Data Strategy & Integration: Rural Women Innovation Compass

A major data gap concerns the insufficient granularity of demographic information for rural areas, particularly when comparing rural and urban contexts. Existing datasets rarely provide systematic rural–urban comparisons disaggregated simultaneously by sex, age, and degree of urbanisation (DEGURBA). As a result, key indicators such as age-specific dependency ratios by sex and settlement type remain underdeveloped, limiting the analysis of demographic pressures and labour supply dynamics in rural territories.

There is also a clear need to strengthen intersectional and life-cycle approaches in demographic statistics. Current data do not adequately capture the combined effects of gender with other characteristics such as age, disability, migration background, or family status. This constrains the ability to reflect the diversity of rural life courses and to understand how multiple forms of disadvantage or opportunity accumulate over time, particularly for rural women.

Several critical demographic processes are only partially observed or not systematically recorded, including:

• Temporary mobility and migration potential, including brain circulation;
• Youth retention and sector-specific age profiles;
• Ageing of the workforce and generational replacement in key rural sectors;
• Rural population sustainability and long-term demographic balance;
• Dependency ratios and future labour force trends;
• Working-age population dynamics, active ageing, and reskilling trajectories;
• The presence of women in leadership, decision-making, and entrepreneurial roles.

Despite their importance for rural development and gender equality, these dimensions are often missing or fragmented across datasets. Consequently, policymakers frequently rely on proxy variables to approximate demographic and social dynamics. Relevant proxies include, for example, the mandatory minimum social allocation (14%) within National and Regional Partnership Plans, which is indirectly linked to population inclusion, support for vulnerable groups, and labour market participation. While useful, such proxies cannot substitute for direct, disaggregated demographic evidence and may obscure intra-territorial inequalities.

Certain sectors supported under EU policies—particularly agriculture and fisheries—are demographically critical, given persistent challenges related to workforce ageing, rural depopulation, and generational renewal. However, the lack of harmonised demographic indicators across these sectors hampers the assessment of policy effectiveness in addressing long-term population change.

To address these gaps, quantitative statistical improvements should be complemented by qualitative and participatory methods, including community-based research, stakeholder consultations, and narrative evidence. These approaches can provide deeper insights into lived experiences, identity dynamics, and decision-making processes that are not captured by conventional statistics, thereby strengthening the evidence base for more targeted, inclusive, and gender-responsive rural policies.

Within the knowledge and skills domain, one of the most significant gaps relates to identity-related dimensions, which remain largely absent from European monitoring systems. Factors such as cultural belonging, migration background, ethnicity, language use, intergenerational norms, and gender roles strongly influence rural women’s access to education, training, digital skills, and lifelong learning opportunities, yet they are rarely captured in existing data frameworks.

• formal and non-formal education
• digital skills
• vocational and farm-related training
• opportunities to participate in innovation ecosystems

Despite their importance, these factors are not systematically recorded in existing datasets, limiting the ability of policymakers to design targeted, inclusive interventions.

Policymakers often must rely on proxy variables to approximate identity dynamics. Relevant proxies can include:

• participation in informal or community-based learning activities
• involvement in cultural or local association networks
• integration and inclusion indicators
• mobility and migration patterns
• informal caregiving responsibilities
• women’s engagement in local leadership, civic initiatives or community groups

To complement statistical proxies, qualitative and participatory methods—such as rural women’s surveys, focus groups, and community-based case studies—are essential. These approaches provide the contextual insights needed to interpret quantitative indicators and reveal how identity-related barriers obstruct women’s learning pathways, digital inclusion and innovation capacity.

Improving measurement in the Identity domain is fundamental for developing more inclusive, culturally responsive and territorially grounded rural policies. A better understanding of identity-related factors will make it possible to:

• recognise the diversity of rural women’s experiences
• identify hidden or structural barriers affecting education, skills and participation
• support tailored policies that enhance women’s roles in rural innovation ecosystems

Significant data gaps persist in the Employment & Business domain, limiting the ability to design precise policies and evaluate the role of women in rural and agricultural economies. While useful indicators exist in areas such as employment, entrepreneurship, self-employment and business demography, essential information is still missing to adequately capture women’s participation in rural value chains, entrepreneurial performance, leadership, innovation activities and labour conditions.

