Rural Resilience from a Gender Perspective

APRIL 2025 RURAL RESILIENCE FROM A GENDER PERSPECTIVE D 4.2 Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.UK participants in the GRANULAR project are supported by UKRIGrant numbers 10039965 (James Hutton Institute) and 10041831 (University of Southampton). Funded by the European Union | 1 D4.2 RURAL RESILIENCE FROM A GENDER PERSPECTIVE Project name GRANULAR: Giving Rural Actors Novel data and re-Useable tools to Lead public Action in Rural areas Website www.ruralgranular.eu Document type Deliverable Status Final version Dissemination level Public Authors Carlos Tapia, Debora Pricila Birgier, Mari Woien-Maier (NOR) Work Package Leader Nordregio (NOR) Project coordinator Mediterranean Agronomic Institute of Montpellier (IAMM) Citation: Tapia, C., Pricila Birgier, D., & Woien-Maier, M. (2025). Rural Resilience from a Gender Perspective. GRANULAR. https://doi.org/10.5281/zenodo.16981122 This license allows users to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. UK participants in the GRANULAR project are supported by UKRIGrant numbers 10039965 (James Hutton Institute) and 10041831 (University of Southampton). | 2 Table of contents Executive summary ....................................................................................................................................... 5 1. Introduction ........................................................................................................................................... 7 2. Resilience in social sciences ............................................................................................................... 8 2.1. Scoping resilience .............................................................................................................................. 8 2.2. Resilience and time ............................................................................................................................ 9 2.3. Resilience and space ....................................................................................................................... 11 2.4. Conceptual fuzziness ....................................................................................................................... 12 3. Resilience in practice .......................................................................................................................... 13 3.1. Resilience in regional policy ............................................................................................................. 13 3.1.1. The role of hegemonic discourse in regional development policy ............................. 14 3.1.2. Alternative perspectives on regional (economic) resilience ...................................... 15 3.2. Rural resilience: an emerging concept ............................................................................................. 17 3.2.1. Economic resilience in rural areas ............................................................................ 18 3.2.2. Community resilience and the role of farming in rural Europe .................................. 20 3.2.3. Social-ecological resilience ....................................................................................... 23 3.2.4. Indicators of rural socio-economic resilience in rural areas ...................................... 24 4. Characterizing rural resilience ........................................................................................................... 25 4.1. Rural resilience as a process ........................................................................................................... 25 4.2. Rural resilience as an enabler of sustainable rural development ..................................................... 25 4.3. A working definition of rural resilience .............................................................................................. 26 5. A gender perspective on rural resilience .......................................................................................... 27 5.1. Introduction ...................................................................................................................................... 27 5.2. Theoretical background .................................................................................................................... 28 5.2.1. Gender gap in labor market outcomes by rurality ..................................................... 30 5.3. Comparison strategy and expectations ............................................................................................ 31 5.4. Data, variables and methods ........................................................................................................... 32 5.4.1. Data and sample ....................................................................................................... 32 5.4.2. Dependent variables ................................................................................................. 32 5.4.3. Independent variable ................................................................................................. 33 5.4.4. Controls ..................................................................................................................... 33 5.4.5. Methods..................................................................................................................... 33 | 3 5.5. Results ............................................................................................................................................. 35 5.5.1. Employment .............................................................................................................. 36 5.5.2. Part time employment ............................................................................................... 39 5.5.3. Care responsibility ..................................................................................................... 43 6. Concluding remarks ............................................................................................................................ 47 7. References ........................................................................................................................................... 49 Annexes ........................................................................................................................................................ 59 Annex 1. Bibliometric searches ................................................................................................................ 59 Annex 2. Annex to gender perspective on rural resilience ....................................................................... 62 | 4 Acronyms AROPE At-Risk-of-Poverty-or-social-Exclusion COEFFY Yearly weighting factor DEGURBA Degree of urbanization EFTA European Free Trade Association ESF+ European Social Fund Plus EU European Union EU-LFS EU Labour Force Survey EVS European Values Survey FTPT Full-time / Part-time distinction GDP Gross Domestic Product ILO International Labor Organization ISCED International Standard Classification of Education LAU Local Administrative Unit LS3 Local Smart Specialization Strategies LTVRA Long Term Vision for Rural Areas NEET Not in Employment, Education, or Training SME Small and Medium Enterprise S3 Smart Specialization Strategies UN United Nations | 5 Executive summary The exploration of ‘rural resilience’ as a concept reveals its relatively recent emergence, typically characterized by a blend of restructuring and resistance, incorporating both change and permanence. This multifaceted concept manifests at various levels: place, community, individual, and over time. Despite a wealth of information, a uniform conceptualization of rural resilience remains elusive in the reviewed literature. Academic and policy discussions suggest inherent interconnections between economic, social, and ecological perspectives, aligning with the sustainability triangle. Contemporary discussions increasingly recognize rural resilience as a context-specific, evolving process of future-proofing that integrates economic, environmental, and social sustainability at the individual, community, and regional levels—particularly under conditions of uncertainty. This broader perspective highlights the importance of incorporating social factors into resilience frameworks. By adopting a critical perspective on the concept of rural resilience, this report aims to raise awareness of the normative values associated with it. Recognizing that nuanced understanding is essential for consistently applying the concept across Europe’s uneven and heterogeneous rural areas, we propose a working definition of rural resilience as contextual and spatially bound evolutionary process of future proofing that accounts for the interlinkages between the economic, environmental, and societal sustainability of the place, the community and the individual in a context of uncertainty and unpredictability. Adopting a systems’ perspective, rural resilience is operationalized through complex, non-linear processes. Furthermore, this definition frames resilience as a context-dependent notion in which time, space, and perceptions all play a crucial role in determining what is considered ’resilient’. Finally, the definition explicitly acknowledges that resilience within one domain can significantly impact other domains. Acknowledging these interdependencies, we aim to incorporate gender as an essential concept into our understanding of socio-demographic and socio-economic resilience in rural areas, and by extension, rural resilience. Our work focuses on uncovering the nature of gender and rurality gaps in employment outcomes across 27 European countries, providing insights into the intersection of gender, rurality, and welfare state typologies. Drawing on data from the European Labor Force Survey (EU-LFS) for 2013, 2018, and 2023, we investigate changes over time in labor markets' rurality and gender gaps, analyzing trends between 2013 and 2023. The work also explores cross-country variations by examining welfare typologies across Europe. Our findings reveal persistent labor market disparities faced by rural women, highlighting critical intersections between gender, urbanization, and rural labor markets across the EU. The study's findings emphasize three key dimensions corresponding to each of the outcomes analyzed: ▪ First, the analysis reveals persistent gender gaps in employment across all examined contexts. While the average rural-urban employment gap among women appears negligible at the aggregate level, substantial cross-country variation emerges. In 11 out of the 27 countries studied, rural women face compounded disadvantages compared to their urban counterparts. However, this pattern is not uniform: in Continental and Nordic countries, rural women often exhibit higher employment probabilities than those in urban areas, while in Southern and Eastern Europe they remain substantially less likely to be employed. Notably, temporal trends point to a gradual narrowing of rural-urban gaps, particularly in Southern Europe, where rural women have seen modest but consistent employment growth. These findings suggest a more differentiated landscape than commonly portrayed in literature, highlighting both persistent barriers and emerging improvements in rural women’s employment. ▪ Second, the findings on part-time employment reveal a pronounced disparity in employment patterns. Women in rural areas of Continental and Nordic countries are significantly more likely to work parttime compared to their urban counterparts, with large gender gaps in part-time employment probabilities. This is especially evident in countries such as the Netherlands and Austria, where cultural norms and welfare policies appear to reinforce gendered labor market participation. This suggests that in urban settings partners more equally share both paid and unpaid labor, contrasting with the dominant "one-and-a-half model" more predominant in rural areas. By contrast, in Southern | 6 and Eastern Europe rurality plays a less significant role in determining part-time employment, with both urban and rural women showing similar probabilities of part-time work, indicating that the prevalence of part-time employment is generally lower in Southern European countries. ▪ Third, caregiving responsibilities emerge as a critical factor influencing employment patterns in most Continental countries. Women in rural areas of these countries report significant employment constraints due to caregiving responsibilities, with larger rurality and gender gaps compared to urban areas. This contrasts with findings from Southern and Eastern Europe, where caregiving responsibilities show minimal variation by rurality and gender, suggesting that other structural and cultural factors may drive the lower employment probabilities of rural women in these regions. These results underscore the significance of welfare state typologies in shaping labor force outcomes. In Continental welfare regimes, overall female employment levels are higher for women in rural areas compared to their urban counterparts, while part-time work is more common among rural women. This might indicate that these regimes tend to reinforce traditional gender roles through policies and societal norms that encourage part-time employment, with women's employment often constrained by caregiving responsibilities. Nordic countries, while more egalitarian, also show rural-urban distinctions in part-time employment, though this is less pronounced than in Continental countries. In Nordic countries, consistent with the principles of the universal welfare state and the widespread availability of childcare services, the higher prevalence of parttime employment among rural women does not seem to stem from caregiving obligations. In Southern and Eastern Europe, rural disadvantages are more likely structural. Limited job opportunities, which might relate to less service-oriented and more agrarian local economies alongside traditional gender norms, appear to play a more significant role in shaping employment outcomes than caregiving responsibilities. The study’s findings demonstrate that the intersection of gender and rurality generates distinct patterns of disadvantage, with significant variations across European countries. These patterns reflect differences in welfare state policies and the broader socio-cultural and economic contexts within which rural and urban women navigate the labor market. Consequently, addressing gender disparities offers a key pathway to enhancing rural resilience. Integrating gender-sensitive measures into policy frameworks is not simply a corrective to existing inequalities, but a strategic imperative for fostering rural communities’ long-term adaptive capacities. The report concludes by highlighting the importance of an intersectional and place-sensitive approach in developing policies that promote gender-inclusive and sustainable rural development. | 7 1. Introduction EU rural areas are facing challenges due to aging populations and outmigration, particularly among young educated women (EC, 2021a; Eurofound, 2023). Outmigration of working-age residents undermines the local demographics and exacerbates labor shortages in key sectors. Such a process can trigger a self-reinforcing cycle of decline, further weakening rural resilience, understood as rural areas' capacity to 'futureproof'. If outmigration of younger individuals, particularly women, is sustained over long periods of time, the collapse of local businesses, services, and community institutions is a likely outcome, as social, economic, and institutional foundations are compromised. In addition to their role in maintaining population replacement levels, young women play a crucial role in maintaining rural communities and ensuring local institutions function. Rural women contribute to diversify household livelihoods by engaging in employment and entrepreneurial activity, and to leverage social capital by providing social support and organizing community life. Consequently, as young rural women emigrate, the burden of care work and community leadership increasingly shifts onto those who remain, placing further strain on local resources and resilience. Labor market integration of women is hence crucial for preserving rural communities' social and economic resilience. Rural women need access to good quality jobs, opportunities for entrepreneurship and selfemployment, and community leadership that empower them and provide a sense of purpose. When empowered, rural women can enhance their individual and household wellbeing, strengthen the local economy, and make rural living an attractive option compared to urban centers. This requires balancing work and family responsibilities with availability of community-based support systems. The European Long Term Vision for Rural Areas specifically recognizes the importance of rural women's roles and seeks to incentivize their participation in labor markets, entrepreneurship, decision-making, and work-life balance services (EC, 2021b). Against this framework, the specific objectives of the study are twofold: First, the report aims to shed light on the concept of resilience. Building on a comprehensive review of academic literature and policy documents, the report introduces and critically discusses the different applications and operationalizations of the notion of resilience in social sciences and related disciplines, with a focus on their implications for rural areas. Based on this review, the report provides a working definition of rural resilience that informs the analytical work. The second and most relevant objective of the study is to critically examine the concept of rural resilience through a socio-economic lens, with a specific focus on how gender dynamics intersect with rurality to shape labor market outcomes across Europe. By integrating theoretical insights from resilience literature with an empirical analysis of microdata from the European Union Labour Force Survey (EU-LFS), this strand seeks to expand current understandings of rural resilience beyond economic indicators, to include social and institutional dimensions. Through this lens, the report contributes to a more comprehensive and equity-focused framework for understanding and operationalizing rural resilience across diverse European contexts. The report is organized as follows: Sections 2–3 provide a comprehensive review of the concept of rural resilience based on relevant academic literature and selected policy documents. Based on the reviewed literature, Section 4 traces out a working definition of rural resilience, and from there identify key perspectives that may help operationalize the concept in a rural setting. Section 5 builds on this definition to explore gender differences in the labor market outcomes by rurality. Section 6 presents some general conclusions stemming from the empirical analysis. The two Annexes include complementary materials about the bibliometric searches performed in this work (Annex 1) and additional figures, descriptive data, and