Over at the Woodrow Wilson Center’s New Security Beat, I have a new post about a recent Science article by several economists on the connections between climate change and conflict. That article, which featured a reanalysis of sixty studies, sought to unambiguously demonstrate the climate effects contribute to conflict across a range of time periods, levels of conflict, and countries (see my previous Duck post with links to other critiques). However, as I suggested, the lack of serious attention to causal processes and the lumping together of quite distinct phenomena under a broad umbrella missed the point that climate effects are likely to have heterogeneous links to conflict. Rainfall abundance, rainfall scarcity, extreme weather events, and temperature may operate through different processes and in some cases may help ameliorate conflicts rather than foment them. Now, there is a section of that blog post that ended up on the cutting room floor, and I wanted to try to get some of it out here, mostly to elicit feedback from more quantitatively inclined social scientists who might be able to weigh in and explain whether what they did was kosher. Hsiang and his co-authors make an interesting choice with respect to statistical controls. They suggest that if we include variables like income per capita as controls that might also be correlated with conflict, we run in to the problem that climate indicators themselves affect income and thus inclusion of income would underestimate the climate effect. Their choice, if I read their article correctly, is not to include those standard controls but to try to account for them through fixed effects, bothlocation-specific fixed effects and temporal ones. Here they write about fixed effects:
If different locations in a sample exhibit different average levels of conflict – perhaps because of cultural, historical, political, economic, geographic or institutional differences between the locations – this will be accounted for by the location-specific constants μ (commonly known as “fixed effects”). Time-specific constants θ (a dummy for each time period) flexibly account for other time-trending variables such as economic growth or gradual demographic changes that could be correlated with both climate and conflict.
By dumping in all the things we as political scientists care about into dummy variables, what can we learn about causal processes and the conditions under which climate effects lead to violence? They elaborate on their choice:
Nonetheless, some studies use cross-sectional analyses and attempt to control for confounding variables in regression analyses, typically using a handful of covariates such as average income or political indices. However, because the full suite of determinants of conflict are unknown and unmeasured, it is likely impossible that any cross-sectional study can explicitly account for all important differences between populations. Rather than presuming that all confounders are accounted for, the studies we evaluate only compare Norway or Nigeria to themselves at different moments in time, thereby ensuring that the structure, history and geography of comparison populations are nearly identical.
So, I get that they don’t want purely cross-sectional studies but they are not including just studies of single countries. I guess the fixed effects are doing the work to render a clean shot at isolating the climate effects in different time periods. But, by not including traditional statistical controls and solely relying on fixed effects, it occurs to me that we cannot know much about the relative role played by critical indicators associated with adaptive capacity that are likely to mediate between climate indicators and conflict. In another paper, Burke, Miguel, and co-authors defend the choice of fixed effects:
One potential downside of the fixed effects approach is that it is difficult to estimate the direct impact of other potential explanatory variables of interest (e.g. perhaps consistently democratic countries have less conflict). The advantage – overwhelming, in our opinion – is that our coefficients of interest are unlikely to be biased by either omitted variables or endogeneity.
I see a bigger downside. Understanding whether and how differential economic growth rates, infant mortality, and governance affect the likelihood of conflict are absolutely critical because these are the areas, outside of a dramatic climate change mitigation effort, where human agency can make a difference. Given that some of these indicators are also influenced by climate, I see that they want to isolate the magnitude of climate effects on conflict, but aren’t there other ways to deal with endogeneity other than fixed effects models like two-stage models or instrumental variables approaches? Maybe I just have a poor grasp of what fixed effects can do for you, but to me, it’s what bundled under the umbrella of fixed effects that are interesting and possibly the source of interaction effects and other substantively meaningful causal connections.