Gender Equality Objectives and their Impact on Gender Discrimination in the Hiring Process

Last registered on December 02, 2019

Pre-Trial

Trial Information

General Information

Title
Gender Equality Objectives and their Impact on Gender Discrimination in the Hiring Process
RCT ID
AEARCTR-0004986
Initial registration date
December 02, 2019

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
December 02, 2019, 3:05 PM EST

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

Locations

Region

Primary Investigator

Affiliation
Centre National de la Recherche Scientifique

Other Primary Investigator(s)

PI Affiliation
University of Warwick & Institut des Politiques Publiques
PI Affiliation
Sciences Po (OSC) & Laboratoire interdisciplinaire d'évaluation des politiques publiques (LIEPP) de Sciences Po
PI Affiliation
Université Paris 1 Panthéon Sorbonne (Centre d’Economie de la Sorbonne) & Laboratoire interdisciplinaire d'évaluation des politiques publiques (LIEPP) de Sciences Po
PI Affiliation
Université Paris 1 Panthéon Sorbonne (CES), Paris School of Economics (PSE), Institut des Politiques Publiques
PI Affiliation
PJSE (Paris Jourdan Sciences Economiques), Institut des Politiques Publiques

Additional Trial Information

Status
In development
Start date
2019-11-24
End date
2020-12-31
Secondary IDs
Abstract
Previous correspondence studies conducted in France and internationally conclude that there is on average no or little gender discrimination in the hiring process. These findings might however simply reflect the coexistence of positive and negative discrimination instead of revealing no discrimination. Discrimination in hiring may indeed be stronger or weaker against certain groups of women (e.g. age of having children) and in well-identified situations (e.g. for positions of responsibility). We thereby aim to answer to what extent gender diversity objectives effectively lead to taking into account the candidate's gender in recruitment decisions, but also under what conditions and for which types of occupations and application profiles this applies in particular. In that regard we disentangle two factors of heterogeneity which are likely to affect the interdependence between the objectives of diversity and non-discrimination: 1. firm characteristics (i.e. economic situation, level of diversity,etc), 2. candidate's characteristics (age, origin, etc), which we experimentally vary in fictitious applications.
External Link(s)

Registration Citation

Citation
BREDA, THOMAS et al. 2019. "Gender Equality Objectives and their Impact on Gender Discrimination in the Hiring Process ." AEA RCT Registry. December 02. https://doi.org/10.1257/rct.4986-1.0
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Experimental Details

Interventions

Intervention(s)
We are conducting a large-scale correspondence study in order to produce new evidence on hiring discrimination in France. This correspondence study is designed in a way to be merged with firm-level data in the aim of measuring the effect of the firm context on employment opportunity hoarding mechanisms.
Intervention Start Date
2019-11-24
Intervention End Date
2020-12-31

Primary Outcomes

Primary Outcomes (end points)
1. Whether application received a callback, i.e. dummy variable equal to one if the application received a callback and zero otherwise.
2. Whether application received an invitation for a job interview, i.e. dummy variable equal to one if the application received an invitation and zero otherwise
Primary Outcomes (explanation)
A callback is defined as a positive personalised phone, or e-mail contact by a potential employer. This is usually a request for an interview, but employers also contact applicants asking for additional documents/information or for a call-back by the applicant.

An invitation is defined as a personalised phone or e-mail contact in which the potential employer expresses interest in conducting an interview.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment is based on a correspondence study design involving a matched-pair (quartet) design.
The results of the testing will be systematically matched at the firm level with administrative data.
Experimental Design Details
In the standard correspondence study, matched pairs of qualitatively identical job applications are sent to employers that have advertised a job opening. The only difference between the fictitious applications is the name of the applicant, which usually signals ethnicity or gender. The degree of discrimination in hiring is then quantified by calculating the difference in the callback rate (i.e., the fraction of invitations) to a job interview between the groups.
In order to test the interaction between gender discrimination (measured by the difference in call-backs between applications according to the gender of the candidate signaled by the first name) and all other dimensions considered relevant, we opted for the following experimental protocol.
The occupations selected for applications differ on the following 3 dimensions:
1. the level of qualification of the occupation (3 categories: low skilled / medium skilled (without management function) / high skilled (with management function);
2. the degree of feminisation of the occupation (3 categories: predominantly male / balanced / predominantly female);
3. the degree of labour market tightness of the occupation (2 categories: low labor market tightness / high labor market tightness).
Based on these categories, the study covers a total of 12 occupations: 4 low-skilled occupations, one of which has a high labor market tightness; 4 medium-skilled occupations (without managerial functions), one of which has a high labor market tightness; 4 high-skilled occupations (with managerial functions). The 10 occupations with low labor market tightness were chosen according to their degree of feminisation, by retaining in each qualification level a predominantly male occupation, a balanced one and a third predominantly female one. As for the occupations with high labor market tightness, they were chosen preferably from predominantly male and/or balanced occupations.

