Commission Staff Working Paper Gender pay gaps in European labour markets -Measurement, analysis and policy implications - EU monitor

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Tekst

COUNCIL OF Brussels, 10 September 2003 THE EUROPEAN UNION

12378/03

SOC 334

COVER NOTE

from: Secretary-General of the European Commission, signed by Mrs Patricia BUGNOT, Director

date of receipt: 8 September 2003

to: Mr Javier SOLANA, Secretary-General/High Representative

Subject: Commission Staff Working Paper

Gender pay gaps in European labour markets

-Measurement, analysis and policy implications

Delegations will find attached Commission document SEC(2003) 937.

________________________

Encl.: SEC(2003) 937

COMMISSION OF THE EUROPEAN COMMUNITIES

Brussels, 4.9.2003 SEC(2003) 937

COMMISSION STAFF WORKING PAPER

Gender pay gaps in European labour markets

- Measurement, analysis and policy implications -

COMMISSION STAFF WORKING PAPER

Gender pay gaps in European labour markets

- Measurement, analysis and policy implications -

Summary and Conclusions .........................................................................................................3

  • 1. 
    Introduction...................................................................................................................6
  • 2. 
    Monitoring gender equality: the gender pay gap ..........................................................7

2.1 Definition ......................................................................................................................7

2.2 Data sources..................................................................................................................8

2.3 Empirical evidence .....................................................................................................10

  • 3. 
    Analysing factors related to the gender pay gap.........................................................13

3.1 Explanatory factors .....................................................................................................13

3.2 Results from cross-country analyses ..........................................................................14

3.3 Results from decomposition analyses.........................................................................14

  • 4. 
    Policy implications .....................................................................................................17

4.1 Implications for the gender pay gap indicator ............................................................17

4.2 Implications for data provision ...................................................................................18

4.3 Implications for policy responses ...............................................................................18

  • 5. 
    References...................................................................................................................20

Appendix 1: Gender gaps in European labour markets – descriptive evidence .......................22

Appendix 2: Analysing factors related to the gender pay gap – summary of main findings from selected national studies.......................................................................34

S UMMARY AND C ONCLUSIONS

The EU has a long-standing commitment to promoting gender equality, enshrined in the Treaty since 1957, in particular the principles of equal pay and equal treatment . Furthermore, the Lisbon strategy highlighted the importance of reducing structural imbalances in the labour market including the gender pay gap as a precondition to the achievement of the overall objectives. Progress in this area is measured by a set of structural indicator, including the "gender pay gap (in unadjusted form)", and reported annually to the Spring European Council.

The focus on gender equality notwithstanding, important gender gaps in labour market participation, employment, earnings and career progression do remain in European labour markets. Consequently, the Employment Guidelines of 2003 ask Member States "through an integrated approach combining gender mainstreaming and specific policy actions, [to] encourage female labour market participation and achieve a substantial reduction in gender gaps in employment rates, unemployment rates, and pay by 2010. "With regard to the gender pay gap Member States are asked to pursue policies which, "with a view to its elimination, (…) will aim to achieve by 2010 a substantial reduction in the gender pay gap in each Member State, through a multifaceted approach addressing the underlying factors of the gender pay gap, including sectoral and occupational segregation, education and training, job classifications and pay systems, awareness raising and transparency."

The purpose of this Staff Paper is threefold:

· firstly, it reviews the definition of the structural indicator "gender pay gap" to

monitor the overall size and evolution of the gender pay gap and provides empirical evidence on the extent and the evolution of gender imbalances in

European labour markets;

· secondly, it summarises recent work analysing factors related to the gender pay

gap by the Commission Services and the European Commission's Expert Group on

Gender and Employment following various political mandates;

· and thirdly, it discusses the implications of the results of these analyses for

measuring both the gender pay gap and progress to reduce it, for data provision and

for policies to tackle gender pay gaps.

The main results can be summarised as follows:

· Despite progress in recent years, important gender gaps remain in European labour

markets: women's activity and employment rates remain significantly below those of men (18 percentage points at EU-level); in most Member States, the female unemployment rate exceeds the male one considerably. Furthermore, women, on average, continue to receive significantly lower gross hourly earnings than men,

with women's average earnings in the EU 16% below those of men.

· Various analyses of the determinants of gender pay gaps in the European labour

markets - in the form of both cross-country comparisons and decomposition analyses - provide important evidence on the factors associated with these gender pay gaps. They also show to what extent these factors and their impact on gender gaps vary across Member States. As a result, these analyses generally identify both gender segregation by sector and occupation and the under-valuation of work in female-dominated sectors and occupations as major contributors to the gender pay gap. They also highlight the importance of differences in male and female labour

market participation for the gender pay gap.

· The results from cross-country analyses show that labour market participation,

wage structures and the relative evaluation of female-dominated employment account for a large part of the cross-country differences in gender pay gaps. While lower female labour force participation rates tend to be associated with lower gender pay gaps, higher degrees of wage inequality and the less favourable relative earnings position of women compared to men in their respective wage distribution

are associated with larger gender pay gaps.

· The results from decomposition analyses show that various effects contribute to the

gender pay gap, notably: differences in the composition of the male and female workforce ('composition effect'), differences in the remuneration of the personal and job characteristics between men and women ('remuneration effect') as well as differences in male and female labour force participation behaviour ('selection effect'). Although composition effects can in general only account for less than half of the overall gender pay gap, differences in the male and female workforce composition with respect to certain characteristics can have an important impact on the gender pay gap. Both potential direct and indirect discrimination and the related statistical problems (selectivity, endogeneity and unobserved heterogeneity)

have to be taken into account when monitoring and analysing the gender pay gap.

The main implications of these analytical results are the following:

· The gender pay gap is a multidimensional phenomenon and no clear borders exist

between the various effects (composition, remuneration and selection effects). The gender pay gap indicator in unadjusted form gives an overall picture of inequality in pay in the whole economy. It has therefore so far been chosen as structural indicator to monitor the overall size and evolution of the gender pay gap in an economy. Concrete policy measures need to take into account the evidence on the various factors underlying the gender pay gap, identified by means of in-depth analyses based on comprehensive microdata on the basis of adjusted indicators and

comprehensive microdata.

· Both the provision of coherent, comparable, complete and timely data on gender

imbalances in the labour market and a full and easy access to the underlying microdata are crucial for ensuring a high quality measurement of the gender pay

gap and for enabling further necessary in-depth analysis.

· The reduction of gender gaps in the labour market requires both a gender

mainstreaming approach and specific policy actions, addressing the main factors related to gender pay gaps: differences in male and female labour market participation and career structures; differences in male and female wage structures

and gender effects of pay and promotion policies; and horizontal segregation in general and the concentration of women in low paying sectors and occupations in particular. Particular attention needs to be given to reconciling work and family life.

1. I NTRODUCTION

The EU has a long-standing commitment to promoting gender equality, enshrined in the Treaty since 1957. The goal of eliminating inequalities and promoting equality between women and men is set out in Articles 2 and 3(2) of the Treaty. The principle of equal pay has been enshrined in Community law from its origins, amplified by the Equal Pay Directive 75/117/EEC which introduced the concept of equal pay for work of equal value (article 141 of the EU Treaty). Community legislation has established the principle of equal treatment as regards: access to employment; vocational training

and promotion; working conditions; and matters of social security 1 .

The Lisbon strategy has stressed the need to address gender inequality in the EU labour markets, including the gender pay gap, and put forward an employment rate target for women. Structural indicators measuring the female employment rate and the gender pay gap were included. Subsequent European Councils asked for further indepth analysis in the Commission Spring Report.

In its Communication to the Spring European Council in Barcelona 2002, the Commission announced it would "launch an overall assessment in 2002 on the

reasons why differences leading to a gender gap, including in pay levels, exist." 2 In

the joint report “Increasing labour force participation and promoting active ageing”, the Council and the Commission called for a "strong initiative to reduce gender disparities in both public and private sectors" which should involve "an overall assessment of the reasons – including differences in productivity – explaining the

presence of pay gaps between men and women in each Member State." 3 The 2003

Spring European Council insisted, more generally, on strengthening the structural indicators and analytical tools for assessing progress.

Consequently, the Employment Guidelines 2003 4 , which integrate the Employment

Strategy fully into the Lisbon agenda, ask Member States "through an integrated approach, combining gender mainstreaming and specific policy actions, [to] encourage female labour market participation and achieve a substantial reduction in gender gaps in employment rates, unemployment rates, and pay by 2010." With regard to the gender pay gap Member States are asked to pursue policies which, "with a view to its elimination, (…) will aim to achieve by 2010 a substantial reduction in the gender pay gap in each Member State, through a multi-faceted approach addressing the underlying factors of the gender pay gap, including sectoral and occupational segregation, education and training, job classifications and pay systems, awareness raising and transparency."

1 See European Commission (1999) for the relevant Community legislation.

2 European Commission (2002), "The Lisbon Strategy – Making Change Happen", Communication

from the Commission to the Spring European Council in Barcelona, COM(2002) 14 final, 15.01.2002

3 European Commission (2002), Report requested by the Stockholm European Council "Increasing

labour force participation and promoting active ageing", Report from the Commission to the Council, the European Parliament, the Economic and Social Committee and the Committee of the

Regions, COM(2002) 9 final, 24.01.2002

4 European Commission (2003), Proposal for a Council Decision on Guidelines for the Employment

Policies of the Member States, COM (2003) 176, 26.05.2003

The purpose of this Staff Paper is threefold: firstly, it reviews the definition of the structural indicator "gender pay gap" to monitor the overall size and evolution of the gender pay gap and provides empirical evidence on the extent and the evolution of gender imbalances in European labour markets; secondly, it summarises the results of recent work analysing factors related to the gender pay gap – including the work

undertaken by the Commission Services following the above mandates; 5 and thirdly,

it discusses the implications of the results of these analyses for measuring both the gender pay gap and progress to reduce it, for data provision and for policies to tackle gender pay gaps.

2. M ONITORING GENDER EQUALITY : THE GENDER PAY GAP

In addition to gender gaps in labour market participation, employment and unemployment, the gender pay gap is the most important indicator of gender imbalances in the labour market. Despite progress in recent years, according to these indicators, important gender gaps remain in European labour markets: women's activity and employment rates remain significantly below those of men (18 percentage points at EU-level); in most Member States, the female unemployment rate exceeds the male one considerably; and women continue to receive, on average, significantly lower gross hourly earnings (16% less) than men (see appendix 1 for an overview of the labour market situation of men and women in the European Union).

2.1 Definition

The gender pay gap (in unadjusted form) - measuring the difference in average gross hourly earnings between men and women across the whole economy and all establishments – is one of the structural indicators to monitor progress in the framework of the Lisbon Strategy (see box 1 for the exact definition). It is also one of the key indicators of quality in work (dimension 3: gender equality), to be complemented by adjusted gender pay gaps as context indicators, taking into account compositional differences in the male and female work force regarding age, sector and

occupation. 6

5 As part of the overall assessment, numerous initiatives have been undertaken, including: the

regular monitoring of related policies in the framework of the European Employment Strategy as reviewed in the Joint Employment Reports 1999-2002 ; detailed analyses of the extent of gender pay gaps and the underlying factors in a number of reports, notably Employment in Europe 2001 (European Commission (2001c)) and Employment in Europe 2002 (European Commission (2002a)), Annual Report on Equal Opportunities (European Commission (2002b)), and The Social Situation in Europe 2003 (European Commission (2003a)), as well as in a follow-up study on "Methodological issues related to the analysis of gender gaps in employment, earnings and career progression" (European Commission (2003b). Moreover, the Expert Group on Employment and Gender provided a whole range of analytical reports on the gender pay gap, including a critical assessment of standard decomposition techniques in the analysis of factors related to the gender pay gap (European Commission (2002c)) and a synthesis report on gender pay equality in EU Member States (European Commission (2002d)), complemented by national reports on the unadjusted and adjusted gender pay gap for all EU Member States (European Commission

(2002e)).

