outcomes are missing in many studies and measured in very inconsistent ways. We therefore limit our analysis to the amount of cultivated area, fertilizers, pesticides, seeds, and a risk index – whose components are available for all studies and measured in fairly consistent ways. We note that these outcomes are not identical from a theoretical point of view. Assuming that financial constraints are not too binding and that farmer preferences are characterized by decreasing absolute risk aversion, the provision of insurance should affect positively cultivated area, fertilizers, seeds, and crop riskiness, whereas it is not obvious that more insurance would foster the use of pesticides (more pesticides increase production costs but tend to decrease production risks).

Most of these outcomes have straightforward definitions. For the risk index, we draw on Gehrke (2019) and define it for household _i_ in study _k_ as _Rik_ = ∑ _rmkdimk_ / ∑ _dimk_ , where _rmk_ is the coefficient of variation of the yield of crop _m_ in study _k_ , and _dimk_ is the amount of land devoted by household _i_ to the cultivation of crop _m_ in study _k_ .<sup>19</sup> We derive _rmk_ using time series of agricultural yields provided by the FAO at the country level.<sup>20</sup> For India, given the possibly large differences in crop riskiness across regions, we use sub-national data on crop yields from the Directorate of Economics and Statistics, Ministry of Agriculture.<sup>21</sup> For studies in which the insurance products are based on rainfall exclusively (see Table 2), we enrich the index with the term _fmk_ to take into account the rainfall sensitivity of crop _m_ in study _k_ . More specifically, we use _fmk_ to multiply _rmk_ and deflate the contribution to the index of crops which are less sensitive to rainfall. The idea is to prevent the influence of other production risks, such as pests or diseases, which are not covered by rainfall insurance. We use monthly rainfall data drawn from the Climatic Research Unit, and simply define _fkm_ as the correlation between total rainfall over the three wettest months and the yield of crop _m_ .<sup>22</sup> Overall, this index captures how insurance provision affects farmer crop portfolio choices in light of the traditional riskproductivity trade-off outlined in the literature.

Table A1, taken from our pre-analysis plan, provides more details on how we derived each

> 19We view the allocation of land to each crop as perhaps more appropriate than the allocation of other inputs such as fertilizer because it is more ex-ante with respect to rainfall realization, and because different crops may have different fertilizer needs.

> 20FAOSTAT provides annual statistics on harvested areas, production quantities and yields for 173 crops over the 1961-2017 period, covering the production of all primary crops for all countries – see http://www.fao.org/faostat for more details.

> 21See https://aps.dac.gov.in/ for more details. To our knowledge, India is the only country in our sample providing yearly sub-national crop production statistics over a sufficiently large time frame.

> 22As a robustness check, we also add a quadratic term to take into account the possibility of non-linear relationships and find similar results.

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