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15 03

Are America’s Inner Cities Competitive?
Evidence from the 2000s
Daniel A. Hartley, Nikhil Kaza, and
T. William Lester

FEDERAL RESERVE BANK OF CLEVELAND

Working papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to
stimulate discussion and critical comment on research in progress. They may not have been subject to the
formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views stated
herein are those of the authors and are not necessarily those of the Federal Reserve Bank of Cleveland or of
the Board of Governors of the Federal Reserve System.
Working papers are available on the Cleveland Fed’s website at:

www.clevelandfed.org

Working Paper 15-03

March 2015

Are America’s Inner Cities Competitive? Evidence from the 2000s
Daniel A. Hartley, Nikhil Kaza, and T. William Lester

In the years since Michael Porter’s paper about the potential competitiveness of
inner cities there has been growing evidence of a residential resurgence in urban
neighborhoods. Yet, there is less evidence on the competitiveness of inner cities
for employment. We document the trends in net employment growth and find that
inner cities gained over 1.8 million jobs between 2002 and 2011 at a rate comparable to suburban areas. We also find a significant number of inner cities are
competitive over this period—increasing their share of metropolitan employment
in 120 out of 281 MSAs. We also describe the pattern of job growth within the inner city, finding that tracts that grew faster tended to be closer to downtown, with
access to transit, and adjacent to areas with higher population growth. However,
tracts with higher poverty rates experienced less job growth, indicating that barriers still exist in the inner city.

JEL Classification: R12 , O18
Keywords: Urban Labor Markets, City and Suburban Employment

Suggested citation: Hartley, Daniel A., Nikhil Kaza, and T. William Lester, 2015.
“Are America’s Inner Cities Competitive? Evidence from the 2000s,” Federal
Reserve Bank of Cleveland, working paper no 15-03.

Daniel A. Hartley is at the Federal Reserve Bank of Cleveland (daniel.hartley@
clev.frb.org); Nikhil Kaza is at the University of North Carolina, Chapel Hill
(kaza@unc.edu); and T. William Lester is at the University of North Carolina,
Chapel Hill (twlester@unc.edu). The authors extend special thanks for excellent
research assistance to Daniel Kolliner.

1. Introduction
In the years since Michael Porter’s seminal paper about the potential competitiveness of inner
cities, two narratives have emerged about the overall pattern of urban economic development. The
first, which we call the “comeback cities” narrative, states that the decades of the 1990s and 2000s were
a renaissance for cities as flows of population, jobs and investment shifted back from suburbs and
exurbs to urban areas, particularly downtowns (Grogan & Proscio, 2000; Sohmer & Lang, 2001). The
literature on gentrification, as well as the oft-cited creative class theories of Richard Florida underscore
this narrative by highlighting the pro-urban preferences and consumption patterns of a new middle class
(R. Florida, 2003; Neil Smith, 2002; Zukin, 1982). The second narrative that has taken shape is that of an
uneven geography of growth in the last few decades. Morreti’s The New Geography of Jobs (2012) and
the literature on high-technology regions argues that contemporary US economic development has
taken on a distinctly uneven pattern that leads to a polarization between so-called “innovative” regions
and “backward” regions, which in turn drives inequality and a divergence in outcomes across
metropolitan areas (Moretti, 2012; Pastor, Lester, & Scoggins, 2009; Saxenian, 1994). i The implication
of this second narrative is that the type of inner-city renaissance described in the first narrative will only
occur in growing, innovative regional economies.
Is this necessarily the case? Can inner-city economic growth occur in declining regions? Recent
research has demonstrated an empirical link between gentrification and neighborhood job growth
(Lester & Hartley, 2014). Is the type of consumption-based growth that is fueled by gentrification in
growing regions like New York or the San Francisco Bay Area, the only mechanism to bring jobs back to
urban neighborhoods? In addition to private market-driven development, policy makers have
employed a host of economic development tools and distributed millions of dollars in funding targeted
towards business development and job growth in inner-city neighborhoods. Have tools such as

targeted tax credits (e.g. Empowerment Zone/Enterprise Community designation, Low Income Housing
Tax Credit (LIHTC)) influenced the pattern of inner city employment growth? In this paper, we explore
these intertwined narratives by describing the pattern of neighborhood based employment changes at a
national scale. We then test the validity of a number of competing claims about the competitiveness of
inner-city neighborhoods in terms of economic development during the 2000s.
First, using data at the census tract level from the Local Origin-Destination Employment Statistics
(LODES) program at the US Census Bureau, we begin by providing an overview of the extent and broad
characteristics of employment growth of inner-cities, Central Business Districts (CBDs), and suburban
areas of all metropolitan areas in the U.S. Surprisingly, we find that the rate of job growth between
2002 and 2011 1 in inner-cities—defined as non-CBD tracts in large principal cities within a metropolitan
area—was on par with that of suburban areas (6.1% versus 6.9%) and even surpassed suburbs in the
post-Great recession recovery (2009-11). This trend is consistent across broad census regions and
industrial sectors.
Next, we explicitly test the question of inner-city competitiveness by considering 120
metropolitan areas, which had net positive employment growth and an increase in the share of jobs
located in the inner city (these two criteria form our working definition of competitive inner cities). We
find that the regions with competitive inner-cities are diverse geographically, but, compared to other
metropolitan areas, tend to have above average growth in high-wage jobs, less racial segregation, and
less job sprawl.
Finally, we provide a descriptive analysis of the spatial determinants of inner-city growth at the
tract-level within inner-city areas. Specifically, we find that inner-city employment growth is positively

2002 and 2011 are the earliest and latest years available in our primary dataset. While the choice of study period
may impact the results slightly, we test for the robustness of the period choice by examining the post-recession
period (2009-2011) separately.

associated with neighborhoods closer to downtown, with nearby population increases, recent
residential construction, and other indicators of gentrification. We also find that employment grew
faster in areas with mixed uses and greater employment diversity. There is some evidence that
empowerment zone designation is associated with more employment growth. However, tracts with
high poverty levels have lower job growth.
The remainder of the paper is organized as follows: Section two reviews the research on the
competitiveness of inner cities, and puts our empirical analysis in the context of the literature inspired
by Porter’s work. Section three describes the main datasets and analytical methods used in our analysis.
Section four presents the descriptive analysis of the patterns of inner city job growth in aggregate and
describes our analysis of the characteristics of regions with competitive inner cities. Section five,
presents our model of tract-level correlates of inner-city employment growth. The final section
concludes the paper and summarizes our rich descriptive analysis of the nature of inner-city job growth
in the 2000s.

