Driving forces behind the sectoral wage costs diﬁerentials in … · 2008. 7. 21. · Rudolf Zwiener Macroeconomic Policy Institute (IMK), Duesseldorf [email protected]

Driving forces behind the sectoral wage costs differentials in

Europe∗

Camille Logeay∗∗

Macroeconomic Policy Institute(IMK), Duesseldorf

[email protected]

Sabine StephanMacroeconomic Policy Institute

(IMK), [email protected]

Rudolf ZwienerMacroeconomic Policy Institute

(IMK), [email protected]

This version: July 18, 2008

∗We thank Simon Sturn for helpful comments, the usual disclaimer applies.∗∗Corresponding author, Camille Logeay, IMK, Hans-Boeckler-Str. 39, D-40 476 Duesseldorf, Ger-

many; phone +49/211/7778 334, fax +49/211/4 7778 334.

1

Abstract

In 2004, Eurostat starts publishing new figures on hourly wage costs for all Eu-ropean countries. These figures are new in several respects: It is the first time thatinternationally comparable hourly figures on wage costs are available covering a quiteimportant time period (1995-2005), so that not only cross-country comparisons butalso dynamic analyses are possible. Furthermore, these figures are fairly detailedat the sectoral level, therefore allowing for inter-sectoral comparisons. ConcerningGermany, the Eurostat statistics provide quite unexpected insights; the gap betweenwage costs in the manufacturing sector and the (private and business) services sectoris much larger than in other countries. This study aims at giving some explanations.According to theory, various explanations are possible. First, the neo-classical the-ory emphasises factors affecting or indicating the level of individual productivity,as well as firm or sectoral productivity; indicators corresponding to this approachare tested. Second, dropping the assumption of perfect competition on both labourand goods markets allows for other factors (mark-up, market power) to influencethe wage costs levels; these potential determinants are also tested. Finally, we thinkthat the structure of demand (driven by domestic or foreign demand) could alsohave a major impact on wages in the industry and the services sector and indeedthis factor seems to play an important role. This paper is structured as follows:First, the new Eurostat statistics is presented focussing on some interesting descrip-tive results. In the second section, we present a list of potential determinants ofwage differentials between the industry and the services sector derived from theoryand literature. A bivariate analysis (correlation) is then performed and conclusionsare drawn. In a third step, a multivariate analysis (panel estimation) is performed.The final section concludes.

JEL: J31, C23, E24

Keywords: Wage differentials, Europe, sectoral level, macroeconomic panel.

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Contents

1 Introduction 4

2 Overview of the literature 5

2.1 Theory: several competing explanations . . . . . . . . . . . . . . . . . . 5

2.2 Empirics: Strong evidence for productivity and rent-sharing factors . . . 6

3 Bivariate analysis: A correlation assessment 9

3.1 The data to explain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Individual factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 Firm and sectoral factors . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.4 Country specific factors: institutional settings . . . . . . . . . . . . . . . 18

3.5 Growth composition indicators . . . . . . . . . . . . . . . . . . . . . . . 20

4 Multivariate analysis 23

4.1 Static regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5 Conclusions 26

References 27

6 Annex: Overview of the estimators used 29

6.1 Pooled OLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6.2 Static Fixed Effects (SFE) . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6.3 Static Random Effects (SRE) . . . . . . . . . . . . . . . . . . . . . . . . 30

7 Annex: Scatter diagrams of the data 32

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1 Introduction

In 2004, Eurostat starts publishing new figures on hourly wage costs for all Europeancountries. These figures are new in several respects: It is the first time that internation-ally comparable hourly figures on wage costs are available covering a quite importanttime period (1995-2005), so that not only cross-country comparisons but also dynamicanalyses are possible. Furthermore, these figures are fairly detailed at the sectoral level,therefore allowing for inter-sectoral comparisons.

Concerning Germany, the Eurostat statistics provide quite unexpected insights; thegap between wage costs in the manufacturing sector and the (private and business)services sector is much larger than in other countries. This study aims at giving someexplanations. According to theory, various explanations are possible. First, the neo-classical theory emphasises factors affecting or indicating the level of individual produc-tivity, as well as firm or sectoral productivity; indicators corresponding to this approachare tested. Second, dropping the assumption of perfect competition on both labour andgoods markets allows for other factors (mark-up, market power) to influence the wagecosts levels; these potential determinants are also tested. Finally, we think that thestructure of demand (driven by domestic or foreign demand) could also have a majorimpact on wages in the industry and the services sector and indeed this factor seems toplay an important role.

This paper is structured as follows: First, the new Eurostat statistics is presentedfocussing on some interesting descriptive results. In the second section, we present alist of potential determinants of wage differentials between the industry and the servicessector derived from theory and literature. A bivariate analysis (correlation) is thenperformed and conclusions are drawn. In a third step, a multivariate analysis (panelestimation) is performed. The final section concludes.

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2 Overview of the literature

2.1 Theory: several competing explanations

There are different theories explaining persisting wage differentials. The neo-classicaltradition focuses on factors affecting or indicating the level of individual productivity aswell as firm and sectoral productivity respectively. Theories dropping the assumption ofperfect competition on both labour and goods markets consider additional determinants(mark-up, market power) influencing wage cost levels. We think, however, that thestructure of demand (domestic versus foreign demand) could also play an importantrole in determining wages in specific sectors.

Schramm (2004, chap. 3) distinguishes three different explanations for cross-sectoralwage differentials: First, the neo-classical approach assuming perfect competition onthe labour market and explaining wage diffrentials due to different labour/job qualityor compensation for non-monetary remunerations. Second, the efficiency wage theorybroadens the neo-classical view assuming that wages above the neo-classical equilibriumgive workers an incentives to work in a more efficient manner. In doing so they producea rent which can be divided among employers and employees (rent-sharing). More-over, employers act reasonable if they pay more than the equilibrium wage because ofmoral hazard problems. The third approach focuses on non-competitive features on thelabour and goods markets due to institutional set-ups that give rise to rent-sharing forboth sides, independently from the productivity level. Sociological aspects are also putforward to explain the persistence of the differences over the business cycles.

Schettkat (2006, chap. 2) quotes the same explanations and puts more emphasison the assumption of a monopsonistic labour market (Manning 2003). In this model(contrary to the efficiency wage) the actual wage is fixed below the optimal level. Alot of empirical case studies, esp. in relation to the introduction of a minimum wage inthe UK (Metcalf 2007), show that several sub-branches of the service sector are bettermodeled as a monopsony rather than within a perfect competition framework (Card andKrueger 1994, Dickens and Katz 1986, Card 1996, Machin and Manning 2002). Thus notonly productivity differences but also the structure of the labour market could explainwage differentials across sectors.

The efficiency wage theory assumes that the wage level directly affects workers’ ef-

5

forts and that the information about the strength of this effort is asymmetric; workersknow, employers can only guess (Shapiro and Stiglitz 1984, Krueger and Summers 1988).Consequently, moral hazard problems occur. This is the reason why employers have in-centives to take workers’ interests into account. Wages above the optimal wage level(without efficiency wage consideration) produce involuntary unemployment but reduceturn-over (hiring/firing costs) and provide incentives for high-productive workers to ap-ply and stay in the firm and to increase their work-effort. Therefore, cross-sectionalwage differences can be due to different productivity/qualifications of workers as statedin the neo-classical theory; but they can be modified according to the social consensus– if society prefers a more equal income distribution, we would face a smaller wagedispersion (due to a higher wage compression) than the one occuring according to pro-ductivity/quality differences in the specific sectors.

Nowadays, institutional factors are also put forward; especially the presence of acoordinated wage bargaining system should reduce wage dispersion across sectors andqualifications. Thus, a high union density or a high coverage of union agreements shouldgo hand in hand with a smaller wage differential (Layard, Nickell, and Jackman 1991,“the battle of markups”). The market power of trade unions, however, probably dependson the firm size; Very small firms could be able to resist trade unions’ demands moreeasily than bigger ones (where strikes could be more likely and costly)1. Not only thesize of the firms may play a role but also the amount of the rent the owner receives.A higher rent could encourage workers to organize in a trade unions in order to forcethe owner to share it. In the insider-outsider theory the more institutions protect theinsider, the higher the wage they can achieve (Lindbeck and Snower 1988, Lindbeck andSnower 2002).

2.2 Empirics: Strong evidence for productivity and rent-sharing fac-

tors

According to the neo-classical theory, only differences in the labour productivity or inthe job-conditions can explain wage differences. Schramm (2004), however, reports thatempirical studies on job-conditions (health, ecological, hardness, job-security, working-time and vacations, non-monetary rewards, ...) come to the conclusion that the onlyrobust determinant explaining wage differences is a higher death probability. Thus, dif-

1The idea is also that workers in smaller firms may be less attainable by trade-unions.

