retail decentralization

Urban Studies, Vol. 39, No. 8, 1307–1327, 2002

Fiscalisation of Land Use, Urban GrowthBoundaries and Non-central Retail Sprawlin the Western United States

Robert W. Wassmer

[Paper � rst received, February 2001; in � nal form, January 2002]

Summary. Do the ways that local governments raise own-source revenue and/or use urbangrowth boundaries exert distinct in� uences on the occurrence of retail activity outside ametropolitan area’s central places? This question is addressed in this paper through a regressionanalysis that also accounts for economic factors that provide clear reasons for retail activity tolocate in non-central places. Results indicate that state-wide reliance by municipalities on someforms of own-source revenue exert signi� cant positive in� uences on retail sales in non-centralplaces in metropolitan areas in the western US. ‘Excessive’ retail decentralisation generatedthrough this ‘� scalisation of land use’ is presented within the widely discussed concept of ‘urbansprawl’. The continuing presence of one form of urban growth boundary is also found to reduceretail decentralisation.

1. Introduction

Perhaps diffusion is too kind a word … Inbursting its bounds, the city actuallysprawled and made the countrysideugly … uneconomic [in terms] of servicesand doubtful social value (Earle Draper,Tennessee Valley Authority, 1937).

The pejorative use of the term ‘sprawl’ hasbeen traced by Black (1996) back to this1937 quote made to a national conference ofplanners. Urban planners have retained thisterm as part of their lexicon and apply it toundesirable patterns of urban land use. Be-ginning in the early 1990s, such disparate

groups as the Sierra Club and the NationalAssociation of Homebuilders took an activestance against sprawl and embraced an urbandevelopment agenda based on the concept of‘smart growth’.

Given the renewed national interest in theUS in spatial patterns of urban growth,prominent urban economists such as Gordonand Richardson (1997), Mills (1999) andBrueckner (2000) have weighed in on theissue with articles that summarise an econ-omic approach to de� ning what constitutessmart urban growth. These economists em-phasise the point that the metropolitan decen-

Robert W. Wassmer is in the Graduate Program in Public Policy and Administration, California State University, Sacramento,California 95819– 6081, USA. Fax: 916 278 6544. E-mail: [email protected]. Support came from a sabbatica l leave from CaliforniaState University and grants from the Lincoln Institute of Land Policy, the California State University Faculty Research Fellows inassociation with the California Senate Of� ce of Research, and the California Institute for County Government. The paper has bene� tedfrom comments received from Jan Brueckner, John McDonald, Stanley Longhofer , two anonymous referees and seminar participant sat the Lusk Center for Real Estate at the University of Southern California and a Lincoln Institute conference on ‘The Property Tax,Land Use and Land Use Regulation’. The views expressed here are those of the author.

0042-0980 Print/1360-063X On-line/02/081307-21 Ó 2002 The Editors of Urban StudiesDOI: 10.1080/0042098022014265 5

ROBERT W. WASSMER1308

tralisation of people and economic activity inthe US has occurred for well over 50 yearsand has been driven in large part by popu-lation increases, real income increases anddecreases in the real cost of automobile use.To most economists, decentralisation is onlyan undesirable pattern of urban land use ifthe total costs it imposes upon a metropolitanregion are greater than the total bene� ts gen-erated from it.

Conversely, other analysts like Ewing(1997), Downs (1999) and Myers and Kit-suse (1999) have pointed out that a purelymarket-based approach to de� ning excessivespatial growth ignores the institutional en-vironment in which economic actors in ametropolitan area make land-use decisions.These analysts highlight the fact that govern-ment institutions in� uence local land-use de-cisions and can generate or slow urbandecentralisation. The objective of this paperis to determine whether the state-wide sys-tem of local public � nance and the use ofurban growth boundaries work to further ordeter retail decentralisation in a metropolitanarea.

‘Fiscalisation of land use’ implies that thesystem of local public � nance exerts anin� uence on local land-use decisions. Re-gression analysis is used here to test whetherthe real dollar value of retail activity in ametropolitan area’s non-central places is in-dependently in� uenced by relevant measuresof local government revenue reliance. Thismeasure of the � scalisation of land use isappropriately sought after controlling fornon-central-place population, income, agedistribution, farmland price, previous growthrate and the presence of different forms ofurban growth boundaries. The explanatoryvariables contained in the regression analysis(except those representing local revenuereliance and urban containment policies) rep-resent factors that—many would argue—� ttingly drive the non-central location ofretail activity in a metropolitan area. Fromthe social perspective of what is best for theentire region, local revenue reliance is a poorbasis for determining retail location deci-sions. If local revenue reliance is found inde-

pendently to increase non-central retail salesin a metropolitan area, then it can be con-sidered a driver of excessive retail decentra-lisation or sprawl. At the same time, if thepresence of an urban growth boundary re-duces non-central retail sales, then it hasachieved its stated policy goal of slowing theamount of retail decentralisation that wouldhave occurred without it.

As a complete reading of this paper re-veals, the regression analysis shows thatstate-wide measures of reliance by munici-palities on two forms of own-source revenueexert a positive in� uence on the amount ofnon-central retail activity in metropolitanareas in the western US over the period1977–97; while the continuing presence ofone form of urban growth boundary reducesthe amount of non-central retail activity.

The concept of urban retail sprawl is de-veloped in the next section of the paper in adiscussion of the ways that both planners andeconomists have thought about it. Section 3grounds the regression analysis in theory byreviewing previous literature on the determi-nants of retail location in a metropolitan area.This section also contains a discussion ofwhy the way that local governments raiserevenue in a state could in� uence the in-trametropolitan location of retail activity inthat state. Section 4 of the paper offers adescription of differences in the degree ofretail decentralisation in 54 metropolitan ar-eas in the western portion of the US for theyears 1977, 1987 and 1997. Section 5 pro-vides background on the regression test usedto determine if state-wide averages for mu-nicipal revenue reliance and/or the presenceof urban growth boundaries exert measurablein� uences on the location of retail activity ina metropolitan area. Section 6 contains adiscussion of the regression results. The im-plications of the research are in the conclud-ing section 7.

2. Urban Retail Sprawl

Urban planners, and increasingly the generalpublic, use the term sprawl to mark undesir-able forms of urban land use. Observing this

FISCALISATION OF LAND USE 1309

now-common application, researchers havedeveloped a list of land-use characteristicsthat are most often associated with beinglabelled sprawl. Downs (1999) de� nes urbansprawl by observable traits such as unlimitedoutward extension of new development, low-density developments in new areas, leapfrogdevelopment, transport dominance by privateautomobiles and strip commercial develop-ment. Myers and Kitsuse (1999) frame theissue of sprawl in terms of patterns of urbanpopulation density that produce undesirablesocial outcomes.

Ewing (1994) surveyed academic articleswritten on sprawl between 1957 and 1992and found that low-density, strip, scatteredand ‘leapfrog’ are the forms of urban devel-opment most often labelled sprawl. His re-view points out the subtle distinctionbetween undesirable non-compact develop-ment in a metropolitan area (sprawl) anddesirable polycentric development. Polycen-tric development, which now characterisesmost large metropolitan areas in the US, isoften more ef� cient—in terms of clusteringland uses to reduce trip lengths and reducecongestion—than development in a compactcentralised pattern. This is also the line ofreasoning offered by economists who havewritten on this issue. Both planners andeconomists recognise that there are sociallybene� cial reasons for activity to locate innon-central locations of a metropolitan area.A reasonable position to take is that decen-tralised development should only fall underthe pejorative label of sprawl to the degreethat it is being driven by reasons that areunlikely to promote social optimality.

