# ‘Metrics Monday: Combining Bits and Pieces of Likelihood to Study Behavior

I have mentioned a few times that there is an unspoken ontological order of things in applied work, wherein one first needs to take care of the problem of identification before one should worry about properly modeling the dependent variable’s data-generating process. In other words, before you obsess over whether you should estimate a Poisson or a negative binomial regression, your time is better spend thinking about whether the effect of your variable of interest on your dependent variable is properly identified.

This week, however, I wanted to move away from my usual focus on the identification of causal effects to look at the modeling of DGPs.

Let us take an example from the first article I ever published (and which, to this day, remains my most-cited article). In that article, my coauthor and I were interested in the marketing behavior of the households in our sample. In some time periods, some households happened to be net sellers (i.e., their sales exceeded their purchases), some households happened to be net buyers (i.e., their purchases exceeded their sales), and some households happened to be autarkic (i.e., their neither bought nor sold). Continue reading

# ‘Metrics Monday: Fixed Effects, Random Effects, and (Lack of) External Validity

Very early mornings, before our entire households is awake, are when I get all of my professional reading done. Last Monday, I read a recent published paper in my discipline. I am remaining purposely vague about that paper, because the research question was interesting and the findings pretty useful; it’s just that the econometrics weren’t great.

Anyway, at some point the authors make the following argument:

• Our random effects findings are almost identical to our fixed effects findings;
• Random effects should be used with a random sample from a population of interest and fixed effects in the absence of such a random sample;
• This means our (small, highly selected sample) is representative of the population of interest;
• Thus, this means we can use findings from our (small, highly selected sample) to make inferences about the population as a whole.
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# Farmers Markets and Food-Borne Illness, Finally Finished

A little over a year ago, I published an op-ed in the New York Times titled “Farmers Markets and Food-Borne Illness.”

That op-ed was based on the findings of a similarly titled working paper of mine, which one of the New York Times editors had gotten wind of after I first discussed it on this blog during the summer of 2015.

In my op-ed, however, I mentioned that I would soon post an updated version of our paper. But things got busy, and though I worked quite a bit on it here and there, I did not get to finish it until a few weeks ago.

(And by “finish,” I mean “stop working on it until it is returned to us with reviewer comments about how to improve it before it can get published.”)

Here is the new version. The major innovation is that we now exploit both the longitudinal nature of the data as well as a source of plausibly exogenous variation for the number of farmers markets in a given state in a given year. This obviously makes for much stronger results than we used to have. Here is the abstract of this latest version: Continue reading