Isolating the Effect of Natural Amenities on Population Change at the Local Level

15 Pages·2011·2.14 MB·English

RegionalStudies,Vol.45.4,pp.491–505,April2011
Isolating the Effect of Natural Amenities
on Population Change at the Local Level
GUANGQING CHI(cid:2) and DAVIDW. MARCOUILLER†
(cid:2)Department of Sociologyand Social Science Research Center, Mississippi State University, 207 Bowen Hall, Hardy Road,
Mississippi State, MS 39762, USA. Email: [email protected]
†Department of Urban and Regional Planning, University of Wisconsin-Madison, Madison, WI 53706, USA.
Email: [email protected]
(ReceivedSeptember2008:inrevisedformSeptember2009)
CHIG.andMARCOUILLERD.W.Isolatingtheeffectofnaturalamenitiesonpopulationchangeatthelocallevel,RegionalStudies.
Aslatentproductioninputs,naturalamenitiesarethoughttoserveasthebasisforavarietyofregionaleffects.Inthisstudy,natural
amenityandsocio-economicconditionswereanalysedwithrespecttopopulationgrowthempiricallyestimatedatthemunicipal
1 level in an amenity-rich lakes region of the North Central United States from 1970 to 2000 with a specific focus on spatial
1
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2 relationships.Thelimitedresultssuggestmixed effects thatconfirmanincreasedamenity influenceasoneofmanyfactorsthat

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21 Naturalamenities Populationchange Economiccondition Spatialeconometrics

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: [ CHI G. et MARCOUILLER D. W. Isoler l’effet des ame´nite´s naturelles sur lavariation de´mographique au niveau local, Regional
By Studies. On pense que les ame´nite´s naturelles, en tant que facteurs de production latents, sont a` l’origine d’effets re´gionaux

