OCS Study MMS 2000-061

Final Report

An Economic Assessment of the Sport Fisheries for Halibut,

Chinook and Coho Salmon in Lower and Central Cook Inlet

by

Mark Herrmann*

Department of Economics

University of Alaska Fairbanks

Fairbanks, AK 99775-6080

ffmlh@uaf.edu

S. Todd Lee

Alaska Fisheries Science Center

National Marine Fisheries Service

7600 Sand Point Way NE

Seattle, WA 98115-0070

Charles Hamel

North Pacific Fishery Management Council

605 West 4th Avenue, Suite 306

Anchorage, AK 99501

Keith R. Criddle*

Department of Economics

Utah State University

Logan, UT 84322-3530

kcriddle@econ.usu.edu

Hans T. Geier

Joshua A. Greenberg

Carol E. Lewis

Department of Resources Management

University of Alaska Fairbanks

Fairbanks, AK 99775-7200

April 2001

*Corresponding authors
ii

Project Organization

Mark Herrmann and Keith Criddle were responsible for overall project management. Todd Lee and Mark

Herrmann adopted the data from the Alaska Sea Grant sponsored UAF saltwater sportfishing survey to

meet the information requirements of this project. Todd Lee led in the specification and estimation of the

participation rate model. Mark Herrmann spearheaded derivation of the compensating variation estimates

with assistance from Todd Lee. Hans Geier and Charles Hamel developed, groundtruthed, and modified

the IMPLAN input–output model to more accurately reflect patterns of economic activity on the Kenai

Peninsula. Charles Hamel, Mark Herrmann, and Todd Lee developed the Cook Inlet Region Marine

Sportfishing Economic Assessment program. Keith Criddle, Mark Herrmann, Charles Hamel, and Todd

Lee developed alternative management scenarios and estimated the corresponding impacts. Keith Criddle

and Mark Herrmann drafted the quarterly and annual project reports. Mark Herrmann, Keith Criddle,

Todd Lee, and Charles Hamel drafted the final project report. Joshua Greenberg contributed to the

development of the input–output model specification. Carol Lewis and Joshua Greenberg also

participated in drafting and review of the annual and final project reports.

Any views expressed are solely those of the authors, and should not be construed as representing those

of the institutions by which they are employed.

The tangible results of a good day’s work (play)—Homer, Alaska.
iii

Table of Contents

Project Organization .......................................................................................................................................... ii

Table of Contents ............................................................................................................................................. iii

List of Tables ...................................................................................................................................................... v

List of Figures .................................................................................................................................................... ix

1. Executive Summary ..................................................................................................................................... 1

2. Introduction .................................................................................................................................................. 2

3. Baseline Expenditures ............................................................................................................................... 10

Number of sportfishing-days ................................................................................................................ 10

Average daily fishing and non-fishing expenditures ............................................................................ 14

Days spent on the Kenai and in Alaska ................................................................................................ 17

Total expenditures assuming that all trip expenditures are attributable to fishing .............................. 18

Total expenditures assuming that some trip expenditures are not attributable to fishing ................... 24

4. Participation Rate Model ........................................................................................................................... 28

Econometric model ................................................................................................................................ 29

Coefficient estimates ............................................................................................................................. 30

Simulation model ................................................................................................................................... 33

Compensating variations ....................................................................................................................... 43

5. Input–Output Model .................................................................................................................................. 46

Economic impact analysis and input–output ........................................................................................ 46

Input–output model ............................................................................................................................... 47

Application of the IMPLAN database and model ................................................................................ 49

6. Impact Simulations .................................................................................................................................... 51

Baseline expenditures ............................................................................................................................ 51

Simulations ............................................................................................................................................ 53

Summary of results ................................................................................................................................ 67

7. Conclusions ................................................................................................................................................ 69

Limitations of the results and the need for future research .................................................................. 74

8. Acknowledgements ................................................................................................................................... 75

9. Study Products ........................................................................................................................................... 75

Publications ........................................................................................................................................... 75

Working papers ..................................................................................................................................... 76

Presentations .......................................................................................................................................... 76

