Affiliation:
1. University of Chicago and NBER (email: )
2. The University of Hong Kong (email: )
3. UC San Diego and London School of Economics and Political Science (email: )
4. Stanford University and National University of Singapore (email: )
Abstract
We examine the introduction of automatic air pollution monitoring to counter suspected tampering at the local level, a central feature of China’s “war on pollution.” Exploiting 654 regression discontinuity designs based on city-level variation in the day that monitoring was automated, we find an immediate and lasting increase of 35 percent in reported PM10 concentrations post-automation. Moreover, automation’s introduction increased online searches for face masks and air filters, which are strong predictors of purchases. Overall, our findings suggest that the biased and imperfect information prior to automation led to suboptimal investments in defensive measures, plausibly imposing meaningful welfare costs. (JEL D82, O13, P28, Q53, Q55, Q58)
Publisher
American Economic Association
Cited by
92 articles.
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