Labor Market Gender Gaps and the Great Recession: Evidence from European Union

Labor Market Gender Gaps and the Great Recession: evidence from European Union

Work in progress - May 2017

Carole Brunet

LED, Universit´eParis 8

Esther Jeﬀers

CRIISEA, Universit´ede Picardie Jules Verne

Abstract

This paper examines the evolution of labor market gender gaps between 2003 and 2015 in the 15 European Union countries. We focus on participation, employment, unemployment and wage gender gaps, and study the effect of the Great Recession on gender labor market differentials. Women are more vulnerable to recession than men due to their higher concentration in part- time and temporary work. But male and female workers are not equally present in the sectors most affected by the crisis. Our results indicate overall that gender gaps have decreased during this time span. However, this did not always reflect an improvement in the situation of women in the labor market: often it was the deterioration of men’s conditions that was responsible for the narrowing of gender gaps. Keywords: Gender gap, part-time work, temporary work, crisis, added worker effect, buffer effect JEL classification : J16, J31.

[email protected] (Carole Brunet), [email protected] (Esther Jeﬀers) 1. Introduction and related literature

Much has been written about the global crisis that started in 2007-2008. Arguably one of the hardest hit regions was Europe. European economies are still battling depression-era levels of unemployment and the risk of deflation is not so remote. In normal times, most countries recognize that women’s participation in the labor market is a factor of growth and that equal rights for men and women should exist (e.g. Löfström,2009). Progress in that direction has undoubtedly been made over the last three decades. Regulations intended to

ﬁght discrimination and programs granting women equal rights in the labor market have been adopted. But when a crisis of such magnitude hits economies, what happens to these principles?

A number of studies have looked into how the economic and financial crisis and ensuing policies have affected gender inequalities (Karamessini and Rubery, 2013; Rafferty and Rubery,

2013). This period indeed constitutes a crucial field of research from a gender perspective. At least two dimensions linked to gender equality can be studied: first, the possible differentiated impact on women and men in the labor market; and, secondly, the impact of the economic and

ﬁnancial crisis on policies directly or indirectly aﬀecting gender equality, including in terms of austerity measures and public spending. Macroeconomic policies are fundamental instruments to reorient the economy but they are not gender blind. It is therefore a legitimate question to ask how the crisis and programs adopted to counter it impacted gender inequalities. Did they contribute to the narrowing or the widening of the gender gap?

Among the most important questions are the disparities in the labor market, which can be difficult to examine because they differ according to countries, regions, and sectors. Men and women tend to have different average unemployment, labor participation, and wage rates, and to cluster in different occupations. No matter which country is under study, the participation rate for women is typically lower than that for men, but differences vary widely. Undoubtedly, improvements in the ratio of female to male labor force participation rates have occurred in

2 most countries in the past. In many cases we have witnessed drastic reductions of male-female wage gaps in recent decades. But despite this progress, gender differentials persist. Over time, these and other labor market differences have been narrowing but have endured. To what extent did a crisis of such magnitude as the Great Recession affect this process? The literature that focuses on the impact of a crisis in the evolution of gender-based differences on the labor market is relatively small compared to the much broader literature on the general impact of crises. The analysis of the forces behind gender differences in the evolution of labor force participation rates can be viewed through the three following angles, which we will use for our study.

The first approach focuses on the behavior of women seen through the added worker effect or the “discouraged worker effect”. “Added worker effect” is broadly defined as an increase in the spousal participation in the labor market in response to an income shock. The increase in the female labor force participation rate may reflect a distress response to the crisis

(Lundberg, 1985; Cerrutti, 2000; Sabarwal et al., 2011), when wives enter the labor force as a response to spousal job loss. In contrast, the “discouraged worker effect” describes a situation where unemployed individuals stop looking for jobs when they anticipate their search has a low probability of success. In particular, married women may become discouraged when the unemployment rate is high, leading them to leave the labor force. Both effects exist and are responses to the worsening of the macroeconomic environment. The question is which one is dominant in time of crisis, since they have opposite effects on the participation of women in the labor force (Gong, 2011).

The second approach relates to firms’ behavior, again with two contradictory effects. On the one hand, management may be tempted to hire women because they provide cheaper labor than male workers. We refer to this as the “substitution effect” (Bruegel, 1979; Rubery and

Tarling, 1982). On the other hand, women can be considered as a ﬂexible labor reserve, and a decrease in employment opportunities can thus lead to more women being laid oﬀ or otherwise

3 leaving the labor force. This is the “buﬀer eﬀect”, leading to fewer women being part of the labor force during a downturn (Rubery, 1988).

Finally, gender segregation is the third approach through which shifts in the participation of women in the labor force can be analyzed. Men and women do not occupy the same jobs and are not present in the same sectors (Bettio et al., 2009). Workforce job data is limited in that it does not indicate patterns of occupational vertical or horizontal segregation within sectors.

However, trends at the level of the industrial sector make it possible to analyze whether gender segregation of men and women in different sectors influences patterns of male and female job losses. Men and women appear to be in “protected” or “buffer” jobs based on their job losses within sectors being proportionate or disproportionate to the level of sector representation.

