Relational contracting, health externalities, and labor supply in Bangladeshi apparel factories

Last registered on January 17, 2022


Trial Information

General Information

Relational contracting, health externalities, and labor supply in Bangladeshi apparel factories
Initial registration date
November 18, 2021

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
November 21, 2021, 5:01 PM EST

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

Last updated
January 17, 2022, 9:29 PM EST

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



Primary Investigator

University of Washington

Other Primary Investigator(s)

PI Affiliation
Columbia University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Manufacturing can provide high-paying jobs for workers in low-income countries, but health issues or caretaking often drive workers (especially women) out of the labor force. Paid sick leave can prevent the spread of disease and keep workers in the workforce, but workers often fear retribution for taking sick leave. We are conducting a stratified, cluster-randomized experiment in collaboration with a large garment manufacturing firm in Bangladesh. All workers receive a phone survey in which they are reminded that their responses will be kept private; treated workers will receive an additional message over the phone: treatment (1) provides a message that the firm cares about their health, (2) provides treatment 1 + a message that respondents are entitled to sick leave and (3) provides treatment 2 + information about recourses if a request for sick leave is denied.

We will estimate the effects of treatment on workers’ reported health (mental and physical), job satisfaction, and absenteeism. Reported health and job satisfaction will be asked after the health messaging in the phone survey. We will also conduct a follow-up survey 2 to 3 weeks after the initial survey, which will provide the information on absenteeism. We will also re-measure health and job satisfaction to assess whether any changes in these outcomes are persistent.
External Link(s)

Registration Citation

Boudreau, Laura and Rachel Heath. 2022. "Relational contracting, health externalities, and labor supply in Bangladeshi apparel factories." AEA RCT Registry. January 17.
Experimental Details


Treatment (1) provides a message that the firm cares about their health, (2) provides treatment 1 + a message that respondents are entitled to sick leave and (3) provides treatment 2 + information about recourses if a request for sick leave is denied.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Job satisfaction, mental health
Primary Outcomes (explanation)
Job satisfaction: Construct an index following Anderson (2008) of the following variables:
• How satisfied are you with your job overall? (Select one: Very dissatisfied; Dissatisfied; Neutral; Satisfied; Very satisfied)
• For the following statements, please state whether you strongly agree, agree, neither agree nor disagree, disagree, or strongly disagree.
a) For me this is the best of all possible organizations for which to work.
b) I find that my values and the organization’s values are very similar.
c) I feel very little loyalty to this organization
d) Often, I find it difficult to agree with this organization’s policies on important matters relating to its employees.
e) I am proud to tell others that I am part of this organization.

Mental health: Construct an index following Anderson (2008) of the following variables. In the past 7 days, how often have you… Select one: Not at all or less than 1 day; 1-2 days; 3-4 days; 5-7 days.
a. ...Have you felt nervous, anxious, or on edge?
b. ...Have you felt depressed?
c. ...Have you felt lonely?
d. ...Have you felt hopeful about the future?
e….. Being so restless that it is hard to sit still
f……. Becoming easily annoyed or irritable
g……. Feeling afraid, as if something awful might happen

Secondary Outcomes

Secondary Outcomes (end points)
absenteeism, physical health, direct reporting of covid symptoms
Secondary Outcomes (explanation)
Absences: In the most recent full month, how many days of work did you miss? (dropping responses of “I don’t know’”)
Physical health: Construct an index following Anderson (2008) of the binary occurrence of the following symptoms in the past 12 weeks
a. Fever Select one: Yes; No.
b. An unusual dry cough: Yes; No.
c. Difficulty breathing/shortness of breath? Select one: Yes; No.
d. Loss of a sense of smell? Select one: Yes; No.
e. Headache? Select one: Yes; No.
f. Diarrhea? Select one: Yes; No.
g. Sore throat? Select one: Yes; No.
h. Unusual fatigue? Select one: Yes; No.
Direct reporting of Covid symptoms
To your knowledge, since the beginning of the COVID-19 pandemic, have you had any of these symptoms or tested positive for COVID-19?
To your knowledge, since the beginning of the COVID-19 pandemic, have any workers on your sewing line/in your section had symptoms of or tested positive for COVID-19? Select one. Yes; No.

