Hiring discrimination against women : distinguishing
taste based discrimination from statistical discrimination
Emmanuel Duguet, Loic Du Parquet, Pascale Petit
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WORKING PAPER
N° 2017 - 11
HIRING DISCRIMINATION AGAINST WOMEN: DISTINGUISHING
TASTE BASED DISCRIMINATION FROM STATISTICAL
DISCRIMINATION
EMMANUEL DUGUET, LOIC DU PARQUET, PASCALE PETIT
www.tepp.eu
TEPP - Institute for Labor Studies and Public Policies
TEPP - Travail, Emploi et Politiques Publiques - FR CNRS 3435
ISSN 2110-5472
Hiring discrimination against women: distinguishing taste
based discrimination from statistical discrimination*
Emmanuel Duguet1,4,6 , Loïc du Parquet3,5,6 and Pascale Petit2,4,6
1
Université Paris Est Créteil
2 Université Paris Est Marne-la-Vallée
3 Université du Maine
4 ERUDITE (EA 437)
5 GAINS (EA 2167)
6 FR TEPP CNRS 3435
December 2017
Abstract
We propose a new method which allows for measuring separately taste based discrimination from statistical discriminations in the hiring process. We consider two types of
statistical discriminations against women: first, when a recruiter doubts the productivity
of the workers; second, when a recruiter anticipates a maternity leave. We show that the
experimental data that we have collected can be represented by an over-identified model,
which can be estimated optimally by the Asymptotic Least Squares. The method is applied
to experimental data on three construction jobs: masonry, plumbing and electricity. We
find that each job exhibits a different discrimination type. Furthermore, the most obvious
forms of discrimination may not be the most harmful.
JEL: C51, C93, J16, J24, J71 .
Keywords: Hiring discrimination, Field experiments, Gender, Asymptotic Least Squares,
France .
* We thank Souleymane Mbaye for research assistance. We also thank the participants to the 6th TEPP Winter
School (Aussois, 2015) and to the 13th TEPP Conference on Policy Evaluation (La Réunion, 2016). Corresponding
author: Emmanuel Duguet, Université Paris Est, Mail des Mèches, 61 avenue du Général de Gaulle, 94010 Créteil
Cedex, France. emmanuel.duguet@u-pec.fr
1
Introduction
Over the last decades, women have increased their qualification level and their activity rates
(OECD, 2012). However, the strong inequalities between women and men in the labour market
remain a major societal challenge (Bertrand, 2010). Inequality persists in terms of working
time, working conditions, occupation and sector. This leads to income inequality.
Women employment suffers both from a vertical segregation, the glass ceiling, and from
a horizontal segregation, gendered occupations. Therefore, despite of the development of
new technologies, women and men continue to specialize in different occupations in the USA
(Hegewisch et al., 2010) and in Europe (Bettio and Verashchagina, 2009). For instance, according to OECD (2012), more than 70% of the workers in education, health and social services
are women. This is of consequence in France, since the half of the gender hourly wage gap,
estimated at 18.4%, can be explained by occupation differences (Chamki, 2015).
Notwithstanding some progress in occupation balancing, only 17% of occupations in the
French labour market included at least 40% of women or men (Naves and Wisnia-Weill, 2014).
These occupations represented 16% of French employment over 2009-2011. One of the sector were women are especially at a disadvantage is construction. In 2014, they represented
5% of this sector’s total employment, even though the environment was favourable. Indeed,
the Construction Federation had announced its will to triple women employment, counting
on a change in mentalities, strong needs of qualified personnel and less physically demanding
working conditions. At the same time, communication campaigns by public authorities are accompanying this decision by the Construction Federation. The goal of these private and public
actions is to correct the over-representation of men in the construction sector. It originates
both from demand and supply factors.
On the supply side, the disequilibrium could come from the under-representation of women
in the construction training fields. Since few women choose the construction training channels, few candidate and get the jobs. And girls indeed do different choices than boys. According
to the French Ministry of Education (2012), girls represented 1% of the pupils or apprentices
in the building-construction-roofing trainings in 2010, and 9% in the building-constructionfinishing trainings. The same year, the proportion of girls in office secretary training reached
92%, and 91% in health and social occupations. On the demand side, a low proportion of
women could come from hiring discrimination (Heckman, 1998). Several discrimination mechanisms may be at work. First, taste discrimination from employers, the other workers or customers may reduce the access of women to some occupations (Becker, 1957). The second
mechanism, statistical discrimination, is based on beliefs about the discriminated group (Arrow, 1972; Phelps, 1972; Spence, 1973). At the hiring stage, an employer may not observe the
productivity of the candidate perfectly well and, for this reason, forms an anticipation from
both objective and subjective elements. At the hiring stage, women may be discriminated because of an increased risk of career interruption (maternity leaves) and, in the construction
sector, because of the beliefs of the employer regarding the productivity of women in this field.
