Human Capital in Cities and Suburbs

Abstract

Research on human capital generally focuses on the regional level, and neglects the relative effects of its distribution between center cities and surrounding suburbs. This research examines the effects of this intra-metropolitan distribution on economic performance. The findings indicate that this distribution matters significantly to US regional performance. Suburban human capital matters more than center city human capital. However, this varies by regional size. Suburban human capital has the biggest effect on regional economic performance in smaller and medium size metros. Center city human capital has a relatively larger effect on economic performance in regions with over one million people.

Full text

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CESIS Electronic Working Paper Series
Paper No. 264
Human Capital in Cities and Suburbs
Kevin Stolarick
Charlotta Mellander
Richard Florida
January 2012
The Royal Institute of technology
Centre of Excellence for Science and Innovation Studies (CESIS)
http://www.cesis.se

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Human Capital in Cities and Suburbs
Kevin Stolarick, Charlotta Mellander*, and Richard Florida
Stolarick is Research Director of the Martin Prosperity Institute in the Rotman School of Management,
University of Toronto, kevin.stolarick@rotman.utoronto.ca.
Mellander is Research Director of the Prosperity Institute of Scandinavia, Jönköping International
Business School, charlotta.mellander@ihh.hj.se.
Florida is Director of the Martin Prosperity Institute in the Rotman School of Management, University
of Toronto, florida@rotman.utoronto.ca.
*Corresponding author
ABSTRACT
Research on human capital generally focuses on the regional level, and neglects the
relative effects of its distribution between center cities and surrounding suburbs. This
research examines the effects of this intra-metropolitan distribution on economic
performance. The findings indicate that this distribution matters significantly to US
regional performance. Suburban human capital matters more than center city human
capital. However, this varies by regional size. Suburban human capital has the
biggest effect on regional economic performance in smaller and medium size metros.
Center city human capital has a relatively larger effect on economic performance in
regions with over one million people.
JEL: O3 R1 R2 J24
Key words: Human Capital, Density, Intra-metropolitan distribution, Income,
Housing prices

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Introduction
In his classic work on The Wealth of Nations (1776) Adam Smith long ago
identified the “acquired and useful abilities of all the inhabitants or members of the
society” as something akin to a “fourth factor of production” (e.g. Samuelson and
Nordhaus, 2004) operating alongside land, labor and production, noting that: "The
greatest improvement in the productive powers of labour, and the greater part of the
skill, dexterity, and judgment with which it is anywhere directed, or applied, seem to
have been the effects of the division of labour”(Smith, 1776; book 1, page 7). Jane
Jacobs (1969) later argued that the clustering of talented and energetic people in cities
is the fundamental driving force of innovation and economic development, more
important even than the efficiency gains associated with the deepening division of
labor within firms. Lucas (1988) formalized Jacobs' insights advancing the construct
of “external human capital” or “Jane Jacobs’ externalities” as playing a fundamental
role in the “mechanics of economic development. Cross-national studies (Barro,
1991; 1997) have documented the role of human capital in national economic
development, while urban economics and regional research (Florida, 2002; Berry and
Glaeser, 2005; Florida et al. 2008) has found that human capital also plays a key role
in the growth and development of metropolitan regions.
Virtually all studies of the association between human capital and urban and
regional development focus on the relationship between the two at the regional or
metropolitan level. Human capital is measured broadly across metropolitan regions

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on the whole. But following the ideas of Jacobs (1969) and Lucas (1988) on the role of
human capital concentration, density and externalities in economic growth and
development, it is likely that it is not just the overall level of human capital that
matters but its distribution within regions as well.
Metropolitan regions which combine central cities and their surrounding
suburbs vary widely: They come in a wide variety of shapes and sizes. Some have
concentrated central cities or cores – for example within the greater New York
metropolitan area, Manhattan has heavy concentrations of business and also
significant concentrations of higher income, higher skill higher human capital
individuals. Other regions are more sprawling, take Los Angeles for example, with
higher-income, higher skill, higher human capital individuals residing mainly in the
suburbs.
This is the core issue our research takes up. We focus on the effects of the
distribution of human capital within regions– specifically between cities and suburbs
– on regional economic performance. To get at this, we empirically examine the
effects of the distribution of human capital across core cities and suburbs for 331 US
metropolitan regions. We utilize two measures of human capital: the distribution of
human capital per population or per capita measured as the percentage of adults
with a bachelors’ degree or greater, and the density of human capital, that is the
number of adults with bachelors’ degree or greater per square kilometer. We
examine the effects of the distribution of human capital between cities and suburbs

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on both measures on two key measures of regional economic performance: average
metropolitan income and median metropolitan housing prices, while controlling for
other factors. We run a series of regression models to capture relative explanatory
power between of average human capital level across metros and the distribution of
human capital between city centers and suburbs. We examine the effect of the
overall human capital level, city and suburban human capital, and other key control
variables on our two key measures of regional economic performance. We run the
regressions for all metro regions, and also separate into major regional size
categories.
The key findings indicate that the distribution of human capital within metros
– that is between cities and their surrounding suburbs – matters significantly to
regional economic performance. Our variables which measure the distribution of
human capital shares for center cities and suburbs explain more of the variation in
regional income than the variable for metropolitan human capital overall. Human
capital density matters relatively less overall, but increases for smaller regions. The
intra-regional distribution of human capital density also adds more to the
explanatory power in the housing value regressions. Generally speaking, our
findings indicate that suburban human capital matters more than center city human
capital. But this varies by regional size. Suburban human capital has the biggest
effect on regional economic performance in smaller and medium size metros. But,

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city human capital has a relatively larger effect on economic performance in the
largest metropolitan regions, those with over one million people.
Theory and Concepts
The literature on human capital and regional economic performance is
considerable. Ullman (1958) was among the first to highlight the role of human
capital on regional development. Jacobs (1969) argued that cities are formed by
geographic concentration of diverse activities and highly skilled people. She also
drew attention to the distinctiveness of cities vis-à-vis industrial firms. She argued
that while firms specialize and divide labor into more specified and productive uses,
cities organize natural, industrial and human inputs to facilitate innovation and
production. Therefore in Jacobs’ view, cities become the key arena for the clustering
combination and recombination of skilled individuals that give rise to new
innovations which create new work and spur economic development. Lucas (1988)
later refined Jacobs’ contributions regarding cities and the collocation of skilled
people, casting them in terms of the locational advantages that stem from “human
capital externalities” – essentially the ancillary benefits that come from the
collocation of talented, ambitious and entrepreneurial people. He formalized the role
of dense urban areas which localize human capital and information, create
knowledge spillovers, and become engines of economic growth. Cities reduce the

