3. Understanding the Welsh territorial puzzle in the context of megatrends
Chapter 3 begins with a snapshot of Welsh territorial and socio-economic conditions. It then explores the regional divide in productivity and inclusive growth in Wales, highlighting disparities in labour productivity growth and putting this in the context of different productivity growth models. It identifies challenges and opportunities to enhance productivity across the full Welsh territory, with a focus on the role of cities, transport networks, demographics, skills, research and development (R&D) and business dynamics. The chapter also assesses well-being performance, noting that well-being outcomes in Wales are often higher than or equal to OECD standards but lower than United Kingdom (UK) averages. Youth unemployment and income deprivation are examined, as well as how inequalities can exacerbate a population’s vulnerability to environmental and health risks, such as the COVID-19 pandemic. The chapter concludes with the importance of a place-based approach to regional development for addressing territorial inequalities in economic performance and for generating well-being throughout Wales.
Wales, like several of its peer OECD regions, is currently facing global megatrends related to demographic (declining population and ageing), technological (digitalisation and automation) and environmental change (pollution, loss of biodiversity and climate change). Addressing these global shifts will require new policy approaches, as well as building stronger institutional capacity to manage public investment and ensure effective governance. These megatrends can also have important consequences at the local level, with the risk of exacerbating territorial inequalities. Such risk calls for action from all levels of government and for a place-based approach (OECD, 2019[1]).
In addition to the uncertainty generated by megatrends, persistent regional inequalities have led to an increasing mistrust in institutions and discontent with the economic, social and political status quo, particularly in regions that are lagging behind (Rodríguez-Pose, 2018[2]). There are many examples where sharp within-country inequalities reinforced the mistrust and discontent of the population in less developed regions, which struggle to catch up (OECD, 2019[1]). In 2016, among OECD countries, the UK had one of the largest regional disparity in gross domestic product (GDP) per capita, with the richest top 10% of its regions showing a GDP per capita 4 times higher than the bottom 10% of regions (OECD, 2018[3]). What is more, this ratio increased to 4.7 in 2018, with the bottom 10% of regions in the UK including 4 out of the 12 small regions of Wales (small regions refer to NUTS-3 or TL3 regions, see Box 3.1), namely the Isle of Anglesey, Central Valleys, Gwent Valleys and Powys.
The UK’s regional disparities in labour productivity are also among the starkest in the OECD. In 2018, Greater London presented levels of gross value added (GVA) per worker of around USD 110 000 (in 2015 purchasing power parity [PPP]), a level 70% higher than the labour productivity of Wales and Yorkshire and The Humber – the two UK large regions (NUTS-1 or TL2) with the lowest GVA per worker. Similarly, within Wales, the GVA per worker in the area of Cardiff and Vale of Glamorgan1 was approximately 43% larger than that of Powys in the same year. Beyond productivity, well-being outcomes, such as having at least tertiary education or having a job, can also highlight important regional inequalities within Wales. For example, while Cardiff and Vale of Glamorgan display shares of the labour force with at least tertiary education above 45% and employment rates around 60%, Blaenau Gwent and Neath Port Talbot present shares of the labour force with at least tertiary education below 30% and employment rates around 52%.
In the context of the UK, the persistent within-country and within-nation inequalities might have contributed to radical political responses such as Brexit, where the most lagging regions tended to support leaving the European Union (EU) (Dijkstra, Poelman and Rodríguez-Pose, 2019[4]). For example, Blaenau Gwent, the Welsh local authority with the highest support for Brexit during the EU referendum (Electoral Commission, 2020[5]), is a region facing population decline over the last 17 years, with high shares of its population living in income deprivation and with one of the lowest educational levels for its workforce compared to other Welsh local authorities.
In light of the challenges posed by demographic, technological and environmental megatrends, as well as the uncertainty generated by the process of Brexit, the Welsh Government is adopting a place-based approach to regional policy that focuses on inclusive growth and well-being throughout its territory. This approach requires enhancing multi-level governance processes with national, local and UK authorities, as well as the participation of other stakeholders – including international co-operation (Welsh Government, 2020[6]) and partnerships with European institutions.
After examining the geography of socio-economic conditions of Wales, this chapter provides an assessment of two major and interlinked policy areas that are at the core of the Welsh development agenda: i) productivity and inclusive growth; and ii) well-being today and for future generations. To provide insight into Wales’ situation from an external perspective, every section provides international comparisons relative to other OECD regions (NUTS-2 or TL2 regions).2 Subsequently, to understand the Welsh territorial puzzle and to identify potential areas of improvement, each section further analyses outcomes and challenges across the Welsh local authorities or Welsh small regions (NUTS-3 or TL3 regions).
Wales is one of the UK’s four nations, together with England, Northern Ireland and Scotland. It has a population of around 3 million people and an area of approximately 20 000 km2, which represents 5% of the total UK population and 9% of the total UK area. This makes Wales a very low-density region (151 people per km2) compared to the UK average (275 people per km2). This can be challenging for the delivery of public services, particularly in remote rural areas, and limits the potential of agglomeration economies. In 2018, Wales represented only 3.4% of the total GDP of the UK and 4% of its employment (a slightly lower portion than its share in terms of the UK population).
Wales has not been catching up with the UK average levels of economic prosperity but economic growth rates have been improving since the 2007-08 financial crisis
In the past 18 years, Wales has not been catching up with the UK average levels of GDP per capita. From 2000 to 2018, the GDP per capita in Wales remained the lowest of the 4 UK nations, representing only 72% of the UK average (Figure 3.1, panel A). Although long-term growth rates in GDP per capita have remained sluggish in both Wales and the UK (at an annual average of 1.1%), it is worth noting that growth rates in Wales slightly increased after the 2007-08 financial crisis – they shifted from 0.7% before the crisis (2000-09) to 1.5% for the post-crisis period (2009-18) (Figure 3.1, panel B).
Note: GDP per capita is expressed in USD PPP at constant prices of 2015.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Large (TL2) and small (TL3) regions
The OECD classifies its regions on two territorial levels reflecting the administrative organisation of countries. The 393 OECD large (TL2) regions represent the first administrative tier of subnational government, for example, the Ontario Province in Canada. The 2 256 OECD small (TL3) regions correspond to administrative regions, with the exception of Australia, Canada and the United States. These TL3 regions are contained in a TL2 region, with the exception of the United States for which the economic areas cross the states’ borders. For New Zealand, TL2 and TL3 levels are equivalent and defined by regional councils. All regions are defined within national borders.
This classification – which, for European countries, is largely consistent with the Eurostat NUTS 2016 classification – facilitates greater comparability of geographic units at the same territorial level. These two levels, which are officially established and relatively stable in all member countries, are used as a framework for implementing regional policies in most countries.
