The causal relationship between living in a peripheral area and real estate investment decisions is currently unexplored in empirical literature. Using survey data on 2711 Italian respondents collected between 2022 and 2023, we show that living in a peripheral area has a negative impact on the...

Environmental policies such as the European Union Emissions Trading System (EU ETS) raise concerns about their impact on local employment and competitiveness. Yet, existing EU ETS studies focus on firm-level outcomes during the initial phases of the program. We construct a panel dataset of about...

We study the regional distribution of primary care physicians in Germany to learn about the extent and possible reasons of geographic maldistribution. For this aim, we apply a greedy capacitated algorithm on very fine spatial data. We compare this reference allocation of primary care physicians...

This paper provides quantitative evidence on the geography of regional readiness to tackle climate change using data from France, Germany, Italy, Poland, and Spain. Following Cappellano et al. (2022), we estimate a composite indicator that reports the situation of regions in these countries...

Environmental policies such as the European Union Emissions Trading System (EU ETS) raise concerns about their impact on local employment and competitiveness. Yet, existing EU ETS studies focus on firm-level outcomes during the initial phases of the program. We construct a panel dataset of about...

This paper investigates the role of banking networks in the transmission of shocks across borders. Combining banking deregulation in the US with state-level idiosyncratic demand shocks, we show that geographically diversified banks reallocate funds from economies experiencing negative shocks to...

As China advances toward its "Dual Carbon" goals, clarifying the role of the digital economy (DE) in reducing urban carbon emissions is of growing importance. This study uses panel data from 274 Chinese prefecture-level cities (2011-2022) and applies benchmark regression, the Spatial Durbin...

Concerns have been raised regarding the equity and efficiency of Chinese healthcare systems, where improved transportation is expected to be a significant factor. The positive externalities of transport have been confirmed in many domains, but there is a lack of evidence in the field of...

We investigate how strongly the local environment beyond the family can contribute to understanding the formation of children's economic preferences. Building on precise geolocation data for around 6000 children, we use fixed effects, spatial autoregressive models and Kriging to capture the...

Submarine cables enable international connectivity and are essential for high- speed internet access. This paper examines their impact on internet access price, focusing on price reductions driven by cost savings and competition intensity. Using a dataset of submarine cable capacity and internet...

Spatial econometrics deals with econometric modeling in the presence of spatial dependence and heterogeneity, where observations correspond to specific spatial units such as points or regions. Traditional estimation techniques assume independent observations and are inadequate when spatial...

This study applies a heteroscedastic spatial Durbin panel data model to investigate how sociodemographic and socioeconomic factors influence regional commuter mobility in the Greater Stockholm Area. Commuter mobility, defined as the flow of people to and from workplaces across regions and over...

This study examines regional income and inequality in the People's Republic of China from 2001 to 2020 using high-quality nighttime light (NTL) data to help proxy for the gross domestic product (GDP). The NTL-based dataset for 332 Chinese prefectural cities and 2,813 county-level units that...

Purpose This research investigates the effect of accessibility to points of tourist interest (buffer) and direct and indirect spatial spillover effects of agglomeration economies on tourism industry revenues in Spain. Design/methodology/approach Data were collected from the Bureau van Dijk's...

Recent studies harnessing geospatial big data and machine learning have significantly advanced poverty mapping, enabling granular and timely welfare estimates in traditionally datascarce regions. While much of the existing research has focused on overall out-of-sample predictive performance,...

This paper explores the concepts and computational methods used to measure spatial inequality, emphasizing a reproducible approach that social scientists can apply to their research. The analysis focuses on geographic income disparities at the sub-national level, using Mexico as a case study. By...

The impact of current changes in technology, society, and the environment demand innovative research approaches that can accurately capture the increasing complexity of modern geographic data. To this end, this article provides an introduction to the use and implementation of spatial network...

This paper builds a spatial model of trade with supply-chain links to examine the effect of economic links and anti-COVID policies on the spread of the COVID-19 pandemic during the first wave across NUTS2 regions of the European Union (EU) and the UK. We find that the effort to reduce infection...

This study evaluates the potential for Transit-Oriented Development (TOD) in Khartoum, Sudan, a rapidly urbanizing city facing critical transportation challenges. By integrating Geographic Information Systems (GIS) and Spatial Multi-Criteria Analysis (SMCA), we analyze bus terminals' spatial...

In the recent past, a growing number of voices within the spatial sciences have called for a departure from traditional economic considerations related to the current market and growth paradigm. Instead, they favour a post-growth economy approach. This can convey the impression that there is an...

We introduce a new time-varying parameter spatial matrix autoregressive model that integrates matrix-valued time series, heterogeneous spillover effects, outlier robustness, and time-varying parameters in one unified framework. The model allows for separate dynamic spatial spillover effects...

We investigate how strongly the local environment beyond the family can contribute to understanding the formation of children's economic preferences. Building on precise geolocation data for around 6000 children, we use fixed effects, spatial autoregressive models and Kriging to capture the...