In this paper, we focus on labour demand. We examine whether there is a gender discrimination in three occupations of the construction sector. We consider three types of discriminations: taste discrimination, statistical discrimination on productivity beliefs and statistical
discrimination on career interruptions. We have performed correspondence tests designed to
address this issue (Firth, 1982; Neumark et al., 1996; Riach and Rich, 2002). In order to distinguish the three discrimination types, we use the differences between them. Taste discrimination should apply to all the women. Statistical discrimination on productivity beliefs should
2
apply to women with standard qualifications only. The most difficult case is that of statistical discrimination on career interruptions. We can obviously not cancel the ability of young
women to have children by modifying a CV template. Still, we can select offers for which there
is no extra cost associated with a maternity leave. In France, the only cost for the firm is the
replacement cost since the replacement revenues are paid by the Sécurité Sociale. Therefore,
there should be no gender discrimination on the shortest contracts, since the replacement issue is the same for women and men.
The first section presents the experiment which produces the main statistics. The second
section is about the identification of the discrimination coefficients, because the different discrimination types are entangled. In the third section we present an original method for exploiting the results of correspondence tests. We conclude to the presence of different discrimination
types in the last section.
1 The experiment
We have collected data about the construction sector in the Paris area between February and
July 2015. We have replied to all the full-time jobs posts for electricians, plumbers and masons.
This includes both short term and long term contracts.
Candidates. Four candidates were sent in reply to each offer: a woman and a man with the
standard qualification, and a man and a woman with an excellence qualification. Table 1 indicates the identity of the candidates. All the candidates have French sounding first and last
names,1 are either 23 or 24 years old, childless and own a driving licence and a car.2 . They had
a vocational training certificate (CAP) in the same profession, in 2008 or 2009 (depending on
their age). They have three professional experiences in small companies, did not experience
any unemployment period and are searching on the job.3 . They live inside the city of Paris,
in districts ("arrondissements") with comparable socio-economic characteristics.4 Overall, we
have given to our candidates the characteristics which are the most favourable in the French
labour market according to the previous applied discrimination literature, except for the three
characteristics that the wish to test (gender, qualification, maternity). We did not use any photograph to avoid appearance biases (see Rich, 2017), used standard and impersonal hobbies
(sport, cinema, reading, music) and have included some differencing elements in the CV layout. We have randomized the differencing elements among the candidates.
The CAP (Cerfificat d’Aptitude Profesionelle, literally "professional capacity certificate") is
a vocational training certificate which grants a qualified worker/employee degree. It exists for
around 200 professions. 188,386 CAP have been granted in France in 2015. We focus on three
professions: electricity, plumbing and masonry. This basic diploma is often demanded by the
construction firms in their posts. We indicate it as the "required" level in our statistical model.
In order to implement our methodology, we need to distinguish short and long contracts.
After examining the distribution of the contracts durations, it appears that our method is applicable if we keep, on the one hand, the contracts of 6 months or less and, on the other hand,
1 A foreign origin can reduce the callback rates in the French labour market, see Duguet, du Parquet, L’Horty and
Petit (2015).
2 Mobility can influence the callback rates, see Duguet, du Parquet, L’Horty and Petit (2017)
3 Past unemployment period can interfere with the callback rates, see Duguet, Le Gall, L’Horty and Petit (2017)
4 For evidence of address discrimination, see Duguet, Gray, L’Horty, du Parquet and Petit (2017).
3
Table 1: Experiment candidates
Candidates
Woman, Standard
Woman, Excellence
Man, Standard
Man, Excellence
Plumbing
Aurélie DUVAL
Pauline LEMAIRE
Thomas ROUX
Jonathan MOREL
Masonry
Juliette LEROY
Laura BONNET
Alexandre PETIT
Jérémy MOREAU
Electricity
Anaïs DUBOIS
Elodie FOURNIER
Julien GUERRIN
Anthony DURAND
Standard: standard vocational training diploma (CAP, "Certificat d’Aptitude Professionnelle")
Excellence: "best French apprentice" (MAF) or "Worldskills competition" (OM) regional laureates
the contracts of 12 months or more (including the permanent contracts).5
Excellence qualifications (MAF and OM). The MAF ("un des Meilleurs Apprentis de France",
literally "one of the best French apprentices") is a prize created in 1986 by the MOF National
Society (MOF, "un des Meilleurs Ouvriers de France" means literally "one of the best French
craftsmen"), in 104 professions. Its goal is to "promote among apprentices ... the taste of well
done work, asserting their personality, their passion, spirit of initiative, progress in their applied competencies, obtaining the fair reward of their effort and to testify with pride about the
efficiency of their training to manual professions". Depending on their profession, the candidates must either make a craft work during 5 months or pass a trial. The best apprentices
obtain a medal. The contest takes place in three steps: first, at the département level.6 The
best apprentices get a bronze, silver or gold medal. The silver and gold medal at this level can
compete at the regional level.7 . A the regional level, the best apprentices can get a gold or silver
medal. Finally, the regional gold medals can compete at the national level, were gold medals
only are awarded. In 2015, there were 5196 participants (in all professions). There were 3472
laureates at the departement level, 1517 at the regional level. 817 apprentices went in final, and
316 were rewarded. The national level of the competition would send too strong a signal and
cause detection because the laureates are invited to a ceremony by the President of the Senate. We indicate a gold medal at the regional level, which represent 13% of the total number of
candidates.