6 European Council (2001), Indicators of Quality in Work, report by the Employment Committee to

the European Council, 14263/01, 23.11.2001

Box 1: Definition of the structural indicator "gender pay gap"

The gender pay gap is defined as the ratio of the average gross hourly earnings of female and male paid employees aged 15-64 who work at least 15 hours per week, covering all sectors and firm sizes of the economy. It is calculated on the basis of data from the European Community Household Panel (ECHP) except for France, Sweden and the Netherlands. In the case of France it is calculated on the basis of the Labour Force Survey (LFS), and in that of Sweden and the Netherlands on appropriate national sources, including administrative data.

This definition involves the following questions in the ECHP:

Q048: What are your normal monthly earnings from your main job, including earnings from any overtime you normally do? (You are requested to give the gross amount, that is the amount before tax and contributions to social insurance are deducted, and the net amount, i.e. the take home pay.)

Q049: How many hours per week do you work in your main job, including paid overtime if any?

Hourly earnings are obtained by dividing gross monthly normal earnings from the main job by four times the number of worked hours per week in the main job, including normal

overtime, but excluding bonuses, irregular overtime, any 13 th month payments and the like.

2.2 Data sources

Measures of the gender pay gap are sensitive to the data source, the sample restrictions and the choice of the dependent variable (hourly or monthly earnings; net or gross earnings; inclusion of bonus payments; etc.). The scope for analysis further depends on the availability of informative microdata, allowing to link earnings information to the relevant individual, job-related, firm-level or institutional variables.

In this context, the Directors of Social Statistics, in their April 2002 meeting, drew attention to the necessary criteria for defining the structural indicator "gender pay gap". The indicator ought to be based on data covering the whole economy, including all sectors and firm sizes (so-called 'completeness condition'), including possibly also those working less than 15 hours a week. In the April 2003 meeting of the Directors of Social Statistics, in particular the question of accessibility to the microdata used for the calculation of the gender pay gap was discussed. Improvements in this respect would enable the Commission to respond to various political mandates from the European Council and the Member States, in particular an in-depth analysis of wage

formation systems in general and factors related to the gender pay gap in particular. 7

Clearly, all potential data sources need to be evaluated on the basis of the above criteria, completeness and accessibility (see box 2 for an evaluation of potential data sources).

7 Definitive Minutes of the annual meeting of the European Directors of Social Statistics, 7-8 April

2003, Luxembourg, Eurostat/E0/03/DSS/6/11/EN

Box 2: Potential data sources for calculating the gender pay gap

The European Community Household Panel (ECHP) is based on harmonised EU-wide surveys for the years 1994-2001. Despite relatively small sample sizes, all sectors of the economy are covered, notably the public sector. It also includes information on employees in agriculture and on the self-employed. Data collection for the ECHP stopped in 2001, and the whole survey will be replaced in 2004 with a new instrument, EU-SILC (Statistics on Income and Living Conditions). During the transition period, problems may arise regarding the provision and comparability of data. No fully comparable data on income and living conditions at European level will, for instance, be available for the years 2002, 2003 and – for a large number of countries – 2004. Longitudinally linked data from the EU-SILC will not be available before 2007.

While EU-SILC might represent an adequate data source for the measurement of gender pay gaps in the EU as from 2004 onwards, the scope for econometric analyses of the gender pay gap on the basis of EU-SILC will be more restricted than in the case of the ECHP due to the design of the data set, in particular the separation of cross-sectional and longitudinal information, the lack of detailed information on the occupational employment structure and the characteristics of small jobs and, finally, the lack of harmonised information on gross monthly or hourly current earnings for all Member States.

Since, by definition, the gender pay gap needs to cover all sectors and establishments of the economy, and since further analyses require full and timely access to comparable microdata, other often less frequent and restricted Community surveys such as the Structure of Earnings

Survey (SES) 8 or the Structural Business Statistics (SBS) 9 are no adequate sources for

measuring the gender pay gap or for analysing factors related to it:

· The self-employed, people working in local units with less than ten people and employees

in a large number of sectors (agriculture and fishing, public administration and defence, education, health and social work, other community, social and personal service activities, private households and extra-territorial organisations) are not covered by the SES. In particular sectors with large female employment shares, such as health, education and personal services, are not covered by this data source. As a consequence, the SES cannot provide information on nearly one-third of all wage-earners in the EU, almost half of all

employed women and one fifth of all employed men.

· The SBS data are based on employer surveys and do not provide information on

individuals and do further not contain any information on important related factors such as educational attainment levels of the workforce. They do also not cover the agricultural and

service sectors and non-market activities.

Finally, data sets linking individual-level employment information (such as the Community Labour Force Survey (LFS)) with earnings information from business surveys or data from national sources, including administrative records, could represent adequate data sources if they meet the necessary completeness and accessibility conditions discussed by the Directors of Social Statistics in their meetings of April 2002 and April 2003. The use of nonharmonised national sources, on the other hand, to calculate indicators such as the gender pay gap should be limited.

8 The Structure of Earnings Surveys is an in-depth survey which covers large samples using data

from existing surveys, social security records and business surveys. So far, it was conducted in 1995 and 2002 (data available late 2004) only, and it is planned to repeat it every four years from

now on. (FOOTNOTE TO BE TRANSFERRED INTO BOX)

9 The Structural Business Statistics are enterprise-based surveys conducted every four to five years.

Their main focus is on business and production variables as well as trade statistics. (FOOTNOTE TO BE TRANSFERRED INTO BOX)

2.3 Empirical evidence

According to the above gender pay gap indicator, in 2000, women in the EU had, on average, 16% lower hourly earnings than men, ranging from below 10% in Portugal and Italy to 20% or more in Austria, Germany, the Netherlands and the UK (table 1).

TABLE 1: Gender pay ratios in the European Union, 1995-2000

1995 1996 1997 1998 1999 2000 private public

B 88 90 90 91 89 88 85 107 DK 85 85 87 88 86 85 84 87

D 79 79 79 78 81 79 79 80

EL 83 85 87 88 87 85 78 91

E 87 p 86 p 86 p 84 p 86 p 85 77 97

F 87 87 88 88 88 87 na na IRL 80 79 81 80 78 81 77 85

I 92 92 93 93 91 95 85 100

L 81 82 na na na na na na

NL 77 77 78 79 79 79 na na

A 78 80 78 79 79 80 76 86

P 95 94 93 94 95 92 72 117

FIN na 83 82 81 81 83 85 75

S 85 83 83 82 83 82 na na

UK 74 76 79 p 76 p 78 p 79 74 82

EU 83* 84* 84* 84* 84* 84 79 88

Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available.

For Germany, the 'number of hours' used for calculations include 'paid' and 'unpaid' overtime. For France the gender pay gap is based on net hourly earnings

Source: Eurostat, ECHP UDB, version June 2003; for France, Netherlands and Sweden: National Statistical Institutes; these latter have not yet provided breakdowns of the gender pay gap by public and private sectors.

The difference in earnings between men and women was generally smaller in the public sector than in the private sector – reaching at EU-level 11% in the former

compared to 22% in the latter. 10 The gender pay gap also varied with personal and job

characteristics as well as across sectors and occupations: It was found to be particularly high among older workers (25%), the high-skilled (22%) and those employed with supervisory job status (17%) as well as in financial services (29%), manufacturing (27%) and among craft workers (32%). Men were both more concentrated in higher paid sectors and occupations and more likely to hold

supervisory responsibilities within these sectors and occupations (chart 1). 11

10 According to data from the ECHP, the only exceptions are Finland, the Netherlands and Germany.

In these countries, the gender pay gap is of a similar magnitude in both public and private sectors or, as in the case of Finland, even larger in the public sector.

10 CHART 1: UPDATED CHART 25, EiE2002 (personal and job characteristics)

Gender pay gaps in the EU by personal and job characteristics, 2000

% of men's respective average gross hourly earnings

-5 0 5 10 15 20 25 30 35

Total

by age:

15- 24

25- 54

55- 64

by educ ation:

High- skilled

Medium- skilled

Low- skilled

by job status:

Supervisory

Intermediate

Non- supervisory

by public /private:

Private sec tor

Public sec tor

by sec tor:

Agric ulture

Mining and energy

Manufac turing

Construc tion

Retail and trade

Hotels and restaurants

Transport and c ommunic ation

Financ ial servic es

Public admin., health, educ ation

by oc c upation:

Legislators, managers

Professionals

Tec hnic ians

Clerks

Servic e workers, shop assistants

Skilled agric ultural workers

Craft and related workers

Plant and mac hine operators

Elementary oc c upations

Source: Eurostat, ECHP UDE, version June 2003, wave 7 (2000)

Remarks: The 'positive' gender pay gap in the construction sector shown in the chart might be due to the fact that the few women working in this sector are predominantly in high skilled occupations and supervisory functions which tend to receive higher pay.

Women, on the other hand, were over-represented in the lower part of the (gross hourly) earnings distribution in all EU Member States. While in the EU on average, women accounted for only 32% of all employed in the highest earnings quintile, they made up almost 60% of all employed in the lowest quintile. While the earnings of 15% of all employed men were in the lowest quintile, those of one quarter of all employed men were in the highest earnings quintile. For women, the situation is exactly the opposite, with only 15% in the highest, but 25% in the lowest earnings quintile (chart 2). The relative earnings position was least favourable in those countries with the highest gender pay gap. Notably in Austria and the UK, two thirds of those employed with the 20% lowest earnings were women.

CHART 2:

Employment shares in the bottom and top earnings quintiles by gender in the EU, 2000

men - bottom men - top women - bottom women - top

% of male and female employment, respectively 35

30

25

20

15

10

5

0

EU B DK D EL E F IRL I L NL A P FIN S UK

Source: Eurostat, ECHP UDB version June 2003, wave 7 (2000)

Although no strictly comparable data are available for the Accession Countries, there

12

is evidence that the gender pay gaps in the Accession Countries are similar to, or smaller than, those prevailing in the current European Union Member States. Women tend to be over-represented in low paid occupations such as teaching and health care in the Accession Countries, too. There is also some evidence that, contrary to the gaps in employment rates between men and women, the gender pay gaps in the Accession Countries have been declining over the last decade. Recent significant increases in wage inequality in the Accession Countries, however, could well offset these narrowing trends.

12 Finally, and although no strictly comparable data exist, gender pay gaps in other, non EU OECD countries are found to be of a similar order than those in the EU, ranging from 11% in Australia and 16% in New Zealand to 20% or more in Canada , Switzerland and the US (OECD (2002)).

3. A NALYSING FACTORS RELATED TO THE GENDER PAY GAP

As shown above, the gender pay gap varies considerably by individual, job and firm characteristics. While these findings provide important information on the nature of gender imbalances in the labour market, they do not allow conclusions as to the relative impact of the various factors on the overall gender pay gap. Further analyses are needed to identify the factors related to the gender pay gap.

