URBAN GROWTH IMPACTS ON RURAL POPULATION AND
EMPLOYMENT IN EXPORT AND SERVICE INDUSTRIES IN
FUNCTIONAL ECONOMIC REGIONS
Bertrand SCHMITT1, Directeur de Recherche
UMR INRA-ENESAD en Economie et Sociologie Rurales,
Institut National de la Recherche Agronomique (INRA), Dijon, FRANCE
Mark S. HENRY, Professor,
Department of Agricultural and Applied Economics,
Clemson University, Clemson, USA
Virginie PIGUET, Ingénieur d’Etudes
UMR INRA-ENESAD en Economie et Sociologie Rurales,
Institut National de la Recherche Agronomique (INRA), Dijon, FRANCE
Mohamed HILAL, Ingénieur de Recherche
UMR INRA-ENESAD en Economie et Sociologie Rurales,
Institut National de la Recherche Agronomique (INRA), Dijon, FRANCE
Abstract: The purpose of this paper is to examine how the spatial pattern of urban growth in a
functional economic region influences the interplay of rural export employment, rural services
employment, and population change in rural areas. Using an extension of the Boarnet (1994) model,
we find that urban spread effects to rural areas in France are more likely than urban backwash. If the
urban core is declining and urban fringe expanding, urban population growth involves an increase in
rural export and services employment. We also find spatial linkages between changes in urban export
employment and rural service employment -- the export base multiplier effect with a spatial
dimension. If the urban core and fringe are growing, we observe an urban spread effect from the urban
export sector to rural population change and from urban population growth to rural service
employment.
JEL: C39 R11 R12
Key Words:
1
Spatial Econometric Models - Rural Urban Linkages - Urban Spatial Expansion
Corresponding author: UMR INRA-ENESAD en ESR, 26 bd Dr Petitjean, BP 87999, 21079 Dijon Cedex,
France. Ph (33) 380 77 25 76; FAX (33) 380 77 25 71; email [email protected].
URBAN GROWTH IMPACTS ON RURAL POPULATION AND
EMPLOYMENT IN EXPORT AND SERVICE INDUSTRIES IN
FUNCTIONAL ECONOMIC REGIONS
I. INTRODUCTION
Recent empirical evidence suggests that urban to rural spread effects are present in regions of
France, the US and Denmark (Henry et al., 1997 & 1999; Goffette-Nagot and Schmitt, 1999; Schmitt
and Henry, 2000). Urban population and employment growth affects rural places in different ways
depending on the size of the urban core and fringe and their patterns of change. But these approaches
have considered employment as homogeneous and have not examined how urban growth might affect
export and service employment in proximate rural areas. The purpose of this paper is to examine how
the pattern of urban spatial expansion influences rural export and services employment, and population
change in rural areas of functional economic regions.
The direction of causality in the "jobs follow people or people follow jobs?" literature (Steinnes
and Fischer, 1974) was addressed by Carlino and Mills (1987) by constructing location equations as a
two equation simultaneous system. Underlying wage and land price structural equations are not
specified since variation in amenities across space is assumed to be capitalized into local wages and
rents (see Roback, 1982). Then, use of amenity variables in the two-equation system reflects these
wage and rent effects across space.
As suggested by Henry et al. (1997), to incorporate the interplay of employment and population
changes into the spatial development process within a region, urban growth and size influences on
development in proximate rural areas are assumed to follow the monocentric tradition, with some
contemporary twists. First, consider a set of functional economic regions (FERs) -- each with an urban
core, urban fringe (suburbs and edge cities), and a rural hinterland. The spatial extent of the region is
limited by commuting costs from the urban core and fringe to proximate rural villages.
Second, population and two kinds of economic activity, export and service (North, 1956), may
locate in each of the three sub-areas of the region. Households require trade and service goods within a
2
shopping trip distance that is shorter than the distance they are willing to commute to employment
opportunities. Accordingly, the demand for service goods comes primarily from households within the
sub-area where the service establishment is located. Service employment depends on the size of the
local population.
Third, demand for the export commodity comes from outside the functional economic region so
its level is exogenous. However, the marginal cost of producing the export commodity is influenced
by spatial externalities within the functional economic region. Establishments located in functional
economic regions with higher densities of employment in the export industries and of population may
have lower costs. Examples of agglomeration economies that reduce production costs in urban areas
compared to rural areas are: "deeper labor markets" or labor pooling that provides lower transactions
and labor costs in urban areas; lower input costs from supplying industries as these industries achieve
scale economies; and spillovers of knowledge that are more likely in urban areas (Kilkenny, 1998, p.
265).
However, rising urban land rent within FERs with high population and export employment
density plays a dispersive force. As urban land rents increase with proximity to the urban center and
increased competition for fixed urban land resources, these added costs may offset other reductions in
production costs from spatial agglomeration economies. Rising housing costs push households away
from their job location until increasing commuting cost offsets the land rent rise (Fujita, 1989;
Goffette-Nagot, 1999) – causing population dispersion within functional economic region as well as
between functional economic regions (Tabucchi, 1998). If land rents in the urban center are high
enough (relative to rural areas) to offset cost savings from urban agglomeration economies, export
sector firms and households (and associated service firms) seek new locations in the urban fringe or
rural hinterland as export demand expands.
Negative spatial externalities, like congestion costs, can also induce urban "spillovers" or spread
from the urban core to the fringe and rural hinterlands. Moreover, with improvements in
transportation, rural villages become more attractive to residents who commute to jobs in the urban
core or fringe. In all cases, the impact of spatial externalities on rural villages can be expected to decay
with distance from the urban center.
3
In sum, there are three agents (firms in export and in population services and households) seeking
sites in one of three sub-areas in each functional region (urban core, fringe and rural hinterland). The
empirical model is focused on employment and population change in rural villages within the
hinterland sub-area. Size and growth of employment opportunities in urban areas proximate to the
rural village are hypothesized to affect the desirability of rural places as a residential site and thus rural
service employment growth. Variations in amenity levels across rural places also shape the
attractiveness of rural places as residential sites and as sites for export and service employment. Export
employment in rural hinterland areas is positively related to spatial agglomeration economies that will
increase with size and growth of the urban core and fringe in the functional economic region.
