Journal of Transport and Land Use

Employment concentration, dispersion, and the changing commute in the San Francisco Bay Area

Evelyn Blumenberg

University of California, Los Angeles

https://orcid.org/0000-0001-6767-2686

Samuel Speroni

Institute of Transportation Studies, University of California, Los Angeles

https://orcid.org/0000-0003-4364-6162

DOI: https://doi.org/10.5198/jtlu.2024.2456

Keywords: commute distance, employment clusters, housing, regional planning, San Francisco Bay Area

---

Abstract

In the first decade and a half of the twenty-first century, the San Francisco Bay Area experienced rapid job growth (17% from 2002 to 2015). Employment growth greatly exceeded housing production, resulting in rising housing prices. The mismatch between jobs and housing potentially contributed to an increase in commute distance, as workers relocated to outlying neighborhoods in search of affordable housing. In this paper, the authors analyze changes in commute distance over time, with a focus on the spatial location of employment and, in particular, downtown job growth. They find that commute distance increased slightly between 2002 and 2015 throughout the Bay Area (from 17.2 to 17.8 mi.), with the greatest increase among workers in job centers located in outlying parts of the region (from 19.1 to 20.8 mi.). Increases in census tract jobs was by far the strongest predictor of commute distance increase, though this overall relationship in the region was likely moderated by the increase in employment in downtown San Francisco (44%) where, all else being equal, workers travel shorter distances (14.4 mi. in 2002 and 15.4 mi. in 2015) relative to other workers. This relationship may be due to the demographic composition of San Francisco residents: high-wage, young, single workers who are able to afford high-priced housing close to downtown. A better balance between jobs and housing would allow workers the option of self-selecting into neighborhoods closer to their jobs, underscoring the importance of policies to spur housing production in high-cost metropolitan areas.

---

References

Alonso, W. (1964). Location and land use. In Location and land use. Cambridge, MA: Harvard University Press. https://www.degruyter.com/document/doi/10.4159/harvard.9780674730854/html

Angel, S., & Blei, A. M. (2016). The spatial structure of American cities: The great majority of workplaces are no longer in CBDs, employment sub-centers, or live-work communities. Cities, 51, 21–35. https://doi.org/10.1016/j.cities.2015.11.031

Arribas-Bel, D., & Sanz-Gracia, F. (2014). The validity of the monocentric city model in a polycentric age: US metropolitan areas in 1990, 2000 and 2010. Urban Geography, 35(7), 980–997. https://doi.org/10.1080/02723638.2014.940693

Barrera, J. (2016, April 6). In search of cheaper housing, Silicon Valley workers face long commutes. San Mateo, CA: Peninsula Press. http://peninsulapress.com/2016/04/06/silicon-valley-long-commutes-cost-of-living/

Benner, C., & Karner, A. (2016). Low-wage jobs-housing fit: Identifying locations of affordable housing shortages. Urban Geography, 37(6), 883–903. https://doi.org/10.1080/02723638.2015.1112565

Berry, B. J. L., & Kim, H.-M. (1993). Challenges to the monocentric model. Geographical Analysis, 25(1), 1–4. https://doi.org/10.1111/j.1538-4632.1993.tb00275.x

Bloom, N., Han, R., & Liang, J. (2022). How hybrid working from home works out (Working Paper 30292). Cambridge, MA: National Bureau of Economic Research. https://doi.org/10.3386/w30292

Blumenberg, E., & Wander, M. (2022). Housing affordability and commute distance. Urban Geography, 44(7), 1454–1473. https://doi.org/10.1080/02723638.2022.2087319

Britschgi, C. (2021). The number of “super commuters” explodes in America’s housing-starved metros. Reason. https://reason.com/2021/08/27/the-number-of-super-commuters-explodes-in-americas-housing-starved-metros/

California Department of Industrial Relations. (2022, April). California Consumer Price Index (1955-2022). Sacramento: State of California Department of Industrial Relations. https://www.dir.ca.gov/oprl/CPI/EntireCCPI.PDF

