Bidding for industrial plants : does winning a "million dollar plant" increase welfare?

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Bidding

for Industrial Plants:

Does

Winning a

"Million

Dollar Plant"

Increase Welfare?

Michael Greenstone

Enrico

Moretti

Working

Paper 04-39

November, 2004

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Bidding

forIndustrial Plants:

Does

Winning

a

'Million

Dollar

Plant'

Increase

Welfare?

Michael Greenstone

MIT, American

Bar Foundation and

NBER

Enrico

Moretti

University

of

California,

Berkeley

and

NBER

November

2004

We

thank AlbertoAbadie, Michael Ash, HalCole,

David

Card,

Gordon

Dahl,

Thomas

Davidoff, Michael Davidson,Rajeev Dehejia, Stefano Delia Vigna.

Mark Duggan,

Jinyong Hahn, Robert

Haveman, Vernon

Henderson, Ali Hortacsu,

Matthew

Kahn,

Tom

Kane, Brian Knight, Alan Krueger, SteveLevitt,

Boyan

Jovanovic,

David

Lee,Therese

McGuire,

Derek

Neal,

Matthew

Neidell.

Aviv

Nevo,

John Quigley,Karl

Scholz,

Chad

Syverson,

Duncan

Thomas

and seminar participants at Berkeley,

Brown,

Chicago,

Columbia, Illinois, Michigan,

NYU,

NBER

Summer

Institute. Rice. Stanford,

UCLA,

Wharton, and Wisconsinforveryhelpful discussions.

Adina

Allen,

Ben

Bolitzer.JustinGallagher,

Genevieve

Pham-Kanter,

Yan

Lee,

Sam

Schulhofer-Wohl, AntoineSt-Pierre, and William

Young

provided outstanding

research assistance. Greenstone

acknowledges

generous funding fromthe

American Bar

Foundation.

Bidding

forIndustrialPlants:

Does Winning

a'MillionDollarPlant*Increase

Welfare?

Abstract

Increasingly,localgovernments

compete by

offering substantial subsidiesto industrial plants to

locate within their jurisdictions. This paper uses a novel research design to estimate the local

consequences ofsuccessfully bidding for an industrial plant, relativeto biddingand losing, on labor

earnings, public finances, and property values.

Each

issue of Ihe corporate real estatejournal Site

Selection includesanarticle titled

"The

Million DollarPlant" that reports thecounty

where

a large plant

chosetolocate(i.e.,the 'winner'), as well asthe

one

or

two

runner-upcounties(i.e.,the'losers').

We

use

these revealed rankings of profit-maximizing firms to form a counterfactual for

what would

have

happened

in thewinningcountiesintheabsenceofthe plant opening.

We

find that the plantopening

announcement

is associated witha

1.5%

trend break in laborearnings inthe

new

plant's industry in winningcounties, as well as increasedearningsinthe

same

industryincountiesthatneighborthewinner.

Further, thereis

modest

evidenceof increased expendituresforlocalservices,suchas public education.

Property values

may

provide a

summary

measure

ofthe netchangeinwelfare,becausethecosts

andbenefitsofattracting aplantshould becapitalized into the priceofland. Ifthewinners andlosersare

homogeneous,

asimple

model

suggeststhatanyrentsshould bebid away.

We

findapositive,relative

trend break of approximately 1.1-1.7%in propertyvalues. Sincethe winners andlosers have similar

observables inadvance oftheopening

announcement,

thepropertyvalueresults

may

be explained by heterogeneityinsubsidiesfrom higherlevelsof

government

(e.g.,states)and/or systematic underbidding.

Overall, the results

undermine

the popular

view

thatthe provision oflocal subsidies to attractlarge

industrialplantsreduceslocalresidents' welfare.

Michael Greenstone

MIT

Department

of

Economics

50

Memorial

Drive,

E52-359

Cambridge.

MA

02142-1347

and

NBER

mgreenst@,m

it.edu

EnricoMoretti

UniversityofCalifornia,Berkeley

Department

of

Economics

Berkeley,

CA

94720-3880

and

NBER

• _Introduction

Over

the past 30 years, stateandlocal

governments

have

assumed

a greater responsibility for

economic

development. For example,they frequently offer substantial subsidies tobusinessesto locate

within their jurisdictions. Theseincentivescan includetax breaks,low-costor free land, theissuanceof

tax-exempt bonds,trainingfunds, theconstructionofroads, andother infrastructureinvestments.

These

policiesare controversial: local politicians andthe subsidized

companies

usually extol the benefits of

these deals,whilecriticscomplainthatthey areawasteofpublicmonies.' Itisdifficult toevaluate these

competing

claims,becausethereis littlesystematicevidenceontheconsequences ofthesepolicies.^

I'hetraditionalapproachtoevaluating policiesdesignedatattracting

new

plantsistocalculate the

number

of jobs gained andthe costofthe taxbreaks

awarded

to firms. For example, itiswidelycited that

Mercedes

received a

$250

million ($165,000 per job) incentive package for locating in Vance,

Alabama,

the

Toyota

plant in

Georgetown, Kentucky was awarded $200

million($80,000 perjob)and

Boeing

was

given$50million($100,000perjob)intaxabatementstolocates itscorporateheadquarters

in

Chicago

(Mitol2001;

Trogen

2002). This"accounting"approach produces eye-catchingstatistics,but

ithas

two

importantlimitations. First,these calculations are

done

ex-anteandare rarely verified ex-post.

Second,and

more

fundamentally,thisapproachdoesnot offer a

framework

fordetermining whetherthe

policiesincreaseordecrease welfare oflocal residents. Forexample,is

$165,000

perjoba

good

dealfor

the residentsof Vance,

Alabama?

Economic

theoryprovides

mixed

resultsonthisquestion, as

emphasized

by Glaeser (2001) ina

recent survey.'

On

theone hand,

some models

suggestthatthe attraction of

new

businesses generates

positive spillovers and/or increases producer surplus. In these cases, local subsidies

may

be welfare

enhancingor, at least,neutral forlocalresidents.

On

the otherhand,there aremodels thatindicatethat

local subsidies

may

reflect

government

officials' private interestsby providing for their

own

financial

gainor satisfyingLeviathan goals. Inthese settings, local

government

officialsgrant subsidies

whose

coststolocalresidents are largerthanthe benefits

and

thereforereduceresidents" welfare.

-Thispaperempirically assesses theconsequences forcounties ofsuccessfullybidding forlarge

industrial plants on county-level earnings, property values, and

government

finances.

The

empirical

'_{For example,arecent}

front-pagearticleinthe

New

York Timesdescribes theexperienceofseveral localities that

providedgeneroussubsidies to attract largemanufacturing firmsinthe 1980sand 1990s and apparently obtained

limited benefits to the localeconomy.("PayingforJobsDoesn'tAlways PayOff,

New

York Times,

November

10,

2003).

"_{In a}recentsurvey ofthe literature,Glaeser (2001) concludesthat,althoughlocationbased-incentives"seemtobea permanent partofthe urbaneconomic landscape, economists donotyet

know

why

these incentivesoccur and

whethertheyareinfactdesirable". FurtheraStandard

&

Poor's publicationstates,"Economic development warsof

recentyearshave not developeda sufficienttrackrecordtoassesstheirtruecost-benefit ratio"(Standard

&

Poor's

challengeisthat plantschooseto locate

where

theirexpectedprofits are highest,

which

area functionof

their location-specificexpectedfuture costsof production

and

anysubsidies.

A

plant'sexpectedfuture

costsof productionina county

depend

on

many

unobservablefactors,includingthepresenceofa suitable

transportation infrastructure, the availability of workers withparticular skills, andthe local regulatory

environment.

