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Archive for the ‘methodology’ Category

One of the most significant developments lately in terms of framing libertarianism has been the advent of the “Bleeding-Heart Libertarian” blog. I know most of the contributors personally (and I’m electronically-acquainted with all of them), and there’s not one I don’t respect. Their mission statement says they are “libertarians who believe that addressing the needs of the economically vulnerable by remedying injustice, engaging in benevolence, fostering mutual aid, and encouraging the flourishing of free markets is both practically and morally important.” The reason that’s a great point to make is that often, people who advocate the moral superiority of the free society are accused of not caring about the poor. One response to that is to bite the bullet and say “right: I don’t care about the poor qua poor, I care about all people qua people, and all people’s rights must be protected.” That’s a legitimate stance, but it’s not hard to see why some critics of liberalism find it less than compelling. So it’s helpful to say, as they do, “no, you don’t get it: we do care about the poor – that’s why we advocate free markets and individual liberty.” To be sure, there is an intra-libertarian debate to have about philosophical justification: is a system of individual rights ultimately justified because it accrues the best results for the poor, or is it justified for some other reason(s), and has the beneficial characteristic of accruing the best results for the poor? This is not unlike Socrates’ second refutation of Euthyphro: the pious is loved by the gods, but that’s an attribute, not a definition. As President Clinton (correctly!) put it, it depends on what the meaning of the word “is” is. I think it’s right and important for political philosophers to have that argument (and for the record, I say it’s the latter), but inasmuch as we ultimately want to persuade as many people as we can of the good sense of our position, I think this sort of debate should not overshadow the many ways in which we can show that good sense.

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Over at 538, Nate Silver has an excellent discussion of the perils of “overfitting” statistical forecasting models. It’s good enough that I could see assigning it to my students in methods courses. Incidentally, I would argue that the opposite peril (“underfitting” if you will) is more common in standard, hypothesis-testing political science research. Because the goal is to establish a particular hypothesis, authors face a temptation to exclude relevant control variables and maximize degrees of freedom.

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At The Monkey Cage, Andrew Gelman takes issue with my post on union density and tax collections by state. I argued that states with higher percentages of workers covered by collective-bargaining contracts have higher tax collections as a percentage of personal income, and that the relationship is probably causal. Gelman argues that it is inappropriate to infer causation from a correlation among observational data. My UB colleague Phil Arena offers a qualified defense of my post.

I more or less agree with the points Phil makes, as well as Gelman’s main point. Yes, correlation does not automatically mean causation, and in my original post I moved very quickly between the two without acknowledging the difference – not the sort of thing I would do in a journal article. Nevertheless, the most natural interpretation of my results is indeed causal. It does not seem plausible that higher taxes cause higher union densities (I can think of no reason why this should be the case). On the other hand, it is quite plausible that higher union densities cause higher taxes: in my state the education unions have been lobbying heavily against spending cuts and a proposed property tax cap. What about endogeneity due to omitted variables? Well, the most plausible one would be ideology: liberal states have higher unionization rates and higher taxes. But I controlled for ideology, and indeed even “overfitted” taxation to ideology with a squared term.

Finally, the dynamic analysis showed a correlation between unionization rates in 2000 and change in tax burdens over the next eight years, although it was not quite statistically significant. But because it’s a short time period, we shouldn’t expect taxes to change all that much. Most of the dependent variable is going to be statistical noise. The effect found is also substantively impressive, even if not statistically significant, and as Ziliak and McCloskey remind us, that’s often what we really want to know.

So yes, Gelman is right that correlation doesn’t automatically imply causation, but I nevertheless contend that the most plausible interpretation of the relationship between union densities and tax levels in the states is that the former are affecting the latter.

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A piece by Jim Manzi in the City Journal explains why Keynesians and non-Keynesians will still be debating stimulus programs in the 22nd century: social science is inherently limited in situations where there is no counterfactual, because each data point is sui generis. In other words, a causal relationship that might hold under one set of circumstances may not hold under another set. In criminology research, even randomized experiments have not generated many reliable conclusions. Some examples of conclusions Manzi believes do hold up fairly well:

[W]ithin this universe of programs that are far more likely to fail than succeed, programs that try to change people are even more likely to fail than those that try to change incentives. A litany of program ideas designed to push welfare recipients into the workforce failed when tested in those randomized experiments of the welfare-reform era; only adding mandatory work requirements succeeded in moving people from welfare to work in a humane fashion. And mandatory work-requirement programs that emphasize just getting a job are far more effective than those that emphasize skills-building. Similarly, the list of failed attempts to change people to make them less likely to commit crimes is almost endless—prisoner counseling, transitional aid to prisoners, intensive probation, juvenile boot camps—but the only program concept that tentatively demonstrated reductions in crime rates in replicated RFTs was nuisance abatement, which changes the environment in which criminals operate.

HT: RealClearPolitics

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