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Industrial diversification is widely acknowledged for its capacity to sustain long-term economic growth by compensating for the decline of traditional industries (Frenken & Boschma, 2007). While existing research in evolutionary economic geography primarily highlights the positive impacts of industrial diversification—such as economic growth, enhanced employment opportunities, and technological advancements—its potential adverse effects on urban dynamics and social well-being remain underexplored (Pinheiro et al., 2022). This study addresses this gap by investigating the dark side of industrial diversification, specifically its impact on commuting patterns in Shanghai, one of China's most economically dynamic megacities.
Utilizing multi-source microdata on firm registrations and human mobility, this research examines 164 subdistricts within Shanghai’s suburban ring. Employing a relatedness and complexity framework rooted in evolutionary economic geography (Balland et al., 2019), the study analyzes how intra-urban industrial diversification influences both the duration and distance of commutes. Industrial diversification is measured using two primary indicators: Entry Relatedness Density (ERD), which quantifies the extent to which new industries are related to existing ones, and Standardized Complexity Change (SCC), which captures shifts in industrial complexity over time. Commuting patterns are assessed through changes in average commuting distance and duration, derived from mobile phone signaling data and estimated using the Baidu Maps API.
The empirical analysis employs Ordinary Least Squares (OLS) regression models and spatial regression techniques to explore the relationship between industrial diversification and commuting patterns. Findings reveal that industrial diversification unrelated to a subdistrict’s existing industrial base would significantly increase both commuting duration and distance. Furthermore, diversification into more complex industries exacerbates these commuting burdens, suggesting that new industries requiring specialized skills attract workers from outside the local labor pool. This likely intensifies job-housing mismatches and contributes to longer commutes. Conversely, related diversification into less complex industries is associated with shorter commuting distances and durations, as these industries are more likely to employ the existing local workforce, thereby facilitating easier access to jobs.
A Bivariate Local Indicator of Spatial Association (LISA) analysis further uncovers spatial heterogeneities in the impact of industrial diversification on commuting patterns. This supplementary analysis highlights the role of housing supply gradients and price differentials in shaping residents' job-housing trade-offs, providing a deeper understanding of how industrial dynamics influence commuting behaviors across different urban areas.
These results underscore the dual nature of industrial diversification: while it fosters economic dynamism and industrial upgrading, it also introduces substantial commuting challenges. Although high-skilled workers typically enjoy higher incomes and greater mobility, allowing them to tolerate elevated commuting costs and prefer trading higher commuting burdens for better living quality (Zhu et al., 2024), the inevitable time consumption associated with long-distance commutes can negatively affect residents' quality of life due to time scarcity (Lorenz, 2018). Recognizing these challenges, it is essential for urban planning strategies to proactively address the spatial impacts of industrial diversification. By integrating considerations such as housing accessibility, public transportation efficiency, and balanced job-housing distribution, planning efforts can mitigate the commuting burdens associated with economic transformation, ensuring that the benefits of industrial upgrading do not come at the expense of workers' well-being.
This research contributes to the broader understanding of the socio-economic impacts of industrial diversification by highlighting its complex relationship with urban mobility patterns. However, the focus on Shanghai limits the generalizability of the findings, suggesting the need for further studies across diverse urban contexts. Future research should employ more sophisticated econometric models to elucidate the underlying mechanisms and explore additional dimensions of human behavior affected by industrial dynamics, such as physical and mental health, consumer preferences, and lifestyle changes.
References
Balland, P. A., Boschma, R., Crespo, J., & Rigby, D. L. (2019). Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification. Regional Studies, 53(9), 1252-1268.
Frenken, K., & Boschma, R. A. (2007). A theoretical framework for evolutionary economic geography: industrial dynamics and urban growth as a branching process. Journal of Economic Geography, 7(5), 635-649.
Lorenz, O. (2018). Does commuting matter to subjective well-being? Journal of Transport Geography, 66, 180-199.
Pinheiro, F. L., Balland, P. A., Boschma, R., & Hartmann, D. (2022). The Dark Side of the Geography of Innovation. Relatedness, Complexity, and Regional Inequality in Europe.
Zhu, W., Wang, J., & Liu, T. (2024). Trade time for space: Does living space moderate the relationship between commuting duration and mental health in Beijing? Journal of Transport Geography, 121, 104017.
| Keywords | Industrial Diversification; Commuting Patterns; Urban Mobility; Evolutionary Economic Geography; Shanghai |
|---|---|
| Best Congress Paper Award | Yes |