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ferent wages in the industrial and the service sectors can only be attributed to differencesin the ability and productivity of workers in these sectors.

Regarding Europe, Genre, Momferatou, and Mourre (2005) give a broad descriptiveanalysis of the wage differentials within the Euro Area. Using essentially OECD data,their wage data differ from our data. Nevertheless, they find similar results: the rankingof the countries did not change much over time. On average higher wages are paidin the industry compared to the service sector. This cannot be fully explained bypart-time, self-employment or age effects nor by qualification effects (that even have anopposite effect). Gender appears to play no role. The individual productivity-approachhowever cannot fully explain the differences. The alternative theories provide variablesas capital intensity, average firm size and sectoral labour productivity. This means thatrent-sharing theories are in line with the observed data.

Haisken-DeNew and Schmidt (1999) confirm with more recent data for Germany andthe US that even after controlling for human capital components, job characteristics,status and geographical factors, the inter-industry wage structure is very persistent onboth sides of the Atlantic.

Freeman and Schettkat (2001) compare the skill distributions among the unemployedand the employment in USA and Germany, as well as the wage distribution in these twocountries and come to the conclusion that the productivity-theory, i.e. the neoclassicalone, is not able to explain the better employment performance in the USA.

In most studies focussing on the USA the inter-industry wage differentials are foundto be stable over time, even after controlling for unionization and observable character-istics of the workers and job characteristics. Since these differences are highly stableover time, they cannot be attributed to transitory supply or demand shocks affecting aspecific industrial sector. Thus, the competitive theories seem to be unable to explainthese differentials. Some authors argue that non-standard theories would do a betterjob; efficiency wage theories for example can explain why the quit rate in high-wage-industries is lower (Krueger and Summers 1988), or rent-sharing theories can explainwhy profitability seems to be higher in high-wage industries (Dickens and Katz 1986).On the other side, some authors argue that non-observed abilities of workers may affectthe inter-industry wage differentials even in the long-run, since these abilities may bejudged or perceived differently across industries (Gibbons and Katz 1992). In this latterexplanation the competitive model is not challenged anymore, however, the empirical

7

evidence is not that clear.

Martins, Scarpetta, and Pilat (1996) find that market power exist in the studiedindustrial sectors and differ across products.

Suedekum and Blien (2007) use a German panel for 326 regional districts and 28industries & services categories between 1993/5-2002 and find that 70% of the wagedifferential across regions and industries can be explained by industries and area typefixed effects as well as time-varying characteristics such as qualification (low, mediumand high), firm size (small, medium and large), age and gender structure. In a secondstep they regress the employment growth rate on the unexplained regional wage differen-tial and find out that the elasticity is strongly negative for export-oriented and exposedsectors such as manufacturing and insignificant (and even positive) for rather domes-tically oriented service sectors (gastronomy and household-related services). Thus, forexport-exposed industries, the supply-side effects of wages are dominant whereas for theservice sector the demand-side aspects of wages have a bigger impact (however the au-thors claim that the supply-side effects dominate for both type of industries). It is alsoremarkable that for Germany the qualification variable is right-signed (branches andregions with higher workers pay higher wages too), the effect of age is slightly positive,as the firm size and the proportion of men.

Erdil and Yetkiner (2001) perform a macroeconomic panel using the STAN-OECDand UNIDO databases. After showing that the wage differentials within the manufac-turing sector is constant over the whole available sample period (1970-1992), explanationfor these differentials are put forward. For the OECD countries only labour productiv-ity and gender have the right sign and robust effects. Profitability and competitivenessseem to play a role only in 1990. However, their dataset is quite restrictive regardingpossible control-variables; working-time, nationality or race, qualification, country sizecannot be accounted for example.

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3 Bivariate analysis: A correlation assessment

3.1 The data to explain

3.1.1 Sources and definitions

The labour costs analysed in this study are taken from the Labour Cost Statistics(Eurostat)2.

The wage costs per hour include social security contributions of both employees andemployers, as well as the personal income tax paid by the employees. Some additionaland quite negligible costs paid by the employers are also included in the statistics (work-ing clothes, ...).

The countries covered are the EU-27 countries; i.e. EU-25+2: Euro Area(12) + UK,Denmark, Sweden and the new 10 Eastern countries + Romania and Bulgaria3. WhileIreland is completely missing, others have very few data (Hungary, Malta and Italy forexample).

Note, that for the EU-15 only those employees are considered which are workingin firms employing at least 10 employees, whereas for the new member states all areconsidered. This implies that the wage cost levels are probably over-estimated in theEU-15, since small firms pay lower wages on average (see detailed Eurostat statistics forthe NMS-10 grouped by firm size).

In this study we focus on the sectors C to K with CDE = Industry (without con-struction) and GHIJK = business services (without state and near-state services likehealth care or education)4.

The study is based on annual data covering the years 1995 to 2005. For somecountries the data base is imcomplete, i.e. we face an unbalanced panel.

2See: http://epp.eurostat.ec.europa.eu/ → Data → Labour Market → Labour costs → Labour costsannual data → hourly labour costs.

3As the data ends in 2005, Slovenia is not a member of the Euro Area but belongs to the 10 newmember states.

4More and more data for the other sectors (AB and LMNO) are available. However, these sectorsare either negligible in size or cannot be considered as market-driven and are thus not considered in thisstudy.

9

Chart 1: EU-27-Country ranking for the hourly wage costs in e, in 2005 and for themarket economy (C-K) (Data-update: Aug. 2007).

3.1.2 Two interesting results

1. Contrary to the broadly accepted view (Sinn 2007, Schroder 2007) Germany is not thecountry with the highest wage costs. In fact, several Northern and Western Europeancountries form a bulk of high-wage countries, while Germany has a rather middle positionwithin the EU-15. The Southern countries and especially the new member states arewell below the Western average. See Chart 1 and a detailed descriptive analysis inDuthmann, Hohlfeld, Horn, Logeay, Rietzler, Stephan, and Zwiener (2006) and Horn,Logeay, Stephan, and Zwiener (2007).

2. The second interesting result is the fact that in Germany the wage costs in theindustry sector differ largely from those in the service sector. Explaining this puzzlingresult is the aim of this paper.

Thus, the dependent variable in our empirical study is the hourly wage costs inthe industry (CDE) in relation to those in the market service sector (GHIJK). A valueabove 1 indicates that the average wage costs in the industry are higher than those in

10

the service sector. Germany has the highest value with 1.25, meaning that industrysectors pay on average 25% more than service sectors! At the opposite the Portuguesewage costs in service sector are on average about 20 to 30% higher than those in theindustry5. As we can see in Chart 2, this result is stable over the available years. Adistinction between the “old” European countries (EU-15) and the others (EU-27) ismade.

Chart 2: Relative wage costs in Europe: Industry/Services

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3.2 Individual factors

As introduced above, we focus on different categories of explanatory variables. Thefirst set which is in line with all theories are the individual features. These variables

5Interestingly the contribution of domestic demand to GDP growth was 0.8%-points in Germany and3.7%-points in Portugal between 1995-2005!

11

are indicators for the individual productivity (stemming from working experience oreducation), or for some inherent social discrimination (that per se says nothing aboutthe individual productivity but rather how high social discrimination is or may indicatesome hidden specific job characteristics). The correlations are shown in Table 1 (p. 14).

3.2.1 Age/Experience

Data for the age composition of the sectoral labour force is available from the LabourForce Survey (Eurostat). Two variables are constructed: a variable for young employ-ees (15-24 years old: alter 1524) and one for elder employees (more than 50 years:alter 50+). The variables are constructed similar to the wage costs: a value above onemeans that the proportion of younger resp. elder employees in the industry is higherthan in the service sector.

alter 1524 =Young employees in the industry/All employees in the industryYoung employees in the services/All employees in the services

(1)

alter 50+ =Elder employees in the industry/All employees in the industryElder employees in the services/All employees in the services

(2)

The age can be regarded as a proxy for the working experience and thus the ac-quired qualification of the employee. Usually, a hump-shaped productivity-age-curve isassumed. However, in empirical studies the curve is rather flat at the end, so that areal decline at higher ages is probably not significant. Therefore, a negative sign for the15-24-variable is expected and a presumably negative albeit not significant correlationfor the 50+-variable.

Actually, the correlations are not significant, an indication that either the age playsno role, or it is a rather poor indicator for acquired qualification or of course othercounter-acting effects linked to the age are offsetting the presumed effects. However, itis worth noting that from the scatter-plots in the annex (p. 33), Luxembourg seems tofollow another logic than the rest of the EU. If we exclude this country, the correlationbetween alter 50+ and the relative wage costs is significantly negative for both theEU-15 and EU-27 with ca. -35%. Therefore, the hypothesis of an hump-shaped age-productivity-curve is not true or there are strong institutional features (seniority clauses)of the wage bargaining inducing higher wages for elder employees, independently fromtheir productivity.

12

3.2.2 Gender

Data for the gender composition of the sectoral labour force is available from the LabourForce Survey (Eurostat). The variable (gender) is constructed similar to the wage costs:a value above one implies that the proportion of men in the industry is higher than theproportion of men in the service sector.

gender =Male employees in the industry/All employees in the industryMale employees in the services/All employees in the services

(3)

There is no “theoretical” explanation – along the productivity-theories – why womenshould earn less than men. But it is a stylized fact reported in various and numerousgender studies, that women are discriminated with respect to wages. Thus, omitting thisvariable could yield to a severe bias in quantifying the effects of the other variables. Thisvariable is correlated with the part-time proportion, probably indicating a difference injob quality between men and women.

The sign of the expected effect is positive. And indeed the correlation is strong andpositive.

3.2.3 Qualification

Data for the formal qualification composition of the sectoral labour force are availablefrom the Labour Force Survey (Eurostat), according to the ISCED classification. Theemployed persons are divided into three groups according to the highest level of edu-cation attained: low, medium and high qualifications6. For each sub-sectoral group,a weighted mean was calculated. Again, if the variable (qualification) is above one itmeans that on average employees in the industry are (formally) better qualified thanthose in the service sector.

qualification =Weighted mean of the qualification categorical variable in the industryWeighted mean of the qualification categorical variable in the services

(4)6Low: At most lower secondary (ISCED 0-2); Medium: Upper secondary (ISCED 3-4); High: Tertiary

(ISCED 5-6). See:http://forum.europa.eu.int/irc/dsis/employment/info/data/eu lfs/f lfs statistical classifications.htm.

13

As for the acquired qualification, a positive effect (more qualification should go alongwith higher wages and thus higher wage costs) is expected here. On the other side, as forthe acquired qualification, only the individual qualification is taken into account here.Labour productivity gains induced by useing specific technologies or capital inputs in abroad sense may not be well reflected in this variable.

Actually, the correlations are significantly negative, which is in contrast to standardtheory. This result was also found by the ECB (Genre, Momferatou, and Mourre 2005).We interpret this result in a way that either this variable is a poor indicator for individualand formal qualification, or that this variable is correlated with some sectoral specificfeatures that distroy the true (positive) presumed correlation. In any case, this resultremains puzzling because one would expect that the necessary bias induced by thesebivariate considerations should not be so severe as to even produce a counter-intuitivesign.

Table 1: Correlation between individual factors and the wage costs differential betweenIndustry and Services for the EU-27 and the EU-15.

EU-27 EU-151995-2005

Relative wage costsALTER 1524 -4.2% -2.2%(15-24 years/all employees) 53.5% 80.2%

219 135ALTER 50+* 28.2% 31.8%(50+/all employees) 0.0% 0.0%

209 125GENDER 42.0% 59.0%(Men/all employees) 0.0% 0.0%

225 135QUALIFICATION -33.5% -27.9%(weighted mean: 1-low, 2-mid, 3-high) 0.0% 0.1%

216 132

Bold figures are significant at the 1%-level. The first number is the correlation coefficient (ρ),the second the probability associated with H0: ρ = 0 and the last number is the number ofobservations.*: w/0 Luxembourg due to big outlier.

14

3.3 Firm and sectoral factors

The second set of possible explanatory variables are the firm and sector-specific variables.They are also indicators for productivity in the sense that they are in fact not linked tothe personal abilities of the workers, but through capital-intensity still affect the overalllabour productivity. They can also be interpreted in line with the rent-sharing theories;the bigger the firm, the more likely it possesses some market power and therefore receivesa monopolistic rent that can be shared with the workers, leading to wage differences thatcannot be explained due to productivity differences. The correlations are shown in Table2 on p. 17.

3.3.1 Firm size/capital intensity

Data on firm size are very difficult to obtain and are even more difficult to get for a longtime period. Data from Eurostat could be collected essentially for the middle of thesample (1999-2001; Eurostat, annual Structural Business Statistics – SBS). Because of alack of data, some calculations based on the national accounts statistics were performed.Therefore, one should not rely too much on this variable since the measurement erroris likely to be important. The variable (firmsize) is also constructed similar to the wagecosts variable. A value above one means that firms in the industry are larger on averagethan those in service sector.

firmsize =Average number of employees per firm in industryAverage number of employees per firm in services

(5)

This variable is a proxy for capital intensity or the degree of market power in thesector: the larger a firm, the bigger the economies of scale, and thus the larger theamount of capital per head but also the more likely the fact that the firm possessesmarket power. The first explanation yields a positive effect on labour productivity andhence on wages, the second through the rent-sharing among employers and workers apositive effect on wages too. Thus, in both cases, a positive correlation is expected butcannot be shown for the EU-15. Due to the small number of observations, it is difficultto state significance at all.

For Germany, however, the Federal Statistical Office (Destatis 2006, p. 15, Chart 4)shows that the size of firms and the wage costs are positively correlated and that this

15

is a long-term feature (1992-2004). Capital intensity (measured as GCF/head) is alsopositively correlated through the German branches in 2004 with the amount of the wagecosts.

3.3.2 Self-employment ratio/capital intensity

Data on self-employment and employment in a sectoral disaggregation can be takenfrom the national account statistics (Eurostat) but we choose the labour force survey asit is more complete (at least for the EU-15 countries). The variable (selfemploy) takesa value above one if the proportion of self-employed in the industry is larger than thosein the service sector.

selfemploy =Self-employed/All employment, in the industrySelf-employed/All employment, in the services

(6)

This variable is also a proxy for the firm size (the correlation between the two vari-ables is indeed significantly negative with about -65%) and consequently for the capitalintensity and/or for the market power of the firms. Because the link is reversed (a largerproportion of self-employed goes along with a smaller average firm size), we expect anegative sign. Indeed the correlation is negative, however it is not significant.

3.3.3 Part-time

Data for part-time jobs on a sectoral level are available from Eurostat only for the year2000 and from the labour costs statistics. Thus this variable is quite restricted. Againthe variable takes values above one if the part-time ratio is higher in the industry thanin the service sector7.

teilzeit =Part-time employees/All employees, in the industryPart-time employees/All employees, in the services

(7)

Similar to gender, there is no “theoretical” justification for an influence of this vari-able, as the labor costs are already corrected for the hours worked. However, like forgender, it is a stylized fact that the hourly wages for part-time employees are lower than

7This was calculated for employees of firms which employ at least ten employees.

16

those of full-time employees (OECD 1999, p. 22-25). Thus, one should take this intoaccount. This variable is negatively correlated with the gender variable (-30%, meaningthat if the fraction of female employees is high, part-time jobs are high too) as men-tioned above, so that this variable may be an indicator for social discrimination and forjob quality. This would yield a negative correlation.

The correlation is indeed negative for the EU-27 (albeit not significant) but is unex-pectedly positive for the EU-15 (but not significant either). These correlations shouldbe interpreted with caution because of the very small sample size.

3.3.4 (Labour) Productivity

Productivity measures are only available on a per-capita basis, because figures for hoursare not available for all countries. Working time reductions have reduced the produc-tivity per head but increased wages per hour in the past. Besides, taking the ratio ofsectoral gross value added to the sectoral employment levels is thought to be highlyendogenous and may yield to strong endogeneity problems. Therefore, productivity percapita cannot be used here.

Table 2: Correlation between firm-specific factors and the wage costs differential betweenIndustry and Services for the EU-27 and the EU-15.

EU-27 EU-151995-2005

Relative wage costsFIRMSIZE 12.0% -13.1%

31.9% 42.8%71 39

SELFEMPLOY* -15.8% -33.9%(Selfemployed/all employment) 2.3% 0.0%

207 125TEILZEIT -12.1% 15.9%(Part-time/all employees) 57.3% 58.7%

24 14

Bold figures are significant at the 1%-level. The first number is the correlation coefficient (ρ),the second the probability associated with H0: ρ = 0 and the last Number is the number ofobservations.*: w/0 Luxembourg due to big outlier.

17

3.4 Country specific factors: institutional settings

The third set of possible explanatory variables are the country-specific variables. Clearly,they are indicators for failures of the neo-classical framework, as they indicate someinstitutional features of the labour market and show whether market power plays asignificant role. The correlations are shown in Table 3.

