Skip to main content
SpringerLink
Log in

European countries’ competitiveness and productive performance evolution: unraveling the complexity in a heterogeneity context

  • Regular Article
  • Published:
Journal of Evolutionary Economics Aims and scope Submit manuscript

Abstract

This paper is developed around two major arguments. First, the European Country Specific Industrial Structures (CSIS) technology gap defined in a metafrontier framework, captures the productive performance of the examined European CSIS conditional on the country-specificities, and therefore mirrors the micro-component of country competitiveness. Second, the evolution patterns of technology gaps, incorporating a time varying and group specific path dependence process as well as the features of the corresponding growth curve, reflect the contribution of the micro-component on the foundational country competitiveness. We use a balanced panel dataset that includes thirteen industries from seventeen European countries industries covering an 8 year period and employ a two stage approach. First, we employ a metafrontier framework for the estimation of productive performance and, at a second stage, we adopt and further develop a Growth Mixture Autoregressive Latent Trajectory model. This empirical strategy allows for the identification of heterogeneous groups with respect to the evolution patterns and for the consideration of group specific and time varying path dependence of European CSIS technology gaps evolution patterns. Empirical findings confirm the existence of two distinct groups that, over time, become more divergent but also more concentrated around their center. Finally, empirical evidence suggests that the micro component of foundational competitiveness, captured by CSIS technology gaps evolution patterns, exerts a differential impact on EU country overall foundational competitiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. The authors link competitiveness components with a country’s endogenous ability to create its own technology and also its ability to exploit externally available knowledge, as well as exogenous factors such changes in world demand and relative prices in common currency. In our case we control for these five components through the employed methodology for the estimation of EU countries industrial structures technology gaps

  2. Namely, (i) inactivity is allowed, (ii)“free lunch” is not allowed, (iii) technology is convex and (iv) there is strong disposability of inputs and outputs

  3. Hayami (1969) and Hayami and Rutan (1970) were the first to propose the concept of a meta-production distance function “… as the envelope of commonly conceived neoclassical production functions”. Meta-production focuses on the heterogeneity of production technology associated with different decision-making units (DMU) and reflects region, type, scale and other inherent attributes. Then, the DMUs are a priori divided into groups according to the different sources of technological heterogeneity. Each group can form a production frontier, i.e. group frontier.

  4. Such kind of technology differentials may include the structure of national markets, national regulations and policies, cultural profiles and legal and institutional frameworks (Halkos and Tzeremes 2011), different ownership types (Kontolaimou and Tsekouras 2010; Casu et al. 2013) and different rate of access and adoption of General Purpose Technologies (Kounetas et al. 2009).

  5. However, the development of the “best method” for the analysis of change has been a continuing concern in many research disciplines other than economics. For instance, in the context of behavior and developmental research, Baltes and Nesselroade (1979) outlined five key objectives for longitudinal research: (i)direct identification of intra-individual change, (ii) direct identification of inter-individual differences in intra-individual change, (iii) analysis of interrelationships in change, (iv) analysis of causes (determinants) of intra-individual differences and (v) analysis of causes (determinants) of inter-individual differences in intra-individual change.

  6. This particular family of methods is very popular in other disciplines such as sociology and behavioral sciences but remains practically unknown within the realm of economics.

  7. Hamaker (2005) shows that an ALT model that has an equal autoregressive coefficient and is not written with the first wave outcome as predetermined to be mathematically equivalent to an alternative growth curve model with autoregressive disturbances. However, in the autoregressive disturbance model, there is no direct influence of the lagged value but instead the interest shifts in the correlation of the disturbances. This equivalency does not hold if the autoregressive parameter differs across time or if the first time of the outcome is treated as a predetermined variable, as recommended in Bollen and Curran (2004).

  8. Indeed, Ou et al. (2016) have criticized ALT model for treating the initial outcome variable as predetermined and present alternative model specifications where the initial outcome variable is treated as endogenous.

  9. In defining the industrial sectors we follow the International Standard Industrial Classification (ISIC). More specifically, nine of them belong to the manufacturing (food and beverages, textiles, wood, pulp, chemicals, other non-metallic minerals, basic metals, transport equipment) and four to the transportation sectors (land transport, water transport, air transport and supporting and auxiliary transport activities).

