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Transcript of Residential ammenities firm_location_economic_development
Urban Studies, Vol. 32, No. 9, 1995 1413±1436
Residential Amenities, Firm Location andEconomic Development
Paul D. Gottlieb
[Paper ® rst received, April 1994; in ® nal form, March 1995]
Summary. Amenities are regarded as increasingly important to the location decisions of certain
types of ® rm. Yet they are often ignored in economic development research because of the
assumption that they attrac t only workers, and that this workforce, in turn, attracts ® rms. This
paper argues for a reduced form model of the impact of amenities on corporate locatio n. When
testing such a model at the intra-metropolitan scale , it will be necessary to measure amenities not
only at the potential worksite, but also where employees are likely to live . This paper tests such
a ® rm locatio n model using a sample of municipalities in northern New Jersey . Results support
the hypothesis that ® rms evaluate certain amenities with respect to the likely residential locations
of their employees .
Introduction
Residential amenities may be de® ned as
place-speci® c goods or services that enter the
utility functions of residents directly. If ® rms
can pay their workers a lower wage because
of the existence of such amenities, then they
are a potential location factor for ® rms as
well as for workers. Providing residential
amenities could prove to be a usefulÐ and
politically attractiveÐ economic develop-
ment strategy.
The notion that residential amenities
attract ® rms is widespread in the survey
literature, in which business executives are
asked to rank location factors (Schmenner,
1982; Foster, 1977; McLoughlin , 1983;
Lyne, 1988) . It is also prominent in the
literature on high technology, in which
academics speculate on the kinds of locations
that should be attractive to scientists and
engineers (Malecki, 1984, 1986; Markusen et
al., 1986, p. 134; Herzog and Schlottmann,
1991).
In the econometric literature on urban
and regional development, however, the
idea that amenities can attract ® rms remains
virtually untested. It is not that the amenity±
® rm location link is ignored; it is just that
the causal connections are compart-
mentalised. Residential amenities are said to
attract only residents: corporate amenity
orientation is therefore viewed as attraction
to a pre-existing labour force.
Indeed, one would have to piece together
several different literatures in order to de-
velop a coherent picture of amenity orien-
tation on the part of ® rms. Migration studies
Paul D. Gottlieb is at the Center for Regional Economic Issues, Weatherhead School of Management, Case Western Reserve University,10900 Euclid Avenue, Cleveland, Ohio 44106-7208, USA. Funding for this research was provided by the Center for Domestic andComparative Policy Studies and the Woodrow Wilson Society of Fellows of Princeton University, and by the Center for RegionalEconomic Issues, Weatherhead School of Management, Case Western Reserve University.
0042-0980/95/091413-24 1995 Urban Studies
PAUL D . GOTTLIEB1414
examine the impact of amenities on popu-
lation movements (Cebula and Vedder, 1973;
Liu, 1975; Graves, 1980) ; studies of ® rm
location focus on the reaction of ® rms to
labour and policy variables, such as unionisa-
tion or tax rates (Bartik, 1983; Carlton, 1983;
Wasylenko and McGuire, 1985) ; while a
number of demographic-economic studies
look at the interaction between population
and employment growth (Steinnes, 1982;
Greenwood and Hunt, 1984; Carlino and
Mills, 1987; Crown, 1991)
The Present Study
In this paper, I argue that this chain of cau-
sation can be collapsed. Amenity-oriented
® rm location can be analysed in the reduced
form, without demanding that a migration
link be speci® ed. Such an approach can be
justi ® ed on both theoretical and practical
grounds.
If we are to build an empirical model in
which ® rms respond to residential amenities,
we need to account for the fact that these
amenities are typically enjoyed not at the
worksite itself, but around the worksiteÐ
where employees live. We must simulate a
decision process in which the ® rm selects a
site so as to maximise amenities on behalf of
employees in its commuter-shed.
This paper develops such a technique for
weighting amenity variables spatially. These
weighted variables are then employed in an
empirical model of high-technology employ-
ment concentrations in a sample of 365 New
Jersey municipalities.
Location patterns of eÂlite corporations are
mostly agglomerative at this scale. However,
the results also show evidence of amenity
optimisation over hypothe tical commuter-
sheds, supporting the hypothesis of the ® rm
as an amenity-maximising agent. Violent
crime is one of the few amenities or disa-
menities that in¯ uences ® rm location when
evaluated at the worksite itself. A likely ex-
planation is that only the most distressing
disamenities matter at the place of work,
while surrounding residential locations must
pass a higher amenity standard. The paper
ends with a discussion of implications for
economic development policy and research.
Amenity-oriented Firm Location: A Direct
Approach
The most important reason why ® rm location
researchers ignore amenities is that they are
hypothesised to attract residents, not ® rms.
Corporate amenity orientation is typically
viewed as attraction to a pre-existing labour
force. The solid arrows in Figure 1 depict the
standard story. These solid arrows are a
reasonable way to analyse the impact of resi-
dential amenities on ® rm location, especially
if the full range of amenities is included and
temporal lags are properly speci® ed. Empiri-
cal models that adopt this structure include
Erickson and Wasylenko (1980), Carlino and
Mills (1987) Crown (1991) and Boarnet
(1994).
However, the standard model suggests that
® rms respond directly only to a pre-existing
labour force. This means that any direct
amenity orientation on the part of ® rms (as
depicted by the dashed arrow in Figure 1) is
effectively ignored. Possible justi ® cations for
the existence of a more direct path of cau-
sation include the following:
Ð In the survey literature, executives consist-
ently rank both labour supply and quality
of life as top location factors, raising the
possibility that amenities are viewed as a
separate factor, possibly even a `non-econ-
omic’ one.1
Ð Firms may locate in high-amenity areas,
not only to tap an existing labour force,
but also to recruit a new one. The eÂlite
® rm may act as an amenity-maximising
agent, blurring the distinction between res-
idential and non-residential location be-
haviour.
Ð Amenities presumably affect ® rm location
through compensating wage differentials.
If the price rather than the quantity of
skilled labour is key, then a focus on
migration or labour supply could be mis-
leading.
Firm location andemployment growth
Populationgrowth
Amenities
overlap
Businessfactors
RESIDENTIAL AMENITIES AND FIRM LOCATION 1415
Figure 1. Amenitie s and ® rm location : causal connecti ons.
Reduced Form Model
A logical response to these objections would
be to add a new path to the empirical analysis
of this systemÐ i.e. the dashed arrow in Fig-
ure 1. Rather than increase the level of com-
plexity , however, I will move in the opposite
direction and estimate a reduced-form model
of the system in Figure 1. The model will
omit population (labour force) aggregates en-
tirely, and focus on the long-run relationship
between residential amenities, traditional
business factors and employment location.
Amenity variables will be carefully speci® ed
in order to proxy existing and potential resi-
dential locations of employees. Thus implic-
itly at least, the direct and indirect paths for
corporate amenity orientation described in
Figure 1 will both be included.
The advantages of such a model are
mostly practical. It will have smaller data
requirements and increase the likelihood that
amenity data are incorporated explicitly into
economic development research. Indeed, be-
cause the model will be estimated cross-sec-
tionally, and data on the residential locations
of workers are not included, this study will
be unable to con ® rm the existence of the
direct amenity behaviour depicted by the
dashed arrow in Figure 1.2
My more limited goal is to explore a class
of ® rm location models that incorporate resi-
dential amenities more completely than has
been done to date. I will be particularly keen
to explore the spatial implications of a model
in which the ® rm evaluates amenities on
behalf of potential employeesÐ that is, in the
hypothe tical commuter-shed surrounding
each potential site.
Amenities in the Commuter-shed: An Em-
pirical Model
For purposes of this study, I will accept the
notion that high-technology ® rms are the
most likely to consider residential amenities
in their location decisions. The reason typi-
cally given is that residential amenities are
normal goods, so that af¯ uent employees
demand more of them (Power, 1980, p. 93;
Pacione, 1984). Presumably, skilled profes-
sionals also have greater leverage when
negotiating the terms of their employment
(Lyne, 1988) . This negotiation can include
the attributes of the work location, nearby
residential amenities and the length of the
commute that connects the two (Gottlieb,
1994a, ch 4). Thus we might expect ® rms
employing skilled professionals to be amen-
PAUL D . GOTTLIEB1416
ity-oriented when selecting sites within
metropolitan areas.
The cross-sectional location of `eÂlite’ em-
ployment will therefore be the dependent
variable of interest. In this paper, I explore
empirical models of the following type:
Y 5 X b 1 WX g 1 « (1)
where Y is an n 3 1 vector of high-technol-
ogy or professional employment concentra-
tions (for example, gross densities) in a
sample of municipalities; X is an n 3 k ma-
trix of independent variables partitioned be-
tween amenity variables and a smaller group
of business variables hypothesised to be im-
portant to the high-tech sector. These vari-
ables are measured within the same area unit
as the dependent variable Y; W is an n 3 n
spatial weight matrix, with zero diagonal, for
all municipalities in the sample; b and g are
parameter vectors with dimension k 3 1; and
« is assumed to be an identical, indepen-
dently distributed error.