3.1 Explanatory factors

The observed gender pay gaps could be due to a whole range of factors, including:

· personal characteristics such as age, educational background, family background,

presence of children, experience in the labour market, previous career interruptions

and tenure on the job;

· job characteristics such as occupation, working time, contract type, job status,

career prospects and working conditions ;

· firm characteristics such as sector, firm size, work organisation, recruitment

behaviour and the firm's compensation and human resources policies;

· gender segregation by occupation or sector;

· institutional characteristics including education and training systems, wage

bargaining, wage formation and tax and benefit systems, industrial relations, parental leave arrangements and the provision of childcare facilities before and

during compulsory school years; as well as

· social norms and traditions regarding education, labour market participation, job

choice, career patterns and the evaluation of male- and female-dominated

occupations.

The above personal, job and firm characteristics reflect systematic differences in the composition of the male and female workforce. They can reflect objective differences in productivity - which in turn would lead to differences in wages - as well as differences in incentives, opportunity costs and preferences for job and firms characteristics – which would explain systematic differences in labour market participation, job access and career progression

Many of these labour market characteristics, however, could themselves be the outcome of discriminatory processes, including institutional settings, pay policies and social norms and traditions. It is obvious that, in the presence of such ‘indirect discrimination’, gender pay gaps do not just reflect systematic pay differences due to differences in choice behaviour, productivity or labour market characteristics.

Moreover, women or men could also be subject to ‘direct discrimination’ by receiving lower pay than workers with the same characteristics and job performance.

It is therefore one of the main challenges to distinguish pay differences resulting from different labour market characteristics, on the one hand, and differences due to indirect or direct discrimination, on the other, including the societal differences in the evaluation of work in male and female dominated sectors or occupations.

To examine the relative importance of these for the overall gender pay gap, two types of analyses can be employed:

· cross-country analyses aim at identifying the impact of labour market

characteristics and institutional arrangements on the gender pay gap by exploiting the differences in gender pay gaps, labour market characteristics and institutional

variables across countries;

· decomposition analyses aim at identifying, in general at country level, the relative

importance of, firstly, differences in the composition of the male and female workforce (‘composition effect’), secondly, differences in the remuneration of men and women with similar characteristics (‘remuneration effect’), and thirdly, differences in the labour market participation behaviour of men and women

(‘selection effect’) for the overall gender pay gap.

3.2 Results from cross-country analyses

Among the various studies undertaken to assess factors related to the gender pay gap 13

the Expert Group on Employment and Gender has reviewed in particular to what extent cross-country differences in female labour market participation, wage structures and wage formation systems are associated with differences in the gender pay gap across countries.

It is generally found that differences between countries in labour market participation, wage structures and the relative evaluation of female-dominated employment account for a large part of the cross-country differences in gender pay gaps. Lower wage dispersion, possibly as a result of centralised wage formation systems , and lower female labour force participation rates are associated with lower gender pay gaps. On the other hand, higher degrees of wage inequality and the less favourable relative earnings position of women compared to men in their respective wage distribution are associated with larger gender pay gaps.

3.3 Results from decomposition analyses

Various statistical decomposition techniques can be applied to decompose observed differences in earnings between men and women into, firstly, differences in the composition of the male and female workforce (‘composition effect’), secondly, differences in the remuneration of men and women with similar characteristics

14 (‘remuneration effect’), and thirdly, differences in the labour market participation

behaviour of men and women (‘selection effect’). 14

Most studies only consider the first two effects, however, sometimes interpreting them as 'explained' and 'unexplained' components of the gender pay gap, respectively. The latter of these two effects is often considered as the part of the gender pay gap due to ‘direct discrimination’, and is equivalent to the gender pay gap ‘adjusted’ for differences in the composition of the male and female workforce.

It is important to understand, however, that this interpretation is generally not valid in the presence of indirect discrimination with respect to labour market characteristics such as educational attainment, occupation and job status. In this case, the 'explained' part of the gender pay gap could also reflect inequalities in the societal evaluation of comparable work or (indirect) 'discrimination' related e.g. to education and training, occupational choice or firms' recruitment and promotion behaviour.

While recognising that there is generally a wide variation of results across countries, among studies within a particular country and over time, according to the various

studies undertaken to assess factors related to the gender pay gap, 15 both differences

in the composition of the male and female workforce and differences in the remuneration of the personal and job characteristics between men and women contribute to the overall gender pay gap. A whole range of factors are found to have

an impact on the gender pay gap. 16

The single most important factors contributing to the gender pay gap in the EU are: earnings differences between men and women with family responsibilities; gender segregation by sectors and occupations, with a higher concentration of women in low paying sectors and occupations; and, in particular, relatively lower earnings of women in female-dominated sectors and occupations that cannot be explained by productivity differences between sectors and occupations.

14 Sometimes a fourth effect is calculated to take account of the problem of unobserved

heterogeneity, i.e. the differences in unobservable characteristics, such as intelligence or motivation, which might be related to both earnings and labour market characteristics. See European Commission (2003b) for decompositions of the gender pay gap including an

'unobservables effect'.

15 See appendix 2 for an overview of results from selected national studies.

16 See European Commission (2002a, 2003b) for a detailed list of various factors and their

contribution to the gender pay gap.

Although the remuneration effect is generally found to account for the largest part of the overall gender pay gap, small effects due to gender differences in the workforce composition are found to hide important 'composition effects' related to specific factors. In particular, differences in the male and female workforce composition across sectors and occupations and differences in male and female career patterns are found to

contribute significantly to the gender pay gap (chart 3). 17

CHART 3:

Contributions of 'explanatory' variables to the EU-level gender pay gap Due to differences in composition

Due to differences in remuneration -4 -2 0 2 4 6

Age

Family situation and children

Educational attainment and training

Career interruptions

Tenure on the job

Contract type

Working time

Public sector

Firm size

Job status

Sectors

Occupations

Gender concentration

-4 -2 0 2 4 6

Note: The bars show the contribution to the gender pay gap of the factors included in the model presented, split into the part due to differences in the workforce composition between men and women (dark bars) and that due to differences in the remuneration of the various characteristics (light bars). The contributions of all the variables in the model (country-year effects not included in the chart above) add up to the observed gender pay gap of 16%. See European Commission (2002a) for details on the data source, variables and decomposition method.

Source: Eurostat, ECHP, waves 2-5 (1995-98)

17 By means of an example, the chart is to be interpreted in the following way: Both variables, 'job

status' and 'career interruptions' each contribute almost two percentage points to the overall gender pay gap. The respective importance of differences in composition and differences in remuneration, however, differs strongly for the two variables. In the case of 'job status', the effect on the overall gender pay gap is mainly due to the fact that women are less often in supervisory positions than men, and not to differences in the remuneration between men and women in supervisory functions. The effect of 'career interruptions' on the overall gender pay gap, on the other hand, is almost entirely due to the fact that women seem to receive lower earnings than men with similar career interruptions, and not to differences in the incidence or duration of such interruptions between men

and women.

The differences in labour market participation behaviour between men and women further tend to decrease the overall gender pay gap. Indeed the ‘selection effect’ is generally found to be large and positive, implying that labour market participation and wages are negatively correlated (European Commission (2003c)). These results indicate that, in many countries, those women who are active in the labour market have, on average, more favourable labour market characteristics (such as higher education and better work-related skills) compared to women outside the labour market – and hence higher wage prospects. Without such 'positive selection', gender pay gaps in European labour markets would probably be considerably larger than

those observed. 18

Decomposition analyses that correct for cross-country differences in wage structures and women’s relative position generally corroborate the findings above (World Bank (1999), OECD (2002)).

4. P OLICY IMPLICATIONS

4.1 Implications for the gender pay gap indicator

The above analyses on the determinants of gender pay gaps in the European labour markets provide important evidence on the factors associated with the gender pay gap and show to what extent these factors and their impact on gender gaps vary across Member States. They also highlight the role of both direct and indirect discrimination for the gender pay gap and the related statistical problems due to e.g. (endogenous) educational or occupational choice and (selective) labour market participation in measuring and analysing the gender pay gap, thus clarifying the difficulties encountered when adjusting gender pay gaps and interpreting the results.

The results summarised above confirm in particular the complex nature of gender equality and gender gaps in labour markets as multi-facetted social and economic phenomena, including the working of education and training, job classification and wage formation systems as well as social norms and traditions. In particular no clear borders exist between composition effects and remuneration effects. Furthermore, selection effects might complicate the analysis. The gender pay gap (in unadjusted form) gives an overall picture of gender pay equality and has therefore so far been chosen as structural indicator to measure gender inequalities in earnings and express the related challenges. Concrete policy measures need to take into account the evidence on the various factors underlying the gender pay gap, identified by means of in-depth analyses based on comprehensive microdata .

18 While no analyses on comparable data for the Accession Countries are possible, previous analyses

by UNICEF and the World Bank on the determinants of the gaps in monthly earnings between men and women have resulted in similar conclusions: the largest part of the gender pay gap cannot be 'explained' by differences in personal and job characteristics, thus suggesting an important degree of "wage discrimination" (World Bank (2002)) between men and women" in the Accession

Countries. There is also evidence of an important 'selection effect' in these countries.

4.2 Implications for data provision

The provision of adequate, complete, comparable and timely data for both coherent policy monitoring over time and for detailed analyses of factors related to the gender pay gap has to be ensured. This applies in particular at a time when the increased political demand for an assessment of progress on the Lisbon strategy and for an improvement in statistical and analytical tools will be hampered by the transition to new statistical data sources such as the continuous Labour Force Survey (LFS) and the new reference database for statistics on income and living conditions (EU-SILC).

In the transition period, alternative data sources will have to be employed. In particular data sets linking individual-level employment information (such as the LFS) with earnings information from business surveys or data from national sources, including administrative records, could represent adequate data sources. On the other hand, the use of non-harmonised national sources to calculate indicators such as the gender pay gap should be limited.

In any case it has to be ensured that – in addition to meeting the necessary criteria for the definition of the indicator - the Commission Services have full and timely access to the underlying microdata for analytical purposes. This data should be transmitted via Eurostat in harmonised format and be available according to the delays foreseen for the provision of EU-SILC data.

These specific data requirements for the calculation and analysis of the gender pay gap should be duly reflected in the Commission response to the specific request by the Brussels Spring 2003 European Council "to report in time for the 2004 Spring European Council on how the use of structural indicators and other analytical tools for assessing progress on the Lisbon strategy could be strengthened" and to describe which "improvements in the quality, in particular the comparability over time, countries and regions, of statistical and analytical tools [are needed] so as to provide better analytical foundations for the design and monitoring of policies".

4.3 Implications for policy responses

As stressed by the Employment Guidelines 2003, gender gaps in the labour market need to be progressively eliminated, if the EU is to deliver full employment, increase quality in work and promote social inclusion and cohesion. This requires both a gender mainstreaming approach and specific policy actions to create the conditions for women and men to enter, re-enter, and remain in the labour market.

Three key elements of policy relevance stand out in the results summarised above: first, the importance of differences in male and female labour market participation and career structures; second, the differences in male and female wage structures and gender effects of pay and promotion policies; and third, horizontal segregation in general and the concentration of women in low paying sectors and occupations in particular.

The reduction of gender pay gaps hence calls for overall across-the-board policy responses to eliminate gender bias and to avoid any under-valuation of work in women-dominated sectors and occupations. To achieve a substantial reduction of gender pay gaps, a multi-faceted approach is needed, addressing the underlying factors of the gender pay gap, including sectoral and occupational segregation, education and training, job classifications and pay systems, awareness raising and transparency, without calling into question the principle of wage differentiation according to productivity and labour market situation. All actors concerned, including social partners, have to be involved in this process.