In the next section, we offer a new version of the Boarnet (1994) and Henry et al. (1997) models
that captures the interplay of the three agents as they decide whether or not to expand in rural parts of
the functional economic regions of eastern France. In section III, data needed to estimate the model
and econometric issues are presented. Empirical results for the selected French regions are examined
and discussed in section IV. A summary of findings and rural policy implications are provided in
section V.
II. EMPIRICAL MODEL
Boarnet's model of local development is modified in two ways. First, to capture possible urban
growth and size influences on nearby rural places, urban center and urban fringe growth rates are
allowed to interact with rural labor and residential zones as suggested by Henry et al. (1997). Second,
employment is disaggregated into a service sector and an export sector.
Consider i areas that belong to the rural hinterland of different functional economic regions
(FER). Each FER has an urban center and an urban fringe. Call P rural population, and E1 and E2 rural
employment in two sectors -- an export (or industrial) sector and a service (or population-serving)
sector. Rural population acts on rural service employment density through demand for local goods and
on the rural export sector through labor pooling effects. Rural and urban employment in each sector
influences changes in rural population density by providing job opportunities and urban employment
acts on rural population by increasing land use competition in the urban center. Urban and rural
4
employment in each sector is allowed to influence changes in urban and rural employment density in
the other sector through customer and/or supplier-driven linkages. Accordingly, both rural and urban
components of economic activity are expected to influence rural changes.
In addition, proximity characteristics and community amenities will influence residential choices
(better schools, shopping opportunities, proximity of hospitals, etc.), export firm locational choices
(better access to transport nodes, skilled labor pools, etc.) and service sector firm community choices
(better access to customers, distance to competing stores in urban centers, etc). These proximity and
amenity effects are captured in sets of local attributes, called Ai, Bi and Ci for residents, export firms
and service firms, respectively. Proximity characteristics and amenities also are capitalized into local
rental or housing prices. For example, rural villages closer to the urban center are expected to
command higher rents than more distant villages. Accordingly, varying proximity characteristics and
amenity levels serve as a proxy for variation in rents across rural places. If local rents fully reflect
varying proximity characteristics and amenity levels, then the attraction of more favorable locations
and higher amenity levels may be concealed by the countervailing effects from higher rents.
Finally, because of commuting and shopping trips, we construct residential and labor market
zones surrounding each rural place. The decision to reside or to locate a business in a particular rural
village is influenced by the characteristics of both the rural village and the places within its commuting
zone.
The expected population and employment interdependencies and amenity influences are
introduced in the simultaneous equation model shown in equations (1a), (1b) and (1c):
(
Pi*,t = Ψ Ai , EMPi*,1,t , EMPi*,2,t , f1 , f 2 , g1 , g 2 ,
E i*,1,t = Φ Bi , POPi*,t , , EMPi*,2,t , h1 , h2 , g1 , g 2
E i*,2,t
(
= Ξ (C , POP
i
*
*
i ,t , EMPi ,1,t , h1 , h2 , f1 , f 2
)
)
)
(1a )
(1b)
(1c )
where Pi*,t , Ei*,1,t and Ei*,2, t are equilibrium population and employment in export and service sectors
in the ith rural area at time period t; POPi*,t , EMPi*,1,t and EMPi*, 2,t are equilibrium population and
employment in local residential and labor market zone (i.e. in the rural area and its nearby commuting
areas); f1 (f2) is the urban center (urban fringe) employment growth rate in the export sector for the
FER to which the ith rural area belongs; g1 (g2) is the urban center (fringe) growth rate in the service
5
sector; h1 (h2) is the urban center (fringe) population growth rate.
The cross-activity effects on local changes (i.e. local population effects on local employment, etc.)
are captured by the POP and EMP variables that include values of the ith area Pi (E i) and of its nearby
areas. The POP and EMP variables are found as (I+W) P and (I+W) E, where I is the identity matrix
and W a “queen” spatial contiguity matrix2, dimension n x n where n is the number of places in the
sample. Wij is nonzero for places that are proximate to each other, except Wii is set to zero. This
proximity measure may simply be a one, as assumed here, for places within a 30 km commute, or it
may be based on a gravity model index as in Boarnet (1994). I is the identity matrix so that (I+W)
represents a cluster of places within a 30 km commute of each other.3
Adopting linear forms of (1a), (1b) and (1c) and assuming interactions between rural population
(or employment in each sector) and urban employment in each sector (or population) growth yields
equations (2a), (2b) and (2c).
Pi*,t = α1 + α 2 . Ai + (α 3 + α 4 . f1 + α 5 . f 2 )(
. I + W ).Ei*,1,t + (α 6 + α 7 .g1 + α 8 .g 2 )(
. I + W ).Ei*,2,t + ν 1
( 2a )
Ei*,1,t
Ei*,2,t
(2b)
= β1 + β 2 .Bi + (
)(
)(
)
(
β 3 + β 4 .h1 + β 5 .h2 . I + W .Pi*,t + β 6 + β 7 .g1 + β 8 .g 2
γ 3 + γ 4 .h1 + γ 5 .h2 . I + W .Pi*,t + γ 6 + γ 7 . f1 + γ 8 . f 2 . I
= γ 1 + γ 2 .Ci + (
)
(
)(
)(
)
. I + W .Ei*,2,t + ν 2
+ W .Ei*,1,t + ν 3
)
(2c)
where ν1, ν2 and ν3 are random disturbance terms that are assumed to be normally and independently
distributed with zero mean and constant variance; α2, β2 and γ2 are vectors of ‘local characteristics’
parameters; etc.
