Comparative Analysis of Regional Gender Disparities

Abstract

The analysis of gender equality at country level is a key information tool to assess the impact of gender policies as well as the trends of change over time regarding this social reality. In this chapter we would like to deepen this type of analysis by investigating at the regional level (NUTS2) to what extent differentiated behaviour can be observed within each country. We have chosen Italy, Spain and France as case study, covering a total of 62 regions, to analyse gender equality with the different indicators proposed in the GEI and to be able to observe what particularities emerge from a disaggregated territorial analysis, essential for the development of local gender policies. We carried out two types of analysis: a) the construction of the index and the analysis of the six main domains that define the measure: Work, Money, Time, Power, Knowledge and Health, with the aim of typifying the regions analysed, showing specific structures and dynamics that allow us to enrich the conclusions of the analyses; b) to relate the results obtained in the RGEI to elements linked to other indicators of economic, social, cultural, political and demographic development. The relevance of this analysis is inscribed in the importance of the persistent regional disparities observed in Europe. Monitoring these regional gender differences is essential in order to identify processes of convergence (or divergence) that are urgently needed and for the evaluation and implementation of policies at regional level.

1 Introduction

The analysis of gender equality at the country level is a key information tool for assessing the impact of gender policies as well as the trends of change over time regarding this social reality. The European Institute for Gender Equality (EIGE) has been conducting this function since 2013, producing several interesting results (see Chapters “Gender Equality, Equity, and Equal Opportunities”, “The Main Indicators of Gender (in)Equality”, and “Gender Equality as EU Strategy” in this book). In the context of the ReGEM project, we would like to deepen this type of analysis by investigating at the regional level (NUTS2) the extent to which differentiated behaviour can be observed within each country. We chose Italy, Spain, and France as case studies, covering a total of 62 regions, to analyse gender equality with the different indicators proposed in the GEI and to observe what particularities emerge from a disaggregated territorial analysis, essential for the development of local gender policies. We constructed a disaggregated measure as close as possible to the national-level indicator produced by EIGE: Regional Gender Equality Index (RGEI) obtained from Eurostat in the form of micro-data.

Chapter “Gender Equality, Equity, and Equal Opportunities” conceptualises the phenomenon of gender (in)equality, while Chapter “The Main Indicators of Gender (in)Equality” presents the main proposals on its measurement, particularly the GEI. Chapter “Sub-national Level Data to Measure Gender (in)Equality in the EU: Opportunities and Limitations of Official Datasets” shows the need to work at the subnational level and the limitations encountered in doing so. Considering these different reflections, this chapter presents the results of a comparative analysis of the indicators available at the regional level according to the NUTS2 division in the four countries chosen in this study. We carried out two types of analysis: construction of the index and analysis of the six main domains that define the measure, work, money, time, power, knowledge, and health, with the aim of typifying the regions analysed. The analysis will also provide relevant insights into the role of national boundaries in gender equality when physical separators determine them (in particular, Spain, France, Italy, and France). Internal disparities are also relevant, such as the north/south division in each country.

The European Union (EU) is considered to be one of the world’s most advanced political systems with regard to the promotion of gender equality. In 1957, the construction of an initiative at the European level in the field of gender equality was not self-evident, and continuous work for 40 years led the EU to impose a series of norms and values on gender equality on member states. The policies were then developed through successive treaties and the gradual extension of community competence within the Council and the Commission, and the driving role played by women, such as Jacqueline Nonon, Éliane Vogel-Polsky, Barbara Helfferich, and Eryl McNally (Jacquot, 2014).

Until 1997, Article 119 (today, Article 157 of the Treaty on the Functioning of the European Union) was the sole foundation for European activity in the area of gender equality; we can say that a substantial policy for combatting inequality and promoting gender equality was built on this single basis. In general, the policy was based on different instruments (treaties, directives, jurisprudence) to guarantee a variety of rights and was the central pillar during 1975 and 2010.

In 2006, the Parliament and Council established an important body, the European Institute for Gender Equality, thought regulation number 1922, which became fully operational in 2010. The objective was to contribute to and strengthen the promotion of gender equality, including gender mainstreaming in all community policies and the resulting national policies, and the fight against discrimination based on sex, and to raise EU citizens’ awareness of gender equality by providing technical assistance to community institutions, in particular, the Commission and the authorities of the Member States.

A report on progress on equality in all areas, specifically in business leadership in 2010, stated that the progress was not so good ‘despite a general trend towards more equality in society and on the labour market, progress in eliminating gender inequalities remains slow’ (European Commission, 2011a: 17).

