# **1. Introduction**

Agriculture plays a key role in the economies of Sub-Saharan Africa. Across the continent, the sector contributes approximately 18 percent to GDP and accounts for 56 percent of the employed population (World Bank 2017, ILO 2016). Agriculture serves as a key livelihood strategy for many poor families in rural areas, where labor-intensive, smallholder farming is the predominant source of income. At the macrolevel, agricultural growth has been found to be more effective than non-agricultural growth in reducing extreme poverty (Christiaensen, Demery and Kuhl 2011).

Designing policies to improve living conditions of smallholder farmers requires data on outputs and inputs. One of the most important inputs into agriculture in developing countries is the labor provided by family members on their own farms, denoted in this paper as _farm labo_ r and commonly measured in hours per person or hours per person per plot over an extended reference period, such as the last season or the last year. Data on farm labor are important for a range of literature strands in development economics – such as analyses of agricultural productivity (e.g. Restuccia and Santaeulalia-Llopis 2017; Gollin and Udry 2017), agricultural household models (e.g. LaFave and Thomas 2016), rural labor markets (Dillon et al 2017) and gender differences in agriculture (O’Sullivan et al 2014; McCarthy et al 2016).

Measuring farm labor is, however, fraught with empirical difficulties. The most common approach is to ask survey respondents to recall the amount of time each member of the household spent on farm activities during the previous agricultural season ( _end-of-season recall_ ).<sup>1</sup> While this extended reference period minimizes the impact of seasonality, it can lead to difficulties for respondents to remember how much time various members of the household worked on the farm over the entire season. Recall is further hindered by the informal nature of smallholder agriculture, where working hours are highly irregular, with periods of lower labor activity after planting and prior to harvest. In addition, since irrigation is limited and rain-fed agriculture the norm across much of Africa, there is added unpredictability with the reliance on weather. These features render measurement of farm labor in developing countries extraordinarily challenging.

While several recent papers highlight shortcomings in agricultural statistics in developing countries (Fermont and Benson 2011; Gollin, Lagakos and Waugh 2014a,b; de Janvry, Sadoulet and Suri 2016), there are very few empirical studies that evaluate the reliability of survey-based measures of agricultural labor. An exception is Arthi et al (2018), who show that end-of-season recall overestimates farm labor per person per plot by more than 200 percent in the Mara region of Tanzania. Their study also documents competing forms of recall bias, as end-of-season recall simultaneously leads to underreporting of cultivated plots and household farm workers. Because of these two counteracting effects, recall bias disappears if hours are aggregated to the household level.

Using a similar study design, we conducted a survey experiment in the Ashanti and Brang Ahafo regions of Ghana over the 2015-16 rainy season. One group of farmers was interviewed weekly about farm labor for each of the preceding seven days, what we consider the resource-intensive benchmark. Another group was interviewed at the end of the agricultural season (i.e. after harvest) about farm labor for the entire

> 1 Variants of this approach are used, for example, by the surveys conducted under the umbrella of the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative.

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