Right in the Short Run

the 5 best models of drug  (incl. alcohol) consumption?

Steve Levitt writes a reply to Noam Scheiber’s hit piece on Levitt and other eminent Economists. It is a great read, here is an excerpt:

It seems to me that Scheiber is tied up in knots to the point that he no longer knows what he believes. He seems to instinctively like clever research, but feels such guilt about it that he is compelled to denounce it. The incredibly important point that he misses is that often being clever is the way one cracks an important problem. He can denigrate the questions I have made progress on tackling, but it seems to me that understanding how crime responds to punishment, why crime fell in the 1990s, why Blacks are lagging Whites so badly educationally and economically, whether firms profit maximize, whether campaign spending affects election outcomes, and whether elected officials follow the will of the electorate are pretty reasonable topics for an economist to study. Sure, my approach to each of these was different than what everyone else was doing, but the questions I have asked have (usually) had both serious policy questions and economic issues at their heart, and I delivered some answers when others were not.

Joshua Angrist also responds:

Second, in his rush to tar some up-and-comers with the “cute-o-nomics” brush, Scheiber misses a central feature of the clean-identification research agenda, best explained by example.  One of the enduring scientific and policy questions in Labor economics is the sensitivity of hours worked to changes in pay (this matters for tax policy, for example).  The best evidence labor economists have on the relation between wages and hours worked comes from a small experiment (by Ernst Fehr and Lorenz Goette) involving the wages of bicycle messengers in Switzerland.  The second best comes from a study of stadium vendors by Gerald Oettinger.  Who cares about the riders of Veloblitz or snack sellers at Camden Yards?  We care because economics is predicated on the notion that a few simple principles explain behavior in many settings.  These studies produce results that are convincing and may well be general, though, as always in science, it will take replication to know for sure.

Earlier this year Andrew Gelman and co. came up with a new argument solving the voting paradox, based on social preferences (the paradox of voting: if you are rational, you will realize that the probability of affecting the outcome of a vote is negligible, while the costs are often considerable. Yet still people vote regularly).

Social preference is basically the idea that individuals incorporate other people’s utility into their considerations.

Gelman argues that if individuals take into consideration the impact of a vote’s outcome on other individual’s in society, as well as themselves, voting may indeed be rational.

A recent experimental paper by Fisman, Kariv and Markovits shows that some people do indeed have social preferences.

Voters, congratulations.

Reading Robert Frank’s reply to Greg Mankiw’s post, I came across a really interesting passage. I’ve always wondered how we could say that a nation “prefers” to supply labor in some way or another when the options individuals face on the labor market are so dependent on the simultaneous (and competitive) actions of others. It turns out that there’s an economic concept addressing this very issue, called “context externalities”.

For present purposes, by far the most important externalities are those stemming from the link between context and evaluation. As decades of behavioral evidence clearly demonstrates, virtually every evaluation is heavily shaped by local context. As Richard Layard put it, “In a poor society a man proves to his wife that he loves her by giving her a rose, but in a rich society he must give a dozen roses.” Because evaluation drives consumer choice, context is an important determinant of consumer demand. The upshot is that almost every consumer choice generates significant context externalities.
Consider, for example, a job applicant’s decision about how much to spend on an interview suit. His goal is to make a favorable impression. But his ability to do so depends far less on the absolute quality of his suit than on how it compares with those worn by other applicants. And when he spends more on a suit, he shifts the context within which other candidates will be evaluated.

Context externalities are pervasive. A good school, for instance, is one that compares favorably with other schools in the same local environment. The amount parents must spend to ensure that their children attend such a school is thus an increasing function of the amounts spent by other parents. The evaluations that guide an employer’s promotion decisions are similarly dependent on context. A worker’s odds of promotion depend less on his absolute performance than on how well he performs relative to his coworkers.

