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This is to certify that the

thesis entitled

Recreational Expenditures in Gede Pangrango National
Park and Their Regional Economic Impacts

presented by

Adi Susmianto

has been accepted towards fulfillment
of the requirements for

Masters degreein Forestry

 

 

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2/17 203 Blue FORN S/DaIeDueForms_2017.indd - p95

RECREATIONAL EXPENDITURES IN GEDE PAN GRANGO NATIONAL PARK
AND THEIR REGIONAL ECONOMIC IMPACTS

By

Adi Susmianto

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Forestry

1995

ABSTRACT

RECREATIONAL EXPENDITURES IN GEDE PAN GRANGO NATIONAL
PARK AND THEIR REGIONAL ECONOMIC IMPACTS

By

Adi Susmianto

Recreational economic impacts of the Gede Pangrango National Park (GPNP)
have been recognized, but not quantified. An analytical framework and related data are
needed to estimate the impacts. To accomplish this, an input-output model was explored
for analyzing the impacts. A non-survey technique, the simple location quotient (SLQ),
was used to regionalize Indonesia's 1990 66-sector input-output model for estimating the
regional output multipliers (Type I multipliers). On-site interviews were conducted in
early 1994 to collect primary data for twenty-eight trip-related spending categories. Then,
eight domestic visitor segments were developed for assessing the spending profiles and the
impacts (output, income, and employment). Three districts surrounding the park, Bogor-
Cianjur-Sukabumi (3-08), were defined as the impacted region. By segment, the
estimated total average trip-related spending varied from Rp 6,889 to Rp 21,812 per
person per day (U .S. $ 1.0 = Rp 2,150 in early 1994). Based on GPNP records, over
58,000 visitors came to the park between April 1, 1993 and March 30, 1994. Thirteen
sectors were affected by the spending with impacts totaling Rp 470,769,881 of output or
sales, Rp 80,115,334 of income, and 155 persons employed. The economic impacts were

minor within the region and did not reflect the GPNP's social and ecological importance.

ACKNOWLEDGMENTS

This study was accomplished due to immense contributions of several dedicated
persons. I am especially indebted to Professor Larry A. Leefers who served as my major
professor and mentor throughout my years in graduate school for providing inspirations, a
variety of subjects in resource economics, academic and technical guidance on research
issues, and editorial assistance. He also imparted much wisdom, patience, and sacrifice on
directing me in writing this study. His visit to Indonesia where I was conducting the
survey expressed his wise responsibility for enabling me to do the research. Professor
Daniel J. Stynes and Professor Dennis B. Propst, my committee members, deserve special
acknowledgment for providing many useful materials or references associated with input-
output methods and economic impact analyses. Their support, constructive criticism, and
thorough correction improved the quality of this thesis. I would like to express my
gratitude to all of them.

This study was also facilitated through cooperation from Wahyudi Wardojo, Gede
Pangrango National Park Manager, with his subordinates. Special thanks go to my
research assistants (enumerators) who worked hard in the data collection. This
acknowledgment is also due Colin MacAndrews (Chief of the Indonesian Natural
Resources Management Project) and Patricia S. Link (Manager of Training Programs,
Institute of International Education), who provided financial support during my study in
the US. My generous companion, Herr Soeryantono, is also deserved of this dedication

for his assistance and gift of computer use.

iii

Finally, the persons to whom I owe a great debt for making it all possible are my

parents, my wife (Nina), and my daughter (Gita). To all, thanks and may God bless you.

iv

TABLE OF CONTENTS

Page No.

LIST OF TABLES ................................................................................................. viii
INTRODUCTION ..................................................................................................... 1
Background ............................................................................................................... 1
The Importance of the Study Site .................... ~ ........................................................... 3
The Problem Statement .............................................................................................. 5
The Study Area ......................................................................................................... 6
Objectives of the Study ............................................................................................. 8
Definitions ................................................................................................................. 8
Organization of Thesis ............................................................................................. 10
LITERATURE REVIEW ........................................................................................ 11
Introduction ............................................................................................................. 11
Recreational Expenditures ....................................................................................... 11
Measurement of Economic Impacts ......................................................................... 15
Input-Output Model ................................................................................................ 19
General Overview of Input-Output Model .................................................... 19
Input-Output Modeling ................................................................................ 21

Flow or Transaction Table ............................................................... 21

Technical or Direct Coefficient ........................................................ 23

Direct and Indirect Coefficient Table ............................................... 25

Input-output Multipliers .................................................................. 26

The Strengths and Limitations ......................................................... 29

Assumptions in Input-Output Analysis ............................................. 31

Regional Input-Output Models ..................................................................... 32
Location Quotient Technique .......................................................... 33
Supply-Demand Pool Technique ..................................................... 35

Regional Purchase Coeficient (RPC) Technique ............................. 36

Defining Regions ..................................................................................................... 38
The Linkage Between Recreation Expenditures and Input-Output Analysis .............. 41
RESEARCH METHODS ........................................................................................ 44
Introduction ............................................................................................................. 44
Visitor Segments ..................................................................................................... 44
Spending Categories ................................................................................................ 46
Survey Instrument ................................................................................................... 46
Defining the Region ................................................................................................. 48
Procedure of Data Collection ................................................................................... 49
Primary Data ................................................................................................ 49
Secondary Data ............................................................................................ 51
Enumerators Recruitment ............................................................................. 51

TABLE OF CONTENTS (cont'd)

Page No.
Developing Spending Profiles .................................................................................. 52
Available Input-Output Models ................................................................................ 54
Impacts Estimate Procedure ..................................................................................... 55
RESULTS AND DISCUSSION .............................................................................. 60
Introduction ............................................................................................................. 60
Results ..................................................................................................................... 60
Sample Sizes and Response Rates ................................................................ 60
Visitor Segmentation .................................................................................... 65
Trip-related Spending ................................................................................... 71
Average Trip-related Spending by Categories .................................. 72
Average Trip-related Spending by Segments ................................... 72
Total Trip-related Spending by Segments ........................................ 77
Total Trip-related Spending by Activity Categories ......................... 80
Total Trip-related Spending by Occupation Categories .................... 80
Total Trip-related Spending by Residency Categories ...................... 82
Regional Output Multipliers ......................................................................... 84
Margining and Bridging Processes ................................................................ 90
Regional Economic Impact Estimates ........................................................... 94
Discussion ............................................................................................................... 97
Unit of Measure ........................................................................................... 97
Sample Sizes ................................................................................................ 98
Visitor Segmentation .................................................................................. 102
Trip-related Spending Profiles .................................................................... 103
Developing Regional Output Multipliers ..................................................... 105
Economic Impact Estimates ....................................................................... 106
SUMMARY AND CONCLUSIONS ..................................................................... 109
Introduction ........................................................................................................... 109
Summary ............................................................................................................... 109
Conclusions ........................................................................................................... 1 16
Recommendations .................................................................................................. 1 18
Applications ........................................................................................................... 120
APPENDICES
Appendix A
A—l. English version of the questionnaire, GPNP ....................................... 123
A-2. Indonesian version of the questionnaire, GPNP .................................. 129
A-3. Summary of the primary data, GPNP ................................................. 136

TABLE OF CONTENTS (cont'd.)

Page No.
Appendix B
B-la. Visitation data in GPNP for the last five years, GPNP ...................... 139
B- lb. Visitation data in two recreation sites near the GPNP, GPNP ........... 139
B2 The 66 sector classification for the Indonesian input-output
table 1990, GPNP ............................................................................ 140

B-3. Average trip spending (Rp. per person per day) within the

B-C-S region for 28 detailed spending categories brought by

the resident visitors (4 visitor segments), GPNP ............................... 142
B-4. Total trip spending (Rp, total p0pulation) within the B-C-S

region for 28 detailed spending categories brought by the

resident visitors (4 visitor segments), GPNP ..................................... 143
B-5. Total trip spending (Rp, total population) within the B-C-S

region for 5 aggregate spending categories brought by the

resident visitors (4 visitor segments), GPNP ..................................... 144
3-6. Technical coefficients table for sectors in the B-C-S region at

purchasers prices in 1990, GPNP ..................................................... 145
B-7. "Margining" spending for selected retail goods in the B-C-S

region, GPNP ................................................................................... 152
B-8. National income- and employment-output ratios in 1990 by

selected sectors, GPNP .................................................................... 153
Appendix C
C-l. UCRIHS approval letter .................................................................... 154
C-2. Map of the study location, GPNP ...................................................... 155

REFERENCES ...................................................................................................... 156

vii

LIST OF TABLES

Page No.

1. A simplified transaction table (in US. dollars), GPNP ......................................... 22
2. A simplified table of direct coefficients (purchases over dollar of output),

GPNP ................................................................................................................ 24
3. A simplified table of direct and direct coefficients, GPNP .................................... 26
4. The 28 detail spending and their 5 aggregate categories, GPNP .......................... 47
5. On-site interview sample sizes and response rates, GPNP .................................... 61
6. Sample and population distributions by visitor category, GPNP ........................... 62
7. Sample and population distributions by 12 visitor segments, GPNP ..................... 64
8. Distribution of the sample and population dropped in four visitor

segments, GPNP ................................................................................................ 66
9. Sample distributions by the possible visitor segments, GPNP ............................... 68
10. Population distributions by the possible visitor segments, GPNP ........................ 70
11. Regional distribution of the sample and the population by eight visitor

segments, GPNP ................................................................................................ 71
12. Average trip spending (Rp per person per day) within the B-C-S region

for 28 detailed expenditure items, GPNP ............................................................ 72
13. Average trip spending (Rp per person per day) within the B-C—S region

for 28 detailed expenditure items brought by the nonresident visitors (4

visitor segments), GPNP .................................................................................... 74
14. Average spending patterns (ranks) by segment for 5 aggregate spending

categories, GPNP ............................................................................................... 76
15. Total trip spending (Rp, total population) within the B-C-S region for 28

detailed expenditure items brought by the nonresident visitors (4 visitor

segments), GPNP ............................................................................................... 78
16. Total trip spending (Rp, total population) within the B-C-S region for 5

aggregate spending categories brought by the nonresident visitors ...................... 79

viii

LIST OF TABLE (Cont’d.)

Page No.

17. Total trip spending (Rp, total population) within the B-C-S region for 5

aggregate spending categories by the activity categories, GPNP ......................... 81
18. Total trip spending (Rp, total population) within the B-C-S region for 5

aggregate spending categories by the occupation categories, GPNP ................... 83
19. Total trip spending (Rp, total population) within the B-C-S region for 5

aggregate spending categories by the residency categories, GPNP ...................... 83
20. The 1990's gross domestic products by industrial origin at current market

prices (billion rupiahs) and the value of location quotients (LQs), GPNP ............ 86
21. National and regional output multipliers in 1990 (66 sectors), GPNP ................. 87
22. Bridge table derived from the 1990's national input-output table, GPNP ............. 92
23. Final demand changes within the B-C-S region by sectors, GPNP ...................... 93
24. Total regional output multipliers and the impacts on regional output,

income, and employment by related sectors, GPNP ............................................ 96
25. Selected statistics for trip-related spending by 28 detailed spending

categories, GPNP ............................................................................................. 100
26. Selected statistics for teip-related spending by segment, GPNP ........................ 101

27. Ranks or patterns of spending for eight visitor segments, GPNP ....................... 104

ix

INTRODUCTION

Background

Two of the purposes in the fifth Five-year Development Plan of Forestry
(1989/1990-1993/1994) in Indonesia are: (1) to increase national income, employment and
business opportunities including the benefits of value-added production and (2) to
improve the prosperity and development of income sources for people who live within and
around the forests. These purposes still strongly underlie the direction of forestry
development policies addressed in its sixth Development Plan (1994/ 1995-1998/ 1999).
Through the filth plan, timber production has been and is one of the country's leading
sectors providing income and generating employment. About 60 million ha (hectares) of
production forests (out of 144 million ha of forest lands) have been utilized for timber
production. So far, many efforts have focused on this sector rather than on the roles of
non-timber resources as other valuable assets.

Recognizing that, aside fi'om 60 million ha of production forests, the country has
set aside almost 19 million ha as terrestrial parks and reserves, about 30 million ha as
permanent protection forests, and another 30 million ha as designated (23 locations) and
proposed (200 locations) marine conservation areas. The sixth Development Plan of
Forestry tries to give higher priority to utilizing non-timber products as other alternative
resources upon which, in fact, so many of the country's economic activities depend.
Properly managed, these areas will be sufficient not only to protect Indonesia's biological
diversity and its critical ecosystems, but also to encourage economic development (World
Bank, 1994). Regarding protected areas management, one suggestion by the World Bank,
for the medium term, is to accelerate national parks management and develop feasible
options for income-generating activities related to the areas, such as tourism and
recreation activities, in the context of ICDPs (Integrated Conservation and Development

Projects).

This approach seems to be relevant recognizing that presently Indonesia has 31
designated national parks (part of 303 terrestrial conservation areas) and proposes an
additional 9 national parks in the next five years. It is also relevant realizing that a large
number of people, depending much on the existence of the areas, live near (or even within)
park boundaries. Tourism and recreation industries might elevate local people's concerns
about protected areas because local people could participate in businesses generated by
the visitor's activities. Thus, the approach is in line with the initiative of ICDPs, that is, to
ensure the conservation of biological diversity by reconciling the management of protected
areas with the social and economic needs of local people (Wells and Brandon, 1992).

The roles of tourism and recreation in national parks in Indonesia have been
recognized since the 1980s for their contributions to economic development. Tourist
visitations to the parks have increased through the years. The average increment of annual
visits between 1984/1985 and 1988/1989 for domestic tourists was 18%, whereas, for
foreign tourists it reached an average annual increment of 47% (Ministry of Forestry of
Indonesia, 1989). It is now clearly recognized that the tourism industry can generate
income through expenditures brought by tourists to the areas and that these expenditures
benefit local people and the region. As a result, the Ministry of Forestry of Indonesia
(MFI) has tried to implement a strategy for accelerating development of national parks
through establishment of concessions at 20 national parks; however, it has not been well
implemented and reasons for this are unclear. The World Bank (1994) indicates that the
economic benefits of tourism activities in national parks in Indonesia are difiicult to assess
due in part to the lack of information on their market values. This, in turn, results in
difliculties for both planners and the managers in making decisions.

This study focuses on the problem of assessing economic role of a national park
and its possible efi‘ects on a region due to recreationists' activities. To be specific, this
study focuses on the analysis of the Gede Pangrango National Park (GPNP) located in
West Java, Indonesia (see map in Appendix C, Figure 1).

The Importance of the Study Site
The GPNP, with a total area of 15,196 ha, is one of the first five national parks in

Indonesia; it was designated in 1980. As a protected area, the GPNP fits in at least two

management categories defined by the IUCN (International Union for Conservation of

Nature and Natural Resources) in 1985: (1) strict nature reserve, and (2) national park

(Wells and Brandon, 1992). These categories are based on various laws and regulations

governing protected areas, and on the roles provided by the areas. In the case of the

GPNP, there are at least four important roles characterized by the area (GPNP, 1994):

(1) Biodiversity reserve." The area is dominated by a mountain tropical rain forest
ecosystem which is claimed as the representative in Java island. A mountain swamp
ecosystem is also found in the area. The exact number of flora and fauna species is
not well recorded yet; however, it is reported that about 900 native and 30 exotic
plant species, 245 bird species, 4 primate species, 2 wild boar species, 1 big predator
(Pant/rem pardus), and other mammals are found in the area.

(2) Hydrological reserve.-- Due to the mountainous character of the area, in which 9
mountains with the elevation of more than 2,000 m above sea level exist, the GPNP
forms a good water catchment area from which about 60 big and small rivers flow.
Also, it is reported that the area generates subsurface run-off. This hydrological
system altogether provides vital water resources for the districts directly surrounding
the area (Bogor, Cianjur and Sukabumi) and even for the country's capital (Jakarta).

(3) Natural features or sceneries reserve." The primary natural features found in the
area consist of waterfalls (20 sites), lakes (2 sites), caves (3 sites), volcanic craters
(3 sites), and high flat open spaces in between of two mountains (2 sites). Not all of
these sites are open for public use.

(4) Scientific, educational and recreational use.-- The natural heritage of the area

provides many advantages to the people, especially to the local people. The area is

strategically located in populous districts (three-angle region: Bogor, Cianjur and
Sukabumi) and in between two big cities (Jakarta and Bandung); GPNP is very
attractive to people. In terms of recreational use, the GPNP might be able to
contribute to the regional economic development by attracting tourists in the region.

As one of the most pOpular national parks in Indonesia, the GPNP is gaining
recognition for the role it presently plays and the future promise it offers to the region's
economy. Based on five years of data (Appendix B, Table B-l), it was reported that at
least about 50,000 visitors annually entered the park. The highest visitation occurred in
the year of 1991/ 1992 in which 76,565 visitors were recorded at the park. Of these, 98%
were domestic visitors and 2% were foreign visitors (GPNP, 1994). Scientific research to
estimate recreational expenditures in GPNP and its impact to the region is not yet
available (Bogor Agriculture University, 1986). The same situation is also reported by the
World Bank (1994) for other protected areas in Indonesia. A recent study conducted by
Purwita (I993 ), however, does focus on the identification and the possibility of ecotourism
business in the GPNP. An important finding of his study is the attractiveness of the park
is the key for generating more visitors and, in turn, more spending in the region.

One measure of the park's contribution is the amount of recreational expenditures
made by visitors. These visitor expenditures, in tum, may stimulate a considerable amount
of local economic activity that affects output, income, and/or employment in the economic
region around the park. To ensure the achievement of the policy objectives addressed
previously, quantitative information on recreational expenditures and their economic
effects to the region are necessary. Moreover, Bergstrom et a1. (1990) stated that
information on the economic value of outdoor recreation is useful not only for assessing
economic efi‘ects to the region, but also for gaining increased political support for outdoor

recreation management programs and alternatives.

The Problem Statement

Most Indonesians still depend on agriculture and agro-industry for their livelihood;
these are two particularly important sectors of the Indonesian economy. This
phenomenon also occurs in the region of the GPNP where about 75% of the population
surrounding the park depends on agriculture and agro-industry sectors as the source of
their livelihood. About 40% of them are landless farmers; they work as laborers in
agricultural sectors. The average farm size is less than 0.25 hectare per household
(GPNP, 1994). On the other hand, total farmland has been decreasing because of
conversion to other purposes (Bogor Agriculture University, 1986). Without attempts to
create alternatives, the unemployment and loss of income generation in the region may
become problematic in the future.

Outdoor recreation activity associated with the existence of the GPNP may be an
important alternative for encouraging economic growth due to the trend of increasing
visitors. The increase will bring revenues to the region in particular sectors/industries that,
in turn, will generate output, income, and/or employment. These may benefit both the
local people and the park as the protected area. Nonetheless, the existence of the GPNP
would also generate costs of development such as trafiic congestion, crowding at
recreation and tourists' facilities, environmental degradation, higher housing costs,
crowding of public services, and a general increase in the cost of living (these are beyond
the scope of this study). Therefore, estimating the nature and the magnitude of the
economic effects of the recreational visits to GPNP on the regional economy as an element
in regional planning is important to determine.

To encourage economic growth, regional planning should be accomplished with
full recognition of the linkages and interdependencies between the many sectors
represented in the economic region. In the case of the GPNP, planning for economic
development of the region may be seriously handicapped by a deficiency in available

analytical information, a data set of recreational expenditures and the corresponding

economic efi‘ects of recreational visits to the park. This study helps address these
problems by developing an exploratory investigation which provides findings for improved

regional planning.

The Study Area

Local governments often face the difiicult decision of how best to use their scarce
natural areas. Also, there is often substantial pressure to convert and exploit these natural
areas. The GPNP is an example of a scarce natural areas, and because of its important
roles, the GPNP is designated as a protected area. Based on regulations (Act of the
Republic of Indonesia No. 5 of 1990: Conservation of Living Resources and Their
Ecosystems), the uses of this protected area are: (1) keeping the area relatively intact, thus
ensuring the survival of the living organisms it contains, (2) providing opportunities for
recreation and tourism, and (3) maintaining other roles such as watershed protection. This
may be a reasonable land use for those who want the area protected and the local people
who have limited economic stakes in other land uses.

Economics is ofien defined as the study of the allocation of scarce resources
among competing ends to produce goods and services (Nicholson, 1985). To economists,
recreation is a part of the overall economic problem of how to manage people's activities
so as to best meet their needs and wants with scarce resources (Walsh, 1986). This
implies that recreation economics is concerned with allocating limited resources for
recreation activities. On the basis of this definition, many studies focus on how to
measure the outputs of recreation industries stemming from recreationists' expenditures
injected into a region. For instance, what portion of the expenditures is for the recreation
use of a particular park and other recreation sites? Recreation activities involve many
characteristics of tourists (occupations, group sizes, etc.), activities (sports or
entertainment), sites (indoors or outdoors), purposes of visits (primary or secondary), and

visit times. Recreation expenditures cover the payment for transportation services, food

and beverages, lodging, equipment, attraction fees, insurance, and a variety of other
products and services.

To regional decision makers, the ripple effect of recreation expenditures is an
interesting subject to explore since this, in tum, may affect the economy in their regions.
Economists often study regional economic impacts, which are defined as the regional

economic activities generated by the recreation use of resources.

"It is a measure of the secondary effects of the actual expenditures by individual
consumers and managers of private and public recreation resources (Walsh, 1986;
p. 373)".

Specifically, primary (direct) expenses to recreationists become secondary (indirect) gains,
in part, to the regional suppliers of recreation goods and services. These impacts could
affect business output or sales, employment, and net income. Knowledge of the regional
economic impacts and much quantitative information could be provided by the use of
appropriate methods. The information includes, for example, the magnitude of the
impacts, how sectors relate to the activities, interdependencies among the sectors, which
sectors receive more gains than others, and which sectors contribute more to sales,
employment and income in the region.

Measuring regional economic impacts also requires the definition of a geographic
region since the magnitudes of the impacts relate to the size of the relevant regional
economy associated with a particular site. In addition, regional economic impact applies
not only the benefits (gains) side, but also the costs (losses) side; social and environmental
disturbances are part of these costs.

An ideal study pertinent to recreational economic activities should consider all
issues described above. However, it requires tremendous effort, time, and resources. This
study only takes a part of those issues covering the following aspects:

(1) It is concerned only with economic effects of recreationists' expenditures related to

their recreation activities.

(2) It concentrates on one protected area, that is, Gede Pangrango National Park (the
GPNP area).

(3) The region of interest is the three districts surrounding the park: Bogor, Cianjur and
Sukabumi (the B-C-S region).

(4) It accounts for domestic visitors only.

(5) It includes only trip-related expenditures of visitors and ignores durable goods
spending. Also, government's expenditures for park operations and local costs
associated to the park are not included.

(6) Impact estimates (output, income and employment) are based on the Type I (direct
and indirect) multipliers derived fi'om the national input-output model.

The reasons for dealing only with these aspects are presented in Chapter 3, Research

Methods.

Objectives of the Study
The overall purpose of this study is to provide an analytical fi'amework for estimating
impacts of GPNP visitors' trip-related expenditures in the B-C-S region. To address this
overall purpose, this study's objectives are:
(1) Estimate spending profiles generated by visitors to the park resulting in a set of
spending averages by categories, and

(2) Calculate regional economic impacts of this spending.

Definitions
To facilitate the readers' understanding of this study, the following important
definitions are used:
(1) Domestic visitors.-- These are visitors who live within Indonesia.
(2) Visitor segment.-- This connotes a subgroup of visitors based on their actions or

similarities so as to distinguish their spending patterns.

(3) Spending profile- This represents a vector of the amount spent by an average
visitor across a set of spending categories.

(4) Direct (technical) coefficient— This quantifies input requirements to be purchased
by a given sector fiom all other sectors within the processing sector of an input-output
table to produce a unit of output.

(5) Direct and indirect (interdependence) coefficient.-- In an input-output table this is
the total value of input from each sector required to provide $1.00 (in this study is Rp
1.00) of output to the final demand for sectors of concern.

(6) Economic multiplier.-- This provides information on the total amount of output,
income or employment generated by each sector of the regional economy when its

final demand is increased by one dollar (or in this study, by one rupiah).

(7) Final demand.-- This term is defined as the consumption expenditure on goods and
services made by GPNP's visitors (usually measured on an annual basis).

(8) Economic impact.-- This term is defined as the output, income and employment
effect of the rupiahs spent by GPNP's nonresident visitors on the economy of the
region.

(9) Economic significance.-- Contribution of visitors’ spending to the local economy
including spending of both resident and nonresident visitors within the region.

(10) Employment.-- The term employment is defined as the number of people (citizens)
10-years old or older who work, full or part-time, at least one hour continuously per
day during the previous week.

(1 1) Employment coefl'icient.-- This coefficient represents the number of employees in a
sector needed to produce one unit of its output.

(12) Total employment coefficient.-— This coefiicient represents the total number of
additional employees needed as a result of a one unit change of its final demand in a

given sector.

10

(13) Gross value added.-- In an input-output table, this represents the primary input of a
sector which is equal to the difi‘erence of its output and intermediate input. In this
study, value added consists of wages and salaries, business surpluses, depreciation,

indirect taxes, and subsidies.

Organization of Thesis

Chapter Two summarizes various literature related to this study. The flow of
topics described in this chapter follows the study‘s objectives sequentially. To clarify the
use of theories, some examples and related studies are also addressed in this chapter. The
third chapter deals with research methods and covers topics such as data collection
procedures, instruments and analytical tools used, and data analysis procedures. Reasons
of using or applying those subjects are also presented in this chapter. The next chapter is
devoted to the presentation and discussion of the study's results. The last chapter,
Chapter Five, summarizes the findings, draws conclusions, addresses possible

recommendations for the regional planners, and makes suggestions for fixture research.

LITERATURE REVIEW

Introduction

To facilitate the readers' understanding of this study, the topics presented in this
chapter are sequentially developed corresponding with the study's objectives. The part on
recreational expenditures includes its roles and characteristics in a region's economy, the
profiles, and the market segments fi'om which the expenditures are generated; these are
addressed in the first part of this chapter. The next part, measurement of economic
impacts, presents general information on several approaches or methods commonly used
to estimate economic impacts. Topics of the input-output model are the following part.
These cover general overviews of the model, input-output modeling, and regional input-
output models. Prior to a summary the linkage of the topics, as the last part of this

chapter, topics related to defining the impacted region are described.

Recreational Expenditures

Tourism, recreation development, and related businesses are often regarded as
particularly attractive investments and means of economic support for communities
(Clawson and Knetsch, 1966). In some countries, the tourism industry has grown rapidly
in recent years following a prolonged period of little or no growth. A recent study
(Hannigan, 1994) showed that the tourism industry has been identified by many European
governments and within the European Community as a means of job creation and regional
development; the industry has grown rapidly since 1987. Similarly, the tourism industry in
New Zealand seems to be the most appropriate economic alternative to agricultural
products due to the reduction of farm subsidies that resulted in an economic depression in

most rural areas (Oppermann, 1994). From the standpoint of the local economy tourism is

11

12

an export industry; that is, it engages in the export of goods and services being marketed

and sold to non-resident consumers rather than sales to local residents.

”The incomes of those so engaged form part of the local economic base, just as a
manufacturing payroll does, thereby serving to stimulate that sector of the
economy which provides goods and services to local residents (Garrison, 1974; p.
8)".

The economic impacts of recreation in an area are the gross additions to residents'
income and wealth resulting from the spending of recreationists. Normally, this
employment and related income as well as rising real property values will result from
recreationists' expenditures in the area (F rechtling, 1987). To count, the expenditures
should truly occur in the area under study. These expenditures include major consumer
durables such as recreational vehicles and vacation homes and minor items such as tennis
rackets and camping equipment, transportation, accommodation, food consumption,
entertainment and recreation, and incidental purchases such as souvenirs. There is no
agreement about what specific expenditure categories should be included in a given study.
A typical tourist expenditure profile from a simple travel spending survey might only
include lodging, food and beverages, transportation, recreation and miscellaneous
expenses, but other tourist expenditures could cover more than the 5 categories. Stynes
and Propst (1992) suggested that it will generally be more efficient to measure spending in
more categories, generate detailed profiles, and then aggregate into larger groupings after

the survey, if necessary.

In terms of regional development studies, knowing the profile of these
expenditures is very important. However, notions as to what happens to the recreationists'
money are often sketchy. For a better understanding it is important to know how much of
the expenditure stays in the local area, who in the area receives it and who benefits most

from recreation expenditures, and probably of most concern, communities are interested in

13

the employment and wages that are generated by these expenditures (Clawson and

Knetsch, 1966).

One common deficiency of most studies about tourism expenditures is that market
segmentation analyses of the spatial effects of the various markets are rarely performed
(Oppermann, 1994). Therefore, in order to expand their usefulness for regional planners,
expenditure profiles and associated final demand estimates should ideally be developed
according to a number market segments. Recreation activities, types of accommodation
used, recreationists' residency status, and, preferably, substate regions and season are

potential segmentation variables (Pedersen, 1990).

For economic impact analyses (BIA), market segments should be easily
interpreted, reasonably homogenous in their spending patterns, and relevant to the
purposes of management, planning, and marketing decisions (Tyrrell, 1985; Stynes and
Propst, 1992). Stynes and Propst (1992; p. 39) described three important roles of

segments in an EIA system:

"First, segments are the means by which an analyst identifies who is afi‘ected by a
particular action. The more precisely a user can identify impacted market
segments, the more the economic impact estimates can be tailored to the
particular situation. Second, by forming segments that are relatively
homogenous in their spending patterns, we reduce variations and sampling errors
in survey-based estimates of spending. Finally, segments are the primary vehicle
for explaining why one site or region may generate a different pattern of spending
and impacts than another".

More detail in defining spending categories is suggested to spread tourist
expenditures across detailed sectors (Stevens and Rose, 1985). There are at least four
advantages of detailed spending categories; they: (1) allow selective choice and adjustment
of a set of profiles to fit a particular application, (2) permit flexibility in the level of

aggregation, (3) facilitate the process of bridging tourist spending data to sectors of an

l4

input-output model, and (4) reduce aggregation errors in applying input-output models
(Stynes and Propst, 1992).

In order to estimate recreational expenditures, several approaches (direct or
indirect) are suggested in the literature. For example, the direct estimation of final
demand in recreation and tourism economic impact studies traditionally takes one or both
of two approaches: (1) demand related (expenditures by consumers), or (2) supply related
(receipts of firms in the industry). The two approaches should give identical results and,
in practice, one approach may provide a cross-check on the accuracy of the other (Tyrrell,
1985). In the demand-related approach, Frechtling (1987) offered three methods:

(1) actual observation of purchasing goods or items by following recreationists around,
(2) survey of recreationists while traveling or in their home, or (3) household surveys. Of
these methods, he claimed, the second is the most popular method since the result from
questions on expenditures can then be projected to produce estimates of business receipts
in various types of businesses. Furthermore, he mentioned that surveys of travelers can be
conducted as they enter the area (entry surveys), as they leave the area (exit surveys), or
while in the area under study (on-site survey). Previous studies such as Garrison (1974)
and Stynes and Propst (1992) have applied the on-site survey method. In the case of

hunting or fishing surveys, Stevens and Rose (1985; p. 21) recommended that:

" ..... it is preferable to conduct the survey at the destination of trips, both to
increase the probability that respondent will be able to provide accurate estimates
of expenditures (at least up to that point in the trip) and to provide accurate
information on where the expenditures were made".

Besides the demand-related approach, Brown and Connelly (1992) used a supply-

related approach for their study on Assessing Changes in Tourism in the Northeast, that is,

15

by the use of lodging receipts as the index of tourism. They claim the rationale for this is
that: (l) of all the Standard Industrial Codes (SIC) of economic sectors pertaining to
tourism, only the lodging sector has an insignificant amount of trade fiom local residents,
and (2) most tourism studies find lodging receipts to be a significant portion of total trip

expenditures.

Indirect approaches of assessing tourism expenditures are usually based upon
databases prepared by other institutions. In the US, some studies on tourism used data
derived from the Census of Selected Services or Census of Retail Trade, publications of
the Bureau of the Census, US. Department of Commerce. To gain appropriate analyses,
users usually make some adjustments due to the difl‘erences or changes in the rate of

inflation, definition of services, classification of related sectors, time period of data, etc.

