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regional potential index

a revised methodology

Author: Carlos Tapia
DATA AND MAPS: Gustaf Norlén and Kamila Dzhavatova
Photos: Unsplash, iStock, iStock

Introduction 

Regional development and innovation processes are multifaceted phenomena that encompass various factors related to infrastructure, institutions, technologies, social capital and more (Isaksen et al. 2022). These factors play various roles in regional development and interact in complex ways, which makes interpretation challenging. It is, therefore, helpful to employ composite indicators or synthetic indices to summarise multidimensional information. This both simplifies communication and facilitates more straightforward benchmarking and trend analysis (Nardo and Saisana 2009). Multidimensional indicators of this kind are widely utilised in policy- and decision-making, including in regional policy (Saisana et al. 2005).
Nordregio’s Regional Potential Index (RPI) enables cross-regional comparison of development potential and illustrates the regional balance between the Nordic countries (Grunfelder et al. 2020a). The State of the Nordic Region report included RPI for the first time in 2016, and the index was subsequently updated in the 2018 and 2020 releases (Lindberg et al. 2017; Grunfelder et al. 2018; Grunfelder et al. 2020b). The purpose of this multidimensional index is to summarise the current and past performance of the Nordic regions across major policy domains. The index helps to identify regions that have high potential and those in need of further support to boost their potential and meet existing challenges. It provides policy-makers with a comparative learning tool that informs the design of effective regional development strategies at Nordic level.
Nordregio’s RPI is a multi-item measurement scale that incorporates information about the demographics, labour market and economic output of the Nordic countries’ 66 administrative regions. It consists of eight indicators
The original RPI had nine indicators (see explanation below).
.classified into four main groups and eight subgroups (see Table 10.1). These components and indicators were originally selected on the basis of their relevance for regional development (Lindberg et al. 2017).
The original RPI was constructed as a simple weighted average of the contributing indicators. In the 2018 and 2020 editions, the RPI allocated a maximum of 75 points to each indicator in the demographic domain, up to 100 points to each indicator in the labour market dimension, a maximum of 200 points to regional GDP per capita, and up to 100 points to R&D investment per capita.
The weights were manually assigned based on expert judgement, in line with their intended policy relevance. However, this interpretation is not appropriate when using a fully compensatory aggregation function, such as the weighted arithmetic mean. The nature of the additive aggregation method entails full compensability, which implies that a decline in one policy domain (e.g. gender balance) could be completely offset (compensated) by progress in another one (e.g. total production). This is undesirable since it might encourage decision-makers to focus on policy areas in which results can be more easily achieved rather than those with a greater need for intervention.
Domain
Subdomain
Indicator
Justification
1. Demography
1.1. Degree of urbanisation
Proportion of population living in urban areas of 5,000+ inhabitants
Medium-sized and large cities offer relatively good access to jobs (especially in the tertiary sector), healthcare, culture, environmentally friendly transport and other services due to a critical mass of population.
1. Demography
1.2. Gender balance
Gender ratio of the total population
In a balanced situation, the regions offer education and workplaces for both genders. An unbalanced situation is often the result of the outmigration of women for education or work purposes, which contributes to the intensification of demographic shrinkage (e.g. lower fertility rates and ageing).
1. Demography
1.3. Age balance
Demographic dependency ratio
This highlights the economic burden on the working population (those who have the potential to earn their own income) in supporting non-working members of the population (young people and pensioners).
2. Labour market
2.1. Employment level
Total employment rate
Relatively high employment contributes to higher tax revenues, the overall regional economy and its production. It also indicates that the region’s population has the skills sought by employers. A high employment rate also contributes to both social cohesion and life satisfaction.
2. Labour market
2.2. Youth unemployment
Youth unemployment rate
A low rate of youth unemployment highlights good conditions for entering the labour market.
3. Labour market
2.3. Educational attainment
Population with tertiary education
A high proportion indicates a more skilled workforce and a better chance of being an innovation leader. It also tends to improve the quality of jobs and, consequently, the life satisfaction of the region’s inhabitants.
3. Economy
3.1. Total production
GDP per capita (PPS)
This provides an indication of the level of production of goods and services in the region. More generally, it also provides a fairly reliable measure of the performance of the regional economy.
3. Economy
3.2. Innovation investment
Total R&D investment per capita
This indicates the region’s preparedness in relation to future development and is seen as a tool for translating innovation into economic growth.
Table 10.1: The RPI framework’s data model.
In this edition, we adopt a new methodology that maintains a similar set of indicators but applies a more robust statistical process to the construction of the RPI.
In brief, our methodology consists of a pre-processing stage, in which the input data is prepared for analysis, and a processing stage, in which the indicators are weighted and aggregated.
The pre-processing stage entails imputing missing values (Step 1), removing statistical outliers (Step 2), transforming the data to reduce skewness (Step 3), and standardising the values for aggregation as adimensional constructs (Step 4). The data-processing stage involves assigning a data-driven definition of weights to different variables (as presented in Table 10.2) based on their contribution to the overall variance (Step 5), and the aggregation of this information in an adimensional index, using a weighted geometric mean (Step 6).
Finally, there is also a post-processing stage (Step 7), in which a sensitivity analysis evaluates the influence of each preceding step on the final outcome.
Indicators
PCA FA weights
Proportion of population living in urban areas of 5,000+ inhabitants
0.15
Gender ratio of the total population
0.18
Demographic dependency ratio
0.05
Total employment rate
0.14
Youth unemployment rate
0.11
GDP per capita (PPP)
0.15
Total R&D investment per capita
0.08
Population with tertiary education
0.14
Table 10.2: Weights assigned through Principal Component Analysis (PCA) and Factor Analysis (FA)
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As part of the extensive methodology review, we also chose to exclude the net migration rate indicator. The purpose of this was to ensure the conceptual coherence of the index. Net migration rate was the only indicator in the original data model that was expressed as a rate of change, whereas the other indicators provided a static snapshot of conditions across various domains. Removing the net migration rate from the data model, therefore, enhanced the conceptual coherence of the RPI. At the same time, it also improved the temporal stability of the resulting index, especially during the COVID-19 years.
Consequently, the revised methodology produces more stable and consistent results while also minimising data redundancy (double-counting) during the weighting phase and mitigating compensatory issues during the aggregation phase. For further information on the construction of robust multidimensional indices, see the European Commission’s Competence Centre on Composite Indicators and Scoreboards (European Commission 2023).