ed multiples.Danscettee´tudesur unere´giondelacsricheename´nite´sduCentreNorddesEtats-Unis,lescaracte´ristiquessocio-
d
a
o e´conomiques et d’ame´nite´s naturelles ont e´te´ analyse´es par rapport a` l’e´volution de´mographique estime´e au niveau communal
l
n
w sur la pe´riode 1970–2000, avec un ciblage spe´cifique des relations spatiales. Les re´sultats limite´s sugge`rent des impacts mitige´s
o
D
quiconfirmentuneffetame´nite´ accrumaisparmid’autresfacteursquiinfluencentlavariationdepopulationlocalise´e.
Ame´nite´snaturelles E´volutionde´mographique E´tate´conomique E´conome´triespatiale
CHI G. und MARCOUILLER D. W. Isolation der Auswirkung natu¨rlicher Attraktionen auf demografische Vera¨nderungen auf
lokalerEbene,RegionalStudies.Natu¨rlicheAttraktionengeltenalslatenteProduktions-InputsundalsGrundlagefu¨rdieverschie-
denstenregionalenAuswirkungen.IndieserStudieanalysierenwirdienatu¨rlichenAttraktionenundsozioo¨konomischenBedin-
gungen hinsichtlich des auf Gemeindeebene empirisch gescha¨tzten Bevo¨lkerungswachstums in einer attraktionsreichen
SeenregionimmittlerenNordenderUSAimZeitraumvon1970bis2000unterbesondererBeachtungderra¨umlichenVerha¨lt-
nisse.DiebegrenztenErgebnisselassenaufgemischteAuswirkungenschließen,dieeinengestiegenenEinflussderAttraktionenals
einenvonzahlreichenFaktorenbesta¨tigen,diezulokalisiertendemografischenVera¨nderungenfu¨hren.
Natu¨rlicheAttraktionen DemografischeVera¨nderungen Wirtschaftsbedingungen Ra¨umlicheO¨konometrie
CHI G.yMARCOUILLER D.W.Aislamientodelefectodecomodidadesnaturalesenelcambiodepoblacio´nanivellocal,Regional
Studies.Seconsideraquecomoaportacionesdeproduccio´nlatente,loscomodidadesnaturalessirvendebaseparaunaseriedeefectos
regionales.Enesteestudio,seanalizaronlosserviciosnaturalesylascondicionessocioecono´micasconrespectoalcrecimientodelapobla-
cio´ncalculadoemp´ıricamenteanivelmunicipalenunaregio´ndelagosricaencomodidadesenlazonacentralnortedelosEstadosUnidos
de1970a2000conunenfoqueespec´ıficoenlasrelacionesespaciales.Losresultadoslimitadosindicanefectosmixtosqueconfirmanun
aumentoenlainfluenciadelascomodidadescomounodelosmuchosfactoresqueimpulsanelcambiolocalizadodelapoblacio´n.
Comodidadesnaturales Cambiodelapoblacio´n Condicionesecono´micas Factoreseconome´tricosespaciales
JELclassifications:R11,R14,R23
0034-3404print/1360-0591online/11/040491-15#2011RegionalStudiesAssociation DOI:10.1080/00343400903496394
http://www.regional-studies-assoc.ac.uk
492 Guangqing Chi and David W. Marcouiller
INTRODUCTION etal.,1997;FUGUITTandBROWN,1990;HUMPHREY,
1980; JOHNSON, 1989; JOHNSON and BEALE, 1994;
Recent literature focused on the role of natural ame-
JOHNSON and PURDY, 1980). Further, previous work
nities in the development of regions has dealt with an
has identified natural amenities as latent factor inputs
ongoing debate over causal elements behind local
to the local production of goods and services
demographicchange.Somearguethatnaturalamenities
(GRAVES, 1979, 1980, 1983; KNAPP and GRAVES,
are important resources that provide a direct attractant
1989; MARCOUILLER, 1998; PORELL, 1982), with
for local population change, while others argue that
particular importance to rural regions.
economic conditions such as income and employment
Rural ‘growth engines’ exist as complex interwoven
opportunitieshavelongbeenthemostimportantdeter-
markets including traditional primary factor inputs of
minants of migration in modern society and still
land, labour, and capital combined with latent, or
dominate its influence. As a result of this debate, a
hidden, factor inputs characterized by natural amenities
growing number of empirical studies attempt to
(for example, bucolic landscapes, open space, forests,
isolate the role of natural amenities in explaining
water bodies, climate, etc.) and built and/or socio-
migration and population growth. The results of these
cultural amenities (for example, parks and other rec-
empirical studies are often contradictory.
reational sites, shopping opportunities, eating/drinking
These contradictory findings often result from
establishments, built attractions, etc.). Equilibrium
the simple fact that studies reflect incomparable
theory argues that the main determinants of migration
time frames and local conditions in which natural
come from differences in amenities rather than from
amenities operate to affect population redistribution
differences in economic opportunities (GRAVES, 1979,
(cf. BOARNET, 1994a; BROWNING and GIBBS, 1971;
1 1983; GRAVES and LINNEMAN, 1979). Further, the
201 CLARK and HUNTER, 1992; CLARK and MURPHY, life-cycle literature suggests key relationships between
ch 1996; FRENTZ et al., 2004; FUGUITT and BROWN, amenities and demographic structure. Specifically,
ar 1990; GREENWOOD, 1975; GUSTAFSON et al., 2005;
M regional amenities increase in importance as people
21 and TREYZ et al., 1993). There is also, however, the becomeolder(CLARKandHUNTER,1992;HUMPHREY,
7 veryrealpossibilitythatexperimentaloversighthasgen-
:4 1980). A combination of increased affluence, develop-
17 eratedspuriousempiricalresults.Theseoversightscould
mentofthetransportationinfrastructure,active regional
t: result from errors in model specifications that did not
