max-entropy approach advocated by Jaynes (1957).

The reweighting procedure consists of two steps. First, we use assets, demographic and education variables observed in the NFHS-2015 (as well as the CPHS) to reweigh all CPHS rounds from 2015 to 2019<sup>17</sup> . Second, we use demographic, education and labor market indicators observed in the PLFS rounds of 2017, 2018 and 2019 to further adjust the sampling weights in each round of the CPHS<sup>18</sup> . The second reweighting step allows us to account for changes in socio-economic indicators over time.

For the selection of target variables (on which to reweigh), we prioritize non-expenditure indicators that exhibit comparatively large biases in the CPHS relative to the benchmark surveys that are assumed to be nationally representative. An example of such a target variable is the share of undereducated adults (comprising of illiterate and below primary levels of education). We deliberately do not include all indicators that are shared between the CPHS, PLFS and NFHS in the set of target variables. This facilitates convergence of the max-entropy procedure (Zhang and Yoshida, 2022), and more importantly, sets aside a set of indicators that can be used to validate the reweighting exercise.

The adjusted sampling weights are obtained by matching the weighted means of the target variables between the CPHS and the benchmark representative surveys at the state-rural or urban levels (max-entropy minimizes distances between the weighted means obtained in the CPHS and the benchmark surveys). Following existing practices (e.g. Chen et al., 2018; Haziza and Beaumont, 2017; Kolenikov, 2014), the adjusted individual level weights obtained are winsorized at the 0.25<sup>th</sup> and 99.75<sup>th</sup> percentile level. We achieve national level representation by multiplying the resulting normalized weights with the rural and urban population populations of each state. The population estimates are obtained from the NFHS-2015 for 2015 and 2016 rounds; and from the PLFS 2017 to 2019 for the remaining periods. Finally, the household level weights are reconstructed by dividing the adjusted individual level weights by the household size observed in the survey.

17 We use the following set of target indicators for reweighting at the first step: dummy variables for ownership of air conditioners, cars, computers, refrigerators, television sets, two-wheelers, washing machine; dummies for household sizes 1 and 2, sizes 3 and 5; dummy variables for hindu, muslim, scheduled caste, schedule tribe, other backward classes households; total number of members less than 10 years old, over 60 years old; and, total members with below primary level of education, primary level and secondary level of education.

18 We use the following set of target indicators for reweighting at the second step: dummy variables for female headed household; scheduled caste, scheduled tribe and other backward classes households; dummy variables for household sizes 1 to 5; total members working in casual, salaried and self-employed jobs; total number of members less than 10 years old, over 60 years old; and, total members with below primary level of education, primary level of education and secondary level of education.

12