question the honesty of the results as a consequence.

I don't really have much to add here, except that the problem noted by Roberts (it's hard to know whether to believe a statistical study) is even more of a problem with non-statstical empirical studies (i.e., anecdotes). I think Roberts might be overstating the problem because he is focusing on issues where he already had a strong personal opinion even before seeing data analyses. (He mentions the examples of concealed handguns and anti-theft devices on cars.) But there are a lot of areas where we have only weak opinions which can indeed be swayed by data (see here for some examples). These cases are important in their own right and also can serve as benchmarks for the success of statistical analysis, so that we can trust good analyses more when they're applied to tougher problems. This is one way that applied statistics proceeds, by exemplary analyses of problems that might not be hugely important on their own terms but serve as useful templates. Consider, for example, the book by Snedecor and Cochran: it's full of examples on agricultural field trials. Sure, these are important, but these methods have been useful in so many other fields. This is a great example, actually: Snedecor and his colleagues worked on agricultural trials because they cared about the results--these were not "toy examples" or thought experiments--and the resulting methods endured.

This is fun because, as an outsider to both fields, I can just stand back and watch. Dan Goldstein writes:

Shane Murphy writes,

I am a graduate student in political science (interested in economics as well), and I was reading your recent blog posts about significance testing, and the problems common for economists doing statistics. Do you know of and recommend any books to students learning econometrics or statistics for social science? Also, just in case your answer is your own book, "Data Analysis Using Regression and Multilevel/Hierarchical Models," is this book an appropriate way to learn "econometrics" (which is just statistics for economists, right?)?

My reply: Yes, I do recommend my book with Jennifer Hill. I also think it's the right book to learn applied statistics for economics. However, within economics, "econometrics" usually means something more theoretical, I think. You could take a look at a book such as Wooldridge's, which presents the theory pretty clearly.

After I posted this discussion of articles by McCloskey, Ziliak, Hoover, and Siegler, I received several interesting comments, which I'll address below. The main point I want to make is that the underlying problem--inference for small effects--is hard, and this is what drives much of the struggles with statistically significance. See here for more discussion of this point.

Scott Cunningham writes,

Today I was rereading Deirdre McCloskey and Ziliak's JEL paper on statistical significance, and then reading for the first time their detailed response to a critic who challenged their original paper. I was wondering what opinion you had about this debate. Is statistical significance and Fisher tests of significance as maligned and problematic as McCloskey and Ziliak claim? In your professional opinion, what is the proper use of seeking to scientifically prove that a result is valid and important?

Steve Sailer sends in some more thoughts on the issue of cost-of-living adjustments. (See here for background.):

Here's the link to the latest Accra numbers, which are a little different from my [Sailer's] table from a couple of years ago. My vague impression is that Accra sells its cost of living data to large corporations concerned about transferring employees and adjusting their pay so that they feel they aren't being disadvantaged by the move.

Andrew Oswald sends along this updated version of his paper with Blanchflower on happiness over the life course:

A comment on Paul Krugman's blog led me to this chart compiled by Steve Sailer of median income and cost of living by state. Sailer worked with median income for families of 4 and the Accra cost-of-living index and found the highest cost-adjusted incomes to be in Minnesota, Illinois, and Wisconsin, and the lowest in Hawaii, California, and New Mexico.

Bill Fulton has this interesting discussion of a debate about urban planning:

I'm not old enough to be a cranky old man but I'm old enough to be just plain cranky, as can be evidenced by my irritation at this passage:

By the early 2000s, Whitestone was again filling up with young families eager to make homes for themselves on its quiet, leafy streets. But prices had soared. In October 2005, the Sheas sold the house, for which they had paid $28,000 nearly 40 years ago, for more than $600,000.

The inflation calculator here reveals that prices have indeed soared, but not quite as much as implied by the gaudy comparison of nominal prices.

OK, this is great (see below). What I want is a cleaner graph (a horizontal dotplot, please, instead of a *&^!@&*#@ 3-d color barplot), on the logarithmic scale (base 10, please), also including some other liquids of interest, such as fresh water (e.g., divide the total cost of maintaining the NYC water system divided by the total amount used each year), mercury, pig's blood, olive oil, Coca-Cola, epoxy, liquid nitrogen, etc etc.

Robin Hanson points out that biological systems that have a useful function are not necessarily optimal when put in new environments. This reminds me of an interesting interesting article by Witold Rybczynski where I learned that the structural engineer Ove Arup agreed:

...The idea that the correct functional, the correct structural and the best possible aesthetic solutions are one and the same thing must, I am afraid, be abandoned together with the older philosophers' dream about the harmony and ultimate identity of truth, goodness, justice and beauty.

For example, Orup wrote:

A wall like the one at Highpoint would have been cheaper to build with bricks, but [Lubetkin] claimed it was functional and economic. It wasn't functional at all: it had to be "Modern." Functionalism really became a farce. What is wrong with a sloping roof? They can't afford to pay what it costs to make a flat roof really waterproof. Lubetkin didn't care. He just cared for the picture in the architectural magazines.

G. K. Chesterton writes, at the end of his celebrated book on George Bernard Shaw:

I know it is all very strange. From the height of eight hundred years ago, or of eight hundred years hence, our age must look incredibly odd. We call the twelfth century ascetic. We call our own time hedonist and full of praise and pleasure. But in the ascetic age the love of life was evident and enormous, so that it had to be restrained. In a hedonist age pleasure has always sunk low, so that it had to be encouraged. How high the sea of human happiness rose in the Middle Ages, we now only know by the colossal walls that that they built to keep it in bounds. How low human happiness sank in the twentieth century our children will only know by these extraordinary modern books, which tell people that it is a duty to be cheerful and that life is not so bad after all. Humanity never produces optimists till it has ceased to produce happy men. It is strange to be obliged to impose a holiday like a fast, and to drive men to a banquet with spears. But this shall be written of our time: that when the spirit who denies beseiged the last citadel, blaspheming life itself, there were some, there was one especially, whose voice was heard and whose spear was never broken.

Chesterton was a Catholic conservative of the early 1900s, Shaw was a socialist, and both were famous for expressing their ideas in paradox.

Shaw, the leftist, associated progress with material happiness, while Chesterton, the rightist, said things were better in the Middle Ages. Nowadays, the debates usually go in the other directions, with people on the left being less positive about material progress and people on the right saying that things are great now and are getting better. (See, for example, Will Wilkinson's skeptical take on happiness
research.)

I don't have anything to add here except to note the interesting switch of polarity, which reminds me of my thoughts here and here on the changing views of left and right regarding science.

I was having an interesting discussion with Seth about his claim that "the overall benefits of health care are probably minor." The basis of his claim is evidence cited by Aaron Swartz:

In the 1970s, the RAND Corporation picked out 7700 people in six cities and gave half of them free health care. Those lucky ones took advantage of it (spending 30-40% more on average) and they spent it on reasonable things (as judged by medical observers), but they didn’t seem to get any healthier. . . . The RAND study was by far the biggest study of this kind, but other studies find similar results. One analysis found that regions whose Medicare programs give out more money (when the underlying healthiness of the residents is held constant) see no increase in survival rates. A replication found the same results in VA hospitals. Cross-national comparisons find “the impact of public spending on health is … both numerically small and statistically insignificant”. Correlational studies find “Environmental variables are far more important than medical care.” And there are more where that came from.

Several discussants (including myself) at Seth's blog were skeptical about his skeptism, citing various successful medical treatments (in my case, fixing a compound fracture of the wrist; others mentioned cancer treatment, etc.). Seth responded:

The RAND study, of course, is limited — but is there a better attempt to figure out the overall value of medicine? I don’t know of one. if you can point me to a study that shows the more-than-minor value of modern medicine I’d love to look at it. . . . when the overall effectiveness of medicine has come under scrutiny, it has not fared well — and the RAND study is a good example.

Total vs. marginal effects

I have not looked at the Rand study so can't comment on the details, but my first thought is that the marginal benefits from additional health care will be less than the benefits from good existing care. So, even if more is not much better, that doesn’t mean that the overall benefits of existing care are “minor.”

From a policy standpoint, it is the marginal effects that are the most interesting, since nobody (even Seth?) is proposing to zero out medical treatment. Presumably there are diminishing returns, and the benefit/cost ratio for additional treatment is less than that for existing treatment. (And, indeed, some medical care can make things worse, even in expected value; for example, you can get catch the flu in the doctor's waiting room.) But, unless I'm missing something, Seth and Aaron are confusing marginal with total effects.

P.S. Also see Robin Hanson's discussion (with lots of links), which explicitly distinguishes between marginal and total effects. Here I'm not expressing any position on the marginal effects of health care (given my ignorance on the topic), just pointing out that Robin's position seems to have become overstated by others.

P.P.S. See Jake Bowers's comments below. Also more discussion here.

OK, I think you know the answer I want youall to give to the above question . . . anyway, I got the following email from someone who will be an assistant professor teaching econometrics to masters students:

I have not yet decided on a textbook. I've been reviewing books like Stock and Watson, though, and Greene's econometrics textbook, but I'm undecided. I purchased your book with Jennifer Hill the other day, and absolutely love it. I love how readable it is, and how practical it is in its orientation, but at the same time, how rigorous it is. Ordinarily, I am selecting among textbooks that are practical and readable but not technical, or (as is usually the case) technical but not aimed towards the practitioner and hardly readable. That said, I was wondering that since I'm not finished with the book, whether you could advise me about the appropriateness of your book for a masters level econometrics course in an economics department?

My quick answer is, Yeah, I think it would be excellent for an econometrics class if the students have applied interests. Probably I'd just go through chapter 10 (regression, logistic regression, glm, causal inference), with the later parts being optional.

But what do others think? My book does have a blurb from an economist, but is there essential info for an econometrics class that's not in our book?

P.S. to readers: I'm usually pretty good at trimming the gratuitous compliments from these emails--I kept them in here only because they are, as they say in the movies, essential to the plot.

Stephen Dubner writes about a professor of economics who makes zillions of dollars consulting. I'm not surprised by this, because my impression is that legal consulting is an extremely inefficient market. In several of the cases I've consulted on, the statistical expert (typically not actually a statistician) has been truly incompetent, in one case not being certified as an expert in the relevant area by the court, and in another case--which was truly memorable--dividing by N (the population size) rather than n (the sample size) in computing the estimation variance from a random sample. (I was really looking forward to the exchange with this guy in the courtroom--I mean, what could he say, he's a sampling expert and divided by the wrong N?--but, like most cases, it got settled before reaching court.)

I can give other stories, but the key point is that lawyers hire incompetent statistical experts, even in cases that are important. It's gotta be worth their money to hire better consultants, but presumably they can't find them. Actually, I think that I've probably cost less than the opposing consultant in every case I've worked on, since, despite my high hourly rate, I'm trying to minimize my consulting hours, whereas I imagine that professional consultants are, if not trying to maximize hours, at least to keep their business going. But most clients don't know to hire me (or the equivalent)--I think they pretty much get their consultants by word of mouth or through some casual search. (I still can't figure out why the Gore team in the 2000 election hired a statistical consultant who had, as far as I know, never worked seriously on election data before, given that there are so many quantitative political scientists out there. (I don't actually know who the Bush team hired, but since they won, I guess they got their retrospective money's worth.))

So, anyway, to get back to the econ professor guy: it's probably worth clients' money to hire this guy--I imagine his team has a minimum level of professionalism that's much better than what's usually out there. Given the high stakes in many legal cases, and the relative simplicity of the statistical questions that arise, I'm surprised that clients can't do a better job in finding competent statistical experts.

Ubs pointed me to this entry at Mental Floss linking to this article by Graham Walker, who's described as "a co-author of the Official Rock Paper Scissors Strategy Guide (published by Simon and Schuster) and five-time organizer of the World Rock Paper Scissors Championships." Hey, I wanted to organize a RPS tournament one winter in college but everybody thought it was a silly idea. Credit goes to those who put in the effort.

Anyway, here are Walker's suggestions. I thought it was just going to be a joke--"rock always ins" and all that--but they actually look pretty good to me. The comments at the end of the article are interesting too.

The secret to winning at RPS

Basically, there are two ways to win at RPS. First is to take one throw away from your opponent options. ie - If you can get your opponent to not play rock, then you can safely go with scissors as it will win against paper and stalemate against itself. Seems impossible right? Not if you know the subtle ways you can manipulate someone. The art is to not let them know you are eliminating one of their options. The second way is to force you opponent into making a predictable move. Obviously, the key is that it has to be done without them realizing that you are manipulating them.

Most of the following techniques use variations on these basic principles. How well it works for you depends upon how well you can subtly manipulate your opponent without them figuring out what you are doing. So, now that the background is out of the way, let's get into these techniques:

1 - Rock is for Rookies

In RPS circles a common mantra is "Rock is for Rookies" because males have a tendency to lead with Rock on their opening throw. It has a lot to do with idea that Rock is perceived as "strong" and forceful", so guys tend to fall back on it. Use this knowledge to take an easy first win by playing Paper. This tactic is best done in pedestrian matches against someone who doesn't play that much and generally won't work in tournament play.

2 - Scissors on First

The second step in the 'Rock is for Rookies' line of thinking is to play scissors as your opening move against a more experienced player. Since you know they won't come out with rock (since it is too obvious), scissors is your obvious safe move to win against paper or stalemate to itself.

3 - The Double Run

When playing with someone who is not experienced at the RPS, look out for double runs or in other words, the same throw twice. When this happens you can safely eliminate that throw and guarantee yourself at worst a stalemate in the next game. So, when you see a two-Scissor run, you know their next move will be Rock or Paper, so Paper is your best move. Why does this work? People hate being predictable and the perceived hallmark of predictability is to come out with the same throw three times in row.

4 - Telegraph Your Throw

Tell your opponent what you are going to throw and then actually throw what you said. Why? As long as you are not playing someone who actually thinks you are bold enough to telegraph your throw and then actually deliver it, you can eliminate the throw that beats the throw you are telegraphing. So, if you announce rock, your opponent won't play paper which means coming out with that scissors will give you at worst a stalemate and at best the win.

5 - Step Ahead Thinking

Don't know what to do for your next throw? Try playing the throw that would have lost to your opponents last throw? Sounds weird but it works more often than not, why? Inexperienced (or flustered) players will often subconsciously deliver the throw that beat their last one. Therefore, if your opponent played paper, they will very often play Scissors, so you go Rock. This is a good tactic in a stalemate situation or when your opponent lost their last game. It is not as successful after a player has won the last game as they are generally in a more confident state of mind which causes them to be more active in choosing their next throw.

6 - Suggest A Throw

When playing against someone who asks you to remind them about the rules, take the opportunity to subtly "suggest a throw" as you explain to them by physically showing them the throw you want them to play. ie "Paper beats Rock, Rock beats scissors (show scissors), Scissors (show scissors again) beats paper." Believe it or not, when people are not paying attention their subconscious mind will often accept your "suggestion". A very similar technique is used by magicians to get someone to take a specific card from the deck.

7 - When All Else Fails Go With Paper

Haven't a clue what to throw next? Then go with Paper. Why? Statistically, in competition play, it has been observed that scissors is thrown the least often. Specifically, it gets delivered 29.6% of the time, so it slightly under-indexes against the expected average of 33.33% by 3.73%. Obviously, knowing this only gives you a slight advantage, but in a situation where you just don't know what to do, even a slight edge is better than none at all.