A competitiveness, globalization, and environmental
] sufficiently control for driving factors of population
ng awareness/sensitivity have driven development into a
qi growthorfromdevelopingmodelswithoutaccounting
g post-industrial (some say post-consumer) phase which
an for spatial process effects.
Gu has qualityof life and amenities as one of the important
The present researchcontributes tothe literature by
Chi, employing spatial econometric models to examine and determinants of population change (BUTTEL, 1995;
: [ compare the effects of natural amenities and economic CASTLE, 1993; FREUDENBURG, 1992; GALSTON and
By BAEHLER, 1995; THOMPSON et al., 2006). At the
conditions on population change at the local level.
ed regional level, narrow income gaps between non-
ad Thepaperisorganizedintoseveralsubsequentsections.
o metropolitan and metropolitan areas and lower living
nl It begins with an overview of elements that influence
ow expenses in non-metropolitan areas could result in
D human migratory patterns within an amenity context.
populationgrowthinnon-metropolitanareas(FUGUITT
In the next three sections the approach is outlined and
et al., 1989; JOHNSON and BEALE, 1994; LYSON and
empirically tested by examining population growth
GILLESPIE,1995).
from 1970 to 2000 at the minor civil division level in
Nevertheless, some scholars argue that economic
the US state of Wisconsin accounting for spatial
forces have long been and are still the dominating
process effects. Finally, the paper closes with a section
factors in stimulating migration. Migration flows from
that summarizes the findings and it draws general
areas with few economic opportunities to those with
conclusions relevant to regional planning and policy
many opportunities, and thus it functions to balance
analysis. Limited evidence is provided that natural
population size and economic opportunities at the
amenities remain an increasingly significant influence
aggregate level (FRISBIE and POSTON, 1975). The
within the context of local population change.
mostimportanteconomicfactorsthataffectpopulation
redistribution (at least for persons of working age)
appear to be income and employment (TREYZ et al.,
POPULATION GROWTH AND NATURAL
1993). Economic theory would suggest that potential
AMENITIES
migrants select destinations where the expected net
Thereexistsagrowingliteraturesuggestingthatnatural benefits are maximized (GREENWOOD, 1975). Such
amenitieshaveandcontinuetoplayanimportantrolein benefits can be best measured by wage income that is
regionaleconomicandsocio-demographicchange.For usually the largest portion of real income received by
example, the post-1970 ‘turnaround migration’ was households (SMITH et al., 2000).
thought,inlargepart, tobeafunctionoftheattractive Interestingly, ROBACK (1982, 1988) suggests that
power of natural amenities in rural America (BROWN the presence of amenities likewise serve as regional
Isolating the Effect of Natural Amenities on Population Change at the Local Level 493
benefitstobecapitalizedintohouseholdutilitymaximi- alternatively specified models. It is important to note
zation decisions creating an inverse relationship with that the theory behind linking amenities with
household income. Subsequent Roback-type models migrationdoesvaryandthatasinglegeneralizablecon-
confirm thiswage–amenity trade-off whileidentifying struct does not exist.
the significance of amenities in stimulating economic For the present study, four categories of variables
growth (DELLER et al., 2001). were used to compare the effects of natural amenities
Increased availability of local employment opportu- and economic conditions on population growth. First,
nitiescancreatepopulationgrowthinnon-metropolitan it has long been understood that demographic
areas(FUGUITTandBEALE,1996)duetothestimulation characteristics of a population are important determi-
of household labour use and its effect on household nantsofpopulationchange.Themostimportantdemo-
incomes. Empirical studies on regional development graphic characteristics that have been shown to affect
often stress the interdependencies between household population change include population density, age
residentialchoicesandfirmlocationdecisions,especially structure, racial and ethnic composition, institutional
in the literature identifying causality between employ- populations, educational attainments, household
ment and population change (HENRY et al., 1997). demographic characteristics, and sustenance organiz-
CARLINOandMILLS(1987)arguethattheperformance ation (BROWNING and GIBBS, 1971; FRIEDMAN
of a functional market economy determines migratory and LICHTER, 1998; FRISBIE and POSTON, 1975;
causality; namely, that in regions exhibiting high- HUMPHREY, 1980; MINCER, 1978).
performing markets, jobs follow people, while during Second, socio-economic conditions known to have
periods of poor performance, people follow jobs. important impacts on population change include
Many studies treat population and employment school performance, crime, public infrastructure, local
1
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20 changes to be endogenous (for example, BOARNET, efforts to expand public services, retail sales, and real

