This week, we’re going to think about non-experimental study design and about interpreting results of statistical analysis/hypothesis testing.
Discuss the following questions as a group. Nominate a member of your group to communicate your thoughts/conclusions to the class as a whole when we reconvene.
Two economists, Grainger and Schreiber, built a model of the probability that an air quality monitor is placed in a specific site. A “site” in their study is a 0.05x0.05 degree grid cell—basically, they cut the country up into a bunch of tiny boxes and model the probability an air quality monitor gets put into any specific box.
They model this probability as a function of many factors: air pollution levels, the demographics of the box, home prices in the box, whether the county has been designated as “non-attainment” (has excess measured air pollution levels, in violation of the relevant regulations), and idiosyncratic spatial and time-varying effects (the “fixed effects”).
A note on terminology: “indicator” variables are discrete variables which are either 0 or 1. For example, “Attainment” is an indicator variable which is 1 if the county is “in attainment” (not in violation) or 0 if the county is “in non-attainment” (in violation).