Steven P. Lanza

English

Research Papers in Economics

SPRING 2007  THE CONNECTICUT ECONOMY 7
BY STEVEN P. LANZA
Historically cities were population
magnets, but since World War II their
attraction has steadily declined as
residents moved to the suburbs and
beyond.  But the 2000 census
revealed that some American cities,
New York and Boston among them,
are enjoying something of a renais-
sance, as urban professionals and
empty nesters rediscover the advan-
tages of urban living.  Might such a
rebirth be possible in Hartford and
other Connecticut cities, too?  What,
if anything, might give the Nutmeg
State’s metros new allure?
Many factors, like proximity to
the shoreline and local zoning policy,
combine to explain the distribution of
the state’s population across its land-
scape.  But population estimates sug-
gest that the towns seeing the most
growth since the last census are those
offering residents better economic
opportunities and recreational ameni-
ties.  And the cities may have an ace up
their sleeves.  Evidence suggests that
population density itself may boost
economic efficiency and so serve as an
important urban resource.  
WHERE WE ARE
Connecticut is among the most
densely populated states in the coun-
try, ranking 4th from the top in the
number of people per square mile of
land area, according to data from the
last census.  New Jersey is tops, Rhode
Island is runner-up, and Massachusetts
ranks 3rd.  
But as anyone familiar with
Connecticut’s population sprawl prob-
lem will attest, residents aren’t exactly
packed like sardines into the state’s
urban areas.  Our urban areas are far
less crowded than those of most states.
Of Connecticut’s entire land area of
4,845 square miles, 36% of it, or
1,757 square miles was densely popu-
lated enough to be classified as “urban”
in 2000.  Three million of the state’s
3.5 million residents lived within that
urban land area, which translates into
urban population density of just 1,701
people per square mile.  By compari-
son, the average population density of
U.S. urban areas was 2,404.  New York
and California ranked first and second
among the states, with 4,215 and
4,041 urban residents per square mile,
respectively.  Connecticut placed 38th,
right behind New Mexico and just
ahead of Alaska.
What explains Nutmeggers’ choic-
es of places to live? A regression equa-
tion relating population density by
town in Connecticut, using 2000
Census figures, to a number of possi-
Beacons of Light
for Connecticut’s Cities?
Variable Elasticity Explanation
*Distance from NYC -0.81 A 10% decrease in distance to NYC increases population density 8.1%.
*Coast 0.23 A coastal locale raises a town's population density 23%.
*Interstate 0.21 Towns bisected by an interstate have 21% greater population densities.
*Minimum Lot Size -0.45 A 10% increase in minimum lot size reduces population density 4.5%.
*Equalized Net Grand List per Capita -1.02
A 1% increase in a town's per-capita grand list is associated with a 1% decrease in 
population density.
*Per-Pupil Spending -0.63
A 10% increase in per-pupil spending is associated with a 6.3% decrease in 
population density.
*Unemployment Rate 0.35
A 3.5% increase in population density is associated with a 10% increase in the 
share unemployed.
*Median Home Value 1.28
A 1% increase in the median home value is associated with a 1.3% increase in 
population density.
*Crime Rate 0.42 A 4.2% increase in the crime rate is associated with a 10% increase in population.
*Leisure Index Population Under 3.5K -0.28
A 2.8% decrease in population density is associated with a 10% decline in the  
leisure index ranking.
Leisure Index Population 3.5K - 6.5K -0.14 No statistically significant association.
Leisure Index Population 6.5K - 10K -0.09 No statistically significant association.
*Leisure Index Population 10K-15K 0.03
A 0.3% increase in population density is associated with a 10% decline in the 
leisure index ranking.
*Leisure Index Population 15K - 25K 0.15
A 1.5% increase in population density is associated with a 10% decline in the
leisure index ranking.
*Leisure Index Population 25K - 50K 0.17
A 1.7% increase in population density is associated with a 10% decline in the 
leisure index ranking.
*Leisure Index Population Above 50K 0.18
A 1.8% increase in population density is associated with a 10% decline in the 
leisure index ranking.
* Statistically significant.
DETERMINANTS OF TOWN POPULATION DENSITY, 2000
Population density 
may boost 
economic efficiency, 
and so serve as 
an important 
urban resource.8 THE CONNECTICUT ECONOMY SPRING 2007
ble determinants may offer some clues
(see the first table).  
Some fixed features of the towns
themselves seem to have clear effects.
One-quarter of the state’s residents live
in Fairfield County, and nearly one-