Actions that will give an important boost to gender equality were established in 2010. In that sense the European Commission boosts three strategic plans to promote the advancement of women and objectives for strategic engagement for gender equality: Strategy for Equality between Women and Men 2010–2015¹; Strategic Engagement for Gender Equality 2016–2019²; and Gender Equality Strategy 2020–2025.³

Particularly in research and innovations, the Vademecum on Gender Equality in Horizon 2020 (2014) was very important, promoting gender equality at each stage of the research cycle: measures before the call, during the process of selection, and in the execution action of research.

The European Regional Development Fund (ERDF) has certainly been a key body in promoting gender equality at the regional level. All programs funded between 2000 and 2006 had specific requirements in this regard (European Commission, 2010).

Among the main requirements concerning the integration of these actions in the ERDF were a clear reference to the intention to ensure the contribution to the elimination of inequalities and promote equality between men and women; to promote community policies and operations concerning equality between men and women; the quantification of global objectives for the reduction of inequalities; a clear demonstration of consistency in planning and strategising about equal opportunities of the relevant national employment plan; how the balanced participation of women and men would be achieved within the monitoring committees; to construct key monitoring indicators broken down by sex; to perform ex-ante evaluations of the situation in terms of equality between men and women; to detailed objectives and quantify targets on equal opportunities between women and men for those priorities, and to measure them; and to adopt measures and an outline of how equal opportunities would be considered in the management and control arrangements for selecting and monitoring operations.

An evaluation carried out in 2010 by the European Commission has shown significant gender equality effects of such policies, especially on childcare and elderly care services, which were more effective than specific measures in both creating employment opportunities for women and improving work-life balance. The impact of social infrastructure and regeneration projects in areas experiencing depopulation or deprived urban areas has also been shown to be indirectly beneficial for women’s employment and quality of life, with potential positive effects on fertility rates. The smallest impact has been on demographic changes, which takes a long time to observe. Among the lessons learned, it can be underlined that: (a) the local dimension is relevant to demographic change and gender equality, but, while necessary, it is not sufficient; (b) to address the multiple dimensions of demographic change and gender equality, it is necessary to adopt integrated and multi-level policy solutions; (c) to deliver positive effects on gender equality, it is not enough to set this as a horizontal principle but to complement it with sound implementing measures; and (d) to improve the effectiveness and sustainability of ERDF interventions, appropriate management capacities are necessary at the regional and local levels (European Commission, 2010).

More recent reports on ERDF have shown its important role in the promotion of gender equality principles in several intervention areas (European Commission, 2018). This investment has been particularly relevant, considering that these services faced cuts in public funding at the national, regional, and local levels. Approximately EUR 1.25 billion was programmed for investment in childcare infrastructure during the 2014–2020 period. The ERDF has also supported the mainstreaming of gender equality with specific measures and contributed to the reduction of gender segregation in occupations and sectors (González Gago, 2019: 46). However, as the same author notes, mainstreaming equal opportunities and gender equality principles into all ERDF intervention areas is still perceived as very difficult by almost 30% of managing authorities; as a result, little attention has been paid to the specific needs of women in the intervention design, and this lack of focus reduces the effectiveness of ERDF-funded actions with respect to gender equality.

In a more general framework, we examine the performance of regional disparities. In this regard we can summarise four stages: (1) Before the Great Recession, the regional disparities were in a decline between EU member states, to the extent that the EU was nicknamed a ‘convergence machine’, as Monfort (2009) showed. (2) A second stage shows a negative impact with great effect at the regional level, as a consequence of the neoliberal model and its policies, as noted by Perrons and Dunford (2013). (3) A sudden stop in the convergence process during the Great Recession, a crisis that also had a deep impact on economic and social cohesion in the EU, was frequently cited as one of the main causes for the lack of popular support for the project to build the European Union (Montfort, 2020). (4) The COVID-19 crisis in 2020 demonstrated that a shock of such magnitude, even if temporary, can have permanent or at least long-lasting effects on regional convergence (Montfort, 2020), which also generates a significant delay in the catching-up process.

In the course of this overall process, the other convergence (‘gender equality’) had its own vicissitudes. Without any doubt, ‘gender mainstreaming’ played a fundamental role in the policy transformations in the 1990s and marked the so-called exception model by Jacquot (2015). This model was followed by the ‘anti-discrimination model’ until the mid-2000, where the emblematic image is a female victim of discrimination in the public sphere, and, finally, the ‘rights model’, which ran from the 2000s to the 2010s, wherein the image is a woman whose fundamental right to equality is not respected (Jacquot, 2015: 176).

