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1
Does unobserved Heterogeneity Matter? A Panel Data Analysis of the
Gender Pay Gap*
DRAFT
AMYNAH GANGJI
♦
, KRISTIAN ORSINI
♣
AND SALIMATA SISSOKO
♠
Abstract:
This paper provides evidences on the effects of unobserved individual heterogeneity on
estimated gender pay differentials. Using the European Community Household Panel
(ECHP), we present a cross-country comparison of the evolution of unadjusted and adjusted
gender pay gaps using both cross-section and panel data estimation techniques. The analysed
countries differ greatly with respect to labour market legislation, bargaining practices
structure of earnings and female employment rates.. Once adjusting for unobserved
heterogeneity, we find a narrowed male-female pay differential, as well as significantly
different rates of return on individual characteristics. In particularly, the adjusted wage
differential decreases by 7% in Belgium, 14% in Ireland, between 20-30% Germany, Italy, the
Netherlands and Spain and of 41% and 54% in the UK and in Denmark respectively.
Keywords: gender wage gap, panel data, discrimination
JEL-Classification: J16, J31, J71
*We thank Michele Cincera, Robert Plasman, Rodrigo Ruz-Torres, François Rycx, Jean-Luc
Demeulemeester, and participants of the DULBEA-ETE internal seminar, SOLE (2004),
EPUNet (2004) and AEA (2004) conferences for helpful comments and discussions on earlier
drafts.
♦
Université Libre de Bruxelles, Department of Applied Economics (DULBEA), CP140 –Av.
F.D. Roosevelt 50, 1050 Brussels, Belgium. Tel: +32 (0)2 650.49.53, Fax: +32 (0)2
650.438.25, e-mail: amgangji@ulb.ac.be
♣
KULeuven, Departement Economie, Naamsestraat 69, 3000 Leuven, Belgium. Tel +32
(0)16 326806, Fax +32 (0)16 326796, e-mail: kristian.orsini@econ.kuleuven.ac.be
♠
Université Libre de Bruxelles, Department of Applied Economics (DULBEA), CP140 –Av.
F.D. Roosevelt 50, 1050 Brussels, Belgium. Tel: +32 (0)2 650.41.24, Fax: +32 (0)2
650.438.25, e-mail: nsissoko@ulb.ac.be
IZA, Bonn, Germany
2
3
Introduction
A large number of studies have documented a generalised tendency towards a reduction in the
gross gender pay gap in the European countries during the 70s. The following decades
nevertheless have produced a more scattered pattern: while some countries have continued to
witness a reduction in the gender wage inequalities, others have shown a stationary trend. The
gender gap hence remains at an important level, although significant differences across
countries may be observed.
Empirical research has pointed out several reasons that may lay behind the dynamic of the pay
differential. The factors identified mainly concern labour market mechanisms such as changes
in human capital endowments, overall wage structure, wage setting arrangements, as well as
legislation on equal opportunities (see e.g. Rosholm and Smith, 1996, Dolton, O’Neill and
Sweetman, 1996, Blau and Khan, 1997, Rice, 1999).
Most pay gap analyses rely on the Oaxaca-Blinder (1973) technique. This method
decomposes the gender pay gap into a part due to differences in productive characteristics
(education, potential work experience, tenure, etc), and a part shaped by non-productive
characteristics (such as gender, race, etc).
Earlier studies have shown that estimating wage equations by Ordinary Least Squares (OLS)
technique may produce biased results due to the heterogeneity bias (see e.g. Hausman and
Taylor, 1981). The heterogeneity bias arise because unobserved characteristics (motivation,
ability, etc.) may be correlated with observed individual characteristics (work continuity,
education, etc). In particular if motivation is correlated with intermittent labour force
participation then estimates of the effects of intermittency might be picking up motivation and
not earning power losses caused by intermittency (Moon-Kak and Polachek, 1994). This issue
4
is crucial for policy purposes, since the estimated rate of return on observed individual
characteristics determines the extent of wage discrimination.
Fewer studies, however, have addressed the problem of endogeneity when estimating the
gender pay gap. The only exceptions are Cornwell and Rupert (1988), Baltagi and Khanti-
Akom (1990), Moon-Kak and Polachek (1994) and Hansen and Wahlberg (1997).
Endogeneity occours when one or more explicative variables are not exogenously assigned,
but determined by some other characteristics of the individual. A typical example of
endogeneity is education. If the latter is not exogenously assigned, but determined by the
extent of own abilities and motivation, the estimated rate of return on education will be
biased.
The aforementioned studies, however, do not provide international comparison of the gender
pay gap adjusted for unobserved heterogeneity and, more importantly, do not attempt to
explain through which channels the inclusion of individual heterogeneity affects the gender
pay gap in a particular country.
The main purposes of this paper is to estimate the adjusted gender pay gap over time for 8
European countries, to analyse the effect of incorporating unobserved heterogeneity, and
finally to evaluated the impact of changes in individual characteristics on the evolution of the
pay differential.
In this paper we estimate the adjusted gender pay gap with the Oaxaca (1973) decomposition
technique. We also use the panel data estimator proposed by Hausman and Taylor (1981).
This estimator is preferred to the traditionally within-group fixed effect estimators because it
avoids a well-known drawback of the within estimators, namely that all time-invariant
variables are eliminated by the data transformation – which implies that their coefficients
cannot be estimated – and that the estimator is not fully efficient .
5
As far as we know, this study is the only one to use recent European panel datasets to present
a comparison of the (un)adjusted gender wage gap overtime estimated with both cross-
sectional and panel-data econometric techniques. Our results confirm the common findings of
previous studies. Gender pay differentials are higher in more liberal economies and in
economies providing lower supports for female employment (UK, Ireland, Germany and
Spain). Although Italy and Spain are usually clustered together in the Mediterranean
typology, these countries differ in the extent of their gender pay differential. Italy presents a
smaller pay gap, probably owing to its concentrate wage structure and a high level of public
supports for female employment (see Gornick et al, 1997 and Rice 1999, Blau and Khan,
1996).
Adjusting for individual heterogeneity, we find an increase in the rates of return of potential
experience and education for both men and women. In addition, compared to the reference
category, the wage differentials due to subordinate occupations, determined spell contracts
and relatively small enterprise size decreases. Furthermore, in accordance with the previous
national studies, we observe a decrease of the adjusted wage differential. We observe that the
decrease is related to the gender differences in our endogenous variables i.e. education and to
a lesser extent to work experience. This fall ranges from 7% in Belgium to 41% and 54% in
UK and Denmark respectively. Finally, our results suggest that the narrowing of pay
differentials between men and women goes with a convergence of observed productive
characteristics between men and women in all countries.
The remainder of the paper is structured as follows. Section 1 describes our data. Section 2
presents national studies on gender wage gap, section 3 and 4 present the estimation methods
and the results and section 5 concludes.
6
1 Data and Descriptive statistics
The European Community Household Panel Study (ECHP) is a convenient dataset for
estimating panel regressions. The data gathering has been planned for 9 years and started in
1994. In that year, the survey was conducted in 12 member states and collected information
on a representative sample of 60.500 households, corresponding to 170.000 individuals. Since
then, Austria (1995), Finland (1996) and Sweden (1997) have joined the survey.
Wage equations are estimated for 8 European countries: Belgium, Denmark, Germany,
Ireland, Italy, The Netherlands, Spain, and United-Kingdom. This subset allows comparison
of countries with welfare state of the conservative-corporatist model (Belgium, Germany,
Italy, The Netherlands and Spain), the Scandinavian model (Denmark) and the liberal welfare
model (Ireland and UK). Finally, according to Gornick et al (1997) these countries differ
according to their level of public support to childcare arrangements, maternity and parental
leave provision. High support is encountered in Denmark and Belgium, medium support in
West-Germany, Italy and The Netherlands and low support in Ireland, Spain and United-
Kingdom.
