An interesting post from Nicolas Baumard (with a great quote on large N vs. small n studies):

On the Use of Natural Experiments in Anthropology

Jared Diamond and Harvard Economist James Robinson have just edited a book on Natural experiments in history. This book reviews eight comparative studies drawn from history, archaeology, economics, economic history, geography, and political science. The studies cover a spectrum of approaches, ranging from a non-quantitative narrative style in the early chapters to quantitative statistical analyses in the later chapters. (…) The conclusion of the book struck me, as I had the feeling that it could have been written for students of culture.

Every field of scholarship, not just human history, experiences tension between narrowly focused case studies and broader synthesis or generalization. Practitioners of the case study method tend to decry syntheses as superficial, coarse-grained, and absurdly oversimplified; practitioners of syntheses tend to decry the case studies as merely descriptive, devoid of explanatory power, and unable to illuminate anything except one particular case study. Eventually, scholars in mature fields come to realize that scholarly understanding required both approaches. Without reliable case studies, generalists have nothing to synthesize; without sound syntheses, specialist lack a framework within which to place their case studies. (…) Continue reading →

This weekend I will be attending the Midwest Political Science Association Conference, in Chicago 22-25 April, 2010. You can find the program and some papers here. These are the abstracts of the papers that we will deliver at the conference.

Subsidized democracy? The effect of public funding to political parties in electoral competitiveness

Javier Aparicio and Jacaranda Pérez (IFE)

[Session 22-19 Comparative Electoral Analysis. Saturday, April 24 10:25 am]

Public funding to political parties has been adopted in a large number of democracies over the last several decades. Public funding seeks two major goals: 1) to level the playing field in electoral races, which should make elections more competitive; and 2) to reduce the entry costs for new political parties, which should enhance the representativeness of the electoral system.  However, there is scant evidence on whether or not any of these objectives are actually achieved. Using a panel dataset of national elections in OECD countries over the 1945 to 2008 period, we estimate the effect of public funding on the competitiveness of both parliamentary and presidential elections.  Our results indicate that public funding is associated with narrower margins of victory and a larger effective number of political parties.  On the other hand, public funding may be associated with lower turnout levels.

The Mexican Supreme Court: Judicial Activism in New Constitutional Courts

Jenny Guardado (NYU) and Javier Aparicio

[Session 3-24 Political Institutions in Contemporary Mexico. Saturday, April 24 12:45 pm]

Under what conditions do newly established constitutional courts exercise their judicial review powers? Recent studies of judicial decision-making have ignored the political process leading to the ling of a case, which creates sample selection problems: Are constitutional courts more active because they receive more cases that are “likely to succeed”, or because the political conditions of each case have a role in the court’s final rulings? We address this issue by exploiting the longitudinal variation in a novel dataset of all constitutional cases presented by sub-national actors to the Mexican Supreme Court from 1995 to 2005. We use sample selection and propensity score matching models to estimate the likelihood of a positive ruling from the court, conditional on the factors that led to the filing of a case in the first place. Our results indicate that sub-national political pluralism is associated with a higher likelihood of judicial activity. Specifically, the Court’s rulings are more likely to change the status-quo when political fragmentation or competition is higher. Moreover, we find that this effect varies depending on the likelihood of presenting a case. Finally, we find that the partisanship of the actors involved have a lesser impact.

If you think vote buying/pork-barrel politics can only be studied in developing countries or young democracies, think again. This is precisely the research agenda of Jowei Chen, a young professor at the University of Michigan.  I met him in APSA 2009 and, as you can see, it is really great stuff (the following links are from his own website):

1. Vote Buying in United States Elections:

2. The Geographic Targeting of Pork Barrel Projects:

3. The Electoral Geography of Legislative Districting:

We conduct legislative districting simulations using only the apolitical criteria of drawing compact and contiguous districts. We show that the Republican party naturally wins a disproportionately large share of legislative seats in Florida, even without gerrymandering. This result emerges because Democratic voters tend to live in highly concentrated, urban cores, thus “wasting” their electoral strength on a small number of landslide Democratic districts. Republican voters are geographically dispersed more evenly throughout the hinterlands, allowing the Republican party to win a disproportionate share of districts by a slight margin.

This is from The New York Times (August 6, 2009):

For Today’s Graduate, Just One Word: Statistics

Taleb’s classical metaphor: “A turkey is fed for a 1000 days—every days confirms to its statistical department that the human race cares about its welfare ‘with increased statistical significance’. On the 1001st day, the turkey has a surprise.”

Some quotable quotes from Taleb’s essay:

“Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the “logic of science”; it is the instrument of risk-taking; it is the applied tools of epistemology; you can’t be a modern intellectual and not think probabilistically—but… let’s not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now.

“By the “narrative fallacy” the turkey economics department will always manage to state, before thanksgivings that “we are in a new era of safety”, and back-it up with thorough and “rigorous” analysis. And Professor Bernanke indeed found plenty of economic explanations—what I call the narrative fallacy—with graphs, jargon, curves, the kind of facade-of-knowledge that you find in economics textbooks. This is the find of glib, snake-oil facade of knowledge—even more dangerous because of the mathematics. (…) I have nothing against economists: you should let them entertain each others with their theories and elegant mathematics, and help keep college students inside buildings. But beware: they can be plain wrong, yet frame things in a way to make you feel stupid arguing with them.

What Is Wise To Do (Or Not Do) In The Fourth Quadrant

(NB: The 4th quadrant basically refers to heavy-tailed or unknown probability distributions with complex or nonlinear payoffs)

1) Avoid Optimization, Learn to Love Redundancy. Psychologists tell us that getting rich does not bring happiness—if you spend it. But if you hide it under the mattress, you are less vulnerable to a black swan. (…) Biological systems—those that survived millions of years—include huge redundancies. (…) Historically populations tended to produced around 4-12 children to get to the historical average of ~2 survivors to adulthood.

2) Avoid prediction of remote payoffs—though not necessarily ordinary ones. Payoffs from remote parts of the distribution are more difficult to predict than closer parts. A general principle is that, while in the first three quadrants you can use the best model you can find, this is dangerous in the fourth quadrant: no model should be better than just any model.

3) Beware the “atypicality” of remote events. There is a sucker’s method called “scenario analysis” and “stress testing”—usually based on the past (or some “make sense” theory).

4) Time. It takes much, much longer for a times series in the Fourth Quadrant to reveal its property. At the worst, we don’t know how long. Things that have worked for a long time are preferable—they are more likely to have reached their ergodic states.

5) Beware Moral Hazard. Is optimal to make series of bonuses betting on hidden risks in the Fourth Quadrant, then blow up and write a thank you letter.

6) Metrics. Conventional metrics based on type 1 randomness don’t work. Words like “standard deviation” are not stable and does not measure anything in the Fourth Quadrant. 70-90% of the Kurtosis in Oil, SP500, Silver, UK interest rates, Nikkei, US deposit rates, sugar, and the dollar/yet currency rate come from 1 day in the past 40 years.