To measure gender inequalities in the labor market, we use four different indicators. The employment rate is one. Historically, within the framework of the EU, which includes of course the Eurozone, the number of women who are part of the workforce and employed has risen. This has been uneven from country to country, but the trend has been clear1. However, one of the most important effects of the crisis has been its major impact on employment levels of both female and male workers, which cannot be limited to the initial impact of the downturn but needs to be studied on a mid to long-term period. Falling employment rates for women are not necessarily accompanied by similar increases in unemployment rates, since discouraged persons are no longer counted as unemployed but rather as ”not in the labor force”: for this reason, we have also included in the analysis activity rate differentials according to gender. Gendered unemployment patterns clearly vary not only by region, but also according to the structures of particular economies. Another manifestation of women being considered a voluntary flexible reserve is the fact that they have to move into part-time work as an

1An example is the case of Sweden, where 77% of women are employed, in contrast to Greece, where only 43% are.

4 alternative to full-time employment, or into temporary work in the face of reduced permanent employment opportunities.

Another measure we use is the pay gap. In an economy hit by a sharp recession, especially for countries in a currency union such as the euro area, “wage moderation” is used to regain competitiveness when the nominal exchange rate is ﬁxed and to combat current account deﬁcits.

This is referred to as “internal devaluation”. Even in good economic times, there is a diﬀerence between the earnings of men and women. Despite the fact that the European employment strategy includes reducing the gender pay gap as one of its core objectives, more than thirty years of equal pay legislation, and real progress in the situation of women in the labor market, the gender pay gap persists, with women’s gross hourly earnings on average 16.3% below those of men in the European Union (EU-28) and 16.8% in the euro area (EA-18)2. This gender pay gap captures the enduring gender inequalities that exist on the labor market.

How do we explain the existence of the gender pay gap? Existing literature highlights different possible reasons. One is that professions and sectors with low-level wages tend to be dominated by women (Bettio et al., 2009) . Another is that women are much more likely than men to be employed part-time (OECD 2016), this form of work being considered more suitable for women nearly everywhere. Maternal employment rates may also affect women’s average salaries. In this paper we test these hypotheses and measure the relative impact of different factors on the gender pay gap. What is the effect of the crisis on gender pay differentials?

Women are more vulnerable to recession than men due to their higher concentration in part- time and temporary work. But male and female workers are not equally present in the sectors most aﬀected by the crisis (Eydoux et al., 2014). How does this aﬀect the gender pay gap?

Are women and men not affected in the same way? Or have they been affected at different stages during the crisis?

2Eurostat data from March 2017.

5 Cutbacks in public spending on services, austerity policies, and job cuts in the public sector, can also be viewed through the lens of gender equality. Women are particularly aﬀected by public service cuts, directly and indirectly, both as public service workers - they make up the majority of the labor force in the public sector - and as the principal users of such services.

The object of this paper is to study the evolution of gender gaps in 15 European Union countries before and after the outbreak of the crisis. We consider the different effects described above and expect them to impact dependent gender related variables. These effects can happen simultaneously, and since they can have contradictory impacts, the outcome is not clearly predictable as to which effect dominates the other. However this analysis can be useful to help us better understand the mechanisms at work.

1. Women’s behavior - Hypothesis 1a: the added worker eﬀect will translate into more

women looking for work in case of spousal job loss. The expected eﬀect is therefore

an increase in women’s participation in the job market, including settling for part-time

work, and an increase in the wage gap, since they would have to accept lower wages.

2. Women’s behavior - Hypothesis 1b: The discouraged worker eﬀect will translate into

fewer women looking for work. The expected eﬀect is therefore a decrease in their

unemployment rate and in their participation in the labor market, hence a decrease

in the unemployment gap and an increase in the participation gap.

3. Firms’ behavior - Hypothesis 2a: The substitution eﬀect translates into hiring cheaper

labor. The expected eﬀects are more women participating in the labor market, including

in part-time work, and therefore a decrease in the participation gap. The wage gap may

increase.

4. Firms’ behavior - Hypothesis 2b: The buﬀer eﬀect means less hiring in times of crisis,

and women being easier to ﬁre. Expected eﬀects are a decrease in women’s participation

and probably an increase in part-time work.

6 5. Gender segregation - Hypothesis 3a: The protected sector eﬀect suggests that women

initially worked in sectors less aﬀected by the crisis at an early stage. Thus at ﬁrst

we expect to see a decrease in the participation gap. However, this can change in the

medium or long-term if the male-dominated sectors proﬁt from governmental subsidies.

6. Gender segregation - Hypothesis 3b: Public spending eﬀects can translate directly into

cuts of public jobs where a majority of the employees are women, leading to a decrease

in the number of employed women; expected eﬀects are an increase of the labor market

participation gap . However, women use more social services and during times of auster-

ity, when public services are cut back, the result is that women are less available to take

jobs. Expected eﬀects are a decrease in women’s participation in the labor market, or

more part-time jobs.

The rest of the paper is organized as follows. Section 2 presents our data and the sample descriptive statistics. Section 3 discusses the econometric speciﬁcation and estimation, before turning to the results and their main implications in section 4. The last section concludes.

2. The Data

We use data from the former Eurostat database, and our sample is made up of countries from the formerly EU-15, from 2003 to 2015.3. We choose not to include more recent members in order to keep a balanced panel of relatively homogeneous countries (see Jaba et al. (2015)) for a study conducted with Eastern European countries)

Our dependent variables include gender gaps in labor market participation, employment and unemployment rates, and wages. The period under study is 2003-2015 for the three former indicators, while it is restricted to 2006-2015 for the wage gap, because of data availability

3Our sample includes Austria, Belgium, Denmark, Finland, Germany, Greece,France, Ireland, Italy, Luxem- bourg, The Netherlands, Portugal, Spain, Sweden and United Kingdom.

