The gender wage gap in Europe: women, men and the public sector

Transcription

1 Direction des Statistiques Déographiques et Sociales F0502 The gender wage gap in Europe: woen, en and the public sector Sophie Ponthieux (Insee) Doinque Meurs (Eres) Docuent de travail

2 INSTITUT NATIONAL DE LA STATISTIQUE ET DES ÉTUDES ÉCONOMIQUES Série des Docuents de Travail de la DIRECTION DES STATISTIQUES DEMOGRAPHIQUES ET SOCIALES Départeent des ix à la consoation, des ressources et des conditions de vie des énages N F0502 The Gender Wage Gap in Europe: Woen, Men and the Public Sector Sophie PONTHIEUX (Division Conditions de vie des énages) et Doinique MEURS (ERMES, Université de Paris2) Février 2005 Ces docuents de travail ne relètent pas la position de l'insee et n'engagent que leurs auteurs. Working papers do not relect the position o INSEE but only their authors' views.

3 Ce docuent est égaleent disponible dans la série des docuents de travail de l ERMES, sous le n 05-04

4 Abstract In this paper, we opose a decoposition o the gender onthly wages gap, with a particular ocus on the coponents o the part o the gap due to dierences in characteristics. The analysis is done or 10 countries o the EU: Austria, Denark, France, Gerany, Greece, Ireland, Italy, Portugal, Spain and United-Kingdo. The data set is the ECHP, in its 7th wave (2000). The ethod used or the decoposition is that oposed by Oaxaca & Ranso (1994), copleted with a Heckan s two-steps ocedure to correct or selectivity inspired ro Oaxaca & Neuan (1998). The results show, irstly, that countries are not coparable whether in the size o the gap, or in its coposition, but the ipact o the dierences in working hours between woen and en is crucial in all countries. The other coponents contribute variously to the gap, but on average, the public sector generally appears ore avorable to woen. This leads to another decoposition in which the total wage gap is taken as ade o three gaps: a gap between en and woen in the ivate sector, a wage gap between the public and the ivate sector aong en, and a wage gap between the public and the ivate sector aong woen. This decoposition shows that ater the ivate sector wage gap, the ain contribution to the total gap, which reduces its size, is that o the public/ivate wage gap aong woen. An interetation o these results or purposes o public policy has to be careul; but at this eliinary step, they suggest that the general trend to downsize the public sectors could contradict the objective o greater oessional equality between en and woen, and could widen the gender wage gap. Keywords: gender wage gap, decoposition, public sector. JEL code: J31, J45, J71 Résué Ce docuent opose une décoposition de l écart des salaires ensuels entre ees et hoes, en portant une attention spéciique à la part de cet écart qui résulte des diérences entre les caractéristiques. L analyse est réalisée pour 10 pays de l Union européenne : Autriche, Daneark, France, Alleagne, Grèce, Irlande, Italie, Portugal, Espagne and Royaue-Uni. Les données sont celles de la vague 7 (année 2000) du panel counautaire de énages. La éthode de décoposition utilisée est celle oposée par Oaxaca & Ranso (1994), coplétée d une correction de la sélection suivant Heckan telle qu appliquée dans Oaxaca & Neuan (1998). L analyse ontre en eier que les pays dièrent tant en ce qui concerne le niveau de l écart des salaires, qu en ce qui concerne sa coposition. Par contre, l ipact de la diérence du nobre d heures de travail entre les ees et les hoes est partout crucial. Les autres diérences de caractéristiques contribuent de açon variable à l écart selon les pays, ais, en général, le ait d occuper un eploi dans le secteur public est plutôt avorable aux ees. Ce constat nous conduit à réaliser une seconde décoposition, dans laquelle on considère que l écart des salaires entre ees et hoes est la soe d un écart ees/hoes dans le secteur ivé, d un écart public/ivé entre les ees, et d un écart public/ivé entre les hoes. C est l écart dans le secteur ivé qui contribue le plus à l écart total ; au contraire, l écart public/ivé entre les ees contribue à sa réduction. On ne peut qu avoir une interétation udente de ce résultat, ais il suggère néanoins que l eort général en Europe de réduction de la taille des secteurs publics pourrait contrarier l objecti d égalité oessionnelle entre les ees et les hoes en contribuant à ré-ouvrir l écart des salaires. Mots-clés : écart de salaires, décoposition, secteur public. Codes JEL : J31, J45, J71

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6 Table o contents page Introduction 3 1. Men and woen in the EU labour arket Occupational status Eployent characteristics Education, experience, aily structures 7 2. Coposition o the wage gaps Methodological aspects The breakdown o the gender wage gaps The evaluation o earnings equations The gap due to characteristics, the gap due to returns To which extent does public sector eployent reduce the wage gap? Decoposition o the three gaps Results 21 Conclusions 23 Reerences 25 List o tables and graphs Graph 1 - Activity status o en (1.a) and woen (1.b) in 10 EU countries Table 1 Salaried eployent structures (%) Table 2 Distribution o en and woen by education levels (%) Table 3 - Distribution o en and woen by aily characteristics (%) Graph 2 - Distribution o weekly working hours Graph 3 - Distribution o onthly earnings (in PPP, logariths) Graph 4 Average gender wage gap Graph 5 Dierences in returns and onthly wage gap Graph 6 Coposition o the dierences in characteristics Graph 7 Overall and ivate sector gender wage gaps Graph 8 - Public/ivate wage dierential Table 4 Public/ivate coposition o the onthly wage gap APPENDIX A - DETAILED RESULTS Table A1 Probit odel o selection in salaried eployent Table A2 Wage equation, irst decoposition Table A3 Coposition o the gender wage gap - decoposition 1 Table A4 Coposition o the gender wage gap - decoposition APPENDIX B - DECOMPOSITION OF THE GENDER WAGE GAP - FULL-TIME WORKERS 45 APPENDIX C - GENDER AND PUBLIC/PRIVATE WAGE GAP: DECOMPOSITION WITH A CORRECTION FOR SECTORAL SELECTION 47 Table B1- Probit odel - Selection in the ivate sector - reduced speciication Table B2 - Probit odel - Selection in the ivate sector - coplete speciication Table B3 - Public/ivate selection corrected with speciication 1 Table B4 Public/ivate selection corrected with speciication

