Recently in Economics Category

I don't like the term "risk aversion" (see here and here). For a long time I've been meaning to write something longer and more systematic on the topic, but every once in awhile I see something that reminds me of the slipperiness of the topic.

For example, Alex Tabarrok asks, "Why are Americans more risk averse about medicine than Europeans?" It's a good question, and it's something I've wondered about myself. But I don't know what he's talking about when he says that "the stereotype is that Americans are more risk-loving" than Europeans. Huh? Americans are notorious for worrying about risks, with car seats, bike helmets, high railings on any possible place where someone could fall, Purell bottles everywhere, etc etc. The commenters on Alex's blog are all talking about drug company regulations, but it seems like a broader cultural thing to me.

But I'm bothered by the term "risk aversion." Why exactly is it appropriate to refer to strict rules on drug approvals as "risk averse"? In a general English-language use of the words, I understand it, but it gets slippery when you try to express it more formally.

The questions are no big deal, but what I find interesting is that medical school do personal interviews at all. No place where I've ever worked has interviewed grad school applicants. It's hard for me to see what you get from it, that it would be worth the cost. I guess there must be quite a bit of psychology literature on this question.

From Aaron Swartz, a link stating that famous sociologist Peter L. Berger was a big-time consultant for the Tobacco Insitute:

Peter L. Berger is an academic social philosopher and sociologist who served as a consultant to the tobacco industry starting with the industry's original 1979 Social Costs/Social Values Project (SC/SV). According to a 1980 International Committee on Smoking Issues/Social Acceptability Working Party (International Committee on Smoking Issues/SAWP) progress report, Berger's primary assignment was "to demonstrate clearly that anti-smoking activists have a special agenda which serves their own purposes, but not necessarily the majority of nonsmokers."

Lane Kenworthy, Yu-Sung Su, and I write:

Income inequality in the United States has risen during the past several decades. Has this produced an increase in partisan voting differences between rich and poor? We examine trends from the 1940s through the 2000s in the country as a whole and in the states. We find no clear relation between income inequality and class-based voting.

This article will appear in a special issue of Social Science Quarterly on the topic of "Inequality and Poverty: American and International Perspectives." We have some pretty graphs, some of which appeared in the Red State, Blue State book and some of which didn't.

P.S. "We find no clear relation . . .": That works great in an academic article but I don't think we'll be grabbing the headlines anytime soon.

The sequel is already assured of box-office success, so now's the time to start thinking about what's gonna be in volume 3. Here are a few models that Levitt and Dubner could consider, in no particular order:

Dubner defends himself here. No word on the drunk driving advice, but he has some backstory on the interviews that he and Levitt did regarding global warming. It seems pretty clear that their approach to writing Freakonomics 2 was much different than the original book: the first Freakonomics was all about Levitt's work, whereas the most prominent part of the sequel is a discussion of the ideas of others. As I noted yesterday, this creates a huge selection issue--how did they decide whom to interview?--which is much less present in the first book. I'm also still confused that Dubner describes global warming as "a very difficult problem to solve," given that on his blog the other day he seemed to be endorsing the view that future trends are "virtually assuring us of about 30 years of global cooling."

My guess is that Levitt/Dubner's views on the topic are not completely coherent (by which I mean, not that Levitt and Dubner disagree with each other, but that between them they have a bunch of partly conflicting attitudes on the topic). As a political scientist, I'm the last person to criticize attitudes for being incoherent, and given that neither Levitt nor Dubner is an expert on climate change, it's probably a good thing that their attitudes are fluid and not so easy to pin down. The difficulty comes when they feel the need to defend everything that they've written so far. Again, this is tougher to do here than in the Freakonomics 1 examples, partly because Levitt was much more of an expert on his own research than on others' research, and partly, I suppose, because you'll get a lot more flak in the major news media if you question global warming than if you write about the beneficial consequences of abortion.

P.S. But see the second blurb here!

The above title is a joke. I haven't actually seen the book. As a big-time blogger, I get some books in the mail to review, but maybe this one is sitting in my NYC office. Anyway, the backlash has begun, so maybe this is the right time to buy low and be the first to offer the contrarian claim that, despite what everybody's saying, the book is awesome.

From a short-term economics standpoint, the controversy has gotta be good for the book. So far, Levitt and Dubner have put the words "GLOBAL COOLING" on the cover of their book, they've endorsed a report saying that future trends are "virtually assuring us of about 30 years of global cooling," and that "even if man is warming the planet, it is a small part compared with nature," and they've written that "we believe that rising global temperatures are a man-made phenomenon and that global warming is an important issue to solve." That last bit should do the job of ticking off anybody who was with them so far! (I was actually surprised when reading the comments on that last quote--where Levitt assures us that they do believe in global warming--that pretty much all the global-warming-skeptics among the commenters still seem to think that Levitt is on their side. I guess half a loaf is better than none at all, politically speaking, but I'm surprised that more of them didn't get angry at Levitt for saying that.)