The OM ("Olympiade des Métiers" or "Worldskills Competition") is organized by the Regional Councils in partnership with the professional and training organisms in 50 professions.
It is a three-stage contest. First, at the French regional level; second, at the national level where
a team is made for each profession; third, the national teams are sent at the Worldskills Competition, organized every two years. In 2015, the competition took place in Sao-Paulo. There
were 1000 finalists from 59 countries. About 6000 French apprentices have participated to this
competition. In order to avoid detection, we cannot use the national level selection, because
the team is received by the President of the French Republic. We indicate a selection at the regional level, which represent 800 candidates out of 6000. The selection rate is therefore 13.3%,
close to the MAF selection level. Overall, our excellence qualifications indicate candidates if the
first decile of the competitors. This achievement could be used for international comparisons
because it exists in many countries.
5 Therefore, we have excluded the contracts of 7 to 11 months from all the computations
6 Metropolitan France is divided into 95 département.
7 Metropolitan France is divided into 22 regions
4
Experiment and protocol tests. Table 2 provides some sample statistics. We have data 133
posts for masonry, 141 for electricity and 288 for plumbing. This corresponds to the dispatch of
2248 CVs (4× 462 posts). The global callback rates are high, at least one of the four candidates
gets a callback in 29.8% of the cases for electricity, 36.1% for plumbing and 24.8% for masonry.
Table 2: Sample Statistics
The table reports the p values for all the tests.
Profession
Electricians Plumbers
Short term
72
108
Long term
69
180
Total
141
288
Observations
564
1152
Call-back rate
0.298
0.361
Qualifications callback equality test
Women
0.896
0.215
Men
0.278
0.149
Independence test:
Sending order
0.571
0.982
Masons
70
63
133
532
0.248
0.472
0.907
0.986
We need to perform two tests in order to validate our experimental protocol. First, we check
that the rotation of CVs was efficient, by performing a chi-squared test of independence between the sending order of the candidates and their callback status. Second, in order to avoid
detection, we cannot send two MAF or two OM candidates. We send two CAP, one MAF and
one OM candidates. This raises the issue of differentiated effects of MAF and OM candidates,
and we need to test this hypothesis.
The tests are reported in Table 2. We never reject the independence assumption between
the sending order and the callback status at the conventional levels (p-value between 0.57 and
0.98). In order to test the homogeneity of the excellence characteristics, we have performed the
following test. We first compute the callback rate difference between the MAF candidates and
the CAP candidate on the same posts; this difference eliminates (additive) unobserved heterogeneity. We get an estimate of the MAF advantage over the standard qualification. Then,
we perform the same operation for the OM candidate and get an estimate of the callback rate
advantage of the OM candidate over the standard candidates on the same posts. Finally, we
test the equality of these two differences, separately for each gender and profession. This is a
difference-in-differences test. The results are presented in Table 2: we never reject the homogeneity effect at conventional levels of significance (p-values between 0.149 and 0.907). For this
reason, we will regroup the OM and MAF characteristics under the "excellence" appellation in
the statistical model.
The raw callback rates are reported in Table 3. They mix all the effects of the components
model and, for this reason, are hard to interpret. Overall, men tend to be called more often than
woman, so that some discrimination may be at work. In electricity, men enjoy an advantage on
long term contracts, but not women. Something must restrain the hiring of women on long
term contracts. For masonry, women are almost always ranked behind their male counterpart.
For plumbing, men are not preferred to women for the strongest qualification, but this can
happen for low qualifications. Something reduces the hiring of standard qualification women.
5
Table 3: Call-back rates
Proportions.
Profession
Electrician
Short term contracts:
woman, required
0.111
woman, excellence
0.139
man, required
0.167
man, excellence
0.153
Long term contracts:
woman, required
0.116
woman, excellence
0.145
man, required
0.246
man, excellence
0.261
Global
0.298
Plumber
Mason
Reference∗
0.148
0.231
0.120
0.167
0.057
0.100
0.143
0.171
Pr(s, w, r )
Pr(s, w, e)
Pr(s, m, r )
Pr(s, m, e)
0.161
0.206
0.222
0.200
0.361
0.111
0.063
0.079
0.159
0.248
Pr(ℓ, w, r )
Pr(ℓ, w, e)
Pr(ℓ, m, r )
Pr(ℓ, m, e)
∗: reference used in the components model.
2 Identification and Estimation
We wish to distinguish taste discrimination from statistical discrimination. There are two types
of statistical discrimination against young women: first, the employer may question their professional skill; second, the candidate could be pregnant in a near future. Our methodology
allows for estimating these two types of statistical discrimination separately.8
The identification of these discrimination components relies on the differences between
them. Taste discrimination, like sexism, should apply to all women. It is not the case of the two
other discriminations. Statistical discrimination on skills may only apply to the women with the
standard skills, not to the women with excellence qualifications. Here, we use two excellence
markers, which indicate workers at the top of their profession. Therefore we can assume that
statistical discrimination on skills apply to the standard qualification level only. There remains
to isolate statistical discrimination on pregnancy. Here, it should apply to all the women, like
taste discrimination, but there is one way to isolate its effects. We use information about the
length of the labour contract. If the contract is very short, the probability that a maternity
could affect the firm is negligible, since the firm has anticipated the end of the contract. We
consider contracts whose duration is lower than 6 months, and compare them to contracts
of 12 months and more. Therefore we do not suppress the maternity characteristic from the
candidates, since it is impossible, but its consequences for the firm.
Notice that this method can be easily applied to all correspondence tests: adding an extra
qualification to some candidates is straightforward, and the term of labour contracts is indicated in almost all job offers. In order to clarify the important identification issue, we use a
component model. We also use this model in order to discuss the issues of unobserved heterogeneity and optimal estimation.
8 This is the first paper where we have developed this method. Two variants of this method, which do not address
the issues of the discrimination types, have been used later in Duguet, Gray, L’Horty, du Parquet, Petit (2017) and
Duguet, Le Gall, L’Horty and Petit (2017).
6
2.1 Identification
We model the probability of a positive answer. Since the data are experimental, we will consider
a components model. There are eight probabilities since there are two types of labour contracts
(less than 6 months, 12 months and more) and we send four candidates in reply to each of
them. The four candidates correspond to the two genders times the two qualifications. Gender
is denoted by w and m (woman, man) and the qualification by r and e (required, excellence)
and the length of the labour contract by s and ℓ (short, long). The probabilities for the short
term contracts are described by the following equations:
Pr(s, m, r ) = θs + u s,i
Pr(s, m, e) = θs + θe + u s,i
Pr(s, w, r ) = θs + δT + δQ + u s,i
Pr(s, w, e) = θs + θe + δT + u s,i
where u s,i is the job level heterogeneity. It may be specific to each job post and is likely to be
correlated with all the other components of the model. We will eliminate this component by
differencing from a benchmark case. Since the model includes a constant term, θs , we can
assume E(u s,i ) = 0 without loss of generality. The parameter θs represents the job opportunities in the labour market for short term jobs, labour market tightness, since it represents the
average probability of success for a man with the required qualification. By definition, this candidate should not suffer from discrimination and provides a natural benchmark. The second
probability concerns the excellence candidates. The effect of excellence is denoted θe , it is not
a discriminatory component, and measures the potential increase in the recruitment probability caused by an excellence qualification. We expect θe ≥ 0. Then come the probabilities for
women. By definition, they include the gender based discrimination components, denoted δ.
The first component, δT , is the taste discrimination coefficient. Since discrimination reduces
the hiring probability, we expect δT ≤ 0 in the presence of a Beckerian discrimination. The statistical discrimination on the labour quality also contributes to the hiring probability, and we
denote it δQ . By definition, it only affects women with the standard qualification, not the other
women. Finally, there is no effect related to pregnancy in this first block of equations because
they deal with short term contracts. For the long term labour contract we must account for
statistical discrimination related to the maternity leaves (denoted δM ). We get:
Pr(ℓ, m, r ) = θℓ + u ℓ,i
Pr(ℓ, m, e) = θℓ + θe + u ℓ,i
Pr(ℓ, w, r ) = θℓ + δT + δQ + δM + u ℓ,i
Pr(ℓ, w, e) = θℓ + θe + δT + δM + u ℓ,i
where E(u ℓ,i ) = 0 without loss of generality since there is a constant term θℓ . Here we should
be careful that the unobserved heterogeneity term u ℓ,i can have different distributions for long
run and short run contracts, so that we cannot take a direct difference between the probabilities
of the long and short run contracts. One simple reason is that we provide an application in
the construction sector, where the duration of a contract may be related to the duration of a
construction site. We must take the differences inside each category of labour contract before
to go any further. These differences eliminate the fixed effect heterogeneity.
