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Building on the literature underscoring the importance of lending relationships and the role of banks' structural and organisational features in favouring firms' access to credit, we investigate the effect of branch closures on firms' birth rates in the Italian local credit markets in 2000-2020. Despite the potential relevance, the implications of de-branching activity have received modest attention from scholars thus far. By employing various measures of branch closures and econometric methodologies, we find that the de-branching process negatively affects the establishment of new firms, and this detrimental impact appears stronger as the closures involve older branches. The results also show that our main findings seem ascribable to the closures carried out by large/medium-sized banks. As a policy implication, our work suggests that - although driven by technological transformations and banks' necessity to rationalise costs - authorities should consider the potential negative consequences that bank de-branching may entail for new business creation.

This paper seeks to study work-related and geographical conditions under which innovativeness is stimulated through the analysis of individual and regional data dating from just prior to the smartphone age. As a result, by using the ISSP 2005 Work Orientations Survey, we are able to examine the role of work flexibility, among other work-related conditions, in a relatively more traditional context that mostly excludes modern, smartphone-driven, remote-working practices. Our study confirms that individual freedom in the work place, flexible work hours, job security, living in suburban areas, low stress, private business activity, and the ability to take free time off work are important drivers of innovation. In particular, through a spatial econometric model, we identified an optimum level for weekly work time of about 36 h, which is supported by our findings from tree-based ensemble models. The originality of the present study is particularly due to its examination of innovative output rather than general productivity through the integration of person-level data on individual work conditions, in addition to its novel methodological approach which combines machine learning and spatial econometric findings.

The software for spatial econometrics available in the R system for statistical computing is reviewed. The methods are illustrated in a historical perspective, highlighting the main lines of development and employing historically relevant datasets in the examples. Estimators and tests for spatial cross-sectional and panel models based either on maximum likelihood or on generalized moments methods are presented. The paper is concluded reviewing some current active lines of research in spatial econometric software methods.

This paper aims to reveal the role of entrepreneurial universities in the regional economic development of Türkiye. By using spatial econometrics tools including cluster maps, namely LISA and Geary, and spatial regression models for NUTS 3 regions (81 provinces), the effect of the entrepreneurial universities on regional development indicators, i.e., gross domestic product (GDP), GDP per capita (GDPPC), and the number of enterprises is examined from 2012 to 2017. Both the number of entrepreneurial universities and their presence is effective on all three regional development indicators. Additionally, investing in education, promoting export activities, and developing physical infrastructure should be prioritized in provincial development plans to overcome regional disparities. Este artículo pretende revelar el papel de las universidades emprendedoras en el desarrollo económico regional de Turquía. Mediante el uso de herramientas de econometría espacial, como mapas de clústeres, como LISA y Geary, y modelos de regresión espacial para el nivel NUTS 3 (81 provincias), se examina el efecto de las universidades emprendedoras en los indicadores de desarrollo regional, a saber, el Producto Interno Bruto (PIB), el PIB per cápita (PIBPC) y el número de empresas, entre 2012 y 2017. Tanto el número de universidades emprendedoras como su presencia influyen en los tres indicadores de desarrollo regional. Además, la inversión en educación, la promoción de las actividades exportadoras y el desarrollo de infraestructura física deben priorizarse en los planes de desarrollo provinciales para superar las disparidades regionales.

Online food delivery (OFD) services have developed rapidly. By extending service areas, OFD platforms allow restaurants to reach more customers, shifting the focus from the restaurant’s location to the customer’s location. This study investigates the influence of geographic proximity between customers on repeat purchase behavior at a restaurant chain via an OFD service and examines the moderating role of location-specific characteristics from both the supply and demand sides. We analyze 93,987 purchase records from 26,503 customers over a five-month period, sourced from a restaurant chain partnered with an OFD platform. Using a spatial econometric model, we find that customers who live close to one another exhibit similar repeat purchase behavior, which is consistent with prior research on adoption. We extend prior research by showing that on the supply side, offline competition from food retailers like supermarkets or from physical restaurants weakens the proximity effect. In contrast, online competition from other restaurants that offer home delivery intensifies the proximity effect. On the demand side, business density amplifies the proximity effect. Our findings can help restaurants and OFD platforms better leverage geographic proximity between customers to promote repeat purchases. [Display omitted]