Urban-rural typology
Traditionally the OECD has classified TL3 regions as predominantly urban (PU), intermediate (IN), or predominantly rural (PR) regions. This typology is mainly based on population density in each local unit, combined with the existence of urban centres where at least one-quarter of the regional population reside. An extended regional typology has been adopted to distinguish between rural regions that are located close to larger urban centres and those that are not. The result is a fourfold classification of TL3 regions: PU, IN, predominantly rural regions close to a city (PRC) and predominantly rural remote regions (PRR). The distance from urban centres is measured by the driving time necessary for a certain share of the regional population to reach an urban centre with at least 50 000 people (OECD, 2018[3]).
Functional urban areas (FUAs)
The definition of FUAs was developed in collaboration with the EU. Using population density and travel-to-work flows as key information, an FUA consists of a densely inhabited city and of a surrounding area (commuting zone) whose labour market is highly integrated with the city. The ultimate aim of the EU-OECD approach to FUAs is to create a harmonised definition of cities and their areas of influence for international comparisons as well as for policy analysis on topics related to urban development.
Using FUAs allows designing policies at the right scale. For example, for mobility and accessibility to services. At the same time, FUAs provide a harmonised methodology to compare similar urban units in size and function. This is particularly relevant in the context of the Sustainable Development Goals (SDGs), a universal global agenda that requires comparability across the world in order to track progress towards sustainable development (OECD, 2020[8]).
Source: OECD (2018[3]), OECD Regions and Cities at a Glance 2018, https://doi.org/10.1787/reg_cit_glance-2018-en.
The largest cities in Wales close to England concentrate most of the economic activity, yielding an unbalanced spatial development
According to the OECD urban-rural typology (Box 3.1), Wales has 2 main urban clusters (the area encompassed by contiguous PU regions), 1 in the northeast and 1 in the southeast, separated by a distance of about 180 km (close to a 3-hour drive). The urban cluster in the southeast has its centre in Cardiff. It covers the area from Swansea to Newport and Monmouthshire and is bordered by Carmarthenshire and Powys to the north. The urban cluster in the northeast is composed of Wrexham and Flintshire, bordered by Powys to the south and the intermediate areas of Conwy and Denbighshire to the west. These 2 urban clusters, which represent less than one-fourth of the Welsh territory, are home to 70% of the Welsh population and produce 75% of Wales’ GDP. On the other hand, the rural remote areas of Wales, which account for more than two-thirds of the Welsh territory, host 23% of the population and produce 20% of the GDP (Figure 3.2).
Note: For more details, see Box 3.1. This typology refers to a methodology based on small regions (NUTS-3 or TL3). For this reason, within small regions, differences in urbanisation patterns might not be reflected.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
The main cities of Wales – concentrating a large part of the nation’s economic activity and reaping the benefits of agglomeration economies – yield an unbalanced spatial development. This is characterised by a divide between the highly urbanised southeast and northeast areas on one side, and the rest of the nation, typified as predominantly rural remote regions (PRR). The main cities (high-density areas) in the southeast urban cluster are Cardiff, Newport and Swansea, while the densest areas in the northeast are the counties of Flintshire and Wrexham. Table 3.1 shows that the highest economic performance in Wales is concentrated in the regions that include the cities of Cardiff, Newport, Swansea, Flintshire and Wrexham – with levels of GDP per capita that range from the USD 33 000 (in 2015 PPP) to USD 44 000. Nevertheless, Swansea is lagging in terms of GDP per capita relative to the southern Welsh regions that include Cardiff and Newport, which among other things, might be benefitting from their physical proximity to English markets.
The Welsh local authorities
There are currently 22 local authorities in Wales, which have statutory duties in sectors that influence and enhance regional development and the well-being of their population. The mandate of these municipalities covers a large range of public policy interventions. Their responsibilities encompass the education system (until 16 years old), the delivery of social care services (including for children and elderly people), housing, waste collection, road maintenance and public protection (including the population’s environmental safety). To deliver these services, Welsh local authorities can partner with each other or with other entities, such as third-sector organisations. Local authorities are also legally required to promote equality of opportunities, including the eradication of discrimination, and to take into consideration the well-being of future generations in their policy choices. Table 3.2 lists the 22 Welsh local authorities and their correspondence to the 12 small statistical regions (NUTS-3 for Eurostat or TL3 for OECD). In this chapter, these 12 TL3 regions are also referred to as the Welsh small regions.
Economic regions for economic policy
Following the Economic Action Plan (Welsh Government, 2017[9]), the Welsh Government introduced three “economic regions” for which regional economic frameworks will be established and implemented in collaboration with local authorities and stakeholders. Figure 3.3 shows the three potential economic regions for Wales, namely North Wales, Mid and South West Wales, and South East Wales. One concern with this delineation of regions is the placement of Powys and Ceredigion into South West Wales due to sharp sectoral inequalities and low connectivity between regions, among others. For this reason, a second potential delineation of regional units proposes treating Powys and Ceredigion as another economic region denominated as Mid Wales (Table 3.2). Chapter 6 of this report examines this question more closely.
Source: Map: Welsh Government (2017[9]), Prosperity for All: Economic Action Plan, https://gov.wales/sites/default/files/publications/2019-02/prosperity-for-all-economic-action-plan.pdf.
Source: Text: Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
In Wales, certain economic sectors and types of workers are highly spatially concentrated, partially explaining current regional differences in GDP per capita levels. The sectoral composition of the local economy reflects those differences, as some economic sectors – notably high-tech and knowledge-intensive services – are associated with higher levels of value-added per input (OECD, 2020[11]).
While the Cardiff area has developed a public sector- and service-based economy, the urban cluster in the northeast is highly specialised on manufacturing. Since the 1970s, the UK economy has gradually shifted from secondary to tertiary economic activities (OECD, 2020[11]). This pattern is observed in the Cardiff area but not in Wrexham and Flintshire. While the urban cluster in the northeast relies on manufacturing (32% of total GVA), the urban cluster of the southeast such as Cardiff, Newport and Swansea are specialised in the public sector (administration, education and health) and services (wholesale and retail, transportation and accommodation) – which generates 35% or more of their total GVA. On the other hand, the economy of rural remote regions, such as the Isle of Anglesey, Gwynedd, Powys and South West Wales, relies to a significant extent on primary activities (agriculture, forestry, fishing, mining), energy and water – from 8% to 11% of their total GVA (Figure 3.4). Delineating strategic economic regions for a place-based approach requires considering the characteristics and distribution of industries and workers across the territory.
To implement its place-based approach to economic policy, the Welsh Government is delineating three “economic regions” (see Box 3.2). To ensure the success of this strategy, sectoral differences across local authorities should be considered. For instance, while in Flintshire and Wrexham manufacturing represents 32% of the total GVA, in Conwy and Denbighshire – areas that would integrate the new region of North Wales – it represents less than 7%. Although having different predominant industries does not imply incompatibility between regions, complementarities or relatedness between local economies can be relevant to drive the re-grouping of small regions (Figure 3.4).
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Delineating economic regions should also consider the location of workers, by sector and type of skills. In Powys, around 14% of the employed population works in agriculture, forestry and fishing, whereas in Swansea – which would be placed in the same region as Powys to form the new region of Mid and South West Wales – this is the case for less than 1% of the employed. Targeting policies to a scale that combines very different areas in terms of sectoral structures and settlement types (e.g. rural and urban) could make it harder to ensure an integrated approach with inclusive outcomes for all types of workers and population groups (Annex Figure 3.A.1). What is more, regional inequalities could be further accentuated if the mobility of the less-favoured workers across these regions is low3.
