Discretion in Referrals: Evidence from a Tanzanian Export Garment Factory

Last registered on July 10, 2023
View Trial History

Pre-Trial

Trial Information

General Information

Title
Discretion in Referrals: Evidence from a Tanzanian Export Garment Factory
RCT ID
AEARCTR-0011641
Initial registration date
June 29, 2023

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 10, 2023, 8:49 PM EDT

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

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Name
Yuen Ho
Affiliation
University of California Berkeley
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Yihong Huang
PI Affiliation
Harvard University
Contact Investigator

Additional Trial Information

Status
On going
Start date
2023-06-22
End date
2024-12-31
Keywords
Behavior, Firms & Productivity, Labor
Additional Keywords
Discretion, Job Referrals, Professional Development Opportunities, Job Training
JEL code(s)
O15, M51
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Developing country contexts are often characterized by informality and clientelism. In such contexts, can making processes more meritocratic improve efficiency? We study these questions with a field experiment in partnership with a large export garment manufacturing firm in Tanzania in the context of an on the job leadership training program offered by the firm. In the status quo system, supervisors and workers have a lot of discretion in who they informally recommend for professional opportunities. As in a classical principal-agent problem, on the one hand, such discretion can lead to more efficient outcomes if supervisors and workers have valuable private information not otherwise available to the firm. On the other hand, such discretion can reduce efficiency if supervisors and workers are biased, make mistakes, or have preferences that are not aligned with the firm’s objectives. To study whether discretion is efficient in this setting, we implement a two-part field experiment. First, we randomize whether supervisors and coworkers are informed of a performance–based referral bonus either before or after they recommend workers for the training program. If discretion is driven by taste-based preferences, or if incentives are misaligned between the firm and employees to begin with, then ex-ante bonus announcements should reduce inefficient discretion and result in higher quality referrals. If discretion is instead driven by genuine mistakes, statistical-based preferences, or if incentives are already aligned between the firm and employees, then ex-ante bonus announcements will have no effect or could even worsen the quality of referrals. To study the labor supply-side response to discretion, we also randomize whether applications to the training program emphasize that selection will be based on supervisor discretion (referrals) or objective, meritocratic measures (past performance records). Taken together, our study examines the efficiency of discretion in a labor context in which informal social relationships are paramount.
External Link(s)

Registration Citation

Citation
Ho, Yuen and Yihong Huang. 2023. "Discretion in Referrals: Evidence from a Tanzanian Export Garment Factory." AEA RCT Registry. July 10. https://doi.org/10.1257/rct.11641-1.0
Sponsors & Partners

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
Experimental Details

Interventions

Intervention(s)
Our partner firm is offering an on-the-job training program for promising workers, focusing on technical production, communication, leadership, and management skills. The program will be designed and implemented by our partner firm.
Intervention Start Date
2023-06-22
Intervention End Date
2023-08-31

Primary Outcomes

Primary Outcomes (end points)
The key outcome variables of interest are the types and quality of the workers that apply and that are referred under the different referral experimental conditions, including gender, religion, past performance, and training assessment scores.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Part 1 - Supervisor and coworker referrals: Supervisors and a random sample of workers will be given the opportunity to refer other workers for the training program. All supervisors and referring coworkers will receive a referral bonus if the worker(s) they recommend for the training successfully completes the training and scores within the top 50% of the training cohort in an assessment of their technical knowledge and soft skills. Prior to giving referrals, supervisors and referring coworkers will be randomized into one of two groups:
- Ex-ante bonus announcement: Respondents will be informed about the performance-based referral bonus prior to giving their referrals
- Ex-post bonus announcement: Respondents will be informed about the performance-based referral bonus after they have given their referrals

Part 2 - Worker applications: All eligible workers will be given the opportunity to apply for the training program. Specifically, by production teams, workers will be invited to an application session to learn about the training program and receive applications where they can indicate whether they want to apply to the training program or not. In these information sessions, workers will be randomly assigned to receive either one of two versions of the application form. The application forms are identical except for a difference of two words. Specifically, in the "Status Quo" version, the application form states that selection for the training program will be based on many criteria, including supervisor referrals. In the "Meritocratic" version, the application form states that selection for the training program will be based on many criteria, including past performance records.
Experimental Design Details
Not available
Randomization Method
For the referrals experiment, supervisors will be invited to attend a session for the referral process by facility. Within each facility meeting, supervisors will be randomized in person, into two groups, with assignments alternating consecutively based on the order in which supervisors are seated during the meeting. Within each facility, the referral process will then be conducted separately for each group, with one group receiving the ex-ante bonus announcement and the other group receiving the ex-post bonus announcement. The exact same randomization process will be used for the coworker referrals. For the application experiment, application forms will be pre-sorted in an alternating pattern (C/T/C/T/...etc) and handed out to workers consecutively based on how they are seated during the application information session for their production team. Both randomization methods are comparable to the randomization procedure used in Bursztyn and Jensen (2015).
Randomization Unit
For both experimental parts, randomization is conducted at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Sample sizes:
(1) Worker applications: 1,200 workers
(2) Coworker referrals: 250 randomly selected workers
(3) Supervisor referrals: 120 supervisors
Sample size: planned number of observations
(1) Worker applications: 1,200 workers (2) Coworker referrals: 250 randomly selected workers (3) Supervisor referrals: 120 supervisors
Sample size (or number of clusters) by treatment arms
(1) Worker applications: 600 in the "Status Quo" form group; 600 in the "Meritocratic" form group
(2) Coworker referrals: 125 in the "Ex Post Bonus" group; 125 in the "Ex Ante Bonus" group
(3) Supervisor referrals: 60 in the "Ex Post Bonus" group; 60 in the "Ex Ante Bonus" group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The main power constraints come from the coworker and supervisor referrals. A sample size of 120 supervisors will allow us to detect a 0.5 standard deviation (SD) difference in the average performance of workers referred by supervisors in the "Ex-Ante Bonus" and "Ex-Post Bonus" groups, with an alpha level of 0.05 and a power of 80%. Assuming the average performance of the referred workers in the "Ex-Post Bonus" group is at the 50th percentile with a standard deviation of 20%, a 0.5 SD difference means that the average performance of the referred workers in the "Ex-Ante Bonus" group would be at the 60th percentile. Similarly for coworkers, a sample size of 250 workers has been chosen to ensure that a 0.35 SD difference in the average performance of workers referred by coworkers in the "Ex-Ante bonus" and "Ex-Post bonus" groups can be detected. Assuming the average performance of the "Ex-Post bonus" group is at the 50th percentile, a 0.35 SD difference would correspond to a shift of 7 percentile points to the 57th percentile.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of California, Berkeley
IRB Approval Date
2023-05-26
IRB Approval Number
2023-03-16172
Analysis Plan