Voter decisions on eminent domain and police power reforms

Voter decisions on eminent domain and police power reforms
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  Voter decisions on eminent domain and police power reforms Kwami Adanu a, ⇑ , John P. Hoehn b , Patricia Norris b , Emma Iglesias c a Department of Economics and Finance, GIMPA Business School, Accra, Ghana b Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, USA c Department of Economics, Michigan State University, East Lansing, USA a r t i c l e i n f o  Article history: Received 16 March 2011Available online 7 May 2012  JEL classification: Q15Q24R38R52 Keywords: Eminent domainPolice powerReformsVotingLogistic regression a b s t r a c t One unresolved issue arising from the use of eminent domain power involves how theperceived benefits and costs of eminent domain power affect people’s positions on thereform of eminent domain and police power law. The paper addresses this issue by esti-mating a voting model that explains voters’ decisions on eminent domain and policepower reform referenda in the US. Estimates indicate that eminent domain referendumoutcomes hinged on voters’ fundamental values and ideology, and voters’ immediateself-interest. Voters’ fundamental values and ideology affects referendum outcomesinsofar as educational attainment in a county has a statistically significant effect on sup-port for reform. Despite the greater incidence of eminent domain in low income andpoorer communities, success of reform referenda in this study was found to be greaterin counties with higher incomes and lower unemployment rates. This implies thatwhatever asymmetry exists in the exercise of eminent domain law across incomegroups does not affect voter reaction to eminent domain reforms. Moreover, countieswith high unemployment rates consider the larger potential benefits from urbanrenewal projects in vote decision-making providing a link between self-interest and vot-ing behavior.   2012 Elsevier Inc. All rights reserved. 1. Introduction Eminent domain and police power are two principalavenues by which governments exercise control over landresources. Eminent domain refers to the power of govern-ment to take private property for public use. Public useherereferstopublicserviceslikehighways,publicutilities,communitycenters,schools,andotherfacilitiesthatcanbemade available for use of the entire community (Merrill,1986). Police power on the other hand describes the rightof government to enact and enforce laws that restrict landusetoensureorderlydevelopment, safety,health, andpro-tection of the general welfare of the public (Sax, 1964).Good examples of the use of police power include zoninglaws, building and health codes, and environmentalregulations that impose limits on land use by private own-ers without depriving them of ownership rights over theproperty.Court decisions have however gradually broadened thedefinition of public use to include development initiativesundertaken to provide public benefit (US Supreme Court,1954, 2005; Michigan Supreme Court, 1981). In 1954, theUS Supreme Court affirmed the use by the District of Columbia of eminent domain to eliminate blight and rede-velop blighted area, including the sale or lease of con-demned properties to private entities that wouldundertakeredevelopment(USSupremeCourt,1954).Then,in 2005, the US Supreme Court upheld the decision of theConnecticut Supreme Court in the famous  Kelo v. NewLondon  case that under the US Constitution governmentsare permitted to use eminent domain to take propertyandtransfer itsusetoother privatepartiesas longas thereis a public benefit, such as economic development (USSupreme Court, 2005). 1051-1377/$ - see front matter   2012 Elsevier Inc. All rights reserved. ⇑ Corresponding author. E-mail address: (K. Adanu). Journal of Housing Economics 21 (2012) 187–194 Contents lists available at SciVerse ScienceDirect  Journal of Housing Economics journal homepage:  The broadening of the definition of public use has gen-eratedconsiderablepoliticalresponse.Opinionpollsonthe Kelo  decision for instance show that more than 80% of respondents disagreed with the decision of the Court (Na-dler et al., 2008). Nadler et al. (2008) review opinion polls over the last three decades that suggest that  Kelo  struck atcore American values. Nadler et al. cite poll finding that70% of respondents agree with the statement that ‘‘Theright of property is sacred’’ and 88% agree