The term of “territorial attractiveness” is now shared by economists and economic geographers to identify a series of assets with which the territories are equipped. The intensity of individual assets and, even more importantly, a favorable combination of different assets can represent an attractive factor to direct preferences towards a given territory rather than another for residential and productive settlements, respectively of private citizens (residential attractiveness) of foreign and national investors (productive attractiveness).

Less universally accepted is the use or rather the abuse of the concept of territorial competitiveness. Unlike the concepts of “utility” and “efficiency”, competitiveness is not a basic construct in economics and analyses of competitiveness have in general no fundamentals that are strictly anchored to economic theory.

From the macroeconomic point of view, various official definitions of territorial (country) competitiveness can be found featuring at least one of the following elements:

  • economic performance, in terms of productivity growth rate and real income;

  • international trade in goods and services;

  • sustainability, understood as long-term sustainable achievements.

In the European Competitiveness Report (2000) we find the following: “An economy is competitive if its population can enjoy high and rising standards of living and high employment on a sustainable basis. More precisely, the level of economic activity should not cause an unsustainable external balance of the economy nor should it compromise the welfare of future generations.”

If at the sectoral level the adaptation of the concept does not present any problems whatsoever, at the macroeconomic level some conceptual dyscrasias arise.

The basic idea of ​​the supporters of extending the micro concept of corporate competitiveness to the whole country is that this can be considered as the sum of the companies that operate there, or as a single large company that is operating on international markets with an ever-increasing number of competitorsFootnote 1. It is precisely because of the similarity between company and country that economists consider the translation of the concept from the micro to the macro level as unacceptable. On closer inspection, the implicit analogy between business and territory is for many economists meaningless, as competition between countries cannot, for obvious reasons, lead to the expulsion or suppression of the less competitive one. On the contrary, the success of a territory (like a country or a region within a country) may in general benefit its neighboring territories thanks to the effects of positive spillovers. In essence, the competitive game between countries is not zero-sum, but rather a plus-sum game. Among the economists, the fiercest opponent to the concept of competitiveness of a country is Krugman (1994) who defines country competitiveness as a dangerous obsession with politicians when they claim to put it at the top of their priority agenda. The main argument of the MIT professor is that competitiveness is in itself an empty word and acquires its meaning only by referring to productivity (“… a poetic way of saying productivity … “). In fact, the most commonly used single indicator of competitiveness at the country level is the labour cost per unit of product (ULC) calculated as the ratio between unit labour cost (per worker or per hour worked) and labor productivity (added value for worker or per hour workedFootnote 2).

If productivity is certainly to be considered as a key factor of a country’s competitiveness, the link between competitiveness and well-being is a mutual one. Empirical evidence highlights the virtuous circle between productivity-competitiveness and income per capita, considering that the most competitive countries in international rankings are also those characterized by a higher standard of living measured by per capita income. The difficulties in defining the concept of territorial competitiveness are directly linked to the complexity of the concept, which also affects its measurement.

Measurement of territorial competitiveness (Camagni, 2002; Annoni & Dijkstra, 2017) involves assessing various factors that contribute to a territory’s ability to attract investment, stimulate economic growth, and create favorable conditions for businesses, residents, and visitors. Territorial competitiveness, like other socio-economic phenomena, is complex and multidimensional (Alaimo and Maggino, 2020; Alaimo, 2021) and there’s no single comprehensive measure, but researchers and policymakers often consider systems of indicators to gauge territorial competitiveness. In social field, the measurement process is associated with the development of indicators. The latter is a normative exercise per definition, since indicators are related to a conceptual definition of the phenomenon and a phenomenon can be defined in different ways (Alaimo, 2022). Consequently, in order to describe a phenomenon like territorial competitiveness, different group of indicators can be selected. Starting from such systems of indicators, the objective is to obtain a unique measure of the concept. Composite Indexes (CIs) are increasingly recognized as useful tools in making informed policy (OECD, 2008; Maggino, 2017)decisions. The number of CIs in existence around the world has been exponentially growing in the past decade or so, with the search ‘composite indicator’ in Google Scholar providing more than 1.800.000 results in May 2020. A composite index is meant to provide a quantitative measure of multi-dimensional concepts that cannot be directly observed and cannot be captured by a single indicator, as the concept of territorial competitiveness.

The approach adopted by the European Commission (EC) for measuring regional competitiveness in the European Union, the Regional Competitiveness Index (RCI), builds on the Global Competitiveness Index developed annually by the World Economic Forum (WEF). According to both WEF and the EC, national/regional competitiveness is a latent concept that can only be measured through a series of observable indicators each capturing one aspect (dimension) of competitiveness.

In recent years the research field of statistical methods for measuring competitiveness has grown, exploiting the time-varying nature of available measures, like CIs observed over time on a set of territorial units, and/or contiguity relations, of spatial type even if not exclusively, among territorial units.

Due to the recent adverse events linked to the COVID-19 crisis, it has become even more important to construct scientifically robust measures for assessing and monitoring the capacity of a territory to recover and bounce back.