These

factors are typicallydifficult to

measure and

their relativeimportancevariesacross

plants based on plants' unobserved production functions. Similarly, the subsidies are likely to bea

functionofa

number

of

unmeasured

factors, includinganypotential spillovers andthedegreeoflocal

politicians'malfeasance.

Heterogeneity in the factors thatdeterminevariationincostsof production andsubsidies across

countiesislikelytobiasstandardestimators.Validestimatesoftheimpact ofa plantopeningrequire the

identificationof a countythatisidentical to thecounty

where

the plantdecidedtolocateinboth expected

future production costs andthe factors thatdeterminesubsidies.

We

have little faith in ourabilityto

ascertainand

measure

allthesefactors.

As

a solution,

we

rely onthe revealed rankings of profit-maximizing firmsto identify a valid

counterfactual for

what would

have

happened

intheabsence ofthe plantopening.

These

rankings

come

from thecorporaterealestate journal Site Selection,

which

includes a regular featuretitledthe"Million Dollar Plants" that describes

how

a large plantdecided

where

to locate.

When

firms areconsidering

where

to

open

a large plant, they typically begin

by

considering dozens ofpossible locations.

They

subsequently narrowthelisttoroughly 10sites,

among

which

2 or 3 finalistsare selected.

The

"Million DollarPlants"articlesreport thecountythat the plant ultimatelychose(i.e.,the 'winner'), as well as the

one or

two

runner-up counties (i.e.. the 'losers').

The

losers are counties that have survived a long

selection process, butnarrowlylostthecompetition.

Our

identifyingassumptionisthat the losers

form

a

valid counterfactual for thewinners,afteradjustmentfor differencesinpre-existing trends.

Even

ifthis

assumption is invalid,

we

suspect that this pairwise sampling approach is preferable to the leading

alternativeof

comparing

winnerstothe other3,000U.S. counties.

Consistent with our assumption,

we

find that beforethe

announcement

ofthe winner county, winning and losing counties have similar trends in

wage

bill,

employment,

per capita

income

and employment-population ratio. Several other observablecharacteristics arealso similar.

By

contrast,

trendsinthewinningcounties tendtobedifferentfromtrendsinallother

US

counties. Notably inthe 8

yearsbeforethe

announcement

ofthewinner,the trends inproperty valuesinwinning andlosingcounties

are virtually identical.

Under

the plausibleassumptionthatproperty marketsare forward-looking,this

finding suggests that not only are winning and losing counties similar in the years before the

'

Seealsotheclassic Gates (1Q72) and Zodrovv and Mieszkowski (1986) papers ontax competitions andthe

annouiiceincnt. but theexpected Futurechanges in

economic

activitytliat arc capitalized intoproperty valuesare alsoex-antesimilar. Overall, these findings lend credibilitytotheidentifyingassumptionthat

the losersforma valid counterfactual for the winners.

The

primai7

employment

resultisthat inthe

new

plants' I-digitindustry the

wage

billincreased

bya statisticallysignificant$16.8 millionper yearinwinningcounties (relativetolosingones)afterthe

opening

announcement

(relativetotheperiod before). Thisis

1.5%

oftheaverage

wage

billinthe plant's

1-digitindustry inwinningcounties intheyear beforetheannouncement.

Taken

literally,thisimplies

that sixyearslaterthe

wage

bill inthe

new

plant's I-digitindustryisroughly

$100

millionhigherdueto

the plantopening thanpredictedbypre-existing trends.

We

also findevidence ofpositive trendbreaksin

thetotal

wage

billinthe

new

plant'sindustryincounties thatneighborthe winner.'

These

resultsprovide

some

ofthe first ex-post estimates ofthe increase in local

economic

activity due to the successful

attraction ofa large,

new

plantand indicate that the

new

activitydoesn't

crowd

out existing activity

locally.

However, by

themselves,these findings are notinformativeaboutwhetherthesubsidiesincrease

localresidents'welfarebecause theydonotaccountfor theircost. Directinformationonthe costofthe

subsidies is unavailable.

We

follow the stylized

Roback

(1982)

model

and arguethatthe netwelfare

effectofattractinga plantwillbereflectedinproperty values. Thisisbecause propertyvalues capitalize

theincreased

economic

activityinthecounty(i.e.the benefitofthesubsidy)andtheincreaseinproperty taxes—or reductioninpublicservices— necessarytopayforthesubsidy(i.e.thecostofthe subsidy).'

We

derive a simple

model

that demonstrates that in the presence of bidding for plants by

counties,asuccessful bid

may

causeproperty values to increase,decreaseorremain unchanged.

When

countiesare

homogeneous

andpoliticianssolely

maximize

residents' welfare,the successful attractionof

aplantwill leaveproperty values unchanged. This is because countiesraise their bidsuntilthe costs

equalthe benefits

and

theyare indifferentaboutwinningor losing.

When

countiesarenot

homogenous

orifstatespayforpartofthesubsidy, the successful attractionofa plant

may

resultinincreasedproperty

values.This isbecausethecountythathas the mostattractivecharacteristicsor the largest contribution

fromstate

government

canbidlessthanthesecondbestcounty,andstill

win

theplant.Finally

when

local

politicians deriveprivate benefits fromgranting subsidies, they will overbid, and property values

may

decrease.

',',,,

.,, • , , .

SeeHanson(1998)forevidencethat localshocksaffectneighboringjurisdictionsandthat this effect dissipates

withdistance.SeeChapter 2ofBartik(1991)forareviewofthe effectsofstateandlocaleconomic development programs on employmentandeconomic growth in localareas.

On

a related topic,Evans andTopoleski (2002)

examinethe socialandeconomics consequencesoftheopeningofan Indiancasino.

'_{Gyourko andTracy}

(1991)demonstratethatthe efficientprovisionof publicservicesiscapitalized into land.

They

Ultimately, the questionofthe effect ofsuccessfully attracting a planton property values isan

empirical one. Using a unique self-collecteddatafile,

we

find thatthesuccessful attraction ofa plant

resultsinincreasedpropertyvalues. Specifically,

we

findthat inwinningcounties thereisa relativetrend

breakofapproximately 1.1-1.7%inannual property valuesaftertheplantopening announcement.Ifour model'sassumptionsare valid,thisfindingcan beinterpretedasanincreaseinwelfarefor localresidents.

Even

iftheassumptionsare notvalid,theproperty valueresults appearto

undermine

the popularview

thattheprovisionoflocalsubsidiesto attractlarge industrialplantsdecreaseswelfare.

The

finalpart

of

thepaperestimates the effectofwinningaplantonlocal

government

finances.

A

widespread concern abouttheprovision ofsubsidies isthat localgovernments pay for

them

bycutting

important services, such aseducation and police protection.'' Usingdata

from

the

Annual

Survey of

Governments,

we

find that localgovernmentsinwinningcountiesexperiencedpositiveand roughlyequal

trendbreaksinrevenuesandexpenditures. Notably,thereisa substantial increaseineducationspending

and

no

changeinpoliceexpenditures. Overall, thereislittleevidenceofdeteriorationintheprovisionof

vitalpublic servicesincountiesthat

win

'"MillionDollar"plants.

We

want

to

emphasize

thatthefocusofthispaperisthe credibleestimation oftheconsequences

for acountyofsuccessfullybiddingfora

new

plant,relativetobiddingandlosing. Therearea

number

of

related policyand/or welfarequestions,only

some

of

which

this papercanaddress. Forexample,the

analysis does not provide evidence on whether states or the nation as a

whole

benefit from tax

competitions

between

local governments to attract

new

business. Further, it is not informativeabout whether individual counties should pursue industrial

development

policies at all.

However,

since

virtuallyalllargeplantopenings inducetaxcompetitionsbetweenjurisdictions,the salientquestionfora

local

government

interestedinattractingone ofthese plants isnotwhethertobidatailbut

what

arethe

consequencesofsuccessfully bidding.

