NUTS2 Level Research Articles

Assessing Regional Variation in Support for the Radical Right-Wing Party ‘Alternative for Germany’ (AfD)—A Novel Application of Institutional Anomie Theory across German Districts

The paper at hand aims to address a research gap by examining the spatial impact of economic and non-economic institutions on regional variation in the support for the German populist far-right party ‘Alternative for Germany (AfD)’. Adopting an interdisciplinary approach and drawing on institutional anomie theory, the study explores the relationship between economic dominance—economic inequality and economic strength—and regional AfD vote share, as well as the role of non-economic institutions in shaping support for the party. Using various regional indicators, the study assesses the strength of different institutions and their impact on the AfD’s electoral results in the year 2021 across 401 German districts (NUTS 3-level). The results point to significant differences between eastern and western Germany in the analysis of regional populist voting patterns. Economic dominance emerges as a stronger predictor of the AfD’s vote share in eastern regions. However, the findings on the protective effect of strong non-economic institutions against AfD voting are ambiguous and partly contrary to theoretical expectations. Overall, the study’s findings show that the newly introduced indicators deduced from institutional anomie theory contribute to the existing literature to help better understand regional variances in AfD support. Furthermore, the study contributes to the existing literature on the rise of contemporary radical right movements by employing Karl Polanyi’s framework of ‘disembedded’ markets. In doing so, it sheds light on the complex interplay between social change, anomic tendencies, and the growth of radical right movements in capitalist societies.

Regional economic growth and unemployment in the European Union – a spatio-temporal analysis at the NUTS-2 level (2013–2019)

The study aims to verify the relationship between the unemployment rate and economic growth in European Union (EU) regions. As the most important macroeconomic relationship, the significance of the dependence between the labour market situation and the output growth is widely known and considered. Analysis in this research was conducted using data for 229 EU regions on the NUTS-2 level in the years 2013–2019. In order to verify the relationship between the unemployment rate and the output growth, the spatio-temporal models for pooled time series and cross-sectional data (TSCS) were estimated. The Fitted Trend and Elasticity Method of verifying Okun’s law was used in the analysis, wherein the deterministic trend factor was enriched with the spatial element. Educational attainment as the additional explanatory variable was included in the models. The neighbourhood between regions was quantified based on two criteria: (1) common border criterion – related to the possibility of population migrations, and (2) similarity of the unemployment rate criterion – related to the imitation effect in the issue of introduced rules and regulations on the labour market by regional governments. One of the hypotheses verified in the investigation is the superiority of the economic neighbourhood over the geographical neighbourhood.

The impact of convergence of inequalities in the European Union since 2004

On the basis of a complex methodology,the study shows that territorial disparities in the European Union have been decreasing since 2004, and, at the same time, examines the claim that the intense development and convergence of countries go hand in hand with regional divergences within these countries (at NUTS2 level). The results show that convergence occurred mainly during the recovery phases between crises causing significant downturns, and the development and catching-up tendencies of convergence countries have also contributed to that. While inequalities have been reduced in the EU, there seem to be increasing gaps between countries and regions, with the top performing better and better, while other countries and regions are becoming poorer compared to the average. When examining regional disparities within countries, divergence was mainly found in convergence countries and above- average performers achieving outstanding development.

Spatial Patterns of Recent Ukrainian Refugees in Germany: Administrative Dispersal and Existing Ethnic Networks

Since the beginning of the Russian invasion of Ukraine on February 24, 2022, many people have fled the war and left their home country. By the end of January 2023, more than one million Ukrainian refugees had been registered in Germany alone. In contrast to refugees from other countries of origin in Germany, Ukrainian citizens can choose their place of residence if they have either found private accommodation with family members or friends or do not claim state support. However, little information exists on where within Germany Ukrainian refugees have moved and why certain regions are potentially more attractive than others. There exists a substantial literature on the location choices of migrants in general, showing that the existing level of immigrant concentration is an important determinant, while economic factors have a smaller effect – if not in the initial location choice after immigration, then at least in later location decisions. Whereas these studies mainly focus on labour migrants, research on refugees’ location choices is still scarce, because refugees are usually assigned to specific places of residence by the authorities in many European countries. In the context of forced migration, spatial patterns may therefore largely be related to administrative decisions. In this paper, we aim to answer the question of the settlement patterns of recently arrived refugees from Ukraine in Germany by using current data from the Central Register of Foreigners. These patterns are modelled on the NUTS-3 level and consider the proportion of previous Ukrainian migrants living within those regions as well as additional economic, demographic, and geographical factors. Spatial regression models show that, on the one hand, Ukrainian refugees indeed settle where the number of Ukrainians is already high. The empirical analyses also indicate a correlation between the spatial patterns of refugees in general and Ukrainian refugees, suggesting that dispersal policies may play a role in explaining settlement patterns. Furthermore, affordable housing and lower rents are important explanatory variables.

