Rural Sociology 75(1), 2010, pp. 58–89
Copyright © 2010, by the Rural Sociological Society
The Impacts of Highway Expansion on Population
Change: An Integrated Spatial Approach*
Guangqing Chi
Department of Sociology and Social Science Research Center
Mississippi State University
Abstract The effects of highways on transforming human society and
promoting population change have been investigated in several disciplines,
including geography, sociology, economics, and planning. Currently, the
primary highway construction activity in the nation is highway expansion;
however, this expansion has not been the focus of much of the existing
literature. This research examines the role that highway expansion plays in
the process of population change. Specifically, this research proposes an
integrated spatial regression approach to study the impacts of highway expansion on population change in the 1980s and 1990s in Wisconsin at the minor
civil division level. The integrated approach thoroughly considers the factors
that influence population change, appropriately examines the spatial variations of their impacts, simultaneously incorporates spatial lag and spatial
error dependence, and systematically selects the optimal neighborhood structure. The findings suggest that the impacts of highway expansion on population change differ across rural, suburban, and urban areas: There are only
indirect effects in rural areas, both direct and indirect effects in suburban
areas, and no statistically significant effects in urban areas. Overall, highway
expansion serves as a facilitator of population change within the framework of
growth pole theory and location theory.
Highways have served an important function in transforming human
society and affecting population change (Baum-Snow 2007; Vandenbroucke 2008). In 1956, the Interstate Highway Act was passed to
advance the development of new highway infrastructure. Through the
years, highway construction has caused fervent protest from environmentalists and local communities. Objectors have disputed highway
projects on the grounds of negative effects resulting from highway construction, such as inequalities in highway access (Deka 2004), spatial
mismatch (Boustan and Margo 2009; Ong and Miller 2005), less fre* Earlier versions of this article were presented at the annual meeting of the Population
Association of America, April 17–19, 2008, New Orleans, Louisiana, and the annual
meeting of the Southern Demographic Association, October 12–13, 2007, Birmingham,
Alabama. I thank Paul R. Voss, John R. Weeks, Marcia C. Castro, Cindy S. Michaels, Paul A.
Gilbert, Michael D. Schulman, and four anonymous reviewers for their many helpful
comments. I thank Balkrishna D. Kale for providing the highway expansion data. The
research was supported in part by grants from the Office of Research and Economic
Development of Mississippi State University, the National Center for Intermodal Transportation (36301–01–00), and the Wisconsin Agricultural Experiment Station (Hatch
project no. WI04536).
Highway Expansion: An Integrated Spatial Approach — Chi
59
quent family interactions (Loeber et al. 2000), and transportationrelated stress (Song et al. 2006). In contrast, most governmental decision
makers have supported highway expansion by arguing that such construction promotes economic growth and development, decreases traffic
congestion, increases travel safety, and assists in the more efficient use of
existing roads (Moore and Thorsnes 1994). Because of the different
views on highway effects, several theories have been presented, each
supported by numerous empirical studies, to explain the effects that
highways have on economic and population change. The widespread
literature regarding this topic is located within several diverse disciplines, including geography, sociology, economics, and urban and
regional planning (Chi, Voss, and Deller 2006).
Presently, most planned highway and interstate systems have been
completed. Current highway construction activities, therefore, primarily
expand or improve existing highways, rather than build new highways.
In 2002, the executive director of the National Academies’ Transportation Research Board said that “[m]uch of the existing highway systems,
particularly interstates and primary arterial highways, must be reconstructed in the coming years” (Skinner 2002:34). Moreover, in 2009 the
administration of President Barack Obama decided to invest heavily in
transportation infrastructure as part of the American Recovery and Reinvestment Act. Therefore, because of the upcoming efforts in improving
and expanding highways as well as the inevitable conflicts between protesters and proponents of highway construction, it is essential to comprehend the impacts of highway expansion on population change as well
as economic growth and development.
While researchers in several disciplines have studied highway effects,
they have rarely addressed the impacts of highway expansion. This study,
therefore, will contribute to the body of literature on understanding the
function that highway expansion1 serves in affecting population change
by revisiting and advancing the Voss and Chi (2006) study, which was
probably the first to concentrate on highway expansion rather than
newly built highways. Specifically, this research has two main goals. One
is to develop an integrated spatial regression approach and apply it to
investigate the impacts of highway expansion on population change
from 1980 to 2000 at the minor civil-division level in Wisconsin. The
other is to discuss the role that highway expansion plays in affecting
population change within the regional economic theoretical framework
of growth pole theory and location theory.
1
In this study, “highway expansion” refers to travel lanes added to existing highway
segments, for example, expanding a highway from two lanes to four or more lanes.
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This article has five sections. The first section reviews highway impacts
on population change and examines the Voss and Chi (2006) study. The
article then proposes an integrated spatial approach to investigate
highway expansion impacts on population change. The next section
introduces data, variables, and methods. The findings section reports
the impacts of highway expansion on population change. Finally, the
article concludes with a summary and discussion section.
Prior Research
Highways
It is essential to recognize the impacts of highway expansion on population change and economic growth and development because current
highway construction activities primarily expand or improve existing
highways rather than build new highways (Skinner 2002). However,
existing literature has rarely addressed the impacts of highway expansion. Reviewing the sizable literature on the impacts of highways provides a preliminary understanding of the impacts of highway expansion,
which can assist in designing an effective research procedure for investigating those impacts. A vast literature pool, distributed across several
disciplines, as noted earlier, has resulted in a multifaceted mixture of
theoretical and empirical approaches to describing the effects that highways have on population change. This literature suggests several definitions and explanations of the function that highways serve in affecting
that change. Two regional economic theories robustly describe the
effects of highways on population and economic growth: growth pole
theory and location theory.
Although there is no consensus on a single definition of growth pole
theory, from the perspective of urban form and population redistribution, it refers to a “growth pole” (usually an urban location that has
benefited from economies of scales and agglomeration) interacting with
its surrounding areas to rebalance growth and development among them
(Darwent 1969). The theory uses the ideas of spread (growth in one
location results in growth in surrounding areas) and backwash (growth
in one location results in decline in surrounding areas) to explain the
mutual geographic dependence of metropolitan areas and their surrounding rural areas in regard to economic growth and development,
which in turn causes population change (e.g., Henry, Barkley, and Bao
1997). The more dependent the growth is on highways, the stronger the
effects of spread and backwash will be. However, an improved highway is
merely a catalyst of change and is neither necessary nor sufficient for
causing population growth or decline on its own (Thiel 1962).
Highway Expansion: An Integrated Spatial Approach — Chi
61
Location theory argues that firms determine their business locations
by anticipated revenue and input and transportation costs. In order to
achieve profit maximization, firms will choose locations for cost minimization and/or demand maximization. Scholars use various location
theories (including firm-location theory, firm-migration theory, and
central-place theory) to interpret the role of highways in three ways.
First, transportation can be understood as a means of importing inputs
into and exporting outputs out of a location and is therefore seen as a
factor of production itself (Vickerman 1991). According to this view,
highway infrastructure is an important factor in firms determining their
locations. For example, firm-location theory helps explain choice of
manufacturing locations where firms seek profit maximization by minimizing the transportation costs for raw materials and retail locations
where firms achieve profit maximization by minimizing the transportation costs for consumers to purchase their goods. Second, location
theory understands highway infrastructure as a necessary but insufficient
means for local economic growth and development, and considers other
factors such as a quality labor pool equally important (Halstead and
Deller 1997).
Third, transportation infrastructure is a facilitator for flows of raw
materials, capital, finished goods, consumers, and ideas among central
places and their neighborhoods as well as a barrier to these flows, as
transportation takes time and money (Thompson and Bawden 1992).
