gender3

The data were collected by the TEPP team (FR CNRS 2042) between February and
July 2015. A candidate is defined by the variables ("gender","educ").<ul>
<li>offer: add number</li>
<li>gender: Woman or Man</li>
<li>age: 23 or 24 years old</li>
<li>fname: first name (forename)</li>
<li>lname: last name (family name, surname)</li>
<li>educ: education, CAP = vocational training certificate before the A
level, MAF = CAP + "One of the best French apprentice", OLY = CAP +
participation to the Worldskills Competition</li>
<li>zip: ZIP code</li>
<li>cont: length of labour contract, STC = short term contract (&lt;=1 year),
 LTC = long term contract (&gt;1 year with no ending date)</li>
<li>recgender: gender of the recruiter</li>
<li>cv: CV template, A or B</li>
<li>ansmode: answering channel, email or ordinary mail ("omail")</li>
<li>sentorder: order in which the application was sent</li>
<li>hours: weekly work time</li>
<li>callback: TRUE if there was a non negative callback</li>
<li>date: between February and July 2015</li>
</ul>

datasets

In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various statistics are computed. This package can also be used for analyzing the results from random experiments.

Emmanuel Duguet

callback

Computes Statistics from Discrimination Experimental Data

David Gray

Loïc du Parquet

Yannick L'Horty

Rémi Le Gall

Noam Léandri

Pascale Petit

Florent Sari

gender3 function

A data frame with 532 rows and 15 variables

Format

Gender/Maternity discrimination
(masons) — gender3

Gender/Maternity discrimination
(masons)

gender3: Gender/Maternity discrimination (masons)

Description

Usage

Arguments

Format

References