MEASURING HOUSEHOLD LABOR ON TANZANIAN FARMS Presented by Amparo Palacios-Lopez apalacioslopez@worldbank.org Co-authors: Vellore Arthi, Kathleen Beegle and Joachim De Weerdt Presented at the 6th Conference of the European Survey Research Association Reykjavik, Iceland, July 2015 “Preliminary, please do not cite”

The Issue In low-income countries, a large share of people work on their farm Characterize the work that people do Most small-holders do not hire labor Characterizing farm labor productivity Scant attention to the quality and robustness of the information on family farm labor Little evidence on approaches to measuring HH farm labor Noise Bias

How do we measure household farm labor in large household surveys (if at all) 7 day recall for each type of labor 12 month for each type (recall) LSMS-ISA: intensive recall ILO standard In 2008 the Lsms team received a Grant from the gates foundation to work with 6 African NSO’s to improve and expand the ag content of their national hh surveys All the ISA surveys include a hh farm labor module, here is one example for Malawi, it asks….

Objective Develop and validate methods on improving data collection on the quantity and demographics of household labor in farming in low-income settings

What is the truth? What is a good benchmark/gold standard The price of getting the truth Alternatives that are feasible for large household survey efforts How do agricultural labor measurements recalled at the end of the season compare to measures gathered weekly throughout the season? What is the price of getting the truth: we conducting the survey with resident enumerators (that live in the village) with high level of supervision and that is expensive for sample size In addition to seeing how much recall measurement error we have, we also want to test alternatives that would be feasible for the NSOs to do with their nat rep hh surveys

Design of Study Research conducted in 2014 Masika season in 18 communities in rural Mara region, Tanzania Fieldwork January -September 2015 Mara region, Tanzania

Survey Experiment Weekly-Visit Design Interview-Type Number of Households Weekly-Visit weekly in-person visits for the duration of the main season 212 Weekly-Phone weekly phone interviews for the duration of the main season Recall-Short end-of-season survey, short module (days in activity & hours 218 Recall-Long end-of-season survey, standard module (weeks, days & hours,

How Might Recall Bias Manifest? The accuracy of labor measurement depends on the accurate recall of many components, including: Who worked on what plot Hours Days Weeks Ease/accuracy of recall may be affected by factors such as the regularity of work Individuals who did not often work a plot may forget to report that work individuals who worked irregularly may have difficulty computing the average amount of time over the season

Preliminary Findings: Over-reporting of person-plot hours by recall households Total person-plot hours are 140-180% higher in recall than in weekly interviews Hours per day are quite similar But days and weeks worked are over-reported in recall Under-reporting of workers per hh and plots per hh by recall households Roughly 50% fewer people reporting any agricultural labor in recall Roughly 50% fewer plots reported under cultivation in recall Together, these competing sources of bias nearly cancel each other out in the aggregate: household-level hours are similar across arms of the study At each level of aggregation (by plot, by person), gap closes slightly, before settling, when aggregated at the household level, to 1-1.3x higher in recall Variability of activities over the season Weekly-phone households tend to report more hours per-person in each visit than weekly-visit households

Agricultural Labor at a Glance TOTAL SEASON UNITS   Weekly-Visit Weekly-Phone Recall-Short Recall-Long Number of hours per person-plot 56.74* 65.90* 137.24 160.99* Number of hours per person 201.02* 228.25* 313.68 389.46* Number of hours per plot 183.02* 223.09* 363.8 452.42* Number of hours per household 848.64 977.59 865.94 1104.06 Number of hours per hectare 843.09* 987.05* 1608.2 2343.12* People working per household 4.22* 4.28* 2.76 2.83 Plots per household 4.64* 4.38* 2.38 2.44 Household hectares under cultivation 1.65* 1.60* 0.84 0.87 * p < 0.05; Comparisons made with respect to Recall-Short Note: conditional on any work

Preliminary Findings: An Overview Variability in work over the season appears to result in several sources of bias Over-reporting of total hours/days, and under-reporting of people and plots Lack of a consistent or typical routine or person-plot relationship contribute to hours bias Frequent and repeated questioning via weekly surveys recovers plots and workers forgotten earlier in the season and may help drive the gap between plots/workers reported by recall vs weekly households

Activities Over Season Land Preparation Weeding Ridging Harvesting

Preliminary Findings: Sources of the Hours Discrepancy Where does reporting bias in intensity of work come from? Recall households seem to report peak work patterns as average work patterns The time units with the greatest variability over the season (weeks/days worked) are harder to recall correctly Recall individuals report working roughly 2x the weeks or days per plot of their weekly counterparts Hours worked per day are both consistent across the season and similar across arms of the study; as such, these figures do not account for the season-wide hours discrepancy

Preliminary Findings: Sources of the Hours Discrepancy Which plots are being forgotten? Exclusion of late-added and early-dropped plots does not appear to account for the gap in hours, nor the gap in total household plots Plot characteristics do not predict likelihood of exclusion from recall reports Distribution of plots reported by plot characteristics (e.g. proximity to home, ownership status, crops) similar across recall and weekly plots Who is being forgotten? Exclusion of household members who do not report work , household members who report infrequent or highly variable work, and household members who are non-farmers does not appear to account for the gap in hours, nor the gap in total household workers

Take Aways Strong evidence of recall bias in the reporting of family labor Labor collected from weekly visits or phone calls are strikingly similar, as were the labor data reported by recall short or long More research is needed, experiment on the field in Ghana and next year in Ecuador