Ethnicities in Post-Communist Romania: Spatial Dynamics, Fractionalisation, and Polarisation at the NUTS-3 Level

Scholars have shown a special interest in discovering and studying the role of ethnic diversity and spatiality at the country and region levels. This study contributes to the theoretical debate on the spatial dynamics of ethnicities, with the aim of (1) determining the evolution of ethnic dynamics in post-communist Romania and (2) applying ethnic fractionalisation and polarisation indices. The study uses a mixed methods approach based on a descriptive statistics analysis and applies the fractionalisation and polarisation indices to Romania’s NUTS-3 (i.e., county) level. The findings suggest that the ethnic spatial dynamics in post-communist Romania have shown a decrease in all ethnicities due to migration and low birth rates, with the exception of the Roma ethnicity, for whom the trend is increasing. Additionally, polarisation and fractionalisation indices have different evolutionary manifestations depending on the dynamics of the ethnic groups present in certain geographical areas. Although neither of the two analysed indices has witnessed profound change at the spatial level, these small changes in spatial and short-term ethnic diversity can help us advance knowledge about co-existence in ethnically diverse societies. Higher values of the two indices are obvious in several counties where ethnic Hungarians cohabitate with Romanians and other ethnicities. This discovery can inform policy-makers to implement more policies for the further peaceful co-existence of Hungarians, Romanians, and other ethnic groups in Transylvania and other western counties in Romania. Furthermore, as the population growth trend for the Roma ethnicity is upward, Romania has to implement proper policies and build better government infrastructure to counter social inequality against the Roma people. This will help curb potential conflicts between the Roma and other ethnic groups at the local level. Finally, as most ethnicities decreased in number in post-communist times, further attention needs to be paid to the erosion of ethnic diversity in Romania because this could have a negative impact on economic development, social trust, and democracy.

Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets

CONTEXTThe assimilation of Remote Sensing (RS) data into crop models improves the accuracy of yield predictions by considering crop growth dynamics and their spatial heterogeneity due to the different management practices and environmental conditions. OBJECTIVEThis study proposes a new method for performing sub-regional yield predictions (Nomenclature of territorial units for statistics, NUTS-3 level) using RS time series data and crop models. The primary objective was to release a procedure whereby the heterogeneity of the agricultural landscape (i.e. the agrophenotypes) observed from RS is used to drive crop model calibration. METHODSFine-resolution barley and maize distribution maps (100 m) and related crop calendars have been used to derive the “where” and “when” of the 8-day MODIS NDVI time series data, located in Apulia, Tuscany, and Veneto in 2018–2019. Principal Component Analysis and Hierarchical Clustering have been applied to NDVI seasonal profiles to identify the agrophenotypes, which have been used to derive crop growth and leaf area index dynamics. These data served as the reference to optimize the most relevant parameters of the WOFOST_GT model, using gridded weather data as input (0.25° resolution, Copernicus ERA5). Parameter distributions from multiple automatic calibrations have been sampled to characterize the agricultural heterogeneity within 22 NUTS-3 administrative units. Yield statistics from the Italian National Institute of Statistics have been used as reference data to test the accuracy of the yield simulation by the crop model WOFOST_GT. RESULTS AND CONCLUSIONSThe agrophenotypes reflected the wide north-south latitudinal gradient experienced in Italy by the two crops, leading to a gap of 15–30 days in barley and maize flowering and harvest dates across the study area. Average Nast-Sutcliffe modelling efficiency in reproducing LAI dynamics from RS (0.6) and Relative Root Mean Square Error (RRMSE) in predicting yield data (12.1% for barley, 3.7% for maize) in hindcast simulations demonstrated the effectiveness of our approach. The average RRMSE was reduced by 32.7% (barley) and 8.5% (maize) compared to baseline, decreasing by 0.7–1 Mg ha−1 absolute yield errors on both crops. SIGNIFICANCEThe inclusion of local agrophenotypes in the yield prediction workflow reduced errors in yield prediction compared to unsupervised simulations at NUTS-3 level. The script for agrophenotypes extraction and the model parameter sets are released to the scientific community, to foster improvements and further applications to other crops, ecoclimatic regions, satellite sensors and spatial scales.