Particular attention needs to be given to reconciling work and family life, notably through the provision of full day care services for children and other dependants, encouraging the sharing of family and professional responsibilities and facilitating return to work after a period of leave. Disincentives to female labour force participation need to be addressed, and childcare provision needs to be improved, in line with the Barcelona European Council agreement that Member States should provide childcare by 2010 to at least 90% of children between three years old and the mandatory school age and at least 33% of children under 3 years of age.

5. R EFERENCES

European Commission (1994), "Memorandum on equal pay for work of equal value", COM(94) 6 final, Luxembourg

European Commission (1998), "Equal opportunities for women and men: European Community Acts", Luxembourg

European Commission (2000a), Social Policy Agenda, COM(2000) 379 final, Brussels

European Commission (2000b), Communication from the Commission to the European Parliament, the Council, the Economic and Social Committee and the Committee of the Regions on a community framework programme on gender equality (2001-2005).

European Commission (2001a), "The Lisbon Strategy - Making Change Happen", Synthesis Report, Communication from the Commission to the Spring European Council in Barcelona, COM(2002) 14 final, 15.01.2002, Brussels

European Commission (2001b), Communication from the Commission to the Council, the European Parliament, the Economic and Social Committee and the Committee of the Regions on "Employment and social policies: a framework for investing in quality”, COM(2001) 313 final, Brussels

European Commission (2001c), Employment in Europe 2001 – Recent Trends and Prospects, July 2001, Employment and Social Affairs DG, Brussels

European Commission (2001d), "Methodological Note: Measuring Gender Pay Gaps in the EU: Theoretical Approaches and Empirical Evidence", Employment and Social Affairs DG, mimeo, November 2001, Brussels

European Commission (2002a), Employment in Europe 2002 – Recent Trends and Prospects, section "Assessing gender pay gaps in the EU", September 2002, Employment and Social Affairs DG, Brussels

European Commission (2002b), Annual Report on Equal Opportunities for Women and Men in the European Union 2001, COM(2002)258 final, Brussels

European Commission (2002c), "The adjusted gender pay gap: a critical appraisal of standard decomposition techniques", paper prepared by D. Grimshaw and J. Rubery as part of the work by the co-ordinating team of the Group of Experts on Gender and Employment commissioned by DG Employment and Social Affairs, March 2002, Brussels

European Commission (2002d), "The gender pay gap and gender mainstreaming pay policy: Synthesis report of gender pay equality in EU Member States", report prepared by the Group of Experts on Gender and Employment commissioned by the Employment and Social Affairs DG, mimeo, November 2002, Brussels European Commission (2002e), "National reports on the unadjusted and adjusted gender pay gap", reports prepared by the Group of Experts on Gender and Employment commissioned by the Employment and Social Affairs DG, mimeo, September 2002, Brussels

European Commission (2003a), Social Situation in Europe, Employment and Social Affairs DG, Brussels

European Commission (2003b), "Methodological issues related to the analysis of gender gaps in employment, earnings and career progression", report prepared by the Centre for European Economic Research (ZEW) commissioned by the Employment and Social Affairs DG, forthcoming, Brussels

European Commission (2003c), Employment in Europe 2003 – Recent Trends and Prospects, forthcoming, Employment and Social Affairs DG, Brussels

European Council (1999), Review of the implementation by the Member States and the European Institutions of the Beijing Platform for Action : Women in the decision making process, Council of the European Union, 11829/1/99.

European Council (2001), Indicators on gender pay equality: The Belgian presidency’s report, Brussels

European Council (2002), Barcelona Council Conclusions

European Council (2003), 2003 Spring European Council Conclusions, Brussels

European Foundation for theImprovement of Living and Working Conditions (2002), Gender, jobs and working conditions in the European Union, report prepared by C. Fagan and B. Burchell, Dublin

Eurostat (2002), “The life of women and men in Europe. A statistical portrait”, Panorama of the European Union (Population and social conditions), Luxembourg

OECD (2002), Employment Outlook 2002, chapter 2: "Women at Work: Who are They and How are They Faring?", Paris

World Bank (1999), Gender earnings differentials: The European experience, Policy Research Report on Gender and Development by P. Rice, published as Working Paper No. 8, Washington

World Bank (2002), Gender in transition, Washington

A PPENDIX 1: G ENDER GAPS IN E UROPEAN LABOUR MARKETS DESCRIPTIVE

EVIDENCE

Labour market participation, employment and unemployment (tables A1-A4):

· While the difference in activity and employment rates between men and women is

diminishing, both activity rates and employment rates for women remain systematically lower than for men in the vast majority of countries. In 2002, 60.9% (55.6%) of all women in the age group 15-64 were active (employed), compared to 78.4% (78.8%) of all men in that age group. In terms of full-time equivalents, the gap in employment rates amounts to almost 25 percentage points. In virtually all Member States, the gap in activity and employment rates between the sexes is

smallest among the young generation.

· Further differences in employment rates do exist between women with and without

dependent children. Mothers aged 25-44 with at least one young child (aged 0-5) are less likely (57.3%) to be employed than women of the same age without a young child (72.5%). The gap between these two groups is largest in Germany and the United Kingdom. It is s till much bigger in some of the Accession Countries, notably the Czech Republic, Hungary and Slovakia. In contrast, in Portugal as well as in

Slovenia the two rates are almost identical.

· Unemployment among women also remains higher than for men. While women

form around 43% of the EU labour force, they account for half (50.4%) of the unemployed. The unemployment rate in 2002 was higher for women than men in

most parts of the Union, averaging 8.7% as against 6.9%.

Working time and contract status (table A5):

· EU-wide, 33.5% of women in employment are working part-time against only

6.6% of men. Female part-time work is particularly prevalent in the Netherlands (72.8%), the United Kingdom (44.0%) and Germany (39.5%). Among full-time employees, women work fewer hours than men in all Member States although in Austria and Sweden the difference is less than one hour. In contrast, the gender gap

is more than 4 hours in the United Kingdom.

· Female employees (14.3%) are also more likely than their male counterparts

(12.1%) to have a fixed-term contract. In all EU Member States except Germany, women's share in temporary employment exceeds the overall female employment

share.

Skills and access to training (table A6)

· The share of high skilled women in employment (27.7%) exceeds that of high

skilled men (25.2%). This finding applies to all EU Member States except Germany, Austria, Luxembourg and the Netherlands. The share of low skilled, on the other hand, amounts to 25.8% of all employed women compared to 29.6% of all employed men. At the same time, according to data from the Labour Force Survey, women seem somewhat more likely to receive training or further education

on the job than men.

Vertical segregation by gender (table A7):

· Women have problems in gaining access to the labour market and particularly to

managerial and supervisory positions: less than 4% of all women in employment occupy managerial posts compared with more than 7% of all men in employment.

Women also have supervisory responsibilities much less frequently than men: 19

16.6% of men in paid employment in the EU had supervisory responsibilities and an additional 17.9% intermediate responsibilities in 2000 compared to 9.2% and 14.8%, respectively, of women. Men were overall twice as likely to occupy such

supervisory functions.

Horizontal segregation by gender (tables A8-A10):

· In 2000, in all EU Member States, women are more likely to work in the public

sector than men. At EU-level, according to data from the European Community Household Panel, 19% of all male employees and 32.5% of all female employees work in the public sector. This share ranges from 14% or less in Portugal, Spain and the UK to 26.8% in France for men, and from around 20% in Portugal and

Spain to more than 50% in Sweden and Denmark for women.

· As regards the EU-wide sectoral employment structure, women are concentrated in

the growing services sector (83.7% of all employed women against 57% of all employed men) whereas men are employed disproportionately in agriculture and industry, areas where more restructuring has taken place. Employment shares of women range from 25% or less in industry and transport and communications to more than 75% in health and social services and more than 90% in services in

private households.

· With respect to the occupational employment structure, women tend to be overrepresented

in low paying occupations. At EU-level, 34.4% of women work in low paying occupations compared to 19.2% of men. Female employment shares range from around 10% among craft workers, 20% among plant and machine operators and 30% of managers to more than 67% among clerks, service workers and shop assistants. In many Member States, women also work more often in elementary –

i.e. unskilled manual - occupations than men.

19 In the ECHP, individuals are asked whether they supervise or co-ordinate the work of any

personnel and whether they have any say in their pay or promotion. In this case, their job status is classified as "supervisory". If they supervise or co-ordinate others' work without having a say in their pay or promotion, their job status is classified as "intermediate". Without any co-ordinating

function, it is classified as "non-supervisory".

TABLE A1: Activity rates in the European Union and the Accession Countries by gender, 2002

15-64 15-24 25-54 55-64

men women total men women total men women total men women total

B 73.1 56.2 64.7 38.9 32.4 35.7 91.3 72.4 81.9 37.6 18.4 27.8 DK 83.6 75.5 79.6 70.7 66.4 68.6 91.9 83.7 87.8 67.0 52.9 60.4 D 78.7 64.2 71.5 53.1 47.7 50.4 93.2 78.0 85.7 52.3 33.9 43.0 EL 76.6 50.1 63.1 39.0 33.3 36.1 93.9 62.9 78.1 58.1 25.5 41.4 E 79.0 52.8 66.0 47.8 37.7 42.9 92.1 63.9 78.1 62.2 24.4 42.7 F 75.7 62.7 69.1 41.7 32.9 37.3 93.8 78.7 86.1 41.8 31.9 36.7 IRL 79.0 57.8 68.4 56.0 48.6 52.4 91.1 67.1 79.1 66.7 31.6 49.3 I 74.3 47.9 61.1 39.9 31.0 35.5 91.0 60.3 75.7 43.0 18.1 30.2 L 77.1 53.7 65.5 38.3 31.1 34.7 95.1 66.9 81.1 37.9 18.6 28.3 NL 84.5 68.3 76.5 74.5 73.0 73.7 93.6 75.7 84.8 55.8 30.6 43.3 A 80.1 66.0 73.0 60.9 51.1 56.0 94.5 81.8 88.1 42.9 21.5 31.8 P 79.5 65.0 72.1 52.9 42.2 47.6 92.6 78.3 85.4 63.5 43.5 52.9 FIN 77.0 72.8 74.9 52.1 50.9 51.5 90.5 85.5 88.0 53.0 51.2 52.1 S 79.4 75.8 77.6 48.5 49.7 49.1 89.8 85.5 87.7 74.3 68.2 71.2 UK 82.7 68.3 75.6 67.2 60.6 64.0 91.4 76.4 84.0 65.4 45.7 55.4 EU-15 78.4 60.9 69.7 51.3 44.1 47.7 92.4 73.2 82.8 53.4 32.5 42.8 CY 81.0 61.7 71.0 40.7 38.3 39.4 95.1 75.0 84.7 68.8 33.7 50.8 CZ 78.7 62.8 70.7 42.4 35.3 38.8 94.8 81.4 88.2 59.4 27.3 42.5 EE 74.6 64.4 69.3 40.4 27.9 34.2 90.1 81.0 85.4 63.7 49.8 55.7 HU 67.7 52.9 60.1 37.8 30.9 34.4 84.3 69.5 76.9 38.2 18.9 27.5 LT 73.6 65.8 69.6 35.2 26.6 30.9 90.5 86.7 88.5 59.8 37.2 46.9 LV 74.1 63.9 68.8 44.6 33.4 39.1 89.2 82.3 85.7 57.1 38.2 46.3 MT na na na na na na na na na na na na PL 70.6 58.7 64.6 41.6 34.1 37.8 87.2 75.8 81.5 38.7 20.9 29.1 SI 72.5 63.0 67.8 40.4 32.5 36.6 91.2 84.9 88.1 36.7 14.5 25.2 SK 76.7 63.2 69.9 47.5 39.2 43.4 93.4 83.9 88.6 46.3 11.1 26.9 EU-25 77.4 60.7 69.0 49.4 42.0 45.7 91.8 73.9 82.9 52.3 31.1 41.4 BG 66.4 57.5 61.9 34.1 27.6 30.9 83.0 78.4 80.7 43.7 21.5 31.8 RO 70.4 56.6 63.4 41.5 33.4 37.4 86.4 70.8 78.6 43.9 32.8 37.9 EU-27 76.9 60.4 68.6 48.7 41.3 45.0 91.4 73.8 82.6 51.8 31.0 41.1 Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available