10. References .................................................................................................................................................. 77
iv

APPENDIX A

Appendix to Section 3. Total Expenditures .................................................................................................... 85

Adjustments made to total expenditure estimates for multipurpose trips ............................................... 85

Total expenditures assuming that some trip expenditures cannot be attributed to fishing .................... 87

Appendix to Section 4. Estimated Trip Attributes by Sportfishing Mode, Residency,

Target Species, and Region ...................................................................................... 92

Appendix to Section 5. Input–Output ............................................................................................................. 93

Input–output theory .................................................................................................................................. 93

The transactions table ........................................................................................................................... 93

Multipliers ............................................................................................................................................. 96

Ratio multipliers and response coefficients ....................................................................................... 101

Assumptions and limitations .............................................................................................................. 102

Specific changes to the baseline model ................................................................................................. 102

The Kenai–Nikiski model ................................................................................................................... 104

The Homer model ............................................................................................................................... 105

The Anchor Point and Ninilchik model ............................................................................................. 107

Soldotna and balance of northern Kenai Peninsula model ................................................................ 108

IMPLAN baseline data ........................................................................................................................... 110

Appendix to Section 6. Impact Analysis ...................................................................................................... 117

APPENDIX B

Software Manual for $FISH ........................................................................................................................... 125
v

List of Tables

Table 1. Angler-days fished from lower and central Cook Inlet ports during 1997................................. 11

Table 2. Person-days fished in lower and central Cook Inlet during 1997

by fishing area, residency, and sportfishing mode ..................................................................... 12

Table 3. Person-days fished by residency category and sportfishing mode ............................................. 13

Table 4. Effort distribution (%) by residency category and sportfishing mode ....................................... 13

Table 5. Average daily expenditures for lower and central Cook Inlet sportfishing trips,

by residency and sportfishing mode ($/day) ............................................................................... 14

Table 6. Average (across residency categories) daily expenditures for lower and central

Cook Inlet sportfishing trips by sportfishing mode ($/day) ....................................................... 15

Table 7. Ratios of trip length to lower and central Cook Inlet fishing-day

for nonresidents by travel and sportfishing mode ....................................................................... 17

Table 8. Ratios of total days spent on the Kenai and elsewhere in Alaska per

lower and central Cook Inlet fishing day .................................................................................... 17

Table 9. Total expenditures by local residents fishing for halibut and salmon from

the lower and central Cook Inlet shore ....................................................................................... 18

Table 10. Total expenditures by local residents fishing for halibut and salmon from

private boats in lower and central Cook Inlet ............................................................................. 19

Table 11. Total expenditures by local residents fishing for halibut and salmon from

charter boats in lower and central Cook Inlet ............................................................................. 19

Table 12. Total expenditures by Alaskan (non-local) residents fishing for halibut and

salmon from the lower and central Cook Inlet shore .................................................................. 20

Table 13. Total expenditures by Alaskan (non-local) residents fishing for halibut and

salmon from private boats in lower and central Cook Inlet ....................................................... 20

Table 14. Total expenditures by Alaskan (non-local) residents fishing for halibut and

salmon from charter boats in lower and central Cook Inlet ....................................................... 21

Table 15. Total expenditures by nonresidents fishing for halibut and salmon from

the lower and central Cook Inlet shore ....................................................................................... 21

Table 16. Total expenditures by nonresidents fishing for halibut and salmon from

private boats in lower and central Cook Inlet ............................................................................. 22

Table 17. Total expenditures by nonresidents fishing for halibut and salmon from

charter boats in lower and central Cook Inlet ............................................................................. 22

Table 18. Total expenditures by all fishers fishing for halibut and salmon in

lower and central Cook Inlet ....................................................................................................... 23

Table 19. Total expenditures by all sport fishers for halibut and salmon in lower

and central Cook Inlet, by residency and sportfishing mode ..................................................... 23

Table 20. Primary purpose of trip to Alaska ............................................................................................... 24

Table 21. Assumed response to cancellation of the lower and central Cook Inlet

sportfishing portion of their trip .................................................................................................. 25
vi

