Does Decentralized Governance Lead to Less Scientific Output? A Fuzzy Set Analysis of Fiscal Decentralization and Determinants of National Innovation Capacity

  • Blaž Zupan University of Ljubljana, Faculty of Economics
  • Aleš Pustovrh University of Ljubljana, Faculty of Economics
  • Stanka Setnikar Cankar University of Ljubljana, Faculty of Administration
Keywords: national innovation systems, QCA, innovation policy, fiscal decentralization


his paper argues that national innovation infrastructure is closely linked with the scientific output and that some other factors, such as decentralized governance, can in certain combinations with innovation infrastructure lead to a higher scientific output. It demonstrates this by using a set-theoretic fsQCA method to analyze the data on national innovation capacities, fiscal decentralization, and science policy output. Results confirm that while a national innovation infrastructure is a necessary condition for successful scientific output, decentralization in certain, but not all cases leads to successful scientific output and that the paths to a successful scientific output in different countries are diverse.

Author Biographies

Blaž Zupan, University of Ljubljana, Faculty of Economics


Aleš Pustovrh, University of Ljubljana, Faculty of Economics


Stanka Setnikar Cankar, University of Ljubljana, Faculty of Administration



Akai, N. & Sakata, M. (2002) Fiscal Decentralization Contributes to Economic Growth: Evidence from State-Level Cross-Section Data for the Unites States, Journal of Urban Economics, 52(1), pp. 93-108.
Allen, M. M. C., & Aldred, M. L. (2011) Varieties of capitalism, governance, and high-tech export performance: A fuzzy-set analysis of the new EU member states, Employee Relations, 33(4), pp. 334–355.
Abdel Latif, L., Martinez-Vazquez, J. & Musharraf R. C. (2013) Is fiscal decentralization harmful to business climate? (Georgia: International Center for Public Policy Andrew Young School of Policy Studies, Georgia State University).
Asheim, B.T. & Coenen, L. (2005) Knowledge bases and regional innovation systems: Comparing Nordic clusters, Research Policy, 34, pp. 1173-1190.
Bathelt, H., Malmberg, A. & Maskell, P. (2004) Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation, Progress in Human Geography, 28(1), pp. 31-56.
Blöchliger, H. & Égert, B. (2013) Decentralisation and Economic Growth - Part 2: The Impact on Economic Activity, Productivity and Investment. In: OECD Working Papers on Fiscal Federalism, No. 15 (Paris: OECD Publishing).
Cankar, F., Deutsch, T., Zupan, B. & Setnikar Cankar, S. (2013) Schools and promotion of innovation, Hrvatski časopis za odgoj i obrazovanje, 15(Sp. Ed. 2), pp. 179-211.
Edquist, C. (2001) The Systems of Innovation Approach and Innovation Policy: An account of the state of the art, paper presented at DRUID Conference Aalborg, Retrieved from
Edquist, C. (2011) Systems of innovation: Perspectives and Challenges, African Journal of Science, Technology, Innovation and Development, 2(3), pp. 14-43.
European Commission (2010) Europe 2020. A strategy for smart, sustainable and inclusive growth (Brussels: European Commission).
Feld, L.P., Kirchgassner, G. & Schaltegger, C.A. (2004) Fiscal federalism and economic performance: Evidence from Swiss cantons (Marburg: Philipps-Universität).
Filippetti, A. & Cerulli, G. (2015) Are Decentralized Regions Ruled Better? Evidence from European Regions Using a Dose-Response Approach, available at: (January 10, 2017).
Filippetti, A. & Sacchi, A. (2016) Decentralization and economic growth reconsidered: The role of regional authority, Environment and Planning C: Government and Policy, 34(8), pp. 1793-1824.