Literature Review
Michael Porter made a strong and influential argument that inner-city areas had important and

“unrecognized” competitive advantages as a business location. Specifically, he called for a privatesector-led economic development strategy which leveraged the strategic location of inner city
neighborhoods (near the CBD and key infrastructure), along with existing regional economic strengths,
as well as the local purchasing power and human resources of inner-city residents (Porter, 1997).
While he recognized a significant role for government (and non-profits), he also helped to highlight
regulatory barriers of high-taxes and red tape that prevented further private sector investment.
Looking back at his strategic recommendations and comparing them to current practice, it is easy to see
how influential they have been, as few contemporary urban economic developers or planners would
3

find much to disagree with. However, it is important to recall the context in which he was writing.
While the mid-1990s was a period of significant economic growth for the U.S., it followed nearly two
decades of economic restructuring which significantly altered the economic role of central cities and
changed the geography of employment opportunities throughout most metropolitan areas in the
country.
The decades of the 1970s and 1980s were characterized by a pattern of economic restructuring
that featured the dual trends of massive manufacturing job losses coupled with the continued
suburbanization of population and employment. These trends significantly reduced the base of job
opportunities for residents of inner city neighborhoods, which once housed many of the goods
producing jobs and a predominantly working-class workforce. The problem of declining employment in
older, inner-city neighborhoods and growth in emerging suburban areas was first recognized in the late
1960s by scholars like Kain (1968) who argued that housing discrimination coupled with lack of
opportunity in urban areas led to a persistently high unemployment of minority workers in inner cities.
While the “spatial mismatch” hypothesis has been a widely debated topic in the social sciences (see
Chapple, 2006; Ihlanfeldt & Sjoquist, 1989; Teitz & Chapple, 1998), the declining employment within
inner-city neighborhoods was widely viewed as a critical problem. To get a sense of how profoundly
scholars viewed the problem of the inner-city in the mid-1990s, we recall here the opening lines of
Galster and Killen’s (1995) article on the geography of metropolitan opportunity as follows:
“Horatio Alger lies dead in the streets of the inner city. For millions of Americans, the rags-toriches fable has been reduced to ashes just as surely as have many blocks in South Central Los
Angeles and other desperate inner-city communities. What once was a spring board of
socioeconomic mobility for generations…has for too many been transformed into a pit in which
perpetual deprivation and social dysfunction reign” (Galster and Killen, 1995, 7)

Scholars from a wide variety of disciplines attempted to diagnose the problems associated with
lack of inner-city employment opportunities, linking it broader issues of neighborhood decline including
high crime, persistent poverty, segregation and changing attitudes toward work (Kasarda, 1993; Katz,
1993; Wilson, 1987, 1996).
The issue of declining inner-city employment and population losses coupled with continued
suburbanization and sprawl also spawned concerns that declining central cities could pose a drag to an
entire region’s economic growth. This, in turn, ignited a series of studies specifically focused on the
question of whether or not suburbs could prosper without their central cities (Hill, Wolman, & Ford,
1995; Ledebur & Barnes, 1993; Voith, 1992, 1998). The question seems appropriate in light of Pack’s
(2002) comprehensive analysis of long-term trends in metropolitan economic performance which finds
that between 1960 and 1990 the share of income earned by central city residents declined from 45% to
30% and rose in suburban areas from 55% to 70% (Pack, 2002, 3). Although a great deal of empirical
work focused on the issue of inner city competitiveness and the inter-dependency of suburbs and cities,
eventually a consensus emerged supporting the idea that the economic health of both areas was closely
linked by regional factors. Voith (1992) concluded, “decline in central cities is likely to be associated
with slow-growing suburbs. Even if the most acute problems associated with urban decline do not arise
in the suburbs, central city decline is likely to be a long-run, slow drain on the economic and social
vitality of the region.” (Voith, 1992, 31)
Just as the attention of federal policy makers shifted away from defining economic challenges in
in stark urban versus suburban terms, the academic literature shifted in the following decade to
questions of the determinants of overall metropolitan economic competitiveness. The key question
here was what factors explained the relative economic health and resilience of some metropolitan
regions, particularly those with a growing, high-technology industrial base. The work of Saxenian (1994),
Storper (1997) and others argued that metropolitan areas that featured regionally-based networks of
5

firms and supporting institutions that foster accelerated innovation were ultimately more resilient to
economic restructuring and, as a result, are more competitive in terms of employment and income
growth. This emphasis on innovation and regional competitiveness in the economic development
literature had a profound impact on practice (Clark, 2013) and shifted the focus away from intrametropolitan disparities and instead highlighted the overall uneven pattern of metropolitan growth in
the 1990s and 2000s. Recently, Moretti (2012) makes a strong argument in The New Geography of Jobs
that there is a growing disparity between what he calls “innovative regions”—which will garner a
disproportionate share of employment and income growth in the coming decades—and “non-innovative
regions” which will continue to be plagued by stagnant economic growth. The divergent trend was
identified empirically by (Pastor, et al., 2009).
Starting in the early 2000s, a new narrative began to emerge on “comeback cites” as many
scholars used newly available census data to identify a growing trend of residential growth particularly
focused in the downtown and nearby areas of older central cities (Sohmer & Lang, 2001). Much of this
research highlighted shifting demographics such as the aging of the population (i.e. empty nesters
without children) and changing preferences for high-amenity locations like downtown as the causes of
residential resurgence of downtown areas. This research is largely congruent with a pre-existing
literature on the causes and consequences of gentrification. What began as a niche field that focused
on select neighborhoods in places like the Lower East Side in New York (Smith, 1996) or the South End in
Boston and was initially considered a relatively small trend (Wyly & Hammel, 1999), has now grown to
be an active literature drawing scholarship from a wide variety of disciplines. While much of the
empirical debate in the gentrification literature focused on measuring the extent of displacement
(Freeman, 2005; Marcuse, 1985; Vigdor, 2002) within individual cities, there is growing consensus that
gentrification is part of a broader demographic shift that results in the influx of better educated and high
income households to formerly low and moderate income inner-city neighborhoods. The drivers of this
6

trend are seen to involve changes in the consumption and locational preferences of what some
sociologists called a “new middle class” (Ley, 1996) and what Florida (2002) later termed the “creative
class.” Regardless of their moniker, members of this demographic sub-group favor urban living and the
greater accessibility it affords over the suburban dream of previous generations. According to these
scholars, such preference shifts also drive gentrification by increasing demand for urban entertainment
and consumption spaces for the new high-income residents (Lloyd & Clark, 2001; Zukin, 1982).
While scholars continue to debate how widespread and significant gentrification is as a
demographic trend and what it will ultimate mean for inner cities, there is a growing literature that has
examined the impact of gentrification on employment within inner-city neighborhoods. Curran (2004;
2007) focused on a single neighborhood-Williamsburg in Brooklyn—and found that new residential
growth led to displacement of nearby industrial jobs. More recently Lester and Hartley (2014)
examined the impact of gentrification at the census tract-level using detailed employment data for 29
large cities in the US and found that gentrifying neighborhoods had faster employment growth and a
more rapid shift between traditionally blue collar work and locally oriented services such as restaurants
and entertainment. Beyond these studies, there have been relatively few papers that specifically look
at the nature of employment growth in inner-cities. There have been individual case studies such as
Hutton’s (2004) description of the emergence of new high-tech industry clusters in Vancouver, BC. In
addition, there are two new reports that focus on the long-term residential shifts of poor neighborhoods
in U.S. metropolitan areas, which suggest that the gentrification or “back to the city” trend may be
limited, or is bypassing high-poverty neighborhoods. Specifically, Cortright and Mahmoudi (2014) find
that 69 percent of census tracts with high poverty levels (30%) in 1970 still had high poverty levels in
2010. Aliprantis, Fee, and Oliver (2014) examine patterns of tract-level income change between 1980
and 2010 and find considerable stability in tract-level income quartiles over time. However, they also
find that tract-level income growth varied widely by metropolitan characteristics, as tracts that
7

