that are reported to be consumed. The experiment version of the same module only includes the yes/no question, asked of only a sub-set of food items. Similar adjustments were made to the modules on non-food consumption and shocks in the context of the experiment. Hence, if standard questionnaire respondents realized the higher likelihood for follow up questions conditional on answering yes to the screening question and intentionally underreported with respect to their counterparts subject to the experiment, this could potentially explain our findings. We do not, however, believe that this possibility applies to our case. As noted above, the experiment was piggybacked onto the second round of a panel survey that used essentially the same standard questionnaire 3 years prior. In addition, the survey treatment effects in Table 5 are not necessarily greater by restricting our analysis to the second half of the fieldwork that included experiment households that had received the standard questionnaire 3 months prior.

Another mechanism at work could be that enumerators may have exerted different levels of effort while administering different questionnaires. One could speculate that with a shorter list of items that are not coupled with follow/up questions, enumerators may have been more dedicated. Since survey treatment effects in Table 4 did not change after including interviewer fixed effects in our regressions, such variation in effort would have to be similar for all interviewers. This variation in effort would also be a source of bias in a typical proxy-based poverty measurement exercise that relies on a different set of interviewers at two different points in time for different questionnaire instruments.

Finally, two broader points relate to direct consumption measurement in household surveys. First, in the case of Malawi, we have shown that the standard questionnaire modules on food and non-food consumption that we seek to proxy take less than 25 minutes to administer as package at the median. Thus, with respect to a household survey for proxy-based poverty measurement, collecting consumption data, in and of itself, may not be as complex and costly as commonly perceived. Here, “perceived” is the operative word as the cost savings in implementing household surveys with a poverty focus net of consumption data is not rigorously documented due to lack of or weaknesses in comparative budgetary and survey process data.

Second, although we do not directly measure consumption in the experiment as well as standard samples, the differences in the propensity to consume food and non-food consumption items suggest that consumption in the standard sample might have been different from consumption in the experiment sample. While we do not have evidence on the relative accuracy of reporting from the experiment and standard samples, under-reporting of consumption is usually assumed to be the main problem in the literature (see, for instance, Beegle et al., 2012). In our case, consumption in the standard sample would appear to be under-reported. Counter examples of systematic over-reporting might exist, though we are unaware of any from general populations in developing countries.

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