work every day, but I usually work every three days” or “I typically work four days a week”) may produce inaccurate estimates of season-long labor.

Having postulated that irregularity in work schedules is likely to contribute to inaccuracy in labor measurement and that errors increase as schedules become more irregular and having shown evidence of few systematic patterns in smallholder work schedules, we turn to the question of how people arrive at the labor figures they report.

Several possibilities are raised in the cognitive psychology literature. First, people might infer their labor by extrapolating from salient episodes of work, such as the busiest workweek. Second, they might base their inferences on the most recent workweek. Third, as de Nicola and Giné (2014) find in the case of earnings, people might attempt to calculate a total from their knowledge of averages. In all cases, the seasonwide total is built on the basis of some subset of the season.

To examine which subset of the season may be used as the reference period for their seasonwide inferences, we use the weekly data. Here, we compare the recall data with the seasonlong extrapolations based on the person-level average, peak, recent, and harvest work periods in the weekly visit and weekly phone data. These estimates are presented in table 6. Unlike de Nicola and Giné (2014), it appears that farmers in our data do not base season-long estimates on the average workweek. This finding is consistent with the idea that it may be difficult for people to calculate an average mentally in the absence of a clear, regular work pattern. There is little evidence that respondents are inferring their season-long labor based on their average weekly labor at the harvest, the scaled total of which is much higher than the reported data in recall NPS and recall ALT. Nor does it appear that respondents are making inferences based on their busiest week, the totals of which would be two or three times higher than those reported in the recall survey designs. Instead, although none of these reference periods provides a close approximation of the recall-reported person-season hour totals, the totals inferred from the most recent work experiences appear the closest to those obtained by recall, a finding consistent with those in Schwarz and Oyserman (2001).<sup>20</sup> Alternately, it might be that people look to the units or levels of aggregation that are intuitive or meaningful to them to form estimates of labor. If farmers tend to think about labor at the person level rather than the plot level, then they may erroneously substitute their person-level estimates for their person-plot level labor. Simulations using weekly data show that such a scenario may indeed be the case. Thus, we arrive at a total of 187 hours worked over the season in the weekly visits and 212 hours in the weekly phone data. These calculations are close to those reported at the person-plot level for recall NPS and recall ALT (121 hours and 146 hours, respectively) rather than those these groups report at the person level. Similarly, if, as weekly visit

> 20 In our context, the most recent period coincides with both the peak work period and a particularly culturally and economically salient one, namely, the harvest. For this reason, it is difficult to disentangle the effects of recency from those of work intensity or salience. For instance, if recall NPS and recall ALT household members reported labor based on the work they performed during the last weeks of the season, this could be because the most recent work performed is the easiest to remember, because this is a peak and time-bound work period, or because the work period coincides with the harvest, where work is most salient in terms of income gains.

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