rch 1994a, 1994b). In addition, a number of empirical estatevalues(BERNDTandHANSSON,1992;CARLINO
a
M studies demonstrate that both natural amenities and and MILLS, 1987; CLARK and MURPHY, 1996;
1
2 economic conditions are important driving factors of DALENBERG and PARTRIDGE, 1997; DUFFY-DENO
7
7:4 migration and population growth (for example, and EBERTS, 1991; HAMALAINEN and BOCKERMAN,
1
: CUSHING,1987;andSHIELDS etal.,2005). 2004; HULTEN and SCHWAB, 1984; MESSNER and
t
A ANSELIN, 2004; MORRISON and SCHWARTZ, 1996;
]
qing Explanatory variablesrelated to population growth THOMPSON etal.,2006).
g Third, transportation accessibility is important for
n
a
Gu An exhaustive literature review of population-related local economic growth and development as well as

i, theories that was conducted for the present research associated population growth. Accessibility can influ-
h
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[ resulted in approximately seventy variables that were ence population change indirectly through economic

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By considered significantly relevant to population growth, employment change, altered social structure,

ded change. A similar selection of variables is found and environmental change (CHI et al., 2006). There
a
o throughout the empirical literature on population aremanytheoreticalandempiricalstudiesonaccessibil-
l
n
ow change specific to amenity effects (cf. CARLINO and ity in the fields of regional economics, transportation
D
MILLS, 1987; CARRUTHERS, 2003; DALENBERG and planning, rural sociology, demography, etc. Relevant
PARTRIDGE, 1997; GLAESER and SHAPIRO, 2003; factors include residential preference, highways, traffic
NAUDE and KRUGELL, 2006; PARTRIDGE, 2005; volume, distance to access highways, journey towork,
POUDYAL et al., 2008; and SHUMWAY and DAVIS, local capital expenditures on transportation, etc.
1996). These variables can be characterized within (BARTEL, 1979; BROWN et al., 1997; FUGUITT and
the broad categories of demographic characteristics, BROWN, 1990; SMITH et al., 2000; VOSS and CHI,
socio-economic conditions, physical infrastructure, 2006).
environmental and geophysical factors, cultural Finally,populationgrowthislimitedbythepotential
resources, and potential legal constraints. However, for land conversion and development. The residential
these variables are often not adequately controlled for developability of land in a region is determined by its
in multivariate analyses (LICHTER and FUGUITT, geophysical characteristics (water, wetland, slope, and
1980). A wide range of spurious results are possible tax-exemptlands),theextentofbuilt-uplands(existing
by omitting relevant variables from a model residential, commercial, and industrial developments as
(DALENBERG and PARTRIDGE, 1997). Such omissions well as transportation infrastructure), cultural and
potentially bias parameter estimates for those variables aesthetical resources, and legal constraints (including
that are included in the model specification. It is land-use planning legislation and programmes such as
important to note that a dichotomy exists between comprehensive plans, ‘smart growth’ laws, zoning
developing models using a comprehensive set of ordinances,farmland-protectionprogrammes,environ-
variables versus using a more parsimoniously specified mentalregulationssuchastheCleanWaterAct,shore-
set of variables derived from purely theoretical means. land and wetland zoning, etc.). The relevant literature
Indeed, alternative theories can and do lead to relating to the role of developability on migration
494 Guangqing Chi and David W. Marcouiller
includes the extent of land development (COWEN and DATA DESCRIPTION
JENSEN, 1998), qualitative environmental corridors
Thepresentresearchfocusedongeographieswithinthe
(LEWIS, 1996), quantitative environmental corridors
US state of Wisconsin and covered population change
(CARDILLE et al., 2001), and work dealing with growth
from 1970 to 2000 at the minor civil division (MCD)
management (LAND INFORMATION & COMPUTER
level(Fig.1).Itis importanttonote that Wisconsinisa
GRAPHICS FACILITY,2000,2002).
‘strong MCD’ state that contains a mix of rural areas,
small cities, and large cities found in throughout the
Spatial process effects United States. MCDs (towns, cities, and villages) in
Wisconsin are functioning governmental units with
Populationgrowthisademographiccharacteristicoften
electedofficialswhoprovideservicesandraiserevenues.
found to be spatially correlated within its regional
TheMCDgeographyconsistedofnon-nested,mutually
neighbourhood. While most demographic theories
exclusive, and exhaustive political territory from 1837
and many current demographic studies lack a spatial
MCD units with an average size of 29.56 square miles
perspective (TIEFELSDORF, 2000), spatial distribution (Fig.1).1TheadvantageofusingMCDsistheirrelevance
and differentiation of population have long been