Table 22. Reduction in fishing or visitation rates for a 100% reduction in fishing effort (days) .............. 26

Table 23. Total expenditures by all sport fishers fishing for halibut and salmon

in lower and central Cook Inlet that are directly attributable to the

saltwater halibut and salmon fishing trip .................................................................................... 27

Table 24. Expenditures attributable to sportfishing for halibut and salmon in

lower and central Cook Inlet, by residency and sportfishing mode ........................................... 27

Table 25. Hypothetical trip attribute levels ................................................................................................. 29

Table 26. Statistical summary of respondents’ socioeconomic characteristics ......................................... 31

Table 27. Random effects probit model parameter estimates ..................................................................... 32

Table 28. Mean attributes of lower and central Cook Inlet sportfishing trips ........................................... 34

Table 29. Average daily charter halibut catch for all Kenai Peninsula sport fishers ................................. 35

Table 30. Average daily halibut charter catch for all Kenai Peninsula area sport fishers

to the west of Gore Point ............................................................................................................. 35

Table 31. Percentage changes in days fished in response to changes in the cost

per sportfishing-day ..................................................................................................................... 36

Table 32. Percentage changes in days fished in response to changes in catch,

with ±90% confidence bounds .................................................................................................... 37

Table 33. Changes in days fished from shore in response to changes in catch,

with ±90% confidence bounds .................................................................................................... 39

Table 34. Changes in days fished from private boats in response to changes in catch,

with ±90% confidence bounds .................................................................................................... 40

Table 35. Changes in days fished from charter boats in response to changes in catch,

with ±90% confidence bounds .................................................................................................... 40

Table 36. Changes in days fished in response to changes in catch,

with ±90% confidence bounds .................................................................................................... 40

Table 37. Changes in days fished in response to changes in catch by sportfishing mode ......................... 41

Table 38. Percentage changes in days fished in response to changes in catch by

sportfishing mode ......................................................................................................................... 41

Table 39. Compensating variations response to changes in catch, with ± 90% confidence bounds .......... 44

Table 40. Changes in compensating variation in response to changes in expected catches ...................... 45

Table 41. Parameters values for the estimated average production function for the

marine charter sector .................................................................................................................... 50

Table 42. Absorption sectors and coefficients for sportfishing expenditure categories ............................ 50

Table 43. Total Kenai Peninsula area expenditures by Alaskans (non-local) and

nonresidents that can be attributed directly to lower and central Cook Inlet

halibut or salmon sportfishing trips ............................................................................................. 52

Table 44. Regional economic impacts of a 10% decrease in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 58
vii

Table 45. Final demand impacts of a 10% decrease in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 59

Table 46. Regional economic impacts of a 20% decrease in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 60

Table 47. Final demand impacts of a 20% decrease in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 61

Table 48. Regional economic impacts of a 30% decrease in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 62

Table 49. Final demand impacts of a 30% decrease in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 63

Table 50. Regional economic impacts of a 10% increase in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 64

Table 51. Final demand impacts of a 10% increase in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 65

Table 52. Regional economic impacts of a 20% increase in Cook Inlet sportfishing catches .................. 66

Table 53. Final demand impacts of a 20% increase in lower and central Cook Inlet

sportfishing catches ...................................................................................................................... 67

Table 54. Changes in compensating variations (CV) and regional economic impacts in

response to changes in catch ........................................................................................................ 74

Table 55. Primary purpose of visit to Alaska for lower and central Cook Inlet

halibut and salmon sport fishers (by trip) ................................................................................... 85

Table 56. Primary purpose of visit to Alaska for lower and central Cook Inlet

halibut and salmon sport fishers (by fishing days) ...................................................................... 85

Table 57. Primary purpose of visit to Alaska for lower and central Cook Inlet

halibut and salmon sport fishers (by Kenai days) ....................................................................... 86

Table 58. Assumed effects of the cancellation of the saltwater fishing portion of the

Kenai Peninsula trip ..................................................................................................................... 86

Table 59. Assumed net effect of the cancellation of the saltwater fishing portion of the