transitioned from poor to non-poor were more likely to be located in growing metropolitan areas that
were relatively large and densely populated and which experienced greater immigration.
Given the potentially conflicting evidence about demographic trends affecting the inner city and
the relative paucity of research on recent inner city employment trends, we argue that there is a need
for a comprehensive analysis of job growth in America’s inner cities over the past decade. Porter (1997)
recognized this need early on, but lamented that there was no single source of localized, workplacebased employment statistics to track the changing economic role of inner city neighborhoods and to
assess how much private investment “already recognized” the competitive potential of the inner city.
Now we have such a data source; namely the Local Origin-Destination Employment Statistics (LODES)
(see below). Ultimately, this paper will use a descriptive approach that revisits some of the key
questions in the preceding literature. First, we assess the actual extent of job growth that has occurred
in America’s inner cities relative to suburban areas and CBDs between 2002 and 2011, highlighting key
differences by broad geographic regions, industrial sector, and tract poverty status. Next, we return to
the question of inner city competitiveness by defining a new method for identifying which regions have
competitive inner cities and what distinguishes them from the rest of the metropolitan pack. Finally, we
test some of the predictions of the gentrification literature and build a simple descriptive model of inner
city job growth at the tract level.

3. Data Sources and Methods

We primarily use data from the US Census Bureau’s Longitudinal Employment and Household
Dynamics (LEHD) dataset. Specifically we use special tabulations of the LEHD data created for local
transportation and workforce development analysis called the Local Origin-Destination Employment
Statistics (LODES) program. The dataset is available at a 2010 block-group-level geography. Total
8

employment and employment by broad industry sector from 2002 through 2011 is summarized to a
tract level for the purposes of this analysis. While the dataset is available for the most of the United
States, some states are missing from the analysis because of data non-availability for the full period of
analysis. These include Arizona, Arkansas, the District of Columbia, Mississippi, New Hampshire and
Massachusetts which began participation in the LEHD at various years throughout the period and
therefore do not figure in the current analysis.
While the LODES data is also available on a worker residence basis, we use workplace-based
counts of employment, as we are primarily interested in the changing geography of employment
between inner-city tracts and other components of metropolitan areas. The LODES dataset is a
synthetic dataset derived from confidential data sources such as unemployment insurance records,
TIGER line files and additional administrative data from the US Census and the Social Security
Administration. Noise is then infused into the workplace totals to protect employer and employee
confidentiality. These data production methods and caveats should be considered while evaluating the
evidence presented in this analysis. For a more complete description of the LODES dataset and its
differences with the standard Census products such as the American Community Survey (ACS) refer to
Graham et.al (2014).

3.1 Identifying the Inner City

As discussed above, while there is significant research on the competitiveness of inner cities, it is very
hard to find a commonly accepted definition in the literature as to what constitutes an inner-city area.
Generally speaking, inner cities are understood as relatively poor areas with high concentration of
minorities within large central cities. While nearly all scholars distinguish the inner city from suburban
areas and traditional downtowns, there is little agreement on the essential characteristics of inner-city
neighborhoods. Porter implies that these areas are “distressed neighborhoods, in which, in most cases,

African Americans and other people of color represent the majority of the population (Porter, 1997. p.
11)”. Yet, more recent studies, such as Hutton (2004), simply look at all non-downtown portions of the
central city. Ultimately, the literature lacks a systematic delineation of the geographic or jurisdictional
extent of inner cities.
As a first approximation, we define inner cities as areas of the largest central city or main cities in a
Metropolitan Statistical Area (MSA) that are not part of the Central Business District. To identify the
main central cities in each MSA, we consider the official set of Principal Cities ii within an MSA (as defined
by the US Census) and select the largest of those principal cities that collectively make up more than half
of the principal city population within the MSA. Such identification of main cities in an MSA eliminates
classifying older suburban satellite cities (e.g. Schaumburg, IL) as inner cities but still retains the
flexibility of having multiple inner city clusters within an MSA. For example, in Minnesota both
Minneapolis and St. Paul are considered the main cities and the tracts that are not within the CBDs of
these cities are considered inner city areas. In general, a vast majority of the 281 MSAs considered in
this analysis have only one main central city from which we draw our definition of inner-city tracts.

3.1Identifying the CBD

In order to classify census tracts as inner-city or not, we needed to clearly define the central

business district (CBD) or downtown of each principal city. In addition to lack of definition of inner city,
there is also no accepted current definition and delineation of a CBD. The last known delineation of the
CBD was done in 1982 in the Census of Retail Trade. To update this identification we first identify all
employment centers in an MSA. We then identify the cluster of employment centers that overlap the
point definitions of CBD provided by Fee & Hartley (2011) and call them the central business districts
(CBDs) within the MSA.

The employment centers are identified using methods detailed by McMillen (2001, 2003).
Briefly, a locally weighted regression is constructed using the employment densities at a tract level. The
weighting is a smoothing function that accounts for the spatially nearest 50 percent of the density
values. A tract is identified as an employment center if the residuals are significantly greater than 0,
accounting for the standard error of the estimate. This non-parametric method has been used to
identify employment centers in a number of studies (Garcia-López, 2010; Suárez & Delgado, 2009).
Once the tracts that have a higher than expected residuals are identified within an MSA, a contiguity
matrix is constructed using ‘spdep’ (Bivand, 2015). The contiguity matrices are converted to a graph,
where nodes are the identified census tracts and a pair of nodes have an edge if the corresponding
contiguity matrix element is non-zero using `igraph’ (Csardi & Nepusz, 2006). Once the graph is
constructed, standard graph theoretic methods are used to decompose the graph into maximally
connected components. If any of the census tracts within a maximally connected cluster overlaps with
the CBD point, then we designate the entire cluster a central business district.
[FIGURE 1 ABOUT HERE]

3.2 Other Data Sources

To conduct our descriptive analysis comparing metropolitan regions with competitive inner

cities to other regions, and for our tract-level determinants of inner city job growth we also draw upon
several other data sources. The two main sources of additional data beyond the LODES dataset are the
Smart Location Database (SLD) produced by the U.S. Environmental Protection Agency (EPA) iii and the
Building Resilient Regions (BRR) database. The BRR database is a comprehensive dataset on
demographic, economic, and policy variables for all metropolitan areas in the U.S. (mainly derived from
Census data) and was produced by the MacArthur Foundation’s Building Resilient Regions research
network (see Pastor et. al, 2009 for more information).

