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Applied Economics Letters
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Discriminating factors of women's employment
Angela Cipollone
a b
& Carlo D'Ippoliti
c
a
Department of Economics and Institutions , University of Rome ‘Tor Vergata’ , Rome, Italy
b
Department of Economics and Business Sciences , LUISS Guido Carli , Rome, Italy
c
Department of Social, Economic, Actuarial and Demographic Studies , University of Rome
‘La Sapienza’ , Viale Regina Elena 295, 00161, Rome, Italy
Published online: 01 Jun 2009.
To cite this article: Angela Cipollone & Carlo D'Ippoliti (2010) Discriminating factors of women's employment, Applied
Economics Letters, 17:11, 1055-1062, DOI: 10.1080/00036840902762712
To link to this article: http://dx.doi.org/10.1080/00036840902762712
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Applied Economics Letters, 2010, 17, 1055–1062
Discriminating factors
of women’s employment
Angela Cipollonea,b and Carlo D’Ippolitic,*
a
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Department of Economics and Institutions, University of Rome
‘Tor Vergata’, Rome, Italy
b
Department of Economics and Business Sciences, LUISS Guido Carli,
Rome, Italy
c
Department of Social, Economic, Actuarial and Demographic Studies,
University of Rome ‘La Sapienza’, Viale Regina Elena 295,
00161 Rome, Italy
Italy exhibits a dramatic level of territorial heterogeneity in terms of socioeconomic dynamics and in the economic position of women. We employ
this territorial variance to assess the impact of selected policies and
institutions on men’s and women’s employment using microeconomic
data. Such an analysis provides results partly different from what was
expected on the basis of cross-country aggregate evidence on industrialized
countries. Aggregate growth and tertiarization of the economy are surprisingly found beneficial only to men’s employment, while culture and
discrimination are relevant for women’s. Social Assistance is found highly
significant too, with the provision of services being more beneficial to
women’s employment than monetary transfers.
I. Introduction
An extensive and growing literature exists on
women’s employment in developed countries and in
Europe in particular. We refer to Boeri et al. (2005)
for an international assessment of Italy’s specific
situation, and to Battistoni (2006) for a historic
national perspective.
This article aims at contributing to the debate,
employing the dramatic territorial heterogeneity
exhibited by Italy’s Regions as an internationally
relevant case study.1 We show that territorial differences, in terms of (i) macroeconomic conditions,
(ii) social and cultural environment and (iii) regional
policies and institutions, provide relevant additional
information to explain gender differences in the
labour market.
First, concerning macroeconomic variables, the
impact of tertiarization and Gross Domestic Product
(GDP) growth for the promotion of women’s
employment in Italy was most recently discussed by
Boeri et al. (2005) and Simonazzi (2006). Second, the
impact of discrimination was analysed by Goldin
(2002) and the literature on the New Household
Economics developed after Becker (1965 and 1973),
while Barker and Kuiper (2003) and Fernandez
(2007) estimate the role of cultural constraints and
gender roles.2 Third, Del Boca and Locatelli (2006)
*Corresponding author. E-mail: [email protected]
1
For extensive evidences of socioeconomic differences within Italian regions, see Putnam (1993) and Tabellini (2006).
2
Researches showed that countries with more liberal attitudes toward gender roles, higher work orientation of women and
higher acceptance towards young children’s working mothers, show higher women’s employment rates (compare Reimers,
1985; Vella, 1994; Pencavel, 1998; Antecol, 2000; Fernández et al., 2004; Fernández, 2007).