model estimates corresponding to the empirical work (Annex 2). | 8 2. Resilience in social sciences 2.1. Scoping resilience The term resilience, and its Latin root resilire, refers to the ability of rebounding: the elasticity of systems to recover after shocks or disturbances (S. Davoudi et al., 2012; R. Martin, 2012). The concept has been widely used in the natural sciences, as well as in medicine and psychology for decades (Rocha et al., 2023). The notion of resilience has been developed in different contexts and has evolved over time, as it was applied in different fields and disciplines. In many instances, resilience is defined as a system’s coping capacities or “capacities to adapt” (Martin and Sunley, 2015), inferring the ability to continue operating and developing systems despite disruptions. As McManus et al (2012) write with regards to the understanding resilience in a social and economic context, “resilience is understood as the ability to embrace change, with a capability to adapt seamlessly to largely exogenous events (such as technological change) in a form termed stable adaptation” (McManus et al., 2012, p. 21). Departing from this general understanding of resilience as a ‘capacity’ or an ‘ability’, for many scholars the notion of resilience is a ‘contextualized idea’ in many ways, which implies that ‘recovery’ and ‘survival’ sits at the heart of its discourse (Hu and Hassink, 2015). For others, resilience serves first and foremost as a metaphor (Pendall et al., 2010). In theory, resilience is described as an “ideal end goal” indicating a point in the future where all parameters align. However, as Roberts, Anderson, Skerratt, & Farrington (2017) write, “no community is fully resilient or fully vulnerable but displays aspects of both, and these are temporally and spatially changeable” (Roberts et al., 2017, p. 373). This begs the question of what resilience inherently is and what it is not and how it can be measured. Should resilience only be understood as a quality of systems in place that can resist and recover from (often exogenously driven) shocks, or is a resilient system one that comprises a set of qualities that enables change over time, in an evolutionary way? This discussion leads to the differentiation between ‘resilience’ and ‘robustness’. According to Martin and Sunley (2015), ‘robustness’ is often interpreted as an unbendable quality, a characteristic that remains unaffected regardless of shocks and disturbances. It suggests stability and resistance, but also the ability to adapt certain parts of the structure it has created, to retain core functionalities”, and continue: “This notion generally refers to a situation in which there are identical or similar components or subsystems (modules) which can replace each other when one fails (Martin and Sunley, 2015, p. 7). Resilience is what allows the structure within which these components to be replaced, without systems failure. Considering robustness, the focus is not on systems per se, but on the maintenance of key functions in these systems. Resilience and robustness are not mutually exclusive, however, but subsumes aspects of one another: robustness allows for some change to retain key functions, and resilience concerns the plasticity of the structure within which robustness operates. Moreover, “it is not a simple dichotomy between continuity (no change) and (complete) change” (Martin and Sunley, 2015, p. 10). In their study on the applications of rural community resilience in Scotland, Currie et al. (2023) delve into this issue by suggesting that resilience is a dual discourse centered upon ‘everyday resilience’ and ‘emergency resilience’, and that these have emerged because of neo-liberal agendas to work with “short term damage reduction to the detriment of longterm adaptive capacities” (Currie et al., 2023, p. 199). Both should be recognized and embraced as part of resilience studies rather than focusing solely on finding one definition. In general terms, the literature emphasizes that resilience should be considered at the very least as a ‘process’ or a ‘multifaceted process’ that is not static or singular (Martin and Sunley, 2015, 2015). Nor does it have fixed characteristics. The process of resilience, according to Roberts et al (2017), occurs in different contexts, culturally and politically. Moreover, the addition of prefixes, such as ‘regional’, ‘rural’, or ‘community’ creates a new notion and ideas of resilience altogether, which in turn sets out specific ideas of these processes. How time and space is treated and constructed, is one of the key divides among resilience scholars as both are constructing regions and manifested these based in “human action and social relation”, while also being in an evolving situation of transitions (Christopherson et al., 2010, p. 4). | 15 3.1.2. Alternative perspectives on regional (economic) resilience Resilience is primarily seen in light of its relative productivity performance and economic development, which includes socially inclusive and environmentally conscious aspects, but remains strong in adverse economic crisis (Bristow, 2010). Although mentioned and accounted for, socio-economic aspects play second fiddle to economic performance, although such socio-economic issues support (regional/rural) economic development. This is particularly clear when seen in light of the emerging wellbeing economy. Although socioeconomic issues are resonant in resilience literature, human resources are primarily seen in light of their economic usefulness, rather than understanding resilience in terms of the complexity that supports socioeconomic prosperity. Moreover, rural resilience cannot solely rely on either social capital or local leadership, even if the former is crucial for creating new economic opportunities and diversification through e.g., entrepreneurship, as they tend to compartmentalize communities and the individuals within them (Li et al., 2019; McManus et al., 2012). Rural resilience therefore requires a deeper understanding of what the traditional regional resilience discourse has to offer: It requires more nuance, and different parameters to measure economic performance of industrial structures beyond GDP. We cannot compare complex socioeconomic systems with e.g., disruptive economic activity because socio-economic systems are heterogenous and more unpredictable and cannot be ‘locked in’ to the same degree as industrial structures and radical and transformative technologies (Hassink, 2010; Simmie and Martin, 2010). Competitiveness and sustainability remain part of the dominant hegemonic discourse of what constitutes resilience, but resilience relies on much more than merely these aspects. Martin and Sunley (2015) point out that considering resilience in a socio-economic context, it cannot be separated from its normative meanings. They suggest including a more encompassing definition of resilience; one that accounts for the local and regional economies’ ability to recover from shocks or crises, whether competitive or environmental, to resume its developmental growth path—and include adaptive changes if necessary to its structures. This would either bump the economy back on its previous path or pave a new way which incorporates its human and natural resources in a more productive way. In this reading, the value of an e.g., the regional or rural economy’s sustainability and competitiveness is dependent on the economy to remain resilient to disruptions and shocks over time (Scott, 2013; Simmie and Martin, 2010). Here we clearly see how regional development strategies have been forged under a hegemonic competitiveness discourse that “reflects the status of competitiveness as a key discursive construct” (Bristow, 2010, p. 153). Furthermore, discourses of competitiveness are ‘placeless’ and global, which Bristow (2010) argues is too narrowly constructed for regional development, and that needs to be interrogated. Various contributions challenge the hegemonic discourses in regional economic resilience by constructing “’alternative discourses’ that are grounded in alternatives to the social relationships that underpin hegemonic imperatives, although the degree of opposition and radical (counter-hegemonic) change they may represent remains an open question” (Bristow, 2010, p. 157). This is an important part of deconstructing and understanding regional development policies. The powerful and hegemonic ‘economic imaginaries’ at play will lay out specific paths to follow and particular indicators for measuring success. In a world where faced with an increasing uncertainty and insecurity, understanding what ‘success’ is based on, e.g., often ‘neoliberal’ imaginaries, is helpful for constructing alternative discourses, including alternative takes on how we can live and work differently, such as e.g. alternative indicators seen in the well-being economy. These alternatives are also based in and on social relations, but not necessarily ‘mainstream’ relations: It is rather about development than unlimited growth, use value rather than exchange value, less about property rights, capital accumulation and pursuit of competitiveness (Bristow, 2010, p. 158). Different narratives create different outcomes and policies. Scott (2013) argues that it is crucial to examine the underlying ideological and political content of dominant resilience discourses. For example, the USA’s narratives surrounding resilience is tightly connected to their ideas of self-reliance, distrust in the state and government and great emphasis on individualism. This makes a difference for what we expect to see, our preconceived notions of what ‘resilience’ means, and what we can do about our situation to achieve resilience. Seen in relation to regional and rural resilience, the conceptualization of the term is shaped by the dominant discourse of the space and time we find ourselves in, and how alternative understandings and interpretations of ‘resilience’ might emerge by being retained and reinforced. Bristow and Healy (2014) use their paper on regional resilience to place emphasis on agency in understanding and theorizing about regional economic | 16 resilience. They outline why agency matters, and finally how they are organized and how they act, as well as how the focus on agency might help elucidate the conceptualization of resilience, as well as how to measure it empirically and operationalized. Human agency plays an integral role in the complex systems that we find ourselves in, but how collective actions by individuals and others behave in the face of a shock, or indeed what drives them to make certain decisions and choose a specific line of actions to adapt, is an aspect that requires deeper interrogation. This is why understanding the composition of actors and existing links between them matters for understanding how regional or local economies work and might adapt to disturbances, as systems change, and human agents’ behavioral influence happen in tandem. Among the more disruptive and non-conventional perspectives on regional economic development and resilience, the approaches grounded on de-growth and wellbeing economy are probably the most relevant contributions. Both perspectives strive to move ‘beyond GDP’ as a measure of prosperity and both measure impact on and recovery by communities in a new way, especially considering the uneven impacts shocks and crises, such as e.g. the global Covid-19 pandemic (Crisp et al., 2023). In both of these paradigms, human and social capital, as well as environmental and natural assets are considered as part of a multidimensional economic system. These dimensions impact and bolster e.g., business activity, as well as addressing spatial and social inequalities, calling for a differentiated approach in policy making, moving towards place-based policy considerations. Still, both perspectives have important discrepancies too, particularly as regards economic growth and capitalism, towards which the wellbeing economy discourse shows ambivalence in that its proponents hold conflicting views or abstain from taking a clear position (Buch-Hansen, 2025). The wellbeing economy has gained particular traction in Europe, with the Nordic countries paving the way (Birkjær et al., 2021). With low levels of corruption, well-functioning state institutions, social benefits in place, high income levels, individuality, freedom, and social trust, the Nordic Region often tops international classifications on well-being and happiness. As Birkjær and colleagues (2021) note, albeit not explicitly stated, the intention for actively safeguarding individual freedom, preventing corruption and ensuring trust on both a government and societal level, is most likely promoting the well-being among citizens. To have a “well-oiled” society would arguably result in a more prosperous economic future, and therefore be beneficial for the state in general. However, a large number of Nordic people struggle with for example, mental health disorders, low life-satisfaction, as well as reported discrimination and high suicide rates (Birkjær et al., 2021). Other than the degrowth and wellbeing paradigms, following the numerous crises in Europe over the past 15 years, several alternative agendas for how to approach urban economic development. Examples include: Inclusive Growth, understood as an economic system which enables the greatest number and range of people to participate in economic activity and to benefit from economic growth (Gupta et al., 2015); the Doughnut Economics, defined as an ecologically safe and socially just space (the Doughnut) in which humanity can thrive (Raworth, 2017), alongside other circular economy discourses (Calisto Friant et al., 2020); Community Wealth Building, conceptualized as local economies organized so that wealth is broadly held and generative of income, opportunity, dignity and wellbeing for local people (Dubb, 2016); the Foundational Economy, as an expanded neo-endogenous development approach (Mackinnon et al., 2022). People, places and local powers are important aspects in these alternative approaches to economic development. For example, the Foundational Economy framework rests on three main ideas that all break with the norm of measuring economic aspects to determine progress and success. The framework suggests that the well-being of citizens and their social consumption of essential services and goods, access to basic goods and services provided through public policy, and ensuring that the careful practice of policy with an aim to disrupt top-down technocratic policies, will all help to diversify and enable multiple economies, rather than focusing on the mainstream idea of what ‘the economy’ is (The Foundational Economy Collective, 2020). In short, “society [is] strengthened by focus[ing] and invest[ing] in the infrastructures that make civilized everyday life possible” (Crisp et al., 2023, p. 6). While acknowledging all these views on regional economic development and resilience it is important to keep it in mind that the current discourse on regional economic resilience is still forged on a neoliberal growth paradigm and upon the conventional narrative build upon regional economic competitiveness, often under stereotypical views and pre-defined roles for different types of regions, according to their degree of urbanization (Lundgren and Ljuslinder, 2024). Alternative discourses on regional economic development and resilience struggle to become mainstream not only because of the inertia embedded in such underlying | 17 economic principle but also because the mainstream concept of regional resilience is somewhat vague and elusive. 3.2. Rural resilience: an emerging concept With profound advancements in information technology, the proliferation of digital tools, and the widespread availability of consumer products and services online, both in rural and urban areas, the unique characteristics of these locales are undeniably fading (Bæck, 2004) and they tend to co-evolve over time (Mærsk et al., 2023).This can be seen as the ‘digital’ expression of a transformation that Hompland already termed as ‘urbanization’ back in the 1980s (Hompland, 1984). Sucha a cultural transformation exerts an influence on ‘regional variations’, leading to the decline of traditions and the emergence of a more homogenized population across countries. It also implies that the local community no longer serves as the primary normative center (Bæck, 2004) and that cultural identity is shaped elsewhere, displaying a certain malleability (Barcus and Brunn, 2010). In this way, rural areas have become tightly interwoven with the rest of the world in terms of the ‘dissipation of culture’ (Cras, 2018). Despite rural and urban areas becoming increasingly similar, the idea of ‘rural’ still serves as a baseline against ‘modernity’, and therefore the rural imaginary represents the opposite to urban areas, symbolizing tradition and (slow) continuity (Roberts et al., 2017). In spite of this cultural derive, rural areas are not homogeneous. As highlighted by Quaranta & Salvia (2014), rural areas are intensely heterogenous, with some areas struggling with depopulation and abandonment and others rife with activity and land used for food production. Moreover, rural spaces are in constant transition, whether experiencing divestments, investments, depopulation or population growth. At the same time, regions and rural areas do exist in multi-scalar situations, where decision-making by political and economic actors impact on the development of rural regions, or rural areas (Christopherson et al., 2010). Against this dynamic and heterogeneous framework, the exploration of ‘rural resilience’ as a concept has revealed its relatively recent emergence, and the notion of it being a blend of restructuring and resistance, adaptation and transformation, involving both change and permanence, seems to capture some of the essence of the concept (Li, 2023). According to Scott (2013) there are primarily two perspectives on resilience that emerge from the rural studies literature: the first one frames it as a component of social-ecological resilience. The second one interrogates community resilience as it happens in rural areas. The Author concludes that rural resilience is primarily used in connection to economic uncertainty and ecological crisis, within negative parameters of rural decline. Here, ‘rural resilience’ to some extent presupposes a state of socio-economic ‘improvement’, rather than departing from a sense of ‘rural bolstering’ (Scott, 2013). Anthopoulou et al. (2017) present a slightly different perspective, as they argue that public discourse is also framing ‘rural’ as a “resilient milieu of solidarity”, and of social innovation. Skerratt (2013) also supports this more possibilistic view, as rural communities are posited as ‘proactive’ and ‘active’ and takes strides to develop processes for their resources and capacity building. Ashkenazy et al (2018) highlight the ways in which regions and its rural residents can use new circumstances to improve their situation. In this light, rural resilience could be seen in light of “capacity to