1. Emily Oster and Rebecca Thorton have an interesting new paper that uses individual-level randomization to understand, among other things, how peers affect a woman's decision to utilize newly introduced menstrual cups in Nepal.
2. Enrico Moretti looks at the importance of social learning from peers in consumption decisions - particularly the decision to see different movies. I really like this paper: Moretti starts with a theoretical model and uses the uniqueness of the film industry to test it. It's great stuff. And he goes on to find that social learning is non-trivial:
Overall, social learning appears to be an important determinant of sales in the movie industry, accounting for 32% of sales for the typical movie with positive surprise. This implies the existence of a large “social multiplier” such that the elasticity of aggregate demand to movie quality is larger than the elasticity of individual demand to movie quality.
3. Behavioral economics strikes again! Apparently a good way to save money is to carry around Benjamins over Abes and Georges.
4. The Economist is right on about the decision to move the Indian Premier League cricket matches to South Africa because of the upcoming election in India. What kind of aspiring superpower justifies moving a thriving capitalist enterprise by claiming that they cannot guarantee the safety of the players and spectators? Isn't this exactly what terrorists want to happen?
Monday, March 30, 2009
Thursday, March 26, 2009
Is the Row Over AIG Bonuses Getting Ridiculous?
Yes.
Certainly, handing out a bonus package running in the hundreds of millions of dollars during a recession seems like poor form. Especially so when the firm involved played a big role in bringing the house of cards down. However, the public vitriol over this mess has taken on a disturbing character.
A recent open resignation letter by a former AIG VP printed in the New York Times does a pretty good job of laying out the argument. Basically:
(1) Salaries at AIG are low and most people make money through bonuses.
(2) The people responsible for the failure of AIG are no longer working there. The contended bonuses weren't meant to be given out to people in unrelated divisions doing unrelated things.
(3) The bonuses were part of a contractual obligation to get good workers to stay on during tough times. But more fundamentally, the bonuses were part of a compensation package promised to employees before AIG became the demon.
My beef with the whole row hinges on (3). It's ridiculous for people to demand the bonuses to be paid back (or to try and tax these at the rate of 90% or something like this). Nobody should be able to meddle with contracts retroactively. This is because this kind of activity could discourage people from generating real wealth during these tough times: why would anyone try to make money in this climate if they believe they are going to be demonized and that the government will try to take their money away. The bonuses row could serve as a huge disincentive for undertaking the kind of economic activities that we desperately need now.
Reason (2) also deserves some attention. While I don't see it as the best argument against the retroactive penalities (the whole company as a team argument), we need to think about how a few people could derail an entire financial system despite being around a majority of people who were engaged in activities that ostensibly generated real wealth. Perhaps the Geithner regulatory plan, to be announced sometime soon, will address this in a constructive way that doesn't hamper wealth creation.
Whatever the case may be, it is time to put down the pitchforks and start thinking about these issues in a more constructive (and less obviously destructive) manner.
Certainly, handing out a bonus package running in the hundreds of millions of dollars during a recession seems like poor form. Especially so when the firm involved played a big role in bringing the house of cards down. However, the public vitriol over this mess has taken on a disturbing character.
A recent open resignation letter by a former AIG VP printed in the New York Times does a pretty good job of laying out the argument. Basically:
(1) Salaries at AIG are low and most people make money through bonuses.
(2) The people responsible for the failure of AIG are no longer working there. The contended bonuses weren't meant to be given out to people in unrelated divisions doing unrelated things.
(3) The bonuses were part of a contractual obligation to get good workers to stay on during tough times. But more fundamentally, the bonuses were part of a compensation package promised to employees before AIG became the demon.
My beef with the whole row hinges on (3). It's ridiculous for people to demand the bonuses to be paid back (or to try and tax these at the rate of 90% or something like this). Nobody should be able to meddle with contracts retroactively. This is because this kind of activity could discourage people from generating real wealth during these tough times: why would anyone try to make money in this climate if they believe they are going to be demonized and that the government will try to take their money away. The bonuses row could serve as a huge disincentive for undertaking the kind of economic activities that we desperately need now.
Reason (2) also deserves some attention. While I don't see it as the best argument against the retroactive penalities (the whole company as a team argument), we need to think about how a few people could derail an entire financial system despite being around a majority of people who were engaged in activities that ostensibly generated real wealth. Perhaps the Geithner regulatory plan, to be announced sometime soon, will address this in a constructive way that doesn't hamper wealth creation.
Whatever the case may be, it is time to put down the pitchforks and start thinking about these issues in a more constructive (and less obviously destructive) manner.
Wednesday, March 18, 2009
Experiments, Natural Experiments and Learning about Development Policy - I
A while back I blogged about the Jameel Poverty Action Lab, a non-profit organization started and run by economists carrying out randomized field experiments all over the developing world. The purpose of these experiments is to build an evidence base to inform policy-making, and randomization as a tool towards this end has become quite popular of late. Proponents of randomization, now called "randomistas", argue that, as with medical clinical trials, field experiments are the "gold standard" in development policy evaluation.
