International Regional Science Review · ‘‘boom’’ in manufacturing plant location, the prospect of a prison siting led ... tion base through the counting of prisoners in local

http://irx.sagepub.com/

ReviewInternational Regional Science

http://irx.sagepub.com/content/30/3/274The online version of this article can be found at:

DOI: 10.1177/0160017607301608

2007 30: 274International Regional Science ReviewAmy K. Glasmeier and Tracey Farrigan

Poor Rural PlacesThe Economic Impacts of the Prison Development Boom on Persistently

Published by:

http://www.sagepublications.com

On behalf of:

American Agricultural Editors' Association

can be found at:International Regional Science ReviewAdditional services and information for

http://irx.sagepub.com/cgi/alertsEmail Alerts:

http://irx.sagepub.com/subscriptionsSubscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

http://irx.sagepub.com/content/30/3/274.refs.htmlCitations:

at MASSACHUSETTS INST OF TECH on October 14, 2010irx.sagepub.comDownloaded from


http://irx.sagepub.com/content/30/3/274

http://www.sagepublications.com

http://www.ageditors.com

http://irx.sagepub.com/cgi/alerts

http://irx.sagepub.com/subscriptions

http://www.sagepub.com/journalsReprints.nav

http://www.sagepub.com/journalsPermissions.nav

http://irx.sagepub.com/content/30/3/274.refs.html


THE ECONOMIC IMPACTS OF THE

PRISONDEVELOPMENT BOOM ON

PERSISTENTLY POORRURAL PLACES

AMYK. GLASMEIER

Department of Geography,The Pennsylvania State University, [email protected]

TRACEY FARRIGAN

Independent Scholar,[email protected]

Prison construction experienced explosive growth over the 1980s and 1990s. Many poor ruralcommunities invited prisons into their environs, anticipating jobs and economic development.However, with one notable exception, no ex post empirical studies exist of the economic effectsof prison construction on rural counties. Following an extensive review of the literature, thisresearch uses a quasiexperimental control group method to examine the effect of state-runprisons constructed in rural counties between 1985 and 1995 on county earnings by employ-ment sector, population, poverty rate, and degree of economic health. Analysis suggests a lim-ited economic effect on rural places in general, but may have a positive impact on povertyrates in persistently poor rural counties, as measured by diminishing transfer payments andincreasing state and local government earnings in places with relatively good economichealth. However, there is little evidence that prison impacts were significant enough to fosterstructural economic change.

Keywords: rural development; prisons; persistent poverty; quasiexperimental methods

Over the last twenty-five years, U.S. states have constructed more than six hun-dred new prisons. More generally, at the beginning of the twenty-first century,the number of prisons in the nation had literally doubled to 1,023 from 592 in1974 (Lawrence and Travis 2004). The ‘‘three strikes’’ law and longer and moresevere prison terms for first-time drug-related offenses exponentially increasedstates’ prison populations. Prison overpopulation led some states, and regionswithin them, to decide to construct new prisons and operate them as a businessand economic strategy. The reasons for the strategy are clear: in addition to theincreased felon population and stricter law enforcement, since the early 1990s,many rural communities, facing limited growth opportunities in traditionalindustries, bid for and acquired prisons (Beale 1993, 1996, 1998; Beck and

DOI: 10.1177/0160017607301608

! 2007 Sage Publications

INTERNATIONAL REGIONAL SCIENCE REVIEW 30, 3: 274–299 (July 2007)



Harrison 2001). According to the Population Reference Bureau, in the 1990s,over half of all new prisons opened in nonmetro counties (Tarmann 2003; seefigure 1).1

As occurred among economic development agencies during the competitive‘‘boom’’ in manufacturing plant location, the prospect of a prison siting ledfinancially strapped rural communities to engage in bidding wars with othertowns for incarceration facilities. To be competitive, communities offered eco-nomic incentives in the form of tax breaks, infrastructure subsidies such as roadsand sewers, and free land. In some cases, communities constructed the prisonsthemselves (Mattera and Khan 2001; National Conference of State Legislatures1999). For some communities, attracting a prison resulted in new jobs and newresidents. However, there is equally compelling evidence that prison construc-tion has not always led to the type of economic development activity thatreduces unemployment, raises local residents’ incomes, and reduces poverty—typical justifications for public investment of scarce resources (King, Mauer,and Huling 2003). There also is evidence of unanticipated negative social andcommunity consequences related to rural prison construction and the incarcera-tion of prisoners from urban areas far away from their families and communities(Huling 1999).

Of major significance now is the slowing rate of growth of the nation’s prisonpopulation. According to recent estimates, this growth peaked in 2000 frommore than 4 percent to 2.6 percent based on the latest U.S. Department of Justice

FIGURE 1. Prisons Constructed between 1985 and 1995Source: U.S. Department of Justice, Bureau of Justice Statistics (1998).

Glasmeier, Farrigan / PRISON DEVELOPMENT 275



figures (Clement 2002a; Willing 2004; CNN 2006). Some communities arereporting difficulty in filling newly constructed prison facilities, while others areconsidering the future of their current facility (Clement 2002b, 2002c). Giventhe possibility that the supply of prison beds could ultimately outstrip demand,rural communities need to look carefully at prison construction as a viable eco-nomic development strategy by weighing the pros and cons of this form of pub-lic investment.

Incarceration facilities go by many names, including prisons, correctionalfacilities, prison work camps, and other similar terms. Ownership is variable,with facilities owned and operated by public and private enterprises. They houselegal offenders under the jurisdiction of local, state, and federal courts. In thisarticle, we examine the case of state correctional facility location. Differentiat-ing among the three types of facilities is an important and understudied area ofeconomic development research. Its absence is due to both data challenges andthe episodic nature of correctional facility assessments.

With these introductory comments in mind, this article has three parts. In partone we discuss the debates surrounding prison development impacts as repre-sented in the popular, scholarly, and policy literatures. In part two we describeour approach to the analysis of prison development impacts using the pairedcomparison method. Part three presents the empirical results of our nationalstudy of prison development, including the years 1985 to 1995. In this article weanalyze the extent to which prison construction contributes to a positive changein economic conditions in nonadjacent rural counties using a quasiexperimentaldesign approach that compares the development trajectory of prison recipientcounties with ‘‘twins’’ that demonstrate comparable conditions at the start of theperiod of investigation. We consider how and whether rural communities benefitfrom prison construction, especially those communities facing high poverty andunemployment and possibly low incomes. Following Isserman and Rephann’smethodology (Rephann and Isserman 1994; Isserman and Rephann 1995) usedto investigate the development effects of the Appalachian Regional Commis-sion, we compare pairs of counties that are similar in all respects, save that onlyone received a new prison facility over the 1985 to 1995 period. Like Issermanand Rephann’s earlier work, our goal is to go beyond assessing just the changeitself, to include examination of the change that would have occurred in the reci-pient county in the absence of prison construction.

PART I: REVIEWING THE STATE OF THE FIELD

Anecdotal evidence, scholarly research, and policy analysis reveal a range ofperspectives on the efficacy of prison development as an economic developmentstrategy. Here, we examine three perspectives on prison development, based firston anecdotes, then on scholarly reports, and finally on the basis of policyanalyses.

276 INTERNATIONAL REGIONAL SCIENCE REVIEW (Vol. 30, No. 3, 2007)



POPULAR PRESS

Reports in the popular press showcase the range of existing perspectiveson the impact of correctional facilities on recipient communities (Doyle 2002;Carlson 1992; Hawes 1985; Stanley 1978). Prison development has both criticsand boosters. Critics argue that prisons

. . . increase crime in communities that attract them; attract family members whodiffer from local citizens in the host community; reduce local property values; failto generate much economic development; bring only low-skill jobs to host com-munities; place demand on local social, health, and public services; can cost localcommunities funds for the construction of infrastructure in support of the prison,such as roads and utilities; and reduce the number of jobs in the local public sec-tor (e.g., employment of low-risk prisoners in jobs such as landscaping of publicfacilities). (Doyle 2002, p. 4)

Boosters take the opposite perspective, arguing that new prison construction

. . . creates demand for local goods and services; brings new residents to a recipientcommunity; brings middle-class incomes and additions to the tax base; bringsancillary services to the host community that serve prison needs; often leads to ser-vices upgrades in support of the prison; can attract external government funds thatimprove local roads and utilities; and can lead to an increase in the local popula-tion base through the counting of prisoners in local and state censuses, thus helpingcommunities of certain sizes to qualify for additional federal and state develop-ment and infrastructure funds. (Doyle 2002, p. 5)

An examination of the popular press suggests that both sides of the argumenthave some credibility. A number of examples underscore the variety of out-comes that accompany prison development. These examples highlight both sidesof the popular debate.

