Abstract
Agricultural statistics derived from remote sensing data have been used primarily to compare land use information and changes over time. Nonclassical measurement error from farmer self-reports has been well documented in the survey design literature primarily in comparison to plots measured using Global Positioning System (GPS). In this paper, we investigate the reliability of remotely sensed satellite data on nonrandom measurement error and on agricultural relationships such as the inverse land size–productivity relationship and input demand functions. In our comparison of four Asian countries, we find significant differences between GPS and remotely sensed data only in Viet Nam, where plot sizes are small relative to the other countries. The magnitude of farmers’ self-reporting bias relative to GPS measures is nonlinear and varies across countries, with the largest magnitude of self-reporting bias of 130% of a standard deviation (2.2-hectare bias) in the Lao People’s Democratic Republic relative to Viet Nam, which has 13.3% of a standard deviation (.008-hectare bias). In all countries except Viet Nam, the inverse land size–productivity relationship is upwardly biased for lower land area self-reported measures relative to GPS measures. In Viet Nam, the intensive margin of organic fertilizer use is negatively biased by self-reported measurement error by 30.4 percentage points. As remotely sensed data becomes publicly available, it may become a less expensive alternative to link to survey data than rely on GPS measurement.
Highlights
More than 70% of the world’s poor reside in rural areas of developing countries and rely on agriculture as their main source of livelihood (IFAD 2010)
Nonclassical measurement error from farmer self-reports has been well documented in the survey design literature (Carletto, Savastano, and Zezza 2013; Dillon et al 2017, among others), primarily in comparison to Global Positioning System (GPS)-measured plots
We investigate the reliability of remotely sensed satellite data on nonclassical measurement error and on agricultural relationships such as the inverse land size relationship and input demand functions
Summary
More than 70% of the world’s poor reside in rural areas of developing countries and rely on agriculture as their main source of livelihood (IFAD 2010). A second chain of literature focuses on missing markets for land, labor, credit, and insurance markets which leads to differences in land productivity between households (Assunação and Ghatak 2003, Barrett 1996, Carter and Wiebe 1990, Eswaran and Kotwal 1986) Omitted variable bias, such as inability to accurately capture soil quality or farmer ability serves as a third explanation (Barrett, Bellemare, and Hou 2010; Bhalla and Roy 1988; Chen, Huffman, and Rozelle 2011). As technology advances and image resolution improves along with affordability, the use of this method becomes more feasible, and is likely to hold promise for the measurement of large plots.” While papers such as Carletto, Savastano, and Zezza (2013) and Dillon et al (2017) have explored the relationship between survey design, land measurement bias, and their implications for econometric specifications, little evidence to date has been provided to draw. The final section discusses the implications of this study and the prospects for integrating remote sensing into national household surveys
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