that ‘‘‘allowingpeople to own private property’ is a major contributor tomaking America great’’ (p. 291). Consistent with these val-ues, polls data show that disapproval of   Kelo  was indepen-dent of political affiliation.Although eminent domain and police power are relatedin the sense that both affect land use, they represent twofundamentally different perspectives about propertyrights. The exercise of eminent domain involves forcefultransfer of property rights and, as established by the fifthamendment of the US Constitution, requires payment of compensation. Police power is exercised to prevent the ac-tions of property owners from creating some public harm.Because affecting others in some negative way is not partof land ownership rights, regulatory action to protect thepublic does not require compensation (Flick et al., 1995;GoldsteinandWatson,1997).Nevertheless,effortstomakecompensation for the exercise of police power a legalrequirement began in 1995 when the US House of Representatives passed a property rights bill calling forcompensation of property owners whenever federal regu-latory actions decrease property values by more than20%. The bill however failed to pass the Senate (Goldsteinand Watson, 1997). Subsequently, the issue was addressedin some states through legislation and ballot initiativesrequiring compensation for police power.Following the 2005 US Supreme Court ruling in  Kelo v.New London , several more states initiated referenda toban the use of eminent domain for economic developmentpurposes or restrict the circumstances under which emi-nent domain could be used (Orthner, 2007; Sandefur,2006; Berliner, 2003). Several statesalsoproposedlimitingthe exercise of police power by requiring compensation incertain instances. In November 2006, 13 states (Arizona,California, Florida, Georgia, Idaho, Louisiana, Michigan,NewHampshire,Nevada,NorthDakota,Oregon,SouthCar-olina, and Washington) presented special ballots onreforming eminent domain and/or police power to voters.All but three (California, Idaho, and Washington) were ap-proved (Table 1). In general, two main types of ballot mea-sures were presented: eminent domain only ballots, andeminent domain and police power compensation ballots.States with eminent domain only ballots generally calledfor banning or restricting the use of eminent domainpower for economic development purposes while eminentdomain and police power compensation ballots combinerestricted use of eminent domain power with requirementfor police power compensation.The research reported here examines voter decisions toidentify the factors that influenced eminent domain andregulatory compensation referenda outcomes. The empiri-calanalysisapplieslogisticregressiontocounty-levelvoterreturns in 10 states with reform measures on the 2006ballot. Estimates indicate that eminent domain referen-dum outcomes depend on voters’ fundamental valuesand ideology insofar as educational attainment in a countyhas a statistically significant effect on support for reform.However, the results also show that counties with highunemployment rates consider the larger potential benefitsfrom urban renewal projects in vote decision-making thusprovidinga link between self-interest and voting behavior.Moreover, despite the greater incidence of eminent do-main in low income and poorer communities (Carpenterand Ross, 2009), success of reform referenda in this studywas found to be greater in counties with higher incomes.The remainder of the paper is ordered as follows. Thenext section presents the conceptual framework and re-search hypotheses of the paper. This is followed by theresearch data description and economic model sections.Discussionoftheresearchresultsandconclusionsthenfol-low in that order. 2. Framework and hypotheses The rational voters model suggests that voters’ deci-sions on public good provision can be treated as a deriveddemandof howmuchpublic goodvoters want to consumeat the optimum (Downs, 1957; Deacon and Shapiro, 1975;Matsusaka, 1993; Kotchen and Powers, 2006). This impliesthat voters make voting decisions on the provision of pub-lic goods to maximize utility derived from the consump-tion of private and public goods subject to an incomeconstraint. The analysis of vote outcomes on eminent do-main and police power compensation is therefore treated  Table 1 Summary of results for all eminent domain ballots in 2006.  