Construction of the matrix W will be de-
scribed below. The important thing to re-
member is that the term X b signi ® es the
impact of independent variables measured
within the same geographical unit as the
dependent variable, while the term WX gsigni ® es the impact of independent variables
measured in the commuter-shed surrounding
each municipal observation. It includes spa-
tial weighted averages of business variables
that are consumed outside the central munici-
pality, and of amenities measured at likely
residential sites.
If the independent variables are measured
such that `more is better’ , then we can rea-
sonably expect coef® cients b and g to be
positive in this model. There will be at least
two exceptions: (1) when the very existence
of an employment concentration creates an
amenity `depression’ in the central munici-
pality (e.g. traf® c congestion near an of® ce
park); and (2) when ® rms demand fewer
amenities in the central jurisdiction than else-
where (e.g. because employees are less par-
ticular about environmental condition s at the
worksite, or because the ® rm is more tax-
sensitive there). In both cases, the coef® cient
b k is likely to be less than the coef® cient g k
for a given amenity variable k, and might
even be negative. Speci ® c hypotheses for
each of the 21 independent variables are
described in Table 2.
The Study Area
The study area consists of 365 contiguous
municipalities in 13 counties covering close
to 10 000 sq km of northern New Jersey (see
Figures 2±4). Since the 1990 decennial cen-
sus, the entire study region has been placed
within the New York Consolidated Metro-
politan Statistical Area, and so is considered
to be in New York’ s sphere of in¯ uence.
This region is regarded as an attractive
place in which to live and work. It is well
served by trains, airports (especially Newark
International), interstates and authority high-
ways. The economy is diverse. Skilled pro-
fessionals are abundant: the region is among
the highest in the nation in terms of per
capita income and housing costs (Hughes
and Sternlieb, 1989, pp. 8±12). Prominent
industr ies include pharmaceuticals, chemi-
cals, insurance and ® nancial services.
Spatially, northern New Jersey is among
the most polycentric of the nation’ s metro-
politan regions, with economic activity in-
creasingly independent of the large cities
located just beyond its borders.3
Municipali-
ties in the study area are small (27 sq km on
average), and have considerable autonomy in
land-use regulation and the provision of ser-
vices. One must be cautious about transfer-
ring results to regions where the political
economy is less fragmented. US sunbelt cit-
ies, for example, may share some of New
Jersey’ s economic characteristics, but lack its
multitude of service-provid ing general
government jurisdictions.
From the point of view of the present
research, the polycentric nature of the study
region is fortuitous. Local amenities clearly
vary over the sample, and businesses regard
a wide range of locations as potential sites.
1417RESIDENTIAL AMENITIES AND FIRM LOCATION
Fig
ure
2.
Th
est
udy
are
aw
ithin
the
No
rth
east
reg
ion
.
PAUL D . GOTTLIEB1418
Table 1. Descriptive statistic s for standard ised dependent variable s (N 5 365)
NumberStandard of zero
Dependen t variable s Mean Median deviation values
SIC 87 employment density (Jobs/sq ml)a 51.94 17.45 123.63 27SIC 87 employment proporti on (percent age) 3.88 2.3 6.58 27
aFrom here on, all variable s are reported in US units, because that is how they were measured for purpose sof the regression analysis .
Dependent Variable
Collecting employment data on high technol-
ogy typically requires the use of four-digit
Standard Industrial Classi® cation (SIC)
codes (see US Congress Of® ce of Technol-
ogy Assessment, 1984) . This level of sectoral
detail is generally not available at the mu-
nicipal level from public sources. However,
the New Jersey Department of Labor does
make available two-digit SIC employment
data for each municipality under the Federal
Employment and Wages programme, also
known as the ES-202 programme (for `estab-
lishment survey’ ). While it would be dif® cult
to identify all high-technology employment
using these data, it should be possible to
select one or two two-digit industries that
have the appropriate employment character-
istics.
I will therefore use employment in SIC
category 87, engineering and management
services, as the dependent variable for this
study. In addition to virtually all professional
service establishments outside of law and
medicine, this category includes commercial
research, non-commercial research and labo-
ratory testing. Although SIC 87 does not
include manufacturing, the proportion of
af¯ uent professionals is likely to be high.
This makes SIC 87 an appropriate industry
for an investigation of amenity orientation.
Standardisation of the Dependent Variable
The dependent variable must be standardised
to control for the size and economic base of
each municipality. I construct two such de-
pendent variables. For the ® rst, professional
service (SIC 87) employment in each mu-
nicipality is divided by land area, yielding a
density measure. For the second, professional
service employment is divided by total em-
ployment, yielding a proportional measure (if
you prefer, an unstandardised location quo-
tient).
The proportional variable measures eÂlite
location behaviour relative to other ® rms
only. This can be a considerable advantage in
cross-sectional analysis. Total employment
in any municipality is heavily in¯ uenced by
a number of factors, including agglomeration
economies, competition with residential loca-
tors and zoning . The proportional form of the
SIC 87 variable effectively controls for
® xed-area effects that in¯ uence business lo-
cation in general, rather than the composition
of industry. To the extent that professional
service ® rms are disproportionately amenity-
loving , we may expect more powerful results
when the dependent variable is expressed
this way.
Table 1 provides descriptive statistics for
the dependent variables after standardisation
These two variables are mapped in Figures 3
and 4.
Independent Variables
Table 2 lists the 21 independent variables
used in the study, along with hypothesised
signs for the coef® cients. Summary statistics
and data sources for the variables may be
found in the Appendix.
All independent variables were measured
at the municipal scale for a year close to
1990. Independent variables were selected
from a close reading of the survey literature
1419RESIDENTIAL AMENITIES AND FIRM LOCATION
Fig
ure
3.
Dis
trib
uti
on
of
SIC
87
em
plo
ym
en
t,1
990
.
PAUL D . GOTTLIEB1420
on ® rm location (Foster, 1977; Lyne, 1988;
Gottlieb, 1994b), the econometric literature
on intra-metropolitan ® rm location (Erickson
and Wasylenko, 1980; Boarnet, 1994; Bartik,
1991) , the literature on high-technology ® rm
location (Malecki, 1984, 1986; Markusen et
al., 1986; Haug, 1991) and the literature on
the locational preferences of scientists and
engineers (Herzog and Schlottmann, 1991;
Malecki and Bradbury, 1992). The goal was
to select business and amenity variables that
might matter to professional service estab-
lishments at this scale.
Business Variables
The six business variables were selected to
highlight the locational concerns of the high-
tech or professional service sector. These
® rms are said to be less cost-sensitive than
routine manufacturing establishments
(Malecki, 1984) , so tax rates, utility rates and
land costs are excluded (see also the dis-
cussion of equilibrium prices below). In-
stead, the focus here is on agglomerative and
infrastructure factors. University research
programmes, technology transfer, and mid-
career educational opportunities are proxied
by the number of graduate students in each
municipality. Urbanisation economies are
measured using municipal employment den-
sities over all sectors.
High-speed transport and communication
are also said to be important for this sector,
so three transport modesÐ highways, trains
and air operationsÐ are included The vari-
able ª distance to citiesº (sum of the distances
to Philadelphia and New York) measures the
advantage of locating near the transport cor-
ridor that runs between these two cities, both
of which are located outside the study region.
The expected sign on this variable is negative
because a shorter distance means better ac-
cess.
The expected signs on the remaining busi-
ness variables are positive whether they are
measured inside or outside the central mu-
nicipality. The assumption is that businesses
sometimes use infrastructure systems outside
the municipality where they locate, but they
prefer them to be close rather than far away.
Several resources, such as universities and
transport infrastructure, will also be attract-
ive to workers at their place of residence.
The residential and business uses of these
amenities cannot easily be separated (note
the `overlap’ depicted in Figure 1). Fortu-
nately, the hypothesised signs on these vari-
ables should be the same whether one takes
the business or residential amenity view.
To the extent that professional service
® rms are more likely than ® rms in other
sectors to require universities, air service or
easy transport to large central cities, then we
may expect positive coef® cients on these
variables even when the proportional mea-
sure of SIC 87 employment is used (see the
right-hand side of Table 2). These hypoth -
eses on the relative business preferences of
SIC 87 ® rms should be regarded as tentative.
Note for example, that a disproportionate
preference for highways is not assumed in
Table 2.
Amenity Variables
Including the percentage of blacks measures
avoidance by ® rms of minority residential
areas (see, for example, Markusen et al.,
1986, p. 167). The implicit assumption is that
few professional service ® rms are black-
owned. Negative coef® cients are hypothe-
sised both for percentage black at the
worksite itself ( b ), and also for the area
around the worksite, where eÂlite employees
are expected to live ( g ).