I don't want to get into the substance of climate models, a subject on which I've worked on only a little bit. (The paper we wrote a few years ago never got published--actually, we never finished it enough to submit it anywhere--and our current work on the topic is still in the research-and-writing-up stage.) But I do want to speculate a bit on the political angle.

Just following up . . . this time Dr. McWilliams includes many qualifiers: "I suggested . . . I also suggested . . . Of course, this is only a possibility. I have no numbers to draw on. . . . In any case, it's just a thought."

This helps. As I said before, I have no problem with this sort of op-ed-style reasoning; it just seems out of place on Freakonomics. Anyway, this was part 3 of 3, so I'll have no more to say on the topic.

From Eric Loken:

And I expect we'll see some comments here.

Fernando Hoces De La Guardia writes:

Last night we did the traditional first year econ phd student's skit nite @ Penn.

One particular thing that I noticed was that we had less public that what the upper years told us to be prepared for.

Somebody suggested that it was due to Passover and Good Friday. My immediate reaction was "science & religion don't go usually together". By this I meant a prior of mine that the fraction of religious people is a lot less within a scientific discipline than among the rest of the population.

Two things pop out of my head this morning:

- in which data base can I check that prior?

- if true, are economists more religious than other scientists?

My reply: Usually people look these things up at the General Social Survey, which has a convenient web interface. Good luck!

A few days ago I posted a note about a Freakonomics blog by James McWilliams, who asked, "Do Farmers' Markets Really Strengthen Local Communities?" I was disappointed to see that he offered a historical discussion but no quantitative data or analysis, merely a barrage of subjective impressions and rhetorical questions of the "Who is to say?" sort.

I was hoping for something more in the next installment, but Part Two is unfortunately more of the same. Lots of qualitative quotes but still no data. We get this sort of thing: "Building on this suspicion, she acknowledges that many small farms are indeed more sustainable than larger ones, but then reminds us that "Small scale, 'local' farmers are not inherently better environmental stewards."

"Not inherently better"? That's the best he can do??

Again, if this were an ordinary magazine article or posted in an ordinary blog, it would be fine. Personal impressions make the world go round. But I expect something more when I turn to Freakonomics. Hard-edged data analysis is what makes Freakonomics special. Otherwise it's just the sort of opinionating that anyone can do in their sleep.

Part Three is forthcoming. Maybe we'll see some economic analysis there.

Christopher Rhoads writes:

Interested to know what your comment would be on the following article, which includes the following lines:

Lee Sigelman writes that Senator Coburn of Oklahoma is proposing to zero out funding for the National Science Foundation's political science program. It's hard for me to believe this will even come close to happening, and conflict of interest prevents me from saying anything at all trustworthy on the subject--I've had nearly continuous NSF funding for the past 23 years--but I'll tell ya this: I clicked through to Sen. Coburn's list of NSF-funded projects that he'd like to cut, which included:

- $91,601 to conduct a survey to determine why people are for or against American military conflicts.

- $8,992 to study campaign finance reform, with the stated intent of providing "a basis for assessing future proposed changes to campaign finance regulations.

- $958 for a direct mail survey of the residents of Celebration, Florida regarding their feelings of living in privately operated city.

Following my comments on their article on U.S. military funding and conflict in Colombia, Oeindrila Dube and Suresh Naidu wrote:

Thanks for the comments on our paper. It seemed that you viewed the correlations in the anaysis as an interesting descriptive exercise, but not interpretable as causal. We agree with you that the most interesting social science is often causal, and in this case in particular the causal claims are the main results. The paper's punchline is that military aid needs to be reconsidered when there is collusion between the army and non-state armed groups, and we couldn't make this claim if we thought the results were purely descriptive.

In the paper, we do a lot of sample splitting and parametric time controls to rule out the possibility that this is a spurious effect. For example, our results are robust to including a base-specific time trend, along with a base-specific post-2001 dummy.

Possibly the best evidence against a strict "conflict" time-series interpretation is that there is no effect (positive or negative) of US military aid on guerrilla attacks near Colombian military bases. In other words, its not just an increase in conflict on all sides, but an increase in paramilitary attacks in particular.

The "differential time trend" that could drive our effect would have to be a) steeply nonlinear b) only applicable to paramilitaries in base municipalities, and c) would have to be fairly unique to the base municipalities, given the wide variety of alternate control groups we examine. So we think this is not a likely alternative explanation that can account for the effects.

To which I replied:

First off, I still would prefer associational language followed by causal speculation. But I can respect your different choice of emphasis. Now to get to details: my basic alternative model goes as follows: - Conflict in Colombia increased during the early 2000's. - U.S. military aid, in the U.S. and elsewhere, increased during that period also. - Most of the paramilitary attacks (and, thus, most of the increase in paramilitary attacks) occurred near military bases. Thus, I'm not so impressed by the "differential time trend" argument. It's unsurprising (but nonetheless worth noting, as you do) that there are fewer guerilla attacks near military bases. But that doesn't mean that the paramilitary attacks wouldn't have increased in the absence of U.S. aid.