8 - The Rounder's Ploy

This technique falls into more of a 'cheating' category, but if you have no honour and can live with yourself the next day, you can use it to get an edge. The way it works is when you suggest a game with someone, make no mention of the number of rounds you are going to play. Play the first match and if you win, take it is as a win. If you lose, without missing a beat start playing the 'next' round on the assumption that it was a best 2 out of 3. No doubt you will hear protests from your opponent but stay firm and remind them that 'no one plays best of one for a kind of decision that you two are making'. No this devious technique won't guarantee you the win, but it will give you a chance to battle back to even and start again.

This paper by David Blanchflower and Andrew Oswald (from the Australian Economic Review in 2005) looks interesting. I'm interested in happiness (who isn't?) but this paper particularly interests me because it addresses a special case of the general statistical problem of summarizing multivariate data by indexes. Here's the abstract:

According to the well-being measure known as the U.N. Human Development Index, Australia now ranks 3rd in the world and higher than all other English-speaking nations. This paper questions that assessment. It reviews work on the economics of happiness, considers implications for policymakers, and explores where Australia lies in international subjective well-being rankings. Using new data on approximately 50,000 randomly sampled individuals from 35 nations, the paper shows that Australians have some of the lowest levels of job satisfaction in the world. Moreover, among the sub-sample of English-speaking nations, where a common language should help subjective measures to be reliable, Australia performs poorly on a range of happiness indicators. The paper discusses this paradox. Our purpose is not to reject HDI methods, but rather to argue that much remains to be understood in this area.

I recommend--for the next paper these folks write--to present the results in graphical, not tabular, form, and to order the countries in some reasonable way (for example, in order of per-capita GDP) rather than alphabetically. For example, do we really need to know that Australia has a value of 5.39 for one index and 5.62 for another? These comments apply to the raw data and also the displays of regression coefficients.

Going on to the substance of the paper, I have no particular comments. It is admirably crisp and speaks for itself and modestly focuses on the statistical issues.

Aleks's comments here, in particular the bit about selfishness, reminds me of one of my favorite papers, "The norm of self-interest" by the psychologist Dale Miller. Here's the abstract:

The self-interest motive is singularly powerful according to many of the most influential theories of human behavior and the layperson alike. In the present article the author examines the role the assumption of self-interest plays in its own confirmation. It is proposed that a norm exists in Western cultures that specifies self-interest both is and ought to be a powerful determinant of behavior. This norm influences people's actions and opinions as well as the accounts they give for their actions and opinions. In particular, it leads people to act and speak as though they care more about their material self-interest than they do. Consequences of misinterpreting the "fact" of self- interest are discussed.

(Related work by Noah Kaplan, Aaron Edlin, and myself here, distinguishing rationality from selfishness as motivations for voting.)

Wolfram Schlenker of our economics department is presenting this paper by himself and Michael Roberts on the effects of climate change. The talk is this Thursday, 11:30-1, in 717 IAB. Here's the abstract:

When looking at voting and state income (red states and blue states, etc.), we realized that, although many of the geographic patterns of voting in the U.S. are relatively recent (for example, New England used to be strongly Republican, now is solidly Democratic), the rankings of states as rich and poor has been steady for quite awhile. The rich states now are, by and large, the states that had factories and large cities 50 or 100 years ago. Here are some graphs (using Census data that Justin Phillips gave me):

Apparently this isn't just a U.S. phenomenon. Diego Comin, WIlliam Easterly, and Erick Gong find that the wealth of nations was determined in 1000 B.C. Their paper is interesting and has some cool maps, but the graphical presentation could be improved a bit. I'll offer some suggestions on the chance that they'll have an opportunity to revise:

Tyler Cowen reports that the American Economic Association is introducing four new journals, which presumably will instantly be "top-tier." Cowen writes, "Overall I consider this bad news. It expands the career-making power of one professional association and the editors it nominates. It further encourages overspecialization, and discourages general interest research. . . ."

My first impression is the opposite.

The New York Times has published yet another article about "market" approaches to predicting outcomes: people essentially wager on an outcome (like "Barack Obama wins the 2008 presidential election"). The article points out that on the website Intrade.com, the "market" had decided that the Republicans would lose control of the U.S. Senate by about 2am on election night, even though the news and political pundits were reporting that the Republicans appeared to be holding on by one seat. The article goes on to say the usual good things about markets, and says:

N. Gregory Mankiw, a former adviser to President Bush, who has written about Intrade on his blog, explains it this way: “Everybody has information from their own little corner of the universe, and they’d like to know the information from every other corner of the universe. What these markets do is provide a vehicle that reflects all that information.”

I don't disapprove of these "information markets" and I think they're kinda fun, and potentially even useful. But these sites might not be illustrations of the power of the market to correctly take disparate sources of information into account in order to make a prediction. Instead, they might be illustrations of the fact that if you have information that is not available to other people, you can make a better prediction than they can. Even the name of the site that is mentioned, "Intrade.com", carries the suggestion of "insider trading." For example, suppose you were a poll worker in Virginia and you knew two things that were not known to the media or the public at midnight on election day: (1) most of the highly Democratic precincts have not reported yet, and (2) the ones that have, have been even more strongly Democratic than usual. Of course you would have good reason to think the Republicans would lose the Senate, and you could log into Intrade.com or some other similar site and put your money down. And the market price would indeed reflect your knowledge. But what is at work here isn't the magical power of markets to use all of the available information, it's the magical power of "insider" information to let you make a better prediction. If the same information were available to CNN...or to me, for that matter...we wouldn't need "the market" to tell us the Democrats would win the Senate. I'm not sure the Intrade.com market was actually integrating information any better than the pundits, their bettors/investors might have just had better information.

Chris Anderson popularized the idea that internet will fundamentally change the way media works: while the mass media and retail create a small number of big hits, the internet will "flatten" the field in the sense that there will be a large number of smaller hits. There are particular reasons for short-tailed world:

• limited shelf space - smaller choice, the ubiquity of the "popular"
• mass media broadcasts - coverage of the universally ("lowest common denominator") popular at the expense of the niche interests

Ilya Grigorik used the Netflix data (I have written about his analysis already) to examine the shifts in the taste across years on Netflix. He finds that with years from 2000 to 2005, the head has grown bigger, but the tail thinner:

The blockbusters have increased, while the tail has become thinner, especially among the hits. On the first sight, this would appear to indicate that the long-tailed vision is wrong. I disagree: the reasons lie in marketing. There is a distinct difference between early adopters and late adopters. Early adopters are more experimental in their tastes, which is the fundamental reason why they would go bother with novelty services. Moreover, they cannot find their favorite niche movie in the local Blockbuster, driving them towards mail distribution.

As the early adopters spread the word, as Netflix polishes up their service and reduces the cost, more and more of the late adopters join the ranks of Netflix customers, but these late adopters 1) do not have the itch for the rare 2) are not experimental 3) are still largely governed by the mass media. My guess is that the thinning of the tail is temporary.

There's a lot of talk about the long tail--for example, there are a zillion books out there, each selling a few copies a week (hey--those are my books out there in the tail!), and a zillion blogs out there, each getting a few hits a day (hey--that's our blog out there...). We're no longer in the era of mass consumption, etc etc.

I was wondering: who are the consumers of the long-tail items? I'd conjecture that the people who buy books in the long tail are, on average, buyers of many books. Similarly, I'd conjecture that the rarefied few who read our blog read many other blogs as well. In contrast, the average buyer of a bestseller such as The Shangri-La Diet might not be buying so many books, and, similarly, the average reader of BoingBoing might be reading not so many blots.

Or maybe I'm wrong on this, I don't know. I'm picturing a scatterplot, with one dot per book (or blog), on the x-axis showing the number of buyers (or readers), on the y-axis showing the average number of books bought (or blogs read) per week by people who bought thiat book (or read that blog). Or maybe there's a better way of looking at this.

The question is: is the "long tail" being driven by a "fat head" of mega-consumers?

