Right in the Short Run

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

David Laibson mentioned how if you give people randomly assigned health care, and then give them the options to change two weeks later, they tend not to.  Sendhil Mullainathan mentioned how health care providers systematically recommend the same plan no matter what medical history the caller (a senior citizen) had and no matter what cocktail of medicines (s)he was on.  Thoughts?

I do not know a lot about labor economics, but I have been playing around with this idea for quite sometime now. Can we raise the minimum wage (or institute a minimum wage) and still see an uptake (or a non-decrease) in labor? Well, the standard response seems to be “yes” via a monopsony model, which is great, but not the viewpoint I was going for.

The vantage point I wanted to consider was a little more general. What if the industry is not monopsonistic? My idea stemmed from a discussion in which Steven Levitt was explaining his research about how most firms may not be profit maximizing though this seems to be a common assumption in economics.

The reason we say that increases in wage correspond to a decline in labor uptake and a substitution towards capital is because we assume profit maximization on the part of firms. Essentially, if data (such as Card’s research) points to the fact that labor does not decrease despite these increases in wages, then the change in slope of the hyperplane did not affect its tangency point! This means that capital and labor are perfect complements since the the only way a hyperplane can still be tangent to an isoquant manifold after changing slope is if it has met the manifold at a cusp/kink. Of course, perfect complementarity between capital and labor is probably not the case. What else can be the explanation?

Well, if firms weren’t profit maximizing in the first place, then certainly this could be explained. Differential changes in wage rates would not correspond to substitution away from labor since the firm would not even be at the tangency condition in the first place! Consider a situation in which firms only seek to profit “satisfice” as opposed to maximize. Threshold satisficing could mean that firms don’t respond to increases in wages by firing en masse. (This isn’t to say that one can make large increases in wage rate and still have the firms employing everybody.)

Ultimately, my discussion here is a general comment that when we institute policies based on assumptions such as profit maximization, some of the results may be contingent on that fact. These are not general results, but are rather specific implications of our particular assumptions. Remove those assumptions (perhaps they do not actually happen in real life) and many policy implications may change. Of course, the framework I have discussed makes no claim to understanding how firms decide to target profits … we can discuss further theories about that

Any thoughts on these?

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).

Algebraic statistics: A short course by Seth Sullivant over at the Harvard Society of Fellows. Any potential in econometrics?

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.

There seems to be a lot of recent backlash against “cuteness” in economics paper. Steve Levitt and Ulrike Malmendier, in particular, have taken a lot of heat. So has Emily Oster. In his April 2nd article at The New Republic entitled “How Freakonomics is Ruining the Dismal Science”, Noam Scheiber identifies a “cleverness problem” in economics, arguing that students in the premier economics departments in the US end up playing an “academic parlor game”.

Several thoughts on the matter.

First, there seems to be an assumption that “cute” papers lack academic value. Certainly Levitt’s bagel and donut research led to his investigation about the (lack of) profit maximization by “legit” firms such as airlines. Similarly, Malmendier and DellaVigna’s research on gym memberships offer methodological insights from the perspective of psych and econ. The point is, “cute” research may allow us to learn important fundamental points about more “important” issues. As Matthew Rabin pointed out, decades ago there was short paper about used cars that was chastised for being “cute”.

Second, it isn’t obvious to me that “sexy data-set” papers cannot tackle big issues. Consider Ted Miguel’s utilization of rain as an instrument for income to analyze the relationship between income and civil unrest. Hardly a “small issue”! Now Scheiber also levies a criticism at Oster’s work about hepatitis B and missing women, citing “a snot-nosed grad student from Berkeley [who] pointed out that hepatitis B couldn’t possibly explain the missing women problem”. Fair point, I suppose (I admittedly have not read either papers), but the subject of her research obviously wasn’t a parlor game. Even if she was wrong, at least criticisms levied against her paper help strengthen the point that “Sen feared: [that] people were taking dramatic steps to avoid ending up with two girls”. Female infanticide ain’t no small issue!

In any case, people much much smarter than me have commented extensively on this article. For instance, see Marginal Revolution’s take and subsequent comments.

Once upon a time, Abhijit Banerjee (MIT), Angust Deaton (Princeton), Nora Lustig (UNDP), and Ken Rogoff (Harvard), together with a number of my favorite economists including Daron Acemoglu (MIT), Marianne Bertrand (Chicago), Peter Diamond (MIT), Esther Duflo (MIT), Michael Kremer (Harvard), and Chris Udry (Yale) got together and had a party!

What did they do at this party? Being nerdy genius academic types, they wrote a paper. Specifically, they evaluated World Bank research from 1998 to 2005. Here is an excerpt:

Bank researchers have also done extremely visible work on globalization, on aid effectiveness, and on growth and poverty In many ways they have been the leaders on these issues. But the panel had substantial criticisms of the way that this research was used to proselytize on behalf of Bank policy, often without taking a balanced view of the evidence, and without expressing appropriate skepticism. Internal research that was favorable to Bank positions was given great prominence, and unfavorable research ignored. There were similar criticisms of the Bank’s work on pensions, which produced a great deal that was useful, but where balance was lost in favor of advocacy. In these cases, we believe that there was a serious failure of the checks and balances that should separate advocacy and research.

While that passage is certainly not indicative of the overall findings of the committee, it is the juiciest. Perez Hilton would be proud.

In the first of a two-part post, we look at the reflections of NYU economist Debraj Ray about new developments in development economics, in this old paper from 2000. In the second part we will look at Abhijit Banerjee’s reflections on where development economics is headed as of 2007.

In recent years, the subject has made excellent use of economic theory, econometric methods, sociology, anthropology, political science and demography and has burgeoned into one of the liveliest areas of research in all the social sciences. And about time too: the study of economic development is probably the most challenging in all of economics, and provided we are patient about getting to “the bottom line” and the “policy implications”, it can have enormous payoffs.

The main trend I would like to try and document is a move — welcome, in my opinion — away from a traditional preoccupation with the notion of convergence. This is the basic notion that given certain parameters, say savings or fertility rates, economies inevitably move towards some steady state. If these parameters are the same across economies, then in the long run all economies converge to one another.

Ray then goes on to offer a number of theories that refute the conditional convergence hypothesis. These theories argue that “societies that are fundamentally similar in all respects might behave differently, and persistently so”.

Ray offers two reasons for his criticism of convergence theory. First, he contends that economies can exhibit multiple equilibria. “Simultaneously, such societies may display low savings rates or “cultures of corruption”, but this latter set of features cannot be related causally to the former.”

Second, Ray maintains that historical configurations may be important to development trajectories. In particular, two countries can face almost identical values of parameters relevant in growth models and yet proceed down strikingly distinct trajectories due to their differing initial conditions.

Of course what ultimately matters are the policy recommendations stemming from a theory of development. Ray’s theories promote one-time intervention policies that push the country into a new (and more desirable) equilibrium, while the old-guard convergence theories would require permanent shifts in relevant parameters.