Currently, the result according to the evaluation report of the 2016–2019 strategic plan shows rather moderate progress with a score of 2.6 on a scale ranging from 1 to 5, when considering all thematic areas across all member states (González Gago, 2019: 40).

It is thus evident that the significance of regional disparity, and in particular, the pending effort to be made in terms of gender, is a long work still in progress, and that external vicissitudes play a key role in the reduction or widening of differences. Some efforts, such as those of the European Commission (2021: 17), analyse the indicator of how women perform in relation to the best-performing women (FemAI), and a second index reveals whether women are at a disadvantage compared to men in the same region (FemDI). Four out of five residents of less-developed regions live in a region with below-average female achievement and above-average female disadvantage. Nevertheless, only one in five residents living in a transition or more developed region faces below-average female achievement and above-average female disadvantage.

These approximations are fine, but without regional gender equality indicators, we are blind to the changes that have taken place and cannot assess their progress. Monitoring these regional gender differences is essential for identifying the processes of convergence (or divergence) that are urgently needed. The evidence of increased inequality in regional development with the 2009 and COVID crises highlights the importance of regional development issues and the urgent need to analyse them with reliable information.

Finally, we would like to highlight that, according to recent trends in regional disparities (Montfort, 2020), Europe is a territory with significant internal inequalities that cannot be improved if they are only addressed at the national level. Therefore, the implementation and monitoring of regional policies is imperative, in the same way that gender policies at the national level are insufficient. Consequently, adequate information is required for the implementation, monitoring, and evaluation of policies at the regional level.

2 Methodology for Regional Comparative Analysis

In the following sections, we present a descriptive and typological analysis of the equality measure conceptualised and operationalised by the EGEI both at the country level, comparing the 27 European countries as a whole, and at the regional level, taking into account the NUTS2 division for the territories of Spain, France, and Italy. The results also include information on changes over time, especially between the reference years of 2010 and 2019.

The data presented refer to the overall calculation of the GEI and its decomposition into six domains: work, money, knowledge, time, power, and health. In the disaggregated analysis of the domains, we follow a typological construction methodology called structural and articulated typology (López-Roldán, 1996; López-Roldán & Fachelli, 2015) to complement the analysis of the index with the structuring of the main dimensions that make it up, look for possible specific patterns by country, and compare the different regions with each other to configure general types of gender equality according to the similarities observed. Figure 1 shows a schematic of this process.

Fig. 1

Structural and articulated typology

The final objective was to elaborate a typology of both countries and regions according to the levels of gender equality synthesised in the six GEI domains. To do so, we followed a general four-step procedure. First, we take as a model of analysis the theoretical conceptualisation developed by the EIGE, which is expressed operationally in the six domains of the GEI index (see Chapter “Sub-national Level Data to Measure Gender (in)Equality in the EU: Opportunities and Limitations of Official Datasets”). We then applied a combination of two multivariate data analysis techniques. First, by means of principal component factor analysis, we synthesised a set of six gender equality domain variables into two main factors of differentiation of the units of analysis (countries or regions) based on their intercorrelations and by creating a social space (Bourdieu, 1979; Blasius et al., 2019) to allocate these units. The six variables were expressed as two main factors. Based on these factors, which are small in size, we proceeded to apply the second technique: a cluster analysis using an ascending hierarchical method called Ward, or minimum loss of inertia, with the aim of grouping the countries or regions that are most similar to each other and are differentiated from the others according to a number of groups or types, which are considered suitable for typologically expressing the patterns of behaviour of the indicators used to account for gender equality and according to theoretical empirical validation criteria.

Table 1 presents the data for the 27 European countries for the six domains used in the gender index, as developed by the EGEI for the benchmark years 2010 and 2019. With this information, we perform a typological analysis at the country level, comparing the two reference years 2010 and 2019. Table 2 includes the same variables referring to the NUTS2 territories of Italy, Spain, and France (a total of 62 territories) but from the calculation with the indicators that were collected for the regional analysis in this study (see Chapter “Sub-national Level Data to Measure Gender (in)Equality in the EU: Opportunities and Limitations of Official Datasets”). We also analyse the three countries as a whole and within each country, with the 2019 data, to show the comparative diversity of regional behaviour.

Table 1 Mean, standard deviation, and coefficient of variation (%) of the RGEI and six domains, 2019—regions of Spain, France, and Italy

Table 2 Mean of the variables by the typology, 2019—regions of Spain, France, and Italy

3 Gender Equality in European Countries

First, we present the results of an analysis of the 27 countries considered in the EIGE gender equality index. We will provide an account of the main results obtained in 2010–2019 according to the values of the index constructed and will be complemented with a typological analysis based on the six domains of the equality measure.

