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Abstract
Bande R. and Karanassou M. Spanish regional unemployment revisited: the role of capital accumulation, Regional Studies. This paper provides new evidence for the evolution of regional unemployment rates in Spain over the period between 1980 and 2000. It argues that interactive dynamic systems of labour demand, wage setting and labour force equations (1) allow for a richer interpretation of regional disparities, and (2) can capture the unemployment effects of growing variables such as capital stock. After classifying the seventeen Spanish regions into high and low unemployment groups using kernel and cluster techniques, a structural labour market model is estimated for each group and the unemployment contributions of investment, benefits, taxes and the oil price are evaluated. It is found that the main driving force of regional unemployment swings is capital accumulation.
Bande R. and Karanassou M. 重探西班牙的区域失业：资本积累的角色，区域研究。本文为西班牙在 1980 年至 2000 年期间区域失业率的发展提供新的证据。本文主张，劳动需求、薪资设定与劳动力方程式的互动动态系统 (1) 考虑到对于区域差异更为深刻的解释，并且 (2) 得以捕捉逐渐增长的因素所带来的失业效应，例如资本股票。本研究运用核方法和集群技术，将十七个西班牙区域分类为高失业率以及低失业率的群组，接着为每个群组分别评价结构性的劳动市场模型，并评估投资、获利、税收以及石油价格对失业造成的影响。本研究发现，区域失业潮的主要驱动力是资本积累。
Bande R. et Karanassou M. Un réexamen du chômage régional en Espagne: le rôle de l'accumulation du capital, Regional Studies. Cet article cherche à fournir les derniers résultats à propos de l'évolution du taux de chômage régional en Espagne entre 1980 et l'an 2000. On affirme que des systèmes dynamiques interactifs de la demande de travail, la fixation des salaires et des équations de la population active (1) permettent une meilleure interprétation des écarts régionaux, et (2) peuvent capter les effets chômage des variables croissantes, telles le stock de capital. Une fois classé les dix-sept régions d'Espagne en groupes à chômage élevé ou faible employant les techniques de noyau et de cluster, on estime un modèle structurel du marché du travail pour chaque groupe, et l'impact de l'investissement, des prestations, des impôts et du prix du baril sur le chômage sont évalués. L'accumulation du capital s'avère la force motrice de la variation du chômage régional.
Bande R. und Karanassou M. Ein neuer Blick auf die regionale Arbeitslosigkeit in Spanien: die Rolle der Kapitalansammlung, Regional Studies. Dieser Beitrag liefert neue Belege für die Entwicklung der regionalen Arbeitslosenquoten in Spanien im Zeitraum von 1980 bis 2000. Wir argumentieren, dass die interaktiven dynamischen Systeme der Nachfrage nach Arbeitsplätzen, der Lohnvereinbarung und des Angebots an Arbeitsplätzen erstens eine ausführlichere Interpretation der regionalen Disparitäten ermöglichen und zweitens die Auswirkungen der Arbeitslosigkeit auf wachsende Variablen wie zum Beispiel den Kapitalbestand erfassen können. Nach einer Klassifizierung der 17 spanischen Regionen in Gruppen mit hoher und niedriger Arbeitslosigkeit unter Verwendung von Kernel- und Cluster-Techniken schätzen wir für jede Gruppe ein strukturelles Arbeitsmarktmodell und bewerten die Beiträge von Investitionen, Sozialleistungen, Steuern sowie des Ölpreises zur Arbeitslosigkeit. Wir stellen fest, dass die wichtigste treibende Kraft für die Schwankungen bei der regionalen Arbeitslosigkeit in der Kapitalansammlung liegt.
Bande R. y Karanassou M. El desempleo regional español de nuevo: el papel de la acumulación de capital, Regional Studies. Este trabajo proporciona nueva evidencia sobre la evolución de las tasas de desempleo regionales en España a lo largo del período 1980–2000. Sostenemos que los sistemas de ecuaciones, interactivos y dinámicos, de demanda de trabajo, determinación salarial y oferta de trabajo (1) permiten una interpretación más rica de las disparidades regionales y (2) permiten captar el efecto sobre el desempleo de variables que crecen en el tiempo, como el stock de capital. Después de clasificar las 17 regiones españolas en grupos de alto y bajo desempleo empleando técnicas de análisis kernel y cluster, estimamos un modelo estructural de mercado de trabajo para cada grupo, y evaluamos las contribuciones al desempleo de la inversión, las prestaciones sociales, los impuestos y el precio del petróleo. Encontramos que el principal causante de variaciones en el desempleo regional es la acumulación de capital.
Acknowledgements
The authors acknowledge the useful comments made by four anonymous referees and the Editors of the journal. Roberto Bande also acknowledges financial support from the Xunta de Galicia, through grants 10SEC242003PR and 08SEC005242PR.
Notes
1. The CRT framework was originally developed by Marika Karanassou and Dennis J. Snower in a series of papers. For an overview of the chain reaction approach with comparison with single-equation unemployment rate models, see Karanassou et al. Citation(2010).
2. In 2002 and 2005, the Instituto Nacional de Estadística (INE – Spanish National Institute of Statistics) introduced major methodological changes in the survey, which preclude full comparison of figures before and after 2005. Nevertheless, the general trends in unemployment dynamics are portrayed clearly enough.
3. These estimations used a Gaussian kernel. The findings below are not affected by the use of alternative kernel functions. The results are available from the authors upon request.
4. This analysis has followed the k-means procedure, aiming to partition n observations into k = 2 clusters in which each observation belongs to the cluster with the nearest mean. Detailed results on the cluster analysis are available from the authors upon request.
