Spatial mismatch for distinct socioeconomic groups in Xiamen, China
Keywords:spatial mismatch, job accessibility, blue-collar, pink-collar, white-collar, Xiamen
Studies have found that spatial mismatch is a universal phenomenon, although both their substantive and methodological focus can differ substantially. In China, there is a growing body of literature on spatial mismatch, but few studies have measured the degree of spatial mismatch between local and migrant workers in different occupations. To fill this gap, this research investigates the spatial mismatch for different socioeconomic groups in Xiamen according to their “hukou” status and occupation. As one of the country’s first four special economic zones, Xiamen achieved housing marketization earlier than most other Chinese cities, attracting a large amount of capital and migrants, and shaping different spatial patterns of local workers and migrant workers. The findings show that blue-collar, pink-collar, and white-collar workers, who are further categorized as either locals or migrants, experience varying degrees of job accessibility and spatial mismatch. In addition, even though migrant workers experience less spatial mismatch, they still have disadvantages in terms of commuting time due to their travel mode. The results presented in this paper are helpful for understanding the spatial mismatch for various social groups and facilitating sustainable mobility and social equity.
Apparicio, P., Abdelmajid, M., Riva, M., & Shearmur, R. (2008). Comparing alternative approaches to measuring the geographical accessibility of urban health services: Distance types and aggregation-error issues. International Journal of Health Geographics, 7(1), 1–14.
Bi, L., Fan, Y., Gao, M., Lee, C. L., & Yin, G. (2019). Spatial mismatch, enclave effects and employment outcomes for rural migrant workers: Empirical evidence from Yunnan Province, China. Habitat International, 86, 48–60.
Chai, Y. (1996). Danwei-based Chinese cities’ internal life-space structure — A case study of Lanzhou City. Geographical Research, 15(1), 1–15 (in Chinese).
Chai, Y., Chen, L., & Zhang, C. (2007). Transformation of danwei system: An angle of view on city changes in China. World Reginal Studies, 16(4), 60–69 (in Chinese).
Chai, Y., Xiao, Z., & Zhang, Y. (2011). Urban organization and planning transformation in China: A danwei perspective. Urban Planning Forum, 198(6), 28–35 (in Chinese).
Duan, C., & Yang, G. (2009). Trends in destination distribution of floating population in China. Population Research, 33(6), 1–12 (in Chinese).
Fan, C. C. (2002). The elite, the natives, and the outsiders: Migration and labor market segmentation in urban China. Annals of the Association of American Geographers, 92(1), 103–124.
Fan, Y., Allen, R., & Sun, T. (2014). Spatial mismatch in Beijing, China: Implications of job accessibility for Chinese low-wage workers. Habitat International, 44, 202–210. https://doi.org/10.1016/j.habitatint.2014.06.002
Geurs, K. T., & Van Wee, B. (2004). Accessibility evaluation of land-use and transport strategies: Review and research directions. Journal of Transport Geography, 12(2), 127–140. https://doi.org/10.1016/j.jtrangeo.2003.10.005
Haddad, M. A. (2020). Residential income segregation and commuting in a Latin American city. Applied Geography, 117, 102186. https://doi.org/10.1016/j.apgeog.2020.102186
Hao, P., Geertman, S., Hooimeijer, P., & Sliuzas, R. (2012). The land-use diversity in urban villages in Shenzhen. Environment and Planning A, 44(11), 2742–2764. https://doi.org/10.1068/a44696
Hao, P., Geertman, S., Hooimeijer, P., & Sliuzas, R. (2013). Spatial analyses of the urban village development process in Shenzhen, China. International Journal of Urban and Regional Research, 37(6), 2177–2197. https://doi.org/10.1111/j.1468-2427.2012.01109.x
Hernandez, D. (2018). Uneven mobilities, uneven opportunities: Social distribution of public transport accessibility to jobs and education in Montevideo. Journal of Transport Geography, 67, 119–125.
Hu, L. (2015). Job accessibility of the poor in Los Angeles: Has suburbanization affected spatial mismatch? Journal of the American Planning Association, 81(1), 30–45. https://doi.org/10.1080/01944363.2015.1042014
Hu, L., Fan, Y., & Sun, T. (2017). Spatial or socioeconomic inequality? Job accessibility changes for low-and high-education population in Beijing, China. Cities, 66, 23–33.
Huang, J., Levinson, D., Wang, J., & Jin, H. (2019). Job-worker spatial dynamics in Beijing: Insights from smart card data. Cities, 86, 83–93.
