Geographical Concentration of Unemployment: A Male–Female Comparison in Spain

Abstract

Alonso-Villar O. and Del Río C. Geographical concentration of unemployment: a male–female comparison in Spain, Regional Studies. The aim of this paper is to analyse gender differences in the spatial distribution of unemployment. Specifically, it is explored whether agglomeration can influence gender gaps in unemployment rates. In doing so, tools are used from the literature on economic geography and income distribution and they are adapted to the present case. Using data from Spain, it is shown that the advantage of living in large cities does not affect women and men equally; agglomeration seems especially to favour the female population. The results also suggest that the female employment premium appears only in municipalities of a certain size.

Alonso-Villar O. et Del Río C. La concentration géographique du chômage: une comparaison hommes–femmes en Espagne, Regional Studies. Cet article cherche à analyser les différences par sexe de la distribution géographique du chômage. Plus précisément, on étudie si, oui ou non, la notion d'agglomération peut avoir une incidence sur les écarts du chômage par sexe. Pour le faire, on se sert des outils provenant de la documentation sur la géographie économique et la distribution des revenus, et on les adapte. A partir des données pour l'Espagne, on montre que les avantages d'habiter les grandes villes ne touchent pas de même les femmes et les hommes; il semble que l'agglomération favorise notamment la population féminine. Les résultats laissent supposer aussi que la dividende emploi des femmes n'apparaît que dans les municipalités d'une certaine taille.

Concentration géographique Chômage Sexe Echelle municipale

Alonso-Villar O. und Del Río C. Geografische Konzentration der Arbeitslosigkeit: ein Vergleich zwischen Männern und Frauen in Spanien, Regional Studies. In diesem Beitrag werden die Geschlechtsunterschiede bei der räumlichen Verteilung der Arbeitslosigkeit analysiert. Insbesondere untersuchen wir, ob sich eine Ballung auf den Abstand zwischen den Geschlechtern hinsichtlich der Arbeitslosenquote auswirken kann. Hierfür nutzen wir Instrumente aus der Literatur über Wirtschaftsgeografie und Einkommensverteilung, die wir an unseren Fall anpassen. Anhand von Daten aus Spanien zeigen wir, dass sich die Vorteile eines Lebens in der Großstadt auf Frauen und Männer unterschiedlich stark auswirken; eine Ballung scheint vor allem die weibliche Bevölkerung zu begünstigen. Ebenso lassen unsere Ergebnisse darauf schließen, dass die weibliche Beschäftigungsprämie nur in Gemeinden ab einer gewissen Größe auftritt.

Räumliche Konzentration Arbeitslosigkeit Geschlecht Gemeindegröße

Alonso-Villar O. y Del Río C. La concentracíon geográfica del desempleo: una comparación entre hombres y mujeres en España, Regional Studies. El objetivo de este artículo es analizar las diferencias por razón de género en la distribución espacial del desempleo. En concreto estudiamos si la aglomeración afecta a la brecha en tasas de desempleo entre mujeres y hombres. Para ello utilizamos herramientas de la literatura de geografía económica y de distribución de la renta y las adaptamos al caso que nos ocupa. Utilizando datos para España mostramos que las ventajas de vivir en ciudades grandes no afectan de la misma forma a ambos sexos. La aglomeración parece favorecer especialmente a las mujeres. Nuestros resultados también sugieren que la prima de empleo femenina aparece solamente en municipios que superan un cierto umbral de tamaño.

Concentración espacial Desempleo Género Escala municipal

Keywords:

• Spatial concentration
• Unemployment
• Gender
• Municipality scale

Acknowledgements

Financial support from the Ministerio de Educación y Ciencia (Grant Nos SEJ2005-07637-C02-01/ECON and SEJ2004-07373-C03-02/ECON), from the Xunta de Galicia (Grant Nos PGIDIT06PXIC300184PN and PGIDIP05PXIC30001PN), and from FEDER is gratefully acknowledged. The authors thank the SPEE for access to the data; and Luis Toharia for his help. They especially appreciate comments from two anonymous referees.

Notes

1. Thus, in 2003 in Spain the difference between the highest and lowest regional unemployment rate was 13 percentage points, with Andalusia (21%) and Aragon (around 8%) at the two extremes of the distribution (Toharia, 2005).

2. López-Bazo et al. (2005) analysed other spatial aspects of the distribution of unemployment in Spain at a provincial level.

3. Some of these indices have been used not only to analyse income inequality, but also to examine inequality in the provision of health services (Quadrado et al., 2001) and in levels of industrial activity (Brülhart and Traeger, 2005).

4. Note that the difference between Southern and Nordic countries is impressive since the latter have not only much lower unemployment rates, but also lower gender gaps. Moreover, in Sweden and Finland the gender gap does favour the female population. Perrons (1995) also identifies different degrees of gender inequality in employment.

5. Bazen (2003) documents that only one-third of the gender gap in unemployment rates in France can be explained by differences in characteristics. On the other hand, gender differences in education have not been proved particular important in explaining the gender pay gap in Europe (Rubery et al., 2005).

6. Di Addario (2006) also finds evidence that agglomeration (positively) affects the matching process in Italy, especially for women. In particular, the influence of agglomeration on individuals' chances of finding a job is larger for women.

7. In Maurel and Sédillot (1999) the location of a firm could depend on the natural characteristics of the area, or on the possible externalities due to proximity between plants. In the present case one can interpret the probability of an unemployed person to be in a particular place depending on the characteristics of that area, such as its productive structure, the number of companies, turnover, etc.

8. In the present case, index γ is very similar to index C, as the number of unemployed, N, is very high.

9. One can think of this curve in terms of the cumulative share of the unemployed or the cumulative share of unemployment rates (weighted by population size).

10. The Encuesta de Población Activa survey usually used to analyse the labour market in Spain does not gather any municipal information so it cannot be used at this fine geographical scale.

11. This means that one has the whole set of the unemployed (2 521 595 individuals), but due to confidentiality reasons, the authors have had no access to enough information about these individuals to allow an analysis of the causes of unemployment.

12. As the authors did not have an official figure for the economically active population per municipality, the unemployment rates do not take into account the effect generated by the lower participation rate of women. In any case, note that incorporating this issue would enable one to detect even more differences between the male and female unemployment rates. Female participation rates in Spain are much lower than male rates (55.7 against 81.1%, according to OECD data for 2003). Therefore, by using the working-age group as a reference population, instead of the economically active group, the present female unemployment rate is much lower than the traditional unemployment rate for women. In the male case, this difference is less acute.

13. The average of municipal unemployment rates weighted by municipality size is actually the national unemployment rate (number of unemployed divided by the working-age population).

14. The standard deviation is 5.1 for women and 2.9 for men (7.8 and 5.0, respectively, in the unweighted distributions).

15. The fact that the M-S index has higher absolute values as the size of municipalities increases is not surprising since it is very sensitive to the demographic weight of the units under study.

16. Since the authors did not work with a sample, but with the whole population of unemployed (2 521 598 individuals), statistical inference is not applied.

17. The analysis has also been undertaken by using Theil –1 and Theil 0, yielding the same results. In order to calculate the Theil indices, those municipalities with an unemployment rate equal to zero have to be discarded, as some of those indicators are not defined for such a value.

18. The results are identical when using Theil 0.

19. However, the unemployment male rate in subgroup 2 coincides with the national male average (Table 4), as the proportion of unemployed men in the three first deciles of the distribution is larger (Table 7).