3.4.1 Unemployment rate/strength of workers

Data on the standardized unemployment rate are provided by Eurostat (ILO-concept).The unemployment rate here is the national-wide rate (no sectoral differences are avail-able). This should be an indicator for the market power or relation between employersand employees as predicted by the insider/outsider theory: A higher unemployment rategoes along with a weaker bargaining position of employees. By which extent this shouldaffect employees in industrial sectors more than in service sectors is an open question.But if no other indicators are included, as in the years observed (1995-2004), the struc-tural change operates in favor of services: A higher unemployment rate should weakenthe industry employees more than those in the service sectors on the one hand (negativeeffect), on the other hand a higher unemployment rate may be rather an indication forpoor macroeconomic performance, which could weaken the service sector more than theindustry8 (positive effect).

The correlation is significantly positive which gives more evidence for the secondexplanation.

3.4.2 Minimum wage/institutional factors

A dummy variable takes value 1 if the country has a minimum wage and 0 otherwise.There are only seven countries without legal national minimum wage throughout thesample (Austria, Germany, Finland, Italy, Denmark, Sweden and Cyprus), the UK has

8Industry-firms are thought to be more likely to export their products than Services in general. This isconfirmed by the Input-Output tables for Belgium, Germany, Ireland, Spain, France, Italy, Netherlands,Austria, Portugal, Finland, Denmark, Sweden, UK, Lithuania, Hungary, Slovenia and Slovakia for theavailable years between 1995 and 2004. By this way, industrial firms may compensate domestic slacknessthrough better foreign demand more significantly than firms in the Services.

18

adopted a minimum wage in 1999.

Another interesting variable would be the proportion of employees covered by min-imum wage(s); as we exclude civil servants and employees of the social services, forcountries where the minimum wage is legal and national, this coverage rate will be100%9. More interesting is the proportion covered in the North-South axis of countriesthat do have minimum wages only at sectoral levels and set by collective agreements(Italy, Germany, Austria, Denmark and the three scandinavian countries). From Funkand Lesch (2005) and Husson (2006) some figures are available for some years. It ap-pears that Austria, Denmark, Finland and Italy have high coverage rates (above 85%)whereas Germany and Norway have low coverage rates (around 70% in 2004, but herewe suspect that this concerns only West-Germany10). A closer look at low-pay sectorsthat are all in the Services (textile/clothing, retail, hotel/restaurants, hairdressing) alsoshows that the coverage of the minimum wages set by the collective agreements aremuch smaller in those two countries than in the others. Due to lack of data however,the aim to construct such a variable was abandoned.

A national-wide minimum wage affects (almost) all employees. It should reduce thewage differentials because it increases the very low wages in all sectors. Thus the absenceof a minimum wage should be associated with more extreme values of our dependentvariable and the presence of a national minimum wage with values concentrated aroundone. This non-linear effect can be catched if we divide the sample in two (countries/yearswith a value of the dependent below one and countries/years with a value above one).In the first group a positive coefficient is expected and in the second a negative one.

For the whole sample, a significant negative correlation for both variables can beobserved: the presence of a minimum wage is significant. The higher its level, the lowerthe industry/services wage gap. For the split sample, the expected non-linear effect isnot met. Even counter-intuitive, the negative correlation in the first sub-sample appearsto be twice to three times higher than in the second sub-sample (see Table 4).

9In Belgium the minimum wage concerns only the employees in the private sector. As we only lookat the private sector, excluding construction and the primary sectors, the coverage rate is 100%. InCyprus, the minimum wage concerns only some professions; sales staff, clerical workers, auxiliary healthcare staff and auxiliary staff in nursery schools, creches and schools. Thus the coverage rate is not 100%.

10From the WSI-Tarifarchiv (2006) figures, the coverage rate by collective agreements, where minimumwages are normally defined, varied from 76% to 67% between 1998 and 2005 in West-Germany and from63% to 53% in the East.

19

3.4.3 Union density rate/institutional factors

The union density rate is taken from the OECD-database (Bassanini and Duval 2006).The data set ends in 2003 and covers only Western European countries.

One would expect that the union density is especially low in service sector, as men-tioned above. Thus, a higher union density would rather go along with a smaller differ-ence between union coverages in the service compared to the industry sector. This wouldsupport a negative effect in the labour cost relation between Industry and Services. Thecorrelation here is negative but not significant.

It is remarkable that a non-linear effect can be found (the correlation coefficient issignificant positive for values of the dependent below one and significant negative forvalues of the dependent above one; See Table 4). The explanation is quite straightfor-ward and follows the arguments for a hump-shaped wage/centralization degree of wagebargaining a la Calmfors and Driffill (1988). The stronger the trade-unions, the morelikely they coordinate their actions and promote wage compression over all sectors lead-ing to a reduction of the wage differential. On the other hand, the less representative,the more firm-specific the trade-unions demands will be, yielding a higher wage disper-sion across sectors. This means that values well above and below one in our dependentvariable should be associated with low values of union density, and values about oneof our dependent variable should be associated with high values of union density. Thismeans that the correlation should be positive for values of the dependent below one andnegative for values above one.

3.5 Growth composition indicators

The idea for this fourth and last set of variables is that the demand addressed to asector plays at least as an important role in the wage determination as the relativecompetitiveness of this sector. Thus, we think that the wage costs in a sector are notonly reflecting the (marginal) productivity of this sector net of the employer’s rent dueto imperfect competition but also the structur of the demand (foreign versus domesticdemand) a specific sector is confronted with.

Here, however, a precision has to be made. As the sectoral wage differentials arepersistent over time i.e. over the business cycles, the state of demand, reflected within

20

Table 3: Correlation between country-specific factors and the wage costs differentialbetween industry and services for the EU-25 and the EU-15.

EU-27 EU-151995-2005

Relative wage costsALQ 14.4% 27.9%(Unemployment rate) 3.0% 0.1%

226 135MINWAGE IS -20.1% -15.7%(0: no minimum wage, 1: national MW) 0.2% 6.9%

242 135UDENS -13.5%(Union Density) 19.2%

95

Bold figures are significant at the 1%-level. The first number is the correlation coefficient (ρ),the second the probability associated with H0: ρ = 0 and the last number is the number ofobservations.

Table 4: Non-linear correlation between country-specific factors and the wage costsdifferential between Industry and Services for the EU-25 and the EU-15.

EU-27 EU-15All w < 1 w > 1 All w < 1 w > 1

ALQ 14.4% 28.5% -4.8% 27.9% 36.3% -2.4%3.0% 0.2% 62.1% 0.1% 1.0% 82.5%226 114 110 135 50 84

MINWAGE IS -20.1% -41.7% -30.9% -15.7% -75.2% -26.4%0.2% 0.0% 0.1% 6.9% 0.0% 1.5%242 122 118 135 50 84

UDENS -13.5% 66.3% -12.2%19.2% 0.0% 34.8%

95 33 61


traditional business cycle indicators like the growth rate, cannot explain the wage gap.Here, we think rather in terms of long-term structure or strategies; Germany for examplehas a long tradition of using external trade to foster growth, whereas other Europeancountries push rather domestic demand. In that respect, it is likely that the participationin the Monetary Union has induced a change in preferences in economic policy in those

21

countries which traditionally focus rather on domestic forces than in countries with along experience of de facto monetary bindings like Germany, Netherlands or Austria.

In these respects, we thought that the indicator should be cumulative and relative,i.e. has a chance to persist over-business cycles, and reflects long-lasting differences inthe demand addressed to the industry and service sector. We look at the composition ofgrowth as an explanatory factor for the wage differential; industry sectors are tradition-ally more export-oriented, whereas the service sector typically depends on the domesticmarket (with some exceptions of course). Growth will not have the same impact on thewage opportunities for workers in the industry and the service sector, depending on thedriving forces; the German model of export-led growth should favour the industry morethan the service sector whereas the Portuguese model of domestic demand-led growthshould be more favourable for the services. The correlations are shown in Table 5.

Along this idea, cumulative and relative growth variables for the exports and domes-tic demand are constructed: the growth rate in % of exports resp. of domestic demandresp. of GDP was cumulated from 1996 onwards, calculating an index with value 100 in1995 (Malta and Romania start only 1999 due to lack of data); This index is then putin relation to the one of the Euro Area. A value above 100 means therefore that theaccumulated growth performance since 1995 is above average.

growth namek2i,t =

∏tτ=1996(

namei,τ

namei,τ−1)

∏tτ=1996(

EUR12i,τ

nameEUR12,τ−1)× 100 (8)

With name=exp, gdp or intdd for resp. exports, GDP and domestic demand in constantterms; i the country index and t the time index. The index starts in 1995 with the value100.

We calculate this indicator for exports, domestic demand and GDP at constantprices. The expected effects are positive for the first one, negative for the second one;for the third one it depends on the fact whether the first or the second effect is dominant.The correlations have the expected signs and are significant for the domestic demandand GDP. The results show that development of the domestic demand is much moreimportant than export demand.