None of the above really contradicts your main political story, which is that the Colombian military is involved in paramilitary attacks, and that U.S. aid is an enabler for this sort of violence.

My story above is consistent with your causal story--more U.S. aid, more resources for the military, more paramilitary attacks. It's also consistent with a different causal story, which goes like this: more conflict, more paramilitary attacks, also more U.S. aid which actually serves to stop the situation from getting worse. The argument is, yes, the U.S. is giving weapons to the bad guys, but by doing so, it co-opts them and restrains their behavior.

OK, I'm not saying this latter argument is true, but I think your strongest argument against it is to say something like: "Sure, it's possible that things would be getting even worse in the absence of U.S. military aid. But given that, during the time that aid was higher, violence was also higher--and we're talking here about violence being done by the allies of the recipients of the aid--well, maybe aid isn't such a good idea." That is, you can put the burden of proof on the advocates of aid. Hey, it costs money and it's going to some unsavory characters. You shouldn't have to prove that aid is hurting; I think it would be more defensible, from a statistical/econometric point of view, to show the association and put the ball in their court.

P.S. Just to be clear: I don't have any strong feeling that you're wrong or any goal of "debunking" your paper. It's interesting and important work and I'm trying to understand it better.

And then they shot back with:

Regarding the stylistic point about associations and causal claims, we think this is perhaps discipline-specific, as the style in economics seems to be to make a causal claim and then rule out all the alternative causal stories as much as possible. I'm sure this is probably one of many idiosyncrasies that irks non-economists.

The substantive question is why paramilitary attacks (and paramilitary attacks specifically, rather than other measures of conflict), increase more in places near bases. The account we put forward is that this occurs because the Colombian military funnels a share of its resources to paramilitary groups. Thus, if US military aid translates into more resources for the military which are shared with paramilitary groups, the implication is that in the absence of increases in US military aid, paramilitary attacks would not have increased by as much as they did.

Now the alternative account you put forward is "more conflict, more paramilitary attacks, also more U.S. aid which actually serves to stop the situation from getting worse. The argument is, yes, the U.S. is giving weapons to the bad guys, but by doing so, it co-opts them and restrains their behavior."

It seems like you have two distinct things in mind, that overall conflict is a source of bias, and an associated conjecture that this omitted variable (overall conflict) upward biases our main coefficient since it is positively correlated with paramilitary attacks and positively correlated with the aid shock. First, we explicitly address and rule out potential omitted variables using a number of empirical specifications. But, even if there is an omitted variable correlated with U.S. military aid that differentially affects paramilitary attacks in base municipalities, it is not clear whether the direction of the bias would be positive. As an example, say a change in Colombian government leads the state to become more effective in fighting the guerilla insurgency, and the US rewards the state with more military aid, while paramilitary activity declines differentially in base regions, as this activity becomes less necessary with greater military effectiveness. In this case, the omitted variable (stronger Colombian state) is negatively correlated with paramilitary attacks and positively correlated with the aid shock, and this would lead us to underestimate the true effect of U.S. aid on paramilitary activity.

Moreover, we think we do a good job ruling "conflict in general" at the national, state, or municipality level as a confounding variable. "Overall conflict" variation at the country level is absorbed by year fixed effects, and conflict at the department level is absorbed by the department x year fixed effects. At the municipal level, it is NOT the case that we observe increases in overall conflict, such as total number of clashes amongst all armed actors at the municipal level. (In out data, attacks are one-sided events carried out by a particular group. The fact that we see paramilitary attacks increase means we are specifically observing increases in events that involve only paramilitary groups - e,g, the paramilitaries attack a village or destroy some type of infrastructure. ) Also, in every specification we find no effect on the guerrilla attacks, and we think you are not taking the non-effect sufficiently seriously in terms of countering the overall conflict account. The guerilla non-effect actually provides very robust evidence that the U.S. military aid is not just correlated with any type of conflict, but rather with attacks by a particular group (which has no regional spillovers).

In addition, our base-specific linear trend and post-2001 dummy specification should convince you that our effect is not merely a post-2001 increase in conflict that manifests particularly as paramilitary attacks in base municipalities.

Your alternative account suggests that more aid to paramilitary organizations could actually result in less violence. While it is challenging to know what the counterfactual would have been in the absence of increased aid, Figure 2 shows that when aid rises sharply in 1999 there is a differential increase in aid in the base regions, and when aid decreases in 2001, there is a corresponding closing of differential decrease in the base regions. This seems inconsistent with the idea that lower aid translates into more paramilitary activity. Also, after 2002, when aid rises again, the differential increases yet another time. It is difficult to explain this pattern with the account you put forward, which would have to require additional coincidental reasons why paramilitary attacks should increase more in base regions precisely in 1999, then decline in 2001, and then rise again in 2002. This is possible, but seems unlikely.