third cluster along the coastline, so
proximity to New York City and a
coastal locale are significant popula-
tion draws. Controlling for other fac-
tors, a 10% decrease in the distance to
New York increases a town’s popula-
tion density by 8.1%, and towns that
abut Long Island Sound have 23%
more residents per square mile than
their landlocked neighbors.  And the
state’s network of interstate highways
not only helps move residents from
place to place, it also stimulates resi-
dential development along its path.
Towns traversed by an interstate have
more than 20% more residents than
those that are not.
But public policy may influence
state residents’ location decisions, too.
Residential population varies inversely
with a town’s minimum lot size. Other
things equal, a 10% increase in a
town’s minimum lot size is associated
with a 4.5% decrease in population.
Naturally, a housing development sub-
divided into large properties cannot
accommodate as many homes as one
divided into smaller parcels, so towns
with the largest minimum lot sizes will
tend to have the fewest residents.
But the relationship between pop-
ulation and lot size may also reflect a
more complex two-way link. In more
populous areas, where many residents
vie for limited space, towns may have
no choice but to permit smaller lot
sizes as a local zoning policy.  This so-
called endogeneity problem, where the
direction of causality runs from the
independent variables to the depend-
ent variable and back again, appears
with housing prices and crime, too.
We might expect expensive homes and
high crime rates to depress a town’s
population total, yet the evidence
shows a positive association between
the number of residents and both of
these explanatory variables.  The rea-
son?  A larger population produces
more competition for housing and
bids prices up.  And more crime is one
of the many urban pathologies associ-
ated with higher-density living.
WHERE WE’RE GROWING
We won’t know for sure how the
population distribution across
Connecticut towns is changing until
early next decade, when data from the
2010 Census become available.
Meanwhile, we have town population
estimates for 2005 compiled by
Applied Geographic Solutions (AGS),
a professional demographic forecasting
firm (go to www.cerc.com).  Our cen-
terfold (pages 12-13) maps the per-
centage changes in population by town
since the last census, using the AGS
figures. The pattern points to a contin-
uing trend of exurban sprawl.  But the
data also hint that Connecticut’s cities,
which clustered at the bottom of the
list for population growth during the
1990s, may have moved up in the
rankings since then.
Casual empiricism suggests that
some improvement is at least a possi-
bility.  Hartford, determined to capi-
talize on the new urban boom in evi-
dence elsewhere in the country, has
made a concerted effort to attract new
residents through upscale housing
projects like 55 on the Park and
Hartford 21.  And downtown redevel-
The pattern points to 
more exurban sprawl,
but Connecticut’s cities
may have moved up 
in the population 
rankings.
Variable Coefficient Explanation
*Percent Population Change 1990-2000 0.0551
A 10% increase in population from 1990 to 2000 is associated with a half-point  
growth from 2000 to 2005.
*Interstate -0.0106 Towns with interstates grow 1% more slowly than those without.
*Open Space 0.0033 A one-acre increase in open space per capita increases population growth 0.3%.
*Distance to Boston -0.0003 A ten mile decrease in distance to Boston increases population growth 0.3%.
*Median Age -0.0027 A one year increase in median age reduces population growth 0.3%.
*Unemployment Rate -0.0092 A one-point decrease in the unemployment rate boosts population growth 0.9%.
Leisure Index Population Under 3.5K -0.0003 No statistically significant association.
Leisure Index Population 3.5K - 6.5K -0.0003 No statistically significant association.
Leisure Index Population 6.5K - 10K 0.0000 No statistically significant association.
Leisure Index Population 10K-15K -0.0005 No statistically significant association.
*Leisure Index Population 15K - 25K -0.0010
A 10 position improvement in the leisure index ranking is associated with a 1.0% 
increase in population.
*Leisure Index Population 25K - 50K -0.0010
A 10 position improvement in the leisure index ranking is associated with a 1.0% 
increase in population.
*Leisure Index Population Above 50K -0.0017
A 10 position improvement in the leisure index ranking is associated with a 1.7% 
increase in population.
*Statistically significant.
DETERMINANTS OF TOWN POPULATION CHANGE, 2000-2005SPRING 2007  THE CONNECTICUT ECONOMY 9
opment projects, like Adriaen’s
Landing, are designed to boost the
city’s appeal to residents and non-resi-
dents alike.