22

Table 5: Correlation between growth structure indicators factors and the wage-costdifferential between Industry and Services for the EU-25 and the EU-15.

EU-27 EU-151995-2005

Relative wage costsGROWTH EXPK2 -1.3% 22.5%(cumulated export-growth relative to EUR-12) 84.3% 0.9%

242 135GROWTH GDPK2 -24.6% -29.7%(cumulated GDP-growth relative to EUR-12) 0.0% 0.1%

242 135GROWTH INTDDK2 -23.7% -41.1%(cumulated domestic demand-growth relative to EUR-12) 0.0% 0.0%

242 135


4 Multivariate analysis

The advantage of performing a multivariate analysis which includes both dimensions(time and cross-sections) is twofold: first with an increasing number of observations,we expect to obtain more accurate estimates for the effects of individual variables onthe relative wage differential and second – contrary to the bivariate correlations – ina multivariate analysis, one controls for different effects at the same time and shouldbe able to isolate the genuine effect of a factor, reducing therefore potential omittedvariable bias.

The regressors we use, summarized in Xit, are [alter 1524, alter 50+, gender, qualification,selfemploy, alq, minwage is, growth expk2, growth intdd2k]. We dropped firmsize,udens and teilzeit because these variables had too few data. Still the data set remainsunbalanced. All variables are time-variable, whereas the dummy minwageis actually istime-invariant for all countries but for the UK11.

11It takes the value 0 between 1995 and 1998 and 1 after.

23

4.1 Static regressions

It is not obvious that the model should contain a dynamic term, since the series arequite stable (they are all relative and thus do not have trends). Because our dataset is a macro-panel with countries from the EU-27, it seems also reasonable to ruleout random-effects. At least a discussion about which variable may be correlated withcountry-specific unobserved characteristics should be done.

In principle, all variables may be correlated with the country-effects. But certainlythe unemployment rate, the presence or not of a minimum wage may be very likelycandidates. As we have only the minimum-wage dummy that may be considered asalmost time-invariant, the fixed-effects estimator may be the most appropriate.

lohnkostenit = X ′itβ + λ′DTt + α0 + αi + uit (9)

The results of the standard estimators are reported in Table 6. The Hausman-test (H0:Random Effects are present) is rejected and the Breusch-Pagan test (H0: Fixed effectsare present) is consistently not rejected. This is in line with our intuition that a fixed-effect model would better describe a macro-panel. The pooled estimation is reportedbelow:Nb of obs.: 201. Nb of param.: 20. Using robust standard errorsTime dummies: 10; Nb of individuals: 25longest time series: 11 [1995 - 2005]; shortest: 2 (unbalanced panel)

coefficient H-robust p-valALTER 1524 -4.58E-02 60.2%ALTER 50PLUS -2.51E-03 98.3%GENDER 3.88E-01 1.5%QUALIFICATION -4.70E-02 11.2%SELFEMPLOY -3.21E-02 83.7%ALQ 1.18E-02 3.7%MINWAGE IS -2.60E-03 94.4%GROWTH EXPK2 5.70E-04 45.4%GROWTH INTDDK2 -1.46E-03 19.4%Wald (X): χ2(9) = 34.55 [0.0%] ; Wald (time): χ2(10) = 16.94 [7.6%]AR-1 & 2 rejected at 1%. with PcGive.

24

Tab

le6:

Stat

icre

gres

sion

s:re

sult

sof

the

stan

dard

esti

mat

ors

Use

dpr

ogra

m:

STA

TA

Max

imal

Sam

ple:

26co

untr

ies

ofth

eE

U-2

7av

aila

ble;

1995

-200

5(1

1ye

ars)

End

ogen

ous:

Loh

nkos

ten

xtr

egxtg

eextg

lsxtp

cse

Est

imat

ion

Fix

edR

ando

mR

ando

mR

ando

mf(

gaus

s),i(

id)

pane

ls(h

eter

o)he

tonl

ym

etho

d:eff

ects

effec

tseff

ects

effec

tsro

bust

forc

epa

irw

ise

(wit

hin)

GLS

MLE

GLS?

corr

(exc

h)co

rr(a

r1)

corr

(ar1

)co

rr(p

sar1

)co

rr(a

r1)

corr

(psa

r1)

Alt

er15

2438

.6E

-333

.7E

-333

.5E

-333

.7E

-336

.0E

-354

.2E

-352

.7E

-3∗

25.9

E-3

36.0

E-3

37.1

E-3

Alt

er50

plus

43.2

E-3

39.2

E-3

39.0

E-3

39.1

E-3

41.0

E-3

115.

6E-3

∗∗∗

74.9

E-3

∗∗∗

65.3

E-3

∗∗∗

34.5

E-3

53.1

E-3

∗∗

Gen

der

80.9

E-3

112.

4E-3∗

113.

6E-3∗

112.

9E-3∗

98.3

E-3

117.

0E-3

208.

9E-3

∗∗∗17

3.2E

-3∗∗∗

244.

4E-3

∗∗∗20

8.1E

-3∗∗∗

Qua

lifica

tion

-3.1

E-3

-9.7

E-3

-9.9

E-3

-9.8

E-3

-6.7

E-3

-18.

2E-3

∗∗-4

5.9E

-3∗∗∗

-50.

6E-3

∗∗∗

-31.

1E-3

∗∗-5

5.2E

-3∗∗∗

Selfe

mpl

oy5.

7E-3

-8.2

E-3

-8.7

E-3

-8.4

E-3

-2.1

E-3

-5.8

E-3

-5.7

E-3

15.7

E-3

-10.

4E-3

13.6

E-3

Alq

-3.6

E-3

∗-2

.0E

-3-1

.9E

-3-2

.0E

-3-2

.7E

-31.

6E-3

3.2E

-3∗

1.4E

-37.

2E-3

∗∗∗39

7.7E

-6M

inw

age

is-8

.6E

-3-9

.4E

-3-9

.4E

-3-9

.4E

-3-8

.9E

-366

1.8E

-621

.0E

-344

.5E

-3∗∗∗

-32.

5E-3

∗30

.5E

-3∗∗

Gro

wth

expk

217

7.7E

-621

2.1E

-621

3.3E

-621

2.5E

-619

7.4E

-6-1

10.9

E-6

-40.

6E-6

-60.

8E-6

187.

9E-6

118.

1E-6

Gro

wth

intd

d2-1

.9E

-3∗∗∗

-1.7

E-3

∗∗∗

-1.7

E-3

∗∗∗

-1.7

E-3

∗∗∗

-1.8

E-3

∗∗∗-3

20.0

E-6

-238

.2E

-6-9

98.7

E-6

-583

.7E

-6-5

09.2

E-6

Cou

ntry

effec

tye

sye

sye

sye

sye

sye

sye

sye

sye

sye

sT

ime

effec

tye

sye

sye

sye

sye

sye

sye

sye

sye

sye

snb

.obs

.20

120

120

120

120

116

520

120

120

120

1nb

.par

am.

2020

2020

R2

wit

hin

19.4

%19

.0%

19.0

%R

2be

twee

n3.

5%8.

5%8.

6%R

2ov

eral

l6.

7%13

.6%

13.7

%P

rob>

F1.

2%P

rob>

χ2

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

Pro

b>F

(TD

=0)

25.3

%0.

0%32

.2%

39.6

%0.

0%25

.1%

67.7

%53

.5%

0.0%

0.0%

xtre

g:O

LS

esti

mat

or;

xtge

e:M

L-e

stim

ator

asin

xtre

gbu

tha

ndle

dun

bala

nced

pane

ldi

ffere

ntly

;xt

gls:

GLS-

esti

mat

orw

ith

assu

med

hete

rosk

edas

tici

tybu

tno

corr

elat

ion

acro

sspa

nels

and

aar

(1)-

stru

ctur

eac

ross

tim

e;xt

pcse

:G

LS-

esti

mat

orw

ith

corr

elat

ion

inbo

thdi

men

sion

s(t

ime

and

cros

s-se

ctio

ns)

and

hete

rosk

edas

tici

tyac

ross

pane

ls;

psar

:th

eas

sum

edco

rrel

atio

nis

decr

easi

ngw

ith

the

tim

edi

stan

ce.

?:w

ith

smal

l-sa

mpl

eco

rrec

tion

for

the

stan

dard

erro

rs(S

wam

y-A

rora

)an

dno

n-co

nsta

ntth

eta.

*,**

,***

:si

gnifi

cant

at10

%,5%

,1%

leve

l.

25

5 Conclusions

In this article we analyze the new data provided by Eurostat on hourly wage costs,which are comparable across all European countries. Among other interesting features,a surprising result is the outlier position of Germany where in the industry sector wagesare on average 25% higher than in the service sector. The aim of this paper is to explainthis puzzle.