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cost of knowledge transfer, so ideas move more quickly, in turn giving rise to new
knowledge more quickly.
A wide range of empirical studies have documented the role of human capital
in national and regional growth. Barro (1991), Rauch (1993), Simon and Nardinelli
(1996) and Simon (1998) all confirm the relation between human capital and growth
on a national level. Glaeser (2000) provides empirical evidence on the correlation
between human capital and regional economic growth. Firms locate in areas of high
human capital concentration to gain competitive advantages, rather than letting
suppliers’ and customers’ geography alone dictate their location. Glaeser and Saiz
(2003) find that skilled cities grow, relative to less skilled cities, through increases in
productivity.
Other studies find that human capital is becoming more concentrated. Berry
and Glaeser (2005) document the growing divergence of human capital levels across
cities, finding that the distribution of human capital has gone from relatively even
among US metropolitan areas to increasingly divergent.. There are reasons to believe
that such divergence will continue (Shapiro 2005).
Research also finds that human capital is not only associated with higher
regional incomes but higher housing values (Shapiro, 2005; Gyourko et al., 2006).
Part of this is via an income effect, where higher incomes create higher demand for
housing. This is obvious both in the Manhattan effect which takes place in dense

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urban cores and in a Greenwich effect which occurs in upscale suburbs. But research
has found that other factors also play a role.
Higher income, higher human capital households also exhibit a preference for
amenity (Florida, 2002 a, b, c). Glaeser et al. (2001) indentified an urban amenity
premium that works alongside an urban productivity premium to effect housing
prices. Gyourko et al. (2006) identified a small number of super star cities which
support housing prices far above average, over what their productivity premium
might suggest. Florida and Mellander (2010) find additional evidence for this
amenity effect, finding that factors which proxy for regional amenity (like the
presence of large concentration of artists and cultural creatives) have a significant
effect on housing values.
However, virtually all of these studies operate at the metropolitan level. There
has been little research which empirically examines the effects of the intra-
metropolitan distribution of human capital across cities and suburb on regional
economic performance – measured as metropolitan average income and median
housing values.
There are good reasons however to believe that it does. One strand of urban
economics per Jacobs (1969) and Lucas (1988) would suggest that dense
concentrations of human capital might gain advantages in productivity and
innovation, thus leading to higher levels of economic performance. These denser core

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areas might also be thought to gain advantages in the provision of the sorts of
amenities that have been found to attract highly-skilled, high human capital
individuals and households (Glaeser et al, 2001; Florida, 2002). On the other hand,
many leading high-tech metros like Silicon Valley, the North Carolina Research
Triangle, and even greater Austin are more suburban in nature, defined by industrial
or tech parks and suburban style housing – Kotkin (2000) dubbed them “nerdistans,”
citing a suburban-preference among engineers and high-tech professionals. And,
many upscale suburbs are composed almost exclusively of high-skilled, high-income,
high human capital individuals and households, as noted above.
Urban economics gives us a rationale to understand this. Tiebout’s classic
formulation (1956) shows how individuals and households select specific locations
based on their income and preferred bundles of service and amenities. Muth (1961)
provides the basic framework to understand the tradeoff between income and
housing costs and how it relates to city growth. Cities that increase in income and/or
population can be expected to spread out in geography. Cities with traffic congestion
or high commuting costs will decrease the incentives to move out from the center.
A recent study by Lee (2010) investigates the sorting of human capital
between urban and rural areas. Although the typical expectation is that urban
workers will receive a wage premium from agglomeration effects and as
compensation for higher living costs, Lee finds that in some cases there is actually a
wage premium discount for some urban workers. This is attributed to the greater
availability and variety of consumption opportunities available in urban areas. Some

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workers find increased utility from access to consumption opportunities and don’t
require additional wages. In this case, high skill workers can be less expensive than
lower skill workers and employed with greater frequency. The findings of Lee’s
empirical analysis of the medical profession support this line of thinking. Hospitals
in urban centers have a higher ratio of doctors to nurses, the doctors are paid less,
and the nurses are paid more than their counterparts in rural areas. Urban doctors
are more likely to be specialists and graduates of more highly ranked medical schools
than rural doctors. This is in line with the findings of Glaeser and Saiz (2001) that
show how consumption opportunities can attract urban clusters of high skill
workers. Lee adds to this line of thinking, showing how these workers may have a
rational reason for doing so at a lower wage.
Building from these studies and literature, our research examines the effects of
the distribution of human capital between cities and suburbs on regional economic
performance. The next section details our methodology for doing so.
Model, Variables and Methods
This section describes our model, variables, data and methods. We begin with
our general model. The model is based on the core concepts and theory outlined
above. It is designed to empirically test how the distribution of human capital affects
regional economic performance. To do so, we compare two different models using
the same variables and data. The first model explains regional economic

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performance based on the average level of human capital across the entire metro. The
second explains regional economic performance based on the shares of human
capital made up of by the center city versus its surrounding suburbs. This gives us
two versions of a similar relationship:
(Model a)    

  
(Model b)     

  

  
Variables
The key distinction in this analysis is separating the “city” and the “suburb” in
a metropolitan area. Our data is based on the conventional U.S. Metropolitan
Statistical Area (MSA) designation from the 2000 Census as defined by the Office of
Management and Budget (US OMB, FIPS-55 standard). Only MSAs, or what we refer
to as metropolitan areas or metros are analyzed. Each MSA is spatially divided into
“central city[s]” and the remainder. The remainder is designated “suburb,” The
central city is identified in the geographic header of the 2000 Census Summary File 3
(SF3). The definition used is the one provided by the US Census Bureau:
“[T]he largest place and, in some cases, one or more additional places are
designated as ‘‘central cities’’ under the official standards. A few primary
metropolitan statistical areas do not have central cities. … An MA
[metropolitan area] central city does not include any part of that place that

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extends outside the MA boundary.” Summary File 3 Technical Documentation,
U.S. Census Bureau, 2000 [issued 2003], U.S. Department of Commerce:
Washington D.C., pp. A-16 – A-17.
Once the central city and suburbs have been identified, total area for each is
calculated. Both land and water area is included. The individual variable values and
densities (discussed below) are then calculated for the city and suburban portions of
each MSA.
We populate the model with the following dependent and independent
variables.
Dependent Variables
We employ two commonly used measures of regional economic performance,
average incomes and median housing values.
Average Income: Average income captures regional wealth based on wages and
salaries. This is perhaps the single best measure of the economic status regional
populations relative to one another. Average income includes wage and salary
income including net self-employment income; interest, dividends, or net rental or
royalty income or income from estates and trusts; social security or railroad
retirement income; Supplemental Security Income (SSI); public assistance or welfare

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payments; retirement, survivor, or disability pensions; and all other income . It is
measured on a per capita basis.
Median Housing Value: This is perhaps the best available measure for the
relative demand for space across regions. It tells how much households are willing to
pay for housing in the region. Median housing value is the standard measure of
housing value. For metros that cross state borders, we calculate separate medians for
each state and calculate a weighted average of the medians using the number of
owner-occupied houses valued.
Independent Variables:
Metro Human Capital Share: The conventional measure of human capital is
based on the percentage of adults with a bachelor’s degree and above. Here we use
the standard measure of regional human capital based on the percentage of adults
(age 25 and older) in a metro with a bachelor’s degree or above.
Center City Human Capital Share: This variable captures the share of adults
with a bachelor’s degree or above located in the center city.
Suburban Human Capital Share: This variable captures the share of adults with
a bachelor’s degree or above located in the suburbs surrounding the center city.