4. Employment Trends and the Competitiveness of Inner Cities

4.1 The Nature of Inner City Employment Change in the U.S. in the 2000s.
Compared to the preceding two decades, the 2000s was a period of relatively stable job growth
for America’s inner cities. During the nine year period from 2002—just after a mild recession—to 2011
two years after the end of the Great Recession—inner city census tracts added 1.8 million net new jobs.
Surprisingly, this rate of growth (6.1 percent) was roughly comparable to the rate of growth observed in
suburban areas (6.9 percent). However, suburbs still added nearly twice as many total positions and
maintain the preponderance of all metropolitan jobs Over the study period, inner cities grew faster
than non-metropolitan areas (2.3 percent) and CBDs, which declined by 1.6 percent. As indicated in
Table 1 below, the post-Great Recession period (2009-2011) was particularly favorable to inner cities as
their growth rate actually surpassed the suburban rate (3.6 versus 3.0) and nearly 1 in 3 jobs created
during this period was located in the inner city.
[TABLE 1 ABOUT HERE]

Given some concern in the literature that the “comeback cities” narrative is limited primarily to
only a select set of coastal cities such as New York, Washington, and San Francisco, we examined the
same employment trends in each of the nine census divisions across the country (see Figure 2). Looking
at the full period, this observation holds somewhat. Although inner city growth was positive in all
divisions except the East North Central (which declined as a whole), it outpaced suburban areas in only
the Mid Atlantic (which includes New York) and the Pacific census divisions.
[FIGURE 2 ABOUT HERE]

In the post-recession period however, inner cities were considerably more competitive vis-à-vis the
suburbs throughout the country, growing faster in five out of nine divisions and rebounding strongly
even in the rust-belt East North Central area. The outlier region seems to be West South Central where
suburban job growth consistently swamped both CBD and inner city areas. Although this is a relatively
small period, the post-recession evidence is indicative of a relatively urban-based recovery.
While total employment increased within inner-city tracts in aggregate, there have been
significant industrial shifts occurring within inner cities as they continue to transition away from goods
producing sectors and towards relatively place-bound service sector industries. In Figure 3 we analyze
net employment change for the full period (2002-11) and the post-recession period for all of the inner
city tracts for the U.S. as a whole. Not surprisingly, the greatest losses occurred in manufacturing (782,000 jobs), followed by construction (-224,000), which was particularly hard hit by the housing crisis
and recession. The strongest gaining industries were the so-called “eds and meds” sectors of Health
Care and Social Assistance and Educational Services, which added 1.1 million and 633,000 jobs
respectively. This finding makes sense since many institutions such as universities and hospitals were
founded in the past century in inner-city neighborhoods, have remained in those neighborhoods, and
have proved resilient to the wider economic changes that affected the inner city during the 1970s and
1980s. The economic role of universities and their expanding hospitals is critical in areas like West
Philadelphia (home to the University of Pennsylvania and Drexel University) and Hyde Park (home to the
University of Chicago). Inner city areas also saw strong growth in the Accommodation and Food Services
sector (323,000 jobs), which includes restaurants and is consistent with the findings in the gentrification
literatures on the changing economic role of inner cities from spaces of production to spaces of
consumption.
[FIGURE 3 ABOUT HERE]

Since our definition of inner city is quite broad, including all non-CBD portions of the largest
principal city in each MSA, we also sought to understand if the net positive employment growth was
limited to areas that were initially higher income enclaves within the city. To test this, we categorized
each census tract by its poverty status in 2000. Since much of the literature in the 1990s focused on
high poverty neighborhoods and declining employment therein, we also included the tract poverty
status in 1990.
[TABLE 2 ABOUT HERE]
As Table 2 indicates, the large majority of inner city job creation occurred in areas where less
than 20 percent of residents earned incomes below the poverty line (79% for 1990 and 73% for 2000).
In addition, lower poverty areas maintained a much larger share of total jobs (by a factor of two)
compared to high poverty tracts. What is interesting about this tabulation is that the figures for highpoverty tracts are positive at all, given all the preceding discussion of job flight and neighborhood
decline. Most interesting is the fact that, while they only have a small share of total jobs, the growth
rate of tracts with extreme poverty (over 40 percent) was the faster than low-poverty tracts.
4.2 Inner city competitiveness at the metropolitan scale
The decade of the 2000s was significant in the long-term economic trajectory of inner cities over
the past 40 years because it marked a reversal of the trend of large-scale job losses and decline. But
does this necessarily mean that inner cities are now more competitive locations for business expansion
and job growth compared to suburban areas? We revisit the question of inner city competitiveness by
exploring the nature of inner city job growth in nearly all metropolitan areas in the U.S. and attempting
to determine the extent of inner city competitiveness and the regional factors that influence the
growing competitiveness of inner cities in certain MSAs.

However, the uneven pattern of metropolitan growth itself clearly plays a role for the prospects
of inner city change. In general, larger MSAs experienced larger total employment growth over the
study period. Places like San Antonio, TX and Los Angeles, CA experienced substantial growth in
metropolitan employment and also experienced significant growth in inner city employment. However,
metropolitan area growth does not always coincide with employment growth in the inner city. For
example, in places like Houston and Dallas, TX while the inner city employment growth is positive,
suburban growth overshadows the inner city. Therefore, we wanted to develop a method of defining
inner city competitiveness that accounted for overall metropolitan growth and identified MSAs where
job growth was disproportionately focused on the inner city during the 2000s.
[FIGURE 4 ABOUT HERE]
To identify which inner cities are competitive over our study period, we examined the relative
change in the proportion of inner city employment within its metropolitan area (see Figure 4). Within
each quadrant we plot the 2002 inner city share and 2011 inner city share of total metropolitan area
employment. We then divide the whole data set into four groups based on whether or not total
employment in the metropolitan area grew or declined (horizontal axis) and whether there was net
positive inner city job growth or not (vertical axis). While there are a few inner cities that have grown
despite the overall metropolitan area decline (SW quadrant), the vast majority of observations with
positive inner city employment growth also had positive regional growth. However, since we are
interested in “competitive” inner cities, we focus on those metropolitan areas with both positive
employment growth and where inner cities increased their share of jobs. These metros are above the
45° line in the SE quadrant of Figure 4. Specifically, we find that 120 out of a total set of 281
metropolitan areas (43 percent) have “competitive inner cities.” We label these metros as metros with
competitive inner cities and compare their characteristics with the other metropolitan areas in the

sample. A complete list of these metropolitan areas is provided in Appendix A. The metropolitan areas
that are on this list are quite diverse ranging from large metros to more moderate size ones. In general,
the change in the share of employment in the inner city is modest between 2002 and 2011, except in a
few metropolitan areas.
Next, we compared these metropolitan areas with competitive inner cities to the rest of the
metros in the sample (see Figure 5). There is no difference between proportions of jobs in the
concentrated employment sub-centers between the two groups (as defined using McMillen 2003’s
method). However, high wage job growth both at the metro level and within the inner city stand out.
Competitive inner cities, in general, have experienced significant high wage job growth. Further research
is needed to address the question of whether this high wage job growth is a cause or an effect of
“competitiveness”.
[FIGURE 5 ABOUT HERE]
Metropolitan areas that have a lower black-white dissimilarity index—an indicator of
segregation at the metro-level—are more likely to have a competitive inner city. This is consistent with
the work of Pastor (Pastor, Drier, & al., 2000) and others who argue that regions where segregation is
less pronounced are more likely to produced balanced economic growth. We find that metros with
competitive inner cities have lower average black-white dissimilarity indices in 2000s compared to their
peers. However, the two groups have the similar distribution of dissimilarity indices with the foreign
born and native born, suggesting smaller influence of cross-national migration on competitive inner
cities.
Competitive inner city metropolitan areas had higher poverty rates in 2000, suggesting higher
poverty rates are not a constraint for economic development. There are only small differences in the
means of the median household income between the two groups. However, the means tell only part of
16