The analyses carried out by EIGE since 2013, with the series of data available between 2010 and 2020 (EIGE, 2022), show, in global terms, an improvement in the equality index, albeit relatively moderate, 5.5 points since 2010. As highlighted in the 2021 report ‘it will take nearly three generations to achieve gender parity at the current pace’, which could mean some 60–70 years at that rate get the equality between men and women. The future will tell us what the more or less accelerated rate of growth of the indicator will be, but in the short term, the immediate effects of the COVID-19 pandemic augurs a certain slowdown in the trend, although in 2020, the increase in the level of equality has remained the same as the previous year—that is, a pace of two points per year since 2010 (EIGE, 2022).

The analysis of this period through the six domains of the index (Fig. 2)–work, money, knowledge, time, power, and health–shows the different levels achieved and the different growth rates in each of them (EIGE, 2021, 2022). Thus, the domains of health and money had the highest scores, but health with minimal progress since 2010, except last year, which rose significantly. In the case of money, progress is more important, but with a decelerating trend. The domain of work remained more or less constant over time, and no relevant changes were observed in the aggregate indicator. The contribution to the time domain index score is negative in the long term, and the COVID crisis is expected to increase unpaid care and reproduce more inequalities for women. Knowledge has experienced a moderate increase at the beginning of the decade but has remained static in recent years; in 2020, it has decreased for the second consecutive year. Finally, power is the area of the greatest gender inequality; nevertheless, the overall progress in gender equality is mainly generated by advances in this area.

Fig. 2

Evolution of the GEI index and domains from 2010 to 2020. Source: EIGE (2022)

The 27 countries considered were situated in this particular context. In summary, four main situations and trends that classify countries can be distinguished (EIGE, 2021: 29). Figure 3 shows four patterns: (1) countries that are above the European average in the index value and have slow growth; (2) countries that are also above the average but with a higher rate of change over time than the average for all countries; (3) countries below the European average but with a fast growth rate; and (4) countries with a relatively lower index and a slower trend.

Fig. 3

Evolution patterns of the GEI Index by countries from 2010 to 2019. Source: EIGE (2021)

The three countries that we analyse in a territorially disaggregated manner are located in two of these profiles: France and Spain in Pattern 2, and Italy in Pattern 3.

To complement these results, we applied a combined analysis of dimensionalisation and classification to establish a typology of countries according to the six domains considered in the equality measure. We have done so with the data that takes 2010 as a reference year, comparing them with the most recent data from 2019 (see Table 1 in the Annex).

The principal component analysis yielded two dimensions or factors that accounted for 84% of the variance explained in 2010 and 83% in 2019 (Fig. 4). In both cases, a fairly similar social space is drawn in which the first main factor is configured (with 65% of the variance in 2010, rising to 70% in 2019), which expresses a dimension that contrasts countries with high levels of equality in all domains with countries with low levels. It is a dimension of a lower or higher degree of gender equality that corresponds to the measure expressed by the GEI index, as the correlation between the two variables is 0.938 in 2010 and 0.954 in 2019.

Fig. 4

Principal component analysis. Factor Plot, 2010 and 2019. European countries. Source: Own elaboration with EIGE data

The second factor has less importance (19% in 2010, which was reduced to 13% in 2019) and introduced a second differentiation profile of the countries, showing a small change between the two periods. In the first year under consideration, countries where the money and health domains stand out are contrasted with countries with greater equality in power and work. In 2019, the differences between these domains tended to be reduced, with the health domain standing out at the extremes compared to the power domain.

If we place the 27 European countries in these social spaces of gender equality after applying cluster analysis, we obtain a typology with three significant groups in the 2 years studied (Figs. 5, 6, and 7).

Fig. 5

Cluster analysis: Dendrogram, 2010 and 2019. Source: Own elaboration with EIGE data

Fig. 6

Cluster analysis: Typology in factor plot, 2010 and 2019—27 European countries. Source: Own elaboration with EIGE data

Fig. 7

Country map by the typology, 2010 and 2019. Source: Own elaboration with EIGE data

The typology obtained in 2019 shows, on the one hand, Group 1, which is characterised by having the highest levels of equality in the different synthetic indicators of dominance (running from north to south of Western Europe, in order of the first factor: Sweden, Denmark, the Netherlands, Luxembourg, Ireland, Finland, Belgium, France, Spain, Austria, Malta, Germany, and Slovenia).

