Different Perspectives onEconomic Base

United StatesDepartment ofAgriculture

Forest Service

Pacific NorthwestResearch Station

Research NotePNW-RN-538April 1999

Lisa K. Crone, Richard W. Haynes, and Nicholas E. Reyna

Abstract Two general approaches for measuring an economic base are discussed. Eachmethod was used to define the economic base for each of the counties includedin the Interior Columbia Basin Ecosystem Management Project area. A moredetailed look at four selected counties results in similar findings from bothapproaches. Limitations of economic base analyses are noted.

Keywords: Economic base, functional economies, Columbia River basin.

Introduction Initial comments on the environmental impact statements from the Interior ColumbiaBasin Ecosystem Management Project1 (ICBEMP; USDA and USDI 1997a, 1997b)indicated dissatisfaction with descriptions of the economy and economic conditionsof the Columbia River basin. Much of the dissatisfaction stemmed from the scaleused to portray the economic situation in the area. Most comments expressed dis-agreement with the summary finding that only 4 percent of the region’s employmentis natural resource based (that is, in wood products, ranching, and mining).

LISA K. CRONE and RICHARD W. HAYNES are research foresters,Forestry Sciences Laboratory, P.O. Box 3890, Portland, OR 97208-3890; and NICHOLAS E. REYNA was an economist, Pacific North-west Region, P.O. Box 3623, Portland, OR 97208-3623. Portland,Oregon. Crone is located at the Interior Columbia Basin EcosystemManagement Project, 112 E. Poplar, Walla Walla, WA 99362. Reynais a program manager, State and Private Forestry, 3825 E. MulberryStreet, Fort Collins, CO 80524.

1 The Interior Columbia Basin Ecosystem Management Projectwas organized to develop a scientifically sound, ecosystem-based management strategy for FS- and BLM-administeredlands in the interior Columbia River basin. The project’s ScienceIntegration Team developed an ecosystem management frame-work, a scientific assessment, and an evaluation of alternativemanagement strategies. This paper is one of a series developed asbackground material for those documents. It provides more detailthan was possible to disclose directly in the primary documents.

2

In this paper, we will attempt to clarify the manner in which the information on theeconomic base was derived for the environmental impact statements at the U.S.Bureau of Economic Analysis (BEA) area and county level. We begin by definingwhat an economy is, as described in the economics component of the assessmentof ecosystem components (referred to as the economics assessment; Haynes andHorne 1997). We introduce the economic base concept and present two methods fordetermining the economic base of an area. County-level data are used to describe theeconomic base of the counties in the project area according to each method, with fourcounties selected for illustrative purposes. Finally, we point out the limitations of theseapproaches in describing present and predicting future economic conditions in anarea.

An Economy An economy can be defined as a set of interrelated production and consumptionactivities (Lipsey and others 1984). In the economic assessment, we adopted theBEA definition of functional economies;2 that is, a functional economy is one largeenough to include the bulk of economic transactions or flow of trade. In general,functional economies are larger than a community or county. Projected employ-ment levels for 1995 for the BEA areas (or portions thereof) in the project areaare summarized in table 1.

Note that a region’s economy is not the same as that of a county or community.Some confusion arises because economic data are generally collected and reportedat the county level, even though this is not the scale at which economies operate.Just because counties are the reporting level does not imply that they represent theeconomy in a broader area, or at the community level; for example, while a regionaleconomy may be thriving, the county or community economy could be contractingand vice versa.

Economic Base The economic base of an area consists of those activities that provide the coreemployment and income on which the rest of the local economy depends. Concernsabout what constitutes the economic base and its size are part of trying to under-stand how various activities might affect opportunities for the people living in thearea and those whose well-being depends on the level of economic activity in thearea.

2 For a detailed description of the methodology used by the BEA todelineate these areas, see Johnson (1995). In 1995, the BEA areaswere redefined based mainly on new information on commutingpatterns. This resulted in the aggregation of the Butte BEA regioninto the Missoula BEA region. Because the data reported in theeconomic assessment were based on the previous area definitions,those area definitions are maintained here for consistency.

3

Tabl

e 1—

Pro

ject

ed e

mpl

oym

ent f

or th

e N

atio

n an

d in

terio

r C

olum

bia

basi

n, 1

995

Inte

rior

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ond-

Indu

stry

Nat

ion

aver

age

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kane

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soul

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in F

alls

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Ben

dbB

utte

Per

cent

Man

ufac

turi

ng e

mpl

oym

ent:

Agr

icul

ture

ser

vice

s1.

142.

564.

411.

131.

922.

694.

722.

532.

632.

130.

92M

inin

g.6

6.4

5.2

3.6

1.6

4.8

3.2

6.3

80

01.

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onst

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ion

5.20

4.65

4.21

4.55

5.40

5.38

5.37

5.09

3.48

4.58

3.48

Man

ufac

turi

ng—

14.1

111

.71

11.2

911

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11.5

39.

5911

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12.5

514

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542.

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3.34

4.30

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4.72

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3.76

6.04

Trad

e21

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21.1

421

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22.1

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21.9

321

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20.3

818

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20.7

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7.53

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4.64

6.65

6.22

5.52

5.64

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4.53

5.48

6.78

Se

rvic

es

28.3

825

.02

23.2

126

.79

27.3

627

.24

21.3

624

.17

20.8

623

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31.6

8

Gov

ernm

ent

(all)

:14

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16.3

914

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18.7

615

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15.7

911

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16.3

417

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15.1

423

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Sta

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ourc

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IC 2

4 (w

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ploy

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abor

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tistic

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Beu

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(199

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ecau

se it

is c

alcu

late

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ence

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nclu

des

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ors.

Sou

rce:

Hay

nes

and

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ne 1

997.

4

The fundamental problem is how to identify and measure the economic base of anarea. Several methods have been used to define what constitutes the economicbase; here we discuss and compare two. The first method is called the assumption(or assignment) approach. In this approach, selected industries are assumed tocomprise the economic base. A frequently used assumption is that all manufacturingand agriculture defines the economic base, because these industries traditionallyproduce exports sold outside the local area. The other method is called the locationquotient approach. With this approach, the economic base is defined by those indus-tries that reflect local specialization. Sales, value added, income, and employment canall be used as “units of measure” to identify the economic base of the local economy.Both approaches generally rely on indirect measures, such as income or employmentdata, to determine the basic industries and their sizes, because detailed surveys ofexactly what gets exported are difficult and costly.

In the economic work for ICBEMP, the location quotient approach was used to identifythe industries forming the foundation of a region’s economy. The implication was thatanything that harms or decreases these industries threatens the regional economy.We adopted this approach after vigorous debate at public meetings hosted by ICBEMPstaff early in the development of the project. Much of the debate focused on whatconstituted functional economies and the roles of various industries in a functionaleconomy.

