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Which are the Factors Influencing Innovation Performances? Evidence from Italian Cohesion Policy

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Abstract

Our paper aims at evaluating the role that the local administrations play on Italian regional innovation performance, by using a revised version of Regional Innovation Scoreboard. Carrying out a panel data analysis and a logistic regression, the analysis is based on a combined approach towards institutions, involving socio-political aspects, considered by the institutional quality index, and the capability of regional administrators of expending Structural and Cohesion Funds, by a quantitative index. Firstly, a panel data analysis helped us to highlight the role of institutions and which sectoral themes of EU investments affected the most regional innovative performances in the time span 2007-2015. Secondly, the logistic regression captures which investment is more likely to affect innovation performances within Italian Regions. The results underline that major effect on innovation performance derive from the combined effect of high quality institutions and efficient public expenditure.

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Notes

  1. The EIS and RIS reports distinguishes three “Categories” (Enablers; Firm Activities; Output) and eight “Dimensions” (Human resources; Open, excellent and attractive research systems; Finance and support; Firm investments; Linkages and entrepreneurship; Intellectual assets; Innovators; Economic effects) (European Commission 2016a, b).

  2. According to RIS methodology, when regional data were not available, we used national level data or chose new indicators measuring the same dimensions of the original framework. Seventeen of the 25 Indicators used in the EIS framework are available or easy to estimate at regional level (ISTAT, EUROSTAT and OECD). There other nine indicators have been substituted either with new data assessing the same dimensions (4 indicators) or with national level data (four indicators). The new indicators are: (1) the percentage of Doctorate courses activated by region universities on the total as a proxy of doctoral education in the region; (2) Total expenditure of firms for innovative activities as a proxy of innovation rates comprehending R&D and non-R&D activities; (3) Percentage of researcher working in the enterprises on the total employed as a proxy of in-house entrepreneurial innovation; (4) Regional % of total national Fast Growing Enterprises as a proxy of regional economic dynamism, given the primary role of fast growing enterprises in the innovation processes (Gupta et al. 2015).

  3. Performace groups are calculated as the distance of each regional value from the national average year by year. Then regions are classified as Modest Innovators (if their score had been 50% or more below average), Moderate Innovators (if their score had been between the 10% and the 50% of the average), Strong Innovators (if their score had been higher than 20% but lower than 10% below the average) e Innovation Leaders (if their score had been 20% higher).

  4. It stands for a synthetic thematic division of the projects into 13 areas based on the combination of both EU priority issues and CUP System sectoral classification (http://cupweb.tesoro.it).

  5. Three levels hierarchical project classification (sector, subsector and category) defined by the Public Administration on the occasion of CUP request (http://www.opencoesione.gov.it/opendata/). Sector classification is organised in 11 groups.

  6. Project classification into six groups defined by the Public Administration on the occasion of CUP request (http://www.opencoesione.gov.it/opendata/).

  7. The IQI is the unweighted average of the following 24 indicators: Social Cooperatives, Associations, Election participation, Books published, Purchased in bookshop; Endowment of social facilities, Endowment of economic facilities, Regional health deficit, Separate waste collection, Urban environment index; Economic openness, Local government employees, Business density, Business mortality, Business environment; Crimes against properties, Crimes reported, Trial times, Magistrate productivity, Submerged economy, Tax evasion; Crimes against PA, Golden-Picci Index, Special commissioners.

  8. EU Commission defines microenterprise as companies with fewer than 10 employees and an annual turnover (the amount of money taken in a particular period) or balance sheet (a statement of a company's assets and liabilities) below €2 million (Commission of the European Communities 2003).

  9. The closing date of 2007–2013 funding program, 31st December 2013, does not correspond to the real deadline for implementation of co-financed projects, which is 31st December 2015. Implementations continued for an additional two-year period, applying the current European rules about public balances: the so-called “+ 2” rule; http://opencoesione.gov.it.

  10. We analysed the six groups of investments distinguished by Opencoesione according to the "nature of the investment" (Purchase of Goods, Purchase or implementation of Services, Provision of incentives to production units, Granting of contribution to other units different from production units, Public work projects, Capital initial subscription or increase and tender and venture capital initial subscription or increase) as independent variables.

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Authors and Affiliations

  1. University of Naples “L’Orientale”, 80134, Naples, Italy

    Roberta Arbolino, Raffaele Boffardi & Luisa De Simone

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  1. Roberta Arbolino
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Appendices

Appendix A

See Table 5.

Table 5 Regional Innovation Scoreboard groups of performance
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Appendix B

See Table 6.

Table 6 New Regional Innovation Scoreboard groups of performance
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Appendix C

See Table 7.

Table 7 Data on structural and cohesion funds, 8 synthetic themes. Our elaborations.
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Appendix D

See Table 8.

Table 8 Cohesion funds management index and IQI, Italy. Our elaborations.
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Arbolino, R., Boffardi, R. & De Simone, L. Which are the Factors Influencing Innovation Performances? Evidence from Italian Cohesion Policy. Soc Indic Res 146, 221–247 (2019). https://doi.org/10.1007/s11205-018-1904-5

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