We were thinking of some ideas that would be consistent with your alternative account, of why more aid to paramilitary organizations could actually lower violence. One story here could be deterrence - that stronger paramilitaries deter the guerillas resulting in fewer attacks by guerillas or fewer clashes between guerillas and paramilitaries. But, our results do not show a fall in guerilla attacks or clashes amongst the two groups; rather the coefficient on these other variables is close to 0 and they are statistically insignificant, which is inconsistent with the deterrence account.

Another reason could be dependence, that in the short run U.S. aid increases paramilitary violence, but it also induces paramilitary reliance on the Colombian military for supplies, which increases the sway the government has vis-à-vis this group, potentially leading to future demobilization. Thus in the long-run, U.S. military aid reduces paramilitary violence. While this process could take "long and variable lags" to manifest, it is important to note that we see a dramatic increase in paramilitary activity in 2005, despite a half-decade of huge U.S. military transfers to Colombia. Thus we do not see evidence of this dependence account in our data.

I enjoy reading the Freakonomics blog, but as I've noted previously, I remain puzzled by the presence of two appealing but, to my mind, incompatible forms of reasoning that seem to be used more generally in the world of "freakonomics" (which I'm using in lower-case to indicate not just the famous book and blog, but the larger world of empirical microeconomic analyses intended for a popular audience).

Carlisle Rainey writes:

In an earlier blog post, you suggest: "...do a global search-and-replace to change 'DV' to 'outcome' and to change 'OLS' to 'linear regression'." Would you provide a quick explanation why or point me somewhere to find the answer myself?

My reply:

1. I don't like the term "dependent variable" because of confusion with dependence of random variables. To me, "outcome" makes it clearer that you are choosing which variables to use as predictors and which as outcome. "Predictee" would be ok too, I guess.

2. "OLS" focuses on the optimization task; "linear regression" focuses on the model. I think the model is more important that how it's estimated. To put it another way, "OLS" generalizes to weighted least squares, least absolute deviation, etc. "Linear regression" generalizes to logistic regression, nonlinear regression, etc. I find the latter set of generalizations more important and interesting.

Ole Rogeberg writes:

Saw your comments on rational addiction - thought you might like to know that some economists think the "theory" is pretty silly as well. It's worse than you think: They assume people smoke cigarettes, shoot up heroin etc. at increasing rates because they've planned out their future consumption paths and found that to be the optimal way to adjust their "addiction stocks" in the way maximizing discounted, lifetime utility. To quote Becker and Murphy's original article: "[I]n our model, both present and future behavior are part of a consistent, maximizing plan."

Yeah, right

Here's Ole's article, "Taking Absurd Theories Seriously: Economics and the Case of Rational Addiction Theories," which begins:

Rational addiction theories illustrate how absurd choice theories in economics get taken seriously as possibly true explanations and tools for welfare analysis despite being poorly interpreted, empirically unfalsifiable, and based on wildly inaccurate assumptions selectively justified by ad-hoc stories. The lack of transparency introduced by poorly anchored mathematical models, the psychological persuasiveness of stories, and the way the profession neglects relevant issues are suggested as explanations for how what we perhaps should see as displays of technical skill and ingenuity are allowed to blur the lines between science and games.

I agree, and I'd also add that this problem isn't unique to economics. Political science and statistics also have lots of silly models that seem to have a life of their own.

I just assumed they already were doing this. Did they really used to charge the same price for flights on every day of the year? That would be silly, no? It doesn't make sense to me for people to be angry about differential pricing.

Comments on the linked blog suggest that the problem is a lack of information in the communication of ticket prices. Consumers (such as myself) don't really have any idea what a ticket will cost--we either have to just buy something blind or else do informal statistical inference by running a lot of queries on Expedia or whatever. As a result of this ignorance, airlines have an incentive to advertise super-low fares, which then leads to surcharges etc. What a mess.

On the other hand, I never feel comfortable complaining about airport/airline experiences. I fly a lot and as a result am a big polluter. So, really, anything that makes flying more of a pain in the ass is probably a net benefit to the world.

Fivethirtyeight commenter TGGP links to a news article about zillionaire financier Peter Theil, who "predicted which firms would be bailed out based on whether they leaned Republican or Democratic." In the words of reporter Peter Robinson, Theil "possesses a preternatural ability to spot patterns that others miss."

I'll repeat a bunch of Theil's reasoning, because on one level if's interesting while on another level I find it hard to take completely seriously as it stands..

