Our main finding is that lobbyists connected to US Senators suffer an average 24% drop in generated revenue when their previous employer leaves the Senate. The decrease in revenue is out of line with pre-existing trends, it is discontinuous around the period in which the connected Senator exits Congress and it persists in the long-term. The sharp decrease in revenue is also present when we study separately a small subsample of unexpected and idiosyncratic Senator exits. Measured in terms of median revenues per ex-staffer turned lobbyist, this estimate indicates that the exit of a Senator leads to approximately a $177,000 per year fall in revenues for each affiliated lobbyist. The equivalent estimated drop for lobbyists connected to US Representatives leaving Congress is a weakly statistically signicant 10% of generated revenue.  The equivalent estimated drop forlobbyists connected to US Representatives leaving Congress is a weakly statistically signicant 10% ofgenerated revenue.We also find evidence that ex-sta ffers are more likely to leave the lobbying industry after their connected Senator or Representative exits Congress. (emphasis mine)

They also show that ex-staffers revenues has grown at a faster rate than non ex-staffers since the late 1990′s.

Here’s a graphical representation of the findings from Tyler Cowen:

It’s a nod in the direction of social science, rather than economics per se.  It’s another homage to the New Institutional Economics and also to Law and Economics.  It’s rewarding larger rather than smaller ideas, practical economics rather than abstract theory.

For those interested in a deeper discussion of her work, Henry Farrell at Crooked Timber provides an excellent overview and personal reflection on Ostrom.

As a political scientist, this is especially gratifying and I think reflective of some broader trends in social science, whereby the best insights and research culminate from the cross-fertilization of ideas from multiple disciplines within the social sciences (as well as the hard sciences).   If you look at some of the more recent winners, their work transcended the discipline of economics and had a much broader impact on the study of human behavior and social dynamics broadly.

Personally, my research focused on decision-making, signaling & reputation, and conflict.  The work of recent winners, such as Thomas Schelling (game theory), Daniel Kahneman (behavioral economics), A. Michael Spence (signaling), John Harsanyi & John Nash (game theory), and Douglass North (path-dependence, neo-institutionalism), all played a role in how I approached (and continue to approach) those issues.

Maybe a day will come when the prize is renamed the Nobel Prize for Social Science–another step towards the social sciences getting their day.

While I think his critique of the three books and of political forecasting in general is quite good, I find lacking one of his suggestions for how to improve the practice; namely, crowdsourcing. My issues does not lie with the practice of crowdsourcing, but rather the way that Tetlock describes it.

After his review of the three books (and the requisite approaches to forecasting each represents), Tetlock provides a powerful suggestion for how to improve the prediction business–crowdsourcing political forecasts:

Aggregation helps. As financial journalist James Surowiecki stressed in his insightful book The Wisdom of Crowds, if you average the predictions of many pundits, that average will typically outperform the individual predictions of the pundits from whom the averages were derived. This might sound magical, but averaging works when two fairly easily satisfied conditions are met: (1) the experts are mostly wrong, but they are wrong in different ways that tend to cancel out when you average; (2) the experts are right about some things, but they are right in partly overlapping ways that are amplified by averaging. Averaging improves the signal-to-noise ratio in a very noisy world. If you doubt this, try this demonstration. Ask several dozen of your coworkers to estimate the value of a large jar of coins. When my classes do this exercise, the average guess is closer to the truth than 80 or 90 percent of the individual guesses. From this perspective, if you want to improve your odds, you are better-off betting not on George Friedman but rather on a basket of averaged-out predictions from a broad ideological portfolio of George Friedman–style pundits. Diversification helps.

As Dan points out in his post, this suggestion potentially violates two of the necessary conditions of successful outsourcing, and that is the independence of the experts and diversity of their opinion.

Dan says it best:

One of the accusations levied against the foreign policy community is that because they only talk to and read each other, they all generate the same blinkered analysis. I’m not sure that’s true, but it would be worth conducting this experiment to see whether a Village of Pundits does a better job than a single pundit.

I would actually go farther than Dan here. The problem with approach isn’t simply that political scientists and pundits may conduct their analysis in an echo chamber (although that is definitely an issue), but rather that for the crowdsourcing of these issues to work properly you would want as diverse a crowd as possible–meaning, you would wan to include individuals from outside of political science and the political pundit community.

Outside of an effective aggregation mechanism, James Surowiecki points to three necessary conditions for successful crowdsourcing:

1. Diversity of opinion
2. Independence of those opinions
3. Decentralization (i.e. ability to lean on local knowledge)

Political Scientists and pundits do not hold a monopoly on useful insights into the world of politics. Other actors have an interest in understanding and predicting what will happen politically, including financial analysts, corporations, journalists, and politicians and citizens around the globe. Each of these groups likely brings their own perspective and lens for analyzing political outcomes to the table, and from a crowdsourcing perspective that is precisely what one would want (diversity, independence, and decentralization). The answer isn’t simply to gather more opinion from political pundits, but rather to gather more opinion from additional actors who represent an even greater diversity of opinion.

I agree with Dan that it would be worthwhile to set up some kind of experiment to determine the optimal composition of a political forecasting crowd. I smell a side project a brewin’….

[Cross-posted at The Duck of Minerva]