Groups 2 and 3 had lower levels of equality. The difference between these two groups was attributed to the second factor. Group 2 is characterised by higher values in the domains of power and work and includes mainly Eastern countries (Estonia, Latvia, Lithuania, Croatia, Bulgaria, Romania) together with Portugal. Group 3 was identified by higher values in health and money, including countries in Southeast Europe (Italy, Cyprus, Czechia, Slovakia, Hungary, Poland, and Greece).

This distribution coincides to a large extent with that obtained in 2010, when three countries moved: Malta and Luxembourg were in Group 3 and are now in Group 1, and Poland was in Group 2 and moved to Group 3. Group 1 also shows similar internal differences that tend to attenuate somewhat over time due to the loss of importance of the second factor.

4 Regional Typological Analysis

Country-level analysis provides a basic and fundamental overview of gender equality behaviour that summarises the dynamics of the aggregate whole territory, as well as the expression of the results of action by national, regional, and local governments. Although its informative value is essential, the measurement of a country’s gender equality does not cease to be an average indicator of internal realities that may be notably different, implying specific diagnoses and their own needs for action. To show the internal differences, we chose three countries—Spain, France, and Italy—breaking down the gender equality indicators at the NUTS2 level, as explained in detail in Chapter “Sub-national Level Data to Measure Gender (in)Equality in the EU: Opportunities and Limitations of Official Datasets”.

With the available information for 22 individual variables that configure the domains and sub-domains of the NUTS2 territories, we have the resulting values that show territorial diversity in detail. We wanted to analyse these results in a complementary way by relating the aggregation in the six domains to the 62 regions that it sets the three chosen countries (see Table 2 in the Annex). To do so, we followed the procedure described in the previous section.

First, we establish the main differentiation factors and then apply cluster analysis to elaborate the typology of regions. We approached this descriptive analytical exercise in two different ways: first, by looking at all regions simultaneously, with the aim of positioning them in relative terms from a joint vision between the three countries, and second, we analyse the internal reality of each country separately.

We conclude the analysis by relating the measure of gender equality at the regional level to various variables of interest for which information is available: gross domestic product per capita, the regional competitiveness index, the social progress index and the quality of government index, and population density.

4.1 Overall Intercountry Analysis

We first present the mapped results of the regional gender equality index by grouping territories into quartiles (Fig. 8). Italy shows the clearest internal division of its territory between the north, above Lazio, with high levels of equality (especially in Trentino-Alto, Emilia-Romagna, Valle d’Aosta, Veneto, Friuli-Venezia Giulia, and Adige-Sudtirol), and the south with low values, coinciding to a large extent with the results obtained in Salvatore Alaimo et al. (2019) and di Bella et al. (2021). This territorial pattern is unclear for Spain and France. In the Spanish case, it tends to be centre-north, which shows greater equality (highlighting Navarre, Basque Community, La Rioja, Castile-Leon, Madrid, and Catalonia), but Andalusia is close to the highest levels and Asturias is farther away. In the French case, the highest equality values were located in the northwest (Île-de-France, Centre-Val de Loire, Pays de la Loire, and Britany) and southeast (Provence-Alpes Côte d’Azur, Corsica, Languedoc-Roussillon, and Rhône-Alpes).

Fig. 8

Regional map of the RGEI in quartiles in Spain, France, and Italy, 2019. Source: Own elaboration with EUROSTAT data

Italy shows the greatest internal disparities in the RGEI, with a coefficient of variation of 7.6, compared to Spain’s 6.2, and France’s lower dispersion of 4.5 (see Table 1). If we analyse the six domains that conform to the global measure, work, power, and knowledge show the greatest differences between regions, with the work domain standing out in Italy, knowledge in France, and power in Spain.

Considering the six domains in which the gender equality indicators are aggregated (work, money, knowledge, time, power, and health), we conducted a factor analysis to synthesise the six characteristic variables of the 62 regions (see data in Table 2 in the Appendix) in terms of two main factors of variation that explain 64% of the variance. To some extent, these factors coincide with those previously obtained in the country-level analysis. Above all, because the same main factor is configured to express the dimension of low to high levels of gender equality, the second dimension, unlike the previous analysis, mainly contrasts the domains of power versus work as a second factor of differentiation of the regions. This structure is shown in Fig. 9, on which we proceed to locate and group different regions.

Fig. 9

Principal component analysis: Factor plot, 2019—regions of Spain, France, and Italy. Source: Own elaboration with EUROSTAT data

The first factor also correlated highly with the regional gender equality index (RGEI) developed in this study, with a value of 0.885. Figure 10 shows the linear relationship between the two measures graphically, differentiating the regions of each country.