As suggested by Carlino and Mills (1987), partial adjustment equations to the equilibrium levels
for the ith area are:
(
dPi = λ p Pi*,t − Pi,t −1
(
(
)
)
dEi,1 = λe1 Ei*,1,t − Ei ,1,t −1
dEi,2 = λe 2 Ei*,2,t − Ei ,2,t −1
(3a)
)
(3b)
(3c)
where dPi, dEi,1 and dEi,2 are population change and employment changes in export and service
sectors; λp, λe1 and λe2 are the rates of adjustment to equilibrium levels for population and employment
in sector 1 and 2 respectively.
2
Lesage (1999).
W matrices using a 15 km commute between cantons and a distance decay parameter was also used in
alternative model estimates. Empirical results were more robust over the 30 km commute range.
3
6
By substitution of (2a), (2b) and (2c) into (3a), (3b) and (3c) and simplification, the three
simultaneous equations are:
dP = δ 0 + A.δ 1 − λ p .Pt −1 + (δ 2 + δ 3 . f1 + δ 4 . f 2 )(
. I + W ).E1,t −1 + (δ 5 + δ 6 . f1 + δ 7 . f 2 )(
. I + W ).dE1 +
(δ 8 + δ 9 .g1 + δ10 .g 2 )(. I + W ).E2,t −1 + (δ11 + δ12 .g1 + δ13 .g 2 )(. I + W ).dE2 + ε1
(4a)
dE1 = θ 0 + B.θ1 − λe1.E1,t −1 + (θ 2 + θ 3 .h1 + θ 4 .h2 )(
. I + W ).Pt −1 + (θ 5 + θ 6 .h1 + θ 7 .h2 )(
. I + W ).dP +
(θ 8 + θ 9 .g1 + θ10 .g 2 )(. I + W ).E2,t −1 + (θ11 + θ12 .g1 + θ13 .g 2 )(. I + W ).dE2 + ε 2
dE2 = τ 0 + C.τ 1 − λe2 .E 2,t −1 + (τ 2 + τ 3 .h1 + τ 4 .h2 )(
. I + W ).Pt −1 + (τ 5 + τ 6 .h1 + τ 7 .h2 )(
. I + W ).dP +
(τ 8 + τ 9 . f1 + τ 10 . f 2 )(. I + W ).E1,t −1 + (τ 11 + τ 12 . f1 + τ 13 . f 2 )(. I + W ).dE1 + ε 3
(4b)
(4c)
Note that the δ‘s, θ‘s and τ‘s are reduced form parameters that are products of the structural model
parameters in equation (2a), (2b) and (2c) and the rates of adjustment coefficients are λp, λe1 and λe2..
In the empirical model presented in equations (4a) to (4c), the transmission of urban spread or
backwash effects is captured in two ways -- through urban size and growth. Urban size affects the
values of (I+W) Pt-1 – the size of the labor pool centered on a rural canton – as well as (I+W) E1, t-1
and (I+W) E2, t-1 -- the size of the “job market” centered on a rural canton. Urban growth rates for the
core f1, g1, h1 and fringe f2, g2, h2 interact with both the beginning period sizes and changes in the labor
pools and job markets. Urban growth also affects (I+W) dP, (I+W) dE1 and (I+W) dE2 -- change in
the sizes of the labor pool and “job market” over the study period.
III. DATA USED AND ESTIMATION ISSUES
Data Geography
French data are organized across 429 Functional Economic Regions (FERs) in six regions and
FERs are delineated using commuting flows between communes4 (Schmitt and Henry, 2000). Each
commune is assigned to a FER as either an employment center (the urban core), a “periurban” area
(the urban fringe) or a peripheral rural area (the rural hinterland) in relation to the number of
commuters from commune to the employment center. We first calculate the urban core and fringe
population and employment growth rates (f1, f2, etc.) by aggregation of communal data.
Estimating equations (4a) to (4c) using commune level creates a problem because employment
7
data come from a one-quarter sample of the French censuses. Data are not reliable at this level so use
of cantons (an upper political level in France) is required. Each canton is allocated to a FER and to its
rural hinterland if the population of its communes mainly belongs to the rural hinterland of this FER.
Data are finally aggregated into 191 cantons belonging to rural hinterlands as shown in Figure 1.
Insert Figure 1 about here
Rural cantons are the units of observation for the dependent variables. Explanatory variables for
the rural cantons include: amenity and proximity characteristics of the rural canton, employment and
population in commuting zones centered on each rural canton, and growth rates of population and
employment in the urban core and urban fringe of the functional economic region to which the rural
canton belongs. The data are disaggregated into two subsets: one subset for rural cantons in FERs
with declining urban cores (84 rural cantons), the other set for rural cantons with growing urban cores
(107 cantons). Estimates of 4a through 4c are made for each subset of rural cantons.
As shown in Table 1, the FERs generally exhibit population and service employment spatial
dispersion from the urban core to the urban fringe. However, when the urban core population is
declining, employment in the export sector decreases in both the core and fringe. Alternatively, in
FERs with population growth in the urban core, employment in the export sector increases in both the
core and fringe.
Insert Table 1 about here
Data Sources
The data are assembled from French statistical sources including: Census Files for Population and
Job Data, 1975, 1982 and 1990; Communal Inventory, 1980, 1988; and Taxable Income File, General
Director of Taxes, 1984. Variables used to estimate each equation are defined in Table 2.
Dependent variables
The dependent variables are: dP (or VDP8290), change in rural canton population density from
1982 to 1990,; dE1 (or VDE8290), change in rural canton employment density in industrial (export)
sectors from 1982 to 1990; and, dE2 (or VDS8290), change in rural employment density in shops and
4
French municipalities
8
personal services sectors (service sector) from 1982 to 1990. Densities are used for the employment
and population change variables to control for the geographical size of the cantons. For the same
reason, we also used densities for beginning period (1982) employment and population variables
which appear in the equation (4a to 4c): the ( −λ p .Pt −1 ), ( −λE1 .E1, t −1 ) and ( −λ E2 .E 2,t −1 ) terms.
