# ‘Metrics Monday: How to Systematically Think About Selection

Jeffrey Smith and Arthur Sweetman have a very nice viewpoint article titled “Estimating the Causal Effects of Policies and Programs” in the latest issue of the Canadian Journal of Economics. The article is articulated around three points, viz. heterogeneity of treatment effects, the increased focus on internal validity over the past 20 years, and the use of economic theory to guide empirical work.

It is a good read–one that avoids taking some of the more extreme positions often taken by in that literature–and I plan on including it as a reading for the advanced econometrics course I teach every other year.

In reading Smith and Sweetman’s paper, I learned how to systematically think about selection into treatment when dealing with observational data. Their discussion can be particularly useful when you have survey data and your units of observation–in my case, that usually means individuals or households–are not randomly assigned to treatment but choose to participate on the basis of both their observable and unobservable characteristics, which means that you have to do the best you can with the data you have if you want to make a causal statement. Continue reading

# ‘Metrics Monday: Simpson’s Paradox, or Why “Determinants of…” Papers Are Problematic

From Wikipedia:

Simpson’s paradox, or the Yule-Simpson effect, is a paradox in probability and statistics, in which a trend appears in different groups of data but disappears or reverses when these groups are combined. … This result is often encountered in social-science and medical-science statistics, and is particularly confounding when frequency data is unduly given causal interpretations. The paradoxical elements disappear when causal relations are brought into consideration.

What does this mean, specifically? Suppose you are estimating the equation

(1) $y={\alpha}+{\gamma}{D}+{\epsilon}$

with observational (i.e., nonexperimental) data, and you are interested in the causal effect of $D$ on $y$. Suppose further that after estimating equation (1), you find that $\hat{\gamma}<0$. Continue reading

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# The Contract Theory Nobel (and Some Grad Student Advice)

Yesterday, the Royal Swedish Academy of Sciences announced that the 2016 Nobel Memorial Prize in Economic Sciences was given to Oliver Hart and Bengt Holmström, respectively of Harvard and MIT, for their work on contract theory.

As far as I am concerned, it was about time those two got the Nobel. But then again, I have been a big fan of Holmström and Hart for a long time–I went into graduate school wanting to work on applications of contract theory, and I did just that by writing a dissertation titled, rather un-enticingly, Three Essays on Agrarian Contracts.

In 2004, I spent eight months in Madagascar to collect primary survey data for my dissertation. Because I arrived during cyclone season, my field site was not accessible for some time, and so I decided to go through the entire syllabus for a course in applied contract theory that was then taught at the European University Institute by Pascal Courty. In the process, I read a number of things by Hart and Holmström, all of which were very enlightening. Continue reading