being based only on length (i.e. ropes), and farmers struggling to think in terms of a two dimensional measure (i.e. hectares) with increasing land scarcity.

Keita and Carfagna (2009) provide a discussion on the performance of different GPS devices in comparison to the rope and compass alternative, which they assume to be the ‘gold standard’ for land area measurement. The authors document that while GPS technology allows for the measurement of 80 percent of the sample plot areas with negligible error, it tends to, on average, underestimate plot areas marginally with respect to the gold standard.<sup>4</sup> Possibly the most important problem with relying on GPS-based area measures, however, relates to the inability of obtaining these measures for all agricultural plots owned and/or cultivated by sample households. The inability is typically underlined by field logistics- and cost-related considerations. Some plots may be distant from the place of the interview (usually the sample household’s dwelling), and respondents may not have time or willingness to accompany the enumerators to these locations. Even if this constraint does not hold, the operational costs, in terms of time and financial resources, required to record GPS-based area measures for all sample plots are often perceived as prohibitive in most survey operations. This raises analytical concerns, in the form of biased estimates, if the plots that are not measured are systematically different from the ones whose areas are captured with GPS technology.

Carletto et al. (2013) use the data from the Uganda National Household Survey (UNHS) 2005/06 to document (i) the determinants of the discrepancy in land areas when using GPS-based versus farmer-reported assessments, and (ii) how the discrepancy varies systematically with plot and farm size.<sup>5</sup> Although the primary focus of their study is to document whether the use of farm size measures based on GPS-based versus farmerreported plot areas weakens, reverses or strengthens the inverse farm size-productivity relationship, they are not able to address the possible selection bias stemming from restricting their sample to households/farms that do not have any plots with missing GPSbased area measures (i.e. discarding approximately 50 percent of the agricultural household sample). Our study explicitly deals with this problem by employing approaches developed over the years by statisticians to overcome issues that are conceptually and practically similar in nature.

> 4 Keita and Carfagna (2009) posit that the plot slope, density of the surrounding tree canopy cover, and the weather conditions at the time of the measurement could be among the factors driving the observed discrepancies. However, their validation was also carried out in less-than-ideal conditions, resulting in a very small sample size.

> 5 Carletto et al. (2013) document that the magnitude of discrepancy between GPS-based and farmer-reported farm size measures increases monotonically while moving from the first decile of the farm size distribution (- 97 percent) to the tenth decile (19 percent). The bias is, on average, negative throughout the 60 percent of the farm size distribution, and becomes positive in the top three deciles, leading the authors to deduce that smaller farmers tend to over-report their land relatively more than larger farmers and that larger farmers tend to under-report land size.

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