# **Appendix: Data issues**

Details on the data used in the country-specific studies can be found in the relevant papers cited. This Appendix provides only an overview of the main issues related to the comparisons of country performance over time. Unless noted otherwise the poverty and inequality measures reported here are from _PovcalNet_ and other data are from the World Bank’s Development Data Platform.

_Household surveys:_ The main data needed for assessing progress against poverty come from household surveys of consumption or income, supplemented by data on prices. But there is more than one way of doing a survey, and the heterogeneity amongst existing data sources can cloud comparisons over time and across countries.

There are problems in the available household surveys for all three countries, but it is India where those problems are most worrying. The biggest concern is that there is a rising gap between aggregate household consumption as measured from India’s National Sample Surveys (NSS) (the main household surveys used to measure poverty) and the PCE component of domestic absorption in the NAS (Table 1). There are reasons why these two data sources should not agree, even if both are correct, given differences in what is being measured.<sup>46</sup> However, the extent of this discrepancy for India is large by international standards, with aggregate consumption based on the sample surveys being not much more than half of the household consumption component of the NAS. (For developing countries as a whole, the survey-based mean consumption is about 90% of consumption as measured in the NAS.) There is no sound basis for assuming that the national accounts are right and the surveys are wrong. But this data issue does cast a cloud of doubt over India’s progress against poverty.

One possible approach to this problem is to simply re-scale the survey means to be consistent with the national accounts, which are assumed to be comparable and accurate. In one version of this method, Bhalla (2002) replaces the survey mean by consumption from the NAS, but keeps the survey-based distribution; in other words, he re-scales all survey-based consumption (or income) levels by the ratio of NAS consumption to the survey mean.<sup>47</sup> However, there is no obvious basis for assuming that the discrepancy between the survey mean and NAS consumption per capita is solely due to underestimation in the surveys or that the survey measurement errors are distribution neutral; the surveys may well under-estimate the mean but also under-estimate inequality. Instead, Karshenas (2004) replaces the survey mean by its predicted value from a regression on consumption per capita from the NAS. So instead of using NAS consumption, Karshenas uses a stable linear function of NAS consumption, with mean equal to the overall mean of the survey means. As in Bhalla’s method, this assumes that NAS consumption data are comparable and ignores the country-specific information on the levels in surveys. That is a questionable assumption. However, Karshenas assumes that the

> 46 Further discussion of these differences can be found in Ravallion (2000, 2003b) and Deaton (2005).

> 47 Bourguignon and Morrisson (2002) and Sala-i-Martin (2006) also re-scale the mean, although they anchor their measures to GDP per capita rather than to consumption.

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