Since we send four candidates on each offer, we can use differencing from the benchmark
case: a man with the required qualification. These probabilities measure the difference of treat7
ment from the benchmark candidate (s, m, r ):
D(s, m, e) = Pr(s, m, e) − Pr(s, m, r ) = θe
D(s, w, r ) = Pr(s, w, r ) − Pr(s, m, r ) = δT + δQ
D(s, w, e) = Pr(s, w, e) − Pr(s, m, r ) = θe + δT
Women with the required qualifications can be discriminated both because they are woman
(δT ) or because the recruiter doubts their qualification (δQ ). Women with an excellence qualification can be discriminated only because they are woman (δT ). Using the terminology of
asymptotic least squares (henceforth ALS), it is clearly possible to invert the previous system
and write the parameters of interest (θe , δT , δQ ) as functions of the auxiliary parameters (the
three probability differences). We get:
θe = D(s, m, e)
δT = D(s, w, e) − D(s, m, e)
δQ = D(s, w, r ) − (D(s, w, e) − D(s, m, e))
the excellence qualification effect is measured by comparing the two male candidates, since
their probabilities are not affected by discrimination; taste discrimination is measured by comparing the two excellence candidates, since statistical discrimination is absent from both probabilities, and statistical discrimination on skills is obtained by a difference in differences, equal
to Pr(s, w, r )−Pr(s, m, r )−(Pr(s, w, e)−Pr(s, m, e)) after simplification, which represents the variation of the difference of treatment caused by excellence. For the long term contracts, we take
the differences from the benchmark case (ℓ, m, r ):
D(ℓ, m, e) = Pr(ℓ, m, e) − Pr(ℓ, m, r ) = θe
D(ℓ, w, r ) = Pr(ℓ, w, r ) − Pr(ℓ, m, r ) = δT + δQ + δM
D(ℓ, w, e) = Pr(ℓ, w, e) − Pr(ℓ, m, r ) = θe + δT + δM
the first equation clearly indicates an overidentification case since it is a new way to estimate
θe . In order to get δM , there are also two different ways to proceed. First, one can compare
the probability differences for short and long run contracts, for the women with the required
qualification. Second, we can use the equivalent quantity on the excellence qualification. This
gives the following overidentification constraint:
δM = D(ℓ, w, r ) − D(s, w, r )
δM = D(ℓ, w, e) − D(s, w, e)
Overall, we have six probability differences, which represent the auxiliary parameters in the ALS
terminology. From these probabilities, we estimate four structural parameters (θe , δT , δQ , δM ).
The degree of overidentification equals 6 − 4 = 2. It allows for an overidentification test and an
optimal estimator.
8
2.2 Estimation
The optimal estimation of the structural parameters is given by the Asymptotic Least Squares
method9 . The six probability differences can be rewriten:
|
D(s, m, e)
D(s, w, r )
D(s, w, e)
D(ℓ, m, e)
D(ℓ, w, r )
D(ℓ, w, e)
{z
π
=
}
1
0
1
1
0
1
|
0
1
1
0
1
1
0
1
0
0
1
0
0
0
0
0
1
1
{z
A
|
}
θe
δT
δQ
δM
{z
β
}
or
π = Aβ
This system simply represents the identification constraints. π is the auxiliary parameter
and is easily estimated from the data, β is the interest parameter, and is not directly observable.
In order to estimate β, we need to replace π with an estimate π̂. Let:
π̂ = π + ω
where ω is the estimation error on the auxiliary parameter. Substituting into the identification
constraints, we get an equation that can be used for estimation:
π̂ = Aβ + ω
where π̂ and A are observable, so that a minimum distance estimation is feasible. Let Ω =
V(ω), its diagonal elements are the variances of the auxiliary parameters estimators, and the
off diagonal term, the covariance between the estimators. They are correlated because the
answers to all the candidates come from the same firms. The optimal estimator of β is the
Feasible Generalized Least Squares (FGLS) estimator:
β̂ = (A ′ Ω̂−1 A)−1 A ′ Ω̂−1 π̂
with estimated covariance matrix:
V(β̂) = (A ′ Ω̂−1 A)−1
where Ω̂ is a consistent estimate of Ω. The overidentification statistic, denoted S, is simply an
estimate of the norm on the identification constraints, we get:
S = ω̂′ Ω̂−1 ω̂
with ω̂ = π̂ − A β̂. Under the null hypothesis (H0 : π = Aβ), it is χ2 (2) distributed.