7 Introduction It is widely known that en s and woen s eployent characteristics and structures are dierent, at least in two diensions: the nuber o working hours, and the distribution by industry and occupation. Beyond the unequal distribution by industry, another diension is the sead o the public vs. ivate sector. These dierences o course have an ipact on the gender wage gap. First, part-tie work, which alost exclusively involves woen, leads to a signiicant gap in working hours, and consequently, in onthly earnings. For instance, in Europe in 2000, woen s hourly wages reesented between 80% and 95% o en s hourly wages (European Coission, 2003, p.10), and between 65% and 80% o en s onthly wages. Next, since it sees that the gender wage gap is saller in the public sector than on average, then the size o the public sector, and the oportion o woen who work in it have also an ipact on the overall gender wage gap. Even though there have been nuerous studies devoted to the public wage eiu (see a survey o these studies in Gregory & Borland, 1999), ew o the cobine it with a gender perspective (Datta Gupta & al., 1998). In this paper, we opose an evaluation o the ipact o these dierent actors on the coposition o the gender wage gap or ten countries in the EU. The data we use is drawn ro the seventh wave o the European Counity Household Panel (ECHP), conducted in The advantage o this source is that the data has been haronised at a European level. However, certain countries or which the data were too incoplete (Sweden, Belgiu and the Netherlands) or or which the saples were too sall (Finland, Luxebourg) have had to be oitted ro our survey. Finally, we are let with ten countries or which we have been able to carry out a coplete analysis: Austria, Denark, France, Gerany, Greece, Ireland, Italy, Portugal, Spain and United Kingdo. We start in section 1 with a brie overview o soe structural characteristics o these countries. In section 2, we run a irst decoposition o the gender wage gap, in order to evaluate the relative contribution o returns and individuals and jobs characteristics. These characteristics are in turn broken down into our coponents: huan capital, sector o activity/oessional category, working hours, and public sector. Not surisingly, the respective iportance o these coponents varies greatly ro one country to another. In ters o public policy to reduce the gender wage gap, this eans that the ephasis ust be placed on dierent iorities in each country. This irst decoposition also shows, as expected, that the public sector coponent generally appears to have a negative ipact on pay inequality. This leads to a second 1 See Eurostat, 1996 or a coplete esentation o the EC Household Panel. 2

8 decoposition (section 3); this tie, the overall wage gap is considered as ade o three gaps: the wage gap between en and woen in the ivate sector, the wage gap between public and ivate sector aong en, and the wage gap between public and ivate sector aong woen. The results ro this last decoposition suggest that the general trend to downsize the public sectors could contribute to a widening o the gender wage gap. 3

9 1. Men and woen in the EU labour arket The EU countries share any coon rules and objectives in the ield o eployent and oessional equality between en and woen. However, the respective situations in these countries are ar ro equivalent. Aong the nuerous reasons or this, we can cite three which stand out: irstly, woen s participation in the labour arket varies in scale, history and social acceptance ro one country to another; secondly, institutional easures, notably in ters o tax systes, ake it ore or less attractive or woen who are arried and/or have children to work; lastly, easures relating to systes o childcare and parental leave can also either encourage or penalise the participation o woen in eployent, particularly in relation to ull-tie work. To achieve greater cross-national coparability in the populations o the countries studied, we have liited our ield throughout the paper to the 25 to 55 year-old age range. We have chosen this relatively restrictive range to liit the ipact o national dierences both in the duration o the studies and in retireent conditions. It has the added advantage o oviding greater ecision in taking the nuber o children into account: in the ECHP (as in any other sources), only the children actually esent in the household are taken into account; by setting an upper age range liit o 55, we obtain a greater obability that children will still be living with their parents Occupational status The oiles o the distribution o en and woen in ters o occupational status vary considerably between the dierent EU countries, shaped by the cobination o actors involved in institutional diversity. In this paper, we have chosen to work with our possible occupational statuses: salaried work, sel-eployent, uneployent and inactivity. For en (graph 1.a), the cross-country dierences in oile appear largely to be due to the dierent oportions o sel-eployent; this is uch higher than average in the Southern countries, bearing witness to the act that the iary sector has reained stronger here than it has in the other countries. The second actor o dierentiation is, o course, uneployent. [Graph 1 - Activity status o en (1.a) and woen (1.b) in 10 EU countries] Much greater national contrasts arise when we exaine the situation or woen (graph 1.B). A irst level o coparison, in relation to the situation or en, shows that the rate o activity is considerably lower or all the countries analysed except Denark. This observation is 4

