Data and Code for: Can Technology Solve the Principal-Agent Problem? Evidence from China’s War on Air Pollution

Principal Investigator(s): View help for Principal Investigator(s) Michael Greenstone, University of Chicago, Kenneth C. Griffin Department of Economics; Guojun He, The University of Hong Kong; Ruixue Jia, University of California-San Diego, and London School of Economics and Political Science; Tong Liu, Stanford University, and National University of Singapore

Version: View help for Version V1

Name	File Type	Size	Last Modified
Ado			07/09/2021 03:35:AM
Code			04/11/2021 10:53:AM
Data			08/03/2021 11:19:AM
Results			04/11/2021 10:56:AM

Project Description

Summary: 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.

Scope of Project

Subject Terms: air pollution; environmental monitoring; automation

JEL Classification:
Q53 Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
Q55 Environmental Economics: Technological Innovation

Geographic Coverage: China

Universe: Air pollution in China

Data Type(s): observational data; program source code

Methodology

Data Source: China’s Real-Time Air Quality Reporting and Analysis Platform

Unit(s) of Observation: monitoring stations

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Published Versions

V1 [2022-02-22]

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DDI 2.5

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