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This paper focuses on the euro area wage structure and its potential determinants from a sectoral viewpoint. Merging information from the OECD Structural Analysis database with data from the EU Labour Force Survey, we construct a cross-country panel of 22 industries in 8 euro area countries for 1991-2002. Data inspection confirms the existence of a fairly stable inter-industry wage structure that is similar across countries. We then apply panel data techniques to identify factors explaining inter-industry wage differentials in the euro area. Both workforce characteristics (e.g., human capital variables) and firm-related characteristics (e.g., capital intensity, productivity) contribute significantly. However, considerable wage heterogeneity across sectors remains. Idiosyncratic sector and country specifics, reflecting different socio-cultural and institutional backgrounds, appear to bear a major role. While our empirical analysis only uses direct evidence from workforce and firm-related characteristics, we also try to relate the remaining heterogeneity to institutional characteristics, based on related literature.

This study compares the structure and determinants of inter-industry wage differentials in Eastern and Western European countries (namely Belgium, Italy, the Netherlands, Norway, Portugal and Spain compared with Latvia, Lithuania, the Czech Republic, Poland and Slovakia). To do so, we use a unique harmonised, linked employer–employee data set, the 2002 European Structure of Earnings Survey. Findings show substantial differences in earnings across sectors in all countries, even when controlling for a wide range of employee, job and employer characteristics. The hierarchy of sectors in terms of wages appears to be quite similar in Eastern and Western European countries. Among high-wage sectors, we find the energy (coke, petroleum, gas, electricity and nuclear power), chemical, financial and computer industries. In contrast, it is in the traditional sectors (wood and cork industry, textile, clothing and leather industry, hotels and restaurants, and retailing) that wages are lowest. Further results suggest that the dispersion of inter-industry wage differentials fluctuates considerably across countries. It is relatively small in Norway and Belgium, large in the Netherlands, Italy, Spain, Poland and the Czech Republic, and very large in Portugal, Latvia, Lithuania and Slovakia. Our findings support the hypothesis of a negative relationship between the dispersion of inter-industry wage differentials and a country’s degree of corporatism., info:eu-repo/semantics/published

This study analyses the interaction between inter-industry wage differentials and the gender wage gap in six European countries using a unique harmonised matched employer-employee data set, the 1995 European Structure of Earnings Survey. Findings show the existence of significant inter-industry wage differentials in all countries for both sexes. While their structure is quite similar for men and women and across countries, their dispersion is significantly larger in countries with decentralised bargaining. These differentials are significantly and positively correlated with industry profitability. The magnitude of this correlation, however, is lower in countries with centralised and coordinated collective bargaining. Further results show that in all countries more than 80% of the gender wage gaps within industries are statistically significant. Yet, industries having the highest and the lowest gender wage gaps vary substantially across European countries. Finally, results indicate that industry effects explain between 0 and 29% of the overall gender wage gap., info:eu-repo/semantics/published

This paper examines the role of unobserved ability in explaining inter-industry wage differentials, drawing on data on brothers. Such data allow us to account for unmeasured abilities due to common family and community factors shared by siblings. Important advantages of this approach are that we do not rely on assumptions of exogenous job mobility and that estimates reflect long-run wage differentials rather than short-run differences following switch of industry. The data sets come from four of the Nordic countries and the United States. We find that, in the Nordic countries, only a moderate proportion of the variability in industry wages is due to unobserved ability, while unmeasured factors explain as much as half of the industry wage variation observed in the United States. Accounting for such differences, we show that the U.S. inter-industry wage dispersion is similar to those of the Nordic countries.

Many workers believe that personal contacts are crucial for obtaining jobs in high-wage sectors. On the other hand, firms in high-wage sectors report using employee referrals because they help provide screening and monitoring of new employees. This paper develops a matching model that can explain the link between inter-industry wage differentials and use of employee referrals. Referrals lower monitoring costs because high-effort referees can exert peer pressure on co-workers, allowing firms to pay lower efficiency wages. On the other hand, informal search provides fewer job and applicant contacts than formal methods (e.g., newspaper ads). In equilibrium, the matching process generates segmentation in the labor market because of heterogeneity in the size of referral networks. Referrals match good high-paying jobs to well-connected workers, while formal methods match less attractive jobs to less-connected workers. Industry-level data show a positive correlation between industry wage premia and use of employee referrals. Moreover, evidence using the NLSY shows similar positive and significant OLS and fixed-effects estimates of the returns to employee referrals, but insignificant effects once sector of employment is controlled for. This evidence suggests referred workers earn higher wages not because of higher unobserved ability or better matches but rather because they are hired in high-wage sectors.

In this paper, the role of the computer at the workplace will be examined in determining the wage structure in Germany. Following Krueger (1993) and using the German Socio- Economic Panel (GSOEP), cross-sectional wage regression results from 1997 and panel results from 1984-1997 are presented. It is shown that the wage premium attributed to using a computer at work using cross-sectional results for 1997 is around 7%. Further it is shown that computer usage is very heterogeneous depending on which industry one works in. In cross-section, hypothesis tests show that several industries and almost all firm size categories exhibit very different wage differentials depending on computer usage at the workplace. As DiNardo and Pischke (1997) stress the need for panel data to control for unmeasured individual effects, we use GSOEP 1984-1997 panel data, where a random effects and fixed effects estimator were run in the wage estimation. We confirm the results that Entorf and Kramarz (1997) had for France, that in Germany the coefficient for computer usage at the workplace did not remain stable and although just barely significant, was reduced to mere 1% with individual fixed effects. We conclude that there are no computer usage wage differentials worth speaking of, once one controls adequately for unobserved individual heterogeneity.

In this paper, the inter-industry wage structure in West Germany and USA is compared using the German Socio-Economic Panel (GSOEP), the German Mikrozensus (MZ), the American Panel Study of Income Dynamics (PSID) and the American Current Population Survey (CPS) from 1984 to 1996. Using a sample of prime age full-time employed males from the respective datasets, it is shown that the structure of wages has remained remarkably stable over this time period, and that the German structure resembles the American structure strongly. Cross-sectional and panel results are provided for both countries. Controlling for unobserved heterogeneity in the random effects panel estimations reduces the industry wage dispersion by about half. Thus, although the MZ and the CPS provide very large sample sizes, panel data sets (although typically smaller in sample size) are still very important in getting at the essence of the industry wage structure and the absolute level of industry wage dispersion. In calculating inter-industry wage differentials as deviations from a hypothetical employment-share weighted mean, we use the methodology as described in Haisken-DeNew and Schmidt (1997) of calculating exact differentials and their respective standard errors.