Yuliya Rodionova (p), Aksel Sundström, Natalia Alvarado Pachon, Rafael Lopez, Cem Mert Dalli, Victor Saidi Phiri (p)

Alexandros Alexiou

This article presents the Local Quality of Government Dataset. We contribute both conceptually – by developing previous approaches to mapping how geographies of local units evolve – and empirically, by displaying how fine-grained indicators map on to these units. First, we carefully trace and document how the borders of all municipal units in Europe changed over the years 2015-2025. This allows us, for the first time, to create a data-structure for fine-grained statistics that meet the pressing need for nuanced subnational measures. Second, we demonstrate how we can map several forms of data on to this structure. We illustrate this through three types of data: (a) geo-coded data from several sources, (b) official statistics on election outcomes and (c) data on corruption risks, as gauged through harmonized figures on public procurement. We will release our dataset to the public through an online portal free of charge. In sum, we show how this dataset will advance the opportunities for those wishing to capture subnational phenomena in Europe over the past decade.

Alexandros Alexiou (p), Angelos Mimis

Jean-Eudes Beuret

Local government in England provides a wide range of services, including housing, transport, social care, public health, cultural, planning and environmental services. All of these can have a wide impact on local communities through multiple pathways. Local government spending accounts for over £100 billion (2020-21), almost a quarter of all public spending. A significant part of these funds comes from central government.
However, austerity policies implemented during the past decade have forced Local Authorities to make difficult decisions on how to deliver essential public services without the appropriate fiscal support. The overall reduction in central government funding (core spending power) was approximately 50% between 2010-11 and 2020-21, equivalent to £18 billion. Another implication regards the impact of funding cuts on inequalities. Previous research suggests that cuts were larger in more deprived areas, despite a likely higher demand for services there. Furthermore, poorer areas are more reliant on central funding, as they are less able to raise income through local taxation.
Facing such difficulties, municipal authorities in England adopted different strategies to respond to these cuts; we hypothesise that these are reflected by how authorities chose to spend their available budgets on various categories of services. In this novel analysis we aim to use longitudinal area-level data to explore local spending patterns. We compiled data on all 12 spending lines between the years 2007-08 to 2020-21, for all 314 Local Authorities in England using the Local Authority Revenue Expenditure and Financing data which are publicly available. We carry out a multivariate time series clustering using per-capita spending measures. We follow a similar approach to a geodemographic analysis, where we describe the resulting clusters and map the geography of local government spending trends across England. We use the 2019 Index of Multiple Deprivation to explore the relationships between trends in spending and deprivation patterns.
Preliminary results show that cuts were not evenly distributed among service areas. Local government has statutory responsibilities to provide some services –such as social care– and as such they have maintained the level of service in these, whilst cutting discretionary services, such as transport, housing, cultural and planning services. There is significant correlation with deprivation patterns, as more deprived areas tend to cut more on discretionary services than more affluent ones. Some geographical clustering also occurs at Regional level, perhaps reflecting differences in underlying macroeconomic and social conditions; while London Local Authorities form their own cluster.

Jean-Eudes Beuret (p), Adeline Bas, Annaig Oiry

Olga Demidova

In France, the development of offshore wind farms is encountering difficulties of social acceptance, with varied, conflicting and/or cooperative interactions with professional fishing. What are the determinants of the trajectories and levels of acceptance of offshore wind farms by professional fishermen? What are the consequences of these levels and of collaborative and/or conflictual trajectories? To answer these questions, a comparative analysis was carried out on six wind farm projects as case studies on the Atlantic and Channel-North Sea coasts: Courseulles sur Mer, Dieppe Le Tréport, Fécamp, Groix Belle-Île, Saint-Brieuc, Saint-Nazaire. As a result, we identified and prioritized key variables in the acceptance of the project by professional fishermen: the identity of the area (especially the place of fishing in this local identity), the local dynamics of resources and fishing effort, path dependency with regard to previous projects and/or consultations, proximity between representatives and those represented, the quality of dialogue and the direct link between the developer and the fishermen, technological choices, and the management of unforeseen events and trust during the construction phase. We then analyzed the levels and trajectories of acceptance. We observe several ideal types. These are linear trajectories, with either continuous rejection, or continuous acceptance but subject to conditions and pressure. These are breaking trajectories: a turning point marks the transition from cooperation to conflict. The shape of the trajectories and the determinants of turning points or inflections were analyzed. This leads to operational deductions (prevention and treatment of conflictuality) and then theoretical ones: the place based strategies to be developed, and then the dynamic nature of the levels of acceptance and their effects are discussed.

Olga Demidova (p), Diana Turchak

Yuliya Rodionova

The study was prepared within the framework of the project "Mirror Laboratories of the National Research University Higher School of Economics"

Russia is a very large and very heterogeneous country in socio-economic terms, including a large number of regions, municipalities, cities, etc., actively influencing each other. Therefore, when modeling socio-economic processes occurring in Russia using regional or municipal data, the following problems arise: 1) the sample used is not homogeneous, observations for regions or municipalities cannot be considered independent, therefore linear regression models with constant coefficients for factors included in the models and a scalar matrix for errors are not suitable, 2) if the mutual influence of regions is not taken into account, a problem of bias in the estimates of coefficients may arise due to the omission of an essential variable.
To solve these problems, many articles use spatial econometric models or, much less frequently, geographically weighted regressions. We demonstrate the advantages of using geographically weighted regression identifying the factors that influence the share of votes cast for candidates from the ruling United Russia party, several opposition parties, and for independent candidates in the 2021–2022 municipal elections. Three key research hypothe-ses were examined: 1) the presence of spatial autocorrelation in voting data; 2) the influence of economic variables on electoral results; 3) the spatial variability of these economic effects. These hypotheses were tested using linear regression models and geographically weighted regressions on data for 2272 Russian municipalities and received empirical confirmation. The higher the budget surplus, the higher the share of votes for United Russia repre-sentatives. The better developed small and medium businesses are in a municipality, the lower the share of voters supporting United Russia, and the higher the share of voters sup-porting independent candidates and opposition parties. The models of the geographically weighted regression (GWR) allow us to obtain much more detailed results. Some factors, the coefficients of which were insignificant in linear models, turned out to be significant for some municipalities in the GWR models. The results of this study may be useful for policy-makers and other stakeholders interested in promoting a fairer electoral process.