Within this context, Thompson and Bawden (1992) suggested four theoretical tendencies to guide empirical examination of highway impacts on
economic development and population change. One, transportation
improvements increase commutable distance, transforming dispersed,
small central places to fewer but larger central places with more concentrated products and services. Two, because highway improvements generally occur along heavily traveled existing routes and routes linking
cities, improvement initially tends to increase unequal access to highways
between cities and their surrounding areas. Three, when highway
improvements bring surrounding areas closer to the center of activities,
the surrounding areas benefit more than the central cities because the
central cities’ markets therefore become enlarged. Four, previously isolated places can develop specialized production rather than produce all
goods required by their population at the commutable distance.
Although these two regional economic theories acknowledge that
highways play an important role in population and economic growth,
they define the role of highways differently. These differences may be
due to the limited examination and understanding of highway effects.
Many existing studies do not sufficiently control for other influential
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factors of economic and population growth and do not appropriately
take into account the spatial dynamics of highway effects. In response,
Voss and Chi (2006) used spatial regression models to examine the
effects of highway expansion on population change.
Highway Expansion: Findings of Voss and Chi
Voss and Chi (2006), reporting in one of the very few studies focusing on
highway expansion, examined the effects of highway expansion completed between 1970 and 1990 on population change from 1980 to 2000
at the minor civil-division (MCD) level in Wisconsin.2 Their hypothesis
was that highway expansion has positive effects on population change.
However, their findings did not uniformly support that hypothesis—
highway expansion had positive effects on population change from 1990
to 2000 but negative effects on population change from 1980 to 1990.
They explained briefly that the effects of highway expansion on population change depended upon the overall state population change (4
percent growth in the 1980s and 9.6 percent growth in the 1990s) and
that highway expansion served as a catalyst of change to support the
broader demographic trends. Although this explanation seems reasonable, they utilized no theories to support their arguments. This study
aims to expand the findings of the Voss and Chi (2006) study by discussing the role that highway expansion plays in affecting population change
within the regional economic theoretical framework of growth pole
theory and location theory.
Voss and Chi (2006) applied two spatial regression models to examine
the effects of highway expansion. The first, a spatial-lag model, considered population change in neighboring MCDs. The second, a spatialerror model, controlled influential factors that could have strong effects
on population change but are not considered in the model. The two
models improved the model fitting to data and reduced spatial dependence in residuals. Nevertheless, the two spatial models did not appre2
Previous studies indicate that the effects of highways on population redistribution
differ widely across different scales, ranging from communities to municipalities to counties to regions, as a result of the scale effect of the modifiable areal unit problem (for a
summary of the literature, see Chi et al. 2006). The MCD is an appropriate scale to match
the population-highway dynamics in Wisconsin because Wisconsin is a “strong MCD” state
and its MCDs are functioning governmental units (with towns, cities, and villages that have
elected officials who provide services and raise revenues). The MCD geography comprises
nonnested, mutually exclusive, and extensive political territories. The primary advantage
of using MCDs is their relevance to public policy making and planning. In most parts of the
state, census tracts have an average size similar to MCDs and provide an alternative unit of
analysis. However, census tracts are geographic units delineated by the Census Bureau only
for counting population purposes; they have no political or social meanings.
Highway Expansion: An Integrated Spatial Approach — Chi
63
ciably change the coefficient estimates or significance from the original
ordinary least squares model. It is unclear whether the models were
appropriately specified and whether Voss and Chi adequately controlled
the spatial process effects. To improve the understanding gained from
these findings, this study develops an integrated spatial approach (see
the next section) and applies it to revisit the impacts of highway expansion on population change reported in the Voss and Chi study.
An Integrated Spatial Approach
Building upon Voss and Chi (2006), this article presents an integrated
spatial approach to reanalyze the impacts of highway expansion on
population change. Specifically, the integrated spatial approach
includes four components: (1) thorough examination of the determinants of population change, (2) simultaneous incorporation of both
spatial lag and spatial error dependence into one spatial regression
model, (3) appropriate consideration of the spatial variations of the
impacts, and (4) systematic selection of the optimal spatial weight
matrix.
Thorough Examination of the Determinants of Population Change
Voss and Chi (2006) controlled 27 variables3 in an examination of the
impacts of highway expansion on population change. However, most of
the control variables are demographic and socioeconomic by nature.
Many factors from other disciplines that are not typically considered in
formal demographic research can also have significant effects on population change (Chi 2009). These factors fall within the broad realms of
transportation accessibility, environmental and natural-resource characteristics, and land use and development.
Transportation accessibility is important for population redistribution
as well as for local economic growth and development. Pertinent accessibility factors include residential preference, accessibility to airports and
highways, highway infrastructure, and commuting to work (Fuguitt and
Brown 1990; Fuguitt and Zuiches 1975; Humphrey 1980; Voss and Chi
2006; Zuiches and Rieger 1978).
3
Many of these variables are conceptualized more as the explanatory variables of migration than as the variables of population change. I treat these variables as explanatory
variables of population change because migration constitutes a larger fraction of population change at the MCD level than do fertility and mortality. For this reason, this article
uses the terms population change, population growth, migration, and population redistribution
interchangeably.
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The environmental and natural-resource characteristics of an area
also influence population redistribution. Landfills and other noxious
sites, the level and type of resource extraction, and the propensity for
natural disasters are some of the main variables that influence population redistribution. In recent decades, natural-resource characteristics
such as water features, terrain relief (e.g., viewsheds), and landscape
aesthetics (e.g., regional land use and cover) have been recognized as
shaping population growth primarily through the role of natural amenities; rural sociologists and regional scientists consider them one of the
key sources of nonmetropolitan population growth (Brown et al. 1997;
English, Marcouiller, and Cordell 2000; Fuguitt and Brown 1990;
Fuguitt, Brown, and Beale 1989; Johnson 1999; Johnson and Beale 1994;
Johnson and Purdy 1980; Marcouiller 1997).
Land use and development is another factor that affects population
change but is often not included in the typical demographic research.
This potential of land use and development has an effect because it
limits population redistribution. The geophysical characteristics (slope,
water, and wetland), built-up lands (existing residential, commercial,
and industrial developments as well as existing transportation infrastructure), cultural and aesthetic resources, and legal constraints (e.g.,
programs such as comprehensive plans, “smart growth” laws, zoning
ordinances, farmland protection programs, land-use planning legislation, and environmental regulations such as the Clean Water Act, shoreland and wetland zoning) of a region determine the capability to
develop the land (Cardille, Ventura, and Turner 2001; Cowen and
Jensen 1998; Lewis 1996).4
These transportation, natural-resource, and land-development variables should be controlled along with the demographic and socioeconomic variables in examining the effects of highway expansion on
population change, because the results could vary when significant variables are not considered in the model. Therefore, this study adds these
variables to the previous data collected by Voss and Chi (2006) in order
to provide a more thorough understanding of population change.
Simultaneous Incorporation of Both Spatial Lag and Spatial
Error Dependence
Scholars often find patterns of spatial dependence in the causes of
population change and explain these patterns by regional economic
4
Cultural resources and legal constraints are not considered in this study due to data
unavailability and modeling complexity. Future studies can address this issue as well as the
endogeneity among policy instruments, population change, and highway expansion. This
study treats these variables as exogenous to population change.
Highway Expansion: An Integrated Spatial Approach — Chi
65
theories, population geography theories, and the results of residential
preference studies. For example, in studies of residential preference,
rural demographers find that migrants favor locations that are somewhat
rural or truly suburban within commutable distances of large cities
(Brown et al. 1997; Fuguitt and Brown 1990; Fuguitt and Zuiches 1975).
