1. MEASURING HOUSEHOLD LABOR ON TANZANIAN FARMS
Presented by Amparo Palacios-Lopez
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“Preliminary, please do not cite”

2. 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

3. 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….

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

5. 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

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

7. 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,

8. 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

9. Preliminary Findings:
Over-reporting of person-plot hours by recall households
Total person-plot hours are % 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

10. 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
Number of hours per hectare
843.09*
987.05*
1608.2 *
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

11. 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

12. Activities Over Season
Land Preparation
Weeding
Ridging
Harvesting

13. 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

14. 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

15. 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