Insert Table 2 about here
Explanatory variables
For the population equation (4a), a vector of rural canton characteristics, A, is used to control for
the following canton attributes: the density of trade and services available in a canton, distance to the
nearest urban center, distance to the nearest autoroute entrance (“interstate highway”), distance to the
nearest secondary school, distance to the nearest hospital, average canton income, natural population
balance (births-deaths), and change in population density during the previous period, 1975-1982. Note
that we introduce the natural population balance as a control variable; population change can then be
viewed as a result of in or out-migrations.
In both employment equations (4b and 4c), the B and C vectors of variables reflect the following
rural canton characteristics that might be expected to influence firm location and expansion decisions:
distance to the nearest urban center, distance to the nearest urban area with at least 200,000
inhabitants, distance to the nearest “autoroute” entrance, the ratio of “white collar” to “blue collar”
workers, ratio of skilled to unskilled manual workers, share of workers in self-employed occupations,
beginning period labor zone unemployment rate, beginning period residential zone average household
income as a proxy for local average wage levels, and prior period (1975-82) growth in employment
density in the rural canton. We include the beginning period density of tourism accommodations in the
rural canton.in the service sector employment equation (4c)
Spatial lags5 are formed by multiplying dP, dE1, dE2, Pt-1, E1,t-1 and E2,t-1 by a (IN + WN* ) row
standardized (queen) spatial contiguity matrix. WN* is an N by (N+J) matrix to capture the influence of
all cantons that surround each rural canton. N is equal to 191, corresponding to the sample rural
5
These variables are denoted in the empirical results presented in appendix, as WVDPOP, WVDOTH,
WVDSHO, DWPOP82, DWOTH82 and DWSHO82, respectively.
9
cantons. (N+J) is equal to 388 and includes the sample rural cantons and other cantons in the six
regions. The WN* matrix has the 191 rural cantons as rows and 388 cantons in the regions as columns
(see Anselin and Kelejian, 1997, for a discussion of the importance of taking into account a N+J
matrix rather than a N+N one in a spatial configuration like here). The wij* element is
1
∑ wij
if the
j
ith and jth cantons are within 30 km of each other, otherwise wij* = 0. Multiplying the WN* matrix
times the 1 by 388 vector of values for the variable across all cantons forms the spatial lag of a
variable. In this way, local labor market zones and residential zones centered on each of the 191 rural
cantons are constructed.
Estimation Issues
The econometric estimates are obtained in a two-stage process using an instrumental variables
method since there is simultaneity implied between the changes in population density, dP, and changes
in export or service sector employment density, dE1 or dE2, in the rural cantons. The instruments used
for the estimation in the first stage are all the exogenous variables introduced in the three population
and employment equations and, when possible, their spatial lag values.6
As reported by Rey and Boarnet (1998), there are two ways to obtain the predicted values of
endogenous variables needed for the second stage of estimation with spatial lags in the endogenous
variables. The first, suggested by Anselin (1988), is to use predicted values of the dependent variables
obtained by regression on the exogenous variables and then multiply the predicted values by the W
matrix: W [X (X’X)-1 X’y] = W [Xβ]. The second technique is to use the predicted values of the
dependent variables obtained by regression of the spatially lagged dependent variables on instruments:
X (X’X)-1 X’ Wy = Xβw. Rey and Boarnet (2001) and Kelejian and Oates (1989, p. 321) suggest that the
first method involves an inconsistent estimator. In this paper, the second option is used – the ( I + WN* )
endogenous variables are computed before the first stage estimation.
6
Some spatial lagged variables were omitted in the instrument vectors: spatial lag of NATBAL_P, DPBAS90C
(and D2PBAS90C), DSHOP82, DOTHE82 and DPOP82. Finally, we used 39 instruments.
10
To test for the presence of spatial autocorrelation in the errors, the I-Moran test was used as
suggested in Anselin and Kelejian (1997) in models with endogenous regressors. Tests for spatial error
autocorrelation were made with three different spatial weight matrices: the row-standardized matrix of
contiguity between cantons (W0); the row-standardized matrix of contiguity in a radius of 15 km


around the canton (W15); and, the non-standardized matrix of distance inverse  1 d  between canton
ij 

centroids (Wd) where dij is the distance between canton i and j. Because the I-Moran tests did not
detect spatial autocorrelation in our estimation, we did not need to correct for it.
IV. ESTIMATION RESULTS
Complete results from the second stage regressions for equation (4a), (4b) and (4c) are listed in
the Appendix. For the most part, the control variables that were most significant include the prior
period change (from 1975 to 1982) in the dependent variable and the beginning period value of the
dependent variable. Only a few proximity or local amenity variables were statistically significant yet
all were retained to provide control across proximity characteristics and amenities while estimating the
interplay effects between employment and population size and change. The impacts on rural canton
economies from the interplay of employment and population change in the urban core, fringe and rural
hinterland in each FER are summarized in Table 3. These findings are derived from the regression
results for equations (4a), (4b) and (4c) at mean growth rates (f1, f2, etc.) for the urban core and fringe
across the FERs.7
The results in Table 3 reflect the urban growth and size impacts on proximate rural cantons .
Recall there is no direct link from urban population (employment) change to rural population
(employment) change in the model. Rather, urban population growth rates are hypothesized to affect
the growth of population (labor pool) within a commuting range of a rural canton. In turn, if the size of
the labor pool within commuting range of a rural canton increases, the rural place may attract more
7
Their joint t-values are computed as, for example:
var ( δ 11 + δ 12 g 1 + δ 13 g 2 ) = s 22 + 2 g 1 s 23 + g 12 s 33 + 2 g 2 s 24 + g 22 s 44 + 2 g 1 g 2 s 34
where sij are elements in the variance/covariance matrix because ( δ 11 + δ 12 g 1 + δ 13 g 2 ) [ (I + W) dE 2 ] is a linear
combination of parameter estimates (see Aiken and West, 1991, p. 24-26).
11
employers while the added jobs attract more residents to the rural place.