On the Positive Side of the Debate

Appleton, Wisconsin. After years of careful research and planning, a prisonopened in Appleton, Wisconsin, which attracted Economic Development Admin-istration (EDA) funding for infrastructure (Stanley 1978; Doyle, 2002). Thefour hundred-employee facility brought new income to the community and is thelargest taxpayer in the county. The overall assessment of the facility is that itsaved Appleton from extinction.

Virginia counties band together for a regional prison. Like many commu-nities, Grayson, Giles, and Pulaski Counties in Virginia had an outdated localjail facility that was badly in need of upgrading, if not outright replacement(Doyle 2002). Joined by four other surrounding counties, these counties bandedtogether to create the New River Valley Regional Jail Authority. Supported withEDA planning funds, the authority developed a strategy to construct a facility




capable of serving the seven-county region. Using local public funds and a grantof $14 million from the state, the Regional Authority borrowed $25 million andbuilt a 425-bed facility. A total of 150 new jobs were created, all of which wentto local residents. Local opinion suggests the decision to work together substan-tially reduced the cost of public safety for its members, particularly because theauthority was able to pool demand and strike advantageous supply arrangementswith service firms.

On the Opposite Side of the Debate

Rush City, Minnesota prisons may not make good friends. Rush City,Minnesota’s experience with prison development hasn’t yet paid off (Clement2002a). The 950-inmate prison has largely been a good neighbor, though theproperty value of adjacent lands fell soon after facility construction and has yetto recover. The supplies for the prison are still acquired from firms locatedsixty miles away in the Twin Cities. Few businesses have been attracted to fillthe increasingly empty downtown area. Most employees working in the prisonlive within thirty miles of the town, but the hoped-for revival of the local realestate market has yet to materialize. The prison’s location sixty miles north ofthe Twin Cities has led to a preference for the relative calm of a rural area out-side the confines of the big city to the west. A number of citizens are concernedthat this hoped-for boost may not occur because, while the prison may be a goodneighbor, it is not as clear that the urban residents will view it as such.

Prisons a major component of the local economic base in the upperpeninsula. The Upper Peninsula of Michigan has gone for prison developmentin a big way (Clement 2002a). Counties in that region are home to nine prisonsand four minimum security prison camps. Prisoners make up 2.7 percent of theregion’s population. While 2 percent of the state’s population lives in the fifteencounties, 18 percent of the state’s incarcerated persons live in the area. Develop-ment officials uniformly agree that the new jobs brought by the prisons havebreathed life back into the region. But when pressed, these same officialsacknowledge that the concentration of prisons in an isolated area of the state ismaking it difficult to attract other types of development. Officials recognize theneed to put the best face on the situation by finding ways to link the prisons to alimited local economic base.

Summing up the Anecdotal Evidence

These four examples reflect similar stories told by a multitude of commu-nities. They highlight the views, experiences, and outcomes of localities thathave successfully attracted prisons over the past decade. The weight of anecdo-tal evidence suggests that benefits from prison construction cut both ways:in the absence of a substitute, the modest economic impact is better than thealternative—continued economic stasis or decline. While anecdotes suggest thatthe negative and positive consequences of prison development tend to cancel




each other out or, at best, are neutral, such reports are not sufficient to guide pol-icy, particularly about whether scarce public resources should be used to encou-rage prison development. Given that public sector financing is often required,more rigorous assessments, based on statistical analysis, suggest on balance thatprison development is at best neutral, and in other cases may not be an effectiveinvestment of economic development resources, if the goals of local develop-ment policy encompass such concerns as reducing poverty and increasingeconomic diversification.

THE SCHOLARLY, STATISTICAL, AND POLICY LITERATURES

Beyond anecdotes, the scholarly record begins with the basic facts. In a sys-tematic review of the growth of federal prisons in rural counties over the 1980sand 1990s, Calvin Beale of the U.S. Department of Agriculture’s EconomicResearch Service found that from 1980 to 1991, 213 new federal prisons werebuilt in nonmetro counties (Beale 1993, 1996, 1998; U.S. Department of Agri-culture, Economic Research Service 1996). This growth represented 53 percentof new additions to incarceration capacity nationwide. From 1992 to 1993,eighty three prisons opened in rural counties, representing 60 percent of newcapacity added nationally over the two-year period. From 1994 to 1999, prisonscontinued to open in rural areas, further concentrating the nation’s prison popu-lation in rural communities. With these numbers as a backdrop, we now turn toan overview of scholarly evidence concerning prison development in the 1990s.

Like popular press accounts, which conclude that the positive and negativeimpacts of prison development in many cases balance out, the bulk of the policyanalysis and academic literature on prison development suggests that prisonshave limited to no effect on local community economies (Carlson 1991, 1995).In one of the seminal studies of prison development’s positive impacts, the JointCenter on Environmental and Urban Problems in Florida concluded that prisonconstruction has a positive effect on job generation and property values (Abramsand Lyons 1987).

Other studies find limited or no effect in terms of jobs, income, and economicdevelopment across a range of locations, study designs, and time periods. Astudy of consumer expenditures in Texas pre- and postprison constructiondemonstrated no discernable effect (Chuang 1998). Studies of the income effectof prison construction and operations on local residents revealed limited to noimpact on surrounding communities. In a study of prison construction in a singlecounty in Colorado, researchers found no discernable effect on per-capitaincome and local unemployment (Setti 2001). In another study using qualitativeand quantitative approaches, researchers found that prison construction andoperation in Postosi, Missouri, enabled job growth but that jobs went to nonlocalresidents, with only modest changes in unemployment and no changes occurringin poverty or per-capita income (Thies 1998, 2000). In Corcoran, California,jobs in the newly constructed prison went to individuals who lived outside the




region, with many traveling in excess of fifty miles to and from work (Gilmore1998). In Berlin, New Hampshire, officials of a company constructing a pri-vately owned prison notified local residents that while the economic impact ofthe new prison would be positive, the size of the effect would depend on the sizeof the local economy and the availability of goods and services; they could notguarantee that jobs would go to local residents. They also stated that correc-tional officer positions would first be filled by transfers from other facilities inthe company’s prison facility group (Gsottschneider 2002). In a study of prisonimpacts on rural New York counties, King, Mauer, and Huling (2003) used amodified quasiexperimental design that included fourteen of the thirty-eightprisons built between 1982 and 1999. The fourteen rural counties that received aprison were compared statistically to a set of seven control counties. An exami-nation of unemployment rates from 1976 to 2001 revealed no difference in ratesin recipient counties compared with those for the nonrecipient control group orfor the state as a whole. Per-capita income growth for the counties and the statealso was evaluated. Like unemployment, there was no appreciable differenceamong the rural counties, although unemployment was lower in the state com-pared with both groups of counties. Using regression analysis, in a comprehen-sive treatment of national prison construction from 1969 to 1994, Hooks et al.(2004) also found no discernable impact of prison construction in rural counties.

In sum, across a range of studies of the economic impact of prison construc-tion, the weight of evidence suggests that prison construction does little tochange local income and employment or to reduce poverty. In the next sectionwe construct a paired comparison of all rural counties that received a prisonfrom 1985 to 1995.