The desire here is to test whether the� scalisation of land use in� uences theamount of retail decentralisation observed ina metropolitan area. A regression analysisuses the real dollar value of retail activity innon-central places as the dependent variable.This is a practical choice because excessiveretail sales activity, and the ‘big-box’ and/or‘strip-mall’ ways in which it generally occursin US suburbs, represents much of whatplanners and the public perceive as sprawl.Suburban retail activity that is greater than

warranted by economic factors also coincideswith other rudiments of how sprawl has beende� ned: a lower density of development inthe metropolitan area’s central places; greaterpossible leapfrog development at the urbanfringe; greater auto reliance for retail shop-ping; and, greater congestion and air pol-lution generated in getting to retail shops thatare farther away from the customers that usethem (central-place citizens shopping in non-central-place locations).

3. The Location of Retail Activity in aMetropolitan Area

If urban retail sprawl is de� ned as non-central retail activity that is greater than non-central economic factors warrant, thenknowing the relevant economic factors thatdetermine the intrametropolitan location ofretail activity is important. As summarised inDiPasquale and Wheaton (1996) andO’Sullivan (2000), economic theory predictsthat a pro� t-maximising retail � rm chooses alocation in a metropolitan area based uponthe location of its customers, transport costs,agglomeration economies and the degree ofscale economies in its particular form ofretail production. In a metropolitan area withone central city, these factors push retailersthat exhibit high and even moderate scaleeconomies in production to locate primarilyin the central city. Retailers with relativelysmall-scale economies in production basetheir intrametropolitan location on wheretheir customers reside and a division of theregion into pro� table market areas.

Between 1950 and 1990, the proportionof the US metropolitan population living incentral cities fell from 64 to 38 per cent.The fraction of metropolitan retail employ-ment in US central cities accordingly fellfrom about two-thirds in 1950 to a little lessthan one-half in 1990. This suburbanisationof retail activity was caused by the migrationof existing metropolitan residents from cen-tral cities to the suburbs, an overall increasein metropolitan residents and a greater per-centage of them choosing to live in the sub-urbs and falling automobile transport costs


which reduced ties to a central shoppinglocation.1

In a review of the economic thinking onthe causes of metropolitan suburbanisation,Miezkowski and Mills (1993) � nd valuableinsights offered by both the natural evolutionand � scal/social approaches. The naturalevolution approach emphasises thesigni� cance of income, population, transport,and technological changes in determining thedegree of decentralisation in a metropolitanarea. The � scal/social approach is a general-isation of Tiebout’s (1956) model of ‘votingwith one’s feet’ and points to increased sub-urbanisation as partially the result of citi-zens’ desires to form and fund morehomogeneous communities. To do this, sub-urban communities use land-use controls andsubsidies to attract residents and businessthat offer a � scal surplus and do little todamage the local environment. The � scal/social approach is particularly relevant whenconsidering how local revenue reliance couldgenerate retail decentralisation.

Municipal revenue from retail activity,that in most instances requires a relativelysmall amount of local government servicesand generates relatively little environmentaldamage, offers a good choice of funding forlocal services. If suburban communities ac-tively seek retail activity for the purpose ofthe � scal surplus it generates, then greaterstate-wide reliance on a municipal revenueinstrument that generates a local � scal sur-plus through greater local retail activity maygenerate greater retail decentralisation. Local� scal structure is unlikely to induce moreretail activity in a metropolitan area, but itcan induce changes in where it locates.Within a metropolitan area, non-centralplaces draw excessive retail activity from thecentral places where historically it had beenlocated.2

Other researchers have also recognisedthat local � scal factors can contribute to thegeneration of urban decentralisation. Harveyand Clark (1965) assert that local reliance onproperty taxation discourages the designationof land for a non-agricultural use becauseonce done the land is subject to higher tax-

ation. The hesitancy of jurisdictions to desig-nate agricultural land for non-agriculturaluses encourages leapfrog development. Mis-czynski (1986) popularised the use of thephrase ‘� scalisation of land use’ in Califor-nia policy circles to describe what he increas-ingly expected to happen after California’spost-Proposition 13 abandonment of propertytaxation as a discretionary source of localrevenue. Innes and Booher (1999) point tothe complex and fragmented system of local� nance in California, with its heavy relianceon sales taxation as a source of local discre-tionary revenue, as the single most importantfactor driving local land-use decisions in thestate. Atkinson and Oleson (1996) believethe automobile to be the major culprit ofsprawl, but maintain that this would not havebeen possible without complementary local� nance policies. Kotin and Peiser (1997)have looked at public–private partnershipsfor high-volume retailers and the degree thatmunicipalities bene� t from them. In a mono-graph-length study of sales taxation in Cali-fornia, Lewis and Barbour (1999) concludethat local sales tax reliance has in� uencedlocal land-use decisions in the state.

In addition, Brueckner and Kim (2000)demonstrate that the theoretical in� uence oflocal property tax reliance on the generationof metropolitan decentralisation is indeter-minate. Greater reliance on local propertytaxation reduces individual housing con-sumption, which raises population densityand reduces urban sprawl. Concurrently,greater local property taxation reduces theintensity of land development, lowers popu-lation density and encourages urban sprawl.A simulation using reasonable real-worldvalues suggests that the likely in� uence ofgreater local property taxation in generatingurban sprawl, through its in� uence on capitaluse, is slightly positive.

Finally, Brueckner and Fansler (1983)conducted one of the only regression studiesof the determinants of the size of a US urbanarea. Using 1970 data and relying on tra-ditional urban theory, they regressed the cen-sus-de� ned size in square miles of the 40largest urbanised areas in the US against the


urbanised area’s population, median income,rent paid on agricultural land and proxies forcommuting costs. The empirical analysis thatfollows builds upon Brueckner and Fansler’sstudy by including state-wide measures ofmunicipal revenue reliance in a regressiondesigned to explain retail activity in non-central places in western US metropolitanareas. Before the method to do this is de-scribed in greater detail, the next section ofthe paper offers a description of areas in-cluded in the study and differences over timein their non-central place retail activity.

4. Metropolitan Retail Decentralisation inthe Western US

An empirical study of the degree of metro-politan retail decentralisation must beginwith a unit of analysis. For this study, it isthe 61 metropolitan areas in what the CensusBureau de� ned in 1990 as the continentalwestern US, less the 7 metropolitan areas inIdaho, Montana and Wyoming. The analysisis limited to metropolitan areas in the Westfor a few reasons. The � rst is that 6 of the 8states de� ned as western enacted state-wideballot box or legislative restrictions on thelocal use of property taxes between 1977 and1997. Through Proposition 13 (1978), Mea-sures 5 and 47 (1990 and 1996) and Amend-ment 1 (1992), California, Oregon andColorado voters all used the citizen initiativeto limit local property taxation. In Arizona,Nevada and Utah, the state legislature tooksimilar steps. (Sokolow (2000) offers a com-prehensive survey of property-tax limitationin the western US. See Chapman (1998) fora summary of the local public � nance conse-quences of California’s 1978 passage ofProposition 13.) These restrictions, whichSokolow (2000) classi� es as harsher than inany other region in the US, offer naturalexperiments by which to test the in� uence ofchanges in state-wide municipal � scal struc-ture on metropolitan retail decentralisation.Furthermore, most metropolitan areas in thewestern US grew up in an era of risingpopulations, rising real incomes and declin-ing transport costs. Metropolitan areas in

Idaho, Montana and Wyoming are not in-cluded because the metropolitan develop-ment patterns in these three states are verydifferent in comparison to the western statesretained in the sample used here.3

Fifty-four metropolitan areas are used inthe analysis. A metropolitan area consists ofthe relevant component counties in the 1990Census de� nition of either a MetropolitanStatistical Area (MSA) or a Primary Metro-politan Statistical Area (PMSA).4 Since thefocus of this paper is retail activity in subur-ban locations, the suburban area within ametropolitan area is the component countiesin a MSA or PMSA, less the ‘central places’included in the 1990 de� nition of urbanisedareas in a metropolitan area.5 Central placesare considered by the Census to be the domi-nant employment and residential centres ineach urbanised area. For instance, the subur-ban area in the San Diego MSA would beSan Diego County less the cities of Coro-nado, Escondido and San Diego. Table 1contains a list of the 54 metropolitan areas inthe sample, the areas’ component countiesand the central places that are excluded fromthese counties to create the de� nition of ametropolitan area’s non-central places that isused here. This designation of suburbia is anattempt to account for the polycentric natureof most US metropolitan areas through theuse of existing data sources.