In the economic assessment, we used the convention that every industry can bedivided into the proportion that is exported (referred to as “basic” activities) versusthat serving local markets (or “nonbasic” activities). Proponents of economic basemodels argue that basic activities help support nonbasic activities by increasing theflow of money into and within an economic area.

We adopted the view that the economic growth of the project area depends on thoseeconomic sectors producing a significant share of their output for export. This is adeparture from the traditional approach taken by the Bureau of Land Management andthe Forest Service of focusing only on jobs in the ranching or manufacturing sectors.Many exporting sectors would have been missed with the traditional approach, butthey were accounted for with our approach. Because many of these nontraditionalexport industries are growth industries, and many of the traditional ones are not,it is important to portray these evolving industries as accurately as possible.

The economic bases in 1994 for the project area counties, from several variations ofthe assumption method, are shown in table 2. Figure 1 illustrates the amount of naturalresource employment in each county. This figure indicates significant variation amongcounties in the project area in terms of their levels of natural resource employment.

An example of the location quotient approach is shown in table 3. Here, the 1994percentage of county employment in a particular sector (such as construction) iscompared to the 1995 national percentage of projected employment in that sector.If the county percentage exceeds the national percentage, the sector is considereda basic sector.

Determiningthe EconomicBase

5

Table 2—Various assumption method definitions of economic base for 1994

TotalCounty State Manufacturinga SIC 24b Miningc Ranchingd resourcee

Ada ID XAdams ID X X XBannock IDBenewah ID X X XBingham ID XBlaine IDBoise ID XBonner ID XBonneville IDBoundary ID X X XButte ID XCamas IDCanyon ID XCaribou ID X X XCassia ID XClark ID X XClearwater ID X X XCuster ID X XElmore IDFremont IDGem ID X X XGooding IDIdaho ID X XJefferson ID XJerome IDKootenai ID XLatah IDLemhi ID XLewis ID X X XLincoln IDMadison ID XMinidoka ID XNez Perce ID XOwyhee ID X XPayette ID XPower ID XShoshone ID XTeton IDTwin Falls ID XValley IDWashington ID X XDeer Lodge MTFlathead MT XGranite MT X X XLake MT

6

Table 2—Various assumption method definitions of economic base for 1994(continued)

TotalCounty State Manufacturinga SIC 24b Miningc Ranchingd resourcee

Lewis and Clark MTLincoln MT X X XMineral MT XMissoula MTPowell MT X XRavalli MT XSanders MT XSilver Bow MTBaker OR XCrook OR X X XDeschutes OR XGilliam ORGrant OR X X XHarney OR X X XHood River OR XJefferson OR X X XKlamath OR X XLake OR X X XMalheur ORMorrow OR XSherman ORUmatilla OR XUnion OR XWallowa OR X XWasco OR XWheeler OR X XAdams WA XAsotin WABenton WAChelan WAColumbia WA XDouglas WAFerry WA X XFranklin WAGarfield WA X XGrant WA XKittitas WAKlickitat WA X XLincoln WAOkanogan WAPend Oreille WA X XSkamania WA XSpokane WAStevens WA X X

7

Table 2—Various assumption method definitions of economic base for 1994(continued)

TotalCounty State Manufacturinga SIC 24b Miningc Ranchingd resourcee

Walla Walla WA XWhitman WAYakima WA XTeton WY

a X = county has more than 10 percent of its total employment in manufacturing, which includes StandardIndustrial Classification (SIC) 24.b X = county has more than 10 percent of its total employment in the wood products sector (SIC 24).c X = county has more than 10 percent of its total employment in mining.d X = county has more than 10 percent of its total employment in ranching.e X = county has more than 10 percent of its total employment in natural resources (the sum of mining,ranching, and wood products).

Source: U.S. Department of Commerce, Bureau of Economic Analysis 1996.

Figure 1—Resource employment in counties in the interior Columbia basin.

8

Tabl

e 3—

Eco

nom

ic b

ase

of e

ach

coun

ty (

in b

old)

acc

ordi

ng to

an

empl

oym

ent l

ocat

ion

quot

ient

def

initi

ona

Agr

icul

tura

lM

anu-

Tota

lA

rea

Sta

tese

rvic

esM

inin

gC

onst

ruct

ion

fact

urin

gbS

IC 2

4cTr

ansp

orta

tion

Trad

eF

IRE

dS

ervi

ces

Gov

ernm

ent

Farm

Nat

ion

1.14

0.66

5.20

14.1

14.

7621

.49

7.53

28.3

814

.57

2.16

CIB

ave

rage

2.56

.45

4.65

11.7

12.

524.

3021

.14

6.00

25.0

216

.39

7.78

Ada

ID1.

19.1

78.

8912

.64

1.28

4.50

22.3

68.

9726

.89

13.4

0.9

8A

dam

sID

2.36

D

D18

.58

17.1

53.

3413

.41

3.80

13.1

823

.36

21.9

8B

anno

ckID

.87

.15

5.79

6.78

.08

6.66

26.4

66.

7521

.83

22.5

72.

14B

enew

ahID

DD

5.03

27.7

320

.00

6.82

15.1

83.

2017

.92

16.3

57.

77B

ingh

amID

4.09

.28

4.99

14.3

7.1

22.

7822

.21

3.43

18.8

717

.17

11.8

0B

lain

eID

3.70

.63

13.8

42.

81.3

32.

3123

.11

10.8

931

.07

8.71

2.94

Boi

seID

5.50

L8.

9011

.19

6.76

3.89

14.9

31.

9927

.43

21.3

04.

86B

onne

rID

2.82

.53

10.2

916

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8.23

3.82

22.5

96.

6020

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13.2

63.

09B

onne

ville

ID1.

64.0

87.

785.

07.2

63.

0228

.70

5.66

32.7

412

.39

2.92

Bou

ndar

yID

3.03

L7.

3917

.91

10.2

44.

4417

.93

4.07

18.9

518

.26

8.02

But

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.38

DD

19.3

80

.19

3.72

.72

57.5

612

.62

5.43

Cam

asID

DL

3.28

3.11

02.

0719

.69

D13

.13

18.6

540

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Can

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16.1

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6418

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2.08

5.06

19.7

14.

8321

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11.4

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2.60

11.6

86.

9915

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04.

2116

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3.09

11.5

914

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13.4

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assi

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5.27

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4.81

11.1

70

4.46

22.9

95.

6417

.58

13.4

113

.89

Cla

rkID

5.84

DL

D0

1.52

8.88

1.52

3.55

20.3

058

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Cle

arw

ater

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08.3

23.

9922

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16.2

43.

2016

.69

2.96

13.5

828

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4.81

Cus

ter

ID4.