LITERATURE SUMMARY

According to the literature, there is little evidence that prison development hasan appreciable impact on recipient counties. Statistical evidence shows a weak ornonexistent association between the presence or absence of a prison and changein local conditions, including income levels and poverty rates. To the extent thatthere is evidence of income growth, it is seen through the expansion of publicsector income, which would be expected given that prison construction includesfacilities operated by state and local governments. Researchers typically controlfor characteristics of counties, including industry structure, availability of infra-structure, public sector conditions including level of taxation and the fiscal capa-city of the state, human capital, and specific state effects (Campbell and Stanley1963; Bassi 1984; Becker and Porter 1986; LaLonde 1986; Heckman and Holtz1989; Davis 1990; Heckman, Smith, and Clements 1997; Neubert 2000; Blundelland Costa-Dias 2002). The results, while enlightening, do not control for macro-economic events, industrial restructuring, and other external factors. Policy eva-luation requires an additional step to assess what would have occurred versuswhat did occur with the construction of a prison. Rather than asking what the




correlation is between the presence of a prison, the characteristics of counties,and the experience of growth in the county, our research project asks: given simi-lar starting conditions, did counties that received a prison from 1985 to 1995exhibit differences in a host of growth-related and socioeconomic-related factorscompared with a ‘‘twin county,’’ which did not receive the same treatment?

PART II: METHODOLOGICAL APPROACH TO THE STUDY

OF IMPACTS OF RURAL STATE-PRISONDEVELOPMENT

RESEARCHAPPROACH

Both natural experiment and quasiexperimental approaches to impact analysisare based on comparison of treatment and nontreatment groups. The naturalexperiment design, commonly employing the ‘‘difference in difference’’ method,examines the differences between the average behavioral properties of a naturallyoccurring control (nontreatment) group and the treatment group. Quasiexperimen-tal design presupposes that qualitative comparability exists prior to treatment andthat the impacts of treatment exposure would be the same for both groups. Effortsto improve evaluations of the effectiveness of policies or projects intended tofoster economic change in specific places have increasingly led to the applicationof quasiexperimental control group approaches to impact assessment (Cook andCampbell 1979; Isserman and Beaumont 1989; Isserman and Merrifield 1982;Reed and Rogers 2001).

Quasiexperimental designs have been used in the evaluation of regionalemployment subsidies (Bohm and Lind 1993) to measure the impacts of high-ways in rural areas (Broder, Taylor, and McNamara 1992; Rogers and Marsh-ment 2000), and to determine the economic effects of the Appalachian RegionalCommission (Isserman and Rephann 1995), among other economic develop-ment and infrastructure investment initiatives.

STUDYMETHODOLOGY IN THREE STEPS

In this section, we lay out the research strategy we pursued, including the dataand statistical measures employed to describe the paired comparison analysis.The discussion proceeds in three steps: selection and matching characteristics,selection of the matching metric, and construction of the match. In part three wereport the results in two stages: stage one reflects a case study of Pender Countyin North Carolina, which received a prison in 1993; and stage two is a discussionof the general findings for the fifty-five sample counties.

Step One: Selection of the Matching Characteristic

The major requirement in statistical matching is to preserve the marginaldistributions of the variables in their original form. To satisfy this condition,constrained matching is the method most commonly used (Rodgers 1984). In




this analysis, selection of both treatment and control or nontreatment groupswere constrained initially by sequential calipers that serve as proxies for spatialindependence (Isserman and Merrifield 1987). In the case of the treatmentgroup, rural places in either completely rural counties or counties with an urbanpopulation of less than 20,000 not adjacent to a metro area were identified.Within that pool of potential treatment counties, those with one state-run prisonconstructed between 1985 and 1995 were selected for analysis (see table 1). Thepretreatment control group was similarly chosen, with nonmetro county designa-tions meeting the same urban/rural population criteria and the lack of, or pre-sence of zero state-run prisons within the county boundaries.

Since economic impacts on the treatment areas’ economies as a whole, aswell as on their poverty population, were of interest, the model covariates werespecified as measures of growth, industrial structure, and population anddemand (Rephann et al. 1997; Rephann and Isserman 1994; Isserman andRephann 1995). Income shares by major industries (excluding mining, wheredata suppression is a serious problem) serve as measures of industrial structure;change rates in economic health,2 poverty, population, and total personalincome, as measures of growth; and proportions of residential adjustment andtransfer income, and state and local earnings, as measures of population anddemand (see table 2).

Earnings and population data were taken from the Bureau of EconomicAnalysis, Regional Economic Information System (1969 to 1999) and povertyrates were obtained from the Census Bureau’s 1970, 1980, and 1990 decennialcensus and 1998 estimates (U.S. Census Bureau 1970, 1980, 1990, 2000a). Theyear 1970 served as the base year for pretest purposes, except for the growthcovariates, for which the base was the absolute change rate from 1970 to 1980.Using this data for both treatment and potential control counties, propensityscores were estimated through logistic regression.3 Then the distributions of thecovariates were examined. Table 3 consists of descriptive statistics for the cov-ariates and the logit of the estimated propensity score by group. The test statisticused to compare the groups was a two-sample t-statistic. These statistics revealsignificant differences between the treatment and control groups on a number ofthe covariates: rate of change in total personal income and poverty, proportionof farming and proportion of state and local government earnings, andpopulation.

The use of the economic health index for the purposes of classification. Todifferentiate among counties on the basis of their start-point economic condi-tion, we employed an economic health index developed by Glasmeier andFuellhart (1999). This index was created to overcome problems of data timeli-ness. Poverty statistics are based on the decennial census. At a cost, commu-nities can purchase special estimates between the decennial censuses throughthe Census Bureau Small Area Income and Poverty Estimates program (see U.S.Census Bureau 2000a). While the U.S. Department of Commerce, Bureau ofEconomic Analysis, publishes intercensal estimates of state and county poverty




rates, these estimates are available about two years after the year of the povertyestimate and the estimates tend to decline in reliability as they get further fromthe census base year (see U.S. Department of Commerce, Bureau of EconomicAnalysis 1997).

Following the work of Glasmeier and Fuellhart (1999), we use the economichealth index to examine levels of economic distress in our sample counties.According to Glasmeier, Fuellhart, and Wood (2006):

This index simultaneously captures the underlying characteristics of interest(unemployment, income) and additional characteristics that relate to the health andeffectiveness of the local labor market (percent of population that is economicallydependent and the share of income from transfer payments). This index was firstused in 1996 in research examining the efficacy of the ARC Distressed CountiesProgram. The four individual measures are a per-capita income index that com-pares a county’s income level to the national level (PCMIidx); an unemployment

TABLE 1. Prison Locations and Year of Construction in Treatment Counties

City/Town County State Year City/Town County State Year

Freesoil Mason MI 1985 Limon Lincoln CO 1991

Winnemucca Humboldt NV 1986 Manistique Schoolcraft MI 1991

Woodville Wilkinson MS 1986 Ontario Malheur OR 1991Jasper Hamilton FL 1987 Painesdale Houghton MI 1991

Mccormick McCormick SC 1987 Allentown Lehigh PA 1992

Pineville Bell KY 1987 Davisboro Washington GA 1992

Westover Somerset MD 1987 Dilley Frio TX 1992Blountstown Calhoun FL 1988 Duquoin Perry IL 1992

Bristol Liberty FL 1988 Ina Jefferson IL 1992

Ellsworth Ellsworth KS 1988 Pampa Gray TX 1992Lambert Quitman MS 1988 San Saba San Saba TX 1992

Madison Madison FL 1988 Sheldon O’Brien IA 1992

Baker Baker OR 1989 Burgaw Pender NC 1993

Fairfax Allendale SC 1989 Homerville Clinch GA 1993Leakesville Greene MS 1989 Abbeville Wilcox GA 1994

Snyder Scurry TX 1989 Breckenridge Stephens TX 1994

Waurika Jefferson OK 1989 Brownwood Brown TX 1994

Idabel McCurtain OK 1990 Central City Muhlenberg KY 1994Jarratt Sussex VA 1990 Colorado City Mitchell TX 1994

New Castle Weston WY 1990 Haynesville Richmond VA 1994

Oakwood Buchanan VA 1990 Holdenville Hughes OK 1994

Robinson Crawford IL 1990 Morgan Calhoun GA 1994Standish Arenac MI 1990 Alva Woods OK 1995

Trion Chattooga GA 1990 Dalhart Dallam TX 1995

West Liberty Morgan KY 1990 Riverton Fremont WY 1995Baraga Baraga MI 1991 San Diego Duval TX 1995

Iron River Iron MI 1991 Tamms Alexander IL 1995

Larned Pawnee KS 1991




TABLE

2.Cou

nty

Selection

Criteria

TreatmentGroup

Description

Measure

Year

Ruralplaces

in10

0%ruralcoun

ties

orcoun

ties

withan

urban

population

<20,000

notadjacent

toametro

area

Spatialindepend

ence

ERS(Beale7,8,9)