What factors explain which towns
are growing fastest?  To find out, I
regressed the percentage changes in
town population between 2000
(Census) and 2005 (AGS) against the
characteristics most likely to have an
effect (see the second table, opposite).
Fast-growing towns since 2000
tend to be those that grew fastest dur-
ing the 1990s, and those tend to be of
the low-density variety.  Every ten per-
centage points of population growth in
the 1990s is associated with an extra
half point of growth between 2000
and 2005. Towns off the beaten track,
without interstates, are estimated to be
growing a percentage point faster than
those with.  What’s more, every acre of
open space per-capita added about a
third of a point to a town’s growth rate,
and the towns with the most open
space are, unsurprisingly, those least
densely populated.  And being ten
miles closer to Boston adds an addi-
tional third of a point to a town’s
growth rate.  In Connecticut, the
municipalities closest to Beantown are
found in a region that, at least for now,
is still called the state’s “Quiet Corner.” 
But the model also suggests that
cities can potentially manipulate to
their advantage at least two powerful
population levers: a healthy economy
and a rich variety of leisure activities.
Between 2000 and 2005, towns with
the lowest unemployment rates tended
to attract the most new residents.  A
one-point decrease in the unemploy-
ment rate produced a one-point
increase in the population growth rate.
So cities that can boost the perform-
ance of their local economies can, in
theory, offset some of the advantage
that smaller towns otherwise enjoy.
And cities may also be able to sig-
nificantly improve their attractiveness
by offering residents more leisure pos-
sibilities.  Connecticut Magazine com-
poses an annual ranking of the state’s
towns, comparing them across several
dimensions including leisure and cul-
tural amenities (“local library expendi-
tures per capita, the number of the-
aters, museums, festivals, concert ven-
ues, historic sites, colleges and univer-
sities, golf courses, local newspapers,
radio stations, state parks and forests,
voter turnout… and good local restau-
rants”). The magazine sorts towns into
seven groups—from the busiest cities
to the sleepiest burgs—and rates each
locality against its peers.
The regression results suggest that,
for the small towns, leisure amenities
(as measured by Connecticut
Magazine’s rankings), aren’t a signifi-
cant determinant of population
growth, but for the big towns they
clearly are.  For the 17 most populous
towns, a ten-position move up the
rankings would add 1.7 percentage
points to a town’s growth rate.  For
towns in the next two tiers down, with
populations of between 15,000 and
50,000, a move of that magnitude
would add a full point to the growth
rate.
DENSITY: THE CITIES’ SECRET
WEAPON?
The opportunity for social inter-
action that appears to be a significant
draw for Connecticut’s biggest towns
is one of the benefits of urban size that
economists have long attributed to
large cities. But economists see a more
important advantage for cities: urban
living makes agglomeration economies
possible.  Where people and business-
Towns off the 
beaten track, 
without intersates, 
seem to be growing 
a percentage point faster
than those with.
Variable Elasticity Explanation
*Percent of Population with B.A. or 
Better
0.27
A 10% increase in the percent with a B.A. is associated with a 2.7% increase in average 
earnings. 
*Median Age 0.34 A 10% increase in median age is associated with a 3.4% increase in average earnings. 
*Female Percent of Labor Force -1.69
A 10% increase in the percent of the labor force female is associated with a 16.9% decrease 
in earnings. 
*Travel Time to Work 0.31 A 10% increase in travel time to work is associated with a 3.1% increase in average earnings. 
*Median Rent 0.15 A 10% increase in the median rent is associated with a 1.5% increase in average earnings. 
*Unemployment Rate -0.14
A 10% increase in the share unemployed is associated with a 1.4% decrease in average 
earnings. 
*Percent Manufacturing Jobs -0.06
A 10% increase in the percent manufacturing jobs is associated with a 0.6% decrease in 
average earnings. 
*Percent FIRE Jobs 0.05
A 10% increase in the percent FIRE jobs is associated with a 0.5% increase in average 
earnings. 
*Population Density 0.04 A 10% increase in population density is associated with a 0.4% increase in average earnings. 
*Statistically significant.
DETERMINANTS OF AVERAGE EARNINGS ACROSS TOWNS, 200010 THE CONNECTICUT ECONOMY SPRING 2007
es cluster close together, firms can
improve efficiency and boost profits by
sharing suppliers of lower-cost inter-
mediate inputs, by drawing on a more
diversified pool of worker skills, and by
