On the impact of discrimination on job search

Last registered on March 24, 2020
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Pre-Trial

Trial Information

General Information

Title
On the impact of discrimination on job search
RCT ID
AEARCTR-0005485
Initial registration date
March 02, 2020

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
March 02, 2020, 3:24 PM EST

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

Last updated
March 24, 2020, 1:24 AM EDT

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

Locations

Country
United Kingdom of Great Britain and Northern Ireland
Region
This is an online experiment with participants from many countries

Primary Investigator

Name
Gergely Horvath
Affiliation
Duke Kunshan University
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2020-03-01
End date
2020-05-31
Keywords
Labor
Additional Keywords
Discrimination, job search, taste-based discrimination, reservation wages, search effort
JEL code(s)
Secondary IDs
Abstract
This project studies the impact of taste-based discrimination on the job search behavior (reservation wages, search effort) of individuals. The project is based on a lab-style experiment that will be carried out online. We create an infinite horizon search environment whereby participants sequentially search for offers randomly drawn from a known distribution. At the beginning of the experiment participants are randomly assigned to one of two artificial groups. In the baseline treatment, there is no difference between the job search environment of the two groups. In comparison, we introduce two treatments that allow us to study the impact of discrimination. In these treatments one of the random groups faces an adverse search environment: with a certain probability their offers are discounted by a certain amount (the discrimination coefficient). In the simple version of the discrimination treatment, individuals will not know whether their offer was discounted and they decide about acceptance of offers without this knowledge. In the choice version of the discrimination treatment, they will know if their offer was discounted and they can choose whether they accept discriminated offers. In addition, we vary the different parameters of the search environment.

The aim is to study the impact of discrimination on the search effort, reservation wages, obtained wages of workers. For this we compare the baseline treatment to the discrimination treatments. We are also interested in how much percentage of the workers end up working for discriminating employers (that is discounted offers) in the discrimination treatments. We compare the experimental results to the theoretical prediction of risk-neutral optimal search models.
External Link(s)

Registration Citation

Citation
Horvath, Gergely. 2020. "On the impact of discrimination on job search." AEA RCT Registry. March 24. https://doi.org/10.1257/rct.5485-1.1
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2020-03-01
Intervention End Date
2020-05-31

Primary Outcomes

Primary Outcomes (end points)
The experiment intends to measure the following outcomes:
-search effort level
-reservation wages
-probability of finding an offer (constructed from search effort and reservation wage)
-value of accepted offers
-welfare (total payoffs in a round)
-percentage of discriminated workers accepting discounted offers
-fraction of participants accepting discounted offers (only in Discrimination (Choice) treatment)

We compare these outcomes between the two groups: discriminated and non-discriminated, as well as, the outcomes of the different groups in the discrimination treatments to the baseline treatment. We also compare the outcomes to the theoretical prediction from a partial equilibrium search model with risk-neutral agents.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment consists of several rounds, in each round an individual participates in an infinite horizon search environment. This means that within each round there is a random number of periods, with a fixed continuation probability. At the beginning of each period, participants start to search for an offer. They specify a search effort level and a reservation wage. The search effort level determines the probability of receiving an offer, higher effort is more costly which is paid out of an endowment. The reservation wage is the minimum acceptable offer. Then the computer determines if each participant receives an offer or not, and if yes, it randomly draws the offer from a known distribution. As long as the individual receives an acceptable offer, the search is over for the given round. Otherwise, the individual searches again in the next period, if there is one. This sequence of events is repeated in each period. Individuals receive the total payoff from a randomly chosen round.

In the Baseline treatment, participants are randomly drawn into two artificial groups, but there is no meaningful difference between the two groups. In the Discrimination treatments, one of the groups faces a worse distribution of offers: with some probability their offers are discounted by a certain amount. In one of the discrimination treatments participants do not know whether their offer was discounted and specify their search without this knowledge. In another treatment they are aware of discounting and can choose not to accept discounted offers.

Experimental Design Details
Randomization Method
Randomization will be done by computer using o-Tree and Python.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No cluster
Sample size: planned number of observations
Sample size: 120 individuals x 7 treatments = 840 individuals
Sample size (or number of clusters) by treatment arms
120 individuals per treatment, within each treatment two groups (discriminated and non-discriminated): 60+60
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University Ethics Committee, Xi'an Jiaotong Liverpool University
IRB Approval Date
2020-03-02
IRB Approval Number
STA-LRR 19-03-56
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials