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Earnings inequality in Germany has increased dramatically. Measuring inequality locally at the level of cities annually since 1985, we find that behind this development is the rapidly worsening inequality in the largest cities, driven by increasing earnings polarisation. In the cross-section, local earnings inequality rises substantially in city size, and this city-size inequality penalty has increased steadily since 1985, reaching an elasticity of .2 in 2010. Inequality decompositions reveal that overall earnings inequality is almost fully explained by the within-locations component, which in turn is driven by the largest cities. The worsening inequality in the largest cities is amplified by their greater population weight. Examining the local earnings distributions directly reveals that this is due to increasing earnings polarisation that is strongest in the largest places. Both upper and lower distributional tails become heavier over time, and are the heaviest in the largest cities. We establish these results using a large and spatially representative administrative data set, and address the top-coding problem in these data using a parametric distribution approach that outperforms standard imputations.

Shanghai has experienced a rapid process of urbanization and urban expansion, which increases travel costs and limits job accessibility for the economically disadvantaged population. This paper investigates the jobs-housing imbalance problem in Shanghai at the subdistrict-level (census-level) and reaches the following conclusions. First, the jobshousing imbalance shows a ring pattern and is evident mainly in the suburban areas and periphery of the Shanghai metropolitan area because job opportunities are highly concentrated while residential areas are sprawling. Second, structural factors such as high housing prices and sprawling development significantly contribute to the jobs-housing imbalance. Third, regional planning policies such as development zones contribute to jobs-housing imbalance due to the specialized industrial structure and limited availability of housing. However, geographically weighted regression reveals the development zones in the traditional Pudong district are exceptional insofar as government policy has created spatial heterogeneity there. In addition, the multilevel model used in this study suggests regions with jobs-housing imbalance usually have well-connected streets, and this represents the local government’s efforts to reduce excessive commuting times created by jobs-housing imbalance.

The lower digitization among seniors must be understood in the context of the coming together of multiple digital divides. In addition to the obvious generation divide (age is one of the factors most determining digital uses), others also have an influence, such as a lower education or income level, which is characteristic of this group and also strongly correlated with lower use of new technologies. We also find gender differences in the digital uses of seniors (more pronounced than in the population as a whole) and a significant geospatial inequality in several variables. The latter is important due to both the rapid aging of the rural population, greater than that seen in the urban population, and the fact that the geographical areas with a lower income level, where the aging population tends to be concentrated to a greater extent, are also the areas where digitization reaches the least, in terms of both infrastructures and uses. This article addresses the multiconfluence of the aforementioned “digital divides in older people” (or “seniors”), trying to determine the effects and degree of importance of each, identify the main groups at risk of digital exclusion, and to characterize the technological uses of seniors and their main segments. To do this, we have used the microdata from the “Survey on Equipment and Use of Information and Communication Technologies in homes”, produced by the Spanish Statistical Office (INE) for the year 2020.

Purpose>Taking as a case study the city of San Carlos de Bariloche – in northern Patagonia, Argentina – this paper aims to compare its urban structure with previous urbanization models and identify some characteristics of this tourist city that could inspire the construction of an adapted urban model for Latin American tourist cities, particularly those based on natural attractions.Design/methodology/approach>Based on multivariate analysis of population census data and local economic statistics, this paper compares the residential location of different social groups and the location of main economic activities in Bariloche. First, principal component analysis (PCA) is combined with cluster analysis to classify Bariloche’s neighborhoods. Second, different maps are analyzed to study the location of economic activities, in comparison with previous clusters.Findings>The results of this paper show that Bariloche partially adjusts to previous urbanization models, as the landscape and physical environment determine the characteristics of its urban growth, as well as the development of tourist activities. Therefore, this paper then proposes an adapted urban model for the case of Bariloche, which could be also contrasted with other Latin American tourist cities in the future.Originality/value>Bearing in mind that there is no model of Latin American tourist cities so far, this paper tries to analyze to what extent the assumptions and patterns of previous urban models could be adapted to Latin American tourist cities, such as Bariloche, which base their attractiveness and economic dynamism on its natural physical environment.