Moreover, urban growth rates in the model reflect the pattern of change in spatial structure in a
given urban complex (core and fringe). For example, if the fringe is growing rapidly while the core is
stagnant or declining, there is a pattern of decentralization. Rapid growth of the core with fringe
decline yields a more center-oriented urban complex. In both cases, the local labor market areas,
EMPi ,1 and EMPi , 2 or residential zone, POPi for a rural place might be affected. Accordingly, urban
spatial growth patterns may influence the desirability of a rural place as a location choice for a new
firm because the local labor force pool, POPi, may increase with rapid urban fringe growth or decrease
with rapid urban core growth. In equations (4b) and (4c), the relative values of h1 and h2 affect POPi.
For example, a low h1 and a high h2 mean that urban population is spreading to the suburbs. This
suggests that nearby rural places will have a larger labor pool available to potential new firms and
favorably affect Ei,1 and Ei,2, employment in a rural place. Alternatively, the desirability of a rural place
as a location choice for a new household, equation (4a), may increase or decrease as urban spatial
employment growth patterns (f1, f2 and g1, g2) affect the size of the local job opportunity pool, EMPi ,1
and EMPi , 2 .
In each pair of rows in Table 3, the first row represents the impacts of beginning period (1982)
regional employment (population) interacting with urban growth rates on rural growth in employment
(population). The second row in each pair in Table 3 represents the impacts of contemporaneous
change (1982 to 1990) in regional employment (population) interacting with urban growth rates on
rural growth in employment (population).
Insert Table 3 about here
Our key findings include the following. For regions with urban areas that have declining urban
cores, larger urban populations promote faster expansion in rural export jobs but not rural service jobs
(rows 1 and 3 in Table 3). Moreover, larger beginning period levels of urban export and service sector
employment do not affect rural population (see rows 5, 7, 9 and 11 in Table 3). For regions with urban
cores that are in decline, we conclude that larger populations in urban areas promote increases in rural
export employment. But, for these FERs, the urban population size has no impact on rural service
12
employment and the urban employment sizes do not influence rural population change and rural
employment changes in each sector.
For regions with urban areas that have growing urban cores, we find that larger urban
employment in export jobs induces higher levels of rural service jobs (row 7, Table 3) – the export
base multiplier effect with a spatial dimension suggesting a “learning by doing” effect. However, there
is a weak negative effect on rural service jobs from larger urban populations (row 3, Table 3). This
suggests that large urban centers that are growing provide services to proximate rural residents that
substitute for services in rural areas. Growing suburban shopping districts may be replacing jobs
formerly provided by establishments in proximate rural cantons. Note that, for these FERs and
contrary to the previous case, there is no effect of urban population size on rural export employment
change.
Second, if static spatial externalities exist, regression coefficients on contemporaneous change in
employment and population will be significantly different from zero. We find empirical support for
static spatial externalities – both positive and negative – export employment via labor pooling and
knowledge spillovers; and services through added local population.
In urban areas that have a growing urban core, growth in urban export jobs increases rural
population (row 6, Table 3). Moreover, urban population growth increases rural service jobs in these
FERs (row 4, Table 3). In FERs with declining urban cores, urban export job growth reduces the
growth in rural service jobs (row 8, Table 3) and expanding urban service jobs reduce rural export jobs
(row 12, Table 3). Together, these results suggest that expanding urban employment opportunities
draws employees away from proximate rural communities. However, larger contemporaneous change
in urban population increases both rural export employment (row 2, Table 3) via benefits of labor
pooling that increase with urban size, and rural service employment (row 4, Table 3) through added
local population.
V. SUMMARY AND CONCLUSIONS.
In selected regions of France, we find that urban patterns of spatial growth affect proximate rural
growth through several channels. First, urban spatial effects are much more likely in FERs where
13
urban cores are declining and the fringe is expanding. If the urban core is declining, we find that urban
population growth increases rural export and services employment and consequently rural population
increases. This suggests that urban core land rents are rising faster than cost savings from spatial
externalities – labor pooling, knowledge spillovers, etc. – spreading growth to the urban fringe and to
rural areas.
Alternatively, urban backwash affects rural places when larger urban places with
growing employment opportunities induce firms and people from nearby rural places to relocate to the
urban core or fringe. In this case, urban core land supply does not appear to be an important constraint
to core growth and cost savings from urban spatial externalities offset higher urban rents. Overall, we
find that spread effects are more likely than urban backwash.
If the urban core is growing (and the urban fringe is also expanding), we find that urban export
growth leads to rural population growth and urban population growth increases rural service
employment. We also find spatial linkages between changes in urban export employment and rural
service employment -- the export base multiplier effect with a spatial dimension. However, there is
also evidence that growing urban services displace service jobs in rural places. Finally, the results
presented in Table 3 assume average growth rates for the urban core and fringe. As illustrated in
Schmitt and Henry (2000), the model developed can also be used to assess the impacts on rural places
from a wide range of urban spatial expansion scenarios – from rapid urban sprawl to increasing
concentration in the urban core.
REFERENCES:
Aiken L.S. and West S. G. (1991) Multiple Regression: Testing and Interpreting Interactions. Sage
Publications, Newbury Park, CA.
Anselin L. (1988) Spatial Econometrics: Methods and Models. Kluwer Academic Publishers,
Dordrect.
Anselin L. and Kelejian H. H. (1997) Testing for Spatial Error Autocorrelation in the Presence of
Endogenous Regressors. International Regional Science Review 20: 153-182.
Boarnet M. G. (1994) An Empirical Model of Intrametropolitan Population and Employment Growth.
Papers in Regional Science 73: 135-153.
Carlino G. A.. and Mills E. S. (1987) The Determinants of County Growth. Journal of Regional
Science 27: 39-54.
14
Fujita M. (1989) Urban Economic Theory, Cambridge University Press, Cambridge.
Goffette-Nagot F. (1999) Urban Spread beyond the City Edge. In : Huriot J.-M., Thisse, J.F.
Economics of Cities. Theoretical Perspectives. Cambridge University Press, Cambridge.