Source:  NationalConference of State Legislatures: Property Rights Issues on the 2006 Ballot. State Measure # Topic area Pass/failArizona Prop. 207 Eminent domain&police powerPass(64.8%)California Prop. 90 Eminent domain&police powerFail (47.6%)Florida Amendment8Eminent domain Pass (69%)Georgia Amendment1Eminent domain Pass(82.7%)Idaho Prop. 2 Eminent domain&police powerFail (23.9%)Louisiana Amendment5Eminent domain Pass (55%)Michigan Proposal 06-4 Eminent domain Pass(80.1%)Nevada Question 2 Eminent domain Pass(63.1%)NewHampshireQuestion 1 Eminent domain Pass(85.7%)North Dakota Measure 2 Eminent domain Pass(67.5%)Oregon Measure 39 Eminent domain Pass(67.1%)South Carolina Amendment5Eminent domain Pass (86%)Washington Initiative 933 Police power Fail (41.2%)188  K. Adanu et al./Journal of Housing Economics 21 (2012) 187–194  here as one of unveiling the derived demand for publicgoods provided by these two institutions.Voter decision in this study is conditioned not only onself-interest of voters but also on their ideological posi-tions. Ideological position here captures voter responsethat cannot be explained by benefits and costs associatedwith eminent domain and regulatory compensationrequirements. Inadditionto ideological satisfaction, voterscan expect direct benefits from their vote choices(Sandefur, 2006; Lazzarotti, 1999). The direct benefits ex-pected from voting on restricted use of eminent domainpower and police power compensation ballots includethe value at risk (e.g. home values) that voters seek to pro-tect (Sandefur, 2006; Riddiough, 1997), direct transfers(e.g. policepower compensation) to landownersas a resultof government regulatory action (Miceli and Segerson,1994), and nonmarket values (e.g. open space) providedby regulatory actions (Bengston et al., 2004). On the otherhand, there are costs attributable to voter decisions on theuse of eminent domain and police power. Such costs oftentake the form of higher tax obligations that can be ex-pected from some of these decisions (Deacon and Shapiro,1975). For instance, in order to pay the increasedcompensation for eminent domain or police power whenthe average voter supports a ballot on unrestricted use of eminent domain or a requirement for police power com-pensation, voters may have to pay higher taxes to raisethe necessary revenue. The increase in tax obligation re-duces the disposable income of voters and changes the in-come constraint of their utility maximization problem.Explanatory variables included in the study includeballot measure type presented to voters, homeownershiprates, household income, education, unemployment rate,populationdensity,andvoterturnout.Thesevariablescon-trol for the probable incentives and disincentives associ-ated with vote decisions at the polls and differences inthe ballot measure types presented to voters. On ballotmeasure type, summary results on eminent domain andpolice power ballots in the 2006 midtermelection (see Ta-ble 1 in Appendix) suggest that voter support may bedeclining as the ballot measure type presented to votersextends from restricted use of eminent domain to re-stricted use of eminent domain and requirement for policepower compensation. Support for the ballot measure istherefore hypothesized to decline as the ballot measureextends from restricted use of eminent domain to re-stricted use of eminent domain and police power compen-sation. If this relationship turns out to be positive instead,thentheresultwill besuggestiveoftwothings:thatvoterssupporting restricted use of eminent domain power alsotend to support compensation for police power, and voterswho are not supportive of restricted use of eminent do-main power tend to support compensation for policepower strongly enoughto vote yes insteadof no given thattheir decision on these two issues conflict.Next, homeowners are expected to be more concernedabout use of eminent domain power and property regula-tory action than voters living in rented properties. This isbecausehomeowners have morevalueat risk thanrenters.The implication here is that counties with high homeow-nership rates would be more supportive of the ballotmeasure since their net benefits from voting yes to re-stricted use of eminent domain and police power compen-sationexceedsthatforrenters.Thereare, however,equallyrelevant reasons to expect the average homeowner to voteno. For instance, given that a common rationale for emi-nent domain takings for economic development is to com-bat blight (Sandefur, 2006), property