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Using Global Positioning Systems in Household Surveys for Better Economics and Better Policy


  • John Gibson
  • David McKenzie


Distance and location are the important determinants of many choices that economists study. Economists often rely on information about these variables that is self-reported by respondents in surveys, although information can sometimes be obtained from secondary sources. Self-reports are typically used for information on distance from households or community centers to roads, markets, schools, clinics, and other public services. There is growing evidence that self-reported distance is measured with error and that these errors are correlated with outcomes of interest. In contrast to self-reports, global positioning systems (GPS) can determine location within 15 m in most cases. The falling cost of GPS receivers makes it increasingly feasible for field surveys to use GPS to more accurately measure location and distance. This article reviews four ways that GPS can lead to better economics and better policy by clarifying policy externalities and spillovers, by improving the understanding of access to services, by improving the collection of household survey data, and by providing data for econometric modeling of the causal impact of policies. Several pitfalls and unresolved problems with using GPS in household surveys are also discussed. Copyright The Author 2007. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / the world bank . All rights reserved. For permissions, please e-mail:, Oxford University Press.

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  • John Gibson & David McKenzie, 2007. "Using Global Positioning Systems in Household Surveys for Better Economics and Better Policy," World Bank Research Observer, World Bank Group, vol. 22(2), pages 217-241, September.
  • Handle: RePEc:oup:wbrobs:v:22:y:2007:i:2:p:217-241

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    References listed on IDEAS

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    Cited by:

    1. Olivia, Susan & Gibson, John & Smith, Aaron D. & Rozelle, Scott & Deng, Xiangzheng, 2009. "An Empirical Evaluation of Poverty Mapping Methodology: Explicitly Spatial versus Implicitly Spatial Approach," 2009 Conference (53rd), February 11-13, 2009, Cairns, Australia 47651, Australian Agricultural and Resource Economics Society.
    2. Florence Kondylis & Marco Manacorda, 2012. "School Proximity and Child Labor: Evidence from Rural Tanzania," Journal of Human Resources, University of Wisconsin Press, vol. 47(1), pages 32-63.
    3. David McKenzie & Christopher Woodruff, 2014. "What Are We Learning from Business Training and Entrepreneurship Evaluations around the Developing World?," World Bank Research Observer, World Bank Group, vol. 29(1), pages 48-82.
    4. Carletto, Calogero & Savastano, Sara & Zezza, Alberto, 2013. "Fact or artifact: The impact of measurement errors on the farm size–productivity relationship," Journal of Development Economics, Elsevier, vol. 103(C), pages 254-261.
    5. Hai-Anh H. Dang & F. Halsey Rogers, 2016. "The Decision to Invest in Child Quality over Quantity: Household Size and Household Investment in Education in Vietnam," World Bank Economic Review, World Bank Group, vol. 30(1), pages 104-142.
    6. Henry G. Overman, 2010. ""Gis A Job": What Use Geographical Information Systems In Spatial Economics?," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 165-180.
    7. Taupo, Tauisi & Cuffe, Harold & Noy, Ilan, 2016. "Household vulnerability on the frontline of climate change: The Pacific atoll nation of Tuvalu," Working Paper Series 5319, Victoria University of Wellington, School of Economics and Finance.
    8. Fafchamps, Marcel & McKenzie, David & Quinn, Simon & Woodruff, Christopher, 2012. "Using PDA consistency checks to increase the precision of profits and sales measurement in panels," Journal of Development Economics, Elsevier, vol. 98(1), pages 51-57.

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