Jobs–Housing Balance Re-Re-Visited

Abstract

Problem, research strategy, and findings
In many U.S. metropolitan areas housing costs have skyrocketed in recent years relative to average incomes. A worsening shortage of affordable housing in these metros may push households away from job-rich cities and expensive neighborhoods into outlying areas, where housing is cheaper but jobs are more distant. To examine this issue, we revisit the jobs–housing balance, a popular topic of research in the 1990s, with a focus on the relationship between housing and the spatial location of workers relative to jobs. Our analysis draws on data from the Longitudinal Employer–Household Dynamics Origin–Destination Employment Statistics (LODES) for cities in California in 2002 and 2015. In contrast to earlier jobs–housing balance research, we find that California cities are becoming less self-contained over time, defined as a decline in the number of workers who both live and work within a jurisdiction relative to the number of commuters who travel either into or out of a city for work. Statistical models show that self-containment was higher in cities with lower housing costs and, in 2015, in cities with a greater balance between jobs and employed residents.

Takeaway for practice
The deepening housing affordability crisis in many metropolitan areas like those found in California are pushing workers and jobs farther apart, increasing the economic, social, and environmental costs of commuting. Policies to increase the supply of housing in job-rich and high–housing cost areas could help reverse this troubling trend, though they are likely to meet with considerable resistance. Our findings also underscore the importance of efforts that include but extend beyond housing production, such as policies to better match job skills and housing prices to the characteristics of workers.

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Jobs–Housing Balance Re-Re-Visited
Evelyn Blumenberg & Hannah King
To cite this article: Evelyn Blumenberg & Hannah King (2021): Jobs–Housing Balance Re-Re-
Visited, Journal of the American Planning Association, DOI: 10.1080/01944363.2021.1880961
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Jobs–Housing Balance
Re-Re-Visited
Evelyn Blumenberg Hannah King
ABSTRACT
Problem, research strategy, and findings: In many U.S. metropolitan areas housing costs have skyrock-
eted in recent years relative to average incomes. A worsening shortage of affordable housing in these
metros may push households away from job-rich cities and expensive neighborhoods into outlying areas,
where housing is cheaper but jobs are more distant. To examine this issue, we revisit the jobs–housing
balance, a popular topic of research in the 1990s, with a focus on the relationship between housing and
the spatial location of workers relative to jobs. Our analysis draws on data from the Longitudinal
Employer–Household Dynamics Origin–Destination Employment Statistics (LODES) for cities in California
in 2002 and 2015. In contrast to earlier jobs–housing balance research, we find that California cities are
becoming less self-contained over time, defined as a decline in the number of workers who both live and
work within a jurisdiction relative to the number of commuters who travel either into or out of a city for
work. Statistical models show that self-containment was higher in cities with lower housing costs and, in
2015, in cities with a greater balance between jobs and employed residents.
Takeaway for practice: The deepening housing affordability crisis in many metropolitan areas like those
found in California are pushing workers and jobs farther apart, increasing the economic, social, and envir-
onmental costs of commuting. Policies to increase the supply of housing in job-rich and high–housing
cost areas could help reverse this troubling trend, though they are likely to meet with considerable resist-
ance. Our findings also underscore the importance of efforts that include but extend beyond housing
production, such as policies to better match job skills and housing prices to the characteristics
of workers.
Keywords: commute distance, housing affordability, jobs–housing balance
Housing costs in many U.S. metropolitan areas
have skyrocketed in recent years relative to
average incomes. This is particularly true in
metropolitan areas—such as Boston (MA), Los
Angeles (CA), New York (NY), Miami (FL), San Diego (CA),
San Francisco (CA), Seattle (WA), and Washington
(DC)—located on either coast (Emmons, 2018). In
response to these trends, numerous media outlets have
reported on how rising housing prices are pushing
households away from job-rich cities and expensive
neighborhoods into outlying areas where households
can exchange cheaper housing for longer commutes.
For example, in a Los Angeles Times article, Lopez (2017)
tells the story of Carolyn Cherry, who travels 6 h a day
commuting 85 miles each way between the exurb of
Hemet (CA) to downtown Los Angeles; he concludes
that “being close to work is a luxury many in the middle
and working class can no longer afford. …”
The relationship between jobs and housing, called
the jobs–housing balance, was a popular topic among
scholars in the 1990s. However, there have been far
fewer recent studies of this relationship, despite
evidence of worsening affordable housing shortages
and lengthening commutes in many areas. In this study,
we revisited research by Cervero (1996) on the
jobs–housing balance in the San Francisco Bay Area,
this time using data for all California cities. Specifically,
we examined changes in the spatial location of workers
relative to jobs in 2002 and 2015 and their implications
for affordable housing. We first analyzed whether
California cities are less self-contained over time, defined
as a decline in the number of workers who both live
and work within a jurisdiction relative to the number of
workers who either travel into or out of a city, and then
estimated a set of statistical models to examined the
potential role of housing costs and availability in
explaining city self-containment.
We find that California cities are indeed
becoming less self-contained over time. Although wide-
spread, the downward trend in self-containment is
greatest in both balanced and employment-rich cities
where housing costs are highest. These findings indi-
cate that the tendency toward increasing self-contain-
ment identified in previous jobs–housing balance
DOI: 10.1080/01944363.2021.1880961 | ß2021 American Planning Association, Chicago, IL.