Steven Levitt points to a report by Kate Holton comparing self-reported happiness levels in different countries. Holton wrote:

Young people in developing nations are at least twice as likely to feel happy about their lives than their richer counterparts, a survey says. Indians are the happiest overall and Japanese the most miserable. According to an MTV Networks International (MTVNI) global survey that covered more than 5,400 young people in 14 countries, only 43 percent of the world's 16- to 34-year-olds say they are happy with their lives. MTVNI said this figure was dragged down by young people in the developed world, including those in the United States and Britain where fewer than 30 percent of young people said they were happy with the way things were. . . . "The happier young people of the developing world are also the most religious," the survey said. The MTVNI survey took six months to complete and resulted in the Wellbeing Index which compared the feelings of young people, based on their perceptions of how they feel about safety, where they fit into society and how they see their future. Young people from Argentina and South Africa came joint top in the list of how happy they were at 75 percent. The overall Wellbeing Index was more mixed between rich and poor. India came top followed by Sweden and Brazil came last.. . . The 14 countries included in the survey were Argentina, Brazil, China, Denmark, France, Germany, India, Indonesia, Japan, Mexico, South Africa, Sweden, the UK and the U.S.

Levitt basically says he doesn't believe these results because, as he puts it,

Economists have a notion called “revealed preference.” By looking at people’s actions, you can infer how they feel. Applied to this MTV survey, if their measure of happiness or Wellbeing Index were meaningful, then I would expect that we would see a steady flow of unhappy young people from the United States and the United Kingdom immigrating to happy places like South Africa and Argentina and Wellbeing places like India.

History tells us that the flow of immigrants has always been and continues to be in the other direction, which to an economist, is the strongest evidence that whatever people are looking for, developed countries like the United States are where they are finding it.

I don't know the details of the survey; for example, maybe the pollsters have more difficulty reaching unhappy people in some countries than others. But let's assume that it's doing a good job of getting people's attitudes. Levitt writes that "people make many mistakes in forecasting what will or will not make them happy in the future," but I don't see why this invalidates survey responses about current happiness levels. If anything, it would suggest that emigrants to the U.S. are, possibly mistakenly, basing their decisions on estimates of future happiness (or maybe possibilities for their children). I would think that, from an economist's perspective, it would be completely reasonable for a currently-happy person in India (say) to come to the U.S. in anticipation of future happiness for self and family. Even if this anticipation turns out to be wrong, the decision to emigrate will be based on the feelings at that time, not on their future happiness levels.

Beyond this, I wonder if Levitt is falling into a "Simpson's paradox" trap of confusing within-group and between-group comparisons. The Indians who emigrate to America are not a random sample of Indians, and so it is possible for (a) Indians to be happier than Americans, and (b) Indian immigrants to become happier when moving to America. (But, given that people can make mistakes in forecasting their future happiness, I don't know that (b) is true.)

To put it another way, I don't plan to emigrate to India. But, even if the average Indian is happier than the average American, it doesn't mean that I'd be happier if I were to emigrate. It's the difference between correlation (the observed pattern of Indians and Americans) and causation (what would happen to an individual person if he or she were to move).

Anyway, I don't mean to belabor the point, it's just something I'd think an economist would be more aware of. Or, more likely, there's another twist to the argument that I'm missing (for example, some reasoning about equilibria).

In summary . . .

Alex Tabarrok links to this interview with Emily Oster, an economist who is studying ways of mitigating Aids in Africa. This is an area I know nothing about, but the following paragraphs caught my eye:

anthropologists, sociologists, and public-health officials . . . believe that cultural differences—differences in how entire groups of people think and act—account for broader social and regional trends. AIDS became a disaster in Africa, the thinking goes, because Africans didn't know how to deal with it.

Economists like me [Oster] don't trust that argument. We assume everyone is fundamentally alike; we believe circumstances, not culture, drive people's decisions, including decisions about sex and disease.

My quick comment on this is that everyone may be fundamentally alike, but apparently the culture of anthropology, etc., is associated with different attitudes than the culture of economics. (One could make a selection argument, of course, that people with attitudes like Oster's drift toward economics, and that people with the other attitudes drift toward anthropology, etc.--but that wouldn't fit with the assumption that "everyone is fundamentally alike." And I think it would be extreme and implausible cynicism to think that anthropoligists etc. and economists have different attitudes simply because of different incentive structures in their fields.)

A more measured response might be to say that political scientists accept that people sometimes have fundamentally different attitudes and interests (i.e., are not "fundamentally alike" in many social settings) and that social and political institutions can affect how they interact.

Circumstances and culture: are they like weather and climate?

This is not to say that I disagree with Oster on the substance of her argument. The key distinction, using her terminology, seems to be "circumstances" vs. "culture"--and at some level, "culture" is just a series of circumstances (or, conversely, the "circumstances" you see are affected by your culture). Political scientists would throw in the word "institutions" in here somewhere too, but it's the same general point.

Just to be clear: I'm not trying to be critical of Oster here--what I'm trying to do is understand the different attitudes in different social sciences, and the effects these have on research claims. In economics, as in many fields, I think that having a strong methodological preference can be helpful in focusing one's research. (That's the attitude I've always taken about Bayesian methods: if you work hard at constructing a good model, and you check it against data, you can learn a lot. But it helps to have that commitment to pushing the Bayesian approach hard and being willing to work with it.) Similarly, I expect that Oster's strong assumptions about individual behavior and strong affiliation with "economism" (an analogy to "Bayeisanism"?) can help her make progress by clarifying her thinking and giving her the fortitude to work out the full implications of her ideas.

How does this play out in practice? You can take a look at the following two articles:

On Explaining Asia’s “Missing Women”: Comment on Das Gupta by Emily Oster. Paper here.

Cultural versus Biological Factors in Explaining Asia's "Missing Women" by Monica Das Gupta. Paper here.

I have not tried to evaluate the competing arguments (hey, I'm busy too!), just to give these as a possible example of different approaches taken by economists and public-health researchers. I think the quote about people being fundamentally alike doesn't really come into play here, but perhaps these papers do illustrate different ways of studying a social phenomenon.

The article cited here ("Young children aged between two and four years believe that you only have to hide your head to become invisible – if your legs are on view, it doesn’t matter, you still can’t be seen") is cool (and so unsurprising that I'm a bit surprised that it's news), and it's certainly psychology, but why is it characterized as "behavioral economics"? It seems cognitive (not behavioral), and I don't see the connection to economics at all!

Economists sure are competitive (at least, based on this sample of size 1).

Jeronimo Cortina, Rodolfo de la Garza, and Pablo Pinto find, surprisingly, that the ability to speak both English and Spanish has a surprisingly small association with income among Hispanics in the U.S., with the association actually being negative for managerial jobs. They write,

These findings are troubling for several reasons. They suggest that the difference in earnings may be the consequence of discrimination in labor markets. Alternatively, it is plausible that lower wages may reflect the extent to which Spanish-speaking Latinos including those who are fluent in English, receive educational services of lower quality than Hispanics that speak English only only, and even non-Hispanic whites despite similar education attainment levels.

From a statistical perspective, this sort of analysis is interesting because it is of the "dog that didn't bark" variety: not finding an expected effect, which implies that there must be something cancelling the underlying pattern (of better skills--in this case, bilinguilism--yielding higher incomes). The regressions control for a bunch of variables (education, sex, age, citizenship, region, and occupation category). I wouldn't mind seeing an analysis using matching as well. It's a challenging problem to think of causally, since the point is that they're not simply estimating the causal estimate of biligual ability--they're actually trying to demonstrate that the model has a omitted variables.

And, of course, ...

Carrie asks:

If by any chance you're still teaching kids to do surveys, we have a project we could REALLY use help on. . . . we'd love to have a survey of ipod users asking them how many ipods they have owned, how often they used each of them, and how long they lasted before dying. We'd then like to crunch that data to find the likelihood of the ipod dying at given intervals.