Forecasting regional GDPs: a comparison with spatial dynamic panel data models

ABSTRACT The monitoring of the regional (provincial) economic situation is of particular importance due to the high level of heterogeneity and interdependences among different territories. Although econometric models allow for spatial and serial correlation of various kinds, the limited availability of territorial data restricts the set of relevant predictors at a more disaggregated level, especially for gross domestic product (GDP). Combining data from different sources at NUTS-3 level, this paper evaluates the predictive performance of a spatial dynamic panel data model with individual fixed effects and some relevant exogenous regressors, by using data on total gross value added (GVA) for 103 Italian provinces over the period 2000–2016. A comparison with nested panel sub-specifications as well as pure temporal autoregressive specifications has also been included. The main finding is that the spatial dynamic specification increases forecast accuracy more than its competitors throughout the out-of-sample, recognising an important role played by both space and time. However, when temporal cointegration is detected, the random-walk specification is still to be preferred in some cases even in the presence of short panels.

Modeling Soil Organic Carbon Dynamics of Arable Land across Scales: A Simplified Assessment of Alternative Management Practices on the Level of Administrative Units

Regional assessments of soil organic carbon (SOC) trends and the carbon sequestration potential of alternative management practices (AMP) are highly relevant for developing climate change mitigation strategies for the agricultural sector. Such studies could benefit from simplified SOC modeling approaches on the scale of administrative units as this often corresponds to the level of policy-making and data availability. However, there is a risk of systematic errors in such scaling operations. To overcome this problem, we performed a scaling experiment where we simulated the SOC dynamics of the arable soils of the State of Saxony (Germany) across a series of scales using the CANDY Carbon Balance (CCB) model. Specifically, we developed model set-ups on four different administrative levels (NUTS1, NUTS2, NUTS3, and LAU) and evaluated the simulation results of the upscaled models against a 500 m grid-based reference model. Furthermore, we quantified the carbon sequestration potential of selected AMP scenarios (addressing field grass, cover crops, and conservation tillage) across all scales. The upscaled model set-ups adequately simulated the SOC trends of Saxon arable land compared to the grid-based reference simulation (scaling error: 0.8–3.8%), while providing significant benefits for model application, data availability and runtime. The carbon sequestration potential of the AMP scenarios (1.33 Mt C until 2050) was slightly overestimated (+0.07–0.09 Mt C) by the upscaled model set-ups. Regardless of the scale of model set-up, we showed that the use of aggregated statistical input data could lead to a systematic underestimation of SOC trends. LAU and NUTS3 levels were shown to be a suitable compromise for effectively quantifying SOC dynamics and allowed for an acceptable spatial prioritization of AMPs. Such simplified, scale-adapted assessments are valuable for cross-regional comparisons and for communication to and among decision-makers, and might provide a quantitative basis for discussions on the effectiveness of AMPs in various stakeholder processes.

Identification of hickory nuts with different oxidation levels by integrating self-supervised and supervised learning

The hickory (Carya cathayensis) nuts are considered as a traditional nut in Asia due to nutritional components such as phenols and steroids, amino acids and minerals, and especially high levels of unsaturated fatty acids. However, the edible quality of hickory nuts is rapidly deteriorated by oxidative rancidity. Deeper Masked autoencoders (DEEPMAE) with a unique structure for automatically extracting some features that could be scaleable from local to global for image classification, has been considered to be a state-of-the-art computer vision technique for grading tasks. This paper aims to present a novel and accurate method for grading hickory nuts with different oxidation levels. Owing to the use of self-supervised and supervised processes, this method is able to predict images of hickory nuts with different oxidation levels effectively, i.e., DEEPMAE can predict the oxidation level of nuts. The proposed DEEPMAE model was constructed from Vision Transformer (VIT) architecture which was followed by Masked autoencoders(MAE). This model was trained and tested on image datasets containing four classes, and the differences between these classes were mainly caused by varying levels of oxidation over time. The DEEPMAE model was able to achieve an overall classification accuracy of 96.14% on the validation set and 96.42% on the test set. The results on the suggested model demonstrated that the application of the DEEPMAE model might be a promising method for grading hickory nuts with different levels of oxidation.

The extent of windfarm infrastructures on recognised European blanket bogs

Peatland environments are the Earth’s largest terrestrial carbon store and have the potential to act as carbon sinks. However, the development of windfarms on peatlands is affecting their morphology, hydrology, ground-level climate conditions, carbon functions and vegetation, and long-term consequences still need to be assessed. Blanket bogs are a rare type of ombrotrophic peatland that are typical of oceanic areas with high precipitation and low temperatures. Their distribution has been mapped across Europe, where they are mainly located on hill summits where wind energy potential is higher, making them attractive sites for windfarm developments. The promotion of renewable energy is currently a priority given the environmental and economic drive to increase low-carbon energy production. Establishing windfarms on peatland in pursuit of greener energy, therefore, risks compromising and undermining the green-energy transition. Despite this, the extent of windfarm infrastructures on blanket bogs have not yet been reported at the European scale. This research reports the extent of windfarm infrastructures on recognised blanket bogs, with a geographical focus on Europe, where blanket bogs have been mapped systematically. Under the EU Habitats Directive (92/43/EEC), there are 36 European regions NUTS level 2 with recognised blanket bogs. Of these, 12 have windfarm developments, including 644 wind turbines, 253.4 km of vehicular access tracks and an affected area of 207.6 ha, mainly in Ireland and Scotland where the extent of blanket bogs is also higher. However, despite Spain having under 0.2% of Europe’s recognised blanket bogs area, this was the most affected country. In Scotland, a comparison of the recognised blanket bogs under the Habitats Directive (92/43/EEC) with blanket bogs recorded in national inventories indicates that the extent of windfarm developments was higher, with 1,063 wind turbines and 634.5 km of vehicular access tracks. Our results highlight the extent of windfarm developments on blanket bog habitat, both in areas where peatlands are broadly distributed across the landscape, and also in areas where this recognised habitat is particularly rare. There is a pressing need to assess the long-term impacts of windfarms on peatlands to ensure that efforts to meet energy targets result only in carbon sequestration, and do not jeopardise ecosystem services. Blanket bogs represent a particularly vulnerable habitat, the study of which should be prioritised updating national and international inventories to protect and restore this habitat.

„Praga uważa się za „naj...” – konkurencyjność regionów w Republice Cze- skiej w kontekście pragocentryzmu

The aim of the article was to compare NUTS3 level regions in the Czech Republic in terms of their competitiveness understood as an outcome of the four following dimensions: economic, social, innovative and environmental. The study of competitiveness was intended to verify the thesis of the legitimacy of concentrating development policy (especially in terms of spending EU funds) in the capital city of Prague as the region with the highest potential, providing the greatest guarantees of their effective use. In the article, using the Hellwig method (i.e. relative distances from the benchmark), an empirical analysis of regional competitiveness was carried out. The occurring developmental asymmetry was confirmed by empirical data. Prague is the leader outclassing the other regions in economic, social and innovative dimensions. In a country with significant developmental disparities, disadvantaged units are unable to compete and thus become underdeveloped regions. Praguecentrism therefore causes the increase in development divergence in the Czech Republic. This phenomenon will grow, regardless of the regional development policy.

From fork to farm: Impacts of more sustainable diets in the EU‐27 on the agricultural sector

AbstractThe implications of dietary changes for the environment and for human health are well documented, but the impacts on the agricultural sector are less well researched. We fill this gap by specifying scenarios in which European consumers' diets approximate the EAT‐Lancet dietary recommendations to varying degrees and estimate the effects on agricultural production, incomes and emissions using an agro‐economic modelling framework. The combination of different models allows for a detailed assessment of consequences for the agricultural sector from the global through European NUTS2 level to the farm level at different time scales. Shifting European consumption towards the EAT‐Lancet recommendations leads to decreasing production of animal‐based products, while production of fruits and vegetables increases sharply. The results indicate that the agricultural sector could benefit from a dietary shift, though the results are mixed at country, regional and farm levels. In particular, countries and regions that are highly specialised in animal farming are likely to lose income—at least in the short run—while regions with higher shares of vegetable and fruit farms can expect income gains. In Germany, pig and poultry farms may experience losses of up to 34% of their income, whereas farms with a high share of vegetables could gain more than 30% in income. Our results have implications for the policies to assist these extensive structural adjustments in response to widespread dietary changes.