Migratory factors such as improved quality of life and increased employment opportunities not only draw migrants to relocate to an area but
also draw migrants to relocate to neighboring areas because the convenience provided by transportation infrastructure affords migrants the
flexibility to select their residential locations. The influence of highways
as well as other determinants of population change demonstrates spatial
dependence, which should be controlled in empirical models of population change. In spatial econometric terms, spatial lag and spatial error
dependence are the two most frequently referenced forms of spatial
dependence. Within the context of this study, spatial lag dependence
exists when population change in a MCD is affected by those in its
neighboring MCDs. Spatial error dependence exists when model residuals are spatially correlated.
As a contribution to the literature, Voss and Chi (2006) controlled
these two forms of spatial dependence in studying the impacts of
highway expansion on population growth. Their results suggest that
both captured spatial lag and spatial error dependence are significant in
affecting population change. However, the two forms of spatial dependence were considered separately in two spatial regression models, similarly to many other studies that account for spatial dependence. To
improve their models, this study simultaneously considers spatial lag and
spatial error dependence, which is practically achieved in a spatial error
model with lag dependence.5
Appropriate Consideration of the Spatial Variations of the Impacts
Besides spatial dependence, spatial heterogeneity is another type of
spatial process effect. Spatial heterogeneity refers to variations in coefficients over space (LeSage 1999). The effects of highway expansion
on population change might exhibit spatial heterogeneity. Previous
research suggests that highway effects on population change vary across
5
A simultaneous estimation of spatial lag and spatial error dependence can be best
achieved through a spatially autoregressive moving average (SARMA) model (Anselin and
Bera 1998) or through a combination of instrumental variables and a general method of
moments (Kelejian and Prucha 1998, 1999). However, these two methods could not be
employed in this study because current software packages are incapable of embodying
them within a spatial regime context to consider spatial heterogeneity, which the following
section addresses.
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rural, suburban, and urban areas (for a detailed discussion, see Chi et al.
2006). Most studies on rural counties conclude that highways have positive effects on population growth by fostering employment growth and
attracting migrants (Humphrey and Sell 1975; Lichter and Fuguitt
1980). However, a convenient highway can also entice rural residents to
travel to urban areas for employment prospects and urban amenities—a
backwash or negative spillover effect (Boarnet 1997). In suburban areas,
enhanced or newly built highways generally have positive effects on
economic and population growth (Moore et al. 1964). Improved highways more easily connect urban and suburban areas and strengthen the
process of suburbanization. Highway effects in urban areas are more
complex because new or improved highways can either assist or thwart
the development of urban areas depending on numerous other factors
and the net effects of spread and backwash (Boarnet 1998, 1999).
Thus, the spatial variations across rural, suburban, and urban areas
should be considered in examining the effects of highway expansion on
population change. This study applies a spatial regime model to deal
with the spatial heterogeneity issue (Anselin 1990; Patton and McErlean
2003).6 It assumes three regimes exist—one for rural areas, one for
suburban areas, and one for urban areas. The model estimates coefficients separately for each regime. The spatial Chow test diagnoses coefficient stability for each variable and the overall structural stability.
Ideally, we would prefer a model specification of spatial heterogeneity
that simultaneously considers spatial lag and spatial error dependence.
However, to my knowledge, such an ideal model specification has not yet
been developed within existing statistical software packages. Despite
that, the spatial regimes can be practically encompassed within a spatial
error model with lag dependence. This model still estimates coefficients
separately for each regime. It also estimates spatial lag effects separately
for each regime and a spatial error effect for the overall model.
Systematic Selection of the Optimal Spatial Weight Matrix
Selecting an optimal spatial weight matrix for each model is another
possible improvement to the research of Voss and Chi (2006) because
6
Alternatively, the spatial heterogeneity issue can be dealt with by using the geographically weighted regression (GWR) method (e.g., Ali, Partridge, and Olfert 2007) or by
partitioning the study area into several regions that exhibit different spatial patterns (e.g.,
Baller and Richardson 2002). While GWR provides an elegant means of modeling the
spatially varying coefficients, it does not consider the spatial lag and spatial error dependence in the spatial econometric context, making comparison to previous studies difficult.
Partitioning data is often used to deal with spatial heterogeneity in sociological studies.
However, it imposes practical difficulty in controlling spatial dependence because neither
the urban nor the suburban areas are contiguous.
Highway Expansion: An Integrated Spatial Approach — Chi
67
doing so helps capture the maximum spatial dependence within each
model. To account for spatial dependence in spatial regression models,
it is essential to create a neighborhood structure for each location by
specifying its neighboring locations on a lattice (Anselin 1988). Specifically, we need to designate a spatial weight matrix corresponding to the
neighborhood structure such that the resulting variance-covariance
matrix can be expressed as a function of a small number of estimable
parameters relative to the sample size (Anselin 2002). While a spatial
weight matrix is needed for spatial regression modeling, its selection
usually receives little theoretical guidance in practice—many studies
select a spatial weight matrix without sound justification or evaluation. A
spatial weight matrix often is defined exogenously, and comparison of
several matrices should be done before choosing a justifiable one. For
example, we can develop and compare several spatial weight matrices
and choose the one that achieves a high coefficient of spatial autocorrelation in combination with a high level of statistical significance,
although currently there is little theoretical support for this method
(Chi and Zhu 2008).
Voss and Chi (2006) compared 40 different spatial weight matrices7
and selected one to apply to all models. Although their approach is
methodologically sound, the approach could be improved by selecting
an optimal weight matrix separately for each model. The selected weight
matrix should maximally capture the spatial dependence in its corresponding model. In this study, I used 40 different spatial weight matrices
to examine and test the magnitudes and significances of spatial dependence (measured by Moran’s I) independently for each model. The
optimal weight matrix is the one that achieves the highest spatial dependence in combination with a high level of statistical significance. I chose
the weight matrix that encompasses the highest spatial dependence of
population change for running the spatial lag model. I chose the weight
matrix that captures the highest spatial dependence of the standard
regression residuals for running the spatial error model. The spatial
error model with lag dependence needs two spatial weight matrices, one
for the spatial lag dependence and the other for the spatial error dependence. The selection of the former is based on the spatial dependence
of population change; the selection of the latter is based on that of the
residuals after fitting a spatial lag model. In addition, a z-score (the test
7
The spatial weight matrices include the rook’s case and queen’s case contiguity weight
matrices with order 1 and order 2; the k-nearest neighbor weight matrices, with k ranging
from 3 to 8 neighbors; and the general-distance weight matrices and the inverse-distance
weight matrices with power 1 or power 2, from 0 to 100 miles at 10-mile increments based
on the distance between the centroids of MCDs.
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Rural Sociology, Vol. 75, No. 1, March 2010
for the significance of the Moran’s I statistic) is computed as the ratio of
Moran’s I and the corresponding standard error. The p values are calculated using a normal approximation.
In summary, the determinants of population change and their spatial
relationships are often not managed properly or adequately in modeling
highway effects on population change. When the model omits pertinent
factors and spatial interactions between population change and influential factors, the potential outcomes vary (Dalenberg and Partridge 1997).
This study takes an integrated spatial approach that carefully considers
the spatial effects and thoroughly includes the influential factors of
population change to understand the impacts of highway expansion
on population change.