Matt writes,

In a letter published in the latest New Yorker, Douglas Robertson writes,

James Surowiecki, in his column on sports betting, writes, "How much difference is there, after all, between betting on the future price of wheat . . . and betting on the performance of a baseball team?" (The Financial Page, September 25th). Future markets in products such as wheat allow famers and other producers to shield themselves from some financial risks, and thereby encourage the production of necessities. In this sense, the futures markets are more akin to homeowners' insurance or liability insurance than to gambling on sports. But there is no corresponding economic benefit to betting on sports; on the contrary, there are serious costs involved in protecting the sports activities from fixing and other corruptions that invariably accompany such gambling activity.

This is a good point. I enjoy gambling in semi-skill-based settings (poker, sports betting, election pools, etc.), and betting markets are cool, but it is useful to step back a bit and consider the larger economic benefits or risks arising from such markets.

In a comment to this entry on Gardner and Oswald's finding that people who won between £1000 and £120,000 in the lottery were happier than people in two control groups, Tony Vallencourt writes,

Daniel Kahneman, Alan Krueger, David Schkade, Norbert Schwarz, and Arthur Stone disagree with this result. It's funny, yesterday, I came across this post and then across Kahneman et al's result in Tuesday Morning Quarterback on ESPN's Page 2.

I [Vallencourt] wrote it up on my blog. I'm not sure who I believe, but I know that I'd like to have more money myself.

OneI possibility is that regular $ (which you have to work for) isn't such a thrill, but the unexpected $ of the lottery is better.

I actually wonder about the £1000 lottery gains, though, since I suppose that many (most?) of these "winners" end up losing more than £1000 anyway from repeated lottery playing. Even the £120,000 winners might gamble much or all of it away.

Regarding unexpected $, I have the opposite problem: book royalties are always unexpected to me (even though I get them every 6 months!). I've always felt that a little mental accounting would do me some good--I'd like to imagine these royalties as something I could spend on some special treat--but, bound as I am to mathematical rules of rationality, I just end up putting these little checks into the bank and I never see them again. "Mental accounting is said to be a cognitive illusion but here it might be nice. Perhaps I could think of these royalties as poker winnings?

And, yes, I too would prefer to have more money--but I don't know that it would make me happier. Or maybe I should say, I don't know whether money would make me happier, but I'd still like to have more of it. I naively think that, if I had the choice between happiness state X, or happiness state X plus $1000 (i.e., I'm assuming that the $1000 doesn't make me any happier), I'd still like to have the extra $. But maybe I'm missing the point. And, of course, as the Tuesday Morning Quarterback points out, extra money doesn't usually come for free--you have to work for it, which takes time away from other pursuits.

So maybe this is really a problem of causal inference. Or, to put it in a regression context, what variables should we hold constant when considering different values the "money" input variable? Do we control for hours worked or not? Different versions of the "treatment" of money could have different effects, which brings us back to the point at the beginning of this note.

Jonathan Gardner and Andrew Oswald write,

One of the famous questions in social science is whether money makes people happy. We [Gardner and Oswald] offer new evidence by using longitudinal data on a random sample of Britons who receive medium-sized lottery wins of between £1000 and £120,000 (that is, up to approximately US$ 200,000). When compared to two control groups – one with no wins and the other with small wins – these individuals go on eventually to exhibit significantly better psychological health. Two years after a lottery win, the average measured improvement in mental wellbeing is 1.4 GHQ points.

Here's the paper. (Yes, Tables 2 and 3 should be graphs).

Stuart Buck has an interesting story (linked from Tyler Cowen and Jane Galt of a map that was published in the newspaper showing gains and losses in median household incomes. Apparently the graph (from the Detroit Free Press) was mistaken. Buck writes,

Let's take my home state of Arkansas. According to the Census Bureau's page, Arkansas' 1999 median household income -- in 2005 dollars -- was $34,770. Then in 2005, the median household income was $36,658. That's an increase of 5.4%, as opposed to the 7.2% decrease that the Detroit Free Press claims to have found.

How about another state: Utah. In 1999 (again, in 2005 dollars): $53,943. In 2005: $54,813. That's a rise of 1.6%, not a decline of 10.5% as the Free Press claims. . . .

The first journalist then followed up and explained further that the 1999 data came from the 2000 Census (it's available here). They used the inflation calculator recommended by the Census Bureau. And then the 2005 data came from the American Community Survey (here). . . .

Estimates from any one survey will almost never exactly match the estimates from any other (unless explicitly controlled), because of differences such as in questionnaires, data collection methodology, reference period, and edit procedures.

Most importantly here, the American Community Survey seems, for whatever reason, to produce lower results than the official Census figures. For example, in one detailed analysis comparing ACS to the Census in a couple of counties, the Bureau reported:

There were significant differences in the estimation of median household income. In Tulare County, the Census reported a value of $33,983 compared to the ACS estimate of $31,467. This is consistent with Census Bureau research in other ACS sites that generally found lower income values reported in the ACS . . . .

This seems like a great example for a statistics (or policy analysis) class. Of course, the ultimate solution is not to give up but to get parallel series of both surveys (if possible) to better adjust for differences in making comparisons.

The other thing to be considered is uncertainty. Looking at the linked webpage, I see some big standard errors. For example, considering Stuart Buck's example of Arkansas, we see $36,700 +/- 1400 (for 2005) and $34,800 +/- 1200 (for 1999). Assuming independent surveys (which maybe isn't right), the difference is $1900 +/- 1800. That is, a difference of 5.4% +/- 5.2%. With numbers like these, it seems a little silly to be looking at individual states.

There is a statistical message here, too, which is that differences are hard to estimate precisely (unless they are studied using a panel design which keeps the data comparable from year to year).

P.S. See here for a table showing how variable the state estimates are--with color and two significant digits included to make the noise be even more visible! There are many comments on that blog entry, and they all seem to be taking the numbers at face value.

One of the most successful new internet companies, judging by the amount of traffic that they are getting, is Zillow, a real estate data company that specializes in the prices of housing. However, they have provided very interesting plots of home values in several metropolitan areas in the US. Finally, we can throw away the Boston housing dataset.

There's an article by Abhijit Vinayak Banerjee in the Boston Review recommending randomized experiments (or the next best thing, "natural experiments") to evaluate stragies for foreign aid. Also, here's a link to the Boston Review page which includes several discussions by others and a response by Banerjee.

On the specific topic of evaluating social interventions, I have little to add beyond my coments last year on Esther Duflo's talk: randomized experimentation is great, but once you have the randomized (or "naturally randomized") data, it still can be a good idea to improve your efficiency by gathering background inforomation and using sophisticated statistical methods to adust for imbalance. To quote myself on Dfulo's talk:

There are a couple ways in which I think the analysis could be improved. First, I'd like to control for pre-treatment measurements at the village level. Various village-level information is available from the 1991 Indian Census, including for example some measures of water quality. I suspect that controlling for this information would reduce the standard errors of regression coefficients (which is an issue given that most of the estimates are less than 2 standard errors away from 0). Second, I'd consider a multilevel analysis to make use of information available at the village, GP, and state levels. Duflo et al. corrected the standard errors for clustering but I'd hope that a full multilevel analysis could make use of more information and thus, again, reduce uncertatinties in the regression coefficients.

Why don't we practice what we preach?

Nonetheless, I am not sure myself that large-N studies are always a good idea. And, in practice, I rarely do any sort of formal experimentation when evaluating interventions in my own activities. Here I'm particularly thinking of teaching methods, where we try all sorts of different things but have difficulty evaluating what works. I certainly do make use of the findings of educational researchers (many of whom, I'm sure, use randomized experiments), but when I try things out myself, I don't ever seem to have the discipline to take good measurements, let alone set up randomized trials. So in my own professional life, I'm just as bad as the aid workers who Banerjee criticizes for not filliong out forms.

This is not meant as a criticizm of Banerjee's paper, just a note that it seems easier to give statistical advice to others than to follow it ourselves.

I received the following (unsolicited) email:

This item reminds me of the time I was riding on the New Jersey Transit train, sitting next to a 6-foot-2-inch woman. It turned out she played the role of Miss Frizzle on the traveling production of The Magic School Bus. She said the kids on the show are played by short adult actors.