Measurement of Economic Impacts

One characteristic of recreational expenditures is that it has a number of
implications for the economic impact on individual areas located near a recreation site. In
relation to recreation activities, regional economic impact is defined as the economic
activity generated by recreational use of resources. Regional economic impact is a
measure of both primary and secondary effects of the actual expenditures by individual
recreationists and managers of recreation resources (Walsh, 1986). Randall (1981) stated
that an increase in economic activity in one or more business and industrial sectors, will
typically increase activity in every sector of the local and regional economy. Thus, in
general, regional economic impact analysis is concerned with the consequences generated
by dollars injected (e. g., visitor expenditures) to a region in terms of the level of sales or

outputs, income or employment in a region.

16

There are several methods ofl'ered or suggested in the literature to analyze and
estimate how impacts originating in one or more sectors are transmitted throughout the
economy. However, so far there is not widespread agreement about what the ideal
method for economic impacts assessment is. Review of journal articles and other
literature indicates that each method offered has its own problems, whether conceptually
or technically. The three methods most fi'equently used in assessing economic impacts
are: (1) economic base models, (2) econometric models, and (3) input-output models
(Pleeter, 1980). Since about 1980s, shift-share analysis, linear programming, and

simulation methods are also alternatives suggested (Propst and Gavrillis, 1987).

The study by Clawson and Knetsch (1966) titled "Economics of Outdoor
Recreation” was one of the earliest publications addressing recreation economic impacts
using the economic base model (Pedersen, 1990). Kalter and Lord (1968) analyzed the
impacts of recreation investment on the local economy by using the "From-To" model (a
derivative of an input-output analysis). The basic difference between this and input-output
analysis is the transactions matrix developed by this method only shows the inputs from
area industries to other area industries and final demand sales handled by the firms in the
region. The model represents, directly, purely regional flows and focuses attention on

local impacts caused by changes in final demand.

Garrison (1974) attempted to estimate the local economic impacts of recreation
activities at Norris Lake with three counties (Campbell, Claiborne and Union) and a
portion of a fourth (Anderson) as the impacted region. His study used a combination of
three methods to estimate the impacts. The direct effect was based on published data on
recreation expenditures provided by the Tennessee Valley Authority which were presented
by type of expenditure and residence of visitors. The indirect efi‘ect estimation was based
upon relationships reported in input-output studies of other small mral areas (in

Oklahoma) since no input-output data existed for the Norris Lake area. Finally, the

l7

calculation of the induced effect relied upon the construction of a local income multiplier

which was estimated by the use of economic base theory.

Given the use of input-output models to estimate economic impacts, the following
two research reports are interesting to review due to the methodology used. The first is
by Diamond and Chappelle (1981) in the case of Manistee County, Michigan. This study
applied an input-output model based on secondary data. For the purpose of estimating the
impacts in the county level, the authors developed the county 15-sector input-output
model derived from the State of Michigan's 87-sector input-output model by omitting
sectors not found in Manistee County and by further aggregating the less important
sectors. The reason for taking this approach was that Manistee County had a strong
manufacturing and service base similar to the State of Michigan (presented by comparing
the percentage of earnings of the industrial and service sectors). The second study is by
Chappelle et al. (1986) on the case of Michigan Forest Industries. The study applied an
input-output model based on a partially survey-based input-output study. Even though the
study used a combination of primary and secondary data, tremendous efforts were still
needed. To construct forest industry sectors of the input-output transaction matrix,
questionnaires were sent to approximately 1,600 forest industry establishments in
Michigan during the survey. The selection of sectors was based on importance to the state

economy in terms of number of firms and value of production and employment.

A recent study concisely described a system to estimate regional economic impacts
of recreation and tourism (Stynes and Propst, 1992). The lack of consistency in
procedures and measures of previous economic impact studies provided the background
for developing the study. The authors attempted to estimate the impacts of recreation and
tourism activities in the Upper Mississippi River System (UMRS) with the use of an input-
output model derived fiom the micro-computer based input-output modeling system,

called IMPLAN (IMpact analysis for PLAN ning), developed by the Land Management

18

Planning Systems Group of the USDA Forest Service. The authors developed five
elements as the basic system for estimating the economic impacts: (1) market segments,
(2) spending profiles, (3) margining and bridging tables, (4) local input-output models, and
(5) impact estimation procedures. Among these elements, developing market segments
and spending were the most important steps and were described in detail. The authors
stated that the more precisely impacted market segments can be identified, the more
economic impact estimates can be tailored to a particular situation. In addition, more
detailed spending profiles permit flexibility in the aggregation process and facilitate the
process of bridging tourist spending data to IMPLAN sectors. For their study, the authors
defined trip-related spending and durable goods spending for 6 and 4 market segments,
respectively. Furthermore, they classified trip-related spending and durable good spending
into 33 and 32 detailed categories. Two important findings of the study that can be
addressed here are: (l) the use of consistent spending categories (assembled for
appropriate segments), units of analysis, and measurement procedures allows spending
estimates or impacts that are comparable across studies, and (2) the carefiil estimation of
spending patterns of visitors to different sites/regions, and especially the estimation of
spending profiles for homogeneous subgroups of visitors is encouraged.

Aside fiom the IMPLAN model, other computerized models for measuring the
economic impact of recreation and tourism have also been developed. Two examples are
RIMS 11 (Regional Input-Output Modeling System, version H) and REMI (Regional
Economic Models, Inc.). RIMS 11 used the 1972 national input-output model (developed
by the Bureau of Economic and Analysis (BEA) of the US. Department of Commerce)
for the development of regional coefficients. The coefficients were modified by the use of
the Location Quotient technique. Similar to RIMS II, the REMI model was based on the
latest national input-output model prepared by the BEA. The difference is that the REMI
used a technique called Regional Purchase Coefiicients to develop a regional coeflicients

19

matrix. Detailed reviews of these computerized models were presented in Bushnell and
Hyle (1985).

Input-Output Model

General Overview of Input-Output Model

The input-output model, pioneered by Professor W.W. Leontief in the early 19305,
has become quite commonly used to analyze the regional economic impacts in many
developed as well as developing countries (Furukawa, 1986). Input-output analysis aids
in providing a procedure or technique whereby the interactions within the regional
economy can be modeled, and the effect of expansion in one or more sectors upon
employment, output, and income in other sectors and the whole economy can be projected
(Randall, 1981). Diamond and Chappelle (1981) described the input-output technique as
a tool of economic analysis which can be used to estimate economic impacts of changes in
final demand given complete quantitative input-output accounts which express
intersectoral linkages. F urthermore, they described it as an aid in providing quantitative
accounting of regional economic interdependencies if data are organized into sectors
representing the important economic sectors of the region. This accounting system, where
the economy is divided into processing, final demand, and value-added or payment
sectors, is a flow system that includes all monetary transactions occurring in the region

over a certain period of time, usually a year (Chappelle et al., 1986).

An input-output model covers all production, both final and intermediate, and
provides a detailed understanding of the linkages among industries that we can not obtain

from analysis of the value of final production sold to the final buyers alone (Ritz, 1979).

More specifically, in recreation economics, an input-output model provides information on

20

the direct and indirect impact of recreationists’ spending on the output or sales of each

industry in the local economy.

"This provides a more precise calculation of the regional recreationist multiplier as
a weighted average of the multipliers for each local industry where recreationists
purchase goods and services (Walsh, 1986)".

Leontief (1953) stated that an input-output model is based on two sets of basic
conditions: (1) there are balance requirements, i.e., the combined inputs of each
commodity or service must equal its total product, and (2) there is a definite relationship
between quantities of all the input absorbed by one particular industry and the level of its
total output. Randall (1981) noted that input-output analysis for a regional economy is
based on a detailed accounting of the flow of goods and services. Part of this flow is
among industries within the regional economy, part may be between the region and other

regions, and the remainder flows to an exogenously defined "final demand" sector.

Schafi’er (1980) mentioned that in input-output models used for regional impact
analysis, economic change can take two forms: structural change or change in final
demand. Structural change can be interpreted as changes in regional production
coefficients which is caused by changes in technology or changes in marketing patterns.
Furthermore, he stated that accounting for this change requires deep familiarity with the
details of an input-output table which is not the case when accounting for the efi‘ect of
final demand changes. Changes in government expenditure patterns and changes in the
demand by other areas for the goods produced in the region are the basic form of the final

demand change.

21

Input-Output Modeling

Flgw or Transaction Tgble

There are some steps that should be carried out when doing an input-output
analysis. Hushak (1987) noted that the first step is to produce the flow table. This table
describes the demand and supply relationships of an economy in equilibrium by showing
final demand for goods and services and the interindustry transactions required to satisfy
this demand. This is called the transaction table; that is, accounts form which indicates the
dollar value of transactions between each sector and every other sector of the state's or
region's economy over the period of time covered by the table. Rows in the transactions
table indicate distribution of sales by sectors and can be represented by the following
equation:

Xi=x11+xi2+ .................. +xin+Yi=5xji+Yj (1)
where X,- = total output of sector 1', xy- = sector 1' output that is delivered as input to sector
j, Yi = sales to final demand for sector 1', and n = number of sectors in the transactions
table. Columns of the transactions table indicate distribution of costs expended by sectors
to other sectors for inputs and can be presented by following equation:

Xj=x1j+x2j+ .................. +xnj+Ifi=inj+in (2)
where .X} = total outlay of sector j, and V]- = charges against final demand or payments to a
factor of production in sector j. Input-output theory maintains that 23X; = ZX) which is
the sum of all inputs in the economy should be equal to the sum of all outputs (Chappelle
et al., 1986).

To clarify and get an easier exposition of the flow or transaction table, a brief
simplified table is presented in Table 1. This table and the following explanation are
summarized from John P. Blair‘s book entitled "Urban & Regional Economics" (1991; pp.
174-179). Each row in this table shows the annual dollar value of output that each sector

listed in the left-hand column sold to each of the sectors listed across the top. For

22

instance, the table indicates that agriculture sold $3 50 of output to itself, $3 50 to firms in
the manufacturing sector, $3 50 to firms in the service sector, $950 directly to local
households, and $670 to businesses and households in the rest of the world as exports.
The columns show where the sectors listed across the top purchased their input. In this
example, regional manufacturing firms purchased $3 50 from agriculture, $200 fiom each
other, $850 from regional service firms, $350 from regional households (factors of
production, especially labor), and $900 in the form of imports from individuals and

businesses outside the region.

In addition to the interindustry sectors (i.e., what agriculture, manufacturing, and
service sell to each other), Table 1 shows two final-demand sectors. The household
column reflects purchases by residents of the region, and the export column reflects goods
and services that are sold to nonresidents. The table also shows two primary-supply
sectors: households and imports. Households provide labor, entrepreneurship, capital, and
land as inputs, and each of the values in the household row reflects compensation for these
services. The import row shows the dollar value of all commodities imported yearly. For
instance, shown in the table, the service sector was the largest importer, importing $1,900

worth of goods.

Table 1. A simplified transaction table (in US. dollars)?

 

 

 

 

 

Supplied by Sold to Final Demand Total
Output
Agriculture ManufacturinL Service Household Exports

Agriculture 350 350 350 950 670 2,670
Manufacturing 100 200 650 300 1,400 2,650
Service 550 850 600 750 900 3,650
Primary Supply:

Households 970 350 150 50 1 130 2,650

Imports 700 900 1900 600 0 4,100
Total 2,670 2,650 3,650 2,650 4,100 15,720

 

a Modified fi'om Blair (1991; pp. 174-179)

23

The final demand (in this case representing sales outside the region) and primary-
supply sectors are often further disaggregated in more detailed transaction table than
Table 1. Exports and imports could be disaggregated to specify exactly who purchased
the exports and who sold the imports. A sector for government purchases and gross
capital formation could also be included. Note that since the total value of output must be
paid to intermediate suppliers or to the primary factors of production, total gross output
must equal the value of the inputs used in production. Related to recreation, changes in
final demand (export to tourists) mean changes in affected tourists' spending associated

with their characteristics or actions (usually defined as market segments).

Technical or Direct Coefficient Table

The second step in doing an input-output analysis is to understand the technical or
direct coefficient table. The technical coefficient table shows the fixed-coefficient
production firnction for each of the processing or production sectors, that is, the amount
of input required to produce a unit of output (Hushak, 1987). Direct coefficients are
formed by dividing each cell value in a column of the transaction table by total outlays for
that sector, which is simply the sum of column figures (Chappelle et al., 1986). This
operation may be expressed as follows:

{A} = a.) = xr/Ig (3)
where “ij = direct coefiicient that quantifies input requirements to be purchased from
sector 1' by sector j, and [A] = matrix of “if The matrix of [A] may be considered a
quantification of technical production relationships. That is, reading down a column of
this table indicates the percentages of total expenditures that are transacted to each sector

of the regional economy on the first transaction (hence the term ”direct").

24

By using the transaction table presented in Table 1, direct coemcients can be
calculated (Table 2). In this case, the manufacturing sector purchased $850 of inputs from
the service sector in order to produce $2,650 of total gross output (see the transaction
table). Thus, for each dollar of output, the manufacturing firms purchased $0.321
(850/2,650) from firms in the service sector. The direct coefiicient for the manufacturing
column and service row, $0.321, is the strongest linkage among the three industries in the
model economy. Agriculture requires the most resources fiom local households; $0.363

of household inputs is required for each dollar of agricultural output.

Thus, Table 2 illustrates interindustry linkages. For example, if the agricultural
sector were to produce an extra dollar of output, using the same input proportions that
were used when the input-output table was constructed, it would need to purchase $0.131
from other agriculture producers, $0.037 from the manufacturing sector, $0.206 from
services, and $0.363 would go to households to pay for input such as labor. In addition,
$0.262 would be spent on imported inputs of all types. All manufacturing goods, services,
agricultural products, and direct inputs from households that are purchased outside the

region are included in the $0.262 of imports.

Table 2. A simplified table of direct coemcients (purchases over dollar of output)“.

 

 

 

Supplied by

Agriculture Manufacturing Service Households
Agriculture 0.131 0.132 0.096 0.358
Manufacturing 0.037 0.075 0.178 0.113
Service 0.206 0.321 0.164 0.283
Households 0.363 0.132 0.041 0.019
Imports 0.262 0.340 0.521 0.226

 

'Modified fi'om Blair (1991; pp. 174-179).

25

Di ndIni otficin Tbl

The direct coeficient table expresses only initial transactions by producing sectors
as they purchase inputs from other sectors of the regional economy, and it accounts for
only first-round spending effects. Normally we are interested in quantifying the stream of
transactions as the successive rounds of re-spending occur that are initiated by these initial
transactions. Sectors providing inputs to other sectors will require additional output from
their suppliers; suppliers of suppliers will purchase more fiom their suppliers and so forth

(Blair, 1991).

Continuing the example above (see Table 2), if the manufacturing sector increases
its output by $1, $01320 of additional output will be required fiom the local agricultural
sector. But, if agriculture is to increase its output by $01320, agricultural firms must
purchase $0.0173 (0.131 x $01320) fiom other agricultural firms, $00049 (0.037 x
$0. 1320) from manufacturing firms, and $0.0272 (0.206 x $01320) from service firms.
Household income will increase by $00174 (0.132 x $01320) because of the primary
factors of production needed to produce the extra output required by manufacturing. But
household income also increases because of the increases in agricultural and service output
created by the initial increase in manufacturing output. In theory, these ripples would
continue forever. The cumulative size of the various rounds of spending (direct and

indirect) can be calculated mathematically.

Chappelle et al. (1986) noted that the total of direct and indirect purchases can be

represented by the series:
I+A+A2+A3+A4+ .......................... +An (4)

where I = an identity matrix. This series is the power series approximation of the inverse
of the Leontief matrix (I-A). The inverted Leontief matrix (i.e., [1-A1-1 ) is a table of direct

and indirect coefficients or a total-requirements table (Schaffer, 1976). In our example, the

26

results of using the inverted Leontief matrix are presented in Table 3. It shows the total
dollar amount of output that would be required fiom each sector listed on the left in order
to accommodate a dollar’s increase in output from each sector listed across the top. For
instance, if manufacturing increased its output by $1, the total efl‘ect on the agricultural
sector would be to increase output by $0.422. Bromley (1972) mentioned that the matrix
of direct and indirect coefficients indicates how a change in the final demand of one sector
afl‘ects the level of output of its suppliers after all the intermediate adjustments have been
made.

Table 3. A simplified table of direct and indirect coefiicientsa.

 

 

 

Supplied by Sold to

Agriculture Manufacturing Service Household
Agriculture 1.530 0.422 0.300 0.694
Manufacturing 0.274 1.281 0.321 0.340
Service 0.697 0.717 1.467 0.760
Households 0.633 0.359 0.216 1.354

 

'Modified from Blair (1991; p. 174-179).

Input-911mg! Multinligg

The regional policy makers may be interested in the answers to questions such as:
How much additional output will be produced? How much additional income will be
generated by a given program or activity in the economy? How many jobs will be
created? Which industries in the economy will be affected most? Multiplier analysis is a
tool that can help answer such questions, because it can be used to make forecasts and to
perform impact analyses (DiPasquale and Polenske, 1980; Blair, 1991; Block, 1977; and
Stevens and Rose, 1984).

27

Impact coefficients or multipliers are quantitative summary measures of the total
efi‘ects that a change in the final demand for a particular sector has on the output, personal
income, or employment of the regional economy (Hushak, 1987). Normally in economic
development analysis, calculating multipliers will indicate magnitudes of impacts likely to
occur in the regional economy if a certain strategy is pursued instead of some other
strategy. For each dimension of economic life in which we are interested, a multiplier may
be calculated for each sector to measure economic impacts on the regional economy
resulting from an increase of one dollar in final demand for the product mix of the
particular processing sector (Chappelle et al., 1986). Some studies indicated that
multipliers can be based on various economic measurement scales or dimensions of

economic life such as output or sales, income, and employment.

The sales or output multiplier indicates the increases in output per dollar increase
in final demand or, more clearly, the amount of economic activity generated in the
economy by an additional dollar of final demand for the products of the specific sector.
The magnitudes of output multipliers for each sector can be calculated directly from the
inverted Leontief matrix (I - A)'1 because this matrix is basically multiplier matrix itself.
This calculation is accomplished by simply adding coefficients in a given column of this
matrix for a processing sector or the sum of each column of the inverted matrix (Shafi‘er,
1976; Block, 1977 ; and Chappelle et al., 1986). The magnitudes of output multipliers also
indicate the degree of interdependence among industries. The higher the multiplier, the
greater is the interdependence among industries. Furthermore, by using the inverted
matrix, it is possible to form the basis for a projection model such as forecasting
production required to satisfy forecasted final demands, because the following relation

holds:

X=[I-A}"1Y (5)

28

where X is a vector of output or column of the gross outputs of industries and Y is the
vector of final demands for goods in the future year under consideration (Shaffer, 1976;

and Chappelle et al., 1986).

Income multipliers indicate the amount of income generated in the region for each
additional dollar of final demand for sector's output. In the same fashion, employment
multipliers indicate the number of jobs generated in the region for each additional dollar of
final demand for sector's output (Diamond and Chappelle, 1981). Another author stated

that:

..... employment multipliers are derived from output multipliers simply by
converting fiom an output to an employment base through the use of
employee/output ratios. These multipliers are to be interpreted as showing the
number of jobs created by a $10,000 export in their industry (Shafl‘er, 1976; p.
62)".

Employment multipliers are fi'equently included in regional analysis to evaluate impacts on

employment of industrial expansion. This is reasonable because employment is a firnction

of income since changes in employment reflect changes in final demand.

In most cases, there are two frequently used types of multipliers (both for income
and employment) to consider. The first, the Type I multiplier, is based upon the direct and
indirect results of an exogenous change in final demand when the household is part of final
demand (exogenous sector); this means that the household consumption fimction (shown
in the household column of the direct coefficients table) remains fixed and unafi‘ected by
changes in other sectors as an additional dollar of final demand reverberates through the
economy. The second, the Type H multiplier, is based upon the direct, indirect and
induced results of an exogenous change in final demand when the household is part of the
endogenous system of interdependency (like any other endogenous sector); that is, the
expenditure level changes as the production level changes, and final demand consists of

government spending, investment expenditures and foreign purchases (Bradley and

29

Gander, 1969; and Chappelle et al., 1986). The term ”induced" means caused by
household consumption (Shaffer, 1976).

Multipliers have many limitations. In relation to the recreation and tourism
sector(s) of the regional economy, Chappelle (1985; p. 2) described three limitations of

multipliers as follows:

1) "Although multipliers can be derived for each recognized internal sector of the
regional economy, they essentially apply to the average establishment of the
sector at the time the data were collected for the region being studied. It is not
possible to measure differences in impact that vary with scale of proposed
expansion of a sector's capacity. Also, there is no reason to expect that
multipliers necessarily can be extrapolated to other situations".

2) "Multipliers strictly apply only to the next incremental change in the region's
economic structure. This means, theoretically at least, that once a change is
introduced into the economic structure, multipliers may change in magnitude.
Since the input-output model is a static model, it is not sensitive to change.
Multipliers can not reflect changes in economic structure except by
recalculating them after changes have been introduced into the transaction
table or direct coefficients table".

3) "From the standpoint of economic development planning, it would be desirable
to be able to apply weights to the various multipliers in order to rank
prospective opportunities. However, since measurement units differ fi'om one
multiplier to another (e. g., output, income, and jobs), it is not possible to apply
weights and add their products".

Thg Stungths and Limigtiong

Input-output analysis has been widely used because of its major advantages.
Propst and Gavrilis (1987) described two major advantages of input-output analysis as:
(1) it analyzes sectoral linkages (the production and distribution) at a level far more
detailed than economic base or shift-share analysis, and (2) input-output tables can be
produced by data reduction techniques, such as non-survey or semi-survey methods (by

the use of secondary data). Schaft'er (1985) more clearly mentioned that:

”Input-output analysis is the most popular tool for regional impact analysis simply
because it is the most consistent and logical way to trace secondary benefits

30

through a regional economy. Economic-base models and econometric models
sufi‘er from all of the noted deficiencies; in addition, they are grossly aggregated.
Input-output analysis forces us to pay attention to detail; artfully used, it not only
focuses on the issue of measurable secondary economic effects, but it also leads us
to consider environmental and social effects".

By using input-output analysis, the export-base theory can be operationalized in a manner
that shows the interindustry repercussions in detail, which is a useful tool for regional
economic planners. In addition, input-output analysis could also help a community
determine which industries it should promote in order to maximize the total economic

impact (Blair, 1991).

Aside from these advantages, input-output analysis can help show distributional or
equity impacts of an economic change, aids in developing a regional growth plan by
providing a framework for organizing data, and provides a fiamework for testing
alternative policies to gain knowledge of their prospective impacts in terms of regional
production of goods and services, regional income and regional employment. Input-
output analysis has also been extended to integrate economic and environmental systems,
since the inclusion of ecological sectors into the accounting system permits analysis of
environmental impacts flowing from changes in the structure of the regional economy

(Isard, 1975; and Diamond and Chappelle, 1981).

Although a powerful tool for regional impact analysis, input-output analysis is not
free from shortcomings, both conceptually and technically. Production functions for each
industry are modeled using linear models in input-output analysis, which implies that any
changes introduced in the system must consequently cause an equiproportionate increase
or decrease in existing levels of resource use. It also implies the absence of scale
economies, which ignores the important theoretical arguments for the existence of cities,
namely agglomeration and urbanization economies. Other criticisms involve the static
nature of the input-output model; that is, its direct coefficients implies a fixed-input (no

substitution) production filnction which means there is only one recipe for producing the

31

output of each sector. These criticisms take two forms: (1) inputs do not increase in fixed
proportions as output increases, and (2) the coefficients change over time. There are a
number of reasons why direct coefficients may vary as output changes. Relative price or
technological changes may encourage an establishment to substitute a relatively cheaper
input or change the production technique (Armstrong, 1978; Pleeter, 1980; Frechtling,
1987; and Blair, 1991).

Other drawbacks concerning regional input-output analysis were addressed by
Hamilton et al. (1991). They stated that because it traces each sector's purchases from
other sectors of the region's economy, it captures only the portion of secondary impacts
caused by backward linkages from the event being studied. They also criticized that input-
output models are usually based upon political boundaries such as counties or states, while
the fact that the firnctional economic areas within which impacts occur often cross political
boundaries. Furthermore, Blair (1991) described that internal improvements in
productivity, technology, and other sources of grth are usually ignored in input-output
analysis. This is because input-output models are based on the export-base theory of

growth, in which changes in output are normally driven by exogenous changes in exports.

um ti nsinIn - t An

Knowing the possible shortcomings of input-output models, there are several
assumptions that should be considered in applying input-output analysis. Hushak (1987)
and Pleeter (1980) described some important assumptions in input-output analysis as:
(1) fixed-coefficient production function (the rate of technological change is slow enough
by only small amounts from year to year), (2) constant relative wages and prices of inputs
and outputs, (3) no supply constraints exist, and (4) production of homogenous output in
each sector (each industry or sector produces only one product) so that economies and

diseconornies of scale are disallowed. In addition, Diamond and Chappelle (1981)

32

described two basic assumptions in input-output analysis: (1) the level of technology is
stable over time, and (2) fixed rates of substitution between inputs imply that relative use

levels of resources do not change over time.

Regional Input-Output Models

The major problem in constructing interindustry flow tables for regional input-
output analysis is that it requires a considerable amount of cost, time, and effort.
Therefore, to avoid the substantial fieldwork, many studies use national input-output
models or derive models from other regional input-output models as a short-cut method.

The drawbacks of using surrogate coemcients are rather obvious.

Czamanski and Malizia (1969) pointed out the sources of divergence between
national and regional input-output: (l) differences in the industrial mix, (2) differences in
relative importance and structure of foreign trade; foreign trade is more sensitive at
regional level, (3) national coefficients are often several years old, and (4) difierences in
technology and in the relative prices of inputs between regions within a country. To
eliminate these drawbacks, a method was suggested for identifying sectors requiring
surveys which would supplement regional matrices otherwise derived via adjusted national
coefiicients. This method needs only information of regional income and product
accounts disaggregated by sectors, and national technical input-output coefiicients. The
regional row-and-column totals could be obtained when total regional output is multiplied
by adjusted national technical input-output coefficients.

Other techniques, which are called non-survey input-output methods, have been
developed lately to avoid a complete survey-based input-output analysis. To modify
national coefiicients to the regional level, employment or output data are often used
because of the availability and readiness of the data. Among those techniques, the

following section is brief description of three non-survey techniques frequently used

33

(i.e., location quotients, supply-demand pooling, and regional purchase coeficients). To
get more detail and complete information or review of the techniques, see Schafi‘er and
Chu (1969), Leigh (1970), Schafi‘er (1976), Stevens et al. (1980), Bushnell and Hyle
(1985), Miller and Blair (1986), Garhart and Giarratani (1987), and Blair (1991).

Qgfion Qggtient Tghniggg

The Location Quotient (LQ) is a technique for assessing a region's specialization in
an industry or other economic base. The LQ is expressed as a number comparing the
relative share of an industry in the region to its relative share in the nation. To do this,
there are, at least, three variants of using location quotients: (l) the Simple Location
Quotient (SLQ), (2) the Purchase-Only Location Quotient (POLQ), and (3) the Cross-
Industry Quotient (CIQ) approaches. For the SLQ, LQ is generally specified as follows:

LQr' = (xr/x) .- (X.- 00 (6)
where LQi: location quotient for sector 1',

xi: regional output (or employment) in sector 1',

x: total regional output (or employment),

X 1‘3 national output (or employment) in sector 1', and

X: total national output (or employment).

The general idea behind this technique is that outputs of sector 1' must be imported
if the industry has less relative share locally than it has nationally (defined as LQ < 1). On
the other hand, excess output is assumed to be exported if the industry has a large relative
share locally (defined as LQ > 1). A location quotient equal to one (LQ = 1) means that
the region is self-suficient in the industry in question; that is, it has its proper share. This
interpretation is based on the assumption that regional production processes are the same

as national production processes. Ifthe LQ; 2 1, then we set ay- = A ija where ay- are the

34

regional input-output coeficients (defined as xi]- / xj) and A ij are the national production
coeficients (defined as X ij' /)(j). Thus, we may easily define regional interindustry flows

as:
xi]- = (aij) (xj) = (Xij) (xi/Xj) (7)

If LQ; < 1, then the aij for the industry are adjusted downward by the value of location

quotient. Thus, the regional production coefficients in row i may now be computed as:

0y = (LQi) (Arj) (8)

and regional interindustry flows as:

)4} = (01;) (xj) = (er) (LQr') (xj/Xj) (9)

Similar to the SLQ, the POLQ for sector 1' in a region relates regional to national
ability to supply sector i inputs, but only to those sectors that use 1' as an input. Thus,
slightly different from the procedure identified in Formula (6), in estimating POLQ for
sector 1', the devisors x and X are defined as the total regional and national output,
respectively, of only those sectors that use i as an input instead of total gross output as
applied in SLQ. POLQ is used in the same way as LQ to uniformly adjust a national

coefficients table.

In the third variant, the CIQ allows cell-by-cell adjustments rather than uniform
adjustments along each row of the coefiicients table as applied in the SLQ and the POLQ.
CIQ is measured as the relative importance of both selling sector 1’ and buying sector j in

the region and the nation. The formula is defined as:

CIQ? = (x,- /Xi) Z (xj/Xj) (10)

The idea of this method was clearly stated by Miller and Blair (1985) that:

".... if the output of regional sector 1‘ (xi) relative to the national output of i (X i) is
larger than the output of regional sector j (xj) relative to the national output of
sector j (Xj) (CIQ)} > I), then all offs needs of input 1' can be supplied from within
the region. Similarly, if sector 1' at the regional level is relatively smaller than

35

sector j at the regional level (CIQiJ- < 1) , then it is assumed that some offs needs
for 1' inputs will have to be imported".

Sgpnfl-nggng P991 Tghnigue

The Supply-Demand Pool technique is derived fiom the concept of regional
commodity balances to regionalize the national coefficients. This technique follows fi'om
the assumption that, given transportation costs, demand will first be met by local
production. In this manner, sales of regional output will first go to satisfy local demand.
Imports will occur after local production is exhausted and exports will only occur after

local demand is met (Pedersen, 1990).

For application, some steps that should be taken are as follows (summarized from

Miller and Blair (1985; p. 300)):

(1) Take the national technical coemcients (A ij) as the first approximation to regional

coeficients,

(2) Calculate the regional intermediate outputs by sector by multiplying each of these

coefiicients by the appropriate actual output of that sector ((A ,-j)(X)-)),

(3) Similar to step 2), calculate also for the regional final demand sectors by using the
national final-demand input proportions ((cif)(Yf)),

(4) Summing the results of step 2) and 3) to get estimated gross regional output by sector
(2?, ). That is,

X, =ZAJXI+ZCITYf (11)
r r

where cifis the national final-demand input proportions, and lyis the total national

purchases of final-demand sector f

36

(5) Then, calculate the regional commodity balance (b,) as bi =X, — X, , where X ,. is the

regional actual gross output of sector 1'.

Interpretation of this technique is that if b,- is positive (or zero), using national
coeflicients as estimates of regional coefficients does not generate an overestimate of
regional production, then we apply Aij and cij for regional level estimates. However, if b,-
is negative, then A ij and cij should be adjusted or balanced for regional level estimates by
using a multiplicator called the "balance ratio” which is defined as X , / X, . For more
detail about this technique, see Schafi‘er and Chu (1969), Schaffer (1976), and Miller and
Blair (1985).

Rggigngl Pgrchgsg Cgefficient (EC) Technique

An RPC has been defined by several originators of RPC as the proportion of a
good or service used to firlfill intermediate and/or final demands in a region that is
supplied by the region to itself rather than being imported (Stevens et al., 1980; and

Bushnell and Hyle, 1985).

"Unlike the location quotients which are applied to the inverted technical
coefficient matrix, the RPCs are applied to the technical coefficients directly, after
which the technical coefiicient matrix is inverted in the normal way (Bushnell and
Hyle, 1985; p. 47)".

Mathematically, the regional purchase coefiicient for a good in regional L is defined as:
RL = SLL / (SLL + SUL) (12)

where SLL is the amount of a good shipped from region L to itself, and SUL is the amount
of the same good shipped from the rest of the nation to region L. Another alternative

definition used as a basis for fitting an estimating equation for RPCs is defined as:
It‘ =(Qr‘/D.-")Ii" (13)

where Q,‘: the amount of 1 produced in L,

37

D,": the total use ofi in L, and
13‘: the proportion of i produced in L which is shipped to destination in L.