This special issue aims provides a comprehensive set of tools for academics, researchers and policy-makers on this field of applied research. It will consists of a collection of 14 articles on cutting-edge research questions related to the measurement of competitiveness and its enlarged concept, including quality of life and well-being.

In the paper “Measuring Competitiveness at NUTS3 Level and Territorial Partitioning of the Italian Provinces”, D’Urso et al. propose a dashboard of indicators of territorial attractiveness at NUTS3 level in the framework of the EU Regional Competitiveness Index (RCI) and apply the Fuzzy C-Medoids Clustering model with multivariate data and contiguity constraints for partitioning the Italian provinces (NUTS3). The novelty of this study is the territorial level analyzed, and the identification of the elementary indicators at the basis of the construction of the eleven composite competitiveness pillars. The positioning of the Italian provinces is deeply analyzed. The analysis developed and the related set of indicators at NUTS3 level constitute an information base that could be effectively used for the implementation of the National Recovery and Resilience Plan (NRRP).

Scarabozzi et al., in the paper “Measuring Competitiveness: A Composite Indicator for Italian Municipalities”, measure territorial competitiveness at the municipal level in Italy, by proposing a robust composite indicator based on variables not yet used in the literature. The underlying theoretical framework is identified based on the literature on regional competitiveness. The proposed indicator consists of seven dimensions of competitiveness: Education, Job, Economic Wellbeing, Territory and Environment, Entrepreneurship, Innovation, and Infrastructures and Mobility. Data are retrieved mainly from administrative sources, for 2014 and 2015. In the building process, different aggregation methods are compared. All the methods considered agree in identifying Innovation and Entrepreneurship as the most influential pillars in 2014 and 2015, respectively. The detailed geographical focus provides specific insights into territorial competitiveness in Italy. It emerges a rather heterogeneous picture of municipal competitiveness within the Italian regions. Highly competitive municipalities are present in every region, though with different concentration levels.

The study “Regional Competitiveness: A Structural-Based Topic Analysis on Recent Literature” of Grassia et al. reviews the most recent scientific literature concerning the concept of Regional Competitiveness with a bibliometric approach based on topic modelling. The use of a textual-based statistical approach offered an interesting insight into this research domain, highlighting the topics discussed by scholars, showing the patterns emerging across years and from different publication types, and marking the differences between the vocabulary used by authors coming from EU and non-EU countries. Finally, a comparison between the Regional Competitiveness issues coming from the literature analysis with the RC issues defined by policymakers tried to link the two standpoints followed by the institutions and the academia in a more comprehensive conceptual framework.

In the paper “Measuring competitiveness differentials inside the same region: a propensity-score matching approach”, Fantechi and Fratesi analyse regional competitiveness at the subregional level through a novel methodological approach that adopts a matching design. Using data for Lombardy, a large and competitive European region, they compare the performance of similar firms in different parts of the region, detecting whether different places provide different competitive territorial assets. The analysis shows that the different territories of the region are differently competitive in different industries, even when they are similar in terms of total GDP per capita or specialization. The paper also confirms that measuring competitiveness on different indicators (Labour Productivity, TFP, Profitability) can provide different results, and this especially happens when comparing static and dynamic indicators.

Bocci et al. propose the paper “A Regression Tree-Based Analysis of the European Regional Competitiveness”, in which a Regression Tree analysis has been performed for the Eurostat Regional Competitiveness Index (RCI) as response variable by taking the 74 basic indicators used in the 2019 RCI edition as explanatory variables. Being a non-parametric method, suitable for the analysis of large data sets via a recursive partitioning procedure, the Regression Tree allowed to identify (a) the 12 most influential indicators, out of the initial 74, for the overall 2019 RCI, and (b) a classification of the 268 European regions into 15 homogeneous groups. Interestingly, the groups are ranked by their predicted RCI values which correspond to the mean observed RCI values within the groups themselves. The almost perfect correlation between the Eurostat RCI and the predicted RCI within groups confirms the key role of the 12 selected indicators as determinants of the 2019 RCI. These evidences help policy makers to address their strategies towards focused objectives in line with the specific needs of the territories, characterized by an intrinsic heterogeneity and complexity.

In the paper “NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries”, Pennoni and Beata Bal-Domańska propose an analysis of young people’s place in the labor market, a topic of interest to the European Union and national governments. The study analyzes young people who are Not in Employment nor in Education or Training (NEET) and Youth Unemployment (YU) in the European Union member states, through data collected over a period of sixteen years, considering the influence of some macroeconomic factors through an hidden Markov model.