Table 2 offers a comparison of the ratio ofnon-central place retail sales to total metro-politan area retail sales for all 54 metropoli-tan areas in the sample.6 The � rst three datacolumns in Table 2 illustrate the variation inthe degree of retail sales decentralisationacross metropolitan areas and within ametropolitan area over time. The last twodata columns in Table 2 indicate the percent-age change in retail decentralisation for eacharea, for the periods 1977–87, and 1987–97.The top eight data rows in this table reportthe averages for each state using the metro-politan area as the unit of observation.

Metropolitan areas in Arizona, on averagebetween 1977 and 1987, experienced a slight4 per cent decrease in metropolitan retailsales in non-central places. This measure


Table 1. Urban area de� nitions for the western US, as of 1990

Central places (cities)Metropolitan area name Counties in metropolitan area in metropolitan area

Phoenix–Mesa AZ, MSA Maricopa AZ, Pinal AZ Mesa AZ, Phoenix AZ,Scottsdale AZ, Tempe AZ

Tuscon AZ, MSA Pima AZ Tucson AZYuma AZ, MSA Yuma AZ Yuma AZBakers� eld CA, MSA Kern CA Bakers� eld CAChico–Paradise CA, MSA Butte CA Chico CAFresno CA, MSA Fresno CA, Madera CA Fresno CALos Angeles–Long Beach Los Angeles CA Lancaster CA, Long BeachCA, PMSA CA, Los Angeles CA, Pasadena CAMerced CA, MSA Merced CA Merced CAModesta CA, MSA Stanislaus CA Modesto CA, Turlock CAOakland CA, PMSA Alameda CA, Contra Alameda CA, Berkeley

Costa CA CA, Oakland CAOrange CA, PMSA Orange CA Anaheim CA, Irvine CA,

Santa Ana CARedding CA, MSA Shasta CA Redding CARiverside–San Bernardino Riverside CA, San Hemet CA, Palm DessertCA, PMSA Bernardino CA CA, Palm Springs CA,

Riverside CA, SanBernardino CA,Temecula CA

Sacramento CA, PMSA El Dorado CA, Placer CA, Sacramento CASacramento CA

Salinas CA, MSA Monterey CA Monterey CA, Salinas CASan Diego CA, MSA San Diego CA Coronado CA, Escondido

CA, San Diego CASan Francisco CA, PMSA Marin CA, San Francisco San Francisco CA

CA, San Mateo CASan Jose CA, PMSA Santa Clara CA Gilroy CA, Palo Alto CA,

San Jose CA, SantaClara CA, Sunnyvale CA

San Luis Obispo– San Luis Obispo CA Atascadero CA, PasoAtascadero–Paso Robles Robles CA, San LuisCA, MSA Obispo CASanta Barbara–Santa Santa Barbara CA Lompoc CA, SantaMaria–Lompoc CA, MSA Barbara CA, Santa Maria CASanta Cruz–Watsonville Santa Cruz CA Santa Cruz CA,CA, PMSA Watsonville CASanta Rosa CA, PMSA Sonoma CA Petaluma CA, Santa

Rosa CAStockton–Lodi CA, MSA San Joaquin CA Lodi CA, Stockton CAVallejo–Fair� eld–Napa CA, Napa CA, Solano CA Fair� eld CA, Napa CA,PMSA Vacaville CA, Vallejo CAVentura CA, PMSA Ventura CA San Buenaventura

(Ventura) CAVisalia–Tulare–Porterville Tulare CA Porterville CA, Tulare CACA, MSAYolo CA, PMSA Yolo CA Davis CA, Woodland CAYuba City CA, MSA Sutter CA, Yuba CA Yuba CABoulder–Longmount CO, Boulder CO Boulder CO, Longmount COPMSAColorado Springs CO, El Paso CO Colorado Springs, COMSA


Table 1. Continued

Central places (cities)Metropolitan area name Counties in metropolitan area in metropolitan area

Denver CO, PMSA Adams CO, Arapahoe CO, Denver CODenver CO, Douglas CO,Jefferson CO

Fort–Collins–Loveland CO, Larimer CO Fort Collins COMSAGrand Junction CO, MSA Mesa CO Grand Junction COGreeley CO, MSA Weld CO Greeley COPueblo CO, MSA Pueblo CO Pueblo COLas Vegas NV & AZ, Clark NV, Mohave AZ, Las Vegas NVMSA Nye NVReno NV, MSA Washoe NV Reno NVAlbuquerque NM, MSA Bernalillo NM, Sandoval Albuquerque NM

NM, Valencia NMLas Cruces NM, MSA Dona Ana NM Las Cruces NMSanta Fe NM, MSA Los Alamos NM, Santa Santa Fe NM

Fe NMEugene–Spring� eld OR, Lane OR Eugene OR, Spring� eldMSA ORMedford–Ashland OR, MSA Jackson OR Medford ORPortland–Vancouver OR, Clackamas OR, Columbia Portland OR, VancouverPMSA OR, Multnomah OR, WA

Washington OR, YamhillOR, Clark WA

Salem OR, PMSA Marion OR, Polk OR Salem ORProvo–Orem UT, MSA Utah UT Provo UT, Orem UTSalt Lake City–Ogden UT, Davis UT, Salt Lake UT, Salt Lake City UT,MSA Weber UT Ogden UTBellingham WA, MSA Whatcom WA Bellingham WABremerton WA, PMSA Kitsap WA Bremerton WAOlympia WA, PMSA Thurston WA Olympia WARichland–Kennewick– Benton WA, Franklin WA Kennewick WA, PascoPasco WA, MSA WA, Richland WASeattle–Bellevue–Everett Island WA, King WA, Auburn WA, Everett WA,WA, PMSA Snohomish WA Seattle WASpokane WA, MSA Spokane WA Spokane WATacoma WA, PMSA Pierce WA Tacoma WAYakima WA, MSA Yakima WA Yakima WA

rose to a 45 per cent increase between 1987and 1997. By contrast, metropolitan areas inUtah on average experienced a 22 per centincrease in the decentralisation of retail ac-tivity between 1977 and 1987, and hardlyany change between 1987 and 1997. Otherthan that there is a great deal of variation inthe degree of non-central place retail activityoccurring in different western metropolitanareas and over time, it is hard to draw anyspeci� c conclusions from the information inTable 2. A regression analysis of the deter-minants of suburban retail activity is necess-ary to comprehend the observed variation.