Kenai Peninsula saltwater fishing trip on all days applied to Appendix to Section 3 ............... 86

Table 60. Estimated reduction in visitation rates for a 100% reduction in fishing effort (days) ............... 87

Table 61. Total expenditures by local residents fishing for halibut and salmon from the

lower and central Cook Inlet shore that are attributed directly to the saltwater

halibut and salmon fishing trip .................................................................................................... 87

Table 62. Total expenditures by local residents fishing for halibut and salmon from private

boats in lower and central Cook Inlet that are attributed directly to the saltwater

halibut and salmon fishing trip .................................................................................................... 88

Table 63. Total expenditures by local residents fishing for halibut and salmon from

charter boats in lower and central Cook Inlet that are attributed directly to the

saltwater halibut and salmon fishing trip .................................................................................... 88
viii

Table 64. Total expenditures by Alaskans (non-local) fishing for halibut and salmon from

the lower and central Cook Inlet shore that are attributed directly to the

saltwater halibut and salmon fishing trip .................................................................................... 89

Table 65. Total expenditures by Alaskans (non-local) fishing for halibut and salmon from

private boats in lower and central Cook Inlet that are attributed directly to the

saltwater halibut and salmon fishing trip .................................................................................... 89

Table 66. T otal expenditures by Alaskans (non-local) fishing for halibut and salmon from

charter boats in lower and central Cook Inlet that are attributed directly to the

saltwater halibut and salmon fishing trip .................................................................................... 90

Table 67. Total expenditures by nonresidents fishing for halibut and salmon from the

lower and central Cook Inlet shore that are attributed directly to the saltwater

halibut and salmon fishing trip .................................................................................................... 90

Table 68. Total expenditures by nonresidents fishing for halibut and salmon from

private boats in lower and central Cook Inlet that are attributed directly

to the saltwater halibut and salmon fishing trip .......................................................................... 91

Table 69. Total expenditures by nonresidents fishing for halibut and salmon from

charter boats in lower and central Cook Inlet that are attributed directly

to the saltwater halibut and salmon fishing trip .......................................................................... 91

Table 70. Kenai Peninsula trip attributes ..................................................................................................... 92

Table 71. Simplified transactions table for a hypothetical regional economy .......................................... 94

Table 72. General IMPLAN model information: household numbers and mean household

income by zip code based subarea ............................................................................................ 110

Table 73. Output, value added, and employment in the study area. (All variables except

employment are in millions of dollars.) .................................................................................... 110

Table 74. Charterboat expenditure categories, SIC classification, and IMPLAN

sectoral translation ..................................................................................................................... 113

Table 75. Final demand categories, IMPLAN sector descriptions, and sector numbers ........................ 114

Table 76. Regional economic impacts of a 10% decrease in lower and central

Cook Inlet sportfishing catches ................................................................................................. 117

Table 77. Regional economic impacts of a 20% decrease in lower and central

Cook Inlet sportfishing catches ................................................................................................. 118

Table 78. Regional economic impacts of a 30% decrease in lower and central

Cook Inlet sportfishing catches ................................................................................................. 119

Table 79. Regional economic impacts of a 10% increase in lower and central

Cook Inlet sportfishing catches ................................................................................................. 120

Table 80. Regional economic impacts of a 20% increase in lower and central

Cook Inlet sportfishing catches ................................................................................................. 121
ix

List of Figures

Figure 1. Minerals Management Service Gulf of Alaska subregion, Cook Inlet Planning Area ............... 2

Figure 2. Cook Inlet study area ..................................................................................................................... 3

Figure 3. IPHC halibut management areas ................................................................................................... 4

Figure 4. Commercial and sportfishing catches and bycatch mortality of Pacific halibut

in IPHC area 3A ............................................................................................................................. 5

Figure 5. Percentages of Pacific halibut mortality due to commercial and sportfishing catches

and bycatch in IPHC area 3A ........................................................................................................ 5

Figure 6. Numbers of vessels licensed by the IPHC for sport (charter) and

sport (charter)/commercial fishing off Alaska .............................................................................. 6

