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http://usj.sagepub.com/content/early/2014/09/03/0042098014549127
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DOI: 10.1177/0042098014549127
published online 5 September 2014Urban Stud
Chris Benner and Manuel Pastor
America's metropolitan regions
Brother, can you spare some time? Sustaining prosperity and social inclusion in
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Special issue article: Governing for urban resilience
Urban Studies
1–18
ÓUrban Studies Journal Limited 2014
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DOI: 10.1177/0042098014549127
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Brother, can you spare some time?
Sustaining prosperity and social
inclusion in America’s metropolitan
regions
Chris Benner
University of California, Davis, USA
Manuel Pastor
University of Southern California, USA
Abstract
Understanding the factors and processes that help regions sustain economic growth has become
a topic of increasing interest in recent years. We examine factors associated with the length of
‘growth spells’ for the 184 largest regions in the United States from the period 1990–2011. We
find that growth duration is positively related to a number of factors one might expect, including
lower levels of reliance on manufacturing and a higher proportion of the population with middle
education levels. However, we also find that the length of growth spells is strongly related to
lower levels of metropolitan income inequality and to measures of social and spatial segregation,
suggesting that more equitable and more integrated regions are better able to sustain growth.
Keywords
economic growth, growth spells, regional equity, resilience, social inclusion
Received June 2013; accepted November 2013
Introduction
Since the early 1990s, a growing body of
research has been documenting links
between social equity and economic growth
at a metropolitan scale in the United States.
Different measures of equity have been con-
sidered, including income inequality, racial
inclusion, and spatial segregation by race,
income or jurisdiction, with the general pat-
tern being that social equity is correlated
with – and, in some of the work, causally
related to – economic growth.
In all these studies, the focus has been on
the pace of growth. But researchers have
recently turned their attention to ‘resilience’
or the capacity of regions to maintain
growth paths in light of external shocks
(Foster, 2012). Testing for economic resili-
ence requires a different approach to both
Corresponding author:
Chris Benner, Department of Human Ecology, University
of California Davis, 1309 Hart Hall, One Shields Ave,
Davis, CA 95616, USA.
Email: ccbenner@ucdavis.edu
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the theoretical framework and empirical
testing, a task that can be difficult since resi-
lience is a relatively new concept and com-
plicated to measure (Hill et al., 2012).
Fortunately, an analogue in the interna-
tional literature may provide some guidance
to testing for resilience. Turning to that liter-
ature makes sense: much of the early work
on the relationship between equity and met-
ropolitan growth was actually inspired by
findings in the development literature that
countries with more equal distributions of
income tended to grow faster and/or with-
stand macroeconomic shocks. Conveniently,
recent work by researchers at the
International Monetary Fund (IMF) has
focused on not just the growth rate but also
the ability to sustain growth over an
extended period (Berg and Ostry, 2011; Berg
et al., 2012). They argue that lengthening
‘growth spells’ is especially critical for poor
countries of the world, partly because coun-
tries with more sustained growth spells may
create an environment where investors feel
more secure about the future, facilitating a
virtuous cycle. Most relevant to our work
here: the IMF authors find that the duration
of growth spells is strongly related to income
distribution, suggesting that more equal
societies tend to sustain growth longer.
The ability to sustain growth spells is also
of critical interest in the US. The social and
economic costs of periods of recession are
substantial. In addition, with growing global
integration and technological innovation
seems to comes increasing economic volatility
and rapid economic restructuring (Shapiro
and Varian, 1998). One of the critical dimen-
sions of building resilient regions is surely the
ability to sustain economic growth, however
slow, in the face of such economic
restructuring.
What are the factors and processes that
can help explain the ability of regions to sus-
tain economic growth, and what role does
equity play in those processes? We examine
growth spells among the 184 largest regions
in the United States (those with a population
exceeding 250,000 in 2010) from the period
1990–2011. We find evidence that growth
duration is positively related to a number of
factors one might expect, including lower
levels of reliance on manufacturing and a
higher proportion of the population with
middle education levels. But of most interest
here is that growth spells seem to be shorter
when there are higher levels of metropolitan
political fragmentation, higher levels of
racial segregation, and most significantly
(both for theory and in terms of statistical
significance) a higher level of income
inequality.
Below, we review the literature on equity
and growth both internationally and domes-
tically, then take up the specific question of
equity and the length of growth spells. We
then explain our econometric methods and
results. As with Berg et al. (2012), this is
early work in which we do not offer a formal
causal model but rather offer an exploratory
look at the relationships (including first
looking at a series of variables in univariate
regressions before moving on to a multivari-
ate setting). Nonetheless, like those authors,
we offer some tentative explanations why we
think the pattern we find might exist and the
challenge these results pose to traditional
economic paradigms.
Equity, growth and growth spells
Distribution and prosperity: An
international view
One of the first concepts taught in under-
graduate economics is that there is a trade-
off between equity and efficiency, between
fairness and economic growth (Kaldor,
1977; Kuznets, 1955). Yet the notion that a
less skewed distribution of income could kill
the engine of economic vitality was chal-
lenged by a wave of multivariate and multi-
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country studies conducted in the 1990s and
early 2000s (see Aghion et al., 1999 for a
review). Alesina and Perotti (1996), for
example, argued that inequality leads to
social tension and political instability, thus
lowering certainty, investment, and eco-
nomic growth. Rodrik (1999) noted that the
ability of countries to handle external shocks
in large part depends on the strength of
conflict-management institutions, such as
the quality of governmental institutions, rule
of law, and social safety nets, which them-
selves reflect and produce certain
distributions.
