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Wage spillovers across sectors in Eastern Europe

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Abstract

This paper studies the interactions between wages in the public sector and the private traded and non-traded sector in ten transition countries which are members of the European Union, during the period 2000–2011. The theoretical literature on wage spillovers, as well as the Balassa–Samuelson hypothesis, suggests that the internationally traded sector should be the leader in wage setting, with sheltered and public sector wages adjusting. Using a cointegrated VAR approach we show that a large heterogeneity across countries is present, and non-traded and public sector wages are often leaders in wage determination or at least affect traded sector wages in the short run. This result is relevant from a policy perspective since wage spillovers, leading to costs growing faster than productivity, may affect the international cost competitiveness of the traded sector and thus the catching-up process may be accompanied by accumulation of large international imbalances.

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Notes

  1. Relevant examples are Klau and Mihaljek (2004) and Égert (2003, 2007).

  2. Empirical models testing for the B–S effect often estimate a regression having CPI inflation differential as dependent variable and the dual productivity growth differential as explanatory variable. This comes from assuming that traded sector wages grow in step with productivity.

  3. The only exception is Friberg (2007), who applies his empirical model to Sweden.

  4. In particular, he distinguishes between private sector, manufacturing sector, construction, wholesale and retail trade, financial sector, central government, and county/municipal government.

  5. We thank an anonymous referee for making this point.

  6. With three variables, if r is the number of cointegrating vectors, there will be \(3-r\) common stochastic trends which, testing for weak exogeneity, may be identified with \(3-r\) variables of the system.

  7. See the discussion in Lamo et al. (2012), p. 242.

  8. The exact definition of the series and the sources is provided in Appendix 1.

  9. We thank an anonymous referee for stressing this point.

  10. The literature often identifies the open sector with industry and the sheltered sector with all the rest (for example, Égert 2002, 2003; Golinelli and Orsi 2002). Halpern and Wyplosz (2001) identify them, respectively, with Industry and Services. Agriculture is excluded in Coricelli and Jazbec (2004).

  11. Results of the empirical analysis which include Education and Health are available from the author upon request.

  12. For example, Lamo et al. (2012) and Demekas and Kontolemis (2000) use annual compensation per employee. Friberg (2007), instead, uses average monthly wages.

  13. Indeed, the IR analysis in Sect. 5 will show that, depending on the country, it takes from 4 to 8 quarters for wages in following sectors to reach their new equilibrium level after a shock in the leading sector.

  14. The Phillips–Perron unit root test confirmed the results of the ADF–GLS test. Results are available from the author upon request.

    Fig. 1
    figure 1

    Real wages in the CEEC (Scaled)

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    Table 2 Unit root tests
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  15. Moreover, the stationarity test within the VECM allows us to control for breaks in the series which indeed are present in the case of Bulgaria and Estonia.

  16. The presence of a unit root was rejected in all cases when the test was performed on the first difference of the wage series. Thus, all series are I(1).

  17. See Juselius 2006, Chap. 6.

  18. The deterministic specification and the order of the underlying VARs are reported in Appendix 2.

  19. Specification tests [multivariate and univariate normality, autocorrelation AC(4), ARCH(4)] are available at http://sites.google.com/site/gdadamosite/internal-resume/research.

  20. To construct WLI, we order sectors from the most open to the least open. Thus, WLI takes value 1 when T is leader, 2 when N is leader, and 3 when P is leader. When P and N are jointly weakly exogenous it takes value of 2.5. When T is weakly exogenous jointly with either P or N, WLI = 1.5. The qualitative results we obtained were robust to alternative constructions of WLI.

    Fig. 2
    figure 2

    Wage leadership and macro-institutional variables

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  21. This result was confirmed by using a measure of co-ordination in wage bargaining instead of centralization (i.e., the variable WCOORD in the ICTWSS Database). Following Lamo et al. (2008), we also estimated the correlation between an index of Government intervention in wage bargaining and WLI, but found practically no correlation (\(\rho \) = 0.05).

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Acknowledgments

The author gratefully acknowledges financial support from the Spanish MINECO Project ECO2011-30260-C03-01. I am indebted to Riccardo Rovelli, Luca Fanelli, Katarina Juselius, Stephen Cecchetti, Efrem Castelnuovo, and participants at the VIII INTECO Workshop at the University of Valencia, as well as two anonymous referees, for detailed and enlightening comments.

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Authors and Affiliations

  1. Department of Applied Economics II, University of Valencia, Valencia, Spain

    Gaetano D’Adamo

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  1. Gaetano D’Adamo
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Correspondence to Gaetano D’Adamo.

Appendices

Appendix 1

See Table 7

Table 7 Data sources
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Appendix 2

See Table 8

Table 8 Specification of the empirical model
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Appendix 3. Impulse response functions

See Figs. 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12.

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Bulgaria

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Czech Republic

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Estonia

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Hungary

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Latvia

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Lithuania

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Poland

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Romania

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Slovakia

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Slovenia

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D’Adamo, G. Wage spillovers across sectors in Eastern Europe. Empir Econ 47, 523–552 (2014). https://doi.org/10.1007/s00181-013-0744-z

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