Demographic decline and ageing challenge rural viability, especially in remote areas, but economic transformation is underway. There are pockets of rural growth: data shows rural regions near cities are best positioned to capitalise on manufacturing, innovation, and urban spillovers, while remote areas require targeted support to harness resource-based and green economy opportunities. Unlocking this potential hinges on strategic investment in digital infrastructure, skills, and entrepreneurship aligned with place-specific strengths and global transitions. This chapter examines trends across different types of rural regions in population growth, ageing, economic output and specialisation, productivity, innovation, environmental performance, social outcomes, and public sentiment. It then identifies the main drivers and enabling factors of rural performance and competitiveness as well as opportunities and challenges across the green economy and service delivery that are relevant for shaping rural quality of life and satisfaction.
Reinforcing Rural Resilience
Abstract
Rural communities present special challenges, but also unique features and resources that can be leveraged for untapped opportunities. Some trends and challenges are accentuated in rural regions, like more profound demographic change, more limited financial capacity, and constraints in labour supply, education, and service provision. There are also challenges that are specific to rural regions like lack of market density, and it is well known that they tend to underperform vis-à-vis their urban counterparts across several economic outcomes, including on productivity and innovation. Often, the more remote the region, the greater the challenge. But recognising also their unique features like natural and cultural resources, governments seek to identify areas of opportunity and integrate specific, tailored policies.
The objective of this chapter is to assess how rural regions can become more competitive in the modern economy and resilient to global megatrends. To do so, it draws on the latest OECD-wide data and analysis to present a comprehensive evaluation of economic, demographic, social, and environmental trends across different types of rural regions, identifying areas where rural regions have performed well as well as challenges and opportunities. This then allows to identify success factors for the transformation of rural economies and to capitalise on opportunities for economic growth and improved well-being standards.
A key contribution of the analysis is its disaggregated approach, distinguishing between rural areas close to functional urban areas (FUAs) and those that are more remote. This distinction recognises their distinct economic trajectories and policy needs. This granular perspective is crucial: while broad rural-urban comparisons offer valuable insights, they often mask significant variations within rural regions themselves. By leveraging detailed OECD regional data and applying a functional approach to rural economies, this chapter (and the publication) helps fill a critical gap in rural development research and has a more solid data foundation to identify how policy responses can be tailored (covered in further chapters). This more granular approach can better tailor policy responses to different types of rural regions undergoing profound transformations. Another contribution is to analyse how rural trends translate into public sentiment. New data on rural perceptions complements the observed socioeconomic data to show new insight. High-level trends across relevant economic, social and environmental indicators are presented in the main sections of the chapter, whereas more detailed analysis is presented in the Statistical Annex 2.A.
The analysis highlights several key trends shaping rural regions today. Following the introduction, section 2.3 explores demographic and economic trends over the last two decades across different types or rural places – and what those trends mean for rural places and people. Section 2.4 examines economic performance and competitiveness, analysing the sources of growth across rural regions and the conditions that enable innovation and capitalising on rural assets. Section 2.5 identifies specific opportunities in the green economy, in the context of unique environmental challenges in rural regions. Section 2.6 assesses social well-being and access to services, integrating perception data to understand community strengths and vulnerabilities. Section 2.7 concludes with some policy implications.
The unit of analysis focuses on OECD TL3 regions using primarily the OECD extended typology sub-defining rural regions into three non-metropolitan regional categories that recognise the diversity of rural regions (Table 2.1). For the purposes of this paper, the term non-metro and rural are used interchangeably. More details on the data and definition sources are included in the Statistical Annex 2.A.
Country coverage and data harmonisation
To ensure a harmonised and comparable basis for analysing demographic and economic trends at the regional level, this report focuses on 29 of the 38 OECD Members. These countries provide consistent and comprehensive subnational data for both economic and demographic indicators, which is essential for producing robust OECD-wide averages. The remaining countries – Australia, Canada, Chile, Colombia, Iceland, Korea, and Mexico – are excluded from most comparative analyses due to missing or incomplete economic data at the TL3 level. In addition, Israel and Costa Rica are not yet classified under the OECD regional rural typology. These exclusions help maintain consistency in the composition of rural and urban regions across countries and ensure the reliability of OECD-level comparisons. Using a different country sample (for example, when assessing the share of rural regions experiencing economic growth alongside population decline), can lead to misleading conclusions due to shifts in the rural-urban composition. For this reason, the restricted sample is used for most cross-country analyses. However, all countries with available data are included in country-level figures, and for demographic indicators, an OECD average based on the full set of countries is presented where possible.
Levels for 2021 and growth from 2001 to 2021 measured in % (CAGR)
Note: In order to overcome biases on the number of rural TL3 regions, the OECD averages through this chapter are country-weighted, meaning that CAGRs are first calculated at the country level before taking a simple average. Population growth, the old-age dependency ratio, and GDP values are based on the restricted OECD sample without Korea, covering 29 OECD countries, while labour productivity figures are based on 20 OECD countries. Source: Author’s elaboration based on the OECD Regional Database
This section analyses how rural regions have been evolving over the last two decades, particularly with respect to urban metro regions, and what their unique trends and challenges mean for the future. The analysis is based on granular socioeconomic data that distinguishes between the different types of rural regions facing different trends, and thus will need to pursue different opportunities and development paths. The section also explores how public sentiment in rural places compares to their urban counterparts amid these trends.
2.3.1. Rural demographic shifts at different paces
The evolution of rural economies is largely shaped by demographic change. Many are certainly facing deep challenges. Over two out of five of the more remote rural regions and those close to small FUAs are already experiencing depopulation and shrinking, and with ageing accelerating in most.1 Nevertheless, the data also suggest that opportunities exist across all types of rural regions. For a start, shrinking does not necessarily mean economic decline; rural GDP per capita has looked resilient overtime, even among regions shrinking. Furthermore, many rural regions continue to grow,2 with some outpacing metro areas, particularly those near midsize and large cities, suggesting opportunities to leverage rural-urban linkages. There are also pockets of growth among the more remote regions.
Population growth
Rural (i.e. non-metro) regions are experiencing slower population growth that metro regions and are shrinking in some countries. Over 2001-21, the overall population across the 29 OECD countries we focus on in this report increased from approximately 945 million to nearly 1 043 million. In 17 OECD countries, population has been increasing in both rural and in urban regions (with the highest rates in Ireland, New Zealand and Türkiye). But population growth is uneven: in all OECD countries, apart from Greece and Switzerland (and Korea, when considering the full set of OECD countries), population growth has been higher in metro than in non-metro regions (Figure 2.1), thus increasing the population share of metro relative to rural. Nine OECD countries are already shrinking in population, mainly driven by rural populations (while both metro and non-metro populations declined in Lithuania and Latvia).3
Among rural regions, those close to midsize/large FUA are more likely to continue growing. Rural regions represent a higher share of the shrinking regions in all countries with the exception of Japan. Already close to half (47.3%) of rural near a small FUA and 40% of remote rural are shrinking in population – thus many OECD countries are experiencing declines in these types of rural populations.4 This contrasts with a lower share (25.1%) of rural near cities (midsize/large FUA) shrinking in population. Rural places near large FUAs tend to show faster population growth and in 15 OECD countries their populations increased (Figure 2.2).5 With these trends, in countries like Austria, Finland, Italy and Portugal, the population of rural remote regions declined, while rural regions close to cities continued to grow.
Rural remote regions are experiencing the highest share of significant demographic decline. The rate of population decline is important for policy design. If population rates are gradually declining, an adaptive approach can be effective. In contrast when population declines are severe it can drive regions into negative downward spirals, warranting more urgent policy responses. The share of rural remote regions whose decline was larger than 1% annually (which means the population shrinking by more than one-fifth over a 20-year period) was 5.5%, and the share climbs to 15.2% for those experiencing annual declines of at least 0.5% (one-tenth over 20 years) (Annex Table 2.A.1). As a reference point, amongst all the regions that lost population (rural and urban), 40% of them experienced an average decline no higher than 0.25%.
Population change, in %
Note: Population changes are expressed as the compound annual growth rate (CAGR). Changes were calculated by summing population of TL3 regions within each country and distinguishing between metropolitan and non-metropolitan regions. Data covers 36 OECD countries and measures growth from 2001 to 2021, with Korea included from 2012 onward due to data availability. The OECD average is an unweighted country average. The asterisk (*) indicates a restricted OECD average, excluding countries without available GDP data: Australia, Canada, Chile, Colombia, Iceland, and Mexico.
Source: Author’s calculations based on the OECD Regional Database.
Population change by OECD country and average for the whole OECD, in %
Note: Population changes are expressed as the compound annual growth rate (CAGR). Changes were calculated by summing population of TL3 regions within each country and distinguishing between NMR-M, NMR-S, and NMR-R. Data covers 36 OECD countries and measures growth from 2001 to 2021, with Korea included from 2012 onward due to data availability. The OECD average is an unweighted country average. The asterisk (*) indicates a restricted OECD average, excluding countries without available GDP data: Australia, Canada, Chile, Colombia, Iceland, and Mexico.
Source: Author’s calculations based on the OECD Regional Database
Ageing population
Rural regions face a growing elderly dependency challenge. The old-age dependency ratio (OADR, defined as the share of 65+ population relative to the local working-age population) has steadily increased across the OECD in general. Overall, there were 24 elderly individuals for every 100 working-age people in 2001, rising to 33.3 by 2021.6 In this context of ageing populations, the OADR is today higher in rural over metro regions in all OECD countries except Poland.7 Figure 2.3 shows how the difference between the OADR of rural regions against metro regions (generally larger in rural already in 2001) has increased over time. Over the last 20 years, it increased at a higher rate in rural regions than in metro regions in all but 5 countries (Greece, Italy, Poland, Portugal Slovak Republic and Spain). Even in countries like Belgium, Czechia or Switzerland where old-age dependency was lower in rural than in metro regions in 2001, the situation had reversed by 2021 and rural regions now have larger shares of older people.
Gap in absolute values between non-metro and metro regions, by OECD country and average for the whole OECD
Note: This gap is calculated by subtracting the metropolitan ratio from the non-metropolitan ratio. The old-age dependency ratio is defined as the number of people aged 65 or older per 100 people of working age (20–64). Values were calculated using a population-weighted approach by summing the population by age across TL3 regions within each country. The data corresponds to the restricted OECD sample without Korea, covering 29 OECD countries.
Source: Author’s elaboration based on the OECD Regional Database
Among rural regions, it is in the remote rural that the OADR tends to be highest, and projections show that they will continue facing higher pressures of elderly population in the future. In several countries, the differences across region types are currently insignificant (see Annex Figure 2.A.3). But in those countries that showed marked differences across rural types in OADR (a total of 13 countries), it was highest in remote regions in 9 countries (Austria, Denmark, Finland, Hungary, France, Latvia, Norway, Portugal and Sweden, ), highest in rural near a small FUA in three countries (Japan, Slovenia and Switzerland) and only in Belgium was it highest in rural near a midsize/large FUA. Population projections reveal that rural remote regions will face the highest pressures of elderly population in the future. By 2040, 16 countries will have the highest elderly dependency ratios in their rural remote regions (Annex Figure 2.A.2). The average value across all countries estimates that in rural regions near a small FUA the elderly share will stand at 30% followed by 29% in non-metro regions near a midsize/large FUA, against 25% in metro regions.
The increasingly skewed concentration of rural populations in older age groups is transforming the workforce at rapid rates. With respect to urban (metro) populations, rural regions show a deficit in people aged below 50 and a surplus over 55. This translates into lower labour force participation rates and thus smaller workforces. Given the larger share of people aged 65+ in remote regions, the workforce challenges are expected to be greater in these places in particular. Other rural regions are ageing faster than metro regions and are expected to face workforce pressures. In rural near large FUAs, this may be the result of larger growth in older populations (the numerator), perhaps driven by older people moving out of cities into suburbs – while the number of working age people is not increasing as fast (the denominator), perhaps because young people move to city centres. Rural regions have also experienced important transformations in the age structure of their workforce.8 Workers aged 50-64, much less prevalent in 2001 than those aged 20-34 and 35-49, have now become the highest share of the workforce in 2021 (Figure 2.4) – which has not happened just yet in metro areas.
Distribution of working age population in metro (left graph) and non-metro (right graph) regions, in %
Note: The graph illustrates the trends in the share of people aged 20–34, 35–49, and 50–64 relative to the total working-age population (20–64) in metropolitan (Panel A) and non-metropolitan (Panel B) regions from 2001 to 2021. The data is population-weighted, meaning each region is assigned equal weight regardless of which country it belongs to. The data corresponds to the restricted OECD sample without Korea, covering 29 OECD countries.
Source: Author’s elaboration based on the OECD Regional Database
These transformations require action and adaptation, tailored to the circumstances of specific rural places, including for those shrinking and the many other still growing. Trends vary across types of rural region. Given population decline in more remote regions and the accelerated demographic shift, there is urgency for targeted policies to manage shrinking while retaining quality of life. For regions shrinking at steady rates, adaptation policies can be effective to prepare regions for future population scenarios. For rapidly regions shrinking, more drastic measures might be warranted to manage downward spiral effects driven by accelerated demographic declines. By contrast, despite slower population growth and an inexorable process of ageing, there are many rural areas still growing, particularly those close to urban centres. Rural regions near large metro areas (i.e. midsize/large FUAs) are growing three times faster in population than rural near a small FUA. These regions need policies to strengthen their workforces, as well as relevant skills to cater to the growing economic activity. For remote rural regions and those near FUA, it is important to differentiate policy responses between those shrinking at a gradual and slow pace against those depopulating more rapidly.