Fiss, P. C. (2011) Building better causal theories: a fuzzy set approach to typologies in organization research, Academy of Management Journal, 54(2), pp. 393–420.
Fiss, P. C. (2012) Using Qualitative Comparative Analysis (QCA) and Fuzzy Sets (Los Angeles: University of Southern California).
Freeman, C. (1987) Technology Policy and Economic Performance (London: Pinter).
Furman, J. L., Porter, M. E., & Stern, S. (2002) The determinants of national innovative capacity, Research Policy, 31(2002), pp. 899–933.
Guimon, J. (2013) Smart Decentralization of Innovation Policies (Washington: World Bank).
Kreiman, G., & Maunsell, J. H. R. (2011) Nine criteria for a measure of scientific output, Frontiers in Computational Neuroscience, 5(48), pp. 1-6.
Likar, B., Cankar, F. & Zupan, B. (2015) Educational model for promoting creativity and innovation in primary schools, Systems research and behavioral science, 32(2), pp. 205-213.
Lin, G. T. R., Shen, Y.-C., & Chou, J. (2010) National innovation policy and performance: Comparing the small island countries of Taiwan and Ireland, Technology in Society, 32(2), pp. 161–172.
Martinez-Vazquez, J., Lago-Peñas, S. & Sacchi A. (2016) The impact of fiscal decentralization: a survey, Journal of Economic Surveys, forthcoming,
Mason, C. & Brown, R. (2013) Creating good public policy to support high-growth firms, Small Business Economics, 40(2), pp. 211-225.
McCann, P. & Ortega-Argilés, R. (2013) Modern regional innovation policy, Cambridge Journal of Regions, Economy and Society, 9(3), pp. 125-138.
Metcalfe, S. (1995) The Economic Foundations of Technology Policy: Equilibrium and Evolutionary Perspectives. In Stoneman P. (ed.), Handbook of the Economics of Innovation and Technological Change (Oxford (UK)/Cambridge (US): Blackwell Publishers).
OECD (1997) Diffusing Technology to Industry: Government Policies and Programmes (Paris: OECD Publishing).
OECD (2011) Regions and Innovation Policy (Paris: OECD Publishing).
OECD (2013) Science, Technology and Industry Scoreboard,
Pustovrh, A. & Jaklič, M. (2014) National Innovation Policies in the EU: A Fuzzy-Set Analysis, Economic and Business Review, 16(1), pp. 39-62.
Ragin, C. C. (1987) The comparative method (Oakland: University of California Press).
Ragin, C. C. (2000) Fuzzy-set social science (Chicago: University of Chicago Press).
Ragin, C. C., Kriss, A. D., & Davey, S. (2006) Fuzzy-Set/Qualitative Comparative Analysis 2.0. (Tuscon, Arizona: University of Arizona, Department of Sociology).
Ragin, C. C., & Rihoux, B. (2009) Configurational Comparative Methods (Thousand Oaks: Sage).
Rihoux, B., & Grimm, H. (2006) Innovative Comparative Methods for Policy Analysis, available at: (December 14, 2016).
Rodríguez-Pose, A. & Ezcurra, R. (2011) Is fiscal decentralization harmful for economic growth? Evidence from the OECD countries, Journal of Economic Geography, 11(4), pp. 619-643.
Schneider, A. (2003) Decentralization: Conceptualization and measurement, Studies in Comparative International Development, 38(3), pp. 32-56.
Schneider, C. Q., & Wagemann, C. (2012) Set-theoretic Methods for the Social Sciences (Cambridge: Cambridge University Press).
Strump, K. S. (2002) Does Government Decentralization Increase Policy Innovation?, Journal of Public Economic Theory, 4(2), pp. 207-241.
Treisman, D. (2002) Defining and Measuring Decentralization : A Global Perspective. Unpublished Manuscript, (March 2002), available at: (November 20, 2016).
Tödtling, F. & Trippl, M. (2011) Regional innovation systems, In: Cooke, P., Asheim, B., Boshma, R., Martin, R., Schwartz, D. & Tödtling, F. (eds.) The Handbook of Regional innovation and Growth (Cheltenham: Edward Elgar).
Conference Paper