But is this really so? In two recent pieces, Angus Deaton and Martin Ravallion argue that the answer is "no." One of their main arguments centers around the idea of heterogeneity in treatment effects, which basically refers to how policies do not have the same impacts for everyone. Consider an example where we are thinking about implementing some large policy and want to learn whether it might be effective. To do so, we consult data from a recent experiment in which some individuals in the sample have been randomized to receive "treatment." We then compare the treatment and control group outcomes.
Randomization of individuals to treatment gives us confidence that the results of the experiments are not biased. However, the concern is whether one can learn something useful about the policy from this experiment. In most field experiments, individuals in the treatment group are either enrolled in a program or incentivized to participate in some way. In most cases, not everyone complies, and some groups of individuals tend to be more likely to comply than others.
The important thing to note is that the program effects that are recovered from the experiment is most reflective of the returns to the group of compliers. This is referred to as a "local average treatment effect", or LATE. Here is where the problem comes in: the LATE that an experiment recovers may not always be policy relevant and, unlike the issue of limited external validity (experimental results in one setting may not apply to others), it is not clear that replications will help get around this problem. To reiterate, the benefits of the program that infer from an experiment may or may not be informative about this program on a larger scale.
Ultimately, this is problem of experiments being "atheoretical." That is, simply looking at experimental averages is not enough: we have to understand who in the treatment group actually responds to the randomization and takes up treatment and whether this group is of interest to the broader policy picture. Building this understanding brings us back to economic theory: we need a model. In this sense, the argument goes, proponents of randomization who argue that field experiments are "easy" by obviating the need for models or (strong) assumptions are badly mistaken.
I find this argument compelling. Indeed, there is a parallel literature in the "natural experiments" world that makes similar points. Ultimately, policy design and resource allocation decisions require a great deal of information, only some of which we can get from randomized experiments. Experiments that incorporate theory and heterogeneity, Deaton argues, will be good step towards making the method more useful towards policy decisions. In the next post, I will list a few examples of experimental and quasi-experimental studies that take an approach more grounded in theory.
But is this really so? In two recent pieces, Angus Deaton and Martin Ravallion argue that the answer is "no." One of their main arguments centers around the idea of heterogeneity in treatment effects, which basically refers to how policies do not have the same impacts for everyone. Consider an example where we are thinking about implementing some large policy and want to learn whether it might be effective. To do so, we consult data from a recent experiment in which some individuals in the sample have been randomized to receive "treatment." We then compare the treatment and control group outcomes.
Randomization of individuals to treatment gives us confidence that the results of the experiments are not biased. However, the concern is whether one can learn something useful about the policy from this experiment. In most field experiments, individuals in the treatment group are either enrolled in a program or incentivized to participate in some way. In most cases, not everyone complies, and some groups of individuals tend to be more likely to comply than others.
The important thing to note is that the program effects that are recovered from the experiment is most reflective of the returns to the group of compliers. This is referred to as a "local average treatment effect", or LATE. Here is where the problem comes in: the LATE that an experiment recovers may not always be policy relevant and, unlike the issue of limited external validity (experimental results in one setting may not apply to others), it is not clear that replications will help get around this problem. To reiterate, the benefits of the program that infer from an experiment may or may not be informative about this program on a larger scale.
Ultimately, this is problem of experiments being "atheoretical." That is, simply looking at experimental averages is not enough: we have to understand who in the treatment group actually responds to the randomization and takes up treatment and whether this group is of interest to the broader policy picture. Building this understanding brings us back to economic theory: we need a model. In this sense, the argument goes, proponents of randomization who argue that field experiments are "easy" by obviating the need for models or (strong) assumptions are badly mistaken.
I find this argument compelling. Indeed, there is a parallel literature in the "natural experiments" world that makes similar points. Ultimately, policy design and resource allocation decisions require a great deal of information, only some of which we can get from randomized experiments. Experiments that incorporate theory and heterogeneity, Deaton argues, will be good step towards making the method more useful towards policy decisions. In the next post, I will list a few examples of experimental and quasi-experimental studies that take an approach more grounded in theory.
Tuesday, March 3, 2009
Private Equity Firms, Orange Juice, and Other Interesting Links
1. Private equity firms spent much of the last decade throwing around large sums of money buying out companies and selling them out for profit after a series of adjustments. The high profile nature of these buyouts and the sheer amount of capital being thrown around begs the following question: what is/was it all for? In a recent working paper, Philip Leslie and Paul Oyer ask whether private equity firms "create value." Their results depressingly suggest than the answer is "no."
2. Steven Levitt has a great post on the intersection between orange juice, environmentalism, and behavioral economics.
3. Will the financial downturn mean less US money for global health? Karen Grepin reports that these outlays are safe for now.
4. Bouts of occasional stupidity are apparently very good for your development as a researcher (summary of the article here). I have yet to see any returns from this. (HT: Melanie Elliot)
2. Steven Levitt has a great post on the intersection between orange juice, environmentalism, and behavioral economics.
3. Will the financial downturn mean less US money for global health? Karen Grepin reports that these outlays are safe for now.
4. Bouts of occasional stupidity are apparently very good for your development as a researcher (summary of the article here). I have yet to see any returns from this. (HT: Melanie Elliot)
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