This chapter analyses trends in inclusive growth outcomes across cities, focusing on economic dimensions such as household disposable income and employment alongside non-monetary dimensions such as health. It examines patterns across the five pillars of the OECD Inclusive Growth in Cities framework: education, labour markets, housing and the built environment, public infrastructure and services, and fair climate action. The chapter also explores how structural factors of cities – analysing inclusive growth performance by size, population dynamics and economic profile – help explain inclusive growth outcomes disparities across urban areas.
What Works for Inclusive Growth in Cities
Abstract
Measuring inclusive growth in cities requires a wide range of economic, social, spatial and environmental indicators, since no single measure captures the concept in full.
Cities face a convergence of macroeconomic challenges that undermine the potential for inclusive growth: productivity growth has slowed markedly across OECD countries, real disposable incomes have lagged in gross domestic product (GDP) growth, and the combined pressures of wage stagnation and rising housing costs are pushing more working households into poverty risk.
Inclusive growth outcomes are shaped by inequalities across multiple dimensions of urban life. Educational outcomes are shaped by neighbourhood effects and intergenerational poverty, city labour markets face polarisation and uneven participation rates, soaring housing costs erode disposable incomes and entrench spatial inequality, unequal access to transport, services and green space deepen disadvantage, and climate risks fall disproportionately on the most vulnerable residents in cities.
Analysis of more than 600 European Union (EU) cities finds that growth does not automatically translate into broader improvements in living standards and differences emerge according to city type. In large and dynamic urban economies, productivity gains are accompanied by rising housing costs, labour market polarisation and congestion. Shrinking or industry-oriented cities may face weaker labour markets and skills erosion.
The evidence illustrates why inclusive growth policies must be tailored to the structural realities of individual cities. Managing housing pressures and addressing pockets of deprivation is key in large, fast-growing cities. For mid-sized cities, priorities include supporting economic diversification and responding to growing affordability pressures. For smaller, shrinking or industry-oriented cities, managing demographic decline and adapting service provision to rising per capita costs is a central challenge.
In cities, the objective of inclusive growth policies is to improve outcomes across income, health, employment and beyond, including access to education, infrastructure and services. This chapter examines trends in inclusive growth outcomes in cities by analysing household disposable income and other dimensions such as health and employment at the subnational level as well and trends in outcomes across the five policy pillars of the OECD Inclusive Growth in Cities framework: education, labour markets, housing and the built environment, public infrastructure and services, and fair climate action (OECD, 2016[1]).
Concepts, indicators and data considerations
Assessing inclusive growth in cities draws on a wide range of indicators, reflecting the multidimensional nature of the concept (OECD, 2016[2]). There is no single measure that can explicitly capture inclusive growth. In practice, a variety of economic, social, spatial and environmental indicators are used depending on data availability and analytical purpose (Boarini, Murtin and Schreyer, 2015[3]). These include monetary outcomes such as household disposable income and GDP per capita, labour market indicators such as employment and participation rates, and non-monetary indicators such as health, education, housing affordability, service accessibility and environmental exposure. Subjective well-being measures like life satisfaction and perceived quality of life can also provide complimentary insights, although they are not explicitly included in the analysis that follows.
What data can (and cannot) tell us at the city level
At the same time, measuring inclusive growth at the city level remains constrained by data limitations. Policies for more inclusive growth require a solid evidence base, yet city-level data constraints limit the feasibility of constructing consistent composite indicators across cities and countries. As a result, some indicators at this scale, such as for income distribution, health and economic output, are less consistently available at the city level, while others such as demographic or spatially derived environmental indicators are more readily observed. Given these constraints, this chapter draws on a range of subnational indicators, primarily from the OECD Database on Regions, Cities and Local Areas and Eurostat.
Finally, it is important to recognise that different indicators capture distinct dimensions of inclusive growth. Aggregate economic measures such as GDP per capita reflect a city’s capacity to generate prosperity but do not capture how widely these benefits are shared. Indicators on labour market issues, poverty rates, access to services and environmental conditions are therefore essential complements to understand inclusion. In cities in particular, aggregate outcomes can mask significant intra-urban disparities driven by spatial inequalities in access to opportunities, service and living conditions which ultimately shape residents’ lived experience (Melic, Huovinen and Rubin, 2026[4]). The analysis presented later in this chapter provides a quantitative perspective to better understand inclusive growth in cities through city size, demographic trends and economic profile, and influence inclusive growth in cities.
Productivity slowdowns, stagnating incomes and rising poverty risks in cities
Growth in household disposable income is driven by various macroeconomic factors. A key driver of this growth is labour productivity, typically measured as output per worker, which has long been recognised as the engine behind sustained economic growth and rising incomes (Solow, 1956[5]; Krugman, 1994[6]; André and Gal, 2024[7]). However, recent slowdowns in productivity growth across and within countries risk undermining potential for inclusive growth in cities. Data for the European Union show a notable downward trend: while annual productivity growth averaged 2% during the period 1996-2001, in 2001‑2007 this dropped to 1.5%, and finally fell to 1% for the period 2013‑2019. Although productivity gaps have narrowed somewhat between high- and low-performing large regions within OECD countries, suggesting poorer regions are catching up, overall productivity growth remains weak. Between 2002 and 2022, low‑productivity regions (bottom 20%) saw an average annual growth rate of 1.3%, compared to just 1% for high-productivity regions (top 20%) (OECD, 2024[8]). This stagnation in productivity is largely driven by disparities between leading and lagging firms, weaker business dynamism and broader economic conditions (Cho et al., 2024[9]). The productivity slowdown has squeezed living standards and puts pressure on cities to generate rising incomes, improve job opportunities and ultimately drive inclusive growth. Cities experiencing low productivity and weak economic growth can find themselves trapped in a cycle whereby limited generation of good quality jobs and constrained public revenues hinder their ability to invest in services to support inclusion (OECD, 2020[10]).
In addition to this productivity crisis, wage stagnation and rising living costs pose a further threat to inclusive growth in cities. Recent trends show disposable income growth has lagged in GDP growth, particularly in capital regions (Figure 2.1). This indicates that even when GDP per capita does rise, the benefits may not be fully felt by all households. Moreover, in seven out of ten OECD countries, capital regions saw a sharper decline in real disposable incomes between 2019 and 2023. Stagnant wages can increase the risk of in-work poverty. In 12 EU Member states, stagnant wages have contributed to a rise in the risk of in-work poverty since 2019 (Eurostat, 2025[11]). Although inflation has eased in many countries, the cumulative impact of successive price shocks continues to affect city residents, increasing the cost of living and squeezing real incomes, especially for poorer households (OECD, 2024[12]; OECD, 2024[8]). Low wage growth, combined with higher cost of living in cities, undercuts the ability of residents to afford essential goods and services which enable economic participation, including housing, childcare and mobility. Higher housing costs in cities are intensifying poverty risks in some EU countries, including in-work poverty. While patterns vary across countries, high housing costs in urban areas are pushing more working households into poverty and, in some regions, this urban pressure now exceeds that of rural areas. In Belgium, the at-risk-of-poverty rate after housing costs have been deducted1 is 39.9% in cities compared to a national average of 28.8%. Similarly, in Denmark, the urban at-risk-of-poverty rate after housing costs reaches 40.9% versus a national average of 34.0%. In contrast, in Romania, at-risk-of-poverty is far more concentrated in rural areas (45.8%) compared to urban areas (15.0%).
