number of workers who provided family labor on each plot during the last major/minor season.<sup>4</sup> An example of the latter approach is the 2014/15 Tanzania National Panel Survey (NPS), which asks “during the long rainy season 2014, how many days did [NAME] spend on the following activities on this [PLOT]?” This is followed by a question about the “typical number of hours per day worked by an individual on this [PLOT]”. Many other agricultural surveys follow variants of this latter approach,<sup>5</sup> including the 2015/16 Ghana agricultural labor survey used in this study.

A major advantage of collecting data on farm labor by person and by plot (denoted here as _person-plot level_ ) is that it accommodate different analytic approaches at the analysis stage. Table A1 (appendix) shows for several recent studies the main unit at which farm labor is reported and/or embedded in the empirical analysis. Studies concerned with gender differences in agriculture tend to aggregate at the plot-level (sometimes with normalization for plot size), because this allows to compare levels of productivity between plots managed by male and female farmers and assess the role of labor shortages as one of the underlying constraints (Aguilar et al. 2016; Ali et al. 2016; Fisher and Kandiwa 2014; Kilic et al. 2015; McCarthy et al. 2016; Oseni et al. 2016; O’Sullivan et al. 2014; Palacios-Lopez and Lopez 2015). Other analyses of agricultural productivity aggregate either at the plot-level (Gollin and Udry 2017) or at the household-level (Andre et al. 2017; Dillon et al. 2017; Restuccia and Santaeulalia-Llopis 2017). McCullough (2017) aggregates farm labor at the person-level to compare labor productivity between farm and non-farm household enterprises. This illustrates that different levels of aggregation serve different purposes. Collecting data on farm labor at the person-plot level keeps all higher levels of aggregation – i.e. _plot-_ , _person-_ and _household-levels_ – open for analysis.

Despite these advantages, data collection at the person-plot level can be challenging in practice. In a first step, enumerators need to compile a comprehensive list (roster) of household members and agricultural plots. Plot rosters, in particular, are notoriously difficult to compile. While most farmers have an intuitive understanding of what constitutes a plot of land, their understanding does not always align with the surveydefinition of a plot<sup>6</sup> and they may forget to mention marginal plots. Enumerators hence need to probe carefully to ensure that all relevant plots are listed as per the standardized definition used in the survey. Another difficulty is that respondents may not find the concept of farm labor per person per plot intuitively meaningful, and hence face difficulties in reporting on the number of days and hours that individual household members worked on each plot. These challenges can be further compounded by the length of the recall period, as discussed in the next section.

In this paper, the principal unit of data collection and analysis is farm labor per person per plot. However, we also report on higher levels of aggregation, i.e. farm labor per plot, per person and per household, which are, as shown in Table A1, more widely used at the analysis stage. This allows us to

> 4 The Ghana Living Standards Survey (GLSS) 5 uses an even more aggregated approach, asking about the number of male, female and children’s hours of family labor on each plot during the last major/minor seasons.

> 5 Other examples are multiple rounds of nationally representative LSMS-ISA surveys in Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda (World Bank 2015) or specialized surveys with a focus on agriculture (e.g. Walker 2009 - Ghana, Winowiecki et al. 2016 – Tanzania, Uganda). Another example would be the Agricultural Integrated Survey (AGRIS) designed by the Food and Agriculture Organization of the United Nations (FAO). They ask the holding about the total number of months worked within the last N months, followed by questions on the average number of days per month and average number of hours per day. These questions are at the agricultural holding level and asked to every household member.

> 6 This definition may be, for example, a “continuous piece of land on which a unique crop or a mixture of crops is grown, under a uniform, consistent crop management system” (World Bank 2016).

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