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A Regional Account of Flexibilization Across the EU: The ‘Flexible Contractual Arrangements’ Composite Index and the Impact of Recession

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Abstract

The aim of the paper is to present a comparative analysis of the diffusion of ‘flexible contractual arrangements’ (FCA) across the EU. The homonymous FCA Composite Index (CI) is calculated for all 200 NUTS II-level regions of France, Germany, the UK, Denmark, Sweden, Belgium, Greece, Italy, Spain, Portugal, Bulgaria and Romania. The CI is calculated for 2005, 2008 and 2011 to present a clear picture of causal effects leading up to, and arising from, the 2008 financial crisis and ensuing recession. The findings suggest that the crisis had more intense consequences in certain regions than in others, and thus its effects upon regional labour markets were spatially uneven. Such an unevenness runs along, and cuts across, a variety of scales, namely the global, the EU and the intra-EU ones. All regions that are at the top of the FCA CI ranking are either regions that lack advanced economic and social or welfare structures, while at the same time facing important pressures from international and EU competitors, or regions of highly tertiarized service economies. The paper discusses the relation between this regional hierarchy, and the official policies of EU and national authorities which seek to re-regulate employment protection and security norms according to new accumulation priorities. Furthermore, it outlines several flexibilizing mechanisms that had contributed to the de-stabilization of modes of social reproduction across different regions, and reinforced each other, even many years before the current crisis occurred. The paper ends with some comments on the validity and social relevance of CIs when not be considered as a goal per se.

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Notes

  1. The sample studied includes 200 out of the 270 NUTS II-level regions of EU-27. Analyzing data for all regions of all EU members had not been chosen due to the amount of work required as well as the need to focus on differences between the crisis-hit Southern regions and other EU areas. Yet, the twelve countries selected are representative of the EU as a whole as they include members from the ‘advanced North’, the ‘semi-peripheral South’, and the ‘former state-socialist Eastern’ parts of the EU, that have divergent developmental trajectories and differentiated levels of employment protection and social structures (Hancké et al. 2008).

  2. Flexicurity is a concept adopted by the EU officials and labour-policy committees from the Nordic experience and corresponds to “a policy strategy that attempts, synchronically and in a deliberate way, to enhance the flexibility of labour markets, work organisation and labour relations on the one hand, and to enhance security – employment and social security – notably for weaker groups …., on the other hand” (Wilthagen and Tros 2004: 169; EC, 2007).

  3. These are: (1) Lifelong learning (LLL) strategies offering “adaptability” and “employability” to different groups of workers, with a special focus on the excluded or vulnerable ones; (2) Active labour market policies (ALMP) that help the unemployed get back to work and secure safe transitions from one job to another; and (3) Modern Social Security Systems (MSS) that provide social protection (e.g. health insurance and care, unemployment benefits etc) and social provisions (e.g. basic education and childcare, facilities that help combine work with familial duties etc).

  4. Two of the few exceptions are the work of Floridi et al (2011) on the sustainability of Italian regions and the work of Jurado and Perez-Mayo (2012) regarding the economic well-being of the Spanish Autonomous Communities.

  5. Unfortunately, available data does not distinguish between part-time employees and employers. The former are often hired for reducing labour costs and flexibilizing working time patterns as the high involuntary shares of part-time work in many counties declare; while the latter may be individuals that run a small business on a personal basis, thus resembling flexible employees, or may be retired firm-owners that continue to work for a few hours.

  6. The exploratory statistics of these two data series are as follows: sample means = [−0.0568, −0.0653], standard deviations = [0.9697, 0.9763] and variances = [0.9403, 0.9532]. Under the null hypothesis that there is no statistically-significant difference in the variances at the 95 % two-tail level confidence, the Chi squared statistic = 197.6798 (degrees of freedom = 195) and returns a p value of 0.8659 with a confidence interval on the variance = [0.7891,1.1748]. As such the null hypothesis is accepted, i.e. the data gaps do not result in a statistically-significant difference in the variance of the data series.

  7. Overall, a total of 16 CIs were calculated and the respective rankings were thoroughly compared with the initial calculation.

  8. For example, the relative deprivation that inhabitants of Greek and Romanian regions are faced upon has been widely documented. To name but a few cases, among the EU-27 regions with the lowest GDP per capita the mountainous regions of Epirus, Greece and Severozapaden, Bulgaria are listed; also the number of beds available in hospitals are less than 0.4 per thousand inhabitants in almost all Greek regions while it is more than 0.8 in the case of Germany.

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Acknowledgments

This research project is implemented within the framework of the Action ‘Supporting Postdoctoral Researchers’ of the Operational Program ‘Education and Lifelong Learning’ (Action’s Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State (Funding Decision: 11409/31-8-2012). The project is named “The Southern EU flexicurity project,” and it has been awarded to the first author for 2012-2015. The authors are very grateful to Anastasia Christodoulou, Rural and Surveyor Engineer (Dipl) and GIS Specialist (M.Sc.) for designing the maps; Akis Kanelleas, GIS Specialist (M.Sc.) who has provided the geographical data and supported the design of the maps in various ways; Mrs Vicky Katsina for her administrative support throughout the whole project; and Valeria Paul Carril for his strong support during the case-studies conducted.

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Authors and Affiliations

  1. Department of European Civilization, Hellenic Open University, Par. Aristotelous 18, 26331, Patras, Greece

    Stelios Gialis

  2. Department of Geography, University of Georgia, UGA Campus, Athens, GA, 30601, USA

    Stelios Gialis

  3. University of Aegean, University Campus, Mytilene, 83100, Lesbos, Greece

    Stelios Gialis

  4. Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Metaxa & Vas. Pavlou, Penteli, 15236, Athens, Greece

    Michael Taylor

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Correspondence to Stelios Gialis.

Appendix: Statistical Analysis

Appendix: Statistical Analysis

See Figs. 3, 4, 5, 6, and 7.

Fig. 3
figure 3

Inter-correlation matrix for the indicators used to construct the FCA CI for each of the years 2005, 2008 and 2011

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Fig. 4
figure 4

Results of applying principal component analysis to the indicators used to construct the FCA CI for 2005, 2008 and 2011. Note that the sum of the eigenvalues is equal to the number of individual indicators (8). The principal components (PCs) are ranked in order of the percentage of variance in the data that they represent, i.e. the first PC explains the maximum variance of all the individual indicators etc. Also given are the correlation coefficients between the PCs and the indicators (component loadings)

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Fig. 5
figure 5

Pearson product-moment correlation coefficients R between absolute values of the FCA CI or changes in the FCA CI (“dFCA”) excluding (“exc”) or including (“inc”) the unemployment protection index, and absolute values of the GDP and changes in the GDP (“dGDP”) or the ROU and changes in ROU (“dROU”) for the years 2005, 2008 and 2011

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Fig. 6
figure 6

Pearson product-moment correlation coefficients R between absolute values of the GDP and ROU and indicators of the FCA CI for the years 2005, 2008 and 2011

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Fig. 7
figure 7

Change in economic markers across the 200 NUTS-II regions over the periods 2005–2008, 2008–2011 and 2005–2011: a GDP, b ROU

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Gialis, S., Taylor, M. A Regional Account of Flexibilization Across the EU: The ‘Flexible Contractual Arrangements’ Composite Index and the Impact of Recession. Soc Indic Res 128, 1121–1146 (2016). https://doi.org/10.1007/s11205-015-1072-9

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