Missing(Ness) in Action: Selectivity Bias in Gps-Based Land Area Measurements

31 Pages Posted: 20 Apr 2016 Last revised: 2 Jun 2020

See all articles by Calogero Carletto

Calogero Carletto

affiliation not provided to SSRN

Talip Kilic

World Bank - Development Data Group (DECDG)

Calogero Carletto

World Bank; World Bank - Development Research Group (DECRG)

Alberto Zezza

World Bank; World Bank - Development Research Group (DECRG)

Sara Savastano

University of Rome Tor Vergata - Faculty of Economics; International Fund for Agricultural Development (IFAD); World Bank

Alberto Zezza

United Nations - Food and Agriculture Organization (FAO)

Date Written: June 1, 2013

Abstract

Land area is a fundamental component of agricultural statistics, and of analyses undertaken by agricultural economists. While household surveys in developing countries have traditionally relied on farmers' own, potentially error-prone, land area assessments, the availability of affordable and reliable Global Positioning System (GPS) units has made GPS-based area measurement a practical alternative. Nonetheless, in an attempt to reduce costs, keep interview durations within reasonable limits, and avoid the difficulty of asking respondents to accompany interviewers to distant plots, survey implementing agencies typically require interviewers to record GPS-based area measurements only for plots within a given radius of dwelling locations. It is, therefore, common for as much as a third of the sample plots not to be measured, and research has not shed light on the possible selection bias in analyses relying on partial data due to gaps in GPS-based area measures. This paper explores the patterns of missingness in GPS-based plot areas, and investigates their implications for land productivity estimates and the inverse scale-land productivity relationship. Using Multiple Imputation (MI) to predict missing GPS-based plot areas in nationally-representative survey data from Uganda and Tanzania, the paper highlights the potential of MI in reliably simulating the missing data, and confirms the existence of an inverse scale-land productivity relationship, which is strengthened by using the complete, multiply-imputed dataset. The study demonstrates the usefulness of judiciously reconstructed GPS-based areas in alleviating concerns over potential measurement error in farmer-reported areas, and with regards to systematic bias in plot selection for GPS-based area measurement.

Keywords: Labor & Employment Law, Food Security, Educational Sciences, Demographics, Climate Change and Agriculture, Crops and Crop Management Systems

Suggested Citation

Carletto, Calogero and Kilic, Talip and Carletto, Calogero and Zezza, Alberto and Savastano, Sara and Savastano, Sara and Zezza, Alberto, Missing(Ness) in Action: Selectivity Bias in Gps-Based Land Area Measurements (June 1, 2013). World Bank Policy Research Working Paper No. 6490, Available at SSRN: https://ssrn.com/abstract=2281015

Calogero Carletto (Contact Author)

affiliation not provided to SSRN

No Address Available

Talip Kilic

World Bank - Development Data Group (DECDG) ( email )

Via Labicana 110
Rome, Lazio 00184
Italy

Calogero Carletto

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

World Bank - Development Research Group (DECRG)

1818 H. Street, N.W.
MSN3-311
Washington, DC 20433
United States

Alberto Zezza

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

World Bank - Development Research Group (DECRG)

1818 H. Street, N.W.
MSN3-311
Washington, DC 20433
United States

Sara Savastano

University of Rome Tor Vergata - Faculty of Economics ( email )

Via di Tor Vergata
Rome, Lazio 00133
Italy
+390672595639 (Phone)

International Fund for Agricultural Development (IFAD) ( email )

Via Paolo di Dono
Rome, 00142
Italy

World Bank

1818 H Street NW
MSN3-311
Washington, DC 20433
United States

Alberto Zezza

United Nations - Food and Agriculture Organization (FAO) ( email )

Viale delle Terme di Caracalla
Rome, Lazio 00100
Italy

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
66
Abstract Views
707
Rank
613,074
PlumX Metrics