10 already widely known. The coposition o the activity is also very dierent: apart ro a ew exceptions (Austria, Greece, Ireland, the United Kingdo), the level o uneployent is at least as high as that o en, testiying to the general phenoenon o eale overuneployent (see Maruani, 2003). In every country, the oportion o sel-eployent is lower than that observed or en, although or woen too, the rate is coparatively higher in the Southern countries. As or the global oportion o salaried eployent, it is lower or woen than or en in every country except Denark. Finally, part-tie work, alost nonexistent or en 2, generally reesents a signiicant oportion o salaried eployent or woen. A second level o coparison, between the dierent national situations or woen, shows that Spain, Greece, Ireland and Italy are all characterised by a high oportion o inactivity. However, Ireland stands out ro the other ebers o this group because o its higher oportion o part-tie eployent, which reains undeveloped in the Southern countries. A second group o countries can be constituted on the criterion o the overall oportion o salaried eployent; this group coises Gerany, Austria, France and Portugal. Within this group, slight dierences can be observed in relation to the share o part-tie work, which is higher in Gerany and Austria than in France, and alost non-existent in Portugal. There reain the United Kingdo, in which the oportion o salaried eployent is signiicantly higher than in the latter group o countries, with a high level o part-tie work, and Denark, distinguished ro all the other countries by its very low oportion o inactive woen. In addition to the general inequality o their esence in salaried eployent, it is also widely acknowledged that en and woen do not hold the sae jobs: inequalities are particularly esent in ters o oessional category, sector o activity and public eployent. These job characteristics, which ay inluence wage levels, will be exained briely in the next subsection Eployent characteristics Overall, an exaination o job characteristics leads to a conclusion in line with the classic results. The dierences that appear between one country and another are o a structural nature: a varying oportion o skilled jobs, corresponding to dierences in the level o qualiication o the work orce, and a varying oportion o large sectorial coponents. As or the sead o en and woen, woen hold relatively less skilled jobs than en, and they work ore oten in the tertiary sector. 2 Aong en, the oportion o part-tie salaried eployent in the age-range studied varies ro 0.8% (in Belgiu) to 2.8% (in France and Ireland); this igure rises to 4% in the Netherlands. 5

11 It should be noted that the coposition o salaried eployent esented here ay dier ro the usual statistics, because the data or alost hal o the countries in our study (Austria, Greece, Italy and Portugal) do not ovide details o the nuber o hours worked in part-tie jobs o less than iteen hours a week. As we shall be studying the earnings o the population described, we have applied this threshold to all the countries in the survey, and the salaried jobs described here thereore exclude sall part-tie jobs. [Table 1 Salaried eployent structures (%)] Taken as a whole, the ain characteristics o gender inequality that we observe are nothing new, but certain nuances can be brought out: although the oportion o woen holding anagerial posts is generally lower than that o en, this is not the case in Spain, Greece, Italy or Portugal (table 1.A). One reason or this particularity is siply echanical: as there are ewer woen actually in eployent in these countries, the nuber o woen holding anagerial jobs as a oportion o this saller raction o the eale population is naturally higher than is the case or en in these countries, or or woen in other countries. A generation eect ay also be at work here: older woen are less likely to be active, and when they are active, they are ore likely to be sel-eployed, whereas younger woen are both ore likely to be salaried and better educated. Nevertheless, the ain dividing line between white collar workers (the largest relative oportion o woen s jobs) and blue collar workers (the largest relative oportion o en s jobs), can be observed in every country. Equally widesead is the act that the blue-collar jobs held by woen are ore oten unskilled, whereas those held by en are ore oten skilled jobs. As or the sectorial sead o activities, the oportion o woen who work in education and social services ( other services" in the table) is generally uch higher than the oportion o en; in the other tertiary sector activities, the oportion o woen in trade exceeds that o en, except in Denark, France and Portugal, and, reciocally, the oportion o en is higher in counication, inance and real estate activities, with the exception o Spain. Finally, the oportion o woen eployed in the public sector is systeatically higher than that o en. As we shall see, this has a certain interrelation with earnings, or their ethod o deterination can be ore avourable to woen than it is in the ivate sector (see Gregory & Borland, 1999). 6

12 1.3. Education, experience, aily structures The huan capital" characteristics (education and experience) and aily characteristics o individuals constitute two urther diensions o actors that can inluence earnings. Education and experience are undaental variables in the traditional orulation o earning equations (Mincer, 1974; Willis, 1986). The easureent o these two variables esents diiculties o various orders. Firstly, in the ield o education, it is diicult to haronise easureents on an international level. In the case o the ECHP, this has resulted in a very poor variable with only three levels and no indication o specialities. Secondly, a classic oble arises ro the act that very oten (and this is true here) no eective easureent o experience is available. Experience is easured by the nuber o years since leaving education or since irst entering a job, without any possibility o taking into account interediate periods o uneployent or inactivity. However, we know that woen are ore likely to experience interruptions in their careers than en are, because o children. This leads to an overevaluation o woen s oessional experience (see Bayet, 1996; Meurs & Ponthieux, 2000), and neglect o the speciic eect o interruptions, which has been described by any authors (Gronau, 1988; Albrecht et al., 1998; Colin, 1999). To correct this easureent o potential experience, we shall introduce the nuber o children into the earning equation in order to capture the eect o tie possibly spent outside the workplace. Fro another perspective, aily characteristics and education levels are iportant actors in eployent selection echaniss; in short, less well-educated woen have weaker job opportunities and tend to have ore children. The diiculty here is due to endogeneity, or a higher nuber o children reduces the obability o eployent. It should be noted that recent work (Ahn & Mira, 2002) tends to deonstrate a change o sign in the classic correlation between the nuber o children and access to eployent (notably in the Southern countries); it sees as though woen were waiting to get a job beore having children. This phenoenon can be related to the all in ertility rates and the rise in levels o education. Within the liits o this paper, we shall siply reer the reader to the abundant literature about the relative eects o education and labour arket institutions and structures on the joint decision concerning participation in the labour arket and child-rearing (see, or exaple: Journal o Population Econoics, 1996; Del Boca, 2002; Sith et al., 2003). What is the situation o the countries analysed in this paper in relation to these dierent diensions? In ters o education, we can divide the whole saple into three groups (Table 2): countries in which woen have on average a lower level than en (Gerany, Austria, Greece and the United Kingdo), those in which the levels are equivalent (Denark, Spain, 7