1995

Cou

ntieswith1state-runprison

built19

85-199

5Spatialindepend

ence

ICPSR69

5319

95Con

trol

Group

Description

Measure

Year

Presenceof

0stateprison

swithincoun

tySpatialindepend

ence

ICPSR69

5319

95

Ruralplaces

in10

0%ruralcoun

ties

orcoun

ties

withan

urbanpo

pulation

<20

,000

notadjacent

toametro

area

Spatialindepend

ence

ERS(Beale7,8,9)

1995

Cov

ariatesa

RTPI

Totalperson

alincomegrow

thrate

Growth

Chang

erate

70-80

RPOP

Pop

ulationgrow

thrate

Growth

Chang

erate

70-80

RPOV

Pov

erty

grow

thrate

bGrowth

Chang

erate

70-80

RIN

DEconomichealth

indexgrow

thrate

cGrowth

Chang

erate

70-80

PFAR

Proportionfarm

earnings

Indu

strialstructure

Share

totalPI

1970

PAFF

Proportionag,fi

sh,forestrysvcearnings

Indu

strialstructure

Share

totalPI

1970

PCON

Proportionconstruction

earnings

Indu

strialstructure

Share

totalPI

1970

PFIR

ProportionFIREearnings

Indu

strialstructure

Share

totalPI

1970

PMFG

Proportionmanufacturing

earnings

Indu

strialstructure

Share

totalPI

1970

PMIN

Proportionminingearnings

Indu

strialstructure

Share

totalPI

1970

PRTL

Proportionretailtradeearnings

Indu

strialstructure

Share

totalPI

1970

PSER

Proportionserviceearnings

Indu

strialstructure

Share

totalPI

1970

PTPU

ProportionTCPUearnings

Indu

strialstructure

Share

totalPI

1970

PWSL

Proportionwho

lesaletradeearnings

Indu

strialstructure

Share

totalPI

1970

PFED

Proportionfederalearnings

Indu

strialstructure

Share

totalPI

1970

PMIL

Proportionmilitaryearnings

Indu

strialstructure

Share

totalPI

1970

PSTL

Proportionstate&

localearnings

Indu

strialstructure

Share

totalPI

1970

PRES

Proportionresidentialadjustment

Population&

demand

Share

totalPI

1970

284 at MASSACHUSETTS INST OF TECH on October 14, 2010irx.sagepub.comDownloaded from


RTFR

Proportiontransfer

income

Pop

ulation&

demand

Share

totalPI

1970

LPOP

Log

ofpo

pulation

Pop

ulation&

demand

Baseten

1970

PCSL

Stateandlocalearnings

percapita

Pop

ulation&

demand

Basepo

p19

70

Note:

Add

itionaltreatm

entandcontrolgrou

pcoun

ties

excluded

becauseof

dataissues.

a.Unlessnotedotherw

ise,datasource

isU.S.D

epartm

entof

Com

merce,B

ureauof

EconomicAnalysis,(1969-1999;seeU.S

.Departm

entof

Com

merce,

Bureauof

Econo

micAnalysis19

97).

b.SourceisU.S.D

epartm

entof

Com

merce,B

ureauof

Census(197

0,19

80).

c.Sou

rceisGlasm

eier

andFuellhart(199

9).

285 at MASSACHUSETTS INST OF TECH on October 14, 2010irx.sagepub.comDownloaded from


rate index that compares the county-level unemployment rate to the national unem-ployment rate (URTidx); a labor force to total population ratio index (LFPOPidx);and a per-capita transfer payments to per-capita income ratio index (TFPidx). Theuse of these four indicators was designed to shed light on the degree to whichthe experience of individual counties deviates from national norms. We interpretthe index such that high scores indicate economic distress and low scores indicatenational average or better conditions. The inclusion of measures of transfer pay-ments and labor force participation was designed to assess the extent to which thepopulation depends on unearned income (transfer payments) and the share of thepopulation that depends on the labor of others. (p. 6)

The index provides an annual measure of economic distress. It allows compar-isons across counties’ changes in county circumstances over time. Nationally,

TABLE 3. Group Comparisons Prior to Matching

Treatment Group Control Group Comparisons

N= 55 N= 899 2-Sample

Variable M SD M SD t-Stat Sign

RTPI! 0.653 0.062 0.616 0.118 !2.331 0.020RPOP 0.091 0.118 0.077 0.137 !0.694 0.488

RPOV!! !8.195 5.967 !5.929 6.131 2.664 0.008

RIND !0.064 0.183 !0.043 0.227 0.679 0.497

PFAR!! 0.100 0.080 0.160 0.126 3.496 0.000PAFF 0.009 0.015 0.010 0.014 0.657 0.511

PCON 0.041 0.024 0.045 0.041 0.618 0.536

PFIR 0.020 0.009 0.019 0.009 !0.929 0.353

PMFG 0.125 0.108 0.101 0.106 !1.668 0.096PRTL 0.091 0.024 0.089 0.025 !0.382 0.703

PSER 0.087 0.028 0.080 0.044 !1.087 0.277

PTPU 0.048 0.029 0.044 0.028 !1.157 0.248PWSL 0.023 0.022 0.023 0.016 !0.204 0.838

PFED 0.020 0.023 0.022 0.021 0.607 0.544

PMIL 0.010 0.046 0.007 0.027 !0.941 0.347

PSTL! 0.098 0.030 0.088 0.035 !2.077 0.038PRES 0.036 0.107 0.050 0.106 0.915 0.360

PTFR 0.137 0.047 0.131 0.046 !0.912 0.362

LPOP!! 4.137 0.320 4.008 0.318 !2.937 0.003

PCSL 0.284 0.114 0.266 0.106 !1.198 0.231Logit of Propensity Score !0.573 0.146 !0.602 0.148 !1.420 0.156

Population 1970 20,147 34,076 13,453 20,427

Population 1980 23,170 38,360 15,001 23,911

Index Score 1970 137.34 37.68 130.83 38.61

Index Score 1980 130.96 34.37 127.70 35.19Poverty Rate 1970 27.2 13.8 23.7 11.2

Poverty Rate 1980 19.0 8.8 17.8 7.5

Note: See table 2 for acronym definitions.!p< .05. !!p< .01.




many counties had an index score of around 100, signifying comparability withnational averages. The group that mirrors the nation is very stable over time.Discernable breaks are evident between groups of counties that scored between100 and 149 (at or slightly above the national average), 150 and 199, and 200and above.

Step Two: Selection of a Matching Metric

Mahalanobis metric matching. The second step in the analysis was the selec-tion of a metric to match pairs of counties—the treatment and the control group.In this analysis, we use the most extensively documented of those techniques,Mahalanobis metric matching. Mahalanobis metric matching is commonlyaccomplished by randomly ordering the treatment observations and then calcu-lating the distance between the first treated observation and all of the control ornontreated observations, where distance d(i,j), between treated observation i andcontrol subject j is defined by the Mahalanobis distance (u! v!TC! 1"u! v)(D’Agostino 1998). Here, u and v are the values of the matching variables forthe treated subject i and control subject j, and C is the sample covariance matrixof the matching variables from the full control group. In the process that followsthe calculation of the Mahalanobis distance, the control observation with theminimum Mahalanobis distance is selected to match the first ordered treatmentobservation. Then both are removed from their respective groups as a match andthe process is repeated until all matches to treatment observations are made.Errors associated with differences across the matches can lead to biased esti-mates; therefore, they must be adjusted for to reduce selection bias prior todetermining the treatment effect.

Reducing bias with propensity scores. As part of our analysis, we added thepropensity score to reduce bias. This metric allows the simultaneous matchingof covariates on a single variable and is defined as the conditional probability ofreceiving a treatment given specified observed covariates e"X!= pr"Z= 1|X!,implying that Z and X are conditionally independent given e(X) (Rosenbaumand Rubin 1985). In other words, the propensity score is a value between zeroand one that represents the predicted probability of the dependent variable. Ifthe dependent variable is a treatment group, as with this study on rural places inwhich state prisons were opened during the prison development boom, then thepropensity score would be interpreted as the predicted probability of acquiring aprison for each case based on the combined pretreatment characteristics of thecounty, as defined by the covariates selected for the study.4

Step Three: Constructing the Match, Testing for Bias and Differences

between the Treatment and the Control Group

The Mahalanobis metric was calculated using a stepwise discriminant analy-sis. All of the significant covariates and the logit of the propensity score wereincluded in the model. The next step was to calculate the Mahalanobis distances




between the treatment and control counties. Matching was achieved by pairing arandomly selected treatment county to the closest control county, based on theMahalanobis distance, from within a subset of control counties whose propen-sity scores fell no further than four standard deviations away from the treatedcounty’s propensity score. That match was then removed from the selection pooland the process continued until all treatment counties were matched with acontrol.

