Dillon (2012). Contacting all respondents 10 times by phone would increase the cost of the survey by 54 percent, while calling all respondents 30 times would increase costs by 162 percent. Our particular experiment required 24 calls to cover the complete agricultural season, but this is highly context-specific, and other surveys may be able to achieve gains in accuracy with fewer points of contact.

These numbers suggest that, in practice, the use of high-frequency phone surveys to collect more reliable labor data may be quite expensive. Nonetheless, it may represent a viable option in surveys that already use phone calls to respondents for other purposes, such as to ensure the continued participation of respondents, to keep track of respondents who relocate, or to collect data requiring high-frequency contacts or a quick turnaround (Dillon 2012; Garlick, Orkin, and Quinn 2015).

Given the importance of cognitive burdens in driving mismeasurement in labor data obtained by recall, another approach is to design surveys in ways that minimize these burdens. For instance, where the analytical demands on the data make this possible, questions could be posed in ways that are more intuitive relative to and better aligned with the ways farmers remember and make inferences about their work.<sup>23</sup> Similarly, data collectors can attempt to shorten the recall period so that labor reporting is likelier to be based on memory than on inference.

Another approach involves managing and correcting for known shortcomings in recall survey data. For instance, by assessing the degree of irregularity in farming practices in the survey context, data collectors will be able to anticipate more effectively whether and the extent to which the resulting labor data will be reliable. They may also use high-frequency surveys such as the ones used in this study, which dramatically shorten the traditional season-long recall period, as an approach to large-scale data collection or as a means to create a consistent adjustment factor that can be applied to past and future recall surveys in the traditional vein. Of course, whether the latter is a reasonable approach to correcting systematic bias in reporting will depend on the specifics of the research context and the degree of variability in these specifics within a given survey group, for instance, the location by region, the crop, the degree of irregularity in farming, the degree of individual responsibility over plots, the prevalence of other types of economic activity, and the uses to which the resulting data will be put.

The second contribution of this study is to the debate on the agricultural productivity gap (Gollin, Lagakos, and Waugh 2014). Systematically overestimated measures of the amount of work people carry out on smallholder farms leads to underestimates of labor productivity in agriculture. Furthermore, it is likely that the effects are correlated with individual characteristics. For example, we find that more highly educated respondents produce less recall bias in reported family farm labor than do their less well educated counterparts (not reported). If people with greater levels of human capital are less likely to overestimate their labor in recall, perhaps because they are more well able to cope with the cognitive burdens of remembering and inferring irregular labor, then their higher labor productivity may not be entirely attributable to true differences in

> 23 Indeed, survey experiments that test the level—for example, person-plot, person, and so on—at which individuals provide the most accurate labor histories would be a promising area for future research.

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