Productivity is crucial to ensure people’s well-being today and for future generations. Although well-being is multi-dimensional and goes beyond material conditions (OECD, 2014[12]), productivity – which reflects the efficiency in generating valuable output by better combining different inputs – is fundamental to sustain many well-being dimensions. Highly productive regions tend to have more resources and better means to ensure higher material conditions for workers (via high-quality jobs and wages), as well as better public services that enhance citizens’ quality of life (Tsvetkova et al., 2020[13]). Although there are many types and ways to measure productivity, this chapter focuses on labour productivity, which is measured as the GVA per worker.
Labour productivity levels in Wales are still among the lowest in the UK and below the average level of Northern and Western European OECD regions. In 2018, labour productivity in Wales was approximately USD 65 000 (in 2015 PPP), very similar to Yorkshire and The Humber (Figure 3.5). This level of labour productivity is also low if compared to most Northern and Western European regions, where labour productivity in 2018 reached average levels of USD 90 000 (in constant 2015 PPP).
Note: Labour productivity is measured as GVA per worker and it is expressed in USD PPP at constant prices of 2015.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Labour productivity in Wales is diverging with respect to the UK productivity frontier
Labour productivity in Wales is not catching up relative to the UK average level. What is more, in the last 16 years, labour productivity in Wales has been diverging relative to the UK frontier region of Greater London (see OECD labour productivity typology in Box 3.3). In 2018, Welsh labour productivity represented 82% of the UK average, 1.2 percentage points less than in 2002. In contrast, the nations of Northern Ireland and Scotland have closed their productivity gap with respect to the UK average by 0.8 and 7.7 percentage points respectively (Figure 3.6) – although only Scotland is keeping pace with respect to the frontier region of the UK (i.e. Greater London). Northern Ireland is also diverging. On the other hand, it is worth highlighting that productivity growth in Wales has increased in the last 8 years, reaching an annual rate of 1% for the period 2010-18, which is slightly above the average of OECD regions for the same period (Figure 3.7).
Note: Labour productivity is measured as GVA per worker and it is expressed in USD PPP at constant prices of 2015.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Note: Labour productivity is measured as GVA per worker and it is expressed in USD PPP at constant prices of 2015.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Productivity growth is not spread equally across Welsh regions, with around half of the regions experiencing low or negative growth rates in the last 16 years
Although average productivity growth in Wales has been positive in the past 16 years, this growth has not spread equally across Welsh small regions – with almost half of the regions actually experiencing close to zero or negative growth rates. From 2002 to 2018, labour productivity grew in Wales at an annual average growth rate of 0.7% – slightly lower than the UK’s average of 0.8%. However, the Welsh national average masks stark inequalities across Welsh small regions (TL3). Figures 3.8 and 3.9 show that labour productivity growth has been higher than 1% only in the regions of Cardiff and Vale of Glamorgan, Swansea, Bridgend and Neath Port Talbot, Central and Gwent Valleys; while in the Isle of Anglesey, Gwynedd, Powys, South West Wales and Conwy and Denbighshire productivity has been decreasing over the past 16 years.
Notes: Labour productivity is measured as GVA per worker and is expressed in USD PPP at constant prices of 2015. GVA per worker estimates for small areas tend to neglect the economic interdependencies across close areas and are more likely to suffer from measurement biases. The Welsh Government also estimates labour productivity for small areas using GVA per filled jobs (instead of number of employees), which is closer to full-time equivalent jobs. Although using the latter definition of labour productivity generates some deviations with respect to the above figure, the overarching message related to an unbalanced distribution of productivity growth across Welsh regions is maintained (see Annex Figure 3.A.2).
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Note: Labour productivity is measured as GVA per worker and is expressed in USD PPP at constant prices of 2015.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
The main driver of GVA per capita growth in lagging Welsh regions is labour utilisation growth, which is compensating for sluggish labour productivity growth
GVA per capita growth (a close proxy of GDP per capita growth) can be decomposed into two main factors, namely labour productivity growth and labour utilisation growth. While labour productivity (measured as GVA per worker) denotes the average value-added associated with each worker, labour utilisation (measured as the number of workers relative to the total population) captures the level of participation of the population in the production process. According to Table 3.3, GVA per capita has been increasing in all Welsh regions in the last 16 years. Contrary to the predominant pattern observed in the UK, where the main driver is labour productivity growth, in Wales, the drivers of GVA per capita growth are more balanced. In 5 out of the 12 Welsh small regions, GVA per capita growth is mainly explained by positive labour utilisation growth rates in the past 16 years, which have compensated for negative growth rates in labour productivity. Increasing labour utilisation is beneficial for economic performance and well-being, but in order to maximise GVA per capita, it should be accompanied by increases in labour productivity, in particular to sustain growth over time – as labour utilisation cannot grow indefinitely in the long term.
The area of Cardiff and Vale of Glamorgan, Gwent Valleys and Swansea are the only Welsh regions keeping pace with the UK productivity frontier, while the rest of the nation is diverging
Nine small (NUTS-3 or TL3) regions define the productivity frontier of the UK (seven are in Greater London and two in South East England). According to the OECD typology for labour productivity described in Box 3.3, only 23 small regions appear as converging towards the frontier. None of these converging small regions belong to Wales or Yorkshire and The Humber. The small regions of Cardiff and Vale of Glamorgan, Gwent Valleys and Swansea are the only Welsh areas keeping pace compared to the productivity growth of the UK frontier, while the rest of the Welsh regions are diverging. In the UK, out of the 179 small regions, only 9 define the frontier, 23 are converging, 39 are keeping pace and 108 are diverging (Figure 3.10).
It is also possible to classify small regions by their level or type of contribution to the average productivity growth of the country. Annex Figure 3.A.3 shows the UK small regions with a positive contribution to the country’s aggregate productivity growth. Only 70 regions (3 of which are in Wales, namely Cardiff and Vale of Glamorgan, Gwent Valleys and Swansea) out of 179 contribute positively to labour productivity growth in the UK, of which 67 are either frontier, converging or keeping pace regions. On the other hand, 106 out of the 109 regions that have negative or no contribution to the country’s productivity growth are diverging regions.
Note: Based on the methodology of OECD (2016[14]).
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Frontier, converging, keeping pace and diverging regions: The labour productivity typology
The OECD has developed a methodology to classify regions according to their labour productivity levels and dynamics over time. This typology first identifies frontier regions in terms of initial levels of labour productivity and employment size (i.e. most productive regions accounting for 10% of total employment in the country). Using frontier region growth rates as a benchmark, it analyses the growth rates of the non-frontier regions and classifies them as catching up (or converging, if growth rates are at least 5% higher than in the frontier), keeping pace (if growth rates are neither 5% higher nor 5% lower than in the frontier) or diverging (if growth rates are at least 5% lower than in the frontier) (OECD, 2016[14]).
