workers than on the number of hours per listed plot, as the omitted workers are the ones working relatively few hours.

# **6. Conclusion**

Measuring labor in agriculture is a key input to the analysis of agricultural labor productivity, which in turn is an important metric in the design of policies for developing countries. Nevertheless, research on the best methods to measure agricultural labor is scarce. We contribute to the literature on measuring agricultural labor by analyzing the data of a randomized survey methodological study conducted in Ghana. The study was designed to allow for the comparison of estimates of farm labor obtained from a recall survey conducted at the end of the season with data collected weekly throughout the season.

The results indicate that the recall method overestimates farm labor per person per plot by about 12 percent, conditional on differences in baseline characteristics. This recall bias in farm labor is accounted for by listing bias, as households in the recall group report significantly fewer marginal plots and farm workers. Moreover, even though farm labor per person per plot is overestimated, the recall bias is very different at higher levels of aggregation. Because recall households report too few plots and too few workers, the end-of-season recall method _underestimates_ farm labor per plot and total farm labor per household. As a result, plot-level estimates of labor productivity in agriculture are significantly overestimated by the recall method.

We also moved beyond proxy determinants to understand the deeper forces behind recall bias. Consistent with the notion that recall bias is linked to the cognitive burden of reporting on past events, we find that better educated households recall farm labor with greater accuracy. This educational gradient – together with variations in the design of the experiment – most likely explains why this study finds much lower recall bias in farm labor in the Ghanaian context than the previous study found for the Mara region in Tanzania (Arthi et al. 2018). We find that more educated households are better able to accurately list all their plots at endline. Additionally, we find no significant recall bias in hours worked per person per plot in more educated households, even when they underreport the number of farm workers.

Contrary to our expectations, we do not find significant recall bias for female farm workers (as opposed to male farm workers). This is explained by the fact that females work substantially fewer hours per plot compared to males, thus the omitted female farm workers differ relatively less from the listed female farm workers. This finding illustrates that recall bias in farm labor per person per plot is likely to be greater for sub-groups of workers among which there is greater variation in the intensity of farm labor.

Our findings have implications for survey design. First, the differences in results between this paper and the experiment in Tanzania (Arthi et al. 2018) highlight the need to understand the context in which the survey will be implemented. Second, data collection agencies should increase their efforts on ensuring that listings of plots and household members are complete to minimize the effect of listing bias on the collection of labor and other agricultural inputs. Third, it is extremely important to have clarity on the objective of the survey, whether the survey will be used for analysis of employment status, land productivity and/or labor productivity; each objective may require different levels of granularity of the data collected and may be more prone to different approaches for data collection.

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