MEASURING HOUSEHOLD LABOR ON TANZANIAN FARMS Vellore Arthi University of Oxford Kathleen Beegle World Bank Joachim De Weerdt University of Antwerp Amparo Palacios-López World Bank World Bank December 10, 2015

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)

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?

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

Role of Surveys Labor measurement happens primarily through surveys Little guidance on best-practice or reliability of current data Most evidence on reliability of labor data comes from the US (Bound e.a. 2001) Unlikely to be relevant for developing world

A typical survey instrument (Own Farm Labor)

Worries 1.Cognitively burdensome: – High level of granularity – Long recall – Asked to calculate averages on the spot 2.Surveys differ: – Respondent (proxy, self) – Recall period (yesterday, last season, last seven days) – Phrasing, sequencing, screening – Level of granularity (HH, ind, ind-plot, ind-plot-activity) – Totals values, typical values or combination – etc. etc. etc… -> comparability?

Social and cognitive psychology 1. Recall period – Ex. “Did you work on plot X in the past 4 weeks?” Forgetting Telescoping – Ex. “How many times have you been angry today?” vs. Ex. “How many times have you been angry in the past 12 months” Recall period influences inferred meaning

Social and cognitive psychology 2. Assumptions about the world – Undue influence of recent experiences – Subjective theories - Ross and Conway skills training experiment: respondents reconstruct their past guided by their subjective theories about what the training should have done Sequencing of questions ‘how happy are you with life in general?’ ‘how often do you go out on a date?’ – Ex. in low-income survey context: do reports on yields influence reports on labor inputs?

Social and cognitive psychology 3. Respondent – survey interactions: - Social desirability bias Ex. 25% of non-voters report having voted immediately after an election - Strategic answers Ex. Asking about poverty in survey evaluating Ex. Asking about attitudes after attitudes training

Social and cognitive psychology 4. Respondent strategies Ex. “how many visits to Africa since January?” Ex. “how many cups of coffee have you had in the past 7 days?” Recall and count for salient and infrequent events. Rate based estimations for regular events (possibly with corrections)

REGULARITY SALIENCE Recall & Count Rate-based FARM LABOR FORMAL LABOUR Schematically Other info?

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

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

A typical survey instrument (Own Farm Labor)

Golden standard (‘truth’) 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

Alternative: phone 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

Farming Practices Mara region of Tanzania 6.3 HH members 1.6 ha land, spread across 4.8 plots 26 minutes away (52 minute commute) Main farming season Jan-August Primary crops: cassava and maize Secondary crops: beans, sweet potato, sorghum

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% Paid ag labor16% Free ag labor21% Fishing10% Livestock work27% Employment off-farm11% Business activity31% Collecting firewood56% Collecting water72% Schooling27% Sick48% * Not conditional on working that week

A Day with Agricultural Labor

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?

Person-plot level results REPORTED CALCULATED Total weeks Total days Avg. days per working week Avg. hours per working day Total hours per person- plot Revisit Note: averages are calculated across person-plot-visit combinations that have at least one positive observation for the person during that visit.

Person-plot level results REPORTED CALCULATED Total weeks Total days Avg. days per working week Avg. hours per working day Total hours per person- plot Revisit Recall NPS Note: averages are calculated across person-plot-visit combinations that have at least one positive observation for the person during that visit.

Person-plot level results REPORTED CALCULATED Total weeks Total days Avg. days per working week Avg. hours per working day Total hours per person- plot Revisit Recall NPS Recall ALT Note: averages are calculated across person-plot-visit combinations that have at least one positive observation for the person during that visit.

Alternative: phone survey REPORTED CALCULATED Total weeks Total days Avg. days per working week Avg. hours per working day Total hours per person- plot Revisit Phone Recall NPSn/a29.2n/a Recall ALT6.3n/a Note: averages are calculated across person-plot-visit combinations that have at least one positive observation for the person during that visit.