Figure 7. Commercial and sport catches (numbers) of chinook salmon from Cook Inlet .......................... 6

Figure 8. Percentages of commercial and sport catches (numbers) of chinook salmon

from Cook Inlet .............................................................................................................................. 7

Figure 9. Commercial and sport catches (numbers) of coho salmon from Cook Inlet

excluding sport catches from Seward and commercial catches from the

Lower Cook Inlet Eastern District ................................................................................................ 7

Figure 10. Percentages of commercial and sport catches (numbers) of coho salmon from

Cook Inlet excluding sport catches from Seward and commercial catches from

the Lower Cook Inlet Eastern District .......................................................................................... 8

Figure 11. Sportfishing for halibut from a charter vessel in Cook Inlet ...................................................... 10

Figure 12. Person-days fished in lower and central Cook Inlet during 1997 by residency

and sportfishing mode .................................................................................................................. 13

Figure 13. A young sport fisher hooked into a halibut from aboard a charter boat in Cook Inlet .............. 14

Figure 14. Mean daily expenditures, by residency category, for charter fishing in

lower and central Cook Inlet ....................................................................................................... 16

Figure 15. Salmon fishing on the Russian River. Freshwater fishing ranked third highest among

reasons for taking a trip that included fishing for halibut or salmon in Cook Inlet ................... 25

Figure 16. Month in which survey respondents took their most recent trip ................................................ 33

Figure 17. Percentage change in the probability that the average sport fisher will participate

as a function of changes in the cost per sportfishing-day. (All other trip attributes

are set at the survey mean levels reflected in Table 28.) ............................................................ 36

Figure 18. Percentage change in the probability that the average sport fisher will participate

as a function of changes in expected catch. (All other trip attributes are set at the

survey mean levels reflected in Table 28.) ................................................................................. 37

Figure 19. The mean (solid line) and 90% confidence intervals (dotted lines) on the % change

in Alaskan resident sportfishing-days as a function of percent changes in expected

catch. (All other trip attributes are set at the survey mean levels reflected in Table 28.) ......... 38
x

Figure 20. The mean (solid line) and 90% confidence intervals (dotted lines) on the % change

in nonresident sportfishing-days as a function of percent changes in expected catch.

(All other trip attributes are set at the survey mean levels reflected in Table 28.) .................... 38

Figure 21. Percentage change in the probability that the average sport fisher will participate

as a function of changes in the expected catch of halibut. (Halibut size set at

35 pounds, fishing costs set at $100, and the catch of all other species set to zero.) ................ 42

Figure 22. Percentage change in the probability that the average sport fisher will participate as

a function of changes in the expected catch of chinook salmon. (Chinook size set at

30 pounds, fishing costs set at $100, and the catch of all other species set to zero.) ................ 42

Figure 23. Percentage change in the probability that the average sport fisher will participate as

a function of changes in the expected catch of coho salmon. (Coho size set at 7 pounds,

fishing costs set at $100, and the catch of all other species set to zero.) ................................... 43

Figure 24. The effect of increases/decreases in expected catch on the magnitude of total

compensating variations .............................................................................................................. 45

Figure 25. Percentage change in compensating variations as a function of percentage changes

in expected catch .......................................................................................................................... 46

Figure 26. Sample input–output transactions table ...................................................................................... 48

Figure 27. Example simulation model data input interface associated with a 10% decrease

in expected catch .......................................................................................................................... 53

Figure 28. Example simulation screen showing changes in participation and sportfishing-days

associated with a 10% decrease in expected catch ..................................................................... 54

Figure 29. Example simulation screen showing regional economic impacts associated with

a 10% decrease in expected catch ............................................................................................... 55

Figure 30. Example simulation screen showing final demand category impacts associated with

a 10% decrease in expected catch ............................................................................................... 56

Figure 31. Example simulation screen showing compensating variations associated with

a 10% decrease in expected catch ............................................................................................... 57

Figure 32. Industry output impacts from changes in expected catches of salmon and halibut

in the lower and central Cook Inlet sport fisheries ..................................................................... 68