Source: Eurostat, Labour Force Survey (LFS), annual averages

TABLE A2: Employment rates in the European Union and the Accession Countries by gender, 2002

15-64 15-24 25-54 55-64

men women total men women total men women total men women total

B 68.2 51.4 59.9 32.2 26.5 29.4 86.1 66.8 76.5 36.1 17.6 26.7 DK 80.0 71.7 75.9 65.5 61.4 63.5 88.4 79.8 84.1 64.5 50.4 57.8

D 71.7 58.8 65.3 47.2 43.9 45.6 85.5 71.7 78.7 47.1 29.9 38.4 EL 71.4 42.5 56.7 31.3 21.9 26.5 88.6 54.4 71.1 56.0 24.4 39.7

E 72.6 44.1 58.4 39.0 27.4 33.3 85.8 54.2 70.1 58.6 22.0 39.7 F 69.5 56.7 63.0 34.2 26.0 30.1 87.3 71.8 79.5 39.3 30.6 34.8 IRL 75.2 55.4 65.3 51.0 44.8 47.9 87.3 64.9 76.1 65.1 30.8 48.1 I 69.1 42.0 55.5 30.3 21.3 25.8 86.0 54.0 70.1 41.3 17.3 28.9 L 75.6 51.6 63.7 36.4 28.1 32.3 93.4 64.6 79.1 37.9 18.6 28.3 NL 82.4 66.2 74.4 70.6 69.5 70.0 91.8 73.6 82.8 54.6 29.9 42.3 A 75.7 63.1 69.3 55.8 47.7 51.8 89.9 78.4 84.1 39.8 20.9 30.0 P 75.9 60.8 68.2 47.7 36.3 42.1 89.4 74.0 81.6 61.2 41.9 50.9 FIN 70.0 66.2 68.1 41.1 40.3 40.7 83.8 79.2 81.6 48.5 47.2 47.8 S 74.9 72.2 73.6 41.8 43.8 42.8 85.9 82.4 84.2 70.4 65.6 68.0 UK 78.0 65.3 71.7 58.1 54.4 56.3 87.4 73.7 80.6 62.6 44.7 53.5 EU-15 72.8 55.6 64.3 43.7 37.4 40.6 86.8 67.4 77.2 50.1 30.5 40.1 CY 78.8 59.2 68.6 37.3 35.5 36.4 93.1 72.4 82.4 67.0 32.1 49.2 CZ 74.0 57.1 65.5 35.3 29.2 32.3 90.2 74.7 82.5 57.3 26.0 40.8 EE 66.5 57.9 62.0 34.6 21.6 28.2 80.3 73.6 76.8 58.4 46.5 51.6 HU 63.5 50.0 56.6 32.9 27.3 30.1 79.7 66.1 72.9 36.7 18.5 26.6 LT 62.7 57.2 59.9 27.1 20.5 23.8 78.0 75.8 76.9 51.5 34.1 41.6 LV 64.3 56.8 60.4 36.4 25.4 31.0 78.1 74.3 76.1 50.5 35.2 41.7

MT na na na na na na na na na na na na PL 56.9 46.2 51.5 24.2 19.3 21.7 73.0 61.9 67.4 34.5 18.9 26.1 SI 68.2 58.6 63.4 34.4 26.5 30.6 86.7 80.0 83.4 35.4 14.2 24.5

SK 62.4 51.4 56.8 28.7 25.3 27.0 79.5 70.6 75.0 39.1 9.5 22.8 EU-25 71.0 54.7 62.9 40.7 34.4 37.6 85.4 67.3 76.4 48.9 29.1 38.7

BG 53.7 47.5 50.6 20.4 18.4 19.4 69.0 66.1 67.6 37.0 18.2 27.0 RO 63.6 51.8 57.6 31.4 26.1 28.7 79.6 65.9 72.7 42.7 32.6 37.3 EU-27 70.4 54.4 62.4 39.8 33.7 36.8 84.8 67.2 76.0 48.5 29.0 38.5

Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available

Source: Eurostat, Labour Force Survey (LFS), annual averages

TABLE A3: Employment rates in the European Union and the Accession Countries by family status and gender, 2002

Population share of persons Employment share of Employment rate of Employment rate of persons aged

with children aged 0-5 employees with children persons aged 25-44 25-44 without children aged 0-5

aged 0-5 with children aged 0-5

men women total men wome total men wome total men wome total

n n n

B 10.3 10.5 10.4 16.9 17.3 17.1 93,5 65,5 78,8 85,7 73,8 80,0

DK na na na

D 9.8 9.9 9.8 14.7 11.6 13.3 90,8 55,3 72,5 84,8 78,7 81,9

EL 8.0 7.5 7.7 14.4 13.6 14.1 96,2 52,5 73,9 87,8 60,1 73,6

E 10.6 10.3 10.5 16.2 14.3 15.5 93,4 47,8 70,0 83,9 61,6 73,1

F 13.5 13.6 13.6 21.6 18.6 20.2 91,7 61,4 75,7 86,2 78,0 82,1

IRL na na na na na na

I 10.2 9.9 10.0 16.6 15.1 16.0 93,9 50,3 71,3 82,8 58,7 71,0

L 16.0 15.6 15.8 24.0 23.1 23.6 96,0 61,8 78,7 93,3 74,4 84,0

NL 13.5 14.0 13.7 17.4 17.2 17.3 95,3 68,2 81,2 91,9 82,0 87,1

A 10.2 10.3 10.2 15.0 15.0 15.0 93,0 69,7 80,8 89,7 80,7 85,3

P 10.9 10.5 10.7 16.7 17.8 17.2 97,4 76,8 86,8 88,3 77,0 82,6

FIN na na na na na na

S na na na na na na

UK 12.1 13.1 12.6 16.5 13.5 15.1 91,5 58,3 74,3 87,0 80,3 83,8

EU-15 11.1 11.2 11.2 16.9 14.6 15.9 92,5 57,3 74,3 85,5 72,5 79,2

CY 13.8 13.2 13.5 20.1 18.2 19.2 97,8 66,1 81,5 92,6 79,5 85,7

CZ 9.8 9.5 9.6 14.2 6.7 10.7 96,0 35,1 65,2 90,2 83,3 86,8

EE 9.7 8.3 8.9 14.8 8.1 11.4 88,6 45,3 67,1 83,1 78,3 80,6

HU 9.7 9.2 9.4 14.9 7.0 11.2 85,6 32,1 58,6 82,4 76,3 79,4

LT 7.7 6.8 7.2 12.4 10.3 11.3 90,9 74,4 82,8 79,7 76,2 77,9

LV 6.5 6.0 6.2 10.0 7.4 8.7 88,5 59,3 73,3 77,3 78,0 77,7

MT na na na na na na

PL 8.9 8.6 8.7 15.5 10.5 13.1 85,0 52,6 68,7 75,4 68,0 71,8

SI 6.7 6.6 6.6 11.1 12.2 11.6 95,3 86,0 90,5 88,5 85,1 86,8

SK 6.1 6.1 6.1 9.1 5.5 7.4 85,0 40,9 62,4 78,7 74,4 76,6

EU-25 10.7 10.7 10.7 16.5 13.7 15.2 91,9 56,1 73,4 84,5 72,7 78,7

BG 5.1 4.8 5.0 8.5 5.8 7.2 75,9 48,4 62,7 69,8 69,2 69,5

RO 6.6 6.4 6.5 11.4 10.9 11.2 88,9 69,6 79,5 81,7 70,1 76,0

EU-27 10.4 10.4 10.4 16.2 13.5 15.0 91,6 56,4 73,5 84,1 72,5 78,4

Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available. No household data available for Denmark, Sweden, Finland

and Ireland (since 1998).

Source: Eurostat, Labour Force Survey (LFS)

TABLE A4: Unemployment rates in the European Union and the Accession Countries by gender, 2002

Unemployment rate Long-term unemployment Youth unemployment rate Youth unemployment ratio rate

men women total men women total men women total men women total B 6.6 8.2 7.3 3.2 4.3 3.7 18.5 17.7 18.2 7.1 5.5 6.3 DK 4.4 4.6 4.5 0.7 1.0 0.9 9.4 5.8 7.7 6.5 3.9 5.2

D 8.7 8.3 8.6 3.9 4.1 4.0 11.3 7.9 9.7 6.0 3.8 4.9 EL 6.6 15.0 10.0 3.0 8.2 5.1 19.6 34.3 26.4 7.6 11.4 9.6 E 8.0 16.4 11.3 2.3 6.3 3.9 18.4 27.3 22.2 8.8 10.3 9.5 F 7.8 9.9 8.7 2.3 3.2 2.7 18.3 22.2 20.0 7.5 7.4 7.4 IRL 4.6 4.0 4.4 1.7 0.8 1.3 8.8 7.1 8.0 4.7 3.2 3.9

I 7.0 12.2 9.0 4.1 7.2 5.3 24.1 31.4 27.2 9.6 9.7 9.7 L 2.1 3.9 2.8 0.6 1.0 0.7 6.4 10.5 8.3 2.4 3.2 2.8

NL 2.5 3.0 2.7 0.6 0.8 0.7 5.3 5.2 5.2 4.0 3.8 3.9 A 4.1 4.5 4.3 0.8 1.0 0.9 6.5 7.0 6.8 3.9 3.6 3.7 P 4.2 6.1 5.1 1.4 2.2 1.7 9.6 13.9 11.5 5.1 5.9 5.5