13 Ireland and Italy) and those in which woen have a higher average level than en (France and Portugal). The eect o selection in eployent is exessed in the dierence between a certain level o education as a oportion o the whole group and as a oportion o salaried eployees; this dierence is greater or woen than or en in every country, and above all in Spain ( where the oportion o woen possessing a higher education diploa rises ro 29% or the whole group to 47% or the salaried eployees), in Greece (ro 19% to 36%), in Ireland (ro 17% to 26%), in Portugal (ro 15% to 22%) and also, but to a lesser degree, in Italy and Gerany. [Table 2 Distribution o en and woen by education levels (%)] To describe aily characteristics, we have identiied seven classes o individuals, using two criteria: living in a couple or not, with children (distinguished by the nuber and age o the children) or not. For each class, the coparison o aily structures or the whole population and or the salaried eployees brings out a contrast between en, or who the sead by class hardly varies, and woen, or who we can observe, unsurisingly, the underreesentation o woen with at least three children aong the salaried eployees and, conversely, the over-reesentation o woen who live alone or in a couple without children. [Table 3 - Distribution o en and woen by aily characteristics (%)] However, certain countries do esent a ore particular oile. Denark is once again eccentric, since the distribution by class changes very little between the whole population and the salaried eployees, and this is true or both en and woen. In certain other countries (Greece, Ireland, Italy and Portugal), the oportion o woen with young children is higher than average aong salaried eployees; this could be an illustration o the change in sign o the correlation between ertility and participation in eployent entioned above. Faily characteristics can aect the esence o woen in eployent. For woen who do work, they also have an inluence on the nuber o hours they work. Cobined with the characteristics o the jobs occupied, they contribute to a shorter average working week than that o en, and to a greater disparity in working hours or woen. A graphic reesentation o the distributions o the length o working weeks shows that this eect is general (Graph 2). On the whole, the aplitude o the variation is the sae or all countries. As ight be expected, woen s working hours are ore requent at the lower end o the distribution than those o en. In addition, the longest working weeks or woen are lower than those or en are. Above all, the oile o the distributions diers greatly between en and woen. The 8

14 distribution or en is uch ore tightly grouped around the 40-hour week. Lastly, the our Southern countries dier ro all the others in that part-tie work is less developed in the. In the other countries except Denark -, this is illustrated by the alost bi-odal distribution o woen s working hours: around 20 hours and around 40 hours. [Graph 2 - Distribution o weekly working hours] This overview will end with a look at onthly earnings (Graph 3). These are the current earnings declared during the survey; they are converted into purchasing power parities" (PPP), and thereore cross-nationally coparable 3. [Graph 3 - Distribution o onthly earnings (in PPP, logariths)] There again, as could be expected, the woen s wage curve lies to the let o the en s wage curve, and, except or Denark, woen s earnings are ore widely dispersed. Furtherore, the top o the en s wage distribution is considerably higher than that o the woen s wage distribution in every country. Conversely, the botto o the woen s wage distribution is lower than the ale one. We shall now exaine how dierences in access to eployent and in the characteristics o individuals and jobs held can help to explain the lower wage levels o woen. The size o the gap in wages (or subsequent calculations, this is exessed as the ratio o en s earnings to woen s earnings) varies ro 1.2 in Portugal to ore than 1.5 in Gerany, Austria, Ireland and the United Kingdo (Graph 4). [Graph 4 Average gender wage gap] 3 Alost : in France, unlike all the other countries studied here, there is no tax deduction at source. Earnings in France are thereore net o social security payents, but gross o taxes, whereas they are net-net" or the other countries. 9

15 2. Coposition o the wage gaps Aong the various ethods that exist or conducting this type o analysis (or a survey o these ethods, see, or exaple, Beblo et al., 2003), we have chosen a standard one: that oposed by Oaxaca & Ranso (1994). One classic diiculty in the evaluation o earnings equations coes ro the act that earnings are only known or the raction o the population which is actually paid a salary, yet this sub-population ay dier ro the reerence saple in ters o unobserved characteristics, which ay result in biased estiations. Here, we have also chosen a standard ethod, Heckan s two-step ethod (1979), to take this selection eect into account Methodological aspects Following Oaxaca (1973) and Blinder (1973) it is usual to write the average wage gap as ollows: W W = ˆ β ( X X )' + X ' ( ˆ β ˆ β ) (1) where W reesent the average earnings evaluated by an earnings equation, the indices and reesent en s and woen s earnings respectively, X are the average characteristics and βˆ are the estiated returns on these characteristics. The gap in average earnings (exessed as a logarith) can be broken down into a irst part reesenting the dierence between the returns on en s characteristics and the returns on woen s characteristics (oten reered to as discriination ), and a second part that can be attributed to the dierences in the observed characteristics o en and woen. All ethods o decoposition o the wage gap ust deal with the oble o the choice o weighting. In equation (1), dierences in characteristics are weighted by the average ale returns, and dierences in returns are weighted by the average eale characteristics. Here, we have chosen to use the ethod oposed by Oaxaca and Ranso (1994). This involves the construction o a non-discriinatory nor or returns on individual characteristics; the wage gap is then exessed as the su o an advantage or en, a disadvantage or woen, and the dierence between characteristics valued at the nor s returns. There are several ways o setting the nor (Oaxaca & Ranso, 1994). The ost direct one consists in using the population o woen and en pooled, that eans considering eployent as a whole, wether jobs are occupied by woen or en. 10