9 It was originally developed by Gouriéroux, Monfort and Trognon (1985) and Chamberlain (1982, 1984)
9
Application. The results are presented in Table 4. Our method proceeds with backward elimination, which has a different meaning in this context. When a coefficient equals zero, it often
means that candidates can be regrouped. Therefore the number of observations available for
estimation increases, and so does the precision of the estimate. We give a full account of the
application for electricians. The first column of table 4 reports that the lowest Student statistics
(0.19) is associated to θ̂e . Therefore, we set θe = 0 and we find that:
Pr(s, m, r ) = θs + u s,i
Pr(s, m, e) = θs + u s,i
Pr(s, w, r ) = θs + δT + δQ + u s,i
Pr(s, w, e) = θs + δT + u s,i
Pr(ℓ, m, r ) = θℓ + u ℓ,i
Pr(ℓ, m, e) = θℓ + u ℓ,i
Pr(ℓ, w, r ) = θℓ + δT + δQ + δM + u ℓ,i
Pr(ℓ, w, e) = θℓ + δT + δM + u ℓ,i
In order to estimate θs , we simply take the average of the corresponding probabilities, since
the same number of candidates is sent on each offer. This is equivalent to take a new reference
candidate. Instead of (m, r ), we take both (m, r ) and (m, e) for both contract lengths. We get:
1
(Pr(s, m, r ) + Pr(s, m, e)) = θs + u s,i
2
Pr(s, w, r ) = θs + δT + δQ + u s,i
Pr(s, w, e) = θs + δT + u s,i
1
(Pr(ℓ, m, r ) + Pr(ℓ, m, e)) = θℓ + u ℓ,i
2
Pr(ℓ, w, r ) = θℓ + δT + δQ + δM + u ℓ,i
Pr(ℓ, w, e) = θℓ + δT + δM + u ℓ,i
which is the estimation basis of column (2) in table 4. Since the Student statistic of δ̂T is 0.62,
wet set δT = 0 in the previous system and get:
1
(Pr(s, m, r ) + Pr(s, m, e)) = θs + u s,i
2
Pr(s, w, r ) = θs + δQ + u s,i
Pr(s, w, e) = θs + u s,i
1
(Pr(ℓ, m, r ) + Pr(ℓ, m, e)) = θℓ + u ℓ,i
2
Pr(ℓ, w, r ) = θℓ + δQ + δM + u ℓ,i
Pr(ℓ, w, e) = θℓ + δM + u ℓ,i
10
which leads to:
1
(Pr(s, m, r ) + Pr(s, m, e) + Pr(s, w, e)) = θs + u s,i
3
Pr(s, w, r ) = θs + δQ + u s,i
1
(Pr(ℓ, m, r ) + Pr(ℓ, m, e)) = θℓ + u ℓ,i
2
Pr(ℓ, w, r ) = θℓ + δQ + δM + u ℓ,i
Pr(ℓ, w, e) = θℓ + δM + u ℓ,i
and this leads to the estimations reported in column (3) of table 4. The Student statistics of δ̂Q
is 1.36 and wet set δQ = 0 in the previous system. After some simplification we get:
1
(Pr(s, m, r ) + Pr(s, m, e) + Pr(s, w, e) + Pr(s, w, r )) = θs + u s,i
4
1
(Pr(ℓ, m, r ) + Pr(ℓ, m, e)) = θℓ + u ℓ,i
2
1
(Pr(ℓ, w, r ) + Pr(ℓ, w, e)) = θℓ + δM + u ℓ,i
2
the first equation will be useful to estimate θs only and, therefore, will not contribute to the
estimations of the discriminatory component that remains. The two other equations will be
used to perform a difference and identify δM . The result is reported in the fourth column of
table 4.
3 Results
The three professions tested have different market tightness depending on the contract duration. Electricians are preferred on long term contracts (θ̂ℓ = 0.222 > 0.120 = θ̂s ), as well as
plumbers (θ̂ℓ = 0.209 > 0.173 = θ̂s ) and masons on short contracts (θ̂s = 0.157 > 0.119 = θ̂ℓ ).
This may reflect the nature of these occupations. This is consistent with the fact that masons
may be preferred for fixed term contracts because they are more often used during the construction phase, while electricians and plumbers may also be called for maintenance.
The three professions do not seem to value the excellence qualifications. This is surprising at a first glance, but it can be understood if we account for the type of jobs the posts are
about. These are rather basic jobs that any worker with the right training may be able to do.
In this case, the absence of an excellence effect (θe = 0) would simply reflect that the recruiters
do not value excellence when they do not need it. Notice that, contrary to Neumark (2012), our
method does not rely on a monotonicity condition between the call-back rate and the qualification level, so that this result has no consequence for our econometric analysis. This also
explains why we did not apply this method when we had planned to do so.
