quantified their pretraining skills at a lower level than the level at which they had originally assessed their skills prior to receiving the skills training. The authors argue that the students reconstructed their past, guided by their subjective theories over what the skills training ought to have done. If African farmers hold implicit theories about the link between, say, labor inputs and production, then the report on the one may influence the report on the other. For example, in an end-of-season recall survey, labor may be retrospectively overstated during good harvests and understated during bad harvests.

# **3. EXPERIMENTAL DESIGN AND CONTEXT**

The goal of this study is to examine biases of the sort described above in agricultural labor data collected through household surveys. We focus on potential biases introduced by the length of the recall period and the frequency of reporting. We have conducted a large randomized survey experiment among smallholder farming households in rural Tanzania through which we have compared agricultural labor information collected in weekly surveys (our benchmark for the true labor estimates) with that collected in a single end-of-season survey. Understanding farm productivity at the lowest level entails studying inputs and yields on plots. From a broader perspective, questions of productivity in smallholder farming may require analysis of aggregated measures. We focus on both by examining plot-person labor reporting as well as aggregate household measures of family labor.

## _3.1. Experimental design_

We have conducted a survey experiment among 854 farming households in 18 enumeration areas in the Mara Region of rural northern Tanzania. Labor input was measured for the 2014 _masika_ (the main, long-rains season), running roughly from January to June 2014. Households were randomly assigned to one of four survey designs within each of the 18 enumeration areas. The differences in the four survey arms are the manner and frequency with which they are contacted.<sup>7</sup> Two survey designs entailed weekly interviews throughout the entire masika season either in person or by phone.<sup>8</sup> Face-to-face baseline survey was conducted in January 2014 and a faceto-face end line survey was fielded in July-September 2014.

The other two survey designs entailed one recall survey fielded at the end of the agricultural season in July-September 2014. This survey differs from the end line surveys received by the weekly households in that collected information on labor from January to June.

The four alternative survey designs are as follows:

- **_Weekly visit (benchmark):_** _Weekly face-to-face surveys for the duration of the masika_ For weekly visit households, a baseline survey was conducted in January 2014, followed by weekly face-to-face surveys conducted by enumerators through the end of June 2014 and an end line survey (July–September 2014) to collect farm production information. For each plot,

> 7 The data were collected using computer-assisted personal interviewing through the surveybe software program. 8 All weekly visit households received a mobile phone, but recall households did not. Mobile phone ownership is widespread, at 72 percent of households in our sample. Thus, this element is unlikely to influence the results.

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