Traf® c congestion is a persistent complaint
in regions like northern New Jersey, and two
distinct measures are included. Table 2 sug-
gests that eÂlite ® rms will avoid traf® c con-
gestion in the areas where their employees
live, but traf® c congestion will inevitably be
higher in the employment nodes where the
® rms themselves locate, leading to a positive
b for these variables. The second of these
hypotheses only makes sense when the de-
pendent variable is a density measure.
Several studies suggest that high-tech em-
1421RESIDENTIAL AMENITIES AND FIRM LOCATION
Fig
ure
4.
SIC
87
em
plo
ym
en
tas
ap
ropo
rtio
no
fto
tal
em
plo
ym
ent,
19
90.
1422 PAUL D . GOTTLIEB
Ta
ble
2.
Tab
leo
fin
depen
den
tv
ari
able
s,w
ith
hy
po
thesi
sed
sig
ns
for
their
imp
act
on
SIC
87
em
plo
ym
ent
locati
on
Den
sity
mod
el
Pro
po
rtio
ns
mo
del
Ex
pecte
dE
xpecte
dE
xp
ecte
dE
xp
ecte
dsi
gn
on
gam
ma
sig
non
beta
sig
non
gam
ma
sig
no
nbeta
Vari
able
by
typ
e(c
om
mu
ter-
shed
)(w
ork
site
)(c
om
mu
ter-
shed
)(w
ork
site
)
Dem
og
rap
hic
Perc
enta
ge
Bla
ck
22
22
Bu
sin
ess
Gra
du
ate
stu
den
ts1
11
1S
tate
/Au
thori
tyh
ighw
ay
sN
A1
NA
?R
ush
ho
ur
train
s1
11
1A
iro
pera
tio
ns
11
11
To
tal
em
plo
ym
en
t(d
en
sity
)1
11
1D
ista
nce
tocit
ies
NA
2N
A2
Tra
f®c
DV
MT
/are
a2
12
2V
olu
me/C
apacit
y2
12
2
Cri
me
Vio
lent
cri
me
rate
22
22
Pro
pert
ycri
me
rate
22
22
Po
lluti
on
To
xic
em
issi
ons
22
22
Land
®ll
wast
e2
22
2
Recr
ea
tion
Per
cap
ita
recre
ati
on
exp
end
itu
res
12
1?
Acre
so
fst
ate
park
s1
11
1D
en
sity
of
am
use
men
tem
plo
ym
en
t1
11
1D
ista
nce
toP
oco
nos/
sho
reN
A2
NA
2
Pu
bli
cedu
cati
on
Teachers
per
pu
pil
12
1?
Ex
pen
dit
ure
sp
er
pup
il1
21
?
Pu
bli
cse
rvic
es
Per
cap
ita
local
exp
end
itu
res
12
1?
Per
cap
ita
cap
ital
ex
pen
dit
ure
s1
21
?
NA
5N
ot
ap
pli
cab
leb
ecau
sen
osp
ati
all
yw
eig
hte
din
dep
en
den
tv
ari
ab
lew
as
calc
ula
ted
.
RESIDENTIAL AMENITIES AND FIRM LOCATION 1423
ployees place a disproportionately high value
on the environment, recreational opportuni-
ties, local public services and public edu-
cation (see Malecki, 1986; Herzog and
Schlottmann, 1991; Gottlieb 1994b) . Aver-
sion to crime runs across the socio-economic
spectrum, but eÂlite ® rms presumably have the
incentive and the means to make it a priority.
This special aversion to disamenities and
attraction to recreation and public services is
re¯ ected throughout Table 2.
Because public service levels are dif® cult
to quantify, they are measured using per
capita and per pupil expenditures. In the
density model, opposite signs on the
coef® cients are hypothesised for expenditure
variables measured inside and outside the
central municipality. The reasoning is that
eÂlite ® rms will want to economise on tax
payments in the jurisdictions where they lo-
cate, but will opt for more lavish public
services in the municipalities where their
employees are likely to live.4
While Table 2
also suggests that SIC 87 ® rms will be dis-
proportionately interested in outlying public
services, it is neutral on the question of
whether these ® rms are more cost-sensitive
in the central municipality than other ® rms.
Equilibrium Prices
Land prices and tax rates are intentionally
omitted from this location model. If we as-
sume that the urban system is at or close to
equilibrium, then these price measures will
be redundant. Following simple hedonic the-
ory, the price of an acre of land in each
municipality will be a function of the at-
tributes of that municipalityÐ i.e. the vector
of business and amenity factors that are tied
to each place. Since these variables are al-
ready included in the model, adding land
prices may be expected to cause collinearity
or endogeneity bias (see Bartik, 1991, p. 64).
A similar line of reasoning applies to tax
rates. If we assume a Tiebout equilibrium,
then the bundle of public services in any
jurisdiction will be priced at its marginal
cost. Tax rates will only be a separate loca-
tion factor to the extent there is disequi-
libriumÐ i.e. if public service bundles are
`over-’ or `under-priced’ by the tax system.
While it is unlikely that our cross-sectional
snapshot depicts a perfect equilibrium, it is
equally unlikely that today’ s measurable
spread between prices (taxes) and amenity
(service) levels will have much to do with
the legacy of decisions made over many
years, which is what our dependent variables
measure.
Another argument associated with equilib-
rium prices is that eÂlite locators will be indif-
ferent to a wide range of locations, because
amenity levels and the ef® ciency of public
service delivery will be capitalised into land
values. Even under equilibrium, however, lo-
cators with different consumption prefer-
ences and incomes can be expected to
segregate themselves on this basis.5
The driv-
ing question of the present study is not how
eÂlite locators respond to disequilibria, but
what is the revealed preference of this par-
ticular group for amenity and public service
bundles? When too much attention is paid to
equilibrium prices, it is easy to lose sight of
the cross-sectional baselineÐ the fundamen-
tal mapping of income and taste to place.6
Spatial Weight Matrices
Weighted averages for `outside in¯ uences’
are created by standardising a spatial weight
matrix W so that each row sums to one, and
then using this matrix to pre-multiply the
column vector of the relevant independent
variable. This creates a new variable whose
observations are properly weighted and
scaled. For example, the ® rst observation of
the newly weighted variable (corresponding
to central municipality i where i 5 1), would
be calculated as
w11x1 1 w12x2 1 ´ ´ 1 w1jxj 1 ´ ´ 1 w1nxn
where w ij is the standardised spatial weight of
municipality j relative to central municipality
i for a particular amenity or business vari-
able, and x j is the x value for neighbouring or
distant municipality j.
In the regression models reported below,
each w ii is set to zero. The attributes of a
PAUL D . GOTTLIEB1424
municipality are therefore not included in the
spatial weighted average for its own observa-
tion. The locational impact of factors inside
(X) and outside (WX) the central munici-
pality are thus kept separate, and may be
analysed using standard techniques for
nested models.
Spatial Weights for the Independent Vari-
ables
For business variables, the best formula for
weighting surrounding municipalities is
likely to be a gravity formula. Business ® rms
consume resources such as air transport or
train service, and the gravity model has a
long tradition in the explanation of consump-
tion behaviour. The gravity formula used for
business-related variables is:
W ij 51
D kij
Goods that bene® t employees at their resi-
dences are a different matter. We need a
formula that combines information on the
likely spatial distribution of employees
around the worksite with information on the
consumption properties of various amenities.
This formula must be simple enough to be
computationally feasible.
One formula that describes the distribution
of employees around the worksite is the
negative exponential. In a recent paper on the
aircraft industry in southern California, A. J.
Scott (1992) used employee questionnaires to
calculate the density gradient of high-tech
workers around the facility where they work.
Although transport conditions in southern
California are clearly not identical to those in
northern New Jersey, Scott’ s parameters have
two advantages: they cover the proper demo-
graphic group; and they are current. Scott’ s
formula, with appropriate adjustments, is
used here to weight residential amenities
around the municipalities where high-tech
employment variables are measured.
Following Scott, I will use the following
formula to calculate spatial weights for
amenities that are primarily residential:
W ij 5 A j 3 e 2 kD ij (3)
where W ij is the calculated weight of
municipality j with respect to the central
municipality i; A j is the land area of
municipality j; k is a distance-decay par-
ameter; and D ij is the distance between i and
j in miles.
The result of this calculation, W ij, is the
i,j th element of the unstandardised spatial
weight matrix W for all amenity variables
with distance-decay properties described by
k. Note that the variable k must simul-
taneously describe employment density
around the plant (which is assumed ® xed)
and the distance-decay properties of con-
sumption for a given amenity. Thus for each
set of amenities that have the same consump-
tion pro ® le, there will be unique values for k
and for W .
