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.
Highlights
Since the 1980s, advances in research design and analytical tools have increased the scientific impact and policy relevance of applied microeconomics, which Angrist and Pischke (2010) called a “credibility revolution.” The increased use of natural experiments and randomized controlled trials (RCTs) were of particular importance to this develop ment (Duflo et al, 2008; Angrist and Pischke, 2010)
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
3 The full study was ten months long, here we present eight months worth of sensor data, since August is the first month in which every household had the solar light for a full month
Summary
Since the 1980s, advances in research design and analytical tools have increased the scientific impact and policy relevance of applied microeconomics, which Angrist and Pischke (2010) called a “credibility revolution.” The increased use of natural experiments and randomized controlled trials (RCTs) were of particular importance to this develop ment (Duflo et al, 2008; Angrist and Pischke, 2010). We analyze how sensor data compares to household survey data on technology adoption, in this case, solar light usage in rural Kenya. We compare sensor data with survey data, interviewing two different household members from each household The interviews included both detailed (time diary) and global household questions about solar light use, allowing us to learn about social desirability bias, selection bias, mean-reverting measurement error, and random error in survey data.
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