Forecasts: Consumption, Production, and Behavioral Responses

Last registered on January 29, 2020

Pre-Trial

Trial Information

General Information

Title
Forecasts: Consumption, Production, and Behavioral Responses
RCT ID
AEARCTR-0004343
Initial registration date
June 23, 2019

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 09, 2019, 11:35 AM EDT

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

Last updated
January 29, 2020, 4:47 PM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Region

Primary Investigator

Affiliation
University of California, Davis

Other Primary Investigator(s)

PI Affiliation
Williams College
PI Affiliation
Sewanee: The University of the South
PI Affiliation
Lahore University of Management Sciences
PI Affiliation
Political Economy and Governance Initiative, Institute of Development and Economic Alternatives

Additional Trial Information

Status
Completed
Start date
2019-04-01
End date
2019-10-31
Secondary IDs
Abstract
Economic theory predicts that forecasts are an important determinant of welfare. In developing countries, however, agents may have difficulty forming accurate, precise forecasts because of limited information and human capital. This plausibly limits the scope for optimal responses to uncertain future events. We study the effect of two randomized interventions on forecast formation and behavioral responses. The first is the provision of day-ahead air pollution forecasts. The second is training in forecasting techniques aimed at reducing behavioral biases. We estimate impacts on forecast error in air pollution and travel times. Measured responses include willingness to pay for protective face masks and changes in time use. We examine effects on proxies for the variance of utility, which broadly reflects forecasting and responses to uncertainty. Finally we elicit willingness to pay for our forecasts, an important input to cost-benefit analysis of air pollution monitoring.
External Link(s)

Registration Citation

Citation
Ahmad, Husnain F. et al. 2020. "Forecasts: Consumption, Production, and Behavioral Responses." AEA RCT Registry. January 29. https://doi.org/10.1257/rct.4343-1.1
Former Citation
Ahmad, Husnain F. et al. 2020. "Forecasts: Consumption, Production, and Behavioral Responses." AEA RCT Registry. January 29. https://www.socialscienceregistry.org/trials/4343/history/61685
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We implement a randomized controlled trial to study people in their roles as consumers and producers of forecasts. The research design includes two orthogonal treatments. The first entails provision of day-ahead air pollution forecasts delivered by text message. The second entails in-person training designed to reduce behavioral biases in forecasts.

See pre-analysis plan for additional details.
Intervention Start Date
2019-06-15
Intervention End Date
2019-08-31

Primary Outcomes

Primary Outcomes (end points)
Broadly, we are interested in three types of outcomes: 1) consumption, e.g. demand for our forecast product; 2) production, e.g. error in forecasting the time required for a future journey; and 3) behavioral responses, e.g. demand for particulate-filtering face masks. In theory these two treatments could be complements or substitutes. While our design will allow us to measure this interaction, it is not our primary focus.

See pre-analysis plan for additional details.
Primary Outcomes (explanation)
See pre-analysis plan.

Secondary Outcomes

Secondary Outcomes (end points)
See pre-analysis plan.
Secondary Outcomes (explanation)
See pre-analysis plan.

Experimental Design

Experimental Design
Our sample is approximately 1100 households in two lower-middle-income neighborhoods in Lahore, Pakistan. Households are individually randomized into four equal-sized groups: a control arm, and three treatment arms (forecasts only, forecast training only, and both). Following a baseline survey, we buffer for approximately a one month period after which we implement our treatment. We develop two treatments: (i) an air pollution information clearinghouse that uses text messages and a forecast model to inform citizens about air pollution forecasts, and (ii) forecast training session. Our information treatment involves a system developed by the research team with support from OpenCodes that combines a forecast model, a GSM mobile connection and an API based system to treat our respondents with air pollution forecast information every day for a period of 3 months. Whereas the forecast training is implemented once to every househould over a period of one month following the SMS message intervention.

See pre-analysis plan for additional details.
Experimental Design Details
See pre-analysis plan.
Randomization Method
Blocking and randomization will be performed in R using the tools in the blockTools package. See pre-analysis plan for additional details.
Randomization Unit
Individual, blocked on several baseline outcomes. See pre-analysis plan for additional details.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1100 households
Sample size: planned number of observations
1100 households
Sample size (or number of clusters) by treatment arms
275 control households, 275 forecasts only households, 275 forecast training only households, 275 both treatments households.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See pre-analysis plan.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
University of California, Davis
IRB Approval Date
2019-03-04
IRB Approval Number
1336133-1
IRB Name
Lahore University of Management Scicnes
IRB Approval Date
2018-11-01
IRB Approval Number
20180214
Analysis Plan

Analysis Plan Documents

Final PAP prior to endline data collection

MD5: 52f6f7a5ecd9bbd73aa4a430427a132e

SHA1: ed4fa5e4ced8d03ed084615537c356a2381d2071

Uploaded At: January 29, 2020

Forecasts: Consumption, Production, and Behavioral Responses

MD5: ff089817e6b6d4a50dad954ecf0f1875

SHA1: 2980edfd2724fc07ce157f2ef46ae593ee66c4d7

Uploaded At: June 23, 2019

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials