Assessin the Economic

acts of Recreation

and Tourism

Dennis B. Propst, Compiler












































It is easy to extend the ideas of multiplicative models and estimated multiplicands to

the calculation of secondary impacts. The USC of

input-output or multiplier techniques also involvc products of cstimatcd parameters. A common

cxamplc is the use of an average wage-to-salts

ratio to estimate wage impacts from final demand.

TYPES AND SOURCES OF ERRORS

When a sample of data is used to generatc an

estimate of a parameter of the underlying population thereis usually a sampling error. In the

case whcrc final demand by tourists is the population parameter, a common estimate is the product

of the estimated number of tourists and their

estimated average expenditures. The sampling

error varies from sample to sample and, as the

sample size increases and approaches the population size, the sampling error disappears.

Thcrcfore it is convcnicnt to discuss an cstimator's average behavior over many small samples.

The two most common small sample propcrtics arc

the bias and the variance of an estimator. The

bias is the difference bctwccn the true parameter

of a population (say, final demand) and the avcrage value taken by the estimator over many samples.

The variance is a measure of the spread of the

values taken by the estimator around its own

average in different samples. These propertifs

arc related to the sampling error through the

avcragc of its square. That is, the mean squared

sampling error equals the sum of the variance and

squared bias of the estimator. It is convenient

to look at the bias and variance scparatcly since

an estimator may be unbiased with a large variance or biased with a small variance. To judge

overall accuracy, the mean squared error criterion (if it can bc calculated) is superior to

either bias or variance criteria alone.

Errors Due to the Model

The errors made in estimating final demand by

tourists arc from two general sources: the multiplicative model, which relates the sample data

to the population parameter, and the sample data

used in estimation. The accuracy of a model

which is the product of scvcral estimated values

depends on the properties of the individual cstimates as well as the indcpcndcncc of the multiplicands. For cxamplc, when final demand is

calculated as the product of the estimated number

of tourists and the estimated cxpcnditures by the

average tourist, the bias and variance of each

estimate is compounded in the final demand cstimate. Both the bias and variance of their product dcpcnd on the indcpcndcnce of the number of

tourists and the level of their cxpenditurcs. If

the estimates of the number of tourists and their

average expcnditurcs arc unbiased and the two

variables they rcprescnt arc indcpcndent, ther

their product will be unbiased. Othcrwisc, the

cstimatc of total tourist cxpcnditures will bc

biased. For example, if weekend tourists spend

less per day than weekday tourists and the former

outnumber the latter, the two variables arc not

indepcndcnt and the estimate of final demand

based on the product of means from random samples

will be biased downward.

The variance of the final demand cstimato is

more complicated; it is not mcrcly the product of

the variances. An unbiased cstimatc of the variance of a product of independent random variables

is given by the sum of three terms: the variance

of the first variable wcightcd by the squared

sample mean of the second, the variance of the

second variable weighted by the squared sample

mean of the first, and the negative of the product of the two variances (Goodman 1960). When

random variables are not indcpcndcnt, the variancc of their product bccomcs even more complex.

The same properties are true for multiplicative

supply-related models where, for example, average

receipts arc multiplied by the average number of

firms. When models comprise products of three or

more cstimatcs, the compounding of errors continues.

To illustrate the bias caused by the multiplicative model, consider the case of an impact

study of tourism in Wcstcrly, Rhode Island (Tyrrcll

and others 1982a). In this study a preliminary

model was used to estimate the wages paid to

local residents by the 35 hotels in the town.

Estimated seasonal hotel capacity was multiplied

by an estimated occupancy rate to arrive at total

occupant days. The latter was multiplied by

cstimatcd sales per occupant to arrive at total

sales. This was multiplied by an estimated wage/

sales ratio to arrive at total wages. Finally

this was multiplied by the proportion of wages

paid only to Wcstcrly residents by the hotel

industry. By the conclusion of the study, all

hotels had been surveyed and the final impact and

each of the intermediate figures were known.

These data permitted us to compare the calculations of the preliminary multiplicative model

by using very precise multiplicands (assumed to

bc unbiased with zero variance) to the exact

intcrmediatc and final values. The only errors

that would be genzratcd by this process would be

those due to dependence among variables. Thus

the bias caused by the form of the model could bc

examined independently of that caused by the

data. It turned out that the 35 hotels of Wcstcrly

did not constitute a very homogeneous group and

that a significant amount of bias was introduced

bccausc WC had assumed that the variables wcrc

indcpcndent.

The results shown in table 1 reveal that the

first two products wcrc both biased downward; the

computed value for occupant days was 7.2 pcrccnt

below actual and the hotel salts cstimatc was

31.6 pcrccnt below actual. This was caused by a

positive correlation bctwcen variables: larger

hotels had higher occupancy rates and higher room

rates (salts per occupant day). The accumulated

estimate was 36.5 percent below actual from the

two calculations. The third and fourth products

were both biased upward: the incrcmcntal bias in

total wages was +4.8 percent and the incrcmcntal

bias in Westerly wages was +9.0 percent. The

larger hotels had lowc?r wage/sales ratios and

lower Wcstcrly/total wage ratios. The accumulated bias dropped to -33.5 pcrccnt by the

third product and to -27.5 pcrccnt by the fourth

product. This result illustrates the scriousncss

of the problem with the multiplicative model.

41
Table l.--Bias in a model of local wages paid by the Westerly Hotel

industry, Rhode Island

Accumulated Incremental Accumulated

Computation Actual -

cstimatc % bias % bias

Hotel capacity

x Occupancy rate

= Occupant days

x Sales per

occupant day

= Salts

x Wages/sales

= Total wages

x Westerly wages/

total wages

= Westerly wages

202,758

0.7743

169,206 156,996 -7.2 -7.2

$21.01

$5,195,806.

0.0809

$401,064

$3,298,476. -31.6 -36.5

$266,847 +4.8 -33.5

0.9386

$345,424 $250,462 +9.0 -27.5

Even prccisc cstimatcs of the multiplicands did

not overcome the bias caused by the form of the

model which, in this case, was an underestimate

of wage impacts of more than 25 percent. If it

was dcsirablc to USC the multiplicative model one

would need to disaggregate the data into homogzncous groups for which the variables may bc less

dcpcndcnt.

Errors Due to the Data and Their USC

The sources of error in estimating final demand

rclatcd to the USC and collection of data can bc

classified as follows:

Errors due to the model

Errors due to the data and their USC

Sampling errors

Nonsampling errors

Survey design

Insufficient frame

Bias in sample sclcction

Inadcquatc sample size

Survey execution

Nonobscrvation bias

Mcasurcmcnt errors

Data analysis

Processing errors

Improper statistical methods

Faulty intcrprctations

The first category refers to the multiplicative

model dcscribcd previously. In the second cat?-

gory, little can br said about sampling errors

except that if it wcrc possible to intcrvicw

every tourist and obtain accurate, relevant information from each, then results would have no

error. Unfortunately, cost and feasibility usually limit surveys to small samples, and sampling

errors will necessarily exist unless entire populations arc surveyed.

Nonsampling errors, on the other hand, can bc

controlled and minimized to a considerable degrcz.

The eight major sources of thcsc errors result

from the design and execution of the survey, and

the analysis of the data. Sampling methodology

and recommended strategies for overcoming the

eight types of problems arc thoroughly discussed

clscwhcrc (Cochran 1977). Howcvcr, it seems

appropriate to comment on these nonsampling errors as they rclatc to some? of the unique fcaturcs of rccrcation and tourism surveys.

Insufficient frame.--It is obviously important

to identify the population of rccreationists,

tourists, or firms in the industry, but a list or

arca description of the population is frequently

inadequate. It is not a trivial matter, however,

to design a sufficient frame. It is usually

impossible to list tourist populations because of

their size. Access to the population of tourists, for cxamplc, may bc limited to times when

they arc participating in recreation or tourism

activities. Since records of all individual

participants usually arc not kept, counting the

same individual more than once during a season is

unavoidable. Thus, attendance records cannot be

translated directly into a frame. In addition,

it is not advisable to treat an individual counted

twice as two different individuals. Different

behavior and ?xpcnditurcs may bc associated with

rcpcat visitors and one-time visitors.

Bias in sample sclcction.--Error occurs if the

sample is chosen from the frame in such a way

42
that the population is misrcprescnted. Simple

random sampling or stratified random sampling may

bc sufficient to prevent biased sample selection

but it is often difficult to ensure randomness

across a season of visitors due to the expense of

interviewing. Reweighting observations on the

basis of known population characteristics from a

second source can reduce some of this bias.

Inndcquatc sample size.--0ftcn the dcsircd

precision of the cstimatcs cannot be obtained

from the number of observations in the sample.

Increasing the size of a sample is one of the

most commonly discussed methods of reducing crrors when designing or conducting a survey. This

is because the standard deviation of the mean of

the sample has a simple invcrsc relationship to

the square root of the sample size. This means

that the cost of sampling is the only reason not

to reduce errors in this way. It should be cautioncd, however! that most biases cannot be overcome by incrcaslng sample sizes.

To illustrate the relationship bctwccn sample

size and the precision of an estimate, sclcctcd

results from four tourist surveys conducted in

Rhode Island over the past 2 years arc given in

table 2. Each survey had a slightly different

purpose and was conducted in a slightly different

manner. All of the survey instruments included a

question asking for per capita daily food cxpcnditures during a leisure trip or a vacation. The

surveys were of southern Rhode Island beach users

(Tyrrcll and others 1982b), Westerly, Rhode Island's

hotel guests (Tyrrcll and others 1982a), Newport

International Sailboat Show (NISS) patrons and

the Newport Yachting Ccntcr's boat manufacturers'

rcndczvous events participants (Tyrrcll 1984).

It is convcnicnt to refer to the four as the

beach, hotel, boat show, and boating cvcnt survcys. By using the formula for the standard

deviation of the mean, the four sets of results

were used to compute sample sizes necessary for

the same rflativc precision of a per capita food

cxpcnditure cstimatc.

The beach survey was conducted by a single

intcrvicwcr who spent 15 to 30 minutes with each

beach user to complete a multipurpose questionnaire. Considerable care was taken to cnsurc

random sampling. The population of beach users

was cstimatcd to be 64,000, of which 352 were

intervicwcd; the cost per observation of the 272

responses that could be used for estimating avcrage food cxpcnditurcs was $10.79 (including

coding and keypunching).

The hotel survey dcpendcd on volunteer rcspondents to questionnaires handed out by hotel

managers. There was no follow-up survey and

observations wcrc not rcwcightcd to compensate

for nonrespondents. The response rate was low

and the results arc bclicvcd to be biased. The

population was estimated to be 25,500; 200 questionnaires were distributed td the hotels; 21

useful rcsponscs wcrc reccivcd and the cost per

observation was $3.81.

The boat show survey was conducted during 4

days of the NISS by 10 diffcrcnt interviewers who

spent 5 to 10 minutes with each patron. Some

mcasurcs were taken to cnsurc random sampling.

The population was estimated to bc 12,000, of

which 492 were interviewed; the cost per observation was $3.00.

The boating event survey was conducted by mail

from the list of event participants. No followup questionnaire was sent, but the response rate

Table Z.--Sample size and precision of four Rhode Island tourist surveys

Survey

Boat Boating

Variable Beach Hotel show event

Population size (no.) 64,000 25,500 12,000 350

Sample size (no.) 352 21 492 126

Cost/observation ($) 10.79 3.81 3.00 2.67

Mean food cxpcnditurcsl

capita/day ($1 9.86 16.05 16.69 16.86

Standard error ($) 10.28 9.38 19.38 16.88

Number of observations

required for 95% CI

of +lO% mean (no.) 417 131 518 7aa _

Total cost for CI ($) 4,500 500 1,550 208

CI = confidence interval.

aBccausc of the small population relative to the sample size, a finite

population correction factor was used.

43
was very high. The population was 350; 126 rcsponded and the cost per observation was $2.67.

It is not possible to compare the averago responses to the food cxpenditurc questions bccausc

of the difference in the populations surveyed.

Howcvcr, to cxaminc the tradeoffs bctwcen sample

size, precision, and the cost of sampling by

diffcrcnt techniques, the estimated standard

errors can be rclatcd to a +lO percent interval

around the respective means: A slight modification permits the surveys to be compared on

the basis of the numbcr of observations required

for a 95 percent confidence interval (CI) of that

size. Multiplying this numbcr by the cost per

observation gives an indication of the relative

efficiency of the diffcrcnt surveys in producing

an average per capita food expenditure cstimatc

with the same relative precision.

The results of this comparison arc that the

boating event survey would require the fewest

observations and cost the least to produce a 95

pcrccnt CI +lO percent around the mean; the hotel

survey rankzd second in observations rcquircd and

cost; the beach survey ranked third in obscrvations required, but fourth in cost; and the

boat show survey ranked fourth in observations

r-cquircd and third in cost.

The simple interpretation of these results is

somewhat misleading since the results of the

hotel survey wcrc biased. In fact, from other

data on Westerly hotel visitors, it was cstimatcd

that the bias in this survey was considerable,

overwhelming its small variance in its mcan squared

error (MsE). The other survey cstimatcs were

bclicvcd to bc rclativcly free from bias so that

their MSE's arc the same as their variances.

Reranking the survey approaches on the basis of

their MSE's put the hotel survey last and loft

the others in their same rclativc positions.

Because of the size and nature of the bias in the

hotel survey estimate, it was cstimatcd that oven

a sample of 1,000 hotel visitors would not have

produced the desired level of precision.

The most successful survey was the one conducted at the boating event. Its advantage was

the small population sampled and enthusiastic

cooperation of the boaters. The rcsponsc rate

was 36 pcrccnt. The boat show survey was also

rclativcly successful. Its advantages were the

high rcsponsc rate because of the intcrvicw approach and the brevity of the survey. The length

of the questionnaire was the downfall of the

beach survey, which took much time to conduct,

cod?, keypunch, and analyze.

Non-observation bias.--A bias results from a

lack of measurcmcnts for some of the individuals

in the selcctcld sample bccausc of failure to

locate thorn or from refusals to answer questions

by those who wcrc located. This was one of the

problems with the hotel survey. It is also a

problem with most mail surveys; the boating cvcnt

survey was an cxccption. A successful strategy

in cases known to the author has been to conduct

a scrics of follow-up rcmindcrs, questionnaires,

and telephone calls. Even if the respondent does

not answer all the questions, it is usually possiblc to adjust results for biases based on some

knowlcdgc of the characteristics of nonrcspondcnts.

Measurement errors.--The diffcrcnce between

accurate information and the response to a qucstion leads to measurement errors. Such errors

arc commonly caused by a poorly worded question

or the failure of a rcspondont to recall accurate

information. Careful design and cxtcnsivc testing

of a questionnaire is the only solution.

Processing errors.--Thcsc errors occur in cditing, coding, and tabulating results.

Improper statistical methods.--If incorrect

assumptions about the distributions of variables

are made and the statistical proccdurcs are based

on these assumptions, then there will bc errors

in the data analysis.

Faulty intcrprotations.--Errors in data analvsis are made if the results from one sample of

the population are incorrectly cxtrapolatcd to

other samples, or when the meaning of survey

rcsponscs are altered by erroneous induction or

the careless USC of words.

All of these errors arc serious and most can bc

avoided by careful attention to details of the

project.

CONCLUSlONS AND RECOMMENDATIONS

This paper has attempted to cxaminc data collection and USE in estimating final demand by tourists. The approach has been to rcvicw the implications of the traditional multiplicative model

and the proccdurcs by which the multiplicands are

cstimatcd. There has been no attempt to identify

all possible sources of data for this typo of

analysis, which is done clscwhcrc (Goeldncr 1980;

WV Univ. 1981). Furthcrmorc, data availability

is a problem that has no gcncral solution but one

that must bc solved by local research. The focus

hcrc has boon on two types of error, bias and

variance, and on the gcncral sources of thcsc

errors in traditional rcscarch cfforts.

Four recommendations arc offered:

1. On the choice of a model for final demand.

Since tourism is a multigood, multiscrvicc industry, a very complex model is implied. Howcvcr,

limitations of data will usually permit only the

use of simple models. If the traditional multiplicativc model is used, the biases caused by

correlations between variables should be accounted for. The simplest way is to disaggregate

and USC a sum of products over the most homogeneous groups possible.

2 . On sample size. The required sample size

can bc calculated from a desired dcgrcc of prccision and a previous cstimatc of the standard

error of a variable. This calculation does not,

however, account for the bias which may bc present

in the estimate used. Also, a large sample size

will not generally ovcrcomc biases in sampling

procedures.
3. On the use of secondary data. Make USC of

all that is available since it is usually very

inexpcnsivc to obtain. Howcvcr, sclcct only data

that can bc rclatcd to final demand by a rcasonablc and relatively simple model. Also, do

not ncglcct the need for measures of variance in

thcsc data.

4. On survey dcslgn and execution. Bc as

concise as possible in asking survey questions,

and test questionnaires cxtcnsively. The cost of

information somctimcs increases more than proportionately to the length of a questionnaire. FinallY> poor survey designs and executions arc the

major causes of biases. It helps to keep the

mean squared error criterion in mind.

LITERATUKE ClTED

Cochran, William G. Sampling techniques. New

York: John Wiley & Sons; 1977. 400 pp.

Gocldncr, Charles R. Data sources for travel

and tourism research. In: Hawkins, Donald E.;

Shafer, Elwood L.; Rov?lstad, James M., cds.

Tourism marketing and managcmcnt issues.

Washington, DC: George Washington University

Press; 1980: 277-290.

Goodman, Leo A. On the exact variance of products.

Journal of the American Statistical Association

55: 708-713; 1960.

Tyrrell, Timothy J. The economic impact of the

boating events at the Newport Yachting Center

in 1982 on the City of Newport, Rhode Island.

Marine Technical Report 86. Kingston, RI:

University of Rhode Island; 1984. 30 pp.

Tyrrcll, Timothy J.; Emerson, William K.B.;

Molzan, David E. The economic impact of tourism

on Wcstcrly, Rhode Island. Bull. 433. Kingston,

RI: University of Rhode Island, Agricultural

Experiment Station; 1982a.

Tyrrcll, T.J.; Morris, D.M.; Alba, A. The 1981

Rhode Island beach USC and value survey: survey

design and preliminary results. Working Pap.

10. Kingston, RI: University of Rhode Island,

Department of Resource Economics; 1982b.

West Virginia University. Creating economic

growth and jobs through travel and tourism.

Mcrgantown, WV: West Virginia University,

Bureau of Business Rcscarch, Collcgc of Business

and Economics; 1981. 317 pp. (Prepared for the

U.S. Department of Commerce.)

45
Computerized Models for Assessing the

Economic Impact of Recreation and

Tourism

Robert C. Bushnell and Matthew Hyle 1

INTRODUCTION

Many approaches to national economic

modeling have been taken. When the word

"impact" is used, it is generally true

that it is the disaggregate interactions

of the economic process that are being

studied. Simulations and multipleequation econometric models sometimes

fill this role, but most often it is

input-output analysis that is employed.

At the national level, the Bureau of

Economic Analysis (BEA) of the U.S.

Department of Commerce expends much time

and painstaking effort in establishing

the classification and flows of payments

that make up the national input-output

model. While a number of small area

models have been constructed and

utilized at the state and local level,

the effort involved in compiling the

data is usually more costly than the

study area can afford. Hence, in recent

years the attempt to develop regional or

small-area input-output models by using

so-called non-survey methods has

increased. In the last 10 years, a

number of systems have arisen which

generate small area, state, or regional

models from the technical coefficients

matrix of the U.S. National Input-Output

model. Three of these models will be

discussed.

RIMS II Modeling System

The first such system is the so-called

RIMS II, constructed and supported by

the Regional Economic Analysis Division

of the BEA.

RIMS II (Regional Input-Output

Modeling System, version II) uses the

1972 BEA 496 input-output national model

as the basis for the regional

coefficients. The coefficients are

modified by the use of the Regional

Location Quotient (LQ) technique:

the national direct-requirement-coefficients matrix is

1

Associate Professor and Assistant Professor, respectively, Department of

Finance and Business Economics,

School of Business Administration,

Detroit, Michigan 48202.

made region-specific by using

4-digit SIC location quotients.

According to this mixed-LQ

approach, BEA county personal

income data, by place of residence, are used for the calculation of LQ's in the service

sectors, while BEA earnings

data, by place of work, are

used for the LQ's in the nonservice sectors. The LQ's

are used to estimate the

extent to which direct requirements are supplied by

firms within the region.

(Cartwright and others, 1981)

Simple location quotients are defined by

the following relation:

LQ(i) =

E(i,r)/E(*,r)

E(i,n)/E(*,n)

(1)

where: E(i,r) = Earnings in the ith

industry in the rth

region,

* = the sum over all

industries, and

n = the sum over all

regions.

Hence the concept is the ratio of the

proportion of industry i's earnings of

all earnings in region r to the similar

proportion of industry i's earnings in

the nation as a whole. An industry in

which the region specializes will have

an LQ greater than 1; an industry in

which the region does not specialize

will have an LQ less than 1.

If

a(i,j,r) is the proportion of the

total output of the regional industry j that is accounted for by the

purchases of inputs from regional

industry i, and

a(i ,j,n) is the national directrequirements coefficient,

the relationship is:

a(i,j,r) = LQ'(i) * a(i,j,n) (2)

where: LQ'(i) is LQ(i) if LQ(i) is less

than 1 or 1 if LQ(i) is greater

than 1.

This latter condition reflects tile fact

that the supplying industry will

certainly not supply more than the

demanding industry requires, even if the

supplying industry is substantiai.

The household row is derived from the

national row by assuming that the value

added/gross output ratios from the

national model hold for all regions.

The household column is derived from the

46
national vector. The national vector is

scaled down by multiplication first by

(l-T(r)), where T(r) Ls the average

regional tax rate, and then by C(r),

where C(r) Ls the national after-tax

consumption rate as measuredby the ratio

of National Personal Consumption

Expenditures to National Disposable

Personal Income. After these

adjustments, each member of the vector

is multiplied by the corresponding

LQ(i,r) as was described previously for

the industrial columns.

Since the regional A matrix has now

been developed, no estimate of regional

demand or gross output is needed since

simply inverting the (I-A) matrLx will

provide the multtpliers. If the A

matrix does not Include the household

sector, the multipliers include the

direct and indirect effects (Type I

multipliers). If the A matrix includes

the household sector, the multLpliers

include all of the direct, the indirect,

and the induced effects (Type II

multipliers).

The RIMS II system also includes some

"shortcut" methods where persons wishing

to develop overall Impact or earnings

multipliers may do so without

undertaking the Inversion of the full

(I-A) matrix.

The REMI Models

The second model to be considered Ls

maintained by Regional Economic Models,

Inc. (REMI). As is the case with RIMS,

the REMI model is based on the latest

available natLona1 Lnput-output model

furnished by the BEA. It, too, relies

on multiplying each of the national

coefficients by a factor in order to

downscale the multLplLer from national

impacts to figures approprLate for the

smaller region.

The REM1 approach, however, Ls

different; it uses a concept termed

"Regional Purchase Coefficients"

(RPC's). The RPC is the proportion of a

used commodity purchased by a using

industry from within its own region.

Unlike the LQ's which are applied to the

Lnverted technical coefficient matrix,

the RPC's are applied to the techn.Lcal

coefficLents directly, aff_er which the

technical coefficient matrix is inverted

in the normal way. In general, the data

required to estimate the RPC's directly

are not avaClable, therefore they are

estimated by REMI from a regression

equa~.lon.

The basic idea behind the regressLon

estimation is that regional purchases

should be a function of relative

delivered costs where delivered costs

are the sum of production and

transportion costs. RelatLve production

costs should be a function of relative

wages, relative other costs, and a

relative scale of production and

transportation costs, which is a

function of relative average shipment

distances for local versus nonlocal

purchases. Average shipment distance 1s

posited as being a function of the

proportion of shLpper-to-users Ln the

region to shippers-to-users Ln the

nation, and the proportlon of land area

In the region to land area in the

nation.

Using this theoretical structure, a

regression relation was developed for

estimating the log of the RPC for each

of 19 industry groups as a linear

function of the ratLo of industry perworker wages in the region to the

natlon, the ratio of industry employment

in the region to the nation, the ratio

of Industry national output tonnage to

industry total wages, the location

coefficient LQ, (as defLned for the RIMS

model), and the ratio of the land area

of the local area to the land area of

the nation.

SLnce the independent variables, the

RPC's themselves, are not dLrectly

measureable, it was also necessary to

infer values for some of these in order

to estimate the coeffictents of the

model. This was accomplished by REMI

through knowledge of the output of each

commodLty in the local region.

RPC(i,r) = P(i,r) * Q(L,r)

D(i,r) (3)

vJhere: Q(i,r) = amount of the commodity

i produced in r,

D(i,r) = total use of i in r,

and

P(i,r) = proportion of L produced and used in r

The Q's can be measured; the D's are

obtained by applying the technical

coefficients to the Q's and then adding

other use as by households, governments,

capital expenditures, foreign exports,

etc. The limiting factors were the P's.

293 P's for 19 commodities could be

calculated from the information in the

Census of Transportation. Thus 293 P's

were measured to calculated 293

observatLons of the RPC's. An equation

to estimate the RPC for any region based

on relative wages, relative employment,

the LQ, the weight to wage-bill ratio,

and relative land area was calclllated.

In addition, 6 of the 19 comodities have

non-zero dummy varLable weights thaL are

utilized. This equation is then used to

calculate the RPC for any region for any

oE the 500 commoditLes in the Eull model

(for Imore detail, see Stevens and

others, 1980).

47
_-

The IMPLAN Models

The third set of models to be

considered, called IMPLAN, were directed

and funded by the USDA Forest Service

and produced by Engineering Economics

AssocLates, Inc. Like the other models,

this set also depends on a national

model. As part of the effort, however,

the Forest Service had the 1972 national

model updated to 1977 for this project.

The Forest service is continuing this

effort and since the 1977 National

Input-Output model was released by the

BEA in 1984, the 1977 model is beLng

updated to 1982 in the same manner.

IMPLAN Ls different from other models,

however, because the Forest Service

wished to have a model for every U.S.

county (or parrlsh) which would

aggregate into state models which, in

turn, would aggregate into the original

U.S. model. Hence this system produces

a flow or a transactions table for each

county (or aggregation of counties)

whLch Ls then converted into a

transaction matrix and then inverted to

form the multipliers. As with the other

systems, both direct-and-indirect (Type

I) and direct-and-indirect-and-Lnduced

(Type II) multtpliers may be produced.

With this goal, the task of

EnglneerLng Economic AssocLates was to

find justifiable proxies by which to

break down the components of demand to

estimate final demand vectors for:

Personal consumption expenditures

Gross private domestic investment

Foreign exports

Inventory change

Federal government expenditures

State & local government

expenditures

In addition the following other elements

must be estLmated for each sector for

each county:

Gross domestic output

Employment

Components of value added

Employee compensation

Property type Lncome

Indirect buslnes taxes

The task Ls complicated by the

at the state and county level,

Eact that.

most OE

the economic data sets provided by

government agencies are character!zed by

havLng certain elements deleted. This

is due to the legal restrictions on the

release of data gathered by government

agencies in surveys of indlvldual firms.

Therefore, techniques that generate

proxy series were employed. These

proxies could be halanced to known

totals; for example, for the sum of all

the states at the natLona1 level, and

for the sum of all the countLes in a

state.

An advantage of this process is that

the flow table is generated for the

local area. It may be inspected and

altered, if desired, before processing

into technical and inverse form. A

model for any multlcounty area (standard

metropolitan statistical area - SMSA,

BEA region, other aggregation) may be

constructed simply by aggregating the

county data before applying it to the

national coefficient matrix. (Further

information can be obtained from

Engineering EconomLc Associates, Inc.,

1700 Solano Avenue, Berkley, CA 94701.)

ASSESSING THE ECONOMIC IMPACTS

All of these models are, Ln theory,

equipped to assess the economic impacts

of tourism and recreation. In practice,

however, each model was designed with

different goals in mind so that the

appropriateness of a model ~111, in

part, depend on how well the model can

meet the various demands placed on it by

the spec1fLc problem or user. Wh-lle the

criteria for evaluating a model will be

shaped by the particular problem that is

under scrutiny, there are five issues

that should he considered in any

applrcation of a regional Input-output

system: (1) the level of regional

disaggregatlon, (2) the level of

sectoral disaggregation, (3) the

definitional basis of the sectors

(commodity versus industry

classifications), (4) the relation of

the direct requLrements matrix to the

region(s) under scrutiny, and (5) the

measurement of final demand. One should

note that these five issues arise quite

naturally out of the modeling process

and therefore cannot he avoided.

Conseqrlently, the following discussion

should not he construed as a criticism

,,f any particular model or technique but

only as an aid in the evaluation of a

model's suitability In the use of

measuring economic Impacts.

To relate and clarify the Lssues and

to give a review of the basic inputout.put relationships, take the following

hypothetical sltuatlon. A family from

Windsor, Ontario, t.akes a week's

vacation in the Detroit metropolitan

area. They drive their car through the

Detroit-Wtndsor tunnel and stay in a

hotel in downtown Detroit. Each day

they drive around the area visiting

landmarks and parks. TheLr budget of

$2,000 (American) Ls spent on lodging,

gasoline, parking fees, admi.ssion fees,

boa: rentals, and food (purchased either

at restallrants or at grocery stores). A

nat,lr.11 quest Len to ask is "What is this

48
family's economic impact on the Detroit

metropolitan area? What will the

changes be in total output and where?"

In theory, an input-output model can

trace the effects of this family's

expenditures and their repercussions

throughout the Detroit area's economy by

employing a basic input-output identity.

Within an input-output table or model,

the total dollar sales (or output) for

each sector must equal the sales to all

sectors (including itself) for use as

inputs into their production process

(intermediate use) and sales to all

final consumers (final demand). Using

algebraic notation, this basic

definition is written as:

q=Aq +f (4)

where: q = a vector of sectoral output

in dollars,

A = a matrix relating the input

requirements per dollar of

output, and

f = a vector of sectoral final

demand in dollars.

Combining similar terms yields a

solution imbedded in all input-output

models:

q = (I-A)-1 (5)

where: I = the identity matrix.

The importance of equatton (5) is not

the mathematics but that it shows, in

theory, that only final demands and the

direct requirements (A) matrix are

needed to measure total productton.

(See Mierynk, 1965 or Richardson, 1972

for a more detailed explanatlon.) In

practice, equation (5) shows that if

final demands are measured correctly,

and Lf the direct requirements matrix

accurately portrays the interrelationships within the economy, and if the

matrix corresponds to the deflnitfons

and conventions used in measuring the

final demands, then total production can

be measured.

These conditions may seem to be quite

obvious and harmless because each one of

the input-output models discussed in the

fLrst section do give answers to many

types of questions similar in nature to

the example. Yet, the reliabL1Lty and

accuracy of those answers will depend on

how well the chosen model adapts to the

five generic issues. The first issue

(regional disaggregation) provides a

straightforward example of the problem.

Regional Dlsaggregation ---

Ideally, one would hope to have the

most disaggregated model possihle in

order to minimize any errors due to

aggregation problems. However, many

practical considerations conspire to

restrain the manageable level of

disaggregation. Regardless of those

considerations, the model to be chosen

should, as closely as possible, resemble

in its level of regional disaggregation

the requirements of the problem to be

examined. For this example the model

that has, among other features, inputoutput relationships for the city of

Detroit (or at least Wayne and Oakland

Counties) should give the most reliable

measurement of the economic impact. Any

model that has the State of Michigan

as its lowest level of disaggregation

should be avoided in this case since it

will require substantial adjustment in

order to yield reasonable estimates of

the economic impact on the Detroit area.

Sectoral Disaggregation

This issue is very similar to that of

regLona1 disaggregation. Given the

spending pattern of the hypothetical

tourist family, the ideal model should

have among its different sectors Hotel -

Standard Industrial Code (SIC) 7011,

Retail gasoline (SIC 5541), Parking lots

(SIC 7523), Restaurants (SIC 58), and

Grocery stores (SIC 5411).

In practice, the retail trade sector

(any SIC of 5000-5800) presents two

special problems. First, although there

is a wide diversity Ln the types of

retail establishments, most models have

only a few retail trade sectors (mainly

due to data restrictions). This

aggregation may impose some measurement

bias with the extent of the bias

depending on how differently the various

types of retail stores use their various

inputs. Second, within the framework of

input-output analysis, a retaL store

does not produce any commodLties but

only a service by acting as a conduit

between the actual prodicers and final

consumers. Consequently, any commodity

purchased at a retail establishment

should be "stripped" of the "service"

component and counted in the commodity's

production sector. For our example, if

the Canadian family purchased a

hamburger from a restaurant, then the

value of the restaurant service would be

subtracted from the dollar value of the

hamburger and then the final demands of

Meat (SIC 2010) would be increased (see

the discussion on the measurement of

fLna1 demand).

Definitional Basis - -

The third issue is the commodity

versus industry definition of a sector.

Input-output models can use either a

commodity definition, whLch groups

products or services with similar SIC

49
codes into a sector, or an industry

definition, which groups establishments

into a sector on the basis of similar

primary product. Establishments can

produce more than one commodity, but

only one commodity can be the

establishment's primary product

(typically determined by the product or

service which has the largest dollar

volume). A common example of the

multicommodity establishment is the

local Sears store. This store may be

offering, under one roof, Auto repair

services (SIC 7500), Appliance repair

services (SIC 7600), Optometrist's

services (SIC 8042), Upholstery cleanLng

(SIC 7217), Real Estate brokering (SIC

6531 and 6610), Insurance brokering (SIC

6400), and Security brokering (SIC 6200)

along with its traditional retail

operatlons. If the largest dollar

volume of sales Is in auto repair, then

this partLcular establishment would be

counted under the auto repair industry

instead of the department store (SIC

5800) industry. (See ten Raa and

others, 1984, for a discussion of

secondary products in a broader

context.)

As a result, a user should be aware of

the consequences of misapplying the

sectoral definitions. If the user

wishes to estimate the economic impact

caused by the change in demand for a

commodity but is using a model with an

industry definition of a sector, the

estimate could be inflated if that

industry has inputs that are used in the

productLon of other dissimLlar

commodities. From our example, if the

Canadian visitors purchased cheese from

a grocery store and one placed that

cheese purchase in the cheese industry

final demand sector, then one will find

an increase in the use of milk, enzymes,

and sugar since many cheese

establishments also produce ice cream.

Fortunately, the errors stemming from

this definitLona1 problem are likely to

be small when estimating the economic

impacts of tourism and recreation. The

multiproduct establishment Ls most.

commonly found in the manufacturing

sectors while the service sectors (with

the exception of department stores) tend

to provide a single service. Because

%he tourism and recreation industry is

largely composed of the service sectors

(or at least in most polLcy questions

f.his is true), this problem may not

arise. Iq addition, most of the models

h.ave a "make" table (which shows the

distribution of commodities that. each

industry makes for the nation) avaylahle

1.0 transform data from one definition to

another. Still, one is bet_ter off being

aware of the pot_ential complicat?ons In

order to assess possible errors in the

estimates.

Direct Requirements (A) Matrix

The fourth issue revolves around the

applicability of a model's direct

requirements matrix to the regional area

under question. Every model in this

paper uses the direct requirements

matrix for the national economy as a

starting point (see U.S. Department of

Commerce, 1984 for the latest update).

This matrix based on national averages

is then imposed on a region and, in

effect, split into a local matrix (in

order to capture local impacts) and an

"import" matrix, but the overall

requirements always equal the control

imposed by the national average.

Consequently, the regional input m-lx

(regardless of the Lnput's geographical

origin) for a dollar's worth of a

sector's output is assumed to equal the

national average for that sector.

The assumption of identLca1 input

requiremens among different regions may

not be completely desirable, but it is

certainly not unreasonable In the

absence of any additional information.

Yet, this assumption results in some

mismeasurements, with the extent

depending on how much the regional use

differs from the national average. (One

suspects that as the region increases in

size this difference grows smaller.)

For example, the electric utility sector

in the natLona1 matrix combines nuclear

power plants, dams, and coal-fired

plants, but a region (especially a

county or group of counties) will use

electricity from only one type of plant.

Thus the use of a national average may

misrepresent the economic impact. One

may argue that in the tourism and

recreation industry, which is dominated

by services, this effect can be

neglected because servLce sectors

generally use the same technologies.

This is open to question, however,

because some regions wLth relatively

high labor costs may substitute capital

equipment for labor, which should change

the overall input requirements for those

sectors.

If a user has additional Lnformation

about the structure of a region's

economy, then the estimates of an

economic impact could be improved if

that information could be incorporated

into the model. Consequently, another

feature of a prospective model to keep

in mind is that model's capability of

!ncorporat_ing any additional informatlon

about the target. region's economy. No t

only should the capability for Lncorporation of new data be present but the

process should he relatively easy.

50
Measurement of Final Demand

In order for an input-output model to

estimate economic impacts, the

categories of final demand (f vector in

equation (2)) should correspond as

closely to the sectoral definitions as

those of the direct requirements matrix

to ensure a more accurate measurement of

the economic impact. In general, the

closer the correspondence of the two,

the more reliable is the final measure.

As mentioned, a slight technical

problem occurs with purchases from

retail establishments. The most

desirable outcome is to have a large

amount of detailed information

concerning these purchases. Prom the

Canadian visitor example this would mean

that, ideally, one would have an

accounting for each meal by item --

Monday's breakfast was two eggs, three

bowls of corn flakes, etc., and each

retail purchase by item. However, that

detail is not always available. If it

is not, then the model or modeler should

have some well-defined method to "breakup" these types of purchases.

SUMMARY

The discussion has focused on a few

potential pitfalls or issues that should

be addressed by any researcher using

regional input-output models. Becoming

aware of the issues allows the user to

more carefully assess the suitability of

a particular model to the demands of the

analysis. However, these are simply

guidelines and cannot help unless the

problem to be analyzed has been clearly

stated in terms that an input-output

model can handle. There can be no

substitute for careful consideration on

the part of the user in structuring the

research problem. Part of that careful

consideration should include the

limitations and strengths of the user's

particular model, not only in light of

these few guidelines but of the entire

structure of the model.

LITERATURE CITED

Cartwright, Joseph V.; Beemiller,

Richard M.; Gustely, Richard D.

Regional input-output modelling system.

Washington, DC: U.S. Department of

Commerce, Bureau of Economic Analysis,

1981. 139 pages.

Mierynk, William H. Elements of inputoutput economics. New York: Random

House, 1965. 157 pages.

Richardson, Harry W. Input-output and

regional economics. New York: John

Wiley h Sons, 1972. 294 pages.

Stevens, Benajmin H.; Erlich, David;

Bower, James. Estimation of regional

purchase coefficients and their use in

the construction of non-survey inputoutput impact models. Reg. Sci. Res.

Inst. Discuss. Pap. 119. Amherst, MA:

Regional Science Research Institute,

1980. 49 pages.

ten Raa, Thijs; Chakraboly, Debesh;

Small, Anthony. An alternative

treatment of secondary products in

input-output analysis. Review of

Economics and Statistics 66(1):88-97,

1984.

U.S. Department of Commerce. The inputoutput structure of the U.S. economy.

Survey of Current Business 64(5):42-84,

1984.

51
Assessing the Secondary Economic Impacts of

Recreation and Tourism: Work Team

Recommendations

Dennis B. Propst, Dimitris G. Gavrilis,

H. Ken Cordell, and William J. Hansen’

The U.S. Forest Service and U.S. Army Corps of

Engineers are major providers of leisure

opportunities. The approximately 91 million

acres of public land in the National Forest

System include more than 25 million acres of

Wilderness, 640 miles of Wild and Scenic Rivers,

and 24,000 developed and dispersed recreation

sites. Similar figures for the Corps of

engineers include 11 million acres of land and

water, 442 lakes and other project areas, and

over 3,800 recreation areas. The amount of

public consumption generated by this enormous

federal supply of recreation opportunities is

substantial: Forest Service--233 million

visitor days (fiscal year 1983); Corps of

Engineers--469 million recreation days of annual

use.

Although the Forest Service and the Corps are

Integral parts of the leisure industry, little

is known about the secondary impacts of the

federal supply on community, regional, and

national economic development. To clarify that

statement, it is necessary to distinguish

between primary and secondary impacts that

result from federal investment in providing

leisure opportunities.

Primary or direct impacts include benefits to

recreation users and costs to the providers.

These are the measures needed to derive

benefit-cost ratios, which guide investment

decisions. A great deal of research since the

mid-60’s has been directed toward determining

the direct benefits of recreation developments.

Travel cost and contingent valuation methods are

the two most widely used and recommended

procedures (Dwyer et al ., 1977; Walsh, 1984).

A similar effort has not been aimed at

developing concepts and procedures for examining

secondary or indirect economic impacts of

supplying recreational services and facilities.

Secondary impacts include benefits and costs

beyond those to users and providers. Secondary

impacts accrue to communlttes, regions, and the

nation in the form of income, employment, retail

sales, taxes, and development of related

1 Assistant Professor and Graduate Assistant,

Department of Park and Recreatfon Resources,

Michigan State University, E. Lansing, MI 48824:

Project Leader, Southeastern For. Exp. Stn.,

U.S.D.A., Forest Service, Athens, GA 30602; and

Economist, Waterways Exp. Stn., U.S. Army

Engineers, Vicksburg, MS 39180.

industries (recreational equipment, information,

service, and development industries, such as

second homes, condominiums, and resorts).

Agencies like the Forest Service and Corps of

Engineers require information on secondany

economic impacts to make financial allocation

decisions. In addition, demonstration of the

important role that such agencies play in local,

regional, and national economic development

would likely provide more impetus for private

and nonfederal provision of recreation

opportunities on or near Corps projects and

National Forests.

STATEMENT OF THE PROBLEM

A system for deriving estimates of the

secondary economic impacts of recreation is

currently lacking, primarily because available

methods are costly. Ideally, the researcher

would want to generate a unique multiplier for

each economic sector in which first-round

recreation spending occurs. The methodology to

derive unique multipliers exists, but the large

data requirements make this procedure expensive

and time-consuming (Marino and Chappelle, 1978;

Leistritz and Murdock, 1981). The alternative

to collecting a large amount of data over time

is to use input/output (I/O) models developed by

government agencies and assume that their

multipliers are accurate. However, existing I/O

models generaJly are not based on sufficiently

detailed breakdowns of the sectors impacted by

recreation and tourism (e.g., marinas,

recreational equipment). Thus, the reliability

of such multipliers is unknown (Gartner and

Holecek, 1982; Stynes and Holecek, 1982). To

restate the problem, the secondary impact

assessment process for other U.S. industries

(e.g., manufacturing) is reasonably clear and

well-developed, but (a) the appropriate economic

impact assessment procedures for recreation are

unclear, and (b) the necessary data for

conducting such assessments are often missing.

OBJECTIVES

To help solve the problem stated above, the

following objectives were pursued:

1. To evaluate the state of the art in

determining the secondary economic impacts of

federal recreation facilities and services at

local, regional , and national level;

2. To prepare a detailed economic impact

assessment procedure and data collection

methodology to be employed by the Forest Service

and Corps of Engineers in determining the

impacts stated in Objective 1.

SCOPE

A range of methods was needed to achieve these

objectives. Computerized literature searches,

personal communications, and library research

were the primary means of achievfng the first

objective. Objective 2 was achieved through

contacts with key government agency, university,

and industry professionals. These contacts were

52
necessary in order to synthesize a tremendous

amount of information into recommendations for

the appropriate variables, models, procedures,

data sources, and economic sectors to be

employed in the economic impact assessment of

recreation. Some of these contacts were made by

telephone and letters. A majority of the

information obtained for achieving Objective 2,

however, emanated from a technical meeting on

assessing the secondary economic impacts of

recreation and tourism held at Michigan State

University in May of 1984. The goal of this

meeting was to bring together a few key

professionals to identify the best available

technology and data for economic impact

assessment of recreation and tourism. This

paper reports the methodology employed during

the meeting to meet study objectives as well as

the results of the meeting. The meeting’s

methodology was highly successful in transfering

technology and in identifying important

considerations for economic assessment of

recreation. The full report (Propst and

Gavrilia, 1984) contains the results of all

methods used to satisfy the two study

objectives.

PROCEDURES

The technical meeting on assessing the

secondary economic impacts of recreation

included both presentations by invited speakers

and a workshop. A series of steps was followed

inorder to select the invited speakers. During

the fall of 1983, a master list of regional

economics professionals was compiled through

personal communications with resource and

agricultural economics faculty at numerous U.S.

universities. At the same time, Forest Service

and Corps of Engineers researchers, planners,

and administrators, were contacted to compile a

list of issues that both agencies wanted to

resolve. Potential speakers were sent a letter

explaining the purpose of the meeting, listing

the identified issues, and seeking their

interest. In addition, potential speakers were

asked to indicate from the list of issues the

top three on which they would be willing to

prepare a presentation. Potential speakers were

told that the presentations of the invited

speakers would be published and all travel

expenses paid. A list of invited speakers

emerged from this initial wave of correspondence

(see Appendix A). The issues covered in papers

and formal presentations by the eight invited

speakers are listed in Table 1. The formal

presentations took approximately 1 day and

provided the necessar

workshop portion of

background for-the

The workshop portion of the meeting lasted 2

days. During this time, participating Forest

Service and Corps of Engineers researchers with

economics backgrounds became actively involved

in discussions. These participants are also

listed in Appendix A. For 2 days, all meeting

participants were divided into work teams of

four to five members and asked to complete t’he

tasks stated in Table 2. These tasks were

written to be more specific reiterations of the

Table l.-- General issues covered in formal

presentations during the “Technical Meeting on

Assessing Secondary Economic Impacts of

Recreation and Tourism,” Michigan State

University, 14-16 May, 1984.

1. What is the state of the art in developing

multiplier for assessing the secondary economic

impacts of recreation and tourism?

2. What methods besides I/O analysis are

available for assessing the secondary economic

impacts of recreation and tourism?

3. How should regions be defined and sectors

disaggregated in existing I/O models to account

for the secondary economic impacts of recreation

and tourism?

4. What are the data requirements and

appropriate sources of data for assessing the

secondary economic impacts of recreation and

tourism?

5. What are the strengths and weaknesses of

using I/O analysis to assess the secondary

economic impacts of recreation and tourism?

6. What computerized models for assessing the

secondary economic impacts of recreation and

tourism are currently available and what are

their strengths and weaknesses?

7 . What are the data requirements and sources

of data for measuring the economic impacts of

the Forest Service and the Corps of Engineers

supply on leisure/tourism fidustries, such as

recreational equipment, second homes,

recreational vehicles, and boatinq?

issues covered the previous day by the invited

speakers. Work teams were arranged so as to

contain both university and agency

representation. All teams worked on the same

tasks, one at a time, after being given the

following instructions:

“There is a specific environment or mood we

would like to create in each group in order to

be most efficient in satisfying meeting

objectives. This mood can result if you keep

the following points in mind:

a.

b .

C .

we have a series of specific problems to

sol ve ;

all of you have ideas for how to solve

these problems;

the goal for the work teams is not

necessarily to reacn a consensus but to

discover new ways to solve these

problems; how can this be done?

carefully listen to what others have to

say:

feel free to respond in an open,

spontaneous way (the aim is to have an

exciting exchange of ideas;

discuss ideas, do not debate them

because we want to encourage divergent

53
points of view and keep ideas flowing,

not cut them off;

d. the purpose of the work teams is not

necessarily to change anyone’s mind;

e. everyone has useful ideas and

information--we’re here to combine all

this into new ideas.

Thus, the environment just described is a

problem solving mode. In this mode, consensus

is not necessary. I will converge all of your

ideas and recommendations after you leave

here. Then, I will mail what I converge from

your recommendations to you for comment. This

divergence and convergence of ideas will

become part of the proceedings.

So, what you do in these work teams is not

the end but only the beginning of the work

that needs to be done in the economic impact

assessment of recreation and tourism.”

After they discussed a task, work teams were

given 1 to 1 l/2 hours to develop solutions.

GrOUQS were asked to designate someone to jot

down their recommendations on large sheets of

paper and someone to be the spokesperson. At

the end of the allotted time, the spokesperson

of each team (four teams in all) took 5 to 10

minutes to present the team’s recommendations to

the entire audience. A question and discussion

period followed each team’s presentation of the

six task solutions. The large sheets of paper

containing the recommendations remained posted

around the room for the duration of the meeting.

The same process was followed for each of the

six tasks, but the composition of the teams was

changed after each task to give each participant

the opportunity to interact with all others.

All work team recommendations and discussions

were taped. Synthesis of the material contained

in the tapes, teams notes (from the large sheets

of paper) and reviews of this synthesis by

meeting participants provide the results that

follow.

The method utilized in bringing together a

grOUQ of professionals and eliciting solutions

to specific problems was a creative problem

solving QrOCeSS fashioned after Noller et al.

(1976, 1981) and Hare (1982).

RESULTS

Table 2 contains full descriptions of the six

tasks the work teams were asked to complete.

The tasks are restated in abbreviated form in

this section along with specific recommendations

of the work teams for accomplishing the tasks.

Task 1: Short Cut Methods

Work teams were asked to describe “short cut”

methods (methods other than I/O analysis) which

the Forest Service and Corps of Engineers could

use to obtain reasonable estimates of the

economic impacts of recreation. The four work

Table 2.-- Tasks completed by work teams in the

“Technical Meeting on Assessing Secondary

Economic Impacts of Recreation and Tourism,”

Michigan State University, 14-16 May, 1984.

1. Describe and provide reference to other

methods besides I/O analysis for assessing the

economic impacts of recreation and tourism. Are

there one or two “quick and dirty” methods that

Forest Service and Corps personnel could use to

obtain a fairly reasonable estimate of such

impacts?

2. Recommend appropriate ways for the Forest

Service and Corps of Engineers to define regions

and disaggregate sectors in I/O models to

account for economic jmpacts of recreation and

tourism. That is, list the sectors impacted and

recommend ways to separate them from commonly

used sectors. Also, describe the problems

associated with measuring these impacts at the

local vs. state vs. regional vs. national

levels. Recommend areas for future research.

3. Indicate the means (research, funding,

administrative changes, etc.) by which the

Forest Service economic impact model, IMPLAN,

may be modified to account for recreation and

tourism impacts. Describe the cost/accuracy

trade-offs of making such modifications.

Recommend other I/O models that may be modified

in this fashion.

4. Provide a list of variables that should be

assessed and questions that should be added to

nationwide federal estate recreation surveys

(mailback and personal on-site interviews)

relative to economic impacts. The goal here is

to create consistency in data collection and

analyses that federal agencies routinely perform

to evaluate the economic impacts of recreation

and tourism.

5. Describe the role of the prfvate sector in

providing data tha would satisfy the goals of

the Forest Service and Corps of Engineers in

determining the economic impacts of recreation

and tourism. To perform this task, you should

answer the following questions: Are data from

the private sector necessary? What types of

data? What strategies should be followed to

obtain such data given that some of it is

proprietary in nature?

6. Articulate the changes that need to be made

to the data collection and analysis procedures

of the Bureau of Economic Analysis to account

for the economic impacts of recreation and

tourjsm. Also, indicate the pros and cons of a

“Census of Tourism ,” similar to the Census of

Manufacturing or Agriculture.

54
teams described six such methods: response

coefficients, minimum requirements, use of

existing multipliers, brainstorming, Delphi

process, and similar sites.

Response Coefficienct Method

Response coefficients (RC’s) are the same as

traditional multipliers except that RC’s are

reported in different units of analysis to ease

interpretation (personal communications with

Adam Rose, West Virginia University, 1984). For

example , a new industry in a region may generate

500 new jobs in direct employment in that

industry. An I/O analysis may reveal an

employment multiplier of ‘2 indicating that the

total employment impact (direct, indirect and

induced) of the new industry is 1,000 new jobs

(500 x 2). Response coefficients merely

transform the 1,000 jobs figure into number of

jobs per $ expended on an activity. Thus, if

1,000 new jobs were generated and $1 million of

goods were produced, the RC would be 1,000

jobs/$ million or 1 job/$l,OOO output. Nothing

has been done to the multiplier; the only change

occurs in the manner in which the employment

impact is reported. The RC transformation may be

applied to Type I, II, or III multipliers.

The big advantage of RC’s over multipliers is

relative ease of interpretation. Since

multiplier are, in effect, partial derivatives,

they are sometimes ambiguous to interpret and

provide the opportunity for misleading

conclusions. This is because it is not

necessarily true that sectors with high

multipliers have the highest impacts in a

region. For example, Rose et al. (1981), using

I/O analysis, derived multipliers to determine

which alternative solar energy technology would

have the greatest employment impact on the City

of Los Angeles. The employment multiplier for

solar energy was much higher than that of

weatherization. However, standardizing

employment impacts by translating them into RC’s

(number of jobs created per million dollars

spent) revealed just the opposite finding: more

jobs created by weatherization than by solar

energy. The authors explain this discrepancy by

noting that traditional employment multipliers

for solar energy are high partially because

solar energy is expensive to produce and thus

requires more production than weatherization.

However, the respending effects of

weatherization generate more employment than the

production effects of solar energy.

A general mathematical expression of how to

cal.culate regional impacts using RC’s is:

Total regional impact = total expenditures X RC

for income or employment; where RC = the

direct, indirect, and induced effect per

amount spent in dollars.

The conventional multiplier is defined as total

effects (direct, indirect, and induced)

throughout an economy divided by direct effects

in a given sector or the proportion by which

total effects exceed direct effects. By

definition, then, a large multiplier may result

because of a small denominator (direct effects).

In other words, the multiplier may represent a

large multiple of a small base. Furthermore,

the relationship between total effects and

direct effects will vary greatly among sectors,

meaning that there is no standard for comparison

of multipliers by sector. The RC is simply the

numerator of the conventional multiplier

equation (total effects). The RC permits a

standard for comparison across sectors, removes

the ambiguity in mu1 tipliers, and maintains the

basic meaning of the multiplier concept.

Archer (1977) also provides evidence for and

formulates the response coefficient concept.

Instead of the term “response coefficient ,”

however, Archer uses “normal multipliers” noting

that multipliers expressed as partial

derivatives are valueless as planning tools

without additional information which relates

endongenous income (or employment) to units of

exogenous spending.

The advantage of RC’s over traditional

multipliers has already been discussed. There

are also two major drawbacks to the RC method:

(a) total expenditures must be collected as

primary data or taken from secondary sources,

and (b) RC’s must be calculated by a central

research unit with access to a computer and I/O

model. Both drawbacks also apply to I/O

analysis in general. Overall, the RC method is

not a separate impact assessment procedure at

all, but a useful way of reporting impacts

derived by traditional procedures. In light of

its ability to avoid misleading results, the

calculation of RC’s may be worth the minimal

extra effort required.

Minimum Requirements Method

Under this method, the analyst determines the

minimum level of all services (not iust

recreation) for prototypical areas or counties

needed to sustain a local economv~ (resident

population). That is, the analyst determines

the economic base of an area. Any economic

activity above this minimum level would be

attributed to basic income (e.g., expenditures

by nonresidents). In this manner, an economic

base multiplier may be established (see

Leistritz and Murdock, 1981; Bendavid-Val, 1983;

and Propst and Gavrilis, 1984 for further

discussions of the derivation of economic base

multipliers).

A variant of this approach would involve a two

step procedure. First, plot certain economic

indicators such as income or sales tax over

time. Second, compare the sales tax collected

in a month (March, say) when tourism is low with

those in a month during the peak tourist season.

Multiplier “Given” Method

In this method, expenditures by recreationists

must be determined, but previously computed

multipliers are accepted. This method is best

explained by two relationships:

1. Total Area Economic Impact = Mu1 tiplier

(given) X Total Direct Expenditures;

55
2. Total Direct Expenditures = Expenditures per

Recreation Visitor Day (EXP/RVD) X Total

Recreation Visitor Days (RVD) per

Activity.

Measures of participation other than RVD’s may

be equally valid. However, a method for

separating resident and nonresident expenditures

per RVD must be devised because nonresident

purchases represnet new money to the region

while resident purchases do not. If necessary,

the variable, EXP/RVD, may later be adjust by

using participation and supply (quantity and

quality of facilities) as independent variables

in regression analyses.

One word of caution is needed here. If not

already available (usually the case),

expenditure data mist be collected directly from

recreationists. This task is not for the

unskilled or the feint of heart. Thus, in many

cases, the mu1 tiplier “given” method may only

give the appearance of being a short cut

procedure. Yet, the point is well taken that

instead of spending a great deal of effort on

developing new mu1 tipliers, the planner should

be gathering quality expenditure data and

enumerating the costs and direct benefits of

future developments.

“Brainstorming”

In this procedure, experts, user groups (i.e.,

the recreationists or tourists themselves), and

business leaders are assembled and asked to

estimate participation, spending, and leakages.

We do not mean to imply, however, that all of

these groups should be assembled at one place at

one time. This may not be feasible. Rather,

these groups (and individuaJs in some cases) may

have to be contacted at their convenience over

an extended period.

Del.phi Process

The Delphi technique is a means of creting a

consensus of opinion concerning future likely

events from the insights of experts rather than

from a theoretical body of knowledge. Moeller

and Schafer (1983) fully describe the Delphi

technique, the steps in carrying out the

technique, and the applications in recreation.

In terms of economic impacts, the Delphi

process would involve having a group of experts

predict the multiplier effects of current or

future tourism and recreation developments in an

area. Moeller and Schafer state that the Delphi

technique can provide general estimates where no

other techniques are available or appropriate.

However, they warn that the process may require

more effort (time and money) than the analyst

might initially expect. Thus, it may not be a

short cut method in all cases.

Similar Sites

When economic impacts must be computed for a

certain site. it would be extremely useful to

know the results of computations for simil,ar

sites elsewhere. If another site were

sufficiently similar, little or no additional

computation might be required. Unfortunately,

few high quality analyses have been completed at

present, but as experience is gained and

analyses are completed, it is recommended that

they be catalogued for future use. This catalog

would contain surrogate multipliers and spending

profiles with full descriptions of the site

conditions. The development of such a catalog

would be a major undertaking, but once done, it

would make future analyses quite easy.

Task 2: Defining Regions and Disaggregating

Sectors

The teams of meeting participants were next

asked to make recommendations concerning how to

define regions and disaggregate sectors in I/O

models to account for the economic impacts of

recreation and tourism.

Defining Regions

All teams felt that, initially, the region of

interest should be the unit of the decision

maker (members of Congress, governors, state

legislators, etc.) or determined by the specific

problem being addressed. After these initial

considerations, subreeions should be defined as

spatial economic units (counties, SMSA’s, BEA

units, etc.) according to the following general

scheme of increasing regional size:

1. Individual sites--physical attributes (lake,

forest, etc.) where recreational activities and

direct economic impacts occur.

2. Recreation focal area (trade area)--one or

more counties (SMSA’s, etc.) surrounding the

site or facility development which may be

considered a “local” impact zone; likely to be

the source of most direct recreation employment.

3. Travel corridors--from the consumer

residence area to the site and define location

of impacts along the travel route.

4. Substate or multistate regions--portions of

several states or large group of counties

surrounding the site where both direct and

indirect impacts occur; may also be defined as

the site’s market area by inspection of

of visitation data.

5. Consumer residence areas--origins of the

recreationists.

6. Extended region-- national in scope; the

source of all goods imported into any of the

above 5 regions; capital input to recreation at

a given site likely to extend over the entire

nation.

Once the market area (no. 4 above) is

established, the internal boundaries may be

delineated by further analysis of population and

visitation data. This hierarchy of regions is

not intended to result in concentric circles

around individual sites.

56
Maki (1985) and Stevens and Rose (1985)

describe these regions and define their data

requirements in more detail. In general, any

attempts at regional delineation and aggregation

must consider the additivity problem. That is,

there are sometimes differences between impacts

derived from summing over numerous small areas

versus an overall large area impact (the whole

may not be the sum of its parts). This is

primarily a methodological problem which may be

overcome by clearly defining export expenditures

as being outside the region of which the

counties surrounding the site are a part.

Sector Disaggregation

To discuss this topic properly, an important

distinction must be made between intermediate

and final purchases. Final purchases are the

sectors in which consumer expenditures occur

(e.g., tourist spending on food and beverages,

angler purchase of fishing bait). In the

accounting system of an I/O table, final

purchases are enumerated. in the final demand

vector. Intermediate purchases occur when firms

within sectors that produce recreation goods and

services buy from or sell to each other (e.g.,

canoe manufacturer’s sales to a canoe livery).

Intermediate purchases are represented in the

interindustry matrix of an I/O table.

Intermediate purchases.--For intermediate

purchases, meeting participants agreed that the

existing level of aggregation in RIMS, the

national 500-sector I/O model, was appropriate

for all but the retail, wholesale, and service

sectors. For example , there is already a

detailed breakdown of manufacturing at the

4-digit SIC level. Since capital expenditures

for recreation or tourism go into manufacturing,

sufficient disaggregation exists. Such is not

the case for the retail, wholesale, and service

sectors. For example , marinas do not have a

separate 4-digit code and are completely

dominated and subsumed by the boating dealers

sector. Certain manufacturing sectors have

their problems as well. This is especially true

for boat building (i.e., small boats) which is

hidden within the ship building sector. Yet,

boat building and marinas are important elements

in the recreation/tourism industry and have

different input structures that ship building

and boat dealers per se. An example of the

aggregation problem in the service sector is

commercial amusements. This sector is so highly

aggregated that it contains everything from

bowling alleys to ski lifts. In recreation and

tourism, retailing and services are major

components of the economic activity of many

local areas. Thus, being wrong in these sectors

can create more errors in multiplier development

than would be the case for large metropolitan

areas or other areas with diverse economies.

REIS, the national I/O model developed by the

Regional Science Research Institute (Stevens et

al., 1975) overcomes some of these aggregation

problems by providing 34 wholesale and 40 retail

sectors. The 40 retail sectors include RV’s

(recreation vehicles like motor homes and

camping trailers) and most of the categories

that appear in the Census of Retail Trade.

However, REIS does not solve the aggregation

problems in the service sector.

In light of the above discussion, work

teams recommended that existing I/O categories

be used except for wholesale, retail, and

service sectors. These sectors should be

disaggregated further into 2-digit SIC sectors,

perhaps using REIS as a starting point.

Final Purchases.--One recommendation was to use

the 84 consumer expenditure categories from the

National Income and Product Accounts (NIPA),

differentiating between local and nonlocal

expenditures for each sector for each recreation

activity. These 84 categories would become the

sectors in the final demand vector. Under this

recommendation, the NIPA categories would also

serve as the basis for gross private capital

formation, government expenditures, and exports.

For gross private capital formation

(construction of new facilities), one would need

to distinguish between private,

recreation-related construction and other

construction. For federal, state, and local

government spending, it would be necessary to

differentiate between recreation-related

spending (both construction, and operations and

maintenance activities) and spending for other

purposes. For exports, expenditures of visitors

from outside the region of concern would have to

be separated from the expenditures of other

visitors. The 84 consumer expenditure

categories from NIPA can also be used to

transform direct expenditures into I/O

categories. Such a transformation becomes a

movement from purchaser to producer prices.

There are other ways of transforming one

expenditure system to another. One way is to

survey visitors to obtain expenditure

information and then transform the expenditures

into I/O categories through the use of the

Survey of Current Business “Commodity

Composition of Personal Consumption

Expenditures .” This is the procedure currently

being followed by the Forest Service’s IMPLAN

system.

Both the NIPA and the Survey of Current

Business approaches call for the collection of

expenditure data directly from visitors. An

alternative to primary data collection would be

to pay someone to identify and publish an index

of existing sources of visitor expenditure data.

The point is that there are databases and

publications not widely circulated that contain

expenditure information necessary to estimate

the economic impacts of recreation and tourism.

Assembly of these sources might sometimes

preclude the need to collect primary data and

would be an important contribution.

Nonetheless, noncomparability of many databases

would likely be so troublesome that only general

expenditure profiles could be published in such

an index. In terms of accuracy, primary data

collection holds a strong advantage over

procedures involving secondary data.

5 7
Task 3: Variables to be Assessed

To complete this task, work teams provided a list

of variables that should be measured in nationwide

surveys of the economic impacts of recreation and

tourism. There was general agreement among work

teams on the variables that should be assessed.

Differences were based on ways of categorizing or

organizing the variables. One way to organize the

variables is to divide them into those that may be

asked of an entire sample of visitors and those that

may be asked of a subsample on-site or at home after

a trip:

1. General variables to be assessed of entire

sample (necessary for visitor segmentation

purposes):

--origin and destination

--purpose of trip

--type of accomodations where staying overnight

--length of stay

--mode of transportation

--phone number and address to recontact

(recontacting critical to obtaining accurate

assessment of trip home expenses)

--day trip vs. multiday, single destination trip

vs. multiday, multiple destination trip

--number in party and party composition (family,

friends, etc.)

--equipment type (because, for example, those

with RV’s may have different expenditure patterns

than those in family auto)

--demographics

--some expenditure data according to distance

from site (most useful would be food and beverage,

lodging, fees and charges, gasoline, equipment):

exercise caution with equipment expenditures because

some equipment purchases would be made regardless of

existence of a particular site

2. Specific expenditure data to collect from a

subsample of visitors (collect according to distance

from site); list not intended to be comprehensive

(may opt to use some subset of the 84 NIPA

expenditure categories):

--public accomodations

--eating and dining out

--groceries

--liquor stores

--gasoline and related services

--incidental sporting goods (bait, clothing,

etc.)

--car rental

--boat rental

--equipment rental

--public transportation

--personal services

--professional services

--hospital services

--finance services

--camping fees

--licenses

--outfitters and guides

--marinas

--mdvi es

--amusements

--others

Depending on the objectives of a particular study,

there are other ways of classifying these variables.

For example , agencies may want to measure all the

above variables in a given sample. Subsampling for

detailed expenditure data is meant to minimize

survey cost and respondent burden; its applicability

depends on the goal of the survey. The primary goal

of a nationwide expenditure survey might be to

develop a general model from a sample of visitors at

different sites. In that case, the samples would

too small to determine spending patterns at any

given site. Through a relatively small increase in

effort, the national sample could be segmented by

geographic region and other variables as listed in

(1) above. The national spending patterns could then

be applied to any site in the U.S. given some

knowledge of that site’s visitation characteristics

(numbers of visitors, origin, activities, etc.).

Thus, a fairly large sample to obtain the data

listed in (1) above plus a relatively small

subsample to obtain the detailed expenditure

information listed in (2) would meet the goal of

establishing a nationwide recreation expenditure

database.

The use to which the survey data will be put must

be clearly specified before a methodology or survey

instrument can be properly developed. For example ,

do the potential users want measures of a few key

variables from a large sample in order to reduce

sampling errors or do they want detailed expenditure

data from a relatively small sample? The more detail

that is required of respondents, the greater the

likl ihood of increased sampling error.

Due to time constraints, discussion of

methodological details (i.e., specific wording of

survey i terns, sampling procedures) was superficial .

Nonetheless, an important point for consideration

was that listing the variables should not

necessarily be the first step in collecting quality

expenditure data. Instead, the first steps should

be the specification of goals as stated above and

the development of a data collection methodology.

This methodology will then point out the key

variables to be measured and specific survey items

will follow.

There was a divergence of opinion as to the most

appropriate methodology to employ. Recommendations

included the following:

1. Personal, on-site interviews to increase

accuracy by reducing recall -problems.

2. Pay people to keep an expenditure diary of

their trip as is done in states like Massachusetts.

3. Have respondents keep a log of their

expenditures during all trips for 1 year.

4. Conduct mailback surveys especially for the

purpose of obtaining estimates of trip home

expenses.

Since there was no consensus concerning the most

appropriate method, the suggestion was made to

employ a variety of methods and allow the results so

derived to serve as checks of reliability and

validity.

58
There was consensus on two important points:

1 . Federal agencies, states, and private interests

should work together (pool resources) to develop a

methodology and collect quality expenditure data on

a national basis.

7_. A proper database will attract researchers to

do the needed analyses because quality databases of

this nature are difficult and expensive to obtain.

--_

Task 4: Yodif ying IMPLAN

Work teams were asked to recommend modifications

in the Forest Service economic impact model, IMPLAN,

to account explicitly for recreation and tourism

impacts. The general recommendation was to taiJor

IMPLAN to meet recreation and tourism needs.

Specific ways to perform such tailoring follow.

One recommendation was to encourage the Bureau of

Economic Analysis (BEA) to collect data more

appropriate to recreation and tourism therehy making

the national I/O model more realistic in terms of

this industry. Since IMPLAN is a subset of the

nat iona!. model , necessary improvements in IMPLAN

woul d f 011 ow sui t . The results presented under Task

6 to follow provide further detail on this point.

Another recommendation was to develop or improve

certain intermediate purchase sectors in IMPLAN

relative to recreation and tourism. Tourist

expenditures currently are not well represented in

the sectoring of IMPLAN. The key sectors related to

the forest and grazing industries have already been

identified. The same could be done for recreation

by specifying the appropriate retailing and service

sectors (see also previous discussion under Task 2 -

disaggregation of sectors). Much of this

specification of recreation sectors could he done

immediately. Other tasks, such as placing the

hoating industry in the model correctly, could take

much 1 anger .

In terms of final demand modifications, it is

again necessary to differentiate expenditures

specific to recreation and tourism, including

private capital formation and government

expenditures. In other words, retail trade and

services should he di saggregated in the final demand

sectors. 4s a starting point, this disaggregation

might he based on NIPA categories, which are closer

to consumer spending categories than those currently

in IMPLAN. 4n alternative for disaggregation is to

establish standard tourist expenditure vectors on a

total purchase basis (i.e., for now, do not worry.

about where purchases are made or by whom but

establish standard vectors on a per person per day

basis by activity). The next step would be to

regionalize the tourist vectors. IMPLAN current1 y

allows this without additional work hy using

imp1 ici t regional response coefficients generated hy

the suppl y-demand pool ing approach. Possible

improvements would he to adjust these imp1 ici t

response coefficients by regional experts or by

regression estimates of these coefficients using

additional exploratory variables.

The purpose of disaggregating final demand sectors

is to transform final demand categories into

intermediate purchase categories (usual.ly SIC

codes). The NIPA approach would require respondents

to allocate their purchases into categories that are

already very similar to many I/O sectors but may not

he specific to recreation and tourism. The tourist

vector approach would require respondents to state

how much they spent in various categories specific

to recreation and tourism. This latter approach has

the advantage of couching expenditures in terms

relative to the respondent, not the I/O model. The

analyst would still be required to transform

expenditures, via NIPA or other categories, into I/O

sectors. These transformations could he devel.oped

based on several case studies employing the

procedures recommended in Task 3. Once the

expenditure vectors and transformations are

specified, it would not he necessary to co1 lect new

expenditure data for every situation. Instead, one

could predict spending based on data previously

collected and gather new data only on visitor days

of use by activity. Whichever approach is used, it

will still be necessary to differentiate the region

of impact according to resident versus nonresident

spending (i .e., have separate vectors for residents

and nonresidents.

Once the sectors are disaggregated or specified,

IMPLAN’s output relative to recreation can and

should be simplified. That is, the full model may be

reduced to just those sectors impacted by

recreation. This is especially important for the

IMPLAN user because confusion with irrelevant

sectors is avoided.

One of the most serious gaps in the current

capability of IMPLAN relative to recreation is in

the payments sector. That is, there is nothing now

in IMPLAN to specify the location of employees, or

owners of capital. Overal 1, this problem is related

to the lack of adequate data on income generated

versus income retained in region (i .e., in the

payments sector). This problem is important in

recreation and tourism because of the seasonality of

employment and husiness ownership . For exampl e,

how much do college student employees spend in an

area? How much do they save for, say, tuition spent

elsewhere? This is a critical issue hecause the

induced portion of the income multiplier comes from

income respent in the region. 1Jy overestimating

income retained in a region, the income multiplier

is prohahly biased upward. IJsual methods of

adjusting for income generated versus income

retained in a region (e.g., residence adjustments

from BEA, commutation data from the Census) are

probably inadequate due to the transient seasonal

employees in recreation and tourism sectors.

Task 5: Private Sector Data

This task required the work teams to describe the

potential role of the private sector in providing

data useful in determining the economic impacts of

recreation and tourism. Work teams suggested types,

sources, and means of obtaining such data at the

local (individual firm), state, and national levels.

An initial question raised was whether data from the

private sector were even necessary. The response was

that these data were useful at least as a supplement

and method of cross-checking puhl ic expenditure

data. Also, a closer working relationship with the

59
private sector may reveal more efficient economic

impact assessment methods than are in current usage

in the puhl ic sector.

The types of private sector data most needed in

economic impact assessment of recreation and tourism

are:

1. total sales

2. employment

3. payroll

4. tourist clientele sales as a percentage of total

sal es

5. percent of business purchases locally versus

outside an area

6. tourist expenditures (e.g., in private campground

stores)

7. industry inventories (e.g., when and what are the

sizes of boat inventories?)

In regards to the last data type, the point was

raised that industry inventories are often estimated

in I/O tables, a practice that may lead to

inaccurate resul ts. During downward cycl es In an

industry, inventories can accumulate and cushion the

response of increases in an activity. For exampl e,

an increase in boating production may be misleading

if there is no accounting for inventories. That is,

a 15% increase in hoating activity may result in a

5-10X increase in production because of accumulation

of inventory. The difference may be insignificant

if projections are for a relatively short period of

time (5 years, say).

Important sources of private sector data include:

1 . Industry associations representing RV’s, skiing,

hoats, marinas, sports equipment, lodging, sport

fishing, and so on; most of these possess visitor

and capital expenditure data.

3. The American Recreation Coalition (perhaps as a

lead into the various industry associations), the

Travel. and Tourism Research Association’s National

Data Center, the U.S. Travel Data Center, chambers

of commerce, utilities, transportation agencies,

American Automobile Association -- all may at least

provide some purchaser characteristic data;

3. A new Bureau of Lahor Statistics quarterly survey

will include a section on leisure/recreation

purchases, hut it is uncertain when these data will

he available.

4. Special industry studies (may be proprietary in

nature).

5. Consul ting firms that conduct market surveys.

6. Sal es Management Magazine’s annual survey of

buying power.

7. New York Stock Exchange (NYSE) shareowner survey.

The usefulness of the NYSE data wou3d he to track

where profits go and to include an income

distribution analysis in IMPLAN (who wins anti who

loses within and across regions). That is, what

income is generated within versus what flows out of

a region? Is income in the hands of a few or spread

out among many? Often much of the income that is

sT,enerated in a region flows away and is therefore no

longer a benefit. The NYSE shareowner survey

provides data on which to estimate the origin sector

and recipient income class for one portion of

proprietary income -- dividends and payments. The

distribution of the other major income

component--wages and salaries--can he ohtained hy

refining “manpower requirements matrices” published

by the Il.‘?. Bureau of Labor Statistics and Bureau of

Economic Analysis (see Rose, et al ., 1982). The

income mu1 tiplier in IMPLAN is for total income.

This may be an inaccurate indicator of well-being in

a region because wages generated may remain while

dividends and royalties may leak out. Thus, there is

often the need to disaggregate income into

appropriate categories before a multiplier is

applied. In the example where wages remain but all

other forms of income flow out of a region, the

proper analysis would he to apply the income

multiplier to household income alone.

In order to obtain private sector data, two

considerations are mandatory. First, there must he

an assurance of confidentiality. Second, mutual

benefits must he identified (i.e., what are the

advantages to individual firms?). Because of these

two important considerations, a nongovernmental data

collector (university or consulting firm) was

recommended.

Task 6: Changes Needed in BEA System

In the final task, work teams recommended changes

needed in data collection and analysis procedures of

the Bureau of Economic Analysis (BEA). Team members

identified five needed modifications.

First, it was recommended that tourism and

recreation professionals, not BEA staff,

disaggregate the tourism/recreation industry into

finer sectors as per the suggestions made in Tasks 2

and 4. One of these earl.ier suggestions was to

develop 2-digit classifications for services and

retail trade instead of the current combinations of

categories. For example , efforts should be aimed

at: (I) aggregating the no longer appropriate

manufacturing sectors; (2) identifying new

manufacturing sectors (genetics, robotics, etc.);

(3) disaggregating some manufacturing sectors (e.g.,

hoat manufacturing from ship building); and (4)

disaggregating (eliminating the noise) the

amusements sector into major amusement categories,

such as major amusement centers, marinas, ski areas,

gal f courses, t enni s compl exes , and fitness centers.

Second, it was recommended that the BEA present

employment data in full-time equivalents (FTE’s)

instead of the current practice of mixing full-time

and part-time employment. This change is especially

critical to the tourism industry hecause of the high

degree of part-time employment in many sectors.

Thi rd there is need for more consistency in

defini tfon of sectors and employment categories

among the BEA system, County Business Patterns, and

other national models. The current BEA I/O tahl e is

i.nconsi stent with County Business Patterns because

different rules are used to categorize certain

husinesses and these rules <are not clearly

articulated. Furthermore, other national models

(e.g., REIS) else different classification rules than

60
either County Business Patterns or the BEA. For

example, County Business Patterns uses unemployment

insurance figures to develop employment data. As a

result, many small firms not covered by unemployment

insurance are omitted in the analysis. REIS, using

different rules and data, is able to include very

small firms in its analysis; however, these rules

and data are not clearly specified.

Fourth, the BEA should conduct a periodic “Census

of Special Services” adjusting for seasonal

fluctuations in such industries as tourism and

recreation. This census is needed because the

current Census of Services is for selected services

only, not special ones like tourism. Such a census,

especially done in conjunction with transportation

and manufacturing censuses, would overcome many of

the problems associated with estimating the economic

impacts of recreation and tourism.

The fifth recommendation concerns better

interagency cooperation. The BEA should be

represented on tourist association statistical

committees and research branches of recreation

management agencies.

SUMMARY AND CONCLUSIONS

The results represent a convergence of sometimes

diverse opinions from regional economics experts

regarding six key issues which any agency must

face when involved in assessing the economic

impacts of recreation and tourism. Work team

participants were encouraged to present divergent

points of view on the assigned tasks. Before

preparing this paper’s resul t, we wrote a draft of

work team recommendations and mailed it to

participants for further comment. We then

integrated the participants’ comments with an

extensive literature review concurrently

performed under contract with the U.S. Forest

Service and Corps of Engineers (Propst and

Gavrilis, 1984). Our paper, therefore, is the

product of this synthesis of professional

experience and literature.

There are several important conclusions to be

drawn from the results presented herein. First,

“short cut” methods (methods other than I/O

analysis) are appropriate only when the

expertise or resources (i.e., computer and

access to an I/O model ) for performing an T/O

analysis are not availahle. These so-called

short cut methods are likely not “short” in the

sense of time or money. Four of the six methods

discussed (response coefficient , minimum

requi rements, mu1 tiplier given, and similar

sites) require the expertise of a regional

economist and/or the collection and analysis of

primary or secondary visitor expenditure data.

The two remaining methods (hrainstorming and

Delphi) require a relatively large investment in

time in contacting and ohtaining the appropriate

information from individuals and groups. We do

not mean that these methods should never he

empl oyed. We merely wish to point out that the

term “short cut” may be mi sl eadi ng.

4 second conclusion is that I/O analysis

represents the most rigorous, accurate method OF

economic impact assessment. Thi s concl us i on

emanates from the long-term experience of the

meeting participants with I/O analysis, the

availability of I/O models, and the existence of

computers capable of handling the data and

mat hemat i cal requi rements of such models .

Yowever, the rigor of I/O analysis can hecome an

important drawback. That is, a great deal of

experience and training is required before

analysts can understand how to perform an I/O

analysis and interpret its results properly.

The jargon that comes as baggage with any body

of knowledge is particularly voluminous and

confusing. Thus, to the uninitiated, I/O

analysis may appear to he a black box with

volumes of data entering one side and results

(usually mu1 ipliers) exiting the other. This

communication prohlem may be overcome to some

extent by empJoying one or more of the

alternative methods discussed above. These

alternative methods may be less rigorous than

I/O analysis but more readily comprehended by

decisionmakers untrained in quantitative

economic analysis. These alternative methods

may al so serve as a useful check on the results

of an I/O analysis.

Despite their complexity, I/O analysis gives

the most complete picture of the sophisticated

interactions in an economy and they accurately

provide much of the information being requested

by decisionmakers (impacts on income, johs,

etc.). However, a third conclusion to be drawn

from the results is that the Forest Service’s

IMPLAN, or any other I/O model, requires certain

key modifications to estimate precisely and

accurately the economic impacts of recreation

and t ouri sm. The first modification required is

the disaggregation of the retail and who1 esal e

trade and services sectors into categories that

accurately reflect the sellers and the producers

of recreation goods and services. Second, the

payments sector should he modified to reflect

the amount of income generated versus the amount

retained in a region. Thi rd, instead of

expressing muJ tipliers as partial derivatives

(the usual procedure), analysts should express

them in units that relate endogenous income or

employment to exogenous spending, such as total

employment generated per $1 ,000 of lodging

expenditures in a region. The result is

otherwise known as a response coefficient (Rose

et. al ., 1981) or normal muJ tip1 ier (Archer,

1977). The fourth modification is to develop a

matrix of transformation indices that convert

final purchases (visitor expenditures) into

producer prices. Either National Income and

Product Accounts or Survey of Current Business

data may be used to develop this matrix.

Visitor expenditure data may be obtained

directly from consumers or from a compendium of

results from previous studies. Once vi si tor

expenditure profiles and a transformation matrix

are specified, it will no longer he necessary to

collect new spending data for every site or

situation. Fifth, for any economic impact

met hod, not just I/O analysis, resident spending

within a region must clear1 y be separated from

nonresident spending wi thin the same region.

Such separation requires careful de1 ineation of

regional houndaries according to study objectives.

61
Regardless of the method chosen, perhaps the

overriding concern of all meeting participants

was the estahl ishment of a rel iahle and valid

visitor expenditure database from a nationwide

sample. Input/output specialists can make the

aforementioned structural changes in IMPLAN or

other models relative1 y easily. However, the

resul ts obtained (e.g., mulitipliers) will only

be as accurate as the expenditure data entering

the models. Currently, this high quality

expenditure database is lacking. Its

development should proceed in the following

sequence : (1) determine objectives and uses of

data, (2) estahlish detailed methodology, (3)

specify variables to be measured, (4) specify

the measurement instrument and items, (5)

pretest the procedure and instrument, and (6)

revise methodology and instrument. Agreement on

methodology is lacking at this time due to the

absence of data comparing the rel iahility and

val idity of such methods as personal interviews,

mai 1 back surveys, and expendi ture diaries.

Because opinions on appropriate methods vary, we

conclude that, wherever possihl e, a variety of

procedures should he used on subsamples of the

population and results compared and made

available to the academic community for

criticial review. The work teams developed an

initial list of variables (both visitor

segmentation and expenditure variables) for

consideration.

llsing the ahove model for devel aping a qual ity

expenditure data hase, the U.S. Forest Service,

Corps of Engineers, and National Park Service

have launched a nationwide effort at collecting

such data. The Public Area Recreation Visitor

Survey (PARVS) will he conducted in 1985 at

hundreds of federal resource agency and state

park sites across the U.S. A comhi nat i on

on-site and mailhack survey, the PARVS has as a

major objective the collection of detailed trip

and annual expenditures for recreation and

tourism. The end product will be the on1 y

national expenditure data hase of its kind. A

strong recommendation made by meeting

participants was an interagency cooperative

effort (pooling of resources) to estahlish the

heretofore missing national expendi ture

datahase. PARVS is such an effort. However,

there is no implied continuity to the PARVS.

That is there is no guarantee that the s,ame

data wili he collected 5 or 10 years from now.

Therefore, another recommendation of the meeting

participants is that the Bureau of Economic

Analysis establish a periodic “Census of Special

Services” with recreation and tourism as one of

the special services high1 ighted.

A fifth conclusion from the results presented

herein is related to the usefulness and

avnilahility of data from the private sector.

Such data are needed to check some of the

estimates derived from public data (Census,

PARVS, etc.) and to fill in some large gaps in

public datahases (e.g., percentage of tourist

sales in selected firms, inventory data, and

nonproprietary i “come generated and retained i n

a region). Furthermore, such data mdst he

collected in a highly professional manner with

cxtremp cat-p given tn confidentiality ,3;14

henefits to private interests of making this

information available.

A final conclusion concerns the method for

obtaining the results-- the small work

team/creative problem solving format. The method

seems to hold much promise for technology

transfer. We hrought together a group of

regional economics professional s representing

we1 1 over a hundred years of training and

experience and were able to apply their talents

to a specific problem area in a

nonconfrontational manner. Numerous

investigations of the economic impacts of

specific recreation and tourism events and

developments have been conducted. Never hef ore,

however, has there been a concerted effort at

accurately assessing the secondary economic

impacts on a nationwide basis. The approach used

transferred vital technology to federal resource

management agencies, providing the basis for

creating a national expenditure database for

recreation and tourism. At-1 east two of these

agencies have plans to transfer this technology

one step further to their field planning

offices. The method also allowed those with

less experience in economic impact assessment to

learn much from those with more. Thus, as a

training tool, the method was also successful.

There are always improvements that can he made

in any methodological approach. We feel only

minor improvements are needed in the protocol

and format of the work team portion of the

meeting. However, we feel we should have heen

more diligent in obtaining comments on the work

team recommendations from the participants after

the meeting ended. ln sum, we recommend the

method followed herein as an efficient approach

for transferring knowledge from one field to

anot her.

LITERATURE CITED

Archer, B.M. Tourism mu1 tip1 iers: the

state-of-the-art. Bangor Occasional Papers in

Economics No. 11. Bangor, Wal es: University of

Wales Press; 1977. 44 pp.

Bendavi d-Val , A. Regional and 1 ocal economic

analysis for practitioners. New York: Praeger;

1983. 195 pp.

Dwyer, .J.F., J.R. Kel 1 y, and M .n. Bowes. Improved

procedures for valuation of the contribution of

recreation to nati 0naI economic devel opmcnt .

Qesearch Report No. 178. Urbana-Champaign, TL.:

'Jniv. of Illinois Water Resources Cer?tcr: 1977.

718 pp.

Cnrtner, V.C. and D.F. Holecek. The economic impact

of a short-term tourism industry exposition.

Qescarch Report 436. East I_ans'ng, MI: Michigan

State Univ. Agric. Exp. Stn. ; 1982. R pp.

Yare, P.A. Creativity in small groilps. Beverly

Hills, CA: Sage Publications; 1982. 199 pp.

62
Leistritz, F.L. and S.H. Murdock. The socioeconomic

impact of resource development: methods for

assessment. Boulder, CO: Westview Press; 1981.

289 pp.

Maki , W.R. Measuring supply side economic impacts

of tourism and recreation industries. In:

Assessing the economic impacts of recreation and

tourism. 1984 May 14-16; East Lansing, MI:

Michigan State Univ. Published in this report;

1985.

Marino, M.L. and D.E. Chappelle. Lodging and

restaurant establishment spending patterns in

northwest lower Michigan. Research Report 346.

East Lansing, MI: Michigan State Univ. Agric.

Exp. Stn.; 1978. 32 pp.

Moeller, G.H. and E.L. Shafer. The use and misuse

of Del phi forecasting. In: Lieber, S.R.;

Fesenmaier, D.R., eds. Recreation planning and

management. State College, PA: Venture

Publishing; 1983:96-104:

Noller, R.B., S.J. Parnes,

Creative actionbook. New

Sons ; 1976. 399 pp.

Koberg, D. and J. Bagnall.

and A.M. Biondi.

York: Charles Scrihner’s

The universal

traveler. Revised Ed. Los Altos, CA: William

Kaufmann, Inc.; 1981. 128 pp.

Propst, D.B. and D.G. Gavrilis. Evaluation of

methods and models for determining the economic

impacts of public recreation services and

facilities: final report. East Lansing, MI:

Michigan State University Department of Park and

Recreation Resources; 1984. 77 pp.

Rose, A., D. Kolk, and

and urban employment

city of Los Angel.es.

1981.

M. Brady. Energy development

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Energy 6(1(I): 1041-1052;

, B. Nakayama and B. Stevens. Modern energy

region development and income distribution.

Journal of Environmental Economics and Management

9(2): 149-164; 1982.

Stevens, B.H., G.L. Treyz, D.J. Ehrlich, and J .R.

Bower. State input-output models for

transportation impact analysis. Discussion Paper

Series No. 128. Amherst, MA: Regional Science

Research Institute; 1981. 23 pp.

, and A. Rose. Regional input-output

methods for tourism impact analysis. In:

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tourism. 1984 May 14-16; East Lansing, MI:

Michigan State Univ. Published in this

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1984. 609 pp.

63
APPENDIX

Participants in the conference and workshop on

"Assessing the Economic Impacts of Recreation and

Tourism," May 14-16, 1984. Michigan State University, East Lansing.

Dr. Greg Alward

Land Management Planning Unit

USDA Forest Serive

Fort Collins, CO 80526

Dr. Robert C. Bushnell

Dep. of Finance R Business Economics

Wayne State University

Detroit, MI 48202

Dr. Daniel E. Chappelle

Dep. of Resource Development R Forestry

Michigan State University

East Lansing, MI 48824

Dr. H. Ken Cordell

Forest Sciences Laboratory

USDA Forest Service

Athens, GA 30602

Mr. Dimitris G. Gavrilis

Dep. of Park & Recreation Resources

Michigan State University

East Lansing, MI 48824

Mr. William J. Hansen

Waterways Experiment Station

Corps of Engineers

Vicksburg, MS 39180

Dr. Donald Holecek

Dep. of Park and Recreation Resources

Michigan State University

East Lansing, MI 48824

Dr. Matthew Hyle

Dep. of Finance R Business Economics

Wayne State University

Detroit, MI 48202

Dr. Jay A. Leitch

Dep. of Agricultural Economics

North Dakota State University

Fargo, ND 581035

Dr. Wilhur R. Maki

Dep. of Agriculture R Applied Economics

University of Minnesota

St. Paul, MN 551118

Dr. Charles Palmer (deceased)

Resources Planning Assessment

USDA Forest Service

Denver, CO

Dr. Dennis B. Propst

Dep. of Park R Recreation Resources

Michigan State University

East Lansing, MI 48824

Dr. Adam Rose

College of Mineral and Energy Resources

West Virginia University

Morgantown, WV 26506

Dr. William A. Schaffer

Dep. of Industrial Management

Georgia Institute of Technology

Atlanta, GA 30332

Mr. Eric Siverts

Land Management Planning Unit

USDA Forest Service

Fort Collins, CO 80526

Dr. David Snepenger

Waterways Experiment Station

Corps of Engineers

Vicksburg, MS 39180

Dr. Benjamin Stevens

Regional Science Research Institute

P. 0. Box 3735

Peace Dale, RI 02883

Dr. Daniel Stynes

Dep. of Park and Recreation Resources

Michigan State University

East Lansing, MI 48824

Ms. Nancy Tessaro

Natural Resources Management Branch

Chief of Engineers

Department of the Army

Corps of Engineers

Washington, DC 20314

Dr. Timothy J. Tyrrell

Dep. of Resource Economics

University of Rhode Island

Kingston, RI 02881

64
Propst, Dennis B., compiler Propst, Dennis H., compiler

Assessing the economic impacts of recreation and Assessing the economic impacts of recreation and

tourism. Conference and Workshop; 1984 May 14-16; tourism. Conference and Workshop; 1984 May 14-16;

East Lansing, MI. Asheville, NC: U.S. Department East Lansing, MI. Asheville, NC: U.S. Department

of Agriculture, Forest Service, Southeastern for- of Agriculture, Forest Service, Southeastern Forest Experiment Station; 1985. est Experiment Station; 1985. 64 PP=

A collection of eight papers that explore and assess A collection of eight papers that explore and assess

the best available technology to evaluate the econo- the best available technology to evaluate the economic impact on recreation and tourism. Research stra- mic impact on recreation and tourism. Research strategies for meeting methodological and data needs are tegies for meeting methodological and data needs are

recommended. recommended.

KEYWORDS: Technology transfer, problem-solving pro- Technology transfer, problem-solving pro-