The study draws on the BES dataset, the Benessere Equo e Sostenibile framework that the Italian National Institute of Statistics, Istat, has maintained since 2013 as an official dashboard of equitable and sustainable well-being. The framework spans twelve domains, ranging from economic well-being, education and health to environment, innovation, landscape and cultural heritage, politics and institutions, quality of services, security, social relations, subjective well-being, and work and life balance. Because the researchers wanted to compare well-being and gender equality on common ground, they restricted the analysis to the 64 indicators for which gender-disaggregated data are consistently available at the regional level, out of the full set of 85. A robustness check confirmed that this restriction barely matters: indices built from the reduced set correlate almost perfectly with those built from the full set, preserving the main territorial patterns.

The methodological core of the paper is its insistence on keeping two concepts separate that most existing indices quietly blend together. Widely used instruments such as the UNDP’s Gender-related Development Index, the Gender Inequality Index, and the European Institute for Gender Equality’s Gender Equality Index combine information about how well a territory performs overall with information about gaps between women and men. The authors, following earlier critiques by scholars such as Iñaki Permanyer, argue that achievement levels can dominate these supposedly equality-focused scores. To quantify the problem, they replicated the GEI methodology on BES data and decomposed each score into an achievement component and an equality component. The result was striking: in 71 percent of cases the achievement component contributed more than half of the final index value, and on average the equality component accounted for only about 42 percent. That finding sits uneasily with official GEI guidelines, which explicitly recommend that the equality contribution should outweigh the correcting factor for every country and variable.

To build their own gender equality measure, the researchers constructed outcome-based gap indicators, comparing women’s and men’s outcomes directly while disentangling parity from overall achievement. Both composite indices were assembled through a careful pipeline of normalisation, aggregation, and sensitivity testing. For the well-being index, the authors tested sixteen alternative specifications, combining four normalisation methods, including z-scores, min-max rescaling, distance-from-best, and Borda scores, with four aggregation schemes mixing linear additive averages and geometric averages. For the gender equality index, which is bounded between zero and one, four aggregation schemes were tested. Regional hierarchies proved remarkably stable across all specifications, with correlations between baseline and average rankings exceeding an R-squared of 0.99, and trimmed-bounds normalisation designed to guard against outliers changed essentially nothing.

The first headline finding concerns geography. Regional well-being follows the familiar North-South gradient that has defined Italian economic life since unification, with northern regions such as Bolzano, Trento, and Emilia-Romagna at the top and southern regions clustered at the bottom. Gender equality, by contrast, displays a far more fragmented geography, with no comparable clean gradient. This divergence matters because it means policymakers cannot assume that lifting a region’s overall standard of living will automatically close the gaps between women and men, nor that prosperous regions are necessarily the most egalitarian ones.

Principal component analysis sharpened the picture. For well-being, the PCA identified a single broad achievement dimension that dominates regional differences, consistent with the idea that prosperous regions tend to do better across the board. For gender equality, the structure was more multidimensional: a first component captured what the authors interpret as a human development and civic dimension, anchored in social relations, health, education, and politics and institutions, while a second, more cautiously interpreted component reflected quality of life and economic gender equality. A bootstrap stability check based on 2,000 resamples of the 21 regions confirmed that the first component is highly stable and that the second is consistently retained as a separate dimension, though its economic anchor is less certain. Combining the retained components yielded exploratory regional profiles in which high well-being and high gender parity do not necessarily coincide, a result the authors present as descriptive rather than a statistically optimal clustering.

The paper also documents which side of the gender gap is advantaged, and where. Across the overall index, male outcomes were more favourable than female outcomes in 57.9 percent of indicators on average, meaning male advantage remains widespread. The pattern varies sharply by domain: men outperform women in 81.0 percent of economic indicators and 74.3 percent of social relations indicators, while education is the most female-favourable domain, with male prevalence of only 31.7 percent. Health and politics and institutions fall in between. Regional variation in overall male prevalence is comparatively modest, ranging from 51.8 percent in Bolzano to 63.3 percent in Umbria, but within specific domains the spread is much wider, as in politics and institutions, where male prevalence runs from 31.0 percent in Bolzano to 69.0 percent in Basilicata.