Color version available at tandfonline.com/rjpa
Journal of the American Planning Association 2021 | Volume 0 Number 01

analyses of 1980s data has reversed. Our statistical mod-
els show negative associations between housing costs
and city self-containment, an effect that has strength-
ened over time. Further, in 2015, self-containment was
higher in cities with a greater balance between jobs
and employed residents. The findings suggest a role
for increasing housing supply in job-rich cities and
high–housing cost neighborhoods to reduce housing
costs and enable more workers to live closer to their
workplaces if they so choose. They also underscore the
importance of efforts that include but extend beyond
jobs–housing balance, such as policies to better match
job skills and housing prices to the characteristics
of workers.
We begin by summarizing the literature on jobs–
housing balance with a focus on the role of housing
affordability. The subsequent section describes the data
and methodology, including the limitations of the ana-
lysis. We then turn to our findings, first describing
changes in the self-containment of California cities over
time, then reporting the results of our statistical models,
and, finally, illustrating differences across cities by their
relative jobs–housing richness. We conclude by discus-
sing the implications for policy and practice.
Understanding Cities, Jobs,
and Housing
Scholars have long focused on jobs–housing balance—
the spatial location of employment relative to housing
within geographic areas—as a predictor of vehicle miles
of travel and traffic congestion (Salon et al., 2012). The
underlying notion is that improvements in the proximity
of workers to jobs will result in shorter commutes, less
vehicle travel, and, potentially, greater use of modes
other than driving. Using different data and analytical
approaches, a number of scholars have found at least
some evidence for jobs–housing balance in influencing
transportation outcomes (Cervero, 1989; Peng, 1997;
Salon et al., 2012; Sultana, 2002).
One prominent body of research in this area
focuses on how the built environment, including jobs–
housing balance, can influence “excess”travel; this area
of research includes study of the impacts of excess
travel on congestion, transportation-related emissions,
and energy consumption (Fan et al., 2011; Layman &
Horner, 2010). Scholars have argued that increasing
jobs–housing balance has greater potential for decreas-
ing vehicle travel than alternative strategies such as
land use mixing (Cervero & Duncan, 2006). Other schol-
ars have argued that the primary benefit of jobs–hous-
ing balance for consumers lies in increasing the range
of choices they face with regard to transportation and
residential location decisions (Levine, 1998).
Low-income households may realize additional
benefits from jobs–housing balance. First espoused by
Kain (1968), the spatial mismatch hypothesis contends
that limitations on residential choice—particularly
among African Americans—combined with the subur-
banization of employment away from central cities
negatively affect the economic outcomes of Black and
other low-income central-city residents. Although the
findings from the spatial mismatch literature are mixed,
the weight of the evidence shows that distance from
employment disadvantages low-income, central-city
residents (Ihlanfeldt & Sjoquist, 1998). Other studies
show positive relationships between job access and
economic outcomes, including higher employment
rates and earnings, as well as lower welfare usage rates
(Immergluck, 1998; Jin & Paulsen, 2018; Ong &
Blumenberg, 1998). Proximity to employment is particu-
larly important for households without automobiles,
who are more likely to commute by public transit.
Some scholars have suggested a much more lim-
ited role for jobs–housing balance, arguing that factors
other than proximity to employment determine residen-
tial location and employment outcomes (Giuliano, 1991;
Wachs et al., 1993). Giuliano (1991) summarized some of
these factors, which include, among others, the willing-
ness of households to accept longer commutes for
larger homes and lot sizes and improved neighborhood
amenities (e.g., high-quality schools, low crime rates,
availability of parks). With respect to low-income house-
holds, job outcomes may have less to do with proximity
to jobs than with racial discrimination in hiring
(Hellerstein et al., 2008) and access to automobiles
(Gautier & Zenou, 2010).
Although likely not the only or, perhaps, the princi-
pal determinant, jobs–housing balance potentially can
contribute to improved travel or economic outcomes.
However, as Levine (1998) argued, historically the
potential effects of jobs–housing balance have been
hindered by land use policies that restrict residential
densities and, as a result, narrow the location choices of
families who value accessibility. The problem extends
beyond jobs–housing as measured by the ratio
between the number of jobs and housing units. During
the 1980s, suburban bedroom cities in the Silicon Valley,
40 miles south of San Francisco, attracted firms, ultim-
ately achieving a balance of housing relative to jobs
(Cervero, 1996). At the same time, however, rising hous-
ing prices prevented some workers who might have
wished to live in the same city where they were
employed from doing so.
In the last 5 years, the price of housing nationwide
has increased faster than median incomes, putting a
squeeze on the household budgets of lower- and
medium-income households (Schuetz, 2019). The
affordable housing crisis is particularly acute in
Journal of the American Planning Association 2021 | Volume 0 Number 02

expensive metropolitan areas where home prices have
risen steeply, low-income renters outnumber the units
they can afford, and commute distances have grown
(Joint Center for Housing Studies, 2018; Schuetz, 2019).
Land use policies such as restrictive zoning and cumber-
some and expensive permitting processes limit growth in
the supply of housing, exacerbating housing market
imbalances, particularly at the bottom end of the market
(Glaeser & Gyourko, 2018; Kok et al., 2014; Malpezzi &
Green, 1996 ). In addition, gentrification in dense urban
neighborhoods may increase housing costs and further
constrain housing choice, either by displacing existing
low-income residents or by limiting opportunities to
select into high job-access neighborhoods (Vigdor et al.,
2002). As a result, some recent studies have returned to
the question of jobs–housing balance, showing the merits
of focusing on the low-wage jobs–affordable housing fit
(Benner & Karner, 2016)aswellasthepotentialtravel
benefits of such a fit, particularly shorter commute distan-
ces in neighborhoods where workers can live closer to
their jobs (Moos et al., 2018).