the story. The distributions are quite different. The median income distribution of the competitive
metropolitan areas is skewed to the left compared to the rest of the metros. Furthermore, higher
poverty rates, especially in inner cities might suggest redevelopment opportunities.
Metropolitan areas with competitive inner cities, on average, have higher average job
accessibility. Accessibility is measured at the block group level as the percentage of the jobs in the
metro that can be accessed within a 45 minute commute; the figure is then averaged for all block groups
in the metropolitan area. This difference disappears when we compare the average block group
accessibility based on transit service. While we should expect to see higher competitiveness of metros
with high quality transit, this result is likely due to persistent low levels of transit provision and usage in
the United States.
Neither the population distribution, nor the proportion of creative jobs is significantly different
in the competitive metropolitan areas from the rest of the metros. The Theil Index of population density
represents skewness in the population density distribution. Higher Theil index metropolitan areas are
metros with some tracts with large population densities and the rest very low population density, while
a lower Theil index means the metropolitan area has relatively uniform population density. The results
suggest that concentrations of density are not different between the two groups of the metropolitan
areas.

5. Tract-Level Drivers of Inner City Employment Growth
What are the characteristics of inner city neighborhoods that experience employment growth?
In this section, we present census tract-level regressions to examine the correlates of employment
growth during the 2000s; focusing on variation in tract-level measures within these inner cities. Our

sample consists of the non-CBD census tracts of 106 large principal cities (within each metropolitan
area) which had at least 30 census tracts once the CBD tracts were excluded. We use 2010 census tract
boundaries and consider the degree to which changes in log employment from 2002 to 2011 are
associated with a number of explanatory variables.
[1]

∆𝑒𝑒𝑒𝑖,𝑐 = 𝛼𝑐 + 𝛽𝑑 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖,𝑐 + 𝛽𝑒 𝑒𝑒𝑒𝑖,𝑐 + 𝛽𝑟 𝑟𝑟𝑟𝑖,𝑐 + 𝛽𝑙 𝑙𝑙𝑐𝑖,𝑐 + 𝛽𝑝 𝑝𝑝𝑝𝑖,𝑐 + 𝜖𝑖 ,

where the dependent variable, ∆𝑒𝑒𝑒𝑖,𝑐 represents the change in the log of census tract employment

from 2002 to 2011 in tract,𝑖, in city, 𝑐. The explanatory variables are, 𝛼𝑐 , a city fixed effect, 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑖,𝑐 ,
the log of the distance (in miles) from the centroid of the tract to the centroid of the CBD, 𝑒𝑒𝑒𝑖,𝑐 , the
log of tract-level employment in 2002, 𝑟𝑟𝑟𝑖,𝑐 , a vector of variables describing the residential

characteristics of the tract, 𝑙𝑙𝑙𝑖,𝑐 , a vector of location factors which measure the accessibility of the tract
vis-à-vis the transportation network, 𝑝𝑝𝑝𝑖,𝑐 , a vector describing whether certain place-based policies

were in effect in the tract, and an error term, 𝜖𝑖 .

The vector of residential characteristics, 𝑟𝑟𝑟𝑖,𝑐 , includes the log of the tract population in 2000,

the change in the log of the sum of the population in all contiguous tracts, the poverty rate in 2000, the
change in the share of the population with a college or higher degree, the share of occupied housing
units in which the residents moved in between 2000 and 2010, and the share of the housing units that
were built between 2000 and 2010. These variables are included to capture both the overall socioeconomic characteristics of the tract itself as well as provide some indicators of gentrification by
accounting for recent building activity and recent changes in population around the tract in question. It
is important to note that we are simply describing associations between tract-level employment growth
and measures related to gentrification. It is likely that there is some degree of endogeneity between
local employment growth and gentrification.

The vector of location factors, 𝑙𝑙𝑙𝑖,𝑐 , includes the gross residential density of the tract measured

in housing units per acre, an entropy index of the industrial diversity of the tract, a measure of

automobile accessibility (the number of automobile-oriented road links per square mile), a measure of
pedestrian accessibility (the number of pedestrian-oriented road links per square mile), and an indicator
of whether the tract contains any public transit stops. The public transit indicator variable is only
available for 55 of the 106 cities in our sample. We set this variable equal to negative one for all
observations in the cities for which it is missing. Inclusion of city fixed effects ensure that the estimation
of the coefficient on this variable will be due to within-city variation in public transit stop presence in
cities for which we do have public transit data.
The vector of placed-based policies, 𝑝𝑝𝑝𝑖,𝑐 , includes an indicator of whether the tract contains

any Low Income Housing Tax Credit developments and an indicator of whether the tract has been

designated an Empowerment Zone or Renewal Community. iv Appendix B contains a table of descriptive
statistics for all independent variables in our regression sample.
Table 3 presents our tract-level regression results aimed at revealing some of the correlates of
within-city variation in non-CBD inner city employment growth. The table shows four specifications,
with an increasing number of explanatory variables. The specification in column (1) includes on the log
of the distance from the centroid of the tract to the CBD. The coefficient of 0.066 means that tracts that
are twice as far from the CBD have on average 4.6 more log points of employment growth (0.69 * 0.066
= 0.046). The specification in column (2) adds the log of initial year (2002) employment. This variable is
added to help mitigate potential measurement error problems in the tract-level employment data.
Adding this control reduces the magnitude of the coefficient on the distance to CBD measure. Column
(3) adds local demand variables in the form the log of the tracts own initial year population and the
change in the log of the population of all of the tracts that share a border with the tract. In this

specification changes in the local area (neighboring tract) population are correlated with tract-level
employment growth. The coefficient of 0.535 implies that, on average, a 10 log point increase in
neighboring tract population is associated with a 5 log point increase in own-tract employment.
[TABLE 3 ABOUT HERE]
The specification in column (4) contains our full set of tract-level explanatory variables. The first
thing that stands out is that the sign of the coefficient on the log of distance to CBD is now negative and
is statistically different from zero. Conditional on all the other explanatory variables, employment
growth is negatively correlated with distance to the CBD. In other words, controlling for other factors,
neighborhoods closer to downtown added jobs at a faster rate than those further away, indicating the
importance of proximity to the largest concentration of employment in region. The log of initial year
(2000) tract population is now positively related to employment growth. The change in the log
population of neighboring tracts is still positively related to employment growth but conditional on all
the other explanatory variables the coefficient has dropped to about half of its value in column (3).
Higher poverty rate tracts are associated with less employment growth. All else equal, 10 percentage
points higher poverty rate is associated with 2.4 fewer log points of employment growth. Thus
neighborhood poverty still seems to be a deterrent to local employment growth.
The coefficient on the change in the share with a college degree is positive but not statistically
different from zero. While we would expect that this would be an important variable, given the
literature on gentrification and then urban preferences of the creative class, it is likely that the effect of
this variable is usurped by the next two variables, which are also indicators of residential changes.
Specifically, the share of occupied housing units with residents that moved in during the 2000s (an
indicator or recent migration to the area) is positively correlated with employment growth. This higher
residential turnover is consistent with urban re-development. Further evidence that employment
20

