Labor Markets Matching in Indonesia: Estimating Employers’ Preferences with Incentivized Resume Rating

Last registered on June 23, 2022
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Pre-Trial

Trial Information

General Information

Title
Labor Markets Matching in Indonesia: Estimating Employers’ Preferences with Incentivized Resume Rating
RCT ID
AEARCTR-0008559
Initial registration date
February 09, 2022

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 10, 2022, 7:57 PM EST

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

Last updated
June 23, 2022, 1:29 AM EDT

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

Locations

Country
Indonesia
Region

Primary Investigator

Name
Eliana La Ferrara
Affiliation
Universita Bocconi
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Other Primary Investigator(s)

PI Name
Stefano Fiorin
PI Affiliation
Universita Bocconi
Contact Investigator
PI Name
Naila Shofia
PI Affiliation
Universita Bocconi
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Additional Trial Information

Status
On going
Start date
2022-02-21
End date
2022-12-31
Keywords
Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We collaborate with a large job portal website to understand which characteristics of job seekers are valued by prospective employers in different sectors on the Indonesian labor market. We will use a modified version of the Incentivized Resume Rating (IRR) design proposed by Kessler et al. (2019). This tool allows to elicit employer preferences for job applicants’ characteristics without deception. Employers will be offered the possibility to participate in an experiment in which they will be asked to rate a number of resumes of hypothetical candidates. We will generate random resumes of hypothetical candidates by randomizing socio-demographic features of the candidates, typical characteristics used by employers to assess candidates’ quality, and the picture of the applicant. Employers will be told that the resumes are only hypothetical (thus avoiding deception), but will be incentivized to disclose their true preferences because their rating will later be used to select real job applicants who best matches the employer’s stated preferences through machine-learning algorithms.
External Link(s)

Registration Citation

Citation
Fiorin, Stefano, Eliana La Ferrara and Naila Shofia. 2022. "Labor Markets Matching in Indonesia: Estimating Employers’ Preferences with Incentivized Resume Rating." AEA RCT Registry. June 23. https://doi.org/10.1257/rct.8559-1.1
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Experimental Details

Interventions

Intervention(s)
We will use a modified version of the Incentivized Resume Rating (IRR) design proposed by Kessler et al. (2019). This tool allows to elicit employer preferences for job applicants’ characteristics without deception. Employers active on the website of our partner institution (a large Indonesian job portal) will be recruited via email, WhatsApp, and online. They will be offered the possibility to participate in an experiment in which they will be asked to rate a number of resumes of hypothetical candidates. We will generate random resumes of hypothetical candidates by randomizing socio-demographic features of the candidates, typical characteristics used by employers to assess candidates’ quality, and the picture of the applicant. Employers will be told that the resumes are only hypothetical (thus avoiding deception), but will be incentivized to disclose their true preferences because their rating will later be used to select (through machine-learning algorithms) real job applicants from the job portal who best match the employer’s stated preferences.
Intervention Start Date
2022-02-21
Intervention End Date
2022-12-31

Primary Outcomes

Primary Outcomes (end points)
We will ask employers: i) about their interest in the candidate; ii) whether they believe that the candidate would accept the job; iii) whether they would like to invite the candidate for an interview.
Primary Outcomes (explanation)
We ask each participating employer, the following questions about each of the hypothetical candidates:
1. “How interested would you be in hiring [candidate’s name]?”
2. “How likely do you think [candidate’s name] would be to accept a job with your organization?”
3. “Would you invite [candidate’s name] for an interview?”

Questions 1, and 2 are measured on a 0 to 10 likert scale. Question 3 is a binary Yes or No question.

Once all profiles have been rated, the participant will also be asked to rank the 5 profiles with the highest rating.

Secondary Outcomes

Secondary Outcomes (end points)
We will also ask employers: i) what would be an appropriate salary for the candidate; and ii) how well they think the candidate would fit in their company.

More specifically, we ask each participating employer, the following questions about each of the hypothetical candidates:
1. “If you decide to hire [candidate’s name], how much salary you would offer to [candidate’s name]?” if the employer answered yes to the previous question, and “If another company decides to hire [candidate’s name] as a [job_title], how much salary do you think they would offer to [candidate’s name]?” in instead the employer answered no.
2. How well would this candidate fit in the work environment of your company (e.g., with other workers and / or with customers)?

Both version of question 1 are measured in Indonesian Rupiahs. Question 2 is measured on a 0 to 10 likert scale.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will generate random resumes of hypothetical candidates by randomizing socio-demographic features of the candidates (gender and marriage status), typical characteristics used by employers to assess candidates’ quality (level and field of education, GPA, and work experience), and the picture of the applicant.
Experimental Design Details
Aside from analyzing the preferences of employers over various candidate attributes, the main innovative contribution of our study is to explore the interplay between the labor market and social norms. We will estimate the labor market returns to a norm that was slowly disappearing and is now rapidly re-emerging: veiling among Muslim women. In order to do so, among other characteristics we randomize whether female resumes feature the picture of an applicant with a veil or not.
Randomization Method
Randomization will be done in STATA.
Randomization Unit
Employer/Vacancy/Hypothetical Candidate
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We plan to send invitations for our experiment to all employers active on the job portal with open vacancies for entry-level and early-carreer positions. The effective sample size will depend on the take-up rate. Our target is to achieve 500 to 1000 completed surveys.
Sample size: planned number of observations
Each participant is asked to rate 30 hypothetical resumes, so our number of observations will be 30 times the number of participants.
Sample size (or number of clusters) by treatment arms
-
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Ethics Committee Review Bocconi
IRB Approval Date
2021-11-08
IRB Approval Number
FA000334
IRB Name
Pusat Pengembangan Etika Atmajaya
IRB Approval Date
2021-09-30
IRB Approval Number
0029P/III/LPPM-PM.10.05/09/2021
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