Vacancy design and applicant selection

Last registered on October 11, 2022

Pre-Trial

Trial Information

General Information

Title
Vacancy design and applicant selection
RCT ID
AEARCTR-0009993
Initial registration date
August 30, 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
September 08, 2022, 11:26 AM EDT

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

Last updated
October 11, 2022, 4:45 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
IZA - Institute of Labor Economics

Other Primary Investigator(s)

PI Affiliation
Peking University

Additional Trial Information

Status
On going
Start date
2022-05-01
End date
2022-11-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We conduct a field experiment in Addis Ababa, Ethiopia, to study the impact of different aspects of vacancy design on the pool of applicants. For this purpose, we collect real vacancies from firms and randomly vary the content of these job adverts.
External Link(s)

Registration Citation

Citation
Hensel, Lukas and Marc Witte. 2022. "Vacancy design and applicant selection." AEA RCT Registry. October 11. https://doi.org/10.1257/rct.9993-1.1
Experimental Details

Interventions

Intervention(s)
We conduct a field experiment in Addis Ababa, Ethiopia, to study the impact of different aspects of vacancy design on the pool of applicants. For this purpose, we collect real vacancies from firms and exogenously vary the content of these job adverts.

We vary the following information on the job adverts:
1. Whether the advert includes information about the offered monthly salary or wage. When collecting the vacancies from firms, we ask about the monthly wage or salary that they expect to pay for this position. We then randomly vary whether we include this information in the vacancy advert.
2. Whether the advert includes a gender inclusivity statement. When collecting the vacancies from firms, we ask which gender they expect the majority of applicants to be off. Based on this information, we then randomly include a gender-specific inclusiveness statement that reads: "We encourage candidates of all genders to apply for this position, including [GENDER].", where [GENDER] is the expected minority gender. If the respondent does not know, which gender will be prevalent, we instead include the following statement: "We encourage candidates of all genders to apply for this position."
3. Whether the advert includes information about the "general skill" most demanded by this vacancy. Based on this information we randomly include a "Desired skills" category in the job advert that lists the top-ranked skill.

For each vacancy, we will create three different versions of each job advert with each of the above-mentioned components cross-randomized (i.e. independently randomized). We will post each vacancy on three different channels (i) offline job boards across the city, ii) Facebook adverts, and iii) the major online job board (www.ezega.com). We then randomize which version of the vacancy is posted on which channel.

The randomization is thus at the vacancy level and all treatment components are cross-randomized. Assuming a total of 400 different vacancies, we end up with the following treatment and control groups in this experiment:

Wage information treatment:
- 200 treated vacancies with wage information
- 200 control vacancies without wage information

Gender inclusivity information treatment:
- 200 treated vacancies with gender inclusivity information
- 200 control vacancies without gender inclusivity information

Skill requirements information treatment:
- 200 treated vacancies with skill requirement information
- 200 control vacancies without skill requirement information

Since the three different information treatments are independently randomized, we will obtain the following distribution of pieces of information over the 400 vacancies:
- 12.5% of vacancies will contain all three information treatments
- 37.5% of vacancies will contain two information treatments (either wage-gender, gender-skill, or wage-skill)
- 37.5% of vacancies will contain one information treatment (either wage only, gender only, or skill only)
- 12.5% of vacancies will contain no information treatments (pure control)

Our main outcomes of interest are the number and composition of applicants to these differentially treated vacancies.

We plan to write separate papers for each of the treatments.
Intervention (Hidden)
Intervention Start Date
2022-05-01
Intervention End Date
2022-09-30

Primary Outcomes

Primary Outcomes (end points)
(IHS) number of applicants, applicant quality index
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Gender composition, beliefs about the vacancy (including beliefs about offered wages), and preferences
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We conduct a field experiment in Addis Ababa, Ethiopia, to study the impact of different aspects of vacancy design on the pool of applicants. For this purpose, we collect real vacancies from firms and randomly vary the content of these job adverts. We also include some vacancies created as part of our project in the study.

