Relative Pay Comparisons in the Workplace: Field Evidence on Eff ort and Labor Supply

Last registered on February 13, 2015


Trial Information

General Information

Relative Pay Comparisons in the Workplace: Field Evidence on Eff ort and Labor Supply
Initial registration date
February 13, 2015

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
February 13, 2015, 10:04 PM EST

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

Last updated
February 13, 2015, 10:06 PM EST

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



Primary Investigator

University of Pittsburgh

Other Primary Investigator(s)

PI Affiliation
Columbia University
PI Affiliation
Columbia University

Additional Trial Information

On going
Start date
End date
Secondary IDs
A long tradition in economics and psychology has advanced the notion that individuals care about not only their own pay, but also their pay relative to that of their co-workers. We use a field experiment with Indian manufacturing workers to test whether relative pay comparisons affect effort and labor supply. Workers perform individual production tasks, but are organized into distinct teams - defined by the type of product they produce. We randomize teams to receive either compressed wages (where all workers earn the same random daily wage) or heterogeneous wages (where each team member is paid a different wage according to his baseline productivity level). This enables effort comparisons across workers who receive the same absolute wage, but vary in the wages of their co-workers. In addition, we introduce heterogeneity in the extent to which pay differences across co-workers seem justified.
External Link(s)

Registration Citation

, , Supreet Kaur and Yogita Shamdasani. 2015. "Relative Pay Comparisons in the Workplace: Field Evidence on Eff ort and Labor Supply." AEA RCT Registry. February 13.
Former Citation
, , Supreet Kaur and Yogita Shamdasani. 2015. "Relative Pay Comparisons in the Workplace: Field Evidence on Eff ort and Labor Supply." AEA RCT Registry. February 13.
Experimental Details


We recruit workers from nearby villages to participate full-time in a low-skilled manufacturing job. At our worksites, workers are organized into teams of three and trained in one of ten tasks, for which production is an individual activity. They first undergo a training period, during which they are paid the same daily wage. Once the training period is over, we vary the wages paid across teams, and in some cases, across workers within a team.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
(1) Daily production for each worker, measured by a count of the total items produced in a day

(2) Daily attendance of each worker, including time of arrival and departure from worksite

(3) Total labor earnings of each worker

(4) Team-level output
Primary Outcomes (explanation)
Total labor earnings of each worker will be constructed from data from worksite payroll as well as end line survey responses about outside activities on days when workers were absent.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Once the training period is over, workers' baseline productivity is assessed, and each worker is assigned a relative productivity rank within their team. We then randomize teams into one of four wage treatments:

(1) Each team member is paid according to his baseline productivity, with the wages for the lowest, middle and highest productivity workers being w_L, w_M and w_H respectively, where w_training < w_L < w_M < w_H

(2) All team members are paid the same daily wage of w_L

(3) All team members are paid the same daily wage of w_M

(4) All team members are paid the same daily wage of w_H
Experimental Design Details
To test whether relative pay comparisons affect worker effort and utility, we construct a design to accomplish two goals:

(i) for each worker, define a clear reference group of co-workers for relative pay comparisons;
(ii) be able to compare outcomes for workers whose absolute pay levels are the same, but who vary in their co-workers' (reference group) wages.

Workers will be employed full-time at worksites in low-skilled manufacturing tasks. Within each worksite, workers will be randomly divided into up to "teams", with 3 workers in each team. Within a team, all 3 teammates will perform the same production task. Each team at the worksite will have a unique task - for example, one team will make rope, another team will produce incense sticks, another team will weave floor mats, etc. - with a total of up to 10 tasks per worksite. Production will be an individual activity - teammates will sit together but will not need to do any work jointly. Rather, the purpose is that each worker's 2 teammates will be the only other people at the worksite making the same product, and will therefore be the most salient reference group for wage comparisons.

Before assigning treatment status, workers will undergo a "training period." During this period, all workers will receive the same daily wage. At the start of training, workers will be told that their post-training wages may depend on their output during training. Once training ends, workers' baseline productivities will be assessed, given their relative productivity ranks in the team: low, medium, or high.

Teams will then be randomized into one of four wage treatments:

(i) Heterogeneous: Each team member is paid according to his baseline productivity. The wages for the lowest, middle, and highest productivity workers are wL, wM and wH respectively, where w_training < w_L < w_M < w_H
(ii) Compressed_L: All team members are paid the same daily wage of w_L (the "low" wage).
(iii) Compressed_M: All team members are paid the same daily wage of w_M (the "medium" wage).
(iv) Compressed_H: All team members are paid the same daily wage of w_H (the "high" wage).

After being randomized into these treatments at the end of training, each person will work under his assigned wage until his fixed employment contract ends. We are sure to emphasize throughout the contract period that there will be absolutely no offers of future employment.

The design incorporates three important sources of heterogeneity. First, the various tasks differ in how easy it is to observe the output of one's teammates. We will stratify wage treatment assignment by task, generating variation in the observability of co-worker output within each treatment cell. Second, since output is continuous while productivity rankings are discrete, there will be natural variation in how much a worker's productivity level differs from that of his teammates, creating variation in {wage difference}/{productivity difference} ratios within each cell. Lastly, given that workers at each worksite are recruited from multiple villages, there is variation in the strength of pre-existing relationships and social ties between workers across teams.

In our analysis, we aim to separately identify effects on the intensive (effort) and extensive (attendance) margins. Measuring the response on attendance captures the extensive margin. However, isolating the intensive margin is more difficult. If there are attendance differences, then regressions of output on treatment status conditional on attendance will be biased. This is the standard selection problem in Heckman (1979). We propose to solve the selection problem by constructing an instrument for attendance based on weather shocks and the difficulty of traveling to the worksite. When it rains, it becomes much harder for workers to travel to the worksites, thus increasing the likelihood of an absence. However, the exclusion restriction for rainfall alone might not be satisfied; on rainy days, workers who do manage come to the site might be less productive. To solve this problem, we plan to interact rainfall with measures of the distance and material of the roads between each worker's village and the worksite. For each worksite, we recruit workers from at least three villages, thus giving us within worksite-day variation in the difficulty in coming to work.

Randomization Method
Randomization of workers into teams and of teams into treatment is done on a computer.
Randomization Unit
Workers are randomized into jobs at the individual level. Once at the worksite, they are randomized into teams. Randomization of teams into wage treatments is done at the team level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
1200 subjects
Sample size (or number of clusters) by treatment arms
100 teams heterogeneous wage treatment, 300 teams homogenous wage treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Columbia University (Morningside) Institutional Review Board
IRB Approval Date
IRB Approval Number


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