16 The average earnings gap is then coposed o three parts: W W = X' ( β βnor) + X' ( βnor β ) + β nor( X X )' (2) The irst ter reesents the additional return on characteristics, copared with the nor, due to the act o being a an and the second ter reesents the deicit in the return on characteristics due to the act o being a woan. The su o these two ters reesents the part o the wage gap resulting ro dierences in returns (we shall work with the su o the two to lighten our reading o the results). The third ter easures the gap resulting ro dierences in characteristics, using the average return o the whole saple as weighting. O course, the relative sizes o the coponents depend on the choice o independant variables introduced into the earnings equation; when the inoration is ore detailed, the coponent returns is ore likely to diinish, but then it could be that the characteristics cover phenoena o oessional segregation. For this reason, we opose irstly to break down onthly earnings, rather than the hourly wages which are usually taken, and secondly to ake explicit, in the characteristics, the ipact o actors o structural inequality between the characteristics and jobs o en and woen. Thus, we have chosen our diensions o observable dierences between en and woen: one on the level o individual characteristics, which we call huan capital, and three on the level o job characteristics: nuber o hours worked, industry / oessional category and public sector. Another actor which ust be taken into account is that, as we have seen, woen s participation in the labour arket is except in Denark considerably lower than that o en. Neglecting to take this dierence into account could result in biased estiations o returns on individual characteristics, and, consequently, on the easureent o the explained and unexplained parts. The two-step ocedure introduced by Heckan (Heckan, 1979) is a ethod requently used 4 when the population o which the earnings are being studied is not a rando saple drawn ro the reerence population. This involves evaluating the ipact o dierent individual characteristics on the obability o having a salaried job rather than being inactive. All individuals who are not in a position to apply or a job (students, old age pensioners, etc.), those who should have a job but cannot ind one 5 (uneployed) and, o course, those who are not available or a salaried job (sel-eployed) are thereore reoved ro the population. We can then contrast salaried workers with the pure inactives. In this case, the 4 Other ethods o correction could be used (see Beblo et al., 2003). 5 The uneployed reesent a particular case; by oceeding in this anner, we consider that all uneployent is involuntary. Furtherore, or the uneployed, the obability o inding a job involves other selection echaniss that do not interest us here. 11

17 pure inactives are woen, as the oportion o en in salaried eployent is about 98 %. Consequently, the oble o selection only concerns the eale population, except in Denark where the oportion o inactive woen is negligible. The correction consists in evaluating a Probit odel o belonging to the group o salaried eployees; in brie, we seek to estiate a latent variable that is a unction o individuals characteristics, which exesses their opensity to hold a salaried job. This estiation ovides us with an additional regressor (the inverse Mills ratio IMR) to introduce into the earnings equation. This variable enables us to capture the eect on wages o selection in eployent. With a standard earnings equation, we thereore have a odel o the ollowing or: W = ˆ βx ' + θˆ ˆ λ + u ˆ i i i i (3) where λ is an estiator o IMR and θ is the coeicient indicating the eect o selection on the wage. Lastly, to break down the wage gap while taking into account the selection bias, we adapt the speciication oposed by Neuan and Oaxaca (1998), which cobines the Oaxaca ethod and the Heckan ethod. Selection appears in the coponents o the wage gap in the or o a ter ( θˆ ˆ λ θˆ ˆ λ ). Neuan and Oaxaca exaine dierent ways o introducing this ter into the breakdown; one o these ways consists in treating selectivity as a separate coponent. This is the siplest apoach, as it requires no a iori hypothesis about whether selectivity has ore o an inluence on individual characteristics or on the return on these characteristics. We then obtain an equation o the ollowing or: W W = X ' ( ˆ β ˆ βnor) + X ' ( ˆ βnor ˆ β ) + ˆ βnor( X X )' + ( θˆ ˆ λ θˆ ˆ λ ) (4) with, here, θˆ λˆ = 0, because there is no selection aong en. With the breakdown o the characteristics, we inally obtain an exession containing six ters: ˆ β + ˆ β Hnor( XH XH )' + ˆ β Snor( XS XS )' + ˆ β Pnor( XP XP )' W W = Cnor( XC XC )' + X ' ( ˆ β ˆ βnor) + X ' ( ˆ βnor ˆ β ) + ( 0 θλ ˆ ˆ ) (5) where C, H, S and P reer respectively to huan capital, nuber o hours worked, industry / oessional category and public sector. 12

