1. MEASURING HOUSEHOLD LABOR ON TANZANIAN FARMS Presented by Amparo Palacios-Lopez apalacioslopez@worldbank.org Co-authors: Vellore Arthi, Kathleen Beegle and Joachim De Weerdt CSAE March 21, 2016 “Draft”

2. Background Small-holder farming predominant in low-income countries (FAO, 2009; Olinto et al, 2013) Small-holder analysis necessary to assess constraints to agricultural growth in Africa – e.g. low levels of labor productivity in agriculture relative to that in other sectors in Sub-Saharan Africa (see review in Gollin et al, 2014, McCullough, 2015) Serious weaknesses in agricultural statistics persist (FAO, 2008) Much of the available data is drawn from recall surveys, but there is little guidance on best-practice or reliability of current data – Most evidence on labor data reliability comes from the US (e.g. Bound et al, 2001)

3. What We Do We conduct a survey experiment using variation in the recall period to test the extent to which recall bias may exist in traditional end-of-season measures of own-household farm labor Questions: – How do agricultural labor measurements recalled at the end of the season compare to measures gathered weekly throughout the season? – What implications does such recall bias have for measures of agricultural productivity?

4. Contributions One of the only studies examining the quality of reporting of household farm labor Provide evidence that recall bias is likely to be exacerbated where work patterns are irregular Offer plausible alternative methods to improve the accuracy of labor measurement in rural farm households Show that problems in traditional labor measurement may lead to spuriously low measures of agricultural productivity, distorting our view of small-holder agriculture and of the optimal allocation of rural labor

5. Research conducted in 2014 Masika season (January-September) in 18 Enumeration Areas in the Musoma rural and Butioma districts of the Mara region, Tanzania Agricultural labor questions varied across 4 arms of study, 2 recall-based and 2 weekly-based, to which households were randomly assigned Mara region, Tanzania Fieldwork conducted in 2014 Study Design

6. A typical survey instrument Measuring Labor

7. Business as Usual Measuring Labor DesignInterview-Type Number of Households Recall-NPSend-of-season survey, short module (days in each activity, hours per day) 218 Recall-ALTend-of-season survey, standard module (weeks, days & hours) 212

8. Golden standard (‘truth’) Measuring Labor DesignInterview-Type Number of Households Weekly-Visitweekly in-person visits for the duration of the main season 212 Recall-NPSend-of-season survey, short module (days in each activity, hours per day) 218 Recall-ALTend-of-season survey, standard module (weeks, days & hours) 212

9. Alternative: phone Measuring Labor DesignInterview-Type Number of Households Weekly-Visitweekly in-person visits for the duration of the main season 212 Weekly- Phone weekly phone interviews for the duration of the main season 212 Recall-NPSend-of-season survey, short module (days in activity & hours 218 Recall-ALTend-of-season survey, standard module (weeks, days & hours, 212

10. Overview of activities (>10yrs) % individuals engaged in the activity at least 1 day average days worked per week, conditional on being active in the activity* Hours per day in activity, conditional working that day Own farm labor87%1.904.49 Paid ag labor16%0.344.65 Free ag labor21%0.284.38 Fishing10%1.236.38 Livestock work27%1.105.08 Employment off-farm11%1.008.39 Business activity31%1.437.60 Collecting firewood56%0.492.01 Collecting water72%2.751.23 Schooling27%2.767.86 Sick48% * Not conditional on working that week Arthi, Beegle, De Weerdt, Palacios-Lopez Measuring Farm Labor

11. A Day with Agricultural Labor

12. The accuracy of labor measurement depends both on memory and the ability to mentally scale labor to fit standard question formats Respondents must accurately recall many components, including: – An accurate listing of plots – An accurate listing of people working on those plots – Total weeks worked – Average hours worked – Average days worked Ease and accuracy of recall may also 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 How Might Recall Bias Manifest?

13. Person-plot level results Measuring Labor Note: averages are calculated across person-plot-visit combinations that have at least one positive observation for the person during that visit. HoursDaysWeeks Hours per day worked Revisit 39.59.22.54.1

14. Person-plot level results Measuring Labor Note: averages are calculated across person-plot-visit combinations that have at least one positive observation for the person during that visit. HoursDaysWeeks Hours per day worked Revisit 39.59.22.54.1 Recall NPS 121.325.7N/A4.6 11% difference 207% difference 179% difference

15. Person-plot level results Measuring Labor Note: averages are calculated across person-plot-visit combinations that have at least one positive observation for the person during that visit. HoursDaysWeeks Hours per day worked Revisit 39.59.22.54.1 Recall NPS 121.325.7N/A4.6 Recall ALT 146.329.85.74.6

16. Alternative: phone survey HoursDaysWeeks Hours per day worked Revisit 39.59.22.54.1 Phone 48.810.72.64.4 Recall NPS 121.325.7N/A4.6 Recall ALT 146.329.85.74.6 Measuring Labor Note: averages are calculated across person-plot-visit combinations that have at least one positive observation for the person during that visit.

17. Mechanisms Cognitive burdens of scaling work to provide “typical” or “average” figures – This is easy for hours per day, as they are regular – This is hard for days per week, as they are irregular Leads to an exaggeration of days and weeks worked How might they construct an “average”? – Based on recent experiences? – Based on peak labor periods? – Assuming they worked all the time?

18. Measuring Labor REGULARITY SALIENCE Recall & Count Rate-based Farm labor Formal sector labor INSIGHTS FROM SOCIAL AND COGNITIVE PSYCHOLOGY

19. Irregularity of Work Patterns: Modal Days Per Week Modal days worked Frequency (%) Distribution of actual days worked, for a given mode (row %) 1234567 124.455.714.97.86.45.67.32.3 212.117.941.011.48.48.28.54.6 37.214.7 33.811.111.810.03.9 46.411.813.812.834.711.211.83.9 510.312.213.013.611.334.511.73.7 629.09.17.69.011.015.441.86.1 710.56.28.78.49.511.115.340.9

20. Irregularity of Work Patterns: Modal Hours Per Day Modal hours worked Frequency (%) Distribution of actual hours worked, for a given mode (row %) 23456 25.448.913.421.26.610.0 312.511.053.420.59.06.1 448.34.514.857.013.610.2 515.23.210.825.946.513.6 618.63.58.918.316.053.3

21. Conclusion Labor recall modules exaggerate estimates of the total days and total hours worked by person-plot – Likely due to the lack of a “standard” working pattern Phone surveys perform well, but remain expensive If we exaggerate labor inputs, do we underestimate productivity of people? – May help explain the agricultural productivity gap – Preliminary analysis using this data suggests agricultural labor productivity is 78% lower in recall-surveyed households than in weekly-surveyed ones, with the reported hours of own-household farm labor explaining a large portion of this difference Also raises a question: why do people not work more? – Low-employment poverty trap (Bandiera et al., 2015)? – Demand for leisure? – Is our concept of farm labor too narrow?

22. MEASURING HOUSEHOLD LABOR ON TANZANIAN FARMS Amparo Palacios-López World Bank apalacioslopez@worldbank.org CSAE March 21, 2016