0210

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8.86

1.19

03.

4617

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2.98

18.9

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13.3

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1.51

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953.

790

2.87

14.9

94.

6310

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49.5

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4.99

L6.

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362.

904.

1319

.19

2.63

16.6

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18.6

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4.68

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5014

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12.1

43.

8915

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3.67

17.8

215

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18.5

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ngID

7.70

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096.

200

8.11

16.7

90.

0015

.92

16.4

422

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oID

2.14

1.86

5.73

13.1

19.

675.

1918

.21

3.27

16.8

221

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12.1

0Je

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onID

9.46

.25

10.0

611

.18

1.55

3.11

18.2

73.

4510

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16.5

817

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me

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5.32

8.73

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3.71

16.3

712

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983.

5324

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7.65

25.5

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1.48

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4.73

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9

Tabl

e 3—

Eco

nom

ic b

ase

of e

ach

coun

ty (

in b

old)

(co

ntin

ued)

Agr

icul

tura

lM

anu-

Tota

lA

rea

Sta

tese

rvic

esM

inin

gC

onst

ruct

ion

fact

urin

gbS

IC 2

4cTr

ansp

orta

tion

Trad

eF

IRE

dS

ervi

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ernm

ent

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Nez

Per

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5.17

16.9

92.

675.

1923

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25.6

112

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4.18

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121.

192.

380

2.59

17.7

53.

8410

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27.3

927

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ID10

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3.48

3.70

3.56

06.

3513

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2.19

11.7

718

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26.9

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ayet

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3.64

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5.19

18.5

00

7.55

16.8

84.

3117

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12.6

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erID

2.60

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6838

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06.

7812

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1.86

8.43

12.4

012

.89

Sho

shon

eID

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8.15

4.91

6.87

2.34

4.62

21.5

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6.7

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ton

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783.

970

2.68

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28.4

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31.5

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1.69

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2.11

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4.10

23.2

415

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4.31

19.7

54.

3919

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18.8

02.

88M

iner

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660

3.52

11.1

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773.

7325

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3.18

24.9

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3.87

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soul

aM

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821.

626.

3825

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6.44

30.5

817

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ell

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1.77

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3.87

10.1

06.

533.

0315

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4.83

17.7

731

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10.6

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aval

liM

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78.

7610

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4.89

4.21

21.1

45.

7324

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ande

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04.6

16.

289.

917.

526.

5416

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4.34

23.6

619

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10.3

3S

ilver

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MT

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3.45

3.15

3.55

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7.50

27.0

15.

0433

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15.9

3.8

0B

aker

OR

1.84

1.06

5.04

9.62

5.09

4.83

20.3

24.

4223

.23

16.8

912

.74

Cro

okO

R2.

51.2

93.

6026

.97

23.6

74.

9719

.21

3.48

15.8

313

.85

9.30

Des

chut

esO

R1.

56.2

39.

0710

.89

5.17

3.52

25.1

18.

7527

.26

11.2

72.

35G

illia

mO

RD

0.9

12.

210

20.2

710

.52

3.05

10.2

914

.94

37.8

0G

rant

OR

2.46

04.

2414

.29

11.0

94.

0914

.78

2.76

17.5

325

.61

14.2

5H

arne

yO

R3.

39L

3.55

12.1

210

.47

3.53

14.9

72.

8216

.03

22.0

821

.51

Hoo

d R

iver

OR

4.89

D3.

6611

.89

4.06

5.34

22.8

40

25.1

910

.94

15.2

5Je

ffers

onO

RD

02.

7222

.68

15.9

32.

1217

.53

022

.08

16.6

016

.26

Kla

mat

hO

R2.

18.1

04.

3214

.26

9.47

4.46

22.7

65.

2023

.34

15.8

57.

53La

keO

R2.

55.4

83.

0113

.91

13.2

02.

4216

.13

2.53

12.1

521

.41

25.4

0M

alhe

urO

R6.

05.3

63.

307.

480

4.46

23.6

53.

6018

.11

15.3

417

.64

Mor

row

OR

8.72

L2.

5315

.09

3.32

6.17

9.18

2.75

8.15

16.4

031

.02

She

rman

OR

1.93

0L

00

2.99

25.4

2L

9.76

24.7

135

.18

Um

atill

aO

R2.

86L

3.32

15.5

82.

935.

2720

.61

4.85

21.2

016

.02

10.2

8

10

Tabl

e 3—

Eco

nom

ic b

ase

of e

ach

coun

ty (

in b

old)

(co

ntin

ued)

Agr

icul

tura

lM

anu-

Tota

lA

rea

Sta

tese

rvic

esM

inin

gC

onst

ruct

ion

fact

urin

gbS

IC 2

4cTr

ansp

orta

tion

Trad

eF

IRE

dS

ervi

ces

Gov

ernm

ent

Farm

Uni

onO

R2.

12.1

44.

2713

.47

8.04

5.32

22.0

14.

2420

.45

19.4

88.

51W

allo

wa

OR

3.44

L4.

6212

.75

6.58

4.33

16.2

94.

3814

.69

19.5

020

.00

Was

coO

R2.

14.1

02.

6510

.24

1.99

3.11

24.6

14.

7426

.25

17.5

08.

66W

heel

erO

R3.

170

L3.

02.9

12.

1111

.18

2.57

10.4

222

.05

45.4

7A

dam

sW

A7.

65D

D12

.48

05.

1219

.42

3.23

13.4

015

.18

23.5

3A

sotin

WA

DD

8.49

5.10

2.18

2.47

25.5

55.

4932

.13

16.0

04.

76B

ento

nW

A1.

88.1

05.

955.

930

2.22

1.85

4.53

42.2

913

.81

21.4

4C

hela

nW

A6.

92.4

55.

726.

33.4

72.

8123

.73

6.24

21.0

613

.61

13.1

3C

olum

bia

WA

2.94

D1.

9422

.06

0D

12.5

22.

4016

.32

24.6

417

.18

Dou

glas

WA

6.17

L4.

261.

420

4.81

19.6

64.

1916

.96

16.3

926

.16

Fer

ryW

AD

9.48

3.91

11.5

07.

49D

14.4

72.

9316

.46

27.3

413

.92

Fran

klin

WA

6.08

.06

5.19

5.29

.13

5.76

21.4

23.

4520

.90

16.0

915

.75

Gar

field

WA

1.79

01.

110.

940

1.37

20.0

54.

618.

7936

.77

24.5

7G

rant

WA

5.28

D4.

0411

.74

03.

9420

.09

D17

.43

16.8

420

.63

Kitt

itas

WA

1.82

.11

3.43

5.67

.96

4.41

25.5

23.

9620

.57

26.0

68.

45K

licki

tat

WA

0.3

04.

2316

.96

7.53

6.82

11.2

73.