Once matching was completed, t-tests were run on the matched counties toexamine whether or not the bias between the groups had been removed. Table 4contains the descriptive statistics and t-tests for the after-matching comparisons.As exhibited by the calculated means and lack of significance, the matched sam-ple covariates were relatively evenly distributed. Further evidence of bias reduc-tion is given in table 5, which shows percent reduction in bias for the covariateswith the largest initial biases, such as the poverty growth rate, for which the biaswas reduced by 61.5 percent. Essentially, no significant differences remain.Therefore, the matched control group was deemed satisfactory. Hence, the qua-sirandomized experiment was successfully created and the covariates could belikened to the background variables in randomized experiments.

The post-treatment period was estimated from 1980 to 1999 (except 1997 forindex scores and 1998 for poverty rates),5 thereby allowing for a maximum offive years for potential construction effects prior to the date of facility comple-tion. A series of paired-samples t-tests were run on the cumulative change ratesfor each of the covariates by group based on the following data select cases: all,economic health index rank one or two for 1980, economic health index rankthree or four for 1980, poverty rate for 1980 less than 20 percent, poverty ratefor 1980 greater than or equal to 20 percent, and poverty rate for 1980 greaterthan 30 percent.6 The ‘‘all’’ category test was meant to identify whether signifi-cant differences in the mean values of the fifty-five treated counties and theircontrol counties developed during the posttreatment period. None of the factorsexamined were shown to be significantly different except for state and localgovernment earnings (see table 6). That is, on average there was a noticeabledifference in the positive growth in state and local government earnings betweentreatment counties and their controls, suggesting that prison development wasresponsible for that difference.

The other tests were conducted to stratify the treatment county sample byeconomically distressed (index rank three or four) and nondistressed counties(index rank one or two), and persistently poor (poverty rate greater than or equalto 20 percent) and not persistently poor counties (poverty rate less than 20 per-cent) at the start of the impact period. With few exceptions, there were no signif-icant differences between treatment and control counties. Exceptions includedchange in transfer income and state and local earnings for counties with an indexrank of one or two in 1980. In other words, counties that scored well on the eco-nomic health index experienced a noticeable change in income. Of the studygroup, this finding was expected given that counties with a score of one or two




exhibit economic characteristics close to the national average in terms of unem-ployment, income market, transfer payments, and labor force participation rates.Change in transfer payment income for treatment counties also was evident incounties with a poverty rate less than 20 percent. Counties with poverty ratesbetween 20 and 30 percent and in which a prison was constructed experienced adecline in their poverty rate (poverty rate greater than 20 percent). In this case,two explanations are worth considering. On the one hand, it is possible that adecline in the poverty rate resulted from an increase in the income of personsformerly living below the poverty line. On the other hand, the increase in eco-nomic activity locally could have resulted in price pressures that translated intocompetition for low-cost housing. Anecdotal evidence suggests in some casesthat prison construction causes inflation in the local housing market, particularlyin rural areas where there is limited affordable or rental housing. In this case,inflationary price pressure in the housing market could force poor people out ofthe recipient county. This effect occurred in counties where the poverty rate wasonly modestly above the national poverty rate. Counties impacted were likelynot to be the most poor given the lack of a significant difference in the change inpoverty rates for counties in the 30 percent or greater range.

TABLE 4. Group Comparisons After Matching: Independent Samples Test

t-Test for Equality of Means

t Sig. (two-tailed)

RTPI !0.761 0.448

RPOP 0.962 0.338

RPOV 0.730 0.467

RIND 0.978 0.330PFAR 0.907 0.367

PAFF !0.445 0.657

PCON 0.119 0.905

PFIR !1.126 0.262PMFG !0.034 0.973

PRTL !0.700 0.486

PSER !1.105 0.272PTPU !0.628 0.532

PWSL !0.120 0.905

PFED 0.027 0.979

PMIL !1.022 0.309PSTL !0.799 0.426

PRES 1.430 0.156

PTFR 0.029 0.977

LPOP !1.135 0.259PCSL !0.851 0.397

LPRSC !1.155 0.251

Note: See table 2 for acronym definitions.




PART II I: A STUDY OF THE IMPACTS OF RURAL

STATE-PRISONDEVELOPMENT: A CASE STUDY AND

RESULTS OF THE PAIRED COMPARISON INVESTIGATION

This section consists of two parts. The first part introduces our case studycounty—Pender County, North Carolina—which we use to illustrate the studyresults. In the second part we generalize the findings to the treatment and controlcounty analysis. The section ends with concluding comments about the merit ofthis approach for impact analysis.

ILLUSTRATIVE CASE: PENDERCOUNTY, NORTHCAROLINA

Pender County, North Carolina, was the site of the construction of an 808-inmate, medium-security prison in 1993. Prior to the construction of the prison,at the close of the pretreatment period (1980), the county’s population was22,333 and the poverty rate was 21.3 percent. The per-capita transfer paymentlevel was $2,447 and the average earnings per job were $20,465 (both in 2000dollars). The economic base of the county was in the farming sector, which held20.8 percent of total full-time and part-time employment in 1980. Since 1980,the following changes occurred statistically over time (absolute change from1980 to 1999): 84 percent population increase, 7.7 percent poverty rate decrease,61.4 percent per-capita transfer payment increase, 2.6 percent average earningsper job increase, and 15.1 percent farming sector employment decrease. Duringthe same period of time, Pender’s control county (Choctaw, Oklahoma) witnesseda 10.6 percent population decrease, a 1.7 percent decrease in the poverty rate,36.5 percent growth in per-capita transfer payments, and a 27.7 percent decreasein average earnings per job. There was also a minimal .4 percent decrease in farmemployment, which was similarly the dominant employment sector in 1980.

TABLE 5. Percent Reduction in Bias After Matching

1 0 1 0

M M Initial M M Post

Percent

Reductiona

RTPI 0.653 0.616 0.037 0.653 0.645 0.008 78.2

RPOV !8.195 !5.929 !2.266 !8.195 !7.322 !0.872 61.5

PFAR 0.100 0.160 !0.060 0.100 0.012 !0.012 79.6PSTL 0.098 0.088 0.010 0.098 0.091 0.007 32.0

LPOP 4.137 4.008 0.130 4.137 4.058 0.080 38.6

Logit of Propensity Score !0.032 !0.026 !0.006 !0.573 !0.608 0.035

Note: See table 2 for acronym definitions.

a. Percent Reduction= 100(1!(After Match Bias / Initial Bias)).




IMPACT ANALYSIS BASED ON GROWTHRATEDIFFERENTIALS

The three factors found to be significant (state and local government earnings,transfer payment income, and poverty rate) served as the basis for impact mea-surement by way of growth rate differentials, defined as: Djt =Rcjt !Rgjt whereD is the growth rate difference, c is a treatment county (c # 1; . . . ; 55!, g is acontrol county (g= 1; . . . ; 55!, R is the growth rate measured from the base year(b), j is one of the variables under investigation (j= 1; . . . ; k!, and t is the testyear (Rephann et al. 1997). A subset of growth rate differentials for base period1980 and test date 1999 (1998 for poverty) for transfer income, state and localgovernment earnings, and poverty rates are provided in Table 7.