On dynamics, the study applied three complementary convergence diagnostics, testing whether lower-scoring regions caught up, whether cross-regional dispersion declined, and whether regional rankings shifted. The results split cleanly along the two dimensions. Well-being shows a short-run catch-up pattern, with lower-scoring regions growing faster and aggregate dispersion declining, the main exception being health. Gender equality tells a different story: while several individual domains display negative catch-up coefficients, aggregate catch-up is not statistically significant and there is no evidence of overall convergence in dispersion. Notably, the COVID-19 pandemic produced only limited reordering of regional positions, with rank correlations between consecutive years remaining close to their long-run averages, 0.973 for well-being and 0.765 for gender equality across the pandemic transition years.

The authors are candid about limitations. The binary women-men structure of the analysis reflects the form in which BES indicators are consistently available at the regional level, a constraint of the empirical setting rather than a claim that gender inequality can be reduced to a binary comparison, a point that resonates with decades of intersectionality scholarship. Missing data affecting 11 of the 64 indicators were handled by carrying forward last available values at the end of series and imputing internal gaps with adjacent observations. The authors also stress that the five regional profiles are an exploratory refinement of a coarser two-cluster structure that statistical diagnostics actually favour, reflecting the dominant separation between higher- and lower-well-being regions.

The broader implication is methodological as much as substantive. Unified gender-sensitive indices, the authors conclude, are useful for communication but tend to obscure the trade-offs between achievement and parity that matter for policy. Keeping regional well-being and gender equality as distinct measurements, while examining their interaction, reveals patterns that a single blended score would hide: a country where average living standards are slowly converging from South to North, but where the geography of gender equality is scattered, multidimensional, and showing no sign of aggregate convergence. As governments worldwide search for alternatives to GDP in the wake of the Stiglitz-Sen-Fitoussi commission and the 2030 Agenda’s Sustainable Development Goals, this Italian experiment offers a template, and a warning, about what happens when equality and achievement are finally counted separately.

Subject of Research: Measurement of regional multidimensional well-being and gender equality in Italy using composite indicators

Article Title: Regional Well-Being and Gender Equality Beyond GDP: A Dual Measurement Framework

Article References: Mariani, M., Pietropaoli, A., & Rughi, T. (2026). Regional Well-Being and Gender Equality Beyond GDP: A Dual Measurement Framework. Social Indicators Research, 185(1), Article 4. https://doi.org/10.1007/s11205-026-03942-5

Image Credits: AI Generated

DOI: 10.1007/s11205-026-03942-5

Keywords: beyond GDP, multidimensional well-being, gender equality, composite indicators, Italy, regional disparities, BES framework, principal component analysis, convergence analysis, social indicators, Istat, gender gap

The motivation behind the index is grounded in hard geopolitical arithmetic. In 2023, 95 percent of the EU’s imports of rare earth elements came from just three countries: China accounted for 46 percent, Russia 28 percent, and Malaysia 20 percent. Energy tells a similar story of exposure and rapid, painful adjustment. The share of natural gas imported from Russia fell from 48 percent in early 2021 to 17 percent by 2025, with the United States now supplying nearly half of the bloc’s liquefied natural gas. Against this backdrop, the European Green Deal, the Circular Economy Action Plan and the Raw Materials Initiative have all reframed circularity not merely as an environmental strategy but as a pillar of what EU policy calls open strategic autonomy — a deliberate balance between global openness and reduced dependency.

What distinguishes the new research from earlier attempts to rank countries on circularity is its conceptual target. Existing composite indices, the authors argue, essentially ask how circular a country’s economy has become, relying heavily on environmental indicators such as recycling rates, resource efficiency and emissions control. The SRI instead measures resilience-enabling capacity: the structural, productive and innovation-related conditions that determine whether an economy can absorb shocks, adapt and transform. To build it, the researchers standardized seven candidate indicators — circular material use rate, energy import dependency, economic globalization, consumption-based greenhouse gas footprint, environmental patents per capita, the UNCTAD Productive Capacities Index, and an energy diversification measure — and applied principal component analysis, a statistical technique that compresses correlated variables into a smaller set of uncorrelated components.

The statistics delivered a strikingly clean result. A single dominant component explained 56.4 percent of the total variance, with all remaining components showing eigenvalues below one, and a factor analysis confirmed the identical structure. Internal consistency, measured by Cronbach’s alpha, reached 0.80, comfortably above the conventional 0.70 threshold. Energy indicators, despite their obvious theoretical relevance, refused to cohere with the rest: across extensive robustness testing that included renewable energy share and energy intensity, their loadings on the first component ranged only from 0.06 to 0.25, compared with 0.35 to 0.54 for the core five indicators. Energy performance, the authors conclude, constitutes an empirically distinct dimension — a separate energy resilience construct — rather than part of the structural capacity captured by the SRI.

