Poverty rates are calculated for each of the 100 imputations and then averaged across imputations. The standard errors of the poverty estimates are computed following Rubin (2004). All estimates are carried out using version 2 of the Stata SAE package, developed by Nguyen et al. (2018).

In summary, we apply parameters from a model derived using CES 2011/12 to data from the SCS Health survey for 2017/18 to predict Indian poverty rates in 2017/18. We test the robustness of the model specification by varying the variable selection algorithm and the functional form of the rainfall shocks.<sup>14</sup> We test two different functional forms for the rainfall shock which is defined as the quarterly deviation of each district’s rainfall from the historical average (between 1981 and 2018). The first functional form of this variable uses the shock and its square. The alternative specification is a simple linear regression on a spline variable created at the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentile points of the rainfall shock distribution (i.e. a dummy variable that indicates whether the household lives in a district where the rainfall shock falls in any of the four quartiles of the distribution of rainfall shocks). As previously mentioned, the framework may assume normality or allow for non-normality in the error terms. Our analysis allows for non-normality which is more flexible. All models are estimated for rural and urban areas separately. We run four model specifications, two using LASSO and two using Stepwise selection, where rainfall is specified either as a spline or a quadratic function.

The consumption models explain between 34 and 45 percent of the variance of the dependent variable, which is slightly lower than Newhouse and Vyas (2019). The explanatory variables vary across models because of the use of different variable selection algorithms (i.e. LASSO and stepwise), but sign and significance of the demographic variables do not vary notably across specifications. A full description of the econometric results of these four specifications is shown in the Appendix.

# **3.2. Poverty Imputation Results**

We present the poverty rates that result from the imputation exercise explained in the previous section, and from equation 4 above, in Table 2. The poverty rates from the imputed consumption do not vary significantly across models. In fact, the confidence intervals overlap for all models, in national, urban and rural estimates. The point estimates for the national poverty rate in 2017/18 range from 8.47 percent in model 4 to 8.75 percent in model 2. Point estimates for rural poverty vary from 8.38 percent in model 3 to 9.14 percent in model 4, while urban poverty rates are between 6.85 percent in model 4 and 9.18 percent in model 3. There is no a-priori reason to prefer one model to another, although it seems unlikely that poverty rates in urban and rural India have equaled -as in model 1- or even reversed -as in model 3. Hence models 2 and 4 seem more plausible, which result in poverty being higher in rural than urban areas. In this section, we report

> 14 Rainfall shocks are the most important predictor of the change in household welfare in Newhouse and Vyas (2019).

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