For the UK large regions, only Scotland is keeping pace with the growth rates of Greater London – the frontier region – while the rest of the UK large regions (TL2 or NUTS-1), including Wales, are diverging. When applying this typology to the UK small regions (TL3 or NUTS-3) the results are not too different with respect to the frontier but some regional disparities in performance are observed for the rest. For instance, not all small regions in Wales are diverging. While nine Welsh small regions are diverging with respect to the UK frontier, Cardiff and Vale of Glamorgan, Gwent Valleys and Swansea are keeping pace.
The concentrated vs. the distributed model
The labour productivity typology allows studying the contribution of the different regions to the aggregate labour productivity growth of the country among other things. Using the cases of Austria and the UK, Figure 3.11 presents the contributions of large regions to the aggregate labour productivity growth of their respective country.
Note: Period: 2000-18. The (pseudo) contribution of a region is defined as the difference between the national annual average labour productivity growth rate and the same rate excluding the indicated region (OECD, 2016[14]).
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
In the UK, most of the contributions to the country’s productivity growth come from the frontier and keeping-pace regions (Greater London and Scotland respectively), whereas the rest of the regions, including Wales, are contributing very little or even negatively to the aggregate growth. In contrast, the productivity growth of Austria is mainly coming from its eight converging regions, while the frontier region (Vienna) is not the main contributor to labour productivity growth. These countries depict two generalised models of regional contributions to aggregate productivity growth: the concentrated model (e.g. France, the Netherlands, Sweden and the UK) and the distributed model (e.g. Austria, Germany, Poland and Spain).
One challenging trade-off between these two models relates to reducing regional inequalities while maximising aggregate growth. While the distributed model is desirable to achieve regional convergence, it tends to generate lower aggregate growth rates than the concentrated model. To put it differently, while the concentrated model can yield higher productivity gains than the distributed model, it does it at the cost of exacerbating regional disparities, as frontier regions grow equally as or more than the regions already lagging behind (OECD, 2016[14]).
Supporting the tradeable sector can contribute to narrowing the productivity gap
In recent years, the two main characteristics of regions that have managed to catch up with their country’s productivity frontier are: i) having well-functioning links between cities and other regions; and ii) a strong and growing tradeable sector (i.e. sectors that produce goods or services that can be traded). In Europe, catching-up regions increased their share of GVA in the tradeable sector by 8% on average from 2000 to 2014. In contrast, for the same period, the tradeable sector of diverging regions remained without growth. While it is common to think that tradeable sectors expose workers more to the risks of global shocks, there is no robust evidence for this. For instance, OECD regions that experienced larger shifts of employment to the non-tradeable sector before the 2007-08 crisis suffered more job losses (OECD, 2018[15]).
Source: OECD (2016[14]), OECD Regional Outlook 2016: Productive Regions for Inclusive Societies, https://doi.org/10.1787/9789264260245-en; OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
To enhance productivity in urban areas, policies should promote the creation and the proper functioning of agglomeration economies. Agglomeration economies are the benefits that arise when people and firms co-locate (Glaeser, 2011[16]). Technological spill-overs, innovation diffusion, labour pooling – in particular, of high-skilled workers – and intermediate input linkages are the main channels through which agglomeration economies enhance productivity. Along the same lines, cities that specialise in knowledge-intensive business services (KIBS) have higher average levels of productivity (OECD, 2020[11]). Agglomeration economies can attract KIBS firms by supplying larger pools of high-skilled workers and better innovation diffusion channels.
Welsh cities can maximise the benefits of their agglomeration economies by boosting transport performance and by exploiting their proximity to English metropolitan areas
Wales has some opportunities to increase the benefits of its agglomeration economies, in particular by boosting transport performance within its functional urban areas (FUAs) and by exploiting the proximity of its urban centres to English metropolitan areas. Figure 3.12 displays the FUAs of Wales, namely Cardiff, Newport and Swansea in the south, and Flintshire and Wrexham in the northeast (although Flintshire belongs to the inter-regional FUA of Cheshire West and Chester). FUAs are densely populated places where people live, work and gather on a daily basis. For this reason, the identification of FUAs, which go beyond administrative boundaries (e.g. municipalities, regions and even countries), highlights the need for cross-border co-operation not only across local authorities but also across regions. The Welsh Government recognises the importance of functional areas and cross-border co-operation for regional economic development in its Economic Action Plan (Welsh Government, 2017[9]).
Note: Names are placed in the centroid of the FUA; they are not necessarily placed within the boundaries of the homonym municipality.
Source: OECD (2020[17]), Metropolitan Areas (dataset), https://dx.doi.org/10.1787/data-00531-en (accessed on 7 August 2020).
High-performing transport networks improve accessibility within FUAs, which is crucial for well-being and productivity. Good transport networks minimise the commuting time of workers to their place of work, improving well-being through better work-life balance and maximising the pool of workers accessible to firms. Transport infrastructure also contributes to the diffusion of knowledge and technology via increased mobility of people and goods, which are key elements for productivity (Andrews, Criscuolo and Gal, 2016[18]). Figure 3.13 shows a positive correlation between labour productivity and the performance of the public transport network in metropolitan areas – where performance is measured as the ratio between the accessibility to certain amenities (including the number of people) by public transport (i.e. how many amenities can be accessed by 30 minutes of public transport) and the proximity of these amenities (i.e. how many are located in a radius of 8 km) (ITF, 2019[19]). Relative to other European metropolitan areas, the metropolitan areas of Cardiff and West Midlands present very low performance in both public transport efficiency and labour productivity. In contrast, metropolitan areas with high public transport performance such as Helsinki, London and Oslo display the highest levels of labour productivity (Figure 3.13).
Notes: Public transport performance is the ratio between the population accessible within 30 minutes by public transport and the population in a radius of 8 km. Labour productivity is measured as GVA per worker. Correlation coefficient: 0.4, significantly different from 0 at the 95% confidence level. Metropolitan areas are defined as FUAs with more than 250 000 people. The graph includes 79 metropolitan areas of 23 European countries. This indicator does not take into account other factors, such as capacity and reliability, which are important for the performance of public transport networks.
Source: OECD (2020[17]), Metropolitan Areas (dataset), https://dx.doi.org/10.1787/data-00531-en (accessed on 7 August 2020); ITF (2019[19]), Benchmarking Accessibility in Cities: Measuring the Impact of Proximity and Transport Performance, https://doi.org/10.1787/4b1f722b-en.
The performance of the road transport network in most of the Welsh small regions is below the UK average level (Figure 3.14). Based on available data, when looking at the cores of functional urban areas (or cities), it is evident that neighbouring FUAs such as Bristol, Liverpool and Greater Manchester, have higher road transport performance than the Welsh FUA cores of Cardiff and Swansea. It is worth noting that, while this indicator integrates speeds on each of the road segments, it does not take into account congestion.