Measuring the Relationship Between
Housing and City Self-Containment
In this study we tested whether California cities have
become more or less “self-contained”over time with
respect to the location of employed residents relative to
their jobs and, more specifically, the role of housing
costs and supply in explaining this trend. All else equal,
we hypothesized that cities with higher housing costs
and cities with a greater imbalance between jobs and
residents are less self-contained than other cities. We
expected these determinants of city self-containment to
have strengthened over time in tandem with
California’s affordable housing crisis (Taylor, 2015).
Our analysis relies on data from the Longitudinal
Employer–Household Dynamics Origin–Destination
Employment Statistics (LODES), which are state adminis-
trative data assembled by the Center for Economic
Studies at the U.S. Census Bureau (U.S. Census Bureau,
n.d.). The LODES data provide information on the loca-
tion of workers, the location of jobs, and the location of
workers relative to their jobs from 2002 and, at the time
of our analysis, to 2015. We supplemented these data
with data from the 2000 Census and the 2013–2017 5-
year American Community Survey (U.S. Census Bureau,
2000,2017).
The analysis includes 394 California municipalities
over the two time periods. To assemble data by city, we
used geoprocessing tools (primarily the computation of
geometric intersections) in geographic information sys-
tems software (ArcMap; Esri, 2020) to assign all
California census tracts to cities using a shapefile
created by the California Department of Transportation
(Caltrans) and California Board of Equalization (California
Department of Transportation, 2020). We then aggre-
gated the LODES origin–destination data for 2002 and
2015 from the census block level to the tract level for
individual cities and unincorporated areas (based on
worker origins and destinations). A map of the centroids
of these cities and, to orient the reader, the county
boundaries and select city names are included in the
Technical Appendix.
To measure self-containment, we used an independ-
ence index, developed by Thomas (1969) and applied in
Cervero (1996):
Independence Index
¼Internal Work Trips ðwork and live inside cityÞ
External Work Trips ðwork outside city þlive outside cityÞ:
We then estimated ordinary least squares models to
predict the logarithm of the independence index for
California cities in 2002 and 2015. Moreover, we repli-
cated earlier research by Cervero (1996) published in
this journal examining jobs–housing changes in large
San Francisco Bay Area cities in the 1980s. Because the
data are highly aggregated, the models include rela-
tively few covariates to minimize multicollinearity issues.
With respect to housing, we drew on data from the
2000 Census and the 2013–2017 5-year American
Community Survey and developed a housing cost index
representing the median single-family home value rela-
tive to median household income in each city (U.S.
Census Bureau, 2000,2017). Comparing the rise in hous-
ing costs with the rise in median income allowed us to
better examine how the housing cost burden—rather
than just housing costs—has changed. The models also
include a jobs–housing index (balance index) calculated
as the absolute difference between the ratio of jobs to
employed residents and 1:
Balance Index ¼jðJobs=Employed ResidentsÞ–1j,
where a 0 value means that jobs equal employed resi-
dents and greater values mean less balance. This vari-
able measures cities’potential for self-containment,
because not all (or even most) who could live and work
in the same city will choose to do so. Other variables in
the models include the number of resident workers as a
measure of city size and private vehicle availability (in
terms of the number of vehicles per household). The lat-
ter measure serves as an indication of the affluence of
the city as well as the ease of travel.
Finally, we present two models, one with and one
without a set of dummy variables indicating whether a
city is located in one of the state’s five metropolitan
areas with more than 1 million residents: Greater Los
Angeles, San Francisco Bay Area, San Diego,
Jobs–Housing Balance Re-Re-Visited3

Sacramento, and Fresno.
1
We include these dummy var-
iables because the employment options for residents of
smaller stand-alone cities outside of metropolitan areas
are likely more constrained than for residents of smaller
cities in large urbanized regions, who can take jobs in
nearby cities.
There are several limitations to this study. The first
relates to the geographic units of analysis. There is no
perfect unit of analysis for studies of jobs–housing bal-
ance; the jobs–housing balance on a single block is very
low, and on continents it is very high. Large cities—due
to the sheer function of their size—will tend to have
greater balance. In contrast, we would not expect jobs
and housing balance in very small cities in very large
metropolitan areas. For example, among the 191 cities
in the 19-million-person greater Los Angeles region, the
city of Vernon is home to just 112 residents. Our analysis
focuses on cities because of their political importance
for municipal housing and land use policy (Palm &
Niemeier, 2017). Since 1969, California has required all
local governments to develop plans to meet the hous-
ing needs of their residents through the Regional
Housing Needs Assessment process (California
Department of Housing and Community Development,
n.d.). Advocates have argued that meeting these hous-
ing targets necessitates a shift away from land use regu-
lations that favor single-family homes to better align
local job growth and housing production (Perry et al.,
2019). However, to test the robustness of the city-level
findings and to address the problem that cities vary so
much in size, we replicated our 2015 models using
aggregations of census tracts as the unit of analysis by
creating 8-mile buffers around each tract in the state.
The buffer is based on Clark et al. (2003), who found
that households are substantially more likely to adjust
their residential location if they live further than 8 miles
from their jobs. These models are included in the
Technical Appendix.
The measure of jobs–housing balance (the balance
index) rests on the ratio between jobs and employed res-
idents. As other scholars have noted, this is an imperfect
measure because it does not directly assess the availabil-
ity (and the availability at particular price points) of hous-
ing units. Although housing data are available from the
U.S. Census Bureau, they are not readily associated with
the number of workers living in the housing units.