Applied Economics Letters ISSN 1350–4851 print/ISSN 1466–4291 online ß 2010 Taylor & Francis
http://www.informaworld.com
DOI: 10.1080/00036840902762712
1055
A. Cipollone and C. D’Ippoliti
1056
Table 1. Selection and aggregation of context variables
Variable
Description
Source of data
Index
Tertiarization
Share of employment in the Services
Sector (men and women)
Regional aggregate GDP growth rate,
2-years basis
Per capita public expenditure on Social
Assistance
Number of children hosted in publicfinanced kindergartens, per thousands of
children 0–3 years old
Direct public expenditure as a fraction
of total public expenditure on Social
Assistance
Expenditure for in-kind services over
expenditure for cash transfers
As defined by the Employment
Committee of the European Union,
lastly on 27 June 2007
Share of women’s self-employment as
a fraction of total self-employment
ISTAT
Tertiarization
ISTAT
GDP growth
ISTAT
Social Assistance
Fortunati (2006)
Social Assistance
GDP growth
Social Assistance
expenditure
Kindergarten
Direct social
expenditure
Public services
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Gender segregation
Prominent
employment
Per capita GDP
Infant mortality
Culture
Information
Raw infant mortality rate
Average households’ expenditure on
cultural and entertainment activities as
a fraction of average households’ disposable income
Average households’ expenditure on
newspapers and periodicals as a fraction
of average households’ disposable
income
provide an up to date comparative review of
the impact of social policy on women’s employment
in Europe.3
While most of the existing literature investigates the
disjoint impacts of contextual variables on women’s
employment, we propose an estimate of the simultaneous impact of these factors. Cipollone and D’Ippoliti
(2007) show that the heterogeneity of Italy’s Regions is
particularly suited for such an analysis. They highlight
that standard analyses of men’s and women’s employment, based only on individual and household characteristics, proves inferior to estimates allowing for
context-related dynamics.
Our analysis proceeds as follows. First, we propose
an original way of synthesizing Regions’ heterogeneity, by developing three main macro-indicators. Then,
by means of a multilevel modelling approach, we
explore the joint impact of the macro-indicators and
3
ISTAT
ISTAT
ISTAT (LFS)
Gender
discrimination
ISTAT (LFS)
ISTAT
EUROSTAT
ISTAT
Gender
discrimination
MDI
MDI
MDI
ISTAT
MDI
of several micro-characteristics on the employment of
women and men.
II. Multivariate Synthesis of the
Socio-economic Milieu
We collect several variables related to the socioeconomic context that we expect to affect gender
differentials in the labour market at a regional level.
All the variables are normalized by their national
average, and some are aggregated into homogeneous
indexes through harmonic means. Table 1 reports the
original variables, their description and aggregation.
Following this procedure, we distinguish five main
dimensions of heterogeneity across Regions: genderbased discrimination, the nature and extension of
The role of social policies in facilitating the conciliation of work and family lives was analysed, for example, by Anderson
and Levine (1999), Conelly (1991, 1992), Hofferth and Wissoker (1992), Kimmel (1995) and Powell (1998). Apps and Rees
(2004) note that countries supporting motherhood by means of childcare facilities rather than monetary benefits exhibit both
higher rates of women’s labour supply and higher fertility rates.
Discriminating factors of women’s employment
1057
4
0.2173
0.1836
0.2480
0.2201
0.4172
0.8491
0.8828
0.3451
0.6684
Fig. 1.
Center
Piemonte
Lombardia
Liguria
Marche
Veneto
Trentino Alto Adige
South
Val d'Aosta
Friuli Venezia Giulia
Men
Women
Emilia Romagna
Toscana
Lazio
Umbria
Molise
Abruzzo
70%
60%
50%
40%
30%
20%
10%
0%
Basilicata
90%
80%
Sardegna
publicly-provided Social Assistance, Regional GDP
growth, the extent of the Services Sector and a
Modified Index of Human Development (MHD).4
In order to obtain synthetic variables for the macro
characteristics considered in Table 2, we employ a
Principal-Component Factor Analysis (PCA). PCA is
employed to extract the relevant factors, which are
then rotated using the varimax method. In keeping
with common practice,5 three factors are selected,
which satisfy the following requirements: eigenvalues
larger than unity; individual contribution to the
explanation of the overall variance of the data greater
than 10%; cumulative contribution to the explanation of the total variance of the data greater than
60%. Within each of these factors, the single indicators are weighted according to the proportion of
the cross-regional variance explained by the factor.