Beyond FUAs, inter-regional transport networks can also boost productivity in some rural remote areas by increasing proximity to the benefits of metropolitan areas and by facilitating the integration of these regions into the regional economy. Better access and reduced travel times to large metropolitan areas can be a significant driver of labour productivity and economic growth (Ahrend and Schumann, 2014[20]). Apart from facilitating the transport of goods and people – which would result in better regional economic and social cohesion within Wales – high-performing transport infrastructure can facilitate the integration of rural areas into regional or global value chains (Cosar and Demir, 2016[21]). This could be particularly helpful to unlock the potential of rural remote areas such as Carmarthenshire or Powys, which could, for instance, be better connected to Swansea and Cardiff or Wrexham and West Midlands (England) respectively. In the UK, road transport performance is highly unequal across areas. While cities display an average performance of 93 (the ratio between accessibility and proximity), the performance in rural areas is below 76.
Note: Road transport performance is the ratio between the population accessible within 1h30 by road and the population in a radius of 120 km. City stands for the core of an FUA. This indicator takes into account speeds on each of the road segments but it does not integrate congestion. While the road network data refers to 2016, the population grid used is from 2011.
Source: Dijkstra, L., H. Poelman and L. Ackermans (2019[22]), “Road transport performance in Europe”, https://doi.org/10.2776/046835.
Developing transport networks requires assessing the existing and potential commuting flows of people and goods within and between regions and cities. While rural remote areas such as Ceredigion, Gwynedd and Pembrokeshire present commuting flows to other local authorities below 15%, the “Valleys” surrounding Cardiff (Blaenau Gwent, Caerphilly, Merthyr Tydfil, Rhondda Cynon Taf, Torfaen and Vale of Glamorgan) display commuting flows of 40% or more. In the north of Wales, only Flintshire presents commuting flows of this magnitude (Figure 3.15). Transport policies should also consider existing commuting flows and integration between Welsh and other UK regions and cities. Although economic integration within Wales is desirable, Welsh regions and cities should also seek to maximise the benefits of proximity to other UK regions and cities, in particular to English metropolitan areas. For example, in Flintshire and Wrexham, more than 60% of the residents that work outside of their local authority commute to a place outside of Wales (i.e. less than 40% travel to another Welsh region) – most commute to work in North West England (Table 3.4). On the other hand, 37% of the workers that live in but work outside of Powys commute to West Midlands (England). This is particularly relevant in the context of the Economic Action Plan (Welsh Government, 2017[9]), with the economic region that places Powys with Swansea and South West Wales – areas to where Powys’ commuting flows are marginal.
Source: Welsh Government (2020[23]), Detailed Commuting Patterns by Welsh Local Authority, Annual Population Survey (database), https://statswales.gov.wales/Catalogue/Business-Economy-and-Labour-Market/People-and-Work/Employment/Commuting/detailedcommutingpatternsinwales-by-welshlocalauthority.
The megatrend of demographic change can affect the regional economy by changing the available pool of workers. Regions facing declining population are less capable of scaling up or even sustaining existing labour-intensive industries. Ageing can also affect labour productivity through two main channels: a negative one associated with the weakening of the physical abilities of workers and a positive one related to the accumulated experience of workers (Garibaldi, Oliveira Martins and van Ours, 2011[24]).
Population decline and ageing in Wales can challenge productivity and the efficient provision of public services
In the period from 2000 to 2017, the population in OECD countries grew at the very low average annual rate of 0.64% (OECD, 2018[3]). England has been driving most of the UK population increase, with an average annual growth rate of 0.72%, while nations like Scotland and Wales experienced growth rates of 0.4% – lower than the UK average of 0.67%. Such low population growth rates reveal the risk of population decline. In the OECD, 11% of the small (TL3) regions saw a population decrease between 2014 and 2017. For the UK, this number is currently lower than 3% but demographic projections suggest that the percentage of small regions with declining population could go up to 16.5% between 2014 and 2050 (OECD, 2019[1]).
Population growth within Wales is also very uneven. While Cardiff is growing at an average annual rate of 1%, the populations of Blaenau Gwent and Ceredigion are declining (this decline is masked when looking at the Welsh NUTS-3 or TL3 regions). When examining population growth within Wales, it is possible to see that while Bridgend and Cardiff are growing at rates above OECD levels, the populations of Ceredigion and Blaenau Gwent are declining at an annual average rate of 0.15% and 0.08% respectively. At the same time, the population growth rates of Rhondda Cynon Taf and Torfaen in the south, and the Isle of Anglesey and Denbighshire in the north are very close to zero (Figure 3.16).
Beyond labour productivity, population decline in Wales can also hinder the efficient provision of public services. A declining population can pose new challenges for the delivery of public services that are financed through local taxes. Although in Wales, a large share of the local tax base is property based rather than population-based, the demand and thus the value of collected taxes from real estate are directly affected by demographic trends. In this perspective, a declining population could represent a meaningful loss of tax revenue that is fundamental to finance health, social care, public education and transport. This is particularly relevant to the Welsh Government that is taking a strong position on the foundational economy, incorporating it as a pillar of its regional development framework. The foundational economy includes the goods and services that are the social and material infrastructure of civilised life because they provide the daily essentials for all households. They are classified as material (e.g. water, transport and energy) and providential (e.g. education, health and social care). In this framework, the main objective of public policy is to secure the provision of these foundational services (Morgan, 2019[25]), which will be very difficult to accomplish in a context of a declining population and shrinking tax base.
In 2017, the elderly population represented around 16.7% of the total population in OECD countries (OECD, 2018[3]), while in the same year in Wales, the elderly population represented 20.6% of the total population. Similar to population decline, ageing can have a major impact on the labour market and the financing of public services and pension systems. More specifically, given the increasing demand of services from the elderly and the relatively decreasing tax base to finance them, ageing can strongly affect the implementation of certain pension systems, as well as on the expenditure for health services and care for the elderly.
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
While the population growth rate for children is negative in Wales, the elderly population is growing at a faster rate than the working-age population. Figure 3.17 shows a population growth rate for the elderly of around 1.5%, almost 5 times the growth rate of the working-age population (aged 16-64). On the other hand, the child population in Wales (aged 0-15) is decreasing at a rate of 0.3% per year. Northern Ireland and Scotland exhibit very similar patterns, whereas in England, the population of children is growing over time.
The elderly dependency rate in Wales is the highest among the four UK nations, with an elderly population representing one-third of the total working-age population. The elderly dependency rate gives an indication of the pressure on the typically economically active population (aged 16-64 years old) from sustaining the typically retired population (aged 65 and over). In 2017, the elderly dependency ratio was around 33% in Wales, the largest across the 4 UK nations and 8 percentage points above the OECD average (Figure 3.18) (OECD, 2018[3]).
The pressure of the elderly on the working-age population is very unequal across Welsh local authorities. While Cardiff displays elderly dependency rates lower than 25%, Conwy and Powys face elderly dependency rates above 45%. Elderly dependency rates are the lowest in the southern local authorities of Wales, going from Cardiff and Swansea to Newport and Torfaen (which may be partially explained by their student population), and in the northeast in Flintshire and Wrexham (below 35%); while in Conwy and Powys, those aged 65 and above represent almost half of the working-age population (Figure 3.18).