growth and re-development are correlated comes from the positive coefficient on the share of housing
units built during the 2000s. It appears that tracts with 10 percentage points higher share of units built
during the 2000s have, on average, 6.6 logs point higher employment growth.
The coefficient on residential density is negative, though not statistically significant. This is not
surprising as tracts which have mostly residential uses (and thus higher density) have little room left for
commercial land-uses and the jobs located therein. Industrial diversity –measured as the 5 category
employment entropy index—is positively correlated with employment growth over the period.
Automobile accessibility shows a positive association with employment growth while pedestrian
accessibility is negatively associated with employment growth. This finding indicates the importance of
auto accessibility for local employment growth in sectors such as retail. While the negative association
for pedestrian oriented streets is indicative low growth in tracts that are primarily residential. Even in
inner city neighborhoods in older cities, such as Chicago, employment growth is still concentrated in
commercial corridors along arterial roads. Finally, there is a statistically significant association between
the presence of a public transit stop and employment growth. The coefficient implies that tracts
containing public transit stops saw roughly 6.7 log-points higher employment growth than those without
a public transit stop.
There is no clear association between the presence of Low Income Housing Tax Credit
developments and employment growth. There is a marginally statistically significant positive
relationship between Empowerment Zone / Renewal Community status and employment growth. While
we do not consider this strong causal evidence of the effectiveness of EZ/RC policies, it is consistent with
the findings of recent research (Busso, Gregory, & Kline, 2010) . On average, tracts in these programs
saw about 5.3 log-points higher employment growth than other inner city tracts.

The specification in column (4) has an R-squared of 0.23, meaning that our full set of
explanatory variables can explain about a quarter of the variation in tract-level employment growth. In
specifications without the city fixed effect (not shown) the R-squared drops to 0.19 and without the log
of initial year employment it drops to 0.12.

6. Conclusion
For America’s inner cities as a whole, the decade of the 2000s stands in stark relief compared to
the 1980s and 1990s in terms of job growth. Using a dataset that was unavailable in the past (LODES),
we show that inner city tracts (those in the non-CBD portions of the large central cities) added 1.8
million jobs between 2002 and 2011. This trend is not just limited to a few cities and regions, as inner
city growth was positive in nearly all census divisions and even outpaced suburban growth rates in some
areas. The post-recession period has been even stronger for inner cities. While the overall national
trend is encouraging given the scale of job losses in previous decades, this growth is probably not
enough to declare a “renaissance” in urban America.
Clearly, regional growth differentials are important, as the literature on city-suburban dependence
indicates. It is not surprising that New York City and San Francisco have much higher inner city
employment growth as they are located within strong, growing metropolitan areas. However, in places
like Dallas and Houston which also grew, suburban employment continues to outpace inner city
employment, suggesting important differences in characteristics and policies of the metropolitan areas
that result in competitive inner cities. Yet, places known for their suburban dominance such as Los
Angeles and San Antonio showed strong inner city resurgence in the last decade. Thus, competitive
inner cities emerged in some unlikely places. We find that while competitive inner cities are no longer
the exception, they are also not universal. Two fifths (120 out of 281) of the metros studied in this

analysis have seen both increases in overall employment as well as share of inner city employment.
Much of the growth in these metropolitan areas is driven by growth in the high wage sectors.
There are also important differences in the nature of job growth by sector. The inner city
resurgence has been led by Health Care and Educational Services; at the same time losses in
manufacturing and construction jobs continue in the inner city reflecting the twin trends of globalization
and suburbanization of manufacturing. Within inner cities, access to physical infrastructure (e.g.
proximity to CBD, transit), as well as social infrastructure (e.g. population increases nearby) are
associated with significant advantage for job growth. However, if access to infrastructure is one of the
competitive strengths of the inner cities, it is not reflected in the job growth in the sectors that largely
depend on infrastructure (such as manufacturing). Instead, the job growth is driven by residentiary
sectors such as food services supporting some claims from the gentrification literature that inner city job
growth is fueled at least in part by recent residential growth.
However, our findings also indicate that inner city job growth tends to be greater in areas that
are relatively less poor. Thus, even though metros with competitive inner cities have higher poverty
rates, on average, high poverty neighborhoods within inner cities still seem to have major barriers that
limit more robust employment gains. It is here that there may be a continued role for government
intervention. Our finding that tracts designated as either an Empowerment Zone or Renewal
Community grew faster, on average, than other tracts suggests that economic development strategies
that are targeted to high-poverty areas can play a role. Our results suggest that, overall, inner city areas
do have real advantages as locations for employment and are increasingly viewed as an attractive
residential location.

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Tables and Figures

Table 1. Employment Change in CBD, Inner City, Suburban and Non-metro tracts, 2002-11.
Year
Total Employment
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011

CBD

Inner City

Suburban

Non-Metro

Total

9,806,579
9,466,413
9,368,606
9,391,107
9,502,148
9,462,838
9,422,301
9,405,450
9,599,146
9,654,338

29,699,043
29,406,696
29,688,362
30,143,171
30,512,600
31,030,945
31,082,578
30,425,117
30,796,523
31,521,499

59,207,009
59,122,731
60,114,666
61,722,807
62,976,328
64,129,684
64,323,119
61,460,391
61,733,541
63,296,946

15,401,902
15,351,324
15,481,987
15,711,700
15,969,935
16,113,194
16,120,592
15,426,231
15,466,790
15,758,332

114,114,533
113,347,164
114,653,621
116,968,785
118,961,011
120,736,661
120,948,590
116,717,189
117,596,000
120,231,115

Net Employment
(152,241)
1,822,456
4,089,937
356,430
6,116,582
Change (2002-11)
% Change
-1.6%
6.1%
6.9%
2.3%
5.4%
Post-Recession Net
248,888
1,096,382
1,836,555
332,101
3,513,926
Change (2009-11)
% Change
2.6%
3.6%
3.0%
2.2%
3.0%
Share of US Emp., 2002
9.9%
30.1%
60.0%
15.6%
Share of US Emp., 2011
9.2%
30.0%
60.3%
15.7%
Note: Authors analysis of LODES data by tract-type for states with full sample (2002-11).