First, a major limitation is insufficient territorial granularity: many sources only provide data at national or NUTS 2 level, making it difficult to analyse rural realities, depopulating territories, or regions with strong agricultural masculinisation. This gap restricts gender-sensitive analysis of labour dynamics, female migration patterns, youth retention and workforce ageing in rural sectors.

There is also a structural gap in measuring agricultural and rural employment by gender. Although progress has been made through the PMEF and the FADN/FSDN systems, current information still omits key variables such as working time, part-time or temporary contracts, seasonal labour, gender-differentiated wages, care-related leave, unpaid family work and participation in innovation and training systems. These variables are essential to reflect the reality of women’s work in agriculture and related activities, where multifunctional and often invisible labour is dominant.

In the business field, sex-disaggregated and rural-specific data on women’s entrepreneurship are still lacking. Although Eurostat offers datasets on business demography, they do not systematically distinguish between rural and urban businesses, nor between male- and female-led ownership structures. As a result, it remains difficult to assess female access to credit, venture capital, internationalisation pathways, or adoption of green and digital technologies. Furthermore, the evidence base does not yet allow a detailed evaluation of how public investment and EU funding streams influence women-led entrepreneurship in agricultural and rural territories.

A critical gap also concerns gendered labour trajectories and life-course differences. Current data systems do not monitor transitions between education and employment, sectoral exit, accumulated wage inequality, or re-entry after care responsibilities. This lack of longitudinal information prevents deeper understanding of the structural factors behind low female representation in leadership, farm management, and decision-making bodies, all widely recognised barriers in GRASS CEILING findings.

Finally, qualitative and longitudinal evidence remains insufficient. Women’s employment conditions are strongly shaped by cultural norms, gendered care expectations, limited childcare and mobility infrastructure, and male-dominated networks, yet these social dimensions are rarely captured in statistical infrastructures. Developing case studies would be particularly valuable to assess women’s access to markets, performance of female-led rural enterprises, leadership in cooperatives, adoption of digital solutions, roles in value chains, and links between innovation and territorial development .

In summary, evidence needs include:

• expanding the use of FSDN to incorporate employment status, working hours, leave, wages and social variables;
• improving gender- and territory-disaggregated labour and business data coverage;
• integrating entrepreneurship and leadership indicators for women in rural value chains;
• adopting intersectional, life-course and regional approaches to employment statistics; and
• developing qualitative and longitudinal research to contextualise what quantitative systems cannot capture.

These data improvements are fundamental to enable robust monitoring of women’s employment, entrepreneurship and innovation in rural Europe, and to support gender-responsive policymaking going forward.

Significant data limitations continue to affect the Socio-ecological Innovation domain, restricting the capacity to map, compare and assess women’s contribution to ecological transition processes in rural Europe. While multiple indicator sources exist across climate, land use, biodiversity, forestry, energy and circular economy fields, very few indicators contain gender-disaggregated or rural-specific information. This makes it difficult to evaluate how women engage in, benefit from, or lead socio-ecological innovation.

A first major limitation concerns the absence of gender breakdowns across environmental and climate monitoring frameworks. Current CAP-PMEF indicators relevant to carbon sequestration, soil erosion, water and air quality, biodiversity, agroforestry, eco-schemes or climate adaptation do not systematically distinguish outcomes by sex. As a result, it remains impossible to assess women’s participation in green investments or ecological commitments related to farming, forestry and rural business activities.

Secondly, existing innovation and green economy evidence structures rarely capture women’s roles in agroecology, bioeconomy, circular economy and nature-based solutions. Statistical frameworks such as the Community Innovation Survey and eco-innovation indices do not differentiate between rural–urban settings nor identify women-led innovation models, especially in micro-enterprises, care farming, sustainable food systems or community-based initiatives, where women are highly active but statistically invisible.

A further gap relates to insufficient territorial granularity. Many environmental and climate-related datasets are only available at national or NUTS 2 level, which obscures diversity across rural territories, depopulating areas, mountainous regions, and places undergoing land-use transition. Without NUTS 3 or local-scale monitoring, it is not possible to identify gendered patterns in soil use, forestry ownership, landscape protection or climate resilience practices.