18 2.2. The breakdown o the gender wage gaps The evaluation o earnings equations In actical ters, the irst step consists in evaluating, or each country, the obit odel o belonging to the group o salaried eployees (only or woen and excluding Denark see above). For this odel we have chosen a relatively standard set o variables: age (and age squared), the level o education, the aily characteristics category, arital status, the incoe o the household ro which we have excluded, when it exists, the work incoe o the woan -, nationality and the region o living. On the whole, the results o the evaluations (detail in Appendix- 1) are quite classic, and this is true or all the countries in our survey: the obability o holding a salaried job increases with age and with the level o education; it is lower or woen with children than or those without; it is lower or others o young children and large ailies than or the others. In general, arriage has a negative eect on this obability: arital status is not signiicant in Austria, France, Greece, Ireland or the United Kingdo, but it is highly signiicant in Gerany and Italy, two countries in which tax easures are particularly discouraging arried woen s work. The level o incoe o the household (excluding the woan s wages i she has a job) also has a negative inluence on this obability, except in Austria and Ireland. Lastly, the eect o regions, introduced as oxies or local labour arket conditions, is obviously very variable. The speciication o the earnings equation is also relatively classic. We have the logarith o onthly wages (in PPP to obtain estiators in a coparable unit) depending on a set o deterinants covering the our ain coponents o which we want to evaluate the respective eects on the earnings gap: - huan capital (C), evaluated by the level o education and potential experience. The nuber o children has also been introduced, to take into account the eect o aternity on the actual accuulation o experience. The length o tie spent in the job is also taken into account, as a easureent o speciic huan capital; - the nuber o hours worked per week (H); - the jobs held (S), described by industry and oessional category; - eployent in the public sector (P). 13

19 Finally, the eect o selection in eployent is captured by the variable obtained with the obit odel (by construction, its value is zero or the en and zero or woen in Denark). For Gerany, a speciic variable has been introduced to control the possible eect o living in one o the new Länder. The results o the estiations (detailed in Appendix A) are quite edictable: returns on huan capital are higher or higher levels o education, and rise with experience and with the nuber o years spent in a job. Furtherore, we can see that the nuber o children generally has a negative eect or woen (whereas it is ore oten than not positive or en). The nuber o hours worked per week obviously has a strong, positive eect on wages. High levels o qualiication and working in the industrial sector are generally the ost advantageous actors. The eect o the public sector is oten positive and signiicant or woen, whereas it is not signiicant or en. In Gerany, the eect o living in one o the new Länder oves to be negative and signiicant (this result has regularly been obtained in nuerous studies on Gerany). The eect o selection in eployent only appears to be signiicant in three countries: France, Gerany and Italy. This is not surising, since each o these countries esents at least one institutional particularity which can be intereted in ters o unobserved characteristics o woen selected in eployent: in France, these are parental leave and the dependents tax allowance; in Gerany, the aintenance o otection or working others, the absence o childcare systes and the tax syste (based, or couples, on the incoe divided by two); and in Italy, the loss o aily allowance when the woan works. All these actors work to discourage the woen with the lowest earnings perspectives ro entering the labour arket; i we had not taken this selection eect into account or these countries, we would have overestiated the return to the characteristics o salaried woen The gap due to characteristics, the gap due to returns The part o the wage gap which is not explained by the characteristics o individuals and their jobs varies widely ro one country and another, ranging ro 30% in Denark and France to ore than 100% in Portugal (Graph 5; the detailed table o the decoposition appears in Appendix- 3). The part o the wage gap which is unexplained by dierences in characteristics can be intereted in ters o discriination. Fro this point o view, Denark and France would appear in alost equal irst place on the honour roll, ollowed, in this order, by the United Kingdo, Austria, Gerany and Ireland, then Italy, Spain, Greece and Portugal. 14

20 [Graph 5 Dierences in returns and onthly wage gap] However, this interetation is siplistic, or it does not take the size o the gap into account. Yet we observe that a high oportion o the unexplained part o the gap is not generally associated with a large gender wage gap. In act, the ain observation to be ade here is the wide diversity o cobinations. For exaple, Denark and France, the two virtuous countries in ters o the unexplained part, are dierentiated by a dierence o ore than 5 percentage points in ters o the wage gap; the sae is true or Spain and Italy, which have a higher unexplained part and a slightly ore onounced dierence in wage gaps. Thus, ro the point o view o the wage gap alone, Italy is closer to Denark than France is. On the other hand, the group coposed o Gerany, Austria, the United Kingdo and Ireland is airly hoogeneous ro both points o view; the large wage gap and sall unexplained part indicates a relatively high level o segregation in jobs. We shall return to this point later. Portugal is a special case, with an unexplained part which is larger than the total gap; this suggests, o course, that the oductive characteristics o eployed woen are, on average, higher than those o en are. One possible interetation o this disparity in avour o woen is that it could be at least partly due to the high oportion o sel-eployent (see Graph 1), which would drain a considerable oportion o the ost highly qualiied en, as i the echaniss o selection in jobs were ore dependent on the division between salaried work and sel-eployent than on the actors usually taken into account. The diiculty here is o a ethodological nature, or no theoretical odel exists or testing this hypothesis. This oble, pointed out with the case o Portugal, is doubtless o uch wider relevance, notably or countries in which uneployed people could be encouraged to create their own businesses, and also in the study o the creation o sall businesses by iigrants. However, this path reains beset by nuerous ethodological obles (see, or exaple, Blanchlower, 2000). The part o the wage gap explained by dierences in the characteristics easures the eect on wages o the dierences between the characteristics o salaried en and woen. By analysing its coposition, we can see how the dierent groups o wage-deterining actors (huan capital, working hours, industry/qualiication and public sector) contribute to the disparity in wages between en and woen; here, a negative coponent indicates a characteristic that is ore avourable to woen than to en. As expected, the strongest dierence is observed or the nuber o working hours, uch higher or en than it is or woen (Graph 6). This actor reesents at least hal o the explained part o the gap in every country except Portugal. The countries in which it akes 15