California Employment Development Department, Labor Market Information. (2022). Labor force [data]. https://www.labormarketinfo.edd.ca.gov/cgi/dataanalysis/labForceReport.asp?menuchoice=LABFORCE

Cervero, R. (1989). Jobs-housing balancing and regional mobility. Journal of the American Planning Association, 55(2), 136–150. https://doi.org/10.1080/01944368908976014

Cervero, R. (1996). Jobs-housing balance revisited: Trends and impacts in the San Francisco Bay Area. Journal of the American Planning Association, 62(4), 492–511. https://doi.org/10.1080/01944369608975714

Cervero, R., & Duncan, M. (2006). Which reduces vehicle travel more: Jobs-housing balance or retail-housing mixing? Journal of the American Planning Association, 72(4), 475–490. https://doi.org/10.1080/01944360608976767

Cervero, R., & Wu, K.-L. (1997). Polycentrism, commuting, and residential location in the San Francisco Bay Area. Environment and Planning A: Economy and Space, 29(5), 865–886. https://doi.org/10.1068/a290865

Cervero, R., & Wu, K.-L. (1998). Sub-centring and commuting: Evidence from the San Francisco Bay Area, 1980-90. Urban Studies, 35(7), 1059–1076. https://doi.org/10.1080/0042098984484

Chapple, K., & Jeon, J. S. (2021). Big tech on the block: Examining the impact of tech campuses on local housing markets in the San Francisco Bay Area. Economic Development Quarterly, 35(4), 351–369. https://doi.org/10.1177/08912424211036180

Chapple, K., Thomas, J. V., Belzer, D., & Autler, G. (2004). Fueling the fire: Information technology and housing price appreciation in the San Francisco Bay area and the Twin Cities. Housing Policy Debate, 15(2), 347–383. https://doi.org/10.1080/10511482.2004.9521505

Deakin, E., Harvey, G., Pozdena, R., & Yarema, G. (1996). Transportation pricing strategies for California: An assessment of congestion, emissions, energy and equity impacts. Berkeley, CA: University of California Transportation Center. https://escholarship.org/uc/item/723002kt

Dingel, J. I., & Neiman, B. (2020). How many jobs can be done at home? (Working Paper 26948). Cambridge, MA: National Bureau of Economic Research. https://doi.org/10.3386/w26948

Dougherty, C., & Burton, A. (2017, August 17). A 2:15 alarm, 2 trains and a but get her to work by 7 A.M. The New York Times. https://www.nytimes.com/2017/08/17/business/economy/san-francisco-commute.html

Dubin, R. A., & Sung, C.-H. (1987). Spatial variation in the price of housing: Rent gradients in non-monocentric cities. Urban Studies, 24(3), 193–204. https://doi.org/10.1080/00420988720080331

Garreau, J. (1992). Edge city: Life on the new frontier. Palatine, IL: Anchor Books.

Giuliano, G. (1991). Is jobs-housing balance a transportation issue? Transportation Research Record, 1305, 305–312.

Giuliano, G., Redfearn, C., Agarwal, A., Li, C., & Zhuang, D. (2007). Employment concentrations in Los Angeles, 1980–2000. Environment and Planning A: Economy and Space, 39(12), 2935–2957. https://doi.org/10.1068/a393

Giuliano, G., & Small, K. A. (1991). Subcenters in the Los Angeles region. Regional Science and Urban Economics, 21(2), 163–182. https://doi.org/10.1016/0166-0462(91)90032-I

Glaeser, E. L., Kahn, M. E., Arnott, R., & Mayer, C. (2001). Decentralized employment and the transformation of the American City [with comments]. Brookings-Wharton Papers on Urban Affairs, 2001, 48–60.

Golgher, A. B., & Voss, P. R. (2016). How to interpret the coefficients of spatial models: Spillovers, direct and indirect effects. Spatial Demography, 4(3), 175–205. https://doi.org/10.1007/s40980-015-0016-y

Graham, M. R., Kutzbach, M. J., & McKenzie, B. (2014). Design comparison of LODES and ACS commuting data products (CES 14-38). Washington, DC: U.S. Census Bureau, Center for Economic Studies.