Applications sent in response to job offers of these 12 occupations are distinguished by age, gender(male/female) and origin(french/maghreb). Age is determined by work experience in three groups: 4 to 6 years of work experience (23 - 30 years); 14 to 16 years of work experience (33 - 40 years), 29 to 31 years of work experience (48 - 55 years).

Based on these variables we apply to each job offer with 4 distinguished identities which combine gender and origin in a factorial way (1: female/french, 2: male/french, 3: female/maghreb, 4: male/maghreb). To preserve the statistical power of the tests, we respond to each job offer with a fixed age range (all candidates are young in response to the first offer, older in response to the second offer, etc.) including only two age groups per occupation (young and middle for low skilled and middle and older for medium/high-skilled).

Given the large scale of the study (number of occupations, number of observations required, see below, and territorial coverage) we have chosen to not send out more than 4 applications per job offer due to the associated increase in costs and the higher risk of detection.

In order to take into account the possible differentiated effect of the content of CVs according to the gender of the candidates (and possibly their origin), we add signals to all 4 CVs sent in response to an offer (all CV's in one quartet will have the same signal). Given the well documented effect of maternity on gender discrimination we hence include three blocks of signals. The first of which crosses period of inactivity and presence of children; the second block crosses marital status and presence of children; the third block will contain CVs without additional signals. The final signals included are thereby: a: no signal, b: in a couple and 2 children, c: period of inactivity, d: in a couple, 2 children and period of inactivity, e: in a couple, f: single, g: in a couple and 2 children, h: single and 2 children).

A last dimension we include is the variation of gender discrimination with the social status of responding candidates, by varying french and maghrebin names based on their social status (advantaged/disadvantaged).

We further created 4 CV profiles (not including signals or identities) with respective experiences, education etc.

We also provide an ordered list of paired CV's to meet offers with detection risk. For these job offers, we draw from the ordered list of couples rather than the list of quartets. The ordered list of couples contains only identities with french origin and only profiles A and B. The first couple is (A1, B2), the second (A2, B1) then again (A1, B2), etc. Responses to offers with couples do not include signals.
Randomization Method
Randomisation done in office by a computer.
Randomization Unit
Randomisation is taking place to create the 4 CV's with which we apply to each job offer and the assignment of the quartets to each job offer.
For each of the 12 occupations and 2 age groups an application will be randomly characterised by an identity (gender, first name, surname, social class), a profile (experiences, training...), a signal block and a signal.

The randomisation units are hence:
- Matching identities to profiles: a random selection of the 24 possible quartets is drawn in terms of profile*identity.
- Signal assignment: for each quartet, the first offer to which we respond does not include any signal, while the second one includes a signal from block 1 and the third one a signal from block 2.
- Assignment of first names: for each CV, the exact first name that will appear on the CV is randomly drawn with a probability of 0.5 (higher/lower social class).
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
12 (occupations) * 2 (age groups) * 4 (identities) = 96 different CVs.
Sample size: planned number of observations
2400 (job offers) * 4 (identities) = 9,600 sent applications.
Sample size (or number of clusters) by treatment arms
100 observations per occupation and age group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our power calculations are based on a power of 80% and a level of significance of 5%. We assume that the average success rate is 15%. In this context, with a sample size of 100 job offers, the minimum detectable effect (MDE) is a difference of 14 percentage points in call-back rates.
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB de PSE
IRB Approval Date
2019-10-22
IRB Approval Number
2019 018

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