penditure surveys that adopt different recall periods.<sup>6</sup> We consider two approaches to imputing NSS-compatible consumption into the CPHS. Our preferred approach identifies the relationship between CPHS- and NSSconsumption, and then use this relationship to convert observed CPHS consumption into NSS-type consumption (within the CPHS survey). As a robustness check, we also impute NSS-type consumption on the basis of non-expenditure predictors of consumption that are shared between the CPHS and NSS (i.e. demographics, education, employment, dwelling characteristics, and asset ownership). Both approaches yield qualitatively similar results. We validate our estimates of the levels and trends in poverty and inequality by means of an inclusive set of corroborative evidence that brings in every available source of official and non-official data that could help rationalize the trends in mean consumption, poverty and inequality in India over the last decade.

Our findings are as follows. First, the poverty headcount rate in India is estimated to have declined by 12.3 percentage points since 2011.<sup>7</sup> Our preferred estimates suggest that the poverty head-count rate is 10.2 percent in 2019, down from 22.5 percent in 2011. Second, reductions in rural areas are more pronounced than in urban areas. Rural and urban poverty dropped by 14.7 and 7.9 percentage points during 2011-2019. Third, urban poverty rose by 2 percentage point in 2016 (coinciding with the demonetization event) and rural poverty rose by 10 basis points in 2019 (coinciding with a slowdown in the economy). Fourth, we observe a slight moderation in consumption inequality since 2011, but by a margin smaller than what is reported in the unreleased NSS-2017 survey.<sup>8</sup> Finally, the extent of poverty reduction during 2015-2019 is estimated to be notably lower than earlier projections based on growth in private final consumption expenditure reported in national account statistics. Our analysis stops just before the 6Similar methods have been applied to estimate consistent poverty measures when recent household survey data is unavailable and older estimates are considered outdated (Douidich, et al., 2016); to report poverty rates at finer levels of spatial disaggregation (Elbers, et al., 2003); and, to validate official estimates of poverty when comparability of data across surveys is compromised due to changes in instruments (Tarozzi, 2007).

> 7This suggests an extension of the steady progress observed in India over the last two decades, see e.g. Gravel and Mukhopadhyay (2010). However, Dreze and Sen (2012) note that this progress does not extend to all indicators as growth in select nutrition and health indicators, for example, have been more muted. Similarly, Ravallion (2016) notes that despite high growth and a fall in headcount rates in developing countries, the minimum levels of living for the global poor has not moved by much over the past three decades. Castello-Climent and Mukhopadhyay (2013) and Castello-Climent et al. (2018) show that in growth in India is sensitive to changes in tertiary education levels -- suggesting that changes in higher levels of education can impact poverty through the growth channel.

> 8The observed reductions in inequality and poverty are accompanied by major expansion of social security programs in India (e.g. school meals, child care services, employment guarantee, food subsidies, and social security pensions) in the past (see e.g. Dreze and Khera (2017)); and, an expansion of household access to bank accounts, cooking gas, access to toilets, electricity, housing, etc in recent periods, see e.g. Subramanian and Felman (2022).

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