Cross-border Commuting Dynamics: Patterns and Driving Forces in the Alpine Macro-region

ABSTRACT

Cross-border commuting is a spatial phenomenon of rising importance throughout Europe. As one of the most concrete aspects of European integration, it facilitates the use of comparative advantages to live and work on different sides of national borders. But despite a general political appreciation of cross-border integration, neither the statistical knowledge base nor its political implications are very high on the agenda. We explore the cross-border commuting dynamics of the Alpine region on a transnational scale, where seven countries meet and cross-border commuting is a relevant pattern posing daily challenges. Against this background, the paper aims to identify the key drivers and explanatory factors of cross-border commuting. In particular, we explore the role of labor market differences, urbanization, and metropolitan quality as well as the distance to the border. Our investigation is based on regional statistical data mobilized in the context of the Alpine Region Preparatory Action Fund (ARPAF) project on cross-border mobility.

KEYWORDS:

• Alpine region
• cross-border commuting
• spatial integration
• macro-region

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1 https://www.alpine-region.eu/projects/arpaf-cross-border.

2 The typology identifies areas of rural population by analyzing 1 km² grid cells. Cells with a population density > 300 inhabitants/km² and a minimum population of 5,000 inhabitants are classified as urban, while all other cells are rural. In a second step, the percentage of rural grid cells is decisive for the allocation to the categories rural (more than 50% of population), intermediate (20%–50%) and urban (less than 20%). This allocation is adjusted with the presence of cities. The classification of a region as remote is based on the accessibility of city centers (less than half of the inhabitants can reach a city of more than 50,000 inhabitants within 45 min).

3 Unemployment rates 2015 on NUTS2 level in case study region: Basel 2,5% (DE13) / 9,3% (FRF1) / 4,3% (CH03); Brig 10,3% (ITC1) / 7,5% (CH01); Geneva 9,1% (FRK2) / 7,5% (CH01); Jura 9,2% (FRC2) / 7,5% (CH01) / 4,4% (CH02); Kufstein-Rosenheim 3,0% (AT33) / 2,7% (DE21); Lake Constance 3,5% (AT34) / 2,5% (DE13) / 3,0% (DE14) / 3,0% (DE27) / 4,1% (CH04) / 4,0% (CH05); Salzburg 2,7% (DE21) / 3,5% (AT32); Styria 10,3% (SI03) / 4,7% (AT22); Terra Raetica 3,0% (AT33) / 3,8% (ITH1) / 4,0% (CH05); Ticino 7,9% (ITC4) / 6,9% (CH07); Trieste 7,5% (SI04) / 8,0% (ITH4).