The selected sub-sample sample consists in individuals aged between 20 and 60 years, who
are employed in the private sector, are not self-employed and work more than 30 hours per
week. The above restrictions lead to a sub-sample of 9,251 observations (1,905 individuals) in
Belgium to 26,444 in Germany (5,430). The sample is unbalanced. In Belgium, about 36% of
individuals remain in the sub-sample the 8 years. This rate falls to 33% in Germany and about
30% in UK, Italy and Denmark.
The earning measure used in this analysis is the logarithm of the gross deflated hourly wage.
The explanatory variables correspond to employees working conditions and, worker and
employee’s characteristics. We have included the level of education, the potential work
7
experience
1
(in level, squared), the firm size (small, medium and large)
2
), dummy variables
for the occupations (1-digit) and the employment contract (fixed term or permanent contract).
The descriptive statistics (see Appendix1) show that on general women in pay employment
are better educated than men. On the opposite, female work experience is largely lower than
that of men. This difference is partly explained by the average lower age of women relative to
men, but also by different life-cycle employment strategies. Women are also more numerous
in subordinate occupations, as clerk or services workers, in small and medium size firm and
also more to be employed with fixed-term employment contract.
In 1994, the unadjusted gender pay differential was highest in the UK (31.9%), in Ireland
(27.6%) and in the Netherlands (26.0%) and smallest in Denmark (14.4%) and Italy (15.9%).
In 2001, the UK still showed the most important gender pay differentials (26.2%). While
Spain (23.3%) and Germany (22.5%) stagnated at a level close to that of the beginning of the
period. Always in 2001, Belgium presented the smallest gender pay gap (14.3%). Noteworthy
is the case of Denmark, which shows a slight increase in the gender pay differential. The
largest reduction is observed in Ireland (-32%) and in the UK and Netherlands (-18% and -
17% respectively). This ranking is in accordance with most studies using ECHP in the
literature and analysing gender pay gaps in EU (Rice, 1999; Beblo et al, 2003; Rubery et al.,
2003; Plasman et al, 2004).
1
The work experience is computed as follows: age minus age when the individual has started his/her working
life
2
The small firms have less than 20 employees; the medium firm have between 20-100 employees and the large
firms more than 100 employees.
8
Table 1: Average gender wage gap in European countries, 1994 and 2001 (euros)
1994 2001
Male Female Gap Male Female Gap
Belgium
11.96 10.03 16.2% 14.09 12.08 14.3%
Denmark
17.16 14.69 14.4% 20.35 17.15 15.7%
Germany
12.36 9.48 23.3% 13.75 10.65 22.5%
Ireland
9.33 6.75 27.6% 11.71 9.49 18.9%
Italy
7.39 6.21 15.9% 7.66 6.46 15.6%
The Netherlands
15.58 11.52 26.0% 16.18 12.67 21.7%
Spain
6.20 4.77 23.0% 7.02 5.38 23.3%
United-Kingdom
12.25 8.34 31.9% 14.77 10.90 26.2%
Source: European Community Household Panel (ECHP)
Wage gap=
m
fm
w
ww −
with
m
w /
f
w , male/female deflated average hourly wage
2 An overview of the gender wage gap in Europe
According to Naur and Smith (1996), after a period of decrease (1960-1970), the Danish
gender wage gap has been slightly increased since the mid-1980s (also see Rosholm and
Smith, 1996). An important explanation is the decentralization of the wage formation process
and the increased wage dispersion. Datta Gupta, Oaxaca and Smith (2001) show that female
gains in human capital were wiped out by the idiosyncratic increase in observed skill prices,
which hurt women relative to men.
In Germany the gender pay gap has decreased over last twenty years, while female
employment has progressively increased. Nevertheless, this country is characterised by a
dominant “single male breadwinner” model: family and tax policies discourage the labour
9
force participation of married women by imposing high implicit marginal tax rates on labour
income (Rice, 1999). According to Beblo et al. (2000), the typical female employment record
(maternity break followed by part-time employment), results in a substantial lower wage rate.
Kunze (2002) has showed the importance of the gender-biased effect of work interruptions is
in disfavour of women.
The study by Harkness (1996) finds that the reduction of discrimination played a stronger role
than the progressive convergence of human capital (see also Blackaby et al, 1997) in the
evolution of the British gender pay gap. On the opposite, the study by Joshi and Paci (1998)
demonstrates that more than the equal opportunity legislation, the main reason for the
reduction of the British gap since mid-1970s is women catching up with men in measures of
human capital (see also Dolton et al, 1996). Finally, the high level of wage inequalities in UK
explains an important part of the pay gap (Blau and Khan, 1996).
According to Barrett et al. (2000) the Irish adjusted pay gap narrowed significantly between
1987 and 1997. The shift in the wage structure towards greater vertical inequality partly
explains why this fall off in the “discrimination” component was not paralleled to a similar
reduction of the unadjusted gender pay gap. Further, a significant part of the pay gap seems to
be attributable to gender differences in length of work experience and absences from the
labour market (Russell et al., 2002).
According to Villa (2002), the Italian gender wage gap has decreased between 1985-1996.
Wage inequalities has decreased between late 1970s and 1980s and has remained quite
constant in the rest of the decade (Brandolini, Cipollone and Sestito, 2001). On ne comprend
pas bien s’il a augmenté au diminué? Y a-t-il une incohérence entre les deux études ?
The Spanish gender pay gap has narrowed during the 1980s and 1990s. A substantial part of
this differential is due to differences in returns to observable characteristics (Ugidos, A.,
1997; Molto, 2002). Further, according to Molto (2002), the gender gap is also particularly
10
influenced by the over-representation of women on the lower earnings steps. Qu’est-ce que tu
intends pour steps? Cela n’est pas assai claire…
In the Netherlands, overall wage inequality has risen both in the 1980s and the 1990s due to
the increasing return to skills. At the same time the wage differential between men and
women has remained fairly constant (Plantenga et al 2002).
Finally, the study of Plasman and Sissoko (2002) indicates that Belgium has experienced a
decrease in its gender pay differential during 1980s and 1990s and presents nowadays a
relatively narrowed pay gap. This is mainly due to its wage structure: the traditionally high
levels of guarantee minimum income and guarantee minimum wage (introduced in 1974 and
1975 respectively) and a centralised wage bargaining system produce a relatively
concentrated wage dispersion.
3 Estimation Method
3.1 Wage Equations
The wage equations have been estimated taking into account both labour supply
characteristics (chiefly human capital variables such as level of education or potential prior
work experience) and labour demand characteristics (namely the occupations, size of the
establishments, the contract type). We have estimated wage equations with ordinary least
squares (OLS) on cross-section samples (1994 and 2001) and with the Hausman and Taylor
(1981) estimation method on the pooled sample.
We assume that wages are determined according to the following equation (Mincer, 19…):
ln W
it
= β
0
+ β
1
X
it
+ β
2
Z
i
+ α
i
+ ε
it
(1)
where i = 1, …, N indexes individuals and t = 1, …, T indexes time periods, ln W
it
(the
logarithm of hourly wage) is the dependant variable, X
it
represents the time-varying
11
regressors, Z
i
is the matrix of the time-invariant regressors, α
i
is the unobserved heterogeneity
term. It is supposed time-invariant and individual specific. In fact, it measures the effects of
unobserved characteristics such as ability, motivation, ambition or efforts on wages, which
may vary between individuals but remain constant in time. It is assumed to have zero mean
and constant variance σ²
α
conditional on X and Z. Failure to take into account this
heterogeneity term will lead to correlation between the error terms of same individuals.