The results of the factor analysis are presented in
Table 2. We are able to express 74% of the total
variance of the five variables by means of three
factors, which identify socio-economic sub-domains
with rather straightforward economic interpretation.
The first factor is highly correlated with MHD
(þ0.8) and the discrimination index (0.8). Due to
the specific socio-economic differences among Italy’s
Regions, we are inclined to interpret this factor as an
index of Regional Civic Progress (henceforth RCP).
The second factor is negatively correlated with the
public provision of Social Assistance (0.8) and
positively with the extension of the Service Sector
(þ0.7). A plausible interpretation of this factor is the
extent of the Private Provision of Services to households (henceforth PPS). Finally, the third factor is
highly correlated with regional economic growth
(þ0.9) and mildly correlated with MHD (þ0.3),
possibly due to the level of income per capita being
Uniqueness
0.3302
Sicilia
0.8097
0.8399
Factor 3
Puglia
MHD
Gender discrimination
Social Assistance
GDP growth
Tertiarization
Factor 2
Campania
Factor 1
Italy
Variable
Calabria
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Table 2. Principal components factor analysis: rotated factors loadings
North
Employment rates by gender and region, year 2004
one of the components of the latter. Hence, this
factor can be deemed a measure of Regional
Macroeconomic Conditions (henceforth RMC).
At the aggregate level, evidence of the relevance of
the factors in shaping gender differentials in the
labour market is mixed, as shown in Figs 1 to 4.
III. Multilevel Analysis of Employment
Determinants
We estimate individual probabilities of being employed
by means of a multilevel modelling approach, which
allows clustering observations into homogenous
groups, defined both by micro and macro characteristics. Our sample contains 8716 individuals (first level
units), of which 4476 women. The sample contains all
the 20 Italian regions (second level units), whose impact
This last index is different from the Human Development Index (HDI) in two respects: on one side aggregation is obtained
by harmonic instead of arithmetic mean, in order to prevent Regions with high unbalances in the component variables from
obtaining extreme values of the index, and, in turn, to rank higher those Regions exhibiting a more equilibrated development
of all the selected variables (Casadio and Palazzi, 2004). On the other side, the variables chosen for the construction of the
Modified Human Development Index (MDI) are slightly different from those of the standard HDI in order to better catch
relevant differences among Italy’s Regions, and to avoid the inclusion of some variables that in our framework have instead
been adopted as micro determinants in the probability of employment.
5
Nardo et al. (2005), Nicoletti et al. (1999), Kline (1994).
A. Cipollone and C. D’Ippoliti
1058
40%
40%
35%
35%
30%
30%
25%
25%
20%
20%
15%
15%
10%
−2.5
10%
−2.5
−2
−1.5
−1
−0.5
0
0.5
1
1.5
2
2.5
Regional civic development
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Fig. 2. Correlation between gender employment gap and
regional civic development
40%
35%
30%
25%
20%
15%
10%
−3.5
−2.5
−1.5
−0.5
0.5
1.5
2.5
Private provision of services
Fig. 3. Correlation between gender employment gap and
private provision of services
is described by means of the three factors derived in
paragraph 2: RCP, PPS and RMC.6
For the first-level, we select a number of family
variables: marital status, value of the real assets
(in logarithmic form), presence in the household of
at least one child below the age of 3, or of at least
an elderly person above the age of 70; context variables proxied by the dimension of the urban agglomeration; and individual variables: age, educational
attainment, and gender.
6
−1.5
−0.5
0.5
1.5
2.5
Regional macroeconomic condition
Fig. 4. Correlation between gender employment gap and
regional macroeconomic conditions
We implement Generalized Linear Latent Models
to estimate a two-level Random-Intercept Probit
model (RI) and a two-level Random-Coefficient
Probit model (RC), taking into account the nesting
of individuals in their Region of residence. All the
models are estimated for both women and men
together (pooled model), and then for women and
men separately.