Chris Blattman reports on a study by Seema Jayachandran and Ilyana Kuziemko that makes the following argument:

Medical research indicates that breastfeeding suppresses post-natal fertility. We [Jayachandran and Kuziemko] model the implications for breastfeeding decisions and test the model's predictions using survey data from India. . . . mothers with no or few sons want to conceive again and thus limit their breastfeeding. . . . Because breastfeeding protects against water- and food-borne disease, our model also makes predictions regarding health outcomes. We find that child-mortality patterns mirror those of breastfeeding with respect to gender and its interactions with birth order and ideal family size. Our results suggest that the gender gap in breastfeeding explains 14 percent of excess female child mortality in India, or about 22,000 "missing girls" each year.

Interesting. I wonder what Monica Das Gupta would say about this study--she seems to be the expert in this area.

Huh?

The only thing that really puzzles me about Jayachandran and Kuziemko's article is that, on one hand, they produce an estimate of 14%, but on the other, they write:

In contrast to conventional explanations, excess female mortality due to differential breastfeeding is largely an unintended consequence of parents' desire to have more sons rather than an explicit decision to allocate fewer resources to daughters.

But they just said their explanation only explains 14%. Doesn't that suggest that the other 86% arises from infanticide and other "explicit decisions"? The difference between "14%" and "largely" is so big that I think I must be missing something here. Perhaps someone can explain? Thanks.

Sheril Kirshenbaum links to this graph from economists Kasey Buckles and Daniel Hungerman showing differences in who conceives babies in the fall (older, better-educated people) and the spring (younger, less well-educated people):

Pretty stunning. And a nice graph. The repeating pattern over the years is super-clear. I'd also like to see a version that just shows the averages for the 12 months, so I could see the pattern in more detail. Also I'd like to subtract 40 weeks so it shows the data by (approximate) month/date of conception.

P.S. This news article by Justin Lahart is excellent. But I did notice one funny thing (to a statistician):

The two economists examined birth-certificate data from the Centers for Disease Control and Prevention for 52 million children born between 1989 and 2001 . . . 13.2% of January births were to teen mothers, compared with 12% in May--a small but statistically significant difference, they say.

Well, yeah, with n=52,000,000, I'd think that a 1 percentage point difference would be statistically significant! More seriously, with that many cases, it sounds like the next step (if the researcher haven't already done this) is to break things down by subgroups of the population. I wonder what data are available from the birth certificate records. To start with, there's geographic information.

Mark Thoma links to this article by Bill Easterly about the history of economic development in the mid-twentieth century. Easterly writes:

Why does this history matter today . . . I [Easterly] do NOT mean to imply guilt by association for development as imperialist and racist; there are many theories of development and many who work on development (including many from developing countries themselves) that have nothing to do with imperialism and racism.

But I [Easterly] think the origin of development as cover for imperialism and racism did have toxic legacies for some. First, it meant that the concept of development was determined to fit a propaganda imperative; it was NOT a breakthrough in thought by economists. Second, it followed that development from the beginning would stress the central role of Western aid to help the helpless natives. . . And this history also seems strangely relevant with today's "humanitarian" nouveau-imperialism to invade and fix "failed states" like Iraq and Afghanistan.

I defer to Easterly both on the history and the economics of international development, but I do have one criticism of his argument. It is my impression that a lot of ideas in economic development are not just about the interaction between "first world" and "third world" countries (Easterly's focus) but also relate to struggles within individual third world countries. In some countries, the international development people were opposing white elites. This doesn't mean that either side was necessarily correct in its economic assumptions, but it seems a bit extreme to think of economic development experts as supporting white superiority.

Of all the first-world institutions that were influencing poorer countries during those times fifty years ago, I'd think that the international development community was one of the less racist.

I received the following in my inbox, from emily@nextadvance.com:

[Have to leave office before 4 to pick up kids] + [No internet yet at home] = [Can't read email]

This note by Robin Hanson (in which he expresses his irritation with state highway departments that leave cones on the highways too long, thus unnecessarily restricting traffic lanes) reminded me of an idea I had when I moved to Berkeley, California, many years ago. I lived on a residential street that was only a few blocks long. But boy was it wide. Really wide. Here's a recent picture from Google maps:

My thought was: why not narrow the street by about 50% and give the extra land to the owners of the property? The lots are pretty small there and property values are high--higher now than in 1990, I'm sure. So it's basically free money. As a renter, I didn't think too much about this, but I really don't see why nobody's done this. They don't even have to do the whole city, they could do it one street at a time.

John Sides and Joshua Tucker link to a news article by Jeremy Peters that reports that former New York State governor Eliot Spitzer is teaching a course at the City University of New York for "$98.43 an hour, or about $4,500 for the semester." This comes out to 45 or 46 hours--let's say 3 classroom hours a week for 15 weeks.

I noticed a few interesting things in the article.

1. I think it's ridiculous to consider $4500 for a course to be a rate of $98/hour. Teaching isn't just lecturing. You also have to prepare the classes, meet with students, write exams, and grade homeworks. $98/hour sounds like a lot, but it's based on a low denominator.