7 limitations. Our explanatory variables include GDP per capita, growth rate, women’s ducation levels, fertility, part-time and temporary employment, employment sector shares and, the existence and extent of a minimum wage, and yearly time eﬀects. Table A1 and table A2 in appendix A1 give a full account of the deﬁnition of variables and of the descriptive statistics, while table 1 below summarizes gender gap indicators, as well as contrasting pre- and post-crisis

ﬁgures.

Table 1: Gender gaps for EU-15 sample

Source: Eurostat, authors’ calculations

8 We can see in table 1 that averaged labor market gender gaps decreased between 2003 and 2015: participation and employment rate gaps were both over 14 points on average in the period 2003-2008; they declined respectively by over 3 and 4 points. Looking at ﬁgures 1 and

2, the decline in the participation and employment gaps between 2003 and 2015 is continuous, and more pronounced during 2008-2010. The average unemployment rate gap fell from 1.7% to 0.25%, while the wage gap declined from almost 17% to 15.5%. However, diﬀerent gender patterns in terms of labor market outcomes account for these changes4 .

Participation gap in EU−15 2003 − 2015 16 14 12 mpart_gap_year 10

2000 2005 2010 2015 year Source: Eurostat .

Figure 1: Participation gap in EU-15 , 2003-2015

4We can not run the following analysis for the wage gap, which is directly provided by Eurostat without access to men’s and women’s earnings series

9 mur_gap_y mempl_gap_year 0 .5 1 1.5 2 8 10 12 14 16 2000 2000 . Source: Eurostat . Source: Eurostat iue3 nmlyetgpi U1 2003-2015 , EU-15 in gap Unemployment 3: Figure iue2 mlyetgpi U1 2003-2015 , EU-15 in gap Employment 2: Figure Unemployment gapinEU−15 Employment gapinEU−15 2005 2005 2003 −2015 2003 −2015 10 year year 2010 2010 2015 2015 mptime_gap_y mwage_gap_year 24 24.5 25 25.5 26 15 15.5 16 16.5 17 2006 2000 . Source: Eurostat . Source: Eurostat iue5 attm a nE-5,2003-2015 , EU-15 in gap Part-time 5: Figure iue4 aegpi U1 2003-2015 , EU-15 in gap Wage 4: Figure 2008 Part−time gapinEU−15 2005 Wage gapinEU−15 2010 2006 −2015 2006 −2015 11 year year 2012 2010 2014 2016 2015 nrae hrl,rsn o95% ial,tetmoayepomn a elndbecause jobs. declined temporary gap in employment working were temporary men the more Finally, 9.58%. men for to of rate percentage rising employment higher part-time sharply, much the increased a men, while with compared in decline: part-time a rise working then sharp still and a are crisis women shows the 5 of Figure outbreak but the similar, been before is have gap study the part-time women under mean periods that The two fact position. the men’s for the of gap worsening negative unemployment, employment the going from at mainly gap looking stems unemployment men when the with up words, shows catching which other 3, In Figure in 2009. seen in be can as compared unemployment women’s men’s the in to Whereas increase employment. sharper women’s a by in explained increase is an change and gap due unemployment rate is employment men’s decrease in gap employment fall a The both stable. to roughly been has participation men’s while rates, h arwn ftepriiaingpi nfc otdi nices nwmnsactivity women’s in increase an in rooted fact in is gap participation the of narrowing The

mtempc_gap_year 1 1.5 2 2.5 3 2000 iue6 eprr mlyetgpi U1 2003-2015 , EU-15 in gap employment Temporary 6: Figure . Source: Eurostat Temporary employmentgapinEU−15 2005 2006 −2015 12 year 2010 2015 3. Speciﬁcation and Estimation

3.1. Model speciﬁcation

We take advantage of the panel structure of our data, and deal with individual hetero- geneity, like constant through time institutional country specific factors, as well as with year specific shocks common to all countries. We also take into account persistence in the evolution of gender gaps indicators, recognizing the fact that past outcomes influence the current state of the observed dependent variables.

More precisely, for country i and year t, we model each dependent gap variable ylit, where l = a, e, u, w stands respectively for activity (a), employment (e), unemployment (u) and wages

(w), as follows:

ylit = αyli,t−1 + Xitβ + ci + ft + it (1)

where yli,t−1 is the lagged dependent variable, Xit is a (1×k) vector of exogenous, predetermined, and possibly endogenous variables, including a constant term, ci is a country speciﬁc

(and time invariant) effect, ft is a time specific effect (common to all countries), and it is iid ∼ N(0, σ2) for i = 1,...,N and t = 2,...,T .

While the most straightforward estimation techniques for equation 1 would be Ordinary

Least Squares (OLS) or fixed effects estimations, these techniques have been shown to yield biased results in the context of fixed T panels because the lagged dependent variable among right hand side variables violates the strict exogeneity of regressor assumption and induces auto-correlation (Nickell, 1981). Even in the absence of the lagged dependent variable among regressors, endogenous or omitted variable might invalidate standard techniques.