The RI model assumes that the three macroindexes affect region-specific intercepts: in other
words, the factors are supposed to directly affect
individuals’ probability of being employed, uniformly
within each Region but differently across Regions.
Results are reported on the left-hand side of Table 3,
under the headings ‘Random-Intercept Models’.
For all the estimations it seems that urban
dimension does not play a statistically significant
role on the probability of being employed, while
individual characteristics are always significant at the
1% significance level. Mixed evidence is found with
respect to household variables.7 Ceteris paribus,
women exhibit an unexplained lower probability of
being employed (114%), as it emerges from the
pooled estimation.
Married women exhibit a probability of being
employed by 44% smaller than unmarried women.
It is noteworthy that for men, marriage is instead
a significantly positive determinant of employment,
and that this inversion of sign is only detected when
We used the GLAMM module for STATA 9 (compare Rabe-Hecketh and Skrondal, 2005).
We also estimated a fixed-effect probit model including the same first-level variables: we find that under the multilevel
model, the coefficients of individual and household variables increase in absolute magnitude, and some in statistical
significance. In particular, the impact of family-related variables strengthens for both women and men, enhancing the
estimated impact of traditional gender roles and sexual division of labour on the probability of being employed. Further
results are available from the authors upon request.
7
0.0169 (0.0044)***
0.2366 (0.0203)***
0.010 (0.0143)
0.0390 (0.0138)***
4240
20
2981899
0.0668 (0.0111)**
0.1653 (0.0093)***
0.0079 (0.0147)
0.0050 (0.0107)
8716
20
12226610
(0.0699)***
(0.0897)***
(0.1138)***
(0.0162)***
(0.1527)
(0.1392)
(0.0834)**
(0.1379)
(0.0468)***
(0.0006)***
(0.2287)***
(0.2085)***
(0.2115)***
(0.1842)***
(0.0532)***
(0.0876)
(0.0639)***
(0.0157)
(0.0805)
(0.0618)
(0.0615)
(0.1124)
(0.0253)***
(0.0003)
(0.2743)***
(0.3147)***
(0.3276)***
(0.3219)***
0.058 (0.0137)***
0.0567 (0.0085)***
0.0082 (0.0111)
4476
20
7792817
0.0429 (0.004)***
0.4425
0.0526
0.4137
0.0211
0.0986
0.0946
0.0252
0.1137
0.1998
0.0024
1.1061
1.3813
1.8446
2.2271
(0.0595)***
(0.0697)
(0.0718)***
(0.0129)**
(0.0532)
(0.0597)
(0.0628)
(0.1026)
(0.0225)***
(0.0003)***
(0.1899)***
(0.1942)***
(0.1931)***
(0.2574)***
0.1114 (0.0136)***
0.0538 (0.0122)***
0.0291 (0.014)**
8716
20
12312747
0.6506 (0.0898)***
0.3457 (0.0462)***
0.1837
0.0356
0.4430
0.0257
0.0496
0.0446
0.0707
0.0601
0.2607
0.0031
0.8243
1.1209
1.5099
1.8095
0.6021
0.2796
0.3606
0.0577
0.0258
0.0244
0.1682
0.0654
0.3787
0.0046
0.6424
1.1040
1.2300
0.8821
1.1382 (0.0756)***
0.1767 (0.0611)***
0.0590 (0.0747)
0.4276 (0.0767)***
0.0196 (0.0123)
0.0507 (0.0573)
0.0869 (0.0748)
0.0909 (0.0747)
0.0512 (0.1031)
0.2535 (0.0236)***
0.0030 (0.0003)***
0.8223 (0.1915)***
1.0944 (0.2034)***
1.4815 (0.2055)***
1.7637 (0.2487)***
Regional level
Notes: Robust SEs in brackets. Coefficients in bold fonts denote 2nd-level coefficients.
