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Productivity in the euro area: any evidence of convergence?

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Abstract

Sizable prevailing real economic disparities among countries in a currency union potentially involve costs for those countries for which the aggregate policy stance is not appropriate. This paper contributes to the literature by testing for productivity convergence among euro area countries. While no convergence can be found on the aggregate level, selected service sectors and manufacturing sub-industries indicate evidence of convergence. In a search for factors influencing productivity, investments in research and development as well as a high skill level of employees are shown to be beneficial, whereas regulations constitute a burden.

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Notes

  1. While this paper examines real convergence in the euro area, several studies have analysed nominal convergence (see e.g. Busetti et al. 2007). In terms of methodology applied, Byrne and Fiess (2010)) come closest to our analysis. Overall inflation differentials are found to be quite persistent. Altissimo and Mojon (2009) find some convergence looking at disaggregate data, while Fritsche and Kuzin (2011) apply non linearity (using time-varying factor loadings as proposed by Phillips and Sul 2007) and find some regional clustering.

  2. Papdemos, speech delivered at the 7th Biennial Athenian Policy Forum conference ‘Asymmetries in Trade and Currency Arrangements in the Twenty-First Century’, 29 July 2004.

  3. Most recently Rodrick (2013) confirmed this consideration, although for a set of 100 countries, showing that convergence in manufacturing industries can be found, while the contrary holds for the aggregate.

  4. The ratio of hours worked per people employed offers insights into this relationship. For the euro area, it shows, first, an overall decreasing pattern depicting the trend to part-time work. Second, the developments throughout euro area countries are very different, giving rise to the conclusion that a focus on people employed is heavily biasing the results of previous convergence tests (Gardiner et al. 2004).

  5. Thanks to an anonymous referee for suggesting this additional section.

  6. While the European Commission addressed goods market competition in the first stage of integration (primarily in the 1970s and 1980s, factor mobility in financial and other services was targeted in the Single Market Programme from 1988 onwards. Barriers in financial markets were reduced to allow capital mobility (National Institute of Economic & Social Research 1996).

  7. In case of multiple common factors extracted Bai and Ng (2004) suggest to use multivariate cointegration methods to check if there is a long-run relationship between the factors. For this, a Johansen trace test could be applied.

  8. Overall, however, the number of observation used in this paper is similar to the simulations in Bai and Ng (2004) and should therefore not pose a limitation.

  9. A study somewhat different is Battisti and Vaio (2008), who apply a procedure that merges together an endogenous identification of convergence paths and spatial dynamics and finds that only a few regions show (slow) rates of convergence.

  10. For a summary overview of the methodology and construction of the EU KLEMS database see O’Mahony and Timmer (2009).

  11. The EA-12 aggregate comprises all countries which joined the euro area until 2001, i.e. Austria, Belgium, Spain, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, The Netherlands and Portugal. The choice of this narrow definition of the euro area stems from limited data available for the remaining euro area countries.

  12. These sectors (with the NACE 1.1 classification in brackets) cover agriculture (A and B), total manufacturing (D), electricity, gas and water (E), construction (F), distributive trades (G), transport, storage and communication (I), financial intermediation (J), real estate, renting and other businesses (K) as well as other non-market services (L–Q). Mining and quarrying (C) and Hotels and restaurants (H) are excluded given their negligible size in terms of value added.

  13. The manufacturing sub-industries are food beverage and tobacco (15 and 16), textile leather and footwear (17–19), wood and cork (20), pulp, paper, printing and publishing (21 and 22), chemicals, rubber, plastics and fuel (23–25), other non-metallic minerals (26), basic and fabricated metals (27 and 28), other machinery (29), electrical and optical equipment (30–33), transport equipment (34 and 35) and other manufacturing including recycling (36 and 37).

  14. A necessary simplification has to be applied for the sectoral analysis. Given that OECD-Eurostat PPPs from 1970 on are only available on an aggregate level, sectoral price level differences are assumed to be on average similar to developments for the total economy.

  15. According to Götzfried (2005) other machinery, electrical/optical equipment and transport equipment are high-technology (or higher medium-technology) manufacturing industries, whereas the remaining industries are classified to produce low-technology (or lower medium-technology).

  16. Productivity developments in all remaining main sectors are displayed in Annex 2.

  17. The information criterion suggested in Bai and Ng (2002) was applied and in generally favoured one common component in the data. In some cases the information criterion was systematically selecting the maximum number of common components given a priori. This misspecification of the information criterion has already been identified elsewhere in the literature (see Fritsche and Kuzin 2007), and can be largely traced back to the size of the panel. The BIC has been used as alternative.

  18. The lag length has been determined by applying AIC and BIC to the data.

  19. As stated earlier, Greece is included into this list, although the country joint the euro area only in 2001.

  20. The sub-industries are not shown here, but are available from the author on request.

  21. Owing to the possibility for more careful supply chain and inventory management and the collation of more precise information about customers’ purchasing patterns (see ECB Occasional Paper (No. 128/2011) on structural features of the distributive trades).

  22. The same is valid for the sub-industries, not shown here, but available from the author on request.

  23. Thanks to an anonymous referee for suggesting this.

  24. Given its role as robustness check, we refrain here from a detailed discussion of the methodology and refer the interested reader to Maasoumi et al. (2007) or Maasoumi and Wang (2008).

  25. The country distribution in Figs. 1, 2, 3 and 4 is used to derive the two groups, with France, Italy, Finland, Greece, Ireland and Portugual are in the lower productivity group, while the remaining countries are part of the higher productivity group. Results are fairly robust to changes in the selection procedure.

  26. See Burgess (2011) for a discussion on the topic.

  27. The data are taken from the OECD statistics database if not stated otherwise. Moreover, the data are available on sector level, except the regulatory indicator and the data on research and development.

  28. A hausman test suggests that random effects estimator would be inconsistent (see Table 6).

  29. Provided the cost curve is downward sloping, i.e. involving a substantial amount of fixed costs.

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Acknowledgments

Helpful comments received from two anonymous referees as well as the editors of this journal, from participants of two ECB internal seminars, and specifically Reiner Martin are gratefully acknowledged.

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  1. European Central Bank (ECB), Frankfurt, Germany

    David Sondermann

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  1. David Sondermann
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The views expressed in the paper are those of the author and do not necessarily reflect those of the ECB.

Appendices

Annex 1

See Fig. 6.

Fig. 6
figure 6figure 6

Countries’ productivity levels relative to euro area average; manufacturing sub-sectors. a Food/beverages/tobacco, b textile/leather/footwear, c wood and cork, d pulp/paper/printing, e chemicals/plastics/fuel, f non-metallic minerals, g metals, h other machinery, i electrical/optical equipment, j transport equipment, k other manufacturing. SourceEU KLEMS (2009), OECD-Eurostat PPPs and own calculations

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Annex 2

See Fig. 7.

Fig. 7
figure 7

Countries’ productivity levels relative to euro area average; main sectors. a Agriculture b Energy c Construction d Transport and communication e Financial intermediation. Source EU KLEMS (2009), OECD-Eurostat PPPs and own calculations

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Annex 3

See Table 7.

Table 7 Entropy measure results for productivity convergence (Maasoumi et al. 2007)
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Sondermann, D. Productivity in the euro area: any evidence of convergence?. Empir Econ 47, 999–1027 (2014). https://doi.org/10.1007/s00181-013-0762-x

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