DIFFERENTIATION OF THE SOCIO-ECONOMIC DEVELOPMENT OF THE NUTS-3 SUBREGIONS IN THE COUNTRIES OF CENTRAL AND EASTERN EUROPE

The article focuses on the analysis of the level of the socio-economic development of 239 NUTS-3 level subregions of Central-Eastern Europe based on 31 indicators categorized within the 3 subcomponents (factors) of the regional development: the human capital, the natural environment as well as entrepreneurship and innovativeness. The purpose of the article is to specify the variation in the level of the socio-economic development of the subregions within the arrangement of the NUTS-3 units. The level of the socio-economic development as well as the level of the development of its factors shall be presented based on a synthetic gauge exhibiting the taxonomic distance of a particular subregion from the established pattern of the development. In the study, a hypothesis shall be verified according to which the socio-economic development of Central-Eastern European subregions is highly varied and its highest level is registered in the subregions located around the capitals of the researched states. In the first part of the article an analysis of the theories of the regional development was made with a special consideration of the factors of the socio-economic development. Five stages of the research procedure conducted were then discussed. The results received were comprised within table charts, moreover they were presented in the form of choropleths representing the spatial variation of the level of the socio-economic development of the NUTS-3 unit in Central-Eastern Europe.

Mapping European high-digital intensive sectors—regional growth accelerator for the circular economy

Globalization and the Fourth Industrial Revolution or Industry 4.0 act as shocks on regional labor markets and regional economies. The presence of a digital economy has high spillover effects on regional development, job creation, economic resilience, and sustainability; furthermore, it valuates eco-innovation and the clean economy. We believe that the process of digital transformation has a robust impact on the green and clean aspects of the entire economy. The consistency of high digital-intensive (HDI) sectors can be evaluated through high digital-intensive employment, human resources, and technological infrastructure, as these are the main pillars of digital transformation. The shift-share analysis method (SSM) is used in this study on employment growth during 2008–2018 for the EU27, the United Kingdom, and Norway, combined with a second stage of exploratory spatial data analysis (ESDA). The findings on national growth, industrial mix, and competitiveness are presented in GIS mapping system considering the Local Indicators of Spatial Association (LISA) technique at the NUTS2 level. This approach allows us to determine the clustering level of high digital-intensive employment and sectors, resilience based on connectivity and eco-innovation, and the regional potential of digital transformation. Policymakers and political or governmental decision-makers could consider the results of the present study as the starting point for developing and implementing their policies for a sustainable green regional economy and determine the emerging area patches that need to be stimulated.

Regional Differentiation of GDP at the NUTS-3 Level in Selected European Countries after their Accession to the European Union

The issue of regional development is gaining importance due to the disproportions in its socio-economic aspects. The study aims to identify changes in economic development of selected countries which joined the European Union (EU) in 2004. The study examines small NUTS-3 (Nomenclature of territorial units for statistics) regions, which are territories determined for statistical purpose, that are less often analysed in the literature. Moreover, it focuses on spatial aspects, also considering rarely examined urban-rural typology of regions. The value and dynamics of gross domestic product (GDP) changes were presented using the Eurostat data for 2004–2019 on GDP per capita ratio (PPS) and GDP per capita (in % in relation to the EU-28 average). The analysis uses basic statistical and convergence measures; regional disparities were presented on graphs and maps. It was found that the examined EU countries are internally different in terms of economic development. The growth of GDP per capita was most dynamic in the Baltic States, Slovakia and Poland. The dynamics of GDP per capita in relation to the EU average was higher in regions — regardless of the type — where the value of GDP per capita was lower at the time of accession to the EU. In rural regions, the dynamics of development changes was smaller in relation to other types of regions. Convergence (both beta and sigma) is occurring at a very low level. Further research may focus on the reasons for enclosed disparities and factors of the ongoing changes.