price increases thatmay be expected to come with neighborhood improve-ments associated with the use of eminent domain to cleanblight provide good reason for a class of property ownersto vote no to restricting the use of eminent domain. Thehypothesized positive relationship between homeowner-ship rate and eminent domain and regulatory compensa-tion could therefore be neutralized or reversed bybenefits expectedfromuse of eminent domain and regula-tory action.Household income level is linked to vote outcomes onpolice power compensation based on the Kuznets in-verted- U   relationship between income and environmentalquality. This relationship has been examined in severalstudies on vote behavior and environmental quality (Dea-con and Shapiro, 1975; Kahn and Matsusaka, 1997; Popp,2001; Kotchen and Powers, 2006). This implies that highincome voters may support police power actions becauseof their relatively high demand for environmental qualityandopenspaceinurbanandcongestedareas.Thus,itisex-pected that high income voters would reject requirementsfor police power compensation, and vote no on the initia-tive, in order to promote its use. The hypothesized rela-tionship between income and eminent domain is basedon the assertion that the exercise of eminent domainpower tends to disproportionately affect poor communi-ties (Dana, 2007; Somin, 2007; Sandefur, 2003, 2006). Thisimplies that lower income earning voters would be moresupportive of a ballot measure that calls for banning orrestricting the use of eminent domain power for economicdevelopment.Past studies on factors affecting attitude towards theenvironment and natural resource use also consistentlyshowthat the level of educationof voters positivelyaffectsvoters’ attitudes towards resource protection (see, Deaconand Shapiro, 1975; Kahn and Matsusaka, 1997; Kahn,2002; Fischel, 1979). This is because knowledge about thevalue of environmental quality and open space and howthese can be improved, and exposure to research findingson the impact of environmental quality and open spaceonpropertyvalues andhumanhealthare important deter-minants of voters’ positions on the environment. Researchin psychology and related fields has also tied educationalattainmenttosocio-politicalattitudesandideologicalposi-tions in general. For instance, high educational attainmenthas been determined to be positively correlated to liberalideological positions (Ekehammar et al., 1987; Zakrissonand Ekehammar, 1998; Weisenfeld and Ott, 2011). Educa-tion is therefore one factor that can affect the ideologicalposition and subsequent choice of voters on natural re-source-related ballot measures. It is hypothesized, basedonpreviousfindingsoneducation-environment-naturalre-source relationships that support for the ballot measure isdecreasing in educational level of voters. A decrease inyes votes here means increased support for use of eminent K. Adanu et al./Journal of Housing Economics 21 (2012) 187–194  189  domaintopromotedesirablelanduse,andadeclineinsup-port for regulatory compensationto promote its use to im-prove environmental quality.Next, counties with high unemployment rates are ex-pected to be more supportive of eminent domain sinceuse of eminent domain power for economic developmentpurposescanbevaluablefor economicallydepressedareasthat are looking forward to economic expansion and jobcreation(ClarkeandKornberg, 1994;BowlerandDonovan,1994; Sandefur, 2006). Several of these studies indicatethat voter dissatisfaction with bad economic conditionserodes support for ballot proposals because of lowsupportfor government (Clarke and Kornberg, 1994; Bowler andDonovan, 1994). On the other hand, given that policepowerdoesnotinvolveanysubsequentuseofthepropertyto provide jobs or any collective good, unemployment ratemaynothaveasignificanteffectonhowvotersreacttopo-lice power compensation ballots. This implies that highunemployment rate can be expected to decrease the pro-portion of yes votes cast on restricted use of eminent do-main and police power compensation.Population density is another variable that can belinked to the potential direct benefits of eminent domainand police power. Limited land availability and high landprices in high population density areas often imply thatsome publicservices mayonlybe providedbytakingsomeexisting properties and converting them to alternativeuses. For instance, single family homes at good locationsmay be taken and converted to multi-story