Fig. 10

Correlation between RGEI and first factor, 2019—regions of Spain, France, and Italy. Source: Own elaboration with EUROSTAT data

If we analyse gender equality by considering these two factors, not only can the regions be differentiated by the degree of overall equality achieved, but also by the predominance of one of the domains established by the second factor. To this end, we classified the regions and obtained a typology of four types. The results are presented in Table 1 and Fig. 11.

Fig. 11

Cluster analysis: Typology in factor plot, 2010 and 2019—regions of Spain, France, and Italy. Source: Own elaboration with EUROSTAT data

The four types correspond to a large extent to the four quadrants that arise from dividing the scatter plot by the factorial axes. The two on the right, Types 1 and 2, show medium and high levels in the equality index. They are differentiated by the contrast between the work and power domains; the first type stands out for its high values in the work domain, whereas Type 2 stands out for its high values in the power domain. The remaining indicators showed similar values. Types 3 and 4 identify the regions with the lowest gender equality values, separated, as in the previous case, by the differentiated values in work, higher in Type 3, and higher in Type 4.

Which regions are a part of each type? In Fig. 11, the regions have been identified by the type and country to which they belong, and in Fig. 12 on the physical map. The case of Italy, again, is the one that most clearly expresses the differences observed between its regions, dividing its territory fundamentally into two parts: the north and the south of the country, with high and low levels of equality, respectively. This classification is consistent with the previous result, in which we considered only the RGEI value. On the one hand, there is a set of regions with medium and high values of gender equality on the right side of the factorial graph with two differentiated identities. Most of them are part of Type 2 and have high values in the power domain, and two regions, Lombardy and Liguria, are separated as part of Type 1, as they share lower values in the power domain, but higher values in the work domain. On the other hand, there are less equal regions in the south, with the exception of the Abruzzo region which has better indicators in the work domain and joins Type 3. The rest of the regions form an exclusive group of Italian territories, Type 4, positioned on the axis of less equality but whose best indicators are reached in the power domain.

Fig. 12

RGEI typological map, 2019—regions of Spain, France, and Italy. Source: Own elaboration with EUROSTAT data

Spain and France share higher levels of equality with the majority of their regions, both in Type 1 (higher values in the work domain) and Type 2 (higher values in the power domain). Type 3 includes regions in Spain with lower gender equality and higher values in the work domain (Extremadura, Castile–La Mancha, Region of Murcia, Ceuta, and Melilla). France is a profile that identifies only the region of the Poitou-Charentes.

4.2 Intracountry Analysis

The comparative results, we have just seen, reveal the relative positions of the regions in relation to the others, considering the overall performance of the three countries. However, the picture of the internal reality of each country is conditioned by this general context and does not reveal the diversity of each country in the same way, although some characteristics are apparent. Therefore, we will present the results separately.

Figure 13 presents, first, the factor analyses for each country. The interrelationships of the six domains are expressed partially differently among the three countries and in relation to the previous joint analysis. Spain and Italy retain the characteristics of the first factor as a general dimension of equality, but France contrasts the power domain with the rest. The weight of the first factor is also of unequal importance: in Italy (74%) it is greater than in Spain (51%) and France (39%), showing, to a greater extent, the significant inequality that exists among the territories in all the indicators in Italy. The domain of power in this country is the one that appears different and configures the second factor.

Fig. 13

Separated principal component analysis: Factor plot, 2019—regions of Spain, France, and Italy. Source: Own elaboration with EUROSTAT data

Spain maintains the same general pattern as Italy in the first factor, but in the second, it contrasts the domains of power and knowledge with the domain of time. France, on the other hand, together with the variation in the first factor, needs two more factors to achieve a sufficient level of explained variance, showing a greater diversity of profiles. Second, there is an opposition between the regions where money, work, and time stand out as opposed to knowledge and health. Third factor, with a weight of 18%, introduces a differentiation between the regions with a better score in power, work, and knowledge domains versus time domain.

These differentiated patterns among the three countries are consequently expressed in some variations in the distribution of regions (see Figs. 14 and 15). Italy shows the least change and maintains the fundamental division between north and south in two main groups, with the extreme behaviour of Abruzzo and Liguria for the second factor characterised by less equality in the power domain. In the case of Spain, there are small movements in the relocation of the regions according to the two new factors that partly modify the classification obtained in the joint analysis, revealing more clearly the division between the more egalitarian in the north (Type 1), with the exception of Asturias, and the less egalitarian in the south: either with better values in the domains of power and knowledge (Type 2) or in the domain of time (Type 3). Finally, France shows the most changing distribution as a result of the loss of importance of the first factor and diversification of the differentiating profiles of its territories. Type 1 is the most egalitarian. Type 5 was more moderated, with high scores in knowledge and health. Type 2 includes regions around the mean for all the factors. Types 3 and 4 are those with a lower level in the equality index, differentiated by the division of the second factor: three with higher values for money, work, and time, and four for knowledge and health.