Following Hausman Taylor we may divide X
it
and Z
it
: X
it
=(X
1it
, X
2it
) and Zit=(Z
1it
, Z
2it
), with
X
2it
and Z
2it
being correlated with the unobserved heterogeneity term. . ε
it
is the error term
measuring the effects of unobserved variables that vary both across individuals and over time.
It is supposed to be not correlated with X, Z and α
i
, and distributed with mean zero and
constant variance (σ²
ε)
.
As far as the pooled sample estimations are concerned, we know that if α
i
is correlated with X
and Z, Ordinary Least Squares (OLS) and Generalized Least Squares (GLS) estimation
methods would yield biased and inconsistent estimates of the parameters. The fixed effects
(within-group fixed effect, FE) model overcomes this problem by eliminating the individual
effect in the sample and transforming the data with either a first-difference or a mean-
deviation operator. Resulting estimators are unbiased, but important information concerning
time-invariant characteristics (e.g. education, sex) is lost, provoking a loss of efficiency. The
IV/GLS estimation technique applied in this study bias are both unbiased and more efficient
than the fixed effects estimator.
Following Rosholm and Smith (1996), the estimation procedure is as follows. First a fixed
effects model is applied:
ln W
it
- ln W
i .
= β
1
(X
it
- X
i
.)+ (ε
it
- ε
i
.
) (2)
where W
i .
= (1/T
i
)
∑
=
i
T
t
it
W
1
, X
i .
= (1/T
i
)
∑
=
i
T
t
it
X
1
, ε
i
= (1/T
i
)
∑
=
i
T
t
it
1
ε
12
In a second step, the mean individual residuals are computed using the estimators obtained in
the fixed effects equation:
β
ˆ
ln
ˆ
... iii
XWd −= (3)
These residuals are then regressed on time-invariant characteristics (“between effect model”).
)(
ˆ
.. iiii
Zd
εαγ
++=
(4)
If the Z
i
variables are correlated with the unobserved error term, the estimation method
suggested by Hausman and Taylor involving instrumental variables must be employed
3
.
In the third step, the estimates of the variances obtained in the preceding regressions will be
used in order to calculate the weights for the final GLS estimation.
αε
ε
σσ
σ
θ
²²
1
i
i
T+
−= (5)
where
εα
σσσ
²
ˆ
)/1()/1(²
ˆ
²
ˆ
1
∑
=
+=
N
i
ibe
TN
Finally, after having computed the individual weights, it is possible to estimate the following
GLS equation:
Y
it
– θ
i
Y
i
. = (X
it
– θ
i
X
i
.)β + (1- θ
i
) Z
i
γ + (ε
it
- θ
i
ε
i
) (6)
3.2
Cross-section and Panel Data Decompositions
We use the standard Oaxaca (1973) decomposition technique to differentiating the gender gap
into a market component (explained by differences in labour supply and demand
3
The instrumental variables must be strongly correlated with Z
i
and not correlated with α
i
. A procedure
commonly found in the economic literature is to employ the X
1i.
, the mean deviations (X
it
-X
i
.) and Z
1i.
uncorrelated with α
i
as instruments.
13
characteristics) and a residual component. Different specifications are presented: we use the
OLS estimators of 1994 or 2001 with the corresponding average observed characteristics of
men and women and the GLS estimators with instrumentation with the average observed
characteristics of men and women of 1994 or 2001. To perform the first decomposition for
2001 for example, we estimate semi-log wage regressions for men and women separately:
mm
OLS
m
OLS
m
xw
20012001
ln
βα
+= (7)
ff
OLS
f
OLS
f
xw
20012001
ln
βα
+=
(8)
The m and f indexes refer to men and women respectively,
fm
ww
20012001
/ denotes the average
wage of men/women in the sample of 2001,
m
OLS
β
/
f
OLS
β
are the OLS estimators of the
separated wage equations of men/women,
m
OLS
α
/
f
OLS
α
are the intercepts and
fm
xx
20012001
/ the
average observed characteristics of men/women in the sample of 2001.
The gender wage gap has then the following specification:
[
]
)()()(lnln
20012001200120012001
f
OLS
m
OLS
f
f
OLS
m
OLS
fmm
OLS
fm
xxxww
ββααβ
−+−+−=− (9)
In the latter equation, the first term represents the explained part: the differences between men
and women in individual characteristics, x. The second term gives the residual part. This last
term regroups the unobserved characteristic differences and the differentials in return for
equal characteristics between men and women.
As far as the analyse of the impact of changes in productive characteristics between men and
women is concerned, following Rosholm and Smith (1996), we compare the explained part of
the year 1994 with that of 2001:
Explained part (1994):
)(
19941994/
fmm
GLSIV
xx −
β
(10)
Explained part (2001):
)(
20012001/
fmm
GLSIV
xx −
β
(11)
14
Where
m
GLSIV /
β
are the GLS estimators with instrumentation of the wage equation of men on
the pooled sample and
fm
xx /
the average observed characteristics of men/women in the
sample of 1994 or 2001.
We have also tested the significance of the components of the wage decomposition using
Oaxaca and Ransom techniques (1998). Since the elements of the decomposition are non-
linear functions of the estimated coefficients of the semi-log wage equations, these authors
propose the so called “delta method” for estimating the asymptotic standard errors of these
latter differentials
4
..
4 Results
4.1 The wage equations
We obtain wage equations for the whole sample as well as separated equations by gender
estimated by OLS, GLS, FE, IV/GLS on the pooled sample and wage equations estimated by
OLS on the samples of 1994 and 2001 (see Appendix 2 for wage equations)
5
. Adjusting for
individual heterogeneity, applying a variance component model (IV/GLS), reduces the
standard errors compared with those obtained with fixed-effect (FE).
Our separated wage equation by gender for 1994 and 2001 are in accordance with previous
studies using ECHP data (Rice, 1999; Rückert, 1997; Plasman et al., 2002). Estimated
coefficients of education variables confirm the strong positive effect of education upon wage.
Whatever the level, men are better remunerated than women for their level of education. The
wage equations also confirm the positive influence of experience upon wages. In agreement
4
See Oaxaca and Ransom (1998) for more details
5
Due to the large number of wage equations produced and the number of countries, only the wage equations
estimated by OLS and IV/GLS on the pooled samples are presented in appendix 2. The other wage equations are
available from the authors upon request.
15
with the literature, we observe a concave relation between wages and experience. Our results
from the pooled sample indicate that an additional year of experience leads to from about
1.5% higher wage in Belgium, Denmark, France and Spain to 2.1-2.6% in Germany, Ireland,
the Netherlands and UK. For all countries, except Spain, the return of experience is larger for
men. The variables relative to the size of the company and type of contract show a positive
relation between wage and the size of firm or the permanent nature of an employment
contract.
As OLS, GLS estimates assume no correlation between explanatory variables and the latent
α
i.
Although GLS estimators differ from OLS estimates, results remain globally the same. Let
us note the decrease of male-female differential and the increase of the rates of return of
experience, education as well as the reduction of penalty due to low skilled and subordinate
occupations
6
, fixed employment contract and relatively small enterprise size.
The within-groups estimates eliminate the latent variable (α
i
) as well as our time invariant
variables (level of education and sex in pooled male-female samples). Estimators are
unbiased. Comparing fixed-effect estimates with GLS, we see the continuous increase of the
return of experience and fall of penalty relative to subordinate occupations, fixed term
contracts, etc. Finally, using the Hausman and Taylor test (1981) for heterogeneity, we find a
correlation between explanatory variables and the latent α
i
. This is solved by instrumentation
of variable potentially correlated with the heterogeneity term.