(There are exceptions, though. I know of a professor who paid the T.A. $100 per lecture to teach the class when the prof was out of town. It happened several times during the semester.)

2. I thought it was interesting that the commenters identified in the news article seemed to think that $4500 was a high rate of pay. I mean, suppose you teach 8 courses a year at $4500 each. That's $36,000. Hardly Richie Rich territory. This point is made at the very end of the article ("The point is not that Spitzer is paid too much, but rather that most adjuncts are paid too little") but it didn't really come through at first.

3. Sides writes that "This isn't pretty." I don't see what's so bad about Spitzer teaching a class. He knows a lot about politics and would seem to be well qualified to be an adjunct professor. I thought that was the ideal, to have adjuncts who are working professionals who take time off to teach a class.

Pete Lindstrom writes:

I was wondering if you could blog on the points discussed in the WSJ at this link. Apparently, there is a controversy over ways to use clinical data to calculate risks - one method adjusting for time and another using absolute numbers for the entire length of the study.

My (wholly inadequate) reply: This is interesting, but I have to say, I find the article pretty confusing. It's written in the standard journalistic style of going forward and backward in time, rather than in the scientific-journal style of presenting the data and models all in one place. If this was something I had to do, I'd puzzle through what's happening here. Luckily for me, I'm blogging just for fun and so I'll just let the question sit for others to worry about.

I get so irritated when economists and political scientists try to explain every sort of irrational behavior in life as being part of some utility function.

That's one reason I love this paper by Erik Snowberg and Justin Wolfers, "Explaining the Favorite-Longshot Bias: Is it Risk-Love or Misperceptions." They conclude that, yes, it's misperceptions:

People keep asking me about this, so let me explain . . . "4pm" refers to local time, wherever I happen to be.

Tyler Cowen links to an article by economist Ed Glaeser on urban political activists Jane Jacobs and Robert Moses. Moses, who ran various NYC government commissions in the mid-twentieth century, is famous for organizing the construction of bridges and structuring the financing so that he controlled the flow of money from the tolls. This independent source of funding gave him a huge amount of power within the government to do almost whatever he wanted--for awhile, until Jacobs and others mustered the popular support to stop him. Given my experiences at Columbia University, I can appreciate Moses's bureaucratic acumen: in any organization I've been involved in, there aren't so many sources of free money--that is, funds that haven't already been allocated to some expense. Free money is a source of power. I imagine this is true within corporations as well.

That's all tactics, though. What's relevant for Glaeser's article is what Robert Moses did with his money and power, which was to build some highways and attempt to build others that, on the plus side, would make it faster for people to go through New York City on the way to or from other places and, on the minus side, would destroy some neighborhoods and make many of the un-destroyed neighborhoods less pleasant to be in (by being next to a highway, disconnected from the rest of the city, etc).

What about the specifics? Glaeser agrees that Moses's proposed lower Manhattan expressway was a bad idea, as was his highway that destroyed a neighborhood in the Bronx. On the plus side, Glaeser supports Moses's parks and swimming pools and describes his roads and bridges as "not all bad."

One thing that interests me about Glaeser's discussion is that, implicitly, there are two levels of liberal-conservative dispute here.

Lucian Bebchuk writes:

Financial firms seeking to retain talent are reported to be making substantial use of guaranteed bonuses, and the French Economy Minister recently called for limiting such bonuses. While many now focus on how guaranteed bonuses affect the level of pay, my [Bebchuk's] piece focuses on their effect on incentives. I show that guaranteed bonuses create perverse incentives to take excessive risks, and consequently could well be worse for incentives than straight salary. . . .

The above discussion has implications that go beyond the question of guaranteed bonuses. It's now well recognized that bonus plans based on short-term results which may turn out to be illusory can produce excessive risk-taking, and that plans should therefore be structured to account for the time horizon of risks. But even though tying bonus plans to long-term results is desirable, it isn't sufficient to avoid excessive incentives to take risks. Bonus plans tied to long-term results can still produce such incentives if they reward executives for the upside produced by their choices but insulate them from a significant part of the downside. Bonus plans that provide executives with such insulation from downsides - either by establishing a guaranteed floor or otherwise - can seriously backfire. . . .

I can see why the bankers want such incentives--as a tenured professor, I can see the appeal of a system with a floor but no ceiling--but Bebchuk makes a convincing argument that the incentives aren't good. So maybe it's just as well that professors don't get fat bonuses as part of their compensation packages.

Matt Ginsberg writes:

I saw your mention on 538.com [see also this article and this with Edlin and Kaplan]; a long time ago (80's), I [Ginsberg] wrote an article with Mike Genesereth and Jeff Rosenschein about rationality for automated agents in collaborative environments. The punch line, which probably bears on this issue as well, is that the strategy, "Act in such a way that if all the other agents were designed identically, we'd do optimally" is provably a Pareto-optimal way to design such agents. It's a nice result: handles the prisoner's dilemma, why you should vote, throw yourself on the grenade, etc.