toplanningandpublicpolicy-making.Inmostpartsofthe
studied by researchers in other disciplines such as
state,censustractshaveaveragesizessimilartoMCDsand
regional science and population geography. These
serve as alternative units of analysis. However, census
fields have well-established theories for understanding
tracts are geographic units delineated by the Census
spatial association and have developed empirical
Bureauandhavenopoliticalorsocialmeanings.
methods for examining the spatial dimension of
The data used in this study came from a variety of
population growth.
11 sources. Population data originated from decennial
20 Regional economic theories explain the change of
censuses from 1970 to 2000 made available on the
ch land-use patterns, which are almost always associated
ar Geolytics Census CD. Socio-economic data were
1 M with population change (BOARNET, 1998; CERVERO, acquired from the US Census Bureau, the Wisconsin
2 2002, 2003). For example, growth pole theory applies
7 DepartmentofPublicInstruction,theFederalBureauof
:4 the notions of spread and backwash to explain the
17 Investigation (FBI), and the State of Wisconsin Blue
mutual geographic dependence of economic growth
t: Books.Transportation infrastructure datawere provided
A and development, which in turn leads to population
] by the Wisconsin Department of Transportation, the
qing change (PERROUX, 1955). Central place theory places Wisconsin Bureau of Aeronautics, the National Atlas of
g population in a hierarchy of urban places where the
an the United States, and the Department of Civil and
Gu movement of population, firms, and goods is
Environmental Engineering of the University of
i, determined by the associated costs and city sizes
Ch Wisconsin-Madison.Thedataofgeophysicalfactorsand
: [ (CHRISTALLER, 1966). More recently, KRUGMAN naturalamenitycharacteristicscamefromtheWisconsin
By (1991) adds space to endogenous (or ‘new’) growth
Department of Natural Resources, the US Geological
ed theory to study the formation process of city networks
ad Survey,andtwoUniversityofWisconsin-Madisonunits
o over time. Population geography focuses on spatial
nl (the Environmental Remote Sensing Center, and the
ow variation of population distribution, growth, compo-
D LandInformationandComputerGraphicsFacility).
sition,andmigrationanditseekstoexplainpopulation
Population distributions (in terms of population
patternscausedbyspatialpatternsandprocesses(JONES,
density) from 1970 to 2000 at the MCD level are
1990). Spatial diffusion theory argues that population
shown in Fig. 2. In 1970, population was mainly con-
growth tends to spread to surrounding areas
centrated in south-eastern Wisconsin and also some
(HUDSON, 1972), which implies that population
scattered higher-density areas between the Minneapo-
growth is spatially autocorrelated. Thus, spatial process
lis/St. Paul Twin Cities and Eau Claire, as well as the
effects of population growth need to be examined and
La Crosse region and Central Sands of Stevens Point
controlled in modelling and when comparing the
and Wausau. By 2000, urban areas had expanded into
effects of natural amenities and economic conditions
their ex-urban surroundings with an important popu-
on population growth.
lation expansion along the Wisconsin River extending
The models employed in this research jointly
north into the centre of the Wisconsin Northwoods.
incorporate natural amenities, economic conditions,
The state grew by less than 4.0% in the 1980s (repre-
spatial process effects, and the characteristics of local
senting the smallest increase in state history), followed
demographics, socio-economic scenarios, transpor-
bya 9.6% growth in the 1990s.
tation capacity, and geophysical limits. The four
characteristics were used as control variables to explain
population growth and were developed into four
METHODS OFANALYSIS
indices – demographics, liveability, accessibility, and
developability, respectively – in order to reduce data For the empiricalwork reportedherein,thedependent
dimension. The methods involved are detailed in the variable was population growth rate, expressed as the
Methods of Analysis section below. natural log of the later census population over the
Isolating the Effect of Natural Amenities on Population Change at the Local Level 495
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Fig. 1. Counties and minor civil divisions (MCDs) in Wisconsin
Source: National Atlas of the United States and US Census Bureau
earliercensuspopulation.Populationgrowthrateinthree growth from 1980 to 1990. For the entire three
timeperiods(1970–1980,1980–1990,and1990–2000) periods, there was a similar spatial pattern: relatively
attheMCD levelinWisconsinare illustrated inFig.3. highgrowthratesoccurredintheNorthwoods,Central
There were more MCDs with high growth rates from Wisconsin,andsuburbsofmajorcities.
1970to1980thanfrom1990to2000,andthemajority The explanatory variables included both natural