The

analysisisstructuredtoinformthisveryquestion.

The

paper proceedsas follows. Section1reviewsthe theoreticalexplanationsfortheprovisionof

localsubsidiesby local

governments

to attractplantsandthedeterminantsofplants' location decisions.

Italsoprovides a casestudyof

BMW's

decision tolocate a

new

plantinSouth Carolinaandintuitively

describes our research design. Section II reviews the

Roback

(1982)

model

inthecontext ofa plant

openingandpresentsastylized

model

ofcounties' decisionsto bidforaplantandthe effectof winning

onproperty values. SectionIIIdescribes thedatasourcesandpresents

some

summary

statistics. Section

Glaeser (2001)emphasizestheimportanceofansweringthisquestionempirically,"Economistsneedtoestimate

whathappens

when

localitiesare deprivedofthemarginaldollar. Doesthislosslead toeliminatingvery valuable

servicesor are fairlymarginalservices cutoff?" (pp.il-12).

Ifoutcomesincountiesthatbidandlosearesimilartocountieswithoutindustrialdevelopmentpolicies,thenthe analysissheds lighton the broader question of the consequencesof whetherindustrial development policies are

IVexplains theeconometriciikhIcI andSection

V

describes theresults. SectionVI interpretsthe results

and VI1concludes.

I.Local

Subsidy

and

PlantLocation Decisions

Thissectionisdivided into

two

parts.

The

firstreviewstheexisting theoretical literatureon

why

local

governments

use incentives to atti'act

some

plants to tiicirjurisdictions.

The

second subsection providesa casestudyof

BMW's

decision tolocate acarassemblyplant in theGreenville-Spartanburg

areaof northwestern South Carolinaand,

more

generally,describes the intuitionof ourresearch design.

A.

Why

do

Local

Governments

Offer Subsidiesto

Some

New

Plants?

In standard models, the provision ofincentives to firms reduces the welfare ofa locality's

residents.^ Thisisbecausethe incentives lead to inefficientlyhighlevelsoflocalproduction. Yet,inthe

real world, large businesses frequently receive subsidies inexchange for their location decision.

The

implicationisthat thestandardmodels

may

be unabletocaptureanimportant feature

of

these location

decisions. Here,

we

reviewa recentlineofresearch thatprovidesa

number

ofpossibleexplanations for

why

localities provide incentivesto

newly

locating firms. This reviewaimstohighlight

some

ofthe

"structural"forces that are likelytounderlieour "reduced-form"estimatesofthewelfareconsequences of

successfully attracting aplant.

The

firstsetoftheoriesemphasizesthe idea that local

governments aim

to

maximize

only the

welfare oftheir residents.

One

example

isthe case

where

agglomeration

economies

areassociatedwith

thelocationofthe firm. For example,finnsinthe

same

industry

may

experienceproductivity increases

asthe

number

or sizeof geographically concentrated firms increase(Henderson 2003; Garcia-Mila

and

McGuire

2001).

These

spillovers

may

beduetothe sharingofinformation, the

number

of highskilled

workers (Ranch 1993; Glaeseretal. 1995; Moretti

2004a

and 2004b),or that the

new

firms willattract

other firmsinthe future. Regardlessoftheirsource,inthepresenceofthese spillovers,localities willbid

to attractthefirms thatproduce

them

andthe resulting subsidiesallowthefirmtocapturethe spillovers

thatthey produce.

Glaeser (2001) suggeststhatlocal incentives

may

represent bids bycommunitiesto attract firms

that will generate producersurplus for the current residents ofthe

community.

Conventional welfare

analysissuggeststhatthere

may

be welfaretriangles tobegainedby localworkers. In particular,ifthe

'

SeeWilson(1999)for areview ofthe theoretical literaturesontax incentives.

'

laborsupply curve is

upward

sloping,inframarginal workersare

made

betteroffbythe presence ofthe

new

firm.

The

sizeofthe bids willreflectthewelfaregains generated.'"

Another

explanationfortaxincentivesisthattheseupfront

payments

arecompensationforfuture

tax

payments

(Wilson 1996). In particular,oncea plant beginsto operateata siteitcan be costly to

changeitslocation,becauseofthe"sunk" nature of

many

industrialinvestments(seee.g.,

Goolsbee

and Gross

2000

and

Ramey

and Shapiro 2001).

The

resultingimmobility

makes

firmseasytargets for local

tax collectors. Sinceitisdifficult for

governments

tocredibly

commit

inadvancetofuture taxrates,the

upfront tax incentives

compensate

firms for future expropriation.

Allofthe previoustheories areconsistentwith local politicianssolelyacting inthe interestsof

theircitizens,but an alternative

view

isthatlocal incentivesare dueto corruption and influence or a

desire on the politician's part to

maximize

the size ofgovernment. In the case ofcorruption and

influence, theside-payments

may

occur throughdirectpayoffs,contributionstore-election campaigns,or

future

employment

for politiciansortheir friends or family (Glaeser2001).

The

magnitude ofthese

bribes will

depend

on

theprobability ofdetection and the punishment.

An

alternative possibility is

derived fromthe Leviathan

view

of

government

(Brennan and

Buchanan

1980).

As

applied tothiscase,

politicianswillseekto

maximize

thewelfareoftheircitizensandthe sizeof

government

or the tax base.

Forthepurposesof ourpaper,itisworth notingthatthefirstsetof

models— where

localsubsidies aremotivated by agglomeration

economies

or increasesinproducersurplus orcompensationforex-post

expropriation--suggest that subsidies

may

be welfareenhancing or,atleast, neutral forlocal residents.

(We

will

show

laterunder

what

conditionssubsidiescan be welfareenhancingorwelfareneutral.)

The

secondsetof

models— where

localsubsidiesreflect

government

officials' private interests—suggestthat

subsidies

may

be welfaredecreasing.

We

returnto thispointinSection II,

where

we

incorporatethese

two

alternativeviewsinasimpletheoreticalframework.

B.

A

Case Study

ofthe

BMW

Plant

Location

Decision

and

a

New

Research

Design

Inthissubsection,

we

use a concrete

example

to illustrate

how

aparticularfirmselected asitefor

its

new

plant. Inparticular,

we

useinformation fromthe"Million DollarPlant""seriesinthecorporatereal

estateJournal Site Selection to describe

BMW's

1992 decision to site a manufacturing plant in the

Greenville-Spartanburgareaof SouthCarolina. Notably,thisis

one

oftheplantsinour sample.

A

second

goal ofthiscasestudyis tohighlight theempirical difficulties that arise

when

estimating the effectof

plant openings on local economies. Further,

we

use this case study to informally explain

why

our

itisalsopossiblethat the

new

firmgeneratesconsumersurplusforthe residents. The consumergainsarepossible

ifthere are local marketsforoutputs(e.g.,sportsteams).Because

we

focusmainlyon manufacturingplants, this

researchdesign

may

circumventthese identitlcation problems.

A

more

formalanalysisisconducted in SectionII.

Afteroverseeinga

worldwide

competitionand considering

250

potentialsites forits

new

plant,

BMW

announced

in 1991 that they had

narrowed

the listofpotential candidatesto 20 counties. Six

months

later.

BMW

announced

thatthe

two

finalists inthecompetition

were

Greenville-Spartanburg, SouthCarolina,and

Omaha,

Nebraska.Finally,in1992

BMW

announced

thatthey

would

sitetheplantin Greenville-Spartanburgand thatthey

would

receivea package ofincentives worth appioxiniately $1 15

millionfunded bythestateandlocalgovernments.

Why

did

BMW

choose Greenville-Spartanburg? It

seems

reasonable to

assume

thatfirms are

profitmaximizers and chooseto locate

where

their expectationofthepresent discounted valueofthe

stream offutureprofitsisgreatest.

