Engendering
economic statistics
Women and economics:
household, enterprise and decision-making bodies
Cristina Freguja, Stefania Cardinaleschi, Lucia Coppola, Sara Demofonti
Istat
Global Forum on Gender Statistics, Rome, 10-12 December 2007
Introduction
Economics has traditionally been a male-dominated sphere and the
gender dimension has been absent in economic statistics and
analysis
Lack of: data, standard for surveying, comparable sources…..
...but also “simply” to look at the available data without a gender
perspective preclude to draw up a detailed outline of women’s
contribution to economics
A growing informative demand is emerging in this domain and
consequently gender statistics have to face new challenges
Introduction
Challenges
 Use of a gender perspective in data analysis
 Enrichment of existing data sources with gender specific
information
 Integration of available data sources
 Development of new surveys
Some examples:
 women’s contribution to the household income
 women’s participation in enterprises
 women’s representation in economic decision-making bodies
Household income
Data and Methods
EU-SILC 2004 provides standardized information at individual and household
level about income and living conditions for:
Austria, Belgium, Denmark, Estonia, Spain, Finland, France, Greece,
Ireland, Italy, Luxembourg, Norway, Portugal, and Sweden
We consider:
Married and cohabiting couples, composed by partners aged 25-54 years
Partners earnings
Household income
Research questions
To what extent dual-earner model is spread in the EU
countries?
In dual-earner couples, women contribute as much as their
partners to the household economic needs?
Which individual and household characteristics are more likely
to be associated with dual-earner couples?
Among dual-earner couples, which are the characteristics
associated with women’s levels of contribution?
Household income
Data and Methods
We define and compare:
1) man sole provider couples VS. dual-earner couples
2) man main provider (woman earns less than 40% of the couple
earnings);
equal providers (woman earns between the 40% and the 60%
of the couple earnings);
woman main provider (woman earns more than 60% of the
couple earnings).
Household income
An Overview
Distribution of dual earner couples by conutry
100%
90%
80%
70%
60%
Dual-Earner couple
50%
Man Sole Provider
40%
30%
20%
10%
0%
AT
BE
DK
EE
ES
FI
FR
GR
IE
IT
LU
NO
PT
SE
EU
Household income
Lowest % of man sole provider:
Highest % of man sole provider:
Denmark, Finland, Norway, Sweden
Spain, Greece…Luxembourg, Italy, Ireland
(lower than 10 %)
(higher than 30%)
Distribution of dual earner couples by conutry
100%
90%
80%
70%
60%
Dual-Earner couple
50%
Man Sole Provider
40%
30%
20%
10%
0%
AT
BE
DK
EE
ES
FI
FR
GR
IE
IT
LU
NO
PT
SE
EU
Household income
Man Sole Provider vs. Dual Earner Couples
ANALYSES
FINDINGS
Logistic regression
the dual-earner model is more likely
Household and individual characteristics:
to be associated with:
highly educated women
women more educated than their
partners
cohabiting couples
without children in pre-scholar age
medium high levels of household
income
partners age and age difference
partners educational level and
educational level difference
type of union (i.e. cohabitation or
marriage) number and age of children
economic level of the household
When comparing the association between household and individual characteristics
and the dual-earner model, the North-South difference noted in the distribution of
dual-earner couples among EU countries disappears
Household income
An Overview
DUAL EARNER COUPLES: women's contribution to couple earnings by country
100%
90%
80%
70%
60%
Woman main provider
50%
Equal providers
Man main provider
40%
30%
20%
10%
0%
AT
BE
DK
EE
ES
FI
FR
GR
IE
IT
LU
NO
PT
SE
EU
Household income
Woman main provider model:
represents less than 16% of the
couples in all countries
Man main provider model:
represents the more frequent
strategy in most of the countries
DUAL EARNER COUPLES: women's contribution to couple earnings by country
100%
90%
80%
70%
60%
Woman main provider
50%
Equal providers
Man main provider
40%
30%
20%
10%
0%
AT
BE
DK
EE
ES
FI
FR
GR
IE
IT
LU
NO
PT
SE
EU
Household income
Man Main Provider vs.
Equal Providers
the man main provider model is
commonly associated with:
low educated women
women less educated than their
partners
presence of children, especially if
in pre-scholar age
More convenient when the woman has not
invested much in human capital, and her
specialisation in domestic activities
becomes extremely worthy for the
presence of young children.
Woman Main Provider vs.