Fig. 14

Separated typological analysis: Factor plot, 2019—regions of Spain, France, and Italy. Source: Own elaboration with EUROSTAT data

Fig. 15

Separated typological map, 2019—regions of Spain, France, and Italy. Source: Own elaboration with EUROSTAT data

4.3 Relationship with Other Indicators at the Regional Level

In this section, we analyse the relationship between gender equality and various measures available at the regional level (NUTS2). The following indicators were considered:

• 1. The Gross Domestic Product (GDP) per capita in the purchasing power standard in 2019 shows regional disparities related to the standard of living and economic development. The original and natural log-transformed variables were presented.

• 2. The European Regional Competitiveness Index (ERCI) measures the main competitiveness factors for NUTS-2 level regions across the European Union. The index measures more than 70 comparable indicators related to governance; infrastructure, including digital networks, health, human capital, labour market, and innovation; and the ability of a region to provide an attractive and sustainable environment for businesses and residents to live and work (European Commission, 2022a).

• 3. The EU Social Progress Index (EU SPI) is a measure of social development and quality of life at the regional level that goes beyond Gross Domestic Product. The Index measures social progress in European regions at the NUTS2 level, using 12 components described by a total number of 55 comparable social and environmental indicators, deliberately excluding economic aspects. Its components are further aggregated into three broader dimensions describing the basic, intermediate, and more sophisticated aspects of social progress (European Commission, 2022b).

• 4. The European Quality of Government Index (EQGI) measures the average citizens’ perceptions and experiences of corruption, quality, and fairness of three essential public services—health, education, and police—in their region of residence (European Commission, 2022c).

• 5. Tertiary education is an indicator of cultural or educational factors and is defined as educational Levels 5–8 in the International Standard Classification of Education (ISCED).

• 6. Population density as an indicator of the degree of urbanisation of a region.

Table 3 presents the correlations between these variables. GDP per capita, or its logarithmic transformation, is the variable that shows the highest correlation with gender equality at the regional level and a certain tendency in that the higher the economic level of the region, the higher the level of gender equality achieved.

Table 3 Pearson’s correlation between gender equality and several indicators—regions of Spain, France, and Italy

For the remaining indicators, the correlations were moderate. For the regional competitiveness index, the social progress index, the quality of government index, and tertiary education, correlations were positive between 0.40 and 0.57. Thus, the linear trend is somewhat blurred. In the case of population density, virtually no correlation was observed.

The relationship between RGEI and GDP can be seen graphically in Fig. 16, where the regions are distinguished by country.

Fig. 16

Scatterplot between RGEI and GDP per capita—regions of Spain, France, and Italy. Source: Own elaboration with EUROSTAT data

Again, this joint description reveals specific behaviours when distinguishing between individual countries (Table 4). In relation to previous results, Italy notably increases the levels of correlation between the gender equality index and all indicators, except population density. France, on the contrary, reduced them, maintaining moderate or low levels only in GPD per capita. Spain, for its part, also increases its correlation with all the variables except EQI, although it reaches the values of the Italian case, except in tertiary education.

Table 4 Pearson’s correlation between gender equality and several indicators by Region (Spain, France, and Italy)

5 Conclusions

In this chapter, we have sought to show how disaggregated analysis at the regional level enriches research and shows diverse realities in the behaviour of gender equality indicators, thus providing valuable information to inform and monitor policies aimed at reducing gender gaps at both the local and national levels. Through the construction of the regional gender index (RGEI) in Spain, France, and Italy, we contrasted the detailed data with a more general overview, which is also necessary at the country level in Europe as a whole.

The relevance of this analysis lies in the importance of the persistent regional disparities observed in Europe, which have been well recorded by several productions. In particular, conforms to a line of European political action whose most important program has been the ERDF as a European structural and investment fund allocated by the European Union, which aims to transfer money from richer regions and invest it in the infrastructure and services of underdeveloped regions.

The analyses carried out in Europe have justified the importance of joint action due to the finding of significant regional socioeconomic disparities. Political action has been implemented with a strong orientation towards the convergence of different regions, a process that has been achieved over time, but the crises tend to slow down and/or reverse (Montfort, 2020).

The ERDF program has had a great influence on gender issues by including gender mainstreaming among the requirements for receiving funds; thus, it has been an important instrument for regional and local governments to be directly involved in the issue and has had to take measures in this regard to implement all types of work.

