Data and Code for: Can Technology Solve the Principal-Agent Problem? Evidence from China’s War on Air Pollution
Jia, R., Greenstone, M., He, G. & Liu, T.
(2022).
Data and Code for: Can Technology Solve the Principal-Agent Problem? Evidence from China’s War on Air Pollution.
[Dataset]. OpenICPSR.
https://doi.org/10.3886/e125321
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% in reported PM10 concentrations post–automation. Moreover, automation’s introduction increased online searches for face masks and air filters that 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.
| Item Type | Dataset |
|---|---|
| Publisher | OpenICPSR |
| DOI | 10.3886/e125321 |
| Date made available | 22 February 2022 |
| Keywords | automation, environmental monitoring, air pollution |
| Temporal coverage |
From To 2011 2016 |
| Geographic coverage | China |
| Resource language | Other |
| Departments | LSE |
Explore Further
- Greenstone, M., He, G., Jia, R. & Liu, T. (2022). Can technology solve the principal-agent problem? Evidence from China’s war on air pollution. American Economic Review, 4(1), 54 - 70. https://doi.org/10.1257/aeri.20200373 (Repository Output)
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