# **1. Introduction**

This paper describes several methods to estimate poverty in India in 2017. India is likely the country with the largest number of people living below the international poverty line of $1.90 and its latest publicly available household survey dates to 2011/12, giving rise to considerable uncertainty over the recent trend in global poverty. Because of the decision by the Government of India to withhold the most recent household survey (National Sample Survey 2017/18), we use a range of methods to derive a poverty estimate for India in 2017, which can be incorporated in the global poverty counts.<sup>1</sup> We focus on estimating poverty at the international poverty line of $1.90 (using 2011 purchasing power parities).<sup>2</sup> We use two main methodologies. The first method uses a survey-to-survey methodology to impute a consumption aggregate into the 2017/2018 Survey on Social Consumption (SCS) on Health. While this survey collects information on covariates that predict consumption, it does not collect a comprehensive consumption aggregate that could be used to measure poverty directly. Our approach is closely related to Newhouse and Vyas (2019) who impute consumption into the 2014/2015 National Sample Survey.<sup>3</sup> This approach builds on the small area estimation methods developed by Elbers et al. (2003), who impute a welfare aggregate into a census. More recently, Douidich et al. (2016) impute a consumption aggregate into a labor force survey to estimate quarterly poverty rates.

The second method assumes that household survey consumption follows the growth in national accounts, adjusted downward by a pass-through factor.<sup>4</sup> The adjustment factor accounts for the fact that survey growth is systematically lower than growth in national accounts, e.g. see Ravallion (2003), Deaton (2005), Pinkovskiy and Sala-i-Martin (2016), Lakner et al. (Forthcoming), Prydz et al. (Forthcoming). The pass-through factor is estimated using a machine-learning algorithm to account for systematic variation in pass-through rates between sub-samples of the data. We report

> 1 The government decided to indefinitely withhold the survey citing concerns over data quality. See Jha (2019)  and Press Information Bureau Government of India, Ministry of Statistics & Programme Implementation issued on November 15, 2019.

> 2 We use the revised 2011 PPPs published in May 2020. Following the World Bank’s global poverty measures, we use different PPPs for urban and rural areas to account for spatial price differences (Atamanov, et al. 2020). Throughout the paper urban and rural poverty are estimated separately and aggregated to the national estimate using the population weights in the World Development Indicators (WDI).

> 3 Using the CES surveys collected in 2004/05, 2009/10 and 2011/12, which collect a consumption aggregate as well as covariates that are also present in the 2014/15 survey, Newhouse and Vyas (2019) estimate several models of household consumption per capita. These models are then used to project household consumption into the 2014/15 CES, which did not collect information on aggregate household consumption, and hence estimate poverty. This poverty estimate underpins the World Bank’s global poverty estimate for 2015, see Chen et al. (2018) and World Bank (2018). We use a different set of variables, and different training and target data sets, but a methodology similar to Newhouse and Vyas (2019).

> 4 This is similar to the way surveys are brought to a common reference year in the World Bank’s global poverty measures, see Chen and Ravallion  (2010), Prydz et al. (2019) and World Bank (2015).

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