A network-based explanation of inequality perceptions

Across income groups and countries, individual citizens perceive economic inequality spectacularly wrong. These misperceptions have far-reaching consequences, as it might be perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The generating mechanism can replicate all of aforementioned stylised facts simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption and voting behaviour

A network-based explanation of inequality perceptions

Across income groups and countries, individual citizens perceive economic inequality spectacularly wrong. These misperceptions have far-reaching consequences, as it might be perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The generating mechanism can replicate all of aforementioned stylised facts simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption and voting behaviour

The evolution of coworking spaces in Milan and Prague: Spatial patterns, diffusion, and urban change

During the last two decades, the advanced economies' labor market has changed—the increased use of short-term contracts and higher flexibility in terms of working spaces and work organization. Due to ongoing processes of globalization and the industry 4.0 revolution, distance, location, and time are often no longer considered necessary conditions to do business (McCann 2008; Anderson 2012). In this context, we have witnessed the development and diffusion of coworking spaces. Under the framework of the COST Action CA18214 “The Geography of New Working Spaces and the Impact on the Periphery,” this chapter investigates and compares the development, typology, and dynamics of the spatial distribution of coworking spaces in two Alpha global cities, Prague and Milan. Using two original geo-referenced databases, the chapter firstly proposes time-space quantitative mapping of coworking spaces within Basic Settlement Units in Prague and Local Identity Units in Milan. Local spatial autocorrelation statistics are used to identify spatial clusters in given years. Local Spatio-temporal autocorrelation statistics are applied to identify whether changes in spatial patterns over time are spatially clustered. Based on these findings, the chapter highlights similarities and differences in spatial patterns, spatial diffusion, and evolution of coworking spaces in the two cities Milan. Secondly, the chapter discusses the micro-location of coworking spaces in relation to the internal urban spatial structure and its transformation (urban core commercialization, inner-city urban regeneration, and gentrification) and thereby the transition to the polycentric city model