ensure continuity”, improvements or betterments (Ashkenazy et al., 2018, p. 211), whether of a farm or of the traditional cultural character of the region. These Authors identified five main categories that can express three dimensions of resilience—‘persistence’, ‘adaptability’, and ‘transformability’: 1) Valuing traditions and local capacities; 2) Promoting economic diversification; 3) Utilising technological innovation and cost efficiency; 4) Increasing cohesion between different social groups within the region and outside 5) Optimising the use of public support. This is also in line with Roberts et al (2017), for whom rural resilience can be a useful framework to scoping policies for developing rural areas in the EU, to understand how small businesses in those areas innovate and learn, and to study how farmers and town communities work together. Adam-Hernández and Harteisen (2020) argue that there are many challenges imposed by economic disruptions, social changes and ecological transformations across Europe today, and that these are particularly visible in the evolution of rural communities and villages. In light of this, rural resilience studies look at how peripheral village communities | 18 in Europe shape change particularly focusing on those that are able to adapt and adjust their development path. By combining resilience research as it is found in social ecology, community development and psychology, it is possible to conceptualize rural and village resilience (Adam-Hernández and Harteisen, 2020). Steiner and Atterton (2014) conceptualize rural resilience as the ability to handle disruptions and rearrange a system during these disruptions, while keeping the same basic function, structure, identity, and interactions, is how. In a rural community development perspective, these Authors argue that resilience should be understood as the way people are able to utilize and build their capabilities and capacities to thrive in system that is always changing—it is therefore a continuous process. In essence, they write that resilient rural communities encompass various elements, including a sustainable local economy, a strong sense of belonging, social capital, and a high-quality local environment. A similar holistic operationalization of rural resilience is adopted by Tao et al. (2025) in their appraisal of rural resilience in the Chinese province of Ganus. To determine a ‘successful’ rural community, however, might go beyond these factors, as it also related back to our previously mentioned ideas of networks, leadership and partnership that exist in our multi-scalar environment and layers of governance structures ” (Steiner and Atterton, 2014). In sum, the resilience narrative offers interesting perspectives to rural studies. It provides alternative analytical methods for exploring ‘path dependencies’ in local development and identifying entrenched interests and ‘institutional apathy’ that leads to ‘locked-in’ development trajectories, which in turn allows for timely vulnerability assessment and appropriate policy interventions (Scott, 2013). More importantly, the resilience discourse provides an alternative policy narrative for rural development, reframing the debates and emphasizing conscious actions and opportunities for rural development. Crises may in fact lead to new opportunities if deliberative responses are made to involve relevant stakeholders in taking care that the place is bouncing forward (Scott, 2013). By involving networks of stakeholders, and using adaptive network governance as a guide, rural development practice can address the questions of what, to what, and for whom. This is placing agency in the hands of the local community rather than in the overstanding structures (Paniagua, 2013), allowing for a more dynamic approach to governance of a space that is constantly changing and adapting to the changing framework conditions. 3.2.1. Economic resilience in rural areas Steiner and Atterton (2014) have explored the contribution of rural enterprises to local resilience and found that the private sector is contributing to local resilience, both socially and environmentally, both directly and indirectly. Rural businesses make a direct contribution to rural resilience is seen in e.g., job creation, placebased services, and local product development. But local business contributions extend beyond the direct outcomes: The more indirect influences are seen in the added value of diversifying the local business structure, of enhancing local resilience by leveraging economic, social, and environmental resources. Singh et al. (2023) claim that actors in the rural business ecosystem and related interaction spaces have become adrift due to urban-centric development paradigms, emphasizing the role of digital platforms in strengthening rural resilience by onboarding missing actors and augmenting proximity. Torre et al. (2023) identify five specific knowledge-based economy components that have contributed to rural innovation in France, including (1) the governance of agricultural lands, (2) the territorial attractiveness and well-being, (3) the agroecological transition in the territories, (4) the territorialized food systems, as well as (5) the bioeconomy and circular economy. Nonetheless, like in any other region, businesses in rural regions need to both cooperate and compete to build a resilient economy and to prosper. This same idea is put forwards by several other research contributions (see eg. Anthopoulou et al., 2017; Steiner and Atterton, 2014). However, the ability to develop a robust local economy characterized by a diversity and growth of businesses, including tourism and ample employment opportunities for all depends on the interdependency of rural communities and businesses, and the recognition of their mutual interdependence, and their complementary nature in a given context (CuéllarFernández et al., 2024; McIntyre and Roy, 2023; Steiner and Atterton, 2014). As Steiner and Atterton write: “progress in one of these areas brings progress in the other; consequently, if economic resilience declines, social resilience is also likely to decline” (Steiner and Atterton, 2014, p. 241). Generational renewal is just one | 19 challenging part of rural decline, which also includes wider social, environmental, economic, and cultural issues (Murtagh et al., 2023). A paramount example of these links between sociodemographic and economic trends is provided by the relationship between demographic resilience and labor markets in Europe’s rural areas. Many rural areas in the EU are characterized by aging populations and outmigration of younger generations, particularly of young women (Deimantas et al., 2024; Lasanta et al., 2017; Mascherini et al., 2023). In most countries across the EU young educated women are more likely than their male counterparts to leave rural areas in search of better economic and social opportunities elsewhere (Ghio et al., 2023; Leibert and Wiest, 2016a; Perpiña Castillo et al., 2024). Inevitably, the outmigration of young women undermines the capacity of rural areas to ‘futureproof’ and weakens their level of resilience by skewing the local population towards older, less economically active residents (EC, 2021a) and by exacerbating labor shortages in key sectors (Seuneke and Bock, 2015). The loss of working-age residents can fundamentally undermine the social, economic, and institutional foundations that have historically sustained these regions (Curtale et al., 2025), leading to the collapse of local businesses, services, and community institutions (see e.g. Bański et al., 2020; Esparcia, 2024 for examples of these dynamics in different spatial contexts). When demographic shifts related to population decline, aging, and outmigration in rural areas reach a certain threshold (e.g. a ‘demographic tipping point’), they can trigger a self-reinforcing cycle of decline. Other than contributing to keep birth rates within population replacement levels, young women often serve as the ‘social glue’ holding rural communities together, maintaining kinship networks, organizing community life, and providing vital social services (Bock, 2004). Rural women have demonstrated remarkable resourcefulness, diversifying household livelihoods and leveraging their social capital to sustain local institutions and support systems (Raue et al., 2024; Seuneke and Bock, 2015). So, as young rural women depart, the burden of care work and community leadership increasingly falls on those who remain, further straining local resources and resilience (EC, 2021a). When rural women have access to meaningful employment and professional development opportunities, it not only enhances their individual and household wellbeing, but also strengthens the overall vitality of the local economy and community (Raue et al., 2024; Seuneke and Bock, 2015; Shortall, 2015). Consequently, labor market participation of rural women is key to maintain the social and economic resilience of rural communities (Leibert, 2016; Shortall and Marangudakis, 2022; Unay-Gailhard, 2016; Wiest and Leibert, 2013). Labor market integration of rural women requires that rural women have access to good quality jobs in rural areas, alongside opportunities for entrepreneurship, self-employment, and community leadership, which also empower them and provide a sense of purpose and agency (Bartekova and Janikovicova, 2025; Bock, 2004; Seuneke and Bock, 2015). Additionally, the ability to balance work and family responsibilities, as well as the availability of community-based support systems, can make rural living a more attractive option than urban centers (O’Sullivan et al., 2022; Shortall, 2015). In general, the evolution of labor markets in rural areas can be considered a reflection of rural community’s ability to adjust to shifts in the social and economic surroundings. On this point, the literature highlights the role of internal factors at firm level and external factors –regional characteristics, milieu –, which are also relevant for the study of resilience (García-Cortijo et al., 2019; Rodríguez-Gulías et al., 2021). Generally, it seems that local areas need to have a good amount of entrepreneurial activity to be economically resilient. Entrepreneurship is also a way to diversify an otherwise undynamic labor market (Huggins and Thompson, 2015). However, economic shocks can affect this activity, and some places can keep more entrepreneurial activity than others. Small and Medium Enterprises (SMEs) and entrepreneurial activity are crucial for local economic resilience, but they can also be affected by broader economic conditions, because they determine the capacity of a business ecosystem to adapt or adjust (Huggins and Thompson, 2015). Given that local economies are becoming increasingly connected to global markets, they are more likely to be exposed to external shocks, and there is no guarantee of continued economic success. In their investigation about the financial crisis in Greece, Anthopoulou et al (2017) found that process seemed to have generated some level of counter-urbanization, and write that this process triggered new ideas of “idyllic rurality”. Rural business owners, influenced by their surroundings, demonstrate entrepreneurial behavior, and play a vital role in addressing unique challenges, ultimately contributing to overall community development and resilience. If a local economy can keep or rebuild entrepreneurial activity after a shock, it could be considered entrepreneurial resilience, but it is important to see it as a dynamic process—more entrepreneurial innovation in new businesses can improve the adaptability of a place (Huggins and Thompson, 2015). | 20 Business diversification in rural areas is also key for both business as well as community resilience. Looking at the role of industrial structures in creating resilience, Ženka, et al. (2019) find that the regional and local context mattered more for resilience than the old industrial structures in Czech regions. Old industrial regions were more resistant and resilient than initially expected in their case studies in Czech Republic, and that rural regions reacted in very different ways depending on whether or not the region had previous experience with economic shocks, as well as the internal composition of the industrial and economic landscape. This implies, that there is a gap between empirics and theory in economic development, as this is primarily built on ideas of structural diversity, actors, and institutional contexts, while many regions in at least the Czech Republic are dominated by foreign-owned manufacturing plants, rather than these dense networks and local SMEs. This diversity of regions demonstrated that it is important to be mindful of the spatial aspects of resilience, to avoid creating ‘one-size-fits-all’ policy designs for achieving resilience. This points to the need to understand contextual issues and understand the extra-regional factors that may also influence regional development (Ženka et al., 2019). As Martin et al. (2016) write, the industrial structures in a region tell us less about a regions’ resilience than the regional context and composition does. This is echoed by et al (2022) in their case study from Spain, where rural areas that offer jobs are able to trigger processes that ultimately are positive for rural resilience, by strengthening the area to become more competitive, socially dynamic and economically viable. In a different paper looking at the same area in Spain, Castilla-La Mancha, De la Cruz and Olmo (2022) show how heritage resources can be positively employed to promote competitiveness, as well as the development and implementation of territorial development strategies. The current limitation of the economy is struggling to revamp and combat outmigration, which leads to landscape transformations and the disappearance of unique heritage. The attempts to use heritage as a re-energizing frame for creating rural resilience is reminiscent of the discussion above on the inherent role of rural resilience versus that of national resilience—whether they are mutually reinforcing or excluding. Along these lines, recent literature places the emphasis in the role of creativity and artistic expression as important means for rural revitalization and resilience. Qu and Zollet (2023) promote a neo-endogenous perspective to examine how socially engaged art represents an effective tool for revitalizing communities and strengthening the resilience, from the perspective of three remote Japanese islands. Their research shows how increased place recognition resulting from an exogenous art initiative triggered endogenous community responses in terms of increased entrepreneurship and social innovation, facilitating the emergence of neoendogenous revitalization processes (Qu and Zollet, 2023). In conclusion, the resilience of rural economies to external shocks and disturbances depends on a multitude of factors (Martin et al., 2016), and to understand resilience therefore requires investigations from a variety of angles - more than merely the place or region’s industrial structure, including the institutional context (practices, conventions and policies) . As previously stated, regional and local capacities and capabilities are uneven across the mosaic of European rural areas, and the role of community and the dominant community culture impact on a broader societal level, including a community’s ability to cope and its impact on entrepreneurship (Huggins and Thompson, 2015). From this, it seems that resilience cannot be only connected to the discourse of competitiveness and economic viability, whether on a rural, local or regional scale. Bringing in aspects that go beyond this narrow focus may be beneficial. Moreover, understanding that rural resilience in essence cannot be separated from place and context, we would do well to recognize the role hegemonic discourses play in shaping the dominating narrative of being resilient or vulnerable in an area. Therefore, looking into the cultural and institutional specificities, or even social-ecological aspects, of rural areas, is an essential requisite to acknowledge their ability and capacity to change, adjust, absorb and adapt (Bristow, 2010). 3.2.2. Community resilience and the role of farming in rural Europe Moving beyond the business, entrepreneurial and economic perspectives, ‘community resilience’ is also prominently featured in rural resilience studies. Li et al. (2019) describe resilient communities as those that “possesses the capacity to prevent unwelcome challenges in the face of external circumstance, and to adapt to the changing external environment in such a way that a satisfactory standard of living is maintained” (Li et al., 2019, p. 139). As with any conceptualization, also community resilience is situated in political and cultural context (Wilson, 2012) and its definition will thereby differ depending on the situation. Because of its | 21 conceptual ambiguity, there is a lack of consensus of how to measure it, and it is therefore applied to the particular situation investigated (Paniagua, 2013). As any system that involve individuals, the notion of community resilience is not an entirely ‘neutral concept´ although often portrayed as being value-free (Mulligan et al., 2016; Roberts et al., 2017). Sociologists have debated the word “community” for decades, and it is interesting to observe how the word is always used in a favorable, soft way. It paints the idea of a place of belonging, which is part of a deep-set desire in human nature (Mulligan et al., 2016). By using the word ‘community’ we are nevertheless entering a space of various contextual and social aspects. The word presents a myriad of different developmental trajectories and governance scales. Mulligan et al. (2016) present community resilience along a continuum (see Figure 1). The figure presents the complex community making process, and nods to the tensions and interplay between exclusion and inclusion, and the role of individuality and solidarity. Community resilience, they write, brings forth the tensions that may surge in planning efforts when the community is undergoing change (or continuity). Mulligan et al. (2016) also comment on the use of the word ‘community’ in relation to resilience, stating that the way community is used in this setting is creating a “stronger public appeal” to build resilience at all levels. Roberts et al. (2017) write that “by bracketing ‘resilience’ with ‘community’ naturalizes resilience as a common project, because ‘community’, as a construct, can privilege one group or set of values over another, and diverts attention from the other scales of action impacting resilience of communities. ‘Community resilience’ seeks to mobilize a collectively; yet, in the process, generates a ‘discourse of equivalence’ that suppresses social inequalities and hierarchies (…) within and between places” (Roberts et al., 2017, p. 375). These Authors drew up a diagram of what they considered critical motifs in resilience literature, as they connect to community resilience (Figure 2). The motifs are concerned with the multiscalar