Two

factorsdeterminetheirexpectedfutureprofits.

The

firstisthe

plant'sexpected future costsof production inalocation,

which

isa function ofthe location'sexpected

supplyofinputsandthefirm'sproduction technology.

The

secondfactoristhepresentdiscounted value ofthesubsidyitreceivesatthesite.

The

BMW

case provides arare opportunitytoobservethedeterminants ofthese

two

key

site-selection factors. Consider first the county's expected supply of inputs. According to

BMW,

the

characteristics that

made

Greenville-Spartanburg

more

attractive than the other

250

sites initially

considered were: lowuniondensity,asupplyofqualifiedworkers;the

numerous

global firms,including 58

German

companies, in the area; the high quality transportation infrastructure, including air, rail,

highway,andportaccess;and accesstokeylocal services.

For our purposes,theimportantpointto note hereis thatthese countycharacteristics are afirst

potential sourceofunobservedheterogeneity.

While

these characteristics are well

documented

inthe

BMW

case,they aregenerally

unknown.

Ifthese characteristics also affect the

outcomes

ofinterest(i.e.,

labor earnings, property values and countyfinances), astandard regression that

compares

Greenville-Spartanburgwith the other

3000

US

countieswillyieldbiasedestimatesoftheeffectofthe plantopening.

A

standard regression will overestimate the effect ofplant openings on outcomes, if for example,

countiesthathave

more

attractivecharacteristics (e.g.,better transportation infrastructure)tend tohave

better

outcomes

(e.g.,higherearnings).

Now,

considerthesecond determinant of

BMW's

decision, the subsidy.

The

BMW

"Million Dollar Plant" article explains

why

the Greenville-Spartanburg and South Carolina governments

were

willingto provide

BMW

with

$115

million insubsidies." According to local officials, the facility's

accountforopportunitycost).

As

apartofthis$2billion,the plant

was

expectedtocreate2,000direct

jobsandleadtoanother 2.000 jobsinrelated industriesbythelate 1990s.''

As

we

arguedinsubsection A, Greenville-Spartanburg's subsidy for

BMW

may

be rationalized by the

2000

"spillover" jobs

indirectly created bythe

new

plant.

As

anexample.

Magna

International beganconstructionon an

$80

millionplant that

was

toproduceroofs,side panels,doorsandothermajorpieces for the

BMW

plantin

1993.'^

It is notablethat

some

counties

may

benefit

more

from a particularplant, depending on their

industrialstructure,labor force skills,

unemployment

rate

and

alltheother factorsthat affect spillovers.

Forthis reason, the factors thatdeterminethetotalsizeofthe spillover(andpresumablythe sizeofthe

subsidy) represent a second potential source of unobserved heterogeneity. If this unobserved heterogeneity is correlated with outcomes, standard regression equations will be misspecified

due

to

omittedvariables, just asdescribedabove. Forexample, ifcounties thathave

more

to gain intermsof

spillovers(andtherefore offer

more

generoussubsidies) also

have

betteroutcomes,thenaregression that

compares

thewinners withtheother

3000

US

counties willoverestimatethe effectofplantopenings

on

outcomes.

Inorderto

make

validinferencesinthepresenceofthese

two

formsofheterogeneity,

knowledge

oftheexact form ofthe selection rule thatdeterminesplants' locationdecisions isgenerallynecessary.

As

the

BMW

example

demonstrates, the

two

factors that determine plant location decisions

—

the

expectedfuturesupplyofinputsinacountyandthe

magnitude

ofthesubsidy—aregenerally

unknown

to

researchersandinthe rarecases

where

theyare

known

theyaredifficulttomeasure. Inshort,

we

have

littlefaith inourabilitytoascertainand

measure

allthisinformation. Thus,the effectofa plantopening

is very likely to be

confounded

bydifferences in factors thatdeterminethe plants" profitabilityat the

chosenlocation.

As

a solution to this identification problem,

we

rely on the revealed rankings of

profit-maximizing

firmstoidentify a valid counterfactual for

what

would

havehappenedin theabsence ofthe

plantopening. In particular,the"MillionDollar Plants"articlestypically report thecountythatthe plant

chose(i.e.,the 'winner'), as well asits2"''choice(i.e.,the'loser'). For example,inthe

BMW

case,the

loseris

Omaha,

Nebraska. Inthesubsequentanalysis

we

assume

that thewinning andlosingcounties are

Ben

Haskevv,chairman ofthe Spartanburg

Chamber

of

Commerce,

summarizedthe localview

when

hesaid,

"Theadditionofthe

company

will furtherelevatean already top-ratedcommunityforJobgrowth"(Venable, 1992,

p.630).

'"_{Interestingly,}

BMW

laterdecidedtoopenasecondplantintheGreenville-SpartanburgareaandrelocateditsU.S.

headquartersfrom

New

JerseytoSouth Carolina.

''

Althoughthe

Magna

Plantwasslated tohire300workers,stateandlocalgovernmentsonly providedabout $1.5

million in incentives. Interestingly, the incentives offered to

Magna

are substantially smaller (even on a

proportional basis) than those received by

BMW,

implying that local governments appear to be judicious in

identical in expected t'utiiie proHts. controlling for dilTcrcnccs in pre-existing trends. Although this assumption is unlikely to hold exactly,

we

suspect that this pairwise approach is preferable to using

regressionadjustmentto

compare

thewinnerstothe other3,000 U.S. counties or amatching procedure

based

on

observable variables.'' In Section V,

we

present empirical evidence thatsuggests that this

identifyingassumption

may

bevalid.

II.

Laud

Prices

and Wellare

When

Counties

BidsforPlants

Thissection presents asimple

framework

thatguidestheempirical analysisandhelpstointerpret

the resulting estimates.

One

ofthe paper'sempirical goalsisto testwhetherthe successful attractionofa

new

plant affects housing prices.

The

tlrst subsection presents a stylized

model

thatspecifies

some

assumptions under

which

the

change

in landvaluesinduced bythe

exogenous

opening ofaplantcan be

interpreted as a

change

inresidents' welfare. Inthesecondsubsection,

we

allowcountiestobid for plants

andspecify

some

assumptions aboutthebidding process.

The

goalofthissubsection isto

show

under

what

conditionsasuccessful bidfora plantwill result inhigher or lowerproperty values(andtherefore welfare).

A.

Land

Prices

and Welfare

Here,

we

followthe stylized

Roback

(1982) model,

which

isoftenused to

model

firm location

decisions.

We

assume

that individualsareperfectlymobile,haveidenticaltastesandafixedlaborsupply.

Further,theyrentlandfor

homes

inthecounty

where

theywork. Allfirms are

assumed

tohave constant

returnstoscaletechnologiesandthere isa fixedsupply oflandineachcounty.''^ Inequilibrium, firm's

unitcostsequalthe nationally determined productprice andindividuals' utilitycannot be increasedby

moving

toadifferentcounty.

Now.

supposethatacountyisexogenouslv assigned a

new

plant. This case

may

beunrealistic,

butitisauseful startingpoint forexpository purposes. Importantly,allotherproducersandall workers

continuetochoosetheir location to

maximize

profitsandutility,respectively.

The

openingofthe

new

plantcauses landvaluesandnominal

wages

to increase inthecounty.