Table 2. Inner city Employment Change by Tract Poverty Status
Employment Measure
Total Employment, 2002
% of Inner City Jobs, 2011
Total Employment, 2009
% of Inner City Jobs, 2011
Total Employment, 2011
% of Inner City Jobs, 2011
Net Employment Change
(2002-11)
% Change
Net Employment Change
(2009-11)
% Change

Tract Poverty Status, 1990
Low Poverty
High
Extreme
(<20%)
Poverty
Poverty
(>20%)
(>40%)
19,843,121
9,855,922
2,879,470
66.8%
33.2%
9.7%
20,581,454
9,843,663
2,936,604
67.6%
32.4%
9.7%
21,296,609
10,224,890 3,183,065
67.6%
32.4%
10.1%

Tract Poverty Status, 2000
Low
High
Extreme
Poverty
Poverty
Poverty
(<20%)
>20%
(>40%)
19,779,094 9,919,949 2,177,597
66.6%
33.4%
7.3%
20,440,333 9,984,784 2,206,598
67.2%
32.8%
7.3%
21,116,880 10,404,619 2,352,930
67.0%
33.0%
7.5%

1,453,488

368,968

303,595

1,337,786

484,670

175,333

7.3%
715,155

3.7%
381,227

10.5%
246,461

6.8%
676,547

4.9%
419,835

8.1%
146,332

3.5%

3.9%

8.4%

3.3%

4.2%

6.6%

Source: Authors analysis of Local Origin-Destination Employment Statistics (LODES) data, 2002-2011.

Table 3: OLS Regression Results: Predictors of Tract-level Employment Growth, 2002-2011.
Variable
log Distance to CBD

(1)

(2)

0.066*** 0.037***
(0.009)
(0.009)
-0.152***
(0.006)

log Employment 2002
log Population 2000
Change in log pop in neighboring tracts, 2000-2010
Poverty Rate, 2000

(3)

0.006
(0.009)
-0.15***
(0.006)
-0.007
(0.014)
0.535***
(0.045)

(4)
**

-0.029

(0.012)
***

-0.202

(0.007)
0.076***
(0.014)
0.256***
(0.042)
-0.24***
(0.072)

Change in Share with College Degree, 2000-2010

0.107
(0.098)

Share of occupied housing with new residents, 2000-2010

0.246***
(0.065)

Share of housing units built between 2000 and 2010

0.662***
(0.066)

Residential density (Units/Acre)

-0.001
(0.001)

Industrial diversity index (5 category entropy index)

***

0.464

(0.035)

Automobile Accessibility (links per square mile)

0.01***
(0.003)

Pedestrian Accessibility (links per square mile)

***

-0.008

(0.002)

Public transit stop in tract?

0.067*
(0.041)

Low Income Housing Tax Credit Development in Tract?

0.008
(0.014)

Empowerment Zone/Renewal Community?

0.053*
(0.031)

0.2309
R
11,837
11,837
11,837
11,837
N
Note: Robust standard errors in parentheses below estimate. *Significant at 10%. **Significant at 5%.
***Significant at 1%.
0.0753

0.1648

0.1864

Figure 1. Examples of Delineation of Inner City in the Cleveland, MSA

Figure 2. Employment Change by Tract-Type and Census Division, Full Period and Post-Recession

Employment Growth (2002-2011)

30.0%
25.0%
20.0%
15.0%
10.0%
5.0%

CBD

0.0%

Inner City
Suburban

-5.0%
-10.0%

Census Division

7.0%
Employment Growth, 2009-2011

6.0%
5.0%
4.0%
3.0%
2.0%
1.0%

CBD

0.0%

Inner-city

-1.0%

Suburban

-2.0%
-3.0%

Census Division

Figure 3. Inner-City Employment change by Major Industry Sector (NAICS)- Full Period (2002-11) and
Post Recession (2009-11).
Net Employment Change
(1,000,000) (500,000)

500,000

1,000,000

1,500,000

Health Care and Social Assistance (62)
Educational Srvs. (61)
Accommodation and Food Srvs. (72)
Professional and Technical Srvs. (54)
Public Administration (92)
Finance and Insurance (52)
Other Services (81)
Management of Companies and Ent. (55)
Administrative and Support Srvs. (56)
Arts, Entertainment, and Recreation (71)

Full Period
(2002-11)

Mining and Extraction (21)
Transportation and Warehousing (48-49)

Post-Recession
(2009-11)

Agriculture (11)
Utilities (22)
Real Estate and Rental and Leasing (53)
Retail Trade (44-45)
Information (51)
Wholesale Trade (42)
Construction (23)
Manufacturing (31-33)

Source: Authors analysis of Local Origin-Destination Employment Statistics (LODES) data, 2002-2011.

Figure 4. Relationship between Inner city proportion of employment in 2002 and 2011.

Source: Authors analysis of Local Origin-Destination Employment Statistics (LODES) data, 2002-2011.

Figure 5. Characteristics of Metropolitan Regions with Competitive Inner Cities Versus All other
Metros

Notes: Figure presents the difference in distribution of various indicators for metropolitan areas with competitive inner cities
and to the distribution for all other metropolitan areas. Sources: LODES (panel 1-3); Building Resilient Regions (BRR) Database
(panels 4-8); EPA Smart Location Database (Panels 9-11). All variables calculated at the metropolitan (CBSA) level. N=281.

A. Appendix: List of Regions with Competitive Inner Cities
CBSA
Palm Coast, FL
Ames, IA
Athens-Clarke County, GA
Blacksburg-Christiansburg-Radford, VA
Morgantown, WV
Lawrence, KS
Kokomo, IN
Columbia, MO
Jackson, TN
Appleton, WI
Bismarck, ND
Logan, UT-ID
Bowling Green, KY
Johnson City, TN
Jackson, MI
Clarksville, TN-KY
Ann Arbor, MI
Glens Falls, NY
Chattanooga, TN-GA
Springfield, IL
Springfield, OH
Atlantic City, NJ
Sumter, SC
Anchorage, AK
New Haven-Milford, CT
Port St. Lucie, FL
College Station-Bryan, TX
Laredo, TX
Salinas, CA
San Antonio, TX
Joplin, MO
Lexington-Fayette, KY
Lake Charles, LA
New York-Northern New Jersey-Long
Island, NY-NJ-PA
Sacramento–Arden-Arcade–Roseville,
CA
Oshkosh-Neenah, WI
Goldsboro, NC

Share of employment in the
inner city
2002
2011
0.392
0.628
0.109
0.305
0.569
0.726
0.063
0.213
0.145
0.273
0.468
0.593
0.265
0.386
0.492
0.602
0.573
0.681
0.250
0.340
0.300
0.373
0.249
0.319
0.629
0.698
0.338
0.405
0.210
0.274
0.359
0.419
0.483
0.542
0.046
0.105
0.523
0.580
0.473
0.529
0.392
0.448
0.077
0.133
0.220
0.275
0.807
0.860
0.104
0.156
0.159
0.210
0.183
0.226
0.674
0.716
0.101
0.142
0.650
0.690
0.122
0.162
0.614
0.652
0.277
0.315

Difference in
shares
0.236
0.196
0.156
0.150
0.128
0.125
0.121
0.111
0.108
0.090
0.074
0.070
0.069
0.066
0.064
0.061
0.059
0.058
0.057
0.056
0.056
0.056
0.055
0.053
0.052
0.050
0.043
0.042
0.041
0.040
0.040
0.038
0.038