21 the greatest contribution are Gerany, Austria, the United Kingdo and Ireland, which are the countries in which the oportion o part-tie work or salaried woen is the highest (see graph 1), and also those in which pay inequality between en and woen is the ost onounced 6. Another very widesead eect, working this tie in avour o woen, is that o eployent in the public sector. The only exceptions are Austria, where this coponent is insigniicant, and Denark, where it works in avour o en. The other coponents then coe into play, having very diverse eects ro one country to another. The part o the wage gap due to dierences in huan capital works in avour o woen in France and the United Kingdo, where the eect is weak, in Italy, where it is slightly stronger, and in Portugal, where it is ore onounced. It is relatively high in avour o en in Gerany, Austria, Ireland and Spain. We can put orward two hypotheses here: irstly, this ight deonstrate an eect, ore unavourable here than in the other countries, o aternity-linked interruptions in oessional activity; secondly, it ight be the result, especially in Spain, o a larger gap in education and experience than in the other countries, to the detrient o the older woen in the survey, thus exessing a generation eect. Finally, the industry and skill level o the jobs held shows a arked contrast between the Northern countries, in which structural dierences are unavourable to woen, and the Southern countries, in which, on the contrary, these dierences work in the avour o woen. This calls to be intereted in ters o segregation: in both North and South, there are signiicant dierences between the characteristics o the jobs held by woen and those held by en. However, in the Southern countries, one cannot disiss the hypothesis o a selection eect, on the en s side, between salaried workers and sel-eployed. [Graph 6 Coposition o the dierences in characteristics] Ultiately, the overriding observation resulting ro this coparison is one o huge diversity, both in the size o the wage gap between en and woen and in the coposition o this gap: the part unexplained by characteristics varies greatly ro one country to another, as do the relative shares o the our coponents corresponding to dierences in the oductive 6 The decoposition has been done also on the population restricted to ull-tie workers. The results are esented in Appendix B; the ain dierence (apart ro the act that it lowers the size o the gap) ro those obtained with the ull population is o course in the oportion o the gap explained by the dierences in working hours; but it shows also a greater share o the dierences in returns to characteristics. This dierence between the coposition o the wage gap when estiated on the whole population and on the ull-tie workers only is usual. It relects the correlation between the (sall) nuber o hours and soe types o jobs. Keeping only the ull-tie workers or the analysis, also eans to keep only the (relatively) best jobs, which lowers the eect o the jobs characteristics on the wage gap. But in counterpart, the unexplained coponent o the wage gap is greater. 16

22 characteristics o en and woen and the jobs they hold. More than the actual levels o each coponent, it is their diversity which calls or our attention, or it signiies that the coon objective o reducing wage gaps cannot be attained by the sae policies in every country. By isolating the public sector as a speciic coponent, we have shown that this is generally, though weakly, ore avourable to woen. To explore this result urther, we shall now opose another way to decopose the gender wage gap. 17

23 3. To which extent does public sector eployent reduce the wage gap? As our evious results suggest, the coponent public sector contributes to a reduction o the gender wage gap. This is clearly illustrated by coparing the overall wage gap to that observed in the ivate sector, and the public/ivate dierential aong en and aong woen: irstly, the gender gap is signicantly greater in the ivate sector than on average(graph 7), and secondly, as underlined in several studies (Elliott et al., 1999; Gregory & Borland, 1999; Oecd, 2000) the public/ivate wage dirential is greater aong woen than it is aong en (Graph 8). [Graph 7 Overall and ivate sector gender wage gaps] [Graph 8 Public/ivate raw wage dierential] This ipact could be due to a coposition eect, or to a return eect, or both. The coposition eect would result ro the act that the public sector attracts and retains ost o the highly educated woen; this would ean, in turn, that these woen cannot ind coparable jobs in the ivate sector (either in pay or in quality, including the possibility that the public sector could be ore aily-riendly than the ivate sector). The returns eect would result ro a better valuation o individual characteristics in the public sector, especially or woen; this explanation is supported by nuerous studies whether on European countries or other Oecd countries. These two eects (coposition and returns) can o course be cobined, in various oportion according to the country. In order to shed soe light on these points, we turn to another way o decoposing the average gender wage gap, as the cobination o three gaps: the wage gap between en and woen in the ivate sector, the wage gap between en in the public sector and en in the ivate sector, and the wage gap between woen in the public sector and woen in the ivate sector. The interest o this orulation is to allow or the estiated returns to charactereistics to be dierent in the ivate and in the public sectors. 3.1 Decoposition o the three gaps We start with an exession o the average wage as the su o the average wages in the public and in the ivate sectors, weighted by the oportion o workers in each sector, as ollows: pu W = PW. + (1 P). W pu i.e: W = W + P.( W W ) (6) 18