Some authors argue for a longer-term
political economy view, suggesting that
when access to productive resources is more
equal, the median voter will perceive that s/
he may benefit from growth and so be more
interested in protecting property rights and
a favourable investment climate (Alesina
and Rodrik, 1994; Persson and Tabellin,
1994). Building on these insights and the
regression results that support them, other
researchers have argued that directly target-
ing poverty and inequality in the developing
world may actually be essential to promot-
ing growth, especially through policies that
increase the productive nature of the poor,
such as investments in education or more
widespread access to finance (Birdsall et al.,
1995; Deninger and Squire, 1996).
As for economic sustainability per se,
Dymski and Pastor (1991) provided an early
view of the role of equity with their study of
the relationship between bank lending and
debt crises in Latin American countries. In
that work, they found that those countries
that were more unequal in their distribution
of income tended to be favoured by private
lenders (accounting for other factors such as
GDP growth and trade openness) but that
those countries were also more likely to
experience payments crises later on. Since all
the other factors that had a positive effect on
lending also had a negative effect on crises,
they labelled the inequality measure a ‘mis-
leading signal’ and argued that strong priors
about the trade-offs between equity and
growth on the part of bankers (and econo-
mists) were possibly one reason why the rela-
tionship between higher inequality and less
sustainability was not well-recognised (a
point to which we return in the conclusion).
Equity and economic growth in America’s
metropolitan areas
It is only recently that the notion of a posi-
tive relationship between equity and long-
term growth – beyond the usual Keynesian
notions about the stimulative effects of pla-
cing money in the hands of less well-off con-
sumers – has made its way into the
discussion of the overall US economy
(Boushey and Hersh, 2012; Stiglitz, 2012).
1
However, one arena where the economic
argument for coupling equity and growth
has gained some analytic, policy and politi-
cal ground for a longer period of time is at
the level of US metropolitan regions. Early
studies of the equity-growth relationship
paralleled the findings in the international
development field but paid insufficient atten-
tion to multivariate controls and issues of
simultaneity (Gottlieb, 2000). Both Voith
(1998) and Pastor et al. (2000) attempted to
address these issues, with Voith finding a
positive association of suburban growth
with city growth and Pastor et al. finding
that various measures of inequality had a
negative impact on per capita income
growth over the 1980s in 74 regions, even
with multivariate controls and considera-
tions of simultaneity (Pastor et al., 2000;
Voith, 1998).
Examining 341 regions in the US and
controlling for other variables that should
promote growth, Pastor later found that real
per capita income growth was negatively
affected by such distributional measures as
the ratio of city to suburban poverty, the
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percentage of poor residents in high poverty
neighbourhoods, the ratio of income at the
sixtieth percentile to household income at
the twentieth percentile, and the index of dis-
similarity between blacks and whites at the
metro level (Pastor, 2006). Again, the results
held up to challenges of simultaneity, sug-
gesting that the causal direction from equity
to growth exists (as well as its obverse). In
more recent work, Pastor and Benner (2008)
found that the dragging effect of inequality
on growth held even in what might be
termed ‘weak market’ metros – places where
some would say that anaemic growth is an
excuse for making attention to equity a sort
of luxury concern.
Federal Reserve economists conducted a
related analysis for nearly 120 metropolitan
areas throughout the US (Eberts et al.,
2006). Using factor analysis, the researchers
identified eight key variables that influence
economic growth on the regional level,
including a region’s skilled workforce, active
small businesses, ethnic diversity and minor-
ity business ownership, level of racial inclu-
sion, costs associated with a declining
industrial base, income inequality (measured
by income disparity and number of children
living in poverty), quality of life variables
(including universities, recreation, and trans-
portation), and concentrated poverty in core
cities. The results: a skilled workforce, high
levels of racial inclusion and progress on
income equality correlate strongly and posi-
tively with economic growth.
It’s a matter of time: Equity and growth
spells
While this work on US metros has looked at
economic growth rates, the international lit-
erature that helped inspire it has moved on
to look at how to sustain economic growth.
One of the striking characteristics of growth
in developing countries over the last 50 year
has been its lack of persistence, and a
growing body of literature has tried to
explain both what helps countries shift from
economic decline towards economic growth,
and what causes an end to growth periods
(Aguiar and Gopinath, 2007; Hausmann et
al., 2005; Hausmann et al., 2006;
Jerzmanowski, 2005; Jones and Olken, 2008;
Pattillo and Gupta, 2006; Rodrik, 1999).
Recent work by IMF researchers has
looked specifically at what is important in
explaining a country’s ability to sustain eco-
nomic growth and forestall a downturn
(Berg et al., 2012). To do this, the authors
first identify a total of 104 distinct ‘growth
spells’ (periods of at least five years of
annualised growth) in a total of 140 coun-
tries (both industrial and developing) since
the 1950s. They then examine a series of fac-
tors that might help explain the likelihood
that a country could fall out of a growth
spell, including: external shocks; political
and economic institutions; inequality and
fractionalisation; social and physical indica-
tors; levels of financial development; levels
and types of globalisation; patterns of cur-
rent account, competitiveness and export
structure; and patterns of macroeconomic
stability.