All the other differences between the call-back rates of the candidates come from discrimination. However, we find that the discrimination type differs from one profession to another.
This justify ex post the usefulness of our estimation method. The taste based discrimination
appears in the mason profession. Women have a lower call-back rate than men by -5.8 pp (tstat: 2.69). This results may come from preconceptions in a predominantly male activity, which
relied on physical force for a long time in the past.
11
Table 4: Discrimination components estimates
Estimation by the Asymptotic Least Squares method. Asymptotic Student statistics between parentheses (critical
values: 1.645 at 10%, 1.96 at 5%)
Profession
Model
Levels
θs
θℓ
Differences
θe
δT
δQ
δM
Overidentification
p-value
(1)
0.167
(3.78)
0.246
(4.62)
Electrician
(2)
(3)
0.160
(3.97)
0.254
(5.27)
0.153
(4.14)
0.254
(5.27)
Plumber
(6)
(7)
(4)
(5)
0.142
(4.25)
0.254
(5.27)
0.120
(3.76)
0.222
(7.11)
0.071
(2.06)
-0.059
(3.15)
-0.067
(1.57)
0.033
(1.33)
-0.059
(3.14)
0.315
0.175
-0.005
(0.19)
-0.017
(0.49)
-0.036
(1.07)
-0.088
(1.52)
-0.021
(0.62)
-0.028
(1.04)
-0.088
(1.52)
-0.036
(1.36)
-0.104
(2.03)
-0.128
(2.67)
0.007
(0.35)
0.068
(1.86)
-0.053
(1.81)
-0.063
(1.48)
0.876
0.982
0.809
0.443
0.163
0.144
(4.85)
0.211
(7.80)
0.144
(4.85)
0.211
(7.80)
Mason
(10)
(11)
(8)
(9)
0.173
(6.08)
0.209
(8.82)
0.143
(3.30)
0.079
(2.28)
0.143
(3.30)
0.079
(2.28)
0.157
(4.32)
0.119
(3.55)
0.157
(4.32)
0.119
(3.55)
0.055
(1.58)
-0.107
(2.91)
0.044
(1.17)
0.059
(1.37)
0.021
(1.08)
-0.080
(2.78)
-0.081
(2.77)
-0.058
(2.69)
0.058
(1.35)
0.051
(1.16)
0.068
0.080
0.116
-0.038
(2.20)
0.509
(12)
0.130
Statistical discrimination on labour qualification is found for plumbers. Women are at a
disadvantage by -3.8 pp (t-stat: 2.20). In this case, the recruiters may trust women less than
man because they believe that women would work less well.
Last but not least, we find a maternity leave discrimination for electricians. Women are at
a strong disadvantage since their loss reaches -12.8 pp (t-stat: 2.67). In this case, the fear of
career interruption by the recruiters may drive the results.
Conclusion
We find evidence of hiring discrimination against women in the construction sector. However,
the discrimination types really are different from one profession to another, as well as their
magnitude.
The Beckerian explanation seems to hold in masonry, where taste-based discrimination is
at work. In this context all women should suffer from discrimination. Persuasive advertising
campaigns could help in changing the attitudes in this field, as well as legal sanctions. The
situation is different in the two other jobs that we have tested.
The occupation of electrician is characterized by a statistical discrimination on qualification. Here, the employer may doubt the competences of the female candidates. Informative
advertising campaigns should give the employers the right information and contribute to reduce the discriminatory hiring gap. Increasing the internship length during the training of the
apprentices may also contribute to reduce information asymmetry.
Eventually, we find that plumbing exhibits another type of discrimination. The anticipation
of a maternity by the employer may hinder the chances of young women to get a job. Since
the full cost of the maternity leave is incurred by the Sécurité Sociale, the issue may be about
the birth-related career interruptions of women. This issue is difficult to tackle. One solution
would be to increase the parental leave by men, so as to bring nearer the career interruptions
of women and men.
12
A last result is about the magnitude of the discrimination coefficients. We find that the
stronger effect is about maternity, far above the taste based discrimination. This suggests that
the most obvious forms of discrimination may not be the most harmful.
References
[1] Arrow, K. J., (1973). The Theory of Discrimination, in O. Ashenfelter and A. Rees (eds.), Discrimination in Labor Markets, Princeton, NJ: Princeton University Press.
[2] Becker, G. (1957), The Economics of Discrimination. The University of Chicago Press., 2nd
ed: 1971, ISBN 0-226-04116-6.
[3] Bertrand, M. (2010). New Perspectives on Gender. Handbook of Labor Economics, Volume
4b. Elsevier, 1545-1592.
