REGION

A Systematic Literature Review on the Relations between “Firm-Region Nexus” and Firm Productivity

2025-07-31T13:23:08+00:00

An increasing number of studies focus on the impact of firm location on firm productivity. Here, location refers to localised resources or externalities available to firms. These studies have become possible due to advancements in firm-level data availability and productivity estimation methods. There is a growing need to better identify the effects of localised externalities -- "the firm-region nexus" -- on firms, which are the primary targets of many regional development policies. However, relying solely on administrative regions risks committing the ecological fallacy: attributing to all firms in a region the effects observed for a region as a whole.

This article presents the state of the art as of 2025, highlighting key issues in this area of research. A total of 165 articles were selected, mainly published in the past decade. From a methodological perspective, there is no real consensus on the models utilised, whether to estimate productivity or to investigate the relationships between the "firm-region nexus" and firm productivity. Spatial integration is still not adequately taken into account: (a) Total Factor Productivity estimations do not account for spatial dependence among methodological concerns; (b) 70% of the articles do not discuss methodological issues linked to the use of firm locations. A deeper grasp of firm location, particularly the "head office bias", emerges as critical to improving the robustness of analyses.

Quantifying the "firm-region nexus" remains heterogeneous across national and local contexts (diverging points of interest, data availability, etc.). Comparing the effects of various types of externalities across countries therefore appears ambitious. Some articles focus on the effect of one category of localised externalities, aiming not only to identify a relationship but also its type: spatial or temporal effect, linear or non-linear relationships, and threshold effects.

Regional Disparities in Italy: Developing a Composite Indicator of Physical Activity, Health, and Mobility

2026-01-16T12:25:38+00:00

Quality of Life in urban settings is intricately tied to active transportation, public health, and physical activity, capturing the growing societal emphasis on sustainable mobility and well-being. This study develops a composite indicator to evaluate regional Quality of Life across Italy, utilizing data from the 2021 ISTAT (Italian National Institute of Statistics) survey “Aspects of Daily Life.” Employing Principal Component Analysis for weighting, the indicator identifies key dimensions of urban well-being, including active transportation, physical activity, and general health. The results reveal pronounced disparities between central-northern and southern Italy, driven by differences in infrastructure, access to health resources, and socio-economic conditions. Northern regions consistently outperform southern counterparts, where structural challenges hinder improvements in Quality of Life. These findings underscore the urgent need for targeted, equity-focused policies to address regional disparities and enhance urban environments, with a particular focus on improving accessibility and public health in underperforming regions. The study provides valuable insights for policymakers aiming to promote sustainable and inclusive urban development.

Street View Imagery Analysis in a Computational Notebook

2025-02-06T06:39:16+00:00

Street view imagery, capturing detailed streetscapes at human eye-level, has received significant attention in the past decade. Street view images can be leveraged to observe the built environment from both element- and scene-levels. This chapter provides an introduction to methodologies for street view image-based analytics, including downloading street view images using Google Places API, and employing advanced computer vision techniques such as Deep Convolutional Neural Networks to detect and quantify urban elements and scenes. Particularly, this chapter introduces the use of image semantic segmentation to identify distinct urban elements, and image classification techniques to categorize and predict urban scene types. Further, an example of the calculation of the Green View Index (GVI) is provided to demonstrate how street view imagery analysis could contribute to urban data analytics. Through these methods, street view imagery not only helps model the digital environment, enhances our understanding of urban environments, but also offers a variety of insights into geography and urban studies.