Perhaps unsurprisingly, external shocks
and macroeconomic volatility are associated
with shorter growth spells while ‘good’ polit-
ical institutions are associated with longer
growth spells. The authors also utilise a vari-
ety of other indicators – including competi-
tive exchange rates, external capital
structures weighted towards foreign direct
investment, and export product sophistica-
tion. But what is particularly interesting for
our work – and what the researchers them-
selves describe as a ‘striking’ result – is that
the length of growth spells is strongly related
to income distribution, with more equal soci-
eties tending to be able to sustain growth
over a longer period. Across their sample, a
one percentage increase in a Gini coefficient
of income inequality is associated with an
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11–15% reduction in the expected duration
of a growth spell. In their summary model
combining a range of indicators, while there
are many that remain significant, one of their
conclusions is that ‘income inequality is
among the most robust predictors of dura-
tion’ (Berg et al., 2012: 160).
We have seen no similar studies of the
length of growth spells in the US; the work
of Hill et al. (2012) is a notable exception in
some ways but there the focus is on a variety
of different notions of economic resilience.
While the general focus of the literature on
overall growth rates is understandable, in
recent years, researchers have come to
appreciate that spells of unemployment, for
example, have lasting effects on people’s life-
time earnings long after they are once again
able to secure employment (Mroz and
Savage, 2006) and that the lasting effects also
include psychological distress and decline in
life satisfaction (Daly and Delaney, 2013).
There is also evidence that new graduates
entering the labour market during a reces-
sion also experience long-term earnings
declines when compared with those entering
the labour market during growth periods
(Kahn, 2010; Oreopoulos et al., 2012).
We thus focus here on sustained growth
or ‘growth spells’. We acknowledge that
many researchers and policy advocates ques-
tion whether growth – either its rate or its
length – is the best metric for understanding
economic well-being. The Genuine Progress
Indicator, for example, tries to account for
the value of unpaid labour and subtracts the
value of environmental pollution from eco-
nomic growth figures (Hamilton, 1999;
Lawn, 2003; Talberth et al., 2006). The
Human Development Index provides a more
comprehensive picture of socio-economic
welfare, and a number of indicators incorpo-
rate subjective measures of happiness and
social well-being (Abdallah et al., 2012;
Bates, 2009; Sagar and Najam, 1998). We
support this broadening of the research
agenda but we also think that growth spells
matter for people’s lives and that is our
focus here.
Explaining sustained metropolitan
growth in the US
Defining growth spells
As noted above, much of the previous work
on the relationship between metropolitan
equity and growth was inspired by work
being conducted in the developing country
context – and we follow suit here. Building
on the work of Berg et al. (2012), we decided
to examine growth spells in the largest 184
metropolitan regions in the US (all CBSAs
(Core Based Statistical Areas) that had a
population of 250,000 or more as of the
2010 census). For our measure of economic
growth, we used data from the Quarterly
Census of Employment and Wages
(QCEW), which has a consistent measure of
monthly employment starting in 1990. We
look at quarter-to-quarter average employ-
ment, rather than month-to-month employ-
ment, given the volatility in monthly
employment figures. We considered a region
to have experienced growth in a quarter
if the total average employment in that
quarter was greater (by any amount) than
the same quarter in the previous year; the
year-over-year measure was used as a way of
adjusting for seasonal variation in
employment.
We considered a region to be experien-
cing a full growth spell if it experienced at
least 12 quarters of uninterrupted quarter-
to-quarter employment growth in this mea-
sure. At the time of our analysis we had the
full employment data from 1990 to 2011, for
a theoretical possible maximum length of
growth spell of 84 quarters. We resulted with
a database that included 324 growth spells
in 181 of the 184 regions (the three regions
with no growth spell of at least 12 quarters
Benner and Pastor 5
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in this time period are: Buffalo-Niagara
Falls, NY; Merced, CA; and Sarasota-
Bradenton-Venice, FL).
2
Do growth spells really matter? One
might argue that the length of growth is of
little consequence – perhaps a ‘boom and
bust’ economy is volatile but it will deliver
high employment and wages over time.
Table 1 takes the 181 regions which had
growth spells and breaks them into cate-
gories based on the number of quarters in
the overall period that a region was in a
growth spell; the categories are chosen to
create bands that are non-arbitrary but
somewhat similar in terms of the number of
regions that falls in each and the basic results
are not sensitive to our particular choice of
bands. Note that the minimum is 12 – one
needs that to have experienced a growth
spell at all – and the maximum that any
region spent in growth spells over the whole
period is 70 quarters. We then calculate the
growth in employment and real weekly earn-
ings (also from the QCEW data) over the
whole period. The data suggest that more
time in growth spells generates more overall
employment growth and generally higher
earnings (although the earnings effect seems
to taper off in the higher bands).
3
What about the impacts on employment
and earnings of the spells themselves (rather
than the length of time any particular region
spends in a spell)? Table 2 shows those
results; note that the longest single growth
spell was 69 quarters (Go, Ogden, Utah!)
and that the number of growth spells rang-
ing from 12 to 69 is the 324 discussed earlier.
One feature of Table 2 is that we are also
able to offer a view of performance for peri-
ods which fall out of growth before our 12
Table 1. Growth spells and regional outcomes.