5. State-wide Local Revenue Choices andRetail Decentralisation

The dependent variable for the empiricalstudy discussed next is the real value of retailsales in non-central places for the 54 metro-politan areas described in the previous sec-tion, for the years 1977, 1987 and 1997.7 Thepooling of cross-sectional and time-seriesdata allows for variation in non-central retailsales to occur across metropolitan areas andwithin an area over time. These data enable aregression test of whether state-wide aver-ages for pertinent forms of own-source


Table 2. Distribution of retail sales and changes in retail sales for non-central places in metropolitan areasin the western US

Percentage change inNon-central place retail non-central place retailsales/metro retail sales sales/metro retail sales

Metropolitan area name, 1990 1977 1987 1997 1977–87 1987–97

Arizona average for MSAs 0.190 0.180 0.260 2 4.28 45.39California average for (P)MSAs 0.461 0.461 0.477 0.52 4.57Colorado average for (P)MSAs 0.257 0.258 0.282 0.00 5.99Nevada average for MSAs 0.423 0.439 0.406 5.71 2 8.25New Mexico average for MSAs 0.140 0.123 0.117 2 9.41 2 1.54Oregon average for (P)MSAs 0.412 0.422 0.397 2.30 2 3.13Utah average for MSAs 0.403 0.470 0.492 22.04 2 0.69Washington average for (P)MSAs 0.406 0.420 0.463 1.66 6.75

Phoenix–Mesa AZ, MSA 0.202 0.190 0.238 2 5.83 25.31Tucson AZ, MSA 0.160 0.177 0.216 10.34 22.41Yuma AZ, MSA 0.209 0.173 0.326 2 17.33 88.44Bakers� eld CA, MSA 0.445 0.431 0.436 2 3.05 1.09Chico–Paradise CA, MSA 0.581 0.555 0.343 2 4.45 2 38.32Fresno CA, MSA 0.417 0.364 0.412 2 12.76 13.24LA–Long Beach CA, PMSA 0.524 0.557 0.610 6.42 9.38Merced CA, MSA 0.410 0.383 0.388 2 6.73 1.36Modesta CA, MSA 0.250 0.250 0.355 2 0.11 41.74Oakland CA, PMSA 0.720 0.748 0.808 3.89 8.06Orange CA, PMSA 0.757 0.773 0.754 2.16 2 2.54Redding CA, MSA 0.272 0.227 0.167 2 16.54 2 26.41Riverside–San Bernardino CA, PMSA 0.598 0.691 0.702 15.58 1.59Sacramento CA, PMSA 0.694 0.722 0.768 4.05 6.41Salinas CA, MSA 0.394 0.395 0.436 0.26 10.17San Diego CA, MSA 0.450 0.477 0.467 6.02 2 2.18San Francisco CA, PMSA 0.532 0.555 0.598 4.20 7.79San Jose CA, PMSA 0.326 0.299 0.261 2 8.27 2 12.67SLO–Atasc–Paso Robles CA, MSA 0.436 0.353 0.533 2 19.03 50.89San Barb–Santa Maria–Lom CA, MSA 0.263 0.323 0.285 22.75 2 11.82Santa Cruz–Watsonville CA, PMSA 0.355 0.452 0.553 27.33 22.31Santa Rosa CA, PMSA 0.446 0.312 0.340 2 30.04 8.97Stockton–Lodi CA, MSA 0.330 0.289 0.375 2 12.34 29.57Vallejo–Fair� eld–Napa CA, PMSA 0.133 0.193 0.176 44.72 2 8.61Visalia–Tulare–Porterville CA, MSA 0.691 0.689 0.693 2 0.27 0.58Ventura CA, PMSA 0.760 0.761 0.807 0.14 6.02Yolo CA, PMSA 0.280 0.248 0.288 2 11.35 16.13Yuba City CA, MSA na 0.466 0.379 na 2 18.65Boulder–Longmount CO, PMSA 0.225 0.211 0.204 2 6.10 2 3.60Colorado Springs CO, MSA 0.097 0.080 0.069 2 17.13 2 14.08Denver CO, PMSA 0.632 0.711 0.735 12.41 3.45Fort Collins–Loveland CO, MSA 0.401 0.361 0.411 2 10.15 14.00Grand Junction CO, MSA 0.105 0.156 0.162 49.19 3.85Greeley CO, MSA 0.271 0.222 0.338 2 18.21 52.30Pueblo CO, MSA 0.069 0.063 0.054 2 9.99 2 13.99Las Vegas NV & AZ, MSA 0.607 0.617 0.576 1.72 2 6.75Reno NV, MSA 0.238 0.261 0.236 9.71 2 9.75Albuquerque NM, MSA 0.154 0.117 0.168 2 24.10 43.84Las Cruces NM, MSA 0.098 0.104 0.098 6.84 2 6.24Santa Fe NM, MSA 0.167 0.149 0.086 2 10.98 2 42.23Eugene–Spring� eld OR, MSA 0.362 0.212 0.235 2 41.42 10.85


Table 2. Continued

Percentage change inNon-central place retail non-central place retailsales/metro retail sales sales/metro retail sales

Metropolitan area name, 1990 1977 1987 1997 1977–87 1987–97

Medford–Ashland OR, MSA 0.308 0.438 0.370 42.31 2 15.54Portland–Vancouver OR, PMSA 0.593 0.670 0.612 12.98 2 8.67Salem OR, PMSA 0.386 0.368 0.371 2 4.68 0.86Provo–Orem UT, MSA 0.249 0.338 0.273 35.96 2 19.33Salt Lake City–Ogden UT, MSA 0.557 0.603 0.711 8.12 17.94Bellingham WA, MSA 0.309 0.325 0.327 5.29 0.33Bremerton WA, PMSA 0.400 0.597 0.731 49.09 22.39Olympia WA, PMSA 0.450 0.407 0.472 2 9.71 16.14Richland–Kennewick–Pasco WA, MSA 0.344 0.139 0.097 2 59.44 2 30.51Seattle–Bellevue–Everett WA, PMSA 0.541 0.626 0.663 15.68 5.94Spokane WA, MSA 0.339 0.326 0.420 2 3.71 28.91Tacoma WA, PMSA 0.490 0.535 0.601 9.25 12.31Yakima WA, MSA 0.375 0.401 0.395 6.81 2 1.48

municipal revenue reliance exert signi� cantin� uences on the amount of non-central retailsales in a state’s metropolitan areas. A modelof what determines non-central retail sales ina metropolitan area is necessary to formulatethis regression test. The model that followsbuilds upon the earlier work of Brueckner andFansler (1983).

Economic theory indicates that the realdollar value of retail sales in the non-centralportion of a metropolitan region increases asnon-central population and real householdincome increase. Suburban retail activity maybe slowed by a higher acquisition price foragricultural land upon which to build newretail centres. The availability of transportoptions can also in� uence where people in ametropolitan area shop. These four factors(population, income, price of agricultural landand transport options) are what Bruecknerand Fansler expect to in� uence the size of anurbanised area.8 With the exception of proxiesfor transport options, the model of suburbanretail sales used here also relies on these samecausal factors. Metropolitan transport optionsare excluded for two reasons: demographics,population and income largely determine thetransport options available in a metropolitanarea; and, the in� uence of transport options

on suburban retail activity is not the focus ofthis investigation.9 Further re� nement ofBrueckner and Fansler’s model of urban sizeis necessary to assess accurately the in� uenceof local government � scal institutions on non-central retail activity. This is in the form ofcontrolling for demographic differences in thetype of population located in non-centralplaces, the previous decade’s growth in non-central population and any forms of urbangrowth controls that may be present. Themodel used to delineate the regression analy-sis is thus

Retail salesi, t 5

f (Incomei, t, Populationi, t, Previous dec-ade’s population growthi, t, Presence ofthe urban containment policyi, t, Price ofagricultural landi, t, Percentage popu-lation less than age 18i, t, Percentagepopulation greater than age 64i, t,Percentage state-wide discretionary mu-nicipal revenue from property taxesi, t,Percentage state-wide discretionarymunicipal revenue from general salestaxesi, t, Percentage state-wide discre-tionary municipal revenue from othertaxesi, t)


where, i 5 1, 2, 3, … , or 54 (for each metro-politan area’s non-central places); andt 5 1977, 1987 or 1997.

The degree of state-wide reliance on dif-ferent forms of own-source municipal rev-enue can in� uence the amount of retailactivity in non-central metropolitan placesthrough local � scal zoning and the offeringof local economic development incentives.Municipal revenue reliance for a speci� c cat-egory is calculated as a percentage of locallygenerated revenue from sources most likelyto be in� uenced by local land-use decisions.The term used here to describe this form oflocal revenue source is ‘discretionary’. Dis-cretionary revenue sources include locallygenerated property taxes, sales taxes, othertaxes and user charges/special assessments.10

The chosen term discretionary also refers tothe fact that if a state-wide policy were insti-tuted to reduce percentage reliance on one ofthese local revenue instruments, the percent-age reliance on the others would be likely tohave to increase. Only three of the fourdiscretionary revenue sources are accountedfor in the regression model because the third,Percentage state-wide discretionary munici-pal revenue from charges, equals 100 percent less the sum of the included three.