Equal Providers
the woman main provider model is
commonly associated with:
highly educated women,
women more educated than their
partners
the poorest quintiles of the income
For a woman, becoming the main
provider, might be not only the result
of high investments in human capital,
but also of the need for supporting
household economics.
Enterprises
An appropriate combination of results from different data sources
may provide evidence of relevant gender dynamics
Study by ISTAT on women entrepreneurs (2001)
Data from
1. Industry and Services Census carried out in 1997,
2. Labour Force survey
3. Multipurpose survey on Everyday Life
Enterprises
1. only a quarter of enterprises were managed by women and their
enterprises were generally smaller and concentrated in services to families
2. women-run enterprises were less integrated into the market: they made
fewer agreements, received and requested fewer orders, had lower
average yields and smaller sales-costs ratios
3. 53,3% of entrepreneur or self-employed women worked more than 60
hours per week, when considering work both within and outside the family;
the same percentage for men was 26% (on average, men and women
work respectively 54 and 64 hours)
BUT……
1. smaller proportion of time devoted to the enterprise by women, in spite of a
higher total number of hours worked (58.5% of male entrepreneurs work
46 hours and more per week, while only the 40.6% of female
entrepreneurs do so)
Enterprises
The activities inside and outside home lead to double burden, and the
overload of work prevents female entrepreneurs from dedicating
appropriately to their enterprises
The situation does not seem to show signs of important improvements in
last years
The results from the Labour Cost Survey (LCS) carried out in 2004, that
for the first time collected information on the sex of the entrepreneur,
confirm that the women-run enterprisers play a role that is still
secondary respect to the men’s ones
Results from the time budget survey show a persistent asymmetry
between the commitment of women and men in terms of familiar work,
even if we can observe that participation of men to the domestic work
is slowly increasing
The workload inside home continues to have a big relevance in explaining
the reality of women-run enterprises
Economic decision-making
The last twenty years have seen a huge increase in the number of
women participating in the labor force almost everywhere and in all
sectors…
…but the women’s representation at a decision-making level is
much lower then men’s in major institutions
The participation of women in high level economic decisionmaking is fundamental to give women and men an equal share
of power and influence in policy making processes
This is not only a demand for simple justice or democracy but can
also be considered as a necessary condition for women's interests
to be taken into account (Beijing Platform, 1995)
Economic decision-making
Economic decision makers
are those who occupy institutional positions in decision-making
bodies, they are actively involved in the deliberation and
determination of economic policies and they are responsible for
implementing them on behalf of the State or the institution they
represent
Economic decisions
made by either private or public actors, determine both present and
future economic performance and assets, with obvious implications
for everyone’s daily life
Economic decision-making
Aims
 Adoption of adequate measures on the basis of the most appropriate
monitoring indicators
 Institutionalization of formal requirements to collect and provide data
by sex
 National Statistical Institutes responsible for pre-testing and revising
the data collection instruments, designing and supervising the data
collection process as well as the data validation and analysis
Economic decision-making
Indicators
Developed by the EU Italian Presidency to measure the representation
of women and men in economic decision-making bodies
The proportion and the number of women and men among…
 Governors and deputy/vice-governors of the Central Banks
 Members of the decision-making bodies of the Central Banks
 Ministers and deputy ministers/vice-ministers of the Economic Ministries
 Presidents and vice-presidents of the Labour Confederations
 Total governing bodies of the Labour Confederations
 Presidents and vice-presidents of the Employer Confederations
 Members of total governing bodies of the Employer Confederations
 Chiefs of executive boards of the 50 top firms publicly on the national stock exchange
 Members of executive boards of the 50 top firms publicly on the national stock
exchange
Summarizing
New challenges are emerging for official statistics at international level:
1. look at the available data in a new perspective: as an instance, an appropriate
analysis of EU-SILC based on a “couple perspective”, allows for understanding the
interrelationship between partners and the balance between gender roles;
2. enrich existing data sources with pertinent variables and integrate different
data sources: for example, as far as the enterprises are concerned, countries
should collect sex disaggregated data about the entrepreneurs, collect information
that allow to highlight critical aspects women have to face; to measure the
contribution of women work in the household, by attributing an economic value to
the familiar work;
3. carry out new surveys: for instance, referring to the economic decision-making
bodies, it would be very important to guarantee the collection of data through the
National Statistical Institutes, adopting a set of indicators able to measure the
representation of women and men in this domain.