Quantifying Causes and Consequences of Air Pollution in China

Paper Session

Sunday, Jan. 8, 2017 1:00 PM – 3:00 PM

Hyatt Regency Chicago, Plaza B
Hosted By: American Economic Association
  • Chair: Maximilian Auffhammer, University of California-Berkeley

Willingness to Pay for Clean Air: Evidence From Air Purifier Markets in China

Koichiro Ito
,
University of Chicago
Shuang Zhang
,
University of Colorado-Boulder

Abstract

This paper provides among the first revealed preference estimate of willingness-to-pay (WTP) for clean air in developing countries. We use product-by-store level transaction data on air purifier sales in Chinese cities and city-level air pollution data. Our empirical strategy leverages the Huai River heating policy, which created discontinuous quasi-experimental variation in air pollution between the north and south of the river. Using a spatial regression discontinuity design, we estimate the marginal willingness to pay for removing 1 ug/m3 PM10. Our findings provide important policy implications for optimal environmental regulation.

Do Local Government-Industry Linkages Affect Air Quality? Evidence From Chinese Cities

Jing Cao
,
Tsinghua University
Valerie Jean Karplus
,
Massachusetts Institute of Technology
Xingyao Shen
,
Massachusetts Institute of Technology

Abstract

We investigate the relationship between local government-industry linkages and pollution outcomes in Chinese cities over the period 2000 to 2010. For identification we rely on the administrative rotation of city mayors, which is determined by political career considerations and retirement age cutoffs but unrelated to environmental record. These transitions act as a plausibly exogenous shock that disrupts the relationship between the local government and enterprises at the city level. Wefind correlations between mayoral characteristics and city-level environmental outcomes, focusing specifically on SO2 pollution, SO2 pollution intensity, and end-of-pipe SO2 removal. The tenures of mayors promoted from within the local government and mayors on the verge of retirement are associated with reductions in a city's SO2 emissions intensity and an increase in end-of-pipe SO2 removal rates. We find evidence that higher removal rates result from the enhanced application of end-of-pipe removal technology among domestic non-state owned enterprises.

The Marginal Cost of Traffic Congestion and Road Pricing: Evidence From a Natural Experiment in Beijing

Shanjun Li
,
Cornell University
Avralt-Od Purevjav
,
Cornell University
Jun Yang
,
Beijing Transportation Research Center

Abstract

Leveraging a natural experiment and big data, this study examines road pricing, the first-best policy to address traffic congestion in Beijing. Based on fine-scale traffic data from over 1500 monitoring stations throughout the city, this paper provides the first empirical estimate of the marginal external cost of traffic congestion (MECC) and optimal congestion charges based on the causal effect of traffic density on speed, a key input for measuring the MECC. The identification of the causal effect relies on the plausibly exogenous variation in traffic density induced by the driving restriction policy. Our analysis shows that the MECC during rush hours is about 92 cents (or $0.15) per km on average, nearly three times as much as what OLS regressions would imply and larger than estimates from transportation engineering models. The optimal congestion charges range from 5 to 38 cents per km depending on time and location. Road pricing would increase traffic speed by 10 percent within the city center and lead to a welfare gain of 1.4 billion and revenue of 40 billion Yuan per year.

Air Pollution, Avoidance Behavior and Happiness in Chinese Cities: Evidence From Social Media Data

Siqi Zheng
,
Tsinghua University
Jianghao Wang
,
Chinese Academy of Sciences
Matthew E. Kahn
,
University of Southern California and NBER
Xiaonan Zhang
,
Tsinghua University

Abstract

Existing studies have shown that air pollution not only has direct impacts on health and life expectancy, but also has a variety of negative social costs, such as reducing workers' productivity, peoples outdoor activities and related social interactions, and overall life satisfaction. Another issue is that people may actively be engaged in avoidance behaviors such as choosing cleaner places to live, work and play. Therefore the given air pollution and peoples active self-protection behaviors work together to affect their subjective happiness. This paper constructs several big data pieces from the social media platforms Weibo (Chinese Twitter) and WeChat. We will study how air pollution affects people's happiness, measured using Weibo data and the machine-trained sentiment analysis algorithms from computational linguistics. We will also examine people's avoidance behaviors on polluted days using Weibo and WeChat data, e.g., less people appear in big parks, and more people appear in indoor public spaces. On our first step analysis, we test the relationship between peoples daily mood and air pollution in 71 cities in China from January to November in 2014. Using a fixed effects model for panel data and controlling daily weather variables, the results show that people are more upset on polluted days. Specifically, one standard deviation increase in PM 2.5 concentration leads to 0.04 standard deviation decrease in people's mood. Further, people are more angry if the polluted days are weekends. In ongoing work, we are instrumenting for air pollution using neighboring cities pollutant emissions. We will also merge the WeChat data with our current data sets to study the avoidance behaviors and the resulting impacts on people's happiness.
Discussant(s)
Jing Cao
,
Tsinghua University
Shanjun Li
,
Cornell University
Shuang Zhang
,
University of Colorado-Boulder
Valerie Jean Karplus
,
Massachusetts Institute of Technology
JEL Classifications
  • Q5 - Environmental Economics
  • R1 - General Regional Economics