The new Regional Potential Index

The RPI was calculated retroactively for the 2015–2023 period. However, the focus in this section is on 2022 – the most recent year in our time series with full data coverage. Map 1 shows the redesigned RPI for that period. In line with the principles of accumulation and agglomeration that drive the global economy, the RPI highlights the role of the Nordic countries’ largest cities and city regions. The top performing region in 2022 was Oslo, the Capital Region of Norway, followed by Stockholm County and the capital regions of Denmark and Iceland.
On the other end of the RPI spectrum, we find regions such as Greenland, the Eastern Region in Iceland, and South Savo and Päijät-Häme in Finland. All of these regions are characterised by sparse populations, rural economies, and in some cases, remoteness.
Greenland has the lowest overall RPI, with low rankings for most indicators but not all. For instance, Greenland boasts one of the lowest demographic dependency ratios in the Nordic Region, and youth unemployment does not appear to be a significant issue. However, the region consistently underperforms in indicators such as gender balance, educational attainment and degree of urbanisation. This description is also largely applicable to the Eastern Region of Iceland.
Like several other areas across the Nordics, regions in Eastern Finland have one of the fastest-ageing populations, and increasing life expectancy is leading to growth in the elderly population (see Demography section). Concurrently, even though Eastern Finland’s economy is developing, the labour market presents discouraging figures, with comparatively lower employment rates and higher youth unemployment rates than the national and Nordic averages. This could explain why, unlike other rural Nordic regions, several landlocked Finnish regions near the Russian border are struggling to counter some of the challenges posed by an ageing population, such as a shrinking labour supply and escalating public-sector costs. The same situation applies to Södermanland County in Sweden, even though this region also struggles with one of the Nordics’ highest youth unemployment rates.
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Map 10.1: Regional Potential Index (2022)