In addition to its many advantages, there are sev-
eral drawbacks with the LODES data, which are sum-
marized in Graham et al. (2014). The LODES data are
derived from administrative employment records, which
are the source of many of the data anomalies. However,
we expected these issues to be present in data for both
time periods, so the distribution of errors should be
consistent over time. With respect to our use of these
data, the largest drawback is the inability to analyze
workers by wages. The U.S. Census Bureau provides
information on three wage groups (<$1,251/month,
$1,251–$3,333/month, and >$3,333 month). They do
not adjust these wage categories over time for inflation,
limiting our ability to track changes in the effects of the
housing variables specifically for lower-wage workers
who, no doubt, experience the greatest housing con-
straints. This issue also limited our ability to match low-
wage workers over time to suitable housing.
Finally, the aggregate nature of the analysis pre-
sented additional issues. One is endogeneity. For
example, the models include the number of automo-
biles per person. Yet vehicle ownership may be predi-
cated on city self-containment, which may allow
households to live closer to their jobs and to rely on
modes other than the automobile. We mitigated this
issue by our use of cities as the unit of analysis as well
as evidence that socioeconomic characteristics likely
have a greater influence on mode choice than charac-
teristics of the built environment (Ewing & Cervero,
2010). Finally, to minimize multicollinearity, the models
are parsimonious, which could have caused us to
underspecify our models and thereby overestimate the
effects of the independent variables.
Cities Are Becoming Less Self-
Contained Over Time
Table 1 provides descriptive statistics on changes in jobs
and employed residents in California cities. From 2002 to
2015, California cities experienced an increase in both the
number of jobs and the number of workers or “employed
residents.”On average, cities have more employed resi-
dents than jobs. However, during this 13-year period, the
number of jobs grew at a faster rate than that of
employed residents—22% compared with 10.5%—bring-
ing the numbers of jobs and workers more in line. So
between 2002 and 2015, the ratio of jobs to employed
residents, one measure of jobs–housing balance,
increased by less than 2%.
As Cervero (1996) noted, the ratio between jobs to
residents indicates only the potential for jobs–housing
balance. To examine the extent to which this potential
is realized, we (like Cervero) calculated the number of
locally working residents (the share of employed resi-
dents who work in the same city in which they live) and
the number of locally residing workers (the share of
workers who live in the same city in which they work).
From 2002 to 2015, the mean percentage of locally
working residents declined from 12.9% to 11.3%, pos-
sibly reflecting increased housing prices near employ-
ment centers as well as higher job growth in outlying
areas. The trend is similar for locally residing workers,
with a decline from 14.9% to 13.2%. These percentages
Journal of the American Planning Association 2021 | Volume 0 Number 04

are noticeably lower than the results obtained by
Cervero in his analysis of cities in the San Francisco Bay
Area in the 1980s, which suggests that the spatial loca-
tion of jobs and housing has changed as the state’s
population and employment continue to increase.
However, the data show that in both time periods most
workers did not live in the same city where
they worked.
The independence index, a measure of self-contain-
ment, was low in 2002 (0.075) and declined further by
2015 (0.063). As we note above, the data did not allow
us to analyze the trends for lower-wage workers over
time because the wage categories are not adjusted for
inflation. We did, however, use the 2015 data to see
whether lower-wage workers remained more likely to
live closer to their workplaces than higher-wage work-
ers, as previous research suggests (Blumenberg & King,
2019; Schleith et al., 2016), and therefore are more likely
to be self-contained. The data in Table 1, rows 6a and
6b, show that lower-wage workers are indeed more
likely to live and work in the same city.
In both 2002 and 2015, the ratio of jobs to
employed residents is negatively related to the percent-
age of locally residing workers in a given city. In other
words, cities with higher ratios of jobs to employed resi-
dents tend to have smaller percentages of locally resid-
ing workers. The percentage of locally residing workers
declined modestly over time. The decline in locally
residing workers may be due to employment-rich cities
that attract workers who prefer to live elsewhere. For
example, many high-technology workers in Silicon
Valley live in San Francisco, a finding supported by the
substantial ridership on employer shuttles from San
Francisco to Silicon Valley in Santa Clara County
(Metropolitan Transportation Commission, 2016).
However, this decline also may reflect workers who
would like to live in close proximity to high-amenity job
centers but simply cannot afford the high costs of hous-
ing there. Finally, because we did not have data on
household size, the trend may also reflect the displace-
ment of local workers in larger households by smaller,
more affluent households, one of the trends associated
with gentrification (Couture & Handbury, 2015; Moos,
2016). Regardless of the exact mechanism underlying
these dynamics, the data indicate that fewer workers
over time live and work in the same city.
For comparison, we analyzed the 23 cities included
in Cervero’s(1996) study of jobs–housing balance in the
Bay Area;
2
the full descriptive results for these cities are
in the Technical Appendix. This subsample shows some
significant differences between the 1980s and
2002–2015. In both periods, the number of jobs and
workers grew. These increases were widespread across
Bay Area cities in the 1980s in Cervero’s analysis but less
so between 2002 and 2015. In recent years, Bay Area
job growth appears to have outpaced changes in the
number of employed residents, particularly in cities with
large and well-established employment centers. In
many other Bay Area cities, growth in employed resi-
dents outpaced job growth.