Energy consumption and industrial activities are the primary sources of carbon emissions. As the “world’s factory” and the largest carbon emitter, China has been emphasizing the core role of technological innovation in promoting industrial structure upgrades (ISU) and energy efficiency (EE) to reduce carbon emissions from industrial production and energy consumption. This study investigated the mechanism (through ISU and EE) and spillover effect of technological innovation on carbon emission reduction using the panel dataset of 30 Chinese provinces from 2008 to 2019 and spatial econometrics models. The study concluded that (1) technological innovation had a negative direct effect on provincial carbon emissions, while it also showed a spatial spillover effect on neighboring provinces; (2) technological innovation had an indirect effect on provincial carbon emissions reduction through the mediation of energy efficiency improvement, while the mediation effect of industrial structure upgrading is not yet significant; and (3) the effect of technological innovation on carbon emission reduction showed heterogeneity in the eastern, central, and western regions of China. This study provided empirical and theoretical references to decision-makers in China and other developing countries in promoting technological and carbon control policies. More specifically, direct technology investment and indirect investment in industrial structure upgrades and energy efficiency could help with regional carbon emissions reduction.

The Strategy of Spatial Development of the Russian Federation until 2025 aims at the economic growth acceleration and reduction of the intra-regional socio-economic differences. Therefore, the factors affecting the economic growth of regions, convergence of regions, spillover effects from the neighbouring regions are of importance. Russian regions are very different and do not converge to a unique equilibrium path. 80 Russian regions were divided into the groups of poor, middle and rich regions. Three main hypotheses were considered, based on the differences in the 1) convergence speed, 2) influence of the same factors, 3) different mutual influence of regions. They were tested using a modified spatially autoregressive model for the three groups using the Russian regional data for 2000–2017. Beta-convergence was found only for the middle and rich regions, the rate of convergence was higher in the rich regions. The poor regions did not grow faster than the other regions, confirming the relevance of the Strategy of Spatial Development. The similarities and differences were identified in the factors ensuring the economic growth of regions belonging to the three groups. The growth in all regions is stimulated by the regional economy openness. The growth of rich regions can be achieved by increasing the investment and reducing the investment risk. However, the investments in the poor and middle regions are not effective. The poor and middle regions receive positive spillovers from the growth of the neighbouring regions. It is possible to expect reduced differences in the living standards between the poor and rich regions.

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super efficiency slacks-based measure (SBM) model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: “stationary period”, “recession period” and “growth period”. However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted ‘U’ shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

This paper aims to test the existence of the environmental Kuznets curve (EKC) hypothesis using SO2 measurements in Turkish provinces between 2004 and 2019. The existing studies concerning the EKC hypothesis for Turkey either use a country-level analysis or panel data techniques covering provincial data that do not account for the spatial dimension. To account for the spatial dependence and overcome the biases resulting from the existence of such spatial spillovers, this paper combines the traditional panel data methodology with the recent advances in spatial econometrics. Our findings confirm the presence of a non-linear link between regional economic prospects and environmental degradation. However, unlike the core expectations of the EKC hypothesis, our results demonstrate a U-shaped relationship between economic development and SO2 levels. Moreover, these findings are robust to the inclusion of a spatial battery which highlights the existence of regional spillovers. Overall, our results show that the post-2000 epoch calls for a different action plan to mitigate the rising impact of environmental degradation in Turkey.

Correct understanding of the positive role and mechanism of green finance in promoting ecological environment is an important premise and guarantee for promoting green finance to better serve the improvement of ecological environment. Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2009 to 2017, this paper constructs a spatial Dubin model based on the distance weight matrix and empirically analyzes the impact of green finance on the ecological environment and its spatial spillover effects. The empirical results show that (1) the development of green finance promotes the improvement of the ecological environment in this region and (2) the influence of green finance on the ecological environment has a significant positive spatial spillover effect, that is, the development of green finance in this region will promote the improvement of the ecological environment in the surrounding areas.