English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

When publishing socioeconomic survey data, survey programs implement avariety of statistical methods designed to preserve privacy but which come atthe cost of distorting the data. We explore the extent to which spatialanonymization methods to preserve privacy in the large-scale surveys supportedby the World Bank Living Standards Measurement Study - Integrated Surveys onAgriculture (LSMS-ISA) introduce measurement error in econometric estimateswhen that survey data is integrated with remote sensing weather data. Guided bya pre-analysis plan, we produce 90 linked weather-household datasets that varyby the spatial anonymization method and the remote sensing weather product. Byvarying the data along with the econometric model we quantify the magnitude andsignificance of measurement error coming from the loss of accuracy that resultsfrom protect privacy measures. We find that spatial anonymization techniquescurrently in general use have, on average, limited to no impact on estimates ofthe relationship between weather and agricultural productivity. However, thedegree to which spatial anonymization introduces mismeasurement is a functionof which remote sensing weather product is used in the analysis. We concludethat care must be taken in choosing a remote sensing weather product whenlooking to integrate it with publicly available survey data.

Privacy Protection, Measurement Error, and the Integration of Remote Sensing and Socioeconomic Survey Data