To proceed in estimating equations for RPCs in the form of a regression analysis,
several steps should be developed. Following several systematic assumptions related to
comparative delivered costs which are dependent upon relative production costs, industrial
concentration, weight-to-value ratios, and spatial density of suppliers, we come up with an

estimating equation defined as (an example in the United States):
RiL = K(WiL /wiU )bl(eiL /ef1)b2(mu /[eiUWiU ])b3(LQ{L )b4(AL / AU )b5 (14)

where w!" , w,” : average annual wages per worker in industry i in region L and in the US,

e,‘ ,e.”: total employment in industry i in region L and in the US,

,U: total tonnage of i shipped domestically in the US,
AL , A”: land area of region L and the US,
LQf: employment location quotient for industry i in region L,
bj: the elasticity of response of R to a change in the ratio for variable j, and
K: a constant.

Procedures of estimating RPCs are not as simple as those described above. There
are at least two important steps must be taken before proceeding the regression analysis
(Stevens et al., 1980): obtaining a sample of RPCs to use as the dependent variables, and
revising the right-hand side of the equation in order to take into account of data
availability for the independent variables. However, the details of these steps need not be

given in this study.

Once the RPCs are obtained, then the regional technical coefficients can be directly
estimated by multiplying these RPCs by the national technical coemcients. That is,
A' = RPC A", where A' and A" are the regional and the national technical coefficients

38

matrix, respectively, and RFC is an n-element vector of regional purchase coefficients.
For detailed information and reviews of the regional purchase coeficient technique, see
Stevens et al. (1980), Miller and Blair (1985), Bushnell and Hyle (1985), and Stevens et
al. (1986).

Of all non-survey methods described above, so far, there is no agreement among
experts on what method is the best to use for regionalizing the national models. Pertinent
to this study, the author uses the Simple Location Quotient technique to adjust the
national coefiicients for regional analysis. The reason is based largely on the availability of

secondary data to support the use of the method.

Defining Regions

Economists look at the behavior of business enterprises, consumers, and financial
and governmental institutions mostly from a national standpoint, and prefer to analyze an
economy as if it were located at a point. They usually neglect the spatial arrangement of
the social order or the order of the economy. Regional economists, on the other hand,
examine households, retail stores, wholesale outlets, manufacturing plants, banks, farms,
and mines located in a region as an order and pattern that can be studied and understood
(Nourse, 1968). More broadly, Isard (1975) mentioned that the regional scientist is
concerned with all of these topics and others only when they relate to locations, local
areas, cities, and regions. To regional scientists or economists, then, a region is not merely
an arbitrarily demarcated area; rather it is an area that is meaningful because of one or

more problems associated with it which they want to examine and solve.

Concerning regional economics, Blair (1991) described how regional economists
distinguish among types of regions. The first is "functional regions", which are

distinguished by the degree to which they are integrated or the extent that their component

39

parts interact. The second is "homogenous regions", which are defined on the basis of
internal similarity such as economic similarities (e. g. common economic development
problems), cultures, climates, or other common activities. The third is "administrative
regions", which are formed for managerial or organizational purposes. Compared to
firnctional or homogenous regions, administrative regions are normally more clearly
delineated because they are formed to clarify spheres of activity for businesses or
governments, and they fiequently become the basis for policy. However, administrative

regions may not be distinct from homogenous or functional regions.

Similarly, Hoover (1975) distinguished two different types of regions, the
homogenous and the nodal. A homogenous region is demarcated on the basis of internal
uniformity. What is true for one part of the region is true for the other parts, and the
various part resemble each other more than they resemble areas outside the region. In a
nodal region, fimctional integration is the basis of the correlation or community of interest
within such a region. For instance, a city and its surrounding commuting and trading area
make a nodal region. The parts with the main concentration of business and employment
are in sharp contrast to the residential areas, but they are tightly linked to them by flows of
labor, goods, and services. Typically, in a nodal region, there is a single main nucleus,
perhaps some subordinate centers, and the mral remainder of the territory in which the
internal change of goods and services is very tight. By contrast, in a homogenous region,
the usual basis is common exportable outputs; the whole region is a surplus supply area
for such an output, and consequently its various parts have little or no reason to trade

extensively with one another.

In reviewing economic impact studies of recreation and tourism, defining the
impacted region has become a practical issue. Most studies define their impacted regions
in administrative or political terms, such as one of the standard regions or a local authority

area, even though they show some economic rationale (Johnson and Thomas, 1990; and

40

Diamond and Chappelle, 1981). The study by Kalter and Lord (1968) selected Walworth
County as the impacted region. The reason was that the county possesses a diversified
economy sufiicient in size to encompass a wide range of economic activities and because
recreational services were mostly exported (users of recreational facilities are largely fi'om
other regions). Theoretically, these delineations impose a slight arbitrariness because the
economic impact of a project is normally one of diminishing geographical response rather
than a sudden cut-off at some boundary. The size of the multiplier will, of course, vary
with the size of the reference region; the smaller the region the larger are the import

leakages and the smaller the multiplier (Johnson and Thomas, 1990).

Stevens and Rose (1985) provided more detail on this topic. They mentioned that
the delineation of the impacted region depends greatly on the policy issues being examined
and the hierarchical rank of the policy maker, and should consider characteristics of the
region. A region is typically specified according to a single or a mix of major
characteristics in the physical, political, or economic realm. In the case of assessing
economic impacts of recreation and tourism, they defined an impacted regions into five
hierarchical levels. The first is a "recreation site", the smallest area, distinguished primarily
on the basis of physical attributes (a lake, forest, ski slope, etc.). The second level is an
"economic support area", an area in which most expenditures take place in close proximity
to the recreation site (this area covers both direct and indirect impacts). To establish a
meaningfirl cutoff point, the authors suggested two possible descriptions for support area.
One is the "trading area", an area in which the majority of economic transaction occurs
between any of its subareas. Another delineator is the "labor service area", an area in
which the majority of the pertinent labor force, including commuters, resides. The third
hierarchical level is the "travel corridor", an area from the consumer residence to the
recreation site in which the impacts along travel route take place. The fourth is the

"consumer residence area", an area of recreationists' origin. Typically, it is larger than the

41

support area, however in the case of smaller sites, it need not be. The fifth level is the
"extended region" or national level, the ultimate source for all the goods imported into any
of the other areas. A similar discussion of this topic was also presented by Propst et al.
(1985)

In defining a region in economic impact analysis, it is important to consider that:

..... any attempts at regional delineation and aggregation must consider the
additivity problem. That is, there are sometimes difl‘erences 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) (Propst et al., 1985; p. 57)".

The Linkage Between Recreational Expenditures and Input-Output Analysis.

To estimate regional economic impacts of tourism or recreation activities, basically
there are two tasks that should be accomplished: (1) estimating visitor expenditures or
spending, and (2) estimating regional economic models (this study uses an input-output
model). One of the objectives of estimating visitor spending is to develop a final demand
vector. In this case, tourism spending bridged to the input-output model is treated as a
final demand sector. The problem is how to convert visitor spending which is estimated in
broad categories into a final demand vector relevant to the sectors present in the region.
The step is known as "margining" and "bridging" processes (Stynes and Propst, 1992).
The basic idea behind this step is to delineate the impacted sectors in order to get a
suitable bridge table containing sectors appropriate to the input-output model of the
region for transforming spending categories to specific sectors. The economic impacts,
then, are obtained by applying the final demand vector to the input-output model for the
region being studied.

Accurate estimation of visitor spending profiles for recreation or tourism market

segments is very important, because it critically underlies the level of direct, indirect,

42

induced sales, value-added, income or employment impacts generated by the spending in

the region. Related to this, Pedersen (1990; p. 6) mentioned that:

" ..... in light of the importance of the final demand estimates for input-output
analysis, the reliability of recreation participation levels and spending profiles may
be at least as important and in need of review as is the input-output model used to

generate the economic impacts estimate".

Using the models constructed by Stynes and Propst (1992), total spending can be

estimated as follows:

S, = N t"; M, * s, (15)
where Sj: total spending in category j, j = 1, ...J,

N: total number of visitors,

m: number of segments,

Mi: segment is share of total visits, i = 1,...m, and

Sij3 average spending of a member of segment i on category j. These are the

"spending profiles".

When using an input-output model, the final demand vector and the impacts are

formulated as:
FD], = 5:]- * Bjk (16)
I = R * FDk (17)

where FD: a final demand vector of spending changes,
B: a bridge table to convert from spending categories j to sectors k,
R: represents an input-output model multipliers (formally R would be the
Leontief inverse matrix), and
I: impacts, expressed as changes in output, income, or employment resulting

from the change in final demand.

Recognizing the strengths, limitations, and requirements of procedures or
techniques addressed above, and realizing the availability of data and information pertinent

43

to the expected study's objectives, this study is directed at applying the input-output
analysis to the Gede Pangrango National Park. The Simple Location Quotient (SLQ)
technique is utilized for regionalizing the national input-output model. Furthermore, based
on the characteristics of the study area, the on-site or direct interview is applied for
primary data collection involving visitation and spending profiles. Details of these

procedures are sequentially explored in the next chapter.

RESEARCH METHODS

Introduction

In estimating recreational spending as well as the corresponding impacts, several
steps involved in this chapter are developed sequentially with the study's objectives.
Therefore, describing procedures for estimating spending profiles and their appropriate
requirements are presented in the first part of this chapter. These steps consist of:
(1) developing visitor segments, (2) developing spending categories, (3) developing
instruments used in the survey, (4) defining the region, (5) data collection procedures
covering primary data collection, secondary data collection, and enumerators recruitment,
and (6) developing spending profiles. Then, in the last part of this section, a procedure for
estimating the economic impacts in the B-C-S region based upon an available input-output
model is addressed. This involves converting and matching the spending profiles to the
final demand vector (i.e., bridging and margining process) and applying this final demand

vector to the input-output model to estimate the impacts.

Visitor Segments
The purpose for developing visitor segments in this study is to get relatively

homogeneous spending patterns within the aggregates of visitors. Furthermore, spending
patterns developed properly for visitor segments will facilitate selectively choosing and
adjusting a set of profiles to fit a particular application (Stynes and Propst, 1992). This
study assumes that spending patterns will vary across the residencies, the occupations and
the types of activities among the visitors. To be appropriate for spending profiles analysis
as well as impact analysis, the visitors were separated:
(1) By residency: residents and nonresidents,
(2) By occupation: students and nonstudents, and
(3) By activity: hiking, recreation, and other activity.

44

45

Such segments were developed to get linkage between primary and secondary data used in
this study. Residents are the visitors who come from the B-C-S region, and nonresidents
are those who come fi'om outside the B-C-S region. The term "student" means anyone
who has been in formal education until the time of the survey and does not have
employment yet.

Visitor segments by activities were derived and adjusted fi'om categories used by
the park management in its visitors' registration process. In the process of getting a
permission document, visitors were registered into 1 of 4 categories based on their
activities which were: (1) hiking, (2) recreation, (3) education/research, and (4) other
activities. The "hiking" category covers the visitors whose activities takes place in the
area of 2,000 m above sea level or more (including those who hike to the top of the park).
The "recreation" category represents the visitors whose activities include sightseeing
(diversity of flora and fauna), photographing, visiting the lakes and waterfalls,
relaxing/pleasure, and picnicking. These activities take place in the area lower than 2,000
m above see level. This study combined "education/research" and "other" categories into
one category, called "other" category, because of small portion of visitors participating in
education/research activities (approximately of 1.4 % during 1993/ 1994). Thereby, in this
study, the "other" category included activities such as camping, researching/ studying,
visiting holy places, making movie fihns, and other activities.

Realizing that visitors may visit more than one recreation site, besides the park,
during their trips, this study also grouped the visitors based upon the purpose of visit
(primary or secondary visit). Visitors' expenditures were divided by the number of sites
visited. Thus, only a portion of the multi-site expenditures were attributed to GPNP. By
combining those four visitor categories, a preliminary set of 24 visitor segments was

resulted.

46

Spending Categories

Developing and recording detailed spending categories permits flexibility in the
process of data aggregation and facilitates the process of bridging spending data to an
input-output sector. This study accounted for trip-related spending (Table 4), which
covers spending at home, en route, and at the destination. Twenty-eight categories were
combined into 5 major groups: (1) transportation expenses (7 categories), (2) lodging
expenses (2 categories), (3) food and beverage expenses (2 categories), (4) outside market
expenses (5 categories), and (5) other expenses (12 categories). Outside market
categories represent expenses on agricultural products (fi'uits, vegetables, other food
crops, fishes, and plants) purchased outside the marketplaces. These expenses are not
uncommon in the vicinity of the park. The amount of trip-related spending for each
category recorded during the survey is the sum of spending made before and after leaving

the destination (on the return home).

Survey Instrument

Though used extensively in the United States, a mailback questionnaire is not
commonly used in the area of the study. The author predicted the survey responses would
be very low with the use of an unfamiliar mailback questionnaire approach. Instead, this
study used a five-page on-site questionnaire for primary data collection. Seventeen
questions in the questionnaire focused on getting information about types of visitors, types
of activities, origins, occupations, trip characteristics, and trip-related spending made by
the visitors (see questionnaire in Appendix A). With direct interviews, it took
approximately 10 minutes to fill out the questionnaire. The questions were structured in
such a way that there was no possible open-ended answers besides spending amounts.
The questions were also constructed in a manner to meet the requirements of developing
visitor segments, spending profile estimates, and impact analysis. To capture detailed

information on spending for each category, respondents had to identify type of spending

47

Table 4. The 28 detail spending and their 5 aggregate categories, GPNP.

 

 

 

 

 

 

Detail spending categories Aggregate categories
1. Gas and oil Transportation Expenses
2. Repairs and washing
3. Spare parts
4. Parking fees and toll (road, bridge, etc.)

5. Fares/tickets
6. Rent
7. Fares for recreation vehicles
8. Hotels, motels, rental homes, cottages, etc. Lodging Expenses
9. Campgrounds (outside the park)
10. Grocery stores, convenience, liquor, vendor, soft Food and Beverage
drink, and other food and beverage stores (for eating Expenses
and drinking off-premises)
11. Restaurant, diners, catering, bars, and other eating and
drinking places
12. Fruits Outside Market Expenses
13. Vegetables
14. Other food crops
15. Plants (including ornamental plants)
16. Fishes (including ornamental fishes)
17. Rental fees for recreation equipment, rental horse, etc. Miscellaneous Expenses
18. Cigarettes
19. Insurance fees

20. Fees for guide services

21. Admission to recreation sites

22. Admission to tourist attraction

23. Other recreation expenses (billiard, golf, movie,

fishing)

24. Camera fihn or video tape purchase

25. Film developing or video tape processing

26. Souvenirs or gifts

27. Clothing (including footwear)

28. Other expenses

 

 

48

(group or individual spending), the spending within the region being studied, and the
spending outside the region.

Besides the questionnaire, a brief map and a list of spending categories were
distributed to the respondents during interviews to assist them in locating places where
they make expenditures and in tracking systematically the categories being asked. In
addition, these instruments were pretested and evaluated before the survey was conducted.
This took place in the main entrance gate (Cibodas) and involved the author, a rrriddle stafi‘
of the GPNP, and four enumerators (one fi'om each entrance gate where the survey would

be conducted).

Defining the Region

Spending profile estimates and impact analysis require the development or
delineation of the region of interest. GPNP is located in West Java province (similar to a
state in the US.) and surrounded by three districts: Bogor, Cianjur and Sukabumi (B-C-S).
These districts, with the total area of about 1,094,120 hectares (West Java, 1992), are
located in three out of seven difi‘erent regional development areas defined by the
province's government. Bogor, whose population was 3,953,521 in 1990, is a part of the
Botabek (Bogor, Tangerang and Bekasi) regional development area; Cianjur, whose
population was 1,662,067 in 1990, is a part of the Bandung Raya (Bandung and Cianjur)
regional development area; and Sukabumi with a 1990 population of 1,968,190 is in
Sukabumi regional development area. For these three regional development areas, the
province's government focuses outdoor recreation development on B-C-S districts (Bogor
Agriculture University, 1992).

In terms of outdoor recreation, the B-C-S districts have similar natural resources
for supporting tourism development. The B-C-S is also known as a popular area because
in part of its good accessibility and of its strategic location, that is in between of two
populated big cities (Jakarta with 8,222,515 of population and Bandung with 5,258,247 of

49

population; both are in 1990). Another characteristic of the region is that it also provides
diverse opportunities such as hotels or motels, restaurants, grocery stores, transportation
agencies or services, and souvenir shops for supporting recreation and tourism activities.
The B-C-S region was selected as the region concerned in this study. Thus, the
terms "residents" or "nonresidents" and spending "within" or "outside" the region refer to
the region defined in this section. Related to literature reviews described in the previous
chapter, this region refers to "homogenous region" categorized by Blair (1991) and
Hoover (1975). However, the term "administrative region" also applies to this region

because its border is demarcated on the basis of its administrative geography.
Procedure of Data Collection

Primary Data

To obtain demographic, trip characteristics and trip-related spending data, on-site
interviews were conducted at four main entrance gates to the park (Cibodas, Situgunung,
Selabintana, and Gunung Putri) simultaneously between January 22 and April 17, 1994.
Interviews were conducted on every week-end day (Saturday and Sunday) and on a
selected week day (Monday through Friday). Using a systematic random sampling
technique, individual visitors (the sampling unit) to the park were selected. The reasons
for this approach are: (l) to get a linkage with secondary data used in this study (visitor
data basis), (2) to capture the direct portion of tourism expenditures related to the
recreation site (GPNP); tourism expenditures can be collected by using business receipts
fiom the establishment's owners; however, then techniques to separate proportionately
expenditures stemming fiom recreationists and non-recreationists, or recreationists of a
particular recreation site and those of other sites are required, and (3) this study is

concerned only with recreation impacts of a small geographic area stemming from a

50

particular recreation site (see detailed explanations in F rechtling (1987) and Brown and
Connelly (1992)).

The selection of sampling units (respondent) was divided into two procedures.

For the weekend days' survey, each tenth visitor to the park was selected as a respondent.
However, for the weekdays' survey, the selection was developed based upon group size:
(1) if the group consisted of 50 persons or more, every tenth person was selected,

(2) between 10 to 49 persons, every fifth was selected, and (3) less than 10 persons, every
second person was selected. The reason of developing such procedures is because of
different participation patterns of visits during weekend days and weekdays. In the case of
GPNP, the visitation is very low during weekdays (approximately 10%) and high on
weekend days.

The advantages of using the on-site interview technique in this study are: (1) the
respondents still remember the activities and trip-related spending that has been made (at
least up to the interview); (2) if the spending was made in group, respondents could easily
get the information, in the case the respondent did not recall, because the group was still
there; (3) the technique could reduce problems with respondent illiteracy; and
(4) problems of blank responses (in the questionnaire) could be eliminated. Thus, the on-
site interviews could increase the probability that the respondents would be able to provide
accurate estimates of spending and information on where the spending was made.

During the interviews, the respondents were asked to answer questions provided in
the questionnaire. A brief map and a list of spending categories were also distributed to
the respondents to facilitate them in tracking the subjects being asked by the enumerators.
To meet the needs of impact analysis, the respondents were requested to report the
purpose of visit as primary, secondary or unimportant. In addition, they were also asked
to provide information on a regional basis. For instance, the respondents were asked to
report if the trip-related spending was being made within or outside the B-C-S region.

They were also asked, for example, if they were students or nonstudents to assist in

51

segmenting visitors. To estimate detailed categories of trip-related spending, the
respondents were requested to differentiate some spending information into separate
categories. For example, spending for food and beverages was tallied separately for
restaurants and grocery stores. Furthermore, group and individual spending was

separated.

Secondary Data

The survey only covers data on the first quarter of the year (1994). Thus, the
survey period does not allow for reporting of results on an annual or multi-seasonal basis.
To estimate trip-related spending on an annual basis, annual visitation data are required.
These data were derived and recorded from visitor registration documents for 1993/1994
(April 1, 1993 to March 30, 1994) administered by the GPNP office. Information
recorded from the documents contained the dates, purposes of visit, places of residence,
occupations, group sizes, and the gates through which the visitors enter the park. Using
these secondary data, the total number of visitors (on annual basis) by origins,
occupations, and primary activities was developed. Thus, there was a linkage between
primary and secondary data in terms of visitor segments. Other secondary data such as
visitation data in other recreation sites surrounding the park, demographic and economic
data of the region, and development policies of the park were also collected to support
this study.

Enumerators Recruitment

For conducting the survey, thirteen GPNP stafi‘ members were recruited as
enumerators (8 of them are permanent enumerators and the rest are additional
enumerators), and they were distributed at the four gates where the survey was conducted.
The additional enumerators were utilized in case of the permanent enumerators could not

handle the interviews during peak visitation times. The recruitment was based upon

52

consultations with and recommendations from the park manager, Mr. Wahjudi Wardojo,
M.Sc. Several stafl‘ members had experience in conducting this kind of survey, and in
addition, some of them were certified to do so. In these circumstances, intensive training
was not really needed. The location of survey was also the place where they work so they
were extremely knowledgeable about the local situation. In addition, the enumerators also
live very close to the survey location so that transportation costs did not handicap the
conduct of the survey. Another advantage was that the supervision could be very effective
because the park manager was also involved.

Regardless of these benefits, detailed explanations about the survey and the survey
instrument were presented. To test and evaluate the capability of the enumerators as well
as the efl‘ectiveness of the survey instruments, a pretest was conducted a week before
actual survey work started. Also, the author did supervise and evaluate the results weekly
to identify opportunities for improvement. Finally, given their dedication, a certificate was
presented to each enumerator after the survey was completed. As staff members, this
certificate is very valuable for their careers because it means an additional credit point they

save in pursuing the next rank.

Developing Spending Profiles

To develop trip-related spending profiles, the unit of measure used in this study is
rupiahs (the Indonesian currency: US. $1.00 equal to Rp 2,150.00 in April 1994) per
visitor per day. There are four units of measures commonly used to develop spending
profiles: (1) dollars per party per trip, (2) dollars per person per trip, (3) dollars per party
per day, and (4) dollars per person per day (Stynes and Propst, 1992; and Chadwick,
1987). The use of rupiahs per visitor per day as the unit of measure is based upon the
following rationale:
(1) Visitors who come to the park are mostly day users (approximately 85%), so that

fewer visitors spend nights during a trip to the park.

53

(2) Most visitors depend less on private transportation than on public transportation.
Thus, transportation costs more likely relate to transportation fares than to gasoline
expenses.

(3) Group types of visitors are more likely dominated by friends or business associates
than by families or relatives. This implies that the trip-related spending is more likely
shared by individuals rather than an individual paying the group expenses.

(4) Few young children (six years old or less) participate in the trips. In this case, the
effect in calculating spending per person basis due to children participation would not
be a big problem. Moreover, children are free from admission ticket charges for entry
into the park.

To develop trip-related spending profiles and spending totals, the step-by-step
procedure undertaken in this study is summarized below:

(1) Calculate the spending per visitor per day for each spending category by dividing
reported spending by group members and nights spent.

(2) To take account for expenditures spent by the visitors whose visits to the park are as
secondary purpose, their reported spending was divided by the number of sites visited
to get the spending per visitor per day associated with the GPNP.

(3) Determine the average spending for spending categories by dividing the sum spent by
all respondents by the total number of respondents.

(4) Determine the average spending for a member of each segment for each spending
category. The calculation resulted from dividing the spending for a segment by the
number of segment members. These calculations yielded the spending profiles
(average spending by segment and by spending category).

(5) Find the total spending by segment for detailed spending categories. This resulted
from multiplying the appropriate segment spending profile (step (4)) by the number of
segment members in the population. Here, visitor segmentation for the population

relied on the secondary data derived fiom visitation data administered by the park

54

management (annual basis). In case that segmentation could not be calculated from
the secondary data, the estimation was done by utilizing the proportions of segments
(segment's market share) in the sample (i.e., weighted averages). Summing the total
spending (annual basis) across segments provides an estimate of total final demand
vector by spending category. This vector can be used in impact analysis for the region

(described in next section).

Available Input-Output Models

An input-output model for the B-C-S region is not yet available. In addition,
constructing a regional model or adjusting available input-output models still requires
related data which are beyond the possible time and budget of this study. Therefore, for
estimating regional economic impacts, this study relied on secondary data provided in the
1990 Indonesian Input-Output Model (national level). Of course, the results would not be
the true values or measures of the impacts; however, they provided estimates of the
relative magnitudes of the impacts. The estimates were calculated by the use of an
allocation technique described below.

The 1990 input-output model (published in March 1994) is the fifth table produced
by the Central Bureau of Statistics (CBS) of Indonesia. The previous tables were
produced in 1971, 1975, 1980 and 1985. The characteristics of the 1990 input-output
model are as follows:

(1) It represents two transaction matrices of 66 sectors and 19 aggregated sectors.

(2) It treats imports as both competitive and non-competitive sectors (there are two
difl‘erent transaction tables).

(3) The transaction tables are developed based upon both consumer prices and producer
prices.

(4) Output multipliers developed in this model is Type I output multipliers.

55

(5) The sector classification is based on KLUI (the Indonesian version of industry
classification) and KKI (the Indonesian version of commodity classification). KLUI,
constructed by CBS, is a modified version of the ISIC (International Standard
Industrial Classification). Two main criteria are considered in classifying sectors:
(a) a commodity unit grouping which is based on the criterion of physical similarities
of goods and services, and (b) an activity unit grouping which implies a notion that
goods and services produced by similar activities are grouped in the same sector.

For the purpose of this study, to gain estimates of output multipliers appropriate for the

B—C-S region, the adjustment process relied on the 66-sector transaction table at

consumer prices in which imports were treated as competitive sectors.

Impacts Estimate Procedures
One of the many applications of spending profiles developed in this study is to
estimate the economic impacts to the region. The spending profiles can be applied either
in total or by segment to generate the impacts. To get more accurate impact estimates,
several additional steps (extending from the steps to estimate spending profiles) are
developed as follows:

(1) Adjusting the spending profiles-- This step involves differentiation of spending
generated by resident and nonresident visitors, and spending within and outside the
region. To derive spending profiles applied to impacts estimates, this study used trip-
related spending within the region generated by visitors from outside the region
(nonresidents).

(2) Margining process.- This process is to identify the proportion of the spending that is
associated with the goods that are produced locally (within the region of concern) as
many goods that are purchased by visitors may not be produced within the region.
Only the margin associated with these purchases are included in the impact estimates.

Since there are not available data for estimating this proportion, this study used

56

margins for comparable sectors in the US. (Stynes and Propst, 1992). These
proportion were estimated by using the percentage shares of national (the U. S.)
personal consumption expenditures in the category "other recreation" published by the
Bureau of Economic Analysis, US. Department of Commerce (1984).

(3) Developing a bridge table.-- This step provides a table containing sectors afi‘ected by
tourism activities. The table was derived from the national input-output model
currently available.

(4) Developing a final demand vector by sector.- The spending profiles from step (1)
above contain spending in broad categories. By matching these spending data with the
bridge table, a vector representing spending profiles by sectors is created; it is the final
demand vector by sector. In this case, the final demand vector expresses the additional
nonresident visitors' spending in the region in terms of sectors in the input-output
model or sectors as defined by the model.

(5) Determining appropriate multipliers- Since there is not an input-output model
available for the B-C-S region, for estimating proportional multipliers appropriate for
the region, an adjustment (non-survey) technique was utilized. This study applied
the Simple Location Quotient (SLQ) technique to regionalize the national model in
order to estimate the regional multipliers. Relevant to the characteristics of the
national model, the regional multipliers developed herein are the Type I output
multipliers for the 66 producing sectors. Due to the availability of data, this study
used gross domestic products (GDP) by sector as the weighted factor instead of gross
output or employment by sector usually used. As noted by Miller and Blair (1985),
instead of gross outputs, other economic indicators such as employment, value added,
income, and so on, by sector can also be utilized to estimate the location quotient (LQ)
values. Thus, procedures utilized to estimate the regional output multipliers fi'om

adjusting the national model include:

57

(a) Calculation of the location quotient by sector. -- This was done by applying
Formula (6) in previous chapter, where x,- is a regional GDP in sector i, x is the
total regional GDP, X i is a national GDP in sector i, and X is the total national
GDP. The result was a vector of the location quotients for the 66 producing (row)
sectors.

(b) Calculation of the regional technical coefiicients matrix.-- This table was estimated
by multiplying the national technical coefficients matrix by the vector of location
quotients fiom step (a). However, as described in previous chapter, for the
sectors with LQs 2 I, we apply the national technical coefficients of those sector
directly for the corresponding regional sectors. For the sectors with LQs < 1, the
national technical coefiicients should be adjusted by multiplying these coefficients
by the LQs of corresponding sectors from step (a) to get the regional coefiicients.

(c) Calculation of the total regional output multipliers.-- Prior to the calculation, a
Leontief matrix should be calculated; that is, by subtracting the regional technical
coemcients matrix (step (b)) fi'om an identity matrix which is a matrix with 1 in its
diagonal and 0 in its other cells. Then, the estimated total output multipliers (direct
and indirect efi‘ects) by sector are the column totals of the inverted Leontief matrix
itself. This step resulted in a vector of the total output multipliers for 66 sectors.
There was no aggregation or disaggregation made in this step.

(6) Estimating impacts.-- There were three regional economic impacts estimated in this
study: output, income, and employment, which were measured by sectors. The
procedures are as follows:

(a) Impacts on outputs (X ,-(,)).- The values of these impacts represented the total
output (direct and indirect effects) of each sector in the B-C-S region due to the
changes in its final demand. To obtain the measures, the final demand vector by
sectors fiom step (4) was multiplied by the vector of total output multipliers for

the region developed in step (5).

58

(b) Impacts on income (Ii(,)).- These values indicate the total payment (direct and
indirect efl'ects) paid by each producing sector to households to satisfy the changes
of its final demand. To estimate the impacts, the study follows an assumption of
linearity relationship of income and outputs, which means that change in outputs
will be followed proportionally by changes of income. To satisfy this relationship
for estimating the impacts, the income-output ratio by sectors should be
determined. These magnitudes represent the amount of payment should be paid by
each sector to household in order to produce one unit of its output. Technically,
the ratios were gained simply by dividing the row cell of wage and salary by the
row cell of the total output addressed in the input-output table. Since these ratios
are not available for the B-C-S region, this study assumes that the national ratios
are appropriate for the B-C-S region, though these probably are not entirely true.
Then, the impacts on income were estimated as:

’i(r)="i(r)xXi(r) (18)
where 1“,): total income generated by sector i in region r (B-C-S),

i,~(,.): income-output ratio of sector 1' in region r, and

X ,-(,-): total output (direct and indirect) of sector i in region r from step (a).

(c) Impacts on employment (Ei(,)).-- These values imply the total employment
needed by each sector in order to fulfill the changes of its final demand. To
estimate the impacts, this study also follows an assumption of linearity relationship
of employment and outputs, which means that change in outputs will be followed
proportionally by changes of employment. To satisfy this relationship for
estimating the impacts, the employment-output ratio by sectors should be
determined. These ratios represent a number of employees needed by each sector
in order to produce one economic unit of its output. Similar to the income-output
ratios, the employment-output ratios for the B-C-S region are also not available.

To estimate these ratios, this study also assumes that the national ratios are

59

representative for the B-C-S region. The ratios were calculated by dividing the
number of employees by the total output derived from the input-output table for
each corresponding sector. Then, the impacts on employment were estimated as
follow:
Ei(r) = ei(r) x Xi(r) (19)
where Em): total employment generated by sector i in region r (B-C-S),
e K"): employment-output ratio of sector i in region r, and
Xi(r)3 total output (direct and indirect) of sector i in region r from step (a).
Additional assumptions for applying the procedures used to estimate the
impacts are that the level of technology and the productivity level of sectors are the
same for both in the nation and the region, and the ratios (income-output and

employment-output ratios) remain stable over time.

RESULTS AND DISCUSSION

Introduction

This chapter is divided into two parts: results and discussion. The results are
divided into six major sections. The first section provides information and discussion
about sample sizes and response rates. The second section discusses selection and
development of visitor segments and their distributions, both in the sample and in the
population. Section three describes how the findings correlate to trip-related spending
followed by their distributions in the visitor segments. The next section reports the
process for developing output multipliers for sectors in the region of interest (derived from
the national input-output model). Section five presents the results of margining and
bridging processes pertaining to estimation of the impacted sectors. And the last section
presents the regional economic impact estimates by sector stemming from visitors' trip-
related spending. Additional results are summarized in Appendix A, A-3. The second
major part of the chapter, the discussion, focuses on additional aspects of the results and

the overall conduct of the study.

