Estimating Employer Preferences Over Workers in India

Last registered on October 17, 2023

Pre-Trial

Trial Information

General Information

Title
Estimating Employer Preferences Over Workers in India
RCT ID
AEARCTR-0012153
Initial registration date
October 04, 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 17, 2023, 10:47 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
Stanford University

Other Primary Investigator(s)

PI Affiliation
University of Pennsylvania
PI Affiliation
Dartmouth College
PI Affiliation
Stanford University

Additional Trial Information

Status
In development
Start date
2023-10-05
End date
2025-01-05
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Employer preferences over worker characteristics are key determinants of employment outcomes. In this project, we will estimate employer preferences in high-skilled labor markets in India. We partner with a large job matching platform that helps women in India find jobs after career breaks. We will use Incentivized Resume Rating (IRR) to elicit the preferences of recruiters who use the platform to find workers. We will estimate preferences over a range of candidate characteristics - including gender, caste, educational qualifications and skills, work experience, and career breaks - and in several different sectors.
External Link(s)

Registration Citation

Citation
Low, Corinne et al. 2023. "Estimating Employer Preferences Over Workers in India." AEA RCT Registry. October 17. https://doi.org/10.1257/rct.12153-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2023-10-05
Intervention End Date
2024-07-05

Primary Outcomes

Primary Outcomes (end points)
(1) Lowest salary recruiter thinks candidate would accept

(2) Highest salary recruiter would be willing to pay candidate

(3) Whether recruiter would interview candidate
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will create resumes of fictitious candidates for each of several different job titles, working closely with our partner to ensure the fictitious resumes are realistic. We will invite job recruiters connected to our partner to evaluate the hypothetical resumes in an online (qualtrics) survey. This will be done through Incentivized Resume Rating (IRR): recruiters will know the resumes are fictitious, but we will match them with real candidates based on their answers, which incentivizes them to carefully and honestly rate the resumes.

We will randomly vary key elements of the resumes to estimate recruiter preferences over various candidate characteristics. More specifically, each recruiter will rate 43 resumes of candidates in their field. The first resume will be the same for all recruiters looking to fill a given position and will present a “neutral” candidate (a male with no career break and average qualifications); this initial resume is meant to reduce priming effects and evaluations of it will be excluded from our analyses. Each of the subsequent 42 resumes will be based on an underlying template with key details of the resume randomized across employers. The templates will be based on real resumes our partner shared with us and will all reflect candidates with an undergraduate degree and two work experiences. The order in which the 42 templates appear will be randomized across recruiters.

We will randomize the following elements on top of the templates: the candidate’s name (which embeds gender and caste), years since completion of undergraduate degree, the prestige of the undergraduate institution, the undergraduate grade percentage, whether the candidate has a masters degree, the duration of the more recent work experience, whether the more recent employer is a prestigious employer, whether the skills the candidate lists are basic or advanced, whether the candidate has a career break, and details of the career break. The break details to be randomized depend on the gender of the candidate. For women, we will randomize whether the break is “resolved” (i.e. happened before the more recent work experience) or “unresolved” (i.e. there has been no work experience since the break), and whether the woman explicitly calls the break a maternity break or says nothing but has a gap in her experiences. Among the women who label the break as a maternity break, we will further randomize whether women say the break was to care for two daughters or a daughter and a son, and whether women did any “upskilling” (i.e. some sort of course or training) during the break. For men, we will randomize whether the break is resolved or unresolved, but nothing else; any breaks on male resumes will not be labeled and will simply be a gap in experiences. Additional details of the randomization are attached in a supporting document.

We will ask recruiters to complete a brief survey after rating the resumes that asks about recruiter demographics and characteristics of their firms, among other topics.
Experimental Design Details
Not available
Randomization Method
Randomization done in qualtrics
Randomization Unit
Randomization at the recruiter x resume level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Same as number of observations since design is not clustered
Sample size: planned number of observations
2100-4200 recruiter x resume combinations (evaluations of 42 resumes from each of 50-100 recruiters)
Sample size (or number of clusters) by treatment arms
See attached document
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
Randomization Details
Document Type
other
Document Description
This document contains details on the randomization plan.
File
Randomization Details

MD5: eddf3b32840b7e1bd92ca203a8f671e9

SHA1: e5781d7e6c7b6f8f40e561f5d030c525a001e221

Uploaded At: October 04, 2023

IRB

Institutional Review Boards (IRBs)

IRB Name
Stanford
IRB Approval Date
2023-03-24
IRB Approval Number
Stanford Protocol Id: 68053