For example, following equation (3) a hy-
pothetical worker density in a distant munici-
pality (j) can be calculated using the negative
exponential formula (e 2 kD ij) with k set equal
to Scott’ s estimated value of 0.12.7
This den-
sity is then multiplied by that municipality ’ s
land area (A j) to yield an `employee poten-
tial’ that is the true target of the ® rm’ s amen-
ity maximisation problem. In other words,
the measured value of amenities available in
municipality j is weighted by the number of
workers who are expected to be there to
enjoy them.
This story is straightforward whenever
an amenity is produced and consumed
within the jurisdiction where workers will
live (e.g. public school quality). But when
amenities are consumed over greater dis-
tances (e.g. state parks), then k 5 0.12
may not be appropriate. Because residential
location is not required for consumption
of this kind of amenity, weighting it only
by expected employee density in the
outlying jurisdiction where the amenity is
measured will be too restrictive. The dis-
tance-decay parameter should be smaller
than 0.12, re¯ ecting the smaller friction of
distance faced by residents (and, by exten-
sion, their employers) when they evaluate the
amenity.
For example: a k of zero in equation (3)
would create a set of weights W ij in which
RESIDENTIAL AMENITIES AND FIRM LOCATION 1425
Table 3. Speci ® cation s for spatially weightin g the indepen dent variable s
Variable by type Weightin g formula W eightin g parameter (k)
DemographicPercentage Black Scott 0.12
BusinessGraduate student s Gravity 1State/Authority highways not weighted NARush-hou r trains Gravity 2Air operations Gravity 0.5Total employment density Gravity 1Distance to cities not weighted NA
Traf® cDVMT/area Scott 0.06Volume/Capacity Scott 0.06
CrimeViolen t crime rate Scott 0.12Property crime rate Scott 0.12
PollutionToxic emissions Scott 0.12Land ® ll waste Scott 0.12
Recreatio nPer capita recreation expendit ures Scott 0.12Acres of state parks Scott 0.01Density of amusement employee s Scott 0.06Distance to Poconos /shore not weighted NA
Public educationTeachers per pupil Scott 0.12Expenditures per pupil Scott 0.12
Public servicesPer capita local expenditures Scott 0.12Per capita capita l expendi tures Scott 0.12
only land areaÐ not distance from the plant
siteÐ mattered. This would simulate a situ-
ation in which residents and ® rms are com-
pletely insensitive to the location of the
amenity. A downtown attraction that is vis-
ited so infrequently that travel time is not
regarded as important might fall into this
category. Of course in this case, the weighted
form of the independent variable would be
identical for all i, so it could just as easily be
omitted. Clearly, the more interesting cases
will be those for which 0 , k , 0.12.
The precise formula and spatial parameters
used to weight each independent variable
in this study are summarised in Table 3.
`Gravity’ in this table means weights are
calculated as in formula (2). `Scott’ means
weights are calculated as in formula (3). The
weighting parameter is k as de® ned in either
(2) or (3).
Estimating the Spatia l Parameters
Following the reasoning laid out above,
Scott’ s estimate of k 5 0.12 is used for all
amenities that are consumed strictly within
jurisdictional boundaries. The ® ve variables
that measure local public services and edu-
cation clearly ® t into this category.
The proper distance-decay parameters for
the remaining eight amenity variables are not
so obvious . The effects of these amenities
typically range over continuous space, both
PAUL D . GOTTLIEB1426
inside and outside the reference municipality.
The same may be said for the six business
variables. Formula 2 suggests a gravity model
for their weights, but it provides little insight
into the proper value of the parameter k.
In principle, it is possible to estimate k
within the empirical location model de-
scribed by equation (1). But the computa-
tional demands of this procedure are such
that only one k parameter can be estimated in
a given model.8 Thus we would be unable to
explore the spatial in¯ uence of different lo-
cation factors outside the central munici-
pality.
For the purposes of this study, it may be
more important to use any exogenous spatial
parameter for each independent variable than
to make sure that each parameter is estimated
precisely. First, providing exogenous spatial
parameters will save valuable degrees of
freedom. Secondly , the use of any distance-
weighted independent variable is an advance
over ® rm location studies that ignore the
impact of condition s in neighbouring munici-
palities (all such studies implicitly assume
that the value of k is zero). Thirdly, the
precise estimation of each parameter in Table
3 would require us to collect data on the
distance-decay properties of consumption
(e.g. frequency of patronage), or physical
geography (e.g. dissipation rates for pollu-
tants) for each independent variable. We
would effectively need to conduct a full geo-
graphical study for each variable; even then,
our estimated parameters would not be inter-
pretable as k in formula (3), and would need
to be re-scaled.
For these reasons, the k parameter for all
non-jurisdictional location factors is esti-
mated heuristically The parameters were se-
lected as follows:
(1) All location factors that are likely to
vary by neighbourhood, such as crime
and racial composition, are given the
`jurisdictional’ parameter of 0.12. Be-
cause we are unable to measure these
variables at a scale smaller than the
municipality, an assumption of jurisdic-
tion-on ly consumption is the best we can
do. The pollution variables are given this
parameter also, under the assumption
that land ® lls and toxic sites in¯ uence
location behaviour either at a municipal
scale or below.
(2) The remaining variables are assigned
nominal k values on the basis of an
educated guess about their ordinal val-
ues. State parks, for example, are likely
to be visited infrequently, and only at
certain times of the year, so they
have been arbitrarily assigned a k of
0.01. Amusement employment includes
bowling, golf courses, arcades and
other attractions that have a retail
character. Assuming them to be more
frequently patronised, I have assigned
them a k of 0.06, one-half of the juris-
dictional parameter of 0.12. The same
0.06 value has been assigned to the two
traf® c variables, to account for the many
non-work-trips that are made outside the
residential jurisdiction, but for which
conditions in neighbouring jurisdictions
are still more important than those far
away.
(3) Gravity parameters for the business fac-
tors were set using a a similar logic. The
distance-decay parameter for rush-hour
trains is assumed to be the largest, since
trains have their greatest utility when
they are a short ride or walk away (the
longer the auto trip required to access a
railway station, the greater the likelihood
the entire trip will be made by auto). Air
service is given the smallest decay par-
ameter because it is used less frequently
than rail.9
Graduate programmes and em-
ployment density suggest regular face-
to-face contact (if not a requirement that
these resources be within taxi distance),
so they receive a decay parameter that is
midway between those of the two trans-
port factors.
(4) Highways do not appear in spatially
weighted form: they are fairly ubiquitous
and distance decays are likely to be con-
tained within municipal boundaries for
business consumption. Statistical consid-
RESIDENTIAL AMENITIES AND FIRM LOCATION 1427
erations also make it unwise to include
the variable `distance to cities’ in its
spatially weighted form.
A sensitivity analysis was conducted on the
empirical location models reported in Table
4. Distance-decay parameters were varied for
all location factors whose parameters could
not be deduced with precision. These include
all of the business, traf® c and pollution vari-
ables, as well as state park acreage and the
density of amusement employees.
Changes in coef® cient estimates in the
sensitivity analysis tended to follow expecta-
tions. For example, when the spatial weight-
ing coef® cient for rush-hour trains was
reduced from 2 to 0.5 in the SIC 87 propor-
tions model (Table 4, Model II), train service
became insigni ® cant in its distance-weighted
form. This outcome might be expected if
distance decay in the consumption of train
service is, as I have argued, quite steep. The
sensitivity of the coef® cient estimates to dif-
ferent ks suggests, however, that the method-
ology for estimating these parameters should
be re® ned. Separate studies on consumption
behaviour will be warranted.10
Regression Speci® cations
Because the density dependent variable is
censored at zero, SIC 87 density was
analysed using a Tobit model. This model
was estimated using the LIFEREG procedure
in SAS, which uses a Newton±Raphson al-
gorithm to estimate maximum likelihood
parameters. Dependent variables expressed
as proportions were analysed using the mini-
mum logit chi-square technique (Maddala,
1983, p. 30; Berkson, 1953) . This technique
is similar to the standard logit transformation
for quantal response data, but is adapted to a
situation where each observation consists of
events, trials, and a percentage outcome. For
this kind of problem, minimum logit chi-
square estimators are consistent, asymptoti-
cally unbiased and equivalent to maximum
likelihood estimators in large samples. The
computational algorithm is a variant of
weighted least squares.
Because of the log transformations under-
lying them, the estimated coef® cients re-
ported below cannot be used to calculate
linear relationships. Signs, standard errors
and p values, however, have the usual inter-
pretations.
Regression Results
Table 4 shows results for the two empirical
models. In Model I, the dependent variable is
the density of SIC 87 employment in each of
the 365 municipalities. In Model II, the de-
pendent variable is SIC 87 employment as a
proportion of total employment in each mu-
nicipality. The signi ® cance of different
groups of business variables, amenity vari-
ables and commuter-shed-weighted variables
are analysed using F and likelihood ratio
tests. The spatial parameters for the weighted
variables may be found in Table 3.