Growth rate differentials are interpreted as the difference between treatmentcounty growth and growth in the control county over the same period of time.Mean growth rate differentials are commonly used; here, mean (average), cumu-lative (additive), and point (absolute) estimates are given as alternative perspec-tives of impact. In reference to poverty rates, and transfer income for the mostpart, negative values represent a positive effect. For instance, the mean growthrate differential for poverty in Pender County, North Carolina, was 5.1 percentbelow that of its matched county. The impact of this difference is evident in thereduction in the poverty rate in the county since 1980, from 21.3 percent to 15percent in 1998 (model-based estimate) to 13.6 percent in 1999 (U.S. Census

TABLE 6. Significance of Cumulative Growth Rates

All

Index Rank

1 or 2

Index Rank

3 or 4

Poverty

Rate< 20%

Poverty

Rate " 20%

Poverty

Rate " 30%

Variable N = 55 n = 33 n = 22 n = 30 n = 25 n = 11

Total personal income 0.757 0.408 0.476 0.616 0.907 0.267

Population 0.116 0.123 0.659 0.276 0.261 0.384

Poverty 0.568 0.374 0.057 0.347 0.020! 0.115Index score 0.124 0.417 0.189 0.178 0.471 0.439

Farm earnings 0.707 0.335 0.353 0.909 0.527 0.143

Ag, fish, forest earnings 0.413 0.341 0.801 0.524 0.165 0.788Construction earnings 0.824 0.865 0.885 0.688 0.501 0.968

Fire earnings 0.659 0.435 0.561 0.477 0.361 0.664

Manufacturing earnings 0.635 0.833 0.334 0.641 0.303 0.454

Retail trade earnings 0.155 0.109 0.851 0.482 0.165 0.823Service earnings 0.704 0.636 0.988 0.904 0.694 0.699

TCPU earnings 0.289 0.393 0.543 0.868 0.164 0.811

Wholesale trade earnings 0.742 0.739 0.969 0.448 0.448 0.807

Federal civilian earnings 0.950 0.791 0.893 0.486 0.361 0.664Military earnings 0.566 0.092 0.357 0.443 0.217 0.374

State and Local Earnings 0.026! 0.021! 0.140 0.128 0.100 0.229

Residential adjustment 0.567 0.401 0.330 0.468 0.698 0.770Transfer income 0.041 0.008! 0.972 0.001! 0.800 0.848

!p< .05.




Bureau 2000a). However, the absolute change in poverty rate from 1980 to1998 was comparatively minimal (!.6 percent differential). In comparison, themean growth rate differential in state and local government earnings for thesame county was only 2.9 percent, but the cumulative value was relatively highat 54.5 percent. This suggests that growth may be substantial in the treatmentcounty in comparison to its control (14.5 percent absolute), but potentially dis-persed over time. Thus, there is a need to look more closely at the temporal fac-tors of change. We provide a graphic example of the change in state and localgovernment earnings in figure 2.

The graph of cumulative growth rates in state and local government earningsfor Pender County reveals that the treatment county began to outgrow its controlcounty at the start of the prison boom, but since the date of construction for theprison was 1993, it would be misleading to attribute that growth to the prison.However, the line graph also illustrates that accelerated growth (increase inchange in comparative slope) has taken place since the time of prison construc-tion, thereby suggesting that some measure of the growth rate differential forstate and local government earnings for the entire study period was likelybecause of the presence of the prison.7 The difference in this measure, taken attwo periods of time (say 1989 to allow for construction effects to be captured,and then again in 1999), would separate out the impact on growth in state andlocal government earnings potentially attributable to the prison, from that whichis likely due to macro trends earlier in the study period. The results presented inthis study are not disaggregated in such a way (for reasons of space), but table 8contains impact measures for comparison across counties that had poverty ratesequal to or greater than 20 percent and an index rank of either one or two in

TABLE 7. Growth Rate Differentials, Treatment Counties, Index Rank 1 or 2 and PovertyRate 20, 1980–1999

Growth Rate Differential

State &

Local Earnings(in percentages)

Transfer

Income(in percentages) Poverty Rate

M Cum Point M Cum Point M Cum Point

Duval, Texas [48,131] 1.9 36.6 17.3 1.8 33.4 10.2 3.7 7.5 0.5

Washington, Georgia [13,303] 1.4 25.7 6.5 0.6 12.0 3.4 0.2 0.5 0.0Pender, North Carolina [37,141] 2.9 54.5 14.5 4.3 82.4 22.2 !5.1 !10.2 !0.6

McCormick, South Carolina [45,065] 1.5 27.8 11.9 2.4 46.2 15.1 !4.2 !8.5 !0.5

Hamilton, Florida [12,047] 2.9 55.3 14.3 1.7 33.2 8.4 !0.4 !0.8 0.0

Madison, Florida [12,079] 3.5 66.3 22.4 0.8 16.0 4.4 !1.4 !2.7 0.2San Saba, Texas [48,411] 2.3 44.0 16.3 0.1 1.5 0.5 2.0 3.9 0.2

Clinch, Georgia [13,065] 0.0 !0.7 !0.2 0.4 !7.3 !1.8 0.2 0.3 0.1

Mitchell, Texas [48,335] 1.5 28.2 7.5 0.1 !2.8 !0.7 2.5 5.0 0.2Liberty, Florida [12,077] 5.2 99.3 24.1 1.1 21.7 5.7 !1.0 !2.0 !0.1




1980 (n= 10). According to these estimates, the greatest impact of prison devel-opment appears to be on poverty rates in Pender, North Carolina, and McCor-mick, South Carolina, where a differential reduction in poverty byapproximately 5.3 percent (mean) occurred over the study period. That repre-sents an absolute change of 6.3 percent and 8.9 percent, respectively, given the1998 estimate, based model (7.7 percent and 9.0 percent in 1999 using U.S.Census 2000 figures; see U.S. Census Bureau 2000a).

Based on the literature, we expected that if prisons were to have any impact atall, it would be in state and local government employment in counties with asufficiently skilled labor pool. This was indeed the case. Like Pender County,counties experiencing a positive change in state and local government sectorincome were those with significantly different levels of education in the basepopulation prior to prison construction (1990)—32.3 percent of the populationtwenty-five years and older had at least some college education (this was slightlyabove average for all treatment counties in the study—mean 30.2 percent—andsubstantially above average for counties at a poverty rate greater than or equal to20 percent (mean 23.9 percent). Counties that experienced a change in stateand local government income, 40 percent of the counties with poverty rates 20percent or greater in 1980 were counties ranked high on the economic healthindex (56 percent ranked three, 4 percent ranked four, and 0 percent ranked one).

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

140.0%

160.0%

180.0%

C80-8

1

C82-8

3

C84-8

5

C86-8

7

C88-8

9

C90-9

1

C92-9

3

C94-9

5

C96-9

7

C98-9

9

Year

Cum

ulat

ive

Rat

e of

Cha

nge

Treatment

Control

FIGURE 2. Cumulative Change Rate in State and Local Government Earnings—PenderCounty, North Carolina, 1980-1999




This means that only 40 percent of the persistently poor counties were among themost economically distressed. A county could be persistently poor with povertyabove 20 percent over time, but still not seriously economically distressedbecause of high levels of transfer payments, labor force participation rates andlow levels of unemployment. Additionally, of that 40 percent, the majority werelocated in the South and 64 percent were in counties with some urban population.This suggests that a link could exist between a treatment county’s preexistingeconomic structure and the location of a prison, which would also challenge theclaim that the prison industry economy is an ‘‘island,’’ such that a prison’simpact may be more exogenously driven locally than the literature suggests. Inother words, the characteristics of the place in which the facility is located has agreater influence on the measure of effect than do the characteristics of the facil-ity itself; however, the current analysis does not support making further infer-ences on that topic.

