indicate an increase in average household consumption per capita, although most of them are either not fully comparable to the official CES, or do not cover a period long enough, or a geographical coverage wide enough, to assess the evolution of household consumption and, more precisely, poverty rates. We then adopt two methods to estimate poverty in 2017/18. First, we use a surveyto-survey methodology to impute consumption into the Social Consumption Survey for Health 2017/18 using a model estimated on the CES 2011/12. Our method builds on Newhouse and Vyas (2019), adding explanatory variables such as household energy consumption and demographic household characteristics, and modifying the functional form of the rainfall shock, but not including a time trend. Second, we project the CES 2011/12 forward using national accounts growth rates combined with a pass-through factor that adjusts for the difference between growth in national accounts and household surveys. Borrowing from Lakner et al. (forthcoming), we use a machine learning algorithm to estimate the pass-through rate and allow for changes in inequality.

Using the survey-to-survey method, we estimate a national poverty rate (at the international poverty line of $1.90 per person per day) of 9.9 percent in 2017, with a 95 percent confidence interval of between 8.1 and 11.3.<sup>35</sup> With the preferred pass-through rate of 0.67, we obtain a national poverty rate of 10.4 percent in 2017, with a confidence interval between 10.0 and 10.8. Despite using very different data sources and methods, the estimated poverty rates are strikingly similar with overlapping confidence intervals. Within urban and rural areas, the differences are somewhat larger, but the confidence intervals again overlap, and there is no evidence that one method is systematically biased in one direction. Using the survey-to-survey method, rural poverty is estimated at 10.5 percent [8.8, 12.0], some 1.5 percentage points lower than the result of the pass-through exercise (12.0 percent, [11.5, 12.4]). In contrast, at 8.5 percent [6.8, 10.1] urban poverty is higher using the survey-to-survey method compared to 7.2 percent [6.9, 7.6] with the pass-through.

Our results are robust to changes in the model used in the survey-to-survey method, plausible alternative pass-through rates and varying the starting year of the pass-through exercise (see Appendix). They are also consistent with the trends using alternative surveys over the same period, which show growth in average household consumption, and in some cases welfare gains across the entire income distribution.

However, neither approach is without limitations. On the one hand, the survey-to-survey method takes advantage of the variation in the survey data to capture changes in the distribution of welfare. But if the imputation is done between periods too far apart, it may fail to capture important changes in the behavior of markets, since the parameters of the consumption model are assumed fixed for a long period of time. Hence, important structural changes in the Indian economy between 2011

> 35 The survey-to-survey estimates refer to 2017/18, the period of fieldwork for the Health SCS. To compare with the results of the pass-through method, these estimates have been brought back to 2017 using growth in HFCE per capita, following the method described in Chen et al. (2018) for the 2014/15 (Newhouse and Vyas 2019) estimates. The text refers to the results from the preferred model (model 2 in Table A.3).

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