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Over the past couple of weeks two studies of the effects of Seattle’s recent minimum wage hikes have been released: one from some researchers at the University of Washington, the other from researchers at Berkeley.  The results have widely been interpreted, including by the authors of one of the studies, as wildly inconsistent.  This post presents a non-technical guide to the problems associated with attempting to estimate the effects of the minimum wage, the statistical methods used in both papers, and how to interpret the results (spoiler: the results are actually not in conflict).

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This post presents a simple explanation of the concept of “local average treatment effects” in the context of instrumental variables estimation.  I borrow shamelessly from the somewhat more advanced presentation in Imbens and Wooldridge’s lecture notes, which is a good place to look for further reading.

The basic idea underlying LATE is to acknowledge that different people (or different units more generally) generally have different causal effects for any given “treatment,” broadly defined.  It is common to talk about “the” causal effect of, say, education on earnings, or interest rates on growth, or pharmaceuticals on health, but if different people respond differently to education or to medical treatments and different countries respond differently to macroeconomic interventions, it’s not clear what we mean by “the” causal effect.  We can still talk coherently about distributions of causal effects, though, and we may be interested in estimated various averages of those causal effects.  Local average treatment effects (LATEs) are one such average.

For concreteness, let’s suppose the government decides to lend a hand to empirical researchers by implementing the following goofy policy: a randomly selected group of high school kids are randomized to get an offer of either $0 or $5,000 to acquire a college degree.  We wish to use this natural experiment to estimate “the” effect of getting a college degree on, say, wages.  We collect data on all these folks comprised of: a dummy variable Z_i which equals one if person i was offered $5,000 and zero if they were offered zero, a dummy variable D_i which indicates the student actually received a college degree, and wages, y_i.

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Canada’s Globe and Mail newspaper has been running a series of articles on income inequality. This post presents some comments on the coverage of income inequality and health in Wealth begets health: Why universal medical care only goes so far by André Picard.

I make three points. First, that the article is much more consistent with the literature if one reads “poverty” every time the article uses the word “inequality.” Second, that the fact that income and health are correlated across people or across regions does not tell us that income causes health. And finally, that the research does suggest that decreasing poverty will increase health, but that we should not expect substantial reductions in health care expenditures as a result. I close with some notes on policy implications.

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Much has been made of a recent Gallup poll showing a majority of Americans now support marijuana legalization. But if a majority support legalization, why do politicians seem so reluctant to support drug law reform?

One explanation for this puzzle is that Americans who vote are less likely to support legalization than those who do not vote. Voters tend to be older, and possibly have other characteristics which are associated with opposition to drug policy reform.

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The 2013 Nobel prize in economics was won by Fama, Shiller, and some other dude, according to most media accounts. Fama and Shiller were pretty easy to explain: one of them is at Chicago and is associated with a theory called “efficient markets,” so he’s the free market guy. Shiller criticized the Chicago guy, so we know where to put him on the political spectrum. But this third guy, Hansen, well, he’s at Chicago, but he does some sort of theoretical econometrics, so if we’re the Guardian we’ll just assume he’s “ultra-conservative” and then ignore him, or if we’re anyone else we’ll skip to just ignoring him (even the Economist gives up, complaining they can’t explain his work without “writing all sorts of equations in our newspaper.”) This post attempts to provide a relatively gentle, albeit with all sorts of equations, introduction to part of the third guys’s research, focusing on applications in causal modeling in microeconomics rather than the examples from finance or macroeconomics.

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Bryant Chen and Judea Pearl have published a interesting piece in which they critically examine the discussions (or lack thereof) of causal interpretations of regression models in six econometrics textbooks. In this post, I provide brief assessments of the discussion of causality in nine additional econometrics texts of various levels and vintages, and close with a few remarks about causality in textbooks from the perspective of someone who does, and teaches, applied econometrics. Like Chen and Pearl, I find some of these textbooks provide weak or misleading discussion of causality, but I also find one very good and one excellent discussion in relatively recent texts. I argue that the discussion of causality in econometrics textbooks appears to be improving over time, and that the oral tradition in economics is not well-reflected in econometrics textbooks.

The Chen and Pearl paper has been around for a while in working paper form and recently came out in the Real World Economics Review, also available here from the authors with much clearer typesetting.

The additional textbooks I discuss below are: Amemiya (1985), Kmenta (1986), Davidson and MacKinnon (1993), Gujarati (1999), Hayashi (2000), Wooldridge (2002), Davidson and MacKinnon (2004), Deilman (2005), and Cameron and Trivedi (2005).

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