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Human Capital Density: This variable is the number of adults with a bachelor’s
degree or above per square kilometer.
Center City Human Capital Density: This variable captures the number of adults
with a bachelor’s degree or above per square kilometer in the center city.
Suburban Human Capital Density: This variable captures the number of adults
with a bachelor’s degree or above per square kilometer in the suburbs surrounding
the center city.
Population Density: This variable is the number of individuals per square
kilometer.
Center City Population Density: This variable captures the total number of
residents per square kilometer in the center city.
Suburban Population Density: This variable captures the total number of
residents per square kilometer in the suburbs surrounding the center city.
The variables and data-set cover 331 metropolitan statistical areas across the
U.S. and are for the year 2000 from the US Census. Descriptive statistics for the
variables are provided in Table 1. Appendix Table A provides descriptive statistics

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for the variables across four regional size groups – metros with over one million
people (size 1), those between 500,000 and one million people (size 2), regions with
250,000-500,000 people (size 3), and regions under 250,000 people (size 4).
(Table 1 about here)
Methods
Our analysis is based on a combination of bivariate correlation analysis and
multivariate regression. We include separate regressions for our two dependent
variables – average income and median housing value. Based on the model outlined
above, we run regressions at the overall metro level first, and then follow with
separate regressions which split the human capital variable into center city and
suburban shares. To test for region size effects, we also run the regressions for four
different size classes of regions: those with million people (size 1); 500,000 to 1
million (size 2); 250,000 to 500,000 (size 3); and less than 250,000 (size 4). In the
regressions, all variables are logged and the coefficients are thereby expressed as
elasticities.
Findings
To orient the analysis, we start with the results of a simple bivariate
correlation analysis (see Table 2, Appendix Table B presents the correlation results
for the metro size groups).
(Table 2 about here)

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The results of the correlation analysis, not surprisingly, reveal a close
association between human capital and both average income and median housing
value at the metropolitan level, with correlations of .676 and .628 respectively. The
correlations become just slightly lower when we employ human capital density, .644
for average income and .576 for median housing value. In both instances, the
correlations for the metro-level human capital measures are higher than for the
metro-level population density measures, .502 and .454 respectively.
We now split the data set by center city and suburban human capital shares.
Turning first to income, the highest correlation is for suburban human capital (.783)
share which is somewhat higher than metro-level human capital (.676) and
substantially higher than for center city human capital share (.298). The measure for
suburban human capital density (.651) also outperforms that for metro human capital
density (.502) and center city human capital density (.454) although the measure of
center city human capital density performs relatively better than for center city
human capital share as per above.
Turning now to median housing values, we find a more muted pattern. The
highest correlation is again for suburban human capital share (.678), but it is only
slightly better than for metro-level human capital share (.628) and center city share
(.584). The pattern changes somewhat when we employ measures of human capital
density. Now the correlations for all three measures – suburb, center-city and metro-
level are all relatively similar. The correlation for center city human capital is highest

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(.584) by a nose, followed by metro-level human capital density (.576) and then
suburban human capital density (.567).
The next question is how metro, center city and suburb human capital levels
might be related to one another. Figure 1 provides two scatter-plots for this
relationship, one for shares, the other for density.
(Figure 1 about here)
The scatter-plots show a clear positive and significant relationship between
human capital shares in city centers and suburbs on both scores, with a correlation of
.405 based on population and .446 based on density. In other words, higher levels of
human capital in city centers increase the probability of finding higher levels also in
the suburbs and vice versa. However, the relationship between human capital in city
centers and suburbs is not perfectly linear, and does not include exactly the same
information. The relationship between the metro averages and the center shares and
density levels are not surprisingly more linear (given that the metro average to a
certain extent is a function of the centers), with correlations of .706 versus .495. In
other words, we find a stronger relation between the human capital shares between
centers and metros on average, than we do for the relation between human capital
density in metros and centers.
While the bottom two scatter plots (Figure 1) show the expected relationship
between metropolitan and center city human capital, the top two show that, while

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very weakly correlated, there is a great deal of variation across metropolitan areas in
both central city and suburban human capital and human capital density. Further,
the weak correlation shows that for any given city the urban and suburban values are
generally independent of each other. This variation means that investigating these
relationships is likely to generate interesting results.
Regression Results
We now turn to the results of our regression models. Based on the model
outlined above, we run separate regressions for average income and median housing
value. We analyze the models based on metro average shares and density levels first,
followed by separate models which distinguish between city center and suburban
human capital. We also run the analysis for four regional size classes: regions over a
million population (size 1); 500,000 to 1 million (size 2); 250,000 to 500,000 (size 3);
and less than 250,000 (size 4), to capture if center city and suburban shares play
different roles based on region size. (An appendix reports the results for all regions
in models that include regional size dummies to check for any results that may be the
result of the smaller number of regions that populate the regional size groups).
The models examine the effects of human capital shares and density on
average income and median housing values. We run each regression for all regions
and each size class. At top of each table, we illustrate the results for the regressions
where metro averages are used as explanatory variables. Below, we illustrate the

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regression results where the dependent variable is explained by the distribution of
human capital shares and density between city centers and suburbs. We also run
population density regressions to rule out that human capital is not just a proxy for
population density. The population density regression results are detailed in the
appendix, but we report for the key results in the text below. Additionally, we
control for the share of the land area the metro centers make up. We add this control
variable to the regressions based on Model B which splits up center and suburb
human capital levels. We report in footnotes under each table for the cases where this
control variable is significant.
Results for Median Income
We start with the results for the regressions for median income summarized in
Table 3.
(Table 3 about here)
The results for these regressions suggest that the distribution of human capital
within metropolitan areas matters. Save for one case – large regions with more than
1 million in population (eq 2), the R2 values increase when we split metro human
capital into center city and suburban shares. The R2 for the overall regression for all
region increases from .457 to .610, while the R2s for the regressions for small and
medium size regions also increase, going from .559 to .754 for size 2 regions, from
.527 to .638 for size 3 regions, and from .293 to .487 for size 4.