Number of quarters
in growth spells
Number of regions
in category
Employment growth
over whole period
Growth in real weekly
earnings over whole period
12–20 18 6.0% 6.5%
21–30 17 10.7% 15.1%
31–40 25 17.3% 14.0%
41–50 31 19.8% 20.2%
51–55 23 22.9% 19.1%
56–60 31 43.3% 20.6%
61–70 36 61.2% 22.1%
Table 2. Growth spell outcomes.
Length of
growth period
Number of growth
periods in each category
Annualised
employment growth
Annualised growth in
real weekly earnings
1–4 332 2.7% -0.8%
5–11 167 1.9% 1.0%
12–16 70 2.2% 0.7%
17–20 69 2.2% 0.8%
21–28 73 2.6% 0.9%
29–38 67 2.9% 1.3%
39–69 45 4.2% 1.3%
Overall 1.0% 0.7%
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quarter mark for a ‘growth spell’. There are
332 growth periods where growth occurred
for less than a year; these were indeed
booms with high employment growth on an
annualised basis but since they are associ-
ated with declining earnings and immedi-
ately fell into recession (and since growth
spells are associated, as in Table 1, with bet-
ter growth for a region over the long haul),
it’s hard to see why this is a desirable out-
come. Starting from growth periods that run
from 5 to 11 quarters, we see that annualised
employment growth generally rises with the
length of the growth spell; the increase in
real earnings also improves in longer growth
spells (starting from 12 quarters on) but the
effect is seemingly less strong. Note that the
overall employment growth figures are lower
than for any of the categories; that is
because the overall figures include years in
which employment is falling. The overall
message from these tables: longer growth
spells are associated with faster growth and
higher employment outcomes over time,
with earnings showing a similar but weaker
relationship to the length of growth spells.
Methodological approach
If growth spells are good, what determines
their length? In their country-level analysis,
Berg et al. (2012) look at a range of indica-
tors, some of which – like inequality and
social conditions – have direct parallels to
regional economies. Others, such as those
related to macro-economic stability or level
of development in financial institutions, are
essentially uniform across the entire US and
thus are not appropriate for an analysis of
regional growth spells. We wind up categor-
ising our variables into the following
domains: external shocks and vulnerability;
political fragmentation; inequality and
separation; social indicators; and economic
structure and institutions.
Aside from similar domains, another way
in which our approach parallels Berg et al.
(2012) is that this is an exploratory exercise.
Berg et al. state:
We sequentially test the relevance of particular
regressors of interest, while including some
minimal controls .At the end, we summarise
by showing the results of a few parsimonious
regressions that control for all or most of the
variables that were found to matter during the
sequential testing process. (2012: 152)
We follow suit, first looking at individual
regressors and then combining them and
offering one more parsimonious specifica-
tion. While, like Berg et al. (2012), this
approach is somewhat unorthodox (there is
no strong model specification prior to explo-
ration), we do offer heuristic rationales for
our variables and view this initial work as
setting the stage for future quantitative and
qualitative work. Partly because of this, we
report not just on the usual significance lev-
els (.01, .05 and .10) but also note when vari-
ables achieve a significance level around .20;
the idea is to point to relationships for which
further research will be needed.
The testing technique specifically used in
this exercise is a Cox regression, a particular
type of survival analysis regression method.
In our case, we are trying to see which fac-
tors are associated with an early exit from
sustained growth. The reported coefficients
are so-called ‘hazard ratios’ that are always
positive; when a coefficient is more than one,
that means the variable being tested is asso-
ciated with falling out of a growth spell and
when the coefficient is less than one, the vari-
able being tested is associated with staying
longer in a growth spell.
4
One key issue in hazard analysis is right
censoring– which occurs when an observa-
tion is terminated before the expected event
occurs. All survival analysis software is
designed to handle this kind of right
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censoring, and in our case only one growth
spell was continuing at the end of our time-
period of analysis (nearly every region even-
tually got knocked off its growth path by the
Great Recession; some did recover more
quickly than others but there was not
enough time to complete a growth spell).
However, we also face another issue: the
data for our analysis start in 1990 and thus
the first complete quarter of year over year
growth is in 1991, and 29 out of our 324
growth spells date their first quarter then.
We thus know a minimum length of these 29
growth spells, but not the actual full length.
This differs from left censoring, in which an
event is known to have happened before
some particular time, so a maximum value is
known, or internal censoring in which an
event is known to happen between two
points in time, but the exact time is unknown
(Allison, 2012; Finkelstein, 1986; Klein and
Moeschberger, 2003).
Because of the uncertainty of length for
that particular set of growth spells and a
lack of clear guidance in the literature on
how best to handle such cases, we ran two
different sets of regressions: one in which we
simply excluded those 29 cases with incom-
plete growth lengths, and one in which we
included them and treated them as regular
growth spells, assuming that the growth spell
did actually begin at the beginning of our
time-period (which may not be problematic
since the US was coming out of a national
recession in that time period). There were
only minor differences in the regression
results so we present the findings below for
the entire sample.
Finally, in terms of right-hand side vari-
ables, unless indicated, these came from a
database assembled for the Building
Resilient Regions network that contains eco-
nomic, civic, social, housing, geographic,
and demographic measures for several
decades for all 934 Core-Based Statistical
Areas (CBSAs) in the United States.
5
One
special feature of the data is that CBSA
boundaries have been made consistent to
compare measures across the 1970, 1980,
1990, and 2000 censuses and recent versions
of the American Community Survey (ACS).
(Here, we just use 1990 and 2000 data.)