  10. In particular, Belgium, Czech Republic, Denmark, Germany, Ireland, Greece, Spain, France, Italy, Netherlands, Austria, Poland, Slovenia, Slovakia, Finland, Sweden, United Kingdom

  11. A presentation of the capital stock estimation may be found in Tsekouras et al. (2016).

  12. Czech Republic, Denmark, Poland, Slovak Republic, Slovenia, Sweden and the United Kingdom

  13. The dummy variable capturing the Construction sector has been used as the reference group

  14. The World Input Output Database (WIOD) disaggregates the labor input in high-, medium- and low- skilled components. Although such information is quite interesting, the estimation of the country technologies and the European metatechnology on the basis of a single-output, six-inputs frontiers would raise the problem of the upward biased technical efficiency, which has been investigated in the context of the non-parametric performance estimation (Grosskopf and Valdmanis 1987; Kneip et al. 1998)

References

  • Abramovitz M (1986) Catching up, forging ahead, and falling behind. J Econ Hist 46:385–406

    Article  Google Scholar 

  • Acemoglu D, Johnson S, Robinson JA (2001) The colonial origins of comparative development: an empirical investigation. Am Econ Rev 91:1369–1401

    Article  Google Scholar 

  • Acemoglu D, Aghion P, Zilibotti F (2006) Distance to frontier, selection, and economic growth. J Eur Econ Assoc 4:37–74

    Article  Google Scholar 

  • Antonelli C, Crespi F, andScellato G (2015) Productivity growth persistence: firm strategies, size and system properties. Small Bus Econ 45:129–147

    Article  Google Scholar 

  • Atkinson AB, Stiglitz JE (1969) A new view of technological change. Econ J 79:573–578

    Article  Google Scholar 

  • Azariadis C, Drazen A (1990) Threshold externalities in economic development. Q J Econ 105:501–526

    Article  Google Scholar 

  • Baltes PB, Nesselroade JR (1979) Longitudinal research in the study of behavior and development. Academic Press, New York

    Google Scholar 

  • Basu S, Weil DN (1998) Appropriate technology and growth. Q J Econ 113:1025–1054

    Article  Google Scholar 

  • Battese GE, Rao DSP, O'Donnell CJ (2004) A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. J Prod Anal 21:91–103

    Article  Google Scholar 

  • Ben-David D (1996) Trade and convergence among countries. J Int Econ 40:279–298

  • Bernard AB, Jones CI (1996) Comparing apples to oranges: productivity convergence and measurement across industries and countries. Am Econ Rev 86:1216–1238

    Google Scholar 

  • Bloom N, Van Reenen J (2007) Measuring and explaining management practices across firms and countries. Q J Econ 122:1351–1408

    Article  Google Scholar 

  • Bogliacino F, Pianta M (2013) Innovation and demand in industry dynamics: R&D, new products and profits. In: Pyka A, Esben A (eds) Long term economic development. Springer, Berlin Heidelberg, pp 95–112

    Chapter  Google Scholar 

  • Bollen KA, Curran PJ (2004) Autoregressive latent trajectory (ALT) models: a synthesis of two traditions. Sociol Methods Res 32:336–383

    Article  Google Scholar 

  • Bollen KA, Zimmer C (2010). An overview of the autoregressive latent trajectory (ALT) model. In Longitudinal research with latent variables (pp. 153-176). Springer Berlin Heidelberg

  • Boltho A (1996) The assessment: international competitiveness. Oxf Rev Econ Policy 12:1–16

    Article  Google Scholar 

  • Bos JWB, Economidou C, Koetter M (2010) Technology clubs, R&D and growth patterns: evidence from EU manufacturing. Eur Econ Rev 54:60–79

    Article  Google Scholar 

  • Caccomo JL (1996) Technological evolution and economic instability: theoretical simulations. J Evol Econ 6:141–155

    Article  Google Scholar 

  • Cantner U, Hanusch H (2001) Heterogeneity and evolutionary change: empirical conception, findings, and unresolved issues. In J. Foster and S. Metcalfe (eds.) Frontiers of Evolutionary Economics, 228–267