A similarly nuanced picture emerges with “at risk of poverty or social exclusion” (AROPE) across different EU territories. First, comparing across regions, the urban AROPE rate is highest in Southern EU cities (23.6%), followed closely by North-Western EU cities (23.2%), and significantly lower in Eastern EU cities (14.6%).2 Second, when comparing AROPE rates within each region by degree of urbanisation, different patterns emerge. In North-Western EU countries, the risk is highest in cities (23.2%), lower in towns and suburbs (19.2%), and lowest in rural areas (15.9%), indicating an urban disadvantage. In contrast, in Southern and Eastern EU countries, the trend is reversed: rural areas show the highest risk (Southern EU: 26.7%; Eastern EU: 27.9%), while cities report the lowest (Southern EU: 23.6%; Eastern EU: 14.6%) (European Union, 2024[13]).
Annual average % growth rate, capital regions and other regions
Note: Average annual growth rate of real household disposable income (USD PPP, reference year 2015), in %. 2014-18 and 2019-23, large regions (TL2).
Health outcomes in cities are essential to measuring inclusive growth outcomes. Health outcomes, calculated based on life expectancy at birth for example, are a core component of multidimensional living standards, a measure of inclusive growth. The relationship between inclusive growth and health is complex; for individuals, poverty can be both a cause and consequence of poor health. Cities generally outperform non-metropolitan areas when it comes to health outcomes, with an average life expectancy 2.4 years higher in metropolitan regions than in remote areas (Figure 2.2). Across 20 OECD countries, metropolitan regions had higher life expectancy than rural regions in all but one country. This advantage reflects greater access to healthcare resources, such as a higher density of doctors and hospitals, as well as differences in healthcare quality, among other factors (OECD, 2024[12]).
However, aggregate advantages in cities can mask significant inequalities within cities themselves. Large disparities in life expectancy, healthy life years and the prevalence of chronic diseases can exist between neighbourhood located only a few kilometres apart, reflecting differences in income, employment, housing quality, environmental conditions and access to services. In London (the United Kingdom), life expectancy can vary by more than five years between neighbourhoods, with widening inequalities reflecting socioeconomic disparities (ONS, 2024[14]). Similar patterns are observed in many other cities, with residents of disadvantaged neighbourhoods experiencing poorer health outcomes than those in more affluent areas, reflecting inequalities in income, housing conditions environmental quality and access to opportunities (OECD, 2018[15]).
While better access to healthcare can contribute to greater inclusive growth in cities, structural challenges in cities persist, such as environmental factors. They include higher exposure to air pollution, linked to respiratory and chronic diseases, or extreme temperatures, which has wide ranging health effects including on mortality rates, cognitive performance and violent crime (Dell, Jones and Olken, 2012[16]).
Population-weighted average
Note: Population-weighted average life expectancy at birth in 2023, small regions (TL3). 2022 for Australia, Czechia, Denmark, Estonia, Finland, France, Hungary, Latvia, Lithuania, Norway, Portugal and Spain; 2021 for Sweden and the United Kingdom; 2020 for Germany, Japan, Korea and Türkiye; 2018 for New Zealand.
Source: OECD (2025[17]), OECD Regions, Cities and Local Areas Database, http://oe.cd/geostats.
Finally, the employment dimension of inclusive growth has shown considerable resilience in recent years. Recent labour market tightness, which has been particularly acute in capital-city regions, can constrain business expansion and dampen local economic potential (OECD, 2024[8]). At the same time, uneven access to employment, persistent skills mismatches and unequal access to reskilling opportunities risk undermining long-term productivity by limiting the efficient allocation and utilisation of labour across firms and sectors (OECD, 2024[8]). Looking ahead, technological change and automation are likely to accelerate these trends, creating a dual challenge for cities: to foster sustained job creation while ensuring that employment remains accessible, provides dignity to workers and meets high standards of quality.
Inclusive growth outcomes are also shaped by five dimensions of urban life, which are also the five policy pillars of the OECD’s Inclusive Growth in Cities framework: education, labour markets, housing and the built environment, public infrastructure and services, and fair climate action. This section examines trends in cities across these dimensions and their impact on different measures of inclusive growth.
Education
Education and skills development generally boost individual’s future employment rates, earnings, productivity, health and social mobility across the life course. Cities have higher levels of educational attainment than rural areas on average (OECD, 2024[18]; Eurostat, 2024[19]), but disparities persist, often mirroring wider socio-economic and spatial divides. Children in deprived neighbourhoods are more likely to attend schools where the majority of students come from low-income backgrounds, limiting educational opportunities due, for example, to fewer educational resources or extracurricular activities (OECD, 2025[20]). In later life, inequalities can persist with children from deprived neighbourhoods having more limited job finding networks, limiting future employment prospects due to “neighbourhood effects” (OECD, 2018[15]). Educational inequalities can also have wider economic implications for cities. Research from the United Kingdom shows that cities with a higher proportion of residents without formal qualifications tend to have lower employment rates, whereas cities with a greater share of highly qualified residents are among the most productive (Thew, 2025[21]).
Educational inequalities also intersect with other forms of disadvantage and discrimination, including race and migration background. Across the European Union, foreign-born students lag behind native‑born peers by the equivalent of nearly 1.5 years of schooling, while 45% lack basic literacy skills (OECD, 2025[22]). Even among native-born children of immigrant parents, around one in three lack basic reading skills at age 15, compared to one in five among students with native-born parents. Educational gaps have significant outcomes for future labour market inclusion: in 2024, rates of young people not in employment, education or training (NEET) were substantially lower among those with tertiary education (OECD, 2021[23]). At the same time, educational attainment does not always translate into equal opportunities later in life. While girls and many students from ethnic minority backgrounds outperform peers educationally in some OECD countries, they continue to face inequalities in labour market outcomes and wider socio-economic opportunities (Farquharson, McNally and Tahir, 2024[24]).
Labour markets
Despite overall employment gains, labour market differences between men and women in cities persist. While female employment rates in most OECD regions are above pre-COVID levels (Figure 2.3), the gap with male employment remains. In EU cities, there is a 9-percentage point (p.p.) gap between men and women (ages 20-64 years old) who are outside the labour force, with 23.6% of women, compared to 14.7% of men (Eurostat, 2023[25]). Although this gap is generally smaller in EU cities than in towns, suburbs or rural areas, it persists even in urban settings with higher labour demand. Closing these male-female employment gaps could yield substantial economic benefits. OECD estimates suggest that eliminating employment disparities between men and women across OECD countries could raise GDP per capita growth by 0.2 p.p. (OECD, 2025[26]). Notably, however, the potential gains vary across places, with smaller effects in cities where female employment rates are already close to the levels for males. For example, in Finland, Latvia and Lithuania, gender gaps in urban areas are among the lowest in the European Union (0.3%, 1.2% and 2.2% respectively) (Eurostat, 2023[25]).