Figure 33. Personal income impacts from changes in expected catch of salmon and halibut

in the lower and central Cook Inlet sport fisheries ..................................................................... 68

Figure 34. Employment impacts from changes in expected catch of salmon and halibut

in the lower and central Cook Inlet sport fisheries ..................................................................... 69

Figure 35. Extramural funding sources and their relationship to components of the

lower and central Cook Inlet sportfishing analyses .................................................................... 70

Figure 36. Willingness to pay, consumer surplus, and total expenditures ................................................... 71

Figure 37. Effect of a decrease in willingness to pay on consumer surplus and total expenditures ........... 72

Figure 38. Compensating variation ............................................................................................................... 73

Figure 39. Sample input–output transactions table ...................................................................................... 93
1

1. Executive Summary

The Cook Inlet Planning Area includes and abuts productive commercial, subsistence, and sport fishing

grounds. Outer continental shelf minerals exploration, development and production activities could

affect the productivity of these fisheries, the quality of recreation opportunities, and the demand for

tourism-related services. The marine sport fisheries of lower and central Cook Inlet are the focus of a

large recreation based economic sector that provides non-monetary benefits to participants and monetary

benefits to tourism-related businesses. This study and a companion study, Lee et al. [1999b], develop a

predictive model of participation rate changes that can be linked to a regional input–output model to

estimate net benefits to sport fishers and the regional economic impact of marine sportfishing on the

Kenai Peninsula economy.

The probability that a typical sport fisher would take a halibut or salmon sportfishing trip in lower or

central Cook Inlet is modeled as a random utility function of the expected trip cost and catch (species,

size, number). The model, estimated using a binary probit estimation technique, allows for declining

marginal utility as well as the interactions between salmon and halibut sportfishing catches. The model

is used to predict changes in participation given changes in expected catch that may result from changes

in mean catch that could arise from changes in biomass (abundance) or changes in catch limits. The

e st imat e d pr o ba bi li t y of ta ki ng a tr i p is t r an sf o rmed i n to a pr ed ic t io n o f ch an g es i n t ot al s po rt fi s hi ng ef fo rt .

Net benefits to sport fishers are measured by compensating variations, the amount of money that could

be added to the price of the trip until the sport fisher would be indifferent to taking the trip. Consequently,

compensating variation is a measure of the consumer surplus from sportfishing and changes in the total

compensating variation are measures of changes in consumer surplus. The total compensating variation

in 1997 ($19.2 million) is the product of the estimated mean compensating variation per day ($97.30)

and the number of sportfishing-days (197,556). Reductions in expected catch reduce the compensating

variation in two ways. First, the marginal participant will drop out of the fishery as the expected benefits

(in terms of catch) decrease, thereby decreasing the total net benefits of the sportfishing. Second, the net

benefit of taking a trip is also reduced for those who continue to participate because the average trip

produces less net benefit when the catch rate declines.

In contrast to net benefits, which are a measure of economic efficiency, economic impact is a measure of

the distribution of economic activity. Changes in sportfishing effort affect regional economies by altering

primary and secondary expenditure patterns. For example, if fishing effort were to diminish, fishingrelated expenditures would also decline. However, estimates of these changes must account for the

possibility that some individuals might engage in other Kenai Peninsula recreation activities as substitutes

for the foregone sportfishing-days. The Cook Inlet Region Marine Sportfishing Economic Assessment

simulation model accounts for these substitution effects and declining marginal utility and allows

estimation of the economic impact of lower and central Cook Inlet sportfishing for halibut and salmon

u nd er v a ry in g c on di t io ns of s to c k ab u nd an ce an d h ar ve st li mi t at io ns [s ee Ap pe nd i x B o r Hamel e t a l. 20 01 ].

Five examples of changes to sportfishing trip attributes are examined: two increases (10% and 20%) and

three decreases (–10%, – 20%, and – 30%) from the baseline (1997) mean catch per fishing trip. The five

scenarios reflect changes in expected harvests that might result from natural stock dynamic processes;

changes in allocation shares among commercial, subsistence, and sport fishers; changes in catch limits; or

population, regulatory, or economic and behavioral responses to environmental damage that might result

from accidents associated with minerals exploration, development, production, or transportation.