Hajrasouliha, A. H., & Hamidi, S. (2017). The typology of the American metropolis: Monocentricity, polycentricity, or generalized dispersion? Urban Geography, 38(3), 420–444. https://doi.org/10.1080/02723638.2016.1165386

Heider, B., & Siedentop, S. (2020). Employment suburbanization in the 21st century: A comparison of German and US city regions. Cities, 104, 102802. https://doi.org/10.1016/j.cities.2020.102802

Hu, L., & Schneider, R. J. (2017). Different ways to get to the same workplace: How does workplace location relate to commuting by different income groups? Transport Policy, 59, 106–115. https://doi.org/10.1016/j.tranpol.2017.07.009

Hu, Y., Wang, F., & Wilmot, C. G. (2017). Commuting variability by wage groups in Baton Rouge, 1990–2010. Papers in Applied Geography, 3(1), 14–29. https://doi.org/10.1080/23754931.2016.1248577

Joint Center for Housing Studies. (2022). The state of the nation’s housing 2022. Cambridge, MA: Harvard University. https://www.jchs.harvard.edu/sites/default/files/reports/files/Harvard_JCHS_State_Nations_Housing_2022.pdf

Klein, N. J. (2020). Subsidizing car ownership for low-income individuals and households. Journal of Planning Education and Research, 44(1), 165–177. https://doi.org/10.1177/0739456X20950428

Kneebone, E., & Holmes, N. (2016). The growing distance between people and jobs in metropolitan America. Washington, DC: Metropolitan Policy Program, Brookings Institution.

Loh, T. H., & Kim, J. (2021). To recover from COVID-19, downtowns must adapt. Washington, DC: Brookings Institution. https://www.brookings.edu/articles/to-recover-from-covid-19-downtowns-must-adapt/

Lucas, M. T., & Nicholson, C. F. (2003). Subsidized vehicle acquisition and earned income in the transition from welfare to work. Transportation, 30(4), 483–501. https://doi.org/10.1023/A:1024724224634

Lufkin, B. (2022). The workers taking on new “super commutes.” BBC. Retrieved from https://www.bbc.com/worklife/article/20220413-the-workers-taking-on-new-super-commutes

Manaugh, K., Miranda-Moreno, L. F., & El-Geneidy, A. M. (2010). The effect of neighbourhood characteristics, accessibility, home–work location, and demographics on commuting distances. Transportation, 37(4), 627–646. https://doi.org/10.1007/s11116-010-9275-z

Manduca, R. (2018). The spatial structure of US metropolitan employment: New insights from LODES data [Preprint]. SocArXiv. Retrieved from https://doi.org/10.31235/osf.io/6sxv5

Metropolitan Transportation Commission. (2016). Fresh data on Bay Area’s vital signs show big jump in freeway congestion for 2015. San Francisco: MTC. https://mtc.ca.gov/news/fresh-data-bay-areas-vital-signs-show-big-jump-freeway-congestion-2015

Metropolitan Transportation Commission. (2021, June 15). Toll bridge traffic nears 90 percent of pre-COVID levels. San Francisco: MTC. https://mtc.ca.gov/news/toll-bridge-traffic-nears-90-percent-pre-covid-levels

Modarres, A. (2011). Polycentricity, commuting pattern, urban form: The case of Southern California. International Journal of Urban and Regional Research, 35(6), 1193–1211. https://doi.org/10.1111/j.1468-2427.2010.00994.x

Morris, E. A., Ettema, D., & Zhou, Y. (2020). Which activities do those with long commutes forego, and should we care? Transportation Research Interdisciplinary Perspectives, 5, 100119. https://doi.org/10.1016/j.trip.2020.100119

Ramani, A., & Bloom, N. (2021). The donut effect of Covid-19 on cities (Working paper 28876). Washington, DC: National Bureau of Economic Research. https://doi.org/10.3386/w28876

Rhoades, S. A. (1993). The Herfindahl-Hirschman index. Federal Reserve Bulletin, March, 188–189.