FIN 9.1 9.1 9.1 2.5 2.0 2.2 21.2 20.9 21.0 11.0 10.6 10.8 S 5.3 4.5 4.9 1.2 0.8 1.0 12.0 11.6 11.8 6.4 6.5 6.4

UK 5.6 4.5 5.1 1.4 0.7 1.1 13.7 10.2 12.1 9.2 6.2 7.8 EU-15 6.9 8.7 7.7 2.6 3.5 3.0 14.8 15.5 15.1 7.6 6.8 7.2

CY 2.9 5.0 3.8 0.4 0.9 0.6 9.3 10.1 9.7 4.0 3.9 4.0

CZ 5.9 9.0 7.3 3.0 4.6 3.7 16.6 17.2 16.9 7.0 6.1 6.6

EE 9.8 8.4 9.1 6.3 4.4 5.4 14.2 22.9 17.7 5.2 5.8 5.5

HU 6.0 5.1 5.6 2.8 2.2 2.6 12.6 11.0 11.9 4.9 3.3 4.1

LT 13.3 13.0 13.1 7.8 6.8 7.3 20.5 22.6 21.4 7.4 6.3 6.8

LV 13.7 11.8 12.8 6.2 4.7 5.5 22.1 27.8 24.6 9.4 9.6 9.5

MT 6.4 9.8 7.4 0.0 0.0 0.0 19.5 17.9 18.7 0.0 0.0 0.0

PL 19.1 20.9 19.9 9.7 12.3 10.9 40.9 42.7 41.7 16.9 14.6 15.7

SI 5.7 6.4 6.0 3.4 3.6 3.5 13.9 17.2 15.3 5.6 5.6 5.6

SK 18.4 18.8 18.6 11.9 12.5 12.2 38.3 36.1 37.3 17.7 14.1 15.9

EU-25 8.0 9.9 8.9 3.4 4.4 3.8 17.4 18.5 17.8 8.7 7.7 8.2

BG 18.7 17.4 18.1 12.5 11.4 12.0 39.0 31.4 35.5 13.8 9.1 11.4

RO 7.3 6.6 7.0 4.8 4.3 4.6 18.6 18.5 18.5 7.4 5.9 6.7

EU-27 8.2 9.8 8.9 3.6 4.5 4.0 17.8 18.7 18.1 8.7 7.6 8.2

Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available

Source: Eurostat, Harmonised unemployment series and Labour Force Survey (LFS)

TABLE A5: Working time and contract status in the European Union and the Accession Countries by gender, 2002

Average working hours Full-time equivalent Share of part-time Share of employees on fixed-term employment rate employees contracts

men women total men Women total men women total men women total B 38.7 31.9 35.7 67.6 43.2 55.4 5.9 37.7 19.4 5.5 10.3 7.6 DK 37.2 32.2 34.8 76.7 63.1 69.7 11.0 31.4 20.6 7.4 10.5 8.9 D 39.0 30.8 35.2 69.9 46.4 58.1 5.8 39.5 20.8 11.8 12.2 12.0 EL 41.5 38.4 40.2 72.0 41.3 56.3 2.3 8.1 4.5 9.8 13.4 11.3 E 40.4 35.8 38.6 72.2 40.1 56.2 2.6 17.0 8.0 29.2 34.2 31.2 F 37.4 32.8 35.2 70.4 50.9 60.4 5.0 29.7 16.2 12.5 16.0 14.1 IRL 39.3 32.1 35.8 74.4 47.0 60.7 6.5 30.5 16.5 4.5 6.3 5.3

I 39.4 34.0 37.2 68.4 39.2 53.6 3.7 16.7 8.6 8.3 12.1 9.9

L 40.1 33.4 37.3 76.0 45.7 60.9 1.8 26.4 11.7 4.0 4.7 4.3

NL 34.9 24.2 30.1 74.7 42.0 58.1 21.5 72.8 43.8 12.2 17.0 14.3

A 39.4 33.2 36.6 74.8 51.4 63.0 5.1 35.7 18.9 7.3 7.4 7.4

P 40.7 37.6 39.3 76.7 58.0 67.1 7.1 16.4 11.3 20.5 23.4 21.8

FIN 38.6 35.3 36.9 69.3 62.4 65.8 8.0 17.1 12.4 13.9 20.5 17.3

S 38.1 34.0 36.0 72.9 63.4 68.1 11.2 32.9 21.4 13.3 17.9 15.7

UK 42.5 31.3 37.2 74.0 50.8 62.1 9.4 44.0 25.0 5.5 6.8 6.1

EU-15 39.4 32.2 36.1 71.2 46.8 58.9 6.6 33.5 18.2 12.1 14.3 13.1

CY 40.0 38.6 39.3 79.5 56.3 67.4 4.0 11.3 7.2 5.8 12.7 9.1

CZ 41.5 39.1 40.4 74.0 55.7 64.8 2.1 8.3 4.8 7.1 9.5 8.3

EE 41.1 38.6 39.8 66.5 55.9 60.9 3.9 9.6 6.7 3.1 1.4 2.2

HU 41.1 39.5 40.3 64.1 49.1 56.5 2.3 5.1 3.6 8.0 6.8 7.4

LT 39.2 36.9 38.0 64.4 56.5 60.3 8.6 11.0 9.8 10.3 4.8 7.5

LV 44.0 40.9 42.4 63.5 56.7 59.9 7.3 11.2 9.3 13.8 9.4 11.6

MT na na na na Na na na na na na na na

PL 42.0 38.3 40.2 56.7 44.9 50.7 8.3 13.4 10.7 16.4 14.6 15.5

SI 41.2 39.6 40.4 67.7 57.6 62.7 5.2 8.3 6.6 12.9 16.7 14.7

SK 41.9 40.9 41.4 61.7 50.0 55.8 1.2 2.7 1.9 5.1 4.4 4.8

EU-25 na na na na Na na 6.5 29.8 16.6 12.0 13.7 12.8

BG 41.3 40.3 40.8 53.9 47.5 50.6 2.4 3.7 3.1 6.8 5.3 6.1

RO 42.1 41.3 41.8 65.1 51.9 58.4 10.2 12.8 11.4 1.0 0.8 0.9

EU-27 na na na na Na na 6.6 28.6 16.2 11.6 13.2 12.3

p

Remarks: * denotes Eurostat estimate; denotes provisional value; na: not available. Average hours refer to average number of usual weekly hours of work in the main job (employees only, full-time and part-time).

Source: Eurostat, Labour Force Survey (LFS), Spring results

TABLE A6: Skills and access to training in the European Union and the Accession Countries by gender, 2002

Share of high skilled Share of medium skilled Share of low skilled Share of employees employed employed employed receiving training

men women total men women total men women total men women total B 31.2 40.1 35.0 36.4 36.1 36.3 32.3 23.8 28.7 7.2 7.8 7.5 DK 26.8 34.3 30.3 57.2 50.9 54.2 16.1 14.8 15.5 16.7 20.2 18.4

D 29.8 22.3 26.5 59.6 63.1 61.2 10.5 14.6 12.3 5.3 5.7 5.5 EL 20.1 26.2 22.4 36.8 35.1 36.1 43.2 38.7 41.5 0.8 1.2 1.0 E 26.8 36.9 30.5 18.5 20.1 19.1 54.7 43.0 50.4 3.5 5.5 4.3 F 25.3 30.6 27.7 46.3 40.3 43.5 28.4 29.2 28.8 1.9 2.9 2.4 IRL 26.7 36.5 30.8 36.3 39.5 37.6 37.1 24.0 31.6 7.9 11.4 9.6

I 12.0 17.1 13.9 37.1 45.2 40.2 50.9 37.7 45.9 3.1 5.0 3.9 L 23.3 21.6 22.6 45.5 44.3 45.0 31.2 34.1 32.4 9.9 8.6 9.4

NL 29.0 28.1 28.6 44.6 46.1 45.3 26.3 25.8 26.1 19.4 19.6 19.5 A 20.5 19.9 20.2 66.9 59.1 63.4 12.6 21.0 16.4 7.2 8.7 7.9 P 8.0 14.7 11.1 11.8 12.6 12.2 80.2 72.7 76.8 1.9 3.3 2.6

FIN 33.1 41.7 37.2 45.0 40.4 42.8 21.9 17.9 20.0 20.0 25.4 22.8 S 24.1 33.3 28.5 57.6 53.9 55.8 18.3 12.9 15.7 13.7 18.9 16.4

UK 32.7 33.6 33.1 55.9 52.2 54.2 11.4 14.2 12.7 21.0 30.0 25.2 EU-15 25.2 27.7 26.3 45.2 46.5 45.8 29.6 25.8 27.9 8.2 11.4 9.6

CY 32.3 35.2 33.6 39.4 36.7 38.2 28.3 28.1 28.2 4.1 5.2 4.7 CZ 15.1 12.5 14.0 79.9 76.6 78.4 5.0 10.9 7.6 6.6 7.2 6.9 EE 26.4 39.8 33.1 64.6 52.5 58.5 9.0 7.8 8.4 4.6 7.6 6.1 HU 16.6 20.5 18.4 68.8 60.2 65.0 14.6 19.3 16.7 3.1 4.7 3.9 LT 40.3 58.6 49.4 48.3 34.5 41.5 11.3 6.8 9.1 2.8 5.5 4.3 LV 19.2 27.0 23.1 65.3 64.3 64.8 15.5 8.8 12.1 6.4 15.9 11.3 MT na na na na na na na na na na na na PL 13.9 20.2 16.8 73.9 67.7 71.0 12.3 12.1 12.2 5.8 8.1 6.9 SI 14.7 21.1 17.7 69.0 58.0 63.9 16.3 20.8 18.4 10.5 12.4 11.4 SK 13.4 15.3 14.2 82.3 76.4 79.6 4.4 8.3 6.2 9.7 11.9 10.7

EU-25 23.9 26.7 25.1 49.3 49.6 49.5 26.8 23.6 25.4 7.9 10.9 9.2 BG 21.8 33.4 27.3 58.4 50.7 54.7 19.8 15.9 18.0 0.8 0.8 0.8 RO 12.3 12.3 12.3 67.7 58.4 63.4 20.0 29.3 24.3 1.1 1.4 1.2

EU-27 23.3 26.1 24.6 50.2 50.1 50.2 26.4 23.8 25.3 7.5 10.4 8.8

Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available. High skilled (tertiary education completed); medium

skilled (upper secondary education completed), low skilled (less than upper secondary education).

Source: Eurostat, Labour Force Survey (LFS), Spring results

TABLE A7: Vertical employment segregation in the European Union and the Accession Countries by gender, 2002

Share of employees in Share of employees in Share of employees in Share of employees in nonmanagerial occupations supervisory positions * intermediate positions * supervisory positions * men women total men women total men women total men women total B 7.6 4.4 6.2 17.6 6.0 12.5 23.3 16.5 20.3 59.1 77.5 67.3 DK 6.7 1.7 4.3 21.5 8.9 15.5 12.5 13.4 12.9 66.0 77.8 71.6

D 4.9 1.9 3.5 na Na na na na na na na Na EL 2.9 1.0 2.1 8.3 2.7 6.1 8.3 4.3 6.7 83.4 93.0 87.3 E 2.7 1.1 2.0 8.6 3.5 6.7 19.1 13.8 17.1 72.3 82.6 76.1 F 5.9 4.0 5.0 17.2 7.1 12.6 22.4 16.7 19.8 60.4 76.2 67.6 IRL 11.1 8.7 9.9 14.5 9.0 12.0 13.8 11.8 12.9 71.7 79.1 75.0

I 2.4 0.8 1.8 11.8 4.9 9.0 16.4 11.7 14.5 71.7 83.4 76.4 L 4.2 1.8 3.2 na Na na na na na na na Na

NL 11.7 4.3 8.4 17.3 6.1 12.8 17.9 13.3 16.1 64.8 80.6 71.2 A 6.4 3.8 5.2 14.3 4.3 9.9 27.3 19.0 23.7 57.4 76.7 66.4 P 1.6 0.9 1.3 5.1 1.6 3.5 5.4 6.4 5.9 89.5 92.0 90.6

FIN 8.9 4.1 6.5 20.2 8.1 14.3 17.3 17.4 17.3 62.5 74.5 68.4 S 5.2 2.6 3.9 na na na na na na na na Na

UK 17.9 9.0 13.7 25.8 19.1 22.6 15.3 16.6 15.9 58.9 64.3 61.5 EU-15 7.3 3.7 5.7 16.6 9.2 13.4 17.9 14.8 16.5 65.5 76.0 70.1

CY 3.8 0.9 2.4 na na na na na na na na na

CZ 5.3 2.7 4.1 na na na na na na na na na

EE 11.7 7.4 9.5 na na na na na na na na na

HU 6.4 4.7 5.6 na na na na na na na na na

LT 7.0 6.8 6.9 na na na na na na na na na

LV 9.2 7.4 8.3 na na na na na na na na na

MT Na na na na na na na na na na na na

PL 6.8 3.7 5.3 na na na na na na na na na

SI 5.2 3.5 4.4 na na na na na na na na na

SK 4.5 2.6 3.6 na na na na na na na na na

EU-25 7.1 3.8 5.6 na na na na na na na na na

BG 5.2 3.7 4.4 na na na na na na na na na

RO 2.5 1.7 2.1 na na na na na na na na na

EU-27 7.0 3.7 5.5 na na na na na na na na na

Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available. Italics denotes unreliable. Managerial occupations

defined according to ISCO-88 (COM); Category 100 (Legislators, senior officials and managers); * data refer to year 2000.