  • Castellacci F (2007) Technological regimes and sectoral differences in productivity growth. Ind Corp Chang 16:1105–1145

    Article  Google Scholar 

  • Castellacci F (2008) Technological paradigms, regimes and trajectories: manufacturing and service industries in a new taxonomy of sectoral patterns of innovation. Res Policy 37:978–994

    Article  Google Scholar 

  • Castellacci F (2011) Closing the technology gap? Rev Dev Econ 15:180–197

    Article  Google Scholar 

  • Castellacci F, Archibugi D (2008) The technology clubs: the distribution of knowledge across nations. Res Policy 37:1659–1673

    Article  Google Scholar 

  • Castellacci F, Los B, de Vries G (2014) Sectoral productivity trends: convergence islands in oceans of non-convergence. J Evol Econ 24:983–1007

    Article  Google Scholar 

  • Casu B, Ferrari A, Zhao T (2013) Regulatory reform and productivity change in Indian banking. Rev Econ Stat 95:1066–1077

    Article  Google Scholar 

  • Casu B, Ferrari A, Girardone C, Wilson JO (2016) Integration, productivity and technological spillovers: evidence for eurozone banking industries. Eur J Oper Res 255:971–983

    Article  Google Scholar 

  • Clark S (2010) Mixture modeling with behavioral data. Doctoral Dissertation, University of California, Los Angeles

  • Crespi F, Pianta M (2008) Diversity in innovation and productivity in Europe. J Evol Econ 18:529–545

    Article  Google Scholar 

  • David PA (1985) Clio and the economics of QWERTY. Am Econ Rev 77:332–337

    Google Scholar 

  • David PA (1986) Understanding the economics of QWERTY: the necessity of history. In: Parker WN (ed) Economic history and the modern economics. Blackwell, Oxford

    Google Scholar 

  • Delgado M, Ketels C, Porter ME, Stern S (2012) The Determinants of National Competitiveness. NBER Working Paper No. 18249

  • Dias JG, Vermunt JK (2006) Bootstrap methods for measuring classification uncertainty in latent class analysis. Springer, New York

    Book  Google Scholar 

  • Dosi G (1988) Sources, procedures, and microeconomic effects of innovation. J Econ Lit 1120–1171

  • Dosi G, Nelson RR (2010) Technical change and industrial dynamics as evolutionary processes. Handbook of the Economics of Innovation. Eur Bus Rev 1:51–127

  • Dosi G, Soete L (1983) Technology gaps and cost-based adjustment: some explorations on the determinants of international competitiveness. Metroeconomica 35:197–222

    Article  Google Scholar 

  • Dosi G, Lechevalier S, Secchi A (2010) Introduction: interfirm heterogeneity--nature, sources and consequences for industrial dynamics. Ind Corp Chang 19:1867–1890

    Article  Google Scholar 

  • Duarte M, Restuccia D (2010) The role of the structural transformation in aggregate productivity. Q J Econ 125:129–173

    Article  Google Scholar 

  • Duncan SC, Duncan TE (1994) Modeling incomplete longitudinal substance use data using latent variable growth curve methodology. Multivar Behav Res 29:313–338

    Article  Google Scholar 

  • Durlauf SN (1994) Path dependence in aggregate output. Ind Corp Chang 3:149–171

    Article  Google Scholar 

  • Durlauf SN, Johnson PA, Temple J (2005) Growth econometrics. In: Aghion P, Durlauf S (eds) Handbook of economic growth. Elsevier, North Holland, pp 555–667

    Google Scholar 

  • Durlauf SN, Kourtellos A, Tan CM (2008) Are any growth theories robust? Econ J 118:329–346

    Article  Google Scholar 

  • Fagerberg J (1996) Technology and competitiveness. Oxf Rev Econ Policy 12:39–51

    Article  Google Scholar 

  • Fagerberg J (2000) Technological progress, structural change and productivity growth: a comparative study. Struct Chang Econ Dyn 11:393–411

    Article  Google Scholar 

  • Fagerberg J, Srholec M (2016) Global dynamics, capabilities and the crisis. J Evol Econ 26:765–784

