Our results have important implications for development policy and fill key gaps in the literature concerning survey methods and the quality of agricultural labor data. Ours is one of the few studies to test the accuracy of agricultural labor data in developing-country settings. While labor data have been an essential ingredient in a broad range of important studies on smallholder agriculture in developing countries, scant attention has been paid thus far to the quality and robustness of the underlying data on family farm labor.<sup>3</sup> By showing evidence that agricultural labor inputs may be substantially overestimated because of recall bias, we challenge the reliability of the traditional end-of-season labor estimates commonly used in development economics.

These findings also contribute to academic and policy debates concerning the agricultural productivity gap and the degree to which rural labor may be misallocated in developing economies. Several studies have been engaged in this debate. Gollin, Lagakos, and Waugh (2014) and McCullough (2015), question the accuracy of current labor measures and reconsider the agricultural productivity gap after adjusting for labor data quality. By conducting comparisons at the per hour level (McCullough 2015) and by adjusting for sectoral differences in hours worked as well as for levels of human capital (Gollin, Lagakos, and Waugh 2014), both studies find that the difference in the productivity between the agricultural and nonfarming sectors is narrower than usually thought. Our study suggests that when surveying irregular labor through recall an upward of hours worked on the farm may result, which would further explain part of the productivity gap.

Although our results call into question the accuracy of current farm labor data, they also propose specific ways to improve the accuracy of labor measurement. The consistency of the labor reported across face-to-face and phone surveys suggests that season-long phone surveys are an option for reducing error in the measurement of rural agricultural labor.

The rest of the paper proceeds as follows. In section 2, we offer background on labor measurement. In section 3, we provide an overview of the empirical approach, including details on the survey experiment. In section 4, we present the results of our experiment on the reporting of labor inputs in family farming and outline the sources of bias in recall data. Section 5 concludes.

# **2. MEASURING LABOR**

## _2.1. Current practice_

The wealth of evidence on the quality and reliability of labor statistics in household surveys comes largely from the United States (for a thorough review, see Bound, Brown, and Mathiowetz 2001). In developing and agriculturally driven countries, for contrast, little is known about the extent to which the design of surveys influences labor statistics. Clearly, it is difficult to extrapolate from studies conducted in the United States to the African context. Moreover, the existing literature on data quality and survey methods in low-income settings rarely pertains to farm labor (see Bardasi

> 3 Examples include farm household models (Barnum and Squire 1979; Benjamin 1992; Rosenzweig and Wolpin 1985; Singh, Squire, and Strauss 1986), shadow wages in family farm labor (Jacoby 1993), trade-offs between hired and household labor (Chowdhury 2010; Deolalikar and Vijverberg 1987; Johnston and Le Roux 2007), the way households allocate labor to farm and off-farm work (Lanjouw and Lanjouw 2001; Matshe and Young 2004; Shapiro 1990), and intrahousehold labor allocation choices (Udry 1996).

3