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    Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data

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    Name:
    Michler_et_al_22.pdf
    Embargo:
    2025-07-01
    Size:
    4.164Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
    Download
    Author
    Michler, Jeffrey D.
    Josephson, Anna
    Kilic, Talip
    Murray, Siobhan
    Affiliation
    Department of Agricultural and Resource Economics, University of Arizona
    Issue Date
    2022-09
    Keywords
    Measurement error
    Privacy protection
    Remote sensing data
    Spatial anonymization
    Sub-Saharan Africa
    
    Metadata
    Show full item record
    Publisher
    Elsevier BV
    Citation
    Michler, J. D., Josephson, A., Kilic, T., & Murray, S. (2022). Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data. Journal of Development Economics, 158.
    Journal
    Journal of Development Economics
    Rights
    © 2022 The Authors. Published by Elsevier B.V.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    When publishing socioeconomic survey data, survey programs implement a variety of statistical methods designed to preserve privacy but which come at the cost of distorting the data. We explore the extent to which spatial anonymization methods to preserve privacy in the large-scale surveys supported by the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) introduce measurement error in econometric estimates when that survey data is integrated with remote sensing weather data. Guided by a pre-analysis plan, we produce 90 linked weather-household datasets that vary by the spatial anonymization method and the remote sensing weather product. By varying the data along with the econometric model we quantify the magnitude and significance of measurement error coming from the loss of accuracy that results from privacy protection measures. We find that spatial anonymization techniques currently in general use have, on average, limited to no impact on estimates of the relationship between weather and agricultural productivity. However, the degree to which spatial anonymization introduces mismeasurement is a function of which remote sensing weather product is used in the analysis. We conclude that care must be taken in choosing a remote sensing weather product when looking to integrate it with publicly available survey data.
    Note
    36 month embargo; available online: 1 July 2022
    ISSN
    0304-3878
    DOI
    10.1016/j.jdeveco.2022.102927
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jdeveco.2022.102927
    Scopus Count
    Collections
    UA Faculty Publications

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