    Article  Google Scholar 

  • Fagerberg J, Srholec M (2017) Capabilities, economic development, sustainability. Camb J Econ 41:905–926

    Article  Google Scholar 

  • Fagerberg J, Srholec M, Knell M (2007) The competitiveness of nations: why some countries prosper while others fall behind. World Dev 35:1595–1620

    Article  Google Scholar 

  • Glaeser EL, La Porta R, Lopez-de-Silanes F, Shleifer A (2004) Do institutions cause growth? J Econ Growth 9:271–303

    Article  Google Scholar 

  • Grosskopf S, Valdmanis V (1987) Measuring hospital performance: a non-parametric approach. J Health Econ 6:89–107

    Article  Google Scholar 

  • Halkos GE, Tzeremes NG (2011) Modelling the effect of national culture on multinational banks performance: a conditional robust nonparametric frontier analysis. Econ Model 28:515–525

    Article  Google Scholar 

  • Hall P, Wylie R (2014) Isolation and technological innovation. J Evol Econ 24:357–376

    Article  Google Scholar 

  • Hamaker EL (2005) Conditions for the equivalence of the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances. Sociol Methods Res 33:404–416

    Article  Google Scholar 

  • Hayami Y (1969) Sources of agricultural productivity gap among selected countries. Am J Agric Econ 51:564–575

    Article  Google Scholar 

  • Hayami Y, Rutan VW (1970) Agricultural productivity differences among countries. Am Econ Rev 60:895–911

    Google Scholar 

  • Kasy M (2011) A nonparametric test for path dependence in discrete panel data. Econ Lett 113:172–175

    Article  Google Scholar 

  • Kelly M (2001) Linkages, thresholds, and development. J Econ Growth 6:39–53

    Article  Google Scholar 

  • Kneip A, Park BU, Simar L (1998) A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econom Theory 14:783–793

    Article  Google Scholar 

  • Kontolaimou A (2014) An efficiency analysis of European banks considering hierarchical technologies. Appl Econ Lett 21:692–696

    Article  Google Scholar 

  • Kontolaimou A, Tsekouras K (2010) Are the cooperatives the weakest link in European banking? A non-parametric metafrontier approach. J Bank Finance 34:1946–1957

    Article  Google Scholar 

  • Kontolaimou A, Kounetas K, Mourtos I, Tsekouras K (2012) Technology gaps in European banking: put the blame on inputs or outputs? Econ Model 29:1798–1808

    Article  Google Scholar 

  • Kounetas K, Mourtos I, Tsekouras K (2009) Efficiency decompositions for heterogeneous technologies. Eur J Oper Res 199:209–218

    Article  Google Scholar 

  • Krugman PR (1985) Increasing returns and the theory of international trade. NBER 1752, National Bureau of Economic Research, Inc

  • Landesmann MA, Stehrer R (2001) Convergence patterns and switchovers in comparative advantage. Struct Chang Econ Dyn 12:399–423

    Article  Google Scholar 

  • Lane PR (2012) The European sovereign debt crisis. J Econ Perspect 26:49–67

    Article  Google Scholar 

  • Lo Y, Mendell NR, Rubin DB (2001) Testing the number of components in a normal mixture. Biometrica 88:767–778

    Article  Google Scholar 

  • Los B, Timmer MP (2005) The ‘appropriate technology ‘explanation of productivity growth differentials: an empirical approach. J Dev Econ 77:517–531

    Article  Google Scholar 

  • Martin R, Sunley P (2006) Path dependence and regional economic evolution. J Econ Geogr 6:395–437

    Article  Google Scholar 

  • McLachlan G, Peel D (2005) Finite mixture models. John Wiley & Sons, Inc

  • Melitz MC (2003) The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica (6):1695–1725

  • Meredith W, Tisak J (1990) Latent curve analysis. Psychometrica 55:107–122

    Article  Google Scholar 

  • Muthén B (2004) Latent variable analysis: growth mixture modeling and related techniques for longitudinal data. In: Kaplan D (ed) Handbook of quantitative methodology for the social sciences. Sage Publications, Newbury Park, pp 345–368

    Google Scholar 

  • Nylund KL, Asparouhov T, Muthén BO (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model 14:535–569