As widely documented, municipalities andunincorporated areas of counties in the USregulate local land uses with an eye on the� scal bottom-line.11 Municipal and countygovernments in the US also use local incen-tives to attract desirable land uses withintheir boundaries.12 Both of these activitiescan result in greater local retail activity in ametropolitan area’s non-central places thaneconomic factors alone would dictate. Differ-ent degrees of state-wide reliance, on differ-ent forms of own-source municipal revenue,could thus yield different amounts of � scalsurplus generated by local land devoted toretail activity. The greater the reliance on amunicipal revenue source that generates alocal � scal surplus from local retail activity,the more likely it will be that local of� cialszone for retail land uses and use local incen-tives to encourage it. Kotin and Peiser(1997), in their study of the � scal bene� ts

that retailers offer cities in California, in-clude local sales taxes, property taxes andbusiness licence fees (which fall into thecategory of other taxes used above) as thethree forms of city revenues that need to beaccounted for.

The US Census of Governments dividesmunicipal own-source revenue into two cate-gories: current charges/miscellaneous rev-enue and taxes. The census describes currentcharges as fees for speci� c local servicesdelivered to a local citizen or business. Forthe purpose of this study, charges equal cur-rent charges plus special assessments. Spe-cial assessments are included with chargesbecause of their census de� nition as“compulsory contributions collected fromowners of property bene� ted by special pub-lic improvements”. The census includes in-terest earnings, special assessments, sale ofproperty and other general revenue under itsde� nition of miscellaneous revenue. With thepossible exception of special assessments,these forms of miscellaneous revenue areunlikely to generate a local � scal surplusthrough greater retail activity and are ex-cluded from the regression model.

The census classi� es municipal taxes inthe forms of property, sales, individual in-come, corporate income, motor vehicle li-cence, and other taxes. None of the eightwestern states under consideration here al-lows local personal or corporate incometaxes. All other forms of local taxation, ex-cept motor vehicle taxes, are accounted for inthe regression analysis because they offer thepotential for a local suburban government tobene� t from a � scal surplus gained throughthe attraction of greater retail activity withinits boundaries.13

Brueckner and Kim (2000) have theoreti-cally shown that the expected in� uence ofgreater local reliance on property taxation onurban decentralisation through capital use isuncertain. Aside from altering capital use,greater local reliance on property taxes canalso encourage local land-use decisions thatare more likely to generate a � scal surplusthrough property taxation (property tax rev-enue greater than the cost of local services


required by the retail property). Thein� uence that this has on suburban retailactivity depends upon how retail does ingenerating a property tax � scal surplus rela-tive to alternative uses (housing or manufac-turing) for a municipality’s land.14

Throughout a state, greater average localreliance on general sales taxation as a sourceof discretionary local revenue offers a reasonfor suburban governments in the state to lureretailers away from traditional business dis-tricts in central-place communities and in-crease the amount of retail sales in thesuburbs. In support, Lewis and Barbour(1999) found through a survey of of� cials in300 California cities, that asked them to rank18 different motivations for evaluating thedesirability of various forms of developmentprojects, that ‘new sales tax revenues’ always� nished � rst or second in terms of the pos-ition most often given. Interestingly, only the36 central-city of� cials in the sample rankedsales tax considerations consistently lower.The lure of collecting other taxes, like alicence fee or other business tax, from retail-ers offers an additional motivation for non-central-place governments to draw retailactivity away from traditional central-placelocations.

Economic theory indicates that suburbanincome and population should exert a posi-tive in� uence on suburban retail sales, whilethe in� uence of the price of agricultural landin the metropolitan area should be negative.After a previous decade’s surge in populationgrowth, retail developers may have not beenable to keep pace with the amount of devel-opment speci� ed by population and retailsales may be smaller, holding other factorsconstant, in an area that previously experi-enced high population growth. Likewise,suburban areas with a higher percentage ofsenior citizens or families with children arelikely to exhibit different retail consumptionpatterns; however, the directions of thesein� uences are uncertain.15

The regression model used to explain non-central retail activity in a metropolitan areaalso includes six explanatory variables thataccount for whether a certain type of urban

containment policy (UCP) exists in a metro-politan area and, if it does, for how long hasit been in existence. UCPs are commonlyreferred to as urban growth boundaries andare designed to slow the degree of decentra-lisation in a metropolitan area that wouldhave occurred over time. The presence of aUCP could thus reduce the amount of non-central retail activity in a metropolitan area.

The regression accounts for the three dif-ferent types of UCP catalogued by Nelson(2001) in his recent examination of thesepolicies. The � rst type is ‘closed-region con-tainment’. Nelson de� nes this as metropoli-tan-wide, explicitly preserving land at theurban fringe and attempting to shift displaceddevelopment back to the centre. The secondtype is ‘open-region containment’. It is alsometropolitan-wide, does nothing explicitly topreserve open space at the fringe, but doesendeavour to shift development back to thecentre of the urban area. The � nal type ofUCP is ‘isolated containment’. By Nelson’sde� nition, a policy of isolated containmentdoes not exist on a metropolitan-wide basis,intends only to preserve limited land outsidesome jurisdictional boundaries and doesnothing to shift development occurring out-side these intrametropolitan boundaries backto the urban core.

As taken from Nelson, a description of thewestern metropolitan areas that had one ofthe three urban containment policies in placein 1997 is given in Table 3. Further investi-gation yielded the recorded information onthe approximate year that each of these UCPsbegan. Since development patterns are morelikely to be constrained by an urban contain-ment policy the longer it has been in place,the explanatory variables in the regressioninclude three dummy variables for whether atype of UCP exists and three other variablesthat account for the number of years since acertain UCP began in the metropolitan area.

The high level of variation in own-sourcemunicipal revenue reliance across states andacross time is indicated by the valuesrecorded in Table 4. For instance, on averagebetween 1977 and 1997, municipal govern-ments in New Mexico drew only 22.4 per


Table 3. Year that a type of urban containment policy (UCP) began in a western metropolitan area

Type of UCP

Western metropolitan areas with Closed-region Open-region Isolatedan urban containment policy (UCP) containment containment containment

Yuma AZ, MSA 1996Chico–Paradise CA, MSA 1983Fresno CA, MSA 1984Sacramento CA, MSA 1993San Diego CA, MSA 1979San Jose CA, MSA 1972Santa Rosa CA, PMSA 1996Vallejo–Fair� eld–Napa CA, PMSA 1980Visalia–Tulare–Porterville CA, MSA 1974San Luis Obispo–Atascadero–Paso Robles CA, MSA 1981Santa Barbara–Santa Mraia–Lompoc CA, MSA 1989Yolo CA, PMSA 1987Yuba City CA, MSA 1989Boulder–Longmount CO, PMSA 1978Fort Collins–Loveland CO, MSA 1980Santa Fe NM, MSA 1991Eugene–Spring� eld OR, MSA 1982Medford–Ashland OR, MSA 1982Portland–Vancouver OR, PMSA 1979Salem OR, PMSA 1981Bellingham WA, MSA 1992Olympia WA, PMSA 1992Seattle–Bellevue–Everett WA, PMSA 1992Tacoma WA, PMSA 1992Yakima WA, MSA 1992

cent of their discretionary local revenue fromproperty taxation. The comparable � gure formunicipal governments in Oregon was 52.6per cent. For general sales taxation over thesame 20-year period, municipal governmentsin Oregon relied on it for none of theirown-source revenue, while municipal gov-ernments in Colorado gained 41.1 per cent oftheir discretionary revenue from it. As well,within-state variations over time for somestates were large. In 1977, local governmentsin California drew 41.7 per cent of discre-tionary revenue from property taxation; by1997, this value had fallen to 25.7 per cent.General sales taxation totalled 12.1 per centof New Mexico’s own-source municipal rev-enue in 1977; by 1997, it had risen to 37.0per cent. Nevada municipalities relied onother taxes for 22.9 per cent of their discre-tionary revenue in 1977; by 1997, this � gurehad fallen to 15.0 per cent.