The

increaseinlandvalues occursfortworeasons. First,the

new

plantwilldirectly increase the

demand

through itslandpurchase and byincreasing the

number

ofworkers

who

need housing. Second,ifthere areagglomerationeconomies,thepresenceofthe

new

plant will lowerthe costs of productionforother

'*

Propensity scorematchingisan alternativeapproach(Rosenbaum and Rubin 1983). Itsprincipalshortcoming

relative toourapproachisitsassumptionthatthetreatment(i.e., winnerstatus)is"ignorable" conditional onthe

observables.As itshould beclearfromtheexample, adjustmentforobservablevariablesthroughthepropensity

scoreisunlikelytobesufficient.

firmsinthecounty. IntheparlanceofRoback,thisis referredtoas aproductive amenity. And,inthe

presence ofaproductive amenity,firmswill bidupthe priceoflandtogainaccesstothe spillovers. In

order to retainworkers,firms

must

pay higher

wages

to

compensate

them

forthe higherrental rate of

land,sothatreal

wages

areunchanged. Thisisnecessary becauseinequilibriumworkers'utilitymust be

constantacrosscounties.

With

thisset-up,the increase in landvalues providesaone-timegaintoproperty ownersinthe

county that receives the

new

plant. This is the only change in welfare experienced by the county's

residents. Thisis becausethe increasein

wages

isoffsetbythehigherprices thatworkersfacetorent

land(e.g., forapartments/houses), leaving theirutility isunchanged. Further,thehigherrental rateof

landcounterbalancesanyspilloversavailable totlrms. Thus, undertheseassumptions,changesin land

values translateone-to-oneinchangesinresidents'welfare.

B.

A

Stylized

Model

of

Bidding

forPlants

and

Land

Values

In theprevious subsection, the opening ofa

new

plant increased property values because

we

assumed

that the county does not have to incur any costs to attract the plant. In practice, local

governments

frequentlyprovidesubsidiesin

exchange

foraplant'slocationdecision. In thissubsection,

andtheremainderofthe paper,

we

considerthe possibility that the3,000 U.S. counties

compete

forthe

new

plant

by

offeringsubsidiesor bids.

We

assume

thatacounty'sresidents electa

mayor

thatacts as theiragenttobidto attractplants

to itsjurisdiction.

A

successful bid involves a trade-off for thecounty.

On

the

one

hand, subsidies are

costly to thecounty,because they involvetheprovisionofservicesand

may

reducethe futurestreamof

taxrevenues.

The

increaseinpublic services includes the special services for the

new

plant stipulatedby

the incentive

package

(e.g.,theconstructionofroads,or other infrastructureinvestments,taxabatements,

job training funds, provisionof low-cost or free land, the issuanceof tax-exempt bonds, provision of

cheap electricpower, etc.), as well as the standardpublic services (e.g., garbage removal and police

protection).

We

assume

thatthecounty'sincentivepackageisfinancedby propertytaxes,so thatitscost

iscapitalizedintoland values.'"

On

theotherhand,the

new

plant directly increases thelevelof

economic

activity inthecounty,

which

raisesthevalueofland.

As

the

BMW

case demonstrates,thepresenceofthe

new

plant

may

also

raisethevalueof landindirectlyby generatingspillovers, if,forexample, itattractsother plantsand/or

lowersthe costsof production forother plants. Thus, property values capitalizeboththe costs(i.e.,the

IntheAnnual Survey of Governmenisdata,property taxesaccountfor

49%

oftotalrevenuesfrom

own

sources.

increasedpropertytaxesand/or reducedservices)and beneHts(i.e., tiie increased

economic

activity)of

attractingtiicplant.

Tiiissubsection's goal isto theoreticallyanalyze

when

thesuccessful attractionofa plant will

increase ordecrease property values (and therefore welfare) in the presence of county bidding.

We

denotepropeityvaluesasP,and

assume

thatthechangeinproperty valueslorthewinning county can be expressedas AP,,

=

Vi|

-

C,j. Vi,denotesthebenefitof

new

plantjforcountyi,anditiseqiiivalenttothe

increaseinpropertyvalues(intheabsence ofa subsidy).

The

sizeofthisbenefitis

exogenous

and

known

tothecounty.

We

also

assume

thatitis

known

toallthe othercountiesbidding

on

theplant.

Cy

isthe costtothecounty ofthesubsidyprovidedtotheplant.

Intherealworld,itisoften thecasethatthestatebears partofthecostofthe incentivepackage. In this case thetotal subsidy received by the plant is B,,

=

Cij + Sj,,

where

Sy denotesthe state's

contribution.

We

assume

thatSis

exogenous

tothecountyandisprovided bythe statetoaccountforthe

benefitstoother countiesinthestate.

The

plantsaretheother sideofthistwo-sided matchingproblem and

we

assume

thatthey will

locateinthecounty

where

theirfutureprofitsaremaximized.

As

describedabove,

two

factorsdetermine

theirexpectedfuture profitsinagiven county:thesubsidyandtheexpectedfuture costsof productionin

thatcounty. Itislikelythatthereis heterogeneityinthe

maximum

subsidythat countiesare willingto

offer,

due

to differences incounties characteristics (recall Section I-B). In orderto obtain thehighest

subsidy,

we

assume

thatthefirmsconduct an Englishauction inthepresenceof independent,or private,

values.

We

further

assume

thatthereisnotanycollusioninthebidding

among

counties.

The

firm'schoicealsodepends onthe location-specific productioncosts. Itislikelythatthereis

heterogeneityinaplant's valuationof a countyduetodifferences intheexpected supply offuture inputs.

Inthe

BMW

case,recallthese factorsincludedthepresenceofqualifiedworkers,thepresenceof

German

companies

andair, rail,highway,andport access.

We

denotethevaluetothefirmofallthese factors as

Z,j.

A

higherZy implies thatproduction costsoffirmj are lower incounty i.

We

assume

that

Z

is

exogenous

tothecountyandis

known

toallcounties.

We

also

assume

each county hasrational expectations aboutthe plant'sproductioncostsinall

other counties,(i.e.,Z,jforall i)andallother counties' bids(i.e.. B,,forall i). This assumption prevents

finns from increasing the subsidy that a count)' offers b\- exaggerating the benefits oflocating in a

differentcounty. If thisassumptionis incorrect, it

would

causecountiesto"overbid." Itisnotevident

why

such overbidding

would

be an equilibriumstrategy.

Overall, thetotalvalueforafirmoflocatingina particularcountyisthe

sum

ofthesubsidyand

the county-specific costadvantages. It

seems

reasonabletopresumethata plant will select thecounty

1.

Homogeneous

Counties. Ingenera],counties differintheirvaluationsofa

new

plant,Vy,and

tiie plants' valuation ofthe county, Z,j. Here,

we

begin by considering the case

where

counties are

homogeneous

in

V

andZ: Vi,

= Vq

andZjj

=

Zqforall i. This

would

be thecaseifour keyidentifying

assumptionthatwinners andlosers are identical

were

valid.

The homogeneity

caseisimportantbecause

inourempirical analysis,

we

willretainthe

homogeneity

assumption

when comparing

winners

and

losers.

Later,

we

analyzethe

more

general

model

thatallowsforheterogeneityin

V

and

Z

toinvestigate

how

our

conclusions differ ifthis identification assumption is not valid.

Under

homogeneity,the firmsimply choosesthecountythatoffers the highest subsidy, B.

We

considerfour cases.

Case

J. This caseis the simplest.

We

assume

thatthecounty's

mayor maximizes

residents'

welfare andthe stateprovides

no

subsidy (i.e.,S

=

0).

The

mayor

raisesthe biduntilsheisindifferent

between

winning andlosing. Formally,theequilibriumbid,B*,isdetermined by

(!)

B* = Vo

Consequently,

AP

= Vo

-

B* =

andthesuccessful attractionofthe plantdoesnotchangeland pricesor residents' welfare.

Case

2.

We

now

allowstatestosubsidizethe incentivesofferedtothe plant butretaintheother

assumptions. Itispossible that different states providedifferentlevelofincentives if,forexample,the

magnitude

ofspilloversinneighboring countiesdiffersacrossstates. Here, thecountythatreceives the

most

generousincentivefromthe state will

win

the

new

plant.