1617

.23

21.1

518

.88

Linc

oln

WA

2.13

L3.

852.

080

2.13

20.5

65.

9714

.26

26.8

922

.13

Oka

noga

nW

A5.

46.3

03.

496.

144.

601.

8621

.15

3.84

18.7

417

.47

21.5

6P

end

Ore

ille

WA

3.01

.73

6.62

14.8

34.

142.

9616

.95

4.47

17.0

926

.72

6.62

Ska

man

iaW

A1.

60D

4.98

10.9

16.

543.

4210

.72

D27

.02

34.4

76.

88S

poka

neW

A.8

3.1

66.

139.

96.6

94.

5023

.68

7.93

30.3

415

.42

1.05

Ste

vens

WA

2.03

.74

4.27

19.3

38.

443.

5416

.19

3.58

24.4

317

.61

8.28

Wal

la W

alla

WA

1.74

.07

3.54

15.5

90

2.65

19.4

74.

1626

.90

17.9

07.

98W

hitm

anW

A1.

36L

2.43

1.49

1.11

2.40

18.6

44.

3518

.68

43.1

07.

55Ya

kim

aW

A4.

85.0

44.

9911

.85

2.00

3.71

9.64

4.92

27.2

614

.78

17.9

6Te

ton

WY

.79

09.

523.

17N

A2.

3823

.81

7.14

41.2

710

.32

1.59

D =

dat

a no

t rep

orte

d du

e to

dis

clos

ure

polic

y; L

= le

ss th

an 1

0 jo

bs; N

A =

not

ava

ilabl

e.a

Per

cent

age

of c

ount

y em

ploy

men

t in

each

sec

tor

with

sec

tors

hav

ing

a la

rger

per

cent

age

than

the

natio

nal p

erce

ntag

e co

nsid

ered

par

t of t

he e

cono

mic

bas

e an

d sh

own

in b

old

.b M

anuf

actu

ring

incl

udes

Sta

ndar

d In

dust

rial C

lass

ifica

tion

(SIC

) 24.

c SIC

24

for

woo

d pr

oduc

ts e

mpl

oym

ent.

d Fin

anci

al, i

nsur

ance

, and

real

est

ate

indu

strie

s.

Sou

rce:

U.S

. Dep

artm

ent o

f Com

mer

ce, B

urea

u of

Eco

nom

ic A

naly

sis

1996

.

11

The following tabulation can be used to compare tables 2 and 3. It shows four countiesin the project area (chosen because they traditionally have been considered timberdependent) and their respective basic employment sectors.

County Agricultural Manu-and state services Farming Mining facturing Government

Lincoln, MT 2.11 2.88 23.24 18.80Grant, OR 2.46 14.25 14.29 25.61Lake, OR 2.55 25.40 21.41Ferry, WA 13.92 9.48 27.34

These data suggest that in all four counties, the level of agricultural3 activity is highenough to be considered part of the economic base, but in only two counties is thelevel of manufacturing high enough to say that it is part of the economic base.

Under a cooperative agreement, Paul Polzin provided an assessment of the economicbase in each project area county.4 Polzin used the assumption approach in determin-ing which industries to include. In general, he included agriculture and agriculturalservices, mining, wood products, and Federal Government as basic sectors, withadditional basic sectors (such as railroad transportation and nonresident travel)differing by county. He computed the percentage of total economic base labor incomeaccruing to each base sector, on average, for the 5 years (1988 to 1992). Polzin’sresults are shown in table 4. We can compare his findings for the above four coun-ties with ours by first converting the above figures from percentage of total countyemployment to percentage of total county base employment. This yields the followingtabulation:

County Agricultural Manu-and state services Farming Mining facturing Government

Lincoln, MT 4.5 6.1 49.4 40.0Grant, OR 4.3 25.2 25.2 45.2Lake, OR 5.2 51.5 43.4Ferry, WA 27.4 18.7 53.9

3 Agriculture includes ranching.

4 Paul Polzin. Economic base profiles Interior Columbia Riverbasin, draft: December 8, 1994. Director, Bureau of Businessand Economic Research, University of Montana, Missoula, MT.On file with: U.S. Department of Agriculture, Forest Service;U.S. Department of the Interior, Bureau of Land Management;Interior Columbia Basin Ecosystem Management Project,112 E. Poplar, Walla Walla, WA 99362.

12

Tabl

e 4—

Eco

nom

ic b

ase

as d

efin

ed in

ass

umpt

ion

appr

oach

use

d by

Pol

zin

Agr

icul

tura

lW

ood

Pul

p an

dM

anu-

Food

Oth

erR

ail-

Non

res.

Sta

teFe

dera

lM

isce

lla-

Cou

nty

Sta

tese

rvic

es

Min

ing

prod

uctio

npa

per

fact

urin

gpr

oduc

tion

basi

cro

ads

trav

elgo

vt.

Gov

t.C

omm

uter

sne

ous

Ada

IDX

XX

XA

dam

sID

XX

XB

anno

ckID

XX

XB

enew

ahID

XB

ingh

amID

XX

Bla

ine

IDX

XB

oise

IDX

XX

Bon

ner

IDX

Bon

nevi

lleID

XX

XX

a

Bou

ndar

yID

XX

XB

utte

IDX

a

Cam

asID

XX

XC

anyo

nID

XX

XX

Car

ibou

IDX

XC

assi

aID

XX

Cla

rkID

XC

lear

wat

erID

XX

Cus

ter

IDX

XE

lmor

eID

XX

Frem

ont

IDX

XX

XG

emID

XX

XG

oodi

ngID

XX

Idah

oID

XX

XJe

ffers

onID

XX

Jero

me

IDX

XX

Koo

tena

iID

XX

XLa

tah

IDX

XX

Lem

hiID

XX

XLe

wis

IDX

XLi

ncol

nID

XX

XM

adis

onID

XX

Xb

Min

idok

aID

XX

Nez

Per

ceID

XX

XO

neid

aID

XX

13

Tabl

e 4—

Eco

nom

ic b

ase

as d

efin

ed in

ass

umpt

ion

appr

oach

use

d by

Pol

zin

(con

tinue

d)

Agr

icul

tura

lW

ood

Pul

p an

dM

anu-

Food

Oth

erR

ail-

Non

res.

Sta

teFe

dera

lM

isce

lla-

Cou

nty

Sta

tese

rvic

es

Min

ing

prod

uctio

npa

per

fact

urin

gpr

oduc

tion

basi

cro

ads

trav

elgo

vt.