The dependence of evaluation on context lays waste to any presumption that individual decisions about how many hours to work or how much to spend on interview suits will be socially optimal. The general result predicted by theory is that if context shapes evaluation more heavily in some domains than others, too many resources will flow to the most context-sensitive domains and too few to the least context-sensitive domains. In my forthcoming book, Falling Behind, I summarize what available evidence says about the extent to which context differs across domains. For the discussion at hand, the relevant finding is that evaluations of leisure tend to be far less context-sensitive than evaluations of income. The implication is that individual valuations of leisure tend to understate social valuations. Thus people work longer hours in the hope of moving higher on the income ladder, only to discover that when others do likewise, their position remains unchanged.

Just wow!

Things to read up on:

1. Relative utility functions. Let’s go find the literature on u_1(x_1) = f_1(x_1,x_-1) where x_-1 = vector of others’ wealth or consumption vectors.

2. Addiction literature. Under what conditions does it make sense to describe drug consumption in the expected utility framework?

3. Causal inference tools employed in labor economics. See this text by Angrist (MIT) and Krueger (Princeton).

Over a year ago, over at Marginal Revolution, Tyler Cowen asked the question, “How quickly should I go through my stock of Battlestar Galactica?”

He argues:

The Hotelling rule tells us to consume a stock so the shadow value rises at a rate commensurable with the rate of interest…or something like that. C’mon, let ’s get real. Here are a few options:

1. Set aside one day for a BSG fest. I would lose the pleasures of anticipation, so no way. (Would you want all non-currents-events-specific MR posts available all at once?) The pleasures of memory would be weaker as well.

2. Have a strict rule, such as one a day.

3. Have a stranger impose a rationing pattern. Sometimes we call this stranger the Science Fiction Channel. But what about the accumulated stock of programs on DVD?

4. Watch it when your wife lets you (not an issue for those that have married well).

5. Refuse to watch the last episode, in an attempt to deny your mortality.

6. Watch them at an increasing rate.

#3 is appealing, but so far I am opting for #6. Comments are open, if you wish to rationalize what you already have done.

Addendum: This question will become more important.

Typically, when I read a good book or watch a good film, I tend to go through it all at once. For one thing, I don’t really enjoy the “anticipation”. I just want to read the damn thing. Moreover, I’m perfectly happy to reuse these goods - I re-read and re-watch things all the time. The benefit of watching/reading things straight through is that it allows me to immerse myself in a fantasy world continuously for a whole day… so I vote #1 …

Lots of value there …

This is a response to chandrasekhar’s post, Cuteness.

There is plenty of material to criticize in Noam Scheiber’s recent TNR article, but one aspect of it annoyed me in particular. Scheiber claims that economics had a mid-life crisis in the 1980’s, and in response economists started thinking in an entirely different way…

By the ’80s, however, the data-crunchers had come down with a crisis f confidence. In one famous episode, the eminent economist H. Gregg Lewis reviewed several studies on unions. What he found was alarming: some papers reported that unions strongly increased wages; others reported exactly the opposite. The difference, in most cases, was simply the assumptions the authors had made.

Critiques like this tipped the discipline into a prolonged bout of soul-searching. The old approach had been sweeping in its ambition. But what good were ambitious goals if the best you could do was “on the one hand/on the other hand”style equivocation or, worse, plain jibberish? “People didn’t believe the estimates being produced,” recalls David Card, then a rising star at Princeton. “They felt the evidence in economics was not very credible.” Economists had long aspired to science. Suddenly they faced a harrowing thought: What if they were no better at pinning down truth than the average critical
studies major?

Yes, this all happened in the 1980’s. In the article, the time period has no relation to the change in methodology — it was the general sense of disappointment in the old ways that made people start looking for “clean ID”:

Having glimpsed this nihilistic vision, many economists ran screaming in the opposite direction. They concluded that the path to knowledge lay in solid answers to modest questions. Henceforth, the emphasis would be on “clean identification,” on sorting out what caused what.

The early practitioners of this approach–Angrist, Krueger, Card–had well-earned reputations as crafty researchers. But, by and large, all three men used their creativity to chip away at important questions. It was only in the late ’90s that the signs of overreach became apparent. To some professors at top departments, clean identification became a fetish. “Almost every student, myself included, had the terrible experience of getting up in front of the [professors] for whom identification is the Holy Grail, and getting cut to shreds when your identification strategy doesn’t pass muster,” recalls a recent Harvard Ph.D.