Base regression specification
In all of our regressions, we include dummy
variables for census region (as did Hill et al.,
2012) and we also include both regional per
capita income and a measure of metro size
as controls. Regional per capita income is
included partly because a parallel starting
income measure is used in Berg et al. (2012);
moreover, the regional economic conver-
gence literature generally controls for initial
regional income to account for convergence
to the mean (see the discussion and parallel
construction for growth equations in Pastor
et al., 2009). The coefficients on initial
income, which we do not report to conserve
space, always pointed in the appropriate
direction (higher per capita income is associ-
ated with shorter growth spells) and remain
significant in our full specification (similar to
the results in Berg et al., 2012). We included
metro size, a familiar control, because larger
metros might be more resilient to shocks and
that is indeed the case in our regressions; our
measure, the log of the metro population
normed relative to the sample, is similar to
the metro size variable used in a recent effort
by Li et al. (2013).
These and all other variables (with one
exception detailed immediately below) are
meant to capture initial conditions as the
growth spell begins. However, spells get
started at different times and so too the ini-
tial conditions. We have selected the time–
year of these variables that is the closest
available data prior to the beginning of the
growth spells (e.g. 1990 census data for
growth spells beginning in the 1990s and
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2000 census data for growth spells beginning
in the 2000s).
External shocks and vulnerability
An external shock, such as a national reces-
sion, is one of the most likely factors to end
a growth spell. We consider here the percent-
age of total quarters within the growth spell
in which the national economy was in reces-
sion. Our notion is that the longer the spell
has been impacted by the recession, the more
likely it is to fall out – and the results are sig-
nificant at the .10 level with the expected sign
(in the tables that follow, any result that is
significant to at least the .20 level is bolded)
(see Table 3). Again, recall how one should
read these coefficients: the 1.018 coefficient
indicates that, holding all other covariates
constant, an increase of one unit (in this case
a percentage point increase in the share of
the region’s growth spell that the nation is in
an overall recession) is associated with a
nearly 2% increase in hazard (or likelihood)
of growth ending.
Another way to get at external vulnerabil-
ity is to consider the likely impacts of globa-
lisation. For this, we calculated the
proportion of gross regional product
accounted for by international exports with
data taken from the Department of
Commerce. 2005 was the first year for which
we had the export data and we averaged the
years 2005 to 2010 to smooth out yearly var-
iations and instead catch the overall struc-
ture. This is a highly imperfect measure,
partly because it is taken from the end of the
period rather than before, a failing to which
we simply plea that we had no other such
variable available to us for the earlier peri-
ods.
6
The direction is as expected – a higher
share of exports is associated with a greater
hazard of falling out of a growth spell – and
is significant at the .03 level.
Political fragmentation
There is now a voluminous literature sug-
gesting that regional collaboration may be
important for promoting economic competi-
tiveness (Cooke and Morgan, 1998; Martin
et al., 2012; Scott, 1998; Storper, 1997).
Regional equity advocates have also argued
that the fragmentation of local government
within metropolitan regions has been an
important driver of inequality and inefficient
public investments (Rusk, 2003). On the
other hand, some recent research has sug-
gested that fragmentation, perhaps because
it promotes Tieboutian competition, can be
consistent with more rapid growth
(Grassmueck and Shields, 2010). Whatever
the perspective, it is clear that metropolitan
fragmentation may be implicated in growth
outcomes.
However, measuring such fragmentation
can be difficult. Some researchers simply
count the number of governments in a metro
region, either in absolute terms or on some
per capita basis (Dolan, 1990; Goodman,
1980; Grassmueck and Shields, 2010; Ostrom
et al., 1974). A second approach, most promi-
nently represented in the Hirshmann-
Herfindal Index, considers the concentration
of expenditures of all governmental units in a
region, and is measured as the sum of the
squared percentage of each player’s share of
the total market (Grassmueck and Shields,
2010; Scherer and Ross, 2009). A third and
newer approach, developed by David Miller
of the University of Pittsburgh, builds on this
Hirshmann-Herfindal Index approach but
Table 3. External shocks and vulnerability.
Hazard ratio Sig.
% of growth spell in
national recession
1.018 0.09
Exports as % of GMP 1.011 0.03
Note: The variables are presented in a single table for
convenience, but each was entered separately.
Benner and Pastor 9
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also incorporates the number of jurisdictions
in the region (Hamilton et al., 2004; Miller
and Lee, 2009).
7
The resulting Metropolitan
Power Diffusion Index (MPDI) is available
for all metropolitan areas in 1987, 1997 and
2007, and is derived by using the square root
of the percentage contribution to total
regional expenditures, rather than the square,
a process that gives greater mathematical
value to the smaller units.
When measured alone (along with the
regional, per capita income and metro size
controls), it turns out that the MPDI is asso-
ciated with the shortening of growth spells –
more diffuse regions are more likely to fall
out of a growth spell – albeit at the .15 sig-
nificance level (see Table 4). As we will see
later, the statistical significance improves
slightly when MPDI is included in regres-
sions with a range of other indicators, sug-
gesting political fragmentation might be a
drag on sustained growth.
Inequality and social separation
Following Berg et al. (2012), we use the Gini
coefficient to look at the role of inequality in
shaping growth spells, deriving the measure
from metropolitan household income data
from the decennial census.
8
We also looked
at the size of the ‘minority middle class’,
considering the proportion of African–
American and Latino households that are in
the middle income brackets for the region,
first separately, then combined; below, we
just present results for the combined mea-
sure to save space.