Source: Eurostat, Labour Force Survey (LFS), Spring results; * European Community Household Panel (ECHP), UDB version June 2003 TABLE A8: Horizontal (sectoral) employment segregation in the European Union and the Accession Countries by gender, 2002

Employment share of Share of employees Share of employees Share of employees working in self-employed working in the private working in the public firms with less than 10 workers sector * sector *

men women total men women total men women total Men women total B 16.9 13.5 15.4 83.0 75.0 79.2 17.0 25.0 20.8 14.2 19.0 16.3 DK 12.1 5.2 8.9 76.6 46.2 62.6 23.4 53.8 37.4 16.3 17.1 16.7

D 13.3 8.3 11.1 81.9 68.3 76.2 18.1 31.7 23.8 12.2 23.1 17.2 EL 41.8 36.5 39.8 79.1 75.0 77.6 20.9 25.0 22.4 28.5 32.2 30.0 E 21.6 15.4 19.3 85.9 78.8 83.4 14.1 21.2 16.6 13.0 22.6 16.8 F 13.3 7.7 10.8 73.2 60.5 67.6 26.8 39.5 32.4 9.3 14.7 11.8 IRL 25.1 7.5 17.7 80.3 73.3 77.6 19.7 26.7 22.4 na na na

I 31.3 21.1 27.4 77.8 66.1 73.6 22.2 33.9 26.4 na na na

L 9.6 5.4 7.9 na na na na na na 11.9 23.8 16.8

NL 13.4 9.5 11.7 81.0 70.2 76.7 19.0 29.8 23.3 na na na

A 14.6 12.0 13.5 79.4 73.6 76.9 20.6 26.4 23.1 na na na

P 28.8 25.4 27.3 87.6 80.8 84.5 12.4 19.2 15.5 16.1 23.1 19.4

FIN 16.5 9.0 12.9 77.2 53.4 66.0 22.8 46.6 34.0 23.8 28.3 26.1

S 15.3 5.7 10.7 81.3 47.8 64.8 18.7 52.2 35.2 17.2 17.3 17.3

UK 15.6 7.0 11.7 86.1 68.2 78.1 13.9 31.8 21.9 14.5 18.4 16.4

EU-15 18.8 11.4 15.6 81.0 67.5 75.4 19.0 32.5 24.6 11.1 17.2 13.9

CY 28.6 15.9 23.0 na na na na na na 26.9 37.6 32.1

CZ 20.1 10.5 15.9 na na na na na na 14.3 23.2 18.5

EE 9.5 4.4 7.0 na na na na na na 16.8 20.0 18.4

HU 16.8 10.0 13.7 na na na na na na 18.0 21.0 19.4

LT 23.7 17.8 20.8 na na na na na na 16.4 13.7 15.1

LV 15.5 10.9 13.2 na na na na na na 24.0 28.3 26.2

MT na na na na na na na na na na na na

PL 30.6 25.4 28.2 na na na na na na 12.5 15.6 14.0

SI 19.0 13.0 16.2 na na na na na na 32.9 30.4 31.6

SK 11.8 4.4 8.4 na na na na na na 17.2 29.6 23.1

EU-25 19.5 12.4 16.4 na na na na na na 11.8 17.7 14.5

BG 17.9 12.2 15.2 na na na na na na 17.7 20.8 19.3

RO 38.3 41.4 39.8 na na na na na na 7.7 14.9 11.0

EU-27 20.4 13.8 17.5 na na na na na na 11.7 17.6 14.4

p

Remarks: * denotes Eurostat estimate; denotes provisional value; na: not available. Self-employment includes family workers; * data refer to year 2000. Source: Eurostat, Labour Force Survey (LFS), Spring results; * European Community Household Panel (ECHP), UDB version June 2003

TABLE A9: Horizontal (sectoral) employment segregation in the European Union and the Accession Countries by gender, 2002

Share of employed in Share of employed in Share of employed in Share of employed in low

industry services high-tech or knowledgepaying sectors 2) intensive sectors 1)

men women total men women total men women total Men women total B 38.9 13.1 27.7 60.0 86.6 71.5 37.4 57.6 46.1 23.0 18.5 21.1 DK 34.8 12.5 23.9 62.8 86.6 74.4 40.6 64.6 52.3 30.3 16.4 23.5

D 47.1 18.4 33.9 51.2 80.6 64.7 38.8 49.8 43.9 26.0 24.2 25.2 EL 35.8 14.2 27.1 62.6 85.1 71.7 26.5 43.0 33.1 33.5 25.1 30.1 E 44.6 15.5 33.1 51.4 82.7 63.7 27.8 44.5 34.3 38.6 26.7 33.9 F 37.0 13.6 26.0 60.9 85.5 72.4 37.0 52.7 44.3 27.6 17.6 22.9 IRL 41.9 14.0 28.7 55.5 85.5 69.6 34.9 56.0 44.8 36.4 26.1 31.5

I 42.3 21.6 33.8 54.6 76.2 63.5 32.7 48.7 39.3 26.4 18.9 23.3 L 30.8 6.6 20.9 68.4 93.2 78.6 30.2 53.8 39.9 28.0 21.2 25.2

NL 31.8 9.0 21.7 66.2 90.0 76.8 39.0 59.6 48.1 30.0 23.0 26.9

A 46.0 14.2 31.5 52.8 84.8 67.4 31.2 47.1 38.4 31.1 30.5 30.8

P 49.1 25.9 38.3 47.6 72.1 58.9 19.5 38.6 28.4 41.5 23.0 32.9

FIN 42.6 14.2 28.3 55.2 84.6 70.0 40.2 60.2 50.3 26.1 19.8 22.9

S 36.5 10.4 23.3 62.0 89.1 75.7 45.2 68.4 56.9 24.9 14.9 19.8

UK 35.0 10.9 23.6 63.8 88.7 75.6 42.0 56.9 49.1 27.5 24.1 25.9

EU-15 40.8 15.2 29.3 57.0 83.7 69.1 36.4 52.4 43.6 28.8 22.2 25.8

CY 34.2 12.9 24.0 63.7 86.1 74.5 23.0 38.0 30.2 42.3 31.3 37.0

CZ 51.4 29.6 41.3 42.5 67.0 53.9 26.3 43.8 34.5 27.9 24.0 26.1

EE 40.3 23.7 31.9 53.9 73.1 63.6 26.1 46.2 36.3 31.8 23.8 27.7

HU 44.7 25.9 35.9 49.4 71.9 60.0 28.3 47.2 37.2 30.8 22.9 27.1

LT 43.9 23.1 33.3 49.0 73.6 61.5 21.2 45.4 33.5 38.0 23.0 30.4

LV 39.6 16.8 28.0 48.8 79.0 64.2 19.1 40.0 29.7 34.8 29.1 31.9

MT na na na na na na na na na na na na

PL 49.0 22.2 36.3 47.9 76.6 61.5 na na na 26.8 22.6 24.8

SI 52.4 31.9 42.6 45.6 67.2 55.9 29.9 43.8 36.5 25.9 23.9 24.9

SK 48.8 26.9 38.4 42.4 68.7 54.9 24.9 42.7 33.4 31.2 24.5 28.0

EU-25 41.8 16.6 30.3 55.7 82.1 67.7 35.5 51.6 42.8 28.9 22.4 25.9

BG 41.0 31.7 36.4 52.3 65.4 58.7 23.7 38.4 30.9 31.8 24.8 28.3

RO 51.8 41.2 47.0 43.3 57.3 49.6 24.3 36.3 29.7 25.2 21.2 23.4

EU-27 42.2 17.7 31.0 55.2 81.0 66.9 34.9 50.9 42.2 28.8 22.4 25.9

Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available; 1) as defined in Employment in Europe 2001; 2) low paying

sectors are defined as those sectors that, at EU level, pay average gross hourly wages of 75% or less of the median gross hourly wages (agriculture, construction, retail and trade, hotels and restaurants; NACE Rev. 1 sections A, B, F, G and H). Source: Eurostat, Labour Force Survey (LFS), Spring results TABLE A10: Horizontal (occupational) employment segregation in the European Union and the Accession Countries by gender, 2002 Share of employed in high skilled Share of employed in low Share of employed in low Share of employed in low

manual or non-manual skilled non-manual skilled manual paying occupations 2)

1)

occupations occupations 1) occupations 1)

men women total men women total men women total Men women total B 34.4 40.3 37.0 6.6 15.8 10.6 23.3 15.2 19.8 17.2 28.1 21.9 DK 40.7 40.9 40.8 7.1 25.9 16.3 24.8 15.0 20.0 22.4 38.2 30.1

D 35.2 41.0 37.8 5.7 20.4 12.5 19.7 12.9 16.6 14.2 31.5 22.1 EL 23.4 32.9 27.2 13.8 20.5 16.5 21.2 16.5 19.4 23.0 34.9 27.8 E 23.7 30.6 26.5 10.9 23.8 15.9 28.3 24.9 27.0 26.2 44.7 33.5 F 35.9 34.2 35.1 6.7 21.5 13.6 23.8 16.1 20.2 15.7 33.2 23.9 IRL 31.2 35.8 33.4 11.6 25.1 17.9 26.3 12.5 19.8 24.2 32.7 28.2

