Measuring Farm Labor: Survey Experimental Evidence from Ghana

Abstract

This study examines recall bias in farm labor through a randomized survey experiment in Ghana, comparing farm labor estimates from an end-of-season recall survey with data collected weekly throughout the agricultural season. Recall households report 10 percent more farm labor per person-plot, which can be explained by recall households' underreporting of "marginal" plots and household workers. This "selective" omission by recall households, denoted as listing bias, alters the composition of plots and workers across treatment arms and inflates average farm labor hours per person-plot in the recall group. Since listing bias, in this setting, dominates other forms of recall bias at higher levels of aggregation (i.e., when farm labor per person-plot is summed at the plot, person, or household level), farm labor productivity is overestimated for recall households. Consistent with the notion that recall bias is linked to the cognitive burden of reporting on past events, there is no recall bias among more educated households.

Employment Data in Household Surveys: Taking Stock, Looking Ahead

“…Arthi et al (2016) find, however, strong evidence of recall bias in agricultural labor input per person-plot in Tanzania, but also show that labor input aggregated at household level yields correct results because respondents overreport labor-input at plot level but underreport the number of plots and people working on each plot. A similar study in Ghana confirms these findings (Gaddis et al, 2019).…”

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“…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. For example, in a similar study conducted in rural Ghana , recall data overestimated the time household members spend on plots by 18 percent (Gaddis et al 2017). These differences call for attention to the context and characteristics of the population under investigation.…”

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“…Without further guidance on the part of the interviewer, it is unclear a priori how farmers perform such aggregation in their response. In addition, survey research shows that cognitively onerous questions can lead to bias in the data, especially among respondents with low levels of education (Arthi et al 2018;Gaddis et al, forthcoming). Conversely, if the intention to sell is measured at the product-level, aggregation is left to the analyst.…”

“…This complements the extensive qualitative and quantitative work carried out by the ILO in piloting alternative survey questionnaires to support the operationalization of the 19 th ICLS standards in labor force surveys (Benes and Walsh 2018). Third, the paper contributes to the broader literature on the sensitivity of labor statistics to survey methodology in developing countries (Bardasi et al 2011;Heath et al, forthcoming;Arthi et al 2018;Gaddis et al, forthcoming).…”