Results

Sample Sizes and Response Rates

A total of 934 respondents, defined as an individual visitors, were interviewed at
the park from January 22 to April 17, 1994 (Table 5). Nine hundred and twenty-six
visitors (99.1%) participated, while eight visitors (0.9%) declined to participate during the
survey. Because of unreliable responses, forty-five out of 926 samples were not usable,
leaving a sampling flame of 881 samples (questionnaires). In total, the author believes
that this high response rate indicates the effectiveness of direct or on-site interviews used
in the survey. Mailback interviews would receive a lower response rate; they are

uncommon in Indonesia.

60

61

Table 5. On-site interview samme sizes and percentage distributions, GPNP.

 

 

 

Description Number of respondents (11) %
Usable interviews 881 94.3
Nonusable interviews 45 4.8
Interview refilsals 8 0.9
Total 934 100.0

 

Distributions of the sample were observed across visitors' activity (hiking,
recreation, and other activity), occupation (student and nonstudent), and residency
(resident and nonresident) categories (Table 6). The "recreation" activity was the highest
(68.3%) as compared to the "hiking" and the "other activity" which were similar in
magnitude. By occupation, the "student" occupation was 52.6% whereas the
"nonstudent" occupation comprised 47.5% of the usable responses. Furthermore, the
"residen " and the "nonresident" categories were 40.9% and 59.1%, respectively.

Population data were derived fiom the annual visitation data from April 1, 1993 to
March 30, 1994 collected by the GPNP management. "Recreation" had the highest
percentages (65.6%) among the activity categories whereas the "student" was more than
three times (71.9%) than the "nonstudent". Furthermore, the "nonresiden " percentages
were ahnost twice the "residents" (61.1% and 38.9%, respectively). The general
distribution pattern within the sample was similar to those of the population.

Compared to the last five years of annual data, the total population in 1993/ 1994
was higher than that in 1992/1993. However, this was lower than the other four recorded
years (Appendix B, Table B-la). Currently, there is no explanation for the fluctuations.
This may have more to do with the accuracy of data recording conducted by the GPNP
management than with wide changes in visitation. Total visitation was also lower as

compared to the total visitation in two recreation sites near the park in 1993. They were

62

Table 6. Sample and population distributions by visitor category, GPNPa.

 

 

 

 

 

Visitor categories Sample Population Sample-
distribution distribution population
ratio
11 % N %

Hiking (T) 143 16.2 15,475 26.3 0.6

Recreation (R) 602 68.3 38,602 65.6 1.0

Other activity (0) 136 15.4 4,752 8.1 1.9

Student (S) 463 52.6 42,270 71.9 0.7

Nonstudent (N) 418 47.5 16,559 28.2 1.7

Resident (I) 360 40.9 22,866 38.9

Nonresident (Ct) 521 59.1 35,963 61.1

Total 881 58,829

 

a Sample data were based on the survey conducted fi'om January 22 to April 17, 1994,
while population data were derived fi'om the visitor registration book administered by
the GPNP management fi'om April 1, 1993 to March 30, 1994.

b Divide column 3 by column 5 for each category (e.g., Hiking (T): 16.2/26.3 = 0.6).

Cibodas Botanical Garden with 372,613 visitors and Manadalawangi Park with 68,169
visitors (Appendix B, Table B-lb).

To observe the degree of representativeness of the sample by category, this
analysis relied on the population distributions by category as the true representative. What
the author means with the representativeness of the sample is the proportional allocation
of the sample by category relative to the population, not the optimal allocation. Based
upon the population distribution, the "hiking" and the "student" were under-represented,
while the "other activity" and the "nonstudent" were over-represented. For the
"residency" and the "recreation" categories, both the sample and the population revealed

similar patterns. The sample representativeness is clearly indicated by the sample

63

population ratios in which the values under 1.0 indicate under-representation in the sample

whereas values greater than 1.0 indicate over-representation.

Combinations of the three categories resulted in 12 segments. When the sample
distributions were observed across the combinations, in general, their patterns indicated a
similar fashion as the categories' representations (Table 7). The categories that were over-
represented resulted in combinations that were also over-represented or at least equal
relative to the population. These were depicted by most segments of the "other activity"
and the "nonstudent" categories (i.e., R/N/I, R/N/Ot, O/N/I, and O/N/Ot segments).
Similarly, the categories that were under-represented generated combinations that
indicated under-representation or at most equal relative to the population. These were
associated with most segments of the "hiking" and the "studen " categories (i.e., R/S/I,
R/S/Ot, T/S/I, and T/S/Ot segments). For the other four segments, their distribution
patterns varied depending on what categories built those segments. Combinations of the
"hiking" and the "nonstudent" categories resulted in the segments with equal distributions
relative to the population (i.e., T/N/I and T/N/Ot segments). However, combinations of
the "other activity" and the "student" categories created the segments that were over-
represented relative to the population (i.e., O/S/I and O/S/Ot segments). The sample-
population ratio values (column 6) give more detailed information.

Several conditions may have affected the distribution patterns of the sample, they
include:

(1) Seasonality (over a year) was not covered by the survey (i.e., it was about three
months) so that the sample distributions did not truly reflect or represent the annual
population distributions.

(2) The survey was conducted during the school season so that fewer students participated
in leisure activities. In the case that the O/S/I and O/S/Ot segments were over-

represented, this was because of high intensity on camping activities, defined as a part

64

Table 7. Sample and population distributions by 12 visitor segments, GPNP.

 

 

 

 

 

Visitor segmentsa Sample Population Sample-
distribution distribution population
ratio
11 % N %
T/S/I 28 3.2 4,745 8.1 0.4
T/N/I 8 0.9 511 0.9 1.0
R/S/I 108 12.3 8,877 15.1 0.8
R/N/I 194 22.0 7,598 12.9 1.7
O/S/I 12 1.4 463 0.8 1.7
O/N/I 10 1.1 672 1.1 1.0
T/S/Ot 93 10.6 9,310 15.8 0.7
T/N/Ot 14 1.6 909 1.6 1.0
R/S/Ot 113 12.8 15,557 26.4 0.5
R/N/Ot 187 21.2 6,570 11.2 1.9
O/S/Ot 109 12.4 3,318 5.6 2.2
O/N/Ot 5 0.6 299 0.5 1.1
Total 881 100.0 58,829 100.0

 

a T/R/O: Hiking/Recreation/Other activity.
SIN: Student/Nonstudent.
I/Ot: Resident/Nonresident.
1’ Divide column 3 and column 5 for each segment.

65

of the “other activity” category, to inaugurate new students for particular schools (the
survey found groups of over one-hundred students).

(3) During the survey, there was one month of fasting (in March, 1994) for Islamic
people. This is common in the area of the survey, and people gather (to celebrate) or
visit holy places both before and after the Fasting month. This might have resulted in
higher samples captured in the survey as represented by the "nonstudent" (resident and
non-resident) with the "recreation" category (the R/N/I and R/N/Ot segments). Their
distributions in the sample were 22.0% and 21.2%, while in the population they were
12.9% and 11.2%, respectively.

(4) Other possibilities were instrument design and human errors (during interviews and
data entries) so that these affected the calculation and manipulation of data.

To show the adequacy of the sample, the statistical analysis is presented in the following

discussion part of this chapter.

Visitor Segmentation

The purpose of visitor segmentation is to capture relatively homogeneous trip-
related expenditures among groups of visitors, so that the variation in expenditures which
can create biases in the total estimates with respect to their patterns can be reduced. In
this study, the author assumes that type of visitor activities results in different spending
patterns. Consistent with visitor activity categories defined by the GPNP management,
this study differentiates visitor activities into hiking, recreation, and other activity
categories. The “other activity” category includes camping, studying/researching, visiting
holy places, and other activities. In order to conduct impact analyses, it is necessary to
separate spending that is generated by resident visitors (inside the region) and by
nonresident visitors (outside the region). Furthermore, the author also assumes that
spending stemming fiom students is different fiom that of nonstudents. Realizing that a

visitor may visit more than one recreation sites in the same trip, this study also groups the

66

visitors based on the purpose of visiting the park, primary or secondary purpose. Thus,
briefly, the preliminary visitor categories utilized in this study consist of the following:
(1) Type of activities: hiking (T), recreation (R), and other activity (0).

(2) Occupations: student (S) and nonstudent (N).

(3) Purposes: primary (P) and secondary (Sc).

(4) Residence: resident (I) and nonresident (Ot).

Other categories that do not fall under the above categories were dropped from the
sampling frame. This resulted in a reduction of the sampling frame fi'om 881 samples to
872 samples due to the existence of 9 samples categorized as occasional visits. These
samples existed in four visitor segments (Table 8). Using the proportion of reduction in
the sample, the population was reduced from 58,829 units to 58,466 units, with 363 units

dropped.

Table 8. Distribution of the sample and population dropped in four visitor segments,

 

 

 

 

 

GPNP.
Visitor segments Sample Population
11 n1 n2 N N1 N2

R/N/Ot 187 5 182 6,570 177 6,393
O/S/Ot 109 1 108 3,318 30 3,288
R/S/I 108 1 107 8,877 80 8,797
R/N/I 194 2 192 7,598 76 7,522
Total 598 9 589 26,363 363 26,000

 

Note: 11: Sample sizes before dropping.
n1: Number of samples dropped.
n2: Sample sizes after dropping.
N: Population sizes before dropping.
N1: Number of population units dropped (e. g., R/N/Ot: 5/187*100%*6,570=177).
N2: Population sizes after dropping.

67

Combining all categories described above yielded the preliminary set of 24 visitor
segments identified in Table 9. This table displays the sample distributions for all possible
visitor segments. Because 17 segments contained small samples (less than 30 samples
each), for reporting and analysis purposes, it was necessary to combine those small
segments into bigger segments (containing more than 30 samples each). The first
combination process resulted in a reduction fiom 24 segments (column 1) to 12 segments
(column 4). In this process the purpose categories (primary and secondary) were
dropped, but the spending data were used in estimating spending profiles. Still, there were
6 segments contained less than 30 samples each. To get larger samples for analysis, the
12 segments were narrowed into 8 segments (column 7). In the process, the occupation
categories (student and nonstudent) in the segments with the "hiking" as well as with the
"other activity" categories were combined into the visitor category (V). For example,
nonresident nonstudents who hike (the T/N/Ot segment) and nonresident students who
hike (the T/S/Ot segment) were combined as nonresident visitors who hike (the T/V/Ot
segment). In brief, the eight visitor segments, followed by the number of samples and the
proportion (to the total sample) for each segment in parentheses, are as follow:

R/N/Ot: recreation, nonstudent, nonresident (n=l82; 20.9%).
R/S/Ot: recreation, student, nonresident (n=113; 13.0%).
R/N/I: recreation, nonstudent, resident (n=192; 22.0%).
R/S/I: recreation, student, resident (n=107; 12.3%).

T/V/Ot: hiking, visitor, nonresident (n=107; 12.3%).

TN/I: hiking, visitor, resident (n=36; 4.1%).

O/V/Ot: other, visitor, nonresident (n=113; 13.0%).

O/V/I: other, visitor, resident (n=22; 2.5%).

In Table 9, within the "recreation" category segments (column 7), nonstudents
dominated the segments (32.3% were residents and 30.6% were nonresidents) as
compared to those of students (18.0% were residents and 19.0% were nonresidents). By

the residency category, residents and nonresidents shared almost equal proportions with

68

Table 9. Sample distributions by the possible visitor segments, GPNP.

 

 

 

 

 

 

 

 

 

 

_ Preliminary Combination1 . fibinationZ.
figment n 96 ' Segment n1 96 V Segment n2 96 "r
R/NlP/Ot 153 17.55 R/N/Ot 182 61.69 BIN/0t 182 30.64
RISIP/Ot 1 00 1 1 .47
BIN/Sc/Ot 29 3.33 BIS/0t 1 13 38.31 HIS/0t 1 1 3 19.02
R/S/Sc/Ot 1 3 1 .49
Subtotal 295 33.83 295 100.00
BIN/PA 169 19.38 R/N/I 192 64.21 BIN/l 192 32.32
RlS/P/l 97 1 1 .1 2
BIN/Sell 23 2.64 HIS/l 107 35.79 R/S/I 107 18.01
HIS/Sell 10 1 .1 5
Subtotal 299 34.29 299 100.00 594 100.00
TlN/P/Ot 14 1.61 TIN/0t 14 13.08 TN/Ot 107 74.83
T/S/PIOt 92 1 0.55
TlN/Sc/Ot 0 0.00 TlS/Ot 93 86.92
T/S/Sc/Ot 1 0.1 1
Subtotal 107 1 2.27 1 07 100.00
TIN/P/l 7 0.80 TIN/l 8 22.22 TN/l 36 25.17
T/SIP/l 28 3.21
TIN/Sell 1 0.11 178/1 28 77.78
T/S/Scll 0 0.00
Subtotal 36 4.13 36 100.00 143 100.00
O/N/P/Ot 4 0.46 OlN/Ot 5 4.42 ON/Ot 1 1 3 83.70
O/S/P/Ot 86 9.86
O/N/Sc/Ot 1 0.1 1 O/SIOt 1 08 95.58
OlS/SclOt 22 2.52
Subtotal 1 13 12.96 1 13 100.00
O/NlP/l 9 1.03 O/N/I 10 45.45 DNA 22 16.30
OISIPII 1 1 1 .26
O/N/Sc/l 1 0.1 1 013/1 12 54.55
O/S/Sc/l 1 0.1 1
Subtotal 22 2.52 22 100.00 135 100.00
Total 472 100.00 872 872
Note: HIT/O: Recreation/Hiking/Other activity.
NIS: Nonstudent/Student.
PISc: Primary/Secondary purpose.
Otll: Nonresident/Resident.
V: Visitor (Nonstudent and Student).

Preliminary: All possible combinations of five visitor categories.
Combination1: Combination of 'P" and "Se“ categories (e.g., R/N/Ot =R/N/P/Ot + R/N/Sc/

Combination2: Further combination of Combination1 where the 'S' and the 'N'

in 'T' and '0' categories are combined (e.g., TN/Ot=T/N/Ot +T/S/Otl.

' Percentages to the total samples.

" Percentages within the subtotal samples (e.g., R/N/Ot=182/295'10096 = 61.6996).

° Percentages within the ”Recreation in)”, "Hiking (T 1", and "Other 101' categories
(e.g., RIN/Ot = 182/594' 10096 = 30.6496).

69

the "recreation" category (50.3% for residents and 49.7% for nonresidents). However, in
all segments with the "hiking" and the "other activity" categories (column 7), nonresidents
dominated the segments (74.8% for the "hiking" and 83.7% for the "other activity"
categories) as compared to those of residents (25.2% and 16.3%, respectively). By the
occupation category, students had higher proportion in each segment with the "hiking"
and the "other activity" categories (column 6). They were 86.9% for the T/V/Ot, 77.8%
for the T/V/I, 95.6% for the ON /Ot, and 54.6% for the O/V/I segments. Conversely,
nonstudents generated more contributions in the segments with the "recreation" category.

Following similar steps as applied to the sample, Table 10 represents the
population distributions by possible visitor segments. Unlike in the sample, student
dominated the "recreation" category segments (column 7) (23.0% for residents and 40.7%
for nonresidents) as compared to those of nonstudents (19.7% were residents and 16.7%
were nonresidents). In the residency category, 42.7% were residents and 57.4% were
nonresidents. However, as in the sample, nonresidents dominated in the segments both
with the "hiking" and the "other activity" categories (66.0% and 76.0%, respectively),
while residents had 34.0% and 24.0%, respectively. Also, by the occupation category,
except in the ON/I segment, students had higher proportions in the segments both with
the "hiking" and the "other activity" categories (column 6). They were 91.1% for the
TN/Ot, 90.3% for the T/V/I, and 91.7% for the O/V/Ot segments.

In terms of regional distributions for the eight visitor segments described above,
there was a similarity between the sample and the population (Table 11). Nonresident
visitors contributed nearly identical proportion in the sample and the population (59.1%
and 61.2%, respectively). The same pattern was also showed by resident visitors (40.9%
for the sample and 38.8% for the population). However, some variations occurred when
the distributions were observed by the individual segment within the subgroups as
identified in column 3 and 6 (Table 11).

70

Table 10. Popuation distributions by the possible visitor segments. GPNP.

 

 

 

 

 

 

 

 

 

 

 

 

Preliminary Combination1 . Combination2.

Segment N‘ as ' Segment m as " Segment N2 as ‘
BIN/PIOt 5,374 9.19 BIN/0t 6.393 29.13 BIN/01 6.393 16.7
RISIPIOt 1 3. 767 23.55
RIN/SclOt 1.019 1.74 BIS/0t 15,557 70.67 BIS/0t 15.56 40.65
BIS/ScIOt 1 .790 3.06
Subtotal 21.950 37.54 21.950 100.00
RINIPII 6.621 1 1.32 BIN/l 7.522 46.09 BIN/l 7.522 19.66
BIS/Pll 7.975 13.64
BIN/Sell 901 1.54 BIS/l 6.797 53.91 BIS/l 6.797 22.99
BIS/Sell 622 1 .41
Subtotal 16.319 27.91 16.319 100.00 38,269 100.00
T/NlP/Ot 909 1 .55 TIN/0t 909 6. 90 TN/Ot 10. 21 9 66.04
TISIPIOt 9.210 15.75
TIN/Sc/Ot 0 0.00 T/SIOt 9.310 91.10
TlS/Sc/Ot 100 0.1 7
Srbtotal 10.219 17.46 10.219 100.00
TIN/Pll 447 0.76 TIN/l 51 1 9. 72 TN" 5. 256 33.96
T/S/Pll 4. 745 6.1 2
TIN/Sell 64 0.1 1 TlS/l 4. 745 90.26
T/S/Scll 0 0.00
Subtotal 5. 256 6.99 5.256 100.00 15.475 100.00
OlN/PIOt 239 0.41 OIN/Ot 299 6. 34 ON/Ot 3.567 75.96
OlS/PlOt 2.616 4.46
OINIScIOt 60 0.10 OIS/Ot 3. 266 91 .66
O/SlSc/Ot 670 1 .15
Subtotal 3.567 6.14 3.567 100.00
OINIPII 605 1.03 OINII 672 59. 21 ONII 1.135 24.04
OlSIP/l 424 0. 73
GIN/Sell 67 0.1 1 O/S/l 463 40.79
OISIScIl 39 0.07
Srbtotal 1.135 1.94 1.135 100.00 4.722 100.00
Total 56.466 100.00 56.466 56.466

Note: BIT/O: Recreation/HiklmlOther activity.

NIS: Non-Student/Student.

PISc: Primary/Secondary purpose.

Otll: Nonresident/Resident.
Visitor (Nonstudent and Student)

V:

° The entries are calculated by multiplying the sample's proportion of each segment (derived
form cohrrnn 2, Table 91 by the entries of the corresponding segment (derived from column 4,
Table 7 or column 6, Table 61. For example. the segment R/N/PIOt (5,374) results from

153/162 multiply by 6,393.

Preliminary: AI possble combinations of five visitor categories.
Combination1: Combination of 'P" and "Se" categories (e.g., BIN/0t =R/N/P/Ot-o-R/N/Scl0tl.
Combinaflon2: Further combination of Combination1 where the 'S' and 'N" in 'T“ and “0"

categories are combined (e.g., TNIOt a TIN/0t + TlS/Otl.

' Percentages to the total population.
' Percentages within the subtotal population le.g.. RINIOt=6.393/21.950°10096 =29.1396l.
' Percentages within the 'Recreation (61". "Top (T1'. and “Other (01" categories

(e.g., NN/0t88.393l38.269°10096 = 16.7096).

71

The eight visitor segrnents identified in Table 11 are used as the base of estimates
in subsequent trip-related spending by segment described in the next section. The trip-
related spending within the region made by the first four segments in this table is the basic
information for the economic impact analysis. In addition, spending generated by the
second four segments in this table could be derived to estimate the “economic

significance” of the GPNP to the B-C-S region (see definition of terms).

Table 11. Regional distribution of the sample and population by segment, GPNP.

 

 

 

 

 

 

Visitor segments Sample Population

11 °/oa ° ob N Iii/03 ° ob
R/N/Ot 182 35.34 20.87 6,393 17.88 10.93
R/S/Ot 113 21.94 12.96 15,557 43.51 26.61
TN/Ot 107 20.78 12.27 10,219 28.58 17.48
ON/Ot 113 21.94 12.96 3,587 10.03 6.14
Subtotal 515 100.00 59.06 35,756 100.00 61.16
R/N/I 192 53.78 22.02 7,522 33.12 12.87
R/S/I 107 29.97 12.27 8,797 38.74 15.05
T/V/I 36 10.08 4.13 5,256 23.14 8.99
ONII 22 6.16 2.52 1,135 5.00 1.94
Subtotal 357 100.00 40.94 22,710 100.00 38.84
Total 872 100.00 58,466 100.00
a Percentages to the subtotal.
b Percentages to the total.
Trip-related Spending

Trip-related spending in this study was the amount spent within the region of
interest (the B-C-S region). Estimates of average trip expenditures were based on the full
sample (n = 872 samples), including the zero spending. Because this study used direct
interviews to collect trip-related spending data, problems of blank responses on spending

(in the questionnaire) were eliminated. Across the sampling frame, the total average trip-

72

related spending for variable costs was Rp 9,505 per person per day (approximately equal
to US. $4.4). By applying the population sizes (by segment), total spending can be

estimated.

Avgngg Trimrelated Spgnding by Categog
Table 12 presents the distributions of the Rp 9,505 average total spending across

28 detailed spending categories and the 5 spending aggregates (identified as subtotals).
Among the aggregates, spending for miscellaneous items was the largest portion (40.0%)
in which about 50% related to admission and insurance fees (27.4% and 14.0%,
respectively). The second largest was spending for food and beverages (29.5%), followed
by transportation (18.4%), outside market (9.0%), and lodging (3.1%). Within the
transportation expenditures, spending for public transportation in terms of fare/ticket
(41.4%) and rental (23.2%) fees was the largest contributor as compared to that for
private transportation in the form of gas and oil expenses (23.1%). Within the food and
beverage expenditures, spending at grocery stores (58.9%) was higher than that at
restaurants (41.1%). Furthermore, visitors also spent more for fi'uits (51.7%) than for

other outside market items.

Av r Tri rel ted endin b en

The detailed trip spending profiles by segment (8 visitor segments) for 28 detailed
spending categories are identified in Table 13 (for the "nonresident" category) and
Appendix B, Table B-3 (for the "resident" category). The 5 aggregate spending
categories are shown as the subtotals in these tables. Trip-related spending variations
ranged from an average of Rp 6,889 for nonresident visitors with the “other activity”

category (the O/V/Ot segment) to Rp 21,812 per day for resident visitors with the

73

Table 12. Average trip spending (Rp. per person per day) within the B-C-S region for 28 detailed
expenditure items, GPNP.

 

 

 

Items Rp. 96 ' 96 6
TRANSPORTATION

Gas and oil 405.1 23.1 4.3
Repairs & washing 46.04 2.6 0.5
Spare parts 5.12 0.3 0.1
Parking 81 tolls 151.98 8.7 1.6
Fares/tickets 725.27 41 .4 7.6
Rent 406.76 23.2 4.3
Recreation vehicle fares 12.09 0.7 0.1
Subtotal 1,752.36 100.0 18.4
LODGING

Hotels 275.37 94.5 2.9
Campgrounds 16 5.5 0.2
Subtotal 291.37 100.0 3.1
FOOD AND BEVERAGE

Grocery 1,648.54 58.9 17.3
Restaurant 1,151.23 41.1 12.1
Subtotal 2,799.77 100.0 29.5
OUTSIDE MARKET

Fruits 443.15 51.7 4.7
Vegetables 172 20.1 1 .8
Other food crops 12.57 1.5 0.1
Plants 222.74 26.0 2.3
Fishes 6.14 0.7 0.1
Subtotal 856.6 100.0 9.0
MISCELLANEOUS

Equipment rental 122.73 3.2 1.3
Cigarettes 451.05 1 1.9 4.7
Insurances 530.68 1 3.9 5.6
Guide fees 34.96 0.9 0.4
Admission fees 1,041.99 27.4 1 1.0
Attraction fees 12.32 0.3 0.1
Other recreation fees 7.17 0.2 0.1
Film purchase 676.37 17.8 7.1
Film developing 441.07 1 1.6 4.6
Souvenirs 207.39 5.5 2.2
Clothing 136.9 3.6 1.4
Other expenses 142.48 3.7 1 .5
Subtotal 3,805.1 1 100.0 40.0
Total 9,505.21 100.0

 

' Percentages within the subtotal average (aggregate) spending.
" Percentages to the total average spending.
Note: The average spending is derived from the entire sample (n = 872).

74

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75

"hiking" category (the TN/I segment) (see the column totals in Table 13 and Appendix B,
Table B-3). For the aggregate spending categories, the average spending patterns varied
across the segments. Most "nonresiden " category segments reported that they spent
mostly for miscellaneous items, followed by food and beverages, transportation, outside
market items, and lodging expenses. Only nonresident students within the "recreation"
category (the R/S/Ot segment) spent more for outside market items than for
transportation expenses. More variation occurred when the spending patterns were
observed across the "resident" category segments. Resident visitors with the "other
activity" category (the ON /1 segment) spent the most on food and beverages, while the
other three segments spent the most on miscellaneous items. Spending on lodging was
identified as the lowest proportion in most segments, except for resident nonstudents
within the "recreation" category (the R/N/I segment); they had the lowest spending on
outside market items. Table 14 summarizes the average aggregate spending patterns by
rank for the eight segments (derived from Table 13 and Appendix B, Table B-3).

The patterns depicted by the average spending by segment confirmed the important
role of segmenting into homogeneous spending groups. Without segmentation, results
may be misleading based on interpreting the average spending; average spending patterns
based on the filll sample (Table 12) were difierent fiom those of utilizing the sample by
segment (Table 13). Other information derived from the survey, in which most segments
reported small spending on lodging, was that visitors to the GPNP were mostly the day

users. The fiequency data (Appendix A, A-3) also support this phenomenon in which only

76

Table 14. Average spending patterns (ranks) by segment for 5 aggregate spending
categories, GPNP.

 

 

Items R/N/Ot R/S/Ot TN/Ot O/V/Ot R/N/I R/S/I TN/I ON/I
Transportation 3 4 3 3 3 2 3 3
Lodging 5 5 5 5 4 5 5 5
Food and bev. 2 2 2 2 2 3 2 1
Outside market 4 3 4 4 5 4 4 4
Miscellaneous 1 1 1 l 1 1 1 2

 

Note: The rank 1 is the highest spending, and conversely the rank 5 is the lowest.

about 10% and 15% of respondents reported spent overnight before and after arriving at
the GPNP, respectively.

Within the transportation category, spending for fare/ticket and rental fees was
higher than that for gas and oil expenses in six out of eight segments. This implied that
public transportation was commonly used by visitors during their trips to the park as
compared to private transportation. From the frequency data (Appendix A, A-3) it is
reported that about 30% of respondents used private transportation to the park.

Within the food and beverage spending, all the "resident" category segments
reported much higher spending at grocery stores than at restaurants (varied fi'om about
5% to 28% in difierences). However, for the segments with the "nonresident" category,
one segment (R/N/Ot) had higher spending at restaurants, two segments (R/S/Ot and
TN/Ot) reported higher spending at grocery stores, and one segment (ON/0t) split
spending equally between grocery stores and restaurants.

Another important finding from the spending profiles estimates was that almost all
the "resident" category segments spent more total average spending as compared to the
"nonresident" category segments (see the row total of Table 13 and Appendix B, Table B-

3). This means that, in average, transfer of spending by most the "resident" category

77

segments is higher than new money injected by the "nonresident" category segments in the

B-C-S region. This situation was, in part, because:

(1) Only a part of nonresident visitors' spending, which was spending inside the B-C-S
region, was included in the estimation.» For some spending categories, this procedure
resulted in only a portion of the spending being captured (e.g., transportation, food
and beverage, and some of miscellaneous expenses).

(2) The small samples contained in some the "resident" category segments.-- This applied
to resident visitors within the "hiking" and the "other activity" categories (the T/V/I
and O/V/I segments) which contained only 36 and 22 samples for each segment,

respectively. The small samples, in turn, could produce high spending averages.

Tot_al_ Trip;related Sggnding by Sggr_nents
For the population, the total spending was Rp 613,616,248 (the sum of grand

totals displayed in Table 15 and Appendix B, Table B-4). By segment, resident visitors
within the “other activity” category (the O/V/I segment) contributed the lowest spending
with about 1.6% fiom the total spending (derived from column 8, Appendix B, Table B-
5). The highest spending was made by nonresident visitors within the "hiking" category
(the TN /Ot segment). The contribution was about 22.0% from the total spending
(derived fi'om column 6, Table 16).

In interpreting the role of segments in generating spending in the region of interest,
considerations should not only focus on the total spending made, but also on the average
spending displayed. For instance, resident visitors within the "hiking" category (the T/V/I
segment) created low total spending, but had the highest average spending among the
segments (column 8, Appendix B, Table B-3). In the filture, with proper management,

this segment might generate the highest total spending for the region.

78

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80

T Tri rel t ndin b A tivi ri

Table 17 presents the distribution of total spending for 5 aggregate spending
categories in the population by the activity categories (recreation, hiking, and other
activity categories). These were derived by adding the total spending by segments
identified in Table 16 and Appendix B, Table B-5 by the activity category. For example,
the total spending on transportation for the "hiking" category , Rp 38,396,526 (column 4,
Table 17), came from the total spending on transportation made by nonresident visitors
who hike (the TN/Ot segment), Rp 25,144,258 (column 6, Table 16), plus the total
spending on transportation made by resident visitors who hike (the TN /1 segment), Rp
13,252,268 (column 6, Appendix B, Table B-5).

Across the activity categories, in the population, the "recreation" generated the
highest total spending (53.7%) as compared to the "hiking" (40.7%) and the "other
activity" (5.6%) categories. The "recreation" also had the highest proportions of spending
across the aggregate spending categories. However, a similar order of aggregate spending
was depicted by the three activity categories in which the highest spending was on
miscellaneous items, followed by food and beverages, transportation, outside market

items, and lodging expenses.

To :11 Tri rel n in Ge u ion at ori

There are two steps were accomplished in estimating the spending by occupation
categories. The first was calculating the proportion of the "nonstudent" and the "student"
in total spending made by the segments that contained these categories. This case applied
to resident and nonresident visitors with the ”hiking" as well as with the "other activity"
categories. They were the TN/Ot and O/V/Ot segments in Table 16 and the T/V/I and
ON/I segments in Appendix B, Table B-5. It was because these segments combined the
”student" and the "nonstudent" categories into the "visitor" category. The calculation was

done by multiplying the percentage shares of the "nonstudent" and the " student"

81

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82

categories (column 6, Table 10) by the total spending of each corresponding segment. For
example, total spending of the TN/Ot segment, Rp 25,144,258 (column 6, Table 16), was
contributed by nonstudents of 8.9% (Rp 2,227,839) and by students of 91 . 1% (Rp
22,906,419). The second step was summing all the segments' spending into two groups of
spending, nonstudents' and students' spending. No adjustment was made in total spending
by the "recreation" category segments because their total spending was already divided
into nonstudents' and students' spending. The results of these summations are presented in
Table 18.

The table indicates that students accounted for 75.7% of the total spending within
the B-C-S region. This was about 50% higher than the total spending incurred by
nonstudents (24.3%). Across the aggregate spending categories, students spent higher in
almost all categories. The most striking feature was that nonstudents spent relatively
higher on lodging expenses when compared to students (73.4% and 26.6%, respectively).
However, both students and nonstudents reported that they spent more on miscellaneous
items, followed by food and beverages, transportation, outside market items, and lodging

expenses.

Total Tri related endin b R iden Cat cries

Table 19 provides the distributions of total spending made by nonresident and
resident visitors. The entries of the table were derived fi’om the total column of Table 16
for the "nonresident" category and total column of Appendix B, Table B-5 for the
"resident" category. In the population, nonresident visitors accounted for 56.0% of the
total spending, whereas the rest, 44.0%, was generated by resident visitors. They divided
their expenses more evenly on transportation (49.8% for nonresidents and 50.2% for
residents). However, resident visitors spent relatively more on lodging (55.3%) compared

to nonresident visitors (44.7%). For the impact analysis, the estimation was derived from

83

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84

the spending within the B-C-S region made by nonresident visitors which was 56.0% (Rp
343,795,012) of the total spending.

Regional Output Multipliers

In assessing total economic impacts of tourism activities, as described in the
literature review chapter, there are three components involved in the calculation: (1) total
number of visitors by segment, (2) average spending per visitor by segment, and (3) a
multiplier (output, income, or employment, usually). In this case, the estimates are
associated with the spending within the region brought by the nonresident visitors. So far,
the first two components have been calculated and totaled in the previous sections (see
Table 15). This section describes the calculation for estimating the regional output
multipliers.

Since there is no input-output table available for the B-C-S region, the regional
output multipliers were developed from the 1990 national 66-sector input-output model
using the Simple Location Quotient (SLQ) method. This method is a normal approach
used in some studies to regionalize an input-output model fi'om the national level. In
reducing from the national to the regional level, data on output by industry are usually
required to estimate the location quotient (LQ) values. However, in the case that these
data are not available (at regional level, for example), other economic indicators such as
employment, value added, income, and so on, by sector can also be utilized (Miller and
Blair, 1985). This study used gross domestic products (GDP) by sector, which were
available for eighteen aggregated sectors to develop the LQ values.

In the process of estimating the regional output multipliers, the number of sectors
were maintained as identified in the national model (66 sectors). As in the national model,
the regional output multipliers are Type I multipliers (direct and indirect changes).

Procedures utilized to reduce the national model to the regional model included:

85

(1) Calculation of the location quotients (LQs) for the eighteen aggregated sectors.-- This
was done by applying Equation (6) with GDP as the weighted factors. The results are
presented in Table 20.

(2) Calculation of the regional technical coefficients table (matrix A) from the national
table- The LQs were available in eighteen aggregate sectors (step (1)); therefore, to
get the LQs for detailed sectors (66 sectors), these LQs were disaggregated into sixty-
six sectors. In this case, sectors that are in the same aggregate are assumed to have
the same LQs. As explained in the SLQ method, for sectors with LQs 2 1, the
national technical coeflicients are directly applied for the regional level (see Equation
(7)). For sectors with LQs < 1, the national technical coeflicients should be adjusted.
The adjustment was accomplished by multiplying each row of the national coeflicient
table by the appropriate LQ of the sectors (see Equations (8) and (9)). Appendix B,
Table B-6 displays the results.

(3) Calculation of the regional output multipliers.-- This was based on the inverse of the
Leontief matrix; it was derived by subtracting the matrix A (step (2)) from an identity
matrix (a matrix with 1 in its diagonal and O in its other cells). The total output
multiplier for a sector, then, can be computed by summing sectoral column entries of
the inverted Leontief matrix (summarized in Table 21).

In interpreting the LQs, Miller and Blair (1985, p. 297) noted that:

”.... the simple location quotient has been viewed as a measure of the ability of
regional industry i to supply the demands placed upon it by other industries in the
region and by regional final demand".

For the sectors with LQs 2 1, it is said that the sectors are more localized or concentrated
in the region, and these sectors are export oriented. Conversely, if the LQs are < l, the
sectors are less localized in the region, and they are import oriented. Thereby, information
derived from Table 20 can be interpreted that 9 out of 18 aggregate sectors were more

localized in the B-C-S region, and their production surpluses were assumed to be exported

Table 20. The 1990’s gross domestic products by industrial origin at current market prices (billion

rupiahs) and the values of location quotients (LQs), GPNP.

 

 

Industrial Origin Regional GDP National GDP LQ‘
(The B-C-S)' (Indonesia)b
Agriculture, Livestock, Forestry and Fishery: 1,329.8 43,062.1 1.424
1. Farm Food Crops 989.1 26,0655 1.750
2. Farm Non Food Crops 46.5 5,056.4 0.424
3. Estate Crops 62.7 1,797.1 1.609
4. Livestock and Products 169.0 4,560.1 1.709
5. Forestry 5.9 1,931.3 0.141
6. Fisheries 56.6 3,651.7 0.715
Mining and Quanying: 18.9 28,7482 0.030
1. Crude Petroleum and Natural Gas 24,6954
2. Other Mining and Quarrying 4,052.8
Manufacturing Industries: 791.5 38,6015 0.946
1. Manufacturing Without Petroleum & Gas 30,2226
2. Petroleum Refinery 3,531.2
3. Liquid Natural Gas 4,847.7
Electricity, Gas and Water Supply 59.6 1,258.1 2.185
Construction 307.5 10,8278 1.310
Trade, Hotels and Restaurants: 971.5 32,1537 1.394
1. Wholesale and Retail Trade 765.5 26,8657 1.314
2. Hotels and Restaurants 205.9 5,288.0 1.796
Transportation and Communication: 195.8 11,0409 0.818
1. Transportation 191.1 9,734.8 0.905
2. Communications 4.7 1,306.1 0.166
Banking and Other Financial Intermediaries 16.4 7,902.3 0.096
Ownership of Dwellings 87.3 4,890.8 0.823
Public Administration and Defense 303.9 12,801.4 1.095
Services 204.8 6,434.1 1.486
Total 4,287.0 197,721.0

 

'Bogor in Figures, 1991; Cianjur in Figures, 1992; and Sukabumi in Figures, 1992.
”Central Bureau of Statistics, Statistical Yearbook ofIndonesia, 1991.

“Regional location quotients.

87

Table 21. National and regional output multipliers in 1990 (66 sectors), GPNP'.

 

 

 

Sector N ationnl Regional Difference
andonesial (B—C-S Region) Magnitude %

1 1.25977 1.19511 0.06466 5.13
2 1.27509 1.22593 0.04916 3.86
3 1.29939 1.23088 0.06851 5.27
4 1.10273 1.08156 0.02117 1.92
5 1.15346 1.12643 0.02703 2.34
6 1.18402 1.14262 0.04140 3.50
7 1.62181 1.47386 0.14795 9.12
8 1.53508 1.41163 0.12345 8.04
9 1.19084 1.14699 0.04385 3.68
10 1.62835 1.48685 0.14150 8.69
11 2.02131 1.81104 0.21027 10.40
12 1.56920 1.50631 0.06289 4.01
13 1.25142 1.19272 0.05870 4.69
14 1.26493 1.19863 0.06630 5.24
15 1.23695 1.17487 0.06208 5.02
16 1.24472 1.18648 0.05824 4.68
17 1.22570 1.16524 0.06046 4.93
18 1.47582 1.40010 0.07573 5.13
19 2.19720 2.12388 0.07332 3.34
20 1.77672 1.68039 0.09634 5.42
21 1.31894 1.23805 0.08089 6.13
22 1.29411 1.23883 0.05528 4.27
23 1.42981 1.28813 0.14169 9.91
24 1.37186 1.25447 0.11740 8.56
25 1.16371 1.11847 0.04523 3.89
26 1.39317 1.30915 0.08403 6.03
27 2.27906 1.96057 0.31848 13.97
28 1.92991 1.82290 0.10701 5.54
29 2.15702 2.08343 0.07359 3.41
30 2.07350 1.95260 0.12090 5.83
31 1.90151 1.76937 0.13214 6.95
32 1.92658 1.81750 0.10908 5.66
33 2.03964 1.84994 0.18970 9.30
34 1.84012 1.72208 0.11804 6.41
35 2.35172 2.08622 0.26550 11.29
36 2.71989 2.41341 0.30648 11.27
37 1.88715 1.40543 0.48172 25.53
38 2.48080 2.18219 0.29860 12.04
39 2.62861 2.20937 0.41924 15.95
40 2.58468 2.25470 0.32998 12.77
41 1.78333 1.12547 0.65786 36.89
42 2.70592 2.42837 0.27755 10.26
43 1.98337 1.48731 0.49606 25.01
44 2.17518 1.50136 0.67381 30.98

Table 21 (contd.)

88

 

 

 

 

Sector National Regional Difference
(Indonesia) (B-C-S Region) Magnitude 96

45 2.34805 2.05577 0.29227 12.45
46 2.33396 1.69378 0.64018 27.43
47 2.53400 2.19300 0.34100 13.46
48 2.84282 2.53068 0.31215 10.98
49 2.53611 2.26112 0.27499 10.84
50 2.31509 2.03229 0.28281 12.22
51 2.30728 1.82493 0.48234 20.91
52 2.44730 1.98157 0.46573 19.03
53 1.30721 1.21157 0.09564 7.32
54 2.00877 1.85749 0.15128 7.53
55 2.15248 1.85437 0.29811 13. 85
56 1.74070 1.56122 0.17948 10.31
57 1.81225 1.58104 0.23122 12.76
58 2.24552 1.92127 0.32424 14.44
59 1.58249 1.42999 0.15250 9.64
60 1.51819 1.31949 0.19870 13.09
61 1.48117 1.28642 0.19475 13.15
62 1.42611 1.31856 0.10755 7.54
63 1.00000 1.00000 0.00000 -
64 1.66400 1.54192 0.12208 7.34
65 2.18585 1.96939 0.21646 9.90
66 2.33096 2.12295 0.20801 8.92

Total 120.05439 107.02963 13.02476 10. 85

 

’I‘ype I output multipliers (total direct and indirect effects).

Note: These multipliers are derived from the 1990's Indonesia 66-sector input-output table.
Sector names are presented in Appendix B, Table B-2.

89

to the rest of the nation. Part of these sectors were wholesale and retail trades, hotels and
restaurants, farm food crops, and services sectors. Other sectors such as transportation,
manufactures, banking and financial intermediaries, and mining sectors were less
concentrated in the B-C-S region, and they were assumed to import some inputs for
producing their products to satisfy the regional demand.

Regional output multipliers in Table 21 varied from 1.0 (for general government
and defense sector) to 2.43 (for manufacture of rubber and plastic wares sector).
Compared to the national level, there were differences ranging from 0.0% (for general
government and defense sector) to 36.9% (for petroleum refinery sector). In total, the
regional output multiplier was lower about 11% than that of the national level.

The procedures described above are not without limitations. This study ignores
sectors that might not exist in the region of interest so that it affects the magnitudes of the
regional technical coefiicients due to biases on the total output by sectors. By applying
the same LQs for sectors in the same aggregate, biases in their technical coefficients might
result, and in turn, their multipliers may be biased. Furthermore, the level of technology
between the national and the regional level might be difi‘erent so that the productivity level
of industries in both levels might be different as well. Therefore, underlying the process of
estimating the regional output multipliers from the national model, the following
assumptions were made:

(1) The sectors identified in the national model existed in the region being studied.

(2) Technical coefficients developed at the national level were representative for the
region of interest.

(3) Industries at the national level represented in particular sectors utilized a similar level
of technology as those in the region of concern.

(4) Direct requirements of industries in the national level remain stable over time.

Margining and Bridging Processes

In tourism-based economic impact analyses, margining and bridging are used to
match and convert total spending within broad categories injected by nonresident visitors
into the region to a final demand vector within specific economic sectors. In a margining
process, the total spending made by visitors is delineated to choose what portion of
spending is truly associated with industries or sectors that exist within the region of
interest since many goods that are purchased by visitors may not be produced within the
region. Spending for retail goods usually requires margining because it reflects charges
for the output of wholesalers, retailers, transporters, and manufacturers.

In this study, ”margining" was applied to total spending for oil and gas, spare parts
(for vehicles), cigarettes, photo films, souvenirs, and clothing. To the author's knowledge,
the manufacturers that produced these retail goods (e.g. petroleum refinery, manufacturer
of transportation equipment and its repair, manufacturer of cigarette, manufacturer of
photographic and optical equipment, manufacturer of artist material, and manufacturer of
textile, wearing apparel and leathe -- all these names referred to the national industrial
classification) are located outside the B-C-S region. After identifying the sectors, the next
process is to estimate charges for wholesale trade, retail trade, and transportation
industries correlated to those retail goods. There is no supporting data available to assess
these charges; therefore, margins from comparable sectors in the U. S. are used to
approach the estimates (see column 4, Appendix B, Table B-7). The charges were
estimated by using the percentage shares of national personal consumption expenditures in
the category “other recreation”. The charges for wholesale and retail trade industries
included operating expenses, profits, and sales taxes. Here, wholesale and retail trades
were combined since these industries were classified as one sector in the input-output
table used (as a trade sector). Thereby the charges were calculated by multiplying the

estimated percentage charges by total spending on such retail goods (derived from the

91

total column, Table 15). These charges were changes in final demand in the region (see
Appendix B, Table B-7).

To develop the final demand vector, a bridge table was constructed. It was done
by matching the detailed spending categories (28 categories) to the sectors contained in
the Indonesia's 1990 66-sector input-output model. The results are presented in Table 22.
In this table, the sector codes are presented in two versions: input-output codes and KLUI
codes (the Indonesian Version of Industrial Classification) which are expressed as a five-
digit code. The next process was aggregating or disaggregating the spending categories
into the appropriate sector or sectors. This process was followed by summing or dividing
their spending amounts (identified in Table 23). There were 13 sectors, within the B-C-S
region, impacted by economic activities stemming from nonresident visitors associated
with the GPNP. One problem that arose in this bridging process concerned the estimation
of final demand changes for sectors resulting from a disaggregated spending category. In
this case, the other food crops' spending category was disaggregated into 3 sectors (maize,
root crops, and other food crops sectors). To estimate the distributions, this study utilized
percentage proportions of national (Indonesia) per capita food consumption (excluding
rice, fiuit, vegetable, fish, and meat) in 1988 (The World Bank, 1994). Assuming that
these percentages remain the same as in 1994, they were accounted using 25.78% for
maize, 56.75% for root crops, and 17.47% for other food crops. In addition of applying
Equation (16), the final demand vector by sector was completely estimated (column 3,
Table 23). This vector was calculated by summing the "nonresident" category segments'
spending rather than by calculating impacts for each segment.

The change in total final demand in the region of interest accounted for Rp
310,493,506 (total of column 3, Table 23). This was Rp 33,301,506 (about 10%) less
than the total spending actually reported by the nonresident visitors, which was Rp
343,795,012 (the total of column 10, Table 16). This reduction was caused by the

margining process. Here, fi'om the total spending of Rp 52,084,070 contributed by retail

92

Table 22. Bridge table derived from the 1990’s Indonesia 66-sector input-output table, GPNP.

 

 

 

Spending Categories Related Sectors
I-O KLUI Code‘ Name of Sectors
Code
Transportation Expenses:
1. Gas and oil 53 62451; 62452 Trade
2. Repairs and washing 65 97110; 97990 Other services
3. Spare parts 53 62477 Trade
4. Parking fees and toll (road, bridge, etc.) 59 71410; 71490 Services allied to transport
5. Fares/tickets 56 7121 1 Road transport
6. Rent 59 82211 Services allied to transport
7. Fares for recreation vehicles 65 96215 Other services
Lodging Expenses:
8. Hotels, motels, rental homes, cottages, etc. 54 64110; 64120; Restaurant and hotel
64200; 64300
9. Campgrounds (outside the park) 54 64400 Restaurant and hotel
Food and Beverage Expenses:
10. Grocery stores, convenience, liquor, 53 62110; 64120; Trade
vendor, son-drink, and other food and 62322; 62326;
beverage stores (for eating and drinking 62329; 62610;
off-premises) 62690
11. Restaurants, diners, catering, bars, and 54 63110-63150; Restaurant and hotel
other eating and drinking places 63200
Outside Market Expenses:
12. Fruits 5 11321; 11322 Vegetables and fruits
l3. Vegetables 5 11311; 11312 Vegetables and fruits
14. Other food crops 3 1 1230 Maize
4 11212; 11211; Root crops
1 131 1
6 11230; 11900 Other food crops
15. Plants (including ornamental plants) 17 11339; 12200 Other agriculture
16. Fishes (including ornamental fishes) 23 18211 Fishery
Miscellaneous Expenses:
17. Rental fees for recreation equipment, rental 65 96139; 97990 Other services
horse, etc.
18. Cigarettes 53 62327; 62610; Trade
62690
19. Insurance fees 61 81411 Financial intermediaries
20. Fees for guide services 65 97990 Other services
21. Admission to recreation sites 65 96133; 96243 Other services
22. Admission to tourist attraction 65 96133; 96243 Other services
23. Other recreation expenses (billiard, golf, 65 96215; 96119; Other services
movie, fishing, etc.) 96219; 96250;
962 12
24. Camera film or video tape purchase 53 62466 Trade
25. Film developing or video tape processing 65 ~ 97910 Other services
26. Souvenirs or gifts 53 62481-89; Trade
62560
27 . Clothing (including footwear) 53 62419 Trade
28. Other expenses 66 00000 Unspecified sector

 

'KLUI: The Indonesian Version of Industrial Classification.

93

Table 23. Final demand changes within the B-C-S region by sectors, GPNP.

 

 

Spending categories Impacted sectors (l-O codel' Final demand (Rp)b 96 Rank

Other food crops Maize (31 125,361 0.04 12
Root Crops (4) 275,959 0.09 10
Other Food Crops (6) 84,952 0.03 13

Fri]?! ' ' ' ' ' ' ' ' ' " ' VEg'érSfia'éZEd’F’rJtE is? """" 3 'i 7665.550" ' " ' 76' "1'5 ' ' 3 ' ' '

Vegetables

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Clothing

Film purchase

Gas and oil

Grocery

Spare parts

Souvenirs

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Hotels

Restaurant

Fa're'sftiEk'et's' ‘ ' ' ' ' ’ ' 3532i iéEsEth'rEEr """""" 3 "7712631? ' ' ' i 1’ 56' ’ ' T ' '

Fa’rfinB’a’tSIFs' ’ ' ' ’ ' ’ Se-rv-ic-e; Rtfieii'tri’r‘ré'n'sriofi'rs'ei ' ’ " 37536355 """ 5 63' ’ ' 'é’ ' '

Rent

Et§u7a7t3e§"""""Fir'ia'r'iaéi Ks‘r'rfi'e'afiri'e; Tail" """ 2 37785553 """ 5 35' "3""

RErrTis'sEifi t'eEs' """ Bt'an’SSrCiEJs'ts'si """""" 5 37435.54'9""i§.ié' "‘2'”

Attraction fees
Equipment rental

Film developing

Guide fees

Other recreation fees
Recreation vehicle fares
Repairs 81 washing

Other expenses Unspecified Sector (66) 4,601,189 1.48 9

 

Total 310,493,506 100.00

' Derived from the 1990's Indonesia 66-sector input-output table.
" Derived from column total (Table 16) with adjustment (margining process).

94

goods, only Rp 18,782,863 (about 36%) remained within the region; it was separated into
two industries: the wholesale, retail trade industry and the transportation industry. F rom
the total change in final demand, the highest proportion of about 25% was contributed to
”trade” (sector 53), followed by "other services” (sector 65) of 18%, "restaurant and
hotel" (sector 54) of 17%, ”road transport” (sector 56) and "services allied to transport"
(sector 59) of 15%, "vegetables and hits" (sector 5) of 10%, and ”financial
intermediaries" (sector 61) of 10%. Less than 5% was the final demand contributed by the
six other sectors (see Table 23).

In economic impact analyses, changes in final demand will afl‘ect the transaction
activities among affected sectors in the regional economy. To provide those changes in
final demand, the producing sectors make transactions with others. Initial transactions
made by those sectors reflect direct impacts of the final demand. In addition to direct
impacts, however, these initial transactions initiate the stream of transactions as the
successive rounds of re-spending occur among the producing sectors. These are what
economists call indirect (or) induced impacts. The following section describes the impacts

of the final demands identified above.

Regional Economic Impact Estimates

In this study, the impacts of trip-related spending in the B-C-S region were
estimated for output (sale), income, and employment. The impacts were measured in
total, which includes the direct and indirect changes; the regional multipliers developed in
this study were Type 1 (direct and indirect) multipliers. The "households" sector was
excluded from the process of developing output multipliers. Furthermore, the term
"income" reflects a number of wages and salaries paid to households by producing sectors
to produce their outputs.

To arrive at impact estimates, so far, two important elements have been estimated:

a set of output multipliers and a final demand vector derived from the trip-related

95

spending. Since income and employment multipliers are needed, the next step is
calculating income-output and employment-output ratios by related sectors. Assuming
that the national 1990 ratios were representative for the B-C-S region in 1993/1994, the
calculation was obtained by dividing income and employment by output for each related
sector. The results are presented in Appendix B, Table B-8. The employment-output
ratio was measured by a number of persons employed per million rupiahs of output. By
multiplying the final demand vector by the appropriate output multipliers and applying the
Equations (18) and (19), the impact estimates for output, income, and employment,
respectively, were developed.

From Appendix B, Table B-8, the first four sectors (i.e., agricultural activities)
showed relatively high employment-output ratios. This provides information that these
sectors create relatively high employment (labor intensive) to produce a unit of output
(million of rupiahs) in the B-C-S region. But, the impact generated by these sectors
depends upon the magnitudes of trip-related spending injected into these sectors.

The total impact (direct and indirect) of outputs, income, and employment
generated by the trip-related spending for the B-C-S region is presented in Table 24.
Trip-related spending by nonresident visitors generated total annual outputs (sales) of Rp
470,769,881 in 1993/1994 for the B-C-S region. In the same year, this output supported
Rp 80,115,334 of annual income and employed 155 persons. The distributions of output
and income displayed a similar pattern across the impacted sectors. With the employment
impacts, however, the variation in ranks occurred (see Table 24). A consistent pattern
was shown by "other services” (sector 65) and "trade" (sector 53); they had a relatively
high output, supported high income, and high employment. Conversely, " vegetables and
fruits" (sector 5) and "unspecified sectors" (sector 66) had a relatively low output,
generated low income, but supported relatively high employment. The "restaurant and
hotel" sector (sector 54) displayed a relatively high output, provided medium income, and

supported very low employment. "Financial intermediaries" (sector 61) presented a

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97

relatively medium output as well as income, but it created very low employment. Finally,
"road transport" (sector 56) identified a relatively medium of all economic indicators

(output, income, and employment).

Discussion
In addition to the findings discussed above, several important issues need to be
discussed in more detail. Through detailed discussions, questions or ideas may arise, and
in turn, these may be important for improvements of this study as well as for the needs of
future studies. The discussion focuses on unit of measure, sample sizes, visitor
segmentation, trip-related spending profiles, developing regional output multipliers, and

economic impact estimates.

Unit of Measure

To measure trip-related spending, this study used "rupiahs per person per day" as a
unit of measure. It was consistent with the measure of sampling units which was an
individual visitor to the park. This decision relied on the assumption that trip-related
spending had more to do with individual expenses than with group expenditures. Also,
this is appropriate when visitors to the park were mostly day users. These phenomena
were identified in the results discussed above.

From the sample, about 78% of respondents reported making trip with fiiends or
business associates. In this type of party, the trip spending was more individual-based
rather than an individual paying the group expenditures. Only 22% of respondents made
the trip with families, relatives, or spouses. Spending for lodging were the lowest in all
segments (Table 13 and Appendix B, Table B-3), as most visitors to the park were day
users.

Estimating expenses on a per person basis reduces variance associated with

different party sizes; for instance, expenditures on food and souvenirs will more likely vary

98

with the party size. Variations for expenses like gasoline and spare parts that do not
depend much on the party size might have small effects in this study. The results (Table
12, 13, and Appendix B, Table B-3) indicated that most respondents used public
transportation rather than private transportation. About 11% from the total average
spending was for public transportation expenses as compared to about 5% for private
transportation expenses. Furthermore, accounting for children (less than 6-year old) is
another complication; it was also negligible in this study as only about 3% respondents
reported having children in their groups. They accounted for about 0.5% fi'om the total

group members.

Sample Sizes .

Because of time constraints, the problem of seasonality (over the year) could not
be detected in this study. As a result, the samples from the survey were not a true
representative for the population as the tirneframe was different. However, information
that could be derived to analyze the sample was the annual distribution patterns of the
population units by visitor category. As identified in Table 6, the distribution patterns of
the sample by visitor category showed a similar pattern as those of the population, which
meant that both in the sample and the population "recreation" dominated the visitor
activities, "student" participated more than "nonstudent", and "nonresident" contributed
more than "resident". The difl‘erences were concerned with the degree of
representativeness of each category between the two fiames. In the sample, applying the
proportional allocation of the samples relative to the population, " student" category was
likely under-represented, while "nonstudent" and "other" categories were likely over-
represented. However, these patterns were more likely a reflection of the true nature in
the survey area during the time the survey was conducted (school season and fasting

month) rather than some integral design flow (sampling and questionnaire designs).

99

The minimum sample size required to estimate the average trip-related spending
for the population depends on the amount of sampling error one can tolerate.
Unfortunately, this sampling error was not available (from previous studies or pilot
surveys) for the GPNP area. As a guide, a similar study conducted in the US. reported
that, by taking into account the likelihood of potential non-sampling errors and the
expected accuracy of use estimates, errors below 0.20 (20%) are reasonable in estimating
trip-related spending (Propst and Stynes, 1992). Table 25 indicates that, for this study,
the sampling error for the average total spending was 3% with a 95% confidence interval
of Rp 8,944 (lower bound) and Rp 10,066 (upper bound). Thus, by applying the 20%
error guideline, the total sample of 872 respondents used in this study was adequate to
estimate the average total spending for the population.

By segment (see Table 26), seven of the eight segments had the error below 20%
or even below 10%. Among these segments, the lowest sample size was 36 respondents
(the TN /1 segment). Only one segment (the ON /1 segment) had the error exceeded the
20% error guideline; it contained the sample size less than 36 respondents. Thus, for trip-
related spending, a reasonable sampling size infuture studies is at least 36 respondents per
segment or 288 respondents for the total sample if the segments are maintained as in this
study. By applying the 36 sample size per segment and utilizing the standard deviations as
presented in Table 26, the errors are still below 20% for each segment (ranging from 8.9%
to 18.7%); the error for the average total spending is about 5%.

Ifone is interested in total samples without segmentation, the minimum sample
size for assessing the population mean can be approached by the following Equation
suggested by Bhattacharyya and Johnson (1977), by assuming that the population

distribution is normal, as follows:
2 2
Z a / 2 0'

n = T (20)

100

Table 25. Selected statistics for trip-related spending by 28 detailed spending categories, GPNP.

 

 

 

 

Items Mean Std. Dev Std. Error Pct. Error 95% CI
Mcan(-) M6800)

Gas and oil 405.10 1,477.66 50.04 12.4 305.02 505.18
Repairs & washing 46.04 414.07 14.02 30.5 18.00 74.08
Spare parts 5.12 97.54 3.30 64.5 0.00 11.73
Parking & tolls 151.98 584.76 19.80 13.0 112.38 191.58
Fares/tickets 725.27 1,459.75 49.43 6.8 626.40 824.14
Rent 406.76 1,533.82 51.94 12.8 302.88 510.64
Recreation vehicle fares 12.09 180.94 6.13 50.7 0.00 24.34
Hotels 275.37 2,193.33 74.27 27.0 126.82 423.92
Campgrounds 16.00 89.52 3.03 18.9 9.94 22.06
Grocery stores 1,648.54 2,703.70 91.56 5.6 1,465.42 1,831.66
Restaurants 1,15123 2,309.32 78.20 6.8 994.82 1,307.64
Fruits 443.15 1,455.56 49.29 11.1 344.57 541.73
Vegetables 172.00 673.46 22.81 13.3 126.39 217.61
Other food crops 12.57 141.29 4.78 38.1 3.00 22.14
Plants 222.74 1,740.77 58.95 26.5 104.84 340.64
Fishes 6.14 91.25 3.09 50.3 0.00 12.32
Equipment rental 122.73 510.22 17.28 14.1 88.17 15729
Cigarettes 451.05 829.14 28.08 6.2 394.89 507.21
Insurances 530.68 934.75 31.65 6.0 467.37 593.99
Guide fees 34.96 724.29 24.53 70.2 0.00 84.01
Admission fees 1,041.99 1,496.92 50.69 4.9 940.61 1,143.37
Attraction fees 12.32 212.10 7.18 58.3 0.00 26.69
Other recreation fees 7.17 46.81 1.59 22.1 4.00 10.34
Film purchases 676.37 1,823.04 61.74 9.1 552.90 799.84
Film developing 441.07 1,754.52 59.41 13.5 322.24 559.90
Souvenirs 207.39 854.51 28.94 14.0 149.52 265.26
Clothing 136.90 729.83 24.71 18.1 87.47 186.33
Other expenses 142.48 666.23 22.56 15.8 97.36 187.60
Total 9,505.21 8,282.27 280.47 3.0 8,944.27 10,066.15

 

Note: Pct Error = standard error of the mean as a percentage of the mean.
Two standard errors yield a 95% confidence interval (CI).

101

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102

where n : estimated sample size,

2,,” : marginal value associated with the level of confidence (l-a ) (derived from

the Standard Normal Probabilities Table),

0': population standard deviation, and

d : margin of error.
With a 20% error, here sample size would be 78. However, maintaining the analysis by
segments to come up with the total sample estimate is suggested. It is because each

segment has a difl‘erent standard deviation and hence a difl‘erent standard error.

Visitor Segmentation

The idea of visitor segmentation is to know who is afl‘ected by a particular action
and reduce variation in spending patterns with segments that are relatively homogeneous
in their spending patterns. Suitable segments may vary from one situation to another. For
economic impact analyses, at least resident visitors should be distinguished from
nonresident visitors. Other categories should be relied on the distinct characteristics of
visitors and what kind of management and planning would be evaluated.

In the application for appropriate analysis and reporting, sumcient segments will be
affected by the sample sizes resulting from the survey. The sample sizes will determine a
number of samples contained by each segment. Insuficient samples by segment could
result in an aggregation of the segments because of statistical analysis requirements. In
this case, based on the Central Limit Theorem, the minimum number of samples
(observations) in each segment should be greater than 30. This sample size is expected to
provide a satisfactory approximation for situations in which the population distribution has
a normal form ( Bhattacharyya and Johnson, 1977).

This study tried to divide visitor groups into 24 segments. However, only a small
portion of several visitor categories had large samples, and through aggregation processes

(2 rounds) these resulted in 8 final segments. As can be seen in Table 6, the "hiking" and

103

the "other" categories had small samples. As a consequence, segments resulting from
using these categories in their combinations would also be small in their sizes. In
statistical analysis, the small size of segments could have higher variances (and hence

higher stande errors) than others.

Trip-Related Spending Profiles

The important role of visitor segmentation, by which homogeneous spending is
gained, was supported in this study. Without segmentation (adjustment), the average total
spending accounted for Rp 9,505 per person per day (Table 14). However, with
segmentation, the average total spending varied from Rp 6,889 to Rp 21,812 per person
per day (Table 13 and Appendix B, Table B-3). One may use the average total spending
without adjustment for their further analyses. The author urges that, for getting more
precise estimates, the average total spending should be handled separately by segment as
the patterns of variances or errors may vary between the two ways and among the
segments as well.

However, one should know that the total spending and hence the total impact
estimates are afi‘ected by segmentation if the sample does not represent the population.
For the population, without adjustment (for segments), the total spending was Rp
555,731,607 (Rp 9,505.21 x 58,466). Using adjustments in this study, the total spending
for the population was reported Rp 613,616,248. This was because the population
distribution was not estimated by using the sample proportion; it was derived from the
secondary data.

Regional planners should be careful in interpreting spending profiles when making
recommendations. From Table 13, Table 15, Table B-3, and Table B-4, it was clear that
the patterns of average total spending and total spending were different across the
segments. A segment with a relatively high average total spending did not mean that its

total spending was also high. Conversely, a low average total spending in a segment did

104

not always reflect that its total spending was also low (see the ranks in Table 27). For
instance, resident visitors within the "hiking" category (the TN /1 segment) had the highest
average total spending; however, its total spending was not the highest. The highest total
spending was spent by nonresident visitors within the “hiking” category (the TN /Ot
segment). These phenomena are due to the difi‘erences in population sizes among the

segments.

Table 27. Ranks or patterns of spending for eight segments, GPNP.

 

Spending R/N/Ot R/S/Ot T/V/Ot ON/Ot R/N/I R/S/I T/V/I ONII

Average 4 6 2 8 7 3 1 5
Total 6 2 l 7 5 4 3 8

 

By activity category, the "recreation" segments dominated total spending for the
population. The high proportion was probably more a reflection of the true nature of the
study area in which facilities for recreation activities were more appropriate. Very few
campgrounds were available in the study area; they were in poor conditions. The ”other
activity" (hiking) needed a particular requirement; only younger people were able to
participate in this activity (the elevation is about 2,000 m above sea level and more).

By occupation category, the " student" spent more higher total spending than that
of the "nonstudent". The excess was about 50%. This was because of the higher
participation of students as compared to nonstudents in the GPNP. As indicated in Table
6, for the population, students accounted for about 72% whereas nonstudents comprised
about 28% (about 50% in difl‘erence).

Furthermore, the total spending by the "nonresidents" was higher than that of the
"residents" (about 12% in deference). The "nonresident" participation was about 61%,

while the "resident” was about 39% (about 22% in difference). Unlike in the occupation

105

category, the percentage excess spending was lower than the percentage excess
participation. This was because the average total spending by the “nonresident” was
slightly lower than that by the “resident”; they were Rp 9,169 and Rp 9,990 per person per
day, respectively (derived fi'om Table 13 and Appendix B, Table B-3).

Developing Regional Output Multipliers

The Simple Location Quotient method used in this study is one non-survey method
for regionalizing coefficients from the national level. Given available data, this study used
the GDP by aggregate sectors as weighted factors. The rationale of using it is that GDP is
a part of domestic gross input or output accounting. GDP is defined as the current market
value of all final goods and services produced by the domestic economy during a year or
time period (Peterson and Esterson, 1992). On the expenditure side, GDP represents the
total purchases of goods and services by consumers and governments, gross private
domestic investment, and net exports of goods and services. On the income side, GDP
shows both the total income created as a result of current productive activity and the
allocation of this income. Thus, in input-output terms, GDP is identified as the total final
demand minus imports or as the total value added plus import sales tax and import duty.
GDP reflects the ability of a region to produce goods and services, so that, in using the
Simple Location Quotient method, GDP is a valid factor (as a weighted factor) for
adjusting the national coeficients to the regional level.

In addition to some limitations described in the results part above, other drawbacks
of procedures used in this study are concerned with time differences. The data for GDP as
well as the national input-output model were available for the year of 1990. By the time
of this study, 1994, these data possibly change. Therefore, the value of LQs are probably
no longer correct. The national technical coefiicients used to estimate the regional
coefiicients might also change as the structural economy and the level of technology

change; the static nature of input-output models is also the limitation. These changes, in

106

turn, could result in biases in the regional output multiplier estimates. Besides the
assumptions addressed in the results part above, therefore, this study also assumes that
proportions of the GDP (regional to national), the structural economy, and the level of
technology remain stable over time. However, if the economy does not change drastically
in the time period in question, the static nature of an input-output table is not a huge

problem (Miernyk, 1965).

Economic Impact Estimates

In the margining process, it was roughly estimated that all manufacturing industries
were located outside the B-C-S region. It is probably not true for particular industries
such as an industry that produces souvenir goods. These products, in the B-C-S region,
were manufactured more locally. Therefore, for future study, accurate information
concerning the existence of manufacturing industries within the B-C-S region should be
collected.

From the foregoing impact estimates described in the results part, one may say that
the GPNP has a minor role for the B-C-S region in terms of its recreational economic
contribution. This might be true if the impacts are compared to the overall economy of
the B-C-S region. In 1990, total GDP for the region (in current market prices) was about
Rp 4,287 billion, and number of people who were seeking jobs in 1992 (based on the
education level) was reported about 71,300 (the B-C-S, 1992). However, one should
realize that the economical role of the GPNP to the B-C-S region is greater than the
impact estimates have been calculated above. The rationale for this conclusion is that:

(l) The impact estimates were based only on the Type I regional output multipliers which
meant only the direct and indirect effects were calculated. The flow of money due to
re-spending fiom households (induced effects) was not included in the estimates. If
so, the estimates must be higher than those identified in this study. Archer (1977)

indicated that the Type H multiplier (including direct, indirect, and induced effects) is

107

more appropriate for assessing the economic impact of recreation and tourism as most
of secondary (indirect and induced) effects of tourists’ spending are induced effects.
The study by Stynes and Propst (1992) indicated that the induced efl’ect for output
(sales), income, value added, and employment is about twice (100%) as much the
indirect efi‘ect.

(2) In estimating the impact on income, this study rested heavily on income-output ratios.
The results indicated that the average ratio was about 16% (derived from Appendix B,
Table B-8). This was likely an underestimate as the income included in the estimation
only covered wages and salaries. Thereby, proprietors income/profit of many family
businesses, for example, may be missing fi'om the estimation. Hence, the impact
derived fiom the income-output ratios as applied in this study may be underestimated.

(3) The impact estimates accounted only for the spending stemming from the nonresident
visitors. The government expenditures for managing the park were excluded fi'om the
estimation. For the year of 1993/1994, these expenditures (from the central
government) were reported to be about Rp 1.2 billion (GPNP, 1994). It was about
four times of the final demand generated by the nonresident visitors calculated
previously. Significant amounts of these expenditures would likely be made in the B-
08 region.

(4) The role of the GPNP in the regional economy is not only reflected by the economic
impact terms per se. Though not included in the impact analysis, the resident visitors'
spending within the region (accounting for Rp 269,821,536) also affected the B-C-S
economy. The economic significance of the park in the B-C-S region is much greater.
In this case, the spending of both resident and nonresident visitors are included in
estimating economic effects.

(5) Finally, this study did not calculate the economic effects of visitors' spending outside
the B-C-S region. Some visitors spent their money outside the region during the trip

to the park, and these expenditures are part of the total impacts -- they are simply

108

outside the B-C-S region. Spending for durable goods that might be linked with some
particular activities was also not estimated.
Even if one may say that the GPNP has a minor role in economic activity for the

B-C-S region, it may have great value in other roles. As a protected area, the GPNP has
at least 4 major roles: (1) a biodiversity reserve, (2) a hydrological reserve, (3) a natural
features scenery reserve, and (4) an area for scientific, educational and recreational use. In
this study, the economic impacts of the last role were calculated. The values of the first
three roles above have not been determined. If needed, several of these values might be
included in a benefit-cost framework. Since there is not a market that reflects these
values, several methods (i.e., the contingent valuation and the hedonic methods) solicit the
benefits by asking the users what they are willing to pay for those roles; this benefits were

beyond the scope of this study, however.

SUMMARY AND CONCLUSIONS

Introduction
The topics associated with this study's objectives have been addressed throughout
the previous chapters. The results are summarized in the first part of this chapter. Then,
conclusions pertinent to the major findings of this study (the strengths and drawbacks) are
presented. Several recommendations addressed next are concerned with the improvement
of this study as well as the needs of firture studies. Finally, possible applications of the

results are presented at the end of this chapter.

Summary

The overall purpose of this study is to provide an analytical framework for
estimating economic impacts of GPNP visitors’ trip-related expenditures in the B-C-S
region. The Indonesian government has encouraged the roles of the GPNP as a protected
area for regional economic development, on one hand, but has lacked methods for
assessing those roles, on the other hand. In this study, no attempt was made to estimate
the value (benefit) of the park to the participants themselves or as a protected area. Also
there was no attempt to include the costs and the environmental and social impacts
generated by those recreation activities.

Consistent with the purpose of this study, topics related to economic impacts have
been addressed. Systematically, these involved developing instrument designs, procedures
of data collection and data analysis, developing market segments and their spending profile
estimates, and assessing the impacts. Since the impact estimates were analyzed by using
an input-output model, these procedures were germane to the requirements of input-

output analysis.

109

110

A primary instrument used in this study was a five-page on-site questionnaire for
primary data collection. Designing a questionnaire is a crucial task as it will determine the
quality of input data which, in turn, will influence confidence in the impact estimates.
Therefore, the seventeen questions addressed in the questionnaire were carefully
stnrctured in a manner to meet the requirements of developing visitor segments, spending
profiles, and impact analyses. Besides the spending information, open-ended answers
were not allowed in the questionnaire. For the needs of visitor segmentation, the
questions also provided linkages with the secondary data obtained from the park
headquarters.

To obtain the primary data, on-site (direct) interviews were conducted on every
weekend day and on a selected weekday between January 22 and April 17, 1994.
Systematic random sampling was used. Thirteen experienced GPNP staff members were
recruited as enumerators to conduct the interviews which resulted in 934 interviews.
About 1% of interviewees refused to participate and about 5% of them were nonusable,
leaving about 94% (881 respondents) as a sampling frame.

Using the Paradox Relational Database Version 3.5 (Borland), the on-site
interview data were organized, validated, and edited. The primary database was
developed in a matrix form with variable names (codes) as the columns (134 columns) and
the respondents' identification numbers corresponding to the date of interviews as the
rows (881 rows). This also applied to the secondary data in which 51 variable names as
the columns and 1,443 dates as the rows were developed. Outliers (4S nonusable
questionnaires) were crossed out fi'om the primary database. For the computations, this
study used the SPSS-PC software to develop spending profiles; all user-defined missing
data (there were 9) were excluded fi'om the computations.

To observe the degree of representativeness of the sample relative to the

population, the analysis relied on the population distribution as the true representative.

111

By using the proportional allocation approach, the sample distributions were analyzed.
The population distribution was derived fiom the secondary data administered by the
GPNP management for the period of April 1, 1993 to March 30, 1994 (annual basis). The
results of this study indicated that, across the visitor categories (residency, activity, and
occupation), the distribution patterns of the sample were similar to those of the
population. However, if the comparison was considered to each visitor category, it was
likely that the sample represented lower proportions for the "hiking" and the "student"
categories and higher pr0portions for the "other activity" and the "nonstudent" categories
as compared to those in the population. For the "residency" category, both the sample
and the population revealed similar patterns (see Table 6). The sample distribution by
category reflected the distribution patterns across the combination of the categories (see
Table 11). These phenomena were due in part to the different timefi'ame between the
sample and the population, and the true nature of the survey area during that survey
period.

From the database, some variables (e.g., residency, activity, occupation, group
size, and spending data by category) were derived to estimate trip-related spending
profiles. Based on the characteristics of visitors and their economic activities, this study
applied rupiahs (the Indonesian currency) per person per day as a unit of measure. To
estimate spending profiles by segments, grouping the visitors into segments was required.
Combinations of four visitor categories used in this study (residency, activity, occupation,
and purpose of visit) resulted in a 24 preliminary visitor segments. Due to small samples
contained in some segments, for the purpose of analysis and reporting as well as statistical
requirements, these segments were reduced (combined) to 8 segments.

To show the adequacy of the sample size for estimating the population average
spending, statistical analyses were performed. The results indicated that, for the total
sample (n = 872), the sampling error was about 3%. By segment, seven of the eight

segments had the error of less than 10% (the lowest sample size was 36); only one

112

segment (the O/V/I segment) had the error of 24% (the sample size was 22). Applying
the 20% error guideline, the total sample and the sample size of the segments were much
more adequate, except for the ON /1 segment. Therefore, for filture studies, the author
suggests that the minimum sample size per segment is at least 36 or 288 samples in total if
the segments are maintained as in this study.

Once visitor segmentation was completed, then the spending profiles were
estimated. There were 28 trip-related spending categories which were grouped into 5
aggregate categories observed in this study. The importance of visitor segmentation was
clearly identified by the results of this study. “Without adjustment (for segments), the
average total trip-related spending within the B-C-S region was Rp 9,505 per person per
day with distributions: 40.0% for miscellaneous items, 29.5% for food and beverage,
18.4% for transportation, 9.0% for outside market items, and 3.1% for lodging. With
adjustment, the average total spending as well as the distributions varied across the
segments. The results indicated that the average total spending was ranging fiom Rp
6,889 for nonresident visitors with the "other activity" category (the ON /Ot segment) to
Rp 21,812 per person per day for resident visitors with the "hiking" category (the T/V/I
segment). The average total spending distributions were also slightly different across the
segments. For example, the average spending for miscellaneous items which was reported
as the highest proportion by the 7 segments was the second highest spent by resident
visitors with the "other activity" category (the ON /1 segment). However, this study
indicated that adjustment did affect total spending. The difference depicted by the total
spending for the population was because the segment proportions in the population were
difi‘erent fi'om those in the sample; the population data were not derived fi'om the sample.

Some important information derived from the spending profiles was that seven out
of eight segments reported that they had the lowest spending on lodging expenses, and six
out of eight segments spent higher on fare/ticket and transportation rental fees than on gas

and oil expenses. This information confirmed the rationale mentioned previously that the

113

visitors to the GPNP were mostly day users and public transportation was more commonly
used to make the trips than private transportation. The information was also supported by
frequency data presented in Appendix A, A-3.

To arrive at the impact analysis, the total trip-related spending (for the entire
population) within the B-C-S region should be determined. This study estimated that this
spending was Rp 613,616,248 from which about 22.0% (the highest) was contributed by
nonresident visitors within the "hiking" category (the TN/Ot segment) and about 1.6%
(the lowest) stemmed from resident visitors within the "other activity" category (the ON /I
segment).

By activity categories, the "recreation" category contributed the highest total
spending (more than 50%) to the B-C-S region, followed by the "hiking" and the "other
activity" categories. Across the aggregate spending, the highest spending was for
miscellaneous items, followed by food and beverage, transportation, outside market items,
and lodging expenses. By occupation categories, the "student" category had higher total
spending than that of the "nonstudent" (75.7% and 24.3%, respectively). The striking
feature was that the "nonstudent" category spent much more on lodging compared to the
" student" category (more than 50% in difference). Furthermore, by residency categories,
the "nonresiden " total spending was higher than the "residen " total spending. They were
56.0% and 44.0% for the "nonresiden " and the "resident" categories, respectively. This
56.0% of total spending was used to estimate the impacts to the B-C-S region; this
spending was basically the final demand.

Once the final demand in broad spending categories was estimated for the impact
analysis, additional steps were taken for determining regional economic multipliers,
computing margin and bridge tables to construct a final demand vector by the appropriate
sectors, and calculating the impacts themselves.

The final demand estimated above was basically the direct expenditures in the B-C-

S region by nonresidents. However, these expenditures were not the only impacts on the

ll4

region. The effect of this spending when re-spent by recipients could produce indirect
impacts. By using economic multipliers, the magnitudes of these impacts (direct and
indirect effects) were estimated. Ideally, economic multipliers for an economy should be
obtained from an input-output study conducted within that economy. If these multipliers
are not available, they can be derived from the national input-output model. To estimate
multipliers appropriate for the B-C-S region, this study relied on the 1990 Indonesian 66-
sector input-output model. Here, the assumptions are that the national level of
technology, level of industrial productivity, and economic structures are appropriate for
the regional level. Another assumption is that the national technical coefficients remain
stable over time. This study applied the Simple Location Quotient (SLQ) technique for
adjusting the national technical coefficients by using gross domestic products (GDP) by
sector as the weighted factor. The regional multiplier was then calculated by inverting the
Leontief matrix derived by subtracting the regional technical coefficients matrix from an
identity matrix. As for the national model, the estimated multipliers were Type I output
multipliers (direct and indirect effects). The results indicated that the total regional output
multiplier was about 11% lower than that of the national level.

The idea of the margining process is to identify the proportion of the spending
associated only to the goods that are produced within the region of concern. Many goods
that are purchased by visitors may not be produced within the region. In this study there
were 6 spending categories that were margined: oil and gas, vehicle's spare parts,
cigarettes, photo films, souvenirs, and clothing. By applying percentage margins used in
the US. (under category of “other recreation”), it was estimated that 36% of the spending
as actually injected to the B-C-S region through two impacted sectors: wholesale-retail
trade and transportation sectors. The next step was developing a bridge table to convert
the final demand vector by broad spending categories to the final demand vector by
appropriate sectors. It was done by matching the detailed spending categories (28

categories) to the sectors contained in the 1990 Indonesian 66-sector input-output table.

115

The results indicated that there were 13 sectors, within the B-C-S region, impacted by the
final demand stemming fiom economic activities of nonresident visitors associated with
the GPNP. The change in total final demand in the B-C-S region accounted for Rp
310,493,506. This was about 10% less than the total spending actually reported by the
nonresident visitors (Rp 343,793,012). This reduction was due to the margining process.

From the total change in final demand, the highest proportion of about 25% was
attributed to "trade" (sector 53), followed by "other services" (sector 65) of 18%,
"restaurant and hotel" (sector 54) of 17%, "road transport" (sector 56) and "services allied
to transport" (sector 59) of 15%, "vegetables and hits" (sector 5) of 10%, and "financial
intermediaries" (sector 61) of 10%. Less than 5% of the final demand was attributed to
the other six sectors (see Table 23).

Impacts were estimated for three economic indicators: output or sales, income,
and employment. To estimate the total impacts (direct and indirect effects) on output, the
estimated regional output multipliers were multiplied by the final demand vector which
was converted to sectors. As the regional income and employment multipliers were not
developed, to estimate the impacts, this study follows an assumption of linearity
relationship of income, employment and output, which means that changes in output will
be followed proportionally by changes of income and employment. Thereby, the income-
output and the employment-output ratios were determined. This study used the 1990
national ratios as the B-C-S ratios with an assumption that the national ratios remained
stable over time and were representative for the region. The total impacts were then
estimated by multiplying those ratios by the total impacts on output for each
corresponding sector.

This study estimated that the trip-related spending injected by nonresident visitors
generated total annual outputs or sales of Rp 470,769,881 to the B-C-S region in
1993/1994. In the same time, this output supported Rp 80,115,334 of annual income and

employed 155 persons. In terms of output and income, the highest impacts were

116

attributed to the "other services" sector (sector 65) which accounted for about 24% and
28%, respectively. However, in terms of employment, the "vegetables and fi'uits" sector
(sector 5) received the highest impact which was estimated at about 32%. Those
magnitudes of impacts should not be considered as a comprehensive description of the
GPNP's contribution to the B-C-S region. It only described economic effects associated
with particular spending (trip-related spending) and visitor (nonresident visitors). One
should realize that spending incurred by resident visitors, spending on durable goods,
governments expenditures for operating the park, and other local costs associated to the

park were excluded from the impact analysis.

Conclusions

Relevant to the objectives of this study, given the data currently available, the
methods or techniques applied herein are sufficient to develop a database consisting of
visitors' expenditures stemming from participation in recreation activities in the GPNP and
to estimate corresponding impacts of these expenditures to the B-C-S region. Three of
the major findings are that the sampling unit (an individual visitor), the method of primary
data collection (on-site interview), and the unit of measure (mpiahs per person per day)
used in this study are relevant to the study area as these reflect the true nature of visitors'
characteristics and their economic activities associate to the park.

By applying the 20% error guideline, the total sample used in this study was much
more sufficient to estimate the average trip-related spending for the population. Even, if
the segments are maintained as in this study, reducing the total sample up to 288 samples
is still adequate, as the errors would remain below 20% (by segment and total). However,
one should consider that each segment should contain at least 36 samples; for a particular
segment (e.g., the O/V/I segment), a sample size of less than 36 would result in a sampling

error of more than 20%.

117

The procedure of estimating the total spending used in this study has reduced
problems with annual seasonality because of utilizing the annual visitation data (secondary
data) derived from the GPNP headquarters. Therefore, spending profiles estimated from
appropriate sample sizes permit calculation for the total spending on an annual basis.
However, it would be a problem if spending profiles in the study area change over the
year. In this case, a short effort survey at difi‘erent times in a particular year may be
needed.

Maintaining the spending profiles by segments is important for further analyses or
applications. In this study, adjustments (for segments) affect the total spending, and hence
the total impact. By segmentation, managers could grasp broader information on spending
profiles and apply them, for example, for designing many programs, planning, or activities
associated with recreation economy.

Given limited secondary data in this study, utilizing the Simple Location Quotient
technique is very useful for regionalizing the national input-output model to estimate the
regional output multipliers. However, errors stemming from secondary data employed by
such a technique (non-survey technique) are unknown. Another problem is that the efl‘ect
of not aggregating or excluding unrelated sectors on the estimated output multipliers (and
hence on the impact results) is also unknown. These were beyond the scope of this study,
however.

In assessing the impacts, the author assumes there is a linear relationship of output,
income, and employment. This assumption implies that more visitors added to the
resource base means equal amount of increase of impacts. However, one should realize
that this is independent of effects on environmental quality and social stability of nearby
communities.

The purpose of impact analysis under the objective of this study is to quantify the
economic efi‘ects in the B-C-S region associated with the GPNP's visitors, but is limited to

nonresidents' trip-related spending. By these limitations the impact results are not enough

118

to express a comprehensive description of the GPNP economic contribution to the B-C-S
economy. There are a lot of impacts and values that are relevant and important but not
covered in this typical input-output study. The author believes that the impact estimates
fiom this study are only a beginning for better understanding a part of the economic roles
of the GPNP.

Recommendations
Regarding the objectives of this study, several positive findings have been
described throughout this thesis. However, the author realizes that many weaknesses still
exist in this study. Therefore, for the improvement of this study as well as for the needs of
future studies, the following are some recommendations that may be useful:

(1) The results of this study indicated that the number of respondents participating in the
survey (response rates) were very high, and the method used for primary data
collection (i.e., on-site interview) reduced the problem of respondents' illiteracy and
eliminated blank responses. Also, visitors to the park were mostly day users.
Regarding these findings, the author recommends using the on-site interview for
primary data collection with an individual visitor as a sampling unit and the rupiahs per
person per day as a unit of measure for future studies.

(2) As indicated previously that the total sample utilized in this study was adequate for
estimating the average trip-related spending for the population. However, there was
one segment that contained insufficient samples (the ON /1 segment). Therefore, for
future studies, improving the sample size may be more to do with increasing the
intensity of sampling to gain sufficient samples for each segment rather than extending
the survey period to increase the total sample.

(3) To get a proper comparison between the spending profiles by the aggregate spending
categories, the aggregation scheme should be considered. The results of this study

indicated that seven out of eight segments reported that their spending for

119

"miscellaneous" were the highest. It might be because the miscellaneous spending
category lumped too many spending items (there were 12 items). Therefore, dividing
this aggregate into two aggregates is recommended. They are: (1) "activity" expenses
which includes insurance, guide services, admission, and other recreation expenses,
and (2) "other" expenses which includes the other items.

(4) The drawback of adjusting the national technical coefiicients for the regional level by
applying the SLQ technique in this study was the use of GDP derived from previous
data (1990). By the time of this study (1994), these data possibly change, and hence
the estimated LQs are probably no longer correct. Therefore, a firrther review of these
values is suggested when more recent GDP data are available even though the national
input-output model may remain the same as being used in this study. In Indonesia, the
national input-output model is usually revised every five years.

(5) In terms of impact estimates, attempting to calculate the impacts by using Type 11
regional output multipliers is a good consideration as this will include the induced
efi‘ects as well as the direct and indirect effects. To do this, the "household" sector
should be treated as endogenous sector, as other producing sectors, in developing
regional output multipliers. In addition to the impact estimation, the author also
suggests assessing the economic significance of visitors' spending to the B-C-S region
as this can provide broader understanding of economic roles of the GPNP in the B-C-
S region. This involves calculation of spending for both resident and nonresident
visitors.

(6) Ifavailable, it is also suggested estimating the impacts by utilizing a narrower area of
an input-output model besides the national model (e. g., the West Java input-output
model). This comparison may be important for providing information on the
magnitude of the impact estimates as the differences in the area coverage and/or the

economical structure of the models.

120

(7) For firture studies in the GPNP, accounting the foreign visitors‘ spending may be
important. Even though these international tourists were only 5% from the total
visitors (in 1992/1993), they may have average spending higher than domestic tourists.
Potential exists for local people to sell art products to foreigners and provide lodging,

restaurants, and other goods and services.

Applications

Many analyses can be performed to support a variety of management and planning
issues in which the results of this study may be applied. From the survey data set, analyses
not related to economic impacts could be carried out. For example, the survey included
data about origins, age classes, and group types of visitors. These data can be used in
analyzing demand patterns of visitors to the GPNP. To gain a complete analysis, of
course, this may require additional data such as demographic (i.e., population data), travel
distance (origin—destination), income per capita by region, and other possible data.
Frequency analyses for addressing general patterns of visitation by gate of entrance and
date (weekdays and weekend-days) could be carried out by exploring the survey data.
From these analyses, many planning and marketing issues or questions may be generated.
The results of these analyses then could be compared to similar analyses in previous
studies (if any) to get general figures about the consistency of sampling units,
measurement procedures, and instrument design used in this study.

Market segments and spending profiles information provide a general guidance for
the park manager and the regional planner for designing programs, planning, or activities
related to the tourism industry and its impacts. From this information, the park manager
will know, for instance, to whom an extension program should be directed, what tourism
facilities or attractions should be developed or improved, and what management issues
need to be coordinated with the regional planners to support regional economic

development. Combined with the impact estimate data, this information may very

121

important in supporting guidelines to the regional planners in performing regional
economic development. Some considerations gained fi'om the information are, for
example, what economic facilities should be improved and what sectors should be
considered most as they contribute more output or sales to the region and income to the
local people. Concerning the unemployment issues in the region (if any), the sectors that
generate more impacts on employment can be considered as a priority emphasis.

By assuming that the spending profile patterns remain the same as those in the last
year, the impact estimate procedures can be used as a tool to evaluate the level of the park
management in terms of economic activities in future years. In this case, the average
spending by segment from this study should be discounted by using appropriate deflators.
The number of visitors by segment are estimated by multiplying the percentage population
distribution by segment from this study by the total population over time. Then, by
comparing the impact estimates over time, the park manager can evaluate if some
improvements are needed.

Another application deals with projections or forecasting. There may be two ways
to forecast the changes of impacts: (1) by estimating the level of final demand for output
of affected sectors change, or (2) by applying the filll carrying capacity of the park, which
means the maximum number of visitors that can be handled by the park. A limitation of
these applications is that if the period of projection gets longer, the accuracy of the impact
estimates tends to decrease, because the technical coefficients of an input-output table
used (and hence the multipliers) may tend to get out of date. However, it is not a big
problem if the regional economy does not change drastically in the period of concern.

Realizing that Indonesian government has designated 31 national parks across the
country, assessing their roles in economic development, in terms of the tourism industry,
may be needed. The World Bank (1994) pointed out that these protected areas, properly
managed, could encourage economic development by generating income and employment

for regions and local people. Applying estimation procedures developed in this study may

122

help to answer those challenges. Ifnot, at least the economic roles of those national parks
could be determined regionally. In turn, some management and planning issues could be
addressed. In implementing procedures, the author recommends using the sampling unit,
unit of measure, instrument design, and procedure of primary data collection as developed
in this study. The visitor and spending categories used to develop spending profiles in this
study should be adjusted to specific characteristics of a particular region. Also, of course,
the regional GDP used to regionalize the national input-output model should be the GDP
of those particular regions. Ifany, the author suggests attempting to use the regional

gross output by sector as weighted factors.

APPENDICES

APPENDIX A

123

APPENDIX A

A- 1. English version of the questionnaire, GPNP.

January 22, 1994

Dear Visitor:

Thank you for agreeing to participate in this survey conducted by Indonesia's
Natural Resources Management Project in coordination with Gede Pangrango National
Park. You indicate your voluntary agreement to participate by completing this personal
interview. We are interviewing visitors to find out about their recreational expenditures
related to their visits to Gede Pangrango National Park. This data will be used to assess
how their expenditures affect the region's economy. To complete this study, we need
information from you based on the questions provided in the following questionnaire. We
believe that your information will not only help us to plan for the needs of visitors like
yourself, but also for the community afi‘ected by recreational activities to the park.

Using a systematic random sample, we have selected you as a survey respondent.
Each 10th visitor to the park is selected. We would greatly appreciate your cooperation in
making this survey a success, by taking 10 minutes to answer our questions. All of your
answers will be kept anonymous and you will not be identified in any way. You may ask
any questions at any time during the interview.

Thank you for your assistance. Please call me at (0255)-512776 if you have any
firrther questions.

Sincerely,

Adi Susmianto

Researcher

J1. Raya Cibodas PO. Box 3 Sdl Cipanas Cianjur
Telp. (0255)~512776

Larry Leefers, Ph.D.

Supervisor

Department of Forestry, Michigan State University
113 Natural Resource Building

East Lansing, MI 48824

124

APPENDIX A, A-l (cont'd.)

RECREATIONAL USE AND EXPENDITURE SURVEY
IN GEDE PAN GRAN GO NATIONAL PARK, INDONESIA

Good morning/afternoon/evening. My name is .............. and I am working for
Indonesia's Natural Resources Management project in coordination with Gede Pangrango
National Park which is surveying visitors to find out about their recreational use of the
park and how their expenditures affect the region's economy. May I talk with you about
your trip? Circle "Y" (Yes) or "N" (No).

If "N", tally as a refusal and thank person for their time. I_I
(Then take the first visitor from the next group as a respondent)
If "Y", record the time and continue. _a.m.lp.m.

 

 

Hand the respondent the cover letter, the map and the list of spending categories, and say:
This letter will give you a general idea about the purpose of the survey, while the map and
the list of spending categories will help you answer a number of the questions that I will
ask. The area marked on the map is the area we are interested in.

 

1. Are you hiking to the top of Gede or Pangrango mountain? (circle) Y/N

2. For this trip to the park, are you coming from: (circle)
a. a permanent home, or
b. a vacation/second home

3. Please tell me:
a. Where is your permanent home? (circle)

1). Bogor 2). Sukabumi 3). Cianjur 4). Other: ............
b. Where is your vacation/second home? (circle)
1). Bogor 2). Sukabumi 3). Cianjur 4). Other: ............

4. What is the primary purpose for trip to the park? You may choose more that one
activities.
a. Hiking/walking e. Pleasure/relaxation
b. Camping f. Flora & fauna watching
c. Studying/researching g. Picnicking
(1. Water fall/lake h. Other: .....................

 

125

APPENDIX A, A-l (cont'd.)

 

Refer to question 1, if the trip started from a:
a. PERMANENT HOME, say: For the rest of this interview, when I say TRIP I am

 

referring to the time from when you left your permanent home until the time you
return there.

. VACATION HOME, say: For the rest of this interview, when I say TRIP I am
referring to the time from when you left the vacation home until the time you return
there or to your permanent home if you are not returning to your vacation home.

 

. What other parks or recreation sites in the region have you visited or will you visit on
this trip? (circle)

a. Cibodas Botanical Garden (1. Selabintana Park g. Telaga Warna
b. Mandalawangi Park e. Situgunung C.G. h. Other: ..............
c. Safari Garden f. Gunung Mas Estate

. Is your visit to the park as: (circle)
a. Primary purpose

b. Secondary purpose

c. Unimportant

(1. By accidence

e. Other: .............

. How many nights have you spent away fiom your permanent or vacation home on this
trip so far?

How many additional nights will you spend away? I_I_I

If none, move to question 10.

If any, continue to question 8.

OVERNIGHT VISITORS ONLY
. How many of these nights are/will be within the area marked on the map?I_I_I

. Which types of lodging have you used or will you use within the area marked on the
map on this trip? (Circle "Y" next to lodging types mentioned and ask how many

nights for each type of lodging.)

Y I_I_I Hotel/Motel Y I_I_I Friend's House
Y I_I_I On-site Campground Y I_I_I Rental House
Y I_I_I Out-site Campground Y I_I_I Second Home
Y I_I_I Family's House Y I_I__I Other: .............

 

126

APPENDIX A, A-1 (cont'd.)

ID # I_I_I
DATE I_I_I_
ALL RESPONDENTS
10. Are you currently a student? (circle) Y/N

(a student means any one who is in a formal education all of the time until now and
doesn't have employment yet)

11. In what kind of transportation do you use during your trip to and from the park?
(circle)
Private transportation: a. Private car b. Motor cycle c. Company car
Public transportation : a. Bus b. Rent-a-car c. Mini-cap d. Other: .......

12. Are you making this trip: (circle)

a. With your immediate family d. With your fiiend or business associates
b. With your spouse e. Alone
c. With relatives 1'. Other: ........

 

 

THE FOLLOWING QUESTIONS CONCERN ABOUT THE TRIP-RELATED
SPENDING MADE BY THE RESPONDENT FROM THE TIME HE OR SHE
LEFT HIS OR HER PERMANENT/VACATION HOME UP TO THE TINIE HE
OR SHE RETURN THERE. Ifthe respondent is making the trip in a group (spending
unit), spending amount for the group is taken into account, and circle "G". If part of this
spending is individually spent, circle "I". The following questions are formed into three
columns. Column 1 is for types of spending, column 11 is for spending within the area
marked on the map, and column ID is for spending outside the area marked on the map.
Ifthere is no trip-related spending, fill 0 (zero) in the appropriate column (don't leave the
column blank).

 

13. Including yourself, how many are in your group (spending unit)?

a. Adults (219 years): I_I_I
b. Teenagers (7-18 years): I_ _I
c. Children or infants (5 6 years): I_I_I

14. For this trip, please tell me how much you have spent and will spend for the following
spending category I will ask: (if staying in a second home, expenditures should relate
only to the park visit, not to maintaining the home, traveling to the second home fi'om
the permanent home, etc.)

 

127

APPENDD( A, A-1 (cont'd.)