Labour market inequalities affect a wide range of population groups beyond gender differences. Young people, older workers, migrants and persons with disabilities often face persistent barriers to accessing stable and well-paid employment, even in economically dynamic cities. Across the European Union, 11% of young people aged 15-29 were NEET in 2025, with rates consistently higher for young women and those from disadvantaged backgrounds (Eurostat, 2026[27]). Persons with disabilities also experience substantial employment gaps: across OECD countries, employment rates for working-age adults with disabilities are on average 27 p.p. lower than for those without disabilities (OECD, 2022[28]). Migrants similarly face weaker labour market outcomes despite strong labour demand in many urban areas. In the European Union, the employment gap between native-born and foreign-born populations was approximately 8 p.p. in 2023, with even larger disparities for migrant women (Eurostat, 2026[29]). Similarly, older workers can face challenges linked to skills transitions, health conditions and age discrimination, particularly as labour markets adapt to digitalisation and the green transition (OECD, 2025[30]).
Labour market polarisation is also intensifying, with high-skilled, high-wage jobs diverging from a growing share of low-paid and precarious work. The rise of temporary contracts, zero-hour roles and platform-based gig work, particularly among vulnerable groups such as youth, migrants and women, contributes to in-work poverty and limits economic mobility. Across OECD countries, non-standard forms of work now account for more than one-third of total employment, including temporary, part-time and self-employed work arrangements (OECD, 2019[31]). Temporary employment alone represents around 12% of dependent employment across OECD countries but rises to more than 25% among young workers (OECD, 2019[31]). Non-standard workers often earn less than standard workers, face higher unemployment risks and have interrupted pension contribution histories (OECD, 2019[31]). These forms of non-standard employment can disproportionately affect groups already facing labour market barriers, including youth, migrants and women, limiting economic mobility and reinforcing wider inequalities.
Source: OECD (2025[17]), OECD Regions, Cities and Local Areas Database, http://oe.cd/geostats.
Housing and the built environment
Across the European Union and beyond, housing conditions increasingly mirror and reinforce social and spatial inequalities. National housing costs have surged ahead of income growth, eroding disposable income and amplifying inequalities. Between 1996 and 2024, real house prices in OECD countries rose by 76%, while GDP per capita grew by only 49% (OECD, 2025[32]). In many cities, wages have not kept pace with rent or purchase prices, pushing more residents into precarious housing situations. This includes rising overcrowding, eviction risk and homelessness, with 20.4% of city residents living in overcrowded conditions in 2025, compared to 14.8% in towns and suburbs and 14.2% in rural areas (Eurostat, 2025[33]). Young people are particularly vulnerable: 26% of EU residents aged 15-29 live in overcrowded dwellings, compared to 17% of the general population (European Commission, 2023[34]).
Increasing differences in housing affordability within and between cities and their wider regions can lead to socio-economic segregation, entrenching inequality and threatening social cohesion and stability (Musterd et al., 2016[35]). After deducting housing costs, city residents in the European Union on average face a notably higher risk of poverty than those living in towns, suburbs or rural areas (Figure 2.4). In cities, the poverty risk is nearly 2 p.p. higher than in towns and suburbs, and 0.1 p.p. higher than in rural areas (Eurostat, 2024[36]). This highlights the deepening impact of rapidly rising urban house prices on living standards of city dwellers. As discussed earlier, this pattern is not uniform across EU Member states and regions, reflecting differences in housing markets and social policies among other factors; however, overall trends point to a growing affordability gap in urban areas. Regional or local anti-poverty strategies, such as those integrating housing support with health and social services or targeting homelessness prevention have shown potential to reduce this risk (OECD, 2024[37]).
The consequences of housing stress extend beyond the financial implications. Poor-quality or overcrowded housing is linked to adverse health outcomes including respiratory problems from damp or poorly insulated buildings (OECD, 2021[38]). It can also hinder children’s educational performance due to a lack of quiet spaces or from chronic stress (OECD, 2023[39]). Overcrowding and housing instability can also increase the likelihood of absenteeism from work or school and reduce the capacity to participate in community life, limiting social mobility. A growing concern with regards to housing is from the “residential accident”, a sudden, temporary loss of housing (often due to eviction) that can have long-lasting effects on economic prospects, mental health and educational attainment. To prevent such episodes, proactive housing and social policy are crucial to avoid a spiral of exclusion.
Energy poverty is also a key challenge, further exacerbated by the 2022 energy crisis. That year, more than 41 million Europeans were unable to keep their homes adequately warm (European Parliament, 2022[40]). Residents living in poorly insulated buildings are acutely affected. While efforts to decarbonise residential buildings such as retrofits and energy efficiency upgrades can reduce the risk or energy poverty in the longer run, they risk placing disproportionate financial burdens on lower-income households in the short run, either through out-of-pocket retrofit costs for homeowners or rent increases that are passed through to tenants.
Demographic shifts, particularly the growing share of older residents, are also reshaping housing needs. As cities age, demand for housing solutions that enable independent living and inclusion in the community, such as step-free accommodation, are rising; cities must address these demands to promote physical accessibility (OECD, 2025[30]). In the European Union, new guidance has been adopted to promote independent living and inclusion of persons with disabilities, which can often occur at an older age. In the housing domain, the guidance underscores the importance of ensuring access to non-segregated and accessible housing within the community, with sustained investments in accessible social housing identified as a key enabler of this objective (European Commission, 2024[41]).
EU countries by degree of urbanisation
Note: The at-risk-of-poverty rate is the share of people whose equivalised disposable income (after social transfers) is below the at-risk-of-poverty threshold, which is set at 60% of the national median equivalised income. Housing costs are accounted for by subtracting housing-related expenditures (e.g. rent, energy bills) from household disposable income. Degree of urbanisation uses the EU Nomenclature of Territorial Units for Statistics (NUTS) and local administrative unit (LAU) level classification, calculated based on population density and settlement size.
Source: Eurostat (2024[36]), “At-risk-of-poverty rate after deducting housing costs by degree of urbanisation”, https://doi.org/10.2908/ILC_LI48 (accessed 5 June 2026).
Public infrastructure and services
Access to high-quality public services, amenities and cultural life is a critical enabler of inclusive growth in cities. However, disparities in physical accessibility, affordability and participation persist and can reinforce wider inequalities across income, age, gender and place. Disadvantaged groups face disproportionate barriers to access services and amenities with wide disparities both within and between cities.
For example, across 28 OECD countries, 36% of the old-age population (over 65 years old) in mid-sized and large3 functional urban areas (FUAs) cannot reach a pharmacy on foot within 15 minutes (OECD, 2024[8]). Lack of proximity to essential services limits independence, increases social isolation and aggravates health risks, particularly for those with limited mobility or without access to private transport.
Access to efficient and affordable public transport and active mobility underpins access to employment, education and healthcare. Public transport helps residents develop economic capital (income), social capital (networks) and human capital (skills) (Kaufmann, Bergman and Joye, 2004[42]). However, access to public transport remains unequal, with 29% of people in mid-sized and large FUAs unable to reach a public transport stop within 10 minutes of walking (OECD, 2024[8]). Lower‑income residents often face longer commutes, greater transport expenditure relative to their incomes and more limited connectivity to jobs, particularly for those working irregular hours (Curl, Clark and Kearns, 2018[43]).