Goffette-Nagot F. and Schmitt B. (1999) Agglomeration Economies and Spatial Configurations in
Rural Areas. Environment and Planning A 31:1239-1257.
Hansen N. (ed.) (1972) Growth Centers in Regional Economic Development. Free Press, New York.
Henry M. S., Schmitt B., Kristensen K., Barkley D. L., Bao S. (1999) Extending Carlino-Mills Models
to Examine Urban Size and Growth Impacts on Proximate Rural Communities. Growth and
Change 30: 526-548.
Henry M. S., Barkley D. and Bao S. (1997) The Hinterland's Stake in Metropolitan: Growth: Evidence
from Selected Southern Regions. Journal of Regional Science 37(3): 479-501.
Kelejian H. H. and Oates W.E. (1989) Introduction to Econometrics. 3rd edition. Harper and Row,
New York.
Kilkenny M. (1998). Transport Costs, the New Economic Geography and Rural Development. Growth
and Change 29: 259-280.
Lesage J. (1999) Spatial Econometrics. In: The Web Book of Regional Science. Regional Research
Institute, West Virginia University.
North D.C. (1956). Exports and Regional Economic Growth: a Reply. Journal of Political Economy,
64(2): 165-168.
Rey S. and Boarnet M. (2000) A Taxonomy of Spatial Econometric Models for Simultaneous
Equation Systems. In: Anselin L. and Florax R. Advances in Spatial Econometrics, SpringerVerlag, Heidelberg.
Roback J. (1982) Wages, Rents, and the Quality of Life. Journal of Political Economy 90(6): 12571278.
Schmitt B. and Henry M. S. (2000) Size and Growth of Employment Centers in French Labor Market
Areas: Consequences for Rural Population and Employment. Regional Science and Urban
Economics 30(1): 1-21.
Steinnes D. and Fisher W. (1974) An Econometric Model of Intraurban Location. Journal of Regional
Science 14: 65-80.
Tabucchi T. (1998) Urban Agglomeration and Dispersion: A Synthesis of Alonso and Krugman.
Journal of Regional Science 44(3): 333-351.
15
APPENDIX
The first two columns in Tables A-1, A-2 and A-3 are parameter estimates for observations on
rural cantons in FERs with a declining urban core; the next two are for rural cantons in FERs with a
growing urban core and the final two columns are for the combined data. First, consider the proximity
and amenity effects and impacts of earlier growth (from 1975 to 1982). Since the impact of urban
spatial externalities on rural communities can be expected to decay with distance from the urban core,
more remote rural villages (greater distance and/or no autoroute access), can be expected to grow
slower than villages proximate to urban cores. Alternatively, villages with lower quality amenities are
expected to grow more slowly than their more appealing counterparts.
Insert Tables A-1, A-2 and A-3 about here
We find few strong proximity or amenity effects on rural population. In each regression in Table
A-1, larger increases in the rural canton population over the 1975 to 1982 period (VDP7582) are
associated with more rural people from 1982 to 1990. In FERs with declining urban cores, rural
population is attracted to higher income rural cantons (INCOM84). Since the natural balance (births
less deaths, NATBAL_P) is a control variable, these population changes can be viewed as a result of
in or out-migrants.
In Table A-2, the determinants of rural export employment growth indicate that again only a
handful of amenities and initial conditions matter. Rural cantons that had more prior period growth
(VDO7582) tended to have less export employment growth. However, larger beginning period export
employment (DOTHE82) increased export employment (the parameter estimate in Table A-2 is
multiplied by –1 as indicated in equation (4b). Higher initial period unemployment rates (RCHOM82)
also tended to boost export employment growth. Increasing distance to the nearest urban area with
more than 200,000 residents (DAGGLOM) increases export employment in rural cantons.
In Table A-3, we find that larger beginning period service sector employment (DSHOP82)
decreases rural canton service employment (again the parameter is multiplied by –1 as shown in
Equation 4c). Moreover, in FERs with growing urban cores, prior period service employment growth
(VDS7582) slows rural service employment growth. Higher density tourist accommodations
(DCAPAC80) also slow subsequent serves sector employment.
16
Figure 1 – Rural Hinterland of FERs in six French Regions
17
18
Table 1 – Growth Rates of Population, Export and Service Sector Employment (82/90)
in Urban Core and Fringe
FERs with declining
FERs with growing
population in urban core population in urban core
Mean
Std dev
Mean
Std dev
% Rate of Population Change in:
Core (h1)
-3.16
2.94
4.49
Fringe (h2)
8.19
4.36
16.30
% Rate of Export Sector1 Employment Change in:
Core (f1)
-2.19
10.59
6.48
Fringe (f2)
-0.07
19.52
25.74
% Rate of Service Sector2 Employment Change in:
Core (g1)
8.35
8.80
16.49
Fringe (g2)
14.52
8.40
27.87
All the FERs
Mean
Std dev
3.81
7.41
1.13
12.74
5.13
8.03
8.55
30.72
2.66
14.39
10.42
29.49
5.09
11.85
12.91
22.00
8.03
28.88
1
This category includes employment in retailing (U08 of INSEE nomenclature in Nap 15), personal services,
hotels and catering, automobile repairs and sales (res. T29, T30 & T33 of Nap 40).
2
Apart from manufacturing sectors in the narrow sense (U02 - U06), this set includes such varied sectors as the
building trade, civil and agricultural engineering (U07), transport and communications (U09), services to firms
(T34 of Nap 40), rentals and real estate leases (U11), insurance (U12), financial institutions (U13) and business
services (U14).