aspects of resilience, the normative understandings of resilience and finally, the integrated policy conceptions of resilience. It connects to how communities can organize their efforts to respond to challenges, as most of the processes that impacts on a Figure 1: The interplay between resistant and adaptive communities: A conceptual framework (Mulligan et al., 2016) Figure 2: Critical motifs in resilience literature (Roberts et al 2017) | 22 community’s ability to adapt, often happen at other (policy) scales—whether exogenous shocks and slow burns or through policy changes impacting on a community’s adaptability. Roberts et al. (2017) indicate that community resilience means little when seen placed in the digital context. There is little room for the community to play a part in building resilience but adopts it as a ‘package deal’. This implies to some extent that the idea of community resilience is a quality that is not inherently within the community itself but is something that must be obtained. Research indicates that the development of community resilience requires the presence of adaptive capacity across three dimensions: social, economic, and environmental/ecological. Indeed, strong leadership, as well as changes across social norms, are required to enable resilience through ‘adaptation’ or ‘adaptability’ (Adger et al., 2005; Li, 2022). Moreover, the integration of various levels of resilience suggests that resilient communities are comprised of resilient groups, with resilient individuals collaborating collectively. ‘Community capitals’ are also important here, and they are often described in terms of ‘community resources’ in literature (Roberts et al., 2017), where community members are developing, renewing, and sustaining the community. Community resilience can therefore be seen as an ongoing process that empowers a community to flourish even in the face of persistent socio-economic changes (Steiner and Atterton, 2014). This is particularly clear in community disaster resilience literature (Cox and Hamlen, 2015), but is also being used as a framework for evaluating the effect of local initiatives (Roberts et al., 2017). In an exploration of the role of digital tools for disaster response in rural areas, Levesque et al. (2024) establish a robust link between municipal digital services and rural resilience, defining a range of technological and cultural barriers that the digitalization of rural municipalities. In a similar vein, some scholars and practitioners have tried to reframe resilience as a bottom-up process, placing the emphasis on producers and consumers and their interaction locally rather than engaging with large, non-local corporations (Bristow, 2010). One example is the Transition Town-movement started in 2005, which seeks to be a “movement of communities coming together to reimagine and rebuild our world through a process of creating healthy human culture”, and builds on ideas of resilience (Transition Network, 2016). One practical implementation of this line of thinking are local energy transitions. When considering energy literacy—awareness, attitudes and behavior—as a road towards rural resilience, Chodkowska-Miszczuk et al (2021) point out that the present energy transition is not only an about the changes that follow technological development, but also about how the energy shift reflects general environmental changes and socio-cultural transformations in local communities. Designing successful energy transition strategies requires awareness, positive attitudes and behaviors in local communities. This in turn impacts on the relative resilience of these rural areas, as the role of a ‘whole-of-community’ approach is important for facilitating change (ChodkowskaMiszczuk and et, 2021). For Ashkenazy, et al. (2018), resilience in the context of farming can be defined as the “capacity to ensure the continuity of a particular value, public and private good, or practice in one form or another, such as for example, the continuity of an agricultural practice, a family farm, or even the character of a region” (Ashkenazy et al., 2018, p. 211). Therefore, the resilience of a farm in rural areas is not the same as rural resilience, and it does not necessarily equate to social resilience, either (Ashkenazy, et al., 2018). This is also observable in Junquera et al.’s (2022) study on structural changes in agriculture and farmer’s social contracts in the Swiss mountain regions. The intensifying of farming, driven by macroeconomic conditions, restructuring, and the subsequent ‘rationalization’ of agricultural supply chains, is seen to affect social relations in these areas. This implies dwindling contact between family and friends and others, but more contact with commercial partners. The increasing workload reduces free time, and it affects farmers’ connections with other people in the area. Assuming that rural resilience, as we have seen, depends on community relations and their ability to adapt together, this process impacts on rural resilience, as social networks erode. Kasimis and Papadopolous (2013) also write about the role of farms in changing the face of rural Greece. They point to a ‘new rurality’ in which agricultural activity is contracting and reorganizing, construction is expanding, and tourism is blossoming, as well as the influx of migrant laborers. This ‘new rurality’ has been particularly seen to be conditioned by economic crises, which induced a ‘back to the land’-movement (Ashkenazy et al., 2018). An influx of new residents, whether retirees, amenity-seekers, or remote workers, can indeed inject new vitality into rural areas (Hedberg and Haandrikman, 2014; Oliva, 2010; Stockdale, 2006). Interestingly, the ‘reverse mobility’ following the ‘back to the land’ combines both modern and traditional elements: new work methods | 23 and organization, as well as a rediscovery of cultures, productions and traditional crops (Ashkenazy et al., 2018). These examples from the Swiss mountain region and the Greek case studies present both similarities and two contradicting ideas: On the one hand, they converge on the multi-scalar impact that macroeconomics, or national economic direction, impact on the individuals in the regions and how these do not necessarily support localized rural resilience. On the other hand, they diverge on the emphasis placed on the value of farms in the rural area itself: in the Swiss mountain regions, farms are seen as an integral part of building rural resilience from a social network point of view, whereas in Greece the farms are assigned less inherent value except for their symbolic presence representing continuity and preservation. Geographical scale might be an important aspect to consider here. As McManus et al (2012) note, the sense of belonging plays an important role from a more localized perspective, as it links to behaviors and the perceptions of actions and impacts— also in terms of building community resilience. Moreover, the engagement between farmers and their local communities are pertinent for ensuring a stable or growing population or indeed a provision of services. In this way, farmers and communities are mutually reinforcing social groups that are important for the environment and the local economy. Resilience in such local communities depends on the interconnectedness between the community, environment, and the local economy—all three sustainability dimensions. Resilience in these areas, therefore becomes something that is reminiscent of equilibrium resilience: Permanence or bouncing back to an ‘imaginary’ of normal with endogenous changes and adaptions, rather than being concerned with development and ‘process’, while resistance and change coexist (Ashkenazy et al., 2018; Paniagua, 2013). Nonetheless, the concepts of 'sustainability’ and ‘resilience’ are not meant to be used interchangeably. As Li et al. (2024) put it, the nuances intrinsic to rural development process “foreground latent sub-rational development models, such as resilient but unsustainable and sustainable but unresilient” (Li et al., 2024). This is interesting when considering resilience issues, particularly those that adopt an evolutionary approach to understanding regional or rural change, as the idea is founded in that these (rural) spaces are constructed by social relations and human action. These rural spaces are in constant transition, whether experiencing divestments, investments, depopulation or population growth (Adisaputri et al., 2023). As Christopherson et al (2010) write, “Regions are manifestations of [human] actions and are in a constant process of transition” (Christopherson et al., 2010, p. 4). 3.2.3. Social-ecological resilience Environmental problems, as we perceive them, are brought on by human activity and influences biophysical processes. As societies are interconnected on a global scale through our economic system—which by and large depend on ecosystems services, grand environmental problems are shared and amplified across regions (Adisaputri et al., 2023; Folke et al., 2010). Understanding social-ecological systems is hence crucial for shaping territorial governance (Adger et al., 2005). Knowledge and information of both the self-organized aspects of social-ecological systems, the design and interaction between them are key to understand socialecological risks and to reduce uncertainty (Anderies et al., 2004). In social-ecological systems research, humans are considered part of nature, and is a major force in global change and ecosystems dynamics, both on a local level and the biosphere at large (Folke et al., 2010). For some scholars, ecological resilience is seemingly an ‘anti-statist’ concept, as it moves away from standard rules for understanding resilience and allows the concept to be more directly influenced by cultural norms and particular habits. However, this can lead to negative results for the adaptability of e.g., a community, as dominant discourses, even though they are grounded in cultural norms, may lead to a myopic approach, or indeed cognitive ‘echo-chambers’ and therefore an inability to adapt and adjust (Hassink, 2010). According to Berkes and Ross (2013), social-ecological systems and resilience both grapple with a range of processes such as ‘adaptability’, ‘relationships’ and ‘learning’, ‘non-linearity’, ‘unpredictability’, ‘scales’, ‘renewal’, ‘system memory’, ‘disturbances’, and ‘feedback’. Such systems are interdependent and coevolutionary (Ashkenazy et al., 2018). Moreover, they exist at many levels and this ‘panarchy’ is key to unpacking the systems’ dynamics as a whole, characterized by processes that cause ripple effects, and | 24 influence the system overall (Gunderson et al., 2002). Social-ecological systems, like rural systems, can exhibit nonlinear dynamics, where gradual changes in conditions can suddenly trigger abrupt and dramatic shifts in the system's structure and function (Folke et al., 2010; Rockström et al., 2009). For this reason, a central concept in the study of social-ecological systems is the notion of ‘tipping points’. Such tipping points represent critical thresholds beyond which the system loses its capacity to recover and reorganize, leading to the collapse of established patterns and the emergence of a new, potentially less desirable, state. Importantly, tipping points can be linked to long-term socio-economic development trajectories—slow burns— and may also be triggered by external shocks, including natural hazards (Di Giovanni and Chelleri, 2019). 3.2.4. Indicators of rural socio-economic resilience in rural areas Measuring rural resilience presents a significant challenge, with various approaches being proposed. Some authors advocate using of multiple indicators (Briguglio et al., 2006), while others prioritize employment or GDP (Sensier et al., 2016). Alternative measures like unemployment rates or household incomes have also been used. Each indicator has strengths and limitations, with employment often viewed as a key indicator due to its social importance and stability in measurement practices (Coyle, 2015). Living conditions are closely tied to rural resilience and also serve as key indicator. A recent Eurofound report explores urban-rural differences in living conditions across the EU, considering material conditions, employment, human capital, and digital skills (Eurofound, 2023). While urban areas generally tend to have higher employment rates, greater tertiary educational attainment, and lower rates of youth not in employment, education, or training (NEET), as well as higher income levels and lower at-risk-of-poverty-or-social-exclusion (AROPE) rates, this pattern does not hold across all member states. In nine of the 27 EU countries—including the Netherlands, Sweden, Germany, Denmark, Malta, Finland, Austria, France, Belgium, and Italy—employment rates are actually higher in rural areas. Moreover, in many EU member states, the gap between urban and suburban employment rates is relatively small (Eurofound, 2023). A recent multi-factor analysis to measure levels of rural resilience in Northern Spain, Hierro and Maza (2024) find that the main factors limiting resilience are related to cultural interest, rural potential, natural endowment, and rural connectivity. | 31 Haandrikman et al. (2021) examined geographical variation in gender contracts in Sweden, using six family, politics, and labor division indicators. Traditional gender roles often feature a divided labor market, with men typically being the primary earners. They found that rural areas are characterized by what they call "traditional gender contracts", with the lowest scores across all gender-related indicators. Fathers are less likely to take parental leave, and women are less likely to be highly educated, employed or earn higher incomes in these areas (Haandrikman et al., 2021). Similarly, a study in Norway showed that urban environments have distinct effects on the working hours of men and women. In compact cities, where workplaces and services are closer together, women are better positioned to work longer hours than those in suburban or peripheral areas. This suggests that urbanization facilitates greater work opportunities for women, particularly in terms of work-life balance and job flexibility (Hjorthol and Vågane, 2014). In a study focusing on the role of local institutions in Italy, Agovino et al. (2019) find that the quality of institutions significantly affects labor market participation for both genders, although it does not impact the gender participation gap. The authors highlight that access to public childcare services plays a crucial role in supporting women's labor market involvement. The studies discussed here indicate that traditional gender norms in rural areas are often associated with lower female labor market participation compared to men. These conservative perceptions are also likely to perpetuate gender disparities reflecting higher male employment rates in rural settings. This could be one reason for the larger employment gaps between men and women in rural areas compared to urban ones. However, these disparities are expected to vary across European countries, influenced by differing social and cultural norms, welfare systems and family-work policies. For instance, female employment rates in rural areas are predicted to be higher in Nordic countries, where universal family policies and care mechanisms might mitigate the effects of urbanization, compared to Southern and Central European countries, where these policies are more limited. In contrast, the interaction between urbanization and gender in labour markets in Eastern European countries remains less clearly defined, requiring further exploration. 5.3. Comparison strategy and expectations The LTVRA defines four Flagship Actions for Resilient Rural Areas. One of those is named “Promoting Social Resilience and Women in Rural Areas”, which specifically addresses gender aspects as a key to enable social resilience in EU rural areas. This flagship action aims to enhance rural women's entrepreneurship, leadership roles, and work-life balance support through the European Care Strategy, alongside targeted networking and research activities. The Strategy emphasizes two main gender-focused areas: supporting care services and promoting women's involvement in decision-making. The European Social Fund Plus (ESF+) also finances efforts to increase women's employment opportunities across Member States, including rural regions. These efforts include actions fostering a gender-balanced workforce, ensuring equal working conditions, and improving work-life balance with access to affordable care for children and other dependents (European Commission, 2021). While addressing rural resilience and gender equality, it is essential to consider the varying definitions and classifications of rural and urban areas, as these distinctions play a crucial role in understanding the socioeconomic dynamics and challenges faced by rural communities across Europe. The boundary between rural and urban areas is not always clear since the urban-rural spectrum can include metropolitan areas, capital regions, cities, medium-sized towns, small towns, peri-urban zones, rural regions and remote areas. This classification varies between countries and evolves over time as the analytical and policy priorities and perspectives change. Such definitions may be based on minimum population thresholds, population density, administrative boundaries, workforce employment in agricultural or non-agricultural sectors, travel times to set sizes of settlements, or the availability of specific infrastructure like health and education facilities (UN, 2019). Despite the limitations intrinsic to territorial typologies, in this work we stick to Eurostat's Degree of Urbanization (DEGURBA) classification. This is a very established territorial typology that was introduced in 1991. In its simplest form, it classifies local administrative units level 2 (LAU2) into ‘thinly populated areas (rural areas)’; ‘intermediate density areas (towns and suburbs/small urban areas)’, and ‘densely populated areas (cities/large urban areas)’, according to the share of local population living in urban clusters and in | 32 urban centers (Dijkstra et al., 2019). 4 However, DEGURBA has limitations in capturing the heterogeneity of rural areas, particularly regarding economic structures, labor market characteristics, and accessibility to services, which might impact employment dynamics (OECD, 2020). For example, rural areas that are geographically remote may face distinct challenges compared to those integrated into urban economies, yet these differences are not always well reflected in DEGURBA classifications (Dijkstra and Poelman, 2014). While acknowledging these limitations, we are still constrained by the information contained within the EULFS microdata files, which utilize the DGURBA classification system. 