The paper “Assessing Response Readiness to Health Emergencies: A Spatial Evaluation of Health and Socio-Economic Justice in Pakistan” of Sajjad et al. propose a Geographic Information Systems-based framework in the context of public health-related hazards and pandemic response, such as in the face of COVID19. Indicators relevant to health system (HS) and socio-economic conditions (SC) are utilized to compute a response readiness index (RRI). The frequency histograms and the Analysis of Variance approaches are applied to analyze the distribution of response readiness. Moreover, spatial distributional models are used to explore the geographically-varying patterns of response readiness pinpointing the priority intervention areas in the context of cross-regional health and socio-economic justice. The framework’s application is demonstrated using Pakistan’s most developed and populous province, namely Punjab, as a case study. The results show that ∼ 45% indicators achieve below-average scores (value < 0.61) including four from HS and five from SC. The findings ascertain maximum districts lack health facilities, hospital beds, and health insurance from HS and more than 50% lack communication means and literacy-rates, which are essential in times of emergencies. This cross-regional assessment shows a north–south spatial heterogeneity with southern Punjab being the most vulnerable to COVID-like situations.

The paper “Sustainable Innovation: The Italian Scenario Studied Through Higher-Order Partial Least Squares-Path Modeling” of Cataldo et al., starting from the concept of sustainable innovation, focuses on the basic criteria (such as indicators and statistical models) required to evaluate the sustainable innovation at the regional level, choosing Italy as a case study. From the elementary indicators of innovation, on one hand, and sustainability, on other hand, a composite indicator of sustainable innovation has been computed. The statistical model used to compute this composite indicator has been a Higher-Order Partial Least Squares Path model. The results obtained applying this model to the Italian scenario are discussed, the ranking of the different Italian regions, and the impact of the composite indicator sustainable innovation on economic results of each region are discussed.

The paper “Testing for Localization with Entropy-Based Measures” of Cerqueti and Cutrini gives statistical significance to the measurement of spatial concentration in the context of entropy-based approaches. The authors simulate confidence intervals based on a null hypothesis able to capture systematic spatial concentration of firms from random patterns, and dissimilarities between the distributions of firms and employees. The paper implements this two-step methodology to the European manufacturing economy, and finds a substantive spatial clustering of establishments whereby the spatial divergence between employees and firms is significant both for small-scale industries typically considered as localized because of industry-specific Marshallian external economies and for those industries characterized by considerable internal scale economies.

In the paper “The Great Recession Index: A Place-based Indicator for Countries, States, and Metropolitan Areas”, Wallace et al. propose and introduce the Great Recession Index (GRI), a place-based composite measure that captures the multidimensional nature of the Great Recession (GR), the devastating economic downturn of 2007–2009. The GRI can be used to examine macro-level outcomes and is especially well-suited for examining the spatial variation and long-term effects of the GR. The proposed index is adaptable to a variety of geospatial units of analysis, and in this article, we develop measures for countries, U.S. states, and U.S. metropolitan areas. Then, using the state-based GRI, the paper provides a research application to demonstrate the utility of the GRI for explaining state-level income inequality in the post-Recession period. The results show that the initial shock of the GR decreased the income share of upper-class households, but the aftershock of the Recession increased their income share, resulting in increased income inequality in the U.S. since the Recession. The paper concludes by considering the feasibility of using similar measures for evaluating the effects of catastrophic events such as wars, civil unrest, climate change, natural disasters, or pestilence on societal outcomes.

Celli et al., in the paper “Does R&D Expenditure Boost Economic Growth in Lagging Regions?”, assess the impact of the EU Regional Policy on regional economic growth by applying a new evaluation strategy, which integrates mediation analysis with a quasi-experimental framework. Using the R&D expenditure as an indicator of innovation capability, the authors evaluate how much of the total effect of the EU Regional Policy is due to R&D in the poorest EU regions. Consistently with the previous literature, the paper found a positive impact of the overall policy on economic growth, but, among the convergence regions, those investing a higher proportion of funds in R&D have the same convergence rate as regions investing more in other priorities. These findings confirm that the EU Regional Policy played an important role in the economic recovery of the poorest regions in the aftermath of the Great Recession.

The paper “Broken Trust. Confidence Gaps and Distrust in Latin America” of Parra Saiani et al. propose an index (LADI) that provides a measure of the level of perceived distrust in the institutions of the different Latin American countries and its variations over the period from 2008 to 2018. The data used for this analysis are of a subjective nature and come from the series of surveys provided by Latinobarómetro. To develop the analysis, the authors use a partially non-compensatory aggregation method, known as Adjusted Mazziotta and Pareto Index. The results show a generalized increase of distrust in the years 2017 and 2018 for several Latin American countries. On the other hand, in countries where the rule of law is more consolidated, a best perception of the functioning of democracy emerges.

The paper “Evaluating Rank-Coherence of Crowd Rating in Customer Satisfaction”of Tomaselli and Cantone proposes an evaluative method to investigate fairness of common measures of rating procedures with the peculiar perspective of assessing linearity of the ranked outcomes. This method is tested on a longitudinal observational case of 7 years of customer satisfaction ratings, for a total amount of 26.888 reviews. According to the results obtained from the sampled dataset, there is a trade-off between loss of (potentially) biased information on ratings and fairness of the resulting rankings. However, computing an ad hoc unbiased ranking case, the ranking outcome through the time-weighted measure is not significantly different from the ad hoc unbiased case.