and 2017 may not be captured by these imputation techniques. In general, this method is more appropriate to estimate poverty for small geographic areas that have no representative samples (e.g. the seminal work by Elbers et al. (2003)), or over short time periods (e.g. Douidich, et al. (2016)).

On the other hand, the pass-through approach assumes that national accounts HFCE growth is accurate and that growth is distribution neutral. Both these assumptions have been the subject of recent debate in India. A. Subramanian (2019) has argued that India’s GDP growth from official sources is overstated, although Goyal and Kumar (2019) have disputed his findings. Regarding changes in inequality, Chanda and Cook (2019) and Chodrow-Reich et al. (2020) find a negative short-term impact of the demonetization introduced in November 2016 among the poorest groups, which dissipates after several months. Lahiri (2020), meanwhile, reports a decline in unemployment shortly after demonetization, which may hide an important decline in labor force participation (also see Vyas (2018)).

The poverty rates estimated for 2017 using the pass-through method would be higher if we allow for increasing inequality, for which there is some supportive evidence in the literature cited above as well as the CMIE data between 2016 and 2017. Assuming a 1 percent annual increase in the Gini index between 2015 and 2017 would lead to a poverty rate of 11.3 percent in 2017, a number still within the confidence interval of the survey-to-survey imputation. If the Gini index were to rise 2 percent per year, the poverty rate would climb to 12.4 percent (compared to 10.4 percent with distribution neutrality) in 2017. If the underlying national accounts growth (in terms of either GDP or HFCE) is reduced by 1.1 or 2.5 percentage points for the period 2015-2017, while assuming distribution-neutrality, we estimate a national poverty rate of 11.0 and 11.8 percent, respectively.

All these estimates are subject to strong assumptions; therefore, considerable uncertainty remains about poverty in India in 2017 and the trend in recent years, and this uncertainty can only be resolved if new survey data become available. Using leaked summary statistics of the withheld 2017/18 household survey, S. Subramanian (2019) estimates that poverty increased significantly between 2011/12 and 2017/18. Himanshu (2019) also finds a decline in average consumption using alternative recent survey data. In contrast, Bhalla and Bhasin (2020) claim that poverty declined significantly between 2011/12 and 2017/18. One additional complication is that different welfare aggregates give very different estimates of poverty levels and potentially also the trend. Using the data from the leaked report, similarly to S. Subramanian, we estimate a level of poverty (15.6 percent in 2017) that is still higher than all the estimates using our regular methods.<sup>36</sup> However, leaked data that cannot be verified are not an acceptable source of information for reliable poverty

> 36 As we explain in more detail in the Appendix (also see Section 2), this is explained by the different consumption aggregates being used. Our main analysis uses the URP aggregate which has been used historically in India and which gives higher levels of poverty than the MMRP aggregate, that is used in the leaked estimates. In other words, projecting a decline in the URP aggregate or an increase in the MMRP aggregate results in levels of poverty that are not too different. This of course does not answer the important question over the direction of poverty in recent years.

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