In only one case (percentage black popu-
lation) does a coef® cient estimate in Table 4
contradict a sign that was hypothesised in
Table 2. Otherwise, those coef® cients that
are signi ® cantly different from zero generally
con® rm expectations. A more detailed dis-
cussion of the results follows.
Commuter-shed Variables
According to the likelihood ratio statistics for
Model I, independent variables measured in
the commuter-shed are not signi ® cant when
the dependent variable is SIC 87 employ-
ment density. By this measure, professional
service employers appear to locate in munic-
ipalities with local rush-hour train service,
agglomeration/urbanisation economies (as
measured by the density of total employees),
amusement employees and a high proportion
of blacks.11 They are repelled by violent
crime and by high municipal expenditures.
These last two variables clearly matter at the
worksite, and their estimated coef® cients are
consistent with our expectations for b in
Table 2.
Overall, when we examine the absolute
rather than the relative location of pro-
fessional service ® rms, agglomerative and
1428 PAUL D . GOTTLIEB
Ta
ble
4.
Reg
ress
ion
resu
lts
for
the
locati
on
of
eng
ineeri
ng
an
dm
an
ag
em
ent
em
plo
ym
en
tin
New
Jers
ey
MO
DE
LI
MO
DE
LII
Dep
end
ent
vari
able
:D
ensi
tyof
Depen
dence
vari
ab
le:
Pro
port
ion
of
em
plo
yee
sem
plo
yee
sin
eng
ineeri
ng
an
din
eng
ineeri
ng
an
dm
anagem
en
test
abli
sh-
man
agem
ent
est
ab
lish
ments
(SIC
87
);m
ents
(SIC
87
);R
egre
ssio
nR
eg
ress
ion
spec
i®ca
tio
n:
To
bit
ML
Esp
eci
®ca
tio
n:
Min
imum
log
itchi-
squ
are
Est
imat
eS
igni®
can
ceE
stim
ate
Sig
ni®
can
ce
Pr.
c2P
rob
.u T
u
Inte
rcep
t2
59
5.9
80.3
94
22
10
.45
0.2
336
Racis
mva
riab
les
1IN
SID
EM
UN
ICIP
AL
ITY
( b)
Perc
enta
ge
Bla
ck
pop
ula
tion
28
0.5
30.0
01
7**
*1
.86
0.0
311
**
1O
UT
SID
EM
UN
ICIP
AL
ITY
( g)
Perc
enta
ge
Bla
ck
pop
ula
tion
28
70.8
80.5
84
92
7.9
30
.68
8B
usi
ness
va
ria
ble
s1
INS
IDE
MU
NIC
IPA
LIT
YG
rad
uat
est
uden
ts2
4.5
0E
-03
0.6
40
72
1.0
0E
-04
0.3
639
Ru
sh-h
ou
rtr
ain
s2.1
30.0
01
2**
*0
.01
0.0
76*
Air
op
erati
ons
2.2
3E
-04
0.6
51
29
.00E
-06
0.0
365
**
To
tal
emplo
ym
ent
den
sity
0.0
50.0
00
1**
*4
.00E
-05
0.2
959
Sta
te/a
uth
ori
tyh
igh
ways
7.9
00.2
32
82
0.0
60
.39
19
Dis
tance
tocit
ies
20.1
10.8
87
62
0.0
20
.05
06
*1
OU
TS
IDE
MU
NIC
IPA
LIT
YG
rad
uat
est
uden
ts0.1
50.5
24
32
5.0
0E
-04
0.8
511
Ru
sh-h
ou
rtr
ain
s2
3.4
20.2
21
80
.09
0.0
059
***
Air
op
erati
ons
0.0
80.0
81
7*
4.0
0E
-04
0.5
078
To
tal
emplo
ym
ent
den
sity
20.0
20.7
06
42
.00E
-04
0.6
936
Tra
f®c
vari
ab
les
1IN
SID
EM
UN
ICIP
AL
ITY
DV
MT
/sq
m2
2.1
0E
-04
0.1
58
31
.00E
-07
0.9
438
1O
UT
SID
EM
UN
ICIP
AL
ITY
DV
MT
/sq
m2
0.0
10.4
85
91
.00E
-04
0.1
748
Volu
me/c
ap
acit
y,
stat
ero
ads
34.0
20.9
53
72
.02
0.7
885
Cri
me
vari
able
s1
INS
IDE
MU
NIC
IPA
LIT
YV
iole
nt
cri
me
rate
21
0.1
60.0
01
8**
*2
0.0
90
.00
31
***
Pro
pert
ycr
ime
rate
20.0
40.9
12
12
0.0
10
.08
03
*1
OU
TS
IDE
MU
NIC
IPA
LIT
YV
iole
nt
cri
me
rate
49.6
60.4
78
70
.65
0.4
149
Pro
pert
ycr
ime
rate
22.3
20.7
40
12
0.1
60
.04
76
**
Poll
uti
on
va
ria
ble
s1
INS
IDE
MU
NIC
IPA
LIT
Y
1429RESIDENTIAL AMENITIES AND FIRM LOCATION
To
xic
emis
sion
s2
1.8
8E
-06
0.7
44
52
6.1
5E
-08
0.2
312
Land
®ll
wast
e2
5.5
2E
-05
0.2
93
52
4.0
0E
-07
0.3
128
1O
UT
SID
EM
UN
ICIP
AL
ITY
To
xic
emis
sion
s2
3.3
7E
-05
0.6
72
92
3.0
0E
-06
0.0
076
***
Land
®ll
wast
e2
2.2
0E
-03
0.0
84
9*
22
.00E
-05
0.1
995
Rec
rea
tion
vari
ab
les
1IN
SID
EM
UN
ICIP
AL
ITY
Per
capit
are
creati
on
ex
pen
dit
ure
s0.1
60.2
20
11
.80E
-03
0.4
439
Acr
es
of
state
park
s2
0.0
10.1
04
21
.00E
-04
0.1
963
Den
sity
of
am
use
men
tem
plo
ym
ent
0.6
30.0
04
5**
*8
.42E
-04
0.7
842
Dis
tance
toP
oco
no
s/sh
ore
21.1
00.1
84
42
0.0
20
.04
08
**
1O
UT
SID
EM
UN
ICIP
AL
ITY
Per
capit
are
creati
on
ex
pen
dit
ure
s0.4
00.7
93
92
0.0
20
.38
86
Acr
es
of
state
park
s2
0.2
10.5
91
62
0.0
10
.21
91
Den
sity
of
am
use
men
tem
plo
ym
ent
33.5
40.2
81
12
0.2
60
.49
56
Pub
lic
edu
cati
on
vari
able
s1
INS
IDE
MU
NIC
IPA
LIT
YE
xpend
iture
sp
er
pu
pil
21.1
1E
-03
0.0
81
8*
28
.00E
-06
0.1
573
Teac
her
sper
pu
pil
40
2.6
50.3
88
42
2.5
30
.62
24
1O
UT
SID
EM
UN
ICIP
AL
ITY
Ex
pend
iture
sp
er
pu
pil
0.0
50.3
18
52
6.4
7E
-04
0.2
763
Teac
her
sper
pu
pil
119
67.0
80.2
43
229
.22
0.0
566
*P
ub
lic
serv
ice
vari
ab
les
1IN
SID
EM
UN
ICIP
AL
ITY
Per
capit
ao
per
ati
ng
expen
dit
ure
s2
0.0
30.0
35
2**
21
.00E
-04
0.2
941
Per
capit
acapit
al
ex
pen
dit
ure
s0.1
80.1
53
36
.00E
-04
0.7
191
1O
UT
SID
EM
UN
ICIP
AL
ITY
Per
capit
ao
per
ati
ng
expen
dit
ure
s2
0.1
40.7
29
62
.00E
-03
0.7
349
Per
capit
acapit
al
ex
pen
dit
ure
s2.0
40.3
32
6.0
0E
-04
0.9
812
N3
63
361
Lo
gli
kel
iho
od
or
adju
sted
R2
220
35.3
20
.185
5P
rob
.F
Ð0
.000
1C
on
stra
ined
mo
del
om
its:
Gro
up
LR
test
sG
rou
pF
test
sB
usi
nes
svari
able
s1
49.7
3,
0.0
01
***
2.5
30
.00
61
***
Tra
f®c
vari
able
s4.0
6.
0.2
0.7
20
.54
11
Cri
me
vari
ab
les
17.7
9,
0.0
1*
**
5.7
30
.00
02
***
Po
llu
tio
nvari
ab
les
4.2
1.