apartmentcomplexes to serve more people and expand the tax base.More densely populated counties are thus expected toshowmoresupportformeasureslikeeminentdomainthatpromise the provision of these much needed services byvoting against restrictions on the use of eminent domain.Further, since properties in urban areas tend to be moreexpensive than comparable properties in rural or lowpop-ulation density areas, voters in high population densitycounties may be more inclined towards voting no to re-quire compensation for use of police power. This reducesthe budgetary burden that voters have to face in the formof additional tax payments to finance such compensationpayments. In summary, voters in counties with high popu-lationdensityarelikelytovotenoonrestricteduseofemi-nent domain, and police power compensation.Finally, previous empirical studies indicate that lowvo-ter turnout correlates strongly with approval of initiativesin referenda (Stone, 1965; Knox et al., 1984; Hadwiger,1992). As turnout rises the proportion of favorable votesdeclines. One explanation offered for this result is thatqualifiedvoterswhoopposeballotpropositionstendtoex-press their protest by boycotting elections (Stone, 1965).Support for the ballot measure is thus hypothesized to bedecreasing in voter turnout. As noted by Hadwiger, this isa result that requires further research to better explainthe finding. 3. Data and variables The paper uses cross-sectional county level data cover-ing yes/no vote outcomes on eminent domain and policepower propositions in the 2006 mid-term elections. Thesamplesizeof189covers10of13USstatesshowninTable1. WashingtonState is not includedinthe dataset becausethe vote initiative involves only police power. Nevada andNorth Dakota are dropped from the study due to missingdata problems. A county is dropped if it has missing datafor one or more variables in the model. High incidence of missing values was observed for educational attainmentand household median income variables. The dependentvariable in the model is the logodds of yes votes as definedin Eq. (1). The source of the vote data is the University of Michigan library government documents center (Univer-sity of Michigan, 2006).Fig. 1 below shows the contribution of the variousstates to the total sample size. Florida has the highestcounty contribution of 18% followed by California with17%. On the lower end New Hampshire and Idaho contrib-ute 3% each to the total sample size.Variations in state contribution to total sample size isinfluenced by the number of reporting counties in a state,and availability of data for variables in the model for thecountyconcerned.Summarystatisticsforallvariablesusedin the model is shown in Table 2 below.The table provides information on the sample size, unitofmeasure,mean,standarddeviation,minimum,andmax-imum values for each variable.Independent variables included in the model are ballottype, homeownership rate, education, income, unemploy-ment rate, populationdensity, andturnout. The ballot typevariable ( ballottype ) is binary and is defined to equal 0 if the ballot question in a given state calls for restricted use County Contribution of States to Total Data Sample N.Hampshire, 6, 3%Louisiana, 15, 8%S.Carolina, 20, 11%Oregon, 14, 8%Georgia, 24, 13%Idaho, 6, 3%Michigan, 25, 14% Arizona, 10, 5%Florida, 33, 18%California, 31, 17% Fig. 1.  Composition of the data by contributing states.190  K. Adanu et al./Journal of Housing Economics 21 (2012) 187–194  of eminent domain only and 1 if a requirement for policepower compensation is added to restricted use of eminentdomain. The source of this data is the National Conferenceof State Legislatures.Homeownership rate ( home ) is defined as the percent-age of occupied housing units that were owner-occupiedin 2006. Data on homeownership rate is obtained fromthe2006USCensusBureau’sAmericanCommunitySurvey.The data is limited to household population and excludespopulation living in institutions, college dormitories, andother group quarters.Level of education is represented by two variables inthis study: percent of county population 25years and overwho have completed at least high school education (in-cludes equivalency) by 2006 ( highschool ) and percent of county population 25years and over who have completedat leastbachelorsdegreeby2006( bachelors ). Thesourceof the education variables is the 2006 American CommunitySurvey data tables (US Census Bureau, 2006). The incomevariable ( income ) is represented by the county-level med-ian householdincome (in 2006 Inflation-AdjustedDollars).The income data is also drawn from the 2006 AmericanCommunity Survey data tables (US Census Bureau, 2006).Populationdensity( density ) is measuredbythenumberof peoplepersquaremilelivingineachcountyandiscom-puted using July 1 2006 population estimates and countyarea (land area in square miles) data. The source of thesedata is the population division of US Census Bureau (USCensusBureau, 2006). Fortheunemploymentvariable( un-emp ), county level unemployment rate data is taken fromthe Bureau of Labor Statistics’ local area unemploymentstatistics. The title of this data at source is ‘‘Labor ForceData by County, 2006 Annual Average (US Department of Labor, 2006)’’. Election turnout ( turnout  ) is measured asthe percentage of qualified voters who voted in thereferendum.Additionalexplanatoryvariablesincludedinthedatasetto allowfor the implementationof Heckmansample selec-tion regression are special interest groups ( ingroup ), exist-ing restrictions on state eminent domain law ( takelaw ),and incidence of eminent domain takings ( incidence ). TheinterestgroupvariableistakenfromThomasandHrebenar(2004). The source of the  incidence  data is Berliner (2003)while  Takelaw  is taken from Castle Coalition (2007) . 4. Model The logistic regression model estimated is specified as, In F  1   F    ¼  b 0  þ  b 1 ballottype þ  b 2 home þ  b 3 home  ballottype þ  b 4 highschool þ  b 5 highschool  ballottype þ  b 6 bachelor þ  b 7 bachelor  ballottype þ  b 8 income þ  b 9 income  ballottype þ  b 10 unemp þ  b 11 unemp  ballotype þ  b 12 density þ  b 13 density  ballottype þ  b 14 turnout þ  b 15 Ariz þ  b 16 Calif  þ  b 23 Ore þ  n  ð 1 Þ where  F   ¼  Number of Yes votesNumber of valid votes , and  F  1  F     represents the oddsof a supporting vote at the polls. The dependent variablein Eq. (1) is therefore the logodds of a supporting vote.The model specification is informed by results of a Chowtest (Chow, 1960) conducted to test for parameter stabil-ity across the two ballot types covered by the data. Thereturned  F  -statistic value of 23.157 for the Chow test iscompared to a 5% critical value of 1.94 to reject the nullhypothesis that the estimated parameters are equalacross the two data sub-samples. Ballot measure typeinteraction terms are thus included in the model to ac-count for shifts in the impact of the explanatory variablesacross the two sub-samples. The model is estimated usingWeighted Least Squares (WLS) to control for inherentHeteroskedasticity.The decision to reform eminent domain and policepower law in a given state may be influenced by severalfactors including presence of special interest groups, andincidence of eminent domain takings. The Heckman(1976) two-step procedure is employed to test for thisselection bias problem. To implement the Heckman proce-dure three other variables are included in the selectionequation to control for the effect of special interest groups(ingroup), existing restrictions on state eminent domainlaw ( takelaw ), and incidence of eminent domain takings( incidence ).  Incidence  here is an example of the so-calledbacklash variables that have been shown to correlate withthe decision to reform eminent domain law across USstates (Lopez and Totah, 2007; Lopez et al., 2009). It is ex-pected that states with higher per capita takings are morelikely to present voters with a proposition to restrict theuse of eminent domain power and require compensationforpolicepower.AtestoftheinverseMillsratioparameterfromthe Heckmanregressionindicated that the variable isnot statistically significant at the 10% level. 1 The model isthus estimated without the Mills ratio variable.  Table 2 Summary statistic of variables. Variable Unit Mean StandarddeviationMin MaxYes votes(logodds)0.931 0.806   1.537 2.223Yes votes Percent 69.600 16.300 17.7 90.200Ballot type 0.249 0.433 0 1HomeownershipratePercent 69.740 8.247 45.200 87.800High school Percent 83.971 6.396 62.300 96.000Bachelors Percent 23.412 8.166 10.000 52.600Income ‘000$ 46.576 9.551 23.119 81.761UnemploymentratePercent 5.048 2.014 2.400 15.300PopulationdensityPop/miles 2 (‘00)2.523 1.988 0.063 9.462Turnout Percent 50.008 12.712 2.032 75.700 1 Results from the Heckman regression are available on request. K. Adanu et al./Journal of Housing Economics 21 (2012) 187–194  191
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