The weighting structure of the final index is where the study delivers its headline finding. The formula reads SRI = 0.209·NCMU + 0.251·NEIG − 0.263·NGHGFP + 0.273·NIC + 0.322·NPCI, with the coefficients derived from the data itself rather than assigned by judgment. Productive capacity carries the heaviest weight at 0.322, followed by innovation capacity at 0.273 and economic globalization at 0.251. The circular material use rate contributes positively but modestly at 0.209, while the greenhouse gas footprint enters negatively at −0.263. Innovation and productive capacity also showed the strongest pairwise correlation in the raw data, at r = 0.741. The message is that the technological and industrial base of an economy — not its recycling bin — is currently the strongest statistical companion of sustainable resilience.

Applied across the EU-26, the index reveals a Union split into three tiers along a pronounced north-west to south-east gradient. The high-resilience group comprises the Netherlands, Denmark, Luxembourg, Germany, Belgium, Finland, Ireland and Sweden, with average scores between 62.0 and 87.9 on a rescaled 0–100 range. A broad middle tier, including Estonia, Austria, France, Czechia, Spain, Italy, Poland and Latvia, clusters around the EU-26 average of 45.7. At the bottom sit Portugal, Lithuania, Greece, Slovakia, Croatia, Bulgaria and Romania, with averages from 5.5 to 28.7 — countries where low circularity coexists with weak productive and innovative capacity, a pattern a circularity-only index would misread as a simple recycling gap.

The longitudinal picture is equally revealing. The cross-country standard deviation of SRI scores fell from 26.34 in 2010 to 18.72 in 2022, a decline of nearly 29 percent, signalling partial convergence driven mainly by catch-up among laggards. Croatia posted the largest gain at +18.83 points, followed by Latvia, Romania, Lithuania and Slovakia. Yet the convergence is partly a story of decline at the top: Finland lost 23.88 points and Luxembourg 22.57, with falling innovation capacity — not environmental deterioration — driving the slide in Finland and Denmark. Slovenia’s drop from mid-table to the lower tier was propelled by a sharp erosion of productive capacity, despite improving circularity and emissions performance. The EU-26 average itself barely moved, fluctuating between 43.8 and 47.2 over twelve years.

Perhaps the most compelling validation of the index comes from its response to crises. Using fixed-effects panel regressions with structural break tests, the researchers found statistically significant downward shifts in the SRI only around the two great global shocks of the period: the COVID-19 pandemic, which produced an immediate drop of 4.44 points followed by a recovery trend of 4.72 points per year, and the Russia–Ukraine war, which coincided with a 5.09-point decline. Neither the European debt crisis nor Brexit registered a significant panel-wide break. The index, in other words, appears to measure exactly what it claims: the capacity of economies to withstand systemic, exogenous disruption rather than localized political turbulence.

For policymakers, the implications cut against one-size-fits-all circularity mandates. In Eastern and South-Eastern Europe, the barriers to a circular transition are structural rather than regulatory, and the authors suggest embedding circular economy conditionalities within existing Cohesion Policy instruments such as the European Regional Development Fund and the Just Transition Fund, rather than pursuing separate funding lines. For mid-tier countries with productive capacity but uneven innovation, cross-border initiatives like the Interregional Innovation Investments mechanism could help. For the leaders losing ground, the priority is sustaining innovation intensity through instruments such as Horizon Europe partnerships. The study also points to accelerating the Critical Raw Materials Act and related regulations as the most direct current bridge between circularity and resilience policy.

So is circular the new resilient? The authors’ own answer, delivered with empirical precision, is: not yet, but increasingly so. The circular economy contributes meaningfully to sustainable resilience, and its weight is expected to grow as the EU pushes toward its target of doubling the circular material use rate by 2030 — an ambition whose full effects fall largely beyond the study’s 2022 cutoff. What the evidence demands, the researchers argue, is not a retreat from circular policy but better sequencing: countries lacking the productive and innovation infrastructure to scale circular strategies cannot be expected to harvest resilience gains from recycling targets alone. The Sustainable Resilience Index offers a benchmarking tool for identifying exactly where those enabling conditions are missing — and, as the EU’s resource dependencies tighten, that diagnostic may prove as valuable as the transition itself.