In a first and descriptive step, a bivariate correlation analysis was performed and thefollowing results were found: only gender is a significant factor at the individual level.Especially the (formal) qualification shows significant wrong-signed correlation, a con-tradiction with the widely asserted low productivity in services. On the contrary, firmspecific factors indicating the importance of capital intensity in explaining wage differ-entials (self-employment share) do not seem to have a great explanatory power. Factors(country specific and demand indicators) that are in accordance with non-standardwage determination theories are found significant (unemployment rate, minimum wage,growth structure).

In a second step a multivariate analysis is performed, allowing to control for eacheffect. The signs found in bivariate correlations are so far confirmed for the elder-age-variable (+), gender (+), qualification (counter-intuitive -) and the domestic demandgrowth variable (-).

From these preliminary results, it seems that our hypothesis is confirmed; the struc-ture of the demand addressed to the sectors is quite an important factor explaning thedevelopement of wages and therefore the EU sectoral wage differentials.

26

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6 Annex: Overview of the estimators used

6.1 Pooled OLS

In the pooled model, there is no country effects and the time effects are common toall individuals. The errors are supposed to be uncorrelated in the time dimension andacross countries and homoskedastic in the cross-sectional dimension:

yit = α∗ + X ′itβ + uit (10)

In STATA, the command is xtreg with time dummies.

6.2 Static Fixed Effects (SFE)

In the fixed effects model, the error term is decomposed in a fixed unit-specific com-ponent (a country-specific intercept) and an observational error term. There existsseveral methods to estimate such a model; Either one adds individual dummies (LSDV-estimator):

yit = α′DIi + λ′DTt + X ′itβ + uit (11)

29

Or one can transform the variables (subtract the group mean), so that the time-invariantvariables are dropped from the model (WITHIN-estimator):

yit − yi = (αi − αi) + λ′(DTt −DT ) + (Xit −Xi)′β + (uit − ui) (12)

This is achieved in Stata with the command xtreg and the option “fe”. In both cases(that yield exactly the same estimates for the β, as the OLS method is applied, we needto have homoskedastic and uncorrelated errors, and that they are unrelated to the αi

and the other exogenous factors, for the estimators to be consistent and BLUE.

6.3 Static Random Effects (SRE)

In the fixed effects model, the error term is decomposed in a random unit-specific com-ponent (a country-specific error term) and an observational error term. As now it isclear that the overall error term will not met the assumption of autocorrelation andprobably also not of homoskedasticity, the GLS-estimator is required. One can showfurther that the GLS reduce to OLS applied to the following transformed model:

yit − θiyi = (1− θi)α∗ + λ′(DTt − θiDT ) + (Xit − θiXi)′β + εit − θiεit (13)

θi stands for 1−√

σ2u

σ2u+Tiσ2

αand εit = αi + uit.

We need to estimate θ, and there exists therefore different estimators , as we canonly perform F-GLS.

In Stata the commands [xtreg, re] and [xtreg, re sa] call the GLS estimator. In bothcases the Swamy-Arora variance estimator based on the within and between regressionsis implemented. The second specification has a small sample correction, that differ fromthe first one in unbalanced panels. Important to note here, is that the uit-error termpossess the usual properties: homoskedastic across individual, uncorrelated between theindividuals and in the time-dimension. It is also unrelated to the individual-specific errorαi and to the other exogenous variables. As this is a F-GLS estimation, test-distributionshold only asymptotically (χ2 rather than F).

It is possible to estimate the F-GLS also with maximum-likelihood [xtreg, mle]. Inthis case, the residual are assumed also to be normally distributed and an iteratedregression is performed. In case the total number of observation (here 201) is smaller

30

than 300 and the data unbalanced (our case too) the [mle] and [re] regressions will yielddifferent results.

If the correlation structure of uit is not as simple as assumed above esp. if sometime-autocorrelation is present, then the GLS estimator needs to be adapted. There isseveral ways to take account of richer variance structure.

[xtgee] is the first possibility embraced in the estimation part. It fits a population-averaged panel-data model; the option [f(gauss) i(id)] consider that the errors are nor-mally distributed and the model linear in the coefficients. This estimator consider thenthe within-group correlation structure Ri:

corr(ui1, ui1) corr(ui1, ui2) . . . corr(ui1, uiTi)corr(ui2, ui1) corr(ui2, ui2) . . . corr(ui2, uiTi)

......

. . ....

corr(uiTi , ui1) corr(uiTi , ui2) . . . corr(uiTi , uiTi)

=

r11 r12 . . . r1Ti

r21 r22 . . . r2Ti

......

. . ....

rTi1 rTi2 . . . rTiTi

Then the within-group correlation structure Ri can take two forms:

• corr(exch): rts =

1 if t = s

ρ if t 6= s. This equivalent to the [xtreg, re] and [xtreg, mle]

commands if the data are balanced, with [xtreg, pa] otherwise.

• corr(ar1):rts =

1 if t = s

ρ|t−s| if t 6= s. This equivalent to the [xtreg, re] command.

[xtgls] is another possibility. It is a F-GLS estimator which allows for ar(1)-autocorrelationwithin panel, as [xtgee], and cross-sectional correlation and cross-sectional heteroskedas-ticity. The variance of the overall residuals (αi + uit) cam be written as the Kroneckerproduct of the within-group variance (σ) and the cross-sectional variance (Ω):

E(εε′) = σ ⊗ Ω =

σ11 σ12 . . . σ1N

σ21 σ22 . . . σ12N

......

. . ....

σN1 σN2 . . . σNN

⊗

Ω11 Ω12 . . . Ω1N

Ω21 Ω22 . . . Ω12N

......

. . ....

ΩN1 ΩN2 . . . ΩNN

If the diagonal of the σ-matrix is filled with different numbers, the individuals do not

31

have the same overall variance (panel-heteroskedasticity). If the off-diagonals are notzero, the individuals are correlated with each other (panel-correlation). If the Ω-matrixis not identity, then the residuals are autocorrelated in the time dimension (within-groupcorrelation). With this estimator several options are possible:

• panels(hetero): state that the Ω-matrix is identity (default if no corr-option spec-

ified) and σ =

σ11 0 . . . 00 σ22 . . . 0...

.... . .

...0 0 . . . σNN

• corr(ar1): state that the Ω-matrix is such that the serial correlation structure is

rts =

1 if t = s

ρ|t−s| if t 6= s

• corr(psar1): state that this correlation factor can be different for each individual:

rts,i =

1 if t = s

ρ|t−s|i if t 6= s

The last estimator used in this paper is [xtpcse]; it calculates a panel-correctedstandard errors for the OLS-estimates (actually Prais-estimator that corrects for firstorder autoregression in the time-dimension, i.e. a GLS estimator!). We use the [pairwise]option that specifies that all information available for an individual should be used tocalculate the covariance matrix. This has influence only when the data are unbalanced.The option [hetonly] is also selected implying that panel-heteroskedasticity is allowed(see above). Two sorts of within-group autocorrelation is then allowed as above, acommon ar(1)-structure (ar1) or individual-specific one (psar1). [xtgls] and [xtpcse]are consistent, and under the correct assumption of the error structure F-GLS wouldbe more efficient. But it is argued that with the typical small samples used in socialscience, xtpcse should be preferred, as it is more conservative.

7 Annex: Scatter diagrams of the data

32

Chart 3: Scatter diagram: Age (15-24) vs Wages, EU-27 and EU-15

at

atat

atatat

atatat

be

bebe

be

bebebe

be

be

be

dededede dedede

dededees

eses

es

eseseseses

es

fi

fifi

fi fifi

fifi

fifi

frfrfrfr

frfr

fr

frfrfr

grgrgrgrgrgrgrgrgr

ititit

it

itititit

itit

lu

lu

lu

lu

lu

lu

lulu

lu

lu

nl

nlnlnl

nlnlnlnlnlnl

pt

ptpt

pt

pt

pt

ptpt

pt

pt

dk

dk

dk

dkdk

dkdk

dkdk

se

sesese

se

sesese

ukukukuk

ukukukukuk uk

cy

cy

cycy

cy

cyczcz

czczczczczcz

czee

ee

ee

eeee

ee

eeee ee

huhu

huhu

huhu

huhuhu

hultlt

ltlt

lt

ltlt

lt

lv lvlv

lv

lvlvlvlv

mt

mtmt

mt

plplplpl

plpl

si

si

sisisi

si

sisi

sisi

sksk

sk

sk

sksksk

sk

bg

bgbgbg

bgbg

.2.4

.6.8

11.