Institutional Settings and Effects on Agricultural Land Conversion: A Global and Spatial Analysis of European Regions

Spatial planning systems and institutions have a significant role in managing non-agricultural land growth in Europe and the assessment of how their implementation impacts on agricultural land consumption is of great significance for policy and institutional improvement. Reducing the area of agricultural land taken for urban development, or eliminating such conversion, is an international policy priority aiming to maintain the amount and quality of land resources currently available for food production and sustainable development. This study aimed to evaluate the impact of land use planning systems and institutional settings on urban conversion of agricultural land in the 265 NUTS2 level EU27 and UK regions. Taking these regions as the unit of our analysis, the research developed and used global and local econometrics models to estimate the effect based on socio-economic, institutional and land use data for the 2000–2018 period. There is limited research focusing on the impacts of institutional settings and planning types of the European countries on the conversion of agricultural land. Furthermore, existing research has not considered the spatial relationships with the determinants of agricultural land conversion and the response variable, therefore, our research aimed to contribute to the literature on the subject. The results showed that the types of spatial planning systems and institution variables significantly impact the conversion of agricultural land to urban uses. Socio-economic indicators and areas of agricultural and urban land have significant impact on agricultural land conversion for any type of spatial planning system. A further result was that decentralization and political fragmentation were positively associated with agricultural land conversion while quality of regional government and governance was negatively associated. A local regression model was assessed to explore the different spatial patterns of the relationships driving agricultural land conversion. The main empirical finding from this model was that there was spatial variation of driving factors of agricultural land conversion in Europe.

Do social capital and the quality of institutions affect waste recycling?

In a social capital framework, the quality of institutions and generalised social trust enhance attitudes towards disposal practices that are less environmentally invasive for the. This study aims to improve our empirical knowledge of this relationship through panel data models to show that waste recycling is affected by social capital and government effectiveness. The study uses Italian annual provincial data (NUTS-3 level) for 2004–2016 period to show that social capital and the quality of institutions are extremely important in determining waste recycling behavior. We find that both social capital and the quality of institutions, which act as ‘substitutes for goods’, increase waste recycling, although with different effects at the territorial level.In the southern and central areas, waste recycling needs to be spurred through policy interventions aimed at improving the quality of institutions, education and trust among citizens, whereas in northern Italy, social capital on its own is sufficient to enhance waste recycling.Hence, our results suggest that policies for waste recycling cannot be uncoupled from the promotion of social cohesion and institutional quality.

Parsimonious stochastic forecasting of international and internal migration on the NUTS-3 level – an outlook of regional depopulation trends in Germany

Substantiated knowledge of future demographic changes that is derived from sound statistical and mathematical methods is a crucial determinant of regional planning. Of the components of demographic developments, migration shapes regional demographics the most over the short term. However, despite its importance, existing approaches model future regional migration based on deterministic assumptions that do not sufficiently account for its highly probabilistic nature. In response to this shortcoming in the literature, our paper uses age- and gender-specific migration data for German NUTS-3 regions over the 1995–2019 period and compares the performance of a variety of forecasting models in backtests. Using the bestperforming model specification and drawing on Monte Carlo simulations, we present a stochastic forecast of regional migration dynamics across German regions until 2040 and analyze their role in regional depopulation. The results provide evidence that well-known age-specific migration patterns across the urban-rural continuum of regions, such as the education-induced migration of young adults, are very likely to persist, and to continue to shape future regional (de)population dynamics.

Measuring Competitiveness at NUTS3 Level and Territorial Partitioning of the Italian Provinces

In this paper we propose a dashboard of indicators of territorial attractiveness at NUTS3 level in the framework of the EU Regional Competitiveness Index (RCI). Then, the Fuzzy C-Medoids Clustering model with multivariate data and contiguity constraints is applied for partitioning the Italian provinces (NUTS3). The novelty is the territorial level analized, 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 clusters obtained with and without contraints are compared. The obtained partition may play an important role in the design of policies at the NUTS3 level, a route already considered by the Italian government. 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).

Regional inequalities as drivers of affective polarization

ABSTRACT This paper investigates divergences in levels of affective polarization across Belgian regions around the 2019 elections. Elaborating on the relative deprivation theory, we analyse the role of current and long-term socio-economic regional inequalities and of geographical distance separating regions. Empirically, we aggregate individual-level measures of affective polarization at the Belgian NUTS-3 level and use a gravity approach to explore the determinants of regional divergences. Our results show that regional variations in affective polarization are best explained by a current rural–urban divide and by how regions have performed economically in the last years. We also show that geographical proximity matters and reinforces the effects of economic deprivation.