The mayor

ofthecountylocated inthe

most

generousstatedoesnotneedto raisethe biduntilsheisindifferentbetween havingtheplant

and

not

havingtheplant.

She

can

win

the plant

by

settingthecounty'sbidatthepointthat

makes

the

mayor

of

thecounty with thesecond

most

generous statesubsidy indifferent

between

winning and losing.

The

optimalbid

B*

is

(2)

B* = Vo +

S™,.,

where

S,„;,^.|istheincentiveprovidedbythesecond

most

generousstate.

Specifically, landvalues increase bythedifference betweenthe statesubsidy provided bythe

most

and 2"''

_most

_generous

states:

AP

= Vq -

(B*-S|„.ix)

=

S^ax -Sn.ax-i>0,

where

S,„axistheincentive

provided bythe

most

generous state. Importantly, thesource ofthe increase inhousingvalues isthe

heterogeneityinthestatesubsidiesandthecounty'scaptureofpartofthestatesubsidy.

Case

3.

We

now

allowforthe possibility that the

mayor ma\

haveher

own

goals. Inparticular,

we

assume

the

mayor

benefits from a higher incentive package because opportunities forgraft or

enlarging

government

are increasing inB.

We

definethispersonal benefit as

T

=

f(B),with

f

>0: the

higherthe subsidy providedbythe

mayor

tothe firm, the larger thekickback.

Due

toan exogenously determined probability ofdetection and punishment, the

mayor

chooses

B

to

maximize

herutility U,

>

0. Inthecase

where

U2

=

0,thereisnoprincipal-agentproblem andthemayor's andresidents'interests

are perfectly aligned. IfU2

>

0,themayor'sobjectivefunction includes residents'welfareaswell asher

own

privategainfromthesubsidy. Forsimplicity,

we

assume

that

U(AP,T)

isseparableinitsfirstand

second

argument

and

T

isafixed fraction yofthebid(i.e.,

T

=

y B),

where

<

y

<

I. Thus,

U

=

AP

+

yB.

We

assume

Ut;<: but reinstate theassumptionthatstatesdonot subsidi/.c the bid(i.e.S

=

0). In

thiscase,the

ma)or

raisesher bidtothepointthat

makes

herindifferentbetween havingtheplantandnot

havingtheplant. Ifallthe

mayors

behaveinthe

same

way,themayor's optimalbidis

(3)

B*-Vo/(l-y)

In this case, the

mayor

overbids, by choosing

B* which

is larger than the value ofthe plant to the

residents. Such overbidding causes landvalues inthe winning countytodeclinerelative to thelosing

county.

The

magnitude ofthedeclinedepends onthemayor's weight on her

own

welfare:

AP=

-_(y/

(1-y))

Vo <

0.

Cose

4.

When

U2 ^

andthereisa positive statesubsidy(i.e.,S,|>0),thechangeinland prices

cannotbesigned.

The

statesubsidyincreases land values, as

shown

aboveincase2,but theinclusionof

themayor's personal gain in the objective function results in adecrease in land values as in

Case

3.

Depending

onthe magnitude ofthese

two

effects, landvalues

may

increase or decreaseinthe winner

relativetotheloser.

'^

2.

Heterogeneous

Counties.

Although

the key assumptioninourempirical analysisis thatthe

winning and losing counties are

homogeneous,

it is important to understand the consequences ifthis assumption isnotvalid. Here,

we

allowforheterogeneityincounties' valuationsofattracting the plant,

V,andplants" valuations ofcounties, Z. Insection I-B,

we

usedthe

BMW

example

toarguethatthe

effectofthe plantopening could be

confounded

bythese

two

sourcesof unobservedheterogeneityacross

counties. Specifically,

we

suspect that a naive estimator that ignores the presence ofunobserved

heterogeneitywillbebiased. This subsection formalizesthispoint.

Forsimplicity,

we

retaintheassumptionsthatS.j

=

andUj

=

0.If

V

and

Z

vary across counties,

the firmchooses

where

tolocatebasednotonlyonthebidB, but alsoon Z. Specifically,the valuefor

firmj of choosing countyi isBii+Z,,.

Assume

for simplicitythatthere areonly

two

levelsof V, high

V

(Vh) and low

V

(Vl);and

two

levelsofZ,high

Z

(Zh)

and low

Z, (Zl). Considerfirstthecase

where

V

and

Z

are positively correlated,sothatthecounty with high

V

alsohas highZ. Thus,one county will

gainthe most fromattracting firmj andisalso theleastcost productionlocation forfirmj. Itsoptimal

bidissuchthatthecounty with

low

Z

and low

V

is indifferentbetween havingthe plantandnothaving

(4)

B* =

Vl-(Zh-Zl).

In this case,thecountythat isthe best

match

enjoys a rent thatis capitalizedintoland values.

Land

valuesincreaseby an

amount

proportionaltothedifferencein

V

andthedifferenceinZ:

AP

= (Vh

-Vl)

+

(Zh -Zl)>0. Importantly, the increaseinlandvaluesisduetotheunderlyingdifferencesin

V

and

Z.

Consider

now

thecase

where

V

and

Z

are negatively correlated.

County

1 hashigh

V

and

low

Z;

whilecounty2hashigh

Z

and low V.If

Vh+Zl >

Vl

+

Zh, county1 winsthe plantby bidding an

amount

B*

that

makes

county 2indifferentbetween havingthe plantandnothavingtheplant:

(5)

B* =

V,.

+ (Zh-Zl)

The

winner county enjoys arent thatiscapitalizedinland values,althoughthe rentislowerthan the rent

inthecaseofpositive correlationbetween

Z

and V:

AP

= (Vh

-Vl)-(Zh-Zl)>0.

A

similarconclusion

appliesif

Vh+Zl

<

Vl

+

Zh-In thiscasecounty 2isthewinner anditsland prices increaseby

AP

=

(Zh

-Zl)-(V|i-Vl)>0. Again,theheterogeneityisthesourceofthechangeinprices.'^

The

implications for

ourempirical analysis arediscussedfurtherintheeconometricssection.

III.

Data

Sources

and

Summary

Statistics

A.

Data Sources

We

implement

thedesign usingdataon winning andlosing counties. Eachissueofthecorporate

realestatejournalSite Selectionincludesanarticle titledthe"Million DollarPlants" thatdescribes

how

a

large plantdecided

where

tolocate."

These

articlesalwaysreport thecountythatthe plantchose(i.e.,the

'winner'),andusually report therunner-upcountyorcounties(i.e.,the "losers").""

As

the

BMW

case

study indicated,the

winner

andlosersareusuallychosen

from

an initialsample of""semi-finalisf"sites

that in

many

cases

number more

than ahundred."'

The

articles tendtofocusonlarge plants, and our

impressionisthatthey providea representativesample ofall

new

large plantopeningsin theUS.

The

articles usually indicate the plant's output,

which

we

use to assign the plant to the relevant 1-digit

industry.

In thiscase,theoptimalbidisB* =(Vo+S^m-i)/(l-y).The changeinlandpricesis

AP=

-_(y/(1-y))

Vo+

S^iK

-(i/(i-y))S„,ax-,.

'^

Inthe unlikely casethat

Vh

+ Zl =

Vl

+Zh,the twocounties areequivalent, and the winneris randomly

determined.

In1985,thejournalIndiistiialDevelopment changedits

name

to Site Selection. Henceforth,

we

refer toitas Site

Selection. Also,insomeyears thefeature ""MillionDollar Plants"wastitled"Location Reports."

"°

Insomeinstancesthe"Million DollarPlants" articlesdonotidentifytherunner-up county. For thesecases,

we

dida Lexis/Nexis searchforotherarticlesdiscussingthe plantopening and in 4caseswereabletoidentifythe

losingcounties. TheLexis/Nexis searcheswerealsousedtoidentifythe plant's industry

when

thiswasunavailable

inSiteSelection.