Mechanisms Regularity of hours per day Irregularity of days per week

Modal number of days/week Modal days worked Frequency (%) Distribution of actual days worked, for a given mode (row %) Notes: mode taken for each member across all working weeks (N=9,508).

modal hours/day Modal days worked Frequency (%) Distribution of actual days worked, for a given mode (row %) Notes: mode taken for each member across all working days. N=38,462.

Mechanisms Regularity of hours per day Irregularity of days per week Assuming respondents do not use recall and count strategies, how might they average? – Based on recent experiences? – Based on peak labor periods? – Assuming they worked all the time?

Scaling exercises (person level) Weekly Visit Weekly Phone Recall NPS Recall ALT Report from survey (no scaling) (196.6)(222.8)(332.5)(436.8) Scaled up hours in busiest week (642.2) Scaled up hours in busiest month (303.3) Scaled up hours in average working week (228.8) Note: The figures for weekly visit and weekly phone individuals are hypotheticals based on scaling up the week or month specified; recall individuals’ hours totals are actual reported hours over the season.

Aggregating to higher levels Weekly VisitWeekly PhoneRecall NPSRecall ALT B. Per person (all household plots) Hours Days C. Per household per plot (all persons) Days Hours D. Per Household (all persons and all plots) Hours Days

Cumulative no. of plots Arthi, Beegle, De Weerdt, Palacios-Lopez Measuring Farm Labor

Plot characteristics Arthi, Beegle, De Weerdt, Palacios-Lopez Measuring Farm Labor

Aggregating across people Weekly VisitWeekly PhoneRecall NPSRecall ALT B. Per person (all household plots) Hours Days C. Per household per plot (all persons) Days Hours D. Per Household (all persons and all plots) Hours Days

Missing workers

Missing workers: gender?

Missing workers: children?

Competing forms of bias Weekly VisitWeekly PhoneRecall NPSRecall ALT B. Per person (all household plots) Hours Days C. Per household per plot (all persons) Days Hours D. Per Household (all persons and all plots) Hours Days

Competing forms of bias Weekly VisitWeekly PhoneRecall NPSRecall ALT B. Per person (all household plots) Hours Days C. Per household per plot (all persons) Days Hours D. Per Household (all persons and all plots) Hours Days times 2.18 times 1.3 times

Exaggerated hours per ha

Labor Productivity RecallRevisit% Difference Statistical Significance Value of output of the plot (tshillings) 110, ,2795% Sum of all household agricultural labor in hours over the plot-season %*** Agricultural labor productivity (tsh/hr) 1,005 2,011100%*** Sum of all household agricultural labor in days over the plot-season %*** Agricultural labor productivity (tsh/day) 3,900 7,00880%***

High frequency phone surveys: cost Weekly Visit Weekly Phone Cost per Household US$1 visit14%6% Cost increase relative to the cost of an LSMS-type survey 10 visits139%54% 20 visits277%108% 25 visits346%135% 30 visits416%162% Table 11: Per household interviewing costs as a percentage of the baseline survey cost

Conclusions 1 Labor recall modules exaggerate estimates of the total days and hours worked by individuals on plots. Likely due to the irregularity of such work Recall can even distort information on the number of plots and the number of people who work on the farm Various forms of bias compete with each other Phone surveys perform well, but remain expensive

Conclusions 2 Misallocation of labor across sectors (Gollin e.a. 2015) If we exaggerate labor inputs, do we underestimate productivity of people? (McCullough, 2015) Also raises a question: why do people not work more? – Demand for leisure? – Market imperfections? – Is our concept of farm labor too narrow?

More to come… From Tanzania – Labor Productivity analysis – Gender aspect Ongoing work in Ghana – Daily rainfall data – Implications of ICLS new definition: intention to sell – Post-harvest follow up of households Peru??

MEASURING HOUSEHOLD LABOR ON TANZANIAN FARMS Vellore Arthi University of Oxford Kathleen Beegle World Bank Joachim De Weerdt University of Antwerp Amparo Palacios-López World Bank World Bank December 10, 2015