Experimental Design

Experimental Design
We conduct a survey experiment with workers at two of the apparel manufacturer's factories, which together employ over 8,700 people. First, we conduct a stratified random selection of workers to participate in the survey experiment. Using the entire list of employees in the two factories, we sample workers from four types of production teams. Among these teams, we chose teams with a sufficient large number of workers to accommodate this survey and a separate survey that we are running for a different project (approximately above 15). We are left with 112 eligible teams and a total of 5,948 eligible workers, out of a workforce of 8,727 people, including 1,000 managers and administrative staff members.

We next stratify workers on eligible teams by their sex, which we identify based on name (male, female, uncertain). We are able to categorize names as male or female for 5,929 out of 5,948 eligible workers. In some cases, there are teams with very small numbers of one group; in this case, we aggregate these workers to the smallest level that yields a sufficiently large groups size (e.g., production section-floor).
We conduct this RCT in coordination with another survey experiment that we are running with the apparel manufacturer (RCT ID: AEARCTR-0007103). Consequently, we randomly sample workers from strata as follows:

1. For workers in the sewing section, for sewing line & female strata, randomly select 11 workers per stratum. For sewing line & male strata in the sewing section, which are smaller, randomly select men in proportion to the strata size. This produced an initial target sample of 574 workers.

2. The remaining 5,374 workers were eligible to be recruited for the other survey experiment, AEARCTR-0007103. For this survey experiment, within strata, the research team randomly selected workers as target participants. We randomly ordered the remaining workers in each stratum so that workers who declined to participate could be replaced. We conducted the second survey experiment, for which we recruited or attempted to recruit 3,638 workers. This left 1,736 eligible workers from the list of 5,374 workers who had not been recruited or attempted to be recruited for AEARCTR-0007103.

3. To arrive at the final target sample for this survey experiment, we appended the list of 574 workers and the list of 1,736 workers, for a maximum total sample size of 2,310. Given our successful recruitment rate of 62% in AEARCTR-0007103, we anticipate achieving a realized sample size of around 1,432.

We randomly assign workers to treatment arms in Stata. We randomly assign workers to each treatment arm with equal probability. To address misfits across strata, we use the randtreat package by Carril (2017). We conducted 10 randomizations and selected the one that performed best in terms of balance on two covariates available to the research team (tenure and skill group).
Experimental Design Details
Randomization Method
We randomly assign workers to treatment arms in Stata.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Maximum total sample size: 2,310. Expected response rate: 62%. Expected final sample size: 1,432.
Sample size (or number of clusters) by treatment arms
Control: 578; Treatment arm 1: 569; Treatment arm 2: 582; Treatment arm 3: 581. Note that we expect the realized sample sizes to be approximately 62% of these sample sizes.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We used data from our pilot survey, which we conducted in the spring of 2021 with garment workers recruited through the community, to inform our power calculations. We construct the job satisfaction and mental health index variables as described above. We assume two primary outcomes and adjust for multiple hypothesis testing by applying the Bonferroni adjustment for two outcomes with alpha = 0.1. We set power = 0.8. As in our pilot, we only observe workers’ factory and gender, we residualize our outcome variables using factory x gender fixed effects. We assume 4 treatment arms. Under these assumptions, we can detect a 0.19 sd effect size for mental health with a sample size of 1364 workers, and the same magnitude effect size for job satisfaction with a sample size of 1424 workers. These MDEs and sample sizes are within our expected ranges.

Institutional Review Boards (IRBs)

IRB Name
University of Washington Human Subjects Division
IRB Approval Date
IRB Approval Number
IRB Name
Columbia University Human Research Protection Office
IRB Approval Date
IRB Approval Number
IRB Name
BRAC University Institutional Review Board
IRB Approval Date
IRB Approval Number


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