0.2
2.9
40
.02
09
**
Recr
eat
ion
var
iable
s1
4.1
4,
0.0
5*
*1
.04
0.4
025
Pu
bli
ced
ucati
on
vari
ab
les
4.7
9.
0.2
1.6
30
.16
62
Pu
bli
cse
rvic
esv
aria
ble
s5.7
0.
0.2
0.3
60
.84
03
All
vari
able
sou
tsid
ecentr
al
mun
icip
alit
y2
3.7
6,
0.2
2.4
20
.00
12
***
Am
en
ity
vari
able
so
uts
ide
cen
tral
mu
nic
ipali
ty1
5.0
6.
0.2
2.5
60
.00
3*
**
**
*si
gni®
can
tat
1p
er
cent
level;
**
sig
ni®
can
tat
5p
er
cent
lev
el;
*si
gn
i®can
tat
10
per
cent
lev
el.
PAUL D . GOTTLIEB1430
business factors appear to dominate. More-
over, it is dif ® cult to detect the in¯ uence of
location factors outside the municipality
where the dependent variable is measured.
When the dependent variable is measured
as a proportion, however, the location behav-
iour of eÂlite ® rms should be more sensitive to
amenities, especially when they are mea-
sured in adjacent municipalities. The results
for Model II suggest that this is indeed the
case. An F test for this model suggests that
distance-weighted amenity variables are
signi ® cant in the aggregate. Among the indi-
vidual variables that are signi ® cant in their
distance-weighted form are rush-hour trains,
property crime, toxic wastes and (just barely)
teachers per pupil .
Model II therefore provides some evidence
that weighting variables to correspond to a
decision on commuter-sheds increases evi-
dence of amenity orientation on the part of
high-tech ® rms. This improved evidence of
amenity orientation appears only when the
dependent variable is measured as the share
of employment that is professional service.
Thus there is little evidence that SIC 87 ® rms
will ignore New Jersey’ s agglomerations of
economic activity in order to ® nd high-amen-
ity locations. Rather, in places with a given
amount of commercial development, pro-
fessional service establishments outbid other
® rms for sites if they expect amenities in
nearby communities to be high.
Spatial Interpretations for Individual Vari-
ables
Without question, violent crime is the most
consistently signi ® cant amenity factor in this
paper. The avoidance of crime, moreover,
has interesting spatial properties. Firms care
about violent crime inside the municipality
where they are located, rather than in the
surrounding commuter-shed. Property crime
and toxic pollution, in contrast, are avoided
only to the extent that they affect workers at
their place of residence (in Model II).
Differences in these amenity variables
may be psychological. Workers spend a
smaller portion of their lives in the immedi-
ate vicinity of their place of work; so they
may ignore threats they perceive as relatively
minor to their health and well-being. Ex-
posure to violent crime near the worksite is
apparently regarded as more seriousÐ cer-
tainly it is much more publicisedÐ than long-
term exposure to toxins near the worksite.
Similarly, property crime is not the same
kind of gut issue at the worksite as is violent
crime.
Note that there is no case in which op-
posite signs are estimated for a given amen-
ity variable measured inside and outside the
central municipality, as hypothesised in
Table 2 for the density model. For example,
I had suggested that a ® rm will contribute to
traf® c congestion in the town where it lo-
cates, while also having a preference for very
little congestion in the town’ s nearest neigh-
bours. Similarly, I had suggested that eÂlite
employment density will be high in com-
munities that have low public expenditures,
but are surrounded by towns with high public
expenditures.
In order for preferences like these to be
realised, at least some communities must
have these peculiar amenity characteristics.
In technical terms, the traf® c and public ex-
penditure variables must exhibit negative
spatial correlation at the municipal scale.
This is a strong condition to impose on any
metropolitan area. It is particularly unlikely
to prevail for traf® c congestion, because of
the tendency for agglomeration economies to
spill over municipal boundaries: for high-
density locations to be positively correlated
in space (see Figure 3). Thus the inevitable
amenity geography of the metropolis may
prevent ® rms from satisfying some of the
more detailed spatial preferences that are
hypothesised in Table 2.12
Conclusion: Implications for Economic
Development Policy and Research
This study is far from de® nitive, but it high-
lights several factors that might be important
for economic development policy:
RESIDENTIAL AMENITIES AND FIRM LOCATION 1431
(1) Table 4 suggests that amenity orientation
for this employment sector is better de-
scribed as avoidance of disamenities
than as attraction to amenities (see also
Gottlieb, 1994b). In particular, violent
crime should probably receive more at-
tention from economic development
of ® cials than it typically gets.
(2) In the present study, residential ameni-
ties were found to affect the composition
of employment, but not its density. Ag-
glomeration is still most important for
the absolute location of professional ser-
vice ® rms.
(3) Development of® cials might consider
classifying amenities into two groups:
those that matter at the worksite (such as
violent crime), and those that matter only
at the site of likely residences (such as
toxic pollution). Adjacent jurisdictions
may wish to co-operate when picking
amenity priorities from among the two
groups. Tax transfers could compensate
a community for implementing an amen-
ity programme that is primarily residen-
tial, with the direct bene ® ts of
commercial development going to its
neighbour.
(4) Clearly, a ® rm may prefer low taxes and
amenity expenditures in the city where it
locates, but much higher expenditures in
surrounding municipalities, or at a higher
level of jurisdiction. This study has not
found clear-cut evidence of this phenom-
enon, possibly because it imposes re-
quirements for spatial proximity that are
too strict. The con¯ icting desire of cer-
tain ® rms for low taxes on the one hand,
and for high residential services on the
other, deserves further exploration.
The complex relationship among agglomer-
ation, urbanisation and amenities is another
fruitful area for additional research. High-
tech ® rms are widely hypothesised to re-
spond both to amenities and agglomeration
in their location decisions; yet we would
expect these two factors to be systematically
correlated in space (Malecki, 1984; Malecki
and Bradbury, 1992) . The failure to account
for this correlation means that we cannot say
with con ® dence that high-tech location be-
haviour is truly agglomerative, as is so often
found in empirical studies. By more carefully
specifying amenity variables, the present
study begins to address this problem, which
should become increasingly important to de-
velopment policy as the information econ-
omy unfolds .
Notes
1. In early work by economists, the `psychicincome’ of the proprietor was regarde d as alocatio n facto r that could be analysed sepa-rately from cost and transport factors . SeeGreenhut (1956 , p. 282); Foster (1977) .
2. Figure 1 describe s a dynamic system, while across-se ctiona l model can only describ e theoutcome of these many behaviours ex post . Ifamenity orientat ion exists at all, then it islikely to manifest itself cross-se ctionally , nomatter whether ® rms or resident s make the`® rst move’ .
3. Although part of the New York CMSA, thestudy region actually has six primary metro-politan statistica l areas, or PMSAs, within it(one of them is so sprawling, it is of® ciallyregarde d as having no centra l city). In theeight countie s closest to New York, 1990employment was 95 per cent of that in Man-hattan , and only 11 per cent of the resident scommuted to New York City. Fifteen munic-ipalitie s in the region had job counts exceed-ing 25 000 in 1990 (Sources : US Departmentof Commerce , 1990 Census; US Departmentof Commerce , Bureau of Economic Analy-sis, Regional Economic Information SystemCD-ROM; New Jersey Department of Labor,1990 ES-202 Series) .
4. The local public service demands of ® rmsare often held to be minimal, consisti ng es-sentially of `safe and clean streets ’ (see Er-ickson and Wasylenko, 1980).
5. Both the monocent ric and Tiebou t models ofurban economics predic t that even in equilib -rium, househo lds will sort themselves intocommunitie s on the basis of both preferencesand income. Under an assumption of agency ,these preferen ces should also be expressed in® rm locatio n behavio ur. For a study thatdevelops formal hypothe ses on the equilib -rium ratio of high- to low-skill worker s incommunitie s with differen t amenity endow-ments , see Roback (1988) .
6. In this sense, the presen t study is reminiscen tof the old social ecology tradition in geogra-
PAUL D . GOTTLIEB1432
phy, excep t that eÂlite employers , rathe r thanresident s, are the group of interest .
7. Scott estimated a multiplica tive constan t of0.66 for his negative exponen tial, but be-cause this scale factor drops out when theweight matrix is standardised, it is omittedhere for ease of expositi on.
8. A descript ion and example of the methodol -ogy for a Logit model may be found inDubin (forthco ming).
9. The assumption is that, unlike air service ,trains are used for commuting . Newark Inter-nationa l Airport is also well connected to therest of the region by the highway system,reducing the time distanc e (if not the Eu-clidean distance) to this valuable resource.
10. In the sensitivi ty analysis , density and pro-portion s models were re-estimated with allten ks set to the highest , lowest and middlevalues that appea r within a givenspeci ® cation in Table 3. Although the magni-tude and signi ® cance of some of theweighted indepen dent variable s changed forthese runs, the ks presented in Table 3 andused in Table 4 are still to be preferre d on apriori grounds , becaus e they incorpor ate thelikely orderin g of k for variable s with differ-ent consumption pro ® les.