5. Like the NRU hypothesis, the structuralist equations are dynamic reduced-form single-equation models. Phelps Citation(1994) offers a comprehensive account of the structuralist theory. For a compare and contrast discussion of the chain reaction and structuralist theories, see also Karanassou et al. Citation(2010).
6. Of course, the employment, wage and labour force adjustment processes may arise for reasons other than the ones given above.
7. For ease of exposition, and without loss of generality, this illustration ignores constants and error terms. However, the augmented version of the labour market model estimated in the fourth section includes constants, other explanatory variables and the second lags of the dependent variables. Furthermore, it can be shown that the above labour market model is compatible with standard microeconomic foundations.
8. The term ‘reduced form’ means that the parameters of the equation are not estimated directly – they are simply some non-linear function of the parameters of the underlying labour market system.
9. The dynamic system (equation 9) is stable if, for given values of the exogenous variables, all the roots of the determinantal equation |A0 – A1L – A2L2| = 0 lie outside the unit circle. Note that all of the estimated equations in the fourth section below satisfy this condition.
10. The estimation of a labour market model in which migration flows are included is a plan for future research, dependent on the availability of a full dataset of migratory flows compatible with the rest of the data (especially the Labour Force Survey data).
11. Maza and Villaverde Citation(2009) find that since the mid-1980s a process of wage convergence has taken place, thus reducing the advantages of firms moving from one region to another.
12. For a detailed exposition of stationary panel data estimation, see, for example, Hsiao Citation(1986) and Baltagi Citation(2008).
13. The reason for restricting the analysis to the period 1980–2000 is twofold. First, the regional capital stock series are obtained from the BD-MORES dataset which covers the period 1980–2000 (for a detailed description, see Dabán et al., Citation2002). Second, in 2005 the methodology of the Labour Force Survey was modified due to various reasons: (1) the need for it to adapt to the new demographic and labour reality of Spain, due mainly to the increase in the number of foreign residents; (2) the incorporation of new European regulations in accordance with the norms of the European Union Statistical Office (EUROSTAT); and (3) the introduction of improvements in the collection method. The induced structural break in the labour force and unemployment rate series implies that the figures for these series are not fully comparable with the ones prior to 2005.
14. Randomness of the spatial distribution cannot be rejected under various schemes of weighting matrices used in the calculation of the statistic. For brevity, these results are available from the authors upon request.
15. This is a 3 × 1 vector representing the error terms of the labour demand, wage setting and labour supply equations in the system.
16. The fixed-effects estimator is also known as the least squares dummy variables (LSDV) estimator, or the within-group or the analysis of covariance estimator. Although the authors have experimented with estimating time-specific effects, the results were not significant and thus did not improve the models. This should come as no surprise, since the events that Spain experienced over the sample period are not one-year events and are thus incorporated in the nationwide exogenous variables.
17. The choice of the national variables to be included in the estimations has been guided by availability, results provided by previous work, and statistical significance and adequacy.
18. Each of the equations in the model was estimated by OLS and GMM, making use of the Arellano and Bond Citation(1991) estimator. Interestingly, the estimated short-run coefficients and the long-run elasticities are similar in both models. This reinforces the prior that the estimations are not subject to Nickell bias. For brevity, these results are available from the authors upon request. Moreover, in order to carry out the empirical task (that is, running counterfactual simulations and computing the dynamic responses of unemployment), one needs the estimated fixed effects for each region, which are eliminated by first differencing the data in the GMM approach.
19. Note that the significant influence of capital stock on unemployment is an empirical fact unveiled by CRT studies for the UK (Henry et al., Citation2000; Karanassou and Snower, Citation2004), the European Union (Karanassou et al., Citation2003), the Nordic countries (Karanassou et al., Citation2008), Spain (Bande and Karanassou, Citation2009; Karanassou and Sala, Citation2009), and Australia (Karanassou and Sala, Citation2010).
20. For example, in the context of the illustrative labour market models (equations 1–4), the restriction for a trendless unemployment rate is:
where Δ(·)LR denotes the long-run growth rates of the right-hand-side variables.
21. The paper only evaluates the contributions of (1) investment, which is the main growth driver, (2) oil price shocks, since these are typically examined in a wide spectrum of models, and (3) benefits and taxes, as these are among the main determinants of the NRU. For reasons of space, the results on working-age population, which were found to have a rather minor influence on the unemployment trajectory, are available from the authors upon request.
22. Note that this is simply a dynamic-accounting exercise, answering the question: How much of the movement in unemployment can be accounted for by the movements in each of the exogenous variables? – it does not tell one what unemployment would have been had the exogenous variables followed different time paths.
23. Note that investment is the dominant factor in the evolution of capital stock growth for modest depreciation rates. This can be seen from the following equation in levels:
where δ is the depreciation rate; It is investment; and Kt is capital stock (in levels). Thus, in exact terms, the growth rate of capital stock equals investment (normalized by lagged capital) minus the rate of depreciation:
Nevertheless, the fact that the essence of capital stock growth is reflected in investment justifies the alternate reference to the two magnitudes.
24. The authors are grateful to an anonymous referee for pointing out this potential driving force of disparities.
25. The finding in the fifth section that the low unemployment rate group benefits more than the high unemployment one in the boom periods (1985–1991, 1994–2000) of capital accumulation implies that the high unemployment group has a relatively lower elasticity of unemployment with respect to capital stock. (Note that the availability of Structural Funds is associated with relatively good times.)