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Also of interest is the pattern of results for the variables for center city and
suburban human capital share. The variable for suburban share is significant in the
models for all regions and for each of the region size classes. The variable for center
city share is significant in only one case, large regions (size 1), those with over one
million people. As this was the only regression where R2 was reduced by splitting
human capital, it may be a spurious result. It is also possible that only the largest
regions have a large enough core with sufficient human capital to influence the
relationship. The latter explanation is somewhat supported by looking at Table A in
the Appendix which shows that the average share of human capital is higher in the
central cities of largest metropolitan areas than all the other size categories.
However, the suburban densities as well as the human capital shares in centers and
suburbs remain approximately at the same level.
We now turn the findings for income and human capital density (see Tables
4).
(Table 4 about here)
The results echo the pattern above to some degree. Overall, the distribution of
human capital density within regions matters significantly to regional incomes.
Again the R2 values increase when metro human capital density is split between
center and suburb human capital density. This increase is .415 to .464 for all regions,
from .316 to .328 for size 1 regions, from .150 to .330 for size 2 regions, from .388 to

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.441 for size 3 regions and from .333 to .423 for size 4 regions. But, the findings here
suggest a slightly different pattern in terms of the relative strength of the center city
and suburban densities of human capital. Whereas the suburban share of human
capital was dominant in the models above, now using the density measure, both
center city and suburban shares are significant in the models for all regions and for
size 2 through 4 regions. Furthermore, center city human capital density outperforms
suburban human capital density for large (size 1) regions, being significant while
suburban human capital density is not. This indicates both city and suburban human
capital density matter to regional incomes for all size classes of regions. In the largest
regions (those with more than 1 million in population) and those with 500,000 to 1
million (size 2) and under 250,000 (size 4) center human capital density explains
more than suburban human capital density.
To rule out that human capital is not just a proxy for population, we re-run the
regressions but substitute human capital density with population density (see
appendix C for these results). The regressions for population density generate
significantly lower R2 values than the human capital density regressions above.
Metro population density is significant in all cases but for size 2 regions (eq 3a).
Splitting population density into center and suburb population density does not add
much explanatory value, as the R2 values are about the same as in the regression
using overall metro-level population density. Both center city population density
and suburban population density are significant in the model for all regions. When
we parse the sample by region size, the results are weaker and mixed. For large

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regions, only suburban population density is weakly significant. For regions between
250,000 and 500,000 (size 3) and those under 250,000 (size 4) suburban population
density is significant. Neither central city nor suburban population density is
significant in the model for medium-size regions (size 2) between 500,000 and one
million people. We also ran the same series of regressions for the entire metro level
sample but using dummy variables for regional size, to check whether our results
were affected by the smaller sizes of some regional groups. The results, summarized
in Appendix Table D, are in line with the results above.
Results for Median Housing Values
This section presents the results for the regressions for median housing values.
We begin with the regressions for human capital shares which we summarize in
Table 5.
(Table 5 about here)
Generally speaking, the pattern for these regressions mimics those of the
regressions for regional incomes (see Table 3), with one caveat: Human capital
explains less of median housing values than it does for regional income. Metro level
human capital is positively associated with regional housing values in each and
every model, for all regions and for each of the four size groupings. Again, when we
split human capital into its center city and suburban shares, the R2 values increase
slightly in four of the five models – for all regions and for size 2, 3 and 4 regions, but

23
not for larger, size 1 regions. And like the earlier analysis, suburban human capital
provides more explanatory power in these models. Suburban human capital share is
significant in the regressions for al metros and size 2, 3 and 4 regions. As earlier,
center city human capital is significant only in the largest (size 1) regions.
We can expect a positive relationship between density and housing values,
since density itself is likely to result when housing values are high or when
expansion at the periphery is not possible or restricted. We now present the results
for our regressions of human capital density and median housing values (see Table
6).
(Table 6 about here)
The key results for these regressions are similar to those for the earlier
regressions on human capital density and income (see Table 4). Human capital
density has a positive and significant relation to median housing values for all
regions and for all regional sizes. However, we note the stronger explanatory power
for size 1 regions where the R2 is .361, than in smaller and medium-size regions
where the R2s range from .150 to .177. Again splitting human capital density into its
city and suburban shares adds significantly to the explanatory power of the models
in four of five cases – in all the regressions except that for the largest ( size 1) regions
where the R2 increase is more minor. For all regions, the R2 increases from .332 to
.465, for size 2 regions it improves from .177 to .306, for size 3 regions it grows from
.204 to .450 (eq 4), and for size 4 regions it increase from .150 to .347. In contrast to the

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result for the share of human capital (Table 5) the results for these density regressions
indicate that city centers play a more important role when it comes to human capital
density. Center city human capital density is significant in all five regressions, and it
is also stronger than the variable for suburban human capital density. It appears that
center city human capital density has a more substantial effect on regional housing
values – which makes intuitive sense since density itself may be a function of higher
housing values. This explanation is also supported by the fact that suburban human
capital density is insignificantly related with median housing values in the largest
(size 1) regions.
To once more rule out that human capital density is not just a proxy for
population density, we re-run these regressions, but with population density as
explanatory variable (see Appendix Table E for these results). Generally speaking,
population density explains less of median housing value than human capital
density, and the R2s are smaller across the board. Metro population density is
positive and significant for all regions and for each and every of the regional size
groups. The R2 values are higher for or all regions (eq 1) and large (size 1) regions
(eq 2) and fall of considerably in smaller regions. Splitting population density into
center city and suburban population density has little effect on the R2 values, and the
R2 levels remain low for small and medium size regions, suggesting that population
density explains very little, and considerably less than human capital density. We
also ran the same regression for all regions but with dummy variables for regional
size (see Appendix Table F). The results are in line with those above.

