plots (Feder, 1985); (ii) missing or incomplete factor markets (Barrett, 1996; Eswaran and Kotwal, 1986); (iii) omitted variables, in particular, controls for farmer ability and land quality (Assuncao and Braido, 2007; Assuncao and Ghatak, 2007; Benjamin, 1995; Bhalla and Roy, 1988; Lamb, 2003), and (iv) errors in land area measurement (Lamb, 2003).

Working backwards, on hypothesis (iv), Carletto et al. (2013) in a Uganda-specific study, and Carletto et al. (2015) in a cross-country study focused on Sub-Saharan Africa, document that land areas, on average, are over-reported by farmers, but that the IR persists even after objective, GPSbased land area measures are used in empirical analyses.<sup>4</sup> On hypothesis (iii), Nkonya et al. (2004) and Barrett et al. (2010) show that controlling for objectively-measured soil quality based on laboratory analyses does not explain the IR at the plot-level. On hypothesis (ii), Ali and Deininger (2015) show that at the farm-level, the IR can be explained by using a productivity measure that nets out the labor input valued at market wages.<sup>5</sup> A more recent hypothesis not reviewed by Eastwood et al. (2010) suggests that land productivity may be greater along the edge of plots as labor may be concentrated in those visible areas (Bevis and Barrett, 2017). Smaller plots have a greater ratio of visible edge area to less-visible interior area, which if edges are indeed more productive, may explain the often-observed IR. Bevis and Barrett (2017) provide support for this hypothesis in the context of Uganda.

In debating the IR, however, the question of potential measurement error in production figures has rarely been examined. This is a profound oversight considering that the studies quoted above all measure agricultural productivity based on farmer-reported production. This is likely attributable (at least in part) to the fact that data on objectively-measured agricultural production via crop cutting is rarely available in large-scale household survey operations due to resource constraints. Yet, severe systematic biases have been found in other farmer-estimated data, such as plot area (see Carletto et al., Forthcoming, and the studies cited therein). Farmer-reported production estimates are exceptionally complicated. The use of non-standard production units, various conditions and states of crop harvests, and the reporting of permanent crop harvests, for starters, threaten the quality of farmer-estimated production. Additionally, humans often exhibit an inclination to round off numbers, as shown for land area measurement in Carletto et al. (2015), which may bias production estimates.

Using unique panel data from a methodological study on maize productivity in Eastern Uganda that includes self-reported, as well as highly-supervised crop-cutting and remotely-sensed production and yield estimates, this study aims to determine whether the IR can be explained by measurement error in self-reported production estimates. The yield measures, irrespective of the approach to production measurement, are anchored in GPS-based plot area measurement. Overall,

> 4 Kilic et al. (2017b) demonstrate that IR persists at the plot-level in the context of Tanzania and Uganda even after accounting for selection bias in missing GPS-based plot area measurements through the use of multiple imputation. 5 Desiere and Jolliffe (2017) aptly argue that the missing/incomplete factor market hypothesis apply strictly to exploration of IR at the farm-level, as opposed to within-farm plot-level.

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