[4] Bettio, F. , Verashchagina, A. (2009). Gender Segregation in the Labour Market: Root Causes,
Implications and Policy Responses in the EU. EU Expert Group on Gender and Employment
(EGGE). Luxembourg: European Commission.
[5] Chamberlain, G. (1982). Multivariate regression models for panel data. Journal of Econometrics, Vol. 18 No. 1, 5-46.
[6] Chamberlain, G. (1984). Panel data. In Griliches, Z., and Intriligator, M. D. (Eds), Handbook
of Econometrics, Vol. 2, North Holland, 1247-1318.
[7] Chamkhi, A. (2015). Les écarts de salaires entre femmes et hommes par zones d’emploi.
Dares Analyses, No 20.
[8] Duguet E., Du Parquet L., L’Horty, Y., Petit, P. (2015). New Evidence of Ethnic and Gender
discriminations in the French Labor Market using experimental data: A ranking extension
of responses from correspondence tests. Annals of Economics and Statistics, No 117-118,
21-39.
[9] Duguet E., du Parquet L., L’Horty Y., Petit, P. (2017). Counterproductive hiring discrimination against women: evidence from a French correspondence test. International Journal of
Manpower, forthcoming.
[10] Duguet E., Gray, D., L’Horty, Y., du Parquet, L., Petit, P. (2017). Labor market effects of
urban riots: an experimental assessment. Working paper #1711E, University of Ottawa, Department of Economics.
[11] Duguet E., Le Gall R., L’Horty Y., Petit P. (2017). How does labour market
history influence the access to hiring interviews?
ERUDITE Working Paper,
https://ssrn.com/abstract=3037419.
[12] Firth, M. (1982). Sex Discrimination in Job Opportunities for Women. Sex Roles, 8 (8), pp.
891-901.
[13] Gouriéroux, C., Monfort, A. and Trognon, A. (1985). Moindres carrés asymptotiques. Annales de l’Insee, No. 58, 91-122.
13
[14] Heckman, J. J. (1998). Detecting Discrimination. Journal of Economic Perspectives, 12(2),
101-116.
[15] Hegewisch, A., Liepmann, H., Hayes, J., Hartmann, H. (2010). Separate and not equal?
Gender segregation in the labor market and the gender wage gap. Institute for Womens’
Policy Research,Washington, C377.
[16] Ministry of Education, 2012. Filles et garçons, sur le chemin de l’égalité de l’école à
l’enseignement supérieur.
[17] Naves, M.-C., Wisnia-Weill, V. (2014). Lutter contre les stéréotypes filles-garçons. Rapport
du Commissariat Général à la stratégie et à la Prospective.
[18] Neumark, D. (2012). Detecting discrimination in audit and correspondence studies. Journal of Human Resources, 47(4), 1128-1157.
[19] Neumark D., Bank, R., Van Nort, K. (1996), Sex discrimination in restaurant hiring: an
audit study. The Quaterly Journal of Economics, 111 (3), 915-941.
[20] OECD (2012). Closing the gender gap: act now. ISBN 978-92-64-17936-3, 296 p.
[21] Phelps, E. (1972). The statistical theory of racism and sexism, The American Economic
Review, 62 (4), 659-661.
[22] Riach, P. A., Rich, J. (2002). Field experiments of discrimination in the market place. The
economic journal, 112(483), 480-518.
[23] Spence, M. (1973). Job Market Signaling. Quarterly Journal of Economics. 87 (3): 355-374.
14
Table 5: Raw data
The table give the cross-products of the call-back dummies. Each number gives the number
of answers common to the two candidates indicated in line and column. The number of
callbacks for each candidate is given by the diagonal of each matrix. These matrices allow
for computing both π̂ and Ω̂.
req.: required, exc.: excellence.
Electricians
woman, required
woman,excellence
man,required
man,excellence
woman, required
woman,excellence
man,required
man,excellence
woman, required
woman,excellence
man,required
man,excellence
short (72)
long (69)
woman
man
woman
man
req. exc. req. exc. req. exc. req. exc.
8
5
6
4
8
6
6
7
5
10
7
7
6
10
6
6
6
7
12
9
6
6
17
13
4
7
9
11
7
6
13
18
Plumbers
short (108)
long (180)
woman
man
woman
man
req. exc. req. exc. req. exc. req. exc.
16
15
6
9
29
24
16
17
15
25
9
11
24
37
17
18
6
9
13
9
16
17
40
25
9
11
9
18
17
18
25
36
Masons
short (70)
long(63)
woman
man
woman
man
req. exc. req. exc. req. exc. req. exc.
4
4
4
3
7
3
3
4
4
7
5
5
3
4
1
3
4
5
10
5
3
1
5
3
3
5
5
12
4
3
3
10
15
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