Meanwhile, transport policies are not always mindful of the specific challenges faced by population groups such as caregivers, women, older adults, people with disabilities or children (Akyelken, 2020[44]; Plyushteva and Schwanen, 2018[45]; OECD, 2025[30]). This results, among other barriers to inclusive mobility, in broader patterns of infrastructure inequality globally. A study of inequality of accessibility in 585 European cities found substantial inequalities in accessibility within cities, with residents’ access to destinations within a 15-minute walk, including jobs, supermarkets, education opportunities or parks, varying considerably across neighbourhoods (Vale and Lopes, 2023[46]). These findings highlight the need to improve accessibility in certain urban neighbourhoods to expand opportunities for high-quality employment and access to healthcare.
Cultural participation helps foster inclusive growth, yet access remains deeply unequal across social and spatial lines in EU cities. In general, people living in EU cities are more likely to engage in cultural activities than those living in rural areas and smaller towns, reflecting disparities in access to infrastructure, transport and opportunities. However, people with lower incomes and lower educational attainment report lower rates of participation in cultural activities, with participation in cultural activities among people with the highest incomes at least twice of those with the lowest incomes in 17 EU countries (Eurostat, 2024[47]). These differences matter, as cultural participation fosters greater social cohesion, bridging groups, stimulating personal development and civic engagement, and driving inclusive growth (OECD, 2022[48]).
Fair climate action
Climate risks are intensifying in cities and disproportionately affect vulnerable populations. Climate shocks such as heatwaves and flooding often compound existing social or spatial inequalities within cities. Larger cities are increasingly exposed to extreme heat, with urban heat island (UHI) effects pushing average EU city temperatures 4-6°C above surrounding rural areas, with peaks reaching 10°C (Iodice et al., 2024[49]). UHI intensity tends to rise with city size due to sealed surfaces, dense infrastructure, limited ventilation and insufficient green space. In the European Union, cities like Bucharest and Sofia in Romania and Turin in Italy experience some of the highest UHI levels (Iodice et al., 2024[49]). These conditions pose serious health risks, particularly for older adults, children, low-income groups and those with pre-existing conditions, while also worsening air pollution and increasing energy demand.
Access to urban green space is also highly uneven. A recent assessment of 862 European cities found that fewer than 15% of urban residents benefit from the full “3-30-300” principle – having three trees visible from every home, 30% canopy cover in each neighbourhood, and access to ahigh-quality green space within 300 metres. At the same time, 21% of residents live in areas meeting none of these benchmarks (Bertassello et al., 2026[50]). The study also identified a pronounced “green divide”, with wealthier neighbourhoods consistently enjoying greater tree cover and proximity to nature than lower-income areas. Southern and lower-income cities including Athens (Greece), Palermo (Italy) and Cordoba (Spain) recorded particularly low compliance rates, reflecting both climatic concerns and disparities in investment capacity.
Urban greenery plays an important role in mitigating heat stress, improving air quality, reducing noise pollution and supporting physical and mental well-being. Yet urbanisation and continued loss of tree cover in many cities risk exacerbating existing inequalities in exposure to climate hazards. Between 2010 and 2020, Europe’s urban population grew by 16%, while green urban areas and tree cover declined slightly, increasing pressure on already limited green infrastructure (Bertassello et al., 2026[50]).
Flood risk is another major concern. In 45 OECD regions across 18 countries, over 20% of the population is at risk of river flooding and in Rotterdam, the Netherlands, this figure exceeds 60% (OECD, 2023[51]). The impacts of such climate risks are rarely felt equally. Marginalised groups are more likely to live in flood-prone areas and face greater difficulty recovering from climate-related losses, while children and chronically ill individuals are more susceptible to waterborne disease following flooding.
Furthermore, while many cities continue to advance decarbonisation plans, few fully integrate equity considerations or explicitly target the needs and constraints of vulnerable groups, even though such approaches have the potential to simultaneously reduce emissions, lower living costs and improve health outcomes. As a result, climate policies risk overlooking the uneven distribution of costs and benefits associated with the green transition. For example, introducing new green spaces can drive up local property values and contribute to the displacement of lower-income households, while housing renovation costs might be passed on to tenants through higher rents.
Neighbourhood effects and urban form can influence inclusive growth in cities
Neighbourhood effects and the clustering of high-status occupational groups in some neighbourhoods reshapes the geography of inequality. These patterns raise concerns about a vicious cycle of spatial and social disadvantage, where neighbourhood conditions shape life chances, particularly for children (van Ham et al., 2011[52]). For instance, the neighbourhood a child grows up in plays a crucial role in shaping their future life chances, with evidence suggesting that its importance for intergenerational mobility has increased over time. Research from the United States highlights that this is resulting in lower intergenerational mobility in denser urban areas, a trend which has worsened since the mid-20th century with the rise of urban sprawl, longer commutes and increased family instability (Connor et al., 2025[53]). Separate research has shown that children raised in high-opportunity neighbourhoods, characterised by quality schools, economic diversity and strong public services, are more likely to achieve better educational and economic outcomes (Chetty and Hendren, 2018[54]; Chetty and Hendren, 2018[55]; Chetty et al., 2022[56]).
Urban sprawl and the growing segregation of cities have deepened spatial inequalities, limiting access to opportunity for children from disadvantaged backgrounds. The shift towards a knowledge-based economy has further reinforced these disparities, as economic success now depends more on skills and education than proximity to jobs, for example (Connor et al., 2025[53]). Similar effects have been found in the European Union. OECD research finds that, in the Netherlands, living in areas with a high concentration of social assistance recipients increases an individual’s own likelihood of receiving social benefits, with this effect particularly strong in larger cities (Moreno-Monroy et al., 2025[57]).
Urban form also shapes neighbourhood effects by influencing the development of social capital through the networks and relationships that connect individuals to job opportunities, mentorship and support systems. Well-connected and mixed-use neighbourhoods can facilitate “bridging” forms of social capital, where diverse groups can share resources, tend to lead to greater resilience and inclusion. However, patterns of urban growth, including car-dependent sprawl and rising segregation can weaken community engagement and deepen inequality. In the face of economic and technological disruption, local networks that help displaced workers retrain and re-enter the labour market are as important as formal policy interventions (Antonietti, Burlina and Rodríguez-Pose, 2025[58]).
Inclusive growth in different types of cities: insights from a quantitative analysis of EU cities
Typologies of cities can be formed around contextual factors and structural conditions that may influence inclusive growth capabilities and challenges for groups of cities. Building on ideas of “club theory” (Rodríguez-Pose et al., 2024[59]), the analysis presented here applies a typology lens to examine how inclusive growth dynamics can vary systematically across EU cities.
Across more than 600 cities in the European Union, inclusive growth outcomes are found to differ according to structural and long-term dynamics. To assess the role of such dynamics, three dimensions were analysed: city size, demographic trajectory and economic profile (Figure 2.5):
City size groups are based on total population in 2023 with FUAs classified as large (more than 1.5 million inhabitants), mid-sized (250 000 to 1.5 million) and small and very small, also known as intermediary cities (hereafter referred to collectively as “small FUAs” of 50 000 to 250 000 inhabitants) (OECD, 2024[8]).
Demographic trends are defined using average annual population change between 2012 and 2022, distinguishing growing FUAs (above 0.2%), stable FUAs (-0.2% to 0.2%) and shrinking FUAs (below -0.2%).
Finally, economic profile captures dominant employment structures in 2022 or the latest available year across four categories of economy: i) service-based; ii) advanced-service and mixed; iii) public sector and administrative; and iv) industry-oriented. Please see more information on the method in Box 2.1.