In the 1980s, the Bay Area cities had a moderate
level of self-containment and were becoming more self-
contained over time, largely due to the increase in the
mean percentage of locally working residents. In con-
trast, from 2002 to 2015, the mean percentage of locally
working residents declined overall, with one exception
(Daly City, immediately southwest of San Francisco). Bay
Area cities, for the most part, appear to be growing less
self-contained in this century. In 22 of the 23 cities, the
independence index decreased and the percentage
change ranged from 4% (Pleasanton) to 35% (Napa).
In short, our analysis indicates that the general trend
toward self-containment in the 1980s identified by
Cervero (1996) had reversed.
Table 1. Median jobs–housing measures, California cities, 2002 and 2015.
Median jobs–housing measures 2002 2015 % Change
1 Total jobs 10,701 13,053 22.0
2 Employed residents 13,861 15,310 10.5
3 Ratio of jobs to employed residents 0.82 0.84 1.8
4%locally working residents
a
12.9 11.3 12.3
5%locally residing workers
b
14.9 13.2 11.6
6 Independence index (all)
c
0.075 0.063 15.5
6a Lower-wage workers and jobs
d
0.078
6b Higher-wage workers and jobs
e
0.046
Notes: a. Locally working residents are the percentage of employed residents who work in the same city in which they live. b. Locally residing workers are the per-
centage of workers who live in the same city as they work. c. Independence Index ¼Internal Work Trips ðwork and live inside cityÞ
External Work Trips ðwork outside city þlive outside cityÞ:d. “Lower-wage”workers are
workers with jobs earning less than $3,333 per month. This category includes both “low-wage”and “mid-wage”workers as defined by LODES specifications.
e. Following LODES, “higher-wage”workers are workers with jobs earning more than $3,333 per month.
Jobs–Housing Balance Re-Re-Visited5

Housing Costs and Availability Matter
We also modeled the determinants of the independ-
ence index to isolate the role of housing costs, housing
availability, and other city characteristics in predicting
the spatial location of workers relative to their jobs.
Descriptive statistics of the outcome measure (the inde-
pendence index) are included in Table 1 and discussed
above. (Descriptive statistics for the independent varia-
bles are included in the Technical Appendix.)
The number of resident workers indicates city size.
In general, larger cities are more self-contained than
smaller ones because they offer more people opportu-
nities to live and work in the same municipality. For
example, large cities such as Los Angeles (3.97 million
population), San Diego (1.41 million), San Francisco
(0.88 million), and Fresno (0.53 million) were among the
most self-contained in the state. Conversely, smaller cit-
ies offer fewer housing and job opportunities and may
or may not be located in proximity to other job- and
housing-rich cities. For example, 72 cities are located
within 10 miles of Los Angeles, the largest city in
California. Between 2002 and 2015, the balance index
more than doubled. In other words, cities grew less bal-
anced over time. Over this same time period, median
household vehicle ownership also increased. These
changes in jobs–housing balance and private vehicle
ownership are consistent with the suburbanization of
households to outlying, lower-cost areas that are more
likely to require the use of automobiles to access oppor-
tunities (Kneebone & Holmes, 2016).
Finally, between 2002 and 2015 there was also a
substantial increase in our housing cost index, from 4.1
in 2002 to 6.7 in 2015, a 63% increase in 13 years. This
change is especially notable given that our study period
includes the housing market volatility of the Great
Recession, a time period in which housing prices
declined widely throughout the state (Public Policy
Institute of California, 2008). In addition, as we note
above, employment increased faster over this period
than the number of resident workers, resulting in a
small increase (1.8%) in the median number of jobs to
employed residents.
Table 2 provides regression outputs for our base
model and one with regional controls added for both
2002 and 2015. In 2002, three of the four variables in
the model are statistically significant. As expected, larger
cities tend to be more self-contained than smaller cities.
Also as expected, higher vehicle ownership rates are
associated with less self-containment. This relationship
makes conceptual sense because increased vehicle
access is both associated with income (and income is
positively related to travel distance) and because cars
enable workers to travel longer distances. These two
variables function in the same way and remain statistic-
ally significant in the 2015 models.
With respect to housing costs and availability, we
hypothesized that housing costs are negatively related
to self-containment, and indeed we found this in all
four models, suggesting that higher housing costs may
motivate some workers to locate farther from their pla-
ces of employment. Increased housing costs are likely a
function of limited housing supply in job-rich areas, per-
haps due to a lag in housing construction or, more
likely, due to local land use policies that limit residential
densities (Glaeser & Gyourko, 2018; Glaeser et al., 2005).
In our base model, the association between hous-
ing costs and the independence index increased in
magnitude over time, in tandem with the substantial
rise in housing costs in the state. In the models with
fixed effects for the major urban regions in the state,
the effect of the housing cost index on self-contain-
ment remains negative, but the coefficient in 2002 is
larger in magnitude than in 2015. In both years, many
of the regional dummy variables—Greater Los Angeles,
the San Francisco Bay Area, and San Diego in both years
and Sacramento in 2015—are negative and statistically
significant, with the largest effect in the largest region
(Los Angeles). This means, controlling for other factors,
cities in the largest urban regions are less self-contained
than cities in other areas of the state. It is likely that
housing plays a unique role in shaping where residents
live and work in these high-cost regions. If high housing
costs deter self-containment, then it stands to reason
that housing costs would have a larger negative rela-
tionship to self-containment in large, coastal urban
housing markets where costs have risen most signifi-
cantly. This interpretation is consistent with findings by
Glaeser and Gyourko (2018), who argued that housing
costs in many coastal metros are exacerbated by devel-
opment restrictions.
