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Do Services Play a Role in Regional Productivity Growth Across Europe?

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Service Industries and Regions

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

The relationship between economic structure and productivity growth has been a subject of increasing interest over recent decades. The innovative focus of this paper concerns the role of the service sector in this relationship at a regional level. Traditionally, productivity has been introduced as explaining factor of tertiarization processes in advanced economies, while it has been simultaneously assessed that services display lower productivity levels and growth rates than other economic industries. Nevertheless, in recent years many papers and authors have refuted or limited these conventional theses.

This paper focuses on the impact of tertiarization on overall productivity growth, using a sample of European NUTS-2 regions in the period between 1980 and 2008. The results partially refute traditional knowledge on the productivity of services. Contrary to what conventional theories suggest, this research demonstrates that several tertiary activities have shown dynamic productivity growth rates, while their contribution to overall productivity growth plays a more important role than was historically believed.

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Notes

  1. 1.

    See, among others, Blackaby (1978) or Gemmell (1982).

  2. 2.

    See, among others, Bell (1974) or Fuchs (1968).

  3. 3.

    See Maroto (2012) for a survey on the relationships between services and productivity. See Towse (1997) for more information on Baumol’s “cost disease”.

  4. 4.

    See, among others, Amiti (1999), Combes and Overman (2003), Ezcurra et al. (2006) or OECD (2009)

  5. 5.

    In the case of Germany and the United Kingdom, we have used NUTS-1 because the dimension of NUTS-2 is too small to make a realistic and accurate comparison. Additionally, Azores Islands (POR), Ceuta and Melilla (SP) and the overseas French territories have been excluded. In the case of Greece, all islands are considered as a single region.

  6. 6.

    Although the dataset provided by Cambridge Econometrics show estimations for later years, these are only forecasting data. For this reason, in this chapter we have decided to handle the data until 2008.

  7. 7.

    See, for example, Salter (1960), Denison (1967), and Chung and Denison (1976).

  8. 8.

    See, among others, Young (1995), Fagerberg (2000), Timmer and Szirmai (2000), Carree (2003), or Krüger (2008).

  9. 9.

    Specifically, the correlation coefficient in the case of employment is −0.5223, significant to 1 % (p-value = 0.0040). Results are robust if the weight of service sector is measured in terms of value added. Then, the correlation coefficient is −0.5838, also significant to 1 % (p-value = 0.0015).

  10. 10.

    Instead of the additive nature which is usually used in this kind of techniques. The reasons are: on the one hand, the elimination of effects of scale originated from the use of several variables with different units. On the other hand, the possibility of combining the three variables under consideration: productivity, added value and employment, in just one indicator, in line with what was previously shown graphically in Fig. 9.1.

  11. 11.

    See: Peneder (2002, 2003) for 28 countries of the OECD; Havlik (2005) for the new Eastern European countries belonging to the EU; Fagerberg (2000) for the manufacturing sectors in 39 countries based on the UNIDO; Timmer and Szirmai (2000) for the manufacturing sectors of four Asian countries; Maroto and Cuadrado (2007, 2009) for Spanish economy, and EU-15 and US, respectively; and van Ark (1995) for a group of 8 countries of the EU and the USA.

  12. 12.

    See: Schettkat and Yocarini (2006) for a review of the literature on the shift to services employment. Fourastie (1949) and Fuchs (1968) have been pioneers introducing this theory. Some empirical applications on this hypothesis are: Peneder et al. (2003); or Gregory et al. (2007).

  13. 13.

    This combined effect of the static and dynamic components is named “structural effect” or simply the “effect of structural change” by some authors (Maddison, 1996), and analyzed together although the analysis is deeper if both effects are distinguished.

  14. 14.

    See, among others, Daniels (2004), Combes and Overman (2003), Midelfart-Knarvik et al. (2003), Ezcurra et al. (2006), Amiti (1999), Haaland et al. (1998), Hallet (2000), and Molle (1996).

  15. 15.

    See, among others, Salter (1960) and Baumol et al. (1989).

  16. 16.

    A standard OLS regression model in a cross-section (for example, in Fagerberg, 2000) has also been implemented. Conclusions, although calculations are not included in the text, do not differ from the conclusions drawn in the paper based on a panel-data regression model.

  17. 17.

    The positive relationship between service growth (regressor) and labour productivity (dependent variable) might be endogenous, so results could be influenced by reverse causation matters. In order to solve this, Granger causality tests were implemented (Granger, 1969). According to our data, the growth of services could explain productivity growth (with the usual number of lags up to 14, null hypothesis that growth of services does not cause productivity growth will be rejected with any usual level of statistical confidence). Nevertheless, reverse causality will not be accepted (null hypothesis that productivity growth does not cause growth of services will not be rejected with any usual level of statistical confidence). Summarizing, likely reverse causation matters seem to be solved in the model regressed here.

  18. 18.

    A standard OLS regression model in a cross-section (for example, in Fagerberg, 2000, or Maroto & Cuadrado, 2009) has also been implemented. Additionally, estimations with subsamples and different time spans have been developed. Conclusions, although calculations are not included in the text, do not differ from the conclusions drawn in the paper based on a panel-data regression model.

  19. 19.

    A time series X is said to Granger-cause Y if it can be shown, usually through a series of F-tests on lagged values of X (and with lagged values of Y also known), that those X values provide statistically significant information about future values of Y.

  20. 20.

    See, among others, Bosworth and Triplett (2007) and Triplett and Bosworth (2004) for the United States; Crespi et al. (2006) for the United Kingdom; McLachlan et al. (2002) for Australia; Maroto and Cuadrado (2009) for a simple of OECD countries; and Maroto and Rubalcaba (2008) for the European Union.

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Authors and Affiliations

  1. Department of Economic Analysis: Economic TheoryC/ Francisco Tomas y Valiente, 5, Autonomous University of Madrid (UAM), 28049, Cantoblanco (Madrid), Spain

    Andrés Maroto-Sánchez

  2. Department of Applied Analysis, University of Alcala, Madrid, Rafael Calvo, 17 – 4 . PD, 28010, Madrid, Spain

    Juan R. Cuadrado-Roura

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  1. Andrés Maroto-Sánchez
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  2. Juan R. Cuadrado-Roura
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Correspondence to Andrés Maroto-Sánchez .

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  1. , Applied Economics, University of Alcala - Madrid, Plaza Victoria 2, Alcalá de Henares, 28802, Spain

    Juan R. Cuadrado-Roura

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Maroto-Sánchez, A., Cuadrado-Roura, J.R. (2013). Do Services Play a Role in Regional Productivity Growth Across Europe?. In: Cuadrado-Roura, J. (eds) Service Industries and Regions. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35801-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-35801-2_9

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