Data, Variables, and Methods
The study applies the proposed integrated spatial approach to revisit the
Voss and Chi (2006) data. Specifically, it investigates the impacts of
highway expansion from 1970 to 1990 (Figure 1) on population change
from 1980 to 2000 (Figure 2) at the MCD level in Wisconsin. The population redistribution process demonstrated diverse patterns in the two
decades: “metropolitan growth” in the 1980s and “rural rebound” in the
1990s (Johnson 1999). This research examines the impacts of highway
expansion separately in two sets of models: The first set evaluates the
effects of highway expansion completed in 1970–1975 and 1975–1980 on
population change in 1980–1990, while the second set evaluates the
effects of highway expansion concluded in 1980–1985 and 1985–1990 on
population change in 1990–2000. Comparing the two different redistribution processes can test the consistency of the effects and provide a
more complete understanding of the effects.
The representation of the response and explanatory variables follows
the depiction specified in Voss and Chi (2006). In both sets of models,
the response variable is a rate of population change, expressed as the
natural log of the ratio of one census population over the previous
census population (Figure 2).8 The explanatory variables are four
dummy variables indicating MCDs within 10 miles of highway expansions finished 5–9 years before the population change period, at a range
of 10–20 miles from highway expansions finished 5–9 years before the
population change period, within 10 miles of highway expansions fin8
The measure of population change does not include natural-amenity-related seasonal
migrants. The census determines population size on April 1 in each decennial year. Most
natural-amenity-related seasonal migrants come to their seasonal houses in Wisconsin
(particularly in northern Wisconsin) for summer vacation.
Highway Expansion: An Integrated Spatial Approach — Chi
Figure 1.
69
Highways and Expansion Segments from 1970 to 1990 in Wisconsin
ished 0–4 years before the population change period, and at a range of
10–20 miles from highway expansions finished 0–4 years before the
population change period (Figure 3). If an MCD fits into a distance
buffer category, it is coded as 1; otherwise it is 0. Voss and Chi (2006)
observed that highway expansion does not have statistically significant
effects on population change by the measure of distance and inverse
distance to highway expansion segments. The dummy representation
better measures the effects of highway expansion on population change
than the distance representation. The data are provided by the Wisconsin Department of Transportation.
The control variables include most of those from Voss and Chi
(2006). This study also includes additional control variables relating to
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Figure 2.
Population Change from 1980 to 2000 at the MCD Level in Wisconsin
transportation accessibility, natural amenities, and land use and development. Table 1 lists these variables as well as their descriptions and data
sources.
The large number of variables could cause a multicollinearity
problem and would not be easily compared across the two sets of models.
This study therefore uses a pooled regression approach to obtain a small
number of variables for both sets of models. Specifically, I first fitted the
pooled data combining the two sets of variables into an ordinary least
squares (OLS) regression model. I excluded insignificant control variables from the regression model until all retained variables are significant and not strongly correlated. The refined regression model contains
4 highway expansion variables and 12 control variables, which are then
used to run two sets of models separately. Both sets of models include six
models: a reduced OLS model with only highway expansion variables
(Equation 1), a full OLS model controlling other influential factors of
population change (Equation 1), a spatial lag model (SLM; Equation 2),
a spatial error model (SEM; Equation 3), a spatial error model with
lag dependence (SEMLD; Equation 4), and a spatial regime SEMLD
(Equation 5).
Pt +10 ⎞
= Xt β + ε
OLS: Ln ⎛⎜
⎝ Pt ⎟⎠
(1)
Pt +10 ⎞
Pt +10 ⎞
= X t β + ρW1Ln ⎛⎜
+ε
SLM: Ln ⎛⎜
⎝ Pt ⎟⎠
⎝ Pt ⎟⎠
(2)
Highway Expansion: An Integrated Spatial Approach — Chi
Figure 3.
71
Wisconsin MCDs within 10 Miles and 10–20 Miles of Highway Expansions
Pt +10 ⎞
= X t β + ε , ε = λW 2 ε + ξ
SEM: Ln ⎛⎜
⎝ Pt ⎟⎠
(3)
Pt +10 ⎞
Pt +10 ⎞
= X t β + ρW1Ln ⎛⎜
+ ε , ε = λW 2 ε + ξ
SEMLD: Ln ⎛⎜
⎝ Pt ⎟⎠
⎝ Pt ⎟⎠
(4)
Pt +10 ⎞
Pt +10 ⎞
= X it βi + ρiW1Ln ⎜⎛
+ ε,
Spatial regime SEMLD: Ln ⎛⎜
⎝ Pt ⎟⎠ i
⎝ Pt ⎟⎠ i
ε = λW 2 ε + ξ
(5)
Proximity to highways
House value
Public water
New housing
Seasonal housing
Retail
Agriculture
County seat
Transportation accessibility
Proximity to central cities
Proximity to airports
Unemployment
Income
School performance
Population density
Young
Old
Blacks
Hispanics
College population
High school degree
Bachelor’s degree
Female-headed families
Socioeconomic conditions
Crime
Demographic characteristics
Previous population change
Variables
Inverse distance to interstate highways
Inverse distance to nearest central city
Inverse distance to nearest major airport
Median house value
Percent housing units using public water
Percent new housing units (ⱕ40 years old)
Percent seasonal housing units
Percent workers in retail industry
Percent workers in agricultural industry
County seat status (dummy variable)
Number of crimes (murder, rape, robbery, assault, burglary, larceny,
motor vehicle theft, and arson) per 100,000 persons at the county
level
Unemployment rate
Median household income
Average American College Test (ACT) score
Number of persons per square kilometer
Percent young population (ages 12–18)
Percent old population (ages ⱖ65)
Percent blacks
Percent Hispanics
Percent college population
Percent population (age ⱖ25) with high school degrees
Percent population (age ⱖ25) with bachelor’s degree
Percent female-headed families with children under 18 years old
Population change rate in the previous decade
Descriptions
Table 1. Variable Descriptions and Data Sources
Census Urban Areas 1990
Wisconsin Department of Transportation and
National Atlas of the U.S.
National Atlas of the U.S.
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
School Performance Report (SPR) series from
Wisconsin Department of Public Instruction
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
State of Wisconsin Blue Book 2001–2002
Federal Bureau of Investigation’s Uniform Crime
Reports
Census 1990 STF3, census 1980 STF3A, and the
Demographic Service Center of Wisconsin
Department of Administration
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Data Sources
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Rural Sociology, Vol. 75, No. 1, March 2010
USGS 1 : 100,000 Hydro Digital Line Graphs
Wisconsin Wetlands Inventory
Digital Elevation Model data of USGS
ArcIMS servers: http://maps.dnr.state.wi.us and
http://maps.botany.wisc.edu
1992–93 Landsat Thematic Mapper Imagery
Digital Elevation Model data of USGS
1992–93 Landsat Thematic Mapper Imagery
a
The study used the five land-development variables to generate land developability, an index that refers to the potential for land conversion and development
developed by the ModelBuilder (®ESRI) function of ArcGIS. The general idea is to identify undevelopable lands (water, wetlands, slopes, tax-exempt lands, and
built-up lands) at the pixel level and create one layer representing undevelopable lands for Wisconsin. This layer is then intersected with a geographic MCD layer
to create a layer that contains the information for undevelopable lands at the MCD level. Using that layer, the study calculated the proportion of undevelopable land
for each MCD and generated the developability index by subtracting the proportion of undevelopable land from 1.