We have first chosen to take the level of education as the only endogenous variable (see
appendix 2 for the wage equations)
7
. The Hausman test
8
indicates that we cannot reject the
6
Low and subordinate occupations concern Clerks (isco 4), Service Workers and Shop and Market Sales
Workers (isco 5), Craft and Related Workers (isco 7), Plant and Machine Operators and Assemblers (isco 8) and
Elementary Occupation (isco 9). High skilled occupations are the following: Legislators and Senior Officials
(isco 1), Professionals (isco 2) and Technicians and Associate Professionals (isco 3).
7
X1= (experience (level, square), dummy variables for occupation, the firm size and the employment contract)
16
null hypothesis of no correlation with the heterogeneity term, with this specification. We have
checked the sensitivity of our results to an alternative choice of the exogenous variable (X
1
).
Appendix 2 also present the wage equations for education and experience (level and squared)
as the endogenous variables
9
. Once again, the Hausman test does not reject the null
hypothesis and the test statistics are even lower than with the first specification. Furthermore,
globally, IV/GLS estimates treating both education and experience as endogenous are very
similar to within-groups estimates.
4.2
Cross-section decomposition over time
We analyse the evolution of the gender pay gap over time using OLS estimates of wage
equations of 1994 and 2004. As mentioned above, a standard Oaxaca decomposition identifies
an explained and a residual part of the wage differential. The former represents the gender
differences in observed characteristics and the latter is constituted by the gender differences in
observed characteristic prices and by the difference in the constant between men and women.
The explained and residual components are significant at 1% for all countries under study.
Table 2: Cross-section decomposition, Oaxaca (1973), 1994 and 2001
1994 2001
Gap Explained
1
Residual
2
Gap Explained
1
Residual
2
Belgium
0.158 0.015** 0.143*** 0.134 0.014** 0.12***
9.7% 90.3% 10.7% 89.3%
8
Hausman test :
)
ˆˆ
())
ˆ
(
ˆ
)
ˆ
(
ˆ
()'
ˆˆ
(²
~
1
GLSwGLSwGLSw
W
ββββββσ
ε
−Ω−Ω−=
−
, which is distributed as a chi-
squared where the degree of freedom corresponds to the number of instruments used minus the number of time-
invariant regressors instrumented
9
X1= (dummy variables for occupation, the firm size and the employment contract)
17
Denmark
0.137 0.047*** 0.090*** 0.157 0.047*** 0.110***
34.1% 65.9% 29.7% 70.3%
Germany
0.269 0.053*** 0.216*** 0.248 0.052*** 0.196***
19.8% 80.2% 20.9% 79.1%
Ireland
0.276 0.107*** 0.169*** 0.193 0.075*** 0.118***
38.7% 61.3% 39.0% 61.0%
Italy
0.168 0.037*** 0.131*** 0.151 0.020*** 0.131***
22.2% 77.8% 13.2% 86.8%
The NLD
0.239 0.067*** 0.173*** 0.213 0.091*** 0.122***
27.9% 72.1% 42.6% 57.4%
Spain
0.258 0.064*** 0.194*** 0.253 0.035*** 0.218***
24.9% 75.1% 13.8% 86.2%
UK
0.365 0.171*** 0.194*** 0.298 0.101*** 0.197***
46.8% 53.2% 33.8% 66.2%
***Statistically significant at 1% level
1
The 3
rd
/6
th
columns give the explained part: )(
fmm
OLS
xx −
β
and
the 4
th
/7
th
columns give the residual the
part:
(
)
(
)
f
OLS
m
OLS
ff
OLS
m
OLS
x
ββαα
−+−
Source: European Community Household Panel (ECHP)
In 1994, the unadjusted gender pay gap is higher than 35% in the UK, between 20-30% in the
Netherlands, Spain, Germany and Ireland, and bellow the 20% in Denmark, Belgium and
Italy. In 2001, the UK still records the highest gender differential with around 30% and the
Netherlands, Spain and Germany a gap between 20-30%. Ireland has fallen bellow the 20%
and Belgium, Italy and Denmark remain below 20%.
These results confirm that gender pay differential is higher in more liberal economies and in
economies providing lower supports of female employment (UK, Ireland, Germany and
Spain). We note that although Italy and Spain are both Mediterranean welfare models, Italy
18
has a higher level of public supports of female employment than Spain and records a lower
gender pay differential.
Moreover, the results indicate that the unadjusted gender pay gap has slightly decreased in all
country with the exception of Denmark and that the explained part has diminished in all
countries, except in Denmark and the Netherlands.
We have analysed the effect of changes in observed individual characteristics on the evolution
of the pay gap over the period 1994-2001. Comparing the results of the Oaxaca
decomposition obtained by using the IV/GLS estimate estimators
10
with the individual
characteristics of the year 1994 or 2001, we see that the convergence of the gender pay gap
goes with a decrease of gender differences in productivity characteristics in all countries. This
also indicates that the increase in the Danish pay gap is not due to a deterioration of female
characteristics (see also Rosholm and Smith, 1996). The effect of the improvement of the
Irish female is noteworthy (see also Barrett et al.,2000).
Table 3: Panel data decomposition, Oaxaca (1973), 1994 and 2001
1994 2001
Gap Explained
1
Gap Explained
1
Belgium
0.158 0.043*** 0.134 0.023***
Denmark
0.137 0.087*** 0.157 0.080***
Germany
0.269 0.104*** 0.248 0.083***
Ireland
0.276 0.134*** 0.193 -0.016***
10
The endogenous time-invariant variable is the level of education, the endogenous time-variant variable is the
experience (level and square) and the instruments are the individual means over time of the time-varying
regressors (X
i.
) and the deviation from the mean transformation of these time-varying regressors (X
it
-X
.
)
19
Italy
0.168 0.064*** 0.151 0.027***
The Netherlands
0.239 0.106*** 0.213 0.101***
Spain
0.258 0.097*** 0.253 0.052***
UK
0.365 0.191*** 0.298 0.171***
***Statistically significant at 1% level
1
The 3
rd
/5
th
columns give the explained part: )(
/
fm
GLSIV
xx −
β
with characteristics of 1994 and 2001
respectively.
Source: European Community Household Panel (ECHP)
4.3
The effects of accounting for individual heterogeneity on the adjusted gender pay gap
We now turn to the impact of individual heterogeneity on the adjusted gender pay gap. Let us
compare the results of the adjusted gender pay gap obtained with OLS, GLS and IV/GLS
estimations on the pooled sample.
20
Table 3: Adjusted gender pay differentials, gender coefficients (std. error in parentheses)
OLS
a
GLS IV/GLS
b
IV/GLS
c
Belgium
-0.142 -0.136 -0.139 -0.132
(0.007)*** (0.012)*** (0.019)*** (0.018)***
Denmark
-0.111 -0.103 -0.044 -0.051
(0.006)*** (0.012)*** (0.022)** (0.022)**
Germany
-0.208 -0.2 -0.153 -0.164
(0.006)*** (0.011)*** (0.015)*** (0.015)***
Ireland
-0.198 -0.203 -0.166 -0.169
(0.009)*** (0.015)*** (0.030)*** (0.028)***
Italy
-0.151 -0.144 -0.131 -0.123
(0.005)*** (0.008)*** (0.011)*** (0.011)***
The Netherlands
-0.148 -0.158 -0.111 -0.114
(0.006)*** (0.010)*** (0.041)*** -0.116
Spain
-0.199 -0.191 -0.173 -0.148
(0.006)*** (0.010)*** (0.016)*** (0.016)***
United-Kingdom
-0.204 -0.237 -0.034 -0.12
(0.006)*** (0.012)*** -0.032 (0.027)***
a
Standard errors of the OLS regressions are inconsistent since they do not account for variance components.
b
X
1
= (experience (level, squared), dummy variables for the occupation, the firm size and the employment
contract)
c
X
1
= (dummy variables for occupation, the firm size and the employment contract)
***/**/*: Statistically significant at 1%/ 5%/ 1%
Source: European Community Household Panel (ECHP)
21
These estimators of the adjusted gender wage gap are significant at 1% level in all models
11
and indicate that, for example, according to OLS estimations on the male-female pooled
sample, Belgian women earn, ceteris paribus, 14.2% less than Belgian men. The estimated
adjusted gender pay gap by OLS is higher in UK, Germany, Spain and Ireland and lower in
Denmark, Belgium and the Netherlands. Our results for the GLS estimators show a decrease
of the male-female wage differential for all countries except Ireland, the Netherlands and UK.
