interviewers, with fieldwork spread over 12 months to balance each module over both time and space. Each interviewer implemented all eight modules in equal proportion in order not to confound module effects with interviewer effects. To prevent potentially uncontrollable crossmodule spillovers within households, each household faced just a single survey design.<sup>3</sup> Since the ‘gold standard’ measure of consumption is not available for each household in the experiment, we use two indirect methods to study the nature of measurement errors in these data.<sup>4</sup> First, we average the logarithm of total household consumption, for each sampling unit (hereafter ‘village’) and each survey design. To the extent that measurement errors in consumption are random, averaging should reduce bias, with estimates converging to the true village average. We find that errors are not random. If the error is calculated by subtracting the benchmark average from the average calculated with each of the other seven survey designs, the error is found to be negatively correlated with the benchmark (true) village average. Exactly the same pattern, found with earnings data, is referred to by Bound and Krueger (1991) as _meanreverting measurement error_ .

Since economists rarely work with village averages, our second exercise for assessing the nature and consequences of the measurement error is to estimate a widely used regression on household consumption data – a food Engel curve. We treat the estimates from the intensively supervised sample given the individually-kept diaries as being closest to what true consumption would reveal. We then compare with coefficients estimated using the data from the other designs, and with patterns of bias expected from simulated random and non-random measurement errors. The food Engel curve is useful because of extant results on expected biases from different types of measurement error that are motivated by an Engel curve puzzle raised by Deaton and Paxson (1998).<sup>5</sup> This exercise also shows that the most plausible model of

- 3 Most previous attempts to compare different consumption survey designs apply them sequentially to the same household (e.g., Ahmed et al, 2010).  Sequential designs suffer not only from lack of balance in timing, but also from _conditioning bias_ , whereby respondents who previously recorded in diaries might be atypically accurate in a recall interview, while those initially surveyed by recall might shirk at the prospect of daily recording in diaries, making these respondents uninformative about how these modules would perform in practice.

- 4 In the typology of validation studies provided by Bound _et al._ (2001: 3743) our methods would be considered ‘macro-level comparisons’ of survey estimates versus estimates generated under preferred survey conditions. None of the problems listed by Bound _et al_ . for such comparisons apply to our experiment given the balance we achieved over time, space, and samples.

- 5 The puzzle is that food shares fall as household size rises at constant per capita consumption. The effective income increase from sharing public goods in larger households should outweigh the substitution effect (public goods are cheaper in large households) so food shares should rise for the

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