Factory safety information and work decisions by garment sector workers in Bangladesh

Last registered on July 02, 2016

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Pre-Trial

Trial Information

General Information

Title

Factory safety information and work decisions by garment sector workers in Bangladesh

RCT ID

AEARCTR-0000818

Initial registration date

September 06, 2015

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

September 06, 2015, 7:13 PM EDT

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

Last updated

July 02, 2016, 7:56 PM EDT

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

Locations

Country

Bangladesh

Region

South Asia

Primary Investigator

Name

Laura Boudreau

Affiliation

Columbia University

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

On going

Start date

2015-08-28

End date

2016-08-20

Keywords

Labor

Additional Keywords

Safety, ready-made garment sector

JEL code(s)

J28, J81

Secondary IDs

Abstract

Workers in developing countries often have limited information about job risks. As a result, workers may not be compensated for bearing workplace risks (Smith, 1979). Furthermore, worker-firm matches may be inefficient, which may generate turnover (Viscusi, 1979), and labor markets may be incomplete. There is a lack of empirical evidence, however, about how imperfect information about job risks affects employment outcomes for these workers. Furthermore, little is known known about their willingness, if informed about job risks, to trade-off wages for exposure to risk. This research aims to provide the first experimental evidence of how limited information about job risks affects workers’ employment outcomes in developing countries and to identify these workers’ wage-safety elasticity. I study Bangladesh’s ready-made garment (RMG) sec- tor, where I document that workers are largely unable to perceive their jobs’ riskiness. I analyze how an information asymmetry about safety between workers and factory owners affects workers’ abilities to make optimal employment decisions. I conduct a field experiment with 309 workers in Savar, a sub-district of Dhaka home to many garment factories. In the experiment, I provide a randomly selected group of workers with information about their factories’ performances, and the performances of numerous factories nearby, on third-party safety audits. I use follow-up phone calls and other methods to determine how the information affects workers’ perceptions about safety and employment decisions.

External Link(s)

Registration Citation

Citation

Boudreau, Laura. 2016. "Factory safety information and work decisions by garment sector workers in Bangladesh." AEA RCT Registry. July 02. https://doi.org/10.1257/rct.818-4.0

Former Citation

Boudreau, Laura. 2016. "Factory safety information and work decisions by garment sector workers in Bangladesh." AEA RCT Registry. July 02. https://www.socialscienceregistry.org/trials/818/history/9237

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Experimental Details

Interventions

Intervention(s)

Treatment group: After participating in a survey (see survey attachment), the treatment group participants receive information about their factory and other factories’ nearby performance on structural, electrical, and fire safety audits conducted by a third-party auditor. Specifically, enumerators follow a script (see treatment group post-survey script attachment) in which they tell the participant how many times their factory does not comply with the “High Priority” safety rules for the audits; they also tell the participant how their factory ranks compared to 71 factories nearby for which the safety audits are available. Then the enumerator provides the participant with a flyer that includes information about the audits and a list of the best and relatively worse performing factories nearby (see attachment). It also includes a phone number that the participant can call for additional information about safety.

Control group: Control group participants also participate in a survey do not receive the information about safety at the end. They hear a short post-survey script and receive a flyer with a phone number that they can call for more information (see control group post-survey script and flyer attachments).

Intervention Start Date

2015-08-28

Intervention End Date

2015-09-28

Primary Outcomes

Primary Outcomes (end points)

Primary outcome variables: Outcome A: Leaving one’s job at the factory where employed at time of intervention between the intervention date and the time of the Round 3 follow-up phone call; Outcome B: Reporting plans to leave one’s job in the near future; Outcome C: Referring one’s family and friends to get a job at the factory where em- ployed at time of intervention between the intervention date and the time of the Round 3 follow-up phone call; Outcome D: Calling the safety information hotline between the intervention date and the time of the Round 3 follow-up phone call; Outcome E: Safety perception index.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

I randomly assign walking routes for enumerators into treatment (factory safety information provided) and control (no factory safety information provided) conditions. Enumerators obtain a random sample of garments along each walking route using a right-hand sampling rule (see attachments). All participants along one walking route are assigned to either the treatment or the control group.

Identification of walking routes: After selecting the location of the field experiment, I mapped the locations of all factories for which the third-party safety audits were publicly available (71 factories). Following a site visit to determine reasonable assumptions for how proximate garment workers’ residences are to their factories, I determined the geographic area to be included in the study. Then I determined the locations of 24 clusters of workers by aiming to maximize distance between walking routes subject to identifying housing units along each route (using Google satellite imagery).

Treatment and control group participants receive follow-up phone calls three and six months after the survey during which they are asked a series of follow-up questions.

Experimental Design Details

Participants’ factories in this study can be grouped into three categories: Top safety performers, intermediate safety performers, and poor safety performers. Participants in the treatment group will learn which type of factory they work at, and therefore their response to the treatment is expected to be different depending on which type of factory they work at. When I test whether the treatment had an effect on the outcome variables of interest, I will specifically test for differential effects across these three groups. The three groups are pre-determined by the study design: 10 factories are listed as top performers, and 17 factories are listed as bottom performers, on the safety information sheet provided to participants. The remaining 44 factories are the intermediate group.

Randomization Method

Randomization was done in the office using the statistical program R.

Randomization Unit

Walking routes through neighborhoods with dense populations of garment workers were the unit of randomization. Each walking route was randomly assigned to either treatment or control.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

24 clusters of 13 workers per cluster. 12 treatment clusters and 12 control clusters.

Sample size: planned number of observations

312 participants were planned. The final number of study participants is 309 participants.

Sample size (or number of clusters) by treatment arms

156 participants in the treatment group and 156 participants in the control group were planned. The final number of participants in each group is 152 participants in the treatment group and 157 in the control group.

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

Supporting Documents and Materials

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