To map gender differences, we begin by analysing the differences between European countries. According to the EIGE data, we start by noting that a relative global improvement of almost five points in the GEI was achieved between 2010 and 2019, with different patterns of evolution between countries.

To deepen these results, we performed a typological analysis in 2010 and 2019 to dimensionalise the main factors based on six domains that make up the index and observe whether the structure of between countries changes or remains constant. The conclusions of this analysis, in accordance with the same results obtained by the European Institute for Gender Equality, are that the structure has remained basically the same in this period, that there have been moderate advances in the value achieved in the gender equality index, but that the levels and rhythms of different countries differ from one another. We show these differing levels in our typological analysis by differentiating the three groups of countries in both periods. There is a fundamental distinction between egalitarians and egalitarians. The latter are differentiated into two: those that score better in the power and work domains, and those that score higher in the money and health domains.

Our main objective is to show how information at the subnational level nuances aggregate analyses by showing specific structures and dynamics that allow us to enrich the conclusions of the analyses and provide us with a very important tool for designing national and local policies on gender equality. On the other hand, disaggregated analyses present shades when the regions of a country are compared to the regions of other countries or when they are compared internally. Both types of logic are of interest: the first approach relativises and compares the situation of a country’s territory with the territories of the rest of the countries considered; the second approach is limited to the sociopolitical reality of the country and is of particular interest for the design and evaluation of national and local public policies.

In the countries chosen for this study and the 62 regions they comprise, the data obtained from the RGEI allow us to conclude, first, that, on an aggregate basis, the highest levels of gender equality are found in France, followed by Spain and Italy. The final values of the index establish the same order as the data elaborated by the EIGE, with certain variations in magnitude given the adaptation made in the construction of the measure (Table 5).

Table 5 GEI and RGEI comparisons

However, internal regional realities are quite diverse. Thus, a greater number of regions in Italy than in the rest of the countries are in the fourth quartile, while others are in the first quartile, showing significant polarisation of the territory. Many regions in France are in the second quartile, while Spain shows a dispersed distribution of regions in the different quartiles. Therefore, important disparities need to be considered in the analysis and policy.

The complementary typological analysis that we have carried out deepens these disparities and shows the diversity of the regions of each country with particular patterns of territorial structuring. When the 62 regions were treated together, the similarity of the first main factor of less or more gender equality in the six regions was reiterated. However, differences appear in relation to the behaviour of the domains in the second factor; in this case, polarising the regions where equality stands out in the domain of power compared to that of work. This dimensionalisation into two principal components determines the typology of the four types of regions that we obtain by grouping them according to the profiles of the four quadrants: medium-high equality/power, medium-high equality/work, low equality/power, and low equality/work.

Regional disparities by sex are also nuanced when a separate analysis is conducted by country. Three distinct patterns were found, one for each country. Italy and Spain maintained the identity of the first factor of less or greater equality, but France changed it by opposing power to the rest in this first dimension, in addition to needing two additional factors to express the diversity of profiles in its territory. Italy and Spain only need a second factor but with differentiated profiles. This shows a territorial distribution marked by the dualisation between northern and southern Italy, the greater diversity of profiles in France, and Spain’s intermediate position between the two above.

Finally, we wanted to relate the RGEI results to elements linked to other indicators of economic, social, cultural, political, and demographic development. Here again, the disparities between countries are shown. Overall, the highest correlation is observed with GDP per capita, but when we separate the countries, the reality is more accentuated for Italy, not very relevant in France, and moderate in Spain. In the case of the cultural indicator, the relationship between tertiary education and gender equality is relatively weak; however, when we separate the countries, it is shown to be an important factor in the Spanish case, somewhat less so in the Italian case, and not very relevant in the French case. The association between the social indicators also shows a weak relationship at the general level, increasing in the case of Italy and Spain and disappearing in the case of France. The weak relationship with political indicators at the general level is important only in Italy and disappears in Spain and France. Finally, there was no strong correlation with the level of urbanisation in the region.

These results demonstrate the importance of working at different territorial levels in research and the design of public policies. Moreover, this requires sufficiently disaggregated sources of information that allow us to measure gender equality at the regional level. With this richer and more detailed information, we can better observe the complex social realities of our societies, and that of gender equality/inequality in particular. This will make it easier for us to formulate better diagnoses and implement better policies, both at the state level, to direct policies to compensate territories and distribute resources more efficiently and to enable political and social action to fight for equality at the local level.

Annex (Tables 6 and 7)

Table 6 Gender equality index by country. Global index and domains—2010 and 2019

Table 7 Gender equality index by region (NUTS2) global index and domains 2019