    Article  Google Scholar 

  • O’Donnell CJ, Rao P, Battese G (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empir Econ 34:231–225

  • Ou L, Chow SM, Ji l, Molenaar P (2016) (Re) evaluating the implications of the autoregressive latent trajectory model through likelihood ratio tests of its initial conditions. Multivar Behav Res. https://doi.org/10.1080/00273171.2016.1259980

  • Pianta M (2014) An industrial policy for Europe. Working Papers 1401, University of Urbino Carlo Bo, Department of Economics, Society & Politics

  • Pittau MG, Zelli R, Johnson PA (2010) Mixture models, convergence clubs and polarization. Rev Income Wealth 56:102–122

    Article  Google Scholar 

  • Porter ME (2003) Building the microeconomic foundations of prosperity: findings from the microeconomic competitiveness index. In: Cornelius P (ed) The global competitiveness report 2002-2003. Oxford University Press, New York

    Google Scholar 

  • Posner MV (1961) International trade and technical change. Oxf Econ Pap 13:323–341

    Article  Google Scholar 

  • Quah DT (1996) Regional convergence clusters across Europe. Eur Econ Rev 40:951–958

    Article  Google Scholar 

  • Rao DP, O’Donnell CJ, Battese GE (2003) Metafrontier functions for the study of inter-regional productivity differences. Centre for Efficiency and Productivity Analysis Working Paper, 1

  • Rodrik D, Subramanian A, Trebbi F (2004) Institutions rule: the primacy of institutions over geography and integration in economic development. J Econ Growth 9:131–165

    Article  Google Scholar 

  • Saviotti PP (1996) Technological evolution. Variety and the economy. Elgar, Aldershot

    Google Scholar 

  • Shepard RW (1970) The theory of cost and production functions. Princeton University Press, Princeton

    Google Scholar 

  • Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manag Sci 44:49–61

    Article  Google Scholar 

  • Simar L, Wilson PW (2000) A general methodology for bootstrapping in nonparametric frontier models. J Appl Stat 27:779–802

    Article  Google Scholar 

  • Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi- parametric models of production processes. J Econ 136:31–64

    Article  Google Scholar 

  • Syverson C (2011) What determines productivity? J Econ Lit 49:326–365

    Article  Google Scholar 

  • Tsekouras K, Chatzistamoulou N, Kounetas K, Broadstock D (2016) Spillovers, path dependence and the productive performance of European transportation sectors in the presence of technology heterogeneity. Technol Forecast Soc Chang 102:261–274

    Article  Google Scholar 

  • Wooldridge JM (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge

    Google Scholar 

  • World Economic Forum (2008) The global competitiveness report. Geneva

Download references

Acknowledgments

We owe special thanks to the Editors and two anonymous referees for their insightful comments and suggestions. The paper has benefited from Dr. Nikos Chatzistamoulou who has provided excellent research assistance in a previous stage. We would like also to thank the participants of the Workshop “Explaining Economic Change”, 12 November 2014, Sapienza Università di Roma and of the Conference “Governace of a Complex World”, 1-3 July 2015, Universite Nice Sophia Antipolis for their useful comments. The usual caveat applies: all remaining errors are the authors’ only.

Funding

This research has been co-financed by the European Union (European Social Fund – ESF) and Greek National Funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: Thales. Investing in knowledge society through the European Social Fund under Grant MIS 380232.

Author information

Authors and Affiliations

  1. Enterprise Research Centre, Warwick Business School, University of Warwick, Coventry, UK

    Areti Gkypali

  2. Department of Economics, University of Patras, University Campus Rio, 26504, Patras, Greece

    Kostas Kounetas & Kostas Tsekouras

Authors
  1. Areti Gkypali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kostas Kounetas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kostas Tsekouras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kostas Tsekouras.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gkypali, A., Kounetas, K. & Tsekouras, K. European countries’ competitiveness and productive performance evolution: unraveling the complexity in a heterogeneity context. J Evol Econ 29, 665–695 (2019). https://doi.org/10.1007/s00191-018-0589-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00191-018-0589-x

Keywords

JEL classification

Search

Navigation