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The Causal Effect of Air Pollution on Anti-social Behaviour
Last registered on October 18, 2019


Trial Information
General Information
The Causal Effect of Air Pollution on Anti-social Behaviour
Initial registration date
October 16, 2019
Last updated
October 18, 2019 10:52 AM EDT

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Primary Investigator
University of Cambridge
Other Primary Investigator(s)
PI Affiliation
University of Cambridge
PI Affiliation
Renmin University of China
PI Affiliation
University of Innsbruck
Additional Trial Information
On going
Start date
End date
Secondary IDs
The aim of this study is to provide causal evidence on the link between pollution and antisocial behaviour. We plan to estimate the causal impact of air pollution on economic decision-making by exploiting exogenous variation in local air pollution in Beijing, China. The primary outcomes of interest are experimental measures of antisocial behaviour obtained using incentivized experimental games including the 'Take Game' (as a measure for crime in the lab), 'Joy of Destruction Game' (as a measure of anti-social behaviour) and a third-party punishment game (as a measure of enforcement of pro-social behaviour). We also collect a range of secondary outcomes, which constitute potential mechanisms for effects on the primary outcomes, including tests of cognitive capacity, revealed measures for risk and time preferences and self-reported levels of mood, stress, anxiety and self-control. Experiments will be conducted with a large sample of students from Universities in Beijing.
External Link(s)
Registration Citation
Gsottbauer, Elisabeth et al. 2019. "The Causal Effect of Air Pollution on Anti-social Behaviour." AEA RCT Registry. October 18. https://doi.org/10.1257/rct.4856-1.0.
Experimental Details
In this RCT we exploit the naturally occurring exogenous variation in air pollution as our treatment intervention. Participants will be randomly assigned to either a low pollution control group or a high pollution treatment group. Using the official Air Quality Index (AQI) classifications as a broad guideline, we define low levels of air pollution for AQI values in the ‘Good’ to ‘Unhealthy for Sensitive Groups’ range and high levels of air pollution for AQI values exceeding ‘Very Unhealthy’ levels of pollution. As pollution episodes generally occur over a series of days, preceded and followed by clean day-time episodes, participants from both treatment and control groups will be surveyed within a timeframe of several weeks. Pollution and weather forecasts will be used to determine the exact distribution date of the experimental survey.

1) Participants in the low pollution control group will be invited to complete a set of incentivised tasks when air pollution levels are objectively low, preceding a forecasted pollution episode.
2) Participants in the high pollution treatment group will be invited to complete the same set of incentivised tasks at the peak of the pollution episode.

Absolute levels of pollution exposure may vary and AQI classifications will serve as a guideline only; however, we aim to choose those days with the largest standard deviation between low and high pollution groups within each sampling period, subject to pollution and weather forecasts. To verify that official pollution levels are precise, we will take additional air quality measurements using modern air pollution measuring equipment located on the Campus of Renmin University.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
As primary outcomes we collect two measures of anti-social behaviour and one measure of enforcement of pro-social behaviour.

1) An experimental measure of crime obtained from the Take Game (Schildberg-Hörisch and Strassmair, 2014).
2) A measure of anti-social behaviour obtained from the Joy of Destruction mini game (Abbink & Herrmann, 2011).
3) A measure of norm enforcement obtained from a modified dictator game (Fehr & Fischbacher, 2004).

Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
As secondary outcomes we collect a range of measures including risk preferences (decision over gambles); time preferences (convex time budgets); cognitive performance (Raven’s matrices and Numerical Stroop) and additional subjective well-being & mental health variables.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The proposed RCT involves approximately 700 student participants enrolled at universities in Beijing, China. Data will be collected via online survey experiments using the Chinese messenger app 'WeChat'. Students will be contacted via direct messages and invited to participate in the study by following a link to an external survey platform. Once participants have completed the online survey, they will be immediately compensated via direct payment to their 'WeChat Wallets'.

We will implement an online between-subject experimental design in which students will be randomly assigned to one of two high pollution treatment groups or one of two low pollution control groups, using a stratified randomisation procedure. The first treatment and control group will be sampled in October/November 2019. The second groups will be sampled in November/December 2019. Participants in the low pollution treatment conditions will be invited to complete the survey when air pollution levels are objectively low, preceding a forecasted pollution episode. Participants in the high treatment conditions will subsequently be sampled at the peak of the pollution episode. The exact sampling dates will be determined based on pollution and weather forecasts.
Experimental Design Details
Not available
Randomization Method
The stratified randomisation will be based on respondent characteristics and performed using a statistical software package. The participants will first be stratified by gender, university, year of study, Hukou status and health status. Within each stratum, every fourth student will be assigned to a given treatment or control group.
Randomization Unit
The randomisation is performed at the individual level
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
700 students (undergraduate & postgraduate)
Sample size (or number of clusters) by treatment arms
The sample will be equally split between treatment and control group. The treatment and control group will be split into two groups, sampled at different time-points throughout the semester.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Department of Land Economy Research Committee (University of Cambridge)
IRB Approval Date
IRB Approval Number