Several alternatives have been advanced in the literature to deal with such dynamic panel data estimation, among which are instrumental variables (IV) and the generalized method of moments (GMM) . We rely on the ”system-GMM” proposed by Arellano and Bover (1995)

13 and Blundell and Bond (1998), which extends the standard ”diﬀerence-GMM” developed by

Arellano and Bond (1991) . While difference-GMM uses variable lagged levels as an instruments for first differenced variables, system-GMM adds to the process the estimation of variables in levels using their first difference as instrument. These additional instruments avoid the instrument weakness problem of first-differenced variables in case of high persistence in the data. 5

3.2. Estimation procedure and speciﬁcation checks

For each of our four dependent variables, we implement the model described by 1. Before turning to the results, we give an overview of the practical details of the estimations. Complete estimation results are presented in tables A3 and A4in appendix A5.

Each estimation is run with robust standard errors clustered by country. Given our relatively small panel dimensions, we pay particular attention to specifying parsimonious sets of instruments. GMM instruments (which account for endogenous regressors) are chosen with a maximum of two period lags, so that not too many observations are dropped from the sample.

Level and diﬀerence instruments (which account for predetermined regressors) are restricted to a one period lag, in order to keep the number of instruments small given our sample size.

Moreover, for GMM instruments, we collect all instrument sets in one aggregated set, which amounts to a reduced number of instruments (e.g. Roodman, 2009).

The dynamics are found to be consistent with the one lag dependency of participation, employment and wage gap indicators, and the auto-regressive coeﬃcient lies in conventional bounds, and in particular is inferior to unity.

5However, system-GMM require additional restrictions on initial conditions: while a thorough discussion is beyond the scope of this paper, Arellano and Bond (1995) and Blundell and Bond (1998) show that the initial conditions requires that yli1 is mean stationary.

14 All estimations exhibit reassuring statistics regarding the choice of instruments. First, all models pass the Sargan6 test with most p-values over 0.40. Second, tests of ”Arrellano-

Bond” type autocorrelation validate the hypothesis of ﬁrst order correlation and reject second order correlation: both tests lend credence to the choice of instruments and the speciﬁed lag structure.

4. Empirical Results and Comments

When significant, per capita GDP levels have negative effects on labor market gender gaps, while the growth rate is correlated with larger gender discrepancies. Even among UE-15 countries, different economic development levels have repercussions on labor market participation differences between men and women, with higher per capita GDP being associated with a lower gender gap regarding activity rates, and, to a lesser extent, with a lower wage gap. Moreover, the level of economic activity tends to exacerbate the participation gap, suggesting that women tend to participate less in the labor market relative to men in good times, while the reverse is true when a recession hits. This evidence is supportive of an added worker effect.

We also see some evidence of an increasing wage gap during good economic times. One explanation is that women’s wages benefit less than those of men from economic growth ; another is that women’s wages are less penalized in an economic downturn. However, this effect vanishes when the minimum wage level is taken into account: an increased minimum wage partly insulates women’s relative wages from economic activity fluctuations.

Besides economic activity, one major determinant of female outcomes in the labor market is the fertility rate. In line with the literature (Fitzenberger et al., 2013; Bloom et al., 2009) ,

6We do not report Hansen statistics, because in our small sample context, very high pvalues were found, due to a relative high instruments count. The Sargan test does not entail this weakness and is performed after robust estimation (Roodman, 2009).

15 we find that the participation gender gap increases with the fertility rate, as women withdraw from the labor market to take care of children more often than men. While we do not find any significant effect of the fertility rate on the wage gap, it increases the employment gap once the minimum wage level is accounted for.

Education also plays a role in the evolution of the labor market gender gaps: female intermediary level attainment (higher secondary and post-secondary non-tertiary education) contributes to reducing the unemployment gap, while women’s superior education reduces the wage gap.

We also consider employment sector shares (in industry or services) as well as the proportion of public employment among gender gap determinants. Interestingly, public employment serves as a protected sector as far as female participation rate is concerned, whereas the industry sector share exerts an opposite effect. We note that the overall sector effect is negative: the protected public sector effect outweighs the industry buffer effect. However, sector effects are also significant in the wage gap equation: public employment as well as employment in the service sector lead to a higher wage gap, and illustrate negative segregation effects.

Male and female unemployment rates enter the participation gap equation, as well as the wage equation. While they do not exert any effect in the latter case, they do have significant associated coefficients in the former. The male unemployment rate reduces the participation gap (discouraged worker effect for men, added worker effect for women), whereas the opposite is found for the female unemployment rate.

Non-regular employment is found to influence gender gaps: first, the female temporary employment share is associated with a lower participation gap; second, the female share of part-time employment exerts a negative effect on the wage gap. The negative effect of the part- time share of women employment seems puzzling at first. Indeed, conventional wisdom is that part-timers have lower earnings than full-timers, whether men or women are concerned (S´ıle

16 O’Dorchai et al., 2007; Bardasi and Gornick, 2008). However, most recent evidence indicates that reduced wage penalty for part-timers is found once workplace characteristics are taken into account (Colella, 2014). Moreover, in a cross-section of European countries, Matteazzi et al. (2013) find that countries with high female part-time rates also have low female part-time vs full-time gaps, as well as a high gender gap. However, they do not include male part-time employment in their account of the gender gap. We interpret our own findings as indicative of a gender differentiated part-time wage penalty, with part-time males being more penalized than part-time women (Simon et al., 2017; Sparreboom, 2014). Because male part-time is not as widespread as female part-time (according to our descriptive statistics, male part-time employment is 8% on average, whereas it amounts to a third of all female employment), men who occupy part-time jobs are confined to low-wage jobs. And according to Matteazzi et al.