Evidence gaps also arise from the limited integration of socio-economic enabling conditions into ecological datasets. Key variables relating to finance, advisory support, green training, AKIS participation and care infrastructure are not routinely linked to women’s ecological activities. Lack of information on credit access, investment uptake or participation in environmental training limits understanding of how women enter green markets or adopt ecological technologies.

Finally, quantitative databases do not capture informal or socially embedded innovation practices. Women’s involvement in community gardens, renewable energy cooperatives, biodiversity volunteering, sustainable tourism and local resource stewardship remains largely undocumented. Qualitative research is required to reveal gendered motivations, care responsibilities, time use and cultural norms shaping ecological participation.

In summary, evidence needs include:

• strengthening gender- and territory-disaggregated environmental indicators across climate mitigation, biodiversity, forestry, circular economy and land-use systems;
• linking ecological datasets to enabling conditions such as finance, AKIS access, skills, and care infrastructure;
• improving rural territorial granularity below NUTS 2 to reveal intra-rural ecological differences;
• incorporating micro-innovation and community-based activities into innovation measurement tools; and
• complementing statistical systems with qualitative and longitudinal research to reflect women’s lived ecological realities.

Major evidence limitations affect the Governance & Participation domain and limit the ability to analyse women’s decision-making presence and influence across rural territories. While European sources offer valuable data on high-level political and economic power, particularly through EIGE’s Gender Equality Index and FemAI/FemDI results, these indicators focus mainly on national and regional institutional structures. They do not capture women’s leadership roles within rural governance mechanisms, local civil society, agricultural organisations, cooperatives, producer groups, advisory committees or community initiatives. As a result, a large part of rural women’s political agency remains statistically invisible.

A first gap concerns territorial precision. Most governance datasets are not available below national or NUTS 2 scale, preventing rural-urban comparisons and masking variation across territories with different demographic trends, governance cultures or masculinised agricultural structures. The absence of local-level evidence complicates efforts to measure women’s representation in municipal councils, parish committees, Local Action Groups (CLLD/LEADER), water and forestry bodies, farm organisations, chambers of agriculture, rural NGOs and community associations. Without this granularity, governance inequalities across declining, remote or peripheral rural areas cannot be assessed.

Secondly, indicators in this domain mainly monitor formal positions rather than informal participation. Rural governance is strongly shaped by volunteerism, self-organised community structures and unpaid civic activity, yet these governance channels are not covered by official statistical infrastructures. Women’s informal influence, through social networks, mutual aid, advisory roles or community leadership, is therefore unrecorded.

There are also structural gaps concerning labour-market-linked participation. Although governance is closely connected to economic autonomy, available indicators do not track whether women involved in care responsibilities, part-time agricultural work or seasonal labour have equal opportunities to hold leadership positions. The lack of intersectional breakdowns (e.g. by age, migration background, disability, socio-economic status) prevents evaluation of whether specific groups of women face stronger governance barriers.

Furthermore, existing European datasets rarely connect governance participation to thematic rural policy areas such as climate, digital transition, food systems, green innovation or environmental management. Decision-making roles in energy cooperatives, climate assemblies, land stewardship groups, forest committees, tourism boards or circular economy initiatives remain largely undocumented.

Finally, qualitative and longitudinal evidence remains insufficient. Rural women’s participation is shaped by cultural norms, gender stereotypes, political trust, digital skills, time poverty and unpaid care burdens, yet these dimensions are largely absent from statistical records. Without narrative data, it is difficult to understand why participation gaps persist despite gender-equality policies.

In summary, evidence needs include:

• expanding governance indicators beyond national and regional levels to cover NUTS 3 and local structures;
• developing sex-disaggregated datasets on women’s representation in agricultural organisations, cooperatives, civil society platforms and CLLD/LEADER governance bodies;
• integrating informal community leadership and voluntary governance into measurement frameworks;
• applying intersectional approaches to participation statistics; and
• generating qualitative and longitudinal research to capture the social and cultural drivers of women’s governance outcomes.

Strengthening these data dimensions is essential for building a gender-sensitive governance evidence base and for recognising women as active decision-makers in rural transformation processes.

Substantial evidence limitations affect the Socialisation & Networks domain, constraining efforts to understand how women connect to, participate in, and benefit from the networks that enable rural innovation. Although community engagement, peer learning and social capital are well-recognised as core drivers of rural transformation, current monitoring systems largely overlook these dimensions. As a result, the extent and significance of women’s social networks, collaboration practices and shared learning spaces remain poorly documented within European datasets.