Population decline and ageing are posing new challenges for the present and future of the Welsh economy and well-being – in particular when focusing on spatial inequalities. Recent literature suggests that new labour-replacing technologies such as automation and artificial intelligence (AI) can help tackle the impact of demographic change (Acemoglu and Restrepo, 2017[26]). Although new technologies can offer a response to declining productivity due to demographic change, the transition to automation, digitalisation and AI has to be strategically managed in order to minimise potential negative externalities, such as the risk of job losses due to automation.
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
Technological change, together with globalisation and other megatrends, has been reshaping the drivers of productivity growth. Higher skills, innovation diffusion and technological progress have become the main determinants of productivity growth in the last decades (Berlingieri, Calligaris and Criscuolo, 2018[27]). Workers’ skills and education are among the most direct channels to affect labour productivity (Tsvetkova et al., 2020[13]). What is more, enhancing skills and human capital at the local level is crucial to favouring the development of regional entrepreneurship, innovation (Charlot, Crescenzi and Musolesi, 2015[28]) and of highly productive sectors, such as knowledge-intensive business services (KIBS).
Increasing the skills of the Welsh workforce can contribute to higher labour productivity
Across the UK, regions with a high-skilled labour force (measured as the share of the labour force with tertiary education) show higher levels of labour productivity. With only 38% of its labour force having tertiary education, Wales displays levels of productivity around USD 65 000 (in 2015 PPP). On the other hand, Scotland and Greater London, with 48% and 59% of their labour force having tertiary education, reach levels of labour productivity above USD 77 000 and USD 110 000 (in 2015 PPP) respectively (Figure 3.20).
Note: Correlation coefficient: 0.9, significantly different from zero at the 95% confidence level. The correlation holds even when excluding Greater London.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Wales presents important disparities in the labour force with tertiary education across its local authorities. Although the educational attainment of the labour force increased everywhere in Wales from 2004 to 2017, more efforts are required in some local authorities such as Ceredigion and Merthyr Tydfil, where the proportion of the labour force with at least tertiary education has increased only by 4 and 7 percentage points in the past 13 years respectively (Figure 3.21). In addition, in 2017, substantial regional disparities in the education of the workforce remained across Welsh local authorities. For example, while Blaenau Gwent and Neath Port Talbot present shares of the labour force with at least tertiary education below the 30% (worst outcomes), Cardiff and Vale of Glamorgan display shares of the labour force with at least tertiary education above 45% (best outcomes) (Figure 3.22).
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
Innovation is the main driver of long-term productivity growth. It allows combining the production factors, including labour, in a more efficient way to maximise economic output. In this sense, investing in R&D is one of the best ways to stimulate innovation and thus productivity growth (OECD, 2020[11]). It is worth highlighting that R&D expenditure alone might not quickly translate into significant increases in patenting, in particular with low levels of investment – as the relationship between R&D expenditure and patents is not linear. Besides, academic research suggests that R&D expenditure can be more effective in increasing patenting when accompanied by high levels of human capital (Charlot, Crescenzi and Musolesi, 2015[28]).
Wales could invest more in R&D with respect to both the UK and OECD standards
In terms of expenditure in R&D as a percentage of GDP, Wales invests the least in innovation within the UK. Figure 3.23 shows that expenditure in R&D in Wales represented only 0.96% of its GDP in 2015, almost 1 percentage point below the UK average. This is particularly concerning when considering that UK expenditure in R&D (1.7%) is also low compared to the OECD average (2.4%). Within-country disparities are large in the UK, as R&D expenditure in East England, the best-performing region, is three and a half times higher than in Wales. A similar gap in R&D expenditure appears in other OECD countries such as Germany, where the region of Baden-Wurttemberg also spends three and a half times more on R&D than the region of Saxony-Anhalt (OECD, 2018[3]).
With the lowest number of patent applications per million inhabitants, Northern Ireland and Wales appear to be the least innovative regions of the UK. In 2015, Northern Ireland and Wales respectively produced 37 and 39 patents per million inhabitants, significantly behind the other large UK regions. Inter-regional inequalities in patent applications are large in the UK as the most innovative region (East of England) applied for 130 more patents per million inhabitants than Wales did in the same year. From an OECD perspective, the level of patent applications in Wales is similar to that of the Central Bohemian region in the Czech Republic, the province of Bolzano-Bozen in Italy and the region of Valencia in Spain (Figure 3.24).
Source: OECD (2018[3]), OECD Regions and Cities at a Glance 2018, https://doi.org/10.1787/reg_cit_glance-2018-en.
Source: OECD (2018[3]), OECD Regions and Cities at a Glance 2018, https://doi.org/10.1787/reg_cit_glance-2018-en.
Innovation also intensifies competition among firms. When a firm innovates – and becomes more productive – it forces existing firms to become more efficient or to exit the market. This creation and destruction of firms (churning) and jobs – which also stimulates a better reallocation of resources – generates higher levels of regional labour productivity. Additionally, if the competition associated with new innovative firms entering the market leads to more productivity through the adaptation of incumbent firms (without firm destruction), employment and labour utilisation are also improved (Diaz Ramirez, Klein and Veneri, forthcoming[29]).
The business churn and net business creation rates in Wales are lower than the UK averages but Welsh areas surrounding Cardiff are reaching or surpassing UK levels
The business churn rate in Wales, measured as firm creation and deaths over total active firms, was 3 percentage points lower than the UK average (of 27%) in 2016. Nevertheless, the regions of Bridgend and Neath Port Talbot, Monmouthshire and Newport, Swansea and the Central Valleys, which might be benefitting from their proximity to Cardiff, present similar or higher levels of firm dynamics compared to the UK average for the same year. Although business churn is good for productivity, it does not always translate into a significantly larger number of firms and employment. Among the aforementioned regions, only the Central Valleys, Monmouthshire and Newport display both large business dynamics and net firm creation.
Firm creation rates in Wales are very similar to the UK average but vary considerably across Welsh small regions. In 2016, on average, the number of newly created firms (including employer and non-employer firms) in Wales represented almost 13% of all already-existing firms, a share close to the average birth rate of firms observed in the UK (15%) (Figure 3.25). Similar to the UK, which is among the most unequal countries of the OECD in terms of firm creation (OECD, 2018[3]), Wales also presents large within-nation inequalities in firm birth rates. The largest gap prevails between Central Valleys (20%) and Gwynedd (9%).
Note: As a percentage of active firms. This figure refers to all type of firms (both employer and non-employer) and from most economic sectors, excluding agriculture and insurance activities of holding companies.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
While firm birth and death rates are very similar to each other in most Welsh small regions, firm creation is substantially larger than firm destruction in Central Valleys – generating a significant net creation of new businesses. In all of Wales’ small regions, except the region of Central Valleys, the birth rate of businesses and the death rate of firms display very similar levels, leading to a low net firm creation rate (average of 2.5%), which is below the UK average by 1.3 percentage points (Figure 3.25). Within Wales, only the regions of Gwent Valleys, Monmouthshire and Newport, and Central Valleys display both strong dynamics for business creation and a relatively low death rate of firms, resulting in a net birth rate of businesses superior to the Welsh average of 2.5%. One possible explanation is that the Valleys (Gwent and Central), and the area of Monmouthshire and Newport are capturing the benefits of proximity to Cardiff (without the high congestion costs), which makes these regions more attractive to new firms.
