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The contribution of women to labor in African agriculture is regularly quoted in the range of 60–80%. Using individual, plot-level labor input data from nationally representative household surveys across six Sub-Saharan African countries, this study estimates the average female labor share in crop production at 40%. It is slightly above 50% in Malawi, Tanzania, and Uganda, and substantially lower in Nigeria (37%), Ethiopia (29%), and Niger (24%). There are no systematic differences across crops and activities, but female labor shares tend to be higher in households where women own a larger share of the land and when they are more educated. Controlling for the gender and knowledge profile of the respondents does not meaningfully change the predicted female labor shares. The findings question prevailing assertions regarding substantial gains in aggregate crop output as a result of increasing female agricultural productivity.

Understanding the constraints to agricultural growth in Africa relies on the accurate measurement of smallholder labor. Yet, serious weaknesses in these statistics persist. The extent of bias in smallholder labor data is examined by conducting a randomized survey experiment among farming households in rural Tanzania. Agricultural labor estimates obtained through weekly surveys are compared with the results of reporting in a single end-of-season recall survey. The findings show strong evidence of recall bias: people in traditional recall-style modules reported working up to four times as many hours per person-plot relative to those reporting labor on a weekly basis. Recall bias manifests both in the intensive and extensive margins of labor reporting: while hours are over-reported in recall, the number of people and plots active in agricultural work are under-reported. The evidence suggests that this recall bias is driven not only by failures in memory, but also by the mental burdens of reporting on highly variable agricultural work patterns to provide a typical estimate. All things equal, studies suffering from this bias would understate agricultural labor productivity. JEL Codes: C8, O12, Q12

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Artículo de publicación ISISin acceso a texto completoThis paper hypothesises that labour and credit market imperfections - by discouraging off-farm income-generating activities and restricting access to inputs, respectively - affect female farm productivity more deeply than male productivity. The paper develops a theoretical model, which decomposes the contribution of various market imperfections to the gender productivity gap. Empirically we show that agricultural labour productivity is, on average, 44 per cent lower on female-headed plots than on those managed by male heads. 34 per cent of this gap is explained by differences in labour market access and 29 per cent by differences in credit access

We calculate statistics for each individual country using the household weights constructed by the World Bank and national statistics offices. The cross-country averages are calculated as simple averages between the 40 country-level values.The COVID-19 pandemic is the worst global macroeconomic shock since the Great Depression. This brief reports which groups of workers have been hit hardest by the jobs impact following the economic fallout of COVID-19 in developing countries. 1 It complements an earlier study by Khamis et al. ( 2021) that shows that the onset of the pandemic had major and pernicious adverse effects on the livelihoods of workers across about 40 developing countries. This brief reveals the following:• Larger shares of female, young, less educated, and urban workers stopped working, with gender differences being particularly pronounced. Although women work in different sectors than men, gender gaps in work stoppage stemmed mainly from differences within sectors rather than differential employment patterns across sectors.

Understanding the constraints to agricultural growth in Africa relies on the accurate measurement of smallholder labor. Yet, serious weaknesses in these statistics persist. The extent of bias in smallholder labor data is examined by conducting a randomized survey experiment among farming households in rural Tanzania. Agricultural labor estimates obtained through weekly surveys are compared with the results of reporting in a single end-of-season recall survey. The findings show strong evidence of recall bias: people in traditional recall-style modules reported working up to four times as many hours per person-plot relative to those reporting labor on a weekly basis. Recall bias manifests both in the intensive and extensive margins of labor reporting: while hours are over-reported in recall, the number of people and plots active in agricultural work are under-reported. The evidence suggests that this recall bias is driven not only by failures in memory, but also by the mental burdens of reporting on highly variable agricultural work patterns to provide a typical estimate. All things equal, studies suffering from this bias would understate agricultural labor productivity.

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.