Gov

t.C

omm

uter

sne

ous

Ow

yhee

IDX

XX

Pay

ette

IDX

XX

XP

ower

IDX

XX

Sho

shon

eID

XTe

ton

IDX

XTw

in F

alls

IDX

XV

alle

yID

XX

XX

Was

hing

ton

IDX

XD

eer L

odge

MT

XX

Fla

thea

dM

TX

XX

Gra

nite

MT

XX

XX

Lake

MT

XX

XX

Lew

is a

nd C

lark

MT

XX

Xc

Linc

oln

MT

XX

XM

iner

alM

TX

XM

isso

ula

MT

XX

XX

Xc

Pow

ell

MT

XX

XX

Rav

alli

MT

XX

XS

ande

rsM

TX

XX

Silv

er B

owM

TX

XX

Xd

Bak

erO

RX

XX

XC

rook

OR

XX

Des

chut

esO

RX

XX

XG

illia

mO

RX

Gra

ntO

RX

XX

Har

ney

OR

XX

XH

ood

Riv

erO

RX

XX

XJe

ffers

onO

RX

XX

Kla

mat

hO

RX

XX

Lake

OR

XX

XM

alhe

urO

RX

XM

orro

wO

RX

XX

She

rman

OR

XX

Um

atill

aO

RX

XX

14

Tabl

e 4—

Eco

nom

ic b

ase

as d

efin

ed in

ass

umpt

ion

appr

oach

use

d by

Pol

zin

(con

tinue

d)

Agr

icul

tura

lW

ood

Pul

p an

dM

anu-

Food

Oth

erR

ail-

Non

res.

Sta

teFe

dera

lM

isce

lla-

Cou

nty

Sta

tese

rvic

es

Min

ing

prod

uctio

npa

per

fact

urin

gpr

oduc

tion

basi

cro

ads

trav

elgo

vt.

Gov

t.C

omm

uter

sne

ous

Uni

onO

RX

XX

Wal

low

aO

RX

XX

Was

coO

RX

XX

XX

Whe

eler

OR

XA

dam

sW

AX

XA

sotin

WA

XX

XX

Ben

ton

WA

Xe

Che

lan

WA

XX

XC

olum

bia

WA

XX

Dou

glas

WA

XX

Fer

ryW

AX

XX

XX

Fran

klin

WA

XX

XG

arfie

ldW

AX

XG

rant

WA

XX

Kitt

itas

WA

XX

XX

XX

Klic

kita

tW

AX

XX

Linc

oln

WA

XO

kano

gan

WA

XX

XP

end

Ore

ille

WA

XX

XX

Ska

man

iaW

AX

XX

Spo

kane

WA

XX

XS

teve

nsW

AX

XX

Wal

la W

alla

WA

XX

XX

XW

hitm

anW

AX

XYa

kim

aW

AX

XX

Teto

nW

YX

X

X =

sec

tor

acco

unte

d fo

r at

leas

t 10

perc

ent o

f the

tota

l bas

e la

bor

inco

me

acco

rdin

g to

ass

umpt

ion

appr

oach

use

d by

Pol

zin.

a Ida

ho N

atio

nal L

abor

ator

y.b

Priv

ate

colle

ge.

c Tra

de c

ente

r.d U

tility

hea

dqua

rter

s.e U

.S. D

epar

tmen

t of E

nerg

y H

anfo

rd s

ite.

Sou

rce:

Pau

l Pol

zin

(see

text

foot

note

3).

15

Polzin’s findings are as follows:

Agriculture

and

County agricultural Wood M anu- Heavy Rail- Nonresident Federal

and state services Mining products facturing construction road travel Government Other

Lincoln, MT 3.5 16.9 53.4 2.8 2.3 1.7 19.4

Grant, OR 13.4 .6 48.6 5.0 32.2

Lake, OR 33.0 2.0 31.7 30.5 2.8

Ferry, WA 27.1 39.1 20.0 10.9 2.8

Several points of clarification between the two tabulations are in order. First, ourfigures for the government sector included all levels of government in the county;Polzin classified Federal Government as basic but state and local governmentas nonbasic. Additionally, the wood products industry was not separated from themanufacturing sector in our tabulation, as was the case in Polzin’s analysis for threeof the four counties. Finally, it is not clear whether farm income is included in Polzin’sagriculture and agricultural services sector in cases where a farm is incorporated.

For Lincoln County, it is worth knowing that in 1993 a copper and zinc mine closed,which caused mining employment and mining labor income to fall by 90 percent and80 percent, respectively, between 1992 and 1994. Breaking out the wood productscomponent of the manufacturing sector for Lincoln County reveals that 33.9 percent ofbase employment was in this sector. In Grant County, the wood products componentof manufacturing accounts for 19.5 percent of base employment. In Ferry County,the percentage of employment in the manufacturing sector in 1994 is less than thenational average; thus, neither it nor the wood products sector is included as part ofthe economic base in the location quotient approach. For Lake County, neither miningnor manufacturing meets the location quotient cutoff; however, the wood productssector does account for 95 percent of employment in the manufacturing sector and13.2 percent of total employment in the county.

With these caveats noted and bearing in mind that we are comparing different years,the overall numbers do not appear to be too different across the two economic baseapproaches. If one is interested simply in examining the existing economic conditionsin the counties, with no predictive emphasis, perhaps the best descriptor is a simplebreakdown of total employment with no distinction in basic vs. nonbasic. In table 5,the employment data from table 3 are reproduced with the sectors having more than10 percent of a county’s total employment highlighted. The top five sectors for eachof the four counties listed above are:

Lincoln: Services (19.84 percent), trade (19.75 percent), government (18.80 percent),wood products (15.96 percent), and other manufacturing (7.28 percent)

Grant: Government (25.61 percent), services (17.53 percent), trade (14.78 percent),farm (14.25 percent), and wood products (11.09 percent)

Lake: Farm (25.4 percent), government (21.4 percent), trade (16.13 percent), woodproducts (13.2 percent), and services (12.15 percent)

Ferry: Government (27.34 percent), services (16.46 percent), trade (14.47 percent),farm (13.92 percent), and mining (9.48 percent)

16

Tabl

e 5—

Per

cent

age

of to

tal c

ount

y em

ploy

men

t in

each

indu

stry

, int

erio

r C

olum

bia

basi

n, 1

994

Agr

icul

tura

lM

anu-

Tota

lC

ount

yS

tate

serv

ices

Min

ing

Con

stru

ctio

nfa

ctur

inga

SIC

24b

Tran

spor

tatio

nTr

ade

FIR

Ec

Ser

vice

sgo

vern

men

tFa

rm

Ada

ID1.

190.

178.

8912

.64

1.28

4.50

22.3

68.

9726

.89

13.4

00.

98A

dam

sID

2.36

DD

18.5

817

.15

3.34

13.4

13.

8013

.18

23.3

621

.98

Ban

nock

ID.8

7.1

55.

796.

78.0

86.

6626

.46

6.75

21.8

322

.57

2.14

Ben

ewah

IDD

D5.

