Journal of Regional Science最新文献

Intercity travel creates more pronounced interregional linkages and spatial effects than migration. However, few studies have integrated travel into general equilibrium models to assess its welfare and general equilibrium impacts. This study quantifies the impact on regional development of travel by developing a spatial general equilibrium model including trade, migration, and travel to quantify and compare the different effects of these three spatial linkages. We structurally estimate the model using data from 287 prefecture-level cities in China. The counterfactual analysis reveals that travel significantly impacts regional economic outputs, with effects roughly equivalent to those of migration. Additionally, travel demonstrates distinct mechanisms and effects on population distribution compared to the other two linkages. This study provides both theoretical and empirical insights into how various types of spatial linkages—particularly travel—affect interregional economic development. The findings are particularly pertinent for evaluating spatial policies, such as transportation infrastructure improvement and urban cluster development.

The rising prominence of e-commerce has raised concerns regarding its potential threat to local businesses. However, existing literature lacks substantial empirical evidence of the impact of e-retailers due to its ambiguous service areas. Our study aims to address this gap by focusing on e-grocery giants Amazon Fresh and Instacart to examine their influences on local brick-and-mortar businesses within the grocery retail industry. We leverage e-grocery's clearly defined service areas at the zip code level to employ a difference-in-difference approach with longitudinal establishment-level records from the National Establishment Times Series (NETS) database in the greater Atlanta metropolitan area. We find that retail competitors experience an annual decline in sales following Amazon Fresh's entry into the local market. In response, these retailers partially adjust operations, reducing their headcount. Notably, the negative impacts disproportionately affect small businesses, and the effects are spatially widespread, irrespective of proximity to major retail clusters anchored by big-box stores. Over time, the presence and continuous operations of Amazon Fresh that offers both sales and delivery of goods exert a heavier burden on local retail stores. In contrast, we do not find such evidence of disruptive effects for Instacart, which is increasingly becoming a dominant grocery e-commerce model in partnership with local store chains over independent operations.

Much research has highlighted the significance of neighborhood effects on individual-level choices and outcomes. But it has proven difficult to disentangle the influence of those that an individual shares a residential space with from that of other peers, such as work colleagues and family members. Neighbors, work colleagues, and family members constitute different sources of information. The decision to accept or refuse a vaccine is intensely personal and involves the processing of information about phenomena likely to be unfamiliar to most individuals. To examine the information effect of different peer groups we use microlevel data on COVID-19 vaccination in Sweden. We investigate the extent to which an individual's decision not to get vaccinated is influenced by the presence of other unvaccinated individuals in their household, workplace, or residential neighborhood. Our findings reveal that workplace peers tend to be most strongly connected to the decision not to get vaccinated. We also find that the role of neighborhood peers tends to be overestimated when we do not control for peers at home and at work.

Climate change and global warming are severe threats that currently affect the daily lives of the world population. One of the human behaviors that can be most affected by weather conditions is that of personal travel, including commuting, an activity that millions of workers worldwide engage in daily. Within this framework, we analyze the relationships between weather conditions and daily commuting in the US. To that end, we use twenty nationally representative American Time Use Surveys, combined with daily weather data at the county level, spanning the period from 2003 to 2023. The analysis reveals significant relationships between daily weather conditions and commuting mode choices. Specifically, rainy days, high temperatures, and elevated snowfall are positively associated with an increased probability of using cars as the primary commuting mode. In contrast, these weather conditions exhibit a negative relationship with alternative modes of transport, such as public transit or walking. Further findings suggest that these estimates are predominantly driven by days characterized by extremely high temperatures, heavy precipitation, and light snowfall. Finally, our results suggest adaptation to higher temperatures in warmer regions. These results are important for the design of policies aimed at mitigating the mobility consequences of climate change.

Attitudes toward immigrants play a crucial role in voting behavior. Such attitudes are shaped by individual characteristics, but also by the environment. This paper examines the role of regional factors in Germany. We use individual-level survey data and district-level administrative data. Specifically, we examine regional differences in economic growth, voting patterns, and immigrant characteristics and their relation to beliefs about and attitudes toward immigrants. We also use an information experiment about the immigrant population and assess its impact on attitudes. We find that regional effects—over and above individual characteristics—are small and depend on the attitudes.

In Italy, organized crime poses a significant economic challenge as it reduces productivity and contributes to regional inequalities. This study investigates the influence of mafia activity on firm efficiency, with a specific focus on civil engineering companies. A composite indicator of organized crime was developed to measure its presence at the municipal level, and stochastic frontier models were employed to assess firm efficiency and input misallocation across Italian municipalities. We also estimate an Institutional Quality Indicator (IQI) to account for the impact of local institutional effectiveness on firm performance, with the results showing that higher IQI levels are associated with lower inefficiency. The findings indicate that criminal activities significantly hinder efficiency, especially in areas where organized crime is pervasive. These results underscore the urgent need for strategies to shield firms from organized crime, foster growth, and facilitate regional development.

This paper examines the relationship between income inequality and municipalities classified as “inner areas” according to the classification proposed by the National Strategy for Inner Areas (NSIA) from 2012 to 2018. This classification categorizes municipalities into different groups based on their distance from essential service provision centers. By adopting a multilevel approach along with Beta Generalized Linear Mixed Models, we obtain two major results. First, inner areas show a lower income concentration index than non-inner municipalities. Ultra-peripheral municipalities manifest the greatest difference. Second, our findings show that location matters. While the sign is negative in southern and northern Italy, we find the opposite result in the central regions, where the ultra-peripheral municipalities show higher income inequality compared to non-inner areas.