Built-up lands
Viewsheds
Land developmenta
Water
Wetlands
Slopes
Tax-exempt lands
Water coverage
Emergent/wet meadow, lowland shrub, and forested wetland
Areas with slope ⱖ20%
Federal and state forests and parks, trails, wildlife refuges, and fishery
areas
Existing residential, commercial, and industrial developments, as well
as transportation infrastructure
Total lengths of lakeshore, riverbank, and coastline divided by square
root of MCD’s area
Proportion of golf course areas divided by distance from MCD’s
centroid to nearest golf course’s centroid
Percent areas with slopes between 12.5% and 20%
Watersheds
Golf courses
Percent water areas
ArcIMS servers: http://maps.dnr.state.wi.us and
http://maps.botany.wisc.edu
U.S. Geological Survey (USGS) 1 : 100,000 Hydro
Digital Line Graphs
USGS 1:100,000 Hydro Digital Line Graphs
Percent forestry areas
Data Sources
National Atlas of the U.S.
State of Wisconsin Blue Books 1979–80 and 1989–90
Census 1990 STF3 and census 1980 STF3A
Census 1990 STF3 and census 1980 STF3A
Descriptions
Total lengths of major roads divided by square root of MCD’s area
Having urban buses or not (dummy variable)
Percent workers using public transportation to work
Percent workers traveling >30 minutes to work
Water
Highway density
Buses
Commute mode to work
Commute time to work
Natural amenities
Forest
Variables
Table 1. Continued
Highway Expansion: An Integrated Spatial Approach — Chi
73
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Rural Sociology, Vol. 75, No. 1, March 2010
where Pt+10 is population in year t+10, Pt is population in year t, Xt is
matrix of independent and control variables in year t, b is a vector of
coefficients of Xt, r is a spatial-lag parameter, l is a spatial error parameter, i refers to different regimes, W1 is a spatial weight matrix for the lag
term, and W2 is a spatial weight matrix for the error term.
The first two OLS models reveal the importance of synthetically considering the influential factors of population change. The following
three spatial regression models encompass spatial dependence, and the
last model considers both spatial dependence and spatial heterogeneity.
I assessed and compared these different models using Akaike’s Information Criterion (AIC) and Schwartz’s Bayesian Information Criterion
(BIC).
Findings
Direct and Indirect Impacts
Highway expansion segments completed from 1975 to 1980 have significant positive impacts on population change from 1980 to 1990 when
only population change in the previous decade is controlled (reduced
OLS; the first column of Table 2). However, the significant effects disappear after the full OLS model controls other influential factors of
population change (the second column of Table 2), which offers a
better model fitting balanced with model parsimony. The better fitting
suggests that it is essential to control other influential factors in examining the effects of highway expansion on population change. Among
the three spatial regression models, the SEMLD is the most appropriate
model to interpret the regression coefficients, judging from the AIC and
BIC values. Highway expansion segments completed from 1975 to 1980
cause slightly statistically significant negative effects on population
change for MCDs within 10 miles of the expansion segments (SEMLD;
the last column of Table 2). The 1980s was the decade with the slowest
growth in the history of Wisconsin; the highway expansion in 1975–1980
may have contributed to this by facilitating population outflows. Population growth rate in the previous decade has positive effects on
population growth, and 1 percent growth in the previous decade contributes 0.104 percent growth. MCDs that underwent rapid growth in the
1970s tended to keep growth into the 1980s.
Both spatial lag and spatial error effects explain population change
significantly. The spatial lag effects come from the spatially lagged population change. Each MCD gains 0.711 percent for each percentage point
of weighted population growth in its neighbors. For the 1980–1990
models, I specified that each MCD has five neighboring MCDs. If each of
—
—
—
—
—
—
—
Bachelor’s degrees in 1980
Female-headed families in 1980
Unemployment in 1980
Income in 1980
Public water in 1980
Seasonal housing in 1980
Agriculture in 1980
0.147***
(0.015)
-2.72e-5
(1.63e-5)
-0.819***
(0.118)
0.042
(0.052)
-0.003
(0.068)
-0.143*
(0.066)
3.43e-6***
(9.51e-7)
0.027*
(0.011)
0.059*
(0.023)
-0.091***
(0.026)
0.196***
(0.014)
—
—
0.012
(0.009)
0.005
(0.008)
-0.009
(0.010)
0.016
(0.009)
Full OLS
0.010
(0.009)
0.002
(0.009)
0.024**
(0.008)
0.033***
(0.009)
Reduced OLS
Young in 1980
Population density in 1980
Explanatory variables
Within 10 miles of highway expansion, finished 5–9
years before population change period
At range of 10–20 miles from highway expansion,
finished 5–9 years before population change period
Within 10 miles of highway expansion, finished
0–4 years before population change period
At range of 10–20 miles of highway expansion,
finished 0–4 years before population change period
Control variables
Population growth rate from 1970 to 1980
0.129***
(0.015)
-2.37e-5
(1.59e-5)
-0.814***
(0.115)
0.024
(0.051)
-0.029
(0.066)
-0.126*
(0.064)
2.89e-6**
(9.31e-7)
0.033**
(0.011)
0.055*
(0.023)
-0.069**
(0.026)
0.009
(0.009)
0.004
(0.007)
-0.010
(0.010)
0.011
(0.009)
SLM
Table 2. Regressions of Highway Expansion on Population Change from 1980 to 1990
0.127***
(0.015)
-2.24e-5
(1.70e-5)
-0.855***
(0.118)
0.038
(0.055)
-0.019
(0.067)
-0.135*
(0.067)
3.13e-6**
(9.97e-7)
0.031**
(0.011)
0.073**
(0.024)
-0.071**
(0.027)
0.012
(0.011)
0.003
(0.009)
-0.005
(0.012)
0.017
(0.010)
SEM
0.104***
(0.013)
-1.87e-5
(1.28e-5)
-0.660***
(0.099)
0.004
(0.040)
-0.072
(0.059)
-0.079
(0.053)
1.58e-6*
(7.53e-7)
0.036***
(0.010)
0.006
(0.018)
-0.041
(0.022)
0.003
(0.006)
0.001
(0.005)
-0.014*
(0.007)
0.003
(0.006)
SEMLD
Highway Expansion: An Integrated Spatial Approach — Chi
75
-2737.98
-2704.89
—
-2872.76
-2778.99
—
0.019
(0.020)
0.024
(0.017)
0.038
(0.027)
—
Full OLS
SLM
-2921.13
-2821.84
0.010
(0.020)
0.025
(0.016)
0.046
(0.026)
0.231***
(0.032)
—
SEM
-2921.43
-2827.66
0.213***
(0.035)
0.011
(0.021)
0.026
(0.018)
0.046
(0.028)
—
SEMLD
-3170.72
-3071.43
-0.006
(0.015)
0.018
(0.013)
0.055**
(0.021)
0.711***
(0.031)
-0.494***
(0.032)
Notes: The study used the 5-nearest neighbor weight matrix in the spatial lag model, the spatial error model, and the spatial lag effects of the
SEMLD. It chose the squared inverse distance (distance decay) within 10 miles weight matrix to control for the spatial error effects of the SEMLD.
AIC = Akaike’s Information Criterion. BIC = Schwartz’s Bayesian Information Criterion.
* Significant at p ⱕ .05 for a two-tail test; ** significant at p ⱕ .01 for a two-tail test; *** significant at p ⱕ .001 for a two-tail test; standard errors
in parentheses.