Actually, it’s a good thing that grad students fear getting cut to shreds if they have a bad identification stategy. When your results depend on your natural experiment being an actual natural experiment, establishing causality rests on good identification. Duh! Why should I bother trusting the conclusion of a paper with bad ID?

The problem is that there are only so many big questions that misgraded tests or arbitrary boundaries can shed light on. If you’re wedded to these techniques, eventually they lead you in obscure directions. “People think about the question less than the method,” says Berkeley professor Raj Chetty, one of the most sought-after Harvard graduates in recent years (and a notable exception to this trend). “They’re not thinking: What important question should I answer?’ So you get weird papers, like sanitation facilities in Native American reservations.”

I suspect Chetty did not intend for his criticism to have as far reaching implications for the discipline as Scheiber claims it does. But that’s beside the point. My sense is that Scheiber has completely brushed past a major cause of the change in the style of Economic research over the past few decades: computers.

After all, how likely is it that most economists would suddenly come to the same realization and change their methods in the same way? Sounds like a coordination problem! The increase in computing power and the ubiquity of the PC do a lot to explain these changes without the psychoanalysis.

PC’s everywhere mean data everywhere. It has become much simpler to keep records, and as a result governments, NGO’s, businesses, and people record tons of information electronically. Remember the Freakonomics bit about the average member of crack gangs making less than a McDonald’s worker? Legend has it that Sudhir Venkatesh, a PhD sociology student at U Chicago, obtained the data for the paper by infiltrating himself into a crack gang and winning over the leader. The gang ended up giving him an Excel spreadsheet file with their finances. Computers allowed the crack gang to easily keep records; these records were easily transferable and already in a format that was ideal for regression analysis.* (Correction at the end)

The processing power of computers has also increased substantially, doubling about every 18 months (It’s called Moore’s Law). In a decade, then, computers become about 100 times faster! If you’re thinking that the instrumental variables regressions that economists use for this “clean ID” stuff probably require lots of mathematical calculations, taking millions of clock cycles, then we’re on the same page.

I’ve worked with some of the data that Scheiber derides as “cute”, and it ends up being huge — tens of thousands of observations spanning several-hundred megabyte files. Even if you could find a hard drive big enough to hold that data back in 1985, and even if you could find enough memory to hold the matrix in temporary storage, no desktop computer from that decade could invert the matrix in a reasonable amount of time.

Yes, there may be other reasons that Economics has embraced the instrumental variables regression, with its holy grail of clean identification. But let’s acknowledge the advance in technology that occurred alongside the growth of these methods, and in my view, is at least partly responsible for the state of the discipline today.

* Oops! This is totally wrong. The gang’s books were physical books! A better example would be the paper on the parking tickets of UN diplomats. There were millions of dollars worth of fines and they were all tracked digitally by the New York City government. In a time without computers, it’s hard to imagine the government keeping such meticulous records or making them so easily available.

Mark Thoma has another extraordinary post at Economist’s View, this time taking on Bruce Bartlett’s claim that “hardly any economist believes what the Keynesians believed in the 1970s and most accept the basic ideas of supply-side economics.”

If you are looking for a quick primer on the two main schools of thought in Macroeconomics, I cannot recommend Professor Thoma’s post enough. Just scroll past the quoting of Bruce Bartlett’s article and look for the pretty charts. Some excerpts:

Real Business Cycle (RBC) theorists believe that most if not all fluctuations in the economy are due to supply side shocks, aggregate demand shocks such as changes in the money supply, changes in taxes, and changes in government spending affect nominal variables such as prices but have little to do with changes in output over time (however, government intervention does causes inefficiencies in these models so that less intervention is generally preferred to more). …

New Keynesians (NK) do not deny that shocks to aggregate supply can affect GDP nor that supply shocks can be large and important. However, New Keynesians also believe that aggregate demand shocks are important…

New Keynesians attempt to stabilize actual output around the natural rate as shown above. Why does NK policy tend to focus on demand shocks rather than supply shocks? The answer is that although it would be ideal if we could use supply-side polices to smooth short-run fluctuations in output arising from supply shocks, the reality is that we cannot do this. As Bartlett notes, supply-side polices are very blunt, slow-acting policies that can affect output in the long-run, but they are all but useless in dealing with short-run fluctuations in the economy (thus, RBC theorists tend to focus mainly long-run growth).