of MCDs actually experienced negative population amenities and economic conditions.2 Natural
496 Guangqing Chi and David W. Marcouiller
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Fig. 2. Population density (persons/km2) from 1970 to 2000 in Wisconsin
amenities3wererepresentedbytheproportionsofforest Several statistical methods were applied in this
and inland water coverage. The economic conditions study. First, the driving factors behind local population
were represented by the median household income growth were categorized using principal factor analysis
and the unemployment rate. Median household (PFA) and the ModelBuilder (ESRIw) function of
income derived from the decennial census was ArcGIS to generate four indices.4 Although as many
deemed to be an appropriate metric for use because it of the driving factors behind population growth as
reflects household income levels rather than individual possiblewereconsidered,largenumbersofindependent
income levels. Household income better reflects variables (driving factors) make interpretation difficult
migratory influence (TUNALI, 2000). and can exacerbate serious and unnecessary
Isolating the Effect of Natural Amenities on Population Change at the Local Level 497
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Fig. 3. Population growth during 1970–1980, 1980–1990, and 1990–2000 at the minor civil division (MCD) level in
Wisconsin
multicollinearity. Using PFA and the ModelBuilder . Accessibility: transportation and community
function,fourindiceswerederivedthatservetocharac- infrastructure.
terize population change: . Developability:thepotentialforlandconversionand
development.
. Demographics: local demographic characteristics.
. Liveability: a measure of social and economic The first three indices were generated by PFA and the
conditions. last one by the ModelBuilder function.
498 Guangqing Chi and David W. Marcouiller
The spatial autocorrelation within each of the four EMPIRICAL RESULTS
indices was then examined on the basis of a Moran’s
An examination of global indicators of spatial
I-statistic. Spatial autocorrelation can be loosely
association for each of the various indices revealed the
defined as the similarity (or dissimilarity) between two
presence of significant spatial process effects at work
values of an attribute with similarity between the two
across Wisconsin. Spatial autocorrelation estimates for
corresponding locations. A positive Moran’s I-value
each standardized variable of interest using the
indicates positive spatial autocorrelation (that is, high
Moran’sI-statisticaresummarizedinTable1.Thevari-
or low values of an attribute tend to cluster in space),
ables were standardized to compare Moran’s I-statistics
and a negative Moran’s I-value indicates negative
because the variances exhibited change over time.
spatial autocorrelation (that is, locations tend to be
Results suggest that population growth rates in each
surrounded by neighbours with very different values).
of the three decades from 1970 to 2000 exhibited
Moran’s I-statistics for residuals in a model are often
significant spatial autocorrelation as indicated by the
used to detect whether the model is appropriately
Moran’s I-statistic (0.231 in the 1970s, 0.183 in the
specified.
1980s, and 0.199 in the 1990s; p , 0.001 in all three
Next, two sets of models were run to examine the
decades). In addition, all explanatory variables (natural
effects of natural amenities and economic conditions
amenities, unemployment rates, and income levels)
onpopulationgrowthinthe1980sand1990sseparately.
and control variables (indices) exhibited significant
The pattern of population redistribution was renewed
spatial autocorrelation. Indeed, results suggest a clear
metropolitan growth in the 1980s and a transition to
need to account for spatial association in the develop-
rural rebound in the 1990s (BROWN et al., 1997;
mentofexplanatorymodelsofmigrationinWisconsin.
11 FUGUITTand BEALE, 1996). A comparison of analyses Indevelopingindicesforthemodellingofmigration,
20 fromthetwodecadescansuggesthownaturalamenities
the first principal factor that represented the best
ch and economic conditions interact with each other to
ar summary of the entire data set and which accounted
M affectpopulationgrowth,whethertheireffectsarecon-
1 for the most common variance in the correlation
2 sistent, and whether their effects are explained by
7 matrix across all the variables was used. The results of
:4 control variables.
17 this analysis are summarized in Table 2. The demo-
Each group of models was first estimated by an
t: graphic index was based on population density, age
A ordinary least-squares (OLS) regression. The existence
] structure, race, institutional population, educational
ng ofspatialdependenceinmodelresidualswasdiagnosed.
qi attainment, mover, family characteristics, and seasonal
g If spatial dependence existed in the residuals, the
an housing. The first principal factor explained roughly