Measures of fit
AIC
BIC
Spatial error effects
Spatial lag effects
-0.024***
(0.004)
—
—
Land developability
Constant
—
Reduced OLS
Commute time to work in 1980
Table 2. Continued
76
Rural Sociology, Vol. 75, No. 1, March 2010
Highway Expansion: An Integrated Spatial Approach — Chi
77
the five neighbors gains 10 percent population growth, the spatial lag
effects contribute 7.11 percent population growth to the MCD. The 7.11
percent growth does not originate from “organic” growth but evolves as
a “gift” from its neighbors. The spatial lag effects can be understood
somewhat as an indirect effect of highway expansion on population
growth.9 Expanded highways provide improved accessibility to connect
the MCDs together. Improved transportation infrastructure provides
people additional autonomy in choosing their residency MCDs. When
population growth in an MCD’s neighbors leads to an increase in
housing prices, this drives some residents from neighboring MCDs to the
MCD where housing prices are lower until they reach equilibrium. In
contrast, when population decline in an MCD’s neighbors leads to a
decrease in housing prices, this attracts some residents of the MCD to its
neighboring MCDs until they reach equilibrium. Thus, highway expansion is best regarded as a facilitator in strengthening the spatial lag
effects of population redistribution. The significant spatial error effects
may be caused by not including some important explanatory variables in
the model. The inclusion of the spatial error effects assists in controlling
those variables.
In the 1990–2000 models, highway expansion segments completed
from 1985 to 1990 reflect significant positive impacts on population
change from 1990 to 2000 regardless of whether the other influential
factors of population change are controlled (Table 3). These two
explanatory variables remain significant in the spatial lag model and
spatial error model, although the regression coefficients and significances are lesser in magnitude. However, none of the four highway
expansion variables are significant in the SEMLD, which is the most
appropriate model to interpret the regression coefficients.
Population change in the previous decade has positive effects on
population change from 1990 to 2000, and each percentage point of
growth in the previous decade contributes 0.160 percent growth. Both
spatial lag and spatial error effects are significant in explaining population change. Each MCD gains 0.551 percent growth for each percentage
point of weighted population growth in its neighboring MCDs. Highway
expansion, through its role as a facilitator, influences population
9
A broad definition of the indirect effects should consider not only the spatial lag effects
of population change but also the spatial error effects (i.e., the indirect effects of all
independent and control variables on population change) as well as other indirect effects
of highway expansion on population change. However, the broad definition is not straightforward for discussing the effects of highway expansion on population change and may
cause confusion. Instead, I used a narrow definition of the indirect effects and referred it
only to the spatial lag effects. The narrow definition helps us discuss highway expansion as
a facilitator of population change.
—
—
—
—
—
—
—
Bachelor’s degrees in 1990
Female-headed families in 1990
Unemployment in 1990
Income in 1990
Public water in 1990
Seasonal housing in 1990
Agriculture in 1990
0.206***
(0.028)
-7.26e-5***
(1.98e-5)
-0.174
(0.126)
-0.097
(0.059)
-0.054
(0.151)
-0.169
(0.100)
2.90e-6***
(6.15e-7)
0.071***
(0.014)
0.240***
(0.024)
-0.155***
(0.038)
0.311***
(0.028)
—
—
0.002
(0.012)
-0.010
(0.009)
0.034**
(0.011)
0.033***
(0.008)
Full OLS
-0.008
(0.013)
-0.009
(0.011)
0.042***
(0.010)
0.041***
(0.008)
Reduced OLS
Young in 1990
Population density in 1990
Explanatory variables
Within 10 miles of highway expansion, finished 5–9
years before population change period
At range of 10–20 miles from highway expansion,
finished 5–9 years before population change period
Within 10 miles of highway expansion, finished
0–4 years before population change period
At range of 10–20 miles of highway expansion,
finished 0–4 years before population change period
Control variables
Population growth rate from 1980 to 1990
0.176***
(0.028)
-6.34e-5***
(1.95e-5)
-0.153
(0.124)
-0.087
(0.058)
-0.102
(0.148)
-0.161
(0.098)
2.55e-6***
(6.06e-7)
0.070***
(0.014)
0.221***
(0.023)
-0.127***
(0.037)
0.003
(0.012)
-0.010
(0.009)
0.026*
(0.011)
0.027***
(0.008)
SLM
Table 3. Regressions of Highway Expansion on Population Change from 1990 to 2000
0.164***
(0.028)
-6.04e-5**
(2.06e-5)
-0.115
(0.124)
-0.082
(0.062)
-0.095
(0.148)
-0.156
(0.101)
2.49e-6***
(6.47e-7)
0.068***
(0.014)
0.244***
(0.025)
-0.145***
(0.039)
0.001
(0.014)
-0.012
(0.010)
0.037**
(0.012)
0.037***
(0.009)
SEM
0.160***
(0.027)
-6.24e-5***
(1.69e-5)
-0.241*
(0.118)
-0.076
(0.048)
-0.155
(0.141)
-0.159
(0.089)
1.99e-6***
(5.24e-7)
0.062***
(0.013)
0.147***
(0.020)
-0.080*
(0.033)
0.003
(0.009)
-0.007
(0.007)
0.012
(0.008)
0.011
(0.006)
SEMLD
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Rural Sociology, Vol. 75, No. 1, March 2010
-1854.96
-1821.86
—
-2059.58
-1965.81
—
0.098***
(0.029)
0.079***
(0.020)
-0.057
(0.031)
—
Full OLS
SLM
-2094.47
-1995.18
0.083**
(0.028)
0.075***
(0.020)
-0.060*
(0.030)
0.185***
(0.030)
—
SEM
-2101.15
-2007.38
0.201***
(0.035)
0.089**
(0.032)
0.081***
(0.022)
-0.056
(0.032)
—
-2162.86
-2063.57
0.062**
(0.023)
0.050**
(0.017)
-0.037
(0.026)
0.551***
(0.032)
-0.380***
(0.034)
SEMLD
Notes: The study chose the 4-nearest neighbor weight matrix for running the spatial lag model and accounting for the spatial lag dependence
of the SEMLD. It chose the 5-nearest neighbor weight matrix for the spatial error model. It chose the squared inverse distance within 10 miles
weight matrix to encompass the spatial error dependence of the SEMLD. AIC = Akaike’s Information Criterion. BIC = Schwartz’s Bayesian
Information Criterion.
* Significant at p ⱕ .05 for a two-tail test; ** significant at p ⱕ .01 for a two-tail test; *** significant at p ⱕ .001 for a two-tail test; standard errors
in parentheses.
Measures of fit
AIC
BIC
Spatial error effects
Spatial lag effects
0.083***
(0.005)
—
—
Land developability
Constant
—
Reduced OLS
Commute time to work in 1990
Table 3. Continued
Highway Expansion: An Integrated Spatial Approach — Chi
79
80
Rural Sociology, Vol. 75, No. 1, March 2010
redistribution indirectly by strengthening the spatial lag effects. The
spatial lag effects are much greater than the temporal effects.
The comparison across the five models in each decade suggests that in
order to understand the effect of highway expansion on population
change, it is essential to consider the influential factors of population
change holistically and incorporate spatial lag and spatial error dependence simultaneously. Highway expansion appears to influence population change directly and indirectly as a facilitator of population
redistribution. The direct impact occurs through highway expansion
itself and can be positive or negative, since highway expansion is a
facilitator of population redistribution. The 1980s was a period of “metropolitan growth” and “rural growth slowdown” in the United States
(Johnson 1999). Highway expansion served as a facilitator of population
outflow out of Wisconsin, which is usually considered a rural state. The
1990s became a period of “rural rebound,” and highway expansion
served as a facilitator of population inflow into Wisconsin. The indirect
impact of highway expansion occurs through the spatial lag effects of
neighboring MCDs’ population change. Population growth (or decline)
in its neighbors is likely to “flow population in” (or “flow population out
of”) an MCD. The indirect impact is always positive.
Spatial Variation in the Impacts
Are the direct and indirect effects uniform over the entire state of
Wisconsin? Previous studies have suggested that highway effects differ
across rural, suburban, and urban areas. Highway expansion effects may
follow similar patterns. Thus, I applied the SEMLD, the best model
among the five regression models, in a spatial regime context to reanalyze the impacts across rural, suburban, and urban areas.10 The results
suggest that both direct and indirect effects of highway expansion
exhibit spatial variation—there are indirect effects in rural areas, direct
and indirect effects in suburban areas, and no statistically significant
effects in urban areas (Table 4).