***, ** and * denote significance at the 1, 5 and 10% levels, respectively.
Women
Regional Int.
Married
Old-aged
RCP
PPS
RMC
Level 1 units
Level 2 units
Log-likelihood
Women
Married
Children
Old-aged
Wealth
Small town
Town
City
Big city
Age
Age2
Elementary
Lower sec.
Higher sec.
Tertiary
Pooled
Men
Pooled
Women
RC model
RI model
Table 3. Determinants of employment status: two-level probit estimation (marginal effects)
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(0.0154)***
(0.1619)
(0.1308)
(0.0833)*
(0.1488)
(0.0556)***
(0.0007)***
(0.2217)***
(0.1997)***
(0.1775)***
(0.1602)***
8.3418 (1.1016)***
0.1988 (0.0838)**
0.0808 (0.0394)**
0.0894 (0.0211)***
0.0603 (0.0265)**
0.0569 (0.0106)***
4240
20
2794929
0.0659
0.0207
0.0445
0.1389
0.0643
0.4173
0.0051
0.9609
1.4453
1.5794
1.2639
0.3755 (0.0712)***
Men
(0.0159)*
(0.0813)
(0.0639)
(0.062)
(0.1079)
(0.0269)***
(0.0003)***
(0.4099)***
(0.4587)***
(0.4703)***
(0.4657)***
5.5201 (0.9014)***
0.276 (0.0343)***
0.0928 (0.0265)***
0.1305 (0.0159)***
0.0586 (0.0124)***
0.0258 (0.0207)
4476
20
7698210
0.0288
0.1069
0.0920
0.0178
0.0743
0.1937
0.0023
1.1947
1.5009
1.9532
2.3658
0.0504 (0.0825)
Women
Discriminating factors of women’s employment
1059
A. Cipollone and C. D’Ippoliti
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1060
running separate regressions, as it cannot be captured
by a pooled regression, even after including a gender
dummy variable. Co-living with an old-aged person
decreases women’s probability of being employed
more than men’s; while the impact of having at least
a child less than 3 years old is significantly positive
for men but puzzlingly it is not significant for women.
Finally, education is found to be the single most
relevant individual determinant of employment, especially for women. Women’s probability of employment, as well as in the pooled estimation, proves
monotonically increasing with educational attainment. By contrast, the impact of education on employment exhibits a peculiar inverse-U shape for men,
as tertiary education is associated with a lower
probability than secondary education.
Concerning the socio-economic environment, PPS
is found to significantly affect only women’s probability of being employed, in a negative direction,
thus suggesting a positive and significant role for the
public provision of social services. RCP exerts
a significantly positive effect in all the sub-samples,
denoting a positive correlation between civic development and employment rates. Differently from what
frequently assumed, favourable macroeconomic conditions (RMC) significantly affect only men’s and the
average probability of being employed (denoted by
the pooled estimation), while the relevant coefficient
is crucially not statistically significant in women’s
estimation.
Finally, Regions’ unobserved heterogeneity terms
are highly significant in all specifications, suggesting
that further relevant Region-specific effects are at
play, which could not be summarized by the three
indexes.
We then estimate a Random-Coefficient model
(right-hand side of Table 3), under the assumption
that the three indexes of regional socio-economic
conditions exert not only a direct Region-specific
effect on individuals’ probabilities of employment,
but also they affect the impact on this probability of
some other (first level) explanatory variable. As a
result, a final compound effect of micro and macro
characteristics on the probability of being employed
emerges, differentiated even across individuals within
the same Region. We selected gender (only for the
pooled estimation); marital status and co-living with
an old-aged person, as the variables whose impact
might be affected by the regional context, in addition
to the Regional intercept.8 A considerable variance
is found in Regions’ unexplained impact, suggesting
that – beyond the variables considered – there is still
variation across Regions in their impact on the
probabilities of employment.