19
Table 2 – List of Variables
Name of Variables
Definition
Dependent variables:
DP = VDP8290
Change in population density in the rural canton 1982 to 1990
DE1 = VDE8290
Change in export employment density in the rural canton 1982 to 1990
DE2 = VDS8290
Change in service employment density in the rural canton 1982 to 1990
Spread/backwash variables:
DWPOP82 (WVDPOP)
Residential Zone population density (Change in Density, 1982)
DWPOPH1 (WVDPH1)
Urban center growth interaction with DWPOP82 (WVDPOPE): h1 * (I+W) P82 and h1 *
(I+W) dP where h1 is the population growth rate in the urban center 1982 to 1990
DWPOPH2 (WVDPH2)
Urban fringe growth interaction with DWPOP82 (WVDPOPE): h2 * (I+W) P82 and h2 *
(I+W) dP where h2 is the population growth rate in the urban fringe 1982 to 1990
DWOTH82 (WVDOTH)
Export Sector Labor Market Zone Employment density (Change in Density), 1982
DWOTHF1 (WVDOF1)
Urban center growth interaction with DWOTH82(WVDOTHE): f1 * (I+W) E182 and f1 *
(I+W) dE1 where f1 is the export sector employment growth rate in the urban center 1982
to 1990
DWOTHF2 (WVDOF2)
Urban fringe growth interaction with DWOTH82 (WVDOTHE): f2 * (I+W) E182 and f2 *
(I+W) dE1 where f2 is the export sector employment growth rate in the urban fringe 1982
to 1990
DWSHO82 (WVDSHO)
Service Sector Labor Market Zone Employment density (Change in Density, 1982);
DWSHOG1 (WVDSG1)
Urban center growth interaction with DWSHO82 (WVDSHOE): g1 * (I+W) E2182 and g1
* (I+W) dE2 where g1 is the service sector employment growth rate in the urban center
1982 to 1990
DWSHOG2 (WVDSG2)
Urban fringe growth interaction with DWOTH82 (WVDSHOE): g2 * (I+W) E282 and g2 *
(I+W) dE2 where g2 is the service sector employment growth rate in the urban fringe 1982
to 1990
Population and Labor market variables:
DPOP82
Population density in each rural canton, 1982
NATBAL_P
Rural canton natural population growth (births-deaths)1982 to 1990
VDP7582
Change in rural canton population density during previous period (1975-1982)
DOTHE82
Export employment density in each rural canton, 1982
VDO7582
Change in rural canton export employment density during previous period (1975-1982)
DSHOP82
Service employment density in each rural canton, 1982
VDS7582
Change in rural canton service employment density during previous period (1975-1982)
"Proximity and amenity" variables for French residents (the "A" vector):
DPBAS90C
Distance from the rural canton to the urban employment center (centroid to centroid)
D2PBAS90C
Distance to the employment center squared
DEQU80S
Index of the density of public services available, 1980
SCHO80D
Distance to the nearest secondary school in 1980
HOSP80D
Distance to the nearest hospital in 1980
D1AUTOR
Distance to the nearest freeway entrance point in 1988
INCOM84
Household taxable income in 1984
Business proximity and amenity variables for the export and service sectors (the "B" and "C" vectors):
DPBAS90C
Distance from the rural canton to the urban employment center
D2PBAS90C
Distance to the urban employment center squared
DAGGLO
Distance to the nearest urban agglomeration greater than 200,000 inhabitants
DIAUTOR
Distance to the nearest freeway entrance point
NOBLU82
Active population in executive and intermediate occupations by active population of
manual and clerical workers in 1982
SKIWO82
Active population of skilled manual workers by unskilled manual workers in 1982
SELFJ82
Percentage of non-salaried jobs in non agricultural sectors in 1982: Non-salaried jobs
% Total jobs
RCHOM82
Unemployment rate in 1982: Unemployed % Active population
Specific variable for the service sector :
DCAPAC80
Density of tourist accommodations in the rural canton
20
Table 3 – Effects of Urban Size and Growth on Rural Cantons in Eastern France
in FERs with urban core:
declining
Parameter
Joint t
in FERs with urban core:
growing
Parameter
Joint t
Effects of size and growth in
Urban Population on:
Rural Export Jobs
Rural Export Jobs
(θ 2 + θ 3.h1 + θ 4 .h2 ).( I + W ) Pt-1
(θ 5 + θ 6 .h1 + θ 7 .h2 ) ( I + W ) dP
0.051
0.320
0.004
0.162
Effects of size and growth in
Urban Population on:
Rural Service Jobs
Rural Service Jobs
(τ 2 + τ 3.h1 + τ 4 .h2 )( I + W ) Pt-1
(τ 5 + τ 6 .h1 + τ 7 .h2 ) ( I + W ) dP
0.004
0.380
-0.021
0.213
Effects of size and growth in
Urban Export jobs on:
Rural Population
(δ 8 + δ 9 .g1 + δ10 .g 2 ) ( I + W ) E1, t-1
1.85
2.12
0.12
2.19
-1.69
2.72
Rural Population
(δ11 + δ12 .g1 + δ13 .g 2 ) ( I + W ) dE1
-0.108
-0.990
Effects of size and growth in
Urban Export jobs on:
Rural Service Jobs
Rural Service Jobs
-0.134
-0.877
0.123
0.122
(τ 8 + τ 9 .g1 + τ10 .g2 ) ( I + W ) E1, t-1
(τ11 + τ 12 .g1 + τ13 .g 2 ) ( I + W ) dE1
-0.63
-1.46
0.16
0.88
-0.77
-1.92
0.050
1.425
0.19
2.32
2.11
0.699
Effects of size and growth in
Urban Service jobs on:
Rural Population
(δ 2 + δ 3. f1 + δ 4. f 2 ) ( I + W ) E2, t-1
(δ 5 + δ 6 . f1 + δ 7 . f 2 ) ( I + W ) dE2
0.323
1.021
Effects of size and growth in
Urban Service jobs on:
Rural Export Jobs
Rural Export Jobs
-0.182
-1.633
0.097
-0.222
(θ8 + θ 9 . f1 + θ10 . f 2 ) ( I + W ) E2, t-1
(θ11 + θ12 . f1 + θ13 . f 2 ) ( I + W ) dE2
1.31
1.33
-1.03
-2.03
Boldface indicates significance at the .10 level
21
Rural Population
-0.278
1.336
-0.99
1.08
0.53
-0.28
Table A-1 –Estimation Results for Rural Population Change Equation
Variable
N
INTERCEP
DPOP82
DPBAS90C
D2PBA90C
NATBAL_P
DEQU80S
DIAUTORO
SCHO80D
HOSP80D
INCOM84
VDP7582
DWOTH82
DWOTHF1
DWOTHF2
WVDOTH
WVDOF1
WVDOF2
DWSHO82
DWSHOG1
DWSHOG2
WVDSHO
WVDSG1
WVDSG2
Adj. R-sq
I-Moran: Wc
W30
Wd
Cantons belonging to:
FERs with declining
FERs with growing
urban core
urban core
Parameter
t
Parameter
t
84
107
-2.692
-1.13
-4.733
-1.23
-0.003
-0.10
0.046
1.06
-0.083
-0.71
0.036
0.28
0.001
0.52
4.4 E-04
0.26
-0.064
-0.99
0.033
0.32
-3.68
-0.93
1.531
0.29
-4.8 E-04
-0.09
0.006
0.49
0.054
0.84
-0.026
-0.27
-0.034
-0.83
-0.036
-0.56
0.062
1.23
0.058
1.72
0.262
1.94
0.462
2.32
-0.106
-0.62
-0.025
0.09
6.0 E-04
0.14
-0.006
-0.80
0.003
1.05
0.003
0.84
-1.096
-1.49
2.233
1.68
-0.049
-1.09
-0.006
-0.09
0.020
0.84
-0.029
-0.62
0.344
1.24
0.899
0.98
-0.008
-0.86
-0.067
-2.06
0.003
0.73
-0.003
-0.15
0.863
0.72
-3.516
-0.88
0.029
0.27
0.265
1.55
-0.006
-0.18
0.017
0.15
0.588
0.82
0.172
0.785
-0.177
1.149
-0.060
0.420
-0.077
0.593
-0.027
0.687
-0.017
0.585
Boldface indicates significance at the .10 level
22
Table A-2 –Estimation Results for Rural Export Sector Employment Change Equation
Variable
N
INTERCEP
DOTHE82
DPBAS90C
D2PBA90C
DAGGLO
DIAUTORO
NOBLU82
SKIWO82
SELFJ82
RCHOM82
VDO7582
DWPOP82
DWPOPH1
DWPOPH2
WVDPOP
WVDPH1
WVDPH2
DWSHO82
DWSHOG1
DWSHOG2
WVDSHO
WVDSG1
WVDSG2
Adj. R-sq
I-Moran: Wc
W30
Wd
Cantons belonging to:
FERs with declining
FERs with declining
urban core
urban core
Parameter
t
Parameter
t
84
107
-0.977
-1.07
-4.734
-2.05
-0.067
-1.09
-0.272
-5.24
0.050
0.75
0.054
0.69
-0.001
-0.77
-0.001
-0.97
0.011
1.03
0.012
2.07
4.7 E-04
0.10
0.006
0.56
0.008
0.69
0.004
0.16
-0.003
-0.98
0.003
0.84
-0.006
-0.77
1.5 E-05
0.00
-0.073
-1.24
0.264
2.28
-0.118
-0.41
-0.408
-1.85
0.064
1.46
0.058
1.68
7.2 E-05
0.04
-7.3 E-04
-0.19
-8.4 E-04
-0.79
-0.003
-1.78
0.281
0.67
0.106
0.18
0.006
0.12
-0.043
-1.02
0.007
0.29
0.015
0.56
-0.206
-1.20
-0.583
-1.08
0.005
0.84
0.015
0.45
-0.002
-0.51
0.016
1.53
-0.385
-0.56
1.151
0.33
-0.085
-1.36
0.083
0.42
0.044
1.98
-0.098
-1.76
0.49
0.25
-0.100
0.401
-0.138
0.765
-0.164
0.951
-0.139
0.956
-0.042
1.006
-0.036
1.043
Boldface indicates significance at the .10 level
23
Table A-3 –Estimation Results for Rural Service Sector Change Equation
Variable
N
INTERCEP
DSHOP82
DPBAS90C
D2PBA90C
DAGGLO
DIAUTORO
DCAPAC80
NOBLU82
SKIWO82
SELFJ82
RCHOM82
VDS7582
DWPOP82
DWPOPH1
DWPOPH2
WVDPOP
WVDPH1
WVDPH2
DWOTH82
DWOTHF1
DWOTHF2
WVDOTH
WVDOF1
WVDOF2
Adj. R-sq
I-Moran: Wc
W30
Wd
Cantons belonging to:
FERs with declining
FERs with growing
urban core
urban core
Parameter
t
Parameter
t
84
107
1.026
1.19
-2.006
-2.12
0.426
3.57
0.210
3.47
-0.038
-0.62
0.044
1.18
4.2 E-04
0.32
-4.3 E-04
-0.74
0.004
0.71
8.9 E-04
0.22
2.2 E-04
0.05
0.006
1.36
-0.031
-2.26
-0.009
-1.81
0.001
0.12
0.003
0.30
-0.003
-0.95
3.6 E-05
0.02
-1.7 E-04
-0.02
0.007
1.35
-0.025
-0.45
0.022
0.51
-0.002
-0.01
-0.367
-2.28
-8.9 E-04
-0.03
-0.002
-0.11
0.001
0.76
8.6 E-04
0.55
0.001
1.01
-0.001
-2.03
0.313
1.29
0.477
1.77
5.1 E-04
0.00
-0.031
-1.69
0.002
0.25
-0.052
-2.10
-0.141
-0.81
0.093
1.76
-0.003
-1.10
0.001
0.49
0.002
1.07
8.5 E-04
0.93
0.419
1.36
-0.971
-2.07
-0.044
-1.44
0.011
0.44
-0.014
-1.19
0.031
1.69
0.61
0.53
0.003
0.014
-0.187
1.126
0.009
0.064
-0.088
0.809
-0.011
0.267
-0.039
1.291
Boldface indicates significance at the .10 level
24