All job adverts contain phone numbers that lead our experimental call center. Job-seekers who are interested in applying call the experimental call center and will complete a short pre-screening survey. They will then be invited to a screening session in a central location in Addis Ababa at a time of their choosing. To proceed with their application, they will have to come to the screening center and complete a screening survey. Once all applications are received and applicants screened, we pass the list of candidates on to the firms that created the vacancies.
Experimental Design Details
We conduct a field experiment in Addis Ababa, Ethiopia, to study the impact of different aspects of vacancy design on the pool of applicants. For this purpose, we collect real vacancies from firms and exogenously vary the content of these job adverts.

We vary the following information on the job adverts:
1. Whether the advert includes information about the offered monthly salary or wage. When collecting the vacancies from firms, we ask about the monthly wage or salary that they expect to pay for this position. We then randomly vary whether we include this information in the vacancy advert.
2. Whether the advert includes a gender inclusivity statement. When collecting the vacancies from firms, we ask which gender they expect the majority of applicants to be off. Based on this information, we then randomly include a gender-specific inclusiveness statement that reads: "We encourage candidates of all genders to apply for this position, including [GENDER].", where [GENDER] is the expected minority gender. If the respondent does not know, which gender will be prevalent, we instead include the following statement: "We encourage candidates of all genders to apply for this position."
3. Whether the advert includes information about the "general skill" most demanded by this vacancy. Based on this information we randomly include a "Desired skills" category in the job advert that lists the top-ranked skill.

For each vacancy, we will create three different versions of each job advert with each of the above-mentioned components cross-randomized (i.e. independently randomized). We will post each vacancy on three different channels (i) offline job boards across the city, ii) Facebook adverts, and iii) the major online job board (www.ezega.com). We then randomize which version of the vacancy is posted on which channel.

The randomization is thus at the vacancy level and all treatment components are cross-randomized. Assuming a total of 400 different vacancies, we end up with the following treatment and control groups in this experiment:

Wage information treatment:
- 200 treated vacancies with wage information
- 200 control vacancies without wage information

Gender inclusivity information treatment:
- 200 treated vacancies with gender inclusivity information
- 200 control vacancies without gender inclusivity information

Skill requirements information treatment:
- 200 treated vacancies with skill requirement information
- 200 control vacancies without skill requirement information

Since the three different information treatments are independently randomized, we will obtain the following distribution of pieces of information over the 400 vacancies:
- 12.5% of vacancies will contain all three information treatments
- 37.5% of vacancies will contain two information treatments (either wage-gender, gender-skill, or wage-skill)
- 37.5% of vacancies will contain one information treatment (either wage only, gender only, or skill only)
- 12.5% of vacancies will contain no information treatments (pure control)

Our main outcomes of interest are the number and composition of applicants to these differentially treated vacancies.
Randomization Method
Randomization done in office by a computer
Randomization Unit
The randomization for each of the three information treatments is done at the vacancy level and all treatment components are cross-randomized independently, as elaborated under "Intervention" and "Experimental design".

In addition to that, we will create three different versions of each vacancy with each of the above-mentioned information treatments cross-randomized. We will post each vacancy on three different channels (i) offline job boards across the city, ii) Facebook adverts, and iii) the major online job board (www.ezega.com). We then randomize which version of the vacancy is posted on which channel.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering: approximately 400 vacancies across 3 posting channels, leading to approximately 4000 (expected) applicants
Sample size: planned number of observations
approximately 400 vacancies across 3 posting channels and 4000 (expected) applicants
Sample size (or number of clusters) by treatment arms
The information treatments (salary, gender, skill) are independently randomized over each vacancy, with a 50% chance for each treatment to be included on a given vacancy (see "Experimental design"). This means that pure control vacancies (no information treatment) represent 0.5^3=0.125 of all vacancies.

All treatments are distributed as follows over the sample of vacancies:
- 12.5% of vacancies will contain all three information treatments
- 37.5% of vacancies will contain two information treatments (either wage-gender, gender-skill, or wage-skill)
- 37.5% of vacancies will contain one information treatment (either wage only, gender only, or skill only)
- 12.5% of vacancies will contain no information treatments (pure control)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Peking University Guanghua School of Management IRB
IRB Approval Date
2022-02-08
IRB Approval Number
#2022-03

Post-Trial

Post Trial Information

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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