Box 10.1: Interpreting the Regional Potential Index

The RPI offers several advantages for policy monitoring and evaluation:
  • It encapsulates multiple dimensions of regional potential into a single metric, providing a comprehensive overview of this phenomenon.
  • It simplifies complex data, making it easier for policy-makers and stakeholders to understand regional potential in a holistic way.
  • It allows for meaningful comparison across different policy priorities, regions, or time periods.
  • It enhances transparency and accountability in policy implementation, thereby helping to identify trends, patterns and anomalies in policy performance over time.
However, much like any other composite indicator, the RPI simplifies reality. While the indicator is a powerful tool for policy monitoring and evaluation, it should be used judiciously – i.e. based on a comprehensive understanding of what the RPI signifies about the measured phenomenon, supplemented by other qualitative and quantitative methods. Furthermore, any interpretation of RPI scores must take into consideration the context and limitations of the data and methodology used.
In essence, the RPI provides a snapshot of regional potential within a Nordic framework. When combined with the various thematic analyses in the State of the Nordic Region, it can facilitate a better understanding of the dynamics, enablers and drivers of this multidimensional phenomenon.

RPI trends over time

The RPI is a relative index that fluctuates both temporally and spatially. This means that the scores depend on the values of all data points within the distribution. The maximum and minimum scores are determined, respectively, by the best- and worst-performing regions across the time series. As new data points are added to the panel, the scores are retroactively recalculated. Consequently, the rankings and their temporal changes offer more insight than the RPI scores themselves.
An examination of the data from 2019 to 2022 (Figures 10.1a and 10.1b) reveals that the top performers during this period were the Southern Region in Iceland, Kronoberg and Kalmar counties in Sweden, and South Karelia, North Ostrobothnia and Ostrobothnia in Finland.
Progress in the top-performing region during this period, the Southern Region of Iceland, was mostly driven by internal population dynamics. This region experienced progress in its relative RPI score, primarily due to the population concentration in the city of Selfoss, which is experiencing sustained growth. This indicates improved access to services for residents in a region that boasts a robust local economy, as well as a relatively low demographic dependency ratio. At the same time, the Southern Region performs fairly well on all indicators – with the exception of gender ratio and share of population with tertiary education, neither of which changed substantially between 2019 and 2022.
Unlike the Southern Region in Iceland, progress in the Swedish and Finnish regions was characterised by substantial improvements in several key indicators. For example, the labour markets in the South Karelia, Kronoberg and Kalmar regions saw considerable enhancements in 2019–2022. Relative to other areas, these regions managed to increase total employment while simultaneously reducing youth unemployment. These improvements coincided with a relative expansion in their regional economies, measured in terms of GDP per capita. All things considered, the labour markets and local economies in these regions demonstrated resilience in the face of the COVID-19 pandemic and the Russian invasion of Ukraine. South Karelia also made substantial strides towards achieving a more balanced gender composition.
In stark contrast to the previously mentioned regions, the Northwestern Region in Iceland, the Faroe Islands, Åland and Västmanlands County in Sweden fell more than ten positions in the RPI ranking during 2019–2022. In the very rural Northwestern Region of Iceland, the decline in the RPI is mostly attributable to a deteriorating gender balance in the total population. This trait is also observed in Åland and Västmanlands County in Sweden. In the case of the Faeroe Islands and Åland, the decline in the RPI is more likely related to the deterioration of local economies during COVID-19, which had a comparatively greater impact on these regions.
In the case of Åland, the process was characterised by a slight relative increase in unemployment rates, particularly among young people. In the Swedish County of Västmanland, youth unemployment also increased substantially in relative terms during 2019–2022. The socioeconomic disruptions caused by the pandemic and the early stages of the war in Ukraine seem to have affected the regional economies to a larger extent than in other areas. This accentuated the ongoing outmigration of young people, particularly women, leading to a deterioration of demographic dependency ratios relative to other regions.
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Figure 10.1a: Top movers 2019–2022.
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Figure 10.1b: Top movers 2019–2022.
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References

European Commission JRC (2023). Competence Centre on Composite Indicators and Scoreboards. Available at: https://composite-indicators.jrc.ec.europa.eu/
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