only 54 observations informing the two means. For this reason, standard errors are not shown. Nonetheless, table 7 shows suggestive results that relate food characteristics and the degree of reporting error.

The frequency of purchase of a particular food appears to convey a salience reflected in the accuracy of the reporting. For frequently purchased items (defined as those above the median purchase incidence of 1.4 times in a two-week period), the incidence error is lower in all five modules investigated, but especially in the usual month, where the error magnitude is less than half as much as the overreporting for infrequently purchased foods. The value error is also far lower for frequently purchased goods among the recall modules. Combined, this suggests that additional attention must be paid to infrequently purchased goods to improve the accuracy in recall surveys of both the reported incidence and the reported value conditional on incidence. For the diaries, the frequency of purchase is not substantially related to the value of the good recorded as we might expect in settings if transactions are recorded soon after they occur, regardless of purchase frequency.

There is no apparent relation of the share of home production to incidence reporting error: across all five modules, the incidence error for goods more commonly consumed from home production and those seldom consumed are close. The same holds true of consumption value error except for the 14- and 7-day recalls. Under the 14-day recall, the value error is positive for foods consumed with a high share of home production, while negative for foods with a low share. On the other hand, the 7-day module exhibits a much higher degree of overreporting for foods with a low share of consumption from home production.

In terms of storability, we define 26 individual food items as storable, e.g. dry goods such as maize flour, and 28 as perishable, e.g. various types of fresh vegetables. There are some apparent differences in errors between the two characteristics. The incidence error is always greater in absolute magnitude for perishable goods: thus, the usual month overstates the incidence of perishable goods, while the other four understate the incidence relative to the benchmark. With respect to consumption value, perishable goods also tend to demonstrate reporting errors of greater magnitude, that is, the overreporting in the 7-day module is greater for perishables, as is the magnitude of underreporting for the usual month and the diaries. The 14-day recall reveals directionally different errors for the two types of goods: overreporting for storable goods and a slight underreporting error for perishables – this is the one module where the absolute value error is greater for storable goods.

# **VI. Concluding lessons for food consumption survey design**

This paper relies on data from a consumption experiment in Tanzania to describe the nature of reporting errors in food consumption surveys. The goal is not merely to document error patterns, but to generate hypotheses for improved consumption survey design. Of course, any such suggestions must be regarded as preliminary as the conclusions are derived from one setting (albeit national in scope) that is largely rural and in which diets are based on a particular staple crop (maize, by caloric intake). More work is needed to establish the generalizability of any findings reported here. However, we hope that our focus on the underlying mechanisms of misreporting error will increase the external validity of our findings.

We use a simple analytic framework of error decomposition that compares consumption incidence and consumption value to a benchmark of intensively supervised individual diaries. This basic decomposition immediately points to clear patterns in module divergence from the benchmark.

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