The top of Table 5 lists the mean andstandard deviation of the regression’s depen-dent variable in millions of dollars. The samedescriptive statistics are given for each of theexplanatory variables in the table’s � rst datacolumn. The non-central-place values of me-dian household income and population arecalculated from the US Department of Hous-ing and Urban Development’s State of theCities Data System.16 Actual income valueswere not available for 1997 and had to beextrapolated from the available 1979 and1989 values. Interpolation from the availabledecennial census years was also necessary todetermine population and income values for1967, 1977 and 1987. The 1997 populationvalue is an estimate provided by the census.Various editions of the US Census City andCounty Databook offer the data necessary tocalculate the desired measures of metropoli-tan age distribution. Interpolation yields the


Table 4. Percentage of state-wide discretionary municipal revenue from component sources forwestern US states

Percentage of state-wide discretionary municipal revenue from

Year and state Property taxes General sales taxes Other taxes Charges

1997 Arizona 15.6 39.0 5.5 39.91997 California 25.7 20.1 11.9 42.41997 Colorado 10.8 40.3 4.1 44.81997 Nevada 24.9 0.0 15.0 60.11997 New Mexico 14.6 37.0 2.7 45.71997 Oregon 46.7 0.0 12.7 40.61997 Utah 24.5 28.9 5.7 41.01997 Washington 24.5 22.0 10.5 43.0



1977 and 1987 values, while extrapolationresults in the values for 1997. A suitableproxy for the real price of agricultural land ina metropolitan area’s non-central places isthe real value of agricultural products sold inthe metropolitan area divided by the numberof agricultural acres in the area. Theseamounts come from the US Census of Agri-culture.

A concern for the regression analysis ishow to control for non-measurable factorsthat are � xed in a given year across all areas,or � xed in a given area for all years, and canin� uence the real value of non-central retailactivity. Since the factors � xed in a givenyear are likely to be related to the position ofthe national economy in the business cycle, adummy variable for observations from 1987and another dummy variable for observationsfrom 1997 are included in all regressions.

To control for factors � xed across all ob-served years, but that vary by metropolitanarea, a few regression options are available(see Kennedy (1992, pp. 222–223) for a fur-ther description of these possibilities). The� rst is the ‘� xed-effect’ method of droppingthe constant term and including a set ofdummy variables representing each of themetropolitan areas in the sample. This allowsdifferent constant terms to control for the� xed contribution of the unmeasured charac-teristics of a speci� c area. A second option isto treat ignorance on the speci� c � xed contri-bution of an area to its retail sales in thesame manner as the general ignorance repre-sented by the regression’s error term. Usingthis ‘random-effect’ method, the regression’serror is composed of the traditional compo-nent plus a second component that varies byeach of the 54 speci� c metropolitan locations

1320 ROBERT W. WASSMER

Tab

le5.

Des

crip

tive

stat

istic

san

dre

gres

sion

resu

ltsus

ing

real

valu

em

etro

polit

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tail

sale

s($

mill

ions

)in

non-

cent

ral

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depe

nden

tvar

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epen

dent

vari

able

’sm

ean

(sta

ndar

dde

viat

ion)

:$3

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12($

7017

.49)

)

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lana

tory

vari

able

sM

ean

(sta

ndar

dde

viat

ion)

Ord

inar

yle

ast

squa

res

Fixe

d-ef

fect

ordi

nary

leas

tsq

uare

sa

Con

stan

t2

185

3.64

not

repo

rted

(196

6.63

)19

87ye

ardu

mm

y2

713.

90**

225

5.37

(365

.06)

(204

.05)

1997

year

dum

my

21

341.

80**

*2

184.

95(5

14.2

6)(2

88.1

4)R

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inco

me

in41

808

0.59

8no

n-ce

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($)

(854

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055*

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6(0

.014

)(0

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)Po

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tion

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lpl

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453

085

1.06

10.

825

(730

899)

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ear

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103

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0)

1321FISCALISATION OF LAND USE

Yea

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ide

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110

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116

116

1R

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tic0.

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ted

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0.97

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rang

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lier

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stat

istic

36.8

1***

Whi

tehe

tero

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astic

ityco

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ted

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a Als

oin

clud

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les

for

each

ofth

e54

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ropo

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sin

clud

edin

sam

ple

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est.

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ndic

ates

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isca

llysi

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cant

intw

o-ta

iled

test

atgr

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per

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gni�

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.


in the sample. A � nal option is to do nothingto account for speci� c area effects. The re-gression results recorded in Table 5 includetwo of these possibilities and the results ofstatistical tests that indicate which is pre-ferred.17

6. Regression Findings

The second column of numbers in Table 5 isthe ordinary least squares (OLS) result andincludes no attempt to calculate a constantintercept term for each metropolitan area.These results, and the � xed-effect regressionresults next to them that include a separateconstant term for each of the 54 metropolitanareas, use White’s method of adjusting theregression coef� cient’s standard errors forpossible heteroscedastic bias from an un-known source.18 The regression entries inTable 5 � rst contain, in bold, the mean elas-ticity values for the statistically signi� cantregression coef� cients. Below these are theactual regression coef� cients and, in parenth-esis, the coef� cient’s standard errors.19 Thestatistical signi� cance of the F statistic,recorded at the bottom of the table, indicatesthat the group of area dummies included inthe � xed-effect model exerts a signi� cantin� uence on retail sales. The statisticalsigni� cance of the Lagrange multiplier stat-istic, also at the bottom of Table 5, indicatesthat the use of the � xed- or-random-effectmodel is preferred to simple ordinary leastsquares. A Hausman statistic, which indi-cates whether the random-effect model ispreferred to the � xed-effect model, could notbe calculated due to correlation between thecalculated random-effect errors and the ran-dom-effect regressors. Such correlation islikely to bias the regression coef� cients inthe random-effect model. Based upon thesetest statistics, the preferred results are fromthe � xed-effect regression model. The ordi-nary least squares regression results are pro-vided to show the difference in magnitudeand statistical signi� cance of regressioncoef� cients after area-speci� c effects are ap-propriately controlled for.

As expected, non-central-place population

exerts a signi� cant in� uence on non-central-place retail sales. In the � xed-effect model, a1 per cent increase in suburban populationfrom its mean results in about a 0.83 per centincrease in real retail sales from its mean forthe average metropolitan area in the sample.Brueckner and Fansler (1983), using ordi-nary least squares for a single cross-sectionof US metropolitan areas recorded a slightlylarger 1.1 per cent increase in urbanised landarea for a 1 per cent increase in urbanisedpopulation. Notice that this is the same as thepopulation elasticity of non-central retailsales recorded in the OLS regression in Table5.

In the � xed-effect regression, householdincome exerted no statistically signi� cantin� uence on real retail sales. However, in theOLS regression, an increase in suburbanhousehold income did result in a statisticallysigni� cant increase in suburban retail sales.20

Another signi� cant in� uence in the � xed-effect regression, that is non-� scal in nature,is that a 1 per cent increase in the price peracre of agricultural land in the metropolitanarea resulted in about a 0.14 per cent de-crease in real retail sales. This is the expectedeffect of higher prices for suburban landslowing down suburban retail expansion.21

Brueckner and Fansler (1983) record ahigher elasticity of –0.20 for a similar ex-planatory variable’s effect on the size of theurbanised land area, but recall this camefrom a regression analysis using a singlecross-section and no controls for � xed ef-fects. In addition, a 1 per cent increase in thepercentage of the non-central population overage 64 yields about a 0.29 per cent increasein non-central retail sales.

