outcome, and which survey questions we used. While all outcomes are measured over the rainy season, Hill et al. (2019) also measured outcomes over the dry season. In this paper, we focus exclusively on outcomes over the rainy season because (i) data over the dry season are incomplete, (ii) insurance products only cover the rainy season, and (iii) payouts for the rainy season could influence production decisions over the dry season (in Bangladesh, rainy season payouts coincided with the planting of dry season crops). We standardize all outcomes to deal with slight differences in measurement across studies. For example, some studies collect fertilizer expenditures, while others collect only purchased quantities with no information about prices. We winsorize all outcomes at the 99th percentile to reduce the influence of outliers.

# **3.2 Covariates of interest**

Household-level covariates may be particularly helpful to understand what drives the heterogeneity in treatment effects. In addition, they may provide useful evidence to guide the targeting of insurance products and identify subgroups for which production decisions are more responsive to the provision of index insurance.<sup>23</sup> We examine the following covariates: a wealth index, household size, household head age, household head literacy, and predicted outcomes.<sup>24</sup> For continuous covariates, we divide households in two groups with respect to their median.<sup>25</sup> We derive the wealth index using the methodology developed by the DHS. We predict each of our five outcomes using an OLS regression with the following set of predictors measured at baseline: chemicals usage, cultivated area, land ownership, household head gender, literacy, education, age (and age<sup>2</sup> ), household size, wealth, livestock, bank account, distance to rainfall station, experience of past weather shocks, and risk preferences.<sup>26</sup> Table A2, taken from our pre-analysis plan, specifies the data we use to derive each covariate. Table A3 shows

> 23Using data from seven studies on microcredit, Meager (2019) finds that microcredit only affects the profits of households with prior business experience, and that the effects for this subpopulation vary largely across contexts. This pattern suggests that business experience of microcredit beneficiaries is a necessary but not sufficient condition for positive effects.

> 24We also specified in our pre-analysis plan that we would analyze heterogeneity with respect to household head’s gender. However, very few households are in fact headed by females, as shown in Table A3.

> 25We stratify the dummy variables by studies. However, for covariates using standardized scales across studies (household head age and household size), we show that estimates are similar with no stratification (Figure A5).

> 26More specifically, we regress each outcome on its predictors using the full sample of control observations, and then use the coefficients from this regression to predict the outcomes of all sample units. In our pre-analysis plan, we specified that we would estimate treatment effects using a repeated split sample approach to limit the bias resulting from overfitting (Abadie et al., 2018), however this is not possible using the _baggr_ R package. Our results on predicted outcomes should therefore be interpreted with caution. In particular, treatment effects for farmers with low predicted outcomes are likely to suffer from an upward bias, whereas treatment effects for farmers with high predicted outcomes are likely to suffer from a downward bias (Abadie et al., 2018).

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