# **1. INTRODUCTION**

Of the 1.4 billion people living in extreme poverty, the majority reside in rural areas and rely on agriculture as a source of income and livelihood (Olinto et al. 2013). In Sub-Saharan Africa, nearly 75 percent of the extreme poor reside in rural areas, and over 90 percent participate in agriculture. Smallholder agriculture is the predominant form of farm organization, with 33 million small farms holding less than two hectares and representing 80 percent of all farms in Africa (FAO 2009). On these farms, agricultural practices are typically labor intensive, and the majority of the labor is provided by household members.

Accordingly, the labor of household members in agriculture is a key asset for poor households, and its accurate measurement is essential to the development of sound policy. Despite the importance of the agricultural sector in reducing poverty and food insecurity (Chen and Ravallion 2007; Irz et al. 2001; Ligon and Sadoulet 2007), serious weaknesses in agricultural statistics persist.<sup>2</sup> In this study, we examine one aspect, measures of family farm labor.

To assess the degree of recall bias in household farm labor, we conducted a survey experiment in Mara Region, Tanzania, over the long rainy season, January–June 2014. Smallholder farming households were randomly assigned to one of four survey designs: (a) households reporting agricultural labor in weekly face-to-face visits; (b) households reporting agricultural labor in weekly phone surveys; (c) households reporting agricultural labor in a single postharvest recall survey, the Tanzania National Panel Survey (NPS); and (d) households reporting agricultural labor in a shorter version of the Tanzania NPS postharvest recall survey. Household labor information collected in weekly visits—our resource-intensive golden standard—was then compared with data reported after the harvest. After establishing the magnitude of recall bias, we investigated the mechanisms by which it arose.

We found strong evidence of recall bias in the reporting of family farm labor, but, because of competing forms of recall bias in the reporting of hours of labor, the number of plots, and the number of active household members, the degree of distortion in reporting depended on the level of data aggregation. Labor data collected on a weekly basis, whether in person or by phone, are similar, albeit sometimes not statistically identical. Likewise, the labor data reported by recall in our two recall designs are also quite alike. However, there are striking and economically meaningful differences between the weekly and recall data. Respondents in recall-style modules reported they worked up to nearly four times as many hours per person per plot, compared with respondents reporting labor on a weekly basis. If hours are aggregated to the household level, however, this discrepancy disappears. This is driven by two factors: underreporting by recall households of the number of both working household members and the plots under cultivation. Evidence suggests that these competing forms of recall bias are driven not only by failures in memory, but also by the mental burdens of computing data on a typical situation if agricultural work patterns are highly variable during the season.

> 2 See ABCDQ (Agricultural Bulletin Board on Data Collection, Dissemination, and Quality of Statistics) (database), Statistical Division, Food and Agriculture Organization of the United Nations, Rome, http://faostat.fao.org/abcdq/.

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