A first major gap concerns the invisibility of informal relationships and trust-based support structures. Rural innovation ecosystems rely heavily on interpersonal exchange, shared problem-solving, community solidarity and mentorship networks, yet these activities are not captured by existing statistical frameworks. Without dedicated indicators, women’s contributions to informal learning environments, social entrepreneurship, or community-based cooperation cannot be assessed.

Secondly, most existing evidence focuses on organisational membership or institutional participation, rather than the quality, density, and impact of social networks. National datasets do not measure the strength of local connections between women innovators, their involvement in cross-sector networks, or the multiplier effects of peer learning and cooperation. Similarly, data on advisory services, supply-chain groups, producer organisations or professional associations rarely consider the gender composition of their networks.

A further limitation is the lack of territorial detail. Social and community networks are highly dependent on local context, yet most relevant indicators are only available at broad territorial scales, masking significant variation between villages, remote communities, depopulating areas and innovation hotspots. Without NUTS 3 or local-level information on social capital, community belonging or organisational density, it is difficult to identify regional inequalities in women’s access to networks or assess how isolation, distance or mobility constraints impact social participation.

In addition, digital transformation and online socialisation remain under-measured. While social media, online learning platforms, female innovation networks and virtual mentoring programmes are increasingly significant, monitoring systems seldom record women’s participation in digital networking spaces. The absence of sex-disaggregated digital engagement data prevents understanding of whether online environments expand or constrain opportunities for rural women to build connections.

Finally, there is very little longitudinal or qualitative evidence explaining why women join networks, how social identities shape participation, and which cultural or structural factors restrict access to collective spaces. Rural women may face time poverty, care responsibilities, mobility limitations or gender norms discouraging public engagement; however, these socio-cultural constraints remain largely undocumented.

In summary, evidence needs include:

• strengthening sex-disaggregated indicators on community participation, advisory access, peer learning and organisational membership;
• capturing informal and trust-based social capital structures, including community support, mentoring, and volunteer networks;
• increasing territorial granularity to reveal local differences in network intensity and access across rural regions;
• integrating digital networking measures into social inclusion and innovation monitoring systems; and
• expanding qualitative and longitudinal research to understand motivations, barriers and identity-based participation patterns.

Significant gaps persist within the science domain, limiting our ability to understand women’s participation in research, development, and innovation ecosystems—particularly regarding their impact in rural contexts, whether through researchers working in these areas or research focused on rural and gender issues. Although multiple European datasets measure gender imbalances in science and STEM education, these indicators do not reflect rural realities. Most available monitoring systems report national-level statistics on STEM graduates, researchers, innovation performance or digital skills, but they do not capture the role of rural women within scientific knowledge systems or research infrastructures. The result is a limited evidence base to evaluate women’s engagement in scientific activities linked to agriculture, food systems, rural innovation or environmental research.

A first major gap concerns the rural dimension of R&I monitoring. Existing datasets—including the Community Innovation Survey, the European Innovation Scoreboard, She Figures, and the new GENDEX—do not distinguish between rural and urban territories. Women working in small rural research units, living labs, advisory structures, bioeconomy testing sites, social science projects or experimental farms therefore remain invisible. Without rural territorial granularity, it is impossible to assess how research participation varies across regions or whether rurality itself creates barriers to scientific careers and innovation engagement.

Secondly, formal science indicators rarely register women’s roles outside higher education or research institutions. Much scientific knowledge in rural settings is produced through practice-based experimentation, innovation in farming, citizen science, field trials, community laboratories, participatory research and data-driven advisory networks. Yet these forms of knowledge are not recognised by science indicators that favour academic publications, patents, or laboratory-based research outputs. As a consequence, women’s contributions to science in agriculture, forestry, nature management or food production remain largely undocumented.

There is also insufficient integration between scientific evidence and agricultural innovation systems. Although advisory services, demonstration farms and AKIS structures are central to scientific exchange, available indicators do not register how women access these systems, contribute scientific knowledge, or benefit from research partnerships. At the same time, major analytical frameworks (such as the Farm Sustainability Data Network or CAP innovation indicators) do not measure gender-disaggregated research participation or scientific leadership.