Gu appropriate spatial regression model was determined
24% of the variance in the 1980 data set and 23% in
Chi, by the Lagrange Multiplier (LM) test (BERA and the 1990 data set. The liveability index consisted of
: [ YOON, 1993) and the robust LM test. The LM test, safety, school performance, public transportation,
By which follows a Chi-square (x2) distribution with one
buses, public water, new housing, county seat status,
ed degree of freedom (1 d.f.), assisted in detecting the
ad and real estate (property) value. The first liveability
o presence of spatial dependence in the form of an
nl factor explained roughly 26% of variance in the 1980
ow omitted spatially lagged dependent variable and/or
D
spatial error dependence (ANSELIN, 1988). The robust
LM test further diagnosed spatial lag dependence in Table 1. Spatial autocorrelation of each variable
the presence of spatial error dependence and spatial
Variables Moran’sI-value p-value
error dependence in the presence of a spatially lagged
dependent variable (ANSELIN et al., 1996). When Populationgrowth
Populationgrowthrate,1990–2000 0.199 0.000
the LM tests for both the lag and error dependence
Populationgrowthrate,1980–1990 0.183 0.000
were significant, the robust LM test suggested the
Populationgrowthrate,1970–1980 0.231 0.000
pertinence of spatial dependence. The potential spatial
Explanatoryvariables
regression models assessed using the LM and robust
Forest 0.739 0.000
LM tests included the spatial lag model, the spatial Water 0.255 0.000
error model, and the spatially autoregressive moving Unemploymentrate,1980 0.345 0.000
average model.5 Based on the spatial dependence Unemploymentrate,1990 0.325 0.000
Income,1980 0.675 0.000
tests, the appropriate spatial regression model was
Income,1990 0.631 0.000
estimated by the maximum likelihood estimation in
Controlvariables
SpaceStat to examine further the effect of natural
Demographicindex,1980 0.560 0.000
amenities on population growth. Finally, the OLS and
Demographicindex,1990 0.588 0.000
the selected spatial regression models were evaluated Liveabilityindex,1980 0.284 0.000
on the basis of Akaike information criterion (AIC) Liveabilityindex,1990 0.096 0.000
and Bayesian information criterion (BIC) to provide Accessibilityindex,1980 0.447 0.000
Accessibilityindex,1990 0.470 0.000
estimates of model fit (AKAIKE, 1973; KUHA, 2005;
Developabilityindex 0.366 0.000
SCHWARZ, 1978).
Isolating the Effect of Natural Amenities on Population Change at the Local Level 499
Table 2. Analysis of variables by principal factor analysis included water, wetland, slope, tax-exempt lands
(FRENTZ et al., 2004), and built-up lands.7
Factorloadings
The effects of natural amenities and economic con-
Variables 1980 1990 ditions on population growth were compared in two
separate groups of models: one for the decade of the
Demographics
1980s (summarized in Table 3) and the other for the
Varianceexplained(%) 24.12 23.45
Populationdensity 0.717 0.663 decade of the 1990s (Table 4). Diagnostics (LM and
Young –0.385 –0.125 robustLMtests)fortheexistenceofspatialdependence
Old 0.138 –0.235 in model residuals suggested that the spatial lag model
Black 0.343 0.327
was the most appropriate spatial regression model for
Hispanic 0.281 0.273
controlling spatial dependence in both groups of
Collegestudents 0.617 0.670
High-schooleducation 0.541 0.663 models. In the group examining population growth in
Bachelor’sdegree 0.726 0.759 the 1980s, the two model fit metrics – Akaike infor-
Mover –0.533 –0.535 mation criterion (AIC) and Bayesian information cri-
Female-headedhouseholds 0.517 0.120
terion (BIC) – indicated that the spatial lag model
Seasonalhousing –0.145 –0.348
exhibited a better fit balanced with model parsimony
Liveability
when compared with the OLS regression model.
Varianceexplained(%) 26.35 24.49
Thus, the spatial lag model was the most appropriate
Safety –0.382 –0.545
Schoolperformance 0.188 0.344 specification for examining the effects of natural ame-
Publictransportation 0.574 0.394 nities and economic conditions on population change
Buses 0.728 0.705 during the period between 1980 and 1990. The
1
201 Publicwater 0.556 0.546 spatial lag parameter was significant – an indication of
Newhousing 0.131 0.281
ch spatial lag dependence in the OLS regression model.
r Countyseatstatus 0.691 0.519
a
M Neitherofthetwonaturalamenityvariableswassignifi-
1 Accessibility
7 2 Varianceexplained(%) 32.72 33.29 cant in affecting population change between 1980 and
7:4 Residentialpreference 0.557 0.546 1990, as indicated by the results of the two models.
1
Highwayinfrastructure 0.606 0.592 The unemployment rate, one of the two economic
:
t
A Accessibilitytohighways –0.003 0.005 factors, was negatively significant in affecting popu-
ng] Accessibilitytoairports 0.487 0.484 lationgrowthatthep , 0.05levelforatwo-tailedtest.
qi Journeytowork 0.293 0.343
g In the group examining population growth in the
n
a
Gu 1990s, the two model fit metrics also suggested a

