Abstract
This study conducts a spatio-temporal analysis of labor flexibilization and GDP per capita change (gross domestic product) with focus on their interrelation in European Union (EU) regions during 2008–2013. Using a composite index to calculate the dispersion of flexible contractual arrangements and spatio-temporal autocorrelation metrics, it shows that changes in overall growth largely affect the distribution of flexible working patterns across space and time. In particular, regions with high increments in the prevalence of flexible work, thus ranking highest in terms of flexibilization, suffered the most as a result of the productivity and financial crisis during the turbulent 2008–2013 period. Further, applying univariate, bivariate, and differential local Moran’s I spatial autocorrelation techniques, this study demonstrates that changes in GDP pca are negatively spatially autocorrelated with flexibilization, while increasing flexibility has no significant positive effect on GDP growth. This major finding contradicts the general belief that more flexible labor environments lead a priori to economic growth, re-emphasizing previous considerations on that matter while also updating existing contributions with a more nuanced and recent account that follows a geographically sensitive methodology. Although many studies have identified that labor flexibilization is not always linked to economic growth, the current study offers recent spatio-temporal evidence that refer to almost all EU regions to complement existing works. Contextualizing these findings, this analysis highlights an increasing schism between the north-central and south-eastern EU regions, with the latter facing poor growth prospects and extensive low-road flexibilization practices. Such a schism, which often transcends national boundaries creating new patterns of sub-national unevenness, challenges the idea of a healthy trade-off between flexibilization and GDP growth and warrants urgent attention.
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Notes
Correlations among indicators used were estimated through the Pearson product-moment coefficient in the initial attempt to establish an FCA CI (Gialis and Taylor 2016) and have been re-verified for the new calculation at hand. The qualitative assessment between ‘very weak’ (i.e., 0.0 < |R| < 0.2) and ‘very strong’ (0.8 < R < 1.0) of the inter-indicator correlation is defined by diving the range of possible values linearly into five equal intervals. The inter-correlation matrix revealed that FCA2_1 and FCA2_3 very strongly anti-correlate across all study years (2005, 2008 and 2013). Nine other sub-index pairs appear to correlate ‘strongly’ (i.e., 0.6 < R < 0.8), including FCA1_2 and FCA1_4. In order to remove or not an indicator that was highly correlated with another, it was necessary to estimate whether both indicators can represent the same phenomenon. For permanent employment (FCA1_4), it was found that a strong negative correlation existed with self-employment (FCA1_2), in turn, related to that high shares of permanent employees are always related to leads minor shares of self-employment. Yet, both indicators were retained as they represent different labor market categories. Also, self-employment captures new trends in flexibility, such as subcontracting or gig-economy work and should be retained. For the redundancy check we applied principal components analysis on the indicators used for the FCA index. It came out that seven of the eight indicators in the correlation matrix have a single strong or very strong component loading, and thus they are accounted for to a large extent by a single principal component As such, there is no opportunity for reducing the dimensionality of the CI and the complete list of indicators has been retained.
The administrative boundaries for EU countries are modeled as polygons at the NUTS-2 level and use the geographic coordinate reference system, European Terrestrial Reference System 1989 (ETRS89), for the reference year 2013 at the 1:60 M scale (GISCO 2015). Spatial data were projected to the WGS84 projected coordinate system because spatial statistics require projected data to accurately measure distances.
The following countries are excluded: Czech Republic, Croatia, Cyprus, Denmark, Estonia, Ireland, Latvia, Lithuania, Luxembourg, Malta, Slovakia, and Slovenia.
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This work was supported by Sun Yat Sen University Starting Research Grant for George Grekousis (37000-18821113).
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Grekousis, G., Gialis, S. More Flexible Yet Less Developed? Spatio-Temporal Analysis of Labor Flexibilization and Gross Domestic Product in Crisis-Hit European Union Regions. Soc Indic Res 143, 505–524 (2019). https://doi.org/10.1007/s11205-018-1994-0
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DOI: https://doi.org/10.1007/s11205-018-1994-0