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

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.

World Bank survey experts Talip Kilic and Siobhan Murray, together with experts from the University of Arizona, explore how spatial anonymization methods 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 the survey data is integrated with remote sensing weather data. Guided by a pre-analysis plan, they produce 90 linked weather-household datasets that vary by the spatial anonymization method and the remote sensing weather product.

Highlights of the article:

Statistical disclosure limitation distorts public use data to preserve privacy.

Matching public use data with remote sensing weather data may generate distortion.

Spatial anonymization methods have limited to no impact on estimates.

Estimates do vary by choice of remote sensing product.

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