Finally, by adjusting for heterogeneity, i.e. using either education or education and experience
as endogenous variables, the gender difference decreases in all countries in comparison with
the OLS and GLS estimations. For instance, taking education and experience as endogenous
produce a reduction of 7% in Belgium, of 14% in Ireland, between about 20-30% Germany,
Italy, the Netherlands and Spain and of 41.2% in UK and 54.1% in Denmark. Therefore, we
first observe that even when we control for unobserved heterogeneity the gender pay gap
remains in all countries. Furthermore, individual heterogeneity seems to pay a significant role
in shaping the gap and taking this element into account produces a different raking of
countries: Denmark, the Netherlands and UK are now the three countries with the lowest
gender pay gap. Belgium looses its second place and gets the fifth and finally Ireland – and
not the UK - records the highest pay differential.
What can explain this shift?
We see that the results are influenced by the choice of the endogenous variables. In this paper,
we have chosen to take the number of years of schooling as well as that of experience as
endogenous variables. Controlling for unobserved heterogeneity with the Hausman-Taylor
(1981) procedure avoids the endogeneity bias. In particular, the returns on education and
experience increase for both men and women with respect to the OLS estimators.
11
exceptions are coefficients of UK (IV/GLS
b
) and of NLD (IV/GLS
c
)
22
We note that the extent of the reduction of the adjusted pay gap is close to the ranking of
countries according to their gender differences in years of schooling and to a lesser extend to
their gender differences in years of experience.
Table 4: Reduction of the adjusted gender pay gap and gender differences in education and
experience
% Decrease of the
adjusted pay gap
1
Rank
Diff. in years of
schooling
Rank
Diff. in years of
experience
Rank
Belgium
7.0% 8 -0.664 8 3.191 3
Denmark
54.1% 1 0.068 2 1.153 6
Germany
21.2% 5 0.298 6 0.460 7
Ireland
14.6% 7 -0.263 5 1.984 5
Italy
18.5% 6 -0.198 4 3.135 4
The Netherlands
23.0% 4 -0.080 3 3.419 2
Spain
25.6% 3 -0.635 7 5.057 1
United-Kingdom
41.2% 2 0.961 1 -0.412 8
1
X
1
= (dummy variables for occupation, the firm size and the employment contract)
Source: European Community Household Panel (ECHP)
The table 4 shows that the impact on the adjusted gender pay gap of taking education and
experience as endogenous variable increases with the level of differences in education
between men and women for all country, except for Spain. In this latter country gender
differences in experience seem to play a more important role than the differences in
education. This suggests that the fewer women are relatively endowed for these characteristics
(or the more they are endowed) the more (the fewer) unobserved characteristics play a
negative role on the gender pay gap.
23
5 Conclusion
Using the European Community Household Panel (ECHP), this paper analyses the evolution
of the gender pay gap of eight European countries between 1994 and 2001. Wage equations,
(un)adjusted gender pay differentials are estimated by cross-section and panel data
techniques.
Our results regarding the analysis over time indicate that the general trend of the explained
and residual part is reduction with some exceptions. We have analysed the impact of changes
in individual characteristics between men and women on the evolution of the pay gap. We
have chosen to use the panel data dimension of the database in order to control for both
observed and unobserved (constant over time) heterogeneity. Using these estimations, we
have compared the explained parts between 1994 and 2001 taking estimators constant. The
results confirm that a part of the reduction of the unadjusted pay gap is due to improvements
in female characteristics over time for all countries.
Finally, we have estimated the impact of controlling for individual heterogeneity on the
gender pay gap. As the other national studies, our results indicate that taking this element into
account reduces the adjusted pay gap significantly: from 7% in Belgium to 41% and 54% in
UK and Denmark respectively. Therefore, the original ranking of the countries is modified.
Moreover, adjusting for individual heterogeneity produce an increase of the rates of the return
of experience and education as well as a reduction of penalty due to low skilled occupations,
fixed employment contract and relatively small enterprise size for both men and women.
These results suggest that unobserved heterogeneity (as motivation, ability, etc.) are in
disfavour of women. Therefore, controlling for this element reduces the residual part of the
pay gap. Furthermore, we observe a link between the level of reduction of the adjusted pay
gap and the ranking of countries according to their gender differences in education and
24
experience. These observed characteristics are actually correlated with the unobserved
heterogeneity, which biases the OLS estimates.
Finally, our results show that the gender pay gap remains even after controlling for productive
individual characteristics and unobserved individual effect. A direct policy recommendation
would be to tackle gender differences in productive characteristics. This would reduce the gap
due to differences in observed characteristics as well as that relative to the unobserved effect
as far as both effects are correlated.