"'

These

data have

two

important limitations. First,the magnitude ofthe subsidy offeredby the

winning counties is in

many

cases unobserved and the bid is almost always unobserved for losing

counties. Thisisunfortunate,because aninterestingcheck ofthe validityof ourresearchdesign

would

be

totestwhetherthe subsidies offered areequalinthewinning andlosing counties. Second,in

many

cases thearticlesdonot report theexpectedsizeoftheplant.

In ordertoconductthe analysis,

we

collectedthemostdetailedand comprehensivecounty-level

data available on

employment, government

finances, and property values available for theperiod from

1970 through 1999.

The employment

data

comes from

the

Census

Bureau'sCounty Business Patterns

(CBP)

datafile.

These

annualdata report the

number

of

employees

andthetotal

wage

billatthecounty

byindustrylevel. Inordertoprotect the confidentialityoftherespondents,thesedata are"zeroed out"for

many

industry by countycells. Consequently,

we

conduct ouranalysisatthe 1-digitindustryby county

level."

The

CBP

data are usedto test whethera plantopeningisassociatedwithchangesin

wage

bill

trends in its industry, as well as other industries.

We

focus

on

thetotal

wage

bill rather than total

employment

since thelattercannotdetectchanges intheskillcontentoflabor. Unfortunately, the

CBP

does notreporthourly wages.

We

also test whether the successful attraction of a plant affects property values.^^

_We

_are

unaware

of anyexisting electronicfilesof annual county-level property valuedata,so

we

createdour

own

county

by

year datafile

on

property valuesfrom

two

sources. First,

we

contactedallthestateand county

governments

inour winnerandlosersamplesdirectly

and

requestedalltheir historicaldataon property

values."''

These

data existbecause governments determinethevalue of propertyintheir jurisdictions for thepurposeof assessing propertytaxes. ,

Second,

we

supplementedthese databy

hand

enteringdatafromthe1972, 1977, 1982, 1987. and 1992

Census of

Governments.

Volume

2 TaxableProperty Values

and

Assessment-Sales PriceRatios.

The

censusesreportsmarket valuesintheyear beforeeach census

was

conducted. In years

where

data

was

unavailable from both sources,

we

estimated county-level property valuesbylinearly interpolating the

Census

of

Government

data,

which

likelycausesthe true variationtobeunderstated.

Forthepurposesoftheanalysis,

we

divide outputintothe5broad"1-digit" industriesdefinedbythe

CBP

for

which uncensored

wage

billdata areavailableinmostyears. Theseindustries are: Manufacturing; Transportation

andPublicUtilities;Trade(Wholesaleplus Retail);Finance, Insurance,andRealEstate; Services. Atthis levelof

aggregation,1

7%

ofthecellsare"zeroed"out.

^^

Ideally,

we

would havealsoexaminedthe effecton propertyrental rates,but

we

wereunabletofindasourcefor

annual or otherhigh frequencyrentaldata.

^''

_We

attemptedtogetthisdatafortheprimarysample of 166countiesdatingbacktothe early1970s.

We

collected

atleast1yearofpropertyvalue datafrom 153counties. Ingeneral,thesegovernmentsdidnothavedatafromthe

earlier years. Forexample,

we

have nonmissing property value datefor68countiesin 1977, 102in 1980,149in

1990,and153in1998.

One

limitationofthesedataisthatourmeasure of property valuesisthe

sum

ofthevaluesofland

andstructuresacross residentialandindustrial land.

The drawback

ofthis

measure

of property values is

thatitwillinclude thevalueofthe

new

plants' structures,

which

mechanically causes

measured

property

valuestoincrease. Unfortunately,aseriesforland valuesonly isunavailablefor

many

ofthecountiesin

oursample. Thisissueisdiscussed

more

extensivelyinthesubsequentdescriptionofthepropertyvalue results,but

we

note herethattheestimatedincreaseinpropeityvaluesappearstobesubstantially larger thanany reasonable estimate ofthe

mean

valueof

new

plants'buildings.

Despite our extensive data collection efforts, the property value data are missing for

some

counties.

Our

preferredpropertyvalue

sample

iscomprised of 30 ofthe82 winnersand62 ofthe 129

losers.

The

Data

Appendix

provides

more

detailsonthepropertyvaluedata.

The

Amntal

Survey

of Governments:

FinanceStatistics Series

(ASG)

is used todetermine the

fiscal consequences for local

governments

of

new

plant openings.

The

ASG

is an annual survey of

governments

that asks detailed questions on governments' expenditures by function (e.g., education,

administration,and publicassistance) and type(i.e., intergovernmentaltransactions, current operations,

andcapital outlays).

The

data alsocontains information on revenues

by

source, indebtedness, andcash

andsecurities holdings.

We

aggregatethese datatothecounty level. This aggregation is

done

onthe

sample

of

governmentalunitsthataresurveyed continuouslyfrom 1970 through 1999so that the units are

held fixed. Inour "Million DollarPlant"

sample

of 166winningandlosing counties,150countieshave

atleast

one

governmental unitthat reportscontinuously.

The

continuousreporterscomprise only

12.5%

of all governmental units in these counties but account for approximately

75%

of revenues and

expenditures. Thisisbecauselarge

government

unitsaresampled withcertainty,while smallerunitsare

sampled with "varying probabilities within an area,type ofgovernment, and size ordering" (Census

Bureau, 1990,p.1-1)."

B.

Summary

Statistics

Table1 presents

summary

statisticsonthesample ofplantlocation decisionsthatformthebasis

ofthe analysis.

The

firstpanel indicates thatinourprimarysamplethere are82separate plantopenings

and an average of1.6 losersperwinnerora totalof 129losers. Thereare 166countiesinthissample,so

According to the data documentation, the following governments are sampled with certainty: "all county

governments withapopulationof 50,000ormore, municipalgovernmentswithapopulation of25,000ormore;

townshipgovernmentinthe

New

England andMiddleAtlantic stateswith a populationof25,000ormore; school

districtswith an enrollment of 5,000ormore;andspecial districtswithlong-term debt outstanding of$10million,or

theaveragecounty appears roughly 1.3times.'"^

_The

_second

panel reports the distributionofthe niuriber

oflosers per winner.

We

refer to the winner and

accompanying

loser(s) associated with each plant

opening

announcement

asa "case." In 57 ofthe 82 cases thereis asingle loserand in 14there are2 losers.

The

table alsoreveals that63 ofthe 82 plantswereinthe manufacturing industry. Thus, our

analysisis

most

informativeabouttheconsequences ofattracting industrialplants.

The

final panel lists

the distributionoftheyearofthe

announcement

abouttheplantopening. 22 ofthe plantopenings

were

announced

in1991 and 1992,suggestingthatthey

may

be countercyclical. Appendi.x Table2 reports the

identityof eachplant,itsindustry,andthewinning andlosing counties.

Although

we

do

not

know

thedeterminants ofthecounties'valuationsofplants(i.e.,V,j)or the

plants' valuations of counties (i.e., Z,j), Table 2 presents the

means

across counties of

some

likely

detenninantsofthese variablesinthe threeyearsbeforethe

announcement

ofthe plantopening.

These

means

arereported forwinners, losers, andthe entire U.S. in

columns

(1),(2), and (4),respectively.^'

Column

(3) presents thet-statisticand p-value fromthetestthatthe entries in(1)and(2) are equal.

We

e.xpectthatwinning andlosingcountiesaresimilar sothatanydifferencesin

columns

(!)and(2)willbe

small. Incontrast,

we

expectthat acomparison ofthewinners(or losers)withall U.S.countiesislikely

toproducelargerdifferences.

