Clouded Judgement: Air Pollution, Priming and Inattention to Energy Efficiency in China

Last registered on July 02, 2018

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Pre-Trial

Trial Information

General Information

Title

Clouded Judgement: Air Pollution, Priming and Inattention to Energy Efficiency in China

RCT ID

AEARCTR-0003096

Initial registration date

June 29, 2018

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

July 02, 2018, 10:27 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

China

Region

Beijing

Primary Investigator

Name

Iza Ding

Affiliation

University of Pittsburgh

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Andrea Nauze

PI Affiliation

University of Pittsburgh

Contact Investigator

PI Name

Graham Beattie

PI Affiliation

University of Pittsburgh

Contact Investigator

Additional Trial Information

Status

On going

Start date

2018-03-11

End date

2018-07-30

Keywords

Environment & Energy

Additional Keywords

Energy efficiency, inattention, air pollution, cognition, priming

JEL code(s)

Secondary IDs

Abstract

We explore the interaction between salience, information and inattention to energy efficiency in China. To date, empirical evidence on the existence of inattention to energy costs comes from the developed world. Our project will establish whether inattention to energy efficiency exists in the developing world. Our experiment will also allow us to explore what drives heterogeneity in inattention. First, we will use plausibly exogenous variation in local air pollution as a measure of the salience of environmental problems to test whether salience has a causal effect on willingness to pay for energy efficiency and whether the effect of information is enhanced or attenuated by salience. Second, we will test whether air pollution, via changes in cognition, has an effect on willingness to pay for energy efficiency and the energy efficiency gap, and whether information is more or less effective when cognition is impaired. Finally, we will test whether priming decision makers to think about environmental or energy costs affects willingness to pay for energy efficiency and estimates of the energy efficiency gap. The results will contribute to our understanding of what drives inattention and will have important implications for the targeting of energy efficiency policy.

External Link(s)

Registration Citation

Citation

Beattie, Graham, Iza Ding and Andrea Nauze. 2018. "Clouded Judgement: Air Pollution, Priming and Inattention to Energy Efficiency in China." AEA RCT Registry. July 02. https://doi.org/10.1257/rct.3096-1.0

Former Citation

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2018-04-11

Intervention End Date

2018-06-30

Primary Outcomes

Primary Outcomes (end points)

willingness to pay (WTP) for LED lightbulb and change in WTP for LED lightbulb

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Participants will be provided with a budget from which they can purchase two lightbulbs. One lightbulb has high energy use and a short lifetime; the other uses less energy and has a longer lifetime. Participants will then be asked to choose between the light bulbs at a range of relative prices. At the end of the experiment, one of the participant’s decisions is chosen to be implemented and they will receive a package of lightbulbs, and any remaining budget that they did not spend. Randomization of the chosen lightbulb will be undertaken by a cell phone app that rolls dice in front of the survey respondent. The surveyor will record the outcome and give the respondent the correct lightbulb and remaining shopping budget. In addition to the lightbulb choice, the surveyors will collect demographic information as well as pro-environmental preferences.

Treatments are a combination of detailed information on the relative lifetime costs of the two lightbulbs and a series of priming questions. In addition, we will implement the experiment on high and low air pollution days, such that we have quasi-experimental variation in air pollution. The treatment groups are:

A: Cost Information
- Intro script
- Light bulb info sheet
- First 15 choices
- Light bulb shape distractor
- Energy cost info sheet
- Second 15 choices
- Cost priming
- Environmental priming
- Time preference elicitation
- Cognition tests
- Post questionnaire

B: Control
- Intro script
- Light bulb info sheet
- First 15 choices
- Light bulb shape distractor
-
- Second 15 choices
- Cost priming
- Environmental priming
- Time preference elicitation
- Cognition tests
- Post questionnaire

C: Cost Priming + Cost Information
- Intro script
- Light bulb info sheet
- First 15 choices
- Cost priming
- Light bulb shape distractor
- Energy cost info sheet
- Second 15 choices
- Environmental priming
- Time preference elicitation
- Cognition tests
- Post questionnaire

D: Environmental Priming + Cost Information
- Intro script
- Light bulb info sheet
- First 15 choices
- Environmental priming
- Light bulb shape distractor
- Energy cost info sheet
- Second 15 choices
- Cost priming
- Time preference elicitation
- Cognition tests
- Post questionnaire

E: Cost Priming
- Intro script
- Light bulb info sheet
- First 15 choices
- Cost priming
- Light bulb shape distractor
-
- Second 15 choices
- Environmental priming
- Time preference elicitation
- Cognition tests
- Post questionnaire

F: Environmental Priming
- Intro script
- Light bulb info sheet
- First 15 choices
- Environmental priming
- Light bulb shape distractor
-
- Second 15 choices
- Cost priming
- Time preference elicitation
- Cognition tests
- Post questionnaire

G: same as A, with lightbulb types reversed in questions X1-15 and X16-30

H: same as B, with lightbulb types reversed in questions X1-15 and X16-30

I: same as C, with lightbulb types reversed in questions X1-15 and X16-30

J: same as D, with lightbulb types reversed in questions X1-15 and X16-30

K: same as E, with lightbulb types reversed in questions X1-15 and X16-30

L: same as F, with lightbulb types reversed in questions X1-15 and X16-30

Experimental Design Details

Randomization Method

The randomization is done by the research team using a random number generator. Enumerators will approach respondents at random.

Randomization Unit

individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

The experiment is implemented via a paper-based field survey in two locations in Beijing. Survey responses will be collected by a team of four enumerators. Our target is to have each enumerator complete 16 surveys per survey day for a total of 32 days. Each day, the enumerators will implement 4 Group A,B, G, H surveys and 2 Group C-F & I-L surveys. At the start of each survey day, the enumerators are provided with a randomized ordering of surveys to be completed. The randomization is done by the research team using a random number generator. Enumerators will approach respondents at random.

Sample size: planned number of observations

1536 people

Sample size (or number of clusters) by treatment arms

• Group A: 192
• Group B: 192
• Group C: 96
• Group D: 96
• Group E: 96
• Group F: 96
• Group G: 192
• Group H: 192
• Group I: 96
• Group J: 96
• Group K: 96
• Group L: 96

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB