household diary than in the frequently supervised one. In one example, the frequently supervised diary yielded a consumption value for Nuts and Seeds 17 percent less than the benchmark, while the corresponding yield of the infrequently supervised diary was only 7 percent less. Because the infrequently supervised diary can be transformed into a recall survey over relatively brief periods between supervisory visits if the household does not fully adhere to the diary format, the larger consumption values for the infrequently supervised diary may point to a similar positive reporting error phenomenon as seen in the 7- day recall survey. This error, in turn, partly compensates for the leave out error, thus bringing the total consumption value from the infrequent diaries closer to the benchmark.

Because household diaries are substantially less resource intensive than the benchmark personal diary, they will remain a commonly used tool. As suggested by these results, steps can be taken to improve accuracy. Thus, even if one household member records all consumption, prompting explicit consultation with all adult household members around the consumption of items commonly consumed alone or outside the home may serve to reduce personal leave out errors. The fieldwork of diary supervisors may also be structured so that more frequent supervision is possible (by optimizing work routes exhibiting a constraint on repeat visits). Where this is not possible, bounding notices such as stickers showing the dates of the past and future visits can call attention to the relevant time periods for the consumption records.

Our analysis focuses on the influence of errors generated by forgetting, telescoping, the use of rules of thumb, and personal leave out errors and proposes changes in survey design that may reduce this influence. We conclude with an additional brief discussion of small design changes unlikely to impact survey costs that may reduce the influence of other types of response error not investigated here. Two examples are given below. We refer interested readers to a more comprehensive treatment of this issue in Smith, Dupriez, and Troubat (2014).

First, intentional error could also stem from interviewers subtly guiding respondents to give answers that minimize interview length or who rush to complete the questionnaire. We can assume that such errors become more likely as questionnaires get longer and if supervision is limited. This type of error has also been observed in high-frequency panels where follow-up survey rounds are likely less accurate than earlier rounds (Halpern-Manners and Warren 2012). Extensive enumerator training and active field supervision that emphasizes adherence to study design can minimize these errors.

Second, in most of the developing world, households do not typically purchase, harvest, or consume their food in standard units (kilograms or liters). Some surveys force reporting in standardized units, and there are doubts about the accuracy of these reports if they are made by people who rarely transact in metric units. Alternatively, consumption surveys can allow the respondent to report in local units, such as bunches, heaps, tins, buckets, or bundles. However, to aggregate food consumption these local units must be converted into standard units. If common foods are more likely to be reported in nonstandard units, that is, pieces of cassava and bunches of bananas, and conversion factors are inadequate, unit conversion error could significantly distort the resulting value estimates. A final relatively low-cost addition to existing consumption surveys would be to ensure that such conversion factors, which are often geographically specific, are locally relevant, well specified, and systematically collected by survey teams.

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