I thought this was amusing.

Phil pointed me to this fun graph:

A guest post by Maria Grazia Pittau:

Rather than revelling in the dolce vita, Italians are battling with the carovita (the high cost of living), Newspaper headlines warn that the "Middle class has gone to hell" and "Italians don't know how they will make it to the end of the month" The Guardian, Tuesday, December 28, 2004.

Considerable progress has been made in empirical research to measure the degree of polarization in the income distribution, not properly captured by inequality indexes. In general, "given any distribution of income, the term polarization means the extent to which a population is clustered around a small number of distant poles" (Esteban, 2002, p.10). How many poles and how distant they are can be regarded as structural features of the whole income distribution. Kernel densities are very good at answering these questions, since features as location, multi-modality and spread can be observed simultaneously. The choice of the bandwidth parameter h is a crucial issue in kernel density since it governs the degree of smoothness of the density estimate. Kernel density estimation can model the data in lesser or finer detail, depending on the extent of smoothing applied.

For income data an adaptive bandwidth is suggested, that is a bandwidth varying along the support of the data-set allows one to reduce the variance of the estimates in areas with few observations (generally, in the tails of the distribution), and to reduce the bias of the estimates in areas with many observations (generally, in the middle of the distribution). The analysis based on kernel density relies to a great extent on the visual impression. When the visual impression seems to corroborate the presence of more than one mode in the distribution, further investigation should be devoted in identifying the sub-populations cluster around the modes.

But are the modes really there? Or are just spurious artifact of the data?

To assess which observed features in the income distribution are "really there", as opposed to being spurious sampling artifact we follow the Sizer approach (Chaudhuri and Marron, 1999, 2000). The SiZer is a graphical tool for the display of significant features with respect to location and bandwidth through assessing the SIgnificant ZERo crossing of the derivatives.

The main advantage of the SiZer is that, for a wide range of bandwidths, it looks at how changes in the bandwidths affect a particular location of the empirical distribution. It searches for the robustness of the shapes at varying bandwidths instead of focusing on a "true" underlying curve.

An important feature is a "bump". The role of SiZer is to attach significance to these bumps. When a bump is present there is a zero crossing of the derivative of the density smooth and the bump is statistically significant (a mode) when the derivative estimate is significantly positive to the left and significantly negative to the right. Analogously, for a dip.

The SiZer approach has two graphical components: A family of nonparametric curves indexed by the smoothing parameter, scale space surface, and the SiZer map that displays significant features with respect to location and bandwidth through assessing the SIgnificant ZERo crossing of the derivatives. The SiZer map displays information about the positivity and negativity of the derivative of the kernel estimator. Each point in the map represents a point indexed by the location in the horizontal axis (x) and by the bandwidth on the vertical axis (h). For a resolution level h, the estimated derivative of the kernel estimation is significantly positive (negative) when all the points within a given confidence interval are positive (negative), that is the (gaussian) kernel distribution is significantly increasing (decreasing) at that location.

Figure 1

Figure 1 represents the scale space surface that is an overlaid family of empirical kernel distributions, each corresponding to a different bandwidth. The family plot gives the idea that no single bandwidth can explain all the information available in the data. The corresponding SiZer map sheds more lights on the crucial question of which modes are statistically significant at any given level of resolution.

Figure 2

Figure 2 reports the net annual disposable Income in Italy in 2002 of all the members of the household after tax and social security transfers for different bandwidths. Household incomes are adjusted for different household sizes. Household incomes are reported in 1995 prices using the consumption deflator of the national accounts.

The SiZer map in Figure 2 has the horizontal axis, y, which represents the household equivalent income, and the vertical axis, log10 (h) represents the bandwidth. The log10 scale used for the bandwidth in the map is chosen to display smooths that are more equally spaced. The horizontal black line represents the optimal (pilot) bandwidth.

The portions of the display are colour-coded: a color, say light gray, when the derivative is significantly positive; a different color, dark gray, when the derivative is significantly negative. The points at which the derivative is not significantly positive or negative appear in the black region of the SiZer map.

The two modes of the Italian income distribution in 2002 are detected for a wide range of bandwidths. These two modes are located around 7,201 euros and 10,254 euros, indicating the presence of two groups of households in Italy: a poor and a rich group. So, the SiZer approach provides a graphical counterpart of measures of polarization to continuous distributions, applied in Duclos et al. (2004). Although number and location of the modes cannot directly linked to the degree of polarization, the emergence of multiple modes, their intensity and separateness, may help relating the changes in the shape of distributions to changes in the polarization measurements.

Seth wrote something here about Jane Jacobs and her relevance to experimental psychology. He mentions her book, Cities and the Wealth of Nations, which I too enjoyed reading.

I have one question, though, which perhaps the economists in the audience can anwer. I recall two messages from that book.:

(1) Certain cities produce great wealth and that it is the natural and unfortunate result of national tax and economic policies to bleed the cities dry until they can no longer be productive, leading to national decline.

(2) Import substitution is a good thing because it leads to dense networks of local factories and suppliers.

But I seem to recall reading somewhere that import substitution is very much out of favor among economists. Was Jacobs wrong on that one?

Here's some discussion by Martin Ternouth and others on organizing office space. I've actually started to use the blog as a way to store interesting ideas. It has the advantage of forcing me to work in full sentences. Storing things in email is a mess.

Statistics and economics have similar, but not identical, jargon, that overlap in various confusing ways (consider "OLS," "endogeneity," "ignorability," etc., not to mention the implicit assumptions about the distributions of error terms in models).

To me, the most interesting bit of terminological confusion is that the word "marginal" has opposite meanings in statistics and economics. In statistics, the margin (as in "marginal distribution") is the average or, in mathematical terms, the integral. In economics, the margin (as in "marginal cost") is the change or, in mathematical terms, the derivative. Things get more muddled because statisticians talk about the marginal effect of a variable in a regression (using "margin" as a derivative, in the economics sense), and econometricians work with marginal distributions (in the statistical sense). I've never seen any confusion in any particular example, but it can't be a good thing for one word to have two opposite meanings.

P.S. I assume that the derivation of "margin," in both senses, is from the margin of a table, in which case either interpretation makes sense: you can compute sums or averages and place them on the margin, or you can imagine the margin to represent the value at the next value of x, in which case the change to get there is the "marginal effect."

This item from Carrie's blog reminds me of a trick I used to do when giving seminars: I'd give out approximately 2/3 as many handouts as there were people in the audience. That way they'd have to share the handouts, and they'd value them more. When I would give enough handouts for everybody, people would just put them in their notebook and not bother looking at them.

I appreciate all the comments here on my article on ANOVA for the New Palgrave Dictionary of Economics. The main suggestion was that I clarify what ANOVA gives you that regression doesn't. I hope this revision does the trick. Here's the revised article and here's the added section:

The analysis of variance is often understood by economists in relation to linear regression (e.g., Goldberger, 1964). From the perspective of linear (or generalized linear) models, we identify ANOVA with the structuring of coefficients into batches, with each batch corresponding to a ``source of variation'' (in ANOVA terminology).

As discussed by Gelman (2005), the relevant inferences from ANOVA can be reproduced using regression---but not always least-squares regression. Multilevel models are needed for analyzing hierarchical data structures such as the split-plot design in Figure 2, where between-plot effects are compared to the main-plot errors, and within-plot effects are compared to sub-plot errors.

Given that we can already fit regression models, what do we gain by thinking about ANOVA? To start with, there are settings in which the sources of variation are of more interest than the individual effects. For example, the analysis of internet connect times in Figure 3 represents thousands of coefficients---but without taking the model too seriously, we can use the ANOVA display as a helpful exploratory summary of data. For another example, the two plots in Figure 4 allow us to quickly understand and compare two multilevel logistic regressions, again without getting overwhelmed with dozens of coefficient estimates.