Huang, J., Levinson, D., Wang, J., Zhou, J., & Wang, Z. J. (2018). Tracking job and housing dynamics with smartcard data. Proceedings of the National Academy of Sciences of the United States of America, 115(50), 12710–12715. https://doi.org/10.1073/pnas.1815928115
Kain, J. F. (1968). Housing segregation, negro employment, and metropolitan decentralization. The Quarterly Journal of Economics, 82(2), 175–197. https://doi.org/10.2307/1885893
Li, L., & Kleiner, B. H. (2001). The legacy of “danwei” and job performance. Management Research News, 24(3/4), 57–66.
Lin, Y., De Meulder, B., Cai, X., Hu, H., & Lai, Y. (2014). Linking social housing provision for rural migrants with the redevelopment of ‘villages in the city’: A case study of Beijing. Cities, 40, 111–119. https://doi.org/10.1016/j.cities.2014.03.011
Liu, C. Y., & Painter, G. (2012). Immigrant settlement and employment suburbanization in the US: Is there a spatial mismatch? Urban Studies, 49(5), 979–1002. https://doi.org/10.1177/0042098011405695
Liu, D., & Kwan, M. P. (2020). Measuring spatial mismatch and job access inequity based on transit-based job accessibility for poor job seekers. Travel Behavior and Society, 19, 184–193. https://doi.org/10.1016/j.tbs.2020.01.005
Liu, T., & Chai, Y. (2015). Daily life circle reconstruction: A scheme for sustainable development in urban China. Habitat International, 50, 250–260. https://doi.org/10.1016/j.habitatint.2015.08.038
Nanjing Municipal Bureau of Statistics. (2020). Nanjing statistical yearbook 2020 (in Chinese). Nanjing, China: Nanjing Municipal Bureau of Statistics.
Ningbo Municipal Bureau of Statistics. (2020). Ningbo statistical yearbook 2020 (in Chinese). Ningbo, China: Nanjing Municipal Bureau of Statistics.
Qi, Y., Fan, Y., Sun, T., & Hu, L. (2018). Decade-long changes in spatial mismatch in Beijing, China: Are disadvantaged populations better or worse off? Environment and Planning A: Economy and Space, 50(4), 848–868. https://doi.org/10.1177/0308518X18755747
Shen, Q. (1998). Location characteristics of inner-city neighborhoods and employment accessibility of low-wage workers. Environment and Planning B: Planning and Design, 25(1), 345–365.
Stoll, M. A. (2006). Job sprawl, spatial mismatch, and black employment disadvantage. Journal of Policy Analysis and Management, 25(4), 827–854. https://doi.org/10.1002/pam.20210
Xiamen Municipal Bureau of Statistics. (2020). Xiamen special economic zone yearbook 2020 (in Chinese). Xiamen, China: Xiamen Municipal Bureau of Statistics.
Xiamen Urban Planning and Design Research Institute. (2015). Xiamen household travel survey in 2015 (in Chinese). Xiamen, China: Xiamen Urban Planning and Design Research Institute.
Zhao, P. (2015). The determinants of the commuting burden of low-income workers: Evidence from Beijing. Environment and Planning A, 47(8), 1736–1755. https://doi.org/10.1177/0308518X15597112
Zhao, P., Lü, B., & De Roo, G. (2011). Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era. Journal of Transport Geography, 19(1), 59–69.
Zhou, S., Liu, Y., & Kwan, M. P. (2016). Spatial mismatch in post-reform urban China: A case study of a relocated state-owned enterprise in Guangzhou. Habitat International, 58, 1–11. https://doi.org/10.1016/j.habitatint.2016.08.003
Zhou, S., Wu, Z., & Cheng, L. (2013). The impact of spatial mismatch on residents in Low-income housing neighborhoods: A study of the Guangzhou Metropolis, China. Urban Studies, 50(9), 1817–1835. https://doi.org/10.1177/0042098012465906
Zhu, P., Zhao, S., Wang, L., & Yammahi, S. A. (2017). Residential segregation and commuting patterns of migrant workers in China. Transportation Research Part D: Transport and Environment, 52, 586–599. https://doi.org/10.1016/j.trd.2016.11.010
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Copyright (c) 2022 Yongling Li, Stan Geertman, Yanliu Lin, Pieter Hooimeijer, Wangtu Xu, Jie Huang
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