Ross, M., & Bateman, N. (2019). Meet the low-wage workfore (p. 68). Washington, DC: Brookings Institution. https://www.brookings.edu/wp-content/uploads/2019/11/201911_Brookings-Metro_low-wage-workforce_Ross-Bateman.pdf

Ruggles, S., Flood, S., Foster, S., Goeken, R., Pacas, J., Schouweiler, M., & Sobek, M. (2021). IPUMS USA: Version 11.0 (American Community Survey). IPUMS USA. R https://doi.org/10.18128/D010.V11.0

Schleith, D., & Horner, M. W. (2014). Commuting, job clusters, and travel burdens: Analysis of spatially and socioeconomically disaggregated longitudinal employer–household dynamics data. Transportation Research Record, 2452(1), 19–27. https://doi.org/10.3141/2452-03

StataCorp. (2021). Stata spatial autoregressive models reference manual (Release 17). College Station, TX: Stata Press. https://www.stata.com/manuals/sp.pdf

Stone, A. A., & Schneider, S. (2016). Commuting episodes in the United States: Their correlates with experiential wellbeing from the American Time Use Survey. Transportation Research Part F: Traffic Psychology and Behavior, 42, 117–124. https://doi.org/10.1016/j.trf.2016.07.004

Storper, M., Kemeny, T., Makarem, N., & Osman, T. (2015). The rise and fall of urban economies: Lessons from San Francisco and Los Angeles (1st edition). Stanford, CA: Stanford Business Books.

Texas A&M Transportation Institute. (2021). Congestion data for your city (Urban mobility report). College Station, TX: Texas A&M Transportation Institute. https://mobility.tamu.edu/umr/congestion-data/

U.S. Census Bureau. (2000). U.S. decennial census. Social Explorer. https://www.socialexplorer.com/explore-tables

U.S. Census Bureau. (2017). American community survey: 2013-2017 (5-year Estimates). Social Explorer. https://www.socialexplorer.com/tables/ACS2018_5yr/R12443803

U.S. Census Bureau. (2019). LODES technical document, Version 7.4. Washington, DC: U.S. Census Bureau.

U.S. Census Bureau. (2022). American community survey: 2022 (1-year Estimates). Social Explorer. https://www.socialexplorer.com/tables/ACS2018_5yr/R12443803

U.S. Census Bureau, Population Division. (2012). Table 2. Intercensal estimates of housing units for counties of California: April 1, 2000, to July 1, 2010 (HU-EST00INT-02-06). Washington, DC: U.S. Census Bureau. https://www.census.gov/data/tables/time-series/demo/popest/intercensal-2000-2010-housing-units.html

U.S. Census Bureau, Population Division. (2020). Annual estimates of housing units for counties in California: April 1, 2010 to July 1, 2019 (CO-EST2019-ANNHU-06). Washington, DC: U.S. Census Bureau. https://www.census.gov/data/tables/time-series/demo/popest/2010s-total-housing-units.html

van Ommeren, J. N., & Gutiérrez-i-Puigarnau, E. (2011). Are workers with a long commute less productive? An empirical analysis of absenteeism. Regional Science and Urban Economics, 41(1), 1–8. https://doi.org/10.1016/j.regsciurbeco.2010.07.005

Voulgaris, C. T., Taylor, B. D., Blumenberg, E., Brown, A., & Ralph, K. (2017). Synergistic neighborhood relationships with travel behavior: An analysis of travel in 30,000 US neighborhoods. Journal of Transport and Land Use, 10(1), 437–461.

Wang, F. (2001). Explaining intraurban variations of commuting by job proximity and workers’ characteristics. Environment and Planning B: Planning and Design, 28(2), 169–182. https://doi.org/10.1068/b2710

Weber, L., Grant, P., & Hoffman, L. (2022, July 7). Big cities can’t get workers back to the office. Wall Street Journal. https://www.wsj.com/articles/office-remote-work-new-york-11657130765#

Wong, G. (2023, April 25). Study: These Bay Area cities among state’s worst commutes. San Francisco Examiner. https://www.sfexaminer.com/news/bay-area-news/study-which-bay-area-cities-have-worst-longest-commutes/article_30f6db76-e395-11ed-9dd3-b39be7764279.html