Considering economy-wide impacts, based on a diversity measure for bothearnings and employment by industry sector, prison development does notappear to be a good way to stimulate diverse economic growth.8 For instance, inour example county (Pender, North Carolina) there was a minor increase inemployment diversity and a somewhat similar decrease in earnings diversityfrom the year prior to prison construction (1992) to 1999. At the same time, thepercentage of total employment in state and local government jobs went almostunchanged (17.2 percent in 1992 and 17.1 percent in 1999), while the percen-tage of total earnings for that sector increased (21.9 percent in 1992 to 24.9 per-cent in 1999). This suggests that the prison impact on the Pender Countyeconomy may not have been through the creation of new jobs per se, but rather

TABLE 8. Estimated Impacts, Treatment Counties, Index Rank 1 or 2 and PovertyRate " 20, 1980-1999 (in thousands of dollars)

Estimated Impacts

State & Local Earnings Transfer Income Poverty Rate

M Cum Point M Cum Point M Cum Point

Duval, Texas [48,131] 193 3,676 1,740 291 5,533 1,697 4.92 0.00 0.15

Washington, Georgia [13,303] 208 3,951 996 140 2,655 753 !0.15 0.00 0.00

Pender, North Carolina [37,141] 349 6,623 1,767 1,036 19,688 5,295 !5.31 0.00 0.13

McCormick,South Carolina [45,065]

97 1,840 784 229 4,358 1,426 !5.29 0.00 0.08

Hamilton, Florida [12,047] 223 4,241 1,093 184 3,490 886 3.23 0.00 0.00

Madison, Florida [12,079] 335 6,362 2,154 161 3,067 852 3.47 0.00 !0.04

San Saba, Texas [48,411] 97 1,844 682 7 125 44 2.15 0.00 0.06Clinch, Georgia [13,065] !2 !30 !7 !29 !545 !133 0.88 0.00 0.00

Mitchell, Texas [48,335] 98 1,868 494 !19 !361 !90 2.09 0.00 0.02

Liberty, Florida [12,077] 127 2,422 588 61 1,165 307 !0.45 0.00 0.03




through the creation of jobs with higher pay than that which existed previouslyin that employment sector.

The results of this analysis suggest that prisons have had a positive effect onpoverty rates (i.e., a decrease), particularly in places with persistently high pov-erty concentrations, although this seems to be tied to a reasonable degree of eco-nomic health in the county. That is, when moving to greater extremes of povertyand economic distress, prisons had virtually no effect on the study sample ofrural places, but for places where poverty is above the national average, someeffect is evident. Furthermore, we note that only a limited number of potentialcovariates were analyzed and that other dimensions, such as migration patternsand income distributions, need to be considered. For instance, looking further atPender County alone, the average number of annual in-migrants from the yearof original prison construction (1993) to 1999 was nearly one and one-half timesthat of out-migrants (3:2) and median income of in-migrants was 13 percentgreater than that of out-migrants ($21,355 to $18,928 in 2000 dollars).9 Thismay explain at least some of the poverty impact for Pender County during thattime period. Yet, interestingly, when compared to 2000 U.S. Census Bureau(2000b) poverty thresholds, the median income of the out-migrating populationfor the impact period was within the poverty-income range for a family of fourwhile that of the in-migrating population was in the poverty range for a familyof five. Therefore, although the in-flow of a higher income population to theout-flow of a smaller number, lower income population is clear, the representa-tion of the poverty population in those movements is not altogether obvious. Assuch, few inferences can be made about the statistical change in poverty in rela-tion to migration without further disaggregation of that population by income.However, these measures do lend some support to the notion that the majorityof prison jobs are filled by transfers from outside the county.

CONCLUSIONS

In conclusion, the economic impacts of the prison development boom on per-sistently poor rural places, and rural places in general, appear to have beenrather limited. Our analysis suggests that prisons may have had a positive impacton poverty rates in persistently poor rural counties as well as an association withdiminishing transfer payments and increasing state and local government earn-ings in places with relatively good economic health. However, based on thenumber of significant covariates for the study sample and the size of the growthrates for individual counties in comparison to their matches, we are not convincedthat the prison development boom resulted in structural economic change in per-sistently poor rural places. It is more likely that the positive impacts are simplyattributable to spatial structure; that is, to the mere existence of a new prisonoperation in a rural place rather than the facility’s ability to foster economy-widechange in terms of serving as an economic development initiative.




NOTES

1. In this article we use the term prison to connote incarceration facilities that are calledvariously correctional facility, jail, work camp, etc.

2. Based on an index developed by Glasmeier and Fuellhart (1999) that incorporates measures of

unemployment, labor force participation, and dependency rates.

3. The logistic regression was done in SAS.4. The propensity score can be estimated using logistic regression or discriminant analysis. The

latter requires the assumption that the covariates have a multivariate normal distribution while the

former does not, but the results in both cases can be used similarly to adjust the measure of the treat-

ment effect, thereby increasing the confidence level that approximately unbiased estimates areobtained (D’Agostino 1998). However, a number of approaches can be employed to reduce bias and

increase precision using propensity scores. One is a case-control matched analysis performed in con-

junction with the propensity score; another is nearest available matching on the estimated propensityscore. A number of other variations exist, many of which include the Mahalanobis metric (Parsons

2000; Rosenbaum and Rubin 1984; Rubin 1979; Rubin and Thomas 1996).

5. These were the latest dates for which data were available for these measures at the time this

study was completed.6. The poverty rate category (20 percent includes those counties that are in the 30 percent range),

otherwise all categories, both index rank and poverty rate, are mutually exclusive.

7. If desired, a measure of that effect can be taken by multiplying the growth rate differential by

the corresponding base level value. Expressed as Ijt ( (Rcjt ( Rgjt)Vcjb where I is the impact estimate,the term (Rcjt ( Rgjt) is the growth rate differential as previously defined, and V is the value of the

factor under examination, which in this case, continuing with the example, would be state and local

government earnings.8. The diversity measure is based on the Herfindahl Index (H ( S1

n(sharei)2), which is used here

as one minus the sum of the squares of the employment shares of all the employment sectors of the

economy and similarly for earnings.

9. Derived from the Internal Revenue Service, Statistical Information Services, Office of theStatistics of Income Division, County-to-County Migration Data (1980–1981, 1983–1984, through

2000–2001 data series).

REFERENCES

Abrams, K. S., and W. Lyons. 1987. Impact of correctional facilities on land values and publicsafety. North Miami, FL: FAU-FIU Joint Center for Environmental and Urban Problems, Florida

International University.

Bassi, L. 1984. Estimating the effects of training programs with nonrandom selection. Review of

Economics and Statistics 66: 36-43.Beale, C. 1993. Prisons, population, and jobs in nonmetro America. Rural Development Perspectives

8: 16-19.

———. 1996. Rural prisons: An update. Rural Development Perspectives 11: 25-27.Beale, Calvin. 1998. Crime and politics in the 21st century. Paper presented at the conference of the

Campaign for an Effective Crime Policy, Bethesda, MD, November.

Beck, A., and P. Harrison. 2001. Bureau of Justice statistics bulletin: Prisoners in 2000. Washington,

DC: U.S. Department of Justice.Becker, H., and A. Porter. 1986. Impact assessment today. Vol. 1. Utrecht, Netherlands: State

University of Utrecht, Research Group on Planning and Policymaking.

Blundell, R., and Miguel Costa-Dias. 2002. Alternative approaches to evaluation in empirical micro-

economics. London: University College London and Institute for Fiscal Studies.Bohm, P., and H. Lind. 1993. Policy evaluation quality: A quasi-experimental study of regional

employment subsidies in Sweden. Regional Science and Urban Economics 23: 51-65.




Broder, J., T. Taylor, and K. McNamara. 1992. Quasi-experimental designs for measuring impactsof developmental highways in rural areas. Southern Journal of Agricultural Economics 24:

199-207.

Campbell, D., and J. Stanley. 1963. Experimental and quasi-experimental designs for research.

Chicago: Rand-McNally.Carlson, K. 1992. Doing good and looking bad: A case study of prison/community relations. Crime

and Delinquency 38: 56-69.

Carlson, K. A. 1991. What happens and what counts: Resident assessments of prison impacts on theircommunities. Humboldt Journal of Social Relations 17: 115-144.

———. 1995. Prisons and rural communities: Making the most and losing the least from a growth

industry. In Rural Development Strategies, ed. J. N. Reid and D. W. Sears, 189-203. Chicago:

Nelson-Hall Publishers.Chuang, Su Chong. 1998. The distribution of Texas State Prisons: economic impact analysis of state

prison siting on local communities. PhD diss., University of Michigan.

Clement, D. 2002a. Big house on the prairie. Minnesota Fedgazette, January. http://www

.minneapolisfed.org/pubs/fedgaz/02-01/house.cfm.———. 2002b. Busted? Fedgazette, January. http://www.minneapolisfed.org/pubs/fedgaz/02-01/

busted.cfm.

———. 2002c. Wanted criminals. Fedgazette, January. http://www.minneapolisfed.org/pubs/fedgaz/02-01/cover.cfm.