The 1979 election heralded a political sea change in the UK, as Thatcherite Two Nation' politics regarded inequality as evidence of a vibrant capitalism. As a result, inter-regional inequalities were exacerbated as the effects of deindustrialisation, capacity reductions and job losses fell most heavily in the 'North'. The lifting of restrictions on capital export precipitated the overseas relocation of private sector manufacturing, with job losses concentrated in the 'North'. Cuts to nationalised industries were also concentrated in the 'North'. Meanwhile the 'South' benefited because of the primacy given to banking and financial services in economic policy priorities and the spatially selective concentration of Government spending on R&D and infrastructure. However, there were also widening intraregional inequalities within both 'North' and 'South', not least because of housing policies. Crucially, post-Thatcher the neoliberal emphases on private sector growth and commodification were adopted by New Labour and Conservative–Liberal coalition governments so that socio-spatial inequality became more deeply embedded in a Divided Kingdom.

The steep rise in U.S. criminal punishment in recent decades has spurred scholarship on the collateral consequences of imprisonment for individuals, families, and communities. Several excellent studies have estimated the number of people who have been incarcerated and the collateral consequences they face, but far less is known about the size and scope of the total U.S. population with felony convictions beyond prison walls, including those who serve their sentences on probation or in jail. This article develops state-level estimates based on demographic life tables and extends previous national estimates of the number of people with felony convictions to 2010. We estimate that 3 % of the total U.S. adult population and 15 % of the African American adult male population has ever been to prison; people with felony convictions account for 8 % of all adults and 33 % of the African American adult male population. We discuss the far-reaching consequences of the spatial concentration and immense growth of these groups since 1980.

•COVID-19 vaccination coverage was modelled using vaccination data from the Kenya Ministry of Health.•The average travel time to a designated COVID-19 vaccination site was a key predictor of COVID-19 vaccination coverage.•Bayesian modelling suggests inequalities in population vaccination coverage for COVID-19 at the sub-national level in Kenya.•Vaccination coverage mapping can be a useful tool for targeting interventions. COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 – 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 – 16.74) – 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 – 36.96) – 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.

Objective: The present work aims to analyze the territorial inequalities of public policies for access to water through the territorial approach.   Theoretical framework: This study is conducted based on theoretical reflections about geographic knowledge about space and territory, the latter as primordial in the analysis of power relations in the management, access and use of water. The theoretical framework uses the contributions of some of the authors who discuss the territory, among them: Haesbaert (2004), Folcault (1989) Saquet (2010) and Araújo (2000).   Method: It is based on the use of data collection with mapping and theoretical discussion. It seeks to analyze the inequalities and diversities of territorialities in the territory(ies), inquiries are made about territorial development through public policies mediated with the contributions of geographic science, data collection on the construction of cisterns is used in the northeast of Brazil, as well as the mapping of areas most affected by droughts in the northeast.   Results and conclusion: As a result, the theoretical analysis of approaches to territorial development and public policies allied to geographic knowledge permeates the production of space and the phenomena caused in rural areas.   Research implications: We understand that the territory is historically built by power relations, the territorial approach to development arises from the proposition of working on intra-regional inequalities with a view to generating social participation and, in parallel, governance of social subjects with power public.   Originality/value: This research has social, economic and scientific value due to the originality of the discussions presented.

Several recent studies have concluded that residential segregation by income in the United States has increased in the decades since 1970, including a significant increase after 2000. Income segregation measures, however, are biased upward when based on sample data. This is a potential concern because the sampling rate of the American Community Survey (ACS)—from which post-2000 income segregation estimates are constructed—was lower than that of the earlier decennial censuses. Thus, the apparent increase in income segregation post-2000 may simply reflect larger upward bias in the estimates from the ACS, and the estimated trend may therefore be inaccurate. In this study, we first derive formulas describing the approximate sampling bias in two measures of segregation. Next, using Monte Carlo simulations, we show that the bias-corrected estimators eliminate virtually all of the bias in segregation estimates in most cases of practical interest, although the correction fails to eliminate bias in some cases when the population is unevenly distributed among geographic units and the average within-unit samples are very small. We then use the bias-corrected estimators to produce unbiased estimates of the trends in income segregation over the last four decades in large U.S. metropolitan areas. Using these corrected estimates, we replicate the central analyses in four prior studies on income segregation. We find that the primary conclusions from these studies remain unchanged, although the true increase in income segregation among families after 2000 was only half as large as that reported in earlier work. Despite this revision, our replications confirm that income segregation has increased sharply in recent decades among families with children and that income inequality is a strong and consistent predictor of income segregation.