9
We also wanted to look
at issues of social separation so we
considered a standard measure of residential
segregation called the dissimilarity index, in
this case calculated in terms of non-Hispanic
whites and everyone else, as well as the ratio
of city to suburban poverty rates.
10
As shown in Table 5, the Gini coefficient
turns out to be extremely significant and
powerful. A one point increase in the Gini is
associated with a 21% increase in the likeli-
hood that a region will fall out of the growth
spell. Our various minority middle class vari-
ables were significant; to conserve space, we
show only the combined measures which
suggest that regions with a higher percentage
of minorities in middle class income brackets
are more likely to have longer growth spells.
The dissimilarity index results suggest that
more segregated regions have shorter growth
spells; a higher city–suburb poverty differen-
tial is also associated with shorter growth but
the result is significant only at the .23 level.
Social indicators
In addition to inequality and social separa-
tion, we sought to look at educational
attainment and levels of immigration. For
education, we looked at two measures: the
proportion of the population 25 years and
older with a bachelor’s degree or higher and
the proportion with at least a high school
Table 4. Regional governance.
Hazard ratio Sig.
Metropolitan Power
Diffusion Index (MPDI)
1.056 0.15
Table 5. Inequality and social separation.
Hazard
ratio
Sig.
Gini coefficient (initial level) 1.213 0.00
% disadvantaged minority
households in middle income
brackets
0.911 0.00
Dissimilarity index, non-Hispanic
whites
1.010 0.12
Ratio, principal city to suburban
poverty rates
1.098 0.23
Note: The variables are presented in a single table for
convenience, but each was entered separately.
10 Urban Studies
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degree but less than a bachelor’s degree; the
default category is the proportion of the
population with less than a high school
degree. For immigration, we looked at the
percentage of the foreign-born (immigrant)
population in each region.
The results are shown in Table 6. Of our
two education measures, the proportion of
the population with at least a high school
degree but less than a bachelor’s degree is
statistically significant (at the .03 level) with
regions with a larger proportion of the
middle-educational population being less
likely to fall out of a growth spell. The pro-
portion of immigrants in the region also has
a significant (shortening) relationship to the
length of growth spell, perhaps reflecting the
sort of ‘immigrant shock’ discussed by
Pastor and Mollenkopf (2012).
Employment structure and institutions
We also looked at three broad measures of
industrial structure in the region, namely the
percentage of workforce employed in con-
struction, in manufacturing, and in public
administration, as well as one set of eco-
nomic institutions, the percentage of the
workforce covered by unions.
11
As shown in
Table 7, the percentage of employment in
public administration is associated with lon-
ger growth spells; manufacturing and con-
struction, each entered alone, do not have a
significant impact on the length of growth
spells. The unionisation variable is associ-
ated with shorter growth spells but the result
is not significant.
Integrated model
Having looked at each of these indicators
separately, we now combine them into a sin-
gle regression; as with the individual regres-
sions, we include controls for census region,
per capita income and metro size. There are
two things to mention before looking at the
results. First, we included only one educa-
tional attainment indicator, due to obvious
high levels of collinearity; we chose the one
that was significant in the stand-alone regres-
sion. Second, in our initial integrated regres-
sion, we found that that variable, indicating
a broadly educated middle, was actually
associated with shorter growth spells, the
opposite of its impact in a univariate con-
text. Since this shift was unusual, we investi-
gated and found with the economic structure
variables and our educational indicator
alone, a broadly educated middle was associ-
ated with longer growth spells (as we found
when it was entered alone), albeit at the .20
level. However, the sign on education flipped
when we introduced the Gini coefficient –
sensible given that inequality would likely be
Table 6. Social indicators.
Hazard
ratio
Sig.
Adult population with BA
degree or higher
1.010 0.44
Adult population with HS to AA
degree
0.975 0.03
% of population foreign born 1.029 0.00
Note: The variables are presented in a single table for
convenience, but each was entered separately.
Table 7. Economic structure and institutions.
Hazard
ratio
Sig.
% employment in public
administration
0.954 0.02
% employment in manufacturing 1.004 0.66
% employment in construction 0.971 0.46
% of workforce covered by a
union contract
1.003 0.73
Note: The variables are presented in a single table for
convenience, but each was entered separately.
Benner and Pastor 11
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larger if there was a lower level of what
might be considered mid-level education.
To deal with this issue, we constructed a
simple linear regression in which the depen-
dent was the original Gini coefficient and
the independent was the share of the popula-
tion with at least a high school degree and
less than a BA. With the regression weighted
by metro population to give a better sense of
the overall relationship, we took the resi-
duals of the regression as a sort of detrended
Gini coefficient – that part of inequality not
directly explained by the single educational
variable we are using in this exercise.
12
The Cox regression results with that mod-
ified Gini coefficient are shown in Table 8.
The first set of columns include all the vari-
ables tested above, while the second set of
columns drops the three least significant
measures. Note first that once we have
accounted for all these structural variables,
the percentage of the growth spell during
which the nation has been in recession is no
longer significant. The export variable is also
insignificant but this measure is, as we have
suggested, imperfect given its timing.
However, both metropolitan diffusion and
higher levels of inequality are associated
with shorter growth spells and the effects are
very significant.
The percentage of minorities in the mid-
dle class is not significant at all, presumably
because of competition with the two residen-
tial segregation measures, the dissimilarity
index and the city–suburb poverty ratio.
