cognitively burdensome than their weekly alternatives and to exacerbate recall bias in the reporting of family farm labor.

# **5. CONCLUSION**

How accurate are data on household farm labor? Our survey experiment finds that recall data collected in the postharvest period leads to overestimates of the time household members spend on specific plots over the course of the season, in some cases by a factor of 3.7. Yet, this overreporting is counterbalanced by considerable underreporting—by up to 50 percent each—in the number of plots worked by household members engaged in family farming. Accordingly, at the household level of aggregation, the total number of seasonal hours worked vary little across recall periods. Recall bias appears to result both from forgetting and from the extrapolation of seasonwide labor from erroneous inferences about past labor. Both of these distortions are rooted in the irregular nature of farmwork schedules and practices in our study region. In the absence of a typical work schedule or a typical and consistent level of engagement among workers and on plots, traditional end-of-season recall surveys force respondents into cognitively taxing calculations. These calculations result in labor inferences that appear to be based on recent rather than representative experiences, the omission of members only intermittently engaged in family farm labor, and the exclusion of plots further from the house and, thus, less salient in memory.

This paper makes two contributions to the literature. The first contribution is to the literature on measurement. If our results hold in other settings, then agriculture-based low-income countries, asking about farm activities 6–12 months after they have ended will tend to exaggerate estimates of the total days and hours household members spend working on their plots and farms. These findings may even hold outside the context of agriculture, for instance, in settings in which some but not other components of the labor calculation face considerable variability (for example, see Dupas, Robinson, and Saavedra 2015).

Clearly, survey designers should tread lightly when asking questions about the frequency of nonsalient, irregular events. But what is the alternative? The benchmark weekly visit approach used here is an expensive one that is unlikely to be a realistic prospect at the larger scale necessary for national labor surveys. A result that comes out forcefully in this study is the strong performance of the phone surveys, which show little difference from the benchmark weekly visit survey. Crucially, given the significantly lower transportation costs involved, phone surveys are also, by design, likely to be less expensive to implement than face-to-face high-frequency alternatives, but how much cheaper?

We use the cost data available through our survey experiment to mimic a scenario whereby an existing household baseline survey adds either short face-to-face surveys or short phone surveys. The results of this costing exercise are presented in table 11. We assume that all fixed costs related to training and preparation have been subsumed in the baseline interview and focus instead on the increase in the variable costs of conducting 1, 10, 20, 25, or 30 visits or phone calls. Phone calls are much less expensive that revisits. .The cost of a single round of phone surveys is 6 percent of the cost of the baseline survey. This estimate is close to the 7 percent reported by 19