Models usually implicitly assume that people are aware of the incentives they face. People in a labor supply model, for example, usually make their decisions based on the actual schedule, not their subjective impression of the schedule. But many people may not even be aware of changes in income taxes schedules: how can they then respond to changes in the schedule? In current research, Raj Chetty, John Friedman, and Emmanuel Saez turn this apparent difficulty into an advantage. They estimate the causal effect of changes in an aspect of the income tax schedule on labor supply. Since the policy they study is Federal, there is not much variation to identify the effects of interest using traditional methods, but the authors show how to recover regional variation in knowledge of the schedule. The interaction of knowledge and the schedule then varies across regions and time even though the schedule only varies over time, so there’s lots more variation to identify the effect of the schedule on labor supply.
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Chuck Manski and John Pepper have issued a new working paper on the effect of the death penalty on homicide rates. If you’re interested in the issue the paper is of course something you should read, but it’s also a great, readable, and not overly technical exposition by way of example of some of Manski’s work over the last couple of decades on partial identification.
Large, heavy vehicles are safe, as everyone knows. If you’re going to be in an accident, would you rather be in a Miata or an Escalade? More large vehicles on the road make us safer, and we should worry about anything which reduces vehicle sizes. Notably, fuel economy standards decrease vehicle size, so we will become less safe as fuel economy standards become more strict. See for example Crandall and Graham (1989).
Or so conventional wisdom goes, but it turns out the truth is more subtle, according to recent research.
Local linear sharp RD estimate: -0.12%, z=-0.05.
Filed under: Exercises in rigorous, pointless causal analyis.
Stephen Gordon laments British Columbians’ failure to ratify the HST, which will reduce our standard of living in B.C. for many years.The case in favour of the HST was overwhelming and no expert opposed the HST in public. Which people voted against their own best interests?
The Globe and Mail’s Chris Hannay presents some graphs showing proportion voting for the HST against certain demographic characteristics at the electoral district level. Districts with higher incomes tended to vote to keep the HST:
and a similar pattern holds for education: unconditionally, districts with more educated people were more likely to vote to keep the HST. Hannay implies that these effects are really the same effect: more educated people also tend to earn more, so the education—vote correlation is just another way of seeing that higher income people voted in their class interest.
An alternate explanation draws on my post from a couple of days ago on education and beliefs that economists understand the effects of taxation on the economy: uneducated people tend to place low weight on expert analysis, certainly including economic analysis. The direct impact of the HST on prices was easy for everyone to observe—they’re printed on cash register receipts. But the indirect effects, the effects on changes in embedded prices, were hard to observe. The HST was not in place long enough to observe effects on investment and growth. One had to yield to expert opinion to draw the correct conclusion that the HST is good policy.
Bryan Caplan speculates on reasons for the decline of economic theory, an apparent reduction over the last two decades in the status of theory in economics. He wonders how to measure changes in the status of theory and, assuming theory has declined, why?
My casual empiricism agrees with Bryan’s. What does actual empiricism say? As a crude first pass, I counted the number of articles in Jstor economics journals that contain the word “regression.” Of course not all articles that contain that word are empirical, nor do all empirical papers contain that word, but it seems a reasonable signal of empirical content. The proportion over time (one observation per decade) looks like
which shows that in the sample of journals tracked by jstor (which changes over time, muddying interpretation) the proportion of empirical papers, or at least papers which for some reason mention regressions, has been steadily increasing, although the rate of increase seems to have been lower 1970-2000 than prior to 1970 or after 2000.