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Conclusion and Discussion
Human capital has been identified as the key driver of regional economic
growth and development. A wide body of empirical research has documented the
close relationship between human capital and regional economic performance
(Florida, 2002; Berry and Glaeser, 2005; Florida et al., 2008). But while theory
suggests that the distribution of human capital within regions is likely to matter,
empirical research has largely ignored this issue. On the one hand, a significant body
of research (Shapiro, 2005; Gyourko et al., 2006) suggests that incomes and housing
values will rise in metros with denser levels of human capital in the center city. On
the other hand, many suburbs are location of choice for high-skill, high-income, high
human capital individuals and households.
Our research took up this question of the effects of the distribution of human
capital between city centers and suburbs on regional economic performance. We ran
regression models to examine the effects of the distribution of human capital
controlling for other factors on two key measures of regional economic performance,
average incomes and median housing values.
Our key findings suggest that the distribution of human capital matters to
regional economic performance. First and foremost, in virtually all permutations of
our models, the results are stronger when we separate center city and suburban
shares rather than for metro-level human capital overall. These results show that the

26
concentration of human capital in central cities and suburbs impacts regional income
differently. The density results show that it’s not just the concentration of human
capital that is important, but also the relative proximity of these high skilled
individuals. The income results are in line with the earlier finding of Lee (2010) that
central cities need not necessarily support higher wages because residents also gain
utility from the diversity of consumption opportunities available in central cities. In
suburban areas, incomes have to be higher to compensate the highly skilled for
lacking consumption opportunities.
Second, the findings further suggest that suburban human capital plays
substantial role. While theory suggests that concentration of human capital at the
center should matter (esp. Jacobs, 1969; Lucas, 1988), the findings indicate that the
suburban human capital share is more strongly related to regional income and
housing values. This is true for the models for all regions and especially for smaller
and medium sized metros. Because of their population size (under one million), most
of these smaller and medium size regions require less density. They can grow and
develop in a less concentrated pattern, and can support much more fluid and less
congested commuting and transportation patterns. Central locations are likely to be
less valued by higher-skill, higher income individuals and households. Regional
economic performance in these locations does not depend on concentration and
density as much as in largest regions.
Third, we find that center city human capital plays a more significant role in
the largest metros, those with more than one million people. For the largest regions

27
(those with more than a million people), the variable for center city human capital
share explains more of regional income than does that for the suburbs. This result is
reinforced by the findings of the regressions for human capital density as well. This
makes sense intuitively and brings us back into line with what we would expect per
urban theory (esp. Jacobs 1969; Lucas, 1988). Larger regions, by virtue of their size,
require denser patterns to accommodate population growth. They are the regions
that suffer most from traffic congestion and burdensome commuting patterns. They
are more likely to see considerable premiums for central locations. For these reasons,
higher-skill, higher-income families are more likely to prefer central locations in
these regions. Our results indicate that when the size threshold of a million people is
crossed, the effects of more highly concentrated and dense human capital at the city
center really comes into play. Human capital densities increase markedly for metros
with more than 3 million people, though the number of these regions is too small to
generate statistically reliable results. Metros with more than 3 million people have
human capital density of 443 high human capital people per square kilometer
compared to 227 high human capital people per square mile for metros between 1 an
3 million people. Human capital in city centers thus appears to play a more
pronounced role in regional economic performance for large regions.
Generally speaking, this research compares the impact of human capital on
regional outcomes in three dimensions. The first is a spatial dimension investigating
central cities and suburbs. The second looks at the intensity of human capital by
analyzing the impact from both concentration (share) and density. Finally, these

28
relationships are investigated at an overall level and within population-based size
groups. The spatial dimension is always found to be significant. Separating central
cities and suburbs generates significant results and increases the explanatory power
of the models. The spatial dimension is stronger when investigating regional income
levels than for housing value, but remains important. The impact of the intensity of
human capital shows similar patterns for regional income and housing values. The
concentration or share of suburban human capital is positively related to increased
incomes and housing values while the share within central cities doesn’t have much
of a relationship. However, when the intensity is ramped up, the density of human
capital in central cities generally outperforms suburban human capital density. For
housing values, suburban human capital density has a more mixed result. When the
results for the final dimension (region size) are considered, income levels and
housing values reveal different relationships. For income, the largest regions (those
over 1 million) reflect an importance for central city human capital that does not
show up for the smaller regions. For housing values, the situation is reversed.
Region size interacts with both intensity and location such that metro areas under 1
million show increases in housing values associated with suburban human capital
density.
Overall our findings indicate that the distribution of human capital within
regions matters greatly to regional economic performance. One limitation of our
analysis is that it deals with human capital based on residence. Findings may differ
if place of work is used. Indeed we might expect to find stronger central city effects.

29
We encourage more research which contrasts intra-metropolitan human capital
between place of residence and place of work. Most of all, we hope our initial
research on this question will encourage further empirical work on this important
subject.

30
References:
Barro, R. J. (1991) Economic Growth in a Cross Section of Countries, Quarterly Journal
of Economics, 106(2): 407-443.
Barro, R. J. (1997) Determinants of Economic Growth: A Cross-Country Empirical Study,
Cambridge, MA: The MIT Press
Berry, C. R., Glaeser, E. L. (2005) The Divergence of Human Capital Levels Across Cities,
NBER Working Paper No. 11617, September 2005.
Florida, R. (2002a) The Rise of the Creative Class, New York: Basic Books.
Florida, R. (2002b) The Economic Geography of Talent, Annals of the Association of
American Geographers, 92(4): 743-755.
Florida, R. (2002c) Bohemia and economic geography, Journal of Economic Geography,
2: 55-71.
Florida, R., Mellander, C., Stolarick, K. (2008) Inside the Black Box of Regional
Development, Journal of Economic Geography, 8: 615-649
Florida, R., Mellander, C. (2010) There Goes the Metro: How and Why Artists,
Bohemians and Gays Affect Housing Values, Journal of Economic Geography, 2: 167-
188.
Glaeser, E. L. (2000) The new economics of urban and regional growth, In The Oxford
handbook of economic geography, ed Gordon, C., Meric, G., Feldman, M, 83-98, Oxford:
Oxford University Press.
Glaeser, E. L., Kolko, J., Saiz, A. (2001) Consumer City, Journal of Economic Geography,
1:27-50.
Glaeser, E. L., Saiz, A. (2003) The Rise of the Skilled City, NBER Working Papers no
10191, National Bureau of Economic Research, Inc.
Gyourko, J., Mayer, C., Sinai, T. (2006) Superstar Cities, NBER Working Paper No
12355, July 2006
Jacobs, J. (1969) The Economies of Cities, New York: Random House.
Kotkin, J. (2000) The new geography, New York: Random House.

