worsening of living conditions for the bottom 15 percent of the population in years 2016 and 2017 with respect to 2015, but improving conditions in year 2018.<sup>9</sup> The unavailability of CP data from 2011/12 prevents a direct comparisons of consumption growth between CP and CES surveys.<sup>10</sup>

# **3. Survey-to-survey imputation**

As described in the previous section, none of the alternative surveys are fully comparable to the CES of 2011/12. The IHDS uses the same measure of consumption as the official surveys but is not nationally representative in recent years. The SCSs are nationally representative and cover a long period but use a different welfare aggregate. The PLB and CP surveys measure a different welfare aggregate and cover a shorter period, preventing a meaningful assessment of the trend in poverty since 2011/12.

In the absence of a comprehensive welfare aggregate covering the period after 2011/12, we use the survey-to-survey imputation methodology originally proposed by Elbers et al. (2003). We closely follow Newhouse and Vyas (2019), who apply this method to India over an earlier period. This method consists of imputing consumption into a survey without consumption data, based on the relationship between consumption and other household characteristics from a survey with consumption data. With the imputed consumption expenditure in the target survey, it is then possible to estimate poverty. A prerequisite for this method is that the two surveys involved in the exercise have a comparable set of explanatory variables. Here we use the Health SCS 2017/18 that includes a series of demographic, economic and locational characteristics that are also included in the previous rounds of the CES. A comparison of the available CES and SCS Health surveys is included in the Appendix.

## **3.1. Empirical Methodology**

This method predicts the conditional distribution of per capita expenditure, ych, for household, h, within cluster, c, of the target data set that is missing actual consumption data (in our case the SCS Health 2017/18). The model is estimated in two steps. The first step is to develop an empirical model that predicts the log of per capita household consumption, ln(ych) from the source (or training) data set, the CES 2011/12 in this case. We adopt a log linear specification relating per capita consumption expenditure to household and district level variables as follows:

> 9 The underlying causes of this evolution are still subject to study. Regarding changes in inequality, Chodrow-Reich et al. (2020) and Chanda and Cook (2019), find a negative short-term impact of the demonetization introduced in November 2016 among the poorest groups, which then dissipates after several months.

> 10 The urban to rural population in CP’s sample is distributed by a ratio of 7 to 3; in contrast, India’s aggregate urban to rural population is distributed by a ratio of 3 to 7. The estimates of consumption reported in this paper are weighted to correct for the oversampling. Moreover, we exclude expenditures on monthly installments, premiums and pocket monies from CP’s consumption aggregate in order to make it as close as possible to CES’ basket of items.

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