0.368

0.403

0.035

0.195

0.229

0.035

0.291
0.407

0.325
0.441

0.034
0.034

Ocean City, NJ
Longview, WA
Lafayette, IN
Bakersfield, CA
St. Cloud, MN
Gadsden, AL
Redding, CA
Saginaw-Saginaw Township North, MI
Vineland-Millville-Bridgeton, NJ
Florence, SC
Warner Robins, GA
Bend, OR
Salem, OR
Corvallis, OR
Merced, CA
Brownsville-Harlingen, TX
Cleveland-Elyria-Mentor, OH
Corpus Christi, TX
Myrtle Beach-Conway-North Myrtle
Beach, SC
Chico, CA
Rome, GA
Florence-Muscle Shoals, AL
Dubuque, IA
San Jose-Sunnyvale-Santa Clara, CA
Auburn-Opelika, AL
Parkersburg-Marietta, WV-OH
Reno-Sparks, NV
Punta Gorda, FL
San Francisco-Oakland-Fremont, CA
Davenport-Moline-Rock Island, IA-IL
Durham, NC
Las Vegas-Paradise, NV
Utica-Rome, NY
Oxnard-Thousand Oaks-Ventura, CA
Bellingham, WA
Spartanburg, SC
Rochester, MN
Bloomington-Normal, IL
Duluth, MN-WI
Longview, TX
Palm Bay-Melbourne-Titusville, FL
Los Angeles-Long Beach-Santa Ana, CA
Tuscaloosa, AL
Fort Collins-Loveland, CO
Winston-Salem, NC
36

0.028
0.048
0.416
0.316
0.361
0.380
0.562
0.199
0.340
0.095
0.240
0.287
0.364
0.373
0.177
0.413
0.146
0.580

0.061
0.082
0.449
0.348
0.394
0.411
0.593
0.230
0.371
0.125
0.269
0.316
0.393
0.401
0.203
0.439
0.171
0.605

0.033
0.033
0.033
0.033
0.032
0.031
0.031
0.031
0.031
0.030
0.030
0.029
0.029
0.028
0.026
0.026
0.025
0.025

0.320

0.344

0.024

0.165
0.253
0.323
0.445
0.371
0.294
0.167
0.449
0.069
0.179
0.304
0.396
0.231
0.213
0.314
0.321
0.095
0.498
0.512
0.275
0.294
0.304
0.231
0.446
0.339
0.600

0.188
0.276
0.345
0.466
0.392
0.314
0.187
0.468
0.088
0.198
0.323
0.414
0.250
0.231
0.332
0.338
0.111
0.513
0.527
0.290
0.308
0.318
0.244
0.459
0.352
0.612

0.023
0.023
0.022
0.021
0.020
0.020
0.020
0.019
0.019
0.019
0.019
0.019
0.018
0.018
0.018
0.017
0.016
0.015
0.015
0.015
0.014
0.014
0.013
0.013
0.012
0.012

Portland-Vancouver-Beaverton, OR-WA
Abilene, TX
Yuba City, CA
Valdosta, GA
Columbus, GA-AL
Jacksonville, FL
Lakeland, FL
Oklahoma City, OK
Columbia, SC
Providence-New Bedford-Fall River, RIMA
Pittsburgh, PA
Louisville, KY-IN
Fayetteville, NC
Pocatello, ID
State College, PA
Yakima, WA
Fairbanks, AK
Las Cruces, NM
Asheville, NC
Niles-Benton Harbor, MI
Wenatchee, WA
Spokane, WA
Springfield, MO
Wilmington, NC
Cape Coral-Fort Myers, FL
Monroe, LA
Scranton–Wilkes-Barre, PA
Modesto, CA
Tyler, TX
Charleston-North Charleston, SC
Salt Lake City, UT
Orlando, FL
Albuquerque, NM
Morristown, TN
Philadelphia-Camden-Wilmington, PANJ-DE-MD
Portland-South Portland-Biddeford, ME
Colorado Springs, CO
San Diego-Carlsbad-San Marcos, CA

0.260
0.583
0.229
0.343
0.642
0.628
0.197
0.567
0.182

0.272
0.594
0.239
0.353
0.652
0.637
0.206
0.576
0.190

0.012
0.011
0.010
0.010
0.010
0.009
0.009
0.009
0.008

0.128

0.136

0.008

0.107
0.413
0.624
0.530
0.098
0.237
0.224
0.368
0.289
0.014
0.159
0.235
0.637
0.443
0.086
0.155
0.090
0.289
0.449
0.165
0.302
0.161
0.732
0.077

0.115
0.421
0.632
0.537
0.104
0.243
0.230
0.374
0.295
0.020
0.165
0.240
0.641
0.448
0.090
0.159
0.094
0.293
0.452
0.168
0.304
0.163
0.734
0.078

0.008
0.008
0.008
0.007
0.007
0.006
0.006
0.006
0.006
0.006
0.005
0.005
0.005
0.004
0.004
0.004
0.004
0.003
0.003
0.002
0.002
0.002
0.002
0.002

0.142

0.143

0.001

0.148
0.697
0.500

0.150
0.698
0.501

0.001
0.001
0.000

Appendix B. Summary Statistics of Tract-level Data
Variable

Observations Mean

Std.
Dev.
0.697
0.760
1.430
0.546
0.221

Min

Max

Change in log Employment 2002-2011
11,837
0.062
-6.672 5.182
log Distance to CBD
11,837
1.526
-4.560 4.029
log Employment 2002
11,837
6.468
0
11.537
log Population 2000
11,837
8.092
1.609 9.865
Change in log Population of Neighboring Tracts 2000- 11,837
0.033
-1.497 5.093
2010
Poverty Rate 2000
11,837
0.183 0.136 0
0.932
Change in Share with College Degree 2000-2010
11,837
0.032 0.079 -1
0.872
Share of Occupied Housing Units with new Residents
11,837
0.706 0.124 0
1
2000-2010
Share of Housing Units Built 2000-2010
11,837
0.083 0.138 0
1
Residential Density (Units/Acre)
11,837
6.669 9.807 0.000 561.963
Industrial Diversity Index
11,837
0.466 0.242 0
1.000
Automobile Accessibility (links per square mile)
11,837
1.479 2.413 0
36.770
Pedestrian Accessibility (links per square mile)
11,837
16.114 5.946 0.245 46.595
Public Transit Stop in Tract?
8,806
0.914 0.280 0
1
Low Income Housing Tax Credit Development in
11,837
0.292 0.455 0
1
Tract?
Empowerment Zone/Renewal Community?
11,837
0.075 0.263 0
1
Note: Summary statistics are presented for inner city census tract sample. These are non-CBD tracts in
the largest principal cities for states with a full sample of data (2002-2011)

Throughout this paper we use the terms region and metropolitan area interchangeably.
Principal Cities are defined for each MSA by the Census Bureau as the largest city in the MSA. Additional cities
may qualify to be a principal city, if it is a census designated place or incorporated place with more than 250,000
residents and 100,000 workers or a place whose employment exceeds the population and both are at least 10,000.
iii
http://www.epa.gov/smartgrowth/smartlocationdatabase.htm
iv
For the list of tracts that were included in EZ/RCs see
http://portal.hud.gov/hudportal/HUD?src=/program_offices/comm_planning/economicdevelopment/programs/.
ii