such as those with low educational attainment, low household wealth, larger numbers of household members, and those located in rural areas. The difficulty of recall to accurately capture consumption is thus exacerbated in more disadvantaged households. The degree of error in value is also attenuated, albeit to a far less degree, by greater education, greater wealth, urban location, and small household size. Survey methods to improve accuracy may thus need to be particularly sensitive to poorer and less well educated households, perhaps with more closely considered and attentive prompting and an enhanced use of images and easily understandable quantity measures.

With respect to item characteristics, recall modules also tend to report lower consumption incidence relative to the benchmark, as well as higher magnitudes of consumption value error, for less frequently purchased items. A separate strategy may need to be employed for the relatively more rarely purchased items. Similarly, recall modules tend to contain incidence errors of greater absolute value for perishable items than for durables (although both types of goods are reported at lower frequency than in the benchmark). For perishables relative to durables, 7-day recall also exhibits greater value errors. For incidence and value reports, these findings suggest there is scope to tailor the recall period to the individual good, although the current knowledge base is not yet sufficient to inform the optimal recall period. Regarding the subset and collapsed recall modules (modules 3 and 4), Beegle et al. (2012) find the time savings from these modules is not especially significant: 41 and 42 minutes to complete these modules, respectively, versus 49 minutes for the long list 7-day module. Given the relative gain in accuracy from the longer list version, only in the most time-constrained surveys (where a savings of seven interview minutes has a real benefit in data quality) should these shorter modules be considered.

Apart from the recall modules rooted in the recent past (7 or 14 days), the usual month approach seeks to encourage respondents to abstract away from a concrete time period and report on consumption for an idealized construct. We have demonstrated that this approach results in a different error pattern whereby consumption incidence is significantly higher than the benchmark. Whereas forgetting any consumption appears to be a problem in standard recall modules, the usual month exercise yields a crowding in of consumption to a large degree, especially for food items that are not commonly consumed in the benchmark. While consumption incidence is biased upward in the usual month approach, the consumption value appears to be significantly underreported for all food groups except Cereals. Compounding these problems, the error magnitudes in both incidence and value are substantially worse among disadvantaged households. Finally, this module took an average of 76 minutes to complete, far longer than the 49 minutes for the 7- day recall. The longer span to completion likely derives from the large cognitive burden placed on respondents by the usual month approach. Because this large cognitive burden also results in a stark divergence from the benchmark in a variety of dimensions, the usual month is not a survey design we can recommend.

The interpretation of the error patterns in the household diaries is somewhat more straightforward because the point of divergence from the benchmark is the reliance on one household member to record the consumption of all members, and, hence, the influence of personal leave out likely becomes more prominent (along with differences in the frequency of supervision in the less frequently supervised module). The household diaries do record 7 percent – 10 percent lower consumption incidence for food goods such as Fruits, Beverages, and Outside Meals, which are more likely to be consumed by individual members alone or outside the household. The reported values are also lower for many of the same commodities. For some food groups, the negative error in consumption value is smaller in magnitude in the infrequently supervised 17