Also associated with shorter growth spells
were the percentage foreign born, the share
of the workforce in manufacturing, and, to a
lesser degree of significance, the share of the
workforce in construction; positively associ-
ated with the length of growth spells is the
percentage of the population with a middle
level of education. Although the percentage
in public administration was associated with
longer growth spells when entered on its
own, it is now associated with shorter
growth spells (although with a relatively low
significance level); it may be that while pub-
lic sector employment can strengthen the
middle class and dampen inequality and
extend growth spells for that reason, when
entered into a regression where inequality is
a direct measure and other dynamic aspects
of the economy are accounted for, a larger
Table 8. Integrated model with GINI residual.
Full model Parsimonious model
Hazard ratio Sig. Hazard ratio Sig.
% of growth spell in national recession 1.001 0.96
Exports as % of GMP 0.996 0.67
Metropolitan Power Diffusion Index (MPDI) 1.096 0.05 1.091 0.04
Gini coefficient (residual) 1.306 0.00 1.300 0.00
% minority middle class 1.000 1.00
Dissimilarity index, non-Hispanic white 1.012 0.19 1.011 0.15
Ratio, principal city to suburban poverty rates 1.166 0.11 1.160 0.12
Adult population with HS to AA degree 0.972 0.12 0.973 0.12
% of population foreign born 1.048 0.00 1.046 0.00
% employment in public administration 1.033 0.25 1.031 0.27
% employment in manufacturing 1.060 0.00 1.057 0.00
% employment in construction 1.090 0.13 1.085 0.14
% of workforce covered by a union contract 1.010 0.40 1.011 0.34
Note: Variables entered in multivariate fashion.
12 Urban Studies
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public sector signals a more rigid econ-
omy.
13
A similar argument could be made
for the shortening impact – albeit insignifi-
cant – of unionisation (although this might
also be associated with an older industrial
structure).
Of course, the big news is that the Gini
coefficient remains highly significant and,
interestingly, the coefficient is essentially the
same as before we did the detrending (every
other coefficient is stable as well which
makes sense since the ‘detrending’ exercise
was only to separate out the education and
Gini factors).
14
This suggests that inequality
does indeed have a dampening impact on
growth spells – although one important
caveat to stress is that this is an exercise cov-
ering the 1990s and 2000s in metropolitan
America. It may well be that inequality can
contribute to growth in some circumstances
and retard it in others – that is, that there is
a U-shaped relationship in which ‘perfect’
equality destroys incentives and hurts eco-
nomic expansion while more extreme levels
of inequality manage to do the same for the
reasons discussed earlier. From this perspec-
tive, it may simply be that we are past a sort
of ‘peak’ level of inequality in contemporary
America – and that we need to rebalance
priorities and strategies to obtain more
inclusive and more robust growth.
Regional resilience and ‘just
growth’
We have been among those suggesting that
an emphasis on regional equity is not simply
about fairness but might also have positive
impacts on economic growth (Boushey and
Hersh, 2012; Treuhaft et al., 2011). Just as
some of our earlier economic work essen-
tially borrowed from international literature
on this question (Pastor, 2006; Pastor and
Benner, 2008), we have borrowed here from
a recent focus by economists at the
International Monetary Fund on not the
rate of growth but the sustainability of
growth. As it turns out, the findings in this
study offer further evidence for pro-equity
proponents: in Cox regressions on the length
of growth spells for nearly 200 metropolitan
regions over the last two decades, we find
that the largest and most significant predic-
tor of shortening a growth spell is the level
of inequality, a result quite close to the find-
ings in the international literature we mimic.
Also of interest are the results for the level
of metropolitan political fragmentation and
residential segregation by race and income.
There are other factors, of course, including
education levels and economic structures
and institutions, that are important to main-
taining growth. But the punchline of this
work is that regions that are more equal and
more integrated – across income, race, and
place – are better able to sustain growth over
time.
If equity may be good for growth, why
don’t regional policymakers pay even more
attention to ameliorating inequality? Of
course, part of the political-economy reason
is that some do benefit (relatively) from less
equitable distributions of opportunity. But
another reason harkens back to the interna-
tional finding in Dymski and Pastor (1991)
that rising inequality was a ‘misleading sig-
nal’, prompting banks to lend more money
to Latin American countries even as those
were exactly the same countries more likely
to fall in a debt crisis where they stopped
payments.
At the time those results were presented,
many economists objected – after all, bank-
ers were considered the exemplar of market-
based rational expectations and it stretched
the neo-classical imagination to suggest that
financiers might make systematic mistakes.
In the wake of multiple waves of overlending
to the developing world, the meltdown of the
savings and loan industry, and a worldwide
crash due to a combination of subprime
loans, credit default swaps, and packaged
Benner and Pastor 13
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derivatives, the idea of a rational financial
sector is, let’s say, at least worthy of further
questioning. But mustering evidence to
change deeply held ‘priors’ can be difficult –
consider the continuing faith that tax cuts
will produce supply-side growth or the reve-
lation that in 2012, 30% of Republicans
believed that President Obama was born
abroad, up from 16% in 2008 (Scherer,
2012).
Priors only shift when the evidence finally
becomes so strong that there is a sort of
‘breakthrough’ in which the norms and pre-
cepts move dramatically and broadly across
a knowledge community (Kuhn, 2012). The
evidence here is tentative but we hope that it
will encourage both more research and fur-
ther theorising about whether more equita-
ble, integrated and spatially connected
regions – which we might prefer for other
reasons as well – may also be able to achieve
more sustainable growth.