First, highway expansion causes indirect but no direct effects on
population change in rural areas. In this study, the indirect effects were
positive in both decades. As discussed in the previous section, the indirect effects can be understood as population growth gained from
10
The classification of rural, suburban, and urban areas is based on the 1990 Census
Urban Areas and 1990 Metropolitan and Micropolitan Statistical Areas (MMSAs) defined
by the U.S. Office of Management and Budget. The MCDs that fall into the Census Urban
Areas are classified as urban areas; the MCDs that fall into the MMSAs but not the Census
Urban Areas are classified as suburban areas; the residual MCDs that fall out of the MMSAs
and Census Urban Areas are classified as rural areas.
Public water
Income
Unemployment
Female-headed families
Bachelor’s degrees
Young
Population density
Explanatory variables
Within 10 miles of highway expansion, finished 5–9
years before population change period
At range of 10–20 miles from highway expansion,
finished 5–9 years before population change period
Within 10 miles of highway expansion, finished 0–4
years before population change period
At range of 10–20 miles of highway expansion,
finished 0–4 years before population change period
Control variables
Population change in previous decade
-0.009
(0.010)
2.10e-4
(0.010)
-0.030*
(0.014)
0.013
(0.011)
0.150***
(0.026)
-4.66e-5
(5.53e-5)
0.024
(0.277)
0.127
(0.090)
-0.145
(0.165)
-0.172
(0.127)
1.27e-6
(1.98e-6)
0.096***
(0.024)
0.054***
(0.016)
-3.26e-6
(2.94e-5)
-0.721***
(0.107)
-0.024
(0.054)
-0.089
(0.064)
-0.092
(0.060)
6.55e-7
(9.70e-7)
0.010
(0.013)
Suburban
Areas
0.005
(0.009)
0.004
(0.007)
-0.013
(0.009)
-0.007
(0.007)
Rural
Areas
1980–1990
0.304***
(0.058)
8.27e-6
(2.58e-5)
-1.374*
(0.636)
-0.225
(0.120)
-0.145
(0.311)
-0.860
(0.494)
5.72e-6
(3.02e-6)
0.057
(0.057)
0.034
(0.023)
0.032
(0.024)
-0.024
(0.031)
-0.054
(0.042)
Urban
Areas
0.015
(0.032)
-5.42e-5
(3.77e-5)
-0.139
(0.127)
-0.020
(0.062)
-0.210
(0.148)
-0.097
(0.094)
2.15e-6**
(7.30e-7)
0.045**
(0.017)
-0.001
(0.011)
-0.002
(0.008)
-0.005
(0.011)
0.006
(0.007)
Rural
Areas
0.455***
(0.056)
-6.20e-5
(6.34e-5)
-0.799**
(0.300)
-0.056
(0.113)
0.078
(0.409)
-0.033
(0.307)
3.08e-6*
(1.25e-6)
0.097**
(0.031)
0.055**
(0.019)
-0.005
(0.014)
0.008
(0.016)
0.036**
(0.014)
Suburban
Areas
1990–2000
Urban
Areas
0.446***
(0.133)
-7.46e-5
(3.84e-5)
0.192
(0.925)
-0.085
(0.182)
-1.830
(1.300)
0.408
(1.041)
5.75e-8
(2.09e-6)
0.062
(0.074)
0.005
(0.032)
0.041
(0.037)
0.084
(0.047)
0.024
(0.051)
Table 4. Spatial Regime Spatial-Error Model with Lag Dependence by Rural, Suburban, and Urban Areas
Highway Expansion: An Integrated Spatial Approach — Chi
81
0.062
(0.058)
-0.064
(0.057)
0.025
(0.034)
0.027
(0.034)
-0.065
(0.061)
0.579***
(0.069)
-0.501***
(0.032)
Suburban
Areas
1.606
(1.938)
3.924***
(1.176)
0.045
(0.184)
-0.024
(0.088)
0.075
(0.149)
0.117
(0.183)
Urban
Areas
94.26 with (36, 1837) degrees
of freedom***
-3193.60
-2895.74
0.011
(0.019)
-0.064**
(0.025)
-0.016
(0.018)
0.025
(0.014)
0.093***
(0.025)
0.740***
(0.036)
Rural
Areas
1980–1990
0.105
(0.068)
0.036
(0.085)
0.032
(0.050)
0.087*
(0.043)
-0.056
(0.069)
0.298***
(0.070)
-0.379***
(0.034)
Suburban
Areas
-5.584
(3.875)
4.018
(2.729)
0.694***
(0.214)
-0.081
(0.112)
-0.013
(0.191)
0.080
(0.160)
Urban
Areas
123.77 with (36, 1837) degrees
of freedom***
-2269.84
-1971.98
0.153***
(0.022)
-0.104**
(0.037)
0.054*
(0.027)
0.056**
(0.018)
-0.060
(0.032)
0.617***
(0.037)
Rural
Areas
1990–2000
Notes: The study measured control variables in the starting year of each model. AIC = Akaike’s Information Criterion. BIC = Schwartz’s
Bayesian Information Criterion.
* Significant at p ⱕ .05 for a two-tail test; ** significant at p ⱕ .01 for a two-tail test; *** significant at p ⱕ .001 for a two-tail test; standard errors
in parentheses.
AIC
BIC
Measures of fit
Spatial Chow test
Spatial error effects
Spatial lag effects
Constant
Land developability
Commute time to work
Agriculture
Seasonal housing
Table 4. Continued
82
Rural Sociology, Vol. 75, No. 1, March 2010
Highway Expansion: An Integrated Spatial Approach — Chi
83
neighbors. A rural MCD will likely gain (or lose) population if its neighbors do. Highway expansion acts as a facilitator of population flows
among neighboring MCDs. However, highway expansion presented no
direct effects in either decade. One possible reason is that the direct
effects in rural areas occur at the regional level rather than the MCD
level—the scale effect of the modifiable areal unit problem (Fotheringham and Wong 1991). Perhaps growth and development in rural areas
relies more on regional growth and development. This explanation can
be implicitly supported by the comparison of indirect effects across
rural, suburban, and urban areas (Table 4). The indirect effects of
highway expansion were the strongest in rural areas, whether in the
decade of metropolitan growth and rural growth slowdown or in
the decade of rural rebound. Rural MCDs benefit the most from neighbors’ growth. Thus, highway expansion plays an important role in facilitating population flows in rural areas and tends to unite rural MCDs into
a region.
Second, highway expansion has both direct and indirect effects on
population change in suburban areas. Highway expansions completed
from 1975 to 1980 have direct effects on population change in the 1980s
for suburban MCDs within 10 miles of expansion segments. Two highway
expansion variables also directly affected suburban population change in
the 1990s. However, these direct effects were negative in the 1980s but
positive in the 1990s. This phenomenon can be well explained by the
spread and backwash effects of the growth pole theory (Perroux 1955). In
the 1980s, the metropolitan areas grew while the nonmetropolitan areas
declined; these changes were mainly caused by economic disruptions
such as the farm debt crisis, deindustrialization, and urban revival
(Johnson 1999). The backwash effects were strong, and highway expansion acted as a facilitator of population redistribution from suburban to
urban areas. In the 1990s, nonmetropolitan areas experienced rural
rebound, in which natural amenities induced more metropolitan residents to move into the recreational counties. Because the spread effects
were strong, the suburban areas benefited dramatically. Suburban areas
have the advantage of location by providing easy access to both job
opportunities in urbanized areas and natural amenities in rural areas.