Ginsberg's papers on the topic are here and here. I like the idea of framing the problem in terms of designing intelligent agents. This bypasses some of the normative vs. descriptive issues that cloud the analysis of rationality in human behavior.

I received the following question in the mail:

Benjamin Kay writes:

I just finished the Stata Journal article you wrote. In it I found the following quote: "On the other hand, I think there is a big gap in practice when there is no discussion of how to set up the model, an implicit assumption that variables are just dumped raw into the regression."

I saw James Heckman (famous econometrician and labor economist) speak on Friday, and he mentioned that using test scores in many kinds of regressions is problematic, because the assignment of a score is somewhat arbitrary even if the order was not. He suggested that positive, monotonic transformations scores contain the same information and lead to different standard errors if in your words one just "dumped into the regression". It was somewhat of a throw away remark, but considering it longer, I imagine he mans that a difference of test scores need have no constant effect. The remedy he suggested was to recalibrate exam scores such that they have some objective meaning. For example, a mechanics exam scored between one and a hundred, one can pass (65) only if they successfully rebuild the engine in the time allotted, but better scores indicate higher quality or faster speed. In this example one might change it to a binary variable to passing or not, an objective testing of a set of competencies. However, doing that clearly throws away information.

Do you or the readers of Statistical Modeling, Causal Inference, and Social Science blog have any advice here? The transformation of the variable is problematic and the critique of transformations on using it raw seems a serious one, but the act of narrowly mapping it onto a set of objective discrete skills seems to destroy lots of information. Percentile ranks on exams might be a substitute for the raw scores in many cases, but introduces other problems like in comparisons between groups.

My reply: Heckman's suggestion sounds like it would be good in some cases but it wouldn't work for something like the SAT which is essentially a continuous measure. In other cases, such as estimated ideal point measures for congressmembers, it can make sense to break a single continuous ideal-point measure into two variables: political party (a binary variable: Dem or Rep) and the ideology score. This gives you the benefits of discretization without the loss of information.

In chapter 4 of ARM we give a bunch of examples of transformations, sometimes on single variables, sometimes combining variables, sometimes breaking up a variable into parts. A lot of information is coded in how you represent a regression function, and it's criminal to just take the data as they appear in the Stata file and just dump them in raw. But I have the horrible feeling that many people either feel that it's cheating to transform the variables, or that it doesn't really matter what you do to the variables, because regression (or matching, or difference-in-differences, or whatever) is a theorem-certified bit of magic.

In the aftermath of linking to my article with Aaron and Nate about the probability of your vote being decisive, Conor Clarke writes:

If your decision to vote is motivated by the sense that "one vote can make a difference," you are being substantially less rational than someone who never leaves the house for fear of being killed by a meteor. Voting is irrational.

I completely disagree with this last statement, and I know that Aaron does also. Here's we wrote on pages 4-5 of our article:

Hal Varian pointed me to this article in The Economist:

Instrumental variables help to isolate causal relationships. But they can be taken too far

"Like elaborately plumed birds...we preen and strut and display our t-values." That was Edward Leamer's uncharitable description of his profession in 1983. Mr Leamer, an economist at the University of California in Los Angeles, was frustrated by empirical economists' emphasis on measures of correlation over underlying questions of cause and effect, such as whether people who spend more years in school go on to earn more in later life. Hardly anyone, he wrote gloomily, "takes anyone else's data analyses seriously". To make his point, Mr Leamer showed how different (but apparently reasonable) choices about which variables to include in an analysis of the effect of capital punishment on murder rates could lead to the conclusion that the death penalty led to more murders, fewer murders, or had no effect at all.

In the years since, economists have focused much more explicitly on improving the analysis of cause and effect, giving rise to what Guido Imbens of Harvard University calls "the causal literature". The techniques at the heart of this literature--in particular, the use of so-called "instrumental variables"--have yielded insights into everything from the link between abortion and crime to the economic return from education. But these methods are themselves now coming under attack.

I don't really think this one is of general interest so I'll put it all below the jump . . .

I published an article in the Stata Journal even though I don't know how to use Stata.

I thought this was funny. I'm not sure if Mankiw is making a joke about what Ken Rogoff thinks is a "dystopia" or whether he's making a more general joke about how economists think, but either way I was amused.

(I have no opinion one way or another on the economic analysis. I just thought it was a funny use of the term "dystopia," which I usually associate more with Mad Max than with inflation or tax increases. Actually, I thought some economists thought that a bit of inflation was a good thing?)

Christopher Beam's recent news article on qalys includes this amazing quote:

QALYs also assume that a year lived by an 80-year-old is worth less than one lived by a 20-year-old. But that's not accurate, says Dana Goldman of the RAND Corp. "It's not taking into account hope, not taking into account the chance of living to see your daughter's wedding, it's not getting at the extra value we put on the end of life." Yes, the U.S. health care system has to rein in costs, says Goldman, but "QALY is not ready for prime time."

