# **I. Literature review**

Collecting detailed consumption or income data is very costly and time-consuming, and SSI can be a cost-effective solution to improve the frequency, timeliness and comparability of poverty data in developing countries. As discussed above, the idea of SSI is simple: suppose consumption or income data are not collected in a particular year, but non-consumption data from other representative surveys are available. Then consumption or income data can be imputed into the non-consumption data sets, using an imputation model calibrated from a cross-section collected in an earlier time period. Since this method relies on data that are already available, there is no additional data collection cost.<sup>3</sup> Projections are needed in this case because household consumption is not observed in the labor force survey, and projections by necessity must be developed from existing data.<sup>4</sup> The key challenges for SSI are that the two types of surveys must be designed in a similar way, and the model parameters must not change over time. The latter assumption satisfies the requirement to assume a value for the unobserved coefficients in the later period, but it is a strong assumption that is impossible to test in the absence of consumption data. Rather than abandoning the use of projections altogether, however, it is more constructive to conduct several tests using past rounds of consumption data, to check how reliable projection models are in practice.

SSI is now a well-established approach for estimating poverty that is growing in popularity. The first case in which SSI played a key role for poverty estimation can be traced to India, and the work of Deaton and Dreze (2002) and Kijima and Lanjouw (2003). The India National Sample Survey Organization (NSSO) made a slight change in the recall period in part of the food consumption module in the 1999-2000 NSSO survey, and it was widely argued that the resulting data likely overestimated household consumption expenditure and therefore underestimated poverty. To project total household expenditures as if the recall periods for food modules were defined consistently over time, Deaton and Dreze (2002) applied a SSI approach. They first created a model to project from the unchanged part of the consumption module to total household expenditures using a previous round of the NSSO survey. This model was then used to project total household expenditures in 1999-2000 from expenditures in the unchanged part of the food module. Kijima and Lanjouw (2003) applied the same approach, but included only non-consumption data in the model. Whether using a portion of consumption or non-consumption characteristics leads to more accurate predictions remains open to debate. Subsequent analysis using consumption data from the thinround household surveys, which are collected annually but are not used to estimate official poverty rates, suggests that the approach adopted by Kijima and Lanjouw (2003) produced more plausible results. However, no further test to examine the reliability of either approach has been conducted.

While the Indian analyses imputed household consumption within the same survey series, they raised the prospect that these methods could be used to impute consumption across multiple surveys. Stifel and Christiaensen (2007) is an early attempt to do this, by applying consumption models based on the 1997 Kenya Welfare Monitoring Survey (WMS) to three consecutive rounds of Demographic Health Survey (DHS) between 1993 and 2003. The 1997 WMS collects both consumption and non-consumption data while the DHS contains only non-consumption data. The paper also provides theoretical guidance regarding the choice of variables to be included when imputing consumption, so as to maintain comparability and reliability of the poverty predictions. It recommends including variables that change over time, but excluding variables whose rates of return are likely to change markedly in the face of evolving economic conditions. This argument makes sense in theory, but it is difficult ex-ante to identify

> 3Ravallion (1996) is one of the early advocates of this approach. He experimented with how well household consumption expenditure can be estimated from information on housing conditions and subjective well-being, which can be collected quickly and cheaply.

> 4 Ahmed et al. (2013) propose an approach that fully overcomes this limitation.

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