i, data set and 24% in the 1990 data set. The accessibility better fit using the spatial lag model when compared
h
C
[ index included residential preference, highway infra- with the standard regression. Thus, the spatial lag

:
By structure, accessibility to airports, accessibility to high- model was most appropriate for examining the effects

ed ways, and journey to work.6 The first factor for ofnaturalamenitiesandeconomicconditionsonpopu-
d
a
o liveability explained about 33% variance in the 1980 lation change during the period between 1990 and
l
n
ow and 1990 data sets. The developability variables 2000. The spatial lag parameter was significant – an
D
Table 3. Modelling population growth in the 1980s
Standardregression Spatiallagmodel
Coefficient (p-value) Coefficient (p-value)
Explanatoryvariables
Forest –0.002 (0.867) –0.006 (0.652)
Water 0.077 (0.075) 0.051 (0.232)
Unemploymentrate,1980 –0.130 (0.047) –0.133 (0.038)
Income,1980 –1.79E–7 (0.814) –9.08E–7 (0.224)
Controlvariables
Populationgrowth,1970–1980 0.159 (0.000) 0.138 (0.000)
Demographicindex,1980 0.012 (0.000) 0.010 (0.002)
Liveabilityindex,1980 –0.022 (0.000) –0.018 (0.000)
Accessibilityindex,1980 0.001 (0.703) 0.001 (0.626)
Developabilityindex 0.028 (0.085) 0.021 (0.195)
Spatiallag – – 0.276 (0.000)
Constant –0.018 (0.327) 0.001 (0.959)
Measuresoffit
Akaikeinformationcriterion(AIC) –2817.63 –2872.21
Bayesianinformationcriterion(BIC) –2762.48 –2811.54
500 Guangqing Chi and David W. Marcouiller
Table 4. Modelling population growth in the 1990s
Standardregression Spatiallagmodel
Coefficient (p-value) Coefficient (p-value)
Explanatoryvariables
Forest 0.052 (0.007) 0.042 (0.023)
Water 0.070 (0.218) 0.011 (0.835)
Unemploymentrate,1990 0.155 (0.136) 0.097 (0.331)
Income,1990 9.38E–7 (0.109) 3.18E–7 (0.571)
Controlvariables
Populationgrowth,1980–1990 0.302 (0.000) 0.228 (0.000)
Demographicindex,1990 0.015 (0.002) 0.013 (0.005)
Liveabilityindex,1990 –0.010 (0.028) –0.009 (0.044)
Accessibilityindex,1990 –0.002 (0.556) –0.002 (0.582)
Developabilityindex 0.029 (0.184) 0.023 (0.273)
Spatiallag – – 0.376 (0.000)
Constant 0.024 (0.352) 0.017 (0.501)
Measuresoffit
Akaikeinformationcriterion(AIC) –1862.37 –1968.51
Bayesianinformationcriterion(BIC) –1807.21 –1907.83
1
1
20 indication of spatial lag dependence in the OLS rural rebound. These findings generally support the

ch regression model. In contrast to the findings from the preconception about migration causality – namely,
r
a
M 1980s, neither of the two economic variables was sig- the hypothesis that neither economic conditions nor

1
2 nificant in affecting population change during the natural amenities can be generalized to determine the
7
:4 1990s, as indicated by the results of the two models. trend of the population-redistribution processes. It
7
1
The proportion of forest area, one of the two natural appears as though effects on population growth follow
:
t
A amenity factors, was positively significant in affecting more macro-redistribution patterns. Population
]
ng population growth at the p , 0.01 level for a two- growth patterns are determined jointly by a variety of
i
q
g tailed test. factors, including demographic characteristics, socio-
n
a
Gu The CHOW (1960) test was used to assess temporal economic conditions, transportation accessibility,

i, instability of coefficients between the 1980s and natural amenities, and land developability.
h
C
[ 1990s. The F-statistic (32.887) was much higher than Thesefindingsechothoseoftwosetsofstudies.The

:
By the corresponding critical F-value (2.97) at the 0.001 first includes the rural demography literature which

ed level of significance with 10 and 3654 d.f., indicating focuses on population redistribution patterns from
d
a
lo that the coefficients between the 1980s and 1990s 1970 to 2000 (BROWN et al., 1997; FUGUITT and
n
ow were statistically different.8 BEALE,1996;JOHNSON andBEALE,1994).Arguments
D
Six additional models were run for both the 1980s from this literature related to the present findings
andthe1990stoexaminewhethertheresultsweresen- includethenotionthateconomicforcesplayedastron-
sitive to the selectionof natural amenityand economic gerroleinthe1980s,aperiodduringwhichtheprocess
variables. The six models assessed included alternative of human settlement involved centralization, while
spatial lag models created by omitting one or two of natural amenities played a stronger role during the
the natural amenity and economic variables. The 1990s,aperiodduringwhichpopulationredistribution
results of this analysis suggested that the coefficients wasmarkedbygeneralpatternsofdecentralization.The
and significance of the natural amenity and economic second set includes the regional economic literature
factors remained relatively constant to those from focused on the causality between population and
Tables 3 and 4: the unemployment rate was significant employment change (CARLINO and MILLS, 1987;
in affecting population growth in the 1980s and forest STEINNES and FISHER, 1974). This literature argues
was significant in affecting population growth in the thatpeoplefollowjobsduringperiodsofrelativeecon-
1990s. (The results are not reported in this paper but omic decline, while jobs follow people during periods
are available from the authors upon request.) of relative economic expansion. The 1980s was a
In sum, results suggested that economic context period of relatively weak regional economic growth.
rather than natural amenities were key to explaining Thus, the causal direction would be expected to be
population growth in the 1980s, a period in which from economic opportunities toward population
the regions experienced renewed metropolitan change. This can be confirmed by a more significant
growth. However, natural amenities rather than econ- unemployment rate with respect to population change
omic context influenced population growth during duringthe1980s.Conversely,the1990scanbecharac-
the 1990s, a period in which the regions experienced terizedasaperiodofrelativelystrongeconomicgrowth