absorbing “knowledge spillovers.”
Described by 19th-century economist
Alfred Marshall as “mysteries of the
trade” that are “in the air,” knowledge
spillovers are ideas in the public
domain that spring up when people
living and working in close proximity
share information and expertise.
If, as economic theory maintains,
concentrating economic activity in
urban settings makes firms and work-
ers more productive, and workers are
paid the value of their marginal prod-
uct, then urban workers should earn a
wage premium above that received by
comparable non-urban workers.  But
testing that idea requires controlling
for the other reasons that earnings
might varying among workers, includ-
ing, among other things, education
and experience.
For the test I regressed average
earnings across towns against a series of
possible explanatory variables (see the
third table, page 9).  As expected, edu-
cation and experience both boost
worker earnings.  A 10% increase in
the share of the population with a B.A.
or better adds 2.7% to average earn-
ings; a 10% increase in the median age
of the population adds 3.4%. 
Other demographic and economic
factors are important, too.  A 1%
increase in the share of female workers
in the labor force is associated with a
1.6% decrease in earnings.  Workers
with longer commutes to work earn
more (proxying, perhaps, for a suburb-
to-city commute).  And as one might
anticipate, earnings are higher where
needed to compensate for pricey rents,
and lower where unemployment is
more of a problem.  Earnings also
depend on an area’s industry composi-
tion, varying inversely with the share
of manufacturing employment and
directly with the share of employment
in finance, insurance and real estate.
With these other influences on
worker earnings held constant, the
relationship between average earnings
and population density measures the
net effect of urbanization on what
Nutmeggers make.  The regression
suggests that a 10% increase in popu-
lation raises pay by 0.4%, and that a
doubling of population lifts earnings
more than 4%.  Admittedly, the regres-
sion doesn’t control for some difficult-
to-measure, invisible factors that could
influence incomes, such as the quality
of education or employee motivation.
Nevertheless, the connection between
urbanization and earnings is consistent
with results in the economics literature
that find earnings rising by between
3% and 8% as population doubles,
and it supports the idea that urban liv-
ing continues to generate significant
agglomeration economies.
WHAT’S IN IT FOR THE CITIES?
Nutmeggers, like Americans
everywhere, once flocked to the cities
to work, to shop, to eat and to live.
But income growth, the ubiquity of
the automobile, and the advent of the
interstate spawned an urban exodus
that, by all signs, continues today.
Connecticut’s cities may no longer lie
at the bottom of the population
growth heap, as they did in the 1990s,
but they’re hardly perched at the top,
either.
Some beacons of light still shine
through the darkness. Economic per-
formance and recreational offerings are
among the most significant factors
conditioning Nutmeggers’ choices of
places to live, according to the analysis
of population growth estimates.  Thus,
policies designed to enlarge the job
base, either by attracting new firms to
an area or encouraging existing firms
to expand could, if implemented suc-
cessfully, help fuel a bit of an urban
renaissance.  
So could a renewed focus on mak-
ing the city environment more livable.
Nutmeggers place a premium on hav-
ing ready access to libraries, theaters,
museums, festivals, concerts, and
restaurants.  Development efforts
focused on improving the leisure activ-
ities available to existing residents can
encourage others to join them.
Most important, town wage data
suggest that cities remain viable eco-
nomic entities.  The pay premium
enjoyed by urban workers likely
reflects the external economies that
accompany urban activity—a felicitous
by-product of sharing input suppliers,
workers and industry knowledge.  
Many of these dynamics are self-
reinforcing.  When a marginal firm
moves in to exploit the existing urban
economies, it enhances the incentive
for others to crowd in.  When a mar-
ginal worker exchanges a rural route
address for a post office box in the city,
others are more rather than less likely
to follow suit.  The hard part is getting
things going in the first place, so that
the momentum is working in the right
direction.
Nobody would suggest that
Connecticut’s cities hold all the cards.
But they at least have a hand worth
playing.

Beacons of Light for Connecticut's Cities?

http://cteconomy.uconn.edu/articles/SL_S2007.pdf

Beacons of Light for Connecticut's Cities?

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