Acknowledgements
We would like to thank Justin Scoggins from the
Program for Environmental and Regional Equity
at USC for his assistance with the data analysis. We
would also like to thank the anonymous reviewers
and the members of the Building Resilient Regions
network, particularly Hal Wolman, for their helpful
and constructive comments.
Funding
We would like to thank the Institute for New
Economic Thinking (grant no. 5409), The Ford
Foundation and The MacArthur Foundation, for
their financial support that helped enable this
paper.
Notes
1. See also the working paper by Levine et al.
(2010) which looks at the effects of income
inequality on ‘expenditure cascades’ and
finds that US counties with the highest levels
of income inequality were most likely to
experience financial distress.
2. While this is the full sample, we lacked key
variables for all 181 regions. We were, for
example, unable to calculate Gini coefficients
for 10 smaller CBSAs (less than 500,000 pop-
ulation), and unionisation rates for 15 smaller
CBSAs are not reported. In regressions with
those variables, we are actually analysing 287
growth spells in 160 regions.
3. For the three cases without any growth
spells, average employment and wage
growth were relatively high but that was due
to averaging one spectacular performer, one
dismal performer and one middle-range
performer.
4. Berg et al. (2012) report time ratios rather
than hazard ratios; the two measures move
in opposite directions but we report hazard
ratios because they might be more familiar
to readers and because the Cox procedure is
built into SPSS, our program of choice for
this exercise. To check our results, we also
did all the regressions in STATA, using the
Streg command with the accelerated-time-
to-failure option (the method used in Berg et
al., 2012). We report those results in a foot-
note when we consider the integrated model;
suffice it to say that everything moves in a
very similar direction.
5. See http://brr.berkeley.edu/.
6. An addition, the gross regional product
measure used as the numerator is taken
from another source and so we get some
unrealistically high export shares; we logged
the variable to reduce that problem and get
a more normal distribution but the result
made little difference to the regression out-
comes and was not parallel with the other
share variables utilised later; hence, we
report on the results for the straightforward
share measure.
7. See http://www.metrostudies.pitt.edu/Projects/
MetropolitanPowerDiffusionIndex/tabid/
1321/ Default.aspx.
8. In particular, we used data on household
income from the 1990 and 2000 5% Public
Use Microdata Samples (PUMS), applying
trapezoidal integration to calculate the Gini
coefficient for each year.
9. We used categorical household income
information by race of householder from
14 Urban Studies
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the 1990 and 2000 censuses, and defined the
middle class to include households with
income between 80% and 120% of the over-
all regional median. All households falling
in income brackets entirely contained within
the resulting income boundaries for the mid-
dle class were included, and, for the income
brackets ‘split’ by the income boundaries,
linear interpolation was used to estimate the
number of households in such brackets fall-
ing in the middle class.
10. The ratio is calculated for the so-called prin-
cipal cities of a metro area relative to other
areas.
11. Most economic institutions do not vary sub-
stantially from region to region across the
US, or are difficult to capture quantitatively
(such as economic development strategies).
There are some state-level differences, such
as minimum wage legislation or right-to-
work legislation (used, for example, in Hill
et al., 2012) but with multiple metro-regions
in single states, and some metropolitan
regions crossing multiple state boundaries, it
seemed more appropriate to consider eco-
nomic institutions that did vary by region:
the percentage of unionisation.
12. We re-ran the individual Cox regressions
with the Gini coefficient and the Gini resi-
dual from this exercise; both were highly sig-
nificant but, as one might expect, the hazard
ratio for the modified Gini coefficient on its
own was lower (since some of the underlying
explanatory power from the education struc-
ture – that is, its role in postponing the
‘hazard’ of an end to a growth spell – has
been set to one side).
13. Because we were worried about the impacts
of the Great Recession on our results – after
all, part of the reason we weren’t worried
about right-censoring is that virtually all the
growth spells were clipped by the end of the
period we examined – we re-ran the full
model with a dummy variable that equalled
one if the growth spell ended in the official
recession period (December 2007 to June
2009, so fourth quarter of 2007 to second
quarter of 2009). The variable itself was not
significant and the only variable that lost
just a bit of its significance was the
percentage of the workforce in construction,
a sensible turn of events given the role
that sector played in the most recent
turndown.
14. As noted earlier, we also ran the model using
Streg with an accelerated-time-to-failure
option in Stata in order to obtain results
more directly parallel to those in Berg et al.
(2012); we specified the Weibull distribution
as did they. The only difference of note in
the individual regressions is that the growth-
shortening effect of the share of the popula-
tion with a BA or better was more significant
(although the high school to AA variables
were still growth-promoting and much more
significant). In the integrated model, virtu-
ally everything was identical in terms of sig-
nificance level with a few minor exceptions:
MPDI and the dissimilarity index were less
significant and the city–suburb poverty ratio
was more significant, suggesting that the
competition for significance of these similar
variables winds up slightly different outside
the Cox specification; the share of the work-
force in construction also became insignifi-
cant although it was signed as expected. One
interesting outcome: the time ratio coeffi-
cient we obtain from this regression for our
Gini measure, the only exactly parallel right-
hand side variable between the Berg et al.
(2012) study and ours, is virtually identical
to the similar measure they obtain.
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