Economic profile types were derived using a cluster analysis of sectoral employment structures across FUAs. Using Eurostat employment data classified according to the Statistical Classification of Economic Activities in the European Community (NACE) Rev. 2, cities were grouped according to similarities in their employment composition. To facilitate interpretation, the original 21 NACE sectors were aggregated into 7 broad categories: agriculture and fisheries; industry; construction; trade, transport and hospitality; advanced services; public administration and related services; and other services. A k‑means clustering algorithm was then applied to sectoral employment shares using 2022 data, or the most recent year available, resulting in four broad economic profile types: service-based; advanced-service and mixed; public sector and administrative; and industry-oriented economies. The analysis includes 267 FUAs across 15 countries with complete sectoral employment data. The number of clusters was fixed at four to balance analytical simplicity with the need to capture meaningful variation in urban economic structures.
While other dimensions such as governance arrangements or geographic function may be potentially relevant, they were not included in this analysis due to data limitations or strong country effects. The characteristics analysed – city size, demographic trends and economic profile – influence employment, housing markets, fiscal capacity and access to services, and therefore condition both the opportunities for, and constraints on, inclusive growth in cities.
Importantly these dimensions do not operate in isolation: rather they reinforce one another and can amplify both structural opportunities and constraints (Figure 2.6). Strong economic performance can attract population inflows and support diversification into advanced services for high-skilled residents, while population decline might increase reliance on public sector anchor institutions for employment. As a result, the analysis should not be interpreted as causal determinants of inclusive growth drivers and outcomes. Instead, they provide an analytical lens to identify systematic associations and recurring problems across cities facing similar structural conditions. Figure 2.6 illustrates how these dimensions tend to cluster in practice. It shows that certain combinations, such as large cities with population growth and advanced services, or small cities with population decline and industry-oriented economies are more prevalent than others. This clustering highlights how structural characteristics can compound opportunities or vulnerabilities, shaping the inclusive growth trajectories available to different groups of cities. Over time, these reinforcing dynamics can widen disparities between cities if not actively managed.
Rather than comparing cities against a single benchmark, this approach identifies recurring patterns across cities with similar structural features, providing a more differentiated understanding of inclusive growth trajectories.
Distribution of EU FUAs across three variables
Note: The figure presents the number of EU FUAs in each category. By city size, there are 35 large, 216 mid-sized and 349 small and very small FUAs. By demographic trajectory, 86 FUAs exhibit strong population growth, 186 moderate growth, 142 stable trends, 53 moderate decline and 53 strong declines. By economic profile, 77 FUAs are classified as advanced-service and mixed, 54 as industry-oriented, 104 as public sector and administrative, and 32 as service-based.
Source: OECD (2025[17]), OECD Regions, Cities and Local Areas Database, http://oe.cd/geostats.
Count of EU FUAs, per variable combination
Note: 265 cities.
Source: OECD (2025[17]), OECD Regions, Cities and Local Areas Database, http://oe.cd/geostats.
Inclusive growth is not automatic: growth and inclusion often diverge in cities
Across EU cities, economic growth does not consistently translate into broader improvements in living standards. A comparison of GDP per capita growth and labour market participation across FUAs shows substantial variation, with many cities experiencing divergence between economic performance and inclusion outcomes (Figure 2.7).
Average annual GDP per capita growth and change in labour market participation rates in EU FUAs, EU countries, 2010-2022
Note: For labour force participation rates, all FUAs use 2022 as the latest available year, except Germany, which relies on 2021 data. The country sample includes Austria (6), Czechia (4), Germany (54), Estonia (1), Finland (4), France (45), Greece (2), Hungary (5), Italy (24), Latvia (1), Lithuania (2), Luxembourg (1), the Netherlands (16), Poland (18), Portugal (3), the Slovak Republic (2), Slovenia (2) and Spain (25).
Source: OECD (2025[17]), OECD Regions, Cities and Local Areas Database, http://oe.cd/geostats.
This divergence can reflect underlying structural conditions and slow-moving trends. Growing cities, whose economic profile is often characterised by a focus on advanced services and mixed economic structures, tend to combine higher income levels (approximately USD 32 000 on average, adjusted for purchasing power parity) with strong labour market participation (around 77%). However, despite their strong economies, these urban areas do not systematically achieve reductions in poverty. In many cases, the benefits of growth can instead be accompanied by rising housing costs and increased cost-of-living pressures, with particularly stark implications for low-income households.
In contrast, shrinking cities, which are often associated with industry and lower-value service-based economies record relatively strong growth in GDP per capita and productivity on average. This GDP per capita growth sometimes exceeds 1.5% annually in shrinking cities, but this could reflect population decline rather than economic dynamism. At the same time, many shrinking and industrial-oriented cities continue to face the long-term effects of deindustrialisation and economic stagnation, including weaker labour market participation (around 68%) and challenges reskilling and upskilling lower-skilled workers displaced by industrial restructuring. The decline or relocation of manufacturing activity can disproportionately affect less-skilled workers, increasing long-term unemployment, barriers to labour market participation and erode the economic base of cities (Draghi, 2024[60]). Similarly, public sector and administrative economies may show relatively stable employment and poverty rates below other economic profiles at around 17%, however this combines with weaker productivity growth, which can constrain long-term improvements in living standards.
These patterns underscore that economic growth left alone is insufficient to support inclusion through a model of trickle-down economics. Instead, inclusive growth depends on how growth is shaped and shared, not just its pace, and on how the underlying economic structure of a city determines the distribution of benefits across workers and households.
Structural trends shaping inclusive growth
Inclusive growth outcomes in cities can diverge systematically according to structural characteristics which in turn shape both opportunities and constraints.
City size
Both economic performance and inclusion outcomes differ across cities depending on their size. Large cities (over 1.5 million people) concentrate economic activity, innovation and services and are more likely to be specialised in advanced services or mixed economies. GDP per capita reaches around 122% of national levels and disposable incomes average 18% above national averages. However, these advantages can be tied to inclusion challenges. Labour markets can be polarised, reflecting strong demand for skilled labour alongside a large base of lower-paid service jobs. Educational attainment levels can also vary significantly in large cities, with 42.5% of the working-age population in large FUAs holding tertiary education qualifications, compared to 22.5 with primary or lower levels of education (Figure 2.8). At the same time, house prices are around 35% higher than national averages, and increased demand has driven house price growth of nearly 60% between 2015 and 2021. Environmental pressures, including exposure to air pollution, are also more acute.
Labour market participation in midsize cities (250 000 to 1.5 million inhabitants) is relatively high, at around 75% on average, and economic growth has been faster in recent years than for large cities, while housing pressures are more moderate. The share of the working-age population with tertiary education is relatively high at 38%, and the share of the population with primary education or below is the lowest of the three groups by city size, at 21% (Figure 2.8). These cities combine scale to support economic diversification with more manageable costs and pressures than large cities. However, outcomes remain heterogeneous and depend on national context and local economic structures.
Small/intermediary cities (50 000 to 250 000 inhabitants) make up the majority of EU urban areas and tend to have lower-income levels but have made relatively stronger progress in reducing poverty over the past decade. Lower housing costs support affordability, although prices have also increased rapidly in recent years. However, these cities face structural constraints including from lower labour market participation (around 68%), lower levels of tertiary education (around 31%, compared with 42% in large cities) (Figure 2.8) and more limited economic diversification, which can constrain long-term growth potential.