There are five categories of expenditures: TRANSPORTATION, LODGING, FOOD
AND BEVERAGE, OUTSIDE MARKET, AND MISCELLANEOUS EXPENSES.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TYPE WITHIN OUTSIDE
THE AREA THE
MARKED AREA
MARKED
A. TRANSPORTATION EXPENSES
PRIVATE TRANSPORTATION:
a. Gas and oil G / I S $
b. Repairs and washing G / I 3 S
c. Spare parts G / I S S
d. Parking fees and tolls (road, bridge, etc.) G / I 3 5
PUBLIC TRANSPORTATION:
a. Fares/tickets G / I 8 $
b. Rent G / I S S
c. Fares for recreation vehicles G / I S S
B. LODGING EXPENSES
a. Hotels, motels, rental homes, cottages, etc G / I S $
b. Campgrounds (outside the park) G / I S S
C. FOOD AND BEVERAGE EXPENSES
a. Grocery, convenience, liquor, vendor, G/ I S S
soft-drink, and other food and beverage
stores (for eating and drinking off-
premises)
b. Restaurants, diners, catering, bars, and G / I S 3
other eating and drinking places

 

 

128

APPENDIX A, A-l (cont'd.)

 

 

 

 

 

 

D. OUTSIDE MARKET EXPENSES

a. Fruits G / I S $
b. Vegetables G / I S S
c. Other food crops G / I S 3
d. Plants (including ornamental plants) G / I S S
e. Fishes (including ornamental fishes) G / I S 3
E. MISCELLANEOUS EXPENSES

a. Rental fees for recreation equipment, G/ I S $

rental horse, etc.
b. Cigarettes G/ I 3 8
c. Insurance fees G / I 5 3
(1. Fees for guide services G/ I S S
e. Admission to recreation sites G / I S S
f. Admission to tourist attractions G / I S S
g. ' Other recreation expenses (billiard, golf, G/ I S 3
movie, fishing, etc.)

h. Camera film or video tape purchase G / I S S
i. Film developing or video tape processing G/ I S S
j. Souvenirs or gifts G/ I S S
k. Clothing (including footwear) G / I 3 S
1. Other expenses (specify) G/ I 3 S

 

15. How many trips did you make between January through April last year to

the park?

I_I_I

16. How many trips did you make during the past 12 months to the park? I_I_I

17 . How many trips do you plan to make during the next 12 months to the

park?

III

Thank you for participating in this survey. THANK YOU FOR YOUR TIME.
End the interview and record:

1. Ending time: a.m.lp.m.
2. Interviewer initial:

 

129

APPENDIX A

A-2. Indonesian version of the questionnaire, GPNP.

22 Januari, 1994
Yth. Pengunjung TN. Gede Pangrango

Terima kasih atas partisipasi anda dalam penelitian yang dilakukan oleh Indonesia's
Natural Resources Management Project bekerja sama dengan Taman Nasional Gede
Pangrango. Anda telah sepakat secara suka rela untuk berpartisipasi dalam penelitian ini
melalui wawancara secara langsung. Kami sedang mewawancarai para pengunjung untuk
mengetahui belanja rekreasi yang berkaitan dengan kunjungan mereke ke TN. Gede
Pangrango. Data tersebut akan digunakan untuk menduga seberapa besar pengaruh
belanja rekreasi dirnaksud terhadap ekonomi regional. Untuk melengkapi penelitian ini,
kami memerlukan inforrnasi dari anda yang didasarkan atas beberapa pertanyaan yang
telah disiapkan dalam kuesioner berikut ini. Kami yakin bahwa inforrnasi anda tidak hanya
akan membantu kami untuk menyusun suatu rencana kebutuhan pengunjung seperti anda,
namun juga kebutuhan masyarakat yang terpengaruh akibat kegiatan-kegiatan rekreasi ke
taman nasional.

Kami memilih anda sebagai responden secara systematic random sample, yaitu
dengan memilih setiap pengunjung yang ke-10 sebagai responden. Kami sangat
menghargai partisipasi anda dengan mengorbankan waktu kurang lebih 10 menit guna
melengkapi wawancara ini. Semua jawaban anda akan kami catat tanpa menyertakan
identitas anda, baik dalam proses pengumpulan data maupun dalam penyajian hasil
penelitian ini. Kami persilahkan anda mengajukan pertanyaan selama wawancara
berlangsung.

Terima kasih atas bantuan anda. Apabila ada pertanyaan lebih lanjut sehubungan
dengan penelitian ini, silahkan menghubungi kami pada alamat tersebut di bawah ini.

Homat kami,

Adi Susmianto

Peneliti

Jl. Raya Cibodas PO. Box 3 Sdl Cipanas Cianjur
Telp. (0255)-512776

Larry Leefers, Ph.D.

Supervisor

Departement of Forestry, Michigan State University
113 Natural Resource Building

East Lansing, MI 48824

130

APPENDIX A, A-2 (cont'd.)

Nomor I I I I
Tanggal I I I I

 

 

SURVEY POLA PEMANFAATAN DAN BELANJA REKREASI
DI TAMAN NASIONAL GEDE PAN GRANGO, INDONESIA

Selamat pagi/siang/sore. Nama saya . Atas narna Indonesia's
Natural Resource Management Project bekerja sama dengan Taman Nasional Gede
Pangrango, saya sedang melakukan survey pengunjung untuk mengetahui penggunaan
taman nasional sebagai tujuan rekreasi dan seberapa besar pengaruh belanja pengunjung
terhadap ekonomi regional. Apakah anda bersedia untuk berpartisipasi dalam survey ini?
Lingkari: "Y" (Ya) atau "T" (Tidak).

Apabila "Tidak", catat sebagai tidak bersedia dan sampaikan terima kasih. I I

(Kemudian pilih pengunjung pertama dari group berikutnya sebagai respondJnT
Apabila "Ya", catat waktunya dan lanjutkan dengan wawancara. I I I

 

 

Serahkan kepada responden surat pengantar, peta dan daftar rincian belanja, dan jelaskan
bahwa: surat pengantar tersebut akan memberikan gambaran umum tentang maksud dan
tujuan survey, sedangkan peta dan dafiar rincian belanja akan membantu responden dalam
menjawab beberapa pertanyaan selama wawancara. Daerah yang diberi tanda pada peta
merupakan daerah (region) yang merupakan objek survey ini.

 

1. Apakah anda akan mendaki ke puncak Gunung Gede atau Pangrango? (lingkari) Y/T

2. Untuk menuju ke taman nasional ini, apakah anda datang dari: (lingkari salah satu)
a. Tempat tinggal permanen, atau
b. Rumah peristirahatan

3. Tolong sampaikan kepada saya:
a. Dirnana tempat tinggal permanen anda? (lingkari)
1). Bogor 2). Sukabumi 3). Cianjur 4). Lainnya: ...........
b. Dirnana rumah peristirahatan anda? (lingkari)
1). Bogor 2). Sukabumi 3). Cianjur 4). Lainnya: ...........

4. Apa tujuan utama anda datang ke taman nasional ini? Anda boleh menyebutkan lebih
dari satu pilihan.

a. Pendakianfjalan e. Sekedar santai
b. Camping f. Melihat aneka satwa/tumbuhan
c. Studi/penelitian g. Piknik

d. Melihat air terjun/danau h. Lainnya: .................

 

 

131

APPENDIX A, A-2 (cont'd.)

Nomor I I I I
Tanggal I I I I

 

 

 

Merujuk pertanyaan l, apabila perjalanan ke taman nasional dimulai dari:

a. TEMPAT TINGGAL PERMANEN, sampaikan: untuk wawancara selanjutnya,
apabila saya menyebut perjalanan berarti mengacu pada selang waktu sejak anda
meninggalkan tempat tinggal permanen anda sampai dengan anda kembali ke tempat
tersebut.

b. RUMAH PERISTIRAHATAN, sampaikan: untuk wawancara selanjutnya, apabila
saya menyebut perjalanan berarti mengacu pada selang waktu sejak anda meninggalkan
rumah peristirahatan anda sampai dengan anda kembali ke tempat tersebut atau ke
tempat tinggal permanen apabila anda tidak kembali ke rumah peristirahatan.

 

5. Berapa banyak tempat rekreasi lain di dalam daerah yang ditandai dalam peta yang
sudah atau akan anda kunjungi selama perjalanan ini? Catat dan lingkari:
a. Kebun Raya Cibodas d. Taman Rekreasi Selabintana g. Telaga Warna
b. Taman Mandalawangi e. Camping Ground Situgunung h. Lainnya: .......
c. Taman Safari f. Perkebunan Gn. Mas

6. Perjalanan anda ke taman nasional ini dapat disebut sebagai (lingkari salah satu):
a. Tujuan utama
b. Tujuan kedua
c. Sambil lalu (kurang penting)
d. Kebetulan saja
e. Lainnya: .........

7. Berapa malam telah anda luangkan sejak anda meninggalkan tempat tinggal permanen
atau rumah peristirahatan anda selama perjalanan ini? I_I_I
Berapa malam lagi akan anda luangkan dalam perjalanan ini? I_I___I
Apabila tidak ada, lanjutkan ke pertanyaan nomor 10.

Apabila ada, lanjutkan ke pertanyaan nomor 8.

KHUSUS UNTUK RESPONDEN YANG BERMALAM

8. Berapa malam, dari total yang telah atau akan anda luangkan, telah atau akan anda
luangkan di dalam daerah yang diberi tanda dalam peta? I_I_I

 

132

APPENDIX A, A-2 (cont'd.)

Nomor I I I I
Tanggal I I I I

 

 

9. Apa tipe penginapan yang telah atau akan anda gunakan di dalam daerah yang diberi

10.

ll.

12.

tanda selama perjalanan ini? Lingkari "Y" disamping tipe penginapan yang digunakan,
dan tanyakan berapa malam pada masing-masing tipe penginapan tersebut.

Y I_ I_ I Hotel/motel Y I___ I_ I Rumah teman

Y I_ I _I Perkemahan di dalam TN Y I__ _I_ I Rumah sewaan

Y I _I__ I Perkemahan di luar TN Y I_ I_ I Rumah peristirahatan

Y I_ I_ I Rumah farnili Y I_ I __I Lainnya ..........

UNTUK SEMUA RESPONDEN

Apakah anda pelajar/mahasiswa? Lingkari "Y" atau "T". Y/T
(yang dimaksud pelajar/mahasiswa di sini adalah mereka yang sedang dalam
pendidikan formal hingga saat wawancara ini dan belum bekerja)

Selama perjalanan menuju dan meninggalkan taman nasional ini, macam transportasi
apa yang anda gunakan? Lingkari salah satu:

Transportasi pribadi: a. Kendaraan pribadi b. Sepeda motor c. Kendaraan kantor
Transportasi umum: a. Bus b. Sewa kendaraan c. Mini-cap d. Lainnya: ..............

Anda melakukan perjalanan ini : (lingkari jawaban yang tepat)

a. Bersama keluarga anda d. Bersama teman atau kelompok sekerja
b. Bersama pasangan anda e. Sendiri
c. Bersama sanak saudara f. Lainnya: ................

 

 

PERTANYAAN-PERTANYAAN BERIKUT BERKAITAN DENGAN BELANJA
YANG DH(ELUARKAN OLEH RESPONDEN SEHUBUNGAN DENGAN
PERJALANANNYA KE TAMAN NASIONAL SEJAK SAAT YANG
BERSANGKUTAN MENINGGALKAN TEMPAT TINGGAL
PERMANEN/RUMAH PERISTIRAHATAN HINGGA KEMBALI KE TEMPAT
TERSEBUT. Apabila responden melakukan perjalanan dalam bentuk grup (spending
unit), jumlah belanja yang diperhitungkan adalah belanja grup; lingkari huruf "G" pada
jenis belanja yang sesuai. Apabila sebagian dari belanja tersebut merupakan belanja
pribadi, lingkari huruf "I" pada jenis belanja yang sesuai. Pertanyaan-pertanyaan berikut
disusun ke dalam tiga kolom. Kolom I adalah untuk tipe belanja (grup atau pribadi),
kolom II untuk belanja yang dibelanjakan di dalam daerah yang diberi tanda pada peta,
dan kolom III untuk belanja yang dibelanjakan di luar daerah yang diberi tanda pada peta.
Apabila responden sama sekali tidak membelanjakan apapun, baik untuk sebagian atau
keseluruhan jenis belanja, isilah 0 (nol) pada kolom yang sesuai (jangan biarkan kolom
tersebut tidak terisi/kosong).

 

 

133

APPENDD( A, A-2 (cont'd.)

Nomor I I I I
Tanggal I I I I

 

 

13. Termasuk anda, berapa orang yang terrnasuk dalam grup (spending unit) anda?

a. Kelompok umur 2 19 tahun: I_I_I
b. Kelompok umur 7-18 tahun: I_I_I
c. Kelompok umur s 6 tahun: I_I_I

14. Untuk perjalanan ini, mohon sebutkan berapa besar belanja yang telah dan akan anda
belanjakan sesuai dengan jenis belanja yang akan saya sebutkan berikut ini: (apabila
responden berangkat dari rumah peristirahatan, belanja dimaksud hanya yang berkaitan
dengan perjalanannya menuju taman nasional dari rumah peristirahatan, tidak terrnasuk
belanja selama tinggal di mmah peristirahatan, belanja perjalanan dari tempat tinggal
permanen menuju ke rumah peristirahatan, dan lain-lain).

Rincian belanja berikut ini dikelompokkan ke dalam lima kategori: TRANSPORTASI,
PENGINAPAN, MAKANAN DAN MINUMAN, DI LUAR PASAR, BELANJA

 

 

 

LAlN-LAIN.
JENIS BELANJA TIPE Di DALAM Di LUAR
DAERAH DAERAH
YANG DIBERI YANG DIBERI
TANDA TANDA
A. TRANSPORTASI
TRANSPORTASI PRIBADI:
a. Bahan bakar dan oli. G/ I Rp. ................. Rp. ..................
b. Reparasi dan cuci kendaraan. G / I Rp. ................. Rp. ..................
c. Perlengkapan kendaraan. G / I Rp. ................. Rp. ..................
d. Parkir dan toll (jalan, jembatan, dll). G/ I Rp. ................. Rp. ..................
TRANSPORTASI UMUM:
a. Karcis/ongkos kendaraan. G/ I Rp. ................. Rp. ..................
b. Sewa kendaraan. G / I Rp. ................. Rp. ..................
c. Karcis/ongkos kendaraan untuk G/ I Rp. ................. Rp. ..................
kegiatan rekreasi.
B. PENGINAPAN
a. Hotel, motel, rumah sewaan, villa, G /I Rp. ................. Rp. ..................
dll
b. Camping ground (luar taman G / I Rp. ................. Rp. ..................
nasional).

 

 

 

 

 

 

134

APPENDIX A, A-2 (cont'd.)

 

 

 

 

 

 

 

 

C. MAKANAN DAN MINUMAN
a. Toko eceran, pedagang kaki lima, G / I Rp. ................. Rp. ..................
pedagang asongan, dan tempat
pembelian makanan dan minuman
lainnya.
b. Restoran, rumah makan, katering, G /I Rp. ................... Rp. ...................
warung, bar, dan tempat makan dan
minum lainnya.
D. BELANJA DI LUAR PASAR
a. Buah-buahan. G / I Rp. ................... Rp. ...................
b. Sayur-sayuran. G/ I Rp. ................... Rp. ...................
c. Tanaman pangan lainnya. G / I Rp. ................... Rp. ...................
d. Tanaman keras (terrnasuk tanaman G / I Rp. ................... Rp. ...................
hias).
e. Ikan (terrnasuk ikan hias). G / I Rp. ................... Rp. ...................
E. BELANJA LAIN-LAIN
a. Sewa perlengkapan/peralatan G / I Rp. ................... Rp. ..................
rekreasi, sewa kuda, dll.
b. Rokok. G / I Rp. ................... Rp. ..................
c. Asuransi. G / I Rp. ................... Rp. ..................
d. Sewa pemandu wisata. G / I Rp. ................... Rp. ..................
e. Karcis/ongkos masuk areal rekreasi. G/ I Rp. ................... Rp. ..................
f. Karcis/ongkos masuk tempat-tempat G / I Rp. ................... Rp. ..................
atraksi di dalam areal rekreasi.
g. Belanja rekreasi dan hiburan lainnya G / I Rp. ................... Rp. ..................
(bilyar, golf, bioskop, mancing, (111).
h. Foto film atau video tape. G/ I Rp. ................... Rp. ..................
i. Cuci & cetak foto/proses video tape. G / I Rp. ................... Rp. ..................
j. Sofenir/buah tangan. G/ I Rp. ................... Rp. ..................
k. Pakaian (terrnasuk sepatu, dll). G / I Rp. ................... Rp. ..................
l. Belanja lainnya (uraikan). G / I Rp. ................... Rp. ..................

 

15. Berapa kali anda mengunjungi taman nasional ini selama bulan Januari sampai dengan
April pada tahun yang lalu? I_I_I

16. Berapa kali anda mengunjungi taman nasional ini selama 12 bulan pada tahun yang
lalu? I_I_I

 

135

APPENDIX A, A—2 (cont'd.)

Nomor I I I I
Tanggal I I I I

 

 

17. Berapa kali anda akan mengunjungi taman nasional ini selama 12 bulan pada tahun
yang akan datang? I_I_I

Terima kasih atas partisipasi anda dalam penelitian ini. TERIMA KASIH ATAS
WAKTU YANG TELAH ANDA LUANGKAN DALAM WAWAN CARA INI.
Akhiri wawancara dan catat:

1. Waktu wawancara berakhir: .....................

2. Paraf pewawancara : .....................

136

APPENDIX A

A-3. A summary of the primary data, GPNP.

A total of 934 respondents were interviewed in the survey conducted fiom January
22 to April 17, 1994. Nine hundred and twenty-six visitors (99.1%) participated, while
eight visitors (0.9%) declined to participate during the survey. Forty-five out of 926
samples were not usable, leaving a sampling frame of 881 samples (questionnaires). The
following data are summarized from the samples presenting the respondents' responses
sequential to the questions addressed in the questionnaire. Spending data are not included

in the summary. The word "res" is used to abbreviate "respondent".

Question 1: Hiking to the top of the GPNP.
Yes = 143 res
N0 = 738 res
Question 2: The respondent' departures.
Permanent house = 824 res
Vacation house = 57 res
Question 3: The origin of respondents.
Bogor = 105 res Sukabumi = 200 res
Cianjur = 55 res Jakarta = 416 res
Bandung = 21 res Tangerang = 46 res
Bekasi = 7 res Depok = 2 res
Purwakarta = 2 res Palembang = l res
Cilegon = 1 res Banten = 1 res
Semarang = 2 res Kerawang = 4 res
Question 4: The primary purpose of visit (a respondent may have more than purpose).
Hiking/walking = 121 res Camping = 134 res
Studying/research = 78 res Waterfall/lake = 562 res
Pleasure = 157 res Flora & fauna watching = 33 res
Picnicking = 304 res Others = 2 res
Question 5: Other recreation sites visited (a respondent may have more than one visit).

Cibodas Botanical Garden = 70 res Mandalawangi Park = 19 res

Safari Garden = 12 res

Situgunung Campground = 78 res

Telaga Warna = 16 res

Selabintana Park = 63 res
Gn Mas Estate = 10 res
Others = 20 res

137

APPENDIX A, A-3 (cont'd.)

Question 6:

Question 7:

Question 8:

Question 9:

Question 10:

Question 11:

The intention of visitation.
Primary purpose = 770 res
Secondary purpose = 102 res
Unimportant = 4 res

By accidence = 5 res

Others = 0 res

Number of nights spent.

Before destination: 0 = 789 res
l = 54 res
2 = 34 res
3 = 2 res
4 = 1 res
6 = l res

After destination: 0 = 700 res
1 = 71 res
2 = 36 res
3 = 71 res
6 = 1 res
7 = 2 res

Number of nights spent within the B-C-S region.

0=646res 1=79res
2=63 res 3=82 res
4 = 7 res 6 = l res
7 = 2 res 8 = 1 res
Type of lodging used.

Hotel/motel = 10 res

Out-site campground = 49 res
F riend's house = 9 res
Vacation house = 13 res

The occupation of respondents.
Students = 463 res
Nonstudents = 418 res

In-site campground = 94 res
Family's house = 6 res
Rental house = 11 res
Others = 36 res

The transportation used (a respondent might use more than one type).

Private transportation:
Private car = 266 res
Office car = 28 res

Motor cycle = 23 res

138

APPENDIX A, A-3 (cont'd.)

Public transportation:
Bus = 199 res Rental car = 292 res
Mini-cap = 187 res Others = 9 res

Question 12: Group type of visitors.
Immediate family = 39 res Spouse = 37 res
Relatives = 136 res Friends or business associates = 669 res
Alone = 0 res Others = 0 res

Question 13: The age class of visitors (number of group members).
Adults (2 19 yrs) = 23,189 (reported by 837 res)
Teenagers (7-18 yrs) = 2,265 (reported by 239 res)
Children or infants (3 6 yrs) = 133 (reported by 128 res)

The rest questions were associated with spending information (not included in this
summary) and were explored throughout the chapters of this study.

APPENDIX B

139

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an: mmhm 5 RN mm 3 ea: venom Nemm mummew .80...
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APPENDIX B.

140

Table B-2. The 66 sector classification for the Indonesian input-output table 1990,

 

 

 

 

GPNP.
I-O code Sector description I-O code
(66 sectors) (161 sectors)

1 Paddy 1
2 Beans 5-7
3 Maize 2
4 Root crops 3-4
5 Vegetables and fruits 8—9
6 Other food crops 10
7 Rubber 11
8 Sugarcane 12
9 Coconut 13
10 Oil palm 14
11 Tobacco 16
12 Coffee 17
13 Tea 18
14 Clove 19
15 Fibre crops 15
16 Other estate crops 20
17 Other agriculture 21
18 Livestock 22-23, 25
19 Slaughtering 44
20 Poultry and its product 24
21 Wood 26
22 Other forest products 27-28
23 Fishery 29-3 1
24 Coal and metal ore mining 32, 35-40
25 Crude oil, natural gas and geothermal mining 33-34
26 Other mining and quarrying 41-43
27 Manufacture of food processing and preserving 45-48
28 Manufacture of oil and fat 49
29 Rice milling 50
30 Manufacture of flour, all kinds 51-54
31 Sugar factory 55
32 Manufacture of other food products 56-61
33 Manufacture of beverages 62-63
34 Manufacture of cigarettes 64-65
35 Yarn spinning 66

 

 

 

 

APPENDIX B.

141

Table B-2 (cont'd.)

 

 

I-O code Sector description I-O code
(66 sectors) (161 sectors)
36 Manufacture of textile, wearing apparel and leather 67-73
37 Manufacture of bamboo, wood and rattan products 74-79
38 Manufacture of paper, paper products and cardboard 80-83
39 Manufacture of fertilizer and pesticide 85-86
40 Manufacture of chemicals 84, 87—93
41 Petroleum refinery 94-95
42 Manufacture of rubber and plastic wares 96-99
43 Manufacture of non metallic mineral products 100-102, 104
44 Manufacture of cement 103
45 Manufacture of basic iron and steel 105-106
46 Manufacture of non ferrous basic metal 107-108
47 Manufacture of fabricated metal products 109-112
48 Manufacture of machine, electrical machinery and 113-120
apparatus

49 Manufacture of transport equipment and its repair 121-126
50 Manufacture of other products not elsewhere classified 127-131
51 Electricity, gas and water supply 132-133
52 Construction 134-138
53 Trade 139
54 Restaurant and hotel 140-141
55 Railway transport 142
56 Road transport 143
57 Water transport 144-145
58 Air transport 146
59 Services allied to transport 147
60 Communication 148
61 Financial intermediaries 149-150
62 Real estate and business services 151-152
63 General government and defense 153
64 Social and community services 154-156
65 Other services 157-160
66 Unspecified sector 161

 

 

 

Source: Indonesian Input-Output Table 1990, Volume: 1, Central Bureau of Statistics,

1994.

 

142

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144

 

 

 

 

 

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.0 02020.3(

APEENDIX B.

145

T0000 M. Tochnicd 000111010110. 008010: 0001010111 1910 B-C-S 1091011 0! 91001100000 071000 in 1900. GPNP.

 

 

900101 j 2 3 4 1 0 7 0 0 10
1 0.0104 0 0 0 0 0 0 0 0 0
2 0 0.003027 0 0 0.000227 0 0 0 0 0
3 0 0 0.021003 0 0 0 0 0 0 0
4 0 0 0 0.000721 0 0 0 0 0 0
0 0 0 0 0 0.010030 0.000200 0 0 0 0
0 0 0 0 0 0 0 040000 0 0 0 0
7 0 0 0 0 0 0 0 027003 0 0 0
0 0 0 0 0 0 0 0 0.077417 0 0
0 0 0 0 0 0.000200 0 0 0 0.004204 0
10 0 0 0 0 0 0 0 0 0 0.004702
11 0 0 0 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0
17 0.000407 0.000070 0.002002 0.000713 0.07000 0 0.000400 0 000-00 0.003023 1 040-00
10 0.001700 0.003701 0.012370 0 000330 0.004203 0.000071 1.07000 0.41000 0.000003 0 001300
10 0 0 0 0 0 0 0 0 o 0

20 0 0.70000 0.00-00 0.000170 0.001730 0 0 0 0 0
21 2.000-00 7.00000 0.00000 4 740-00 3 000-00 0.000300 0.000221 1.22000 0.000240 0 010-00
22 0.01007 3 300-00 0 0 0 0 4 040-00 0 0 0
23 0 0 0 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0 0 0 0
20 0 0 0 0 ’ 0 0 0 0 0 0
20 0 0 0 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0 0 0.000777 0 002440
20 0 0 0 0 0 0 0 o 0 0
30 0 0 0 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0 o 0 0
34 0 0 0 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0 0 0 0
30 0.000403 0.000030 0.000007 0.000447 0 001003 0.001400 0.002407 0.01000 0.000030 0.000410
37 0 0 0.000101 0.000227 0.002000 0.00111 7 0.000040 0 0.000301 0.001101
30 0 o 0 0 0.000104 0.001117 0.000707 0.000100 0.001020 0.004020
30 0.000130 0.004100 0.004003 0.010000 0.034700 0.010004 0.0341 1 0.003100 0.01 1003 0.1 10702
40 0 0 0 0 0.13000 0.000127 0.003001 2.00000 0.150000 0.000200
41 4.00000 0.000100 0.01000 0 100-00 0.001400 0.001400 0.013000 0.00001 0.00233 0.010700
42 0.000301 0 0 0 0.000040 0.001371 0.002102 1.04000 0.002031 0.0017
43 0 0 0 0 0 0 0.000031 0 0 0.000313
44 0 0 0 0 0 0 0 0 0 0
40 0 0 0 0 0 0 0 0 0 0
40 0 0 0 0 0 0 0 0 0 0
47 0.002007 0.001724 0.004330 0 002003 0.001700 0.000700 0.010124 0.001004 0.000200 0.010003
40 4.000-00 0.02000 4.00000 1 420-00 0.000103 0.000740 0.00001 0.013340 0.001034 0.000370
40 0 0 0 0 0 0 0 0.70000 0 0
00 0 0 0 0 2.37000 0.001400 0.000300 2.70000 0.00-00 0.001002
01 0 0 0 0 0 0 0.000223 3.03000 0.000000 0.001700
02 0.000030 0.002000 0.003320 0.000104 0.000073 0.001030 0.010404 0.010004 0.014002 0.010000
03 0 0 0 0 0 0 0 0 0 0
04 0 0.001040 0.002012 0.000200 0.000140 0.001070 0.002020 0.000040 0.003000 0.001004
00 0 0 0 0 0 0 2.140-00 1.700-00 0.070-00 0.000124
00 0.000310 0.000070 0.003300 0.000077 0.000443 0.002420 0.021007 0.004007 0.007170 0.003000
07 0 0.000170 0 3.1000 1.00000 0 0.000301 0 7.01000 0
00 0 0 0.000201 0 4.11000 0 0.000101 0.000200 0.000270 0.001 200
00 0 0 0 0 0 0 0 0 0 0
00 0 0 0 0 3.30000 0 3.17000 1.04000 3.02000 7.70000
01 0.001420 0.000010 0.0012 0.000330 0.02000 0.001002 0.002002 0.002710 0.000370 0.001707
02 0.000204 0.001417 0.00022 0 1.020-00 0.001013 0.000230 0.001010 0.000200 0.000403
03 0 0 0 0 0 0 0 0 0 0
04 0 0 0 0 0.000111 0 2.700—00 7.240-00 0.000140 0
00 0.002041 0.002102 0.004004 0.004107 0.000013 0.000200 0.010700 0.002770 0.00007 0.030307
00 0 0 0 0 0 0 0 0 0 0

 

N000: Thi0 "I100 60 00100100 from "101090'0lndon00i0n 66-000107 input-output t0bl0 by m 010 81mph Location 001011011: m0th0d with GOP . 0
11005ch 100100. Th0 000101 n0m00 .0 10011111100 in Appondix B, T0b|0 8-2.

APPENDIX 8.

T“ 8-8 (cont'd.)

146

 

 

800200 Fl 12 13 14 15 18 17 18 18 w

1 0 0 0 0 0 0 0 0.017851 0 0.000114

2 0 0 0 0 0 0 0 0.003548 0 0.00073

3 0 0 0 0 0 0 0 0.001807 0 0.002503
4 0 0 0 0 0 0 0 0.001723 0 0.001283
5 0.004345 0 0 0 0 0 0 0.002781 0 0.001118

8 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 0 0

8 0 0 0 0 0 0 0 0.02587 0 0

8 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0
1 1 0 008753 0 0 0 0 0 0 0 0 0
12 0 0.245838 0 0 0 0 0 0 0 0
13 0 0 0.01 1578 0 0 0 0 0 0 0
14 0 0 0 0.014355 0 0 0 0 0 0
15 0 0 0 0 0.00238 0 0 0 0 0
18 0 0 8.486-05 0 0 0.002837 0.000808 0.001088 0 0
17 0.000347 0.001318 2.28E-05 0.001322 0.001875 0.000274 0.008578 0.0027 37 0 5 855-05
18 0 004012 0.001888 7.575-05 0.000348 0.0008 0.002212 0.000218 0.000788 0.558228 0
18 0 0 0 0 0 0 0 1.255-05 2 755-05 0
20 0.003708 0 0 0.004582 0 0 0 0.003741 0.183417 0.013158
21 0.000313 0.000353 5.185—05 0.000153 0.001 127 0.000458 0.000155 0.000854 7 21 E-05 3.185-05
22 1.01 E-08 0 0 0 0 0 0 3.78E-07 0 8.08507
23 0 0 0 0 0 0 0 0 0 0.000282
24 0 0 0 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0 8.85-07 0 0
27 0 0 0 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0 0.003385 0 0.000412
28 0 0 0 0 0 0 0 0.038814 0 0.007814
30 0 0 0 0 0 0 0 8.835-05 0 0
31 0 0 0 0 0 0 0 8.875-05 0 0
32 0 0.001 188 0 008807 0 0 0 0 0.088572 0 0.302828
33 0 0 0 0 0 0 0 0 0 0
34 0.004582 0 0 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0 0 0 0
38 0.001588 0.00157 0.000471 0.010318 0 008457 0.002781 0.000151 0.000503 0.000414 0.000125
37 0.008778 0.001728 0.008833 0.001 108 0 002138 0.001541 0.000151 8.885—08 0 0.002588
38 0.002418 0.002484 8.75-05 8.755-05 0.000335 0.000182 0.00024 0.000188 0.00021 1 0.000388
38 0.228188 0.027501 0.038705 0.05481 0.038357 0.04788 0.080853 0 0 4.37 E-08
40 0.000328 0.000205 2.585-05 2.885-05 0 0 0 0.008871 0.0002 0.018332
41 0.018181 0.002332 0.002832 0.00433 0.004428 0.003258 0.001037 0.011871 0.001827 0.008212
42 0.008005 0.001018 0 8.25-05 0.005173 0.000235 0.000208 0.002012 0.000481 0.001021
43 1.135-05 0.000874 1.025—05 5.73505 0 4.885-05 0.000584 0.000285 0 7.08505
44 0 0 0 0 0 0 0 0 0 0
45 0 0 0 0 0 0 0 0 0 0
48 0 0 0 0 0 0 0 0 0 0
47 0.023835 0.00458 0.000583 0.00181 0.005558 0.005478 0.000172 0.001783 0.000121 0.00041 1
48 0.012253 0.001777 0.000848 3.02E-05 1.82505 0.000217 0.000275 0.000501 0.001828 0.000884
48 0 0 0 0 0 0 0 0 0 0
50 0 0.000123 1.025-05 8.885-05 0.0001 15 2.57 E-05 0 0.0001 17 0.00028 0.000128
51 0.004457 0.000575 0.001838 0.000427 0.000101 0.00038 0.000438 0.003827 0.003151 0.002805
52 0.013884 0.002833 0.013258 0.001 878 0.008013 0.017028 0.005803 0.004518 0.000353 0.003222
53 0 0 0 0 0 0 0 0 0 0
54 0.000738 0.002382 0.000827 0.001342 0.001752 0.000583 0.000247 0.000343 0.000428 0.000728
55 8.445-05 2.735-05 8.85E-05 7.81 E-08 0 0 0 4.03E-08 3.74E-05 0
58 0.000184 0.000835 0.000428 0.000715 0.000488 0.002338 7 .23 E-05 0.000181 0.003888 0.000732
57 0 0 0 0 0 0 0 0 0.00148 0
58 0.000882 0.002132 0.01137 0.000483 0.002128 0.00018 5.82E-05 0.000345 0.000334 0.000147
58 0 0.000148 1.475-05 0 0 0 0 0 0.000845 0
80 1.385-05 0.000221 5.845-05 1.735—05 3.875-05 1 .13E-05 1.21E-05 1.81 E-05 1.57 E-05 8.51608
81 0.000874 0.00088 0.000784 0.001284 0.000828 0.000822 0.000225 0.000833 0.000308 0.000215
82 0.007378 0.003871 0.000134 0 0.0005 4.845-05 0 3.885—08 0.0021 15 0.000877
83 0 0 0 0 0 0 0 0 0 0
84 0.004483 0.001573 1.08505 0.001103 0 0 0 0.001011 0 0.00017
85 0.030817 0.005281 0.002788 0.001 1 88 0.005228 0.003827 0.000218 0.003341 0.000878 0.001802
88 0 0 0 0 0 0 0 0 0 0

 

APPENDIX B.

