In a new paper, Jon Mellon reviews 185 papers that use weather as an instrument and finds that researchers have linked 137 variables to weather. You can read it as each paper needing to contend with 136 violations of the exclusion restriction, but the situation is likely less dire. For one, weather as an instrument has many varietals. Some papers use local (both in time and space) fluctuations in the weather for identification. At the other end, some use long-range (both in time and space) variations in weather, e.g., those wrought upon by climate. And the variables affected by each are very different. For instance, we don’t expect long-term ‘dietary diversity’ to be affected by short-term fluctuations in the local weather. A lot of the other variables are like that. For two, the weather’s potential pathways to the dependent variable of interest are often limited. For instance, as Jon notes, it is hard to imagine how rain on election day would affect government spending any other way except its effect on the election outcome.
There are, however, some potential general mechanisms through which exclusion restriction could be violated. The first that Jon identifies is also among the oldest conjecture in social science research—weather’s effect on mood. Except that studies that purport to show the effect of weather on mood are themselves subject to selective response, e.g., when the weather is bad, more people are likely to be home, etc.
There are some other more fundamental concerns with using weather as an instrument. First, when there are no clear answers on how an instrument should be (ahem!) instrumented, the first stage of IV is ripe for specification search. In such cases, people probably pick up the formulation that gives the largest F-stat. Weather falls firmly in this camp. For instance, there is a measurement issue about how to measure rain. Should it be the amount of rain or the duration of rain, or something else? And then there is a crudeness issue of the instrument as ideally, we would like to measure rain over every small geographic unit (of time and space). To create a summary measure from crude observations, we often need to make judgments, and it is plausible that judgments that lead to a larger F-stat. are seen as ‘better.’
Second, for instruments that are correlated in time, we need to often make judgments to regress out longer-term correlations. For instance, as Jon points out, studies that estimate the effect of rain on voting on election day may control long-term weather but not ‘medium term.’ “However, even short-term studies will be vulnerable to other mechanisms acting at time periods not controlled for. For instance, many turnout IV studies control for the average weather on that day of the year over the previous decade. However, this does not account for the fact that the weather on election day will be correlated with the weather over the past week or month in that area. This means that medium-term weather effects will still potentially confound short-term studies.”
The concern is wider and includes some of the RD designs that measure the effect of ad exposure on voting, etc.