5.4. Data, variables and methods 5.4.1. Data and sample The data used in this study, including dependent variables and predictors, are sourced from the European Labour Force Survey (EU-LFS). Specifically, EU-LFS data from 2013, 2018, and 2023 are used to compare labor market outcomes between men and women in rural and urban areas across countries, while controlling for individual characteristics. The EU-LFS is a standardized, large-scale, cross-sectional household survey that provides quarterly insights into labor market participation for individuals aged 15 and older, including those outside the labor force. It captures a wide range of information for all members of the household, including employment status, occupation, education, and other socio-demographic and socio-economic attributes. Currently, the EU-LFS microdata for scientific purposes (Eurostat, 2024, pp. 1983–2023) includes data for all EU countries, along with Iceland, Norway, Switzerland, and the United Kingdom (up to the third quarter of 2020). The decision to focus on the years 2013, 2018, and 2023 was informed by several considerations. First, 2023 represents the most recent year for which data is available. Second, 2013 was selected as a reference point to have a ten-year time frame for analysis. Lastly, 2018 was chosen as mid-point between 2013 and 2023, ensuring a balanced temporal framework for analysis. The sample includes between almost 1.5 and over 2.5 million individuals annually, over 220 thousand participants are women living in rural areas in 2023 (which is the smallest sample of the selected three annual samples). The sample by country and year ranged from about 3900 in Latvia to over 230000 in Italy. The smallest sample of women in rural area is that of Iceland in 2023 amounting to almost 700 women. Our sample was restricted to individuals in their prime working ages (25-64) with complete data for all relevant variables in the models. Similarly, we decided to exclude Malta from the study due to the very small number of cases of sampled women in rural areas (fewer than 100 in 2023). We also excluded Romania due to unstable estimates, which left 27 countries for analysis. Appendix Tables 1.1 and 1.3 in Annex 2 present descriptive statistics of the variables included in the analysis and sample sizes by country for the full and employed samples, respectively. 5.4.2. Dependent variables Labor market outcomes were assessed using three variables. The first variable, employment, is a dummy variable that indicates whether individuals are employed or unemployed/inactive, according to the International Labour Organisation (ILO) definition (Eurostat, 2023c). This variable encompasses the entire analytical sample of individuals in their prime working age (hereafter referred to as full sample). The second variable is an indicator for part-time employment which is derived from self-reported data regarding the level of employment during a typical week (FTPT). This variable is calculated on the sample of employed individuals (hereafter referred to as employed individuals’ sample). The third dependent variable is an indicator for care responsibilities. The literature highlights the increased burden of care responsibilities on women in shaping their work supply (De Meester et al., 2007). To further explore this issue and assess how it is associated with the extent of employment and part time work. The information on care responsibilities is derived from 4 Since 2012, there has been an improved methodology for measuring metropolitan areas., It defines 'cities' (densely populated), 'towns and suburbs' (intermediate density), and 'rural areas' (thinly populated) using geographical contiguity and population density, measured with minimum population thresholds applied to 1 km² grid cells (for further details, see: Dijkstra et al., 2021, 2019; Duleep et al., 2020; Eurostat, 2023b). | 33 responses to the following question in the EU-LFS: “Is the person not searching for a job or working part-time due to specific care responsibilities?” The response options were (1) Suitable care services for children are not available or affordable; (2) Suitable care services for ill, disabled, elderly are not available or affordable; (3) Suitable care services for both children and ill, disabled and elderly are not available, or affordable; (4) Care facilities do not influence decision for working part time or not searching for a job. This variable was transformed into a binary indicator, assigning a value of one if the response indicated the presence of any care responsibility (answers 1 to 3) and zero otherwise. This variable is calculated for the full sample of individuals in their prime working ages. 5.4.3. Independent variable Our main independent variables are sex and the Degree of Urbanization (DEGURBA). Sex is self-assessed and assumes the values of (1) female and (2) male. 5 We recode this to (0) male and (1) female. As described above, DEGURBA distinguishes individuals based on three levels of urbanization of the place in which they live. The categories of DEGURBA are (1) Cities (Densely populated areas), (2) Towns and suburbs (Intermediate density areas), and (3) Rural areas (Thinly populated areas). This classification refers to the degree of urbanization of the municipality where the respondent resides and is included in the dataset. In the analysis we focus on the comparison between cities and rural areas. 6 The second independent variable is that of gender. 5.4.4. Controls In all models, controls were incorporated for demographic characteristics, including age, parental status, having a partner in the household, 7 and the highest level of education attainment, categorized into three groups: (1) lower than secondary education, (2) upper secondary education, and (3) third-level education, which encompasses both academic and non-academic tertiary education. 8 Additionally, given that employment levels among migrants are often significantly lower than those of native-born individuals (OECD and European Commission, 2023), migrant status –defined by place of birth– was included as a control variable. Individuals residing outside their country of birth were classified as migrants. Appendix Tables 1.1 and 1.3 in Annex 2 present descriptive statistics of the variables used in the analysis for the full and the employed individuals samples, respectively, by country and year. Appendix Tables 1.2 and 1.4 in Annex 2 show those descriptive statistics further broken down by degree of urbanization (DEGURBA) and gender. 5.4.5. Methods Since all dependent variables in this study are dichotomous, logistic regression models were employed. Each model is controlled for the aforementioned variables. Following the EU-LFS user guidelines, all analyses adjust for the complex sampling design and unequal selection probabilities and non-response using the annual weighting factor (COEFFY) 9 which is provided in the EU-LFS scientific data files. The primary focus of the analysis was the interaction between gender and the level of urbanization. 5 In this report we refer to self-declared sex as the gender, recognizing that this definition is more inclusive. In addition, it should also be noted that there is some variation between countries regarding the definition of this question in the EU-LFS questionnaire. 6 Further details about the DEGURBA classification are available from (Dijkstra et al., 2019). 7 Note that the variable parental status and having a partner in the household are based on constructing the family relation within the household. Information on parental status was not available for Norway, Iceland, and Switzerland, implying that the models for these countries do not include this control variable. 8 This classification corresponds to the ISCED 2011 aggregated levels of educational classification (Eurostat, n.d.). 9 It is important to note that the design did not incorporate strata (STRATUM) or survey cluster information, as these variables are not available in the scientific data files. | 34 The logistic regression models used in this study can be specified as follows: 𝑙𝑜𝑔(𝑃(𝑌=1) 1−𝑃(𝑌=1)) =𝛽0+𝛽1(𝐺𝑒𝑛𝑑𝑒𝑟)+𝛽2(𝐷𝐸𝐺𝑈𝑅𝐵𝐴)+𝛽3(𝐺𝑒𝑛𝑑𝑒𝑟×𝐷𝐸𝐺𝑈𝑅𝐵𝐴)+𝛽4𝑋1+⋯+𝛽𝑛𝑋𝑛 Where: 𝑃(𝑌=1) represents the probability of the event occurring (i.e., employment, working part-time and care responsibilities); 𝑃(𝑌=1) 1−𝑃(𝑌=1) is the odds of the event occurring. For example, taking the case of employment,𝑙𝑜𝑔( 𝑃(𝑌=1) 1−𝑃(𝑌=1)) represents the log-odds of an individual to be employed, with a set of independent variables; 𝛽0 is the intercept. 𝛽1(𝐺𝑒𝑛𝑑𝑒𝑟) represents the effect of being women on the log-odds of the employment; being men is the omitted category; 𝛽2(𝐷𝐸𝐺𝑈𝑅𝐵𝐴) represents the effect of the degree of urbanization, distinguishing between urban, intermediate, and rural areas. The omitted category is living in urban areas; 𝛽3(𝐺𝑒𝑛𝑑𝑒𝑟×𝐷𝐸𝐺𝑈𝑅𝐵𝐴) captures the interaction effect between "Gender" and "DEGURBA”. This interaction term is essential because it enables the examination of whether the impact of urbanization on employment outcomes differs between men and women. While DEGURBA accounts for the effect of territorial conditions on employment, the interaction term is necessary to explore whether these effects are conditional on gender, as the influence of urbanization may not be uniform across men and women. 𝛽4𝑋1+⋯+𝛽𝑛𝑋𝑛 represents the list of control variables mentioned earlier. 10 After fitting the models on data for each country and year, we estimated the predicted values for the six categories of the interaction between gender and urbanization level. 11 The urbanization variable has three levels, with the analysis emphasizing the gaps between rural and urban areas (i.e. ‘Cities, (densely populated area)’ and ‘Rural area (thinly populated area)’. To facilitate interpretation, a series of graphical representations were produced to illustrate the intersectionality of gender and urbanization. Given our interest in both the gender gap and the disparity arising from place of residence, results are presented as the difference in predicted outcomes between men and women in rural areas (gender gap) on the Y-axis, and the difference between women in urban and rural areas (rurality gap) on the X-axis. This approach enables visualization of the interaction effects between gender and urbanization. For all models we first present the results of all years together and then distinguish them by year and present the results by groups of welfare states (i.e. Continental countries, Southern European, Nordic, and Eastern Europe). 12 This classification of welfare states and geography was informed by the extensive literature exploring the relationship between welfare regimes and women’s labor force outcomes (Chauvel and Bar-Haim, 2016; Esping-Andersen, 1990; Mandel and Semyonov, 2006; Schröder, 2013). 10 Due to multicollinearity, models examining care responsibilities by rurality and gender, including interaction terms, could not be estimated for Cyprus (CY), Greece (EL), Lithuania (LT), Slovakia (SK) and Iceland (IS) in 2013 and 2018. These countries were omitted from the analysis of care responsibility. 11 The model summary, including the coefficients from these regressions, are presented in appendix in Tables 2.1, 2.2, and 2.3, corresponding to the employment, part-time employment, and care responsibilities models, respectively. 12 Due to the United Kingdom's exit from the EU, only one liberal country remains in the analysis—Ireland. Given Ireland's rural demographic and settlement patterns, which share notable similarities with those of Southern European countries (Copus et al., 2006), it was grouped into this category. | 35 5.5. Results For each dependent variable used in the analysis, the results are presented in three visuals. The first figure displays the results for all models, aggregated across all years and countries. This figure enables an examination of temporal trends and cross-country comparisons of the gender and rurality gaps. The Y-axis represents the gender gap, defined as the difference in predicted outcomes between men and women in rural areas, and the X-axis represents the rurality gap, defined as the difference in the predicted outcome between women in urban and rural areas. A value of zero on both axes indicates no gender or rurality gaps. Positive values on the Y-axis signify higher outcomes for men than for women in rural areas in gender comparisons, and positive values on the X-axis signify higher outcomes for rural women than for urban women in rurality comparisons. To facilitate cross-country comparison, two additional reference lines are included in the figure: First, stretching from left to right the mean gender gap in the predicted probability across all countries and years in the sample; second, stretching from bottom to top, the mean rurality gap for all countries and years in the sample. The results for employment gaps in all countries and years are presented in Figure 2. In that figure, each country is represented by three data points, one for each sampled year. Figures 3, 6 and 9 display the results broken down by welfare regime for 2013 and 2023. In those figures, each country is represented by two data points, one for each of these years at the beginning and end of the evaluated time period. These figures present changes over time in rurality and gender gaps within each group of countries, aiming to classify both the patterns of these changes and the factors contributing to them (e.g., the situation of women in rural areas, men in rural areas and women in urban areas). Figures 4, 7, and 10 present the results disaggregated by welfare regime, for 2023. 13 These figures include two data points for each country. The red dot represents the gender gap for women in rural areas, relative to men in rural areas (plotted on the Y-axis), and the rurality gap for rural women relative to women in urban areas (plotted on the X-axis), which was presented earlier. The blue dot indicates the counterfactual outcome, i.e. the gender gap for women in urban areas compared to men in urban areas (on the Y-axis), and the rurality gap for men, comparing rural and urban men (on the X-axis). The distance between the two dots represents the difference-in-difference in both dimensions. This reflects the intersectional effects of gender and rurality that are central to this analysis. A larger distance indicates a greater gap (generally a disadvantage) for women in rural areas compared to the other three groups: rural men, urban women, and, by extension, urban men. For guidance, a sample plot including most of the visual elements described above is provided in Figure 1. 13 The results for the years 2013 and 2018 are available in the Figures included in Annex 2. Figure 1: Exemplary plot of gender and rurality gaps by welfare | 36 5.5.1. Employment Figure 2 presents the predicted probability gaps in employment for all countries and years. 14 The average gender employment gap (marked by a horizontal dashed line) in rural areas across countries is approximately 11.0%, indicating that women in rural areas are generally less likely to be employed compared to men in the same areas. 15 However, there are substantial cross-country variations in the size of these gender employment gaps. The average rurality gap (indicated by a vertical dashed line) across all countries and time points is close to zero, suggesting that, on the whole, women in rural areas of Europe are not significantly less likely to be employed than women in urban areas when controlling for other factors. However, there are notable differences between countries. Some countries, such as Italy, Spain, and Poland, exhibit large gender and rurality gaps. In contrast, countries such as Austria, Germany, and Belgium show relatively small gender gaps and a rurality advantage i.e., rural women are more likely to be employed than their urban counterparts. Although some countries fall into the quadrant of small gender gaps and rurality disadvantages (such as Lithuania and Latvia), the opposite scenario—i.e. small gender gaps with rurality advantages—is less common, with Greece being an exception. On average, the gender employment gap in rural areas across the examined countries decreased from 11.5% to 10.3%, representing a modest decline. 16 However, significant cross-country variation in the patterns of change underscores the need for a more nuanced and in-depth analysis. In some countries, a reduction in the gender employment gap in rural areas is evident, as observed in cases such as the Netherlands, Ireland, Austria, Luxembourg, Belgium, and Portugal (see also Appendix 4.1 in Annex 2). Figure 2: Predicted probabilities of employment gaps by gender, rurality and year, 2013, 2018, 2023 14 Appendix Figure 5.1 in Annex 2 present the same results by year. 15 While both rural and urban women have lower employment rates compared to men in the same areas, the mean gender gap in urban areas across countries and years is smaller at 8.8% (not shown in the figure). 