Highway expansion strengthened these benefits of suburban locations
and facilitated movement into suburban areas, and acted as a facilitator of
in-migration into suburban areas. Highway expansion also produces
indirect effects on population growth in suburban areas, and the effects
were positive in both the 1980s and 1990s. A suburban MCD will likely gain
(or lose) population if its neighbors do. Again, highway expansion can be
understood as a facilitator of population flows among neighboring MCDs.
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Third, highway expansion creates neither direct nor indirect effects
on population change in urban areas. Previous studies (e.g., Boarnet
1998; Boarnet and Haughwout 2000) suggest that highway effects in
urban areas are uncertain because new or improved highways can either
help or hinder the development of urban areas depending on many
other factors as well as the net effects of spread and backwash. The
findings of this study support the previous studies and imply two possible
reasons for why there are no highway effects on population change
in urban areas. One reason is that the effects of highway expansion in
urban areas might be better studied at finer geographic levels such as
census tracts, block groups, or blocks. Urban areas are more densely
populated than suburban and rural areas; highway expansion may be
offensive to immediate neighborhoods but agreeable to neighborhoods
a few blocks away. The other reason is that population change in urban
areas is more liable to be affected by land-use planning and policy
regulations. The divisions of residential, commercial, and other developments combined with zoning regulations complicate population
change and make highway expansion less important in promoting local
growth and development.
Summary and Discussion
Throughout the years, highways have served an important function in
transforming human society and affecting population redistribution.
Because most planned highways are now completed, highway expansion
is one of the main highway construction activities in the United States.
The disciplines of geography, sociology, economics, and urban and
regional planning have presented several theories to explain the effects
that highways have on economic growth and population change (Chi
et al. 2006), yet little research has addressed the impacts of highway
expansion. However, because of the upcoming efforts in expanding and
improving highways as well as the certain conflicts between protesters
and proponents of highway construction, it is essential to comprehend
the impacts of highway expansion on population change as well as
economic growth and development. This study fills the gap in the literature by contributing to the understanding of the role that highway
expansion plays in affecting population change.
Building upon the Voss and Chi (2006) study and other previous
highway literature, this study advances the understanding of the effects
of highway expansion through two main goals. One, it develops an
integrated spatial approach and applies it to investigate the effects of
highway expansion completed between 1970 and 1990 on population
Highway Expansion: An Integrated Spatial Approach — Chi
85
change from 1980 to 2000 at the MCD level across rural, suburban, and
urban areas in Wisconsin. Two, this study discusses the role of highway
expansion in affecting population change across rural, suburban, and
urban areas within growth pole theory and location theory.
The integrated spatial approach employed in this study comprises
four components: (1) thorough examination of the determinants of
population change, (2) simultaneous incorporation of both spatial lag
and spatial error dependence into one spatial regression model, (3)
appropriate consideration of the spatial variations of the impacts, and
(4) systematic selection of the optimal spatial weight matrix. The diagnostic statistics suggest that this integrated spatial approach assists in
improving the overall model fitting balanced with model parsimony and
thus provides a more complete understanding of the impacts of highway
expansion on population change. The simultaneous consideration of
spatial lag and spatial error dependence as well as spatial heterogeneity
provides three advantages over previous studies. One, the inclusion of
spatial lag effects enables examination of the indirect effects of highway
expansion, which are measured by weighted neighbors’ growth. Two,
the inclusion of spatial error effects helps capture the effects of the
variables that have potential influence on population change but are not
included in the model. Three, the use of the spatial regime model, which
deals with spatial heterogeneity, allows for the comparison of the direct
and indirect effects of highway expansion across rural, suburban, and
urban areas. The findings imply that local governments should consider
spatial effects from neighboring places and variations of the effects in
their decision-making and planning processes. Neighboring places are
closely connected physically, socially, and economically. Convenient
transportation integrates neighboring places into a larger region of
economic growth and development. When spatial effects and variations
are not considered, or not appropriately considered, the analysis results
tend to provide “average” estimates that are often misleading.
This study suggests that highway expansion can best be viewed as a
facilitator of population flows, both directly and indirectly; this role can
be supported by growth pole theory and location theory. The study
shows that the effects on population change in each individual MCD
differ across periods with different population redistribution patterns
and vary from rural to suburban to urban areas. Highway expansion
causes direct effects on population change in suburban areas. As a
facilitator of population flows, highway expansion’s effects on suburban
areas are positive when the spread effects are stronger and negative
when the backwash effects are stronger. The net population change
depends on the net effects of spread and backwash. Population growth
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Rural Sociology, Vol. 75, No. 1, March 2010
and decline could both be an outcome. The direct effects can be best
explained by the growth pole theory. First, expanded highways can
increase suburban populations by augmenting the spread effects of
added employment opportunities in or near the surrounding areas.
Second, better highways allow urban families to move to suburban areas.
The reality of lower real estate costs and the perception of a higher
quality of life often drive this trend. Third, convenient highways can also
entice suburban and rural people to travel to urban areas for employment opportunities and urban amenities—the backwash effects.
Indirectly, highway expansion seems to influence population redistribution by strengthening the spatial lag effects through its role as a
facilitator. The indirect effects in Wisconsin in both the 1980s and 1990s
were positive in promoting population growth in suburban and rural
areas. Location theory argues that individuals and industries seek locational advantages created by highway improvement (Audirac 2005). The
improvement of transportation by itself, however, cannot create a comparative advantage when none exists. From the individual perspective,
highway infrastructure operates as a facilitator for people to connect to
their residential locations, work locations, and shopping locations.
Highway expansion decreases travel time among neighboring places,
which in turn lessens the socioeconomic distance between them. From
the industry perspective, highway infrastructure operates as a facilitator
for the transportation of raw materials, capital, finished goods, consumers, and ideas among central places and their neighborhoods (Thompson and Bawden 1992). Highway expansion offers improved accessibility
and tends to incorporate several neighboring places into one larger
place. Population growth (or decline) in one location will cause population growth (or decline) in its neighboring locations until they achieve
an equilibrium.
Future research could contribute to the literature on the effects of
highway expansion on population change by addressing two issues: the
scale issue and the causality issue. First, it is intriguing that highway
expansion produces no direct or indirect effects on population change
in urban areas and no direct effects in rural areas. This may be due to a
mismatch between the scale at which the effects are studied and the scale
at which the effects occur—a phenomenon often called the scale effect.
As urban areas are more densely populated, their land use must be
highly planned. Highway expansion may cause unpleasant effects on
immediate neighborhoods yet create favorable effects for neighborhoods a few blocks away. Thus, the effects of highway expansion in urban
areas may be better determined at finer geographic levels such as census
tracts, block groups, or blocks. In contrast, growth in rural areas relies
Highway Expansion: An Integrated Spatial Approach — Chi
87
more on regional growth rather than their own “organic” growth. The
effects of highway expansion in rural areas may be better evaluated at a
regional level such as the county level.
Second, future research can address the bidirectional causality
between highway expansion and population change within a spatial
structural equation context. Researchers often see the relationship
between highway expansion and population change as a “chicken and
egg” relationship (Hobbs and Campbell 1967; Levinson 2008; Levinson
and Chen 2007). On one hand, expanded or improved highways stimulate population and economic change; on the other hand, population
and economic growth promote demand for higher transportation capacity. Since highway expansion and population change can be seen as
endogenous to each other, future research may examine their simultaneous causal relationship along with the consideration of spatial dependence and heterogeneity within a spatial structural equation context
(e.g., Oud and Folmer 2008).
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