Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/242313 
Year of Publication: 
2020
Citation: 
[Journal:] Development Engineering [ISSN:] 2352-7285 [Volume:] 5 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2020 [Pages:] 1-19
Publisher: 
Elsevier, Amsterdam
Abstract: 
Empirical social sciences rely heavily on surveys to measure human behavior. Previous studies show that such data are prone to random errors and systematic biases caused by social desirability, recall challenges, and the Hawthorne effect. Moreover, collecting high frequency survey data is often impossible, which is important for outcomes that fluctuate. Innovation in sensor technology might address these challenges. In this study, we use sensors to describe solar light adoption in Kenya and analyze the extent to which survey data are limited by systematic and random error. Sensor data reveal that households used lights for about 4 h per day. Frequent surveyor visits for a random sub-sample increased light use in the short term, but had no long-term effects. Despite large measurement errors in survey data, self-reported use does not differ from sensor measurements on average and differences are not correlated with household characteristics. However, mean-reverting measurement error stands out: households that used the light a lot tend to underreport, while households that used it little tend to overreport use. Last, general usage questions provide more accurate information than asking about each hour of the day. Sensor data can serve as a benchmark to test survey questions and seem especially useful for small-sample analyses.
Subjects: 
Hawthorne effect
Measurement error
Self-report surveys
Sensor
Social desirability bias
Technology adoption
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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