11. While percentage black populat ion and den-sity of amusement employee s were designedto measure concept s other than urbanisation ,they are also correlat ed with municipa l den-sities.
12. At least at the municipa l scale. The possibil -ity remains that jobs are clustered in one ortwo parts of the municipal ity, creatin gtraf ® c-free areas for resident ial develop ment(presumably , this is one goal of zoning) .Af¯ uent employees may also be willing tomove to the urban periphery and lengthentheir commutes in order to avoid congest ionat home.
References
BARTIK , T. (1983 ) Business locatio n decision s inthe U.S.: estimates of the effects of unioniza-tion, taxes and other characte ristics of states ,Journal of Business and Economic Statistic s,65, pp. 76±86.
BARTIK , T. (1991) The effects of property taxesand other loca l public policie s on the in-trametropolit an pattern of business location , in:H. HERZOG and A. SCHLOTTMANN (Eds) Industr-ial Location and Public Policy. Knoxvill e, TN:University of Tennesse e Press.
BERKSON , J. (1953 ) A statistic ally precise andrelativel y simple method of estimating the bio-assay with quanta l response , based on the logis -
tic function , Journa l of the American Statistic alAssociation , 48 (September), pp. 565±599.
BOARNET, M. (1994 ) An empirical model of in-trametropoli tan populati on and employmentgrowth, Papers in Regiona l Scienc e, 73, pp.135±152.
CARLINO , G. and M ILLS , E. (1987) The determi-nants of county growth, Journa l of Regiona lScience , 27, pp. 39±54.
CARLTON, D. (1983 ) The locatio n and employ-ment choice s of new ® rms: an econometricmodel of growth with discrete and continuousendogenous variable s, Review of Economicsand Statistic s, 65, pp. 440±449.
CEBULA , R.J. and VEDDER , R.K. (1973 ) A note onmigration , economic opportu nity and the qual-ity of life, Journa l of Regiona l Scienc e, 13, pp.205±211.
CROW N, W . (1991 ) Migration and regiona l econ-omic growth: an origin±destinat ion model,Economic Development Quarterly, 5 (Febru-ary), pp. 45±59.
DUBIN , R. (forthco ming) Estimating Logit modelswith spatia l dependence , in: L. ANSELIN and R.FLORAX (Eds) New Direction s in Spatia lEconometrics.
ERICKSON R. and WASYLENKO , M. (1980) Firmrelocatio n and site selection in suburban munici-palities, Journa l of Urban Economies, 8,pp. 69±85.
FOSTER, R. (1977) Economic and quality of lifefactors in industri al location decision s, SocialIndicators Research, 4, pp. 247±265.
GOTTLIEB, P. (1994a) Amenity-or iented ® rm loca-tion . PhD thesis in Public Affairs , Princeto nUniversity.
GOTTLIEB, P. (1994b ) Amenities as an economicdevelop ment tool: is there enough evidenc e?Economic Development Quarterly, 8, pp. 270±285.
GRAVES, P. (1980 ) Migratio n and climate, Journalof Regiona l Science, 20, pp. 227±237.
GREENH UT, M. (1956) Plant Location in Theoryand Practice. Chapel Hill, NC: University ofNorth Carolin a Press.
GREENW OOD, M. and HUNT, G. (1984 ) Migratio nand interregi onal employment redistrib ution inthe United States, American Economic Review ,74, pp. 957±969.
HAUG, P. (1991 ) Regiona l formation of high-tec h-nology servic e industri es: the software industryin W ashingto n State, Environment and Plan-ning A, 23, pp. 869±884.
HEIK KILA , E. (1988 ) Multicolli nearity in re-gressio n models with multiple distanc e mea-sures, Journal of Regiona l Science, 28, pp.345±362.
HERZOG , H. and SCHLOTTM ANN , A. (1991 ) Metro-politan dimensions of high-tec hnology locatio nin the U.S.: worker mobility and residence
RESIDENTIAL AMENITIES AND FIRM LOCATION 1433
choice , in: H. HERZOG and A. SCHLOTTMANN
(Eds) Industrial Location and Public Policy.Knoxville , TN: University of Tennessee Press.
HUGH ES, J. and STERNLIEB, G. (1989) RutgersRegiona l Report , Volume I: Jobs , Income,Populatio n, and Housing Baselines. New
Brunswick, NJ: Rutger s University.L IU, B. (1975 ) Differenti al net migration rates and
the quality of life, Review of Economics andStatistics , 57, pp. 329±337.
LYNE, J. (1988 ) Quality of life factors dominate
many facility location decision s, Site SelectionHandbook , 33 (August) , pp. 868±870.
MADDA LA, G. (1983) Limited-dependen t andQualitativ e Variable s in Econometrics. NewYork: Cambridge University Press.
MALECKI, E. (1984 ) High technology and localeconomic develop ment, Journa l of the Ameri-can Planning Association , 50, pp. 262±269.
MALECKI, E . (1986) Research and develop mentand the geograp hy of high technology com-
plexes , in: J. REES (Ed.) Technolo gy, Regions ,and Policy . Totowa, NJ: Rowan and Little ® eld.
MALECKI, E . and BRADBURY, S. (1992 ) R&D fa-cilities and professional labour : labou r forcedynamics in high technology, Regional Studie s,26, pp. 123±136.
MARKUSEN, A., HALL , P. and GLASMEIER , A.(1986) High-tec h America: The What, How,Where , and Why of the Sunris e Industri es.Winchester, MA: Allen and Unwin.
MCLOUGHLIN , P. (1983 ) Community conside r-
ations as locatio n attractio n variable s for themanufacturing industry , Urban Studie s, 20, pp.359±363.
PACION E, M. (1984 ) The de® nition and measure -ment of quality of life , in:M. PACIONE and G.
GORDON (Eds) Quality of Life and Human Wel-fare . Norwich: Geo Books.
POW ER , T. (1980) The Economic Value of theQuality of Life. Boulder , CO: Westview Press.
ROBACK, J. (1988 ) Wages, rents , and amenities :
differences among workers and regions , Econo-mic Inquiry , 26, pp. 23±41.
SCHMENNER , R. (1982) Making Business LocationDecisions . Englewood Cliffs, NJ: Prentice -Hall.
SCOTT, A.J. (1992 ) The spatia l organiza tion of a
local labor market: employment and residentialpattern s in a cohort of enginee ring and sci-enti ® c workers , Growth and Change, 23, pp.94±115.
STEINN ES, D. (1982 ) Do `people follow jobs’ or do
`jobs follow people ’ ? A causalit y issue in urbaneconom ics, Urban Studie s, 19, pp. 187±192.
US CONG RESS, OFFICE OF TECHNOLOG Y ASSESS-
MENT (1984 ) De® nition and analysi s of high-technology industry , in: US OTA (Ed.)Technology, Innovat ion, and Regiona l Econ-
omic Development. Washington, DC: USGovernment Printing Of® ce.
WASYLEN KO, M. and MCGUIRE, T. (1985 ) Jobs andtaxes: the effect of business climate on states ’employment growth rates, National Tax Jour-nal, 38, pp. 497±511.
Appendix
Demographic Variables
Percentag e Black. Black and total populat ion bymunicipal ity are taken from the 1990 Census ofPopulation (STF3A).
Business Variables
Graduate students . The number of graduate stu-dents attending universi ties in each munici-pality in the sample. (Graduate student s inprogrammes devoted exclusively to music ortheology were omitted when they could beidenti ® ed.) Data are fall 1989 enrollments fromthe 1988±90 Biennia l Report of the New JerseyDepartment of Higher Education .
Rush-hou r trains . The number of daily inboundand outbound peak-ho ur trains stoppin g in themunicipal ity in 1989 . Inbound peak-ho ur trainsarrive at their easternmost New Jersey terminusbetween 6.30 and 9.30 am; outboun d trainsarrive at their ® rst New Jersey stop during thesame period. Data were obtained from NewJersey Transit Rail Operation Summaries .Equivalent data for PATH (Port Authority )trains were estimated using a peak-hour fre-quency of one train every 7.2 minutes (50 trainseach way).
Air operations. The number of landings and take-offs of scheduled commercia l or air taxi servicein each municipal ity in 1990 . Source: Federa lAviation Administrati on, Airport Operation sDivision.
Highways . The total number of the followinghighways passing through each municipal ity:US Routes 1, 9, 46, 202 and 206; Interstat es 78,80, 95, 195, 280, 287 and 295; the Garden StateParkway and the New Jersey Turnpike .
Total employment density . 1990 tota l municipa lemployment from the ES-202 series divided byland area from the New Jersey Department ofCommunity Affairs , Division of Local Govern-ment Services , Annual Reports: Statements ofFinancia l Conditio n of Countie s and Munici-palities (1987).
1434 PAUL D . GOTTLIEB
Tab
leA
-1.
Desc
ripti
ve
stati
stic
sfo
rin
dep
en
den
tv
ari
ab
les
Nu
mber
Sta
nd
ard
of
zero
Vari
ab
les
by
typ
eU
nit
sM
ean
Med
ian
dev
iati
on
valu
es
Dem
og
raph
ic/G
eog
rap
hic
al
Pop
ula
tio
nd
en
sity
Peo
ple
/sq
ml
382
0.9
62
282
52
21
.61
0P
erc
en
tage
bla
ck
Per
cen
t4
.97
1.3
411
32
Lan
dare
aS
qm
l1
0.4
24
13
.45
0
Bu
sin
ess
Gra
duate
stu
dents
Nu
mb
er
11
4.2
70
759
.98
351
Sta
te/a
uth
ori
tyh
ighw
ay
sN
um
ber
0.7
20
0.9
51
90
Ru
sh-h
ou
rtr
ain
sN
um
ber
4.7
10
11
.25
262
Air
op
era
tio
ns
Nu
mb
er/
year
118
3.8
50
17
997
.96
348
Em
plo
ym
ent
den
sity
Jobs/
sqm
l1
51
6.0
68
61
.56
19
72
.76
0D
ista
nce
tocit
ies
Mil
es
99
.16
97
13
.34
0
Tra
f®c
DV
MT
/are
aD
VM
T/s
qm
l4
574
83
05
69
52
549
1V
olu
me/c
apacit
yR
ati
o0
.81
0.8
0.2
98
117
a
Cri
me
Vio
len
tcri
me
rate
Nu
mb
er
per
10
00
2.5
71
.24
.71
5P
ropert
ycri
me
rate
Nu
mb
er
per
10
00
30
.98
24
.422
.45
1
Po
llu
tio
nT
oxic
em
issi
on
sP
ou
nds/
year
18
324
80
966
964
.52
21
Lan
d®
llw
ast
eT
on
s/y
ear
18
13
4.8
70
117
793
.33
18
Rec
reati
on
Per
capit
are
cre
ati
on
ex
pen
dit
ure
s$/r
esi
den
t3
4.8
524
.25
54
.19
2A
cre
so
fst
ate
park
sA
cre
s22
2.5
10
13
80
.82
344
Den
sity
of
am
use
ment
em
plo
ym
en
tJo
bs/
sqm
l1
3.3
14
.38
25
.65
67
Dis
tan
ce
toP
oco
nos/
Sh
ore
Mil
es
10
7.4
31
01
.81
12
.64
0
Pu
bli
cedu
cati
on
Teach
ers
per
pup
ilT
each
ers
/AD
A0
.07
80
.077
0.0
13
1E
xpen
dit
ure
sp
er
pu
pil
$/A
DA
562
4.9
55
017
.59
87
23
.62
1
Pu
bli
cse
rvic
es
Per
capit
alo
cal
ex
pen
dit
ure
s$/r
esi
den
t57
7.3
14
92
.77
472
.58
0P
er
capit
acap
ital
ex
pen
dit
ure
s$/r
esi
den
t6
5.9
54
.37
64
.99
10
aT
his
isth
en
um
ber
of
mu
nic
ipali
ties
that
hav
en
ost
ate
road
s.
RESIDENTIAL AMENITIES AND FIRM LOCATION 1435
Distance to cities. The sum of straigh t line dis-tances from the centre of each municipal ity tocentra l Manhatta n and centra l Philadelp hia.Distances from shore communitie s to Manhattanwere constrained to go around Raritan Bay,re¯ ecting driving realities . Distance s weresummed to avoid the pure collinea rity of separategeometric measures (see Heikkila , 1988). Thevariable was calculat ed using the ATLAS-GISmapping programme.
Traf ® c Variable s
DVMT/area. Daily vehicle miles travelled(DVMT) is the sum of miles travelle d by allvehicle s in a municipal ity in a given day. Divid-ing by land area yields a rough congest ion mea-sure . DVMT were obtained from the New JerseyDepartment of Transpor tation for 1987; the datawere collecte d using a sampling technique.
Volume:capac ity ratios . Estimated peak-hour,peak-di rection volume-to-cap acity ratios on stateroads in each municipal ity in 1987 . The municipa lV:C ratio is a weighted averag e of the ratios forall state roads in the municipal ity, using state roadmileage s as weights . Volume:capaci ty ratios arecalculat ed by estimating peak-ho ur volumes fromdaily traf ® c counts and dividin g by mid-blockcapacity with a signalis ation factor . Because notall municipal ities contain state roads, this variableis included in distance -weighted form only , inorder to avoid a large number of missing values .Source : New Jersey Department of Transport a-tion, Bureau of Transport ation and Corrido rAnalysis.
Crime Variables
Violen t crime rate . Average of the 1989 and 1990violen t crime rates in each municipal ity. Source:State of New Jersey , Uniform Crime Reports(1990) , Section VII.
Property crime rate . Average of the 1989 and1990 property crime rates in each municipal ity.Source : State of New Jersey , Uniform Crime Re-ports (1990) , Section VII.
Pollution
Toxic emissions . The New Jersey Department ofEnvironmental Protectio n and Energy (DEPE)collect s toxic release data from industri al corpora -tions using a detailed question naire. This ques-tionnair e satis ® es federa l reporting require mentsunder the Superfun d Amendments and Reautho -rizatio n Act of 1986 (Title III, Section 312), andthe New Jersey Community Right to Know Act.
This variable measure s total pounds of all toxicchemicals release d to air, water, and land byplants in each municipal ity in 1990 . Data wereobtaine d in computer-readable form from theNew Jersey DEPE and were totalle d by munici-pality and medium.
Land ® ll waste . Tons of solid waste transferr ed ordisposed of at register ed land ® ll facilitie s in eachmunicipal ity in 1988. Sources : New Jersey De-partment of Environmenta l Protectio n, Divisionof Solid Waste Management, Bureau of Regis-tration and Permits Administration, New JerseySolid Waste Disposal Repor t (December 1988)and Solid Waste Facility Directory (1984 and1988).
Recreatio n and Culture
Per-capit a recreati on expendit ures. Per capitaspending on recreation in each municipal ity in1988 . Source : New Jersey Department of Com-munity Affairs , Division of Local GovernmentServices, Annual Reports : Statements of Finan-cial Condition of Counties and Municipal ities(1988).
Acres of state parks. Total acres of state parks andforests in each municipal ity in 1990. Source: NewJersey DEPE, Division of Parks and Forestry ,State Parks and Forests in New Jersey (brochu re).
Density of amusement employees . Number of em-ployee s in SIC 79: ª amusement and recreationservices º in 1990 , divided by municipa l land area .Source: ES-202 data from the New Jersey Depart-ment of Labor.
Distance to Poconos /shore. The sum of straight -line distance s from each municipal ity to a centra lpoin t in Pennsylv ania ’ s Pocono Mountain s and toBelmar, a centrally -locate d Jersey shore resort . Asin the measure of city distance s, Raritan Bay wasregarde d as a driving barrier .
Public Educatio n
Teachers per pupil . Total public schoo l instruc-tiona l staff divided by averag e daily enrolment(ADA) for the 1985±86 schoo l year. The data arereported by schoo l distric t and are convert ed tomunicipa l scale using an algorith m develop ed bythe autho r for the New Jersey Of® ce of StatePlanning . The raw distric t data were compiledfrom New Jersey Department of Education , Fina-ncia l Statistic s of Schoo l Districts, School Year1985±86 (Thirty- ® fth Annual Report of the Com-missione r of Education).
PAUL D . GOTTLIEB1436
Expenditures per pupil . Calculated like the pre-vious variable , excep t that the numerato r iscurren t operating expendi tures from FinancialStatistic s rathe r than instructi onal staff.
Public Service s
Per capita local expendit ures. Total municipa loperating expendi tures per capita in 1987. Source:Department of Community Affairs , Division ofLocal Government Services , Annual Reports :Statements of Financia l Conditio n of Countie sand Municipal ities (1987).
Per capita capita l expendi tures . Total annual debtservice per capita in 1988 . This is an imperfec t
measure of what locators presumably care about,which is the municipal ity’ s capita l stock . Source :Department of Community Affairs , Division ofLocal Government Services , Annual Reports:Statements of Financia l Conditio n of Countiesand Municipa lities (1988).
Pairwise Distance s
Distance s between each pair of municipal itiesin the sample were calculat ed using latitud eand longitud e co-ordinates of the munici-pality centroids (from ATLAS-GRAPHICS)and a standard cartogra phic formula. Thesedistance s were used to create spatia l weightedaverages .