That’s the title of a new paper by Di Falco et al. in the American Journal of Agricultural Economics (a previous, ungated version can be found here, but note that the two versions differ substantially):
“We examine the driving forces behind farm households’ decisions to adapt to climate change, and the impact of adaptation on farm households’ food productivity. We estimate a simultaneous equations model with endogenous switching to account for the heterogeneity in the decision to adapt or not, and for unobservable characteristics of farmers and their farm. Access to credit, extension and information are found to be the main drivers behind adaptation. We find that adaptation increases food productivity, that the farm households that did not adapt would benefit the most from adaptation.”
This is a very interesting research question and that the core result is interesting (and no, I was not a referee for this paper, nor do I know the authors.) From skimming the paper, however, I’m not sure the relationship between adaptation to climate change and productivity is causal. Because the remainder of this post is pretty technical, I am putting it under the fold.
Indeed, the core empirical specification looks at a whether (i) adapting to climate change leads to (ii) increased agricultural productivity. Of course, this poses the usual identification problem in the social sciences given that (i) and (ii) are jointly determined. For example, farmers may choose to adapt to climate change because they expect to be more productive as a consequence of doing so.
To counter this problem, the authors use the presence of government-provided agricultural extension and farmer-to-farmer agricultural extension; whether the farmer obtains information from the radio information or from neighbors; and whether extension officers provided the farmer with climate-related information (i.e., rainfall and temperature) as variables that affect adaptation to climate change but not agricultural productivity.
But it is likely, however, that extension officers target specific groups of farmers (i.e., low- or high-productivity farmers) with climate information, or that traditionally more productive farmers are those who are sufficiently wealthy to afford a radio. In other words, the variables used to identify the causal impact of adaptation to climate change on productivity likely affect productivity other than through adaptation to climate change.
As I mentioned above, however, even though the identification strategy is far from perfect, this is an interesting research question with an interesting core finding. As such, it is an example of an important research questions relying on less-than-ideal identification.