31
Lucas, R. (1988) On the Mechanics of Economic Development, Journal of Monetary
Economics, 22: 3-42.
Rauch, J. (1993) Productivity Gains from Geographic Concentration of Human
Capital: Evidence from the Cities, Journal of Urban Economics, 34: 380-400.
Samuelson, P. A., Nordhaus,W. D. (2004) Economics, 18
th
ed., New York:
McGrawHill.
Shapiro, J. M. (2006) Smart Cities: Quality of Life, Productivity, and the Growth
Effects of Human Capital, The Review of Economics and Statistics, 88(2): 324-335.
Simon, C., Nardinelli, C. (1996) The Talk of the Town: Human Capital, Information
and the Growth of English Cities, 1861–1961, Explorations in Economic History, 33(3):
384–413
Simon. C. (1998) Human capital and metropolitan employment growth, Journal of
Urban Economics, 43:223-43
Smith, A. (1776) The Wealth of Nations, New York: Random House (2000)
Tiebout, C. M. (1956) A Pure Theory of Local Expenditures, The Journal of Political
Economy, 64:2, pp 416-424.
Ullman, E. L. (1958) Regional development and the geography of concentration,
Papers and proceedings of the Regional Science Association, 4:179-98.

32
APPENDIX:
Table A: Descriptive Statistics by Region Size
Size 1
Size 2
N
Minimu
m
Maximu
m
Mean
Std.
Dev.
N
Minim
um
Maxim
um
Mean
Std.
Dev.
Average Income
61
182967
39796
24368
4124
42
9848
27593
20799
3297
Median Housing
Value
61
78916
502011
16210
9
80955
42
51302
286601
12218
8
45778
Average Metro HC
61
.16
.44
.28
.058
42
.13
.37
.24
.052
Metro HC Density
61
2.08
522.39
69.87
84.36
42
2.52
654.17
37.97
98.97
Metro Population
Density
61
19.22
2711.18
362.74
424.85
42
32.13
3854.40
235.99
579.32
Average Center HC
57
.09
.46
.27
.08
42
.09
.69
.24
.09
Center HC Density
57
43.73
1418.15
272.27
218.07
42
40.38
645.89
177.31
123.51
Center Population
Density
57
285.16
7817.14
1648.0
6
1227.2
4
42
351.46
3767.96
1174.4
4
594.19
Average Suburb
HC
61
.16
.50
.29
.06
42
.07
.35
.23
.06
Suburb HC
Density
61
1.39
265.80
51.41
59.36
42
1.20
662.70
31.69
100.88
Suburb Population
Density
61
13.38
1475.31
254.09
282.61
42
15.35
3943.34
192.52
597.08
Valid N (listwise)
57
42
Size 3
Size 4
N
Minimu
m
Maximu
m
Mean
Std.
Dev.
N
Minim
um
Maxim
um
Mean
Std.
Dev.
Average Income
79
11140
52618
21513
5100
149
10371
34735
19637
3183
Median Housing
Value
79
58630
465154
12766
9
70987
149
50310
260489
10071
1
35262
Average Metro HC
79
.11
.52
.23
.075
149
.11
.48
.22
.079
Metro HC Density
79
1.88
171.20
21.29
27.74
149
.36
55.73
10.20
9.91
Metro Population
Density
79
20.59
643.82
127.41
116.20
149
2.16
373.42
69.64
53.51
Average Center HC
79
.09
.51
.23
.092
148
.04
.69
.25
.105
Center HC Density
79
9.10
433.07
140.69
84.88
148
20.61
642.26
121.05
85.82
Center Population
Density
79
51.24
4041.14
1105.2
9
825.69
148
225.17
3266.14
828.20
435.81
Average Suburb
HC
78
.09
.66
.23
.09
149
.04
.46
.20
.07
Suburb HC
Density
78
.72
142.68
16.20
25.66
149
.04
47.64
5.99
7.92
Suburb Population
Density
78
9.74
482.26
88.48
90.58
149
1.20
226.16
40.61
36.18
Valid N (listwise)
78
148

33
Table B – Correlation Results per Metro Size
Size 1
Size 2
Average
Income
Median
Housing Value
Average
Income
Median
Housing Value
Metro HC Share
.791
***
.637
***
.748
***
.516
***
Metro HC Density
.562
***
.601
***
.387
***
.420
***
Metro Population Density
.423
***
.516
***
.163
.295
*
Center City HC Share
.463
***
.407
***
.331
**
.313
**
Center City HC Density
.540
***
.602
***
.454
***
.489
***
Center Population Density
.296
***
.395
***
.258
*
.316
**
Suburb HC Share
.724
***
.594
***
.867
***
.564
***
Suburb HC Density
.556
***
.558
***
.510
***
.440
***
Suburb Population Density
.448
***
.493
***
.237
.297
*
Size 3
Size 4
Average
Income
Median
Housing Value
Average
Income
Median
Housing Value
Metro HC Share
.726
***
.674
***
.541
***
.560
***
Metro HC Density
.623
***
.451
***
.577
***
.388
***
Metro Population Density
.395
***
.245
**
.391
***
.191
**
Center City HC Share
.209
*
.230
**
.287
***
.382
***
Center City HC Density
.322
***
.530
***
.397
***
.467
***
Center Population Density
.165
.383
***
.186
**
.233
***
Suburb HC Share
.798
***
.697
***
.691
***
.621
***
Suburb HC Density
.610
***
.406
***
.570
***
.424
***
Suburb Population Density
.377
***
.192
*
.391
***
.262
***
*** Indicates significance at the 0.01 level, ** indicates significance at the 0.05 level, and * indicates
significance at the 0.1 level.

34
Table C- Regional Income and Population Density
Av Metro Population Density (a-regressions)
Variables
Eq 1a. All regions
Eq 2a. Size 1
Eq 3a. Size 2
Eq 4a. Size 3
Eq 5a. Size 4
Constant
9.492***
(218.442)
9.645***
(77.074)
9.749***
(56.016)
9.400***
(63.218)
9.558***
(153.837)
Metro Pop Density
0.199***
(10.516)
0.080***
(3.583)
0.037
(1.043)
0.121***
(3.768)
0.079***
(5.158)
Observations
331
61
42
79
149
R2
0.252
0.179
0.026
0.156
0.153
Center and Suburb Population Density (b-regressions)
Variables
Eq 1b. All regions
Eq 2b. Size 1
Eq 3b. Size 2
Eq 4b. Size 3
Eq 5b. Size 4
Constant
9.353***
(93.556)
9.609***
(44.817)
9.296***
(22.235)
9.368***
(42.812)
9.366***
(59.825)
Center City Pop
Density
0.046***
(2.850)
0.030
(0.807)
0.073
(1.091)
0.035
(1.050)
0.046**
(1.981)
Suburb Pop
Density
0.068***
(7.753)
0.049*
(1.757)
0.028
(0.867)
0.083***
(2.976)
0.060***
(4.972)
Observations
326
58
43
79
149
R2
0.249
0.137
0.084
0.155
0.175
***Indicates significance at the 0.01 level; ** at the 0.05 level; * at the 0.10 level.

