Wage flexibility and employment resilience in the Spanish labour market over the Great Recession

ABSTRACT

The Spanish economy is characterized by significant and persistent regional disparities. The Great Recession caused a severe economic downturn, marked by declining wages and rising unemployment, influenced by the internal wage devaluation policies. We investigate the relationship between wage flexibility and regional labour market resilience. We estimate a spatial panel wage curve using microdata from social security records for the period 2002–19 using geographical and time weighted regression techniques. Our findings reveal that regions with higher wage flexibility exhibit higher resilience recovery indices, highlighting the importance of wage flexibility as a short-term adjustment mechanism in response to labour market shocks.

KEYWORDS:

• employment
• unemployment
• wage curve
• resilience
• spatial panel
• geographical and temporal weighted regression (GTWR)

JEL:

• C23
• J31
• J64
• R23

ACKNOWLEDGEMENTS

We are grateful for the comments received at the 13th World Congress of the Regional Science Association International (RSAI), the 60th ERSA Congress, and the XIV Spanish Work Economics Sessions, the 46th of the Spanish Economics Association. We also thank the Directorate General for the Social Security Organization, dependent on the Ministry of Inclusion, Social Security and Migration of the Government of Spain, for providing the Continuous Sample of Working Lives database.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. Other works use alternative indicators to capture resilience: Lewin et al. (2018) use personal income data, Pontarollo and Serpieri (2020a, 2020b) use gross domestic product (GDP) per capita, and several others use composite indicators of economic and employment outcomes (Rizzi et al., 2018; Ubago Martínez et al., 2019).

2. Monastiriotis and Martelli (2021) also adopt a different approach to examine adjustments to shocks in Greek regions during the Great Recession. Using micro-data from the Greek Labour Force Survey (LFS), they first estimate the contribution of various individual and household characteristics to individual unemployment risk during and after the crisis. Next, they apply a decomposition analysis to identify the relative contribution of the shock, compositional effects and price adjustments.

3. Interestingly, most analysis of the impact of the Great Recession has concentrated on the analysis of output or employment measures and not on unemployment, with the exception of those estimating the so-called Okun’s law (e.g., Groot et al., 2011).

4. Considering years before 2002 increases the risk of attrition, because the database is not representative in this time frame, except for in the years where extraction is done. However, from 2002 to 2006, the Spanish economy was in an expansionary phase with no significant regional differences. Besides, the actual series of unemployment from the labour force survey are based in 2002, and extending the series beyond that year could imply some comparability drawbacks.

5. Table A1 in Appendix A in the supplemental data online shows basic descriptive statistics of variables computed from MCVL.

6. Our decision of estimating a GTWR prevents us from dealing with the potential endogeneity of unemployment in the wage regression.

7. We have used the gtwr command of the GWmodel package in R (Gollini et al., 2015; Lu et al., 2019). Figure A1 in Appendix A in the supplemental data online displays the details of parameter selection according to goodness of fit (adjusted R²) and AICc statistics, while Figure A2 displays the relative distance resulting from different parameters of $\lambda$. The final results using different parameters of weighting space and time did not change the main results of the basic specification. Additional results with different combinations of values for these parameters are available from the authors upon request.

8. We have computed a battery of tests of spatial heterogeneity for every quarter separately. As could be expected, in the initial periods, where there are few significant parameters of the wage curve, we observe no significant spatial variability. When the number of significant estimated parameters increases, the spatial heterogeneity becomes relevant. Remarkably, it is at the end of 2009 when we start finding significant heterogeneity in the parameter estimates, and it becomes the norm by 2012.

9. Figure A3 in Appendix A in the supplemental data online displays the box plot over provinces and time of the parameters considered.

10. Such periods try to capture the average wage flexibility anticipating subsequent resilience (resistance and recovery). We considered alternative periods, such as the average between 2002 and 2007, or just 2007 for the resistance period, and the average between 2010 and 2012, 2012 and 2014, or just 2014 for the recovery period. The results did not report major changes.

11. These results are available in the supplementary material online.

 

Additional information

Funding

This work was supported by the Ministerio de Ciencia e Innovación [grant number PID2020-118800GBI00, MCIN/AEI/10.13039/501100011033].