CNN. 2006. Jail, prison populations rise 2.6 percent. May 21. http://www.cnn.com/2006/LAW/05/

21/incarceration.rate/.

Cook, T., and D. Campbell. 1979. Quasi-experimentation, design, and analysis issues for fieldsetting. Boston: Houghton-Mifflin.

D’Agostino, R. 1998. Tutorial in biostatistics: Propensity score methods for bias reduction in the

comparison of a treatment to a non-randomized control group. Statistics in Medicine 17:

2265-81.Davis, H. 1990. Regional economic impact analysis and project evaluation. Vancouver, BC, Canada:

UBC Press.

Doyle, Z. 2002. Does crime pay: Pros and cons of rural prisons. Economic Development Digest 8:1-4.

Gilmore, R. W. 1998. From military Keynesianism to post Keynesian militarism: Finance capital,

labor, and opposition in the rising California prison state. PhD diss., University of California,

Berkeley.Glasmeier, A., and K. Fuellhart. 1999. Building on past experiences: Creation of a new future for

distressed counties. Washington, DC: Appalachian Regional Commission.

Glasmeier, A., K. Fuellhart, and L. Wood. 2006.Measuring economic distress: A comparison of desig-

nations and measures. Measuring Rural Diversity Policy Series 3. Starkville, MS: Mississippi StateUniversity.

Gsottschneider, R. 2002. Berlin prison study. Performed for the Androscoggin Valley Economic

Recovery Corporation. RKG Associates, Inc.

Hawes, J. A. 1985. Cities with prisons: Do they have higher or lower crime rates? Special reportto Senator Robert Presley, Chair Joint Committee on Prison Construction and Operations.

Sacramento, CA: Senate Office of Research.

Heckman, J., and V. Holtz. 1989. Choosing among alternative nonexperimental methods for estimat-ing the impact of social programs. Journal of the American Statistical Association 84: 862-74.

Heckman, J., J. Smith, and N. Clements. 1997. Making the most out of program evaluations and

social experiments: Accounting for heterogeneity in program impacts. Review of Economic

Studies 64: 487-536.Hooks, G., C. Mosher, T. Rotolo, and L. Lobao. 2004. The prison industry: Carceral expansion and

employment in US counties, 1969-1994. Social Science Quarterly 85, 37-57.




Huling, T. L. 1999. Prisons as a growth industry in rural America: An exploratory discussion of theeffects on young African American men in inner cities. Washington, DC: The United States

Commission on Civil Rights.

Isserman, A., and P. Beaumont. 1989. Quasi-experimental control group methods for the evaluation

of regional economic development policy. Socio-Economic Planning Sciences 23: 39-53.Isserman, A., and J. Merrifield. 1982. The use of control group methods for the evaluation of regional

economic development policy. Regional Science and Urban Economics 12: 43-58.

———. 1987. Quasi-experimental control group methods for regional analysis: An application to anenergy boomtown and growth pole theory. Economic Geography 63: 3-19.

Isserman, A., and T. Rephann. 1995. The economic effects of the Appalachian regional commission:

An empirical assessment of 27 years of regional planning experience. Journal of the American

Planning Association 61: 345-64.King, R. S., M. Mauer, and T. Huling. 2003. Big prisons, small towns: Prison economics in rural Amer-

ica. The sentencing project. http://www.soros.org/initiatives/justice/articles_publications/publications/

bigprisons_20030201/bigprisons.pdf (accessed May 15, 2006).

LaLonde, R. 1986. Evaluating the econometrics evaluations of training programs with experimentaldata. American Economic Review 76: 604-20.

Lawrence, S., and J. Travis. 2004. The new landscape of imprisonment: Mapping America’s prison

expansion. Washington, DC: The Urban Institute. http://www.urban.org/UploadedPDF/410994_mapping_prisons.pdf.

Mattera, P., and M. Khan. 2001. Jail breaks: Economic development subsidies given to private

prisons. Washington, DC: Good Jobs First. http://www.goodjobsfirst.org.

National Conference of State Legislatures. 1999. Using prisons and prison privatization foreconomic development. http://www.ncsl.org/programs/fiscal/fac9904e.htm.

Neubert, S. 2000. Social impact analysis of poverty alleviation programmes and projects: A contri-

bution to the debate on the methodology of evaluation in development cooperation. London:

Frank Cass.Parsons, L. 2000. Using SAS software to perform case-control match on propensity score in an

observational study. In Proceedings of the twenty-fifth annual SAS users group international

conference, 1032-1166. Cary, NC: SAS Institute.Reed, R., and C. Rogers. 2001. A study of quasi-experimental control group methods for estimating

policy impacts. Working paper, Department of Economics, University of Oklahoma.

Rephann, T., J. Dalton, A. Stair, and A. Isserman. 1997. Casino gambling as an economic develop-

ment strategy. Tourism Economics 3: 161-83.Rephann, T., and A. Isserman. 1994. New highways as economic development tools: An evalua-

tion using quasi-experimental matching methods. Regional Science and Urban Economics 24:

723-51.

Rodgers, W. 1984. An evaluation of statistical matching. Journal of Business and Economic Statis-tics 2: 91-102.

Rogers, C., and R. Marshment. 2000. Measuring highway bypass impacts on small town business

districts. Working paper, Department of Economics, University of Oklahoma.

Rosenbaum, P., and D. Rubin. 1984. Reducing bias in observational studies using sub-classificationon the propensity score. Journal of the American Statistical Association 79: 516-24.

———. 1985. Constructing a control group using multivariate matched sampling methods that

incorporate the propensity score. American Statistician 39: 33-38.Rubin, D. 1979. Using multivariate matched sampling and regression adjustment to control bias in

observational studies. Journal of the American Statistical Association 74: 318-24.

Rubin, D., and N. Thomas. 1996. Matching using estimated propensity scores: Relating theory to

practice. Biometrics 52: 249-264.Setti, C. 2001. Prisons and their effects on local economics: The Colorado experience. CPEC Center

for Tax Policy Research of Denver 47: 271-291.

Shichor, D. 1992. Myths and realities in prison siting. Crime and Delinquency 38: 70-87.




Stanley, C. E. 1978. The impact of prison proximity on property values in Green Bay and Waupun,Wisconsin. Milwaukee, WI: Wisconsin Division of Corrections and Bureau of Facilities

Management.

Tarmann, Alison. 2003. Fifty years of demographic change in rural America. http://www.prb.org/

rfdcenter/50yearsofchange.htm (accessed May 16, 2006).Thies, J. 2000. Prisons and host communities: Debunking the myths and building community rela-

tions. Corrections Today 62: 136-39.

Thies, J. S. 1998. The big house in a small town: The economic and social impacts of correctionalfacility on its host community. PhD diss., University of Missouri.

U.S. Census Bureau. 1970. Census of population and housing, small area income and poverty

estimates. http://www.census.gov/.

———. 1980. Census of population and housing, small area income and poverty estimates. http://www.census.gov/.

———. 1990. Census of population and housing, small area income and poverty estimates. http://

www.census.gov/.

———. 2000a. Census of population and housing, small area income and poverty estimates. http://www.census.gov/.

———. 2000b. Poverty thresholds. http://www.census.gov/.

———. 2000c. UA Central Places and UC Central Place for Census 2000. http://www.census.gov/.U.S. Census Bureau, Housing and Household Economic Statistics Division, Small Area Estimates.

2001. Table A98-56: Estimated number and percent of people all ages in poverty. http://

www.census.gov/.

U.S. Department of Agriculture, Economic Research Service. 1996. ERS county typology codes.Washington DC: U.S. Department of Agriculture.

U.S. Department of Commerce, Bureau of Economic Analysis. 1997. Regional economic informa-

tion system. Washington, DC: GPO.

U.S. Department of Justice, Bureau of Justice Statistics. 1998. Census of state and federal adultcorrectional facilities, 1995. ICPSR 6953. Ann Arbor, MI: Inter-university Consortium for

Political and Social Research. Washington, DC: U.S. Department of Justice.

Willing, Richard. 2004. USA’s prison population a record, but growth slowing. USA Today,November 7. http://www.usatoday.com/news/nation/2004-11-07-women-prison_x.htm.




Documents

International Regional Science Review · ‘‘boom’’ in manufacturing plant location, the prospect of a prison siting led ... tion base through the counting of prisoners in local