yield better consumption measures (Beegle et al. (2012)),<sup>28</sup> but at a cost. Given that ISAs are spread over 12 months, the surveys will do a good job in recovering average food consumption in the population, but they will potentially do a poor job in measuring annual dispersion as part of this dispersion will be artificially due to seasonal variation. This shortcoming can easily be dealt with using standard deseasonalization techniques (see Section 2.1). Measurement error for other types of consumption, particularly information on durables collected with 12 month recall, is potentially still present, although we note that durable consumption represents a minor share of total consumption (4% in rural areas and 6% in urban areas; see below).

Income is based on recall of the entire production per crop and plot for the past two harvests. The harvest referred to in the questionnaire may have taken place months earlier. We conduct robustness testing for potential recollection bias for production using measures of household income only for those households interviewed within 3 months after the rainy season harvest has been completed (May, June, July); the mean and median of total agricultural production in kilograms is virtually identical for these three months and for the yearly values. The rainy season represents 93% of total agricultural production. In addition to potential recall bias in agricultural production, the underreporting of income is a recurrent issue in household surveys in both rich and poor countries (Deaton (1997)). This is potentially more stringent for the self-employed and, hence, of particular relevance for our rural settings, where most households operate as farms. In this context, it is reassuring that when we focus on rural households that we can categorize as autarkic (i.e, with no production sales) the reported agricultural production (with potentially months of recall bias) and the reported annualized consumption imply very similar quantities (see Section 2.2.1). That is, if we use the measure of food consumption from own production as external validation for our measure of agricultural production, we find only a small scope for measurement error in the ISA’s agricultural production (from recall or elsewhere). Further, in many instances surveys provide checks for internal consistency. For example, interviewees are asked about both total sales, and also sales by crop. The interviewer then must check in situ that the sum of the crops coincides with the total or otherwise reinterview. This design is not applied to nonfarm business income. Hence, the problem of underestimating income potentially persists, in particular, for those households with a large proportion of business income (i.e., urban areas).

Finally, our trimming strategy consists of two steps that mitigate the presence of outliers. As a first step, we exclude households with zero calorie consumption or with an intake per person above the maximum daily of 10,000 Kcal.<sup>29</sup> As a second step, we trim clear outliers after a visual inspection by subitems and then by aggregated measures. We finalize with an implied trimming of

> 28See also Gibson et al. (2014).

> 29We describe in detail how we construct caloric intake in De Magalh ̃aes et al. (2015).

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