25
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29
Appendix 1: Descriptive statistics
Table 1A: Descriptive statistics wave 1
BE DK GE IRL IT NLD SP UK
Hourly wage
Total 11.193 16.287 11.340 8.315 7.004 14.219 5.776 10.473
Male 11.964 17.158 12.360 9.330 7.393 15.582 6.199 12.246
Female 10.031 14.688 9.481 6.754 6.214 11.523 4.773 8.341
Male
Total 0.601 0.647 0.646 0.606 0.670 0.664 0.704 0.546
Male 1.000 1.000 1.000 1.000 1.000 1.000 0.000 1.000
Female 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000
Female
Total 0.399 0.353 0.354 0.394 0.330 0.336 0.296 0.454
Male 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Female 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Experience
Total 16.701 20.295 19.867 15.965 17.299 18.560 19.138 18.694
Male 17.895 19.320 20.086 17.109 18.545 19.623 20.477 18.279
Female 14.902 20.826 19.466 14.207 14.770 16.459 15.959 19.194
Occupation 1
Total 0.048 0.070 0.038 0.071 0.026 0.126 0.027 0.161
Male 0.064 0.092 0.044 0.097 0.036 0.167 0.036 0.196
Female 0.024 0.031 0.027 0.030 0.005 0.046 0.005 0.120
Occupation 2
Total 0.131 0.109 0.063 0.076 0.021 0.109 0.054 0.103
Male 0.116 0.127 0.074 0.083 0.022 0.123 0.043 0.142
Female 0.154 0.077 0.043 0.065 0.021 0.080 0.079 0.057
Occupation 3
Total 0.117 0.182 0.157 0.099 0.075 0.220 0.105 0.107
Male 0.105 0.154 0.125 0.092 0.083 0.192 0.104 0.105
Female 0.136 0.233 0.215 0.108 0.057 0.274 0.107 0.110
Occupation 4
Total 0.255 0.150 0.129 0.150 0.243 0.161 0.125 0.197
Male 0.185 0.050 0.066 0.072 0.180 0.094 0.091 0.082
Female 0.362 0.333 0.242 0.271 0.371 0.294 0.206 0.335
Occupation 5
Total 0.079 0.073 0.101 0.150 0.080 0.101 0.138 0.126
Male 0.042 0.043 0.036 0.083 0.064 0.051 0.099 0.053
Female 0.134 0.127 0.219 0.254 0.112 0.201 0.231 0.214
Occupation 7
Total 0.107 0.202 0.298 0.175 0.307 0.145 0.266 0.134
Male 0.159 0.296 0.416 0.268 0.357 0.208 0.340 0.227
Female 0.028 0.031 0.084 0.032 0.206 0.020 0.090 0.022
Occupation 8
Total 0.101 0.126 0.138 0.160 0.069 0.090 0.112 0.110
Male 0.147 0.149 0.168 0.172 0.092 0.116 0.141 0.137
Female 0.032 0.083 0.084 0.142 0.022 0.039 0.044 0.078
Occupation 9
Total 0.161 0.088 0.076 0.120 0.179 0.047 0.173 0.060
Male
0.182 0.089 0.071 0.099 0.166 0.048 0.146 0.058
Female 0.130 0.085 0.086 0.133 0.205 0.046 0.237 0.063
Fixed contract
Total 0.024 0.013 0.027 0.015 0.030 0.009 0.142 0.006
Male 0.017 0.015 0.027 0.015 0.025 0.006 0.141 0.009
Female 0.034 0.010 0.026 0.017 0.042 0.015 0.144 0.002
Medium size
Total 0.209 0.213 0.192 0.210 0.160 0.241 0.129 0.122
enterprise
Male 0.198 0.219 0.217 0.205 0.172 0.244 0.131 0.126
Female 0.226 0.204 0.146 0.218 0.136 0.234 0.124 0.117
Large size
Total 0.513 0.463 0.549 0.412 0.372 0.558 0.417 0.562
enterprise
Male 0.574 0.480 0.558 0.428 0.380 0.582 0.432 0.608
Female 0.422 0.432 0.532 0.386 0.357 0.510 0.384 0.506
Years of
Total 12.140 11.985 11.886 11.374 10.406 9.635 10.672 11.762
schooling
Male 11.911 11.978 12.017 11.339 10.361 9.559 10.542 12.311
Female 12.486 11.996 11.648 11.429 10.498 9.786 10.981 11.101
Married
Total 0.725 0.547 0.694 0.538 0.642 0.668 0.635 0.609
Male 0.741 0.561 0.733 0.572 0.678 0.730 0.693 0.619
Female 0.702 0.522 0.623 0.485 0.568 0.543 0.497 0.598
Legislators and Senior Officials (isco 1), Professionals (isco 2), Technicians and Associate Professionals (isco 3), Clerks (isco 4), Service
Workers and Shop and Market Sales Workers (isco 5), Craft and Related Workers (isco 7), Plant and Machine Operators and Assemblers
(isco 8), Elementary Occupation (isco 9).
30
Table 1B: Descriptive statistics wave 8
BE DK GE IRL IT NLD SP UK
Hourly wage
Total 13.266 19.25235 12.58062 10.76393 7.260693 14.95621 6.473675 13.14111
Male 14.09 20.34956 13.74723 11.71091 7.659459 16.17508 7.023735 14.77496
Female 12.077 17.1457 10.65236 9.492358 6.461286 12.66908 5.384327 10.90408
Male
Total 0.589 0.6575342 0.6230508 0.573156 0.6671883 0.6523472 0.6644764 0.5779136
Male 1 1 1 1 1 1 1 1
Female 0 0 0 0 0 0 0 0
Female
Total 0.411 0.3424658 0.3769492 0.426844 0.3328117 0.3476528 0.3355236 0.4220864
Male 0 0 0 0 0 0 0 0
Female 1 1 1 1 1 1 1 1
Experience
Total 18.89533 22.43053 21.22441 18.29383 17.80856 20.76971 18.41807 20.05412
Male 20.26552 22.80506 21.23939 18.62025 18.77248 22.15682 20.11681 20.03021
Female 16.92822 21.71143 21.19964 17.85552 15.87618 18.16688 15.05386 20.08687
Occupation 1
Total 0.0863821 0.0831703 0.0464407 0.1039903 0.0182577 0.1527901 0.0291581 0.1959843
Male 0.1155172 0.1011905 0.0484222 0.1139241 0.0265833 0.1860149 0.0370828 0.2107251
Female 0.0445545 0.0485714 0.0431655 0.0906516 0.0015674 0.0904459 0.0134639 0.1758014
Occupation 2
Total 0.1300813 0.148728 0.0966102 0.0725514 0.0292123 0.1452613 0.0640657 0.1104321
Male 0.112069 0.1800595 0.1196953 0.07173 0.0273651 0.1588595 0.0519159 0.1314199
Female 0.1559406 0.0885714 0.0584532 0.0736544 0.0329154 0.1197452 0.0881273 0.081696
Occupation 3
Total 0.1493902 0.2103718 0.1786441 0.1064087 0.1043297 0.2280779 0.1523614 0.11567
Male 0.1310345 0.1770833 0.1305767 0.092827 0.1164973 0.2050238 0.1341162 0.108006
Female 0.1757426 0.2742857 0.2580935 0.1246459 0.0799373 0.2713376 0.1884945 0.1261634
Occupation 4
Total 0.2408537 0.146771 0.1376271 0.1451028 0.2217006 0.1474756 0.0850103 0.1846355
Male 0.1724138 0.0654762 0.0707291 0.0548523 0.1485536 0.0855397 0.0512979 0.0861027
Female 0.3391089 0.3028571 0.2482014 0.266289 0.3683386 0.2636943 0.1517748 0.319545
Occupation 5
Total 0.0945122 0.053816 0.0867797 0.1499395 0.1225874 0.0890168 0.1470226 0.1025753
Male 0.0482759 0.03125 0.0272035 0.0632911 0.0914777 0.0529532 0.0945612 0.0468278
Female 0.1608911 0.0971429 0.1852518 0.266289 0.184953 0.1566879 0.250918 0.1789038
Occupation 7
Total 0.1189024 0.1771037 0.2620339 0.1765417 0.295253 0.108946 0.250924 0.1383675
Male 0.187931 0.2544643 0.3770403 0.2890295 0.3580923 0.155465 0.3362176 0.2228097
Female 0.019802 0.0285714 0.0719424 0.0254958 0.169279 0.0216561 0.0820073 0.0227508
Occupation 8
Total 0.0873984 0.1076321 0.1240678 0.171705 0.124674 0.085031 0.1281314 0.1030118
Male 0.1310345 0.1205357 0.1594124 0.2088608 0.1540266 0.1133741 0.1668727 0.1397281
Female 0.0247525 0.0828571 0.0656475 0.121813 0.0658307 0.0318471 0.0514076 0.0527404
Occupation 9
Total 0.0924797 0.072407 0.0677966 0.0737606 0.0839854 0.0434012 0.1433265 0.0493234
Male
0.1017241 0.0699405 0.0669206 0.1054852 0.0774042 0.0427699 0.1279357 0.0543807
Female 0.0792079 0.0771429 0.0692446 0.0311615 0.0971787 0.044586 0.1738066 0.0423992
Fixed contract
Total 0.0447154 0.0234834 0.0477966 0.0241838 0.0636411 0.0115146 0.2525667 0.0279354
Male 0.0327586 0.0238095 0.0462459 0.0189873 0.0586396 0.0101833 0.2620519 0.0287009
Female 0.0618812 0.0228571 0.0503597 0.0311615 0.0736677 0.0140127 0.2337821 0.0268873
Medium size
Total 0.2154472 0.2152642 0.2033898 0.1970979 0.124674 0.2360496 0.1531828 0.1065037
enterprise
Male 0.2206897 0.2261905 0.2208923 0.1919831 0.1282252 0.2410048 0.1526576 0.1110272
Female 0.2079208 0.1942857 0.1744604 0.203966 0.1175549 0.2267516 0.1542228 0.1003102
Large size
Total 0.5315041 0.4481409 0.5349153 0.4232164 0.3495044 0.5606732 0.3560575 0.5608904
enterprise
Male 0.5706897 0.4553571 0.5565832 0.443038 0.370602 0.5756959 0.3831891 0.5129266
Female 0.4752475 0.4342857 0.4991007 0.3966006 0.30721 0.5324841 0.3023256 0.5959215
Years of
Total 12.65142 11.7407 11.78949 11.13301 10.64424 9.039415 10.97084 11.90223
schooling
Male 12.31897 11.79911 11.87758 10.89241 10.54965 9.039375 10.68789 12.28701
Female 13.12871 11.62857 11.64388 11.45609 10.83386 9.03949 11.53121 11.37539
Married
Total 0.675813 0.6183953 0.6583051 0.5356711 0.6442358 0.6532329 0.6049281 0.5918813
Male 0.7034483 0.6205357 0.6974973 0.5654008 0.6778733 0.7053632 0.6532756 0.6193353
Female 0.6361386 0.6142857 0.5935252 0.4957507 0.5768025 0.555414 0.5091799 0.5542916
Legislators and Senior Officials (isco 1), Professionals (isco 2), Technicians and Associate Professionals (isco 3), Clerks (isco 4),
Service Workers and Shop and Market Sales Workers (isco 5), Craft and Related Workers (isco 7), Plant and Machine Operators and
Assemblers (isco 8), Elementary Occupation (isco 9).