Through the Well-being of Future Generations (Wales) Act 2015, Welsh institutions have made it clear that people’s well-being is at the core of Welsh political values and therefore one of the main pillars of the Welsh development agenda.
This section provides a general overview of well-being in Wales applying the OECD regional well-being framework (OECD, 2014[12]; 2019[30]). Using 13 OECD headline indicators,4 this section benchmarks well-being outcomes in Wales against around 400 OECD large (TL2) regions and provides comparisons to the UK’s average values. Finally, using a set of well-being indicators available at a more granular level, inequalities in well-being across Welsh local authorities are also documented.
Well-being in Wales is higher than or equal to OECD standards in most dimensions but lower than the UK average
Well-being in Wales is above or equal to the OECD median value in 12 out of 13 indicators but below the UK average in most well-being dimensions. Only in the indicator of voter turnout is the Welsh outcome below the OECD median region – by five percentage points. On the other hand, when comparing exclusively to the UK, Wales fares slightly better only on the indicators of the homicide rate, social support network, rooms per inhabitant and exposure to air pollution (PM2.5) (Figures 3.26 and 3.27). With 90% of its households having access to broadband and 95% of the adult population having a strong social support network, Wales is among the top 20% best-performing OECD regions in the dimension of Access to Services and the dimension of Community. In the dimension of Housing (rooms per inhabitant), Wales is in the top quartile of best-performing OECD regions. Together with North East England, Wales is the best-ranked region in the UK in terms of this indicator. In the dimensions of Income, Safety (homicides rate) and Jobs (employment and unemployment rates), Wales is among the middle 60% of OECD regions, but slightly better than the OECD median. Although Wales is in the middle 60% of OECD regions in the dimensions of Environment and Civic Engagement, the exposure of the Welsh population to PM2.5 is just slightly below the suggested levels by the World Health Organization (WHO) (of 10 micrograms per cubic metre), while voter turnout is 5 percentage points below the OECD average.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Inequalities across Welsh local authorities are stark in certain well-being indicators, such as youth unemployment and income deprivation
Regional disparities in well-being dimensions such as youth unemployment and access to high-speed Internet are sharp across Welsh local authorities, where the worst outcomes in two or more dimensions tend to concentrate in the same regions. For example, Blaenau Gwent and Caerphilly face levels of youth unemployment above 16% and only 4% of their premises have access to high-speed Internet (i.e. ultra fibre, >300Mbit/s).
The youth unemployment rate in Wales is lower than the OECD regional average by around five percentage points. Nevertheless, special attention should be paid in South West Wales, where the local authorities of Neath Port Talbot and Swansea display youth unemployment rates above 20%, which implies that out of every 5 youngsters (aged 16-24) that are looking for a job, one of them cannot integrate into the labour market (Figure 3.28).
In 2018, the unemployment rate in Wales was 4.6%, 9.3 percentage points lower than its youth unemployment rate. While most of the variation in unemployment rates comes from comparing local authorities from the different economic regions suggested by the Economic Action Plan (Welsh Government, 2019[10]), the largest disparity in the youth unemployment rate is found within the same economic region. The widest gap in the unemployment rate is found between Powys in Mid and South West Wales (1.4%) and Rhondda Cynon Taf in South East Wales (6.9%). On the other hand, the largest inequality in the youth unemployment rate is observed in Mid and South West Wales, between Pembrokeshire (8%) and Neath Port Talbot (25.2%) (Figure 3.29).
Source: OECD (2020[7]), OECD Regional Statistics (database), https://doi.org/10.1787/region-data-en.
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
Stark spatial inequalities in well-being are also present within local authorities. For example, the average gap in income deprivation between the most and least deprived area within a local authority is 38 percentage points (Figure 3.30). With the exception of Blaenau Gwent, most of the Welsh local authorities have at least one area with an income deprivation below 5%. The granularity of this indicator is very useful to identify areas with concerning income deprivation levels. For example, while the average income deprivation in Wales was 16% in 2017, the worst outcome within a local authority can range from 27% (Cantref 2, Monmouthshire) to 68% (Rhyl West 2, Denbighshire).
Note: The geographical units in this figure correspond to Lower Super Output Area (LSOA).
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
While people are at the centre of well-being policies, ensuring a balanced distribution of well-being in places (where people live) is essential for resilience and sustainable development. For example, large inequalities in income tend to lead to more spatial segregation. In practice, segregation of poor households tends to be problematic when disadvantages in other well-being dimensions concentrate in space and reinforce each other (e.g. education, health and safety), leading to negative lifelong outcomes for residents (OECD, 2018[31]).
Large inequalities across and within regions make some communities and groups of people more vulnerable than others to the adverse effects of environmental or other crises, such as the current COVID-19 pandemic. For example, there is evidence that more deprived areas in England and Wales are experiencing a disproportionate rate of deaths due to COVID-19 compared to less deprived places (Iacobucci, 2020[32]).
From 1 March to 17 April 2020, the most disadvantaged areas in Wales registered around 45 COVID-19 deaths per 100 000 people, while areas with less deprivations experienced close to 23 COVID-19 deaths per 100 000 inhabitants (Iacobucci, 2020[32]) – a similar pattern is also registered for England. Figure 3.31 shows that, for the same period, the most income-deprived areas of Wales were also the most affected by COVID-19. In particular, there is a high spatial concentration of both income deprivation and COVID-19 fatalities in the neighbourhoods of Cardiff, Newport and the Valleys. While density can be an important determinant for the spread of the virus (impacting faster in urban areas), fatality rates are also determined by health system capacity, as well as pre-existing health conditions of people (e.g. high blood pressure, obesity and diabetes), which tend to be correlated to income and education. It is worth noting that the COVID-19 crisis is still an evolving issue. Therefore, statistical evidence at the time of writing – even if suggestive about the particular vulnerability of deprived areas – should not be seen as consolidated.
Note: The geographical units in this figure correspond to Middle Super Output Area (MSOA).
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished and Office for National Statistics (2020[33]), Deaths Involving COVID-19 by Local Area and Deprivation (dataset), https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsinvolvingcovid19bylocalareasanddeprivation.
In the era of digitalisation, having access to Internet is a key determinant of access to opportunities, such as education and jobs. In 2017, broadband access in Wales was 12 percentage points higher than the regional median OECD value of 78%. Additionally, in Wales, inequalities in Internet access are mainly due to high-performing regions such as Cardiff, Newport and Swansea with broadband access levels above 97%, while Ceredigion and Powys present levels slightly below 80% – similar to the OECD median value.
Source: Author’s elaboration based on data from Welsh Government (2019[10]), “Responses to OECD Questionnaire”, Unpublished.
