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
  China_Pollution_Monitoring 07/09/2021 03:35:AM
README.pdf application/pdf 122.5 KB 08/03/2021 07:24:AM

Project Citation: 

Greenstone, Michael, He, Guojun, Jia, Ruixue, and Liu, Tong. Data and Code for: Can Technology Solve the Principal-Agent Problem?  Evidence from China’s War on Air Pollution. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-02-22. https://doi.org/10.3886/E125321V1

Project Description

Summary:  View help for 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.

Funding Sources:  View help for Funding Sources EPIC-UCAS Joint Center for Energy Policy Research in China

Scope of Project

Subject Terms:  View help for Subject Terms air pollution; environmental monitoring; automation
JEL Classification:  View help for JEL Classification
      Q53 Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
      Q55 Environmental Economics: Technological Innovation
Geographic Coverage:  View help for Geographic Coverage China
Time Period(s):  View help for Time Period(s) 2011 – 2016
Collection Date(s):  View help for Collection Date(s) 2011 – 2017
Universe:  View help for Universe Air pollution in China
Data Type(s):  View help for Data Type(s) observational data; program source code

Methodology

Data Source:  View help for Data Source China’s Real-Time Air Quality Reporting and Analysis Platform
Unit(s) of Observation:  View help for Unit(s) of Observation monitoring stations

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V1 [2022-02-22]

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