The three indexes maintain their statistical significance. In particular, while it is confirmed that
macroeconomic conditions increase only men’s (and
the average) probability of being employed, it is
confirmed that the private provision of services
significantly affect women’s employment (negatively),
but in this estimation also men’s (positively). The
impact of RCP (index of civic development and of
women’s nondiscrimination) is confirmed to be significantly positive for all sub-samples.
Compared to the RI model, the coefficients of
education improve in magnitude, maintaining the
monotonically increasing pattern for women and the
inverse-U shape for men. Instead, the unexplained
residual impacts of being woman, being married and
co-living with old-aged person, reduce in absolute
values. Evidently, this result does not imply a reduction of the relevance of gender-specific or care-related
hindrances to women’s employment, but rather a
shift from a completely empirical explanation, based
on observed correlations of residuals, to a much more
informative model, which can provide an explanation
for those correlations in terms of policy (PPS), economic (RMC) and social variables (RCD).
IV. Conclusions
Due to historical reasons, Italy exhibits a dramatic
level of territorial heterogeneity both in terms of
socio-economic context and specifically in the economic position of women. This article uses multilevel
modelling to explore the impact of this geographical
variation on women’s employment status, thus establishing some interesting correlation between men’s
and women’s employment and local policies and
institutions. From a methodological point of view,
we find that such an analysis provides results partly
different from what expected on the basis of crosscountry aggregate evidence or pure microeconomic
databases.
8
In other words, the RI model can be considered as a special case of the RC, where the indirect effects are constrained to zero.
In our estimates, interaction effects – i.e. the impact of contextual conditions on the coefficients of the variables denoting
individual characteristics – are significant, especially for women (detailed results are available upon request). Focusing on
women’s sample, RCP emerges as the macro index that most significantly affects the impact of being married and of co-living
with an old-aged person. Interestingly, the extent of private provision of services differently impacts on women’s and men’s
employment: it significantly affects the probability of employment for married women, while it impacts only on men co-living
with an old-aged person.
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Discriminating factors of women’s employment
For the first, we confirm that ‘women are more
than dummies’ (Figart, 2005), in the sense that their
behaviour cannot be simply modelled through the use
of gender-related dummy variables. Indeed, under
gender-specific estimates, a number of household
and context variables exhibit opposite effects between
men and women, in terms of statistical significance
and sign of the coefficients.
Concerning policy implications, our analysis highlights a relevant role for education, even beyond what
usually implied. Crucially, Italy’s experience suggests
that trust cannot be posed on aggregate growth or
the expansion of the Services Sector as automatically
leading to higher women’s employment rates. Rather,
culture (in terms of gender roles) and discrimination
(in terms of barriers to entry to specific professions
and qualifications) play a crucial role, together with
social policy. Given the construction of our PPS
index, it seems not only that the Social Assistance
expenditure (measured by the direct expenditure as a
percentage of total expenditure), but also its composition can positively affect women’s employment, as
the provision of services appears more effective than
monetary subsidies.
Overall, our analysis shows that women’s employment in Italy is more territorially heterogeneous than
men’s, whose behaviour in the labour market is rather
homogenous across cohorts and Regions. Also, more
than men’s, women’s employment is affected by nonmonetary variables; these results highlight the key
role for regional development programs to address
gender gaps and the social inclusion of women in the
labour market, even beyond individual employability
policies. Finally, not only aggregate growth is found
irrelevant to women’s employment, but even the
acceptance of gender horizontal segregation – implicit
in the reliance on a supposedly positive role for the
growth of the Services Sector – is found detrimental
for women’s employment.
Acknowledgements
We wish to thank Anna Conte, Marcella Corsi, John
D. Hey, Federico Lucidi, Furio C. Rosati, Annalisa
Rosselli for fruitful discussions and for their help. All
remaining errors are responsibility of the authors only.
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