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-


ity-oriented when selecting sites within

metropolitan areas.

The cross-sectional location of èÂlite’ 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

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udy

are

aw

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the

No

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.


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 èstab-

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


Fig

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SIC

87

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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 òverlap’ 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-


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1422 PAUL D . GOTTLIEB

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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

òver-’ or ùnder-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 òutside 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


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 èmployee 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


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


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-


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


Ta

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the

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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:


(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-


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.

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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).


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.


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).


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 .