(2013), where female part-time is widespread, ”women employed in full-time jobs are highly selected in very valued occupations, more than men on average”: since this contributes to narrowing the gender wage gap, it is less surprising to observe that a high female part-time share is associated with a lower gender wage gap.

The existence of a minimum wage tends to reduce the participation gap, whereas it is per se correlated with a higher wage gap. How to interpret the different effects of the minimum wage specification in the participation equation and in the wage equation? The existence of a minimum wage is sufficient to reduce the gender gap in participation rates, whereas the wage gap is reduced when the minimum wage reaches a certain threshold. Below this minimum wage level, the existence of a minimum wage appears to have a detrimental effect on the gender wage gap. One explanation is that all else being equal (in particular for a given level of education), women have a higher reservation wage than men because of child care costs.

One policy implication of this result is that increasing the minimum wage could contribute to narrowing the gender wage gap.

17 Looking at specific yearly time effects, the unemployment gap was first reduced in 2008, and then further in 2009, as was the case for the employment gap, whereas the wage gap widened . In 2010, the participation gap increased. In 2012, the wage gap increased somewhat before falling again.

Finally, we note that determinants of labor market gender gaps seem to intervene mainly when entering the labor market (through the participation rate) as well as when wages are set. ”In between”, employment and unemployment gaps remain pretty much insensitive to socioeconomic variables and mostly driven by past behavior or economic shocks, such as the

2008-2009 recession. This suggests that eﬀorts to foster gender equality on the labor market should concentrate on reducing participation and wage gaps.

5. Conclusion

In recent decades, the situation of women in the labor market has largely improved in the 15

European Union countries studied, when taken as a whole. How did the crisis, which broke out in 2007-08, aﬀect this process? Actually, all the gender gaps we have studied narrowed when comparing 2003 to 2015. They did not do so in the same proportions or using the same path, and this did not always reﬂect an improvement in the situation of women on the labor market.

The per capita GDP level has a negative effect on the participation gap, probably resulting from more gender proactive policies in the more economically advanced countries. In other words, higher per capita GDP is associated with a lower gender gap concerning activity rates, and, to a lesser extent, with a lower wage gap. Other factors that contribute to narrowing some gender gaps in the labor market are education as well as public employment or the existence of a minimum hourly wage. This later factor deserves more study, as it could be used at a certain level as a tool to produce positive effects not only for gender equality in the labor market but to create a virtuous circle benefiting society as a whole.

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21 Appendix

A1. Table of variables

Table A1: Main variables definition

Variable name Definition Data availability and source Participation gap Difference between male and female activity rates, 2002 − 2015, Eurostat calculated among working age population (15-64 years old) Employment gap Difference between male and female employment 2002 − 2015 , Eurostat rates. Employment corresponds to the national scope of employment i.e. resident persons in employment, from 15 to 64 years Unemployment gap Difference between female and male unemploy- 2002 − 2015, Eurostat ment rates. Unemployment definition matches guidelines of the International Labor Organization, for individuals aged 15 to 64 years . Wage gap Difference between average gross hourly earnings 2006 − 2015, Eurostat of male paid employees and of female paid employees as a percentage of average gross hourly earnings of male paid employees (including full-time and part-time workers) Part-time employment Part-time employment definition is based on a 2002 − 2015, Eurostat spontaneous response by the respondent, with the exception of the Netherlands (a 35 hours threshold is applied) and Sweden (a threshold is applied to the self-employed). Temporary employment All workers whose job ends at a specific date or 2002 − 2015, Eurostat with the completion of an assignment, or the re- turn of an employee who is temporarily replaced. It includes all people in seasonal employment; people engaged by an agency or employment exchange and hired to a third party to perform a specific task ; people with specific training contracts.