Particularly notable are the six regressioncoef� cients calculated for the three differentforms of urban containment policies. Thepresence of a closed-region urban contain-ment policy (a metropolitan-wide urbangrowth boundary which preserves land out-side it and attempts to shift demand for re-gional development to within it) is correlatedwith about $1 billion more retail activity inthe metropolitan area’s non-central places.But this effect should not be observed in


isolation, for the � xed-effect regression alsoreveals that, for every year that closed-regioncontainment is in place, the real value ofretail activity in non-central places (holdingother causal factors constant) fell by about$90 million. Although this yearly decrease isnot large relative to the average real value ofnon-central retail activity of $3.8 billion, af-ter 20 years of closed-region urban contain-ment, the resulting $1.8 billion reduction innon-central retail sales is a sizeable amount.

The � xed-effect regression indicates that aregion that institutes a policy of closed-region containment at � rst exhibits morenon-central retail activity; however, after 12years (calculated by dividing 1031 by 90) itbegins to have less. These � ndings are asexpected if regions with greater sprawl aremore likely to adopt closed-region contain-ment and over time this policy reduces de-centralisation. The increased effect over timeis likely to be due to outer developmentpatterns being increasingly constrained thelonger a given closed-region policy has beenin place. Isolated urban containment (openspace preservation in place at only sub-metropolitan jurisdictional boundaries and noeffort to direct development back to centralplaces) exhibited no statistically signi� cantin� uence on the amount of real retail activityin non-central places.

The positive regression coef� cient on thevariable representing the number of yearsthat open-region urban containment has beenin place in the metropolitan area deservesexplanation. Recall that this form of urbancontainment policy is less restrictive than theclosed-region form since it does not attemptto preserve open space outside drawnboundaries. The adoption of such a policy inthe San Luis Obispo–Atascadero–Paso Rob-les and Santa Fe metropolitan areas waslikely to have been a response to a belief thatsprawl is coming and a desire to do some-thing about it. But as the regression indicates,without concentrated efforts to preserve openspace at the fringe, open-region urban con-tainment policies do not reduce the decentra-lisation of retail activity. The positivecoef� cient on years of open-region urban

containment is unlikely to be causal and justpicking up the increased retail decentralis-ation that was anticipated in the earlier adop-tion of this ineffective policy.

Regression coef� cients of equal interestare the ones relating to how state-wide mea-sures of reliance on various forms of own-source municipal revenue affect non-centralretail sales. In the � xed-effect regression, thepercentage of state-wide own-source munici-pal revenue from property taxes exerted nostatistically signi� cant in� uence on the valueof real non-central retail sales. Although thesimulation � nding of Brueckner and Kim(2000), and the additional motivation of sub-urbs seeking � scal surplus, indicate an ex-pected positive in� uence, there is also thepossibility that property tax reliance discour-ages capital consumption, promotes greaterdensity and reduces retail decentralisation.These offsetting occurrences could be thereason for the insigni� cant in� uence that thisvariable exerts on non-central retail sales.

Alternatively, the percentage of state-widediscretionary municipal revenue from generalsales taxation exerted a signi� cant positivein� uence on non-central retail activity. Forevery 1 per cent increase in sales tax re-liance, real retail sales in non-central metro-politan places in the West rose by 0.24 percent. State-wide reliance on other taxes,which includes various types of businesstaxes and franchise/licence fees, also yieldeda signi� cant in� uence. For every 1 per centincrease in reliance on these other forms oflocal taxation, real retail sales in non-centralplaces rose by 0.28 per cent.22

7. Implications

The regression � ndings in Table 5 con� rmthe a priori expectations of economic theory.Population, available land prices and demo-graphics in� uence the real dollar amount ofretail sales observed in non-central places inthe western US. The regression analysis alsogenerated evidence in support of the� scalisation of land use for retail activity andthat over time the most restrictive form ofan urban growth boundary reduces the de-


centralisation of retail sales in metropolitanareas. If urban retail sprawl is de� ned asretail activity that is greater in a metropolitanarea’s non-central places than the non-centralplaces’ population, population growth, de-mographics, land prices and incomes war-rant; this paper has shown that two forms oflocal government revenue reliance contributeto a greater amount of it.

Looking over the data offered in Table 4,New Mexico emerges as one of only twowestern states where discretionary municipalrevenue reliance on sales taxation increasedbetween 1977 and 1997. In 1977, municipal-ities in New Mexico drew 12.1 per cent oftheir local discretionary revenue from gen-eral sales taxation; by 1997, this measure hadincreased to 37.0 per cent. In 1977, NewMexico municipalities concurrently gained8.7 per cent of their discretionary revenuefrom other taxation; by 1997, this measurehad fallen to 2.0 per cent. Over the sameperiod, this state’s municipal reliance onproperty taxes decreased and reliance oncharges increased. As the regression indi-cated, these changes in property tax andcharge reliance should exert no signi� cantin� uence on non-central retail activity. Theincrease in general tax reliance is expected tohave contributed to further non-central retailactivity, while the decrease in other tax re-liance decreased it.

A simulation based on the regression� ndings indicate that New Mexico’s 206 percent increase in reliance in local sales tax-ation resulted in about a 50 per cent increasein the dollar value of retail activity occurringin New Mexico’s non-central metropolitanplaces.23 Also, New Mexico’s 69 per centdecrease in other tax reliance resulted inabout a 20 per cent decrease in non-centralretail activity. The expected end-result ofthese discretionary revenue changes is ap-proximately 30 per cent more non-centralretail activity in New Mexico’s metropolitanareas than would have occurred without it.

A policy lesson that one may be temptedto draw from this analysis is that states inter-ested in reducing urban retail sprawl in theirmetropolitan areas consider reducing their

state-wide reliance on general sales taxationas a source of discretionary revenue andinstead replace it with greater local propertytaxation or charges. However, such a lessonneeds to be tempered by the reality that mostvoters in the US prefer sales taxation toalternate forms of raising local revenue andthe clear movement in the US is away fromproperty taxation.24 The real connection be-tween retail sprawl and local sales taxationcomes from the local retention of a portion ofthe general sales tax revenue generated in ajurisdiction that is greater than necessary tocover the costs of providing additional localservices to retailers. If this � scal surplus iseliminated, then it is less likely that non-central places in metropolitan areas will con-tinue to desire and draw retail activity fromcentral places for purely � scal reasons.

A practical option would be to collect aportion of local retail sales revenue on aregional basis and distribute it back to com-munities in the region on a per capita basis.The � scalisation of land use demonstratedhere could be slowed if this portion was largeenough to reduce the current � scal surplusthat communities enjoy by favouring retail inlocal land-use decisions. The Sacramento re-gion is currently considering the adoption ofsuch a scheme through California state legis-lation (Assembly Bill 680, 2001), but theroute to passage has proved thorny.25 Thepolitical reality is that some jurisdictionshave grown accustomed to the � scal surplusgenerated by local land policies that favourretail activity and loathe losing it.

Avenues for future research on this topicinclude an expansion of the data-set to in-clude other metropolitan areas in the US.Perhaps the in� uence of state-wide local� scal structure is greater in the previouslyless developed and more quickly developingWest than in the rest of the US. It would alsobe valuable to break the level of retail ac-tivity observed in non-central metropolitanplaces into less aggregated categories. If re-tail decentralisation is measured in terms ofnon-central sales of big-box items, the effectsof state-wide reliance on various forms oflocal revenue on retail decentralisation may


even be greater. As a � nal point, as done inBrueckner and Fansler (1983), it would beinteresting to use the Census collected squaremiles in the urbanised area as the dependentvariable in a regression study and check ifstate-wide local � scal structure and the pres-ence of urban growth boundaries exert simi-lar in� uences on the geographical size of ametropolitan area.

