Wage Transparency as a Tool for Increasing Productivity

Last registered on April 23, 2024

View Trial History

Pre-Trial

Trial Information

General Information

Title

Wage Transparency as a Tool for Increasing Productivity

RCT ID

AEARCTR-0012150

Initial registration date

October 03, 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

October 04, 2023, 5:06 PM EDT

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

Last updated

April 23, 2024, 11:41 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Marco Palma

Affiliation

Texas &MUniversity

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2023-11-01

End date

2024-12-31

Keywords

Behavior, Firms & Productivity, Lab, Labor

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Wage transparency in the workplace matters to employers and employees alike. Some countries such as Norway have introduced wage transparency by allowing constituents to search for wage levels through an online search. Employees have several interests that are related to wage transparency, such as knowing that they are receiving a fair wage relative to their coworkers or knowing their relative position in a social or professional ladder. Managers are aware that their employees value this information as a way to “understand and contextualize their workplace,” yet face trade-offs (Collins & Mossholder, 2014). In the interest of encouraging productive employees, increasing or maintaining job satisfaction, increasing trust, and reducing employee turnover, employers and policymakers face the decision on whether to provide wage transparency (Cohen-Charash & Spector, 2001; Ambrose et al., 2002; Reb et al, 2006; Sklaricki & Folger, 1997). Furthermore, transparency discourages public corruption and nepotism, and increases trust in the tax and social security system. This study aims to investigate wage transparency as a tool for increasing productivity and its effect on employees’ motivation to increase effort. Experimental literature shows that transparency is valued when it reduces uncertainty about wages (Brandes & Darai, 2017). Workers also have varying and substantial willingness to pay to learn the wages of peers and their boss (Cullen & Perez-Truglia, 2022). Our study examines conditions under which transparency of wages or productivity may induce workers to exert higher effort when there is no vertical differentiation (similar positions) among them and in a setting where more productive employees are paid more.

External Link(s)

Registration Citation

Citation

Palma, Marco. 2024. "Wage Transparency as a Tool for Increasing Productivity ." AEA RCT Registry. April 23. https://doi.org/10.1257/rct.12150-1.2

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

The interventions we employ involve revealing information in the feedback stage about the outcome(s) of the co-worker in two-person group environments. We compare these interventions to a control where participants receive information only about their own outcome. This question is important because there are different governments and private companies evaluating the implementation of transparent wage policies which could be a tool to promote increased productivity among employees that have similar positions.

Intervention (Hidden)

Intervention Start Date

2023-11-15

Intervention End Date

2024-05-31

Primary Outcomes

Primary Outcomes (end points)

initial performance in the real-effort task, response of real-effort performance to feedback

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

beliefs over outcomes, task/job satisfaction, perception of fairness of the outcomes

Secondary Outcomes (explanation)

Experimental Design

We first run two pilot sessions (14 subjects each) where we randomly assign subjects into groups of 2 for 5 rounds - in total subjects undergo 10 rounds and 2 different pairings. In each round, subjects compete for the higher bonus in a real-effort task - the addition of 4 2-digit numbers. Two bonuses are drawn each round from a uniform distribution with 2 possible outcomes: 5 and 15. If the drawn bonuses are uneven, the more productive subject earns the higher bonus. If the productivities of two subjects in a group for a given round are equal, the higher and lower bonuses are assigned to subjects with equal chance. After each round subjects learn only their own bonus (baseline condition).

To address the questions in our research, we randomly assign subjects within a session to one of four treatments (baseline, wage transparency, productivity transparency, or wage and productivity transparency). Subjects are matched with subjects from pilot sessions to compete for the higher bonus - subjects undergo 10 rounds (or 2 pairings of 5 rounds each) and are matched with the real-effort of two different subjects (25th and 75th percentile) from the pilot sessions in a random order. The real-effort task is to complete as many sums of 4 2-digit numbers as possible. Treatments differ in the amount and type of feedback that is provided about subjects from the pilot sessions.

Experimental Design Details

Randomization Method

Randomization done by a computer. Subjects randomly seated in the lab.

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

308 (28 subjects in 2 pilot sessions, 280 subjects in main experiment).

Sample size: planned number of observations

308 (28 subjects in 2 pilot sessions, 280 subjects in main experiment).

Sample size (or number of clusters) by treatment arms

70 subjects per treatment (baseline, wage transparency, productivity transparency, wage and productivity transparency)

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

To the best of our knowledge there is no study that employs feedback interventions and incentives similar to ours for studying the effect of transparency on real-effort. With 280 subjects (70 subjects/treatment arm) we are powered to detect medium effect sizes (0.48) according to Cohen's d (two-sided, significance level=0.05, power=0.8).

Supporting Documents and Materials

IRB