22 A2. Descriptive statistics

Table A2: Descriptive statistics for EU-15 sample

2003-2007 2008-2015 Variable Mean sd Min. Max. N Mean sd Min. Max. N Participation Participation rate 71.73 5.09 61.6 80.60 75 73.08 4.93 62 81.7 120 Men participation rate 78.94 3.26 72.60 84.60 75 78.7 3.53 72.2 85.3 120 Women participation rate 64.49 7.81 48.7 77 75 67.48 6.92 51.1 79.90 120 Participation gap 14.45 6.33 3.4 26 75 11.22 4.92 2.8 23.4 120 Employment Employment rate 66.77 5.42 56.1 77.40 75 66.12 6.75 48.8 77.90 120 Men employment rate 74.01 3.76 67.09 82.2 75 71.23 5.72 57.9 83.2 120 Women employment rate 59.51 8.27 42.8 73.40 75 61.03 8.43 39.9 74.10 120 Employment gap 14.51 6.95 2.7 28.3 75 10.19 5.14 1.5 25.8 120 Unemployment Unemployment rate 7.07 2.1 3.8 11.5 75 9.56 5.36 3.4 27.5 120 Men unemployment rate 6.34 1.9 3 11.4 75 9.47 5.14 3 25.6 120 Women unemployment rate 8.04 3.08 4 16.3 75 9.72 5.91 3.7 31.4 120 Unemployment gap 1.7 2.56 -1.2 9.6 75 0.25 2.43 -7.2 7.10 120 Wage gap 16.93 5.97 4.4 25.5 30 15.43 5.32 4.9 25.1 113 Part-time employment Part-time rate 18.62 9.31 4.1 46.3 75 21.2 9.48 5.4 50 120 Men part-time rate 7.25 4.8 1.6 22.5 75 9.58 5.07 2.6 26.5 120 Women part-time rate 32.79 15.36 7.5 75 75 34.81 15.35 9.80 77.10 120 Part-time gap 25.55 11.93 5.5 53.2 75 25.23 11.87 5 52.7 120 Temporary employment Temporary employment rate 13.05 6.96 3.1 34 75 13.37 5.42 5.3 29.2 120 Men temporary employment rate 11.74 6.7 2.4 32 75 12.38 5.3 4.7 27.5 120 Women temp. employment rate 14.57 7.42 3.9 36.6 75 14.42 5.68 5.9 31.2 120 Temporary employment gap 2.83 2.04 -1.6 7.7 75 2.04 1.78 -0.9 7.8 120 Growth rate 2.84 1.71 -0.93 8.4 75 0.3 3.74 -9.13 26.28 120 GDP per capita 34.43 13.49 16.3 83.7 75 34.86 14.07 16 81.60 120 Fertility rate 1.62 0.23 1.29 2.01 75 1.65 0.25 1.21 2.06 120 Women education attainment Lower education 37.46 12.37 19.3 73.3 74 31.64 10.51 16.8 67.8 120 Middle education 39.71 10.25 15.3 58.9 74 39.89 9.20 16.9 58.7 120 Higher education 22.83 6.98 9.20 34.4 74 28.47 7.64 13.1 41.3 120 Employment sector shares Industry sector employment 29.54 6.07 19.9 39.7 75 25.58 6.10 17.1 37.9 120 Public sector employment 24.01 5.01 17.5 34.5 75 25.14 4.54 18 33.5 120 Service sector employment 41.88 4.99 32.4 52.9 75 43.69 4.61 36.1 53.9 120 Minimum wage legislation Existence of a minimum wage 0.6 0.49 0 1 75 0.61 0.49 0 1 120 Hourly minimum wage 7.82 2.48 3.72 10.72 45 8.19 2.55 3.91 11.28 73 Source: Eurostat, authors’ calculations

23 A3. Gender gaps by country

Figure A1

Participation gap in EU−15 Employment gap in EU−15 2003 and 2015 2003 and 2015 30 25 20 20 15 10 10 Participation gap Employment gap 5 0 0

Italy Italy Spain Spain Ireland EU−15 Ireland EU−15 AustriaBelgium FinlandFrance Greece AustriaBelgium FinlandFrance Greece Denmark Germany Portugal Sweden Denmark Germany Portugal Sweden LuxembourgNetherlands LuxembourgNetherlands United−Kingdom United−Kingdom

Participation gap in 2003 Participation gap in 2015 Employment gap in 2003 Employment gap in 2015

Source: Eurostat. Source: Eurostat.

(a) (b)

Figure A2

Wage gap in EU−15 Unemployment gap in EU−15 2006 and 2015 2003 and 2015 10 25 20 5 15 0 10 Wage gap (%) Unemployment gap 5 −5 0

Wage gap in 2006 Wage gap in 2015 Unemployment gap in 2003 Unemployment gap in 2015

Source: Eurostat. Note: for Ireland, the wage gap is unavailable in 2015, figure is for 2014. For Greece, the series stopsSource: in 2010 Eurostat.

(a) (b)

24 A4. Gender gaps by group of countries

Figure A3

Participation gap in EU−15 Employment gap in EU−15 2003 and 2015 2003 and 2015 25 20 20 15 15 10 10 5 Participation gap Employment gap 5 0 0

UK UK Ireland Ireland _EU−15 _EU−15

Continental Europe Continental Europe Scandinavian EuropeMediterranean Europe Scandinavian EuropeMediterranean Europe

Participation gap in 2003 Participation gap in 2015 Employment gap in 2003 Employment gap in 2015

Source: Eurostat. Source: Eurostat.

(a) (b)

Figure A4

Wage gap in EU−15 Unemployment gap in EU−15 2006 and 2015 2003 and 2015 6 25 4 20 2 15 0 10 Wage gap (%) −2 5 Unemployment gap −4 0

UK UK Ireland Ireland _EU−15 _EU−15

Continental Europe Continental Europe Scandinavian EuropeMediterranean Europe Scandinavian EuropeMediterranean Europe

Wage gap in 2006 Wage gap in 2015 Unemployment gap in 2003 Unemployment gap in 2015

Source: Eurostat. Source: Eurostat.