16 Note that these percentages reflect the average annual values for gender gaps across countries, whereas the dashed horizontal line in Figure 2 represents the average values of gender gaps across all countries and years. | 37 Figure 3 presents the results of welfare regime comparisons between 2023 and 2013, showcasing trends over time. Figure 4 presents the gaps in the predicted probability of employment by welfare regimes for 2023, incorporating the counterfactual effect explained earlier. The same results for the years 2013 and 2018 are provided in appendices 5.1 and 5.2 in Annex 2, respectively. When comparing the results over time (as presented in Figure 2), it is clear from the three figures that the mean rurality gaps for women across all countries is approximately zero. While this suggests that, on average, there are no significant rurality employment disadvantages for women across Europe, this average may mask significant differences between countries and welfare groups. Regional disparities are likely to be more pronounced, reflecting the varying effects of territorial conditions on women's employment outcomes across different contexts. This variation of country averages between different welfare regimes and over time is evident by looking at the patterns observed in Figure 3. Figure 3: Predicted probabilities of employment gaps by gender, rurality and welfare, 2013 and 2023 In Continental countries, women residing in rural areas generally exhibit higher rates of employment compared to their urban counterparts, with Luxembourg being an exception. The gender employment gap in rural areas of Continental countries is smaller than to that of Southern and Eastern European countries. Over time, gender employment gaps in rural areas have declined in most Continental countries. Specifically, the mean cross-country gender employment gap in rural areas decreased from 10.9% in 2013 to 7.4% in 2023. By contrast, the gender employment gap in urban areas experienced only a slight decline, from 8.7% to 8.6% over the same period. The rural employment advantage for women has increased over time in Austria and Germany. Looking at the gaps between rural women and the counterfactual case (i.e., men’s rurality gap and the urban gender gap presented in Figure 4) reveals that, in 2023, both men and women in rural areas display higher employment levels relative to those in urban areas, as reflected by positive values on the x-axis. Women in rural areas in four of the seven Continental countries—Austria, Belgium, Germany, and France— demonstrated a greater rural employment advantage than men. This is evidenced by the counterfactual scenario being located to the left relative to the employment outcomes for rural women (Figure 4). Gender employment gaps are not consistently wider in rural areas than in urban areas. In Austria, Belgium, Germany, and France, gender employment gaps are larger in urban areas, primarily due to lower employment levels among urban women compared to other groups in these countries. | 38 Similar to Continental European countries, rural women in Nordic countries have exhibited slightly higher employment rates than their urban counterparts. However, this rural employment advantage has been less pronounced than in Continental countries and has diminished over time. Furthermore, the disparity between the outcomes for rural areas relative to the counterfactual is small, particularly in Norway. This can be attributed to higher employment rates for men in rural areas and narrower gender disparities in urban areas within these countries (Eurostat, 2025). For example, in Finland in 2023, the gender employment gap in urban areas was relatively small, at 1.6%, while in rural areas, it was 4.7%, reflecting higher employment rates among men in rural regions relative to urban areas (82.2% compared to 78.6%, see appendix Table 4.1 in Annex 2). Figure 4: Predicted probabilities of employment gaps by gender, rurality and welfare, 2023 The case of Southern European countries highlights significant trends in the employment dynamics of women in rural areas, marked by notable gender employment gaps, uneven changes in rural-urban disparities over time, and considerable deviations from the counterfactual. In all years examined, gender employment gaps for women in rural areas remain substantial. In 2023, gender employment gaps in rural areas range from 9.1% in Portugal to 23.2% in Italy and Greece, with an average gap of 16 % across all countries. Gender gaps in urban areas are lower, ranging from 5.2% in Portugal to 20% in Italy, with an average gap of about 12.2% (see Appendix Table 4.1 in Annex 2 for additional details). While the patterns observed in Figure 3 suggest that gender gaps in rural areas show a small decline over time in Southern European countries, these figures obscure a significant increase in female employment in rural areas between 2013 and 2023. For instance, in Spain, the predicted probability of employment for women in rural areas rose from 50.9% in 2013 to 64.1% in 2023, a 13-percentage point increase. However, over the same period, the employment probability for men in rural areas also increased from 66.9% to 79.2%, reflecting a 12-percentage point rise, resulting in no relative change in the gender gap on average over time. Examining the gaps in relation to the counterfactual (i.e. the rurality gaps for men and the gender gaps in urban areas), men in rural areas of Southern Europe are more likely to be employed than men in urban areas, while gender employment gaps are smaller in urban areas relative to rural areas (see Figure 4). This disparity reflects the combination of the highest employment levels being observed among rural men and the lowest levels among rural women, relative to all other demographic groups. Over time, however, a degree of | 39 convergence is evident, and is marked by a decline in rural-urban employment disparities (along the x-axis), particularly in Spain, Italy and Ireland, alongside a reduction in deviations from counterfactual (see Annex 2, appendix figures 5.1 and 5.2 for the results of 2013 and 2018, respectively). Additionally, the variation in rural employment gaps across Southern European countries has substantially narrowed over the years. In Eastern European countries, rural women are generally at a disadvantage compared to their urban counterparts, except of women in Estonia. More pronounced rural disparities are evident in four of the nine Eastern European countries in scope—Bulgaria (8.7%), Croatia (8.4%), Slovakia (7.3%) and Lithuania (7.2%). In these cases, rurality appears to affect women’s employment opportunities negatively. Additionally, in three Eastern European countries—Poland (16.5%), Bulgaria (12.3%), and Croatia (12.8%)—gender employment gaps are substantial and fall below the average across countries, as indicated by the blue horizontal reference line (Figure 4). However, in Lithuania, Latvia, and Estonia, rural gender employment gaps are smaller relative to the above-mentioned countries, with gaps under 10%. The changes observed over time in Eastern Europe reveal significant cross-country variation and underscore the importance of examining gaps relative to absolute numbers. In seven out of the nine Eastern European countries included in the study, the rurality gaps for women increased between 2013 and 2023. The most pronounced increases are observed in Bulgaria, Croatia, Hungary and Poland. This widening rurality gap is primarily attributed to the more rapid increase in employment rates among women in urban areas compared to rural areas. This does not imply a decline in women’s employment in rural areas over time; rather, the growth in employment among the comparison group (e.g., women in urban areas) was significantly greater, leading to an apparent widening of the gap. For instance, in the case of Bulgaria, the predicted probability of employment for women in rural areas increased from 59.0% in 2013 to 69.3%, reflecting an increase of 9.3percentage points. In comparison, the employment probability for women in urban areas rose from 63.6% to 76.9%, representing an increase of 13.2-percentage points. While rurality gaps have undergone substantial changes over time, gender gaps in Eastern European countries have shown less pronounced variation, with the mean gender gap remaining largely stable at approximately 1011%. For Eastern European countries, a comparison between the main effect and the counterfactual for each country suggests that living in a rural area negatively impacts women’s employment more than men's, although in three cases—Lithuania, Bulgaria, and Slovakia— it disadvantages both genders. In Lithuania and Bulgaria, men experience a rurality-related employment disadvantage of approximately 4–5%; with the equivalent disadvantage for women in these countries considerably greater at 7.2% and 8.6%, respectively. In most cases, rurality appears to have a more pronounced negative effect on women's employment outcomes compared to men. Compared to the counterfactual it is evident that in all Eastern European countries except of Estonia and Czechia, the gender gaps in employment are somewhat larger in the rural areas then in urban areas. Overall, the results demonstrate that welfare regime might be meaningful in understanding women’s employment levels in rural and urban areas, and compared to men. In Continental and Nordic countries, women in rural areas have an advantage over women in cities, even if the rural advantage for women in Nordic countries is somewhat smaller than in Continental countries. In contrast, women in rural areas of Southern Europe and Ireland face significant disadvantages compared to men, resulting in a larger gender gap. Additionally, Southern European countries show a substantial disparity between the situation of women in rural area and the counterfactual case (e.g., the gender gap in urban area and the rurality gap for men). While in Eastern European countries, there is significant variation in the situation of women in rural areas, in most cases, women in rural areas are substantially less likely to be employed than their urban counterparts. 5.5.2. Part time employment Analyzing full-time versus part-time employment is crucial for understanding gender disparities in the labor market. Full-time jobs provide financial stability, benefits, and career advancement. In contrast, part-time employment is often associated with lower wages, limited job security, and fewer opportunities for progression. Research indicates that women in part-time roles are often concentrated in low-paid sectors and occupations (Manning and Petrongolo, 2008; Matteazzi et al., 2018; van Osch and Schaveling, 2020). Additionally, the significant cross-country variations in part-time employment reflect differences in wage- | 40 setting institutions and welfare systems (Anxo et al., 2007; Matteazzi et al., 2018; van der Lippe et al., 2011). Anxo et al. ( 2007) classify national approaches to ‘time policies’ into four broad models, each aligning with distinct welfare regimes. Nordic countries follow a "universal breadwinner" model, ensuring high female employment with minimal marginal part-time work. Southern Europe follows an "exit or full-time" model, with low female employment and limited part-time opportunities. The "modified breadwinner" model (e.g., France) is characterized by some mothers temporarily withdrawing from the labor market. Finally, in the "maternal part-time work" model (Germany, the Netherlands, and the UK), mothers experience a smaller reduction in employment rates compared to those in Mediterranean countries or France, but part-time work remains the norm for mothers, even as their children grow older. Nonetheless, the quality of part-time work varies significantly across countries. In the UK and Southern Europe, part-time employment is often concentrated in low-wage sectors with poor job quality and limited career progression, negatively impacting hourly wages (Manning and Petrongolo, 2008; Matteazzi et al., 2018). In contrast, in the Netherlands and Germany parttime jobs have higher quality with better pay and status (Matteazzi et al., 2018). Figures 5 to 7 illustrate the predicted probabilities of part-time employment gaps. Figure 5 shows the differences in the predicted probabilities of part-time employment across various European countries, segmented by gender and rurality gaps for 2013, 2018 and 2023. 17 The gender gaps in part-time employment probabilities, depicted along the vertical axis, indicate that in almost all countries, women are more likely to work part-time compared to men, with some countries exhibiting minimal gaps and others showing more pronounced differences. The average gender gap in part-time employment in rural areas is approximately 20%, indicating that women in these regions are significantly more likely to engage in parttime work than men. This finding is not unexpected, as previous research has demonstrated that, in general, women tend to have higher levels of part-time employment than men (Matteazzi et al., 2018). The rurality gaps, shown along the horizontal axis, reflect the differing impacts of the degree of urbanization on part-time employment probabilities, with positive values indicating higher probabilities of part-time employment for women in rural areas compared to urban areas. The mean rurality gap across all countries and years is approximately 3.5%, suggesting that women in rural areas are more likely to work part-time than in urban areas. Figure 5: Predicted probabilities of part-time employment gaps by gender, rurality and year, 2013, 2018, 2023 17 Appendix figure 5.4 present the same results by year. | 47 6. Concluding remarks The concept of resilience has become increasingly important in rural studies, offering alternative analytical methods for exploring local development and identifying entrenched interests. By involving stakeholders and using adaptive network governance as a guide, rural development practice can address questions of agency and governance in a rapidly changing environment. However, there is still debate over how to define and appraise resilience, particularly when applying it to policy. Here we propose a working definition of rural resilience as a contextual and spatially bound evolutionary process of future proofing that accounts for the interlinkages between the economic, environmental, and societal sustainability of the place, the community and the individual in a context of uncertainty and unpredictability, which provides a good framework for the exploration of social resilience with a gender angle. Addressing the drivers of female outmigration, such as limited economic opportunities, gender-based discrimination, and recognizing and empowering rural women is in fact a requisite for building resilient and sustainable communities that can weather demographic shifts and economic upheaval. However, in rural contexts traditional gender norms can restrict women’s engagement in the formal labor market, often confining them to unpaid care work or insecure employment. While urban areas typically offer more progressive gender ideologies and greater employment opportunities, rural environments tend to reinforce conservative norms, contributing to persistent gender disparities. Beyond local conditions, national institutions—such as welfare regimes, family policies, and labor regulations—also shape women’s labor market outcomes, alongside prevailing cultural norms. Our findings reveal consistent gender gaps in employment across all welfare regimes. While rural-urban differences among women appear limited at the aggregate level, notable cross-country differences emerge. In eleven of the 27 countries studied—primarily in Southern and Eastern Europe—rural women face heightened disadvantages. Conversely, in several Continental and Nordic countries, rural women demonstrate higher employment probabilities than urban women. Over time, rural-urban disparities in employment have narrowed, particularly in Southern Europe, indicating some progress. Part-time employment also displays regional variation. In Continental and Nordic countries, rural women are notably more likely to work part-time. This is especially evident in the Netherlands and Austria, where welfare systems and cultural expectations reinforce gendered labor patterns. In contrast, Southern and Eastern European countries show minimal differences in part-time work by rurality, consistent with the overall lower prevalence of part-time employment in these regions. Care responsibilities further affect employment, particularly in Continental countries, where rural women report more constraints linked to caregiving. In Southern and Eastern Europe, however, caregiving plays a less central role, with structural labor market limitations and traditional gender norms appearing more decisive in shaping outcomes. These patterns underscore the critical role of welfare state configurations in mediating gender and rural inequalities. In Continental regimes, higher rural female employment often coincides with more part-time work and caregiving-related constraints, reinforcing conventional gender roles. In Nordic countries, while the welfare model is more egalitarian and childcare is widely accessible, some rural-urban differences persist. In Southern and Eastern Europe, rural women’s disadvantages might be associated with structural conditions, including limited job availability, type of industries, and traditional labor divisions. Overall, the intersection of gender and rurality reveals complex, context-dependent disadvantages that vary by institutional and cultural environment. Addressing these disparities is essential not only for social justice but also for enhancing the resilience of rural communities. However, policy responses to these issues remain fragmented. Despite the European Union’s promotion of gender-sensitive rural development policies, most initiatives fall short on this goal, often limiting their scope to separate projects for women rather than addressing systemic gender inequality. Investing in the education, training, and entrepreneurship of rural women is a crucial strategy for building resilient and sustainable rural communities that can withstand the challenges of demographic change. However, our findings show how addressing the intersectional barriers faced by rural women requires indirect interventions, including investments in childcare infrastructure, | 48 enhanced access to education and skill-building programs, and the promotion of flexible work arrangements such as teleworking. Moreover, the transformative potential of rural women as agents of community resilience can be further unlocked by integrating a gender lens into rural development policies, programs and plans. Gender mainstreaming, which integrates gender perspectives across all stages of policymaking, offers a promising approach to addressing these challenges. By ensuring that gender equality remains a central goal of EU’s rural policy, the flagship of the LTVRA of Promoting social resilience and women in rural areas has the potential to foster greater socio-economic resilience in rural areas. Future research should explore the underlying mechanisms driving gender disparities in rural labor markets, including the roles of job availability, childcare infrastructure, and societal gender norms, to better inform policy interventions aimed at reducing employment inequalities. Moreover, where and when possible, more granular analyses should be conducted to capture within-country and within-rural variations and identify barriers to women's labor force participation in rural areas, particularly in relation to remoteness and accessibility. In addition, more research is necessary to evaluate the effectiveness of gender-sensitive interventions across diverse rural contexts and explore the structural and cultural changes required to bridge urban-rural divides. Addressing these gaps is crucial for fostering inclusive labor markets and advancing gender equality across diverse geographic and socio-economic contexts and thereby contribute to the development of resilient rural communities across the EU. | 49 7. References Abels, G., Mushaben, J.M., 2012. Introduction: Studying the EU from a Gender Perspective, in: Abels, G., Mushaben, J.M. (Eds.), Gendering the European Union: New Approaches to Old Democratic Deficits. Palgrave Macmillan UK, London, pp. 1–19. https://doi.org/10.1057/9780230353299_1 Adam-Hernández, A., Harteisen, U., 2020. A Proposed Framework for Rural Resilience–How can peripheral village communities in Europe shape change?. AGER Rev. Estud. Sobre Despoblación Desarro. Rural 28. Adger, W.N., Hughes, T.P., Folke, C., Carpenter, S.R., Rockström, J., 2005. Socialecological resilience to coastal disasters. Science. Adisaputri, G., Khan, A., Cameranesi, M., Ungar, M., 2023. Rural Resilience and Mobility: A Scoping Review. J. Rural Community Dev. 18. Agovino, M., Garofalo, A., Cerciello, M., 2019. Do Local Institutions Affect Labour Market Participation? The Italian Case. BE J. Econ. Anal. Policy 19. https://doi.org/10.1515/bejeap-2018-0077 Anderies, J.M., Janssen, M.A., Ostrom, E., 2004. A framework to analyze the robustness of social-ecological systems from an institutional perspective. Ecol. Soc. 9. Anthopoulou, T., Kaberis, N., Petrou, M., 2017. Aspects and experiences of crisis in rural Greece. Narratives of rural resilience. J. Rural Stud. 52, 1–11. Anxo, D., Fagan, C., Cebrian, I., Moreno, G., 2007. Patterns of labour market integration in Europe—a life course perspective on time policies. Socio-Econ. Rev. 5, 233–260. https://doi.org/10.1093/ser/mwl019 Ashkenazy, A., Chebach, T.C., Knickel, K., Peter, S., Horowitz, B., Offenbach, R., 2018. Operationalising resilience in farms and rural regions - Findings from forteen case studies. J. Rural Stud. 59, 211–221. https://doi.org/10.1016/j.jrurstud.2017.07.008 Bæck, U.