Since supply cannot be managed in the short-run, that leaves demand management policies, i.e. monetary and fiscal policy. …

For those who are already familiar with the tenets of New Keynesian and Real Business Cycle models, the post includes some political insight that’s also worth checking out.

Why do Republicans tend to endorse the RBC (real business cycle) framework? I believe in many cases that belief in the RBC model arises from an honest view that the evidence is most supportive of this class of models. But in other cases I believe it is an ideological marriage. The RBC model has two features that make it attractive.

First, because it says short-run stabilization policy is ineffective, and that government intervention through either spending or taxes generates economic distortions, the RBC framework supports an approach where the role of government in the economy is minimized.

Second, because the RBC framework allows for tax cuts to produce higher growth by reducing inefficiencies, and because it is then possible to argue that tax revenues might increase, it gives two reasons for supporting tax cuts - higher growth and less than a full loss of tax revenue, i.e. a dollar tax cut does not cost a dollar (or, for serious ideologues, the tax-cuts even pay for themselves).

The NK model, on the other hand, supports active government intervention which is at odds with this ideology. In addition, because the focus in NK models is on stabilization of output around the natural rate, not on growth of the natural rate, tax-cuts do not have the dynamic long-run effects as in RBC models (though these can be added) and hence there is not as much ideological support for tax cuts in the NK framework.

Check it out, and read through the comments to catch a glimpse of Paul Krugman butting heads with Bruce Bartlett.

I never cease to be amazed at the power of economics in providing a language to discuss social phenomena. Take for instance, Flavio Cunha and James Heckman’s “The Technology of Skill Formation”. Abstract below:

This paper uses a simple economic model of skill formation to organize this and other evidence summarized below and the findings of related literatures in psychology, education and neuroscience. The existing theoretical literature on child development in economics treats childhood as a single period (see, e.g., Becker and Nigel Tomes, 1986; S. Rao Aiyagari et al., 2002; Roland Benabou, 2002). The implicit assumption in this approach is that inputs into the production of skills at different stages of childhood are perfect substitutes. We argue that to account for a large body of evidence, it is important to build a model of skill formation with multiple stages of childhood, where inputs at different stages are complements and where there is self-productivity of investment. In addition, in order to rationalize the evidence, it is important to recognize three distinct credit constraints operating on the family and its children. (i) The inability of a child to choose its parents. This is the fundamental constraint imposed by the accident of birth. (ii) The inability of parents to borrow against their children’s future income to finance investments in them. (iii) The inability of parents to borrow against their own income to finance investments in their children. This paper summarizes findings from the recent literature on child development and presents a model that explains them.

Thoughts?

There is some pretty interesting research being done in Economics & Psychology over at NYU. Economic theorist Andrew Caplin and Mark Dean are working on an axiomatic approach to neuroeconomics, together with the help of Paul Glimcher.

Reinforcement learning theory has produced important insights into economic behavior. Intriguingly, neuroscientists recently discovered a plausible mechanism through which reinforcement may be encoded in the brain. Yet their resulting “dopaminergic reward prediction error” hypothesis has not yet been incorporated into economics. We develop an axiomatic model that characterizes the empirical implications of this theory for an idealized data set comprising both neuroscientific measurements and choices. Our axiomatization removes the language barrier between economics and neuroscience. This will allow “neuroeconomic” experimental protocols to be developed appropriate to the questions motivating economic, as opposed to purely neuroscientific, interest in learning.

In the words of a good friend, “it’s really strange to see ‘dopamine’ and ‘metric space’ in the same sentence”.

Whether integrating experimental psychology and brain imaging results into economic theory will have practical value remains to be seen. However, in my limited comprehension of the field, it seems ripe with potential. For instance, neuroscience results on inter-temporal discounting and status-quo bias in decision-making could be translated into the language of decision theory. By axiomatizing some of the experimental results into the economic theory, models may better articulate and predict consumer behavior.