Demographic dynamics
Demographic dynamics further shape inclusive growth patterns and reinforce differences across city types. Growing cities (above 0.2% average annual population growth) tend to have higher levels of human capital with close to 40% of the working-age population holding tertiary qualifications, alongside the strongest labour market outcomes with participation rates of around 77% (Figure 2.9) and comparatively narrow gaps in male-female labour force participation. However, these advantages do not consistently translate into improved inclusion outcomes. Poverty rates have remained stable or increased slightly for some, while rapid population growth intensifies pressures on housing markets, with prices rising by around 50% between 2015 and 2021, outpacing income growth.
Average share of the population (25-64 years old) with at most primary or lower education, and tertiary education or higher, by FUA size, 2022 or latest available year
Notes: Data coverage differs between populations with tertiary education and those with primary or lower education.
Tertiary education: Coverage includes 23 EU countries. The number of FUAs per country is: Austria (6), Belgium (14), Bulgaria (17), Croatia (7), Czechia (15), Denmark (4), Estonia (3), Finland (7), France (69), Germany (98), Hungary (19), Ireland (5), Italy (83), Latvia (4), Lithuania (6), Luxembourg (1), the Netherlands (35), Poland (58), Portugal (12), the Slovak Republic (8), Slovenia (2), Spain (81), Sweden (12). FUAs are distributed by size category as large (34), mid-sized (210) and small (321). The reference year is 2022, except for Belgium, Bulgaria, Czechia, Italy, Luxembourg, Poland and Portugal, for which the reference year is 2021.
Primary or lower education: Coverage includes 14 EU countries. The number of FUAs per country is: Bulgaria (17), Estonia (3), Finland (7), France (69), Germany (98), Hungary (19), Italy (83), Latvia (4), Lithuania (3), the Netherlands (35), Portugal (12), Slovenia (2), Spain (81), Sweden (12). FUAs are distributed by size category as large (26), mid-sized (168) and small (250). The reference year is 2022, except for a few German FUAs (2018‑2021), the Netherlands (2020), Portugal (2021) and Sweden (2018).
Source: OECD (2025[17]), OECD Regions, Cities and Local Areas Database, http://oe.cd/geostats.
Meanwhile stable cities show more moderate outcomes but may face risks of stagnation in their economies with more limited improvements in both growth and inclusion indicators. Economic growth in these has been steady, with GDP per capita increasing by around 1.1% annually, slightly higher than for growing cities, while productivity gains remain modest. Disposable incomes are broadly similar to those in shrinking cities (around EUR 29 000), and poverty rates have declined marginally. Labour market participation is around 72% (Figure 2.9) and the male-female participation gap is higher than in growing cities at around 11 p.p., compared to a gap of 7 p.p. in growing cities. The average female participation rate in stable cities is 66%, compared to 73% on average in growing cities. Housing affordability pressures are significant, despite limited population growth, with prices rising by around 60% between 2015 and 2021.
Finally, shrinking cities (below -0.2% annual population change) face a distinct set of structural challenges. Population decline can be driven by low fertility and outmigration, particularly of younger adults, and can lead to reduced labour supply and increasing per capita service costs. Between 2011 and 2021, GDP per capita in shrinking cities grew by around 1.5% annually, while labour productivity (GDP per worker) increased by approximately 0.9% per year, compared to around 0.4% in growing cities. However, overall income levels are below those of growing cities, labour market participation is the lowest of the three demographic trend categories at around 68% (Figure 2.9), and gender gaps are widest by around 14.5 p.p. Educational attainment levels are also lower, with only around 28% of the population holding tertiary qualifications, contributing to the risk of “talent development traps” and limiting long-term growth potential. Despite their declining populations, housing prices have increased sharply, by around 70% between 2015 and 2021, suggesting a disconnect between local demand and price dynamics. Together these dynamics point to increasing risks of economic fragility and exclusion without targeted policy intervention.
Average labour force participation rate, by demographic trend, 2022 or latest available year
Note: Country coverage is 24 EU countries, with 235 FUAs covered. Austria (6), Bulgaria (4), Croatia (2), Czechia (4), Estonia (1), Finland (4), France (45), Germany (54), Hungary (5), Italy (25), Latvia (1), Lithuania (2), Luxembourg (1), Malta (1), the Netherlands (16), Poland (20), Portugal (3), Romania (9), the Slovak Republic (2), Slovenia (2), Spain (25), Sweden (4).
Source: OECD (2025[17]), OECD Regions, Cities and Local Areas Database, http://oe.cd/geostats.
Economic profile
Advanced-service and mixed economies represent the most dynamic profile, combining high productivity, strong employment and diversified economic bases. Concentrated in large and mid-sized cities, these places often benefit from agglomeration economies, deep labour markets and strong connectivity. Their economic performance is reflected in high GDP per capita (around USD 55 000 PPP) (Figure 2.10), typically well above national averages, although growth rates are more moderate (around 0.8% annually), consistent with mature economies. Labour market participation is high (around 77%), and human capital levels are strong, with around 45% of the adult population holding tertiary qualifications. However, inclusive growth challenges arise from elevated poverty rates, at around 18.5%, and housing pressures, which further constrain access for lower- and middle-income households.
Industry-oriented economies are characterised by a strong concentration of manufacturing and related sectors, accounting for over a quarter of employment on average and are predominantly located in Central and Eastern Europe. These cities are typically small and mid-sized and have displayed the fastest GDP growth rates, at around 3.3% annually (2010-2022), which may reflect industrial upgrading alongside economic convergence, as their growth started from a lower base, with GDP per capita around USD 34 000 PPP in 2021 (Figure 2.10). Labour market participation is relatively strong at 75%; however, the gap in female labour force participation is larger than for advanced-service and public sector economies, at 8 p.p. below male participation in 2021.
By economic profile
Note: Country coverage is 12 EU countries, with 114 FUAs covered: Belgium (7), Estonia (1), Finland (4), France (45), Hungary (5), Latvia (1), Lithuania (2), the Netherlands (16), Portugal (1), the Slovak Republic (2), Slovenia (2), Spain (28).
Source: OECD (2025[17]), OECD Regions, Cities and Local Areas Database, http://oe.cd/geostats.
Service-based economies generally capture cities focused on tourism and personal services and tend to exhibit weaker overall economic performance and more acute inclusion challenges. Predominantly located in Southern and parts of Eastern Europe, and more often smaller and mid-sized cities, these economies are characterised by lower GDP per capita of around USD 36 500 PPP (Figure 2.10) and have experienced slow GDP growth rates (2010-2022), at around 0.3% annually. Labour market participation is also lower, at around 71%, with wider gaps in male-female labour force participation. Economic sectors such as tourism can also lead to higher levels of seasonal and non-standard employment, including part-time work. These features contribute to the highest poverty rates of the four profiles (around 25% on average). These cities also tend to have a higher share of low-educated workers, which can limit the capacity to diversify their economic bases.
