2. Twenty years of regional inequalities: Trends in OECD countries

“Suppose that I drive through a two-lane tunnel, both lanes going in the same direction, and run into a serious traffic jam. No car moves in either lane as far as I can see (which is not very far). I am in the left lane and feel dejected. After a while the cars in the right lane begin to move. Naturally, my spirits lift considerably, for I know that the jam has been broken and that my lane’s turn to move will surely come any moment now. Even though I still sit still, I feel much better off than before because of the expectation that I shall soon be on the move. But suppose that the expectation is disappointed and only the right lane keeps moving: in that case I, along with my left lane co-sufferers, shall suspect foul play, and many of us will at some point become quite furious and ready to correct manifest injustice by taking direct action (such as illegally crossing the double line separating the two lanes).” (Hirschman and Rothschild, 1973, p. 545[1])

Economic development is uneven. Places have different levels of growth potential reflecting differences in endowments. These typically result in shifts of people and capital to those places driving growth, which can, in turn, increase regional disparities (Kuznets, 1955[2]; Glaeser and Gottlieb, 2009[3]; Combes et al., 2011[4]; Puga, 1999[5]). Regional development policies, along with redistribution policies (e.g. fiscal or social policies), play an important role in ensuring that all members of society are able to benefit. Whilst regional development has increasingly oriented around mechanisms that can increase the potential of all regions to contribute to growth, there is an increasing tension in designing them based on models that are optimised for national income growth, which has often resulted in tensions between economic policies that prioritise the allocation of labour and capital to places where they are most productive in contrast to models based on reducing territorial disparities.

Whilst the two are not necessarily contradictory, the evidence from many OECD countries suggests that there can be trade-offs in practice and that solely focusing on growth and factor allocation at the national level can perpetuate often entrenched territorial disparities, including in many other dimensions of inequality beyond economic growth.

In many large cities, there is a concentration of top jobs and learning opportunities, in part because of agglomeration effects but also because of attractive amenities and diverse social networks, among other factors that serve as magnets for the higher educated (Moretti, 2012[6]; Südekum, 2021[7]). At the same time, residents of places with more limited access to opportunities often experience lower well-being, worse lifetime achievement and, indeed, partly because of the more limited opportunities, less geographic mobility, hampering their ability to move to places with higher well-being potential (Kemeny and Storper, 2020[8]). These effects not only persist in certain places but also persist over time, as they transmit across generations (Manduca, 2019[9]; Hanushek and Woessmann, 2020[10]; OECD, 2021[11]).

Whilst the costs of these more limited opportunities impact directly on the individuals themselves, there are broader costs to society, through disrupted social cohesion and political instability (Hirschman and Rothschild, 1973[1]; Dijkstra, Poelman and Rodríguez-Pose, 2019[12]), but also to the economy, through the potential costs needed to manage lower resilience to shocks (OECD, 2020[13]; 2022[14]). Indeed, even at the national level, the costs of these “remedial” measures may actually outweigh any potential benefits of place-blind “growth first” policies, which often have relatively short time horizons and, so, may not even be optimal from a longer-term growth perspective.

Bridging differences in access to opportunities is indeed an important part of the solution to reducing regional inequalities. These efforts should go hand in hand with promoting economic dynamism through productivity gains (EC, 2022[15]; OECD, 2020[16]). However, whilst gaps in GDP per capita have narrowed across OECD countries over the past two decades, gaps between regions within many countries have been persistent, with many regions in countries experiencing both growth and stagnation remaining very much in the “rear-view mirror” (OECD, 2020[16]; Diemer et al., 2022[17]; EC, 2022[15]). Indeed, some of the most advanced OECD economies have some of the highest regional disparities.

Whilst there are many factors that can explain the relatively poor performance of some regions, the asymmetric impacts of the knowledge economy and globalisation have certainly contributed, generating a much more complex landscape of sectoral specialisation, firm location and knowledge diffusion (Autor, Dorn and Hanson, 2013[18]; Navaretti and Markovic, 2021[19]; Kemeny and Storper, 2020[8]; OECD, 2021[20]).

In most OECD economies, these gaps have been in large part driven by the performance and agglomeration effects of large metropolitan regions. But relying solely on top-performing regions to boost aggregate productivity is not enough. Even setting aside the costs associated with weaker social cohesion that may arise through significant and persistent spatial inequalities, externalities such as rising congestion may also begin to limit the attractiveness of large cities (Navaretti and Markovic, 2021[19]; Dijkstra, Garcilazo and McCann, 2013[21]). Moreover, the dynamic gains from density, while powerful drivers of growth (Ahlfeldt and Pietrostefani, 2019[22]), do not extend to all cities (Venables, 2018[23]) nor, indeed, benefit all social groups.

In this context, the scope and ambition of regional development policies have evolved too, moving from subsidies to compensate economically weaker regions to an investment agenda aimed at unlocking competitiveness and growth potential in all places and, more recently, growth with an increased emphasis on living standards and well-being (OECD, 2009[24]; 2011[25]; 2012[26]; 2014[27]; 2019[28]). Indeed, several OECD countries including Italy, Korea, Poland and the United Kingdom have shifted their attention and put in place dedicated policies to narrow spatial inequalities and promote more balanced development (OECD, 2022[29]; 2018[30]; UK Government, 2022[31]). That being said, many more countries still do not have dedicated policy frameworks for reducing regional disparities.

This chapter looks back in time to explore the evolution of regional inequalities in OECD countries over the last two decades. The first section looks at the (re)allocation of people across regions, while the second examines trends in regional income inequalities, identifying growth-inequality paths in OECD countries and the role of differences between metropolitan and non-metropolitan regions in driving regional inequality. Chapter 3 then takes a closer look at the nexus between regional income disparities and labour productivity across regions.

Many OECD countries have reached a plateau in their population growth and, in some populations, are declining. At the same time, populations are ageing. However, while these trends are generally true at the national level, they are often more profound or indeed very different within countries, reinforcing the importance of a place-based approach to tackling inequalities and driving inclusive growth.

This section takes a closer look at those demographic challenges, including the role of migration within countries and the impact they have on regional inequalities, and access to key infrastructure and services across regions.

Across OECD countries, metropolitan regions concentrate 70% of the population. The proportion of people living in rural areas within each region type increases from 9% in large metropolitan regions to 52% in remote regions. People living in towns and semi-dense areas (or suburbs) as defined by the degree of urbanisation (OECD et al., 2021[32]) are most common in regions near a metropolitan area (Table 2.1).

Metropolitan regions host a larger proportion of people today compared to two decades ago in all OECD countries except Greece. A key driver behind this change has been the increasing concentration of population in large metropolitan regions, which increased their population share from around 40% in 2001 to 42% in 2021 (across 24 OECD countries with at least 1 large metropolitan region). Meanwhile, the share of metropolitan midsize regions and remote regions decreased slightly (by 0.3 percentage points), while the share of regions near midsize/large FUAs showed the largest decrease (0.7 percentage points) followed by regions near a small FUA (0.5 percentage points). Within regions, from 2000 to 2015, the share of population living in cities (as defined by the degree of urbanisation) increased by around 3 percentage points across the OECD while the share of population in rural areas decreased.

About half of the countries where the share of population in large metropolitan regions increased saw a decrease in the share of people living in midsize metropolitan regions in 2001-21 (Figure 2.1). Population growth has concentrated in the largest FUAs while about one-quarter of all FUAs in OECD countries are shrinking (see Box 2.1). The increase in the contribution of metropolitan regions is especially large, for example, in small countries with one to three midsize metropolitan regions and no large metropolitan regions (Estonia, Finland, Latvia, Lithuania, the Slovak Republic and Slovenia).

The increasing importance of metropolitan regions results from the compounded effect of internal and international migration and natural growth rates (the difference between births and deaths). Across 28 OECD countries with available data, 29 million people (about 3% of the OECD population) changed their region of residence every, year on average in 2016-19. In that period, metropolitan regions and regions close to them gained respectively 10.5 and 7 persons per every 10 000, while regions far from midsize/large FUAs lost 10 persons for every 10 000 (OECD, 2022[36]). The foreign-born population living in OECD countries reached 138 million in 2021 (10.6% of the total population of OECD countries) (OECD, 2022[37]). Both nationals (and especially youth) and international migrants settle disproportionally in metropolitan areas, especially large ones: 8 in 10 migrants live in metropolitan areas compared to 7 in 10 natives (OECD, 2022[36]) (Table 2.1). Migration, therefore, has made metropolitan regions not only larger but also more diverse and younger (OECD, 2022[38]).

Regions that do not manage to attract working-age migrants eventually see their working-age population shrink and their elderly dependency ratios increase. On average, old-age dependency ratios are higher in non-metropolitan regions (Table 2.1), while remote regions represent the highest share of regions with old-age dependency ratios above 50% (6.3%, or 46 in 733) compared to other types of regions (5.4% of regions near a midsize/large FUA, 3.5% of regions near a small FUA, 3.4% of metropolitan midsize regions and 1.4% of metropolitan large regions). Furthermore, a larger proportion of non-metropolitan regions is shrinking compared to metropolitan regions: for instance, the proportion of regions that showed shrinking in 2001-21 was 28 percentage points higher in remote regions compared to large metropolitan regions (Table 2.1). Indeed, most regions with the lowest share of working-age population (and high elderly dependency ratios) shrank at a rate of 1% annually or more in the last 2 decades. In Lithuania, Latvia and Portugal, the top three countries facing the largest population decline, regions with a negative annual population growth of at least 1% represented respectively 80%, 67% and 12% of all regions.

The tertiary educated, i.e. those with a higher education degree, are increasingly concentrated in cities. In 2020, the share of adults with tertiary education was larger in cities than in rural areas in all OECD countries with available data except the United Kingdom (25 out of 26 countries) (Figure 2.3). The gap ranged from 30 percentage points in Hungary to 2 percentage points in Belgium. Compared to 2012, this city-rural gap increased in 19 out of 25 countries with available data, with Poland, Portugal and the Slovak Republic registering the largest increases (about 7 percentage points). However, the share of the tertiary educated increased in all countries during the period and, in all countries except for Hungary, the increase in the city-rural gap was smaller than the increase in the share of the tertiary educated.

People with tertiary education are disproportionally represented in capital city regions in most OECD countries: large capital regions have the highest share of 24-65 year-olds who completed tertiary education in 28 out of 34 OECD countries and accession candidates (i.e. Brazil, Bulgaria, Croatia, Peru and Romania) with available data in 2021 (OECD, 2022[42]). Capital regions can be particularly attractive for the highly educated when they concentrate most of the quality higher education opportunities and a large public sector. However, as discussed in more detail below, they also exhibited the highest income inequalities in half of the 26 OECD countries with available data based on the S80/S20 ratio1 for the disposable income indicator (OECD, 2022[36]).

Rural-urban differences in the share of people with tertiary education may be related to gaps in access to higher education. In 31 OECD countries with available data, 66% of people living in remote regions can access a higher education institution within a 45-minute car trip, compared to 98% for those living in a large metropolitan region. This metric however does not consider the availability and reliability of other means of transportation such as public transport which, when insufficient, can drastically worsen matters, especially for low-income vocational students (OECD, 2022[43]).

Smaller gaps in enrolment in higher education compared to gaps in the share of residents with a higher education degree may point to higher “brain circulation” (i.e. the movement of people for educational purposes). People may get their education outside cities but move to cities in search of quality jobs for professionals. Large city-rural gaps in countries with low access to higher education establishments like Hungary may be related to both poorer access to higher education and lower matching opportunities for professionals outside cities. In contrast, in countries with high access like Portugal, gaps may be mostly related to the concentration of job opportunities in cities (Figure 2.3). Large gaps in the quality of educational offers may not only make the educational offer outside cities less attractive but also lead to worse entrepreneurial or job market outcomes for graduates in rural areas (OECD, 2022[44]; 2022[45]). This may in turn hamper the movement of the tertiary educated to rural areas in search of natural amenities, social and family connections and better housing options.

Establishing evidence of “brain drain” – the increasing flows of educated people from rural areas to cities – nevertheless requires following people over the course of their lives. One of the few studies using cohort data on place of residency versus place of origin of graduates shows that major cities gain and rural areas lose graduates in the United Kingdom, with London consolidated as the main magnet for graduates (Institute for Fiscal Studies, 2021[46]).

Available evidence points to substantial subnational gaps in access to services. In OECD countries, travel time, using motorised vehicles, for people living in remote rural areas to reach a healthcare facility is five times longer than those in metropolitan regions. In Europe, students in remote rural areas have to travel on average five additional kilometres to reach a school compared to students in other areas (JRC, 2022[47]).

These gaps may translate into unequal outcomes. Results from PISA2 show that students in city schools obtained higher scores in reading than their peers in schools located elsewhere in all but two OECD countries with available data (OECD, 2022[14]). Moreover, the importance of enhanced access to rural residents may also be greater. For instance, close to one in three rural residents reported suffering from health problems that prevent them from doing things people their age normally do compared to only one in four city residents (OECD/EC, 2020[48]).

Rural facilities are often required to centralise services and keep minimum quality requirements. In healthcare, for instance, rural facilities often face higher relative costs, lower volumes, poorer overall quality and workforce shortages. Across OECD countries, the number of hospital beds per capita in remote regions fell at an average rate of -0.7% per year since the 2008 GFC, while they slightly increased in metropolitan regions (OECD, 2021[11]). The gap in hospital bed rates between metropolitan regions compared to regions far from metropolitan areas – which stood at 50% in 2020 – increased by 5 percentage points with respect to the pre-pandemic period, because hospital bed rates increased faster in metropolitan regions than in regions far from metropolitan areas (18% vs. 14%) (OECD, 2022[36]). The negative impacts on the distance to care (Hsia et al., 2012[49]) and treatment delays for patients due to hospital closures in rural areas can offset any cost gains, especially in the face of sudden increases in demand for care, like during the COVID-19 pandemic (OECD, 2021[11]; 2020[50]).

While digital provision offers a way to overcome long travel times, lower economies of scale, longer ambulance transportation times and fewer healthcare workers, rural areas that stand the most to benefit from telemedicine often have poor Internet connectivity levels (OECD, 2021[51]; 2021[11]). Data from regulators in 26 OECD countries indicate a persistent rural-urban divide in connectivity speeds: 1 in 3 households in rural areas do not have access to high-speed broadband on average and, in only 7 out of 26 OECD countries, more than 80% of households in rural regions have access to a high-speed connection (OECD, 2020[52]). Across OECD countries, rural areas facing long travel times to healthcare facilities also face below-average access to high-speed broadband: for instance, in Canada and Mexico, people in rural areas face about 200 percentage points longer travel times and about 40 percentage points fewer Internet speeds than the national average.

In the medium and long terms, changes in the distribution of people within countries will happen in the context of stable or decreasing population stocks in half of OECD countries. Populations are expected to decline in 14 OECD countries by 2040 and 18 by 2100, with the largest decreases in East and Southern European countries, Japan and Korea (Vollset et al., 2020[54]). Besides differences in fertility rates – which were already below replacement levels in all OECD countries except Israel in 2021 – differences in international migration and life expectancy drive differences in projected population changes.

The future also holds fundamental changes in age structures across OECD countries: the number of children under 5 years of age could decline from 63.5 million in 2021 to about 59.2 million in 2040 whilst the number of elderly (older than 80 years) is expected to nearly double, from 66.5 million to 114.7 million (Vollset et al., 2020[54]). These population projections have stark implications for elderly dependency ratios in OECD countries: while in 2021, there are about 13 working-age people (15-64 years old) for every elderly person, in 2040, there will be only 7 (Rouzet et al., 2019[55]; OECD, 2019[56]; 2022[14]).

Against this backdrop, metropolitan regions are expected to slightly increase their population share by 2040. In absolute terms, the number of people living in metropolitan regions is expected to remain constant by 2040, while the number of people in non-metropolitan regions is expected to shrink (by 2.8% and 2.3% in regions near and far from a midsize/large FUA) (OECD, 2022[36]).

Within regions, the population living in FUAs in OECD countries is expected to increase from 950 million to 1 billion. Most of this increase will concentrate in large FUAs, which are expected to grow by 5% by 2030 while small and medium-sized FUAs are expected to shrink by 4% and 3% over the same period (OECD, 2022[36]). Areas outside cities are also projected to increase in absolute terms but at a slower pace than cities. By 2050, the population in towns and semi-dense areas is projected to increase from 2.1 billion to 2.3 billion worldwide, while the population in rural areas is expected to expand from 1.7 billion to 1.9 billion (OECD/EC, 2020[48]).

The evolution of regional income inequalities3 hinges upon both the relative evolution of national income per capita levels (linked to national GDP per capita growth) and the redistribution of income within countries. Major economic shocks have an effect not only on national growth rates but also on gaps in income per capita across regions. This is because regions differ in their degree of resilience to shifts and shocks (Rice and Venables, 2020[57]). Structural factors then – and in particular the resilience of those factors to shocks – rather than shocks per se are key drivers of regional inequalities (Garcilazo, Moreno-Monroy and Oliveira Martins, 2021[58]; OECD, 2022[59]).

The 2008 GFC put a halt to regional convergence in many OECD countries (OECD, 2022[36]; 2020[16]; Faggian and Ascani, 2021[60]). In this context, the income gaps between metropolitan and non-metropolitan regions have not closed since then, in part because metropolitan regions have proven to be more resilient to crises than non-metropolitan regions (OECD, 2020[16]). In the past two decades, the gap between GDP per capita in non-metropolitan versus metropolitan regions – of around 68% – did not close across the OECD (OECD, 2022[36]).

This section presents evidence of regional income inequality trends. It focuses mostly on small (TL3) regions in 2000-20, which offer a more granular territorial analysis and allows for classifications based on access to cities (see Annex 2.A). This section does not attempt to draw inferences regarding the effect of the COVID-19 pandemic as current data available (2019-20) are not sufficient to evaluate the impact of and recovery from this shock on regional income inequalities. The section starts by proposing a classification of OECD countries according to their regional income inequality trends. It then identifies distinct growth inequality paths. The section then assesses the importance of metropolitan versus non-metropolitan gaps in driving regional income inequalities across countries. Finally, it explores the role of proximity between regions in driving regional income inequality.

The data used in the analysis have several limitations (see also Annex 2.A). This section assesses differences in income inequalities across regions and FUAs, which reflect underlying structural, including geographical, factors as well as demographic differences (e.g. higher elderly dependency ratios). Whilst there is also interest in identifying spatial differences in income inequalities for similar demographic cohorts (e.g. gender, age, race and sexual orientation), data to assess these are unfortunately unavailable (see Box 2.2).4 As data on disposable income are unfortunately not available for small regions, the section uses regional GDP per capita as a proxy for the typical income of a representative individual in a region (this interpretation applies to the unweighted version of the Theil index, see Gluschenko (2017[61])). This means that the regional inequality measures used in this section are not indicative of the evolution of inter-personal inequalities or the situation of any given income-group (Rey, Arribas-Bel and Wolf, 2020[62]).

Over the last two decades, most OECD regions have seen improvements in their GDP per capita ratios but with significant divergences between small regions at the top and bottom of the income distribution. Top concentration and divergence of bottom regions persisted during the period, except in 2020 for the first year of the COVID-19 pandemic, leading in turn to an increase in the mean-to-median ratio5 (Figure 2.6, Panel B; see Table 2.2).

The structural effects of COVID-19 on regional inequalities will take time to materialise and, as such, it is not yet possible to interpret the impact of the pandemic on bottom convergence and top deconcentration observed in 2019-20 as a new trend of declining inequalities or a temporary consequence of the fall in economic activity during that period.

The Theil index offers a way to observe the variability of regional incomes per capita in OECD countries in a single measure. The index compares the income per capita in each region to the mean of all regions across OECD countries. The index partly captures differences in GDP per capita levels across countries: for instance, if a country experienced faster growth than the OECD average each year while everything else remained the same, the Theil index would decrease, even though the variation in regional incomes stayed the same. The index decomposition into a “between” and a “within” component is useful to assess the changes in the variability of regional income per capita, controlling for the effect of changes in national income per capita (see Box 2.3 and Table 2.2) (OECD, 2020[52]; Elbers et al., 2008[64]).

Separating country-level convergence (between) from region-level convergence (within) is of particular relevance, not least because the evidence does point to a “catching-up” effect in many countries, particularly former industrial transition economies in the European Union, Korea and Türkiye. Ten out of 13 countries with GDP per capita below OECD averages in 2000 saw the gaps narrow in the last 2 decades (Figure 2.7). On the other hand, some countries, notably Greece, Italy, Portugal and Spain saw gaps grow relative to the OECD average. Overall, 19% of the OECD population lived in regions within countries experiencing upwards convergence and 12% lived in countries experiencing divergence.

The broad convergence in overall between-country inequalities capture in Figure 2.7 is also reflected in the Theil “between” measure (see also Box 2.3) in Figure 2.8. However, whilst inequalities between countries have, on the whole, declined over the last two decades, this has not been mirrored with similar progress on within-country inequalities. At the large region (TL2) level for example, within-country inequalities increased from 2000 to 2015 before decreasing, while at the small region (TL3) level, within-country income inequalities slightly increased over the period, with marginal improvements often occurring during severe economic shocks. The absolute levels of within-country inequality stayed in a narrow range compared to changes in between-country inequality, so decreasing between-country inequality drove decreases in overall inequality across large and small regions. In both cases, the decrease in between-country inequality compensated for the rise in within-country inequality.6

Looking only at regional inequalities among small regions (TL3), 15 out of 27 countries with available data since 2000 saw regional income inequalities increase over the last 2 decades (Table 2.3) (see Annex Table 2.B.1 for the full set of results). This means that 70% of the OECD population live in countries (with available data) that experienced increases in regional income inequality. Table 2.3 also differentiates countries on whether they were converging to or diverging from the OECD average (see also Figure 2.7) and further classifies countries on the basis of weather their Theil index followed a mostly linear (increasing/decreasing) or a non-linear trend (e.g. u-shape or inverted u-shape).

Table 2.3 reveals that beyond the general picture, there are different growth-inequality trajectories across OECD countries, confirming there is no single narrative on regional income inequalities (McCann, 2022[65]) (Figure 2.9). Rather, while most OECD countries that showed increasing inequality in the last two decades reached their minimum values of regional GDP per capita inequality before the GFC, they were then set on different trajectories.

  • On the one hand, among OECD countries with income per capita above OECD levels, some such as France and the United Kingdom, experienced a sustained increase in regional inequality since the mid-2000s, while others, such as Germany and Portugal, saw sustained falls.

  • On the other hand, virtually all countries converging towards OECD GDP per capita levels saw within-country inequalities rise over the period as a whole but in a non-linear fashion (mostly as an inverse U). In Poland for instance, inequality started picking up in 2004 and continued increasing until 2020. In Hungary and the Slovak Republic, inequalities also increased rapidly, albeit from relatively low levels, between 2003/04 and 2009, but unlike Poland, fell between 2010 and 2020. In the Czech Republic, Estonia and Lithuania, inequalities plateaued at relatively high levels after decreasing slightly from their peaks in the aftermath of the GFC in 2010.

In most countries, increasing regional inequality went hand in hand with bottom divergence and top concentration (and vice versa, decreasing inequality went hand in hand with bottom convergence and top deconcentration). The correlation between changes in the Theil index and the top-to-mean (bottom-to-mean) was above (below) 0.7 in 21 (22) countries out of 27 with available data. Exceptions include the Czech Republic, Norway and the United Kingdom, which all showed a weak correlation between inequality and bottom region trends but a strong correlation with respect to top concentration.

Growth-inequality paths were counter-cyclical in most countries with declining regional inequality, and pro-cyclical in most countries with increasing regional inequality (Figure 2.10, Panel C). In some cases, including France, Italy and Spain, the pattern switched from counter-cyclical to pro-cyclical after the GFC, in line with findings that the GFC put a stop to regional convergence in these countries (Diemer et al., 2022[17]; OECD, 2020[16]). Greece is the only country with a pro-cyclical growth-inequality trend, as the Theil index closely followed the decreasing trend in GDP per capita in the last two decades.

Polarisation increased in 14 out of 27 OECD countries with available data. Despite decreases in the last two decades, the ratio of top to bottom region in 2020 was still the highest in Türkiye (3.2), followed by the Slovak Republic (2.9) and Poland (2.8).7 Absolute gaps in top versus bottom region incomes however increased in the majority (21 out of 27) of OECD countries with available data in 2010-20 compared to 2000-09 (OECD, 2022[59]) (Figure 2.11, Panel B), including in countries such as Germany, Hungary, Latvia and Türkiye where the relative gaps decreased. Reducing polarisation in both relative and absolute terms requires necessarily that bottom regions grow faster than top regions. In the case of Latvia, for instance, bottom regions would have needed to grow twice as fast as they did between 2000 and 2020, just to maintain the same absolute gap over time.

The evidence on increasing polarisation at the small region level is consistent with findings for disposable income at the large region level. Disposable income and poverty rates differ substantially across OECD countries and within their large regions. In 2020, 11 out of 26 OECD countries with available data had regions with S80/S20 ratios above the OECD average, including most regions in Chile, Colombia, Mexico and the United States. Moreover, the average gap in poverty rates between the worst- and best-performing regions in the same country was 18 percentage points, reaching 50 percentage points in Colombia and Mexico (OECD, 2022[36]).

Regional income inequalities have increased because metropolitan regions have continued to pull apart from other regions in a majority of countries. A decomposition of the Theil index of inequality can shed light on whether differences between metropolitan and other regions matter the most for regional income inequality and whether distance to cities has a role in explaining regional income inequalities (see Box 2.4, (Elbers et al., 2008[64]; Boulant, Brezzi and Veneri, 2016[66]). At the same time, polarisation and bottom divergence are linked to metropolitan versus non-metropolitan gaps because a significant share of top (bottom) regions are metropolitan (non-metropolitan). Proximity to large FUAs is also an important factor: for instance, in some countries all top regions are metropolitan large and in others all bottom regions are far from midsize/large cities (see Box 2.5 for more information on the overlap between top/bottom and metropolitan/non-metropolitan regions).

The decomposition measures the share of inequality explained by differences between groups of regions. As these shares are sensitive to the number of regions in each country, country values are expressed as a ratio of OECD-wide shares. Table 2.4 classifies countries according to the maximum relative share among three possible groupings (see Annex Table 2.B.2 for full results): i) large metropolitan regions versus other regions; ii) metropolitan versus non-metropolitan regions; and iii) regions far from a midsize/large FUA versus other regions. The table also distinguishes between countries where all (large) metropolitan regions are top regions.

Concerning metropolitan versus non-metropolitan differences, the size of cities and distance to a city matter to different degrees across countries, leading to different patterns:

  • The relative differences in the income per capita group of large metropolitan regions and others with respect to OECD levels explain the largest share of regional income inequality in 11 out of 19 countries with large metropolitan regions (8 of which have increasing inequality). In six of these countries, all top regions are also large metropolitan regions.

  • The relative differences in income per capita in the broader group of metropolitan versus non-metropolitan regions mattered the most in 13 out of 27 countries with available data. This included five countries with large metropolitan regions (two of which had increasing inequality) and all eight countries with no large metropolitan regions.

  • Finally, relative differences between regions far from a midsize/large FUA and other regions mattered the most in the three countries (with Italy being the only country in the group with increasing inequality).

Furthermore, comparing changes between 2000-09 and 2010-20 reveals that:

  • The share of inequality explained by differences between metropolitan and non-metropolitan regions increased in 13 out of 27 OECD countries with available data, 7 of which had increasing inequality (the Czech Republic, France, Hungary, Japan, Latvia, Poland, the Slovak Republic and Sweden) (Figure 2.12). This share did not necessarily increase in all countries where income inequality increased: in 8 countries with increasing inequality, including the United Kingdom and the United States, this share was smaller in 2010-20 compared to 2000-09.

  • The share of inequality explained by differences between large metropolitan and other regions increased in 8 out of 19 OECD countries with large metropolitan regions. This included three countries with increasing inequality where the other shares considered did not increase (Denmark, Italy and the United Kingdom).

  • The importance of differences between regions far from cities and others increased in ten OECD countries, six of which had increasing inequalities.

The measures of regional inequality used thus far do not consider the geographical aspect of regions, that is, the fact that regions with similar characteristics may be close or distant to each other (Rey, Arribas-Bel and Wolf, 2020[62]). The spatial Gini index disentangles the effect of proximate versus distant regions in the Gini index (Rey and Smith, 2012[67]). When spatial dependence is high and positive (similar regions show a strong tendency to cluster), differences with distant regions drive inequality, as values are similar among neighbours.

The spatial Gini index for GDP per capita shows that differences across distant regions drive virtually all the variation in regional inequality in large OECD countries including France, Germany and the United States and in countries with high regional divides such as Italy (Figure 2.14). In contrast, in small countries where a large share of inequality is due to concentration in the capital city, there is no evident clustering of regions with high or low income per capita but rather an “oasis” of prosperity.

The existence of localised productivity spillovers tends to translate into a certain degree of similarity in terms of income and prosperity between neighbouring regions. Spatial clustering by income can result in persistent regional income dynamics and the emergence of a two-tier system of regions, with rich regions clustered with other rich regions (“high-high” clustering) and poor ones clustered with other poor ones (“low-low” clustering), unable to learn from “productive neighbours” and stuck into regional development traps (Iammarino, Rodríguez-Pose and Storper, 2019[69]).

“Low-low” clustering appears to be more widespread than “high-high” clustering, except for Southern European countries, where more than half of high-income regions appear to be spatially clustered among regions with a similar level of income (Figure 2.15) (see Box 2.6 for a description of how spatial clustering is measured).8 Furthermore, Italy and Spain also record the highest degree of “low-low” income clustering among the countries considered, with three in every four low-income regions spatially clustered around regions with a similar level of income.

Regions are subnational units below national boundaries. OECD countries have two regional levels: large (TL2) regions and small (TL3) regions. Regional boundaries correspond to administrative divisions defined autonomously by countries using different criteria. Except for the United States, small regions are nested within large regions. However, in Estonia, Latvia and Luxembourg, TL2 borders correspond to national borders, and in Israel and New Zealand, TL2 and TL3 borders are the same. Below small regions, countries define local units such as municipalities, which are usually nested within small regional boundaries. Regional boundaries, especially those at lower scales, may be subject to changes across time as regions merge and split following demographic and political changes.

The comparison of inequality levels and rankings between territorial levels is not meaningful because the way information is scaled down varies across countries.9 The level of administrative fragmentation – that is, how national territories are split by administrative units – varies across OECD countries, especially at the TL3 level. At the TL2 level, the number of TL2 regions per 1 million inhabitants varies from 0.08 in Japan to 5.4 in Iceland. At the TL3 level, it varies from 0.33 in Korea to almost 8 regions per 1 million inhabitants in Canada (that is, 293 regions in approximately 38 million people).

The distribution of population across regions in OECD countries is uneven. Highly urbanised countries such as the United States and sparsely populated countries such as Australia and Spain show more disparity in the distribution of population across regions. Australia has the most unequal distribution of population across small regions, as just 5 of its 51 regions account for 60% of the national population). In contrast, the populations of East European countries, Israel and the United Kingdom are more evenly distributed.

Population concentration is not always associated with large population sizes. Large and highly urbanised countries such as Japan and the United States have numerous regions with at least 1 million inhabitants and many metropolitan (large) regions (Annex Figure 2.A.2). Furthermore, countries may have similar population levels but different concentration levels. For instance, France and the United Kingdom have similar population levels but population and employment in the United Kingdom are less concentrated. The United Kingdom also has only a few small regions with over 1 million inhabitants while France has many. Though surprising when considering both countries have similar sizes, the United Kingdom has higher administrative fragmentation (see Annex Figure 2.A.1). For instance, 21 different small regions make up Greater London while only 8 make up Île-de-France. For this reason, the data in this chapter aggregate values for TL3 regions that are part of the same FUA (i.e. where 50% of the regional population lives in a FUA).

The concentration of population in some countries depends on the territorial scale used. This is due to a combination of larger sparsity and the relative level of administrative fragmentation). For instance, the Gini index for the population in Denmark and Finland, 2 countries with similar population sizes, is 0.2 and 0.3 for large regions. For small regions, the Gini index is still 0.2 for Denmark (1 out of the 11 regions concentrates 15% of the population) but raises to 0.5 in Finland (1 out of the 19 regions concentrates 30% of the population) (Annex Figure 2.A.2). Higher sparsity in Finland, as well as different levels of administrative fragmentation, explain these differences. As disentangling these effects is not possible, this chapter does not make direct comparisons of inequality indicators between small and large regions.

Traditional measures of inequality such as the Theil and Gini indices do not consider the location of regions and fail to acknowledge that economic activity and people tend to cluster in space (Rey, Arribas-Bel and Wolf, 2020[62]). The OECD has developed the concept of functional urban areas – composed of urban centres and their commuting areas – to overcome these limitations. At the regional level, the OECD Access to Cities typology offers a way to overcome the issue of administrative fragmentation and lack of consideration for proximity (Fadic et al., 2019[70]).

The typology classifies small (TL3) regions into metropolitan and non-metropolitan regions according to the following criteria:

  • Metropolitan regions, if more than half of the population live in a FUA. Metropolitan regions are further classified into: metropolitan large, if more than half of the population live in a (large) FUA of at least 1.5 million inhabitants; and metropolitan midsize, if more than half of the population live in a (midsize) FUA of at 250 000 to 1.5 million inhabitants.

  • Non-metropolitan regions, if less than half of the population live in a midsize/large FUA. These regions are further classified according to their level of access to FUAs of different sizes: near a midsize/large FUA if more than half of the population live within a 60-minute drive from a midsize/large FUA (of more than 250 000 inhabitants) or if the TL3 region contains more than 80% of the area of a midsize/large FUA; near a small FUA if the region does not have access to a midsize/large FUA and at least half of its population have access to a small FUA (i.e. between 50 000 and 250 000 inhabitants) within a 60-minute drive, or contains 80% of the area of a small FUA; and remote, otherwise.

Inequality measures are sensitive to the grouping of observations in bins. For instance, measures of inter-personal inequality use income-range bins, which vary in the number of income groups available and censoring of the highest categories. The spatial counterpart of income bins are spatial units, implying that inequality indices will depend on the geographical scale of analysis. The implicit assumption, in this case, is that income in a region is equally distributed across the population in that region (Rey, Arribas-Bel and Wolf, 2020[62]).

The direction of the change in inequality when switching from a larger (TL2) to a smaller scale depends not only on the relative fragmentation at lower levels but also on the distribution of the population. For instance, consider a country with 2 TL2 regions, one with 125 people, 100 of which live in a city and another one with 42 people, 40 of which live in a city. Splitting both TL2 regions into 4 TL3 regions, 2 of them containing the 2 cities, leads to smaller inequality levels at the TL3 level, as the largest region contribution to population goes down from 75% (125/167) to 60% (100/167). Across OECD countries, switching from the TL2 to the TL3 level leads to higher, similar and lower levels of inequality in the distribution of the population (Annex Figure 2.A.3).


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← 1. These is the ratio between the 20% richest and 20% poorest population’s disposable income.

← 2. The OECD Programme for International Student Assessment (PISA) is an international assessment that measures 15-year-old students’ reading, mathematics and science literacy every 3 years.

← 3. The rest of the document uses the terms GDP per capita and income per capita interchangeably.

← 4. Additionally: GDP and population may not be recorded at the same place (place of residency versus place of work); national deflators do not consider higher living costs in cities and GDP value in some services such as financial services and real state may be distorted by where transactions are recorded.

← 5. Values above 1 of this ratio indicate the presence of regions with very high or very low values relative to the mean. This indicator has been used to analyse inter-personal inequalities in OECD countries (Balestra and Tonkin, 2018[71]).

← 6. The absolute value differs because each measure uses all the countries with available data (26 OECD at the TL3 and 29 OECD countries at the TL2 level. The trends are identical when using the same group of 23 OECD countries at both levels.

← 7. The absolute gap in income per head between top and bottom regions can increase even when polarisation measured with the ratio of GDP per capita in top versus bottom regions decreases. Given higher GDP per capita income in top than in bottom regions (e.g. EUR 50 000 vs EUR 10 000), when top and bottom regions grow at exactly the same rate (e.g. 10%) the absolute gap between top and bottom increases (by EUR 400, i.e. EUR 50 500 minus EUR 10 100) and the relative gap stays the same (5 in both cases).

← 8. The geography of spatial clustering presented here tends to diverge from that described in Rosés and Wolf (2018[72]), who report the emergence during the twentieth century of so-called “islands of prosperity” in Europe, i.e. clusters of increasingly rich regions located typically around the largest European metropolitan areas. The main reason behind this divergence has to do with the different geographical units employed in the analysis, TL2 regions in Rosés and Wolf (2018[72]) and TL3 regions (bundling together those belonging to the same metropolitan regions) in the present analysis.

← 9. The technical reason is that no conclusive ranking between distributions can be drawn when Lorenz curves cross (Trapeznikova, 2019[73]).

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