24 where P reesents the oportion o workers in the public sector, and pu and in superscript indicate respectively the public and the ivate sector. When written or en and woen separately, this gives: W W pu = W + P.( W W ) (6) pu = W + P.( W W ) (6) The wage gap between en and woen [(6)-(6)] is then: W pu pu W = W W + [ P.( W W )] [ P.( W W )] (7) where the irst ter reesents the wage gap in the ivate sector, the second ter the weighted sectorial wage gap aong en, and the third ter the weighted sectorial wage gap aong woen. Finally, when each o these ters is decoposed as in (2), we obtain: W P.( W P.( W W pu pu W W ˆ ' ( ˆ) ' ( ˆ ˆ = X β β + X β β ) + ˆ( β X X ) (7a) ) = P [ X ' ) = P [ X ' pu pu ˆ pu ˆ ˆ pu ( β ˆ) β + X ' ( β β ) + ˆ( β X X ) ] (7b) ˆ pu ˆ ˆ pu ( β ˆ) β + X ' ( β β ) + ˆ( β X X ) ] (7c) In the calculation, we distinguish between the characteristics using the sae notation as in (5); o course, there is no ter or the public sector (P), and, in the detail o the speciication, the coponent S takes into account only the oessional category (while the regressors included the sector o industry in our evious decoposition), taken as a oxy or the jobs skill level. There reains to introduce the correction ter or the selection in eployent. This is clearly ore obleatic than in our evious decoposition, because it would be necessary to correct not only or the eployent bias, but also or a selection in ivate vs. public eployent, and this tie or en as well as or woen. While an estiation o a sectorial choice can be ound in several studies (or exaple Godderis, 1988; Hartog & Osterbeek, 1993), it is also well-known that there are any obles in the ipleentation o this correction, going ro speciication and identiication obles (Nawata, 1996; Manski, 1993 & 1995) to the inoration required and not always available in the data sets. At this eliinary step o our work 7, we have only taken into account the selection in eployent, and inally, the exession estiated, cobining (7) and (3) is: W pu pu W = W W + P.( W W )] [ P.( W W )] + [( θˆ ˆ λ ) ( θˆ ˆ λ )] (8) [ 7 Appendix C gives soe additional results obtained with the introduction o a ter o correction or the selection in the public/ivate sector. 19

25 (3) itsel giving: ( θˆ ˆ λ ) ( θˆ ˆ λ ) = [ θˆ + [ P ˆ λ.(( θˆ θˆ pu ˆ λ pu ˆ λ ] ) ( θˆ ˆ λ ))] [ P.(( θˆ pu ˆ λ pu ) ( θˆ ˆ λ ))] but since (c.sua) calculated as: θ ˆ λ ˆ =0, θˆ ˆ λ =0 and θˆ pu ˆ λ pu ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ ) 0 [( ) ( )] [.(( ˆ pu ) ( ˆ pu ( ) ( ˆ θ ˆ λ θ λ = θ λ + θ λ P θ λ θ λ =0, the selectivity ter is in act ))]. 3.2 Results With this decoposition, we wished to analyse better the ipact o the public sector on the size o the gender wage gap, and whether this ipact was due to a coposition eect, or to a return eect, or both. The answer is: both; but ore or less o each ro one country to the other (Table 4; the detailed results are reported in the Appendix A - 4). [Table 4 Public/ivate coposition o the onthly wage gap] In order to sort out the results, we start by looking at the total gap as a cobination o dierences in en and woen returns and en and woen characteristics in the ivate sector, and dierences between public and ivate sector in returns and characteristics aong en and aong woen. In all countries, the irst contribution to the total gender wage gap is that o the gap in the ivate sector. But then, countries dier in the division between returns and characteristics, so that the irst contribution to the total gap is the returns coponent in the ivate sector in Spain, Greece, Italy and Portugal, the characteristics in Austria, Denark, France and United-Kingdo, and returns and characteristics play alost equally in Gerany and in Ireland. It is noticeable that when the characteristics are decoposed, the huan capital coponent ( C in table 4) appears to play a very little role, except in Greece and Portugal. The second contribution to the total gap is that o dierences aong woen between public and ivate sector: it is always positive, which eans that it contributes negatively in the total wage gap, and strong everywhere except in Austria, Denark and Gerany. In all the countries, it is not irstly an eect o the dierences in returns but an eect o the dierences in characteristics. In the detail o characteristics, huan capital coponent accounts or at least one hal o these dierences in ost countries. 20

26 The sallest contribution is that o dierences aong en; its oportion o the total gap is nil in Austria, Denark and UK, and in the other countries, it is at best one hal o the dierences aong woen (except in Greece where it is in the sae range); this disoportion between the size o the public/ivate wage gap aong en and aong woen is not just an eect o the weighting by the respective oportions o en and woen in the public sector. Even though the values are sall, it is interesting to notice that the coposition o the gap aong en, when this gap exists, is only partly siilar to that we have seen or woen: as in the case o woen, it is ostly an eect o dierences in the characteristics; but in the characteristics, it is everywhere due to dierences in huan capital. I we copare now, within the three gaps, the eect o dierences in the other characteristics, that is the jobs skill levels and the working hours ( S and H in table 4), the unsurising result is about the ipact o working hours: it is positive and strong in the ivate sector (where it accounts up to about one third o the gender wage gap in all countries except Spain, Greece and Portugal). The dierence o ipact on the public/ivate wage gap resulting ro dierences in the working hours appears with the expected sign (negative, since it is public-ivate), or en as well as or woen, but it is generally saller aong woen. It is ore diicult to interet the ipact o dierences in the jobs skill level, which could just relect that woen with higher education levels can obtain ore attractive jobs in the public than in the ivate sector. 21