(a) (b)

25 A5. Estimation results

Table A3: Estimation results: participation and employment gaps

Participation gap Employment gap

(1) (2) (3) (1) (2) (3)

L.part gap 0.888*** 0.885*** 0.880*** (0.037) (0.037) (0.040) L.empl gap 0.945*** 0.944*** 0.940*** (0.066) (0.074) (0.072) GDP per capita -0.015*** -0.020*** -0.019* -0.002 -0.014 0.005 (0.005) (0.004) (0.011) (0.023) (0.017) (0.016) Growth rate 0.026** 0.028** 0.027** 0.026 0.033 0.042 (0.011) (0.011) (0.011) (0.036) (0.033) (0.030) Fertility rate 2.747** 2.796** 2.427* -0.002 0.187 1.247* (1.154) (1.191) (1.239) (1.613) (1.769) (0.726) Middle education -0.005 -0.012 -0.008 -0.009 -0.017 0.028 (0.010) (0.010) (0.009) (0.032) (0.036) (0.027) Higher education 0.009 0.004 0.007 0.024 0.022 -0.008 (0.031) (0.031) (0.032) (0.045) (0.052) (0.049) Public sector employment -0.110** -0.115** -0.113* -0.029 -0.040 -0.094 (0.056) (0.049) (0.067) (0.174) (0.180) (0.186) Service sector employment -0.002 0.022 0.017 0.008 0.057 -0.055 (0.065) (0.047) (0.048) (0.119) (0.103) (0.117) Industry sector employment 0.064** 0.050* 0.045 0.100+ 0.082 -0.030 (0.033) (0.026) (0.035) (0.068) (0.088) (0.098) Men unemployment rate -0.251*** -0.231*** -0.214*** (0.050) (0.048) (0.051) Women unemployment rate 0.274*** 0.250*** 0.228*** (0.064) (0.061) (0.068) Women part-time rate 0.019+ 0.016 0.016+ 0.054 0.054 0.041 (0.013) (0.011) (0.011) (0.045) (0.048) (0.040) Women temp. employment rate -0.048** -0.045** -0.046** 0.049 0.048 0.061+ (0.019) (0.019) (0.019) (0.044) (0.050) (0.039) Existence of a min. wage -0.375** -0.156 -0.460 1.054 (0.171) (1.239) (0.496) (1.709) Hourly minimum wage -0.014 -0.150 (0.132) (0.159) 2005 0.284 0.273 0.257 0.141 0.126 0.089 (0.208) (0.205) (0.197) (0.219) (0.214) (0.240) 2006 0.230+ 0.197 0.187 0.270 0.219 0.107 (0.154) (0.144) (0.146) (0.232) (0.250) (0.290) 2007 0.274 0.219 0.207 0.073 -0.004 -0.118 Continued on next page...

26 ... table A3 continued

Participation gap Employment gap

(1) (2) (3) (1) (2) (3) (0.270) (0.256) (0.250) (0.351) (0.370) (0.380) 2008 0.417 0.347 0.331 -0.062 -0.147 -0.237 (0.291) (0.278) (0.269) (0.353) (0.396) (0.364) 2009 0.480+ 0.382 0.331 -1.123*** -1.212*** -1.240*** (0.301) (0.268) (0.264) (0.403) (0.456) (0.477) 2010 0.704** 0.586** 0.545* 0.211 0.061 -0.087 (0.324) (0.280) (0.294) (0.282) (0.340) (0.413) 2011 0.588+ 0.472+ 0.414 0.209 0.053 -0.009 (0.370) (0.309) (0.318) (0.452) (0.473) (0.558) 2012 0.524+ 0.410 0.340 -0.170 -0.330 -0.331 (0.351) (0.294) (0.291) (0.525) (0.538) (0.608) 2013 0.678+ 0.566+ 0.481 0.256 0.086 0.125 (0.448) (0.385) (0.386) (0.485) (0.490) (0.641) 2014 0.754* 0.630+ 0.545 0.392 0.201 0.245 (0.457) (0.395) (0.446) (0.576) (0.562) (0.698) 2015 0.575 0.475 0.384 0.353 0.178 0.241 (0.462) (0.386) (0.438) (0.510) (0.477) (0.641) Constant -2.828 -2.430 -1.611 -5.064 -5.525 0.347 (4.601) (3.649) (5.438) (6.570) (6.946) (10.243) Number of obs. 168 168 168 169 169 169 Nuber of instr. 38 40 42 38 40 42 Model Chi2 727 514 3,004 984 545 1,944 P value 0.00 0.00 0.00 0.00 0.00 0.00 Sargan stat 10.84 11.20 12.20 16.28 16.73 22.30 P value 0.62 0.67 0.66 0.36 0.40 0.17 AB AR(1) Chi2 -2.60 -2.59 -2.59 -2.29 -2.38 -2.39 p value 0.009 0.010 0.010 0.022 0.017 0.017 AB AR(2) Chi2 0.48 0.43 0.41 -0.07 -0.09 -0.22 p value 0.633 0.671 0.682 0.944 0.925 0.825 Robust standard errors in parenthesis: + p < 0.15; * p < 0.1; ** p < 0.05; *** p < 0.01

27 Table A4: Estimation results: unemployment and wage gaps

Unemployment gap Wage gap

(1) (2) (3) (4) (1) (2) (3)