I 26.3 36.9 30.6 10.1 16.1 12.6 23.5 16.2 20.5 20.2 26.9 23.0

L 34.7 36.4 35.4 7.0 17.5 11.3 21.5 19.6 20.7 17.8 36.2 25.3

NL 45.5 44.3 45.0 7.9 20.7 13.7 19.7 12.1 16.3 19.3 31.9 24.9

A 29.4 32.3 30.7 9.1 23.1 15.5 20.0 17.2 18.8 16.9 37.6 26.3

P 17.0 21.3 19.0 9.3 21.0 14.7 27.6 27.2 27.4 23.5 43.4 32.7

FIN 41.1 40.2 40.7 6.8 25.9 16.4 22.6 15.4 18.9 16.7 39.1 28.0

S 42.5 42.0 42.2 8.6 29.6 19.2 22.5 11.2 16.7 15.8 37.4 26.7

UK 42.4 33.4 38.1 8.9 26.7 17.3 25.6 13.2 19.7 23.4 37.0 29.9

EU-15 34.2 36.4 35.2 8.1 22.1 14.5 23.3 15.4 19.8 19.2 34.4 26.1

CY 30.0 29.8 29.9 12.6 16.9 14.7 22.8 25.5 24.1 26.1 39.5 32.6

CZ 30.4 37.6 33.7 7.8 17.6 12.3 26.9 20.7 24.0 14.0 29.0 21.0

EE 31.0 45.1 38.1 6.2 18.5 12.4 29.2 24.3 26.7 16.7 34.7 25.8

HU 24.6 39.1 31.4 9.6 16.2 12.7 25.7 20.0 23.0 19.3 27.9 23.4

LT 24.9 47.6 36.5 6.6 16.5 11.6 31.7 15.8 23.6 21.3 30.6 26.0

LV 29.0 45.5 37.4 8.2 20.5 14.4 32.2 18.1 25.0 23.8 35.5 29.8

MT na na na na na na na na na na na na

PL 28.2 43.1 35.3 8.1 16.7 12.2 26.1 14.9 20.8 17.4 28.9 22.9

SI 30.6 39.9 35.0 9.5 18.4 13.7 26.5 21.9 24.3 15.3 25.3 20.0

SK 25.1 41.5 32.9 7.8 19.8 13.5 32.5 19.9 26.5 18.3 31.8 24.8

EU-25 33.3 37.1 35.1 8.2 21.4 14.2 23.9 15.8 20.2 19.0 33.7 25.7

BG 26.5 38.2 32.3 10.0 18.4 14.1 33.7 20.0 26.9 23.3 30.0 26.6

RO 22.9 35.9 28.8 6.2 17.5 11.3 31.8 16.2 24.8 16.3 25.4 20.4

EU-27 32.9 37.1 34.8 8.1 21.2 14.1 24.3 15.9 20.4 18.9 33.3 25.5

Remarks: * denotes Eurostat estimate; p denotes provisional value; na: not available; 1) as defined in Employment in Europe 2001;High-skilled non-manual

(ISCO categories 100+200+300); Low-skilled non-manual (ISCO 500); Low-skilled manual (ISCO 800+900). 2) low paying occupations are defined as those

occupations that, at EU level, pay average gross hourly wages of 75% or less of the average gross hourly wages (service workers and shop assistants, agricultural workers, elementary occupations; ISCO sections 500, 600 and 900).

Source: Eurostat, Labour Force Survey (LFS), Spring results

8. A PPENDIX 2: A NALYSING FACTORS RELATED TO THE GENDER PAY GAP SUMMARY OF MAIN FINDINGS FROM SELECTED NATIONAL STUDIES

The main focus of the work of the Expert Group to the European Commission on Employment and Gender in 2002 was on the measurement and analysis of the gender pay gap. As part of its work, it has provided a comprehensive review of studies of the gender pay gap at national level the results of which are summarised in the table below.

Country Author Dataset, Independent variables Year Unadjusted % of gap Adjusted Comments

and year year and Personal characteristics Workplace and pay gap explained pay gap sample other

characteristics

Belgium Jepsen (2001) ECHP 1994- Education, qualifications, Employment 1994- 0.16 13% 0.14 MFT v FFT

95 experience, company contract 95

seniority, occupation, 0.09 99% 0.00 FPT v FFT

sector

Ministry of ESES Education, work Employment 1995 0.25 76% 0.06 Employment and experience, seniority, contract, firm size, Labour ISCO (2-digit), working type of economic/ hours, shift/ unsocial financial control hours premia, sector

Plasman (2002) ECHP 1994- Age, nationality, -- 1994- 0.21 37% 0.13 Analysis covers 13 EU 1998 education, family status, 98 member states. Explained property status, gap ranges from 18% experience, sector, firm (Spain) to 66% size, occupation (Netherlands).

Denmark Pedersen and Central Marital status, no. and age Location of 0.17 25% 0.13 Private

Deding (2000) Bureau of of children, education (7 workplace

Statistics levels), age, time in 0.10 84% 0.02 Central gov’t

(1996) training, reason for 0.11 95% 0.01 Local gov’t

absence from labour Study limited to age group market, PT/ FT, hours of 25-59 work, occupation (9 groups), sector (9 and 27 groups)

Germany Seel & Hufnagel GSOEP, W Age, household status, no Estimates the lifetime earnings gap between men and women according to marital status and (2000) Germany of children, FT/PT, hours, motherhood.

net income of partner, interruptions, work experience, job position, sector, education, training

Greece Kanellopoulos Survey of Schooling, seniority, Firm size, 1964 0.38 72% 0.11 Monthly pay

1982 manufacturing, potential experience, employment 1964 occupation (1-digit), growth of firm industry (2 dummies)

Psacharopoulos 1977 survey in Schooling, actual -- 1977 0.35 11% 0.31 1983 nine cities experience

Kanellopoulos Family Wage-related variables: -- 1988 0.24 28% 0.17 and Expenditure age, education, marital

Mavromomaros Surveys, 1988, status, sector, residence, 1994 0.29 29% 0.21

2000 1994 (weekly managerial job, pay) participation variable (function of 10 variables in first step Heckman procedure)

Karamessini and ESES Age, education, tenure, Collective 1995 0.33 73% 0.09 Industry Iokimoglou 2002 individualmarital status, bargaining

level data nationality, overtime, coverage, public/ 0.29 76% 0.07 Services

shift, supervision private ownership, contract, firm size, average

occupational wage

Spain Ribaud and Survey on Education, seniority, 0.19 30% 0.13

Iglesias (1989) Gender experience Discrimination

Caillavet (1990) Survey of 0.40 42% 0.23 Married employees only Living and

Working Conditions (1985)

De la Rica & Survey on 0.17 -14% 0.19 Ugidos (1995) social biography

France Meurs and 1997 survey Working hours, Firm size 1997 0.27 85% 0.04 FT and PT

Ponthieux (2000) ‘Young people education, age,

and careers’ occupation, sector 0.12 52% 0.06 FT

0.27 44% 0.15 Not controlling for hours:

FT and PT 0.12 44% 0.07

FT

Meurs and Employment Education, occupation, 1997 0.13 47% 0.07 FT workers only, includes

Ponthieux (1999) Survey 1991, status (public/ private/ public sector

1997 temporary)

Lemiere et al 1992 pay Education, experience, 1992 0.20 25% 0.15 Banking

1999 structure occupation 0.39 52% 0.19 Research services

survey (3-

sectors) 0.20 36% 0.13 Plastics processing

Ireland Barrett et al Living in Age, education, marital -- 1987 0.20 50% 0.10 Includes FT and PT

(2000) Ireland survey status, presence of

children, work 1994 0.18 62% 0.08

experience, occupation, 1997 0.16 74% 0.05

hours worked, incidence of fringe benefits

Italy ITER (2001) – ECHP, all 1993 0.25 24% 0.19 ECHP data results

CNPO Report years 1995 0.20 20% 0.16

SHIW, all years

Luxembourg Lejealle 2001 ESES Nationality, education, Firm size, 1995 0.85 28% 0.61 potential experience, collective

seniority, occupation, bargaining, % PT supervision, job activity share

Netherlands Spijkerman 2000 Dutch Labour Ethnicity, age, education, Firm size 1993 0.26 65% 0.09 Private sector

Inspectorate experience, FT/PT, job

level, occupation, 1996 0.24 71% 0.07 Private sector

industry 1998 0.23 70% 0.07 Private sector

1998 0.15 73% 0.04 Public sector

SZW 2002 Labour 2000 0.23 78% 0.05 Private sector

Inspectorate 2000 0.15 80% 0.03 Public sector

Wage Index Online pay Education, experience, M-share of job, 2001-02 0.11 72% 0.03 survey, 2001- PT working, children. labour contract,

02 collective bargaining

Austria Boeheim et al Microcensus Age, qualification, Employment status, 1997 0.23 18% 0.19 No significant increase in

(2002) 1983 and 1997 highest level of size of home town,

education, years of province 1983 0.23 21% 0.17

discrimination; different specifications give

employment, sector, discrimination of 66%-81%

Portugal Martins 1998 Quadros de Education, experience 1997 0.21 54% 0.10 Control for education and Pessoal, 1997 experience only

Kiker and Santos Quadros de Education, tenure, Firm size, region 1985 0.29 34% 0.19 Authors note lack of data on 1991 Pessoal, 1985 experience, qualification, family background, IQ,

working hours, sector, motivation, unionisation private/ public

Ribeiro and Hill Quadros de Education, tenure, %F share of 1992 0.29 24% 0.22 Human capital model 1996 Pessoal, 1992 experience occupation, firm

size 0.29 54% 0.13 Comparable value model

Restricted to hotels and

restaurants only

Finland Lilja (1999) Banking Age, banking experience, Work tasks, job 1990 0.44 86% 0.06

sector 1990-97 education, status grade, region 1997 0.51 82% 0.09

Lilja (2000) Employer 3 education levels, career Local region, -- (career start)

assocation phase, job position, wage unemployment

panel; 1980- group, job category, 0.35 43% 0.20 Basic ed

95 (non industry, firm size, 0.33 54% 0.15 Secondary ed

manual, mobility, hours 0.19 32% 0.13 University ed industry)

0.38 45% 0.21 (after 10 years)

0.31 16% 0.26 Basic ed

0.19 5% 0.18 Secondary ed

University ed Vartiainen (2001) Income Age, education, Firm size, region 0.21 54% 0.10

Distribution occupation, sector, no. of Statistics, children Finnish Structural Earnings

Sweden Statistics Sweden Statistics Age, Education, __ 1996 0.17 53% 0.08 All Sectors

(2002) Sweden Working-Time, Sector,

Occupation 1998 0.18 50% 0.09

2000 0.18 56% 0.08

UK Joshi and Paci Birth Cohort General ability at 11 Region 1978 0.31 30% 0.22 Human capital model

1998 studies years, education (5 1991 0.17 7% 0.16

(NDRC, variables), work history, MRC), 1978, family background 1991 (FT only)

Joshi and Paci NDRC 1991 The above variables plus: firm size, 1991 0.18 38% 0.11 FFT/MFT 1998 occupation public/private,

sector, employer 0.60 58% 0.25 FPT/MFT

financed training provision, flexible hours, supervisory responsibility, union member, female share of occupation, fringe benefits, commuting time

Harkness 1996 GHS 1974, Age, educational 1992-93 0.20 10% 0.18 FFT/MFT BHPS 1992- qualifications

93 0.35 17% 0.29 FPT/MFT

Human capital model

Harkness 1996 GHS 1974, Age, educational Union, employer 1992-93 0.20 10% 0.18 FFT/MFT BHPS 1992- qualifications, full-time/ size, region

93 part-time work 0.35 63% 0.13 FPT/MFT

experience, industry, Full model

occupation, children Andersen et al WERS98 Human capital, personal job characteristics 1998 0.22 50% 0.11 All 2001 characteristics, (occupation, job

type, gender 0.16 69% 0.05 FFT/MFT

segregation, 0.34 80% 0.07 FPT/MFT

payment system), workplace characteristics (union, size, age, ownership, gender segregation, parttime share, manual share, industry, gender share of industry, competition, local labour market)

Bell and Ritchie NES Panel, Age, length of time in 1979 0.27 27% 0.20 1977-94 current job, region,

collective bargaining 1994 0.19 18% 0.16

coverage, industry, occupation

Source: European Commission (2003b) based on National Reports

Remarks: "MFT" and "FFT" denote the samples of full-time employed men and women, respectively. "FPT" denotes the sample of women in part-time employment. No information is provided as to whether, and how, the above studies account for the potential problems of selectivity, endogeneity and unobserved heterogeneity.

2.

Originele weergave

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3.

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