2.3.2. Economic convergence and rural pockets of growth
Metro regions generally outperform rural regions in GDP growth. For the entirety of OECD countries, GDP grew by 1.7% annually on average over the 20-years span between 2001 and 2021 – the growth rate when weighted by countries is 2.1%. The growth rate of 2.3% (weighted) for metro regions was superior to the rural rate of 1.8%.
The GDP gap between urban and rural has widened since the 2008 global financial crisis. When compared to 2001, the gap in 2008 widened by 5 percentage points. Since the global financial crisis, the gap more than tripled, widening by 17 percentage points in 2021 relative to 2001 (Figure 2.5). The larger economic activity and GDP growth in metro areas also reflects in larger employment growth in the same period (Annex Figure 2.A.4).
Note: OECD average for metro and non-metro regions is calculated by giving every country the same weight. The data corresponds to the restricted OECD sample without Korea, covering 29 OECD countries.
Source: Author’s elaboration based on the OECD Regional Database
Amid this general trend, the performance of rural areas is diverse across countries, on occasions outperforming metro areas. Though in many countries there are no substantial differences in average GDP growth between rural and metro, in those where gaps exist, metro areas do indeed tend to outperform – which is the case of 17 countries over the last two decades (Figure 2.6). Yet. in three countries (Germany, Portugal and Switzerland) rural regions grew at a faster pace than metro regions between 2001 and 2021, which confirms the presence of pockets of growth in rural regions across several countries. Within countries, rural performance is also diverse with some high performers, and in fact the data suggests that many rural areas are doing well and above national average performance. This may suggest that the general average underperformance of rural regions may actually be severely decelerated by a few left-behind places:
in 11 OECD countries (out of 28), more than half of rural regions outperformed the national average in GDP per capita growth over the 20-year span between 2001-21 (Switzerland and Germany included),
in 6 countries (Portugal included, in addition to Austria, Estonia, Japan and Latvia), over 70% of rural regions outperformed the national average in GDP per capita growth (see Annex Figure 2.A.5).
GDP change, in % (CAGR)
Note: GDP changes are expressed as the compound annual growth rate (CAGR). Changes were calculated by summing GDP of TL3 regions within each country and distinguishing between metropolitan and non-metropolitan regions. For Korea, GDP growth is measured from 2012 onward due to data availability. GDP is measured in constant prices and PPP with base year 2015.
Source: Author’s elaboration based on the OECD Regional Database
Among the different types of rural regions, those near FUAs, especially small FUAs, show stronger economic performance, highlighting the importance of rural-urban linkages and also the role of intermediate cities. On average across OECD countries, GDP increased annually by 1.92% in rural near a small FUA, followed by 1.69% in rural near a midsize/large FUA and 1.49% in rural remote regions.9 Over the last two decades, only in two countries (Switzerland and the United Kingdom) was the growth rate higher in remote regions than in the other two types of rural places closer to urban areas (Figure 2.7). Overall, rural regions closer to cities are more resilient to economic downturns, while remote areas face a higher risk of contraction. Across OECD countries, rural regions near small FUAs and remote rural regions show the highest shares of economic contraction – 19% and 16%, respectively, compared to just 8.5% for rural areas near midsize or large FUAs. However, when excluding Greece and Italy – where national economic conditions affected many rural communities near small FUAs – remote rural regions emerge as the most vulnerable to economic decline (5.6% contracting), compared to 3.9% of rural areas near midsize/large FUAs and just 2.1% of those near small FUAs. These trends highlight that proximity to urban centres, large and intermediate, provides a buffer against economic fluctuations and structural shifts, as rural areas closer to cities benefit from spillover effects, stronger labour markets, and greater economic diversification.
GDP change, in % (CAGR)
Note: The average figures inside countries are weighted by the respective regions’ GDP. For Korea, GDP growth is measured from 2012 onward due to data availability. GDP is measured in constant prices and PPP with base year 2015
Source: Author’s elaboration based on the OECD Regional Database
Pockets of rural growth are concentrated in countries where proximity to Functional Urban Areas (FUAs) and strong manufacturing bases create favourable conditions. The rural regions outperforming national and metropolitan averages are not randomly distributed. They are disproportionately located in countries like Austria, Germany, Switzerland, and to some extent Finland and Portugal. What these countries have in common – particularly the first three – is a high concentration of rural areas close to small or midsize FUAs, where local economies are deeply embedded in high-value manufacturing and tradeable sectors. These areas benefit from access to skilled labour, infrastructure, and knowledge flows associated with agglomeration, without the diseconomies of major metropolitan centres. According to OECD data, rural areas near small FUAs have had the highest GDP growth rates across rural typologies (1.92% annually), highlighting the importance of rural–urban linkages and intermediate city spillovers.
Austria, Germany, and Switzerland have sustained rural manufacturing through long-term policy frameworks and industry linkages. In these countries, rural competitiveness is closely tied to advanced and decentralised manufacturing systems. In Germany, the success of rural regions such as parts of Bavaria or Baden-Württemberg can be linked to the “Mittelstand” (SME-based manufacturing) ecosystem and the country’s Energiewende, which has supported a green industrial base even in rural settings.10 Austria has actively invested in decentralised industrial development, supporting regional innovation hubs and vocational training systems that align rural skills with industrial needs.11 Switzerland benefits from strong integration of rural firms into global value chains, often supported by clustering around small cities with R&D capacity. In all three, the role of polycentric development, co‑ordinated spatial planning, and manufacturing specialisation appear to be key drivers of rural resilience and sustained economic growth.
In Finland and Portugal rural growth is driven by sectoral and structural factors beyond manufacturing – such as trade-related services in Portugal and remoteness-based innovation in Finland. As in Austria and Germany, manufacturing productivity plays an important role in Portugal’s rural performance, contributing to over 32% of GVA in NMR-M regions in 2021. This is further supported by a dynamic services sector, which accounted for around 29% of GVA in NMR-S regions in the same year – particularly in logistics and tourism. Agricultural modernisation and EU investment in rural infrastructure have also played a key role, with productivity increasing significantly across all rural types between 2001 and 2021 – as measured by agriculture’s contribution to GVA relative to its share of employment. In NMR-R regions, productivity rose by 52%, in NMR-M regions by 67%, and in NMR-S regions by 78%. Improvements in irrigation, high-value crop exports, and agri-food logistics in regions like Alentejo have supported job creation and GVA in rural areas close to FUAs like Évora and Beja (OECD, 2024[1]). In Finland, where most rural regions are remote, growth appears to be tied to niche high-tech industries (e.g. forest-based bioeconomy, digital health, and electronics) operating in small towns and benefiting from research linkages and strong public services. Finnish policy has long supported innovation and connectivity even in remote places, helping them overcome the typical disadvantages of distance (OECD, 2017[2]). These examples show that while proximity to urban centres remains a powerful driver, remote rural growth is possible when supported by strategic investments and targeted sectoral strengths.
The experience of growing rural regions provides key policy lessons for other OECD countries. First, investing in manufacturing and tradeable sectors – especially in rural regions near cities – remains a high-return strategy for rural development. But success also depends on aligning these sectors with infrastructure, skills development, and innovation systems. Second, rural resilience can emerge in remote areas when economic strategies are tailored to local strengths, such as agriculture or specialised technologies, and reinforced by strong public service delivery and connectivity. Third, OECD countries must consider polycentric regional strategies, supporting both intermediate cities and their rural hinterlands as integrated economic systems. This means fostering not just rural development, but rural–urban interdependence, backed by spatial planning and cross-jurisdictional governance. Above all, the diversity of successful rural trajectories across countries underscores that place-based policies – rather than one-size-fits-all solutions – are essential to unlocking rural growth potential.
GDP per capita
The rural performance looks more dynamic in GDP per capita growth, which may suggest that capital-labour substitution is leading to some efficiency and productivity gains. In 16 OECD countries,12 the rate of growth in GDP per capita in rural regions was higher than in metro regions over the period covered (Figure 2.8).13 Thus, despite the growing gap between metro and non-metro regions in overall GDP growth, gaps in GDP per capita have been converging in rural regions in 14 countries (see Annex Figure 2.A.6).14 Overall, it is rural near small FUAs that are showing stronger GDP per capita growth across the OECD (1.8% in country weighted average).15
There is substantial variation in rural GDP per capita growth across countries, with a skewed presence of a few countries that stand out over all the others with considerably larger rural growth. The evolution of GDP per capita in rural regions shows higher disparities when compared to metro regions. This may suggest that rural average performance is driven by a strong performance in six countries (see Annex Figure 2.A.7), while rural regions in other countries lag behind. One observation from Figure 2.8 is that rural outperformance is more likely to be seen in rich countries now experiencing low GDP per capita growth overall – suggesting that rural regions are escaping stagnation more effectively (by contrast, in Eastern European countries catching up in GDP per capita, this is still driven by metro areas). But there may also be structural reasons for specific countries like Austria, Finland, Germany, Portugal or Switzerland, or to experience a stronger performance in rural.
GDP per capita change, in % (CAGR)
Note: GDP per capita changes are expressed as the compound annual growth rate (CAGR). Changes were calculated by summing GDP and population of TL3 regions within each country and distinguishing between metropolitan and non-metropolitan regions. GDP per capita is measured in constant prices and PPP with base year 2015.
Source: Author’s elaboration based on the OECD Regional Database
Despite the resilience in GDP per capita of rural regions over 2001-21, metro regions regained the momentum in both absolute and per capita growth after the 2008 financial crisis. The average metro region had higher GDP per capita in 2001 than the average non-metro region, but growth rates were slightly lower between 2001-08 than for non-metro regions (thus rural regions were catching up). Things changed after the 2008 financial crisis, with more metro regions experiencing higher GDP per capita growth over the years that followed (see the gentler slope for metro regions in Figure 2.9). Remote and rural regions near small FUAs were overrepresented among the top 25% performers in GDP per capita between 2001-08, but the picture changed after the 2008 financial crisis, where metro and rural regions near cities started appearing more often among the top performers (Figure 2.9).
GDP per capita change, in %
Note: Cropped graph such that UK31 and UK32 are not depicted.
Source: Author’s elaboration based on the OECD Regional Database.
Productivity
Regional trends confirm that metro regions display higher levels of labour productivity (Figure 2.10). In most OECD countries, rural regions lag behind, with productivity levels around 17% lower than those in metropolitan areas in 2021. However, the size of the gap varies. In Hungary, Korea, Slovenia, Spain, and Switzerland, differences between rural and metropolitan regions were relatively small. Notably, Korea is the only country where labour productivity in rural regions exceeds that of metropolitan areas.
GVA per person employed
Note: 2021 or latest available year: 2020 data for UK; 2019 data for US. Labour productivity is calculated by summing GVA and employment across metro and non-metro regions of each country. GVA is measured in constant prices and PPP with base year 2015. Ireland is not included in the analysis because it has the starkest productivity differences across regions, reflecting in large part the relatively high share of multinational companies with significant intellectual property assets with headquarters in (including those that have redomiciled to) Ireland, and Dublin in particular (Central Statistics Office of Ireland).
Source Author’s elaboration based on the OECD Regional Database.
The high rates of productivity before the global financial crisis were mainly driven by more remote rural regions. Rural regions near small cities and remote rural regions recorded the highest rates of productivity growth, 1.93% and 1.84% respectively, across all types of regions (Figure 2.11). In a sense, there was a process of convergence in these two types of regions before the global financial crisis given their lowest and second lowest productivity levels in 2003 standing respectively at 73% and 81% of the average productivity across all TL3 regions. This process of convergence reverted after the crisis years. These two types of regions experienced the largest drop in productivity growth after the global financial crisis, falling by a full 1.54 percentage points for non-metro close the small cities and 1.32 percentage points in non-metro remote regions.
Note: Productivity measures use equal weights for each TL3 region.
Source: Author’s elaboration based on the OECD Regional Database.
Technological adoption, including automation, has helped maintain rural productivity amidst demographic decline. Rural GDP per capita has remained stable – or even grown—even as overall economic output and population have declined. This is largely due to labour-capital substitution. As emphasised by the OECD Regional Outlook 2023: “While in urban areas productivity and job growth have typically gone hand-in-hand, in non-metropolitan regions a combination of automation and competitive pressures from lower-income economies, have resulted in a lower share of regions generating jobs growth as productivity has grown.” (OECD, 2023[3]). As populations decline and age, rural economies have increasingly adopted capital-intensive production models, which reduce dependency on labour inputs while boosting efficiency. Sectors such as manufacturing and agriculture have embraced precision technologies and digital supply chains, allowing firms to maintain or increase output with fewer workers. Automation is a key enabler, but there are risks. Technology adoption varies by firm size and region; in manufacturing, it is mainly large rural manufacturers that have an innovation edge (OECD, 2023[4]). Furthermore, automation risks displacing lower-skilled workers without corresponding upskilling efforts or reinvestment in local capabilities, thus reinforcing inequalities.
Labour productivity in rural manufacturing has increased, but often through contraction rather than expansion. Over the past two decades, rural regions across OECD countries have generally seen rising labour productivity in manufacturing, despite experiencing a net decline in employment. Between 2000 and 2019, around 92% of rural regions recorded productivity gains. Technology is one potential driver. Though manufacturing shows lower technological intensity in rural areas, it is increasing. The share of manufacturing employees in high technology is twice as high in large metropolitan regions compared to non-metropolitan ones. Yet, from 2008 to 2019, the average share of rural manufacturing employment in high and medium-high technology industries increased from 5.7% to 6.4%. Overall, manufacturing is becoming less labour intensive in both rural and urban settings. But in rural regions this trend adds to a broader process of jobless growth that is also fueled by agriculture – whereas in metro regions agglomeration economies have sustained both employment and productivity growth via other sectors (OECD, 2023[4]). This dynamic reflects a broader trend of "productivity through shrinkage" in rural economies – where output remains stable or increases moderately, but labour inputs fall, particularly as younger and more skilled workers leave for cities (Rodríguez-Pose, 2018[5]).
There are also large disparities in performance, with a smaller set of highly productive farms and businesses driving the overall productivity number. As in manufacturing, also in agriculture, total Factor Productivity (TFP) growth – the efficiency with which producers combine inputs to make outputs – has driven most of the increase in agricultural production growth over the last two decades. However, there continues to be significant productivity gaps across farms, and improving the productivity of farms that lag behind will be a challenge, even in high-performing countries. Moreover, while productivity growth has resulted in less land being converted to farming, progress in the overall environmental performance of agriculture has been uneven across countries.16
Looking ahead, rural productivity will depend on further uptake of smart technologies, targeted investment in digital skills, and integration into innovation networks (OECD, 2022[6]). Without these, the risk of divergence and decline remains significant, particularly in remote and low-density areas. The reality is that technology adoption is still lagging behind urban areas. Sustaining and enhancing productivity in rural regions will require more than technological catch-up. As discussed in Chapter 3, it demands co‑ordinated industrial and place-based policies that improve digital and physical infrastructure, enable skill development aligned with green and digital transitions, and support innovation ecosystems adapted to rural contexts. The current trend of productivity through shrinkage may hollow out rural economies if these opportunities are not seized, reinforcing spatial inequality rather than reducing it.
2.3.3. What all this means for rural regions and people
The pockets of rural growth demonstrate strong economic potential, highlighting opportunities for place-based investment. Although metro regions are indeed growing faster in most countries over the last two decades (17 countries in terms of GDP and 18 countries in GDP per capita), there are pockets of growth in rural regions. For example, in three countries (Germany, Switzerland and Portugal), GDP growth was higher in rural than in metro regions and in in 9 countries (Austria, Estonia, Finland, Germany, Japan, Netherlands, Portugal, Switzerland, and Türkiye,) GDP per capita growth was also higher in rural regions. Productivity growth in rural near small FUA is even higher than in metro areas, which might have been pulled upwards by a few high-performing places.17
This all means that rather than treating rural areas as structurally disadvantaged, their distinct and evolving strengths can be capitalised on. By increasing the competitiveness and attractiveness of diverse regions, the whole country becomes more competitive and prosperous. The actions will depend on the type of rural region. Remote ones are more vulnerable to economic contraction, reinforcing the need for diversification and investment in high-value sectors. Rural regions near midsize/large cities generally perform better economically than those near small cities or remote areas, highlighting the advantages of rural-urban linkages.
Opportunities exists across all types of rural regions, and shrinking does not necessarily mean economic decline. There are many instances of regions with high GDP per capita growth that saw a decline in population. This is more likely experienced in remote regions, which suggest that they appear to be facing stronger transformations in market shredding, more capital intensity, automation, or adapting innovations. Furthermore, the ratio of disposable income per capita relative to GDP per capita tends to be higher in rural regions compared to metro – where a higher GDP per capita does not always lead to a one-to-one correspondence with higher disposable income per capita (see Annex Figure 2.A.9). This may suggest the presence of labour-intensive industries where workers benefit more directly from the fruits of their labour.
Opportunities need to reach local communities, since discontent across rural places seems to be mounting. Recent social tensions in several European countries (e.g. Belgium, France, Germany, the Netherlands, and Spain) signal a degree of dissatisfaction with current policies in some rural places (see results of the perception survey in the Statistical Annex 2.A). However, understanding what drives happiness or discontent in rural regions is an area that requires further exploration.
Well-being includes various dimensions, and perceptions of well-being can differ significantly. In many instances, this dissatisfaction arises from a combination of factors, including a lack of opportunity, declining services, and demographic shifts. Well-being also brings into focus two different types of ways to measure the quality of life in rural areas.
One is more objective and is the more typical approach relying on indicators that focus on resources and opportunities. Specifically, how well the needs of individuals in a society are being met across several spectrums, such as physical, economic, social, environmental, and emotional aspects.
The other is more subjective and places the focus on the individual perceptions and assessments of their own life within society and how individuals perceive their benefit from societal decisions or policies. Data shows that rural communities can show higher community attachment and higher civic engagement.
The following sections examine trends in economic competitiveness as well as in social outcomes, and what they mean for growth and quality of life.
This section examines rural economic activities and what drives competitiveness, including the areas of specialisation across types of rural regions, access to urban linkages, unique resources and the competitive foundations for innovation. It also identifies areas of opportunity amid megatrends.
2.4.1. Rural economic structures and specialisation
Rural regions have traditionally anchored their economies in tradeable goods, such as agriculture, resources sectors such as mining or forestry, tourism and specialised manufacturing (OECD, 2020[7]). This focus on tradeable sectors is essential for rural areas to achieve scale and integration into global supply chains, and they will be the main sources of new economic opportunities.
The share of agriculture, forestry and fisheries in employment18 is the highest in rural remote regions across all countries. Only in New Zealand and Greece was the employment share of these sectors in rural regions close to FUAs close to those observed in remote regions (Figure 2.12). These sectors also had the highest GVA share in remote regions of most OECD countries, except in Hungary, Latvia, and Greece - in these three countries the GVA share was highest in non-metro near a midsize/large FUA. The country-weighted average of the employment contribution of agriculture, forestry, and fisheries to total regional employment across OECD countries is 2.7% in metro regions, 6.6% in non-metro regions near a FUA, and 9.2% in non-metro remote regions. The GVA contribution for each of the three regions is smaller, 1.7%, 4.2% and 5.9, respectively, which is compatible with the lower productivity of these sectors as compared to others – but still more relevant for remote regions.
The left-hand side shows the employment share and the right-hand side the GVA share
Note: The average figure inside countries are population weighted averages.
Source: Author’s elaboration based on the OECD Regional Database
Public administration activities also take an important share of the economy of non-metro regions. These activities take larger shares of employment and of GVA in remote regions, in particular, when compared to non-metro close to a FUA and metro regions (Figure 2.13). The country-weighted average of the employment contribution of public administration to total regional employment across OECD countries is 25.7% in metro regions, 26.1% in non-metro near a FUA, and 27.9% in rural remote regions. In the case of GVA, the country-weighted average contribution was 17.8% in metro regions, 19.4% in non-metro near a FUA, and 21.3% for rural remote regions. Remote regions display the highest share in employment in public administration in 11 countries and the highest share in GVA in 12 countries.19
The left-hand side shows the employment share and the right-hand side the GVA share
Note: The average figure inside countries are population weighted averages.
Source: Author’s elaboration based on the OECD Regional Database
Non-metro regions near a FUA appear to be more specialised in manufacturing activities. This is visible in the data for both the relative employment and GVA shares of manufacturing. In 15 countries, non-metro regions near a FUA featured the highest share of employment in manufacturing – while they featured the larger GVA share of manufacturing in 17 countries.20 By contrast, it was the metro regions the more specialised in manufacturing employment – relative to other sectors – in Spain and Italy and in GVA only in Ireland and Spain (Figure 2.14. The country-weighted average of the employment contribution of manufacturing to total regional employment across OECD countries is 12.9% in metro regions, 16.7% in non-metro near a FUA, and 14.3%% in rural remote regions. For GVA, the average contribution was 17.9% in metro regions, 22.4% in non-metro near a FUA, and 17.7% for rural remote regions.
The left-hand side shows the employment share and the right-hand side the GVA share
Note: The average figure inside countries are population weighted averages.
Source: Author’s elaboration based on the OECD Regional Database
There are, therefore, important differences in economic specialisation amongst types of rural regions, which will drive the opportunities each can pursue. Rural regions near cities can benefit from urban spillovers, supporting high-value sectors like digital services, advanced manufacturing, and innovation hubs. Remote rural areas can build competitive advantages in renewable energy, sustainable agriculture, and nature-based tourism.
2.4.2. Potential drivers of economic performance
The importance of the tradeable sector
The tradeable sector is an important source of competitiveness for rural regions. Evidence shows that employment gains in the tradeable goods and services are positively correlated with productivity gains in OECD TL3 regions. Reallocation towards (away from) tradeable sectors in a region is captured by an increase (decrease) in the share of regional employment in these sectors. Using information on 973 TL3 regions, an annual average increase of 0.1 percentage points in the employment share in the tradeable goods sector over 2001-19 is associated with 0.17 percentage points higher annual average productivity growth in the region. The correlation is weaker for the tradeable services sector but still positive and statistically significant, and equal to 0.07 (Figure 2.15).
TL3-level yearly change in the employment share of tradeable sectors and productivity growth, 2001-19
Note: The 2001 values are obtained as an average between 2001 and 2002; the 2019 values are obtained as an average between 2018 and 2019. The industrial sector includes NACE group B-E, while tradeable services include NACE groups J, K, L, M-N. For Austria, Germany, Poland, Spain and the United Kingdom, tradeable services include G-J, K, L, M-N. Data for the United Kingdom start in 2004 (Northern Ireland missing due to boundary changes). Data for the United States are not included due to the low quality of employment data by sector/TL3 region.
Source: Based on data from OECD (2022[7]), OECD Regional Statistics (database), https://www.oecd.org/regional/regional-statistics/.
Proximity to FUA and rural-urban linkages
Rural regions with linkages to urban areas can capitalise on the strengths, and also the shortcomings, of the these. Cities enjoy from the benefits of economies of agglomeration, leading to productivity and income gains (documented in last section) that can spillover to close regions, particularly by giving access to steady demand (Merenkova, Agibalov and Lubkov, 2019[8]). Agglomerations are also more resilient to economic shocks – more rural regions have faced economic contractions over the past two decades than metro regions (13.8% vs. 4%, based on TL3 data).21 But success can also lead to congestion and other challenges (e.g. higher land and housing prices, rising inequality, and environmental pressures) that makes of rural areas attractive alternatives for economic linkages – particularly those close by. These challenges can generate opportunities for lower density areas close to cities. For example, lower housing prices can be traded-off by higher transportation costs.
In rural areas near FUA’s, the urban and rural spaces can be highly interlinked across economic, social, and environmental dimensions, thus expanding the benefits of agglomeration (OECD, 2013[9]). Rural areas near cities have much stronger linkages in commercial activities, transportation networks, commuting flows, spatial planning, and the provision of goods and services. Stronger linkages and the associated benefits are also often referred to as “borrowed” agglomeration effects from neighbouring cities. It is estimated that for a doubling of the population living – at a given distance – in urban areas within a 300 km radius, the productivity of the city in the centre increases by between 1% and 1.5% (OECD, 2015[10]). This implies that rural near FUA’s can benefit from the borrowed agglomeration effects, but also attract skilled labour via lower housing costs and higher environmental amenities.
The Manufacturing Opportunity
Manufacturing remains an important driver of employment in rural economies, particularly in areas near intermediate cities in the context of rural-urban linkages. There are diverse range of actors in the rural manufacturing ecosystem, ranging from processing firms of agri-food products to medium-sized family businesses and large-scale multinationals. Manufacturing contributes around one out of five rural jobs. Despite rural regions making up less than a third of the OECD population, they accounted for nearly half of manufacturing jobs.22In the average OECD country, 55% of manufacturing workers were working in rural regions in 2019 (Figure 2.16), significantly higher than the share of the OECD population living in rural regions. The contribution is higher in Nordic countries and vast countries such as Australia and Canada. The share of manufacturing employment was 19% in rural near a midsize/large FUA, 23% in rural near a small FUA, and 13% in rural remote.
Note: The OECD average includes only countries for which regional typology or employment data are available at the TL3 level and is calculated as a simple (unweighted) country average. Geographical typology refers to OECD TL3 typology defining metropolitan (large MR-L and medium MR-M) and non-metropolitan regions (near a large city NMR-M, near a small city NMR-S and rural region NMR-R), for further details see Box 2.1. The year for which information is available is 2017 for most of the countries, except Canada, France, Japan, Poland and Switzerland (2016), Belgium, Estonia, Denmark, Hungary, Slovenia, the United Kingdom and the United States (2018), Australia and South Korea (2019).
Source: OECD (2023), The Future of Rural Manufacturing, https://doi.org/10.1787/e065530c-en.
Rural near FUAs have better access to markets and can benefit from borrow agglomerations and also attracting skilled labour force and talent. Rural areas near a FUA already have a higher share of manufacturing activities than in remote regions and are well-positioned to specialise in high-value manufacturing and tradeable goods sectors due to their access to urban labour markets, innovation centres, supply chains and logistic hubs, while often offering cheaper land and rental prices. Rural manufacturing complements metropolitan industry by providing essential goods, diversifying supply chains, and ensuring national economic resilience. These regions can support satellite facilities for urban-based firms, and rural manufacturers can provide crucial inputs for urban industries, particularly in automotive, construction, and consumer goods sectors. They are particularly suited to advanced manufacturing, custom production, and the development of circular economy practices.
Manufacturing looks positioned to continue to be an area of prosperity in rural areas in the future. Manufacturing jobs often pay higher wages compared to other rural industries and reduce outmigration. Rural regions offer in addition distinct advantages for manufacturing:
Lower land and operating costs make large-scale, space-intensive production more viable than in densely populated urban areas, allowing for greater flexibility in facility expansion and automation.
Proximity to key natural resources (such as timber, minerals, and agricultural products) reduces supply chain costs and enhances sustainability.
Fewer zoning restrictions and land-use conflicts – unlike urban centres – enabling manufacturers to develop large, integrated production sites with greater efficiency.
Natural resources advantage
Mining regions, particularly those in remote places, play a critical role in national and local economies, often outperforming national averages in GDP and productivity performance. On average, mining-intensive regions exhibit GDP per capita levels 18% higher than their broader national economies, with labour productivity exceeding national averages by 9% (see Figure 2.17 (OECD, 2023[11]). These figures underscore the economic potential of well-managed resource wealth. High-performing mining regions demonstrate that when resource revenues are effectively reinvested, they can sustain long-term economic prosperity by attracting investment, generating high-wage employment, and strengthening infrastructure development.
Selected TL3 Mining Regions with Corresponding TL2 Regions and Country-Level GDP per capita, 2022
Note: The benchmark is calculated using the location quotient of mining employment at the TL3 level relative to the TL2 region and the country. This ensures that the selected regions have the highest mining specialisation within their respective countries, provided that mining activity is present. The regions included belong to the OECD Mining Regions Benchmark (OECD, 2023[12]).
Source: Author’s elaboration based on the OECD Regional Database
Mining based economy can create jobs, attract investment, and often outperform national averages in GDP and productivity. High-performing mining regions thus lead to higher income levels and contributions to national growth.23 In Chile, Antofagasta records and income that is 3.7% higher than national level in 2022 and contributes to 13.6% of national GDP (against its population share of 3.6%) (OECD, 2023[12]). GDP per capita is three times higher than the national average. Similarly, Nunavut in Canada surpasses the national GDP per capita by 185%, largely due to mining, and the Pilbara region in Western Australia exceeded the national GDP per capita by 166%.
Note: The selection is based on the OECD Mining Benchmark (OECD, 2023[12])
Source: Author’s elaboration based on the OECD Regional Database
The strong economic performances of mining regions, however come with unique challenges including high fluctuations to changes in external prices, high levels of inequality, especially in the housing sector, high use of energy and exposure to pollution. Mining economies are inherently cyclical, subject to fluctuations in global commodity markets, and vulnerable to resource depletion. Data shows that mining regions often have industry diversification levels 15% below their national benchmarks and patenting rates nearly 50% lower, limiting their ability to pivot towards innovation-driven industries. Without proactive economic planning, mining wealth alone does not guarantee sustained prosperity.
The case of Norrbotten in Sweden, or Antofagasta in Chile demonstrates how mining wealth can drive economic success while also increasing vulnerability to price shocks. Mining contributes approximately 20% of Norrbotten’s regional GDP and 72% of Antofagasta’s GDP, providing stable employment and high incomes, making them some of country’s most prosperous regions (OECD, 2021[13]). However, its heavy reliance on iron ore exports makes it highly sensitive to global commodity price fluctuations, posing risks to long-term economic stability. For instance, Antofagasta in Chile, despite achieving the highest GDP per capita in the country - and nearly double the average of OECD mining regions (OECD, 2023[14]), remains narrowly dependent on copper mining, with limited success in diversifying into alternative high-value sectors.
The experiences of both high- and low-performing mining regions underscore the need for strategic policy interventions to ensure resource wealth translates into broad-based, sustainable development. Successful mining regions benefit from strong governance frameworks, reinvestment in skills and infrastructure, and policies that foster economic diversification beyond extractive industries. Without these conditions, mining-dependent regions face the dual threats of economic instability and long-term stagnation. Policy makers must therefore adopt a forward-looking approach that balances resource extraction with investment in innovation, human capital, and industry diversification to ensure mining regions remain competitive in a rapidly evolving global economy.
A similar trend can be observed for forestry since forests are generally concentrated in very few locations in a country. Sweden, despite being a relatively small country, is the fourth largest exporter of sawn softwood, pulp, paper, and board in the world (Royal Swedish Academy of Agriculture and Forestry, 2024[15]). Forests in Sweden are predominantly located in the central and northern parts of the country, contributing significantly to both the local and national economy. This sector employs annually 140 000 people (2% of the total), around nine to twelve percent of the country’s total jobs (Swedish Forest Industries, 2024[16]). This sector is also important in Finland, where forests cover over 75% of land and forest industries account for 18% of exports revenue (MMM.FI, 2024[17]). In Canada, the forestry sector supports over 200 000 direct jobs (Stat Can, 2018[18]) and generates more than CAD 33.4 billion in GDP (Natural Resources Canada, 2025[19]), with strong regional concentrations in the provinces of British Columbia and Quebec.
Publicly owned forests have a potential to play a vital role in local economy. In Ontario, Canada, the dependence on forestry in remote areas on the northern region is relatively high. More importantly, 90% of the province’s forests are located on public lands, which provided USD 5.5 billion to Ontario’s overall GDP in 2022 with total revenues of USD 22.8 billion (Ministry of Natural Resources, Government of Ontario, 2025[20]). The state government grants licenses for commercial purposes to manage forests in compliance with laws and regulations, with the emphasis on communities and Indigenous partnerships. Part of the licenses is given to indigenous-led enterprises for empowering local communities with decision-making, providing a large range of economic and social benefits, while maintaining the unique ecological identity.
Rural specific assets
Rural regions are increasingly driving new sources of economic dynamisms by leveraging their unique assets. In addition to the drivers of growth described – proximity to cities, specialistion in tradeable goods and services and natural resources – rural regions show successful performance when activating their unique assets in strategic and effective ways, whether by joint community initiatives, embracing innovation, turning remoteness into testing facilities or deployment renewable energy project. Traditionally, rural economies were perceived as structurally disadvantaged, overly dependent on agriculture and extractive industries. However, global megatrends (e.g. climate change, digitalisation) are reshaping rural development pathways. The OECD’s Rural Well-being: A Geography of Opportunities (OECD, 2019[21]) highlights that rural areas are not homogenous; some face economic stagnation while others are thriving due to proactive investment in high-value sectors. From large-scale renewable energy projects to military testing and sustainable tourism, rural regions are turning structural challenges – such as remoteness and land availability – into competitive advantages. There are more and more examples of rural places translating specific assets into new sources of economic activity. Specific rural turnaround stories exemplify these opportunities in the Case Study Annex.
Renewable energy is emerging as a game-changer for rural economies, but strategic planning is essential to maximise local benefits. Large-scale renewable projects often require vast, underutilised land, making rural regions ideal locations for wind, solar, and battery storage infrastructure. The Hornsdale Power Reserve in South Australia, developed by Neoen in partnership with Tesla, demonstrates how remote locations can be leveraged for grid stability and clean energy leadership. By addressing energy storage challenges, the project improved electricity affordability and positioned South Australia as a model for large-scale battery storage integration. Meanwhile, the Hornsea 2 offshore wind farm in the UK illustrates how coastal rural regions can benefit from renewable energy through job creation, local infrastructure investment, and community funding mechanisms. However, proactive governance is needed to ensure that profits from energy projects benefit rural communities rather than bypassing them. Policies supporting local workforce development, revenue-sharing mechanisms, and infrastructure reinvestment can make renewable energy a long-term economic driver for rural regions.
Strategic industries that require space, security, or controlled environments are unlocking new opportunities for rural regions, but inclusivity must be prioritised. High-tech and defence-related sectors are increasingly looking to rural locations for research and testing facilities, creating specialised employment and attracting private investment. Sweden’s Vidsel Test Range transformed a remote, economically stagnant area into Europe’s largest overland aerospace testing site, attracting international defence firms and fostering high-tech job creation. An historical reference is the White Sands Missile Range in New Mexico, which leveraged its vast desert landscape to establish a leading military testing facility, generating spillover benefits for local businesses. However, such projects must balance economic benefits with environmental and social considerations, particularly when indigenous land rights or ecological conservation are at stake. Inclusive planning processes and long-term regional reinvestment strategies can ensure that rural communities remain beneficiaries rather than bystanders.
Nature-based tourism is proving that conservation and economic development are not mutually exclusive, but success depends on strong local governance. As global demand for experiential and sustainable travel rises, rural regions with rich natural and cultural assets are capitalising on this shift. The Bracken Bat Cave in Texas and Great Bear Rainforest in British Columbia demonstrate how wildlife conservation can generate significant economic returns while protecting fragile ecosystems. Well-managed wildlife tourism not only creates local jobs and business opportunities but also provides a compelling alternative to environmentally destructive industries such as logging or trophy hunting. Similarly, heritage-based rural tourism, as seen in Linares de la Sierra, Spain, can help counteract depopulation by rebranding remote villages as cultural tourism destinations. However, to sustain these benefits, local governance must ensure that tourism revenues are reinvested in community development, infrastructure, and conservation efforts rather than creating seasonal, low-wage economies that fail to provide long-term resilience.
2.4.3. Rural Innovation as an underlying force for competitiveness
Incremental innovations are key for capitalising on rural assets and for rural prosperity. Pursuing untapped sources of economic value and new rural specialisations requires innovation Rural remote regions which have lower access to markets, higher costs of transportation and are less diversified in their economy can benefit from innovations that can help overcome the distance penalty, access to markets, substitute a declining and ageing labour input and revigorated isolated communities. Incremental forms of innovation can be effective especially in primary activities such as forestry, agriculture, fishing, or mining activities. Innovations to deploy decentralised energy grids can also help rural communities become energy sufficient and less reliant on centralised utilities.
Rural regions do engage in “traditional” innovation linked to science and technology, and disparities with respect to metro areas may not be as large as normally thought. With the right measurement (see Box 2.3), some positive examples emerge. Rural regions that are better positioned for these endeavors are particularly those with linkages to universities and manufacturing sectors. Regions with the highest level of patents per application are often large metropolitan cities, with strong links with research universities, and strong information technology and manufacturing sectors. However, when adjusting for the occupational structure of economies, the innovation performance in rural regions improves substantially. This point is illustrated for the case of the United States. There is a 16-fold decrease in the disparity between non-metropolitan regions and metropolitan regions when adjusting the patent intensity to account for occupational distributions. Grouping metropolitan and non-metropolitan classifications together, regions in large and medium metropolitan regions (MR-L and MR-M) in the United States have approximately 13 times more patents than non-metropolitan regions (NMR-M, NMR-S and NMR-R in Figure 2.19). When we adjust for the occupations prominent in territories, the disparity falls starkly to close to 0.8.
Standard measures of innovation such as patents and R&D statistics are often better at measuring innovation in highly concentrated, urbanised areas. Science and technology indicators, however, are not well-equipped to adequately understand and measure innovation in rural regions due to:
Composition bias: Bias due to the structure or composition of the economy, including the size and sector of rural firms and the occupational structure of rural labour supply. For example, patents and R&D credits are more often filed in larger firms and those in the manufacturing sector than in smaller firms and most firms in the services and agricultural sectors. Small- and medium-sized enterprises (SMEs) are more likely to participate in incremental innovation.
Territorial endowment: Bias due to pre-existing conditions and opportunities in rural regions that are different from those in denser regions.
Headquarter bias: Bias due to the statistical method of gathering information that often centralizes responses from multiple branches to firm headquarters. In most business statistics, data are collected on the enterprise level, associated with the location where business activities are officially declared (headquarters). Often this results in a downward bias for reported activities that is in fact occurring more frequently in less dense areas. Likewise, this includes the location of patents that are often filed at headquarters.
Source: (OECD, 2022[22]) (OECD, 2022[22])
Patents filed, 2015, versus the ratio of patents filed per 1 000 individuals with inventive occupations
Note: Inventive occupations as defined by Dotzel and Wojan (2021[28]).
Source: Dotzel, K. and T. Wojan (2021[28]), “An occupational approach for analyzing regional invention”, https://ncses.nsf.gov/pubs/ncses22202/assets/ncses22202.pdf
While rural enterprises are less likely to participate in high tech innovation, rural context often require other types of innovations. To succeed, rural innovation strategies must go beyond the conventional focus on science and technology, instead aligning with the specific demographics, industries, and community strengths of rural areas. Innovation to address challenges in public service delivery, social or community-based innovation, incremental innovation and innovation in more efficient processes are critical for rural areas. The lack or limited access to public services, challenges in labour and skills shortages and difficulties in linking to other places and firms, accessing finance and general entrepreneurial support suggests that to address rural innovation, a rural lens on direct and indirect innovation policies are critical for rural entrepreneurs.
Innovations that improve access to public services enhance both the competitive foundations of rural places as well as the well-being of residents. Limited access to services is a well-documented challenge for remote rural regions. In addition to digital services, access to education and technical training resources (OECD, 2023[23]; OECD, 2021[24]), access to health services (OECD, 2021[24]) and amenities at large, remain less accessible, and only assured through community or social innovation initiatives (OECD, 2023[25]). Managing the quality of life of rural communities, particularly when shrinking or undergoing demographic disruptions, is a key element of rural prosperity and further examined in Section 5.
Rural innovation needs the right enablers, one being the development of scarce entrepreneurs. Youth start-ups are lagging in rural areas. In 2019, there were proportionately 25% fewer young start-up entrepreneurs in rural areas as compared to cities, according to the analysis from the European Union Labour Force Survey (EU-LFS) on 26 European OECD countries (2011 and 2019).24 In 2011-19, a relative and absolute fall in the number of young founders in rural areas has outpaced peers in cities, suburbs and towns. Lower youth start-up rates are driven the higher outward migration rate of youth to the cities and the age structure composition of the rural workforce that includes a smaller share of youth to the overall workforce. In European OECD countries, young rural entrepreneurs are 8.6% less likely to start a company than those in cities. There is also less dynamism in firms in rural areas, with lower birth and death rates: there are 13% more firms created per 1 000 workers in urban regions as compared to rural regions, and a 9% lower rate of firm closure (OECD, 2022[22]).
In contrast start-up entrepreneurs in rural, although declining at higher rates than in cities, towns and suburbs, they still represent a higher share of the workforce. From 2011 to 2019, the share of start-up entrepreneurs per employed, decreased from than 6 full percentage points in rural areas from 37.7% to 31.4%. This decrease was higher than the decrease recorded in cities (29.1% to 28%) and in towns and suburbs (28.5%to 25.2%) but still remains higher. This higher rate can be driven by necessity, or lack of job alternatives in many rural regions that a less diversified economy.
Most of the rural-urban entrepreneurship gap is explained by socio-economic characteristics such as education, sector of activity, household characteristics and living conditions. There are specific differences in the conditions in which young prospective entrepreneurs operate. In particular, young women in rural areas as well as towns and suburbs are 7.5% less likely to start a firm than young male in rural areas. Young entrepreneurs in cities have a 57% likelihood of having received training the year prior to starting a firm, while those in rural areas, towns and suburbs were only 26% to have received training in the year prior to starting a firm.
2.4.4. State of the competitive foundations
For rural economies to fully capitalise on these emerging opportunities, policies must their enabling foundations right such as skills gaps, lagging entrepreneurship and infrastructure. It is common wisdom that all places, including rural, need the right competitiveness foundations to prosper, including infrastructure, skills, business creation, and mature institutions. Infrastructure investment, particularly in transport and digital connectivity, is essential to link rural economies with larger markets. Skills development programmes must anticipate future industry needs, ensuring that local populations can access high-value, sustainable employment rather than being bypassed by external labour inflows.
Building such foundations or enabling factors is often needed before targeting the growth of specific industries like manufacturing or those based on rural specific assets. Rural places need policies that cater their specific needs and address their competitiveness gaps. Skills development strategies in rural areas, for instance, often focus on building a flexible, multi-skilled workforce to respond to both local and urban industry demands. Productivity and innovation policies may involve closer partnerships between regional businesses and urban research institutions to transfer knowledge and technological know-how.
Skills and human capital
Rural regions continue facing skills gaps and labour shortages. Section 2.3 shows that OECD countries continue ageing, and rural remote regions can be particularly affected, given outmigration of youth to seek educational and professional opportunities in cities. This tends to weaken local labour markets and limit economic growth potential. Rural students can also face more challenges in getting relevant skills.
There are observable disparities in competence between urban and rural regions. PISA scores, which measure students’ competences, show differences between rural and urban areas.25 Scores are reported for both reading and math literacy. Educational performance varies between urban and rural regions, with urban students generally scoring higher, though socio-economic factors play a major role in this gap. PISA data across 31 OECD countries shows that, before adjusting for socio-economic background, urban students outperform rural students in reading by an average of 45 points—equivalent to more than a full year of schooling (Figure 2.20). After accounting for factors such as parental education, household wealth, and school composition,26 the gap shrinks to 21 points, or roughly half a year of schooling. In 12 OECD countries, including Colombia, Hungary, New Zealand and Slovenia, rural students actually outperform urban students once socio-economic differences are considered.
Similar trends emerge in mathematics, where urban students initially score 44 points higher on average, but the gap narrows to 19 points after adjusting for socio-economic background. In 13 OECD countries, including Colombia, Italy, New Zealand and Portugal, rural students exceed urban performance in mathematics when socio-economic factors are accounted for (Figure 2.21). These findings highlight that rural education gaps are largely driven by socio-economic conditions rather than necessarily inherent differences in education quality, suggesting that targeted policies addressing resource allocation, school funding, and student support could help reduce disparities (the performance of service delivery is further examined in Section 2.6).
Source: Author’s elaboration based on the OECD PISA Database (OECD, 2022[26])
Source: Author’s elaboration based on the OECD PISA Database (OECD, 2022[26])
Digital broadband
Digital infrastructure is increasingly vital for economic and social development in rural areas, but gaps remain. Despite advancements in broadband coverage, there are still important gaps in connectivity and download speeds between rural and urban regions in OECD countries. On average one-third of rural households do not have access to high-speed broadband and only 7 out of 26 OECD countries have secured access to a high-speed connection for at least 80% of rural households (OECD, 2023[27]). In addition, rural remote regions have the lowest median download speeds (OECD, 2024[28]).
Broadband speeds can enable or hinder access to opportunities such as education, healthcare, and teleworking – all of which directly influence quality of life and development in rural areas. Figure 2.22 illustrates this trend, showing the median fixed broadband download speeds across OECD countries from Q1 2019 to Q4 2023. Across all regions, there has been an increase in download speeds over time. While metropolitan regions show the highest median download speeds, regions far from metropolitan areas have the lowest speeds, though they have also experienced growth over time.
Q1 2019 to Q4 2023, small regions (TL3) classification by type of region, small regions (TL3), 2022
Source: Going Digital Phase IV, pillar on “Digital Divides, Improving Connectivity” (OECD, 2024[28]). For fixed broadband speed: Calculations based on Speedtest® by Ookla® Global Fixed and Mobile Network Performance Maps. Based on analysis by Ookla of Speedtest Intelligence® data for 2019Q1-2023Q4. Ookla trademarks are used under license and reprinted with permission.
Without direct access to high-speed internet, rural communities face challenges in acquiring knowledge and skills, accessing e-services (like telehealth initiatives), participating in democracy, communicating digitally, working remotely, and creating, or indeed, offering their skills to digitally intensive firms. The digital divide also stifles innovation, business development and the potential for existing firms to grow. Bridging digital divides in access to broadband and in digital skills will be paramount for rural regions to fully leverage the benefits of digitalisation. Investment in digital infrastructure and skills will also help rural areas exploit the benefits of the digitalisation of work and social interactions and in particular, remote working.
Technology digitalisation and remote working
To seize the benefits of digitalisation, access to communication infrastructure needs to be complemented by the widespread adoption of digital technologies and by a minimum level of digital skills. Recent evidence from OECD countries shows that there is still a clear regional divide in the take-up of digital technologies. On average, there is a 7.7 percentage point gap in the share of people using the Internet between the regions with the highest and lowest use. In countries like Ireland, Japan and Türkiye, the gap can be greater than 20 percentage points (OECD, 2021[25]).
The uptake of remote working has been lower in rural regions. The suitability of jobs to remote working depends on the type of skills required to carry out occupational tasks. A large share of workers in essential jobs (agriculture, food processing, etc.), which are the predominant form of employment in rural areas, have a limited capability to work remotely. The share of employees working remotely in 2021, at the height of the first COVID wave, was almost twice as large in urban areas (15.7%) than in rural (8.7%). Indeed, the share of remote workers tripled in capital regions. In contrast in towns and semi-dense areas it doubled and in rural areas it only increased by 70% between 2019 and 2020 (Figure 2.23).
Although rural regions have doubled the adoption of remote working the adoption rate is lower than in other types of regions. Data for remote working are primarily available for European countries at the degree of urbanisation allowing to measure the uptake in remote working across cities, towns and semi-dense areas, and rural areas for 2019-21. Over the three-year period the uptake in remote working doubled – from 4.9% in 2019 to 9.5% in 2021. This increase however was lower than in towns and semi dense areas (from 4.9% to 11.8%) and in cities (from 6.1% to 18.1%) over the same time period.
Source: Özgüzel, C., D. Luca and Z. Wei (2023), "The new geography of remote jobs? Evidence from Europe", OECD Regional Development Papers, No. 57, OECD Publishing, Paris, https://doi.org/10.1787/29f94cd0-en.
2.4.5. Looking ahead: megatrends and future opportunities
A new competitive role for rural regions can be enabled by how well they adapt to global megatrends. In particular, advances in digital connectivity, automation, and the global shift towards sustainability present a new lens for viewing rural competitiveness. Rural economies are no longer defined solely by agriculture or extractive industries; they are increasingly diverse, with growing roles in digital services, advanced manufacturing, and the green economy. Opportunities exist within the main two game-changing megatrends: digital technology and sustainability.
All rural regions can benefit from technology and digitalisation. With diverse sectoral applications, from the use of “smart agriculture” to addresses labour shortages in ageing rural areas to advances in technology in mining27 to improve extraction processes, work conditions, and productivity, widespread use of technology is improving rural economies. Automation offers productivity opportunities, particularly in capital-intensive sectors like agriculture and manufacturing (Box 2.4). The potential is significant in manufacturing in particular. The adoption of automation, digital platforms, and advanced manufacturing can help rural manufacturers remain competitive despite smaller labour pools. Rural regions can adopt cutting-edge technologies such as robotics, AI, and 3D printing to specialise in high-value and customised production.
Automation can become an important development force if some barriers for their proper use (e.g. high logistical costs or access to skills by small businesses) are overcome.
Despite fears of routinised industries and jobs potential being lost, automation can be and opportunity in rural regions. It can enhance rural productivity in both remote rural regions and those close to cities. For example, Canada’s adoption of precision agriculture in the Prairies has boosted productivity, supporting rural competitiveness in global markets.28 Finland, for instance, has integrated automation in its forestry sector, improving productivity and workforce efficiency.29 For rural areas near cities, automation can facilitate high-value manufacturing activities and integration into larger supply chains. Italy’s Veneto region illustrates this through its development of advanced manufacturing clusters that benefit from access to urban labour markets and export channels.30
Source: Author’s elaboration.
All types of regions can benefit from the green economy, including from renewable energy generation. Rural areas lead in renewable energy and are well-positioned to lead in sustainable manufacturing by integrating renewable energy, circular economy models, and low-carbon production techniques. Regions with strong emissions-intensive industries are reconfiguring to meet green standards, offering opportunities in clean technology and energy-efficient production. Germany’s investment in wind energy illustrates how rural areas can become central to national energy strategies while creating local jobs. Rural areas are uniquely positioned to lead in renewable energy, carbon sequestration, and sustainable agriculture. The green economy and its opportunities for different types of rural regions is examined in more detail in Section 2.5.
Rural economies can play a central role in the “twin transitions” of digital and green not just adapting to change but as leaders of transition. Technologies such as automation and digital connectivity offer opportunities to overcome barriers associated with distance and workforce scarcity, enabling rural businesses to compete globally. By fostering local innovation, empowering SMEs, and leveraging natural assets, rural regions can redefine their role in national and global economies (OECD, 2018[29]). What that future would be, and the transformations required accordingly, depend on existing legacy industries, resources and development trajectories. The macro and sectoral trends examined in this section, along with the evolution of megatrends, allow visualising potential futures for rural economies (Table 2.3).
Source: Author’s elaboration
As discussed in Chapter 3, the effective transformation of rural places requires long-term goals, re-imagining the possible in new economic areas. Innovation and the deliberate mobilisation of local resources can make industries emerge and grow where untapped unique assets exist. Manufacturing is a case in point. While rural manufacturing shows the greatest growth and innovation potential in regions close to cities, it also holds strategic value for remote rural areas. Although the concentration of manufacturing activity is typically higher in rural areas near FUAs, some remote rural regions have carved out specialised roles in national and global value chains. For example:
The Orkney Islands in Scotland have become known for renewable energy equipment manufacturing and testing, particularly in marine energy technology.
Similarly, regions of Finland, Canada or Sweden have developed capabilities in wood processing and advanced timber construction, supported by strong regional institutions and alignment with green industrial strategies.
In Finland, the North Karelia Forest Bioeconomy Cluster integrates over 500 companies and research bodies to advance sustainable wood-based solutions (ELMOENF, 2020[30]).
In Canada, organisations such as FPInnovations and the Canadian Wood Fibre Centre foster innovation in engineered timber and low-carbon construction (FPInnovations, 2025[31]).
In Sweden, regional initiatives like The Paper Province and municipal strategies in Växjö promote multi-storey wood buildings using prefabricated components (Växjö, 2013[32]).
These examples illustrate that while scale and proximity to urban markets matter, remote rural areas can still contribute meaningfully to manufacturing through niche positioning and targeted innovation.
This section examines the evolution of the unique environmental challenges in rural regions and identifies areas of opportunity to play a central role in the green transition. These areas, with their lower population densities and greater distance from urban markets, are often seen as both key players in the green transition and as regions in need of proofed policies to adapt to their unique challenges.
2.5.1. The rural green paradox
Rural regions face the following paradox: on the one hand they are significant producers of green energy, and on the other higher per capita emitters. Rural regions contribute over half of the renewable energy produced in OECD countries (OECD, 2021[33]). At the same time, they are also larger emitters largely due to structural inefficiencies in energy systems, a heavy reliance on private transport and an outdated infrastructure (OECD, 2022[34]).
Rural regions must ensure that green energy initiatives taking place in rural regions also benefit local communities to avoid backlash and resistance. In contrast to urban centres, rural areas must overcome the challenge of providing reliable and energy-efficient services over vast, less densely populated spaces. While renewable energy projects, including wind and solar installations, are increasingly located in rural regions (OECD, 2021[33]), benefits of these initiatives do not always reach the local communities hosting them. Instead, benefits often leapfrog rural communities towards external investors or urban areas (OECD, 2024[35]).
In the quest for new sources of growth, rural regions seem particularly exposed to economic and environmental trade-offs. Similarly to energy projects, the exploitation of natural resources, such as mining and forestry, brings economic opportunities, but also leads to environmental degradation, raising concerns about sustainability and the true cost of these developments.
This growing divide – where rural communities are bearing much of the environmental burden without fully reaping the rewards—has resulted in a sense of disconnection and discontent. Key drivers of this issue include:
Rural emissions: While rural areas contribute significantly to renewable energy generation, they still face higher per capita emissions (OECD, 2021[33]), due to factors like energy production accountability, energy inefficiency and car dependency (Partnership, 2024[36]).
Energy and transport challenges: Energy costs and transport emissions are major factors limiting the competitiveness of rural areas, especially as the energy transition accelerates.
AI and technological change. The rise of artificial intelligence (AI) and digital technologies is transforming industries, but it is also creating a divide between rural and urban areas (OpenGlobalRights, 2024[37]). While cities and urban regions benefit from the technological advancements driven by AI (Hsu et al., 2022[38]), rural areas often lack the infrastructure, resources, and skills to fully participate in the digital economy.
Land use and renewable energy: The expansion of renewable energy projects in rural areas often creates land-use conflicts with agriculture and housing, leading to tensions about the distribution of benefits (Kiesecker et al., 2024[39]).
Natural resource exploitation: Mining and forestry in rural regions contribute to economic growth but also cause environmental degradation, including deforestation and habitat loss, which further fuels discontent (OECD, 2023[12]).
These intertwined issues underline the need for policies that consider the unique circumstances of rural areas—policies that not only promote environmental sustainability but also ensure that rural communities directly benefit from these transitions (see Chapter 3).
Higher per capita emissions in rural areas
While metropolitan and urban regions are responsible for the majority of absolute GHG emissions due to higher energy demand and population concentration, rural areas exhibit higher emissions on a per capita basis. In absolute terms, metropolitan areas generate higher total emissions, primarily driven by residential, industrial, and transportation sectors. However, emission intensity – defined as the amount of CO₂ emitted per gigawatt-hour (GWh) of electricity – varies significantly across regions. Rural regions emit notably less CO₂ per unit of electricity produced compared to metropolitan areas. Specifically, rural regions emit 30–42% less CO₂ per GWh than metropolitan areas, with remote rural regions (NMR-R) having the lowest emissions intensity at 187 tCO₂ per GWh, compared to 333 tCO₂ per GWh in large metropolitan areas (MR-L). Despite these efficiencies, rural regions face higher per capita emissions.
Rural regions have significantly higher per capita emissions due to structural economic factors, particularly the dominance of agriculture, energy production, and transportation. Agriculture alone accounts for a substantial portion of emissions in rural areas, particularly remote ones. Consequently, although remote rural regions (NMR-R) produce electricity at 46.3 MWh per capita—more than six times that of large metropolitan regions (7.2 MWh per capita – this electricity is often exported to urban centres, limiting local benefits. Several structural factors underpin higher per capita emissions in rural areas: i) Lower energy efficiency in buildings, attributed to older housing stock and greater heating/cooling needs; ii) longer travel distances and dependency on private vehicles, resulting in higher transportation emissions per capita; iii) smaller-scale economies that increase the cost per capita of energy solutions.
Source: Author’s elaboration based on data from OECD Regional Database.
Nationally, the emissions profile varies considerably. In 7 out of 30 OECD countries, rural regions emit more GHG per capita than the national average, notably in countries such as Switzerland, New Zealand, and Sweden. For example, New Zealand's rural emissions (49.86 tCO₂ per capita) far exceed its urban counterparts (17.5 tCO₂ per capita). Conversely, urban emissions surpass the national average in 23 OECD countries. Poland exemplifies this trend, with metropolitan emissions reaching 644.75 tCO₂ per capita, nearly double those of remote rural areas (329.28 tCO₂ per capita).
2.5.2. Opportunity in renewable electricity production
Rural regions, particularly remote ones, are the backbone of OECD renewable electricity generation. The produce 63% of all renewable energy, with 36% of it coming from the most remote areas (see Figure 2.25). Given that these areas cover 80% of OECD territory and host the majority of land, water, and other natural resources, they are naturally positioned to lead the transition to cleaner energy systems. Hydropower remains the most widely used renewable source, but wind and solar are expanding rapidly due to their scalability and declining costs.
Note: The data cover 36 OECD countries, excluding only Israel and Costa Rica, as these two are not yet classified in the OECD regional rural typology. Renewables include geothermal, hydro, solar, and wind energy sources.
Source: Author’s elaboration based on the OECD Regional Database.
Rural regions play a crucial role in decarbonising OECD economies, as they provide cleaner electricity while cities remain more fossil-fuel dependent. The energy mix in rural and urban regions highlights this divide. Remote regions generate 51.4% of their electricity from renewables, followed by 40.3% in regions near a small FUA and 27.1% in those near to a large FUA. In metropolitan areas, the share was just 22%, and with 15.2% even less in large metropolitan regions. In contrast, large metropolitan areas depend on fossil fuels for 66.9% of their electricity, with 45.3% coming from gas alone.
Energy is a key component for competitive regions and an opportunity for rural regions. Rural areas account for 62.8% of OECD renewable electricity generation, with 34.2% coming from remote areas. Wind, solar, and hydropower are key contributors to this production, offering rural economies a strategic advantage. Access to affordable and reliable energy is essential for economic growth, as it supports industries, attracts investment, and fosters innovation. To capitalise on this opportunity, rural regions need to overcome weak infrastructure, high transmission costs, and regulatory barriers.
Though rural regions often face structural challenges that limit their ability to fully capitalise on this potential, the sharp decline in renewable energy costs is transforming what was once a constraint into an opportunity. Diverse costs reductions, driven by technological advancements and economies of scale, have made renewable energy more competitive than fossil fuels, reinforcing the economic viability of rural regions as energy hubs. Examples of dropping costs include (IRENA, 2024[40]):
Solar photovoltaic costs declined by 80% since 2010 (WEF, 2021[41]).
Onshore wind costs dropped from USD 86 per MWh to USD 53 over the same period (IRENA, 2024[40]).
Battery storage project costs dropped by 89% between 2010 and 2023.
2.5.3. Challenges for new rural economic activities
Existing energy poverty in rural regions
The challenge now lies in ensuring that these cost reductions translate into local benefits for rural communities, strengthening their long-term economic resilience and energy security. Many rural areas face high energy costs and vulnerability to energy poverty (OECD, 2023[27]). Weak infrastructure, outdated grids, and limited market access lead to higher per-unit energy prices than in urban centres. In 91 regions across the Czech Republic, Portugal, and Spain, 38% of non-metropolitan areas experience energy poverty, with another 27% at risk. Rural populations, often older and on lower incomes, struggle to absorb rising energy costs. Many depend on agriculture, forestry, and small industries, making them vulnerable to price fluctuations. Weak grid infrastructure and high transmission costs further drive-up energy prices, particularly in remote areas. Addressing rural energy poverty requires investments in infrastructure, efficiency improvements, and policies that expand affordable access to ensure these regions benefit from the energy transition.
Land-use challenges
While rural areas are at the forefront of the green transition, land is increasingly exposed to competing interests between agricultural, energy, and residential demands – with the environmental impacts they convey. Table 2.4 provides an overview of the current land use dynamics in rural regions. The demand for land for various purposes—such as agriculture, energy infrastructure, housing, and natural resource extraction – has created significant land-use challenges. As renewable energy projects expand and housing demands increase, these regions face growing pressure to balance development with environmental preservation.
OECD regions have lost a tenth of their forests in the last 2 decades. OECD regions have seen a significant loss of forests, with approximately 10% of their forest cover disappearing between 2000 and 2020 (Tesnière, Maes and Haščič, 2024[42]) (see Table 2.4). This decline is driven by a combination of factors, including land conversion for agriculture, urban expansion, and the increasing demand for natural resources. Some countries and regions have experienced even more severe losses, particularly in areas where deforestation and forest fires have been widespread. This loss of forest cover has profound implications for biodiversity, carbon sequestration, and the overall health of ecosystems highlights the changes in forest cover across different OECD countries and regions, providing a snapshot of the environmental challenges faced by rural areas.
Source: Author’s elaboration
This section assesses social well-being and access to services, integrating perception data to understand community strengths and vulnerabilities. Rural areas often face unique challenges in terms of quality, cost, and access to essential services. Limited infrastructure, labour shortages and geographical isolation can hamper the availability and cost of quality healthcare, education, and other essential services, which affects quality of life in the local community.
2.6.1. Social outcomes
Beyond education, rural-urban disparities extend to health outcomes, including life expectancy and health prevalence. Life expectancy at birth remains lower in rural areas, with a 2.4-year gap between remote and metropolitan regions in 2023 (OECD, 2024[43]). In 2022, cardiovascular mortality in remote areas of OECD countries was 19% higher than the national average. Finland had the greatest relative disparity between metropolitan and remote areas, with a difference of 67% (263 versus 440 deaths per 100 000 population), while Hungary had the largest absolute gap (604 versus 813 deaths per 100 000 population) (OECD, 2024[43]). Rural populations also report a greater propensity for health problems, with notable gender disparities. Across different income levels, women in rural areas tend to report worse health outcomes than men, particularly in Europe, Central Asia, and the United States (Figure 2.26). The gap is especially pronounced in the United States and Sub-Saharan Africa, where rural residents are 15 and 11 percentage points, respectively, more likely to report health problems than those in urban areas.
Share of people reporting suffering significantly from health problems, 2016-17
Note: TSA stands for towns and semi-dense areas according to the degree of urbanisation.
2.6.2. Service delivery as element of rural well-being
Access to quality services is a key driver of well-being, but significant disparities persist between rural and urban areas in OECD countries. People living in rural areas often experience lower quality public services or worse outcomes than their urban peers – in education, healthcare, or other essential services – due to factors such as geographical isolation, low population density and poor infrastructure.
Effective service delivery underpins regional well-being and national productivity. Cities, towns and villages form a settlement network that can unlock economic opportunities and enhance access to key services, but demographic shifts like ageing and population decline in remote areas create pressing challenges (OECD, 2024[45]). Factors such as improved transport connectivity, co-location and regional co‑ordination play critical roles in making services more available and affordable. In practice, larger urban areas benefit from economies of scale, whereas remote or sparsely populated regions may need innovative approaches – such as blended digital-physical service delivery and targeted funding. By recognising how density, travel times and resource allocation intersect, policy makers can better balance cost, access, and quality considerations to ensure that rural areas remain viable and attractive places to live (OECD, 2024[45]).
Costs of services
The provision of public services in rural areas presents unique challenges due to low population density, remoteness, and limited economies of scale. In contrast to urban areas, where high population concentrations result in cost savings, rural areas often face higher per capita costs to provide education, healthcare, and other essential services. These cost disparities result from a combination of factors, including sparsity, the need for more extensive and therefore more expensive transport networks, or fixed costs that are more difficult to reduce. Understanding these cost dynamics is essential to designing policies that ensure cost-efficiency in rural areas while guaranteeing equitable access to quality services.
Costs to deliver services tend to be higher in less densely population areas due to sparsity, lower economies of scale, and higher transportation costs. Analysis of cost estimates31 to deliver primary and secondary education at the school level across TL3 regions reveals two key insights. First, the costs to deliver primary education are higher than for secondary education across the five regional classifications, with slight variations by region. Second, costs are inversely correlated with population density, as expected. The differences in costs between urban and rural regions (e.g. metro and non-metro) for primary and secondary education are estimated at around 7% on average, while the cost difference between the most urban type of region (large metro) and the most rural (remote rural) is twice that figure (15%). For instance, the cost per student in large metro regions is EUR 3 739 per student for primary education, rising to EUR 4 297 in remote rural regions (Figure 2.27).
Non-metropolitan regions tend to face higher costs for a range of services, including education (primary and secondary) and healthcare (see Figure 2.28). Remote rural regions often struggle with fixed infrastructure costs and longer travel distances, although exceptions exist: cardiology costs, for instance, can be lower where older populations create steady demand.
Source: Author’s elaboration, data taken from (OECD/EC-JRC, 2021[46])
Note: MR-C corresponds to NMR-S and NMR-M combined.
Source: Author’s elaboration, data taken from (OECD/EC-JRC, 2021[46])
Projections to 2035 also reveal that primary and secondary education costs in rural areas are expected to rise by about 1%, while maternity and obstetrics could increase by 6% and cardiology could decline by up to 10% (Figure 2.29). These trends underscore the importance of tailored policies, including collaborations across settlements and flexible facility placement. Pursuing economies of scale without compromising service quality can improve cost-efficiency, but complementary measures—such as co-locating services and investing in better transport—are essential for ensuring equity of access.
Source: Author’s elaboration, data taken from (OECD/EC-JRC, 2021[46])
Accessibility to services
In many OECD countries, rural inhabitants face greater difficulties than their urban counterparts in accessing key services such as education, healthcare, banking and digital infrastructure. These disparities can have significant social and economic consequences, reinforcing regional inequalities and limiting opportunities for rural populations. OECD analysis shows that settlements close to cities often have fewer services than settlements of a similar size further from cities (OECD, 2024[47]). For example, settlements with no access to a city generally have almost one more school than those with access to the city. Similarly, healthcare services tend to be more prevalent in remote rural areas compared to those near cities. Only 35% of towns less than 30 minutes' drive from a city have a hospital, compared with 78% of regional center towns and 47% of other remote towns. In addition, rural towns close to cities tend to have fewer banks or pharmacies, as residents often travel to neighbouring towns to obtain these services.
Education levels are another area where rural-urban disparities are significant. Individuals with tertiary education, i.e. higher education qualifications, are increasingly concentrated in urban areas. In 2020, the proportion of adults with tertiary education was higher in cities than in rural areas in 25 out of 26 OECD countries for which data is available (OECD, 2023[27]). The disparity ranged from 30 percentage points in Hungary to just 2 percentage points in Belgium. Over time, this gap has widened. Between 2012 and 2020, the difference in tertiary education attainment between urban and rural areas increased in 19 of the 25 countries for which data is available.
Healthcare access is another major challenge for rural communities, particularly in remote regions far from large cities. In OECD countries, more than half of the regions where the number of doctors, nurses and hospital beds per inhabitant is below the national average are far from a midsize/large FUA (67/124 regions). Regions far from a midsize/large FUA have 12% fewer doctors per inhabitant than the national average, while metropolitan regions have 4% more doctors (OECD, 2024[43]).
Residents of metro regions have more health facilities close by. This includes access to healthcare by the percentage of population within a 20-minute drive from a hospital across three types of TL3 regions (Figure 2.30). The data show significant differences in access to healthcare according to regional classification. Across all countries where data are available, metro regions had a higher share of the population with a 20-minute drive from a hospital, followed by non-metro near a FUA and finally rural remote. In all countries, the share of population with better access to a hospital was higher in metro regions except in Canada, where non-metro near a FUA had a higher share. On average across the 34 countries where data are available, 86% of the population had access in metro regions, close to 10 full percentage points (pp) higher than the share in non-metro near a FUA (76%) and 20 full pp than rural regions remote (far from a FUA) (66%) in 2022.
Note: “M” refers to metropolitan regions (MR). “NMR-C” includes only regions near a FUA larger than 250k (NMR-M), and “NM-R” includes both regions near a FUA smaller than 250k (NMR-S) and remote regions (NMR-R).
Source: Regions and Cities at a Glance (OECD, 2022[48])
2.6.3. Public sentiment and rural satisfaction
The well-being of rural residents is influenced by how they feel about their current strengths and challenges. Part of well-being is certainly driven by how well the needs of individuals in a society are being met across several spectrums, such as physical, economic, social, environmental, and emotional aspects. Furthermore, individual perceptions and assessments of their own life within society and how individuals perceive their benefit from societal decisions or policies are determinants of their satisfaction.
Despite unique challenges, rural populations remain attached to their communities. According to the Gallup indicators, the analysis finds evidence of higher community attachment and higher civic engagement in rural and semi-dense areas. In this instance, community attachment refers to the emotional connection residents feel toward their community including pride for where they live, optimism about the future, and their sense of belonging. Civic engagement is the level of individual involvement in activities that contribute to community well-being including voting participation, volunteering, and local political activity. Assessment of the 38 OECD countries highlights that across 14 countries rural regions recorded the highest values in the community attachment index, against 10 countries in towns and semi-dense areas and 14 countries in cities, and in 13 countries rural areas recorded the highest values in the civic engagement index against 10 countries in towns and semi-dense areas, and 15 countries in cities (Figure 2.31).
Perceptions surveys are important analytical tools for understanding attitudes beliefs and options of individuals living in different regions. They can provide valuable qualitative insights that can supplement quantitative analysis. This section presents indicators from perceptions-based surveys like the Gallup World Poll and a trust survey undertaken by the OECD’s Public Governance Directorate. Data are only available cross sectionally, for 2022/23 for the Gallup and 2023 for the trust survey. For Gallup, we present indicators at the degree of urbanisation (DEGURBA) for community attachment, civic engagement, local economic confidence, and corruption. For the trust survey we present indicators across TL3 regions classified by the OECD regional typology that defines predominantly urban regions, intermediate and predominantly rural regions. The indicators presented include trust in national government, satisfaction with health care system and satisfaction with education system.
Source: Author’s elaboration
Community Attachment Index, as an index score from 0-100
Note: The Community Attachment Index measures respondents’ satisfaction with the city or area where they live and their likelihood to move away or recommend that city or area to a friend. Index scores are calculated at the individual record level. The average figures inside countries are weighted by design weights calibrated to age, gender and education or socio-economic status at national level.
Source: Author’s elaboration based on Gallup data and interim result from the project Regional development along the settlement network.
Given strong sense of belonging to rural places, improving services and the competitiveness of these places is crucial for well-being. Studies suggest a variety of reasons why rural and semi-dense areas may experience higher levels of community attachment. These include more distinct boundaries in rural areas that encourage residents to form a stronger sense of belonging, more frequent social interaction with community members that increases networks and social capital, and less transience in rural areas with people staying for extended periods of time (Whitham, 2019[49]). This can lead to increased civic engagement as people feel a sense of pride in contributing to improving well-being in their community. There may also be a correlation between age demographics in rural regions and civic engagement with older adults having more time to engage in these activities (Kafkova, Vidovicova and Wija, 2018[50]); (Stoecker and Witkovsky, 2022[51]).
Yet, rural communities are not optimistic about the future, which hurts their well-being. The local economic confidence index measures perceptions of the economic conditions where a respondent lives and captures people’s thoughts on current economic conditions and outlooks for the future of the economy. Towns and semi-dense areas reached the highest perception of their local economy 19 OECD countries, followed by cities recording the highest value in 12 against 7 countries in rural Figure 2.32). Thus, having some density appears to be correlated with higher confidence in the economy, but the highest confidence is not in the highest-density regions. This suggests that further analysis is needed of the links between the size of towns, community engagement levels and how that is contributing to economic confidence.
Local Economic Confidence Index, as an index score from -100 to 100
Note: Gallup's Local Economic Confidence Index is based on the combined responses to two questions asking respondents, first, to rate economic conditions in their city today, and second, whether they think economic conditions in their city as a whole are getting better or getting worse. The average figures inside countries are weighted by design weights calibrated to age, gender and education or socio-economic status at national level
Source: Author’s elaboration based on Gallup data and interim result from the project Regional development along the settlement network
Rural residents’ satisfaction with services is mixed. Satisfaction with services show interesting results. Predominantly rural regions appear to record the highest satisfaction with the educational system across more countries than in intermediate areas and cities (9 countries with the highest scores in rural areas, 8 countries with the highest in intermediate areas and 5 with the highest in cities across 22 countries). In countries with higher rural satisfaction in the education system, a number of factors might explain this relationship such as smaller class sizes and rural schools having stronger ties to their communities than those in cities, fostering a greater sense of belonging and an emphasis on practical and community oriented education (Schafft, 2016[52]). Alternatively, limited educational alternatives in rural areas might lead to lower expectations for school choices and higher satisfaction than in an urban context (Tine and Tine, 2017[53]).
On the other hand, rural areas are less satisfied with the healthcare system, with 4 countries showing the highest satisfaction in rural areas (across 21 countries), compared with 10 countries that have the highest satisfaction in cities and 7 countries with the highest satisfaction in intermediate areas. Rural areas often experience unique challenges in access to quality healthcare including a lack of proximity to clinics and availability of skilled physicians (Weinhold and Gurtner, 2014[54]). The COVID-19 pandemic may have contributed to this satisfaction, by exposing longstanding gaps in rural healthcare systems, especially staff shortages, leading to a lack of preparedness, rushed interactions, and reduced availability (Hoerold et al., 2021[55]).
Note: The OECD average is not weighted by the number of respondents. The question asked is: "On a scale of 0 to 10, where 0 is not at all and 10 is completely, how much do you trust each of the following? The national, regional, local government" (0-4=Lower trust, 5=Neutral trust, 6-10=Higher trust)”.
Source: Author’s elaboration based on the 2023 OECD Trust Survey
Note: The OECD average is not weighted by the number of respondents. The question asked is: "On a scale of 0 to 10, where 0 is not at all and 10 is completely, how much do you trust each of the following? The national, regional, local government" (0-4=Lower trust, 5=Neutral trust, 6-10=Higher trust)”.
Source: Author’s elaboration based on the 2023 OECD Trust Survey
Key trends outlined in this Chapter highlight both persistent challenges and new opportunities that rural policy must address. Demographic pressures – such as declining and ageing populations – pose a threat to workforce sustainability, particularly in remote areas. However, new economic trends, including automation, reshoring, and the green transition, create opportunities for rural regions to reimagine their economic roles. Rural areas near cities are well-positioned to integrate into manufacturing and innovation networks, while remote regions can benefit from resource-based industries, including renewable energy and sustainable tourism. The key to unlocking these opportunities lies in strategic investment in skills, digital infrastructure, and business ecosystems— – of which are essential to fostering rural entrepreneurship and connecting rural regions to global markets.
Rural regions are in general facing stronger transformations. Furthermore, the three types of rural regions show different trends in socio-economic indicators. This means that each type of region faces have different opportunities and challenges. The analyses across the different sections of this Chapter break down the following high-level trends:
Demographic shifts are driving economic transformation. Population decline is widespread, with 47.3% of rural regions near small cities and 40% of remote regions experiencing depopulation over the past two decades. At the same time, ageing is accelerating, particularly in remote areas where elderly dependency ratios are highest. However, rural areas near midsize and large cities have shown stronger population retention, suggesting opportunities to leverage rural-urban linkages.
Economic performance is highly differentiated across rural regions. While rural GDP per capita generally lags behind metropolitan areas, some rural regions – particularly those near cities – have seen growth in tradeable sectors, including manufacturing and knowledge-based industries. In contrast, remote rural regions remain more reliant on agriculture, public administration, and resource extraction, making them vulnerable to structural economic shifts. There are ‘pockets of growth’ that the data tries to throw light on.
Social and environmental factors play a critical role in rural resilience. Rural communities report high levels of social cohesion and community engagement, yet face challenges in service provision, digital connectivity, and employment opportunities. Environmentally, rural regions are both vulnerable to climate risks and well-positioned to drive the green transition, with significant potential in renewable energy, sustainable tourism, and circular economy initiatives.
Ongoing trends present clear economic opportunities for different types of rural regions. Currently, rural near a small FUA is more specialised in manufacturing activities, rural remote in agriculture, forestry and fisheries and in public administration. Rural areas near cities can benefit from urban spillovers, supporting high-value sectors like digital services, advanced manufacturing, and innovation hubs. Remote rural areas have competitive advantages in renewable energy, sustainable agriculture, and nature-based tourism, but require targeted policies to address labour shortages and service gaps. Across all rural regions, investments in digital infrastructure, skills development, and green technologies will be key to unlocking long-term growth.
The future of rural development depends on harnessing global transitions. These include towards green energy, digitalisation, and sustainable industries, in a way that delivers lasting economic and social benefits. Rural regions are not inherently disadvantaged; rather, they require strategic investment, governance innovation, and adaptive policies to realise their full potential. By aligning policies with local strengths, ensuring inclusive benefits, and addressing structural barriers, rural economies can thrive as competitive and resilient places.
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Rural regions and their underlying economic structures are simply different than in urban places, and nationally. Thus, effective rural policy responses must take into account these characteristics and tailor effective solutions to rural economies and rural places. Rural regions however are also quite diverse between themselves. For example, rural regions that are close to cities and functional urban areas are different to rural remote regions. This differentiation amongst rural regions is also crucial for understanding the different opportunities and challenges that can also lead to more effective policy responses.
Taking these considerations into account, the benchmark and analysis in this chapter provides preliminary analysis using the latest available data. The unit of analysis focuses on OECD TL3 regions using primarily the OECD extended typology sub-defining rural regions into three categories recognising the diversity of rural regions. Thus, rural regions are primarily defined as non-metropolitan regions near FUA larger than 250K (non-metro near a midsize/large FUA), non-metropolitan regions near FUA smaller than 250K (non-metro near a small FUA) and the third remote regions. For the purposes of this paper, the term non-metro and rural are used interchangeable.
Some benchmarks combine non-metro close to metro and non-metro close to a medium/small city into one category: non-metro close to cities, when data are not disaggregated across the three types of rural regions. The final version of the publication will aim at undertaking the analysis as disaggregated as possible. Some indicators also employ the OECD TL3 typology defining TL3 regions as predominantly urban, intermediate and predominantly rural regions. Finally, when data are not available for TL3 regions the analysis also includes indicators based on the degree of urbanisation (DEGURBA) that define smaller areas as cities, towns and semi-dense, and rural areas.
The averages reported throughout the analysis do not use regional weighted average (size effects) but are rather country-weighted.
Source: Adapted from: Fadic, M., et al. (2019), "Classifying small (TL3) regions based on metropolitan population, low density and remoteness", OECD Regional Development Working Papers, No. 2019/06, OECD Publishing, Paris, https://doi.org/10.1787/b902cc00-en.
A functional urban area (FUA) is defined through a systematic, four-step approach that combines densely populated urban centres with surrounding areas linked by commuting patterns:
An urban centre is identified as a contiguous cluster of grid cells with a high population density of at least 1 500 residents per square kilometre. This cluster must include a minimum of 50 000 residents across these contiguous cells.
A city is defined as one or more local administrative units (e.g. municipalities) where at least 50% of the population lives within the boundaries of an identified urban centre.
A commuting zone includes contiguous local units surrounding the city, where at least 15% of employed residents commute to the city for work. This establishes the economic and functional connection between the urban centre and its surroundings.
The FUA is created by combining the city with its commuting zone, representing an integrated region that reflects both residential concentration and commuting patterns, indicating a shared socio-economic space.
Source: Adapted from: Dijkstra, L., H. Poelman and P. Veneri (2019), "The EU-OECD definition of a functional urban area", OECD Regional Development Working Papers, No. 2019/11, OECD Publishing, Paris, https://doi.org/10.1787/d58cb34d-en.
The DEGURBA definition identifies settlements from clusters of adjacent 1 square kilometre (km2) grid cells with medium or high population density. Such clusters meet the criteria for settlements if their total population is also above a certain threshold (see below). The DEGURBA definition also incorporates built-up areas, in addition to population, to avoid the identification of multiple urban centres for a single city. However, with DEGURBA, settlements such as cities are defined by their population density, not including the surrounding commuting areas.
The table below shows the mapping of Level 1 definitions for local area units and Level 2 definitions for grid-based DEGURBA classifications. The Level 2 definition of DEGURBA distinguishes towns and villages, which are settlements, from suburbs and dispersed rural areas, which are not. The minimum population thresholds are shown in the right-most column: villages have at least 500 residents while cities start at 50 000 residents. This report uses the original DEGURBA definition, which defines towns as having at least 5 000 residents.
Source: UNSD (2020), “A recommendation on the method to delineate cities, urban and rural areas”, https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf
Population change, in % (CAGR)
Note: This table shows the number of regions across 29 OECD countries that experienced population decline at different thresholds: any decline (CAGR < 0%), a slight decline (CAGR < -0.25%), a strong decline (CAGR < -0.5%), and a very substantial decline (CAGR < -1%). The numbers in brackets represent the share of regions at each threshold. Source: OECD Regional Indicators.
Population change, in % (CAGR)
Source: OECD Regional Indicators.
Note: “M” refers to metropolitan regions (MR). “NMR-C” includes only regions near a FUA larger than 250k (NMR-M), and “NM-R” includes both regions near a FUA smaller than 250k (NMR-S) and remote regions (NMR-R).
Source: OECD Regions and Cities at a Glance, 2022
Old-age dependency ratio in non-metro regions, in absolute values
Note: The graph illustrates the old-age dependency ratio in non-metropolitan regions (NMR-M, NMR-S, and NMR-R) in 2021 across 29 OECD countries. The old-age dependency ratio is defined as the number of people aged 65 or older per 100 people of working age (20–64). Values were calculated using a population-weighted approach by summing the population by age across TL3 regions within each country.
Source: OECD Regional Indicators.
Employment change, in % (CAGR)
Note: The graph presents employment changes in metropolitan and non-metropolitan regions across 20 OECD countries from 2001 to 2021, expressed as the compound annual growth rate (CAGR). Changes were calculated by summing employment of TL3 regions within each country and distinguishing between metropolitan and non-metropolitan regions. The graph includes two OECD averages: an aggregate-weighted average, where the OECD is treated as a single entity with total employment summed across all countries, and a country-weighted average, where each country is given equal weight when calculating the CAGR. Employment is measured at place of work.
Source: OECD Regional Indicators.
Note: This graph shows the share of rural regions out of all rural regions in a country that grew at a higher level (i.e. GDP per capita CAGR between 2001-21) than the national average.
Source: OECD Regional Indicators.
Gap between rural and national GDP per capita in % (Panel A) and change of gap in percentage points (Panel B)
Note: The rural and national GDP per capita are calculated using a population-weighted approach for every individual country. The gap between those is compared in 2001 and 2021 in Panel A. The change in this gap is shown in Panel
Source: OECD Regional Indicators.
Note: OECD average for metro and non-metro regions is calculated by giving every country the same weight. The grey lines correspond to the individual countries.
Source: OECD Regional Indicators
Note: All panels only cover NMR-M. Panel A and B are showing the CAGR for GDP per capita and population from 2001-21 for the top 25% regions with the highest increases in GDP per capita, as well as for the bottom 25%. Panel C shows, for the top and bottom 25% of regions (91 regions) with the highest, or respectively lowest, GDP per capita growth rate, the average population. The average population rank is calculated by ranking for every year the population growth rate of the selected regions and seeing how this changes over time.
Source: Author’s elaboration based on the OECD Regional Database
Note: The graph displays the ratio of disposable income per capita to GDP per capita for metropolitan and non-metropolitan regions in 17 OECD countries for 2021 or the latest available year. GDP per capita and disposable income per capita are measured in constant prices and PPP with base year 2015.
Source: Author’s elaboration based on the OECD Regional Database
Disposable income per capita and GDP per capita, in absolute values
Note: The scatter plot illustrates the relationship between disposable income per capita and GDP per capita, highlighting the linear trend by regional typology (MR-L, MR-M, NMR-M, NMR-S, and NMR-R). The data covers 17 OECD countries for 2021 or the latest available year. Slopes were estimated using a regression with country-fixed effects, a typology-GDP per capita interaction, and robust standard errors. GDP per capita and disposable income per capita are measured in constant prices and PPP with base year 2015
Source: OECD Regional Indicators.
← 1. With 47.3% of rural regions near small cities and 40% of remote regions experiencing depopulation over the past two decades.
← 2. Rural populations are growing positively in 14 OECD countries.
← 3. Metropolitan areas are not immune to these transformations with 20% of them already shrinking.
← 4. A total of 12 countries experience shrinking in rural remote, against 9 in rural near small FUA and only 3 in rural near midsize/large FUAs.
← 5. By contrast, rural remote increased in population size in 12 countries
← 6. Japan records by far the highest old-age dependency ratio, exceeding one elderly person for every two working-age individuals in 2021.
← 7. Except for Türkiye, Slovakia, Ireland, and Japan, the old-age dependency ratio in non-metro regions generally falls within a range between 30% and 50%.
← 8. Though rural regions near FUA’s have more favourable demographics, with larger population growth and lower elderly dependency ratios, it is also here where dependency ratios are growing the fastest – thus converging in this aspect with other rural regions.
← 9. There are disparities within these averages, though; rural near a small FUA are also more likely to observe cases of economic contraction in the data, which suggest that the average outperformance of these regions is influenced (pull upwards) by some high performers. This highlights the importance for policy to understand what kind of intermediate cities make rural regions more successful, and under which conditions.
← 10. See https://www.bmwk-energiewende.de/EWD/Redaktion/EN/Newsletter/2024/09/Meldung/topthema.html
← 12. That include Türkiye, Estonia, Latvia, Germany, Japan, Netherlands, Finland, Switzerland, Austria, Portugal Spain, Norway, Belgium, the United States, Sweden and Slovak Republic,
← 13. A country-weighted average shows that the country-weighted GDP per capita increased by 1.66%, in non-metro regions by 1.67%.
← 14. Relative to the national average rural regions grew faster than the national average in 14 countries, thus converging to the national average. In 11 countries there was divergence with lower rates by rural regions GDP per capita relative to the national average and no changes in 3 countries (Figure X in Statistical Annex).
← 15. This is followed by rural remote regions (1.51%) and rural near a midsize/large FUA. In six countries the rate of growth in GDP per capita was highest in rural metro near a small FUA and in rural near close to a FUA. In 5 the highest rated occurred in remote regions.
← 16. Source: https://www.oecd.org/en/topics/policy-issues/agricultural-productivity-and-innovation.html . Innovation in the agricultural sector can take many forms. “Process innovations” improve agricultural production techniques, while “product innovations” can lead to the development of healthier foods in downstream industries. "Marketing and organisational innovations” improve performance throughout the supply chain. Lastly, innovations can also occur in institutions and policy design.
← 17. In terms of labour productivity in remote regions, 86% of them increased productivity over the last two decades, similar to rural near a midsize/large FUA (88%) and rural near are a small FUA (78%), however rural remote decreased employment in 45% of regions, a much larger share the other two (around 28%).
← 18. There may be some biases in employment figures due to the measurement of employment as headcount rather than actual working hours. A reduction of employment in rural areas might reflect a shift toward longer working hours, given the difficulty of farmers to find workers. Furthermore, labour force surveys may suffer from low sample sizes in rural areas and could overrepresent agricultural workers vis-a-vis business surveys since they typically include self and informal employment. More analytical work will be conducted to better understand the scope of these biases.
← 19. This is followed by metro recording the highest share in employment in 3 countries (Slovenia, Greece and Netherlands) and the highest share in GVA In 2 (Slovenia and New Zealand). Non-metro regions near a FUA, have the highest share in employment in public administration in Lithuania and Estonia and the highest GVA share in in Estonia and Greece.
← 20. In 5 countries (Norway, Spain, Lithuania, Estonia, and Slovenia) the manufacturing employment share was the highest and in 3 countries (Estonia, Lithuania, Slovenia) the GVA share was the highest.
← 21. Urban economies have a more diversified economic base and thus tend to be more resilient against economic shocks than rural regions. Regions with knowledge across various overlapping industries are more likely to develop new technologies and are less subject to resource dependence and can assist a region in transitioning to industries with steady market demand (Merenkova et al., 2019). Across all TL3 regions, the share of non-metro regions (13.8%) that have faced a GDP contraction over the past two decades, is higher than the average share of OECD TL3 regions (10%) and much above the share in metro regions (4%).
← 22. The manufacturing sector’s direct contribution to rural GVA increased in OECD rural regions from 18.5% to 21.1% from 2000 to 2019 and the sector also supports a significant proportion of upstream service sector jobs, including in metropolitan regions. Source: OECD (2023), The Future of Rural Manufacturing.
← 23. By contrast, lower-performing mining regions struggle to translate resource wealth into sustained economic prosperity, often remaining below national economic benchmarks. In Czechia, Karlovy Vary’s GDP per capita is only 65% of the national average, despite its historical reliance on coal mining. Low wages and limited economic spillovers have hindered local development. Similarly, Huelva in Spain, despite producing nearly 70% of Andalusia’s metallic minerals, suffers from persistent economic difficulties, with unemployment rates exceeding 20%—one of the highest in the country (OECD, 2021[57]). These examples reveal the risks of failing to reinvest resource revenues into economic diversification, infrastructure, and human capital development, which are essential for long-term regional resilience.
← 24. OECD (2022), Unlocking Rural Innovation, OECD Rural Studies, OECD Publishing, Paris, https://doi.org/10.1787/9044a961-en.
← 25. The analysis makes use of the degree of urbanisation (DEGURBA) and the gap captures the differences in scores between rural and urban areas as defined in DEGURBA.
← 26. All the factors controlled for include: parental education, parental occupation, economic resources, cultural and social capital, household wealth, immigration status and school socio-economic composition.
← 27. Source: OECD (2020) Mining Regions and Cities Case of Andalusia, Spain, OECD Publishing, Paris, https://doi.org/10.1787/47062327-en.
← 28. Source: OECD (2024), Enhancing Rural Innovation in Canada. Also see: Government invests in precision agriculture to enhance competiveness and efficiency - Canada.ca
← 29. Source: Improving digital planning and management of forest resources in Lapland, Finland | Rural Pact Community Platform
← 30. Rural regions display a lower intensity of technology in manufacturing activities. Source: OECD, The Future of Rural Manufacturing, 2023
← 31. Costs to deliver primary and secondary education at the school level have been estimated (OECD/EC-JRC, 2021[46]) across different geographies using a two-step method: (1) The first step involves simulating school locations using a thresholds-based, bottom-up algorithm that relies on road networks and fine spatial resolution population grids and assigning student to each school based on a spatial interaction model (2011). (2) The second step estimates school costs based on the estimated number of students per school, broken down by costs on teaching staff, non-teaching staff, and other costs (2035).