in the household; the more adults, the greater the negative error in consumption valuation for goods likely consumed outside the home such as Fruits and Meals Out. This appears true for recall and for diary formats and indicates additional attention is warranted for households with many adults but only one survey respondent. Recall modules administered to households with many children (but not diaries) tend to differ with respect to the benchmark for various basic foodstuffs such as Vegetables, Fruits, and Meat/Fish. In general, the recall modules undervalue consumption for these items, and this undervaluation increases for households with many children, indicating once more the importance of greater attention if recall surveys are administered to larger households.

Urban households administered recall modules also tend to underreport select food groups; this is especially so for the subset and collapsed list recall surveys (modules 3 and 4). However, such households also exhibit overreporting for other food groups. There are some other specific results for various food groups and either the educational attainment or wealth level of the household, but no systematic pattern emerges, making survey design lessons difficult to generalize with respect to value reported and household measures of economic status. This is in contrast to the results shown in table 3, which contains more clear patterns in consumption incidence error and selected household characteristics, whereby more disadvantaged households are more likely to omit the consumption of key food groups entirely.

# _Item characteristics and reporting error_

This section explores the relation between commodity characteristics and reporting error. Investigated commodity characteristics include (1) either commonly or uncommonly consumed items in terms of the consumption incidence as reported in the benchmark individual diary; (2) the monthly value of the consumed item; (3) the frequency of purchase/consumption; (4) the share of the item consumed from home production; and (5) the storability or perishability of the item.

Because most modules record consumption information on up to 58 individual food items, we construct the item-specific incidence and value error for 54 items relative to the incidence and value recorded in the personal diary benchmark.<sup>16</sup> This analysis is not possible for the 7-day collapsed list module (module 4) because that module lacks sufficient disaggregated information. In addition, the 7-day subset list (module 3) only contains item-specific information for the 17 individual items listed and consequently is also removed from subsequent analysis.

For the remaining five modules, figure 1 depicts the consumption incidence error (with respect to the benchmark) as a function of the commonality of the item (as measured by the item incidence recorded in the benchmark) for each of the 54 food items. A fitted linear regression line is also included in the modulespecific plots. Differential patterns in incidence error across modules are readily apparent. The incidence error of modules 1 and 2, while greater in magnitude for less commonly consumed items (especially for those items with a benchmark frequency at less than 0.2), shows no directed relation with the commonality of the consumed item. The fitted regression line with a slope close to zero confirms this lack of a linear relation. In contrast, the usual month module 5 reveals a decidedly negative relationship between consumption incidence and incidence error; the least commonly consumed items in the benchmark report a high positive incidence error. Clearly, the usual month exercise leads to a gross overstatement of

> 16 The long list contains 58 food times. Because of possible confusion in the diary entries among (a) paddy rice and husked rice, (b) maize grain, maize cob, and maize flour, and (c) millet grain and millet flour, these categories are combined for this item-specific analysis, resulting in a total of 54 commodities available for analysis.

13