2al

ter_

1524

: rel

ativ

e pr

opor

tion

of y

oung

wor

kers

(in

dust

ry/s

ervi

ces)

.6 .8 1 1.2 1.4lk_neu: relative hourly wages (industry / services)

at

atat

atatat

atatat

be

bebe

be

bebebe

be

be

be

dededede dedede

dededees

eses

es

eseseseses

es

fi

fifi

fi fifi

fifi

fifi

frfrfrfr

frfr

fr

frfrfr

grgrgrgrgrgrgrgrgr

ititit

it

itititit

itit

lu

lu

lu

lu

lu

lu

lulu

lu

lu

nl

nlnlnl

nlnlnlnlnlnl

pt

ptpt

pt

pt

pt

ptpt

pt

pt

dk

dk

dk

dkdk

dkdk

dkdk

se

sesese

se

sesese

.2.4

.6.8

11.

2al

ter_

1524

: rel

ativ

e pr

opor

tion

of y

oung

wor

kers

(in

dust

ry/s

ervi

ces)


Chart 4: Scatter diagram: Age (50+) vs Wages, EU-27 and EU-15

at atatatatat

atatat

bebebe

bebebebebebebe

dededede dededededede

eseseseseseseseseses

fifififi fi fi fififififr

frfrfr

frfrfrfrfrfr

grgrgrgr

grgrgrgrgr

it ititititititit itit

lulu

lulu

lu

lu

lu

lu

lulu

nlnlnlnl

nlnlnlnlnlnl

ptptptpt

pt

ptpt

pt

ptpt

dkdkdkdk

dk dkdkdkdk

sesesesese

seseseuk

ukukukukukukukuk uk

cy

cy

cy cy

cy

cy

czczcz

czczczczczcz

eeeeeeee

ee

ee

eeee

eehuhu

huhu huhu

huhuhuhu

ltlt

lt

lt

ltlt

ltlt

lv

lv

lvlvlv

lv

lv

lv

mt

mt

mt

mt

plplplplpl

plsi si

si

sisisi

sisi

sisi

sksksk

sk

sk sksk

sk bgbgbgbgbgbg

.51

1.5

22.

5al

ter_

50pl

us: r

elat

ive

prop

ortio

n of

eld

er w

orke

rs (

indu

stry

/ser

vice

s)


at atatatatat

atatat

bebebe

bebebebebebebe



fifififi fi fi fififififr

frfrfr

frfrfrfrfrfr

grgrgrgr

grgrgrgrgr

it ititititititit itit

lulu

lulu

lu

lu

lu

lu

lulu

nlnlnlnl

nlnlnlnlnlnl

ptptptpt

pt

ptpt

pt

ptpt

dkdkdkdk

dk dkdkdkdk

sesesesese

sesese

.51

1.5

22.

5al

ter_

50pl

us: r

elat

ive

prop

ortio

n of

eld

er w

orke

rs (

indu

stry

/ser

vice

s)


Chart 5: Scatter diagram: Gender vs Wages, EU-27 and EU-15

at atatatatat

at at

at

bebebe

bebebebe

bebebe

dededede dedededede

de

eseseseses

eseseseses

fi

fi

fifi fi fi

fifi

fifi

frfrfrfrfrfrfr

frfrfr

grgrgr

grgrgr

grgrgr

it itititit

ititit

itit

lululu

lu

lulululu

lu

lunlnl

nlnlnlnlnlnlnlnl

ptptptpt ptptptptpt

pt

dkdkdkdk

dkdkdkdkdk

sese

sesesesesese

ukukukukukukukukuk uk

cy

cy

cycy

cy

cy

czcz

czczczczczczcz

ee

eeee

eeeeee

eeee ee huhuhuhu

huhu

huhuhu

hu

lt

ltlt

lt

lt

lt

ltlt

lvlv

lvlv

lv

lv

lv

lv

mt

mtmtmt

plplplplplpl

si sisisi

sisi sisi

sisisk

sksksk

sk sksk

sk

bg

bgbgbg

bgbg

.81

1.2

1.4

1.6

gend

er: r

elat

ive

prop

ortio

n of

men

(in

dust

ry/s

ervi

ces)


at atatatatat

at at

at

bebebe

bebebebe

bebebe

dededede dedededede

de

eseseseses

eseseseses

fi

fi

fifi fi fi

fifi

fifi

frfrfrfrfrfrfr

frfrfr

grgrgr

grgrgr

grgrgr

it itititit

ititit

itit

lululu

lu

lulululu

lu

lunlnl

nlnlnlnlnlnlnlnl

ptptptpt ptptptptpt

pt

dkdkdkdk

dkdkdkdkdk

sese

sesesesesese

.81

1.2

1.4

1.6

gend

er: r

elat

ive

prop

ortio

n of

men

(in

dust

ry/s

ervi

ces)


33

Chart 6: Scatter diagram: Qualification vs Wages, EU-27 and EU-15

at atatatatatat atat

bebebebebebe

bebebebe

dedede dededededede


fififi

fi fi fi fifififi

frfrfrfrfrfrfrfrfrfr

grgrgrgrgrgrgrgrgr

it ititititititititit

lulu

lulu

lu

lulu lulu

nlnlnlnlnlnlnlnlnlnl

ptptptpt ptptptptptptdk

dk

dk

dk

dk

dkdkdkdk

sesesesesesesese

ukukukukukukukuk uk

cycycy

cy

cycy

cz

czczcz

czcz

czcz

ee

ee

ee

ee

ee

ee

ee

ee

eehu

hu

huhuhu

huhuhuhu

lt

lt

lt

lt

lt

lt

lt

lt

lv

lvlv

lvlv

lv

lvlv

mtmtmtmt

pl

pl

si

sisi

si

si

si

si

si

sisi

sk

sk

sk

sk

sksksksk

bgbg

bg

bg

bgbg

11.

52

2.5

33.

5qu

alifi

catio

n: r

elat

ive

qual

ifica

tion

aver

age

(indu

stry

/ser

vice

s)


at atatat

at

atat

atat

be

bebe

be

bebe

be

be

be

be

dedede de

dedede

de

de

eseses

eses

eses

eses

es

fi

fi

fi

fi fi

fififi

fifi

frfrfr

frfrfrfrfrfrfr

grgrgrgr

gr

gr

gr

grgr

ititit

ititititit it

it

lu

lu

lulu

lu

lu

lu

lu

lu

nlnl

nl

nl

nlnl

nlnl

nlnl

pt

pt

ptpt ptptpt

ptptpt

dk

dk

dk

dk

dk

dk

dkdkdk

sesesese

sese

se

se

11.

21.

41.

6qu

alifi

catio

n: r

elat

ive

qual

ifica

tion

aver

age

(indu

stry

/ser

vice

s)


34

Chart 7: Scatter diagram: Firm size vs Wages, EU-27 and EU-15

atatat

bebebede dede

esesesfi fi fi

frfrfrititit

lululu

nlnlnl

pt ptptdkdkdk

sesese

ukukuk

cycycy

czczczeeeeee

hu huhu

ltltltlvlv lv

plplplsisisi

sk sksk

bg bgbg

02

46

8fir

msi

ze: r

elat

ive

firm

siz

e (in

dust

ry/s

ervi

ces)


atatat

bebebe

de dede

eseses

fi fi fi

frfrfr

ititit

lululu

nlnlnl

pt ptpt

dkdkdk

sesese

23

45

firm

size

: rel

ativ

e fir

m s

ize

(indu

stry

/ser

vice

s)


Chart 8: Scatter diagram: Self-employment vs Wages, EU-27 and EU-15

at

atat

atatatat at

at

bebebebe

bebebebe

be

bedededede

dededededede

eseseseseseses

eses

esfi

fififi fi fi

fi

fi

fi

fifrfrfrfrfr

frfrfrfrfr

grgrgrgrgrgrgrgr

gr

it itititit

ititititit

lu

lululu

lu

lu

lulu

lu

lu

nl

nlnlnl

nl

nlnl

nlnlnlptptptpt ptptptpt

pt

pt

dkdk

dkdk

dk

dkdkdk

dk sese

sesesese

sese ukukukuk

ukukukuk

uk uk

cy

cycy

cycycy

czczcz

czczcz

czcz

cz

ee

ee

ee

ee

huhuhu

hu

huhuhuhu

huhult

lt

ltltlt

lv lvlv

lv

lv

lvlv

lv

mt

mtmt

mt

plplplplplpl

sisisi

sisisi si

si

sisi

sksk sk

sk

sk

sksksk

bgbgbg

bgbgbg

0.2

.4.6

.8se

lfem

ploy

: rel

ativ

e pr

opor

tion

of s

elf−

empl

oyed

(in

dust

ry/s

ervi

ces)


at

at

at

at

atatat at

at

bebebebe

bebebe

be

be

be

dedede

dededededede

de

eseseseseseses

eses

es

fi

fi

fififi

fi

fi

fi

fi

fifrfrfr

frfr

frfrfrfrfr

grgrgr

grgrgrgrgr

gr

ititit ititititit

itit

lu

lu

lu

lu

lu

lu

lulu

lu

lu

nl

nlnl

nlnl

nlnl

nlnlnlptptptpt pt

ptptpt

pt

pt

dk

dk

dk

dk

dk

dkdk

dk

dk sese

se

sesese

sese

.1.2

.3.4

.5.6

selfe

mpl

oy: r

elat

ive

prop

ortio

n of

sel

f−em

ploy

ed (

indu

stry

/ser

vice

s)