L.ur gap 0.983*** 0.994*** 1.003*** 1.159*** (0.051) (0.055) (0.045) (0.079) L2.ur gap -0.257** (0.106) L.wage gap 0.997*** 0.998*** 0.882*** (0.054) (0.052) (0.074) GDP per capita -0.001 -0.020+ -0.006 -0.024 -0.017 -0.018+ -0.003 (0.015) (0.014) (0.017) (0.019) (0.014) (0.011) (0.012) Growth rate 0.007 0.014 0.016 0.024+ 0.161* 0.164* 0.101 (0.022) (0.020) (0.020) (0.017) (0.094) (0.095) (0.102) Fertility rate -1.330 -0.846 -0.040 -1.107 -0.276 0.038 1.592 (1.286) (1.471) (0.921) (0.875) (2.266) (2.545) (1.823) Middle education -0.029* -0.035** -0.013 -0.012 -0.026 -0.031 0.060 (0.016) (0.015) (0.020) (0.030) (0.041) (0.046) (0.063) Higher education 0.010 0.016 -0.010 -0.002 -0.075+ -0.083+ -0.055** (0.039) (0.038) (0.024) (0.021) (0.051) (0.057) (0.027) Industry sector employment 0.059 0.047 0.004 -0.042 0.055 0.060 0.116+ (0.041) (0.045) (0.043) (0.049) (0.075) (0.079) (0.076) Service sector employment 0.020 0.089 0.055 0.031 0.155** 0.179* 0.276** (0.081) (0.103) (0.067) (0.060) (0.078) (0.094) (0.117) Public sector employment 0.031 -0.021 -0.014 -0.056 0.150*** 0.155*** 0.219*** (0.108) (0.153) (0.098) (0.091) (0.050) (0.048) (0.074) Women participation rate 0.049 0.080 0.060 0.071* (0.039) (0.056) (0.043) (0.039) Men participation rate -0.052 -0.111 -0.037 -0.045 (0.118) (0.114) (0.065) (0.080) Women temp. employment rate -0.008 -0.005 -0.006 -0.009 0.033 0.039 0.053 (0.024) (0.025) (0.022) (0.031) (0.030) (0.031) (0.044) Women part-time rate 0.028 0.036 0.015 -0.006 -0.047*** -0.050*** -0.047** (0.030) (0.037) (0.021) (0.016) (0.016) (0.015) (0.020) Men unemployment rate 0.065 0.070 -0.046 (0.066) (0.064) (0.118) Women unemployment rate -0.116 -0.122+ -0.025 (0.087) (0.083) (0.134) Existence of a min. wage -0.481+ 0.498 -1.678 -0.134 5.637* (0.298) (1.837) (2.185) (0.284) (2.945) Hourly minimum wage -0.090 0.154 -0.627* (0.190) (0.230) (0.326) 2005 -0.287 -0.336+ -0.327+ (0.213) (0.233) (0.221) Continued on next page...

28 ... table A4 continued

Unemployment gap Wage gap

(1) (2) (3) (4) (1) (2) (3) 2006 -0.087 -0.188 -0.211 0.209 (0.210) (0.237) (0.222) (0.182) 2007 -0.267 -0.422 -0.436* -0.070 (0.224) (0.296) (0.263) (0.174) 2008 -0.607* -0.801** -0.810** -0.354 0.427 0.425 0.113 (0.310) (0.401) (0.367) (0.260) (0.571) (0.572) (0.488) 2009 -1.371*** -1.579*** -1.508*** -1.091*** 1.570* 1.589* 1.245 (0.307) (0.428) (0.390) (0.291) (0.931) (0.931) (0.948) 2010 -0.093 -0.355 -0.291 0.168 0.530 0.524 0.536 (0.160) (0.276) (0.243) (0.230) (0.543) (0.536) (0.607) 201100 0.039 -0.255 -0.108 -0.020 0.814 0.828 0.676 (0.276) (0.390) (0.318) (0.264) (0.649) (0.651) (0.536) 2012 -0.383 -0.705+ -0.513 -0.448 1.475* 1.506* 1.198* (0.324) (0.452) (0.398) (0.327) (0.811) (0.818) (0.720) 2013 -0.152 -0.486 -0.236 -0.162 0.639 0.688 0.485 (0.373) (0.399) (0.374) (0.280) (0.864) (0.887) (0.671) 2014 -0.291 -0.673* -0.397 -0.436 0.652 0.698 0.524 (0.313) (0.391) (0.344) (0.326) (0.817) (0.825) (0.745) 2015 -0.239 -0.613* -0.321 -0.378 0.953 1.013 0.936 (0.255) (0.354) (0.317) (0.334) (0.832) (0.850) (0.758) Constant 0.039 1.398 -2.077 3.922 -7.100 -8.449 -21.751** (10.354) (11.249) (6.759) (7.630) (7.308) (8.335) (10.488) Number of obs. 169 169 169 156 119 119 119 Nuber of instr. 42 44 46 45 41 41 41 Model Chi2 458 232 196 155 117,215 680 165 P value 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sargan stat 16.97 19.25 25.71 18.37 18.14 18.07 17.61 P value 0.46 0.38 0.14 0.43 0.51 0.45 0.41 AB AR(1) Chi2 -2.60 -2.59 -2.73 -2.51 -2.02 -2.02 -1.94 p value 0.009 0.009 0.006 0.012 0.043 0.044 0.052 AB AR(2) Chi2 -1.56 -1.56 -1.72 -1.18 -0.42 -0.41 -0.44 p value 0.118 0.119 0.085 0.238 0.678 0.685 0.657 Robust standard errors in parenthesis: + p < 0.15; * p < 0.1; ** p < 0.05; *** p < 0.01