Two sided asymmetric information in labor markets.

Last registered on April 03, 2024

Pre-Trial

Trial Information

General Information

Title
Two sided asymmetric information in labor markets.
RCT ID
AEARCTR-0013100
Initial registration date
April 02, 2024

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
April 02, 2024, 12:51 PM EDT

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

Last updated
April 03, 2024, 1:01 PM EDT

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

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Primary Investigator

Affiliation
University of Chicago

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2024-02-01
End date
2026-01-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Workers and firms in low and middle-income countries operate in a credit-constrained environment characterized by asymmetric information. Firms are unaware of the productivity and trustworthiness of the worker, and workers are unsure whether the firms will pay them on time or at all. Neither the worker nor the firm can commit to a contract. Theory suggests that a consequence of these market failures and the firm's own credit constraints is that firms should offer back-loaded contracts. We will test this theoretical prediction in an experiment where we offer to hire workers for firms with different payment structures. Workers dislike back-loaded contracts because they can't trust the firm to abide by them (due to limited commitment on the firm side) and due to consumption credit constraints. We will test this hypothesis in a take-up experiment where we offer real contracts of different lengths and payment structures to a random sample of workers. Our experiment will disentangle the effect of credit constraints and information asymmetry on the take-up of jobs by workers. Workers and firms who agree to take up the contracts will be matched with each other. The effects of contract structure on worker productivity, worker-firm disagreements will be measured.
External Link(s)

Registration Citation

Citation
K, Varun. 2024. "Two sided asymmetric information in labor markets.." AEA RCT Registry. April 03. https://doi.org/10.1257/rct.13100-2.0
Experimental Details

Interventions

Intervention(s)


Intervention Start Date
2024-02-01
Intervention End Date
2025-10-01

Primary Outcomes

Primary Outcomes (end points)
1) Job take-up on the worker side
2) Probability of acceptance of contract by firms

Primary Outcomes (explanation)
We will construct the worker type variable in two ways
1) Workers who work on fixed work contracts will be classified as high types as these contracts are highly valued
2) Workers who earned earned higher average income in the days before the survey will be classified as high type

Firms will be classified as large if they have higher number of construction sites/labour working under them. Firms which are managed by former masons might be considered as small.

Secondary Outcomes

Secondary Outcomes (end points)
1) Type of worker and take-up of jobs by different types of worker
2) Type of firm and acceptance of offers by different types of firms
3) Worker productivity at the work site
4) Worker-firm disagreements.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The workers are cross-randomized into treatments which offer a job, with or without insurance against wage theft, and with or without back-loading of wages.

The firms are cross-randomized into treatments which offer compensation against worker separation or not, credit to pay workers or not, and with or without back-loading of wages.

Firms and workers which accept the contracts are matched with each other.
Experimental Design Details
Not available
Randomization Method
Worker side: A Unique ID with treatment cells randomly allocated to them is created in the office. Enumerators recruit the workers in the order of unique id.
Firm side: A Unique ID with treatment cells randomly allocated to them is created in the office. Random sample of firms are surveyed in the order of unique id.
Randomization Unit
Randomization on the worker and firm side is done at the individual level.
Workers and firms which accept contracts within a treatment cell are matched with each other randomly to each other.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We will cluster at the individual level for the firms as we observe them hiring more than one workers.
Sample size: planned number of observations
Workers: Approximately 2000. 50% of workers who accept contracts will be provided jobs. Firms: Up to 800 There is a possibility that we might not be able to reach 2000 workers. Similarly, number of firms may be below 800.
Sample size (or number of clusters) by treatment arms
Workers
For the first 600-800 workers we will use the following sample size


1) Daily payment*uninsured* 3 day contracts: 15%
2) Steep Back-loaded* uninsured * 3 day contracts: 15%
3) Smooth Back-loaded* uninsured * 3 day contracts: 25%
4) Steep Back-loaded* insured * 3 day contracts: 20%
5) Smooth Back-loaded* insured * 3 day contracts: 20%
6) Daily payment*insured* 3 day contracts: 5%

For the next 1200-1400 sample we will divide the sample between the 3 and 7 day contracts such as the overall sample has ratio 60:40. The break up of 3 day contracts will be the same as above and the breakup of 7 day contract sample is below:


1) Daily payment*uninsured* 7 day contracts: 15%
2) Steep Back-loaded* uninsured * 7 day contracts: 15%
3) Smooth Back-loaded* uninsured * 7 day contracts: 25%
4) Steep Back-loaded* insured * 7 day contracts: 20%
5) Smooth Back-loaded* insured * 7 day contracts: 20%
6) Daily payment*insured* 7 day contracts: 5%


Firms:
For the first 60-100 firms the sample will be divided as:
1) Daily wage*no credit * no guarantor: 15%
2) Steep Back-loaded * no credit * no guarantor: 15%
3) Daily wage*credit * no guarantor: 15%
4) Steep Back-loaded * credit * no guarantor: 15%
5) Daily wage*no credit * guarantor: 15%
6) Steep Back-loaded * no credit * guarantor: 15%
7) Daily wage*credit * guarantor: 5%
8) Steep Back-loaded * credit * guarantor: 5%

In the remaining sample of firms we will add smooth back-loaded contract as an additional treatment. We might cross-randomize this sample with the type of worker (low or high) type.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Chicago
IRB Approval Date
2024-03-07
IRB Approval Number
IRB23-0207
IRB Name
University of Chicago
IRB Approval Date
2024-04-15
IRB Approval Number
IRB24-0551
IRB Name
University of Chicago
IRB Approval Date
2024-04-11
IRB Approval Number
IRB24-0376