Attracting Talented and Prosocial Applicants: Exploring Trade-offs and Gender Dynamics

Last registered on June 15, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Attracting Talented and Prosocial Applicants: Exploring Trade-offs and Gender Dynamics

RCT ID

AEARCTR-0011501

Initial registration date

June 08, 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

June 15, 2023, 4:27 PM EDT

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

Locations

Country

Ghana

Region

Primary Investigator

Name

Ursa Krenk

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2023-06-08

End date

2023-11-01

Keywords

Behavior, Firms & Productivity, Gender

Additional Keywords

recruitment, personnel

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

How can we attract the most suitable candidates for jobs that require them to be both skilled and prosocial? Is there a trade-off between attracting talented individuals and those with a strong pro-social inclination, and does this vary depending on the applicant's gender?
To explore these questions, I will design an experiment in collaboration with a Ghanaian NGO that recruits recent university graduates for their fellowship program. These fellows are then trained to become teachers, and placed in remote rural areas to teach in secondary schools.

In this experiment, participants will be randomly assigned to different treatment groups, each receiving recruitment messages with varying content. The goal of the treatments is to understand how different types of messages impact the quantity and the composition of the applicant pool.
The main outcome variables I will examine include application submission rates, the quality and measures of prosocial motivation of the applicants. I will also conduct an additional online information experiment to shed some lights on the mechanisms.

External Link(s)

Registration Citation

Citation

Krenk, Ursa. 2023. "Attracting Talented and Prosocial Applicants: Exploring Trade-offs and Gender Dynamics." AEA RCT Registry. June 15. https://doi.org/10.1257/rct.11501-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2023-06-10

Intervention End Date

2023-07-01

Primary Outcomes

Primary Outcomes (end points)

I will use two experimental samples- my main sample will consist of people who already expressed interest in the NGO. I will complement this sample with a sample of people recruited through a survey on social media to record additional intermediary outcomes.

Application submission rates, self-reported likelihood of applying (only in the social media sample)

Applicant quality (performance in the selection process, such as a continuous online application score)
A measure of prosociality/intrinsic motivation

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Supplementary measures of applicant quality (proportion of the application completed, whether the applicant proceeds to the second part of the selection process, score in the second part of the selection process, offer rates)

Secondary Outcomes (explanation)

Experimental Design

There will be two experimental samples:
1) People, who expressed interest in the NGO and have registered on the NGO's webpage, but have not submitted the application yet (they will receive reminder emails to submit the application)
2) People recruited for a survey experiment on social media (they will see the recruitment messages online)

The treatments will manipulate the content of recruitment messages, which will make different aspects of the job salient. These messages will be presented side by side with a female or male photograph (4 treatments)

Experimental Design Details

The treatments will manipulate the salience of the impact of the fellowship on a) the lives of children and the community (“child treatment”) or on b) the career progress of the fellows (“career treatment”). By showing these recruitment messages together with a photograph of a female or a male graduate of the program, the design will additionally enable me to estimate the effects of being exposed to a photograph of own gender.

I intend to make the following main comparisons:
1) I will compare the effects of making salient “helping the community and children” vs. “career progress” on application rates, applicant quality and applicant prosociality for the entire applicant pool, as well as separately for men and women. When conducting this analysis, I will pool messages with female/male photographs. For exploratory analysis purposes, I will also look at the interaction effects between message and the gender displayed on the photograph.

2) Effect of receiving the message with a photograph of a person of own gender (pooled across the two types of messages)

In the NGO sample, my main sample will consist of people who actually opened the reminder emails and those actually eligible to apply.
In the social media sample, my main sample will consist of people who have not heard of the NGO prior to the study.

Randomization Method

Randomisation will be done by the computer.

Randomization Unit

Randomisation is done at the individual level.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

The treatment is not clustered.

Sample size: planned number of observations

The estimate sample size for the participants in the NGO sample is 2500. The estimated sample size for the fb sample is about 1000.

Sample size (or number of clusters) by treatment arms

The estimate sample size for the participants in the NGO sample is 2500. They will be assigned to 4 treatment arms, with about 625 people in each.
The estimated sample size for the fb sample is about 1000, with 250 people in each cell.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB