This chapter analyses the economic performance of intermediary cities. First, it presents an empirical analysis of the factors driving economic prosperity in these cities. Second, it provides contextual information on their sustainability and well-being. Third, it provides an overview of recent demographic and migration trends in intermediary cities. Due to limitations in the availability of comparable data, the analysis in this chapter mostly focuses on the EU countries.
Unlocking the Potential of Intermediary Cities for Regional Development
Abstract
4.1.1. A snapshot of intermediary cities in the national economy
Positioned between major metropolitan areas and smaller towns, intermediary cities have the potential to play an important role in fostering balanced growth, innovation and social cohesion. Their economic performance can reveal important insights into the dynamics of regional economies, particularly in a time of rapid economic transition.
Gross income per capita in intermediary cities is generally lower than the national average in most of the analysed countries, with significant disparities when compared to larger settlements (Figure 4.1). Larger settlements tend to have higher productivity, a more diverse economic base and access to high value-added industries, which contribute to elevated income levels. In contrast, intermediary cities often rely on traditional sectors with lower levels of productivity, limiting their capacity to raise income per capita.
Gross income per capita relative to national average (%), 2019
Note: Bars display the average population-weighted gross income per capita in intermediary cities by country, dots display the corresponding average for larger cities. Dashed line presents the national average, normalised to 100. EU-27 countries are in blue, non-EU-27 countries are in orange.
Source: Calculations based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Larger settlements tend to exhibit higher income levels, mainly due to agglomeration effects. These effects occur when businesses, workers and services cluster in urban areas, boosting productivity and economic efficiency. As a city’s population grows, it becomes more integrated and economically diverse, creating more opportunities for higher-paying jobs. Figure 4.2 illustrates this trend, showing the positive correlation between population size and gross income per capita. The trend line suggests that the relationship is stronger and agglomeration effects are more pronounced for intermediary cities (left of the dotted line). In fact, doubling the population size in intermediary cities is associated with a 21% increase in gross income per capita, whereas doubling the population size in larger cities (with population over 250 000) would result in a 4% increase in gross income per capita. However, it is not entirely clear whether this difference arises from stronger agglomeration effects or from more pronounced sorting, where higher-income individuals are drawn to intermediary cities with larger populations.
Gross income per capita by city population size (2022 USD PPP), 2019
Note: Binned scatterplot with country fixed effects, discontinuity at the 250 000-population threshold (vertical dashed line), the upper bound for intermediary cities. Population and gross income per capita are both expressed in log scales. Linear fitted line in grey, presenting the percentage change in income associated with a one percentage change increase in population. Country sample consists of Estonia, Finland, Germany, Hungary, Italy, Portugal, Slovenia, Spain and Sweden.
Source: Calculations based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Unemployment rates in intermediary cities are often comparable to or lower than in larger cities (Figure 4.3). However, compared to larger cities and the national average, intermediary cities also tend to have lower employment rates (Figure 4.4). This implies, on average, a lower share of the population that is economically active in intermediary cities, highlighting a gap in workforce participation relative to larger urban areas.
Unemployment rate relative to the national average (%), 2021
Note: Bars display the average population-weighted unemployment rate in intermediary cities by country, dots display the corresponding average for larger cities. Dashed line presents the national average, normalised to 100. EU-27 countries are in blue, non-EU-27 countries are in orange.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Employment rate relative to national average (%), 2021
Note: Bars display the average population-weighted employment rate in intermediary cities by country, dots display the corresponding average for larger cities. Dashed line presents the national average, normalised to 100. EU-27 countries are in blue, non-EU-27 countries are in orange.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Intermediary cities generally have a lower proportion of highly educated residents compared to both the national average and larger cities (Figure 4.5). This may be partly due to sorting, where highly educated individuals tend to move to larger cities, which offer more opportunities in high-skilled services and industries. Additionally, the industrial structure in intermediary cities may also play a role. Intermediary cities are often more focused on manufacturing and traditional sectors (see below), which typically require lower levels of formal education. In contrast, larger settlements tend to have more diverse economies with a greater emphasis on high-skilled services, such as technology, finance and research, which attract a more highly educated workforce.
Share of highly educated population relative to national average (%), 2021
Note: The share of highly educated population refers to the number of individuals with education levels classified under ISCED groups 5 to 8 relative to all individuals. Bars display the average population-weighted educational attainment in intermediary cities by country, dots display the corresponding average for larger cities. Dashed line presents the national average, normalised to 100. EU-27 countries are in blue, non-EU-27 countries are in orange.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
The distinct economic specialisation of intermediary cities compared to larger cities is reflected in their sector composition (Figure 4.6), where Wholesale and Retail Trade, and Transportation make up 29%, followed by Low- and Medium-Low-Technology Manufacturing (14%) and High- and Medium-High-Technology Manufacturing (9%). In contrast, larger cities with populations over 250 000 show a more balanced sectoral distribution, with Knowledge and Technical Services making up 29% and Wholesale and Retail Trade, and Transportation accounting for 29%. While manufacturing still plays a role in larger cities, the greater focus on higher-skilled service sectors in these areas reflects their more diversified and innovation-driven economies.
These findings are consistent with the 2021 employment share for knowledge‑intensive business service (KIBS) activities reported in Chapter 5 (Figure 5.1), where the large majority of intermediary cities included in the analysis have lower shares than the national level. The majority of the few intermediary cities with employment shares higher than the national level are located in Italy (19 out of 27).
Employment share by sector (%), 2019
Note: Employment shares by sector refer to the population-weighted share of employed persons by place of work in 2019. Only sectors within sections A to N are included in the calculations. Low- and medium-low-technology manufacturing includes NACE Rev. 2 groups CA, CB, CC, CD, CG, CH and CM. High- and medium-high-technology manufacturing includes NACE Rev. 2 groups CE, CF, CI, CJ, CK and CL. The classification is adapted from Eurostat’s high-tech classification of manufacturing activities (Eurostat, n.d.[1]). Country sample consists of Austria, Finland, France, Germany, Spain and Sweden.
Source: Calculations based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
4.1.2. The evolution of intermediary cities over time
Intermediary cities are more likely to outperform national growth rates than larger cities in the sample of eight EU countries with available data. As shown in Figure 4.7, the share of intermediary cities exceeding the national average growth in gross income per capita is higher than that of larger cities with population over 250 000 and national capitals. Nearly 40% of intermediary cities report growth rates above the national average, reflecting stronger economic performance in these cities despite the challenges they face. In contrast, larger cities with population over 250 000 inhabitants show a slightly lower share of cities outperforming national growth. Capital cities, often the largest city in a country, are less likely to surpass national growth rates, as indicated by the smallest bar in the figure. The results are robust within the eight-country sample but should be interpreted with caution. In a broader OECD context, capital and larger cities tend to be drivers of economic growth (OECD, forthcoming[2]), which limits the extent to which these patterns generalise beyond the countries considered.
Share of cities with gross income per capita growth above the national average growth (%), 2015-2019
Note: Bars represent the share of cities by city category where the four-year annualized growth rate of gross income per capita in constant prices (2015–19) exceeded the national average. The sample consists of 312 cities across eight EU-27 countries: Estonia, Finland, Germany, Hungary, Italy, Portugal, Spain and Sweden. Within this country sample, only Germany’s capital, Berlin, had income growth above the national average in 2015-19.
Source: Calculations based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Figure 4.8 gives an overview of how levels of gross income per capita evolve in time, across intermediary cities, larger cities and capital cities. The data shown confirm that, despite their faster income growth, in our country sample the income gap between intermediary cities and larger cities remains considerable and it underscores the persisting economic divide with major urban centres.
Gross income per capita (2022 USD PPP), 2015 and 2019
Note: Gross income per capita is expressed in 2022 USD PPP. Bars display the population-weighted average gross income per capita by year and city category. Country sample consists of Estonia, Finland, Germany, Hungary, Italy, Portugal, Spain and Sweden.
Source: Calculations based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Despite substantial national gains, the educational attainment gap between intermediary and larger cities remains wide. In 2011, the share of population in intermediary cities with tertiary education (ISCED groups 5 to 8) was on average 2% below the national share across the seven EU countries with available data. By contrast, larger cities had attainment levels well above the national share, with 9% higher in large cities excluding capitals, and 28% higher in capitals (Figure 4.9). Between 2011 and 2021, the share of population with tertiary education increased by over 30% in the seven EU countries in the sample (OECD, 2026[3]). However, this increase has benefitted cities of different sizes to a similar extent in the country sample. Figure 4.9 shows that the gap between intermediary cities and larger cities excluding capitals remains essentially unchanged at around 11% between 2011 and 2021. Intermediary cities caught up slightly with capitals, with the difference in attainment falling from 30% to 25%.
Share of highly educated population (ISCED 5 to 8) relative to the national average (%), 2011 and 2021
Note: The share of highly educated population refers to the number of individuals with education levels classified under ISCED groups 5 to 8 relative to all individuals. Bars display the population-weighted educational attainment relative to the national average which is normalised to 100 by year and country. Country sample consists of Austria, Belgium, Finland, France, Portugal, Spain and Sweden.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Intermediary cities experienced slower population growth than larger cities between 2011 and 2021. Figure 4.10 shows the relationship between city population size in 2011 (x-axis) and 10-year population growth (y-axis).
Population growth (%), 2011-21, against population levels, 2011
Note: Binned scatterplot of 10-year population growth in intermediary cities (between 2011 and 2021) by their initial population in 2011. Dashed line at 250 000 represents the upper bound population threshold of intermediary cities. Country sample is EU-27.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
4.1.3. Drivers of income differences
Intermediary cities can belong to any income group, even within the same country. Figure 4.11 classifies intermediary cities into income quartiles relative to all cities within each country. Cities were split into four income groups based on gross income per capita, with intermediary cities colour-coded according to their respective quartile. The size of each circle is proportional to the population size of the intermediary city it represents: smaller circles correspond to intermediary cities between 50 000 and 100 000 inhabitants, and larger circles represent intermediary cities between 100 000 and 250 000 inhabitants. Around 15% (30) of intermediary cities fall into the top income quartile, while over 30% (66) are in the bottom quartile, reflecting the wide range of economic performance among intermediary cities within national contexts.
Intermediary city gross income per capita quartiles based on all national cities, 2010s
Note: Quartiles are computed based on the distribution of income across all cities within a country, intermediary cities are then assigned to quartiles. Modal quartile between 2010-2019 is reported. The sample of all cities serving as a basis for the income quartile computation consists of 325 cities in 10 countries, made up of 197 ICs and 128 larger cities. EU-27 countries covered: Austria, Estonia, Finland, Germany, Hungary, Italy, Portugal, Spain and Sweden. Non-EU OECD countries covered: Norway.
Source: Calculations based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Cities in the bottom quartile lag behind those in the top quartile not only in income, but also across many other economic dimensions. Table 4.1 shows significant differences in unemployment and employment rates, relative to the national average. Bottom quartile intermediary cities have a much higher unemployment rate compared to top quartile intermediary cities: with unemployment in bottom cities being over twice as large. Similarly, the employment rate in bottom quartile intermediary cities is lower than in top quartile intermediary cities by 15%, showing that these cities fall behind in terms of employment levels relative to the national average.
Economic performance in bottom quartile intermediary cities is also characterised by a lower share of employment in high- and medium-high-technology manufacturing industries compared to top quartile intermediary cities. The share is only 3.6% in bottom quartile intermediary cities, compared to 8.5% in top quartile intermediary cities. Additionally, intermediary cities in the top quartile have higher population sizes and densities and higher net migration rates. These factors point to the more dynamic economies of top income quartile intermediary cities, which also tend to attract more skilled labour and investment. Despite these differences, both groups of intermediary cities share similar demographic characteristics, such as youth and senior population shares, as well as share of urban green areas, suggesting that income disparities are influenced by a mix of economic and industrial factors.
Balance table contrasting bottom and top intermediary cities
Note: a Data from indicated or latest available year. * p < 0.1, ** p < 0.05, *** p < 0.01. Quartiles are computed based on the distribution of income across all cities within a country, intermediary cities are then assigned to quartiles. Modal quartile between 2010-2019 is reported. Variable averages by intermediary city quartile are not weighted by population. Standard errors in parentheses. HHI refers to the Herfindahl-Hirschman index based on employment by two-digit industry, a higher index generally indicates a decrease in competition and an increase of market power. Knowledge intensive service share is defined as the share of employment by place of work in NACE Rev. 2 divisions classified by Eurostat as knowledge-intensive services (Eurostat, n.d.[4]). The share of employment in manufacturing activities according to technological intensity follows Eurostat’s high-tech classification of manufacturing activities (Eurostat, n.d.[1]). Net migration rate is defined as in-migration minus out-migration per 1 000 inhabitants, including internal and international flows. Share with low education is the population share with an educational attainment classified under ISCED groups 0 to 2 in ICs relative to the national average. Distance from the nearest large city, which can be within or outside the country, refers to the straight-line distance (in km) between an intermediary city and the nearest city with more than 1.5 million inhabitants. NO2 exposure is defined as the population-weighted average air pollutant concentration. Sample consists of intermediary cities in Austria, Finland, Germany, Italy, Portugal, Spain and Sweden.
Source: Calculations based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Available data for Austria, Finland, Germany, Italy, Portugal, Spain and Sweden, allow a comparison of income gaps between cities and relative to the largest city in each country, between 2007 and 2022. The income differences observed between intermediary cities and larger cities appear to be primarily driven by differences in industrial specialisation (Ahrend et al., forthcoming[5]). Cities with a higher share of lower-wage industries, such as manufacturing, tend to have lower gross income per capita. When these factors are adjusted for, the income gap diminishes significantly. These findings highlight the importance of industrial composition in determining economic outcomes and illustrate how structural differences between cities, particularly in terms of industry and size, contribute to observed income disparities.
4.1.4. Income patterns in intermediary cities show signs of convergence in the analysed countries
Income patterns in the late 2010s show signs of convergence among intermediary cities, both across and within the countries analysed. First, income dispersion is assessed across a pooled sample of cities from eight EU countries with available data, providing an indication that city income gaps are declining across national contexts. Second, patterns of income convergence are examined within EU countries, capturing the extent to which lower-income intermediary cities are catching up with higher-income peers domestically. The within-country convergence analysis is restricted to countries with more than 40 intermediary cities to ensure robust results. Portugal is included as an exception, as it shows a clear pattern despite having only nine intermediary cities. This second finding should be interpreted with caution: OECD evidence suggests that, while results are consistent for the cities included in the sample, convergence remains limited across a broader set of countries (OECD, 2023[6]; OECD, 2024[7]).
Income dispersion among intermediary cities has declined over time across the pooled sample of eight EU countries. Figure 4.12 shows a reduction in income dispersion among intermediary cities, as measured by the Theil index, between 2015 and 2019. The Theil index indicates a 20% decrease in income dispersion among intermediary cities, while larger cities have remained more stable in terms of income dispersion. The Theil index in larger cities only declined by 14% over the same period. The income convergence among intermediary cities is particularly visible in countries like Germany, Spain and Portugal, where stronger local economic growth, industrial diversification and infrastructure improvements have contributed to this convergence. Although the income gap with larger cities remains, the significant reduction in income inequality among intermediary cities marks a positive step towards an increasing economic similarity across intermediary cities - which to a certain degree could be seen as a contribution towards a more balanced regional development in the countries covered in the sample.
Note: Bar charts display the Theil index of gross income per capita (2022 USD PPP) in intermediary and larger cities. The Theil index is calculated using a pooled sample of cities from all countries in the dataset. A higher Theil index indicates greater income dispersion. Country sample consists of Estonia, Finland, Germany, Hungary, Italy, Portugal, Spain and Sweden.
Source: Calculations based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Figure 4.13 provides evidence of within-country convergence among intermediary cities in Germany, Spain, Italy and Portugal. Lower-income intermediary cities in those four countries have tended to grow faster than their higher income peers. The figure plots the 5-year annualised gross income per capita growth (y-axis) against the initial income level relative to the national average (x-axis), for intermediary cities in Germany, Spain, Italy and Portugal between 2015 and 2020. The downward sloping lines for each country indicate that intermediary cities with lower initial income levels tend to experience higher growth rates, especially for Germany, Portugal and Spain.
5-year annualised gross income per capita growth (%) by initial income level within country, 2015-20
Note: Scatterplot of gross income per capita in 2015 expressed relative to the national average against the 5-year annualized growth rate in gross income per capita. Gross income per capita is measured in constant prices. Each dot represents an intermediary city. The sample of intermediary cities is restricted to EU countries with more than 40 intermediary cities and available data. Portugal is included as an exception, as it shows a clear pattern despite having only nine intermediary cities.
Source: Analysis based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Not only are intermediary cities in the four analysed countries catching up in terms of gross income per capita, but they are doing so at increasing speed. Figure 4.14 shows that the speed of income convergence among intermediary cities increased in the 2010s. It presents the results of separate regressions for Germany, Italy, Spain and Portugal, showing the 5-year annualised growth rates of gross income per capita relative to the log of initial income per capita. The more negative the coefficient, the faster the convergence. Between 2010 and 2017, the convergence rate became more negative, meaning that income disparities between low- and high-income intermediary cities were narrowing faster. For Germany and Italy, convergence was relatively slower in the earlier years of the 2010s, but it accelerated towards the end of the decade, particularly in German intermediary cities. In contrast, Spain and Portugal experienced a sharper and more consistent decline in their income gaps, particularly after 2015, suggesting faster convergence between low‑ and high‑income intermediary cities in these countries during the later part of the period.
Note: 5-year annualised growth rate of gross income per capita is regressed on log gross income per capita, slopes (“beta coefficients”) from that regression are allowed to differ for each year and are plotted on the y-axis. A more negative “beta coefficient” implies faster convergence. Gross income is in constant prices. Separate regressions by country. Country and year fixed effects are included. Sample is ICs only. Country sample consists of Germany, Italy, Portugal and Spain.
Source: Analysis based on national sources and OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Beyond economic performance, sustainability and well-being are important factors in assessing the quality of intermediary cities. Intermediary cities are “neither too big nor too small” and may be best positioned to offer a distinct living environment that balances urban amenities with a more human-scale lifestyle. Such an environment may allow to take advantage of agglomeration benefits, while properly managing diseconomies of scale, such as those linked to urban congestion.
The availability of green recreational spaces, the quality of the environment, proximity of basic services and living costs are some of the key factors that contribute to the overall well-being of city dwellers in intermediary cities. This section provides an overview of some of these factors.
The share of green areas in the urban centres of cities is rather diverse among the intermediary cities analysed (Figure 4.15). Some of the lowest shares, i.e. between 10% and 20% of the city’s surface area, can be found in intermediary cities across Italy, the Slovak Republic, Romania and Spain. Nevertheless, the share of green areas in intermediary cities is on average higher than in the largest city of the respective country, with the exceptions of Bulgaria, Croatia and Lithuania.
The quality of the environment is also more favourable in intermediary cities compared to the largest city in the country. Population exposure to NO2, which is a major air pollutant mainly associated with vehicle traffic, is lower in all intermediary cities analysed compared to the largest city in the respective country (Figure 4.16). For example, exposure levels of city dwellers in intermediary cities in Spain are on average 3 times lower than those of people living in Madrid. Similarly, exposure levels in intermediary cities in Italy and Romania are on average 2.5 times lower than in Milan and Bucharest, respectively. The territorial patterns across countries are also influenced by the distribution of other polluting activities beyond vehicle traffic, such as agriculture and the morphology of the different territories.
Other factors contributing to the quality of life in intermediary cities, such as proximity to basic services, are sub-optimal compared to larger settlements. On average, the share of residents in intermediary cities who can reach a pharmacy within a 15-minute-walk (Figure 4.17). ranges between 30% and almost 65%. Although many intermediary cities are performing relatively well in absolute terms, on average intermediary cities are worse off compared to the biggest city in the respective country.
When it comes to living costs, housing constitutes a large share of the overall living costs of households. Housing seems to be more affordable in intermediary cities in comparison to larger settlements. For the majority of the intermediary cities analysed, the average transaction price of dwellings is lower than the national average, with rather stronger in-country variability for almost all analysed countries (Figure 4.18).
Green area share in urban centres of intermediary cities (%), 2021
Note: Green area shares are computed based on green area within the urban centre of the city (i.e. once the commuting zone has been removed). EU-27 countries covered: Austria, Belgium, Bulgaria, Croatia, Czechia, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, the Netherlands, Poland, Portugal, Romania, the Slovak Republic, Spain and Sweden. Non-EU OECD countries covered: Switzerland, Türkiye and the United Kingdom.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Population-weighted exposure to NO2 within a city (μg/m³), 2023
Note: EU-27 countries covered: Austria, Belgium, Bulgaria, Croatia, Czechia, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, the Netherlands, Poland, Portugal, the Slovak Republic, Spain and Sweden. Non-EU OECD countries covered: Norway, Türkiye and the United Kingdom.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Share of population able to reach the nearest pharmacy within a 15‑minute walk (%), 2024
Note: EU-27 countries covered: Austria, Belgium, Czechia, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, the Netherlands, Poland, Portugal, the Slovak Republic, Spain and Sweden. Non-EU OECD countries covered: Norway, Switzerland, Türkiye and the United Kingdom.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
House transaction price relative to the national average (%), 2023 or latest year
Note: National average is 100%. Transaction prices are per m2 whenever available, else unit transaction prices. EU-27 countries covered: Austria, Belgium, Finland, France, Germany, Hungary, the Netherlands, Poland, Portugal, Spain and Sweden. Non-EU OECD countries covered: Norway and the United Kingdom.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Although intermediary cities fare relatively well across various sustainability and well-being indicators, especially if compared to large metropolises, population ageing could challenge this pattern, making it more difficult for intermediary cities to sustain high-quality public services, infrastructure and employment opportunities (OECD, 2025[8]).
The old-age dependency ratio, which measures the number of people aged 65 and over relative to the working-age population aged 15-64, is rising in nearly all intermediary cities analysed (Figure 4.19). Only a few exceptions exist, notably in Estonia, Slovenia and Spain (EU-27), and the United Kingdom. Meanwhile, net migration rates in intermediary cities vary widely across most analysed countries, often highlighting sharp regional disparities, such as the north-south divide in Italy (Figure 4.20). The net migration rate measures the difference between the number of people moving into a city and the number of people leaving the city. A positive rate indicates more people are arriving than leaving, while a negative rate means the opposite. Net migration rates differ widely not just within countries, but also between countries.
5-year percentage change in old-age dependency (%), 2023 or latest year
Note: Old age dependency is the number of people aged 65+ per 100 working people. EU-27 countries covered: Austria, Belgium, Bulgaria, Czechia, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, the Netherlands, Poland, Romania, the Slovak Republic, Spain and Sweden. Non-EU OECD countries covered: Norway, Switzerland, Türkiye and the United Kingdom.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
Net migration rate relative to the national average (%), 2023 or latest year
Note: National average is 100%. Net migration refers to the sum of net internal and net international migration. EU-27 countries covered: Austria, Belgium, Bulgaria, Czechia, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, the Netherlands, Poland, Portugal, Romania, the Slovak Republic, Spain and Sweden. Non-EU OECD countries covered: Norway, Switzerland, Türkiye and the United Kingdom.
Source: OECD Regions, Cities and Local Areas database http://oe.cd/geostats.
References
[5] Ahrend, R. et al. (forthcoming), “The Economic Performance of Small and Medium-Sized Cities: Evidence from OECD Countries”, OECD Regional Development Papers Series.
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