0327

.73

20.0

06.

8215

.18

3.20

17.9

216

.35

7.77

Bin

gham

ID4.

09.2

84.

9914

.37

.12

2.78

22.2

13.

4318

.87

17.1

711

.80

Bla

ine

ID3.

70.6

313

.84

2.81

.33

2.31

23.1

110

.89

31.0

78.

712.

94B

oise

ID5.

50L

8.90

11.1

96.

763.

8914

.93

1.99

27.4

321

.30

4.86

Bon

ner

ID2.

82.5

310

.29

16.9

98.

233.

8222

.59

6.60

20.0

113

.26

3.09

Bon

nevi

lleID

1.64

.08

7.78

5.07

.26

3.02

28.7

05.

6632

.74

12.3

92.

92B

ound

ary

ID3.

03L

7.39

17.9

110

.24

4.44

17.9

34.

0718

.95

18.2

68.

02B

utte

ID.3

8D

D19

.38

0.1

93.

72.7

257

.56

12.6

25.

43C

amas

IDD

L3.

283.

110

2.07

19.6

9D

13.1

318

.65

40.0

7C

anyo

nID

4.16

.18

7.64

18.7

02.

085.

0619

.71

4.83

21.9

811

.40

6.35

Car

ibou

ID2.

6011

.68

6.99

15.7

30

4.21

16.0

03.

0911

.59

14.7

013

.40

Cas

sia

ID5.

27.7

84.

8111

.17

04.

4622

.99

5.64

17.5

813

.41

13.8

9C

lark

ID5.

84D

LD

01.

528.

881.

523.

5520

.30

58.3

8C

lear

wat

erID

4.08

.32

3.99

22.2

516

.24

3.20

16.6

92.

9613

.58

28.1

34.

81C

uste

rID

4.02

10.2

08.

861.

190

3.46

17.9

52.

9818

.99

18.9

913

.33

Elm

ore

ID1.

51L

3.95

3.79

02.

8714

.99

4.63

10.7

549

.53

7.98

Frem

ont

ID4.

99L

6.09

4.36

2.90

4.13

19.1

92.

6316

.60

23.3

718

.65

Gem

ID4.

68D

6.50

14.0

312

.14

3.89

15.0

13.

6717

.82

15.8

718

.52

Goo

ding

ID7.

70D

6.09

6.20

08.

1116

.79

015

.92

16.4

422

.75

Idah

oID

2.14

1.86

5.73

13.1

19.

675.

1918

.21

3.27

16.8

221

.57

12.1

0Je

ffers

onID

9.46

.25

10.0

611

.18

1.55

3.11

18.2

73.

4510

.17

16.5

817

.47

Jero

me

ID7.

43L

5.32

8.73

07.

6219

.04

3.71

16.3

712

.32

19.4

6K

oote

nai

ID1.

57.3

510

.81

11.1

63.

983.

5324

.53

7.65

25.5

413

.48

1.36

Lata

hID

1.48

.10

4.16

5.83

2.63

3.20

21.3

13.

3122

.56

33.9

94.

07Le

mhi

ID3.

602.

066.

826.

854.

393.

6520

.42

4.04

19.9

520

.47

12.1

3Le

wis

ID3.

08L

4.46

16.8

112

.57

3.79

23.4

12.

9917

.08

17.8

810

.52

Linc

oln

ID4.

62D

2.47

D0

3.47

11.0

91.

4712

.19

28.2

736

.42

Mad

ison

IDD

D3.

6210

.47

1.32

2.13

25.0

24.

3831

.93

12.5

89.

87M

inid

oka

ID7.

94L

4.73

19.1

90

5.06

19.0

52.

4213

.64

14.5

713

.40

Nez

Per

ceID

DD

5.17

16.9

92.

675.

1923

.52

6.73

25.6

112

.61

4.18

One

ida

ID2.

384.

121.

192.

380

2.59

17.7

53.

8410

.48

27.3

927

.88

17

Tabl

e 5—

Per

cent

age

of to

tal c

ount

y em

ploy

men

t in

each

indu

stry

, int

erio

r C

olum

bia

basi

n, 1

994

(con

tinue

d)

Agr

icul

tura

lM

anu-

Tota

lC

ount

yS

tate

serv

ices

Min

ing

Con

stru

ctio

nfa

ctur

inga

SIC

24b

Tran

spor

tatio

nTr

ade

FIR

Ec

Ser

vice

sgo

vern

men

tFa

rm

Ow

yhee

ID10

.26

3.48

3.70

3.56

06.

3513

.65

2.19

11.7

718

.12

26.9

2P

ayet

teID

3.64

.19

5.19

18.5

00

7.55

16.8

84.

3117

.47

12.6

013

.65

Pow

erID

2.60

L4.

6838

.00

06.

7812

.36

1.86

8.43

12.4

012

.89

Sho

shon

eID

0.84

8.15

4.91

6.87

2.34

4.62

21.5

44.

7722

.81

24.7

6.7

2Te

ton

ID3.

920

7.78

3.97

02.

6819

.01

3.55

18.2

919

.17

21.6

4Tw

in F

alls

ID3.

44.2

45.

9411

.62

.62

5.41

24.8

46.

0123

.19

12.9

96.

33V

alle

yID

2.87

1.03

10.5

55.

242.

713.

2124

.67

6.69

21.0

122

.16

2.57

Was

hing

ton

ID8.

95L

3.81

14.0

07.

163.

7218

.68

4.96

15.7

615

.39

14.7

2D

eer L

odge

MT

1.46

1.33

4.28

3.59

03.

8320

.91

3.27

28.4

330

.66

2.26

Fla

thea

dM

T1.

60.3

68.

1311

.89

4.78

5.02

23.8

56.

8328

.75

11.3

82.

19G

rani

teM

T3.

813.

883.

8122

.38

19.5

91.

7718

.44

1.84

12.8

619

.05

12.1

8La

keM

T3.

00.1

96.

698.

322.

932.

8219

.20

4.83

30.6

613

.21

11.0

8Le

wis

and

Cla

rkM

T.7

4.4

94.

463.

84.3

03.

9819

.90

7.55

31.5

925

.76

1.69

Linc

oln

MT

2.11

.58

4.10

23.2

415

.96

4.31

19.7

54.

3919

.84

18.8

02.

88M

iner

alM

T1.

660

3.52

11.1

34.

773.

7325

.36

3.18

24.9

522

.60

3.87

Mis

soul

aM

T1.

05.1

24.

906.

821.

626.

3825

.54

6.44

30.5

817

.29

.88

Pow

ell

MT

1.77

.60

3.87

10.1

06.

533.

0315

.97

4.83

17.7

731

.40

10.6

7R

aval

liM

T2.