Public sector and administrative economies are the most common profile across the economic profiles and are characterised by a high concentration of employment in public administration, education, health and social services. These cities, often regional capitals, university towns or administrative centres, benefit from relatively stable employment and income levels, with GDP per capita around USD 45 000 PPP (Figure 2.10). Economic growth between 2010 and 2022 tended to be more modest, around 0.4% annually, reflecting more limited exposure to high-productivity tradeable sectors. Labour market participation is relatively high at around 75%, with relatively lower poverty rates (17%) than other economic profiles. Human capital levels are also relatively strong, with approximately 40% tertiary attainment. Environmental pressures, measured by air pollution exposure, also tend to be the lowest among the four economic profile typologies, on average and across most countries analysed.
Implications for policy: a need for tailored approaches to inclusive growth
Taken together, the inclusive growth outcomes analysed for different city types underscore that structural context can create trade-offs between economic performance and inclusion, which can be managed through place-sensitive policy decisions. In large, growing and advance-services and mixed-economy cities, high productivity and income levels are often accompanied by rising housing costs, labour market polarisation, congestion and environmental pressures. These dynamics can dilute the benefits of growth, particularly for lower-income groups.
In contrast, industry-oriented and basic-services cities, which are often smaller or shrinking, may experience relatively better outcomes in some dimensions which support inclusion, such as lower housing costs or poverty rates, but face weaker labour markets, lower wages and more limited productivity growth. Over time, these dynamics can constrain economic opportunities and reduce long-term resilience. Meanwhile, public sector and administrative cities may experience fewer short-term fluctuations due to stable employment but can face challenges in generating productivity growth and diversifying economic opportunities. Mid-sized cities, particularly those with a more diversified economic structure, may be able to strike a more favourable balance between growth and inclusion. However, this balance is not guaranteed and depends on broader economic conditions and structural positioning within national systems.
References
[44] Akyelken, N. (2020), “Transport for women: who decides what women need?”, Transport Reviews, Vol. 40/6, pp. 687-688, https://doi.org/10.1080/01441647.2020.1799162.
[7] André, C. and P. Gal (2024), “Reviving productivity growth: A review of policies”, OECD Economics Department Working Papers, No. 1822, OECD Publishing, Paris, https://doi.org/10.1787/61244acd-en.
[58] Antonietti, R., C. Burlina and A. Rodríguez-Pose (2025), “Digital technology and regional income inequality: Are better institutions the solution?”, Papers in Regional Science, Vol. 104/2, p. 100079, https://doi.org/10.1016/j.pirs.2025.100079.
[63] Assari, S. et al. (2021), “Black-White Achievement Gap: Role of Race, School Urbanity, and Parental Education”, Pediatric Health, Medicine and Therapeutics, Vol. 12, pp. 1-11, https://doi.org/10.2147/phmt.s238877.
[50] Bertassello, L. et al. (2026), “Assessing European cities with the 3-30-300 rule underscores the need for enhanced urban greening efforts”, Nature Communications, https://doi.org/10.1038/s41467-026-71523-8.
[3] Boarini, R., F. Murtin and P. Schreyer (2015), “Inclusive Growth: The OECD Measurement Framework”, OECD Statistics Working Papers, No. 2015/6, OECD Publishing, Paris, https://doi.org/10.1787/5jrqppxjqhg4-en.
[55] Chetty, R. and N. Hendren (2018), “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects*”, The Quarterly Journal of Economics, Vol. 133/3, pp. 1107-1162, https://doi.org/10.1093/qje/qjy007.
[54] Chetty, R. and N. Hendren (2018), “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates*”, The Quarterly Journal of Economics, Vol. 133/3, pp. 1163-1228, https://doi.org/10.1093/qje/qjy006.
[56] Chetty, R. et al. (2022), “Social capital I: measurement and associations with economic mobility”, Nature, Vol. 608/7921, pp. 108-121, https://doi.org/10.1038/s41586-022-04996-4.
[9] Cho, W. et al. (2024), “Diagnosis and policy action for sustainable and inclusive productivity growth”, OECD Science, Technology and Industry Working Papers, No. 2024/7, OECD Publishing, Paris, https://doi.org/10.1787/1668f250-en.
[43] Curl, A., J. Clark and A. Kearns (2018), “Household car adoption and financial distress in deprived urban communities: A case of forced car ownership?”, Transport Policy, Vol. 65, pp. 61-71, https://doi.org/10.1016/j.tranpol.2017.01.002.
[16] Dell, M., B. Jones and B. Olken (2012), “Temperature Shocks and Economic Growth: Evidence from the Last Half Century”, American Economic Journal: Macroeconomics, Vol. 4/3, pp. 66-95, https://doi.org/10.1257/mac.4.3.66.
[60] Draghi, M. (2024), The Future of European Competitiveness, https://doi.org/10.2872/9356120.
[41] European Commission (2024), “Guidance on independent living and inclusion in the community of persons with disabilities in the context of EU funding”, https://employment-social-affairs.ec.europa.eu/news/commission-adopts-guidance-independent-living-persons-disabilities-2024-11-20_en.
[34] European Commission (2023), “26% of young people live in overcrowded households”, https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20230607-1#:~:text=In%202021%2C%20in%20the%20EU,the%20total%20population%20(17%25).
[40] European Parliament (2022), “Energy poverty in the EU”, https://www.europarl.europa.eu/RegData/etudes/BRIE/2022/733583/EPRS_BRI(2022)733583_EN.pdf.
[13] European Union (2024), Ninth report on economic, social and territorial cohesion, https://ec.europa.eu/regional_policy/sources/reports/cohesion9/9CR_Report_FINAL.pdf.
[29] Eurostat (2026), Unemployment rates by citizenship (dataset), https://doi.org/10.2908/LFSA_URGAN.
[27] Eurostat (2026), Young persons neither in employment nor in education and training by labour status (NEET rates) (dataset), https://doi.org/10.2908/EDAT_LFSE_20.
[11] Eurostat (2025), In-work at-risk-of-poverty rate (dataset), https://ec.europa.eu/eurostat/databrowser/view/tespm070/default/table?lang=en.
[33] Eurostat (2025), Overcrowding rate by degree of urbanisation - total population (dataset), https://doi.org/10.2908/ILC_LVHO05D.
[36] Eurostat (2024), At-risk-of-poverty rate after deducting housing costs by degree of urbanisation (dataset).
[47] Eurostat (2024), “Culture statistics - cultural participation”, https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Culture_statistics_-_cultural_participation#Cultural_participation_.E2.80.93_general_findings.
[61] Eurostat (2024), “Housing in Europe - 2024”, https://ec.europa.eu/eurostat/web/interactive-publications/housing-2024.
[62] Eurostat (2024), “Living conditions in Europe - poverty and social exclusion”, https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Living_conditions_in_Europe_-_poverty_and_social_exclusion.
[19] Eurostat (2024), “Urban-rural Europe - education and training”, https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Urban-rural_Europe_-_education_and_training#:~:text=More%20than%20half%20(53.3%25),or%20rural%20areas%20(31.7%25).
[25] Eurostat (2023), “Urban-rural Europe - labour market”, https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Urban-rural_Europe_-_labour_market.
[24] Farquharson, C., S. McNally and I. Tahir (2024), “Education inequalities”, Oxford Open Economics, Vol. 3/1, pp. i760-i820, https://doi.org/10.1093/ooec/odad029.
[49] Iodice, S. et al. (2024), “EU cities and heat extremes”, No. JRC137891, European Commission, https://publications.jrc.ec.europa.eu/repository/handle/JRC137891.