The relationship between the jobs–housing balance
index and municipal self-containment is significant in
the 2015 base model. In other words, as California cities
have become less balanced over time, the association
of jobs–housing balance with measures of containment
has strengthened. However, the effect size is small. The
graph in Figure 1 highlights the effect on the independ-
ence index of a 1 standard deviation change in the bal-
ance index below and above the mean, holding all
other model variables at their means. Such a shift in the
balance index would result in an 8% change (or a 0.37
percentage point change) in the independence index.
This result echoes those of Cervero (1996), who also
found weak correlations between balance and self-
containment in the San Francisco Bay Area; other stud-
ies showed stronger relationships between the two
(Stoker & Ewing, 2014). Our findings, like Cervero’s, sug-
gest that efforts to better balance the number of jobs
with the number of housing units in a given city are
Journal of the American Planning Association 2021 | Volume 0 Number 06

Table 2. Determinants of self-containment (log of the independence index), California cities, 2002 and 2015.
2002 2015
Base Base 1Regions Base Base 1Regions
Independent variables Coef. (SE)SD Coef. Coef. (SE)SD Coef. Coef. (SE)SD Coef. Coef. (SE)SD Coef.
Intercept 0.390 (0.371) 0 (0.045) 0.103 (0.354) 0 (0.040) 0.704 (0.323) 0 (0.042)0.004 (0.307) 0 (0.037)
Housing cost index log
a
0.619 (0.116) 0.245 (0.046) 0.497 (0.113) 0.197 (0.045) 0.684 (0.089) 0.322 (0.042) 0.403 (0.088) 0.190 (0.042)
Balance index
b
0.721 (0.457) 0.072 (0.046) 0.554 (0.407) 0.055 (0.041) 0.607 (0.243) 0.106 (0.042)0.422 (0.221) 0.073 (0.038)
Resident workers (10,000) 0.025 (0.005) 0.220 (0.045) 0.030 (0.005) 0.270 (0.041) 0.051 (0.007) 0.316 (0.042) 0.059 (0.006) 0.369 (0.039)
Automobiles per household 1.139 (0.173) 0.301 (0.046) 0.732 (0.164) 0.194 (0.043) 1.127 (0.133) 0.357 (0.042) 0.760 (0.126) 0.241 (0.040)
Major urban regions
Bay Area/San Francisco 0.667 (0.120) 0.310 (0.056) 0.736 (0.098) 0.385 (0.051)
Greater Los Angeles 0.998 (0.096) 0.555 (0.054) 0.810 (0.084) 0.506 (0.052)
Fresno 0.402 (0.284) 0.059 (0.042) 0.452 (0.236) 0.075 (0.039)
Sacramento 0.307 (0.195) 0.067 (0.043) 0.344 (0.163) 0.085 (0.040)
San Diego 0.517 (0.193) 0.117 (0.044) 0.545 (0.167) 0.138 (0.042)
Adjusted R
2
0.189 0.363 0.310 0.442
Notes: Independence Index Internal Work Trips ðwork and live inside cityÞ
External Work Trips ðwork outside cityþlive outside cityÞ.DF¼394; resident workers in 10,000. a. The housing cost index is the median single-family home value relative to median household income in each
city. b. Balance Index ¼j(Jobs/Employed Residents) –1|. p<.05; p<.01; p<.001.
Jobs–Housing Balance Re-Re-Visited7

likely to have only minor effects on the number of resi-
dents who actually live and work in the same city.
Note that most of the relationships between the
model variables—including the effect of housing costs
in our base model—increased between 2002 and 2015.
Our final 2015 model, including the regional fixed
effects, explains almost 44% of the variation in self-
containment across California cities.
Finally, we explored an alternative modeling
approach. The 8-mile radius census tract models high-
light, as expected, the fact that neighborhood dynamics
differ from those that operate citywide. In 2015, the
housing cost index was negative, implying shorter com-
mute distances in neighborhoods with more expensive
housing; however, this variable was not statistically sig-
nificant. The Tiebout hypothesis posits that households
sort themselves into cities according to their public
good preferences, potentially trading high-quality
schools and services in expensive jurisdictions for lon-
ger-distance commutes (Kuhlmey & Hintermann, 2019;
Tiebout, 1956). In contrast, households living in high-
income neighborhoods within large cities may realize
fewer benefits from long-distance commuting.
In contrast, the balance index was significant and
negative, a finding similar to that of our city model.
Commute distances were shorter in neighborhoods in
which the number of jobs better matched the number
of employed residents, providing support for local
efforts to create mixed-use neighborhoods. Finally, with
the exception of neighborhoods in one metropolitan
region, neighborhoods located in the major urban
regions in the state were less self-contained than other
neighborhoods, a finding again consistent with our
city models.
Jobs–Housing Variation by City Type
Like Cervero (1996), we categorized housing-rich cities
as those in which the logarithm of the ratio of jobs to
employed residents is less than 0.5 standard deviation
below the mean. Employment-rich cities are those in
which the logarithm of the ratio of jobs to employed
residents is greater than 0.5 of 1 standard deviation
above the mean. Finally, balanced cities are as those cit-
ies in which the logarithm of the ratio of the jobs to
employed residents falls within 1 standard deviation of
the mean.