35
Table D--Regressions with dummies for metro size (either >1 M or <250 K)
Variables
Eq 1
HC Share
Eq 2
HC Share
Eq 3
HC Density
Eq 4
HC Density
Eq 5
Pop
Density
Eq 6
Pop
Density
Constant
10.498***
(265.767)
10.501***
(310.203)
9.650***
(340.691)
9.403***
(155.705)
9.576***
(169.920)
9.432***
(87.983)
MSA HC Share
0.371***
(14.695)
Center HC Share
-0.009
(-0.475)
Suburb HC Share
0.372***
(18.297)
MSA HC Density
0.107***
(11.754)
Center HC Share
0.078***
(6.095)
Suburb HC Share
0.072***
(10.041)
MSA Pop Density
0.079***
(6.789)
Center Pop
Density
0.039**
(2.426)
Suburb Pop
Density
0.057***
(5.686)
Big MSA Dummy
0.071***
(3.284)
0.039**
(2.055)
0.033
(1.361)
0.014
(0.584)
0.079***
(2.914)
0.071***
(2.626)
Small MSA
Dummy
-0.45***
(-2.735)
-0.013
(-0.883)
0.014
(0.720)
0.026
(1.427)
-0.016
(-0.751)
-0.007
(-0.346)
Observations
333
328
333
328
333
328
R2
0.502
0.619
0.419
0.468
0.275
0.268
***Indicates significance at the 0.01 level; ** at the 0.05 level; * at the 0.10 level.

36
Table E– Regional Housing Values and Population Density
Av Metro Population Density
Variables
Eq 1a. All regions
Eq 2a. Size 1
Eq 3a. Size 2
Eq 4a. Size 3
Eq 5a. Size 4
Constant
10.778***
(116.095)
10.548***
(35.465)
11.069***
(36.303)
10.941***
(33.595)
11.167***
(85.278)
Metro Pop Density
0.186***
(9.252)
0.246***
(4.624)
0.120*
(1.956)
0.155***
(2.215)
0.076**
(2.355)
Observations
331
61
42
79
149
R2
0.206
0.266
0.087
0.060
0.036
Center and Suburb Population Density
Variables
Eq 1b. All regions
Eq 2b. Size 1
Eq 3b. Size 2
Eq 4b. Size 3
Eq 5b. Size 4
Constant
9.863***
(47.894)
10.251***
(19.292)
10.262***
(13.909)
9.675***
(22.070)
10.369***
(32.359)
Center City Pop
Density
0.199***
(5.985)
0.127
(1.380)
0.159
(1.349)
0.279***
(4.146)
0.128***
(2.668)
Suburban Pop
Density
0.101***
(5.558)
0.143**
(2.047)
0.065
(1.138)
0.019
(0.335)
0.078***
(3.124)
Observations
326
58
43
79
149
R2
0.266
0.217
0.030
0.216
0.114
***Indicates significance at the 0.01 level; ** at the 0.05 level; * at the 0.10 level.

37
Table F - Regressions with dummies for msa size (either >1 M or <250 K)
Variables
Eq 1
HC Share
Eq 2
HC Share
Eq 3
HC Density
Eq 4
HC Density
Eq 5
Pop Density
Eq 6
Pop Density
Constant
12.708***
(147.369)
12.697***
(153.875)
11.152***
(177.770)
10.181***
(80.712)
11.036***
(92.052)
10.086***
(45.677)
MSA HC Share
0.708***
(12.823)
Center HC Share
0.085*
(1.928)
Suburb HC Share
0.596***
(12.031)
MSA HC Density
0.182***
(9.062)
Center HC Share
0.259***
(9.725)
Suburb HC Share
0.095***
(6.355)
MSA Pop Density
0.132***
(5.349)
Center Pop
Density
0.184***
(5.537)
Suburb Pop
Density
0.072***
(3.463)
Big MSA Dummy
0.115**
(2.447)
0.068
(1.467)
0.065
(1.208)
-0.007
(-0.139)
0.145**
(2.524)
0.107*
(1.932)
Small MSA
Dummy
-0.133***
(-3.726)
-0.093***
(-2.629)
-0.039
(-0.919)
-0.017
(-0.451)
-0.091**
(-2.013)
-0.068
(-1.538)
Observations
333
328
333
328
333
328
R2
0.449
0.478
0.338
0.465
0.238
0.248
***Indicates significance at the 0.01 level; ** at the 0.05 level; * at the 0.10 level.

38
Tables and Figures:
Table 1: Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Dev.
Average Income
331
9845
52618
21104
4258
Median Housing Value
331
50310
502011
121185
60907
Metro HC Share
331
.11
.52
.24
.075
Metro HC Density
331
.36
654.17
27.37
56.81
Metro Population Density
331
2.16
3854.40
158.55
302.10
Center City HC Share
326
.04
.69
.2447
.11
Center City HC Density
326
9.10
1418.15
159.50
134.94
Center Population Density
326
51.24
7817.14
1083.31
800.22
Suburb HC Share
330
.04
.66
.2253
.08
Suburb HC Density
330
.04
662.70
20.07
48.84
Suburb Population Density
330
1.20
3943.34
110.72
261.75
Valid N (listwise)
325
Table 2: Correlation Results
Average Income
Median Housing Value
Metro HC Share
.676
***
.628
***
Metro HC Density
.644
***
.576
***
Metro Population Density
.502
***
.454
***
Center City HC Share
.298
***
.331
***
Center City HC Density
.484
***
.584
***
Center Population Density
.304
***
.416
***
Suburb HC Share
.783
***
.678
***
Suburb HC Density
.651
***
.567
***
Suburb Population Density
.506
***
.456
***
*** Indicates significance at the 0.01 level.

