consumption frequency for these rare items, which include, for example, Macaroni and Spaghetti or Pork. In contrast, the more frequently consumed items are associated with a much lower incidence error (albeit still positive). Finally, for the diaries, the pattern of error is reversed. The less frequently consumed items are reported at even less frequency in the household diaries, most likely reflecting the influence of personal leave out error for these items, while the more commonly consumed items are captured to a relative degree of accuracy by the household respondent.

Another striking feature underscored in figure 1 relates to the spread of the reporting error. In all five figure panels, there is a clear tendency for the incidence error to be much farther away from zero (in either a positive or a negative direction) for less commonly consumed items and for the spread to narrow substantially at higher consumption incidence. This patterns fits well with the findings of some of the social and cognitive psychology literature, discussed in section II, which highlights salience and regularity as key dimensions in frequency reporting, in particular if respondents do not count and recall but instead use ratebased estimation techniques.

Now that we have established that incidence reports are more accurate for high-incidence items (in terms of less spread in the reporting error), we ask a parallel question concerning the value reported. Here, we may hypothesize that the salience of high-value items in recall surveys leads to lower errors in the value reporting. Figure 2 plots value error as a function of benchmark consumption value. The first thing to note is that, in general, there is no reduction in variance for higher value items, contrary to the case of the incidence reporting of commonly consumed items. Recall that table 5 reveals a mix of over- or underreporting for module 1 (14-day long list), depending on the food group. The relevant scatterplot in figure 2 suggests that the net overreporting error is greater for consumption items of relatively low value. As the benchmark value increases, the net error approaches zero and then turns negative at high-value items, signifying that the items largest in value suffer from underreporting. The same directional pattern is apparent for module 2 (7-day long list), on which each food group in table 5 displays net overreporting. Figure 2 suggests that particularly high relative errors are associated with foods with a consumption value below the median.

The usual month shows the same error gradient as the other two recall modules, although, here, the net error for most food items is negative (as is true of the food groups listed in table 5). The magnitude of this error rises with the benchmark consumption value, suggesting that the highest value consumption items are associated with the greatest degree of underreporting. This likely indicates that either recall error dominates in the value reporting in this module, or rule of thumb value reporting on the usual month induces negative errors that grow in magnitude with consumption value. Finally, for the diary modules, and in contrast to the three recall modules analyzed, there is little systematic reporting error with respect to value. Clearly, the bigger challenge with the household diaries is ensuring the recording of any consumption for some food groups (the incidence), rather than recording accurate consumption values conditional on incidence.

Besides consumption incidence and value, we investigate three other dimensions of the individual food goods: the frequency of market purchase, the share of consumption of home production, and whether the food is storable or perishable. Table 7 lists the mean proportional errors relative to the benchmark for these three characteristics for each of the five modules. Often any difference in mean error across each characteristic is not statistically significant, in part because the power of the test is relatively low: there are 14