In the context of COVID-19 and measures of social distancing, access to high-speed Internet is proving to be a source of social and economic resilience. Digital tools have become fundamental for the continuity of educational programmes and lifelong learning (OECD, 2020[34]), as well as health consultations among others. On top of that, economies founded in sectors and businesses that can re-adapt their productive processes to teleworking systems are more likely to survive the lockdowns and recover faster from the ongoing global recession.
Some Welsh local authorities are leading in the transition to high-speed Internet (ultra fibre, >300Mbit/s). For example, in Cardiff, Neath Port Talbot, Newport and Swansea, the percentage of premises with ultra fibre is above 60%. While the general Internet coverage is good across Welsh local authorities, some areas could improve the quality of their fibre. This is particularly the case in Blaenau Gwent, Caerphilly, Conwy, Merthyr Tydfil and Pembrokeshire, where the ultra fibre coverage is below 5% (Figure 3.32). By working to ensure well-being today and in the future, Wales is building local resilience and contributing to the achievement of the global SDGs
The 2030 Agenda for Sustainable Development, with its 17 SDGs and its 169 targets, recognises that ending all kinds of social deprivations must go hand in hand with economic prosperity and the planet’s protection (OECD, 2020[8]). This aligns with Welsh strategic and policy documents such as the Economic Action Plan and the statutory guidance on the Well-being of Future Generations (Wales) Act 2015 (Welsh Government, 2017[9]; 2016[35]).
To achieve the SDGs, governments need to know where they stand today with respect to the 2030 Agenda. To help policymakers to measure the distance of their country, region or city towards the SDGs, the OECD has developed a visualisation tool. Among other things, the tool aims to foster peer-learning and policy dialogues across similar regions and cities, increase accountability of governments with regards to the SDGs and raise SDG awareness across society at large (Box 3.4).
Under the Territorial Approach to the SDGs programme, the OECD has developed a visualisation tool to help policymakers measure the distance of regions and cities towards reaching the SDGs (see http://www.oecd-local-sdgs.org). The tool covers around 600 regions and 600 cities from OECD and partner countries and includes more than 100 indicators to monitor progress across the 17 SDGs. These indicators can be visualised individually or as a composite index (OECD, 2020[8]).
The web tool allows each region and city to visualise its distance to an end value for 2030, compare it to its country peer regions and the country average. With the objective of enhancing partnerships and the sharing of best practices for the SDGs among regions and cities, the tool also suggests two profiles of similar regions or cities from different countries. The similarity between regions is determined in terms of relevant characteristics (e.g. population size or GDP per capita). However, only the regions or cities that overall are performing better on their path towards achieving the SDGs are considered. The tool also allows visualising the distance of a region or city towards an end value with respect to all OECD regions or cities. This visualisation can be done either by goal index or individual indicator.
Source: OECD (2020[36]), Measuring the Distance to the SDGs in Regions and Cities (webtool), http://www.oecd-local-sdgs.org.
Compared to OECD regions and United Nations (UN) standards, Wales (blue point in Figure 3.33) is performing well in the SDGs related to education (SDG 4), decent work (SDG 8), reduced inequalities (SDG 10), sustainable cities (SDG 11), and peace and institutions (SDG 16). It is worth highlighting that there are still important data gaps (described in the tool) that have to be considered when interpreting indexes. While SDG indexes are useful for communication and visualisation, they are only an entry point to further analyse the whole set of indicators underlying each goal.
Source: OECD (2020[8]), A Territorial Approach to the Sustainable Development Goals: Synthesis report, https://doi.org/10.1787/e86fa715-en.
Diverging productivity across Welsh regions is a driver of low economic performance in the nation and persistent well-being inequalities across local authorities
Wales, similarly to the UK, depicts a regional model of productivity that concentrates most of the economic performance in a few urban clusters, while the rest of the regions are struggling to catch up. For concentrated models such as this one (see Box 3.3), one traditional policy approach is to compensate the lagging regions through financial support and subsidies while promoting labour mobility and public employment opportunities. However, this model can create dependence and, under distressing conditions such as during economic downturns, trigger the “geography of discontent” (McCann, 2019[37]), polarising political responses. What is more, the traditional compensating system tends to be characterised by a top-down approach implemented through large-scale investments in socio-economically weak areas (Rodríguez-Pose, 2018[2]) – with typically low institutional capacity to manage these investments. This can result in inefficient use of financial resources rather than creating long-term development opportunities.
A thriving Welsh economy that generates well-being today and for future generations can be achieved through a place-based approach to regional policy
Over the past two decades, many OECD countries have been refocusing regional development policy to a more integrated, place-based approach to solve productivity growth and well-being issues. Promoting growth and well-being throughout the territory, rather than focusing on high- or low-performing regions, is likely to yield economies that are less vulnerable to external shocks (OECD, 2012[38]). In addition, ensuring that investment reaches all regions, including the less performing ones, is beneficial for sustainable growth. Regions that under-perform can be costly to national budgets as missed growth opportunities go hand in hand with lower tax revenues and lower quality public service delivery. Shifting from an approach that only focuses on transfers and subsidies as generators of growth to one that focuses on identifying and building the productive potential of each region (Figure 3.34) can maximise both regional well-being and national growth (OECD, 2012[38]).
Ensuring the success of a place-based approach requires developing effective multi-level governance systems and public investment capacity
The goal of regional development policy is to ensure that all types of regions are able to thrive and offer their residents a high quality of life (OECD, 2016[14]). However, this framework is just a general guide for implementation. In practice, the success or failure of a place-based approach will be determined, among other things, by the effectiveness of the multi-level governance mechanisms in place and the investment capacity of the managing authorities. Adopting a place-based approach is clearly more demanding in terms of governance because it requires tailored regional strategies and high levels of co-ordination across levels of government and sectors of society, but it leads to more effective investment and sustainable development results.
Source: Oliveira Martins, J. (2019[39]), “The challenges of regional and local development: The OECD perspective”, Presentation at the OECD Seminar “The Future of Regional Development and Investment in Wales”, 27 November, Cardiff, Wales.
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Notes
← 1. Robust measures of GVA per worker are not available at the Welsh local authority level. For this reason, Cardiff and Vale of Glamorgan cannot be split for the analysis.
← 2. At the OECD, TL2 regions correspond to the first administrative tier of subnational government. For Europe, OECD TL2 regions are, in most cases, equivalent to NUTS-2 regions. One exception is the UK, where TL2 regions correspond to the NUTS-1 regions, which include Northern Ireland, Scotland and Wales and nine statistical regions for England.
← 3. For an in-depth discussion on the challenges and opportunities of the economic planning regions, see Chapter 6: “Case study: Considerations for economic regions for Mid Wales and South West Wales”.
← 4. Headline indicators in the OECD regional well-being framework provide an overview of each well-being dimension using one or two statistics. These headline indicators should be interpreted just as an entry point to compare well-being across regions. The main reason for using no more than two indicators per well-being dimension relates to data availability across all OECD regions.
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