A further structural gap is linked to skills pipelines. Current STEM and ICT indicators describe education outcomes but provide little insight into how rural women transition into scientific professions, research institutions, or innovation careers. Without longitudinal tracking, it is not possible to understand why women with STEM training are underrepresented in rural innovation networks or why they move away from science pathways at later stages.

Intersectional and socio-economic gaps compound these limitations. Scientific participation is not analysed by age, socio-economic background, migration status, disability, contract type or career stage, making it difficult to identify which groups face the strongest structural barriers. Rural scientific careers are also shaped by care responsibilities, geographical distance from universities, lack of transport and limited research funding—all factors not reflected in conventional scientific metrics.

Finally, qualitative evidence on scientific participation remains scarce. Barriers such as gender stereotypes in STEM, scientific culture, workplace discrimination, publication bias, networking exclusion, and unequal recognition of research labour are well-documented in urban science contexts, but largely unstudied in rural settings. Without qualitative data on lived experience, drivers of scientific attrition and exclusion remain poorly understood.

In summary, evidence needs include:

• strengthening territorial and gender-disaggregated scientific indicators capable of identifying rural women’s engagement across research fields;
• integrating practice-based, applied and community science activities within innovation measurement frameworks;
• improving linkages between scientific monitoring and agricultural advisory, AKIS and living lab datasets;
• developing longitudinal data on STEM training, career progression, and scientific retention among rural women;
• embedding intersectional variables into science indicators; and
• expanding qualitative research to capture cultural, structural and institutional barriers shaping rural women’s scientific participation.

Building these evidence foundations is essential to understanding how women participate in scientific ecosystems and to ensuring that scientific research and innovation policy in rural Europe recognises, supports and invests in women’s knowledge production.

Despite the central role of policy frameworks in shaping women’s opportunities in rural areas, significant evidence gaps persist in the Policy domain. While numerous monitoring systems track policy outputs, funding allocations and legal developments, these datasets rarely provide gender-sensitive or rural-specific insights. As a result, the effectiveness of policies in addressing structural barriers faced by rural women remains difficult to assess.

A first major gap concerns the absence of systematic gender-disaggregated data on policy beneficiaries. Although Common Agricultural Policy (CAP) indicators capture expenditures, hectares covered and numbers of beneficiaries, they do not consistently identify whether women and men benefit equally from income support, eco-schemes, redistributive payments, innovation investments or advisory services. We understand that these data are anonymised and not made available in a disaggregated form, which prevents gender analysis. This limits the capacity to evaluate whether policy instruments reduce gender inequalities or inadvertently reinforce existing imbalances.

Secondly, policy monitoring focuses largely on financial and administrative outputs rather than outcomes for women. Indicators measuring the adoption of regulations, strategies or funding agreements provide limited insight into how policies translate into improved access to land, income, services, innovation or decision-making for rural women. The absence of gender-sensitive impact indicators hampers accountability and weakens evidence-based policy learning.

A further limitation is the lack of integration across policy domains. Gender equality, rural development, innovation, social inclusion, environmental sustainability and digital transition are often monitored separately, despite being deeply interconnected. The absence of interoperable datasets makes it difficult to assess cumulative or cross-sectoral policy effects on rural women, particularly in areas where multiple policies intersect, such as climate transition, care infrastructure or business support.

Territorial granularity also remains insufficient. Most policy indicators are available at national or programme level, masking significant variation across regions and rural contexts. Without NUTS 3 or local-level data, it is not possible to identify where gender-responsive policies are most effective or where gaps persist in peripheral, depopulating or remote rural areas.

There are also gaps in monitoring institutional capacity and governance quality. Indicators rarely capture whether public administrations possess the skills, resources and gender expertise needed to design, implement and evaluate inclusive policies. Similarly, evidence on stakeholder engagement, consultation processes and the participation of women’s organisations in policy design remains fragmented.

Finally, qualitative evidence is underdeveloped. Statistical indicators do not capture how rural women experience policy frameworks, navigate administrative systems, or perceive barriers related to eligibility criteria, bureaucracy, digital access or information asymmetries. Without qualitative insights, policy monitoring risks overlooking the lived realities that shape access and outcomes.