Maybe this guy is being taken out of context, but . . . "the chance of living to see your daughter's wedding"??? There's always individual variation; that doesn't mean you can't try to capture averages.

Aaron Edlin just sent me this article by Pinar Karaca-Mandic and himself from 2006:

We [Edlin and Karaca-Mandic] estimate auto accident externalities (more specifically insurance externalities) using panel data on state-average insurance premiums and loss costs. Externalities appear to be substantial in traffic-dense states: in California, for example, we find that the increase in traffic density from a typical additional driver increases total statewide insurance costs of other drivers by $1,725-$3,239 per year, depending on the model. High-traffic density states have large economically and statistically significant externalities in all specifications we check. In contrast, the accident externality per driver in low-traffic states appears quite small. On balance, accident externalities are so large that a correcting Pigouvian tax could raise $66 billion annually in California alone, more than all existing California state taxes during our study period, and over $220 billion per year nationally.

Interesting stuff. I don't have it in me right now to check all these numbers, but the argument looks to be laid out clearly enough that the experts in the area can work it out. Also, it all seems to be about accidents to other cars; I'm not sure where they factor in the costs due to running over pedestrians.

Following up on our earlier discussion of the administrative costs of Medicare and private insurers, Robert Book sent me a report on Illusions of Cost Control in Public Health Care Plans, which is full of numbers and argues that "Medicare's administrative costs are a lower percentage of the total not because Medicare has cheaper administration, but because it has more expensive patients." I don't know enough to evaluate these arguments, but I like that he has a lot of numbers and graphs right out there, so that any disputes can be on specific points.

I do have one question, which probably reflects my ignorance of heath-economics terminology more than anything else. Book writes, "Claims processing is the only category that is at all sensitive to the level of health care utilization." From my personal experience with the health care system, I associate "administrative costs" with the many levels of clerks and paper-pushers you have to deal with before you get to see a doctor or nurse. I'm not quite sure how "claims processing" is defined, but I see a lot of full-time employees (as well as, I assume, some higher-paid full-time employees in some back room) who aren't doing anything health-related; they're just minding the store. And this all seems pretty much proportional to health care utilization: I assume that if people are going to the doctor twice as often, or doing more complicated procedures, there are that many extra visits, that many extra forms to fill out, etc. I've been in hospital wards at night where there is no doctor to be seen, maybe no nurse, but three or four administrative employees appear to be continously busy with something or another.

This is not intended as a criticism of Book's argument, just a thought some of these seemingly neutral terms such as "administrative costs" can be confusing.

The counterintuitive style of economic analysis is typically set up to make one of two points:

1. Some seemingly stupid thing that people do actually is rational. (For example, see the notorious "rational addiction" model.) Of course, it's gotta be rational, right? Otherwise why would people do it?

2. Some seemingly reasonable thing that people do actually is irrational. I came across a recent example of this sort of argument in a discussion of the sunk cost fallacy by Dan Reeves on Sharad's blog. Of course people are irrational, right? After all, we're bundles of flesh, not calculating machines.

Both these sorts of points are reasonable (although, I have to admit, I'm pretty skeptical both on the "rational addiction" and the "sunk cost" stories).

But what really interests me is that both sorts of arguments below are, as we say in the social sciences, "normative"; that is, they are about what we should do (in the first case, we "should" be less bothered by certain behavior that seems irrational, we should be less inclined to regulate seemingly irrational or predatory behavior, etc; in the second case, we "should" change our behaviors so as not to violate some key theoretical axiom). And both sorts of arguments make sense. But they go in the opposite direction! And I can easily imagine just about any behavior analyzed in either of these two directions. Obviously, we can analyze addiction by discussing the inconsistency of the actions of an addict; similarly, we can rationalize the sunk-cost examples by postulating more complicated goals.

As I wrote last year:

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 "freakonomics," 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.

P.S. I'm not trying to criticize economics, or economic analysis, in general. I do the stuff myself. (See, for example, this article of ours on cost-benefit tradeoffs in radon measurement and remediation). I'm just pointing out what I see as a difficulty with some of the normative arguments out there.

What with all this discussion of causal inference, I thought I'd rerun a blog entry from a couple years ago about my personal trick for understanding instrumental variables:

Robin Hanson is skeptical of my response in the following exchange:

Hanson: What do the customers who are paying your salary get from you?

Gelman: They learn how to fit multilevel models.

Greg Mankiw links to an article that illustrates the challenges of interpreting raw numbers causally. This would really be a great example for your introductory statistics or economics classes, because the article, by Robert Book, starts off by identifying a statistical error and then goes on to make a nearly identical error of its own! Fun stuff.

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:

A Quantitative 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.