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Field
Abstract
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Before
This research studies how information about working conditions, specifically, workplace safety, affects low-wage workers’ decisions about where to work and where to refer their family and friends to work. I study workers in Bangladesh’s ready-made garment (RMG) sector who face risky conditions at their factories. I test the hypothesis that information asymmetry about factory safety constrains workers’ abilities to make optimal decisions about where to work and where to refer their family and friends to work. I also aim to identify how much workers are willing-to-pay to obtain additional information about safety. To study these questions, I conduct a field experiment with 312 garment workers who live in Savar, a sub-district of Dhaka home to hundreds of garment factories. In the field 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 this information affects workers’ behavior and perceptions about safety in their factory.
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After
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.
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Field
Last Published
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Before
July 02, 2016 07:48 PM
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After
July 02, 2016 07:56 PM
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Field
Primary Outcomes (End Points)
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Before
1. Calls to safety information hotline: (1) Whether a participant places a call to the safety hotline ({0,1}); and (2) the duration of the participant’s call to the safety information hotline. (Exploratory analysis: Which safety information options the participant selects and whether the participant leaves a voicemail (the safety information hotline is a touchtone system where the caller selects different options to receive different safety information).)
2. Employment: Whether participant is more or less likely to report: (1) Having changed jobs in past three months; (2) having left the workforce in the past three months; (3) plans to look for a job with a different employer or to stop working in the near future. If participant responds positively to any of these, (4) whether they report safety as a factor in their decision. If the participant has changed jobs to another garment factory: (5) If the factory is one of the factories for which a safety audit is available, whether the factory performed as well or better than the participant’s former factory on the safety audits; (6) Change in participant’s position and wages from old to new factory; (7) Change in participant’s commuting distance (reported commuting time) from old to new factory; (8) Change in participant’s reported nonpecuniary benefits from old to new factory. If the participant has changed positions within the same factory: (9) Change in positions; (10) Change in wages.
3. Job referral practices: (1) Whether participant is more or less likely to refer family and friends to jobs at their factory and/or (2) other factories nearby compared to participant’s baseline. (3) Whether factories participants reports referring family/friends to are more likely to be top safety performers (less likely to be bottom safety performers). (4) Whether participant is more or less likely to report safety as a factor in their decisions to refer family and friends to jobs at their factory and/or other factories nearby compared to participant’s baseline.
4. Perceived stress and risk at work: Response to survey questions 31, 32, 38, and 39.
5. Prioritization of safety relative to other nonpecuniary benefits: Response to survey question 33.
6. Information sharing with family, friends, and coworkers.
7. Participation in workers’ organizations: Reported participation in trade unions, Worker Participation Committees, and/or Workers’ Welfare Associations (where relevant).
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After
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.
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Field
Planned Number of Observations
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Before
312 participants.
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After
312 participants were planned. The final number of study participants is 309 participants.
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Field
Sample size (or number of clusters) by treatment arms
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Before
156 participants in the treatment group and 156 participants in the control group.
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After
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.
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