Publication: Not Your Average Job: Measuring Farm Labor in Tanzania
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Published
2018-01
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0304-3878
Date
2018-02-01
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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 as 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.
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Publication Not Your Average Job(World Bank, Washington, DC, 2016-07)A good understanding of the constraints on 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 report working up to four times as many hours per person-plot relative to those reporting labor on a weekly basis. If hours are aggregated to the household level, however, this discrepancy disappears, a factor driven by the underreporting by recall households of people and plots active in agricultural work. The evidence suggests that these competing forms of recall bias are 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.Publication What Does Variation in Survey Design Reveal about the Nature of Measurement Errors in Household Consumption?(World Bank, Washington, DC, 2013-02)This paper uses data from eight different consumption questionnaires randomly assigned to 4,000 households in Tanzania to obtain evidence on the nature of measurement errors in estimates of household consumption. While there are no validation data, the design of one questionnaire and the resources put into its implementation make it likely to be substantially more accurate than the others. Comparing regressions using data from this benchmark design with results from the other questionnaires shows that errors have a negative correlation with the true value of consumption, creating a non-classical measurement error problem for which conventional statistical corrections may be ineffective.Publication Measuring Farm Labor(Published by Oxford University Press on behalf of the World Bank , 2020-05-21)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’ under reporting 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.Publication Measuring Farm Labor(World Bank, Washington, DC, 2019-01)This study examines recall bias in farm labor by conducting a randomized survey experiment in Ghana. Hours of farm labor obtained from a recall survey conducted at the end of the season are compared with data collected weekly throughout the season. The study finds that the recall method overestimates farm labor per person per plot by about 10 percent, controlling for observable differences at baseline. Recall bias in farm labor per person per plot is accounted for by the fact that households in the recall group report fewer marginal plots and farm workers, denoted here as listing bias. This listing bias also creates a countervailing effect on hours of farm labor at higher levels of aggregation, so that the recall method underestimates farm labor per plot and per household and overestimates the labor productivity of household-operated farms. Consistent with the notion that recall bias is linked to the cognitive burden of reporting on past events, the study finds that recall bias in farm labor has a strong educational gradient.Publication The Challenge of Measuring Hunger(World Bank, Washington, DC, 2014-01)There is widespread interest in the number of hungry people in the world and trends in hunger. Current global counts rely on combining each country's total food balance with information on distribution patterns from household consumption expenditure surveys. Recent research has advocated for calculating hunger numbers directly from these same surveys. For either approach, embedded in this effort are a number of important details about how household surveys are designed and how these data are then used. Using a survey experiment in Tanzania, this study finds great fragility in hunger counts stemming from alternative survey designs. As a consequence, comparable and valid hunger numbers will be lacking until more effort is made to either harmonize survey designs or better understand the consequences of survey design variation.
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