-D., 2004. The urban ethos: Locality and youth in north norway. Young Stockh. Swed. Bański, J., Wesołowska, M., Łoboda, K., 2020. Disappearing villages – Identification and analysis of selected socioeconomic features. Przeglad Geogr. 92, 175–189. https://doi.org/10.7163/PrzG.2020.2.1 Barcus, H.R., Brunn, S.D., 2010. Place elasticity: Exploring a new conceptualization of mobility and place attachment in rural America. ,. Geogr. Ann. Ser. B Hum. Geogr. 92, 281–295. Bartekova, M., Janikovicova, S., 2025. Sustainable development of women entrepreneurship in selected central European countries, in: Sustainable Entrepreneurship and Family Business for Women’s Empowerment. pp. 41–67. https://doi.org/10.4018/979-8-36938694-1.ch003 Bassett, J., Croft, S., Vaughan-Williams, N., 2013. Introduction; An agenda for resilience research in politics and international relations. Politics 33, 221–228. Berkes, F., Ross, H., 2013. Community resilience: toward an integrated approach. Soc. Nat. Resour. 5–20. Birkjær, M., Gamerdinger, A., El-Abd, S., 2021. Towards a nordic wellbeing economy. Nordic Council of Ministers, Copenhagen. Bock, B.B., 2015. Gender mainstreaming and rural development policy; the trivialisation of rural gender issues. Gend. Place Cult. 22, 731–745. https://doi.org/10.1080/0966369X.2013.879105 Bock, B.B., 2004. Fitting in and Multi‐tasking: Dutch Farm Women’s Strategies in Rural Entrepreneurship. Sociol. Rural. 44, 245–260. https://doi.org/10.1111/j.14679523.2004.00274.x Bock, B.B., Derkzen, P., 2006. Gender and rural development budgets., in: Rural Gender Relations: Issues and Case Studies, CABI Books. pp. 224–240. https://doi.org/10.1079/9780851990309.0224 | 50 Boeckmann, I., Misra, J., Budig, M.J., 2015. Cultural and Institutional Factors Shaping Mothers’ Employment and Working Hours in Postindustrial Countries. Soc. Forces 93, 1301–1333. https://doi.org/10.1093/sf/sou119 Boschma, R.A., 2005. Proximity and innovation: A critical assessment. Reg. Stud. 39, 61–74. https://doi.org/10.1080/0034340052000320887 Brandth, B., 2002. Gender Identity in European Family Farming: A Literature Review. Sociol. Rural. 42, 181–200. https://doi.org/10.1111/1467-9523.00210 Briguglio, L., Cordina, G., Farrugia, N., Vella, S., 2006. Conceptualising and measuring economic resilience, in: Chand, S. (Ed.), Pacific Islands Regional Integration and Governance. ANU Press, pp. 26–49. Bristow, G., 2010. Resilient regions: re-’place’-ing regional competitiveness. Camb. J. Reg. Econ. Soc. 3, 153–167. Bristow, G., Healy, A., 2014. Regional Resilience: An agency perspective. Reg. Stud. 48, 923–935. Buch-Hansen, H., 2025. Why degrowth should be disentangled from the wellbeing economy. Ecol. Econ. 230. https://doi.org/10.1016/j.ecolecon.2025.108532 BusinessEurope, 2023. Reducing Inactivity Rates in Europe. BusinessEurope. Bye, L.M., 2009. ‘How to be a rural man’: Young men’s performances and negotiations of rural masculinities. J. Rural Stud. 25, 278–288. https://doi.org/10.1016/j.jrurstud.2009.03.002 Calisto Friant, M., Vermeulen, W.J.V., Salomone, R., 2020. A typology of circular economy discourses: Navigating the diverse visions of a contested paradigm. Resour. Conserv. Recycl. 161. https://doi.org/10.1016/j.resconrec.2020.104917 Camarero, L., Cruz, F., Oliva, J., 2016. Rural sustainability, inter-generational support and mobility. Eur. Urban Reg. Stud. 23, 734–749. https://doi.org/10.1177/0969776414539338 ČerniČ IsteniČ, M., 2015. Do rural development programmes promote gender equality on farms? The case of Slovenia. Gend. Place Cult. 22, 670–684. https://doi.org/10.1080/0966369X.2013.879102 Chauvel, L., Bar-Haim, E., 2016. Varieties of capitalism (VoC) and varieties of distributions (VoD): How welfare regimes affect the pre-and post-transfer shapes of inequalities? LIS Working Paper Series. Chodkowska-Miszczuk, et, al, 2021. Local communities’ energy literacy as a way to rural resilience - an insight from inner peripheries. Christopherson, S., Michie, J., Tyler, P., 2010. Regional resilience: Theoretical and empirical perspectives. Camb. J. Reg. Econ. Soc. 3, 3–10. Cipollone, A., Patacchini, E., Vallanti, G., 2014. Female labour market participation in Europe: novel evidence on trends and shaping factors. IZA J. Eur. Labor Stud. 3, 1– 40. Copus, A., Hall, C., Barnes, A., Dalton, G., Cook, P., Weingarten, P., Baum, S., Stange, H., Lindner, C., Hill, A., McQuaid, R., Greig, M., Johansson, M., 2006. Study on Employment in Rural Areas (SERA). European Commission. Correia, L.A.T., Alves, M., 2017. Regional employment In Portugal: differences and cyclical synchronisation. Corvers, F., 2023. Place-based transformations throug regional governance: What role(s) for regional authorities? (Webinar). Nordic Thematic Group for Green, Innovative and Resilient Regions, Stockholm. Cox, R.S., Hamlen, M., 2015. Community disaster resilience and the rural resilience index. Am. Behav. Sci. 59, 220–237. Coyle, D., 2015. GDP: a brief but affectionate history-revised and expanded edition. Princeton University Press. Cras, P., 2018. Service i framtidens landsbygdssamhällen, in: Syssner, J. (Ed.), Nya Visioner För Landsbygden. Formas. | 51 Crisp, R., Waite, D., Green, A., Hughes, C., Lupton, R., MacKinnon, D., Pike, A., 2023. “Beyond GDP” in cities: Assessing alternative approaches to urban development. Urban Stud. Cuberes, D., Teignier, M., 2014. Gender Inequality and Economic Growth: A Critical Review. J. Int. Dev. 26, 260–276. https://doi.org/10.1002/jid.2983 Cuéllar-Fernández, B., Fuertes-Callén, Y., Serrano-Magdalena, A., 2024. Factors behind the resilience of rural startups. Technol. Forecast. Soc. Change 206. https://doi.org/10.1016/j.techfore.2024.123521 Currie, M., McKee, A., Glass, J., Markantoni, M., Pinker, A., McMorran, R., Meador, E., 2023. Understandings and applications of rural community resilience amongst Scottish stakeholders: Introducing dual discourses. Community Dev. 54, 187–205. Curtale, R., Stut, M., Alessandrini, A., Deuster, C., Batista, E.S.F., Natale, F., Dijkstra, L., 2025. Outlook and demographic perspectives for EU’s rural regions. A modellingbased exercise. Davoudi, S., Shaw, K., Haider, J.L., Allyson, E.Q., Peterson, G.D., Wilkinson, C., Fünfgeld, H., McEvoy, D., Porter, L., 2012. Resilience: a bridging concept or a dead end? Plan. Theory Pract. 13, 299–333. Davoudi, Simin, Shaw, K., Haider, L.J., Quinlan, A.E., Peterson, G.D., Wilkinson, C., Fünfgeld, H., McEvoy, D., Porter, L., Davoudi, S., 2012. Resilience: A Bridging Concept or a Dead End? “Reframing” Resilience: Challenges for Planning Theory and Practice Interacting Traps: Resilience Assessment of a Pasture Management System in Northern Afghanistan Urban Resilience: What Does it Mean in Planning Practice? Resilience as a Useful Concept for Climate Change Adaptation? The Politics of Resilience for Planning: A Cautionary Note. Plan. Theory Pract. 13, 299– 333. https://doi.org/10.1080/14649357.2012.677124 De Meester, E., Mulder, C.H., Fortuijn, J.D., 2007. Time Spent in Paid Work by Women and Men in Urban and Less Urban Contexts in the Netherlands. Tijdschr. Voor Econ. En Soc. Geogr. 98, 585–602. https://doi.org/10.1111/j.1467-9663.2007.00427.x de Pryck, J.D., Termine, P., 2014. Gender Inequalities in Rural Labor Markets, in: Quisumbing, A.R., Meinzen-Dick, R., Raney, T.L., Croppenstedt, A., Behrman, J.A., Peterman, A. (Eds.), Gender in Agriculture: Closing the Knowledge Gap. Springer Netherlands, Dordrecht, pp. 343–370. https://doi.org/10.1007/978-94-017-8616-4_14 Deimantas, V.J., Şanlıtürk, A.E., Azzollini, L., Köksal, S., 2024. Population Dynamics and Policies in Europe: Analysis of Population Resilience at the Subnational and National Levels. Popul. Res. Policy Rev. 43. https://doi.org/10.1007/s11113-024-09871-w Di Giovanni, G., Chelleri, L., 2019. Why and how to build back better in shrinking territories? Disaster Prev. Manag. Int. J. 28, 460–473. https://doi.org/10.1108/DPM-12-20170322 Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M., Schiavina, M., 2021. Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. J. Urban Econ., Delineation of Urban Areas 125, 103312. https://doi.org/10.1016/j.jue.2020.103312 Dijkstra, L., Poelman, H., 2014. A harmonised definition of cities and rural areas: the new degree of urbanisation. Eur. Comm. Work. Pap. 1, 1–20. Dijkstra, L., Poelman, H., Veneri, P., 2019. The EU-OECD definition of a functional urban area. OECD, Paris. https://doi.org/10.1787/d58cb34d-en Dubb, S., 2016. Community wealth building forms: What they are and how to use them at the local level. Acad. Manag. Perspect. 30, 141–152. https://doi.org/10.5465/amp.2015.0074 Duleep, H., Regets, M.C., Sanders, S., Wunnava, P.V., 2020. Methodological Implications of a Human Capital Investment Perspective, in: Duleep, H., Regets, M.C., Sanders, S., Wunnava, P.V. (Eds.), Human Capital Investment: A History of Asian Immigrants and Their Family Ties. Springer International Publishing, Cham, pp. 45–52. https://doi.org/10.1007/978-3-030-47083-8_5 | 52 EC, 2021a. The demographic landscape of EU territories: challenges and opportunities in diversely ageing regions. Publications Office, LU. EC, 2021b. COM(2021) 345 final. A long-term Vision for the EU’s Rural Areas - Towards stronger, connected, resilient and prosperous rural areas by 2040. Esparcia, J., 2024. Rural Depopulation, Civil Society and Its Participation in the Political Arena in Spain: Rise and Fall of ‘Emptied Spain’ as a New Political Actor?, in: Win or Lose in Rural Development: Case Studies from Europe. pp. 39–63. https://doi.org/10.1007/978-3-031-48675-3_3 Esping-Andersen, G., 2009. Incomplete revolution: Adapting welfare states to women’s new roles. Polity. Esping-Andersen, G., 1990. The three worlds of welfare capitalism. Polity Press, Cambridge, MA. Eurofound, 2023. Bridging the rural–urban divide: Addressing inequalities and empowering communities, Publications Office of the European Union. Luxembourg. Eurostat, 2025. Employment rates by sex, age and degree of urbanisation (lfst_r_ergau). https://doi.org/10.2908/LFST_R_ERGAU Eurostat, 2024. European Union Labour Force Survey (EU-LFS) Microdata, 1983–2023. https://doi.org/10.2907/LFS1983-2023 Eurostat, 2023a. Urban-rural Europe - women and men living in rural areas. Eurostat Stat. Explain. Eurostat, 2023b. Glossary: Degree of Urbanisation. Statistics Explained [WWW Document]. URL https://ec.europa.eu/eurostat/statisticsexplained/index.php?title=Glossary:Degree_of_urbanisation#:~:text=Short%20definiti on%3A%20the%20degree%20of,cells%3B%20each%20LAU%20belongs%20exclusi vely Eurostat, 2023c. Glossary: Employed person - LFS. Eurostat, n.d. International Standard Classification of Education (ISCED). Evans, A., 2019. How Cities Erode Gender Inequality: A New Theory and Evidence from Cambodia. Gend. Soc. 33, 961–984. https://doi.org/10.1177/0891243219865510 Evans, A., 2018. Cities as Catalysts of Gendered Social Change? Reflections from Zambia. Ann. Am. Assoc. Geogr. 108, 1096–1114. https://doi.org/10.1080/24694452.2017.1417820 Evans, A., 2017. Urban change and rural continuity in gender ideologies and practices: Theorizing from Zambia. WIDER Working Paper. Ferragina, E., 2020. Family policy and women’s employment outcomes in 45 high-income countries: A systematic qualitative review of 238 comparative and national studies. Soc. Policy Adm. 54, 1016–1066. https://doi.org/10.1111/spol.12584 Folke, C., 2006. Resilience: The emergence of a perspective for social-ecological systems analyses. Glob. Environ. Change 16, 253–267. https://doi.org/10.1016/j.gloenvcha.2006.04.002 Folke, C., Carpenter, S., Walker, B., Scheffer, M., Chapin, T., Rockström, J., 2010. Resilience Thinking: Integrating Resilience, Adaptability and Transformability. Ecol. Soc. 15. https://doi.org/10.5751/ES-03610-150420 Foray, D., 2014. Smart specialisation: opportunities and challenges for regional innovation policy. Routledge, London. Forsythe, N., Korzeniewicz, R.P., Durrant, V., 2000. Gender Inequalities and Economic Growth: A Longitudinal Evaluation. Econ. Dev. Cult. Change 48, 573–617. https://doi.org/10.1086/452611 Franić, R., Kovačićek, T., 2019. The Professional status of rural women in the EU. García-Cortijo, M.C., Castillo-Valero, J.S., Carrasco, I., 2019. Innovation in rural Spain. What drives innovation in the rural-peripheral areas of southern Europe? J. Rural Stud. 71, 114–124. https://doi.org/10.1016/j.jrurstud.2019.02.027 | 53 Ghio, D., Bosco, C., Natale, F., Loeschner, J., Goujon, A., 2023. Age patterns of net migration and urbanisation dynamics across European municipalities. Popul. Space Place 29, e2599. https://doi.org/10.1002/psp.2599 Goedderz, A., Calanchini, J., 2023. Regional traditional gender stereotypes predict the representation of women in the workforce in 35 countries across five continents. Curr. Res. Ecol. Soc. Psychol. 5, 100138. https://doi.org/10.1016/j.cresp.2023.100138 Grimsrud, G.M., 2011. Gendered spaces on trial: the influence of regional gender contracts on in‐migration of women to rural norway. Geogr. Ann. Ser. B Hum. Geogr. 93, 3–20. https://doi.org/10.1111/j.1468-0467.2011.00358.x Gunderson, L.H., Holling, C.S., et al., 2002. Panarchy: Understanding transformations in human and natural systems. Island press. Gupta, J., Pouw, N.R.M., Ros-Tonen, M.A.F., 2015. Towards an Elaborated Theory of Inclusive Development. Eur. J. Dev. Res. 27, 541–559. https://doi.org/10.1057/ejdr.2015.30 Haandrikman, K., Webster, N.A., Duvander, A.-Z., 2021. Geographical Variation in Local Gender Contracts in Sweden. Appl. Spat. Anal. Policy 14, 679–701. https://doi.org/10.1007/s12061-020-09371-2 Hanley, N., 1998. Resilience in social and eoncomic systems: a concept that fails the costbenefit test? Environ. Dev. Econ. 3, 244–249. Hassink, R., 2010. Regional resilience: A promising concept to explain differences in regional economic adaptability? Camb. J. Reg. Econ. Soc. 3, 45–58. Hedberg, C., Haandrikman, K., 2014. Repopulation of the Swedish countryside: Globalisation by international migration. J. Rural Stud. 34, 128–138. https://doi.org/10.1016/j.jrurstud.2014.01.005 Heijman, W., Hagelaar, G., van der Heide, M., 2019. Rural Resilience as a New Development Concept, in: Dries, L., Heijman, W., Jongeneel, R., Purnhagen, K., Wesseler, J. (Eds.), EU Bioeconomy Economics and Policies: Volume II. Springer International Publishing, Cham, pp. 195–211. https://doi.org/10.1007/978-3-03028642-2_11 Hierro, M., Maza, A., 2024. Toward the identification of laggard rural areas: an evolutionary resilience approach. Scott. Geogr. J. https://doi.org/10.1080/14702541.2024.2378305 Hjorthol, R., Vågane, L., 2014. Allocation of tasks, arrangement of working hours and commuting in different Norwegian households. J. Transp. Geogr. 35, 75–83. https://doi.org/10.1016/j.jtrangeo.2014.01.007 Holm, J.R., Østergaard, C.R., 2015. Regional Employment Growth, Shocks and Regional Industrial Resilience: A Quantitative Analysis of the Danish ICT Sector. Reg. Stud. 49, 95–112. https://doi.org/10.1080/00343404.2013.787159 Hompland, A., 1984. Rurbaniseringsprosesse. Bygdebyen Og Det Nye Nor. Ansikt 9. Hu, X., Hassink, R., 2015. Overcoming the dualism between adaptation and adaptability in regional economic resilience. Pap. Evol. Econ. Geogr. Huggins, R., Thompson, P., 2015. Local entrepreneurial resilience and culture: The role of social values in fostering economic recovery. Camb. J. Reg. Econ. Soc. 313–330. Jaumotte, F., 2004. Labour force participation of women: Empirical evidence on the role of policy and other determinants in OECD countries. OECD Econ. Stud. 2003, 51–108. Junquera, V., Rubenstein, D.I., Grêt-Regamey, A., 2022. Structural change in agriculture and farmers’ social contacts: Insights from a Swiss mountain region. Agric. Syst. Kasimis, C., Papadopolous, A., 2013. Rural transformations and family farming in contemporary Greece. Res. Rural Sociol. Dev. 263–293. Kowalewska, H., 2023. Gendered employment patterns: Women’s labour market outcomes across 24 countries. J. Eur. Soc. Policy 33, 151–168. https://doi.org/10.1177/09589287221148336 Kroll, H., 2015. Efforts to implement smart specialisation in practice -Leading unlike horses to water. Eur. Plan. Stud. 23, 2079–2098. | 54 Lagendijk, A., Pijpers, R., 2013. Beyond the Regional Cradle and Policy Trap: Proximity and Embedding as Development Potentialities. Eur. Plan. Stud. 21, 631–636. https://doi.org/10.1080/09654313.2013.734457 Lasanta, T., Arnáez, J., Pascual, N., Ruiz-Flaño, P., Errea, M.P., Lana-Renault, N., 2017. Space–time process and drivers of land abandonment in Europe. Catena 149, 810– 823. https://doi.org/10.1016/j.catena.2016.02.024 Leibert, T., 2016. She leaves, he stays? Sex-selective migration in rural East Germany. J. Rural Stud. 43, 267–279. https://doi.org/10.1016/j.jrurstud.2015.06.004 Leibert, T., Wiest, K., 2016a. The interplay of gender and migration in Europe’s remote and economically weak rural regions: Introduction to a special issue. J. Rural Stud. 43, 261–266. https://doi.org/10.1016/j.jrurstud.2016.01.007 Leibert, T., Wiest, K., 2016b. The interplay of gender and migration in Europe’s remote and economically weak rural regions: Introduction to a special issue. J. Rural Stud. 43, 261–266. https://doi.org/10.1016/j.jrurstud.2016.01.007 Levesque, V.R., Bell, K.P., Johnson, E.S., 2024. The role of municipal digital services in advancing rural resilience. Gov. Inf. Q. 41. https://doi.org/10.1016/j.giq.2023.101883 Li, H., Xu, T., Yang, C., Fu, Y., Wu, C., Zhang, L., Xu, G., Wang, W., 2024. Towards a dialectical understanding of rural resilience and rural sustainability: bibliometric analysis and evidence from existing literature and China. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-024-05875-w Li, Y., 2023. A systematic review of rural resilience. China Agric. Econ. Rev. 15, 66–77. https://doi.org/10.1108/CAER-03-2022-0048 Li, Y., 2022. A systematic review of rural resilience. China Agric. Econ. Rev. 15, 66–77. https://doi.org/10.1108/CAER-03-2022-0048 Li, Y., Westlund, H., Liu, Y., 2019. Why some rural areas decline while some others not: An overview of rural evolution in the world. J. Rural Stud. 69, 135–143. Little, J., 2002. Rural geography: rural gender identity and the performance of masculinity and femininity in the countryside. Prog. Hum. Geogr. 