In summary, evidence needs include:

• strengthening gender-disaggregated monitoring of policy beneficiaries across CAP, cohesion and sectoral programmes;
• developing outcome-oriented indicators that measure policy impacts on women’s economic, social and innovation opportunities;
• improving integration and interoperability across policy monitoring systems;
• enhancing territorial granularity to reflect diverse rural contexts;
• tracking institutional capacity and gender mainstreaming practices within public administrations; and
• complementing quantitative data with qualitative research on policy accessibility and lived experience.

A first major limitation affecting the Infrastructures & Services domain is the lack of territorial granularity in many key datasets. Most indicators on accessibility, broadband connectivity, service provision, and transport infrastructure remain available only at national or NUTS 2 scale, making it difficult to identify the specific realities of rural communities, depopulating territories, and regions with strong agricultural masculinisation. This restricts the ability to monitor spatial inequalities in access to essential services—such as broadband, childcare, education, mobility and healthcare—and prevents a meaningful gender-sensitive rural analysis.

A second important gap is the limited availability of gender-disaggregated and rural-specific infrastructure indicators. Existing monitoring systems do not routinely provide sex-disaggregated information on access to broadband services, transport patterns, childcare capacity, advisory services, or water and energy infrastructure. This mirrors broader structural weaknesses identified by GRASS CEILING, where women’s roles, needs and constraints in rural economies remain under-represented in official evidence streams.

Evidence gaps are particularly visible in the digital field. While datasets exist for broadband availability and internet access, there is insufficient evidence linking connectivity to women’s participation in rural innovation, entrepreneurship, labour markets, and care responsibilities. The absence of regionalised ICT indicators limits analysis of digital exclusion patterns and women’s digital needs across rural areas. The integration of Regional ICT statistics (t_isoc_reg) is therefore required to improve territorial precision.

Transport and mobility data show similar weaknesses. Current datasets do not capture gender-differentiated use of rural transport, dependency on private vehicles, or unequal time burdens associated with service access. Rural women’s mobility constraints, including care-related travel, are largely absent from official statistics. Using complementary sources—such as EIGE’s time-use data related to mobility and care—could help inform future indicator development.

There are also major gaps regarding the availability of local service indicators, including access to childcare, elder care, healthcare capacity, and cultural facilities. Existing Rural Observatory indicator groups (Accessibility, Health, Well-being, and Attractiveness) remain highly valuable, they lack sufficient territorial granularity. As a result, they fail to capture the lived experiences of rural women or to adequately measure the impacts of policy interventions at the local level.

Against this background, there is a clear need to:

1. Improve methods for identifying areas with poor access to Services of General Interest (SGIs), taking into account the relative nature of access.
2. Develop approaches that reflect the multidimensional complexity of inner peripheries and recognise the dual role of SGIs as both a cause and a consequence of peripherisation processes.
3. Achieve a better understanding of SGI provision and the interrelated policy-making processes that shape access across territories.

In this context, measuring access to SGIs can serve as a powerful diagnostic tool for identifying well-being disparities between rural areas in Europe, as highlighted in recent research on access to Services of General Interest.

In agriculture, new developments under the Farm Sustainability Data Network provide promising avenues to track digitalisation, automation and machinery adoption. However, there is no systematic integration of gender and rural territorial detail in these datasets, especially regarding digital training uptake, advisory access, or the relevance of robotics and precision technology for women-led farming systems. Targeted case studies and micro-level evidence therefore remain essential.

Finally, there is a structural lack of longitudinal and qualitative data capable of capturing how infrastructure, service provision, and digital access influence rural women’s capacity to innovate, stay employed, engage in entrepreneurship, or remain in rural territories. GRASS CEILING evidence confirms that rural women’s innovation pathways are strongly shaped by access to transport, digital networks, land, finance and care structures, yet these mechanisms are not visible in existing European indicators and monitoring frameworks.

Future evidence work should therefore:

• integrate sex- and age-disaggregated infrastructure and service accessibility datasets;
• expand geographical granularity below NUTS 2 using grid-level accessibility models;
• link ICT, transport, care and advisory service indicators to gender outcomes;
• generate case studies on digital skills and participation to complement quantitative sources;

Together, these improvements would enable a robust, gender-sensitive monitoring system for rural infrastructures and services, supporting evidence-based policymaking and giving visibility to women’s contributions and structural constraints in rural transformation processes.