Chart 9: Scatter diagram: Part-time employment vs Wages, EU-27 and EU-15

at

be

de

es

fi

fr

gr

itlu

nl

pt

dk

se

uk

cy

cz ee

hu

lt

lv

si

sk

bg

.1.2

.3.4

.5.6

teilz

eit:

rela

tive

prop

ortio

n of

par

t−tim

e w

orke

rs (

indu

stry

/ser

vice

s)


at

be

de

es

fi

fr

gr

itlu

nl

pt

dk

se

.1.2

.3.4

.5.6

teilz

eit:

rela

tive

prop

ortio

n of

par

t−tim

e w

orke

rs (

indu

stry

/ser

vice

s)


35

Chart 10: Scatter diagram: Unemployment rate vs Wages, EU-27 and EU-15

at atatatatat

at atat

bebebebe

bebebe

bebebe dededede

dededededede

es

es

es

es

eses

eseses

es

fi

fi

fi

fi fifi fifififi

frfrfrfr

frfr

frfrfrfrgrgrgr

gr

grgrgrgr

gr

it itit ititititit itit

lululululululu

lu

lulu

nl

nl

nlnlnl

nlnl

nlnlnl

ptpt

ptpt

ptptpt

ptptpt

dkdkdkdkdk dk

dkdkdk

sese

se

se

sesesese

uk

ukukuk

ukukukukuk ukcy

cy cycy

cy

cz

czczcz

czcz

czcz

eeee

ee

eeee

eeee ee

ee

huhu

hu

huhu

huhuhuhu

hu

ltlt

ltlt

lt

ltlt

lt

lv lvlvlv

lv

lvlv

lv

mt mtmtmt

plpl

pl

pl

pl

plplpl

pl

si sisisi

sisi sisi sisi

sk

sk

sksk

sk

sksk

sk bg

bg

bg

bg

bg

bg

05

1015

20al

q: n

atio

nal u

nem

ploy

men

t rat

e


at atatatatat

at atat

bebebebe

bebebe

bebebe dededede

dededededede

es

es

es

es

eses

eseses

es

fi

fi

fi

fi fifi fifififi

frfrfrfr

frfr

frfrfrfrgrgrgr

gr

grgrgrgr

gr


lululululululu

lu

lulu

nl

nl

nlnlnl

nlnl

nlnlnl

ptpt

ptpt

ptptpt

ptptpt

dkdkdkdkdk dk

dkdkdk

sese

se

se

sesesese

05

1015

20al

q: n

atio

nal u

nem

ploy

men

t rat

e


Chart 11: Scatter diagram: Minimum wage-dummy vs Wages, EU-27 and EU-15


bebebebebebebebebebe



fifififi fi fi fifififi

frfrfrfrfrfrfrfrfrfrgrgrgrgrgrgrgrgrgr


lulululululululu lulu nlnlnlnlnlnlnlnlnlnlptptptpt ptptptptptpt

dkdkdkdkdk dkdkdkdk sesesesesesesese ukukuk

ukukukukukuk uk

cycycycycycy cycycy

czczczczczczczczczcz eeeeeeeeeeeeeeee ee ee huhuhuhu huhuhuhuhuhu ltltltltltltltltltlt lvlv lvlv lvlvlvlvlvmt mtmtmt plplplplplplplplplplsisi sisisisisi sisi sisi sksksksk sksk sk sksksk bgbg bgbgbgbgbgbg

0.2

.4.6

.81

min

wag

e_is

: dum

my

for

exis

tenc

e of

nat

iona

l min

imum

wag

e



bebebebebebebebebebe



fifififi fi fi fifififi

frfrfrfrfrfrfrfrfrfrgrgrgrgrgrgrgrgrgr


lulululululululu lulu nlnlnlnlnlnlnlnlnlnlptptptpt ptptptptptpt

dkdkdkdkdk dkdkdkdk sesesesesesesese0.2

.4.6

.81

min

wag

e_is

: dum

my

for

exis

tenc

e of

nat

iona

l min

imum

wag

e


Chart 12: Scatter diagram: Union density vs Wages, EU-27 and EU-15

at atatatatatat at

bebebebebebebebe

dededede dededede

eseseseseseseses

fifififi fi fi fifi

frfrfrfrfrfrfrfr

it itit ititititit

nlnlnlnlnlnlnlnlptptptpt ptptptpt

dkdkdkdkdk dkdk

sesesesesesesese

ukukukukukukukuk

020

4060

80ud

ens:

uni

on d

ensi

ty (

%)


at atatatatatat at

bebebebebebebebe

dededede dededede

eseseseseseseses

fifififi fi fi fifi

frfrfrfrfrfrfrfr

it itit ititititit

nlnlnlnlnlnlnlnlptptptpt ptptptpt

dkdkdkdkdk dkdk

sesesesesesesese

020

4060

80ud

ens:

uni

on d

ensi

ty (

%)


36

Chart 13: Scatter diagram: Relative export growth vs Wages, EU-27 and EU-15


bebebebe

bebebebebebe



fififi

fifi

fi fififi

fi

frfrfrfrfrfrfrfrfrfr

grgr

grgr

grgrgr

grgrit

itit

ititititit itit

lululu

lululululu

lulu

nlnlnlnlnlnlnlnlnlnlptptptpt

ptptptptptptdkdk

dkdkdk dkdkdkdk

sesesesesesesese ukuk

ukukukukukukukuk

cycycycycycycycycy

czczczczczczcz

cz

cz

cz

ee

eeee

ee

eeeeee

ee

ee

ee

hu

hu

hu

hu

huhu

hu

hu

hu

hu

lt

ltlt

ltlt

lt

ltltlt

lt

lvlv

lvlv lvlv

lvlv

lv

mt mtmtmt

plplpl

pl

plplpl

pl

plpl

sisi sisisisisi

sisisisi

sksk

sksk

sksksk

sksk

sk

bg

bgbg

bgbg

bgbg

bg

5010

015

020

0gr

owth

_exp

k2: r

elat

ive

cum

ulat

ed e

xpor

t gro

wth

(co

untr

y/eu

ro−

zone

)


at atatatatat

at at

at

be

bebebe

be

bebebebe

be

dededededededededede

es

eseseseseses

eseses

fi

fifi

fi

fifi

fi

fifi

fi

frfrfrfrfr

frfrfr

frfr

grgr

grgr

grgr

gr

grgr

it

it

it

itit

it

itit

itit

lulu

lu

lululululu

lu

lu

nlnlnlnlnl

nlnlnlnlnlpt

ptptpt

ptptpt

ptpt

pt

dkdk

dkdkdk

dkdk

dk

dk

sesese

sese

sese

se

6080

100

120

grow

th_e

xpk2

: rel

ativ

e cu

mul

ated

exp

ort g

row

th (

coun

try/

euro

−zo

ne)


Chart 14: Scatter diagram: Relative GDP growth vs Wages, EU-27 and EU-15

at atatatatatat atat bebebebebe

bebebebebe dededede dededededede

eseseseseses

eseses

es

fifififi fi fi fifi

fifi

frfrfrfrfrfrfrfrfrfrgrgrgrgrgrgrgrgr

gr


lulu

lu

lu

lulululu

lulu

nlnlnl

nlnlnlnlnlnlnlpt

ptptpt pt

ptptptptpt dkdkdkdk

dk dkdkdkdk sesesesesesesese ukukukukukukuk

ukuk uk

cycycy

cycycy cycycy

czcz

czczczczczcz

cz

cz ee

eeee

ee

ee

ee

ee

ee

ee

ee

huhu

huhu

huhuhuhuhu

hu

lt

lt

lt

ltltlt

lt

lt

lt

lt

lvlv lv

lv

lv

lv

lv

lv

lv

mtmtmtmt

pl

plpl

plplplplplplpl

sisi

sisisisisi

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ne)


Chart 15: Scatter diagram: Relative internal demand growth vs Wages, EU-27 andEU-15


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try/

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37

Documents

Driving forces behind the sectoral wage costs diﬁerentials in … · 2008. 7. 21. · Rudolf Zwiener Macroeconomic Policy Institute (IMK), Duesseldorf [email protected]