Subject of Research: Measuring the relationship between circular economy performance and sustainable economic resilience across EU member states

Article Title: Is circular the new resilient? Rethinking EU performance through a new sustainable resilience index

Article References: Chovancová, J., & Petruška, I. (2026). Is circular the new resilient? Rethinking EU performance through a new sustainable resilience index. Environmental and Sustainability Indicators, 32, Article 101509. https://doi.org/10.1016/j.indic.2026.101509

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101509

Keywords: circular economy, economic resilience, Sustainable Resilience Index, European Union, principal component analysis, innovation capacity, productive capacity, critical raw materials, energy security, composite indicators, EU policy, convergence

The research, led by Ghinevra Comiti with Paul-Antoine Bisgambiglia and Nathalie Lameta, focuses on Corsica, an island whose sharp contrasts make it an ideal testing ground. A heavily developed coastline sits against a declining rural interior; a tourism-driven economy produces seasonal swings; and demographic growth concentrates in a handful of urban centers. The team built their system on a corpus covering all 361 Corsican municipalities, combining a composite well-being indicator called OppChoLiv, inventories of municipal amenities, and demographic profiles with survey verbatims from 246 respondents across 70 municipalities, qualitative interviews, and aggregated questionnaire scores spanning 16 dimensions of well-being, from transport and health to safety and housing.

At the heart of the system is retrieval-augmented generation, or RAG, a technique that grounds the answers of a large language model in a controlled document corpus rather than in the model’s training data alone. Standard RAG, however, struggles with the kind of questions decision-makers actually ask. A single query may cross several thematic dimensions, several municipalities, and several demographic groups at once. Semantic similarity search over fragmented text chunks handles localized factual questions well but falters when the answer requires a global synthesis, an effect compounded by the well-documented tendency of language models to lose track of information buried in the middle of long contexts.

The proposed architecture rests on three principles. First, the user’s question is explicitly decomposed into five sub-questions, each processed independently with its own retrieval step, so that every semantic dimension of a complex demand gets dedicated coverage. Second, the document index is enriched with 674 precomputed multi-level syntheses built along ten business-driven analytical views, such as a specific well-being dimension in a specific municipality, an approach inspired by the RAPTOR method but replacing its automatic clustering with a controlled partitioning by survey metadata. Each synthesis is attached to a known population of respondents, which makes every summary attributable, addressable, and reproducible, qualities that matter when a synthesis must be presented to territorial actors alongside its scope.

The third principle is a strict typing rule. A factual, quantitative query can only spawn factual sub-questions, and a qualitative query only qualitative ones, preventing the system from fabricating sub-questions for which no documentary support exists. Entity extraction is deliberately deterministic: municipalities are matched against a closed list, thematic dimensions against a fixed taxonomy of 16 categories, guaranteeing perfect precision at no inference cost. Retrieval then proceeds hierarchically, always prioritizing the most specific representation available, and injecting both the aggregated synthesis and the raw chunks behind it, so the generation model sees the big picture and the concrete evidence at once.

The team evaluated the architecture on a benchmark of 109 manually crafted questions spanning seven categories, from simple factual retrieval to causal reasoning, uncertainty handling, and deliberate trick questions about missing data. Answers were scored by an independent language model judge, GPT-4o, whose reliability was calibrated against human annotations and cross-checked against three alternative judges. The headline retrieval result is striking: the full architecture achieved an average Recall of 0.970, against 0.678 for vanilla RAG, a gap of nearly 30 percentage points reflecting a far more exhaustive mobilization of the available evidence.

On overall answer quality, the full system scored 4.62 out of 5, statistically indistinguishable from vanilla RAG’s 4.63, but the aggregate hides a crucial pattern. The advantage concentrates precisely where decisions are hardest: comparative reasoning, causal and counter-intuitive questions, and the delicate crossings between what indicators say and what inhabitants feel. Ablation tests confirmed that both the typing rule and the multi-level syntheses contribute significantly, and that removing the typing rule alone drops the score from 4.30 to 3.72, with the damage concentrated on targeted factual queries where an untyped decomposition spawns qualitative sub-questions that have no matching sources.