The

first

two

panelsreveal that thewinning andlosingcounties are similarinboththe levelsand

pre-announcement

trendsoftheprimary

outcome

variables. Specifically,thefirstpanelreportsthe

means

ofthree

outcome

variables inthe three yearsbeforethe

announcement

ofthe plantopening.

The

mean

total

wage

billinthe

new

plant'sindustryisapproximately $1,127millioninwinningcounties,

compared

to$1,145 million in losing counties. Thisdifference is notstatistically meaningful andindicates that

there

were

similar levelsofactivityinthe

two

setsofcounties.

The

countoffull-time

employees

leadsto

the

same

conclusion. In contrast,thecorrespondingfigures forall the

US

counties are

much

smaller."^

The

third

row

ofthispanel indicates thatthehypothesisofequivalentaggregate property valuesacross

winning andlosingcountiescannot berejectedatconventionalsignificancelevels.

The

second panel

compares

trends in the

outcome

variables in the three years before the

announcement.

The growth

ratesinearnings,

employment

andlandvaluesarenotstatisticallydifferentin

"*

127 counties appearonceinthedata. 33 counties appear twiceand 6 countiesarepresent threetimes.Through

theentireempiricalanalysis,

we

exclude the 7 counties with populationsexceeding 2million. Inthesecounties,it

wouldbedifficult todetecttheimpactofaplantopening.

"^_Thelosing

countyentriesincolumn2 are calculatedinthefollowing manner. First,

we

calculate the

mean

across

allthe losers for agivencase. Second,

we

calculate the overall loseraverageas theunweighted

mean

acrossall

cases sothateach caseisgiven equal weight.

"^_Thefiguresin

thetoppanelofcolumn 4are aweighted averageforyears1982to1993, with weights proportional

to thenumber ofMillionDollar casesineach yearand industry.Thefigures inpanels2to5 ofcolumn4area

weightedaverageforyears1982to1993, with weights proportionaltothenumberof Million Dollar casesineach year(seebottomofTableIforthedistributionof cases acrossyears).

winning

andlosing counties.

We

return tothis issueinSection V,wiiere

we

graphically

show

thatthe

trends in

wage

bill,

employment

and land valuesinthe 8yearsbeforethe

announcement

aresimilarin

winning and losing counties.For

now,

we

notethatthe findingthatboth the levelandgrowth rateof propertyvaluesissimilarin

winning

andlosing countiesisanespecially importanttestofthe validityof

our research design, ifproperty markets are forward looking.

Under

the plausible assumption that

propertymarketsareforwardlooking,this finding indicates that theexpectedfuturechangesinthe level

of

economic

activitycapitalized intoproperty valuesare alsoex-antecomparable. Overall, thefirst

two

findingsprovidereassuringevidenceonthe qualityofthisresearchdesign

The

thirdpanel reports

mean

levelsand changes of socioeconomic and

demographic

variables. In

general, the null hypothesis of equal

means

in winning and losing counties cannot be rejected with

conventionalcriteria. Thisistrue bothfor the levelsofthe variables inthe 3 yearsprecedingthe plant

opening

announcement and

the percent

growth

in those years for selected variables. There are

two

important exceptions

where

thep-valueis .05orless:level of percapita

income

andfractionofcollege

graduates.

However,

forthese

two

variables,the losers"

mean

is closerto thewinners'

mean

thanisthe

mean

acrossallU.S.countiesin

column

(4).Furthermore,forper capitaincome,the percentgrowthisnot

statisticallydifferentinwinners

and

loserscounties. Interestingly, there areimportantdifferences

between

all U.S. counties

and

the

winning

counties. In particular,the winners are richer,have a substantially

larger population,ahigheremployment-populationratio,a bettereducatedpopulation,and fewer people

overtheageof65.

The

fourthandfifthpanels

show

thegeographicdistributionofplantsacross the fourregionsof

theU.S.andtheindustry distributionofthelaborforcewithin the categories. Here,the

means

acrossthe

winning and losing counties are not wellbalanced. For example,

66%

ofthewinnersareintheSouth

compared

to

39%

ofthe losers. This is potentially problematic, since it suggests thatthere

may

be unobserveddifferences(e.g.,uniondensity)across thewinningandlosing counties.

This findingunderscores the valueof our identification strategy's reliance

on

comparisonsof

changesin

outcomes

between

winnersandlosers,ratherthan cross sectional comparisons. In thissetting

cross-sectional differences willonlybiastheresults iflevelsofthese variablesdeterminefuturechanges.

This

would

bethecaseifforexample, uniondensity predictsgrowth in

employment

andproperty values.

As

thenext section discusses,

we

estimate

models

thatincluderegionby yearfixed effectstoaccountfor

the uneven distribution of

winner

and losers across regions.

We

also estimate models on a restricted

samplethatis limitedtocases

where

thewinner and losercounties areboth fromtheSouth.

To

preview

the results, our findings are unaffected by these specification checks, implying that the geographic imbalance doesnotexplainourresults.

IV.

Econometric

Model

In lightofthe firm's selectionrule,thegoalistoestimate the causal effectof winninga planton

county-leveloutcomes. Thissectiondiscusses the 2-stepeconometric

model

usedto estimatethis effect.

Inthefirststep,

we

fitthefollowingequation:

17 17

(6) Y„c,

=

aic+

_Yj

'^wtW.ic,

+

_Yj

^liLijci

+

Hi,

+

iiut+£,.ic.,or

T=-:9 t=-i9

17 17

(6') Y,„

=

ac+

_Y.

":vvtWjcx+

X

J^ltLjcx

+

Ht

+

%

+

^.ic„

where

ireferences industry,j indicates a case, cdenotes county,andt indexesyear, xalsodenotesyear, butitisnormalized sothatforeach casetheyeartheplantopeningis

announced

ist

=

0."'

_{The outcome}

variableinequation(6),Y.jct,istotalwages.

The outcome

variablein(6'),Y,^,, isacounty-level

measure

ofproperty'values or a

government

financevariable, ^ijciand^j^,are the respective stochastic errorterms.

a,c (ttc) is a full set ofindustry x county (county) fixed effects that adjust for

permanent

differencesinthe interceptofthe

outcome

variables.

These

accountforall fixedcounty characteristics.

|i„ ([!,) isa vectorofindicators thatnonparametricallycontrols forindustryx year(year)effects. In

some

specifications the

sample

includes the entire U.S.,while in others

we

restrict it toour "Million Dollar

Plant"sample of 166counties.

The

useofthesmallersample isequivalentto imposingthe restriction

thattheindustryxyear(year) effects arethe

same

inthe 166countiesandtheremainder ofthecountry.

Hut(t1it) isasetofseparate fixed effects foreachofthecasesinteractedwith anindicator forwhetheri

>=

-8andt

<=

5.

We

restrictattention tothesevalues ofi,becausethe sampleis balanced overthis

range.

W,,eT and Wj^^^^are indicator variables. Wjjct(Wj^O equals 1 forobservationsonthe

new

plant's

industr>' inwinning counties (the

new

plant's county)foragiven valueofi. L,|ct and Lj^taredefined analogously forlosing counties. Inthecaseswithmultiplelosers, thisindicatorvariablewillequal 1 for

observationsfrom multiplecountieswithinacase.

The

vectorsttv, and Klaretheparametersofinterest inthese equations.

They

measurethe

period-specific

means

ofthedependentvariablesinwinningandlosing counties, respectively,

where

the

means

are conditionalonalltheindicatorvariablesdescribed above.

The

periodisdeterminedbytheyears since

(oruntil)the plantopeningannouncement. Thus,the effectof

winner

or loser statusisallowedto vary withI. For example,tiwt

when

i

=

3istheconditional

mean

ofthe

outcome

inwinningcounties 3 years