More generally, we think of the analysis of variance as a way of understanding and structuring multilevel models---not as an alternative to regression but as a tool for summarizing complex high-dimensional inferences, as can be seen, for example, in Figure 5 (finite-population and superpopulation standard deviations) and Figures 6-7 (group-level coefficients and trends).

Also, I thanked the commenters who gave their full names. If anyone else has suggestions and also wants to be achknowledged in the article, just let me know your name. Thanks again.

I was asked by the editors of the New Palgrave Dictionary of Economics (second edition) to contribute a short article on the analysis of variance. I don't really know what economists are looking for here (the article is supposed to be aimed at the level of first-year graduate students), but I gave it a try. Here's the article. Any comments (from economists or others) would be appreciated.

P.S. See here for revised version.

Naseem Taleb's publisher sent me a copy of "Fooled by randomness: the hidden role of chance in life and the markets" to review. It's an important topic, and the book is written in a charming style--I'll try to respond in kind, with some miscellaneous comments.

Dsquared's comment on this entry mentioned the economist Deirdre McCloskey, whom I googled and found this paraphrase of a quote from Don Boudreaux, "that no one was ever convinced by raw data of the truth of a proposition that he or she did not already hold to be true."

I wonder what the original form of the quotation was. Or maybe what I really wonder is, what is the point of demarcation for which Boudreaux's statement is true? As written it is certainly false. I know this because I myself get convinced by raw data of propositions on which I held no prior opinion. Here are just a few recent examples:
- Rich people and poor people differ more in their political preferences in poor states than in rich states
- The NYC police department stopped more minorities than whites, even after controlling for neighborhoods and previous crime rates
- Americans have, on average, about 700 acquaintances each
- and many, many others.
And, as a bonus, here's an example where an analysis of raw data left me unconvinced of a hypothesis (that a local election was rigged).

I certainly did not "already hold" these propositions to be true--or false. In fact, in many cases (for example, the rich and poor states), the interesting proposition didn't even come to me until the data whacked me on the head with it.

Different sorts of propositions?

How, then, could Boudreaux (and McCloskey?) say such a thing? There must be different sorts of propositions. The propositions that I study tend to be technical--even the studies of voting and police stops are emprical questions, not value judgments. But for the big social questions, maybe it's hard for people to be persuaded by data because they already have such strong opinions. One thing I like about emprical science (including social science) is that I can be "convinced by raw data of the truth of a proposition that he or she did not already hold to be true"--and it happens all the time.

I received the following one-sentence email from a Ph.D. statistician who works in finance:

For every one time I use stochastic calculus I use statistics 99 times.

Not that stochastic calculus isn't important...after all, I go to the bathroom more often than I use statistics, but that doesn't mean we need Ph.D. courses in pooping...but still, it says something, I think.

During my visit to George Mason University, Bryan Caplan gave me a draft of his forthcoming book, "The logic of collective belief: the political economy of voter irrationality." The basic argument of the book goes as follows:

(1) It is rational for people to vote and to make their preferences based on their views of what is best for the country as a whole, not necessarily what they think will be best for themselves individually.
(2) The feedback between voting, policy, and economic outcomes is weak enough that there is no reason to suppose that voters will be motiaved to have "correct" views on the economy (in the sense of agreeing with the economics profession).
(3) As a result, democracy can lead to suboptimal outcomes--foolish policies resulting from foolish preferences of voters.
(4) In comparison, people have more motivation to be rational in their conomic decisions (when acting as consumers, producers, employers, etc). Thus it would be better to reduce the role of democracy and increase the role of the market in economic decision-making.

Caplan says a lot of things that make sense and puts them together in an interesting way. Poorly-informed voters are a big problem in democracy, and Caplan makes the compelling argument that this is not necessarily a problem that can be easily fixed--it may be fundamental to the system. His argument differs from that of Samuel Huntington and others who claimed in the 1970s that democracy was failing because there was too much political participation. As I recall, the "too much democracy" theorists of the 1970s saw a problem with expectations: basically, there is just no way for "City Hall" to be accountable to everyone, thus they preferred limiting things to a more manageable population of elites. Caplan thinks that voting itself (not just more elaborate demands for governmental attention) is the problem.

Bounding the arguments

I have a bunch of specific comments on the book but first want to bound its arguments a bit. First, Caplan focuses on economics, and specifically on economic issues that economists agree on. To the extent the economists disagree, the recommendations are less clear. For example, some economists prefer a strongly graduated income tax, others prefer a flat tax. Caplan would argue, I think, that tax rates in general should be lowered (since that would reduce the role of democratic government in the economic sphere) but it would still be up to Congress to decide the relative rates. This isn't a weakness of Caplan's argument; I'm just pointing out a limitation of its applicability.

More generally, non-economic issues--on which there is no general agrement by experts--spread into the economic sphere. Consider policies regarding national security, racial discrimination, and health care. Once again, I'm not saying that Caplan is wrong in his analysis of economic issues, just that democratic goverments do a lot of other things. (At one place he points out that the evidence shows that voters typically decide whom to vote for based on economic considerations. But, even thought the economy might be decisive on the margin, that doesn't mean these other issues don't matter.)

Finally, Caplan generally consideres democracy as if it were direct. But I think representative democracy is much different than direct democracy. Caplan makes some mention of this, the idea that politicians have some "slack" in decision-making, but I suspect he is understating the importance of the role of the politicians in the decision-making process.

Specific comments

Ben Cowling asks,

What do you think of the growing area of 'expert trading markets' using expert opinion for predicting future events (as compared to, say, formal mathematical or statistical models incorporating past data in the forecasting process)? From what I can gather the markets produce a form of informative prior so perhaps the whole process might be considered as a kind of simple mathematical model(?)

I'm motivated by the recent article in the economist:

Science and Technology: Trading in flu-tures; Predicting influenza
The Economist: 377 (8448) p. 108. Oct 15, 2005.

But I know these expert markets have been used in other areas; the Iowa Electronic Market is claimed to be good at predicting all sorts of things successfully including elections, which is why I thought you and readers of your blog might be interested.

My response: I first heard of the Iowa markets nearly 15 years ago, when Gary King and I were writing our paper about why pre-election polls vary so much when elections are so predictable. For this paper, all we needed to establish was that elections are predictable, which indeed they are, using state-by-state regression forecasting models (as was done in the 1980s and 1990s by Rosenstone and Campbell, and more recently by Erikson, among others). The Iowa markets also give good forecasts, which isn't a suprise given that the investors in these markets can use the regression forecasts that are out there.

Basically, my impression is that the prediction markets do a good job at making use of the information and analyses that are already out there--for elections, this includes polls and also the information such as economic indicators and past election results, which are used in good forecasting models. The market doesn't produce the forecast so much as it motivates investors to find the good forecasts that are already out there.

As an aside, people sometimes talk about a forecasting model, or a prediction market, "outperforming the polls." This is misleading, because a poll is a snapshot, not a forecast. It makes sense to use polls, even early polls, as an ingredient in a forecast (weighted appropriately, as estimated using linear regression, for example) but not to just use them raw.

P.S. In the comments, Chris points out this interesting article on prediction markets by Wolfers and Zitzewitz.

My colleague Jan Vecer in the statistics department at Columbia gave a talk the other day on "Crash options." His claim was that the introduction of such options could have a socially beneficial effect by allowing investors to plan more effectively in the context of market instabilities. I'm in no position to evaluate this one way or another, but it sounded like a cool idea, so I'm passing it along.

Here's Jan's abstract:

In this paper, we introduce new types of options which do not yet exist in the market, but they have some very desirable properties. These proposed contracts can directly insure events such as a market crash or a market rally. Although the currently traded options can to some extent address situations of extreme market movements, there is no contract whose payo® would be directly linked to the market crash and priced and hedged accordingly as an option.

Here's the paper, and here are the slides from a talk he gave on the topic.