[42] Kaufmann, V., M. Bergman and D. Joye (2004), “Motility: mobility as capital”, International Journal of Urban and Regional Research, Vol. 28/4, pp. 745-756, https://doi.org/10.1111/j.0309-1317.2004.00549.x.
[53] Kimbrough, E. (ed.) (2025), “Big cities fuel inequality within and across generations”, PNAS Nexus, Vol. 4/2, https://doi.org/10.1093/pnasnexus/pgae587.
[6] Krugman, P. (1994), The Age of Diminished Expectations: U.S. Economic Policy in the 1990s, MIT Press.
[4] Melic, T., A. Huovinen and A. Rubin (2026), “Area-based initiatives to transform neighbourhoods: An international policy mapping”, OECD Local Economic and Employment Development (LEED) Papers, No. 2026/04, OECD Publishing, Paris, https://doi.org/10.1787/d85ef103-en.
[57] Moreno-Monroy, A. et al. (2025), “Do neighbourhoods influence the likelihood of receiving social assistance benefits?: Evidence from the Netherlands”, OECD Regional Development Papers, No. 168, OECD Publishing, Paris, https://doi.org/10.1787/c5c2aa17-en.
[35] Musterd, S. et al. (2016), “Socioeconomic segregation in European capital cities. Increasing separation between poor and rich”, Urban Geography, Vol. 38/7, pp. 1062-1083, https://doi.org/10.1080/02723638.2016.1228371.
[30] OECD (2025), Cities for All Ages, OECD Urban Studies, OECD Publishing, Paris, https://doi.org/10.1787/f0c8fefa-en.
[32] OECD (2025), OECD Affordable Housing Database - indicator HM1.2 House Prices.
[26] OECD (2025), OECD Employment Outlook 2025: Can We Get Through the Demographic Crunch?, OECD Publishing, Paris, https://doi.org/10.1787/194a947b-en.
[17] OECD (2025), OECD Regions, Cities and Local Areas Database, OECD, Paris, http://oe.cd/geostats.
[20] OECD (2025), “The importance of monitoring neighbourhood conditions for children’s well-being and development”, OECD Papers on Well-being and Inequalities, No. 34, OECD Publishing, Paris, https://doi.org/10.1787/143ae959-en.
[22] OECD (2025), The integration of children of immigrants in OECD countries, OECD Publishing, Paris, https://doi.org/10.1787/acec9b49-en.
[18] OECD (2024), “Education GPS: Education Attainment”, OECD, Paris, https://gpseducation.oecd.org/revieweducationpolicies/#!node=41755&filter=all.
[12] OECD (2024), “Inflation and cost of living”, OECD, Paris, https://www.oecd.org/en/topics/inflation-and-cost-of-living.html.
[8] OECD (2024), OECD Regions and Cities at a Glance 2024, OECD Publishing, Paris, https://doi.org/10.1787/f42db3bf-en.
[37] OECD (2024), OECD Toolkit to Combat Homelessness, OECD Publishing, Paris, https://doi.org/10.1787/0fec780e-en.
[51] OECD (2023), A Territorial Approach to Climate Action and Resilience, OECD Regional Development Studies, OECD Publishing, Paris, https://doi.org/10.1787/1ec42b0a-en.
[39] OECD (2023), “Confronting the cost-of-living and housing crisis in cities”, OECD Regional Development Papers, No. 49, OECD Publishing, Paris, https://doi.org/10.1787/7a6008af-en.
[28] OECD (2022), Disability, Work and Inclusion: Mainstreaming in All Policies and Practices, OECD Publishing, Paris, https://doi.org/10.1787/1eaa5e9c-en.
[48] OECD (2022), The Culture Fix: Creative People, Places and Industries, Local Economic and Employment Development (LEED), OECD Publishing, Paris, https://doi.org/10.1787/991bb520-en.
[38] OECD (2021), Building for a Better Tomorrow: Policies to Make Housing more Affordable, OECD Publishing, Paris, https://doi.org/10.1787/5d9127d4-en.
[23] OECD (2021), Investing in Youth: Slovenia, Investing in Youth, OECD Publishing, Paris, https://doi.org/10.1787/c3df2833-en.
[10] OECD (2020), Enhancing Productivity in UK Core Cities: Connecting Local and Regional Growth, OECD Urban Policy Reviews, OECD Publishing, Paris, https://doi.org/10.1787/9ef55ff7-en.
[31] OECD (2019), Pensions at a Glance 2019: OECD and G20 Indicators, OECD Publishing, Paris, https://doi.org/10.1787/b6d3dcfc-en.
[15] OECD (2018), Divided Cities: Understanding Intra-urban Inequalities, OECD Publishing, Paris, https://doi.org/10.1787/9789264300385-en.
[2] OECD (2016), Making Cities Work for All: Data and Actions for Inclusive Growth, OECD Publishing, Paris, https://doi.org/10.1787/9789264263260-en.
[1] OECD (2016), Paris Action Plan for Inclusive Growth in Cities, OECD, Paris, https://www.oecd.org/content/dam/oecd/en/about/programmes/cfe/oecd-champion-mayors-for-inclusive-growth/Paris-Action-Plan.pdf.
[14] ONS (2024), Life expectancy for local areas of Great Britain: between 2001 to 2003 and 2021 to 2023.
[45] Plyushteva, A. and T. Schwanen (2018), “Care-related journeys over the life course: Thinking mobility biographies with gender, care and the household”, Geoforum, Vol. 97, pp. 131-141, https://doi.org/10.1016/j.geoforum.2018.10.025.
[59] Rodríguez-Pose, A. et al. (2024), “Overcoming left-behindedness. Moving beyond the efficiency versus equity debate in territorial development”, Regional Science Policy & Practice, Vol. 16/12, p. 100144, https://doi.org/10.1016/j.rspp.2024.100144.
[5] Solow, R. (1956), “A Contribution to the Theory of Economic Growth”, The Quarterly Journal of Economics, Vol. 70/1, p. 65, https://doi.org/10.2307/1884513.
[21] Thew, A. (2025), “How do skills relate to cities’ economic performance”, Centre for Cities, https://www.centreforcities.org/blog/how-do-skills-relate-to-cities-economic-performance/.
[46] Vale, D. and A. Lopes (2023), “Accessibility inequality across Europe: a comparison of 15-minute pedestrian accessibility in cities with 100,000 or more inhabitants”, npj Urban Sustainability, Vol. 3, https://doi.org/10.1038/s42949-023-00133-w.
[52] van Ham, M. et al. (2011), “Neighbourhood Effects Research: New Perspectives”, in Neighbourhood Effects Research: New Perspectives, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-007-2309-2_1.
← 1. The share of the population living in households where disposable income, after deducting housing costs (such as rent, mortgage payments, and utility bills), falls below the poverty threshold. The poverty threshold is set at 60% of the national median equivalized disposable income, adjusted for household size.
← 2. North-Western EU comprises Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Luxembourg, the Netherlands and Sweden; Southern EU comprises Cyprus, Greece, Italy, Malta, Portugal and Spain; and Eastern EU comprises Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, the Slovak Republic and Slovenia.
← 3. Mid-sized FUA (250 000 to 1.5 million inhabitants); large FUA (over 1.5 million inhabitants).