Table 3 reports median containment and housing
statistics for these three city types over time. The per-
centage of locally residing workers was highest in
−4.59 −4.68 −4.77 −4.86 −4.96
−5.00
−4.50
−4.00
−3.50
−3.00
−2.50
−2.00
−1.50
−1.00
−0.50
0.00
Balance index
−1 SD
Balance index
−0.5 SD
Everything
at the mean
Balance index
+0.5 SD
Balance index
+1 SD
Independence Index, 2015
Figure 1. Change in the independence index relative to change in the balance index:
Independence Index¼Internal Work Trips ðwork and live inside cityÞ
External Work Trips ðwork outside city þlive outside city Þ:
Balance Index ¼j(Jobs/Employed Residents) – 1 j.
Journal of the American Planning Association 2021 | Volume 0 Number 08

balanced cities in both 2002 (17.6%) and 2015 (14.7%),
followed closely by housing-rich cities (16.9% and
14.3%). This rate was lowest in employment-rich cities
(8.9% and 8.8%), where median housing prices are high-
est. In contrast, the percentage of locally working resi-
dents was highest in employment-rich cities in both
years (19.5% and 14.9%), possibly reflecting the ability of
some households to self-select into high-cost areas with
many employment opportunities. Overall, and as
expected, the independence index was highest in bal-
anced cities in both years.
The three city types (housing-rich, balanced, and
job-rich) varied with respect to change over time. The
largest increase in housing costs occurred in balanced
cities, followed closely by employment-rich cities, the
two city types that experienced the most significant
declines in their independence indices. The independ-
ence index also declined in employment-rich cities,
though not as much, but in these cities the decline was
due primarily to a significant drop in locally working res-
idents. Thus, rising housing costs may make it increas-
ingly difficult for residents to find local jobs or for
workers to self-select into job-rich cities. Although bal-
anced cities remain more self-contained than both
housing-rich and employment-rich cities, they also
experienced declines in both locally residing and locally
working residents.
Combined, these trends suggest that, overall, work-
ers are locating farther from their places of employment
and that housing costs, although increasing across all
city types, are slightly mitigated in cities with higher
ratios of housing units to employed residents. Specific
jobs–housing patterns, however, vary by city type. In
housing-rich cities, workers appear better able to find
housing as a function of the greater supply of available
units. Many of these cities (e.g., Calabasas in Southern
California and Alameda in the Bay Area) are suburban
and less likely to possess the social, cultural, and phys-
ical amenities present in balanced (e.g., Los Angeles,
San Diego, San Jose) and employment-rich (e.g., San
Francisco, Sacramento) cities that compensate workers
for longer commutes or higher housing prices (Glaeser
& Gottlieb, 2006). In contrast, relatively few workers in
employment-rich cities live in the same cities as they
work, likely due to the high costs of housing. Overall, cit-
ies with a balance of jobs and housing tend to have a
slightly higher independence index and are therefore
more likely to be more self-contained.
The Case for Better Alignment Between
Housing, Jobs, and Workers
California workers are becoming less likely to both live
and work in the same city over time. This finding indi-
cates that the tendency toward increasing self-contain-
ment observed toward the end of the 20th century, in
at least some California cities, has reversed in the 21st
century, likely resulting in growing commute distances,
vehicle miles of travel, and traffic congestion, particularly
into and out of the central parts of already congested,
booming coastal cities. However, the transportation
impacts of jobs–housing imbalance vary across space
and population groups. Workers in booming coastal
economies face intense competition for housing and,
the data suggest, are more likely to live farther from
work where housing is more affordable. However,
lower-wage workers face substantial financial and trans-
portation barriers in commuting long distances to mod-
estly paying jobs. Further, exchanging reduced housing
costs for longer commutes could limit their ability to
find and travel to jobs (Kneebone & Holmes, 2016).
Moreover, if arguments about the benefits of location
efficiency are valid, shifts to outlying, lower-cost residen-
tial areas could increase combined housing and trans-
portation expenditure burdens for lower-wage workers,
resulting in growing financial hardship.
We found that housing costs are negatively related
to city self-containment, a relationship that is strong
and robust across our city models. Generally, larger
Table 3. Jobs–housing and city type, California cities, 2002 and 2015.
Housing rich
(n5117)
Balanced
(n5187)
Employment rich
(n595)
Measures (medians) 2002 2015 2002 2015 2002 2015
Locally residing workers
a
16.9%14.3%17.6%14.7%8.9%8.8%
Locally working residents
b
6.8%7.0%15.0%13.1%19.5%14.9%
Housing cost index
c
3.7 6.0 4.0 6.6 4.9 7.7
Independence index
d
0.053 0.052 0.094 0.081 0.067 0.059
Notes: a. Locally residing workers are the percentage of workers who live in the same city as they work. b. Locally working residents are the percentage of
employed residents who work in the same city in which they live. c. The housing cost index is the median single-family home value relative to median household
income in each city. d. Independence Index ¼Internal Work Trips ðwork and live inside cityÞ
External Work Trips ðwork outside cityþlive outside cityÞ:
Jobs–Housing Balance Re-Re-Visited9

cities provide more land for housing development as a
function of sheer size, but growing economies generate
increased competition for available housing. Housing
supply is, in turn, limited by zoning and other restric-
tions that place caps on market demand for housing
development (Glaeser & Gyourko, 2018; Glaeser et al.,
2005). Limited housing supplies, exacerbated by local
resistance to new housing development in built-up
areas (Pendall, 1999; Scally & Tighe, 2015; Tighe, 2012),
and growing demand for housing combine to inflate
housing prices and encourage workers to locate farther
from their places of work in search of cheaper housing.