T“ 8-8 (cont'd.)

147

 

 

 

”1.1m: 21 22 23 24 m 27 2'! fl
1 0 0 0 0 0 0 4.77E-08 0 0.884718 0
2 0 0 0 0 0 0 0.033711 0.000730 0 0.001700
3 0 0 0.000147 0 0 0 1.27000 0.002211 0 0.000037
4 0 0 7.00000 0 0 0 0.000201 0 0 0.073004
0 0 0 0 020-00 0 0 0 0.010000 0 0 0.004000
0 0 0 1 21000 0 0 0 0.000022 0 0 0.007340
7 0 0 0 0 0 0 0 0 0 0
0 0 0 0 o 0 0 0 0 0 0
0 0 0 0 0 0 0 0.000432 0.1 12003 0 0.007100
10 0 0 0 0 0 0 0.003423 0.240207 0 0
11 0 0 o 0 0 0 0 0 0 0
12 0 0 o 0 0 0 0 0 0 0
13 0 0 0 0 0 o 0 0 0 0
14 0 0 0 0 0 0 0 0 0 0.000000
10 0 0 0 0 0 0 0 0.04000 0 0
10 0 0 0 0 0 0 0.00202 0.000410 0 0.000300
17 0 0 0 0 0 0 3.30000 0.000372 0 0.10000
18 0 0 0 0 0 0 0.022388 0 0 0
10 0 0 0 0 0 0 0.013002 0.001002 0 0.001737
20 0 0 7.72000 0 0 0 0.000320 0.000040 0 0.0003
21 0.000002 1240-00 0.000032 4 030-00 0 0.000232 0 1.4000 210000 0
22 0.000114 2 00000 0.00010 0 0 0 0 4.00000 0 0
23 0 0 0.000722 0 0 0 0.201000 1.2000 0 0.24000
24 0 0 0 0.000070 0 21007 0 0 0 0 0
20 0 0 0 0 0 40000 0 0 0 0 0
20 0 0 0.000101 0 0 2.13000 0 0.70000 0 0
27 0 0 0 0 0 0 0.02001 0 0 0.000040
20 0 0 3.07000 0 0 0 0.01341 0.120200 0 0000040
28 O 0 0.000821 0 0 0 0.00031 0 0.003348 0.0351 33
30 0 0 2.21000 0 0 0 0.003170 0 0 0.102730
31 0 0 7.37000 0 0 0 0.032201 0 0 0.030020
32 0 0 0.02110 0 0 0 0.03713 0 0 0.000270
33 0 0 0 0 0 0 1.00000 0 0 0
34 0 0 0 0 0 0 0 0 0 0
30 0 0 0.001071 0 0 0 0 0 0 0.000307
30 0.000030 0.001170 0.004303 0.001240 0 002304 0.00040 0.000000 0.000207 0.002010 0.001010
37 0 0 0.002040 0 0 0.001377 0.000424 0.000144 0 0.000102
30 0.004030 0.000022 0.000722 0.000707 0.000107 0.001302 0.00227 0.000020 0.23000 0.003700
38 0 0 0.002072 1.04E-08 4.12E-07 1.08505 0 0 0 0.000148
40 0.000000 0.0010 0.000002 0.010473 0.000401 0.032003 0.010020 0.00400 0.34000 0.004024
41 0.030201 0.012740 0.003700 0.027003 0.004100 0.032270 0.014000 0.007000 0.003732 0.01 1 100
42 1.70000 0.00010 0.002300 7.42000 0 0.000432 0.011307 0.001 100 0.0001 14 0.002000
43 0.000107 0.000141 0.000114 0.000101 0 0 0.000337 0.00000 0 0.02000
44 0 0 0 0 0 0 0 0 0 0
40 0 0 0 0 0.000101 0 0 0 0 0
40 0 0.001000 0.000132 0 0 0 1.43000 0 0 0.43000
47 0.000407 0.00433 0.001004 0.00021 0.00040 0.00203 0.02010 0.000020 0 0.000070
40 0.020070 0.002030 0.001207 0.030000 0.000301 0.033007 0.000020 0.001400 0.000070 0.001071
48 0 0 0.018542 4.8E-05 0.000706 0 0 0 0 0
00 0.001 120 0.003310 0.000241 3.70000 2.40000 3.03000 1 .47000 3.7000 0 1.30000
01 0.001070 0.003323 0.000000 0.001040 0.000133 0.000420 0.000030 0.002031 0.000021 0.004004
02 0.010001 0.021 102 0.003000 0.000220 0.000020 0.01 1707 0.001003 0.002407 4.70000 0.001000
03 0 0 0 0 0 0 0 0 0 0
04 0.00737 0.0014 0.002000 0.001 1 10 0.000127 0.01 100 0.000014 0.002400 0.000400 0.002104
00 0.00000 0 1.40000 0.000072 0.04000 0.000203 0.000130 0.22000 1.71000 0.300-00
58 0.003282 0.004878 0.000322 0.008101 0.000782 0.008287 0.013718 0.008178 0.000838 0.00821 8
07 0.001407 0.000100 0.01000 0.000003 0.00014 0 0.000002 0.001000 0.000347 0.001721
00 0.002202 0 0.00013 0.011441 0.002300 0.001000 0.001320 0.000212 0.00000 0.000300
00 0.000241 0 0 0.00024 0.000207 3.70000 0.002322 0.000022 0.00010 0.000720
00 3.73000 7.01000 4.3000 0.27000 0.000120 0.20000 0.000302 0.00010 2.22000 0.000330
01 0.00101 0.000203 0.001300 0.000300 0.000004 0.000330 0.002200 0.00103 0.000703 0.001070
02 0.004002 0.010077 0.000022 0.000003 0.023001 0.004034 0.000003 0.002047 0.23000 0.001720
03 0 0 0 0 0 0 0 0 0 0
04 0 0 0.001107 0.0003 0.000201 0.000133 0.002030 0.000201 1.00000 0.04000
00 0.020701 0.000000 0.001070 0.007430 0.000300 0.010370 0.02412 0.01100 0.000100 0.002220
00 0 0 0 0 0 0 0 0 0 0

 

APPENDIX 8.

Tfib 8-8 (cont'd.)

148

 

 

 

800107 31 32 5'5 34 M 37 if M
1 0 0.000137 0 0 0 0 0 0.000222 0 4.33000
2 0 0.100040 0.002272 0 0 0 0 0 0 0
3 0 0.047002 0.001300 0.00071 0 0 0 0 0 0
4 0.20000 0.001337 0 0 0 0 0 0 0 0
0 0 0.00213 0.014710 0 0 0 0 0 0 0.000020
0 0 0.003110 0.011113 0 0 0 0 0 0 1.70000
7 0 0 0 0 0 2 03000 0 0 0 0.000423
0 0.420740 4.01000 0.02000 0 0 0 0 0 0 0
0 0.0103 0.000307 0 0 0 1 00000 2 02000 0 0 000004 0.1000
10 0 0.00042 0 0 0 0 0 0 0 0.011010
11 0 0 0 0.070400 0 0 0 0 0 0
12 0 0.04741 0 0 o 0 0 0 0 0
13 0 0.022044 0.000130 0 0 0 0 0 0 0
14 0 0.000012 0 0.000010 0 0 0 0 0 0.000700
10 0 3.00007 0 0 0.012000 0 002000 3 02000 0 0 0
10 0 0.012247 0.004401 0.000120 0 2 37000 1 00000 0 0 0.002030
17 0.012000 0.001001 3.14000 0.000244 0 0 0 0 2.30000 0.003100
10 0 0.04000 0.002344 0 0 002204 0.000402 0 0 0 1.10000
10 0 0.000200 0 0 0 0.007010 0 0 0 1.0000

20 0 0.000300 0.000210 0 0 0 0 0 0 0.000130
21 0.000070 0.04000 0 0 0 0 0.040000 0.000043 0 4.03000
22 0 0 0 0.02000 0 0.70000 0.003001 0 3.24000 0.000200
23 0 0.00307 0 0 0 0 0 0 0 3.00000
24 0.01000 0 0 0 0 0 0 0 0 1.30000
20 0 0 0 0 0 0 0 3.72000 0.000000 0
20 0.03000 3.10000 0 0 0 1.37000 7.71007 7.00000 0.001020 0.000200
27 0 0.003240 0.000000 0 0 0 0 0 0 0.000730
20 0.000007 0.022734 0 0 0 0.23000 1.22000 2.00000 4.03000 0.007133
20 0 0.020070 0.002077 0 0 0 0 0 0 0.000200
30 0 0.001010 0.002000 7.01000 0.000300 0.001014 0.000020 0.001300 0 0.001000
31 0.000000 0.020244 0.134000 0.001172 0 0 0 2.04000 0 0.001020
32 0.10000 0.030012 0.002302 0 0.000170 0.20000 0 0 0 0.000201
33 0 2.02000 0.010770 0 0 0 0 0 0 0.000100
34 0 0 0 0.111010 0 0 0 0 0 0
30 0 0.24000 0 0 0.004702 0.200301 0.001327 0.000303 0.003000 0.000300
30 0.000102 0.000330 0.00000 1.10000 0.001270 0.212421 0.001021 4.4000 0.001212 0.000070
37 0.000040 0.000004 0.004210 1.02000 0.00300 0.001000 0.007320 0.001307 0.000037 0.001027
30 0.001 1 17 0.002000 0.000000 0.007000 0.002004 0.00343 0.001024 0.301330 0.021024 0.012700
30 0 0.000310 0.000000 7.40000 0 0.04007 2.30000 0.70000 0.004020 0.000400
40 0.004041 0.000021 0.047070 0.000222 0.320121 0.003733 0.0301 1 0.007371 0.410022 0.430224
41 0.013103 0.014071 0.013210 0.003077 0.030071 0.010077 0.021740 0.03030 0.020044 0.011203
42 0.000140 0.003000 0.003430 0.004000 0.000000 0.000001 0.002234 0.001007 0.0101 10 0.000007
43 0.001007 0.001004 0.021400 0.00000 3.00-00 0.001070 0.000310 0.71000 0.07000 0.01441
44 0 0 0 0 0 0 0 0 0 0
40 0 0 0 0 0 4.4000 0.12000 1.14000 0 0.000100
40 0 0.00000 0 0.000702 0 1 .47000 1 .00000 0.0001 14 0 0.000430
47 0.00112 0.001070 0.033300 0 0.002401 0.000004 0.001010 0.001701 0.002022 0.004000
40 0.000200 0.001300 0.000001 0.001320 0.003707 0.00002 0.000010 0.000000 0.010004 0.00002
40 0 0 0 0 0 0 0 0 0 0
00 1.00000 3.22000 0.04000 1.20000 0.12000 0.001210 1.3000 0.000412 4.40000 0.002721
01 0.000202 0.004041 0.020100 0.001074 0.014420 0.000103 0.004417 0.010200 0.000132 0.01340
02 0.0071 34 0.00143 0.003410 0.000030 0.001101 0.001201 0.001030 0.000030 0.000142 0.003372
03 0 0 0 0 0 0 0 0 0 0
04 0.004337 0.001701 0.007730 0.004300 0.001771 0.002000 0.01732 0.004477 0.000044 0.000020
00 0.70000 0.000124 0.000234 7.00000 0.000140 0.000170 0.000101 0.000707 0.000100 0.000300
00 0.000003 0.000120 0.000002 0.004207 0.002700 0.003070 0.014007 0.007400 0.000402 0.000407
07 0.001240 0.001023 0.001000 0.0012 0.000001 0.001 100 0.003 0.002101 0.000010 0.001704
00 0.000407 0.000370 0.002077 0.001 123 0.000003 0.000773 0.000400 0.000000 0.001702 0.002002
00 0.000000 0.000723 0.00001 0.001032 0.000323 0.000020 0.001420 0.001210 0.002200 0.000003
00 0.000100 0.000104 0.000470 0.000300 0.000200 0.000170 0.000147 0.000007 0.001434 0.001000
01 0.001112 0.001000 0.002401 0.001313 0.001711 0.001072 0.001000 0.002000 0.001143 0.002321
02 0.001772 0.002000 0.000402 0.002003 0.002401 0.003223 0.002002 0.00331 0.013020 0.012024
03 0 0 0 0 0 0 0 0 0 0
04 1.03000 0.000300 0.002243 0.000070 0.001002 0.000400 0.000240 0.004002 0.0012 0.000001
00 0.000300 0.000744 0.002317 0.004410 0.000700 0.000014 0.001000 0.001000 0.004700 0.001003
00 0 0.000204 0.01037 0.040004 0 1.47000 0.01 1200 0.010034 1.00000 0.00001 7

 

APPENDIX 8.

T“ 8~8 Icont'dJ

149

 

 

W 41 42 43 44 H! 40 27 If 40 W
1 0 0 4 330-00 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 o 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0
7 0 0 101300 0 0 0 0 0 0 0 0.000103
0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0.003747
10 0 0 0 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0 0 0.000400
10 0 0 0 0 0 0 0 0 0 0.000703
10 0 0 0 0 0 0 0 0 0 0
17 2.00000 0 0 0 0 0 0 0 0 4.14000
10 0 0 0.000403 0 0 0 0 0 0 4.02000
10 0 0 0 0 0 0 0 0 0 0.00231

20 0 0 0 0 0 0 0 0 0 0.01407
21 0 0 0.000000 0 03000 0 o 0 1.3007 0 00000 0.000130
22 0 0.04007 0 0 0 0 3.71000 7 10000 3.20000
23 0 0 0 0 0 0 0 0 0 7.02000
24 1 730-00 0 0.001400 0.00130 0.000301 0 007132 0 4.01007 0 00000 0
20 0 010301 0 0 0 001000 0.000334 0 0 0 0 0
20 2 44000 1.07000 0.00042 0 000444 3 01000 2 21007 3 10000 4.73007 0 2.01000
27 0 0 0 0 0 0 0 0 0 0
20 0 0 0 0 0 0 0 0 o 0
20 0 0 0.000307 0 0 0 0 0 0 0
30 0 1.04000 0 0 0 0 0 0.30000 0 0.000103
31 0 0 0 0 0 0 0 0 0 0
32 0 0 0 0 0 0 0 0 0 0
33 0 0 0 0 0 0 0 0 0 0
34 0 0 0 0 0 0 0 0 0 0
30 0 0.000707 0 0.001302 0 0 1.00000 4.24000 0.3007 0.004000
30 2.30000 0.020700 0.000443 0.000200 3000 0.00000 0.000170 0.001201 0.001003 0.002300
37 4.04000 7.40000 0.002221 1 30000 4.77000 4.07007 0.000022 0.001031 0.000200 0.01 1010
30 0.000120 0.001400 0.010320 0.040101 0.000100 1.04000 0.004004 0.003301 0.02000 0.003201
30 1.01000 1 .40000 0.000122 0.00001 7 1.21000 1.00000 1 .70000 1 .22000 0 0.00000
40 0.002070 0.270120 0.000002 0 01 2070 0.043304 0.010410 0.020000 0.023003 0.013110 0.124020
41 0.017203 0.010000 0.004003 0.00071 0.001130 0.03100 0 010012 0.003000 0.004003 0.000430
42 0.000020 0.140017 0.003030 0 20000 0.001200 1.04000 0.000703 0.011004 0.012407 0.010004
43 0.03000 0.000100 0.000012 0 0.00330 0.000202 0.000000 0.000344 0.004040 0.01 1 003
44 0 0 0.037700 0 0 0 0.00000 0 0 0.000701
40 2.00000 1.14000 0.000100 0 0.200041 0.000327 0.204100 0.000040 0.000000 0.00007
40 3.20000 2.13000 2.37000 0 0.000433 0.320204 0.130070 0.010040 0.000224 0.100070
47 0.001 100 0.003000 0.000403 0 0.00400 0.000200 0.070004 0.000002 0.022003 0.034040
40 0.000170 0.002001 0.004043 0.002010 0.012302 0.002370 0.002473 0.010000 0.041347 0.001007
40 0.000020 0 0 0 0 0 0 0.000200 0.320330 0
00 0.00000 0.000102 0.000232 0.01000 0.000300 0.000320 0.0001 10 0.010000 0.01024 0.04401 1
01 0.003307 0.004300 0.021270 0.001040 0.040070 0.012774 0.010401 0.001000 0.000702 0.000037
02 0.002000 0.001707 0.004771 0.004401 0.000710 0.001011 0.002300 0.000032 0.001140 0.000103
03 0 0 0 0 0 0 0 0 0 0
04 0.000070 0.00310 0.000031 0.020102 0.010703 0.004001 0.00420 0.002730 0.003200 0.007134
00 3.41000 0.000203 0.000301 0.000040 0.000000 0.000370 0.000241 0.000177 0.000472 0.001073
00 0.001 1 3 0.000073 0.000003 0.031 100 0.012300 0.007203 0.0133 0.000402 0.003034 0.000300
07 0.0001 1 7 0.001244 0.00200 0.002040 0.000000 0.000002 0.00131 0.000003 0.000003 0.001430
00 0.001371 0.000000 0.000003 0.000421 0.0004 0.001033 0.000004 0.00032 0.000300 0.001204
00 0.000144 0.000040 0.00100 0.00100 0.001707 0.001221 0.002020 0.000000 0.000303 0.000000
00 0.000200 0.000200 0.000031 0.001142 0.000220 0.000203 0.00110 0.000107 0.000201 0.000300
01 0.000000 0.000007 0.001240 0.003230 0.001022 0.001033 0.000700 0.000001 0.001204 0.001300
02 0.002240 0.00320 0.000700 0.013004 0.00272 0.000300 0.010132 0.002021 0.000322 0.012730
03 0 0 0 0 0 0 0 0 0 0
04 0.000302 0.000470 0.000414 0.000720 0.30000 0.000020 0.00000 0.00010 0.001270 0.000040
00 0.000137 0.004070 0.002470 0.001007 0.001132 0.000273 0.00332 0.001000 0.000271 0.000070
00 0.004200 0.020447 0.00003 0 0.040041 0.004030 0 0 0 0.020000

 

APPENDIX B.

TJIO 8'8 (cont'd.)

150

 

 

 

jute! 01 02 03 04 00 BI 07 0'5 fl
1 0 0.001020 3.44000 0 0 0 0 0 0 0
2 0 0 0 0.000401 0 0 0.000100 0 0 o
3 0 0 0 0.000001 0 0 0.20000 0 0 0
4 0 0 0 0.004100 0 0 3.07000 0 0 0
0 0 0 1.3000 0.031040 0 0 0 0 0 0
0 0 3 20000 0 0.40000 0 0 0 0 o 0
7 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0.000030 0 0 0 0 0 0
0 0 0 3.0000 0.004002 0 0 0 03000 0 0 0
10 0 0 0 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0 0 0 0
12 0 0 0 0.000104 0 0 0 0 0 0
13 0 0 0 0.07000 0 0 0 0 0 0
14 0 0 0 0.000110 0 0 0 0 0 0
10 0 0 0 0 0 0 3 04000 0 0 0
10 0 0 0 0.002042 0 0 4 00000 0 0 0
17 0 1.1 1000 0 0.000037 0 1.0000 0 0 0 0
10 0 0 0 0.002012 0 0.000170 1.00000 0 0 0
10 0 0 0 0.000011 0 0 0.000202 0 0 0
20 0 0 0 0.007240 0 0 0.000300 0 0 0
21 0 0.003014 0.04007 0.000131 0.000130 0 0 0 0 0
22 0 0.12000 0 0.000110 0 0 0 0 0 0

23 0 0 0 0.024040 0 0 0.000234 0 0 0
24 0.002343 0 0 0 1 03000 0 0 0 0 0
20 0.00133 0 0 0 0 0 0 0 0 0
20 0 0 002044 0.77000 7.01000 0 0 0 0 0 0
27 0 0 0 0.010030 0 0 0.000000 0 0 0
20 0 0 0.70000 0.01070 0 0 4.07000 0 0 0
20 0 0 0 0.000100 0 0 0.000000 0 0 0
30 0 0 0.00000 0.000000 0 0 0.001013 0 0.17000 0.00011
31 0 0 0 0.010031 0.000304 0 0.000017 0 0 0
32 0 0 0.01000 0.027773 0.000420 2.40000 0.001001 0.000140 0.000101 0
33 0 0 1.07000 0.010400 0.000027 0 0.000712 0.000717 0.000733 0
34 0 0 0 0.01020 0 0 0.000200 0.000120 0 0
30 0 0.30000 0.14007 0 0 0 0 0 0 0
30 0.000200 0.001002 0.002207 0.004030 0.003477 0.000077 0.000401 0.000302 0.004721 0 001004
37 0 0.00323 0.001223 0.000304 0 0.03000 0 2.03000 0.001200 0.0002
30 0.003002 0.002037 0.013103 0.000013 0.000404 0.00070 0.001322 0.010401 0.002070 0 013020
30 0 0 1.24000 0.000000 0 4.70000 7.00000 0 3.37000 0
40 0.00003 0.007020 0.000000 0.00337 0.0034 0.00000 0.001337 0.000200 0.003007 0.000070
41 0.201400 0.070222 0.013120 0.021007 0.13007 0.1 10041 0.104020 0.140300 0.041404 0.000407
42 1.00000 0.000042 0.003242 0.001000 0.000230 0.000004 0.000170 0.000200 0.000032 0.000337
43 0.000310 0.000003 0.000200 0.00202 0.000177 0.77000 2.01000 2.00000 0.000103 0.000240
44 0 0.032203 0 0 0 0 0 0 0 0
40 0 0.070017 0 0 2.43000 0 0 0 0 0
40 0 0.013321 0 0 0 0 0 0 0 0.000210
47 0.000000 0.123444 7.30000 0.002024 0.002320 2.12000 0.000140 3.0000 0.001370 0.000240
40 0.000727 0.040020 0.000000 0.003271 0.000222 0.000700 0.00103 0.000444 0.024733 0.000470
40 0 0 0 0 0.120301 0 0.042347 0.100340 0.002207 0
00 7.40000 0.001007 0.000434 0.000000 0.001027 0.000403 0.00100 0.000700 0.000004 0.00031
01 0.140322 0.00040 0.01 1207 0.020230 0.02041 1 0.002034 0.00420 0.00027 0.010204 0.013271
02 0.013123 0.001027 0.000103 0.000173 0.001104 0.001737 0.004311 0.0021 0.040240 0.040040
03 0 0 0 0 0 0 0 0 0 0
04 0.000044 0.004000 0.01 1003 0.00200 0.014420 0.007704 0.001024 0.040041 0.000074 0.01020
00 2.11000 1.10007 0.000200 0.31000 0.000130 0.40000 2.07000 2.30000 0.000320 0.000040
00 0.000100 0.12000 0.017440 0.001400 0.001407 0.010102 0.000100 0 0.002002 0.003130
07 2.07000 0 0.001700 3.04000 0.000000 0.002410 0.007004 0 0.000014 0.001270
00 0.000000 0.000730 0.00307 0.0003 0.0011 0.000132 0.000730 0.001302 0.002007 0.000744
00 1.01 000 0 0.000100 0.000200 0.012301 0.014410 0.102210 0.002421 0.022000 0.001170
00 0.000300 0.000200 0.001070 0.001004 0.001002 0.000000 0.001042 0.000004 0.004442 0.011107
01 0.000471 0.001002 0.002023 0.000430 0.002000 0.001742 0.001 123 0.002720 0.00001 1 0.002747
02 0.004400 0000200 0.010010 0.021300 0.020007 0.01703 0.000043 0.073000 0.033003 0.033034
03 0 0 0 0 0 0 0 0 0 0
04 0.000000 0.000400 0.000240 0.001244 0.010004 0.000013 0.000007 0.003374 0.000074 0.004400
00 0.010410 0.000000 0.000014 0.004001 0.010002 0.100041 0.001021 0.00000 0.010771 0.000103
00 0 3.10000 0.002000 0.000020 0 0 2.03000 0 0 0.000134

 

APPENDIX 8.

T0810 8-8 Icom'd.)

151

 

 

00cm 01 12 03 04 35F 53'
1 0 0 0 0 2.70000 0
2 0 0 0 0.000032 4.00000 0
3 0 0 0 0.00000 0.1000 0
4 0 0 0 0.00000 0.000104 0
0 0 0 0 0.000000 0.000000 0
0 0 0 0 0 3.03000 0
7 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0.000201 1.73000 0
10 0 0 0 0 0 0
1 1 0 0 0 0 0 0
12 0 0 0 0 0 0
13 0 0 0 0 0 0
14 0 0 0 0 0 0
10 0 0 0 1.03000 0 0
10 0 0 0 0.000370 0 0
17 1.47007 2 00007 0 0.01007 0.72000 0
10 0 0 0 0.000740 0.07000 0
10 0 0 0 0.004703 0.000300 0
20 0 0 0 0.002704 0 0
21 0 0 0 2.22000 0 0
22 0 0 0 1.34000 3.10000 0
23 0 0 0 0.001003 7.20007 0
24 0 0 0 0 0 0
20 0 0 0 0 0 0
20 0 0 0 3.40000 0 0
27 0 0 0 0.00100 0 0
20 0 0 0 0.00037 2.00000 0
20 0 0 0 0.007302 0 0
30 0 0 0 0.001000 2.23000 0
31 0 0 0 0.002730 3.40000 0
32 2.00000 0 0 0.001304 0.000000 0
33 0.43000 0.0000 0 0.000103 0.000377 0
34 2.40000 0 0 0 0 0
30 0 0 0 0.001203 0.003100 0
30 0.000107 0.000023 0 0.004004 0.013040 0 002743
37 0 1 .10000 0 0.00002 0.000000 0.001007
30 0.02000 0.004000 0 0.003070 0.003123 0.007220
30 1.04000 0.000304 0 0.0001 10 0.00024 0.000020
40 0.000022 0.002070 0 0.000043 0.014004 0.217021
41 0.001000 0.003040 0 0.000024 0.020203 0.031 107
42 0.000323 0.000040 0 0.002201 0.000102 0.010004
43 1 .00000 0.000200 0 0.000331 0.000044 0
44 0 0 0 0 0 0
40 0 0 0 0 0.00207 0
40 0 0 0 0 0.000300 0
47 1.02000 0.000002 0 0.000004 0.000010 0.000137
40 0.001101 0.022100 0 0.000100 0.003343 0.001077
40 0 0 0 0 0.100000 0
00 0.000710 0.001070 0 0.000020 0.020400 0
01 0.000142 0.00300 0 0.007033 0.010004 0.011037
02 0.014370 0.077113 0 0.01304 0.004000 0
03 0 0 0 0 0 0
04 0.021007 0.004007 0 0.002777 0.004400 0
00 0.000100 0.000122 0 0.20000 7.20000 0
00 0.000001 0.001741 0 0.001270 0.001034 0.000304
07 2.00000 0.000130 0 7.03000 0.000224 0
00 0.000340 0.003020 0 0.000000 0.001 103 0
00 1.02000 0.000121 0 0.74000 0.000100 0
00 0.001770 0.001121 0 0.000374 0.001200 0.001300
01 0.000732 0.002004 0 0.000000 0.000000 0
02 0.04000 0.014002 0 0.00000 0.021412 0
03 0 0 0 0 0 0
04 0.00002 0.007032 0 0.007100 0.001302 0
00 0.010002 0.000027 0 0.007701 0.020010 0.00002
00 0 0.000101 0 0 0.000031 0.220030

 

152

APPENDIX B.

Table 8-7. "Margining“ spending for selected retail goods in the B-C-S region, GPNP.

 

 

 

Retail good Total spending Impacted industry Charges' Charges
(rupiahs) (96) (rupiahs)

Oil and gas 6,523,433.61 Wholesale, retail trade 30.4 1,983,123.82
Transportation 1 .0 65,234.34

Spare parts 30,056.45 Wholesale, retail trade 44.6 13,405.18
Transportation 1.9 571.07

Cigarettes 14.538.860.85 Wholesale, retail trade 1 1.3 1,642,891.28
Transportation 1 .5 21 8,082.91

Photo films 20,082,660.35 Wholesale, retail trade 48.7 9,780,255.59
Transportation 0.3 60,247.98

Souvenirs 6,977,941.84 Wholesale, retail trade 45.4 3,167,985.60
Transportation 0.5 34,889.71

Clothings 3,931,116.60 Wholesale, retail trade 46.0 1,808,313.64
Transportation 0.2 7,862.23

Total 52.084.069.70 18.782.863.34

 

‘ Deived from national (the U.S.l personal consumption expenditures published by Bureau
of Economic Analysis, U.S. Department of Commerce (Stynes and Propst. 1992).

163

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APPENDIX C

 

OFFICE OF
namun"
AND
GNWWUE
mama;

Michigan State UNIVCISIIY
225 Administration BUIIdmg
East Lansing, Michigan
48824-1046

517/355-2180

FAX 517/336-1171

USU IS an ammmve-amon,
ecuareoponum-fy InSlzlulzon

 

154

APPENDIX C

C-l. UCRIHS approval letter.

MICHIGAN STATE

UNIVE

R SI T Y

December 16. 1993

TO: Adi Susmianto
1512-K Spartan Village

RE: IRB 0‘: 93-579
TITLE: RECREATIONAL EXPENDITURES IN GUNUNG
GEDE PANGRANGO NATIONAL PARK AND
THEIR REGIONAL ECONOMIC IMPACTS
REVISION REQUESTED: N/A
CATEGORY: I-C
APPROVAL DATE: 12/13/1993

The University Committee on Research Involving Human Subjects‘ (UCRIHS) review of this project
is complete. I am pleased to advise that the rights and welfare of the human subjects appear to be
adequately protected and methods to obtain informed consent are appropriate. Therefore, the
UCRIHS approved this project including any revision listed above.

Renewal:

Revisions:

UCRIHS approval is valid for one calendar year, beginning with the approval
date shown above. Investigators planning to continue a project beyond one year
must use the green renewal form (enclosed with the original approval letter or when
a project is renewed) to seek updated certification. There is a maximum of four
such expedited renewals possible. Investigators wishing to continue a project
beyond that time need to submit it again for complete review.

UCRIHS must review any changes in procedures involving human subjects. prior
to initiation of the change. If this is done at the time of renewal. please use the
green renewal form. To revise an approved protocol at any other time during the
year, send your written request to the UCRIHS Chair. requesting revised approval
and referencing the project‘s IRB # and title. Include in your request a description
of the change and any revised instruments, consent forms or advertisements that are
applicable.

Should either of the following arise during the course of the work, investigators
must notify UCRIHS promptly: (1) problems (unexpected side effects, complaints.
etc.) involving hum subjects or (2) changes in the research environment or new
information indicating greater risk to the human subjects than exrsted when the
protocol was previously reviewed and approved.

If we can be of any future help, please do not hesitate to contact us at (517) 355-2180 or FAX (5I7)

336-1171.

Sincerely,

UCRIHS Chair

DEW:pjrn

    

avid E. Wright, Ph.D.

cc: Dr. Larry Leefers

155

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