96.8

78.

7610

.46

4.89

4.21

21.1

45.

7324

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13.0

28.

32S

ande

rsM

T2.

04.6

16.

289.

917.

526.

5416

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4.34

23.6

619

.60

10.3

3S

ilver

Bow

MT

.57

3.45

3.15

3.55

.03

7.50

27.0

15.

0433

.00

15.9

3.8

0B

aker

OR

1.84

1.06

5.04

9.62

5.09

4.83

20.3

24.

4223

.23

16.8

912

.74

Cro

okO

R2.

51.2

93.

6026

.97

23.6

74.

9719

.21

3.48

15.8

313

.85

9.30

Des

chut

esO

R1.

56.2

39.

0710

.89

5.17

3.52

25.1

18.

7527

.26

11.2

72.

35G

illia

mO

RD

0.9

12.

210

20.2

710

.52

3.05

10.2

914

.94

37.8

0G

rant

OR

2.46

04.

2414

.29

11.0

94.

0914

.78

2.76

17.5

325

.61

14.2

5H

arne

yO

R3.

39L

3.55

12.1

210

.47

3.53

14.9

72.

8216

.03

22.0

821

.51

Hoo

d R

iver

OR

4.89

D3.

6611

.89

4.06

5.34

22.8

40

25.1

910

.94

15.2

5Je

ffers

onO

RD

02.

7222

.68

15.9

32.

1217

.53

022

.08

16.6

016

.26

Kla

mat

hO

R2.

18.1

04.

3214

.26

9.47

4.46

22.7

65.

2023

.34

15.8

57.

53La

keO

R2.

55.4

83.

0113

.91

13.2

02.

4216

.13

2.53

12.1

521

.41

25.4

0M

alhe

urO

R6.

05.3

63.

307.

480

4.46

23.6

53.

6018

.11

15.3

417

.64

Mor

row

OR

8.72

L2.

5315

.09

3.32

6.17

9.18

2.75

8.15

16.4

031

.02

She

rman

OR

1.93

0L

00

2.99

25.4

2L

9.76

24.7

135

.18

Um

atill

aO

R2.

86L

3.32

15.5

82.

935.

2720

.61

4.85

21.2

016

.02

10.2

8

18

Tabl

e 5—

Per

cent

age

of to

tal c

ount

y em

ploy

men

t in

each

indu

stry

, int

erio

r C

olum

bia

basi

n, 1

994

(con

tinue

d)

Agr

icul

tura

lM

anu-

Tota

lC

ount

yS

tate

serv

ices

Min

ing

Con

stru

ctio

nfa

ctur

inga

SIC

24b

Tran

spor

tatio

nTr

ade

FIR

Ec

Ser

vice

sgo

vern

men

tFa

rm

Uni

onO

R2.

12.1

44.

2713

.47

8.04

5.32

22.0

14.

2420

.45

19.4

88.

51W

allo

wa

OR

3.44

L4.

6212

.75

6.58

4.33

16.2

94.

3814

.69

19.5

020

.00

Was

coO

R2.

14.1

02.

6510

.24

1.99

3.11

24.6

14.

7426

.25

17.5

08.

66W

heel

erO

R3.

170

L3.

02.9

12.

1111

.18

2.57

10.4

222

.05

45.4

7A

dam

sW

A7.

65D

D12

.48

05.

1219

.42

3.23

13.4

015

.18

23.5

3A

sotin

WA

DD

8.49

5.10

2.18

2.47

25.5

55.

4932

.13

16.0

04.

76B

ento

nW

A1.

88.1

05.

955.

930

2.22

1.85

4.53

42.2

913

.81

21.4

4C

hela

nW

A6.

92.4

55.

726.

33.4

72.

8123

.73

6.24

21.0

613

.61

13.1

3C

olum

bia

WA

2.94

D1.

9422

.06

0 D

12.5

22.

4016

.32

24.6

417

.18

Dou

glas

WA

6.17

L4.

261.

420

4.81

19.6

64.

1916

.96

16.3

926

.16

Fer

ryW

AD

9.48

3.91

11.5

07.

49 D

14.4

72.

9316

.46

27.3

413

.92

Fran

klin

WA

6.08

.06

5.19

5.29

.13

5.76

21.4

23.

4520

.90

16.0

915

.75

Gar

field

WA

1.79

01.

11.9

40

1.37

20.0

54.

618.

7936

.77

24.5

7G

rant

WA

5.28

D4.

0411

.74

03.

9420

.09

D17

.43

16.8

420

.63

Kitt

itas

WA

1.82

.11

3.43

5.67

.96

4.41

25.5

23.

9620

.57

26.0

68.

45K

licki

tat

WA

0.3

04.

2316

.96

7.53

6.82

11.2

73.

1617

.23

21.1

518

.88

Linc

oln

WA

2.13

L3.

852.

080

2.13

20.5

65.

9714

.26

26.8

922

.13

Oka

noga

nW

A5.

46.3

03.

496.

144.

601.

8621

.15

3.84

18.7

417

.47

21.5

6P

end

Ore

ille

WA

3.01

.73

6.62

14.8

34.

142.

9616

.95

4.47

17.0

926

.72

6.62

Ska

man

iaW

A1.

60D

4.98

10.9

16.

543.

4210

.72

D27

.02

34.4

76.

88S

poka

neW

A.8

3.1

66.

139.

96.6

94.

5023

.68

7.93

30.3

415

.42

1.05

Ste

vens

WA

2.03

.74

4.27

19.3

38.

443.

5416

.19

3.58

24.4

317

.61

8.28

Wal

la W

alla

WA

1.74

.07

3.54

15.5

90

2.65

19.4

74.

1626

.90

17.9

07.

98W

hitm

anW

A1.

36L

2.43

1.49

1.11

2.40

18.6

44.

3518

.68

43.1

07.

55Ya

kim

aW

A4.

85.0

44.

9911

.85

2.00

3.71

9.64

4.92

27.2

614

.78

17.9

6Te

ton

WY

.79

09.

523.

17N

A2.

3823

.81

7.14

41.2

710

.32

1.59

Bol

d =

em

ploy

men

t sec

tors

with

mor

e th

an 1

0 pe

rcen

t of a

cou

nty’

s to

tal e

mpl

oym

ent;

D =

dat

a no

t rep

orte

d du

e to

dis

clos

ure

polic

y; L

= le

ss th

an 1

0 jo

bs.

a Tot

al m

anuf

actu

ring

incl

udes

Sta

ndar

d In

dust

rial

Cla

ssifi

catio

n 24

.b

SIC

24

for

woo

d pr

oduc

ts s

ecto

r.c F

inan

cial

, ins

uran

ce, a

nd r

eal e

stat

e in

dust

ries

.