Case studies reveal why the design matters. Asked whether objective indicators and perceptions diverge in Bastia, vanilla RAG retrieved only quantitative scores and speculated about verbatims it never saw, while the full system articulated both registers, noting that mobility was rated objectively at 8.65 out of 10 even as residents scored it 2.24 out of 5, complaining of congestion. On a trick question about a municipality with good indicators but low perceived well-being, the full system correctly recognized that the available perception data could not be disaggregated by municipality, refusing to over-interpret a quantitative proxy as a measure of feeling. Yet the architecture has a documented weakness: on questions about missing information, its appetite for assembling rich contexts sometimes tempts it to approximate where a simpler system would refuse cleanly.

The authors are candid about costs and limits. The full pipeline issues roughly six times more language model calls per query than vanilla RAG, with latency about 4.6 times higher, though the offline synthesis construction cost only about $0.16 and is amortized once. Section-level differences often lose statistical significance after correction, and the Corsican corpus is entirely in French. The method transfers to other territories only where surveys carry structured metadata comparable to theirs. The team’s proposed remedy is a two-tier system that routes simple factual questions to cheap direct retrieval and reserves the heavyweight machinery for genuinely analytical queries, alongside an agentic extension in which a controller decides actively whether to answer or refuse, a direction they identify as the immediate next step toward AI assistants that policymakers can actually trust.

Subject of Research: A hybrid quantitative-qualitative retrieval-augmented generation architecture for territorial well-being decision support

Article Title: A hybrid quantitative–qualitative RAG approach for territorial well-being decision support

Article References: Comiti, G., Bisgambiglia, P.-A., & Lameta, N. (2026). A hybrid quantitative–qualitative RAG approach for territorial well-being decision support. Machine Learning with Applications, 26, Article 101005. https://doi.org/10.1016/j.mlwa.2026.101005

Image Credits: AI Generated

DOI: 10.1016/j.mlwa.2026.101005

Keywords: retrieval-augmented generation, large language models, territorial well-being, decision support systems, Corsica, survey data, RAPTOR, query decomposition, hallucination control, public policy, composite indicators, LLM-as-a-judge

The central object of the new framework is the Mahalanobis Country Gender Homogeneity Index, or MCGHI. Instead of averaging national scores, the index measures the distance between the gender equality profiles of EU Member States, where each profile is a vector whose components describe the country’s position on the different dimensions of the Gender Equality Index. The key methodological choice is the distance function itself. The authors adopt the classical Mahalanobis distance, first proposed by the Indian statistician P. C. Mahalanobis in 1936, which weights differences between profiles through a positive definite matrix Sigma. This matrix encodes not only the dispersion of each dimension but also the statistical relationships among dimensions, allowing the index to distinguish, for example, between a gap in knowledge and a correlated combination of gaps in knowledge and money.

This structure gives the MCGHI a set of formal properties that the authors prove in detail. The index satisfies uniformity and country anonymity for any positive definite metric matrix, meaning that the measurement rules apply identically to every country and do not depend on national labels. Dimensional neutrality holds whenever the metric matrix is invariant under permutations of the dimensions; a particularly important special case arises when Sigma is a scalar multiple of the identity matrix, which treats all dimensions symmetrically and identically scales their variability. The index is also invariant under positive affine transformations of the data, provided the metric matrix is transformed consistently, so results do not change if dimensions are rescaled or shifted. In plain terms, the tool is robust to arbitrary units and conventions, which is essential when comparing indicators as heterogeneous as income, political representation and health outcomes.

The choice of Sigma is not innocuous, and the authors make its role explicit rather than burying it. Different specifications can account for scaling, for domain-specific dispersion and for cross-domain dependence among the six core domains and their underlying sub-domains of the Gender Equality Index. Because the empirical covariance structure of gender equality data changes over time and across specifications, the researchers treat the metric matrix as a family of alternatives to be tested, running the analysis under several plausible versions of Sigma and reporting how conclusions respond. This sensitivity discipline reflects a broader methodological literature on composite indicators, which has long warned that weighting and aggregation choices can drive rankings as much as the underlying data do. The MCGHI takes the opposite path from composite aggregation: it refuses to collapse the profile at all, and instead compares whole profiles directly.

The empirical application turns to data from the Gender Equality Index published by the European Institute for Gender Equality, which is publicly available and covers the EU Member States. Using national profiles from 2013 through 2024, the authors track the homogeneity of gender equality across the Union, asking whether countries have grown more alike or more distinct over the decade. The headline finding is a long-run increase in gender equality homogeneity between 2013 and 2024: by the distance-based measure, EU nations’ gender equality profiles have, on average, converged. The result echoes earlier European Commission analyses reporting upward convergence in gender equality, but it arrives through a fundamentally different measurement route, one that preserves the multidimensional composition of each country’s profile rather than compressing it into a single score first.