Notes1. Lang (2000) also writes about the declining

percentage of metropolitan of� ce space inUS central cities and refers to it as ‘of� cesprawl’. In 1979, 74 per cent of US of� cespace was in central cities, by 1999 the cen-tral-city share of of� ce space had dropped to58 per cent.

2. This is a restatement of the most stringenthypothesis that Lewis and Barbour (1999)believe must hold in order to prove empiri-cally that the � scalisation of land use isoccurring.

3. The largest central cities in each of theseexcluded states only had 1992 populations of136 000, 84 000, and 52 000 respectively.

4. A PMSA consists of integrated counties thatare divisible into smaller integrated units thatconsist of one or more counties. A MSAconsists of counties that are not divisible intosmaller integrated units.

5. This approach should be considered con-servative in regard to de� ning excessive de-centralisation because many would considerthe location of retail activity in a place notconsidered a central place in 1977 and 1987,but classi� ed as such in 1990, as sprawl.

6. Information on the dollar amount of retailactivity in metropolitan areas comes from the1977, 1987 and 1997 US Census of RetailTrade.

7. An alternate dependent variable would be thepercentage of retail sales in a metropolitanarea occurring in its non-central places (i.e.the values in the � rst three data columns ofTable 1). A model of what determines thispercentage contains the same explanatoryvariables as included in the model below, butsome of the explanatory variables (popu-lation, household income and demographics)would need to be in percentage form (non-central value relative to total value in themetropolitan area). Such a regressionspeci� cation was tried and the results offeredlow overall explanatory power (R2) and littlestatistical signi� cance of speci� c explanatoryvariables. As described next, using total re-

tail sales in non-central places as the depen-dent variable lends itself to more directmodelling and the resulting regression analy-sis offers quite different results.

8. A mathematical description of the formalurban model that yields these four causalfactors—originally developed by Muth(1969) and Mills (1972)—is contained inBrueckner and Fansler (1983).

9. If metropolitan transport options were in-cluded as explanatory variables in the re-gression analysis, they would need to beconsidered simultaneously determined andappropriately modelled. Brueckner andFansler (1983) found their variable proxiesfor commuting cost (percentage of com-muters using public transit and percentage ofhouseholds owning one or more autos) neverto be statistically signi� cant factors in ex-plaining the size of an urbanised area.

10. In 1997, these 4 sources of local revenueaccounted, on average, for nearly 50 per centof the total local revenue collected in thewestern states in the sample. State-wideaverage reliance on local revenue reliance,rather than metropolitan-wide averages orlocal reliance, is used to ensure the exoge-nous nature of these explanatory variables toeach metropolitan area. Since pertinent land-use decisions are made in the unincorporatedportions of US counties, state-wide munici-pal revenue reliance is intended also to proxyfor the average reliance that counties in astate have upon these forms of local revenue.

11. Fischel’s (1985) book on The Economics ofZoning Laws, especially ch. 14, offers anexcellent introduction to zoning in the USand the use of � scal zoning described here.Ladd (1998) provides a recent summary ofland-use regulation as a local � scal toolwidely used in the US.

12. See Bartik (1991) and Anderson and Wass-mer (2000) for book-length descriptions ofthe use and in� uence of local economic de-velopment incentives in the US. Lewis andBarbour (1999, pp. 73–74) describe thespeci� c forms of local incentives that areavailable to local governments in California.

13. Business taxes and franchise/licence fees areincluded in the explanatory variable categoryof Percentage state-wide discretionarymunicipal revenue from other taxes. In moststates, revenue from businesses makes upmore than half of the amount accounted forin this category, with the other half comingfrom various sources like severance taxes,death taxes and gift taxes. It is impossible toaccount separately for business-related feesbecause distinct business values are notgiven.


14. For the a priori purpose of predicting theexpected in� uence of local property taxationon non-central retail activity, it would beinformative to know the amount of � scalsurplus through property taxation generatedby retail activity, relative to alternative formsof local activity. Unfortunately, a search ofthe literature revealed no previous estimatesof this and a full evaluation would require atleast another paper-length treatment.

15. To account for the spillover of retail cus-tomers between contiguous metropolitan ar-eas, a dummy variable representing suchmetropolitan areas was included in prelimi-nary regressions. This dummy was neverstatistically signi� cant in the OLS and ran-dom-effect models, and could not be in-cluded in the � xed-effect regression modeldue to perfect colinearity. A separate dummyfor whether a metropolitan area is a PMSAyielded similar results. Both of these dum-mies are not included in the � nal regressionanalysis.

16. Available at: http://webstage1.aspensys.com/SOCDS/SOCDS Home.htm.

17. There is also the speci� cation issue ofwhether a log-linear functional form is moreappropriate than the linear form used here. Alog-linear form uses the log of the dependentvariable and allows for non-linear relation-ships between explanatory variables and thedependent variable. This speci� cation wastried and the result was less statisticalsigni� cance for all regression coef� cientsand a few unexpected signs. Thus the � naldecision to use the linear form recorded inTable 5.

18. See Kennedy (1992, ch. 8) for a descriptionof what heteroscedasticity is and the prob-lems it presents for regression analysis.White’s method of correction is described onp. 130.

19. The regressions use only 161 of the possible162 observation (54 areas over 3 areas) be-cause the Yuba City CA, MSA was not inexistence in 1977 and hence explanatory datacould not be gathered for it.

20. The non-signi� cance of the incomecoef� cient in the � xed-effect regression maybe due to the fact that 1997 income valuesare extrapolated. To test this hypothesis, the� xed-effect regression was rerun using onlythe 1977 and 1987 samples and again theregression coef� cient on median householdincome was statistically insigni� cant; thusthe basis for the decision to use the fullsample.

21. The negative impact of higher agriculturalprices on slowing retail decentralisation isonly expected if the price of urban land in a

metropolitan area is held constant. In theregression methodology used here, there isno measure of urban land price and thus it isnot directly held constant—although thereare dummy explanatory variables that controlfor the � xed year and metropolitan areaeffects and these could conceivably serve thepurpose of controlling for differences in ur-ban land prices across metropolitan areas.

22. To measure the independent in� uence ofPercentage state-wide discretionary munici-pal revenue from charges on non-centralactivity, this category replaced the generalsales tax category in another � xed-effectregression run. The result was that the ex-planatory variable representing other taxesremained positive and statisticallysigni� cant; the property tax variable contin-ued to exert a statistically insigni� cantin� uence; while the charge variable also ex-erted no signi� cant in� uence. Consideringthat the legal intent of charges is to generatelittle to no � scal surplus, the insigni� canceof charges to non-central retail activity is asexpected.

23. The 206 per cent increase in sales tax re-liance comes from taking the 1997 relianceof 14.6 per cent, subtracting it from 34.3 percent, and dividing by 14.6 per cent. The 50per cent increase in non-central retail activitythen is calculated by taking the 206 per centincrease in sales tax reliance and multiplyingit by the sales tax elasticity of non-centralretail sales (0.242) recorded in Table 5.

24. For a poll supporting this, see the AdvisoryCouncil on Intergovernmental Relations(1987).

25. See Johnson (2000). California State SenatorDede Alpert, in support of an earlier 2000bill that failed, but would have distributednew local sales tax revenue in all Californiacounties on a per-capita basis instead of thecurrent situs basis, believed that:

Retail sprawl leads to urban sprawl, whichleads to traf� c, pollution, and generally apretty poor quality of life for communities.These communities could otherwise havebeen balanced with jobs and housing lo-cated near each other, full services pro-vided by each level of local governmentand less � ghting and more cooperationbetween local leaders. It is not rocket sci-ence. It is the incentives.

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