shrinks. The number of recall ALT hours is 3.7 times higher than the number among the weekly visit group at the person-plot level, but is only 1.9 and 2.5 times higher at the person and plot levels, respectively.

In looking at total time from the household perspective (in hours, days, weeks), we see too few statistically significant or economically meaningful differences between the four survey designs. It appears that three wrongs make a right: the competing manifestations of recall bias (that is, the misreporting by recall households of days or weeks, plots, and workers) offset each other in average household labor. While the reliability of the labor data generated by recall surveys may be sufficient for household labor supply measures, it may be problematic for other applications, such as plot productivity analysis or the calculation of labor force participation in agriculture.

# _4.2. Irregularity in work patterns_

The dramatic difference between the recall and weekly surveys in the number of hours worked, as well as in the people and plots active in agriculture, is clear, worrisome evidence that recall bias exists in both the intensive and extensive margins of end-of-season labor reporting. What drives these large and systematic gaps? In our context, the competing forms of recall bias, the overreporting of hours per person-plot, and the underreporting of the people and plots active in agriculture nearly cancel each other out in the aggregate. These manifestations of recall bias can be explained by the same phenomenon: the cognitive burdens of irregular work patterns. We discuss in turn the mechanisms by which irregularity drives each of these results.

## **The reporting of the amount of time worked**

If forgetting were the chief mechanism by which recall bias becomes manifest in the person-plot labor data, one might expect weekly interviews to yield higher season-total estimates than end-ofseason interviews. As the direction of the bias runs counter to this explanation, forgetting does not drive our results. Instead, we propose that the hours discrepancy arises from reliance on flawed inferences in the recall interviews. Weekly data show that people do some agricultural work in an average 11 of the season’s 26 weeks and on 46 of the season’s roughly 182 days. Over such a long period, recall NPS and recall ALT respondents are unlikely to use recall-and-count strategies in reporting total days and total weeks, respectively. Thus, rather than attempting to count actual days and weeks worked, respondents may revert to shortcuts, such as inferring seasonwide work from, say, their average workload or their most recent, intense, or otherwise psychologically salient period of work. Such inference may drive the week (recall ALT) and day (recall NPS) estimates. For more granular time units, inference accomplishes this because of the design of the standard survey: for days (recall ALT) and hours (both recall modules), a memory-based recall-and-count strategy is precluded, since the question asks respondents to provide an approximate or typical number.

Whether inference results in an accurate approximation of the true number of hours worked may depend on the degree to which work takes place in regular, predictable, or uniform patterns. For the smallholders in our study, work schedules are both variable (that is, they are different from one week to another) and irregular (that is, there is no systematic or predictable pattern to the 13