Policies to reduce housing costs can take many
forms, including strategies aimed at directly decreasing
the costs of construction through streamlining the per-
mitting process (Reid et al., 2017) and reducing parking
requirements (Manville, 2013); strategies that increase
supply through elimination of restrictive zoning require-
ments (Monkkonen, 2019), including the allowance of
accessory dwelling units (Davis, 2018); and strategies
that increase the market power of renters, such as rental
assistance (Bailey et al., 2018; Pankratz et al., 2017).
Although limits on housing supply and high hous-
ing costs are particularly problematic in large metropol-
itan areas on the Pacific and Atlantic coasts, jobs–
housing imbalances may manifest in other areas for
other reasons, such as property tax incentives that
motivate cities to zone for commercial more than resi-
dential uses. Accordingly, additional research should
compare the self-containment of California metropol-
itan areas with metropolitan areas in the Mountain
West, Plains states, Midwest, or South, where home pri-
ce–income ratios are significantly lower (Hermann,
2018). In these metropolitan areas, declining self-con-
tainment may be more strongly linked to job loss and
suburbanization than to housing prices (Kneebone &
Holmes, 2016).
Further, our models show that potential
jobs–housing balance is associated with self-contain-
ment but the association, although present in 2015, is
not strong. This finding suggests that other types of
interventions are necessary to better match the
availability of opportunities—both suitable jobs and
housing—to the needs and characteristics of workers.
Benner and Karner (2016) made a case for the important
role of affordable housing in enabling lower-wage work-
ers to live in proximity to jobs. As we noted previously,
California cities have in recent years consistently under-
supplied housing and, in particular, affordable housing.
To accommodate the housing needs of workers, hous-
ing production must be better aligned with regional job
growth. Further, research suggests that maximizing
other matches—such as those by income, occupation,
and skill—are also important factors in enabling workers
to live and work locally (Cervero, 1996; Immergluck,
1998; Stoker & Ewing, 2014). In California, cities have for-
mal responsibility for housing their residents. Since
1969, the state has required local governments to
“adequately plan to meet the housing needs of every-
one in the community”through the housing elements
of their general plans (California Department of Housing
and Community Development, n.d., para. 1). Many cities
have not fulfilled this requirement; others have adopted
plans to meet their jurisdiction’s housing needs but
have failed to produce the mandated number of units
(Perry et al., 2019).
Not all workers can or will want to work and live in
the same cities and communities. In fact, recent devel-
opments—the COVID-19 crisis and changes in transpor-
tation technology—may push workers even further
from their places of employment. The current pandemic
has greatly increased the number of employees working
from home (Bloom, 2020), at least for some workers
untethering the link between residential and workplace
location. It is too early to know whether elevated rates
of telecommuting will endure after the pandemic. Data
show a significant decline in full-time telecommuting
since April (Brenan, 2020) and, at the same time, strong
preferences for remote work after COVID-19 (Bloom,
2020; Brenan, 2020). In addition, studies have found that
new transportation technologies—such as connected
and automated vehicles—will lower commuting effort
and costs and, like previous transportation innovations,
likely contribute to the growth of cities and travel dis-
tances (Zakharenko, 2016; Zhang & Guhathakurta, 2021).
Though important, these two trends will have dis-
proportionate effects on the behavior of higher-income
workers. For example, workers in the top income quar-
tile are twice as likely as those in the bottom income
quartile to be able to work from home (Bloom, 2020).
Therefore, it is likely that in high-cost cities an adequate
supply of affordable housing will be critical to ensuring
that low-wage workers—should they choose—can live
in proximity to their jobs, thereby reducing their time
spent commuting and vehicle miles of travel and
increasing opportunities to commute by means other
than driving.
ABOUT THE AUTHORS
EVELYN BLUMENBERG (eblumenb@ucla.edu) is professor of
urban planning and director of the Lewis Center for Regional
Policy Studies at the University of California at Los Angeles
(UCLA); she studies transportation and economic outcomes
for low-wage workers and the role of planning and policy in
addressing transportation disparities. HANNAH KING
(hrking@ucla.edu) is a doctoral student in urban planning at
UCLA who studies transportation finance, travel behavior, and
transportation equity.
ACKNOWLEDGMENTS
The authors are responsible for any errors or omissions. We
thank Mark Garrett, Julene Paul, Madeline Ruvolo, Andrew
Journal of the American Planning Association 2021 | Volume 0 Number 010

Schouten, Brian Taylor, and Jacob Wasserman for their contri-
butions to the larger research project from which this art-
icle draws.
RESEARCH SUPPORT
This research was funded by the State of California through
Senate Bill 1.
SUPPLEMENTAL MATERIAL
Supplemental data for this article can be found on the
publisher’s website.
NOTES
1. We assign cities to regions based on county boundaries for the
metropolitan planning organizations in the state. The San
Francisco Bay Area region includes the following nine counties:
Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo,
Santa Clara, Solano, and Sonoma. The Fresno region includes
Fresno County. Sacramento includes El Dorado, Placer,
Sacramento, Sutter, Yolo, and Yuba counties. San Diego includes
San Diego County. The greater Los Angeles region includes
Imperial, Los Angeles, Orange, Riverside, San Bernardino, and
Ventura counties.
2. The cities include Alameda, Berkeley, Concord, Daly City,
Fairfield, Fremont, Hayward, Mountain View, Napa, Oakland, Palo
Alto, Pleasanton, Redwood City, Richmond, San Francisco, San
Jose, San Leandro, San Mateo, Santa Clara, Santa Rosa, Sunnyvale,
Vallejo, and Walnut Creek.
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