39
Table 3 – Regional Income and Metro, Center and Suburban Human Capital
Av Metro Human Capital Share (a-regressions)
Variables
Eq 1a. All regions
Eq 2a. Size 1
Eq 3a. Size 2
Eq 4a. Size 3
Eq 5a. Size 4
Constant
10.555***
(278.994)
10.880***
(135.055)
10.771***
(89.892)
10.702***
(108.028)
10.270***
(197.266)
Metro HC Share
0.415***
(16.625)
0.612***
(9.941)
0.572***
(7.122)
0.500***
(9.263)
0.254***
(7.799)
Observations
331
61
42
79
149
R2
0.457
0.626
0.559
0.527
0.293
Center and Suburb Human Capital Share (b-regressions)
Variables
Eq 1b. All regions
Eq 2b. Size 1
a
Eq 3b. Size 2
Eq 4b. Size 3
Eq 5b. Size 4
b
Constant
10.529***
(335.143)
10.786***
(129.969)
10.637***
(136.549)
10.646***
(143.138)
10.347***
(227.605)
Center HC Share
-0.011
(-0.622)
0.118***
(2.779)
0.012
(0.471)
0.012
(0.324)
-0.042
(-1.570)
Suburb HC
Share
0.392***
(20.768)
0.428***
(6.953)
0.443***
(10.103)
0.440***
(11.126)
0.317***
(10.692)
Observations
326
58
43
79
149
R2
0.610
0.585
0.754
0.638
0.487
a
When included, the central land share control is negative and significant at the 0.1 level, but center and suburb HC shares
remain significant at the same level.
b
When included, the central land share control is positive and significant at the 0.05 level,
but center and suburb HC shares remain significant at the same level.
***Indicates significance at the 0.01 level; ** at the 0.05 level; * at the 0.10 level.
All results are available from the authors upon request.
Table 4 – Regional Income and Metro, Center and Suburban Human Capital
Density
Av Metro Human Capital Density (a-regressions)
Variables
Eq 1a. All regions
Eq 2a. Size 1
Eq 3a. Size 2
Eq 4a. Size 3
Eq 5a. Size 4
Constant
9.655***
(477.929)
9.733***
(138.937)
9.695***
(105.485)
9.539***
(153.472)
9.670***
(373.769)
Metro HC Density
0.109***
(15.277)
0.093***
(5.222)
0.078**
(2.652)
0.156***
(6.984)
0.103***
(6.522)
Observations
331
61
42
79
149
R2
0.415
0.316
0.150
0.388
0.333
Center and Suburb Human Capital Density (b-regressions)
Variables
Eq 1b. All regions
Eq 2b. Size 1
Eq 3b. Size 2
a
Eq 4b. Size 3
b
Eq 5b. Size 4
b
Constant
9.424***
(167.071)
9.485***
(68.394)
9.306***
(42.858)
9.324***
(72.422)
9.400***
(120.441)
Center HC
Density
0.078***
(6.230)
0.086***
(2.731)
0.094*
(2.005)
0.083***
(3.024)
0.084***
(4.966)
Suburb HC
Density
0.069***
(11.097)
0.037
(1.723)
0.062**
(2.683)
0.106***
(6.118)
0.070***
(8.164)
Observations
326
58
43
79
149
R2
0.464
0.328
0.330
0.441
0.423
a
When included, the central land share control is negative and significant at the 0.05 level, The weakly significant Center HC
Density becomes insignificant in this context.
b
When included, the central land share control is positive and significant at the
0.05 level, but center and suburb HC density remain significant at the same level.
***Indicates significance at the 0.01 level; ** at the 0.05 level; * at the 0.10 level.
All results are available from the authors upon request.

40
Table 5 – Housing Values and Metro, Center and Suburban Human Capital Shares
Av Metro Human Capital Share
Variables
Eq 1a. All regions
Eq 2a. Size 1
Eq 3a. Size 2
Eq 4a. Size 3
Eq 5a. Size 4
Constant
12.808***
(154.628)
13.510***
(59.922)
12.709***
(45.431)
13.035***
(71.430)
12.283***
(121.256)
Metro HC Share
0.801***
(14.655)
1.239***
(6.351)
0.714***
(3.807)
0.963***
(8.003)
0.520***
(8.202)
Observations
331
61
42
79
149
R2
0.395
0.406
0.266
0.454
0.314
Center and Suburb Human Capital Share
Variables
Eq 1b. All regions
Eq 2b. Size 1
Eq 3b. Size 2
Eq 4b. Size 3
a
Eq 5b. Size 4
Constant
12.751***
(164.919)
13.412***
(51.503)
12.594***
(54.346)
12.975***
(70.634)
12.376***
(126.538)
Center HC Share
0.068
(1.533)
0.291**
(2.195)
0.132
(0.987)
0.076
(0.846)
0.055
(0.963)
Suburban HC
Share
0.666***
(14.339)
0.878**
(4.546)
0.486***
(3.720)
0.784***
(8.041)
0.488***
(7.632)
Observations
326
47
43
79
149
R2
0.456
0.397
0.334
0.490
0.391
a
When included, the central land share control is negative and significant at the 0.01 level, but suburb HC density remain
significant at the same level. Center City HC Share remains insignificant. The R2 Adj value increase from .476 to .529 by this
addition.
***Indicates significance at the 0.01 level; ** at the 0.05 level; * at the 0.10 level.
All results are available from the authors upon request.
Table 6 – Regional Metro Housing Values and
Metro, Center and Suburban Human Capital Density
Av Metro Human Capital Density
Variables
Eq 1a. All regions
Eq 2a. Size 1
Eq 3a. Size 2
Eq 4a. Size 3
Eq 5a. Size 4
Constant
11.092***
(247.576)
10.954***
(64.342)
11.198***
(68.444)
11.030***
(74.956)
11.200***
(194.098)
Metro HC Density
0.203***
(12.779)
0.251***
(5.773)
0.154***
(2.930)
0.235***
(4.436)
0.137***
(5.101)
Observations
331
61
42
79
149
R2
0.332
0.361
0.177
0.204
0.150
Center and Suburb Human Capital Density
Variables
Eq 1b. All regions
Eq 2b. Size 1
a
Eq 3b. Size 2
Eq 4b. Size 3
Eq 5b. Size 4
Constant
10.166***
(86.541)
10.143***
(29.578)
10.374***
(25.941)
9.720***
(36.598)
10.360***
(63.195)
Center City HC
Density
0.259***
(9.981)
0.269***
(3.442)
0.216**
(2.512)
0.355***
(6.243)
0.216***
(6.052)
Suburban HC
Density
0.098***
(7.561)
0.088
(1.643)
0.082*
(1.925)
0.110***
(3.075)
0.097***
(5.355)
Observations
326
58
43
79
149
R2
0.465
0.393
0.306
0.450
0.347
a
When included, the central land share control is positive and significant at the 0.05 level, but center HC density remains
significant at the same level. Suburb HC Density remains insignificant.
***Indicates significance at the 0.01 level; ** at the 0.05 level; * at the 0.10 level.
All results are available from the authors upon request.

41
Scatter plot of the relationship
between center and suburb human
capital shares
Scatter plot of the relationship
between center and suburb human
capital density
Scatter plot of the relationship
between center and metro human capital
share
Scatter plot of the relationship
between center and metro human capital
density
Figure 1: Scatter plots of the relationship between metro, center and suburb
human capital levels