Sou

rce:

U.S

. Dep

artm

ent o

f Com

mer

ce, B

urea

u of

Eco

nom

ic A

naly

sis

1996

.

19

Although economic base analysis can be useful as a first approximation for charac-terizing a region’s economy, it is subject to limitations in describing the overall levelof economic welfare and in predicting future economic conditions. Because theseshortcomings have been dealt with extensively elsewhere in the literature, we provideonly a brief summary drawn mainly from the more detailed treatments of Niemi andWhitelaw (1997), Power (1996), Rasker (1995), and Krikelas (1992).

Among the drawbacks of using an economic base model to describe the current levelof economic welfare in an area are the following:

1. A simple measure, such as the number of jobs or amount of labor income, revealslittle about the quality of jobs in the various industries. In other words, even thoughone sector may have more employment or higher wages than another sector, it mayhave higher safety risks, less chance for advancement, and embody fewer job skillseasily transferable to other sectors.

2. Economic base models tend to focus on the industries that export physical goodsand leave out those that export services or attract people who then consume localservices. A classic example of this second type of industry is recreation and tourism.

3. The role of nonbasic industries in stopping leaks from an economy through importsubstitution is not adequately addressed.

4. The importance of nonlabor income to an area is not captured. Rasker (1995)found that nonlabor income accounted for 34 percent of personal income in theproject area in 1993.

5. Implicit in the economic base model is the assumption that people follow jobs; inother words, people locate in an area because of the job opportunities available inthat area. An alternative assumption is that jobs follow people. This is the assumptionbehind the quality of life model, which holds that people locate in high-amenity areasbased on quality of life considerations and that industries follow, believing that workerswill accept lower wages in order to remain in these high-amenity areas.5 This secondthesis regarding the genesis of employment opportunities is not addressed in eco-nomic base models.

6. The externalities (costs not borne by the producers or consumers of a goodor service) associated with various industries are not captured; for example, if anindustry generates a large amount of pollution, the economic benefits of increasingemployment in this industry may be outweighed by increased degradation of theenvironment or the increased costs of maintaining a given quality of the environment.

Limatations ofEconomic BaseModels

5 Niemi and Whitelaw (1997) use the phrase “the secondpaycheck” to represent “the value to residents of thevarious amenities contributing to the quality of life in thearea, including access to social, cultural, and environmentalamenities, access they would not enjoy if they lived elsewhere”(p. 31).

20

Because the economic base is essentially a static portrayal of current conditions,while economies are inherently dynamic, base models fall short for planning purposesor for predicting the future economic structure of a region in the following respects:

1. Base models do not reveal the relative volatility of industries; for example, someindustries may be sensitive to external forces such as macroeconomic cycles, inter-est rates, world prices, and even weather conditions. In contrast, other industriesmay be insulated from these influences.

2. A single snapshot of the basic sectors in an area does not incorporate the dynam-ics of these sectors in the larger context. A county having a large amount of its baseemployment in an industry that is waning, may want to undertake a very differentdevelopment strategy than a county with most of its base employment in an expandingindustry.

3. General trends, such as labor-saving technological change, increasing importanceof education in determining wages and income, and changing demographics, may in-fluence the number and types of jobs created (lost) in the future when a particularindustry expands (contracts). Because such trends are not reflected in economicbase models, predictions based on these models may be skewed.

Conclusions Different definitions of economic base are useful to the extent that they lead to dif-ferent implications about the propensity for change. In this case, little differencewas observed in the general findings from the assumption and location quotientapproaches. An argument can be made that for examining potential growth in anarea, the location quotient approach is preferable because it focuses on sectorswhere specialization has already taken place, presumably due to the comparativeadvantages inherent in those sectors.

The emphasis here has been to illustrate different notions of economic base byusing county data, but the issue remains of what constitutes an economy and howwell these measures describe economic conditions. In the economic assessment,we adopted the BEA definition of functional economies. This choice was frequentlyquestioned, but no reasonable alternative was found that resolved all the variousissues.

Acknowledgments Thanks go to Judy Mikowski for help in preparing the tables.

21

Literature Cited Beuter, J.H. 1995. Legacy and promise: Oregon’s forests and wood products industry.[Place of publication unknown]: [Publisher unknown]; report for the Oregon BusinessCouncil and the Oregon Forest Institute. 56 p.

Haynes, R.W.; Horne, A.L. 1997. Chapter 6: Economic assessment of the basin. In:Quigley, T.M.; Arbelbide, S.J., tech. eds. An assessment of ecosystem componentsin the interior Columbia basin and portions of the Klamath and Great Basins. Gen.Tech. Rep. PNW-GTR-405. Portland, OR: U.S. Departmant of Agriculture, ForestService, Pacific Northwest Research Station: 1715-1869. Vol. 4.

Horne. A.L.; Haynes, R.W. [In press]. Developing measures of socioeconomicresiliency in the interior Columbia basin. Gen. Tech. Rep. PNW-GTR-453. Portland,OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest ResearchStation. 41 p.

Johnson, K.P. 1995. Redefinition of the BEA economic areas. Survey of CurrentBusiness. 75 (Feb.): 75-81.

Krikelas, A.C. 1992. Why regions grow: a review of research on the economic basemodel. Economic Review, Federal Reserve Bank of Atlanta. July/August: 16-29.

Lipsey, R.G.; Steiner, P.O.; Purvis, D.D. 1984. Economics. 7th ed. New York: Harper& Row. 981 p.

Niemi, E.; Whitelaw, E. 1997. Assessing economic tradeoffs in forest management.Gen. Tech. Rep. PNW-GTR-403. Portland, OR: U.S. Department of Agriculture,Forest Service, Pacific Northwest Research Station. 78 p.

Power, T.M. 1996. Lost landscapes and failed economies: the search for value ofplace. Washington, DC: Island Press. 304 p.

Rasker, R. 1995. A new home on the range: economic realities in the interior Columbiabasin. Washington, DC: The Wilderness Society. 59 p.

U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior,Bureau of Land Management. 1997a. Eastside draft environmental impact state-ment. Portland, OR: U.S. Department of the Interior, Bureau of Land Management.1323 p. 2 vol.

U.S. Department of Agriculture, Forest Service; U.S. Department of the Interior,Bureau of Land Management. 1997b. Upper Columbia River basin draft environ-mental impact statement. Portland, OR: U.S. Department of the Interior, Bureau ofLand Management. 1261 p. 2 vol.

U.S. Department of Commerce, Bureau of Economic Analysis. 1996. Regionaleconomic information system, 1969-1994 CD-ROM disk. Washington, DC. [Machinereadable data files and technical documentation files prepared by the RegionalEconomic Measurement Division (BE-55), Bureau of Economic Analysis,Economics and Statistics Administration, U.S. Department of Commerce].

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