31
Appendix 2: Wage equations
Table 2A: Wage equation OLS (male-female pooled sample)
BE DK GE IRL IT NLD SP UK
Potential experience
0.023 0.033 0.029 0.025 0.023 0.025 0.023 0.023
(0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)***
Potential
-0.034 -0.059 -0.052 -0.041 -0.038 -0.042 -0.033 -0.047
experience^2/100
(0.003)*** (0.002)*** (0.002)*** (0.003)*** (0.002)*** (0.002)*** (0.002)*** (0.002)***
Occupation 1
0.258 0.289 0.205 0.245 0.357 0.205 0.476 0.312
(0.014)*** (0.013)*** (0.014)*** (0.018)*** (0.015)*** (0.010)*** (0.017)*** (0.009)***
Occupation 2
0.182 0.285 0.239 0.252 0.063 0.273 0.284 0.339
(0.011)*** (0.011)*** (0.012)*** (0.018)*** (0.014)*** (0.010)*** (0.013)*** (0.011)***
Occupation 3
0.064 0.118 0.034 0.179 -0.021 0.105 0.061 0.201
(0.010)*** (0.009)*** (0.009)*** (0.016)*** (0.008)*** (0.009)*** (0.010)*** (0.010)***
Occupation 5
-0.162 -0.103 -0.221 -0.24 -0.165 -0.149 -0.227 -0.305
(0.012)*** (0.013)*** (0.011)*** (0.014)*** (0.008)*** (0.011)*** (0.010)*** (0.010)***
Occupation 7
-0.14 -0.058 -0.113 -0.025 -0.196 -0.134 -0.176 -0.014
(0.012)*** (0.010)*** (0.009)*** (0.015)* (0.006)*** (0.010)*** (0.009)*** -0.011
Occupation 8
-0.115 -0.12 -0.11 -0.093 -0.146 -0.129 -0.179 -0.141
(0.013)*** (0.011)*** (0.010)*** (0.015)*** (0.008)*** (0.012)*** (0.011)*** (0.011)***
Occupation 9
-0.192 -0.098 -0.192 -0.263 -0.248 -0.205 -0.321 -0.262
(0.012)*** (0.012)*** (0.012)*** (0.016)*** (0.008)*** (0.014)*** (0.010)*** (0.013)***
Fixed contract
-0.071 -0.163 -0.139 -0.03 -0.062 -0.195 -0.128 -0.006
(0.014)*** (0.016)*** (0.011)*** -0.021 (0.009)*** (0.017)*** (0.006)*** -0.018
Medium size
0.131 0.102 0.376 0.294 0.136 0.086 0.264 0.159
enterprise
(0.009)*** (0.007)*** (0.007)*** (0.011)*** (0.006)*** (0.008)*** (0.008)*** (0.009)***
Large size
0.137 0.076 0.181 0.219 0.112 0.097 0.209 0.172
enterprise
(0.007)*** (0.006)*** (0.006)*** (0.009)*** (0.005)*** (0.007)*** (0.005)*** (0.006)***
Married
0.021 0.032 0.063 0.112 0.048 0.097 0.059 0.058
(0.007)*** (0.006)*** (0.006)*** (0.010)*** (0.005)*** (0.006)*** (0.006)*** (0.006)***
Female
-0.142 -0.111 -0.208 -0.198 -0.151 -0.148 -0.199 -0.204
(0.007)*** (0.006)*** (0.006)*** (0.009)*** (0.005)*** (0.006)*** (0.006)*** (0.006)***
Years of
0.033 0.023 0.018 0.034 0.034 0.012 0.032 0.028
schooling
(0.001)*** (0.001)*** (0.001)*** (0.002)*** (0.001)*** (0.003)*** (0.001)*** (0.001)***
Constant
1.71 2.107 1.77 1.356 1.373 2.057 1.12 1.656
(0.024)*** (0.021)*** (0.020)*** (0.031)*** (0.019)*** (0.027)*** (0.019)*** (0.016)***
Number of observations
9251 9819 26444 9896 19106 20216 20801 19593
Number of individuals
1905 2035 5430 2465 4220 4225 4915 3900
R-squared
0.42 0.44 0.33 0.43 0.39 0.29 0.49 0.44
*significant at 10%, ** significant at 5% and *** significant at1%
Legislators and Senior Officials (isco 1), Professionals (isco 2), Technicians and Associate Professionals (isco 3), Clerks (isco 4),
Service Workers and Shop and Market Sales Workers (isco 5), Craft and Related Workers (isco 7), Plant and Machine Operators and
Assemblers (isco 8), Elementary Occupation (isco 9).