Yet the study is equally candid about the limits of that finding. The continuity, timing and magnitude of the homogeneity trend depend on the metric assumptions embedded in Sigma. When dimensions are scaled symmetrically, convergence appears one way; when domain-specific dispersion or cross-domain correlations are taken into account, the shape of the convergence path changes. Some sub-periods show steadier movement than others, and the timing of shifts in homogeneity is not invariant across metric choices. The authors do not present this as a weakness to be hidden but as the honest price of measuring a genuinely multidimensional phenomenon. The practical lesson for policymakers is that claims about convergence should be read alongside the assumptions that produced them, and the MCGHI is designed to make those assumptions visible and testable.

The distinction between level and homogeneity is more than a technical nuance, because the two can move in opposite directions. It is entirely possible for average gender equality across the EU to rise while the spread of national profiles widens, or for profiles to converge at a mediocre level rather than a high one. The overall Gender Equality Index score answers the first question, about levels, but is largely silent on the second, about structural similarity. By complementing rather than replacing the composite score, the new index allows analysts to detect whether progress is happening in a uniform European pattern or whether national gender regimes are pursuing distinct trajectories that happen to yield similar totals. That distinction matters for policy design, since uniform convergence suggests the possibility of EU-wide benchmarking, while persistent heterogeneity argues for differentiated national strategies.

The methodological lineage of the paper is broad. The authors build on the Mahalanobis distance tradition, including earlier work by González-Arteaga, Alcantud and de Andrés Calle that used the same distance in a cardinal dissensus measure, and they engage the substantial literature on composite indicators, from the OECD and European Commission Joint Research Centre handbook to multicriteria approaches and critiques of the Gender Equality Index itself. Scholars such as Permanyer and, more recently, Schmid and Elliot have argued that the EU index conflates corrected gender gaps with achievement levels, a debate that underscores how contested the definition of gender equality measurement remains. The homogeneity index sidesteps part of that controversy by measuring similarity of profiles without asserting what any single country’s score should be, though it inherits the dependence on however EIGE constructs the underlying dimensions.

The timing is significant. The 2024 edition of the Gender Equality Index, titled Sustaining Momentum on a Fragile Path, described progress that is real but uneven, and the 2025 edition promised sharper data for a changing world. Within that context, a tool that quantifies how tightly the Union’s gender equality landscape is knit together offers a new lens on the EU’s stated goal of a Union of Equality. If the 2013 to 2024 trend toward homogeneity continues, future editions of the index could document an increasingly uniform European gender regime; if metric-sensitive reversals appear, they may flag domains where convergence is stalling or where countries are drifting apart. The framework is general enough to be applied beyond the EU wherever multidimensional gender data exist, and the authors’ formal results on permutation invariance and affine robustness make it portable across indicator systems with different dimensional orders and scales.

What the study ultimately delivers is a change of question. Rather than asking which country is most gender equal, it asks how alike the family of European countries has become in the shape of its gender equality achievements, and it answers with a distance measure whose every assumption is stated, varied and stress-tested. The finding of a long-run increase in homogeneity across the EU between 2013 and 2024 is a cautiously optimistic signal that a decade of policy attention has drawn Member States closer together. But the paper’s deeper contribution is methodological: a demonstration that multidimensional social measurement can retain its full structure, respect formal fairness axioms, and remain honest about the metrics that shape its conclusions. For a Union that stakes its credibility on data-driven decision-making, that combination of rigor and transparency may prove as consequential as the headline result itself.

Subject of Research: A distance-based gender equality homogeneity index applied to European Union data

Article Title: A Distance-Based Gender Equality Homogeneity Index: Application to European Union Data

Article References: A Distance-Based Gender Equality Homogeneity Index: Application to European Union Data. (n.d.). https://doi.org/10.1007/s11205-026-03937-2

Image Credits: AI Generated

DOI: 10.1007/s11205-026-03937-2

Keywords: gender equality, European Union, Mahalanobis distance, Gender Equality Index, composite indicators, social indicators, convergence, multidimensional measurement, gender gap, homogeneity index, data-driven decision-making, Distance-Based