# **I. Introduction and Literature Review**

Following the recent economic crisis, which affected both developed and developing countries, policy makers and other stakeholders are demanding more frequent and timely poverty data. During the crisis, food and fuel prices rose sharply and employment opportunities deteriorated substantially. These impacts aggravated the situation of poor and vulnerable groups and likely increased the overall prevalence of poverty in many developing countries. Policy makers and stakeholders need to know to what extent poverty has increased, whether existing poverty alleviation programs are working, and if not, how they can be improved. However, for many developing countries, it is not yet (and may never be) known how much the prevalence of poverty increased simply because poverty statistics were not available during and in the immediate aftermath of the crisis.

Why do so many developing countries fail to publish poverty data regularly and frequently? Producing good quality poverty data requires collecting detailed household expenditure (or income) information at the household level. This is a resource-intensive and time-consuming process. To overcome these constraints, conducting rapid monitoring surveys with highly simplified survey questionnaires and/or shortened survey periods is sometimes suggested. However, many analysts are concerned about the quality of poverty data from such rapid monitoring surveys in terms of their comparability to official poverty data from large-scale full-fledged household surveys. If the data are not comparable, tracking poverty becomes extremely tricky and policy makers and other stakeholders cannot be sure how much (and sometimes even in what direction) poverty has changed.

Imputation procedures offer a potentially cost-effective solution for improving the frequency and comparability of poverty data.<sup>1</sup> The idea is simple: suppose consumption or income data are not collected in a particular year, but nonconsumption data from other surveys are available, then consumption or income data can be imputed into the nonconsumption/income data set using imputation models calibrated on consumption data collected previously. Because we are using data already available, there is no data collection cost (see, for example, Stifel and Christiaensen [2007] or Douidich et al. [2013]). Even if a survey is necessary to collect the nonconsumption/income data, its cost is likely to be low compared to that of a full-fledged consumption survey. Ravallion (1996), one of the early advocates of the imputation approach, 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.

For the imputation approach to work, it is important to assess whether imputation models can accurately predict household consumption/income and produce comparable poverty data. The imputation models are calibrated in a year when both household consumption data and nonconsumption data are available, and then applied to impute household consumption data for years when only the nonconsumption data are available.<sup>1</sup> A potentially serious problem is that there is no guarantee that imputation models will accurately reflect consumption patterns in those periods when only nonconsumption data are available. For example, ownership of cell phones is a good predictor of household wealth, but its relationship with total household expenditure has been changing rapidly in many Sub-Saharan African countries as cell phone coverage increases and cell phone fees decline. Ten years ago, ownership of cell phones was a good predictor of extreme wealth; today such phones are prevalent among the lower-middle-income classes and even among the poor. Using imputation models calibrated 10 years ago with cell phones as covariates would likely underestimate present-day poverty.

Because of these considerations, there is ongoing research and debate among analysts on methods to ensure that imputed poverty data are reliable and comparable. Stifel and Christiaensen (2007) provide

> 1 The imputation method can be used for many other purposes as well. It is used for poverty mapping (e.g., Elbers, Lanjouw and Lanjouw 2003), restoring comparability of consumption data over time (e.g., Deaton and Dreze 2002, Kijima and Lanjouw 2003, Allwine et al. 2013, Revilla et al. 2010), and creating synthetic panel data for vulnerability analysis (Dang and Lanjouw 2013).

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