32
Table 2B: Wage equation IV/GLS
1
(male-female pooled sample)
BE DK GE IRL IT NLD SP UK
Potential experience
0.036 0.06 0.05 0.08 0.033 0.05 0.05 0.07
(0.002)*** (0.002)*** (0.001)*** (0.002)*** (0.001)*** (0.002)*** (0.001)*** (0.002)***
Potential
-0.04 -0.10 -0.08 -0.11 -0.048 -0.071 -0.061 -0.083
experience^2/100
(0.004)*** (0.004)*** (0.003)*** (0.006)*** (0.003)*** (0.004)*** (0.003)*** (0.003)***
Occupation 1
0.028 0.14 0.07 0.05 0.066 0.014 0.114 0.062
(0.014)** (0.018)*** (0.013)*** (0.017)*** (0.017)*** -0.011 (0.016)*** (0.009)***
Occupation 2
-0.015 0.13 0.05 0.06 -0.011 0.023 0.077 0.054
-0.012 (0.017)*** (0.013)*** (0.020)*** -0.014 (0.012)* (0.014)*** (0.011)***
Occupation 3
0.003 0.10 0.02 0.06 0.01 0 0.021 0.038
-0.01 (0.014)*** (0.009)** (0.014)*** -0.009 -0.009 (0.009)** (0.009)***
Occupation 5
-0.015 0.02 -0.10 -0.09 -0.013 -0.041 -0.013 -0.103
-0.012 -0.02 (0.012)*** (0.016)*** -0.01 (0.013)*** -0.012 (0.011)***
Occupation 7
0.04 0.06 0.02 0.04 -0.003 -0.027 0.03 0.037
(0.014)*** (0.017)*** -0.01 (0.018)** -0.01 (0.015)* (0.012)*** (0.012)***
Occupation 8
0.071 0.08 0.04 0.03 0.008 -0.066 0.034 0.017
(0.015)*** (0.017)*** (0.012)*** -0.02 -0.011 (0.015)*** (0.012)*** -0.012
Occupation 9
0.055 0.09 -0.02 -0.04 -0.009 -0.026 -0.015 -0.007
(0.014)*** (0.017)*** -0.01 (0.017)** -0.011 -0.016 -0.012 -0.013
Fixed contract
0 -0.08 -0.02 -0.03 -0.003 -0.039 -0.027 -0.031
-0.011 (0.012)*** (0.008)*** (0.017)** -0.007 (0.015)*** (0.005)*** (0.013)**
Medium size
0.051 0.05 0.15 0.13 0.074 0.03 0.082 0.048
enterprise
(0.011)*** (0.010)*** (0.008)*** (0.011)*** (0.007)*** (0.009)*** (0.008)*** (0.008)***
Large size
0.04 0.06 0.09 0.09 0.044 0.032 0.065 0.052
enterprise
(0.009)*** (0.008)*** (0.006)*** (0.009)*** (0.005)*** (0.008)*** (0.005)*** (0.006)***
Married
0.001 0.01 0.01 -0.02 0.032 0.063 0.023 -0.007
-0.011 -0.01 (0.007)** -0.02 (0.007)*** (0.010)*** (0.009)*** -0.008
Female
-0.139 -0.04 -0.15 -0.17 -0.131 -0.111 -0.173 -0.034
(0.019)*** (0.022)** (0.015)*** (0.030)*** (0.011)*** (0.041)*** (0.016)*** -0.032
Years of
0.134 0.14 0.10 0.21 0.133 0.075 0.161 0.258
schooling
(0.005)*** (0.010)*** (0.006)*** (0.010)*** (0.005)*** -0.071 (0.005)*** (0.011)***
Constant
0.278 0.27 0.54 -1.21 0.095 1.205 -0.726 -1.701
(0.072)*** (0.124)** (0.074)*** (0.129)*** -0.061 (0.670)* (0.060)*** (0.136)***
Number of observations
9251 9819 26444 9896 19106 20216 20801 19593
Number of individuals
1905 2035 5430 2465 4220 4225 4915 3900
Hausman test:
2
28
χ
1.55 6.60 24.40 16.98 3.99 1.91 15.50 16.39
Critical value 95 percent
41.33
28 degrees of freedom
1
X
1
= (experience (level, squared), dummy variables for the occupation, the firm size and the employment contract)
*significant at 10%, ** significant at 5% and *** significant at1%
Legislators and Senior Officials (isco 1), Professionals (isco 2), Technicians and Associate Professionals (isco 3), Clerks (isco 4),
Service Workers and Shop and Market Sales Workers (isco 5), Craft and Related Workers (isco 7), Plant and Machine Operators and
Assemblers (isco 8), Elementary Occupation (isco 9).
33
Table 2C: Wage equation IV/GLS
1
(male-female pooled sample)
BE DK GE IRL IT NLD SP UK
Potential experience
0.039 0.07 0.06 0.088 0.034 0.053 0.057 0.074
(0.002)*** (0.002)*** (0.002)*** (0.003)*** (0.001)*** (0.002)*** (0.002)*** (0.002)***
Potential
-0.038 -0.095 -0.084 -0.105 -0.042 -0.071 -0.058 -0.08
experience^2/100
(0.005)*** (0.004)*** (0.003)*** (0.006)*** (0.003)*** (0.004)*** (0.004)*** (0.004)***
Occupation 1
0.028 0.122 0.056 0.041 0.066 0.007 0.109 0.058
(0.014)** (0.018)*** (0.013)*** (0.017)** (0.017)*** -0.011 (0.016)*** (0.009)***
Occupation 2
-0.01 0.125 0.036 0.068 -0.008 0.013 0.078 0.054
-0.012 (0.018)*** (0.013)*** (0.020)*** -0.014 -0.012 (0.014)*** (0.011)***
Occupation 3
0.004 0.086 0.009 0.057 0.01 -0.005 0.014 0.036
-0.01 (0.014)*** -0.009 (0.015)*** -0.009 -0.009 -0.009 (0.009)***
Occupation 5
-0.019 0.02 -0.097 -0.099 -0.016 -0.038 -0.024 -0.111
-0.012 -0.018 (0.012)*** (0.016)*** -0.01 (0.013)*** (0.012)** (0.011)***
Occupation 7
0.03 0.054 0.014 0.026 -0.008 -0.019 0.011 0.029
(0.014)** (0.017)*** -0.011 -0.018 -0.01 -0.015 -0.012 (0.012)**
Occupation 8
0.063 0.07 0.034 0.012 0.001 -0.059 0.012 0.007
(0.015)*** (0.018)*** (0.012)*** -0.016 -0.011 (0.015)*** -0.012 -0.012
Occupation 9
0.047 0.082 -0.019 -0.047 -0.014 -0.018 -0.032 -0.016
(0.014)*** (0.017)*** -0.012 (0.017)*** -0.011 -0.016 (0.012)*** -0.013
Fixed contract
0.002 -0.072 -0.017 -0.028 -0.002 -0.036 -0.021 -0.032
-0.011 (0.013)*** (0.008)** (0.017)* -0.007 (0.015)** (0.005)*** (0.013)**
Medium size
0.05 0.053 0.142 0.128 0.075 0.027 0.081 0.05
enterprise
(0.011)*** (0.010)*** (0.008)*** (0.011)*** (0.007)*** (0.009)*** (0.008)*** (0.008)***
Large size
0.039 0.059 0.086 0.089 0.045 0.029 0.066 0.053
enterprise
(0.009)*** (0.008)*** (0.006)*** (0.009)*** (0.005)*** (0.008)*** (0.005)*** (0.006)***
Married
-0.007 -0.019 -0.013 -0.085 0.018 0.061 -0.017 -0.025
-0.011 (0.008)** (0.007)* (0.016)*** (0.008)** (0.010)*** (0.009)* (0.008)***
Female
-0.132 -0.051 -0.164 -0.169 -0.123 -0.114 -0.148 -0.12
(0.018)*** (0.022)** (0.015)*** (0.028)*** (0.011)*** -0.116 (0.016)*** (0.027)***
Years of
0.127 0.136 0.112 0.176 0.129 -0.003 0.149 0.194
schooling
(0.005)*** (0.010)*** (0.006)*** (0.010)*** (0.005)*** -0.218 (0.005)*** (0.009)***
Constant
0.325 0.204 0.245 -0.901 0.113 1.9 -0.698 -0.97
(0.071)*** (0.122)* (0.078)*** (0.123)*** (0.061)* -2.05 (0.059)*** (0.120)***
Number of observations
9251 9819 26444 9896 19106 20216 20801 19593
Number of individuals
1905 2035 5430 2465 4220 4225 4915 3900
Hausman test:
2
26
χ
0.96 2.26 9.67 10.14 3.21 0.01 7.60 7.84
Critical value 95 percent
38.89
26 degrees of freedom
1
X
1
= (dummy variables for occupation, the firm size and the employment contract)
*significant at 10%, ** significant at 5% and *** significant at1%
Legislators and Senior Officials (isco 1), Professionals (isco 2), Technicians and Associate Professionals (isco 3), Clerks (isco 4),
Service Workers and Shop and Market Sales Workers (isco 5), Craft and Related Workers (isco 7), Plant and Machine Operators and
Assemblers (isco 8), Elementary Occupation (isco 9).