Unfortunately, his paper has no cool graphs. I've suggested to Jan that he make a graph to show how the crash option could work to stabilize the market. I know he has the ability to make cool graphs; see his paper on tiebreakers in tennis and here for an article about his tennis predictor.

I spent too much of one day last week reading this article and everything it links to. Charles Murray, one of the authors of The Bell Curve, also has a piece in the August 2005 issue of Statistical Science called "How to Accuse the Other Guy of Lying with Statistics" (part of a special section "celebrating" the 50th anniversary of "How to Lie with Statistics"--it's a fun issue).

I haven't read The Bell Curve myself, so I better stop now.

From Chance News, submitted to Chance News by Bill Peterson, based on a posting from Joy Jordan to the Isolated Statisticians e-mail list:

Exploiting the gender gap New York Times, 5 September, 2005, A21 Warren Farrell

Farrell is the author of Why Men Earn More: The Startling Truth Behind the Pay Gap -- and What Women Can Do About It (AMACOM, 2004)

This article was published for Labor Day, and it opens by citing a demoralizing, often-heard statistic: women still earn only 76 cents for each dollar paid to their male counterparts in the workplace. Farrell maintains that such comparisons ignore important lurking variables. He claims to have identified twenty-five tradeoffs involving job vs. lifestyle choices, all of which men tend to resolve in favor of higher pay, while women tend to seek better quality of life.

Here are some the factors discussed in the article. Men more readily accept jobs with longer hours, and Farrell reports that people who work 44 hours per work earn twice as much as people who work 34 hours per week. Similarly, he finds that men are more willing to relocate or travel, to work in higher risk environments, and to enter technical fields where jobs may involve less personal interaction. Each of these choices is associated with higher pay.

Even head-to-head comparisons of men and women working in the “same job” can be tricky. Farrell observes, for example, that Bureau of Labor Statistics data consider all medical doctors together. But men opt more often for surgery or other higher paid specialties, while women more often choose general practice.

As indicated by the subtitle of his book, however, Farrell intends to provide some positive news for women. He claims that in settings where women and men match on his 25 variables, the women actually earn more than men. He also identifies a number of specific fields where women do better. One of these is statistics(!), where he reports that women enjoy a 35 percent advantage in earnings.

I haven't read the book so can't comment on the analysis, but it seems like a great discusison topic for class.

In tomorrow's Applied Micro seminar, Regina Almeyda Duran speaks on "Proximate Literacy, Inter and Intrahousehold Externalities and Child Health Outcomes: Evidence from India." Here's the abstract:

Today's applied micro lunch seminar (which unfortunately I won't be able to attend):

When: Tuesday, September 6th 1:10-2:00pm

Where: International Affairs Building, Room 1027

Speaker: Olga Gorbachev (Graduate Student)

Title: "Have Our Lives Become More Unstable? An Investigation of
Individual Volatility of Welfare in the U.S. over
1980-2000."

Abstract:

Has the individual volatility of welfare changed and if so how? What
events led to these changes and what are the implications for public
policy? We examine the evolution of individual volatility of welfare
over 1980-2000 using data from two surveys: Panel Study of Income
Dynamics (PSID) and Consumer Expenditure Survey (CEX). We find that
on average, micro level data follows macro trends. But, when
specific groups are considered, substantial differences are
observed. Older generations, those born between 1915 and 1944,
experienced increasing levels of volatility over 1980-2000 period,
and those born between 1960 and 1974, encountered decreasing
volatility, independent of their educational attainment. Those born
between 1945 and 1959 saw a decrease in volatility only if they had
some college education otherwise, they experienced an increased
volatility. We propose several reasons for the divergence of the
patterns and conclude by estimating social cost to the society and
to individual groups from changes in volatility measured over
1980-2000 period.

There's a really interesting article in Slate by Steven D. Levitt and Stephen J. Dubner (the authors of Freakonomics) about female births and heptatitis B. The disproportionate number of male births in some Asian countries has been attributed to causes such as selective abortion and infanticide. But, as explained in the paper "Hepatitis B and the Case of the Missing Women", by Harvard Economics graduate student Emily Oster, Hepatitis B infection rates actually explain a lot of the discrepancy. Pregnant women who have Hepatitis B are more likely to bear sons than daughters, and Hepatitis B is more common in those parts of the world where the proportion of male births is so high. Pretty cool.

Again, though, the reason I'm writing about the article doesn't have much to do with its subject matter. What struck me more than anything were the article's opening sentences:

The current most emailed headline on the New York Times website is titled "What Women Want," by op-ed columnist John Tierney. He's writing about a working paper, "Do Women Shy Away from Competition?", by Muriel Niederle and Lise Vesterlund, economists at Stanford University and the University of Pittsburgh, respectively. They conducted an experiment where men and women were first paid to add up numbers in their head, earning fifty cents for each correct answer (referred to as the "piece-rate" task). The participants were eventually offered the choice to compete in a tournament where the person who has the most correct answers after five minutes receives $2 per correct answer and everyone else receives zero compensation. One of the main points of the article was that, even at similar levels of confidence and ability, men were much more likely to enter the tournament than women, i.e., women are less willing than men to enter competition. The results of this study yield another possible theory for why there are so few women in top-paying jobs: Even in a world of equal abilities and no discrimination, family issues, social pressures, etc., women might be less likely to end up as tenured professors or CEOs because the jobs are so inherently competitive.

I noticed the blog of Kevin Brancato. I've been enjoying reading the blog entries, especially since Kevin is a former student of ours at Columbia! His paper on macroeconomic statistics is also interesting (and relevant to some of my work).

Kevin worked as a research assistant for me a few years ago on a project which eventually appeared in the Journal of Business and Economic Statistics under the title, "Regression Modeling and Meta-Analysis for Decision Making: A Cost-Benefit Analysis of Incentives in Telephone Surveys."

Here's the abstract of the paper:

I had always thought of "households with phones" and "households without phones" as two disjoint populations, with only the first group reachable by a telephone survey. In fact, I used this as an example in teaching surveys to distinguish between the "population" of phone households and the "universe" of all households. But when doing the weighting for the NYC Social Indicators Survey, we learned that about as many people in the U.S. have intermittent phone service as have no phone service--and if people with intermittent service have a phone about half the time, then they are indeed represented (although underrepresented) in phone surveys.

A few years ago I was checking an article that was about to be published in a statistics journal and I noticed that the copy editor had made a bunch of stupid changes that I then had to go back and fix. Actually, this has also happened for my two books.

This is a funny thing. A copy editor is a professional editor. All they do (or, at least, much of what they do) is edit, so how is it that they do such a bad job compared to a statistician, for whom writing is only a small part of the job description?

The answer certainly isn't that I'm so wonderful. Non-copy-editor colleagues can go through anything I write and find lots of typos, grammatical errors, confusing passages, and flat-out mistakes. (And check out the long list of errata for the first printing of our book!)

No, the problem comes with the copy editor, and I think it's an example of the pinch-hitter syndrome. The pinch-hitter is the guy who sits on the bench and then comes up to bat, often in a key moment of a close game. When I was a kid, I always thought that pinch hitters must be the best sluggers in baseball, because all they do (well, almost all) is hit. But of course this isn't the case--the best hitters play outfield, or first base, or third base, or whatever. If the pinch hitter were really good, he'd be a starter. So, Kirk Gibson in the 1988 World Series notwithstanding (I was watching that on TV--that gives me credit for being there, right?), pinch hitters are generally not the best hitters.

There must be some general social-science principle here, about generalists and specialists, roles in an organization, etc?

The Monthly Labor Review (a journal published by the Bureau of Labor Statistics) has an online version that features a column called Precis, which summarizes a few research abstracts each month. The subject matter is always economics but also just about always of general interest.

For example, some recent topics: The business cycle and earnings and income inequality; Siblings and earnings inequality; Time stress and its causes; Self-employment around the world; ...

The current (December 2004) issue of Precis is here and the links to all of them (since 1998) are here. This is a great public service and perhaps could be successfully imitated by other agencies.