A straightforward regression framework is used to analyze the relative performance of the seven survey designs in relation to the benchmark module 8. For the specification with respect to consumption incidence, we have the following: where _M_ is a vector of indicators for module type. The individual diary, _m = 8_ , is the excluded category, and the constant ���� therefore represents the mean benchmark incidence. Regressions include survey cluster fixed effects and are estimated with ordinary least squares.<sup>11</sup> Earlier work has demonstrated that household characteristics interact with survey design in nontrivial ways to produce error and, so, may also interact in differential ways with respect to consumption incidence and value. An understanding of the presence of these interaction affects can also inform consumption survey design. An extended regression framework thus includes a household characteristic _X_ – for example the number of adult or child household members, household location, the educational attainment of the household head, or asset wealth – and interacts this characteristic with the survey module indicator, _M_ : In this specification, the coefficient of interest is ����, which relates how module effects on incidence reporting are mediated by the household characteristics.

The same two regression specifications given above are used to explore the survey design effects on the value of consumption (conditional on a positive value) by replacing the dependent variable with the consumption value and dropping all observations that report zero consumption for that specific food item.

The next section first explores consumption incidence and then consumption value. We then extend this analysis by relating module-specific reporting error for a particular food good to select characteristics of that good. It is possible for a survey design that minimizes error with respect to certain types of food goods to be less effective with other food types. Consequently, the analysis compares the design error as estimated above with respect to item-specific features such as consumption incidence (i.e. common or rare items), consumption value, the share of consumption from home production, the frequency of market purchase of a food item, and the storability or perishability of the good. This analysis unveils some of the mechanisms underlying the misreporting and enhances the relevance of our results beyond the specific context of our survey experiment.

# **V. Results**

## _Survey design and the report of consumption incidence_

The consumption decomposition results begin with table 3, which illustrates the consumption incidence for 12 food groups relative to the benchmark module. The consumption incidence estimated by the benchmark is given by the constant term. Several lessons are immediately apparent, beginning with the relative performance of the 7-day and 14-day recall modules. These recall modules record significantly lower consumption incidence among most food groups. For example, while 67 percent of benchmark households

> 11 The results are appreciably similar if binary response models (probit or logit) are used in place of ordinary least squares.

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