26, 665–670. https://doi.org/10.1191/0309132502ph394pr Luca, D., Terrero-Davila, J., Stein, J., Lee, N., 2023. Progressive cities: Urban–rural polarisation of social values and economic development around the world. Urban Stud. 60, 2329–2350. https://doi.org/10.1177/00420980221148388 Lundgren, A.S., Ljuslinder, K., 2024. “County residents take up the fight”: Representing rural resilience. Cult. Unbound 16, 14–39. https://doi.org/10.3384/CU.4285 Mackinnon, D., Kempton, L., O’brien, P., Ormerod, E., Pike, A., Tomaney, J., 2022. Reframing urban and regional “development” for “left behind” places. Camb. J. Reg. Econ. Soc. 15, 39–56. https://doi.org/10.1093/cjres/rsab034 Mærsk, E., Aagaard Thuesen, A., Haartsen., T., 2023. Symbolic mobility capital to fight the social stigma of staying: how young adults re-imagine narratives of ‘leaving’during higher education. J. Youth Stud. 26, 153–169. Mandel, H., Semyonov, M., 2006. A welfare state paradox: State interventions and women’s employment opportunities in 22 Countries. Am. J. Sociol. 111, 1910–1949. Manning, A., Petrongolo, B., 2008. The part-time pay penalty for women in Britain. Econ. J. 118, F28–F51. Markusen, A., 2003. Fuzzy Concepts, Scanty Evidence, Policy Distance: The case for rigour and policy relevance in critical regional studies. Reg. Stud. 37, 701–717. Martin, R., 2012. Regional economic resilience, hysteresis and recessionary shocks. J. Econ. Geogr. 12, 1–32. Martin, Ron, 2012. Regional economic resilience, hysteresis and recessionary shocks. J. Econ. Geogr. 12, 1–32. https://doi.org/10.1093/jeg/lbr019 Martin, R., Sunley, P., 2015. On the notion of regional economic resilience: conceptualization and explanation. J. Econ. Geogr. 15, 1–42. https://doi.org/10.1093/jeg/lbu015 Martin, R., Sunley, P., Gardiner, B., Tyler, P., 2016. How regions react to recessions: Resilience and the role of economic structures. Reg. Stud. 50, 561–585. | 55 Martínez Arroyo, F., Sacristán López, H., Castillo-Valero, J.S., García-Cortijo, M.C., 2022. Rural development programmes: Lessons learnt, and knowledge advancement. A case study in Castilla-La Mancha (Spain). Agric. Econ. Zemědělská Ekon. 68, 393– 402. https://doi.org/10.17221/207/2022-AGRICECON Martini, B., Platania, M., 2022. Are the regions with more gender equality the more resilient ones? An analysis of the Italian regions. Reg. Sci. Inq. 14, 71–94. Mascherini, M., Hyland, M., Hinge, G., Anzillotti, M., Molinuevo, D., Peruffo, E., IvaškaitėTamošiūnė, V., 2023. Bridging the rural-urban divide: Addressing inequalities and empowering communities, Living conditions and quality of life. Publications Office of the European Union, Luxembourg. Matteazzi, E., Pailhé, A., Solaz, A., 2018. Part-time employment, the gender wage gap and the role of wage-setting institutions: Evidence from 11 European countries. Eur. J. Ind. Relat. 24, 221–241. McCann, P., Ortega-Argilés, R., 2015. Smart specialisation, regional growth and applications to european union cohesion policy. Reg. Stud. 49, 1291–1302. McIntyre, S., Roy, G., 2023. Revisiting the dimensions of rural resilience: The CoVid-19 pandemic. J. Rural Stud. 103, 103107. https://doi.org/10.1016/j.jrurstud.2023.103107 McManus, P., Walmsley, J., Argent, N., Baum, S., Bourke, L., Martin, J., Pritchard, B., Sorensen, T., 2012. Rural Community and Rural Resilience: What is important to farmers in keeping their country towns alive? J. Rural Stud. 28, 20–29. https://doi.org/10.1016/j.jrurstud.2011.09.003 Mulligan, M., Steely, W., Rickards, L., Fünfgeld, H., 2016. Keywords in planning; What do we mean by “community resilience”? Int. Plan. Stud. 21. Murtagh, A., Farrell, M., Kuhmonen, T., Weir, L., Mahon, M., 2023. The Future Dreams of Ireland’s Youth: Possibilities for Rural Regeneration and Generational Renewal. Sustain. Switz. 15. https://doi.org/10.3390/su15129528 Muštra, V., 2016. Effects of smart specialization on regional economic resilience in EU. Noback, I., Broersma, L., Van Dijk, J., 2013. Gender-Specific Spatial Interactions on Dutch Regional Labour Markets and the Gender Employment Gap. Reg. Stud. 47, 1299– 1312. https://doi.org/10.1080/00343404.2011.629183 OECD, 2020. OECD Regional Outlook 2020: Leveraging Megatrends for Cities and Rural Areas. OECD Publishing, Paris. OECD, European Commission, 2023. Indicators of Immigrant Integration 2023: Settling In. OECD Publishing, Paris. https://doi.org/10.1787/1d5020a6-en Oedl-Wieser, T., 2015. Gender equality: a core dimension in Rural Development Programmes in Austria? Gend. Place Cult. 22, 685–699. https://doi.org/10.1080/0966369X.2013.879103 Oedl-Wieser, Theresia, 2015. Gender equality: a core dimension in Rural Development Programmes in Austria? Gend. Place Cult. 22, 685–699. https://doi.org/10.1080/0966369X.2013.879103 Oliva, J., 2010. Rural melting-pots, mobilities and fragilities: Reflections on the Spanish case. Sociol. Rural. 50, 277–295. https://doi.org/10.1111/j.1467-9523.2010.00516.x Olivetti, C., Petrongolo, B., 2016. The evolution of gender gaps in industrialized countries. Annu. Rev. Econ. 8, 405–434. O’Sullivan, B., McGrail, M., May, J., 2022. Responsive policies needed to secure rural supply from increasing female doctors: A perspective. Int. J. Health Plann. Manage. 37, 40– 49. https://doi.org/10.1002/hpm.3363 Paniagua, A., 2013. Farmers in remote rural areas: The worth of permanence in the place. Land Use Policy 35, 1–7. Pendall, R., Foster, K.A., Cowell, M., 2010. Resilience and regions: building understanding of the metaphor. Camb. J. Reg. Econ. Soc. 3, 71–84. https://doi.org/10.1093/cjres/rsp028 Perpiña Castillo, C., Ribeiro Barranco, R., Curtale, R., Kompil, M., Jacobs-Crisioni, C., Vallecillo Rodriguez, S., Aurambout, J.-P., Batista e Silva, F., Sulis, P., Auteri, D., | 56 2024. Are remote rural areas in Europe remarkable? Challenges and opportunities. J. Rural Stud. 105, 103180. https://doi.org/10.1016/j.jrurstud.2023.103180 Perrings, C., 2006. Resilience and sustainable development. Environ. Dev. Econ. 4, 417– 427. Pike, A., Béal, V., Cauchi-Duval, N., Franklin, R., Kinossian, N., Lang, T., Leibert, T., MacKinnon, D., Rousseau, M., Royer, J., Servillo, L., Tomaney, J., Velthuis, S., 2024. ‘Left behind places’: a geographical etymology. Reg. Stud. 58, 1167–1179. https://doi.org/10.1080/00343404.2023.2167972 Qu, M., Zollet, S., 2023. Neo-endogenous revitalisation: Enhancing community resilience through art tourism and rural entrepreneurship. J. Rural Stud. 97, 105–114. https://doi.org/10.1016/j.jrurstud.2022.11.016 Quaranta, Salvia, 2014. Sustainability patterns and policy fit: Evidences from a mixed approach applied in a euro-Mediterranean area (Alento Basin, Campania Region, Italy). Raue, P., Fynn, L.-L., Pollermann, K., 2024. The role of women in LEADER: Developments in Germany over time and implications. Eur. Countrys. 16, 589–614. https://doi.org/10.2478/euco-2024-0030 Raworth, K., 2017. Why it’s time for doughnut economics. IPPR Progress. Rev. 24, 217–222. https://doi.org/10.1111/newe.12058 Ray, D.M., MacLachlan, I., Lamarche, R., Srinath, K., 2017. Economic shock and regional resilience: Continuity and change in Canada’s regional employment structure, 1987– 2012. Environ. Plan. Econ. Space 49, 952–973. https://doi.org/10.1177/0308518X16681788 Roberts, E., Anderson, B.A., Skerratt, S., Farrington, J., 2017. A review of the rural-digital policy agenda from a community resilience perspective. J. Rural Stud. 54, 372–385. Rocha, S.F., Waquil, P.D., Schultz, G., 2023. Resilience in rural studies: a bibliometric mapping of the scientific publications. Desenvolv. E Meio Ambiente 62, 499–515. https://doi.org/10.5380/dma.v62i0.82664 Rockström, J., Steffen, W., Noone, K., Persson, \AAsa, Chapin, F.S., Lambin, E.F., Lenton, T.M., Scheffer, M., Folke, C., Schellnhuber, H.J., Nykvist, B., de Wit, C.A., Hughes, T., van der Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P.K., Costanza, R., Svedin, U., Falkenmark, M., Karlberg, L., Corell, R.W., Fabry, V.J., Hansen, J., Walker, B., Liverman, D., Richardson, K., Crutzen, P., Foley, J.A., 2009. A safe operating space for humanity. Nature 461, 472–475. https://doi.org/10.1038/461472a Rodríguez-Gulías, M.J., Rodeiro-Pazos, D., Fernández-López, S., Nogueira-Moreiras, M.Á., 2021. The effect of regional resources on innovation: a firm-centered approach. J. Technol. Transf. 46, 760–791. https://doi.org/10.1007/s10961-020-09811-8 Rural Vision, n.d. Actions for resilient rural areas, Rural Vision. Schröder, M., 2013. Integrating Varieties of Capitalism and Welfare State Research: A Unified Typology of Capitalisms. Springer. Scott, M., 2013. Resilience: a conceptual lens for rural studies? Geogr. Compass 7, 597– 610. Sensier, M., Bristow, G., Healy, A., 2016. Measuring Regional Economic Resilience across Europe: Operationalizing a complex concept. Spat. Econ. Anal. 11, 128–151. https://doi.org/10.1080/17421772.2016.1129435 Serrano de la Cruz Santos-Olmo, M.A., Cañizares Ruiz, M. del C., Ruiz Pulpón, Á.R., 2022. Despoblación rural y revalorización de recursos patrimoniales: Análisis preliminar en el Campo de Montiel (Castilla-La Mancha, España). Ager Rev. Estud. Sobre Despoblación Desarro. Rural J. Depopulation Rural Dev. Stud. 165–197. Seuneke, P., Bock, B.B., 2015. Exploring the roles of women in the development of multifunctional entrepreneurship on family farms: An entrepreneurial learning approach. NJAS - Wagening. J. Life Sci. 74–75, 41–50. https://doi.org/10.1016/j.njas.2015.07.001 | 63 Appendix 4.1: Predicted Probabilities of Employment and Employment Gaps by Year, Country, Rurality, and Gender (2013, 2023) Country year PP Urban men PP Urban women PP Rural men PP Rural women Rurality Gap for women Rurality Gap for men Gender Gap in rural Gender Gap in urban AT 2013 0.729 0.678 0.804 0.723 4.5% 7.5% -8.1% -5.1% AT 2023 0.761 0.685 0.816 0.767 8.2% 5.5% -4.9% -7.6% BE 2013 0.694 0.588 0.767 0.653 6.5% 7.3% -11.4% -10.6% BE 2023 0.765 0.640 0.796 0.708 6.7% 3.0% -8.8% -12.5% BG 2013 0.714 0.636 0.669 0.590 -4.6% -4.5% -7.8% -7.7% BG 2023 0.845 0.769 0.806 0.683 -8.6% -3.9% -12.3% -7.6% CH 2013 0.834 0.748 0.878 0.770 2.2% 4.5% -10.8% -8.6% CH 2023 0.853 0.782 0.890 0.805 2.3% 3.7% -8.5% -7.1% CY 2013 0.748 0.649 0.758 0.645 -0.4% 1.0% -11.3% -9.9% CY 2023 0.857 0.758 0.842 0.741 -1.7% -1.5% -10.1% -10.0% CZ 2013 0.857 0.691 0.843 0.679 -1.2% -1.5% -16.4% -16.6% CZ 2023 0.918 0.783 0.909 0.774 -0.9% -0.9% -13.5% -13.5% DE 2013 0.790 0.711 0.843 0.747 3.5% 5.3% -9.6% -7.9% DE 2023 0.837 0.747 0.854 0.792 4.5% 1.7% -6.2% -9.0% DK 2013 0.765 0.700 0.796 0.718 1.8% 3.1% -7.9% -6.5% DK 2023 0.818 0.745 0.862 0.757 1.2% 4.4% -10.5% -7.3% EE 2013 0.795 0.701 0.788 0.713 1.1% -0.7% -7.5% -9.4% EE 2023 0.869 0.788 0.855 0.803 1.4% -1.3% -5.3% -8.0% EL 2013 0.609 0.445 0.704 0.472 2.6% 9.5% -23.2% -16.3% EL 2023 0.769 0.597 0.841 0.610 1.2% 7.3% -23.2% -17.1% ES 2013 0.653 0.568 0.669 0.509 -5.9% 1.7% -16.1% -8.4% ES 2023 0.773 0.675 0.792 0.641 -3.3% 1.9% -15.0% -9.8% FI 2013 0.775 0.716 0.765 0.708 -0.9% -1.0% -5.7% -5.8% FI 2023 0.786 0.770 0.822 0.775 0.5% 3.6% -4.7% -1.6% FR 2013 0.735 0.660 0.767 0.686 2.5% 3.1% -8.1% -7.5% FR 2023 0.780 0.706 0.789 0.734 2.8% 0.9% -5.4% -7.3% HR 2013 0.602 0.580 0.656 0.529 -5.1% 5.4% -12.7% -2.2% HR 2023 0.744 0.733 0.778 0.649 -8.4% 3.3% -12.8% -1.1% HU 2013 0.710 0.598 0.687 0.582 -1.6% -2.3% -10.5% -11.3% HU 2023 0.876 0.793 0.863 0.758 -3.5% -1.3% -10.5% -8.3% IE 2013 0.719 0.615 0.776 0.598 -1.7% 5.8% -17.8% -10.3% IE 2023 0.848 0.739 0.902 0.747 0.8% 5.4% -15.5% -10.9% IS 2013 0.865 0.782 0.929 0.850 6.8% 6.4% -7.9% -8.4% IS 2023 0.897 0.803 0.907 0.842 3.9% 1.0% -6.5% -9.4% IT 2013 0.702 0.516 0.734 0.493 -2.3% 3.1% -24.1% -18.7% IT 2023 0.771 0.570 0.800 0.568 -0.2% 2.9% -23.2% -20.0% LT 2013 0.784 0.738 0.712 0.680 -5.9% -7.2% -3.3% -4.6% LT 2023 0.848 0.808 0.796 0.736 -7.2% -5.2% -6.0% -3.9% LU 2013 0.800 0.686 0.804 0.658 -2.8% 0.4% -14.6% -11.4% LU 2023 0.817 0.759 0.815 0.727 -3.2% -0.2% -8.8% -5.8% LV 2013 0.750 0.687 0.751 0.679 -0.8% 0.1% -7.2% -6.2% LV 2023 0.813 0.757 0.817 0.744 -1.3% 0.4% -7.3% -5.5% NL 2013 0.805 0.707 0.849 0.713 0.6% 4.4% -13.6% -9.8% NL 2023 0.879 0.788 0.896 0.804 1.6% 1.7% -9.2% -9.1% NO 2013 0.830 0.761 0.833 0.781 1.9% 0.3% -5.2% -6.8% NO 2023 0.852 0.772 0.843 0.775 0.3% -0.8% -6.9% -7.9% PL 2013 0.723 0.605 0.763 0.584 -2.1% 4.0% -17.8% -11.7% PL 2023 0.855 0.748 0.860 0.695 -5.3% 0.6% -16.5% -10.7% PT 2013 0.681 0.626 0.762 0.644 1.8% 8.1% -11.8% -5.4% PT 2023 0.828 0.777 0.834 0.742 -3.4% 0.5% -9.2% -5.2% SE 2013 0.846 0.785 0.852 0.786 0.1% 0.6% -6.7% -6.1% SE 2023 0.858 0.805 0.875 0.799 -0.6% 1.6% -7.5% -5.3% SK 2013 0.789 0.658 0.726 0.593 -6.5% -6.3% -13.3% -13.0% SK 2023 0.855 0.818 0.837 0.744 -7.3% -1.8% -9.3% -3.7% | 64 Appendix 4.2: Predicted Probabilities of Part-Time Employment and Employment Gaps by Year, Country, Rurality, and Gender (2013, 2023) Country year PP Urban men PP Urban women PP Rural men PP Rural women Rurality Gap for women Rurality Gap for men Gender Gap in rural Gender Gap in urban AT 2013 0.141 0.419 0.068 0.505 8.6% -7.3% 43.7% 27.8% AT 2023 0.176 0.481 0.090 0.536 5.6% -8.5% 44.6% 30.5% BE 2013 0.097 0.405 0.075 0.471 6.6% -2.2% 39.6% 30.8% BE 2023 0.118 0.363 0.086 0.413 4.9% -3.1% 32.6% 24.6% BG 2013 0.015 0.022 0.024 0.044 2.2% 0.9% 2.0% 0.7% BG 2023 0.008 0.013 0.018 0.020 0.6% 1.1% 0.1% 0.6% CH 2013 0.187 0.618 0.105 0.669 5.1% -8.2% 56.4% 43.1% CH 2023 0.241 0.580 0.146 0.673 9.3% -9.5% 52.7% 33.9% CY 2013 0.076 0.133 0.081 0.179 4.6% 0.5% 9.8% 5.7% CY 2023 0.054 0.101 0.059 0.113 1.2% 0.5% 5.5% 4.7% CZ 2013 0.029 0.100 0.016 0.076 -2.4% -1.4% 6.1% 7.1% CZ 2023 0.038 0.133 0.025 0.102 -3.1% -1.3% 7.7% 9.4% DE 2013 0.126 0.451 0.059 0.497 4.6% -6.7% 43.8% 32.5% DE 2023 0.145 0.460 0.076 0.511 5.1% -6.9% 43.5% 31.5% DK 2013 0.109 0.267 0.061 0.294 2.7% -4.9% 23.3% 15.7% DK 2023 0.125 0.295 0.089 0.306 1.1% -3.6% 21.7% 17.0% EE 2013 0.050 0.127 0.048 0.098 -2.8% -0.2% 5.0% 7.7% EE 2023 0.082 0.170 0.077 0.145 -2.5% -0.5% 6.8% 8.8% EL 2013 0.058 0.132 0.037 0.122 -1.0% -2.2% 8.6% 7.4% EL 2023 0.034 0.112 0.028 0.134 2.2% -0.7% 10.6% 7.7% ES 2013 0.077 0.249 0.052 0.252 0.4% -2.4% 20.0% 17.2% ES 2023 0.060 0.207 0.043 0.221 1.4% -1.7% 17.8% 14.7% FI 2013 0.085 0.152 0.075 0.161 0.9% -1.0% 8.6% 6.7% FI 2023 0.093 0.191 0.085 0.208 1.6% -0.8% 12.3% 9.9% FR 2013 0.074 0.277 0.053 0.343 6.6% -2.2% 29.0% 20.3% FR 2023 0.072 0.238 0.067 0.291 5.3% -0.5% 22.4% 16.7% HR 2013 0.037 0.038 0.050 0.084 4.7% 1.2% 3.5% 0.1% HR 2023 0.033 0.033 0.020 0.053 2.0% -1.3% 3.3% 0.0% HU 2013 0.048 0.096 0.035 0.083 -1.3% -1.3% 4.8% 4.8% HU 2023 0.024 0.063 0.018 0.051 -1.2% -0.6% 3.3% 3.9% IE 2013 0.100 0.325 0.104 0.354 3.0% 0.4% 25.0% 22.4% IE 2023 0.068 0.231 0.070 0.283 5.1% 0.1% 21.3% 16.3% IS 2013 0.063 0.245 0.014 0.310 6.4% -5.0% 29.6% 18.2% IS 2023 0.063 0.245 0.057 0.339 9.5% -0.6% 28.2% 18.1% IT 2013 0.094 0.327 0.056 0.304 -2.2% -3.9% 24.9% 23.2% IT 2023 0.083 0.324 0.055 0.314 -1.0% -2.8% 25.9% 24.1% LT 2013 0.032 0.069 0.084 0.135 6.6% 5.2% 5.2% 3.8% LT 2023 0.033 0.057 0.045 0.075 1.8% 1.2% 3.0% 2.5% LU 2013 0.048 0.294 0.044 0.395 10.1% -0.4% 35.1% 24.5% LU 2023 0.057 0.221 0.075 0.328 10.7% 1.8% 25.3% 16.4% LV 2013 0.054 0.081 0.044 0.104 2.3% -1.0% 6.0% 2.6% LV 2023 0.069 0.112 0.056 0.079 -3.3% -1.3% 2.3% 4.3% NL 2013 0.220 0.714 0.150 0.803 8.9% -7.0% 65.3% 49.4% NL 2023 0.166 0.578 0.114 0.664 8.6% -5.2% 55.0% 41.2% NO 2013 0.121 0.289 0.086 0.410 12.1% -3.5% 32.4% 16.8% NO 2023 0.109 0.211 0.084 0.318 10.6% -2.5% 23.3% 10.3% PL 2013 0.042 0.098 0.039 0.106 0.8% -0.3% 6.8% 5.7% PL 2023 0.035 0.088 0.022 0.084 -0.4% -1.3% 6.3% 5.3% PT 2013 0.070 0.132 0.094 0.169 3.8% 2.4% 7.5% 6.1% PT 2023 0.046 0.100 0.029 0.097 -0.3% -1.7% 6.8% 5.4% SE 2013 0.116 0.296 0.087 0.425 12.8% -2.9% 33.7% 18.0% SE 2023 0.111 0.216 0.089 0.303 8.6% -2.2% 21.3% 10.5% SK 2013 0.026 0.048 0.041 0.064 1.7% 1.5% 2.3% 2.2% SK 2023 0.018 0.037 0.017 0.042 0.5% 0.0% 2.5% 2.0% | 65 Appendix 4.3: Predicted Probabilities of Care Responsibility Employment and Employment Gaps by Year, Country, Rurality, and Gender for 2013 and 2023 Country year PP Urban men PP Urban women PP Rural men PP Rural women Rurality Gap for women Rurality Gap for men Gender Gap in rural Gender Gap in urban AT 2013 0.007 0.117 0.003 0.146 2.9% -0.4% 14.3% 11.0% AT 2023 0.019 0.167 0.012 0.207 3.9% -0.8% 19.5% 14.8% BE 2013 0.003 0.051 0.004 0.061 1.1% 0.1% 5.7% 4.7% BE 2023 0.010 0.081 0.008 0.106 2.5% -0.2% 9.7% 7.1% BG 2013 0.000 0.004 0.001 0.003 -0.1% 0.0% 0.3% 0.4% BG 2023 0.003 0.034 0.003 0.034 -0.1% 0.1% 3.0% 3.2% CH 2013 0.013 0.109 0.008 0.138 2.9% -0.5% 13.1% 9.7% CH 2023 0.037 0.177 0.029 0.270 9.3% -0.8% 24.1% 14.0% CZ 2013 0.001 0.027 0.001 0.020 -0.8% -0.1% 1.9% 2.6% CZ 2023 0.003 0.104 0.004 0.097 -0.7% 0.1% 9.3% 10.1% DE 2013 0.004 0.069 0.002 0.081 1.2% -0.2% 7.8% 6.5% DE 2023 0.016 0.146 0.010 0.163 1.7% -0.6% 15.3% 13.0% DK 2013 0.000 0.010 0.000 0.016 0.5% 0.0% 1.5% 1.0% DK 2023 0.001 0.011 0.001 0.020 0.9% 0.0% 1.9% 1.0% EE 2013 0.002 0.029 0.001 0.019 -1.0% 0.0% 1.8% 2.8% EE 2023 0.008 0.065 0.005 0.058 -0.7% -0.3% 5.3% 5.7% ES 2013 0.001 0.032 0.001 0.027 -0.5% 0.0% 2.6% 3.1% ES 2023 0.007 0.065 0.009 0.066 0.2% 0.2% 5.8% 5.7% FI 2013 0.003 0.026 0.000 0.024 -0.2% -0.3% 2.3% 2.2% FI 2023 0.011 0.043 0.005 0.046 0.2% -0.6% 4.1% 3.3% FR 2013 0.005 0.059 0.003 0.072 1.3% -0.2% 6.9% 5.4% FR 2023 0.013 0.102 0.010 0.089 -1.3% -0.3% 7.9% 8.9% HR 2013 0.002 0.025 0.002 0.016 -0.9% 0.0% 1.4% 2.3% HR 2023 0.002 0.038 0.002 0.052 1.5% 0.0% 5.0% 3.6% HU 2013 0.003 0.020 0.002 0.022 0.2% 0.0% 2.0% 1.7% HU 2023 0.007 0.034 0.005 0.056 2.2% -0.2% 5.1% 2.7% IE 2013 0.004 0.069 0.002 0.045 -2.4% -0.2% 4.3% 6.5% IE 2023 0.024 0.146 0.020 0.131 -1.5% -0.4% 11.0% 12.2% IT 2013 0.002 0.060 0.001 0.058 -0.2% -0.1% 5.7% 5.8% IT 2023 0.006 0.081 0.005 0.071 -1.0% -0.1% 6.6% 7.5% LU 2013 0.005 0.046 0.003 0.083 3.7% -0.2% 7.9% 4.1% LU 2023 0.011 0.069 0.017 0.097 2.8% 0.6% 8.1% 5.8% LV 2013 0.002 0.022 0.001 0.013 -0.9% -0.1% 1.2% 2.0% LV 2023 0.004 0.040 0.001 0.021 -1.9% -0.3% 1.9% 3.5% NL 2013 0.022 0.194 0.012 0.219 2.5% -1.0% 20.7% 17.2% NL 2023 0.032 0.247 0.021 0.306 5.9% -1.2% 28.6% 21.4% NO 2013 0.004 0.029 0.001 0.058 2.9% -0.3% 5.8% 2.6% NO 2023 0.001 0.022 0.004 0.027 0.6% 0.3% 2.3% 2.0% PL 2013 0.002 0.032 0.002 0.034 0.2% -0.1% 3.2% 2.9% PL 2023 0.005 0.053 0.004 0.061 0.8% -0.1% 5.6% 4.8% PT 2013 0.001 0.012 0.001 0.011 -0.1% 0.0% 1.0% 1.1% PT 2023 0.003 0.032 0.002 0.035 0.3% -0.1% 3.3% 2.9% SE 2013 0.010 0.052 0.006 0.075 2.4% -0.4% 6.9% 4.2% SE 2023 0.007 0.028 0.008 0.048 1.9% 0.1% 3.9% 2.1% | 66 Appendix Figure 5.1: Predicted probabilities of employment gaps by gender, rurality and year (2013, 2018, 2023) | 67 Appendix Figure 5.2: Predicted probabilities of employment gaps by gender, rurality and welfare, 2013 Appendix Figure 5.3: Predicted probabilities of employment gaps by gender, rurality and welfare, 2018 | 68 Appendix Figure 5.4: Predicted probabilities of part-time employment gaps by gender, rurality and year (2013, 2018, 2023) | 69 Appendix Figure 5.5: Predicted probabilities of part-time employment gaps by gender, rurality and welfare, 2013 Appendix Figure 5.6: Predicted probabilities of part-time employment gaps by gender, rurality and welfare, 2018 | 70 Appendix Figure 5.7: Predicted probabilities of care responsibility gaps by gender, rurality and year (2013, 2018, 202) | 71 Appendix Figure 5.8: Predicted probabilities of care responsibility gaps by gender, rurality and welfare, 2013 Appendix Figure 5.9: Predicted probabilities of care responsibility gaps by gender, rurality and welfare, 2018