The results indicate, for example, that for a 10% decrease in expected salmon and halibut catch, net

benefits to sport fishers will decrease by 23% ($4.4 million). The concomitant decrease in participation

can be expected to result in a $2.5 million decrease of direct, indirect, and induced output expenditures

in the Kenai Peninsula region, a $1.0 million reduction in personal income, and a loss of 72 jobs.
2

2. Introduction

Outer continental shelf minerals exploration, development and production activities in the Cook Inlet

Planning Area (Figure 1) could affect the productivity of commercial, subsistence, and sport fisheries,

the quality of recreation opportunities, and the demand for tourism-related services.

Figure 1. Minerals Management Service Gulf of Alaska subregion, Cook Inlet

Planning Area.

The sport fisheries of Cook Inlet (Figure 2) contribute to the economic well-being of residents of the

Kenai Peninsula, Alaska, and the nation. In this analysis, we focus primarily on the sport fishery for

Pacific halibut (Hippoglossus stenolepis). However, because the marine salmon sport fishery is both a

substitute and a complement for the halibut sport fishery, the analysis also addresses the marine sport

fisheries for chinook (Oncorhynchus tshawytscha) and coho (O. kisutch).
3

Figure 2. Cook Inlet study area.

Pacific halibut are managed by an international agreement between the U.S. and Canada. Under the

auspices of this agreement, the International Pacific Halibut Commission (IPHC) establishes overall

harvest limits in ten management zones (2A-C, 3A, B, 4A-E) in the North Pacific, Gulf of Alaska, and

eastern Bering Sea (Figure 3), while authority to allocate catches among competing commercial, sport,

and subsistence interests is delegated to the individual nations. With passage of the Fishery Conservation

and Management Act of 1976 (MSFCMA), the North Pacific Fishery Management Council (Council) was

given responsibility for allocating halibut catches off Alaska. Until recently, the commercial TAC (total

allowable catch) for Pacific halibut has been determined by subtracting the bycatch allowance and

expected non-commercial (sport and subsistence) catches from the ABC (allowable biological catch),

a limit set by the Council consistent with IPHC limits. In February 2000, the Council approved for

recommendation to the Secretary of Commerce, a management structure that sets a guideline harvest level

(GHL) for charterboat-based sportfishing catches of halibut equal to the 1995–1999 average catches (636

metric tons in IPHC area 2C and 1,777 metric tons in IPHC area 3A), with provisions for a reduction in

the GHL if stock biomass declines. Under the proposed regulations, harvests by sport fishers who do not

hire charterboat services will continue to be accommodated through reductions of the commercial TAC.

Subject to Secretarial approval, the new management scheme will be implemented in 2001. The Council

has also indicated an interest in development of an individual fishing quota (IFQ) management structure

as an alternative to the GHL. Under an IFQ, open markets would serve to allocate the combined TAC and

GHL (specified as a percentage of the ABC) among commercial fishers and charterboat operators.
4

Figure 3. IPHC halibut management areas [IPHC 1999].

While the Council has exercised direct management of the commercial catch and bycatch of halibut, it

has relied on the Alaska Department of Fish and Game (ADF&G) to manage the sport fishery under

bag and possession limits established by the Alaska Board of Fisheries. Current regulations stipulate a

1 February–31 December open season with a two fish daily bag limit and a four fish possession limit.

Under the recently adopted management structure, sportfishing bag limits could be reduced if the

charterboat GHL is exceeded.

Although salmon management is also subject to international agreement and federal oversight outside of

state waters, for all practical purposes the Alaska Board of Fisheries controls salmon catches off Alaska.

ADF&G manages the salmon fisheries off Alaska according to guideline harvest ranges established by the

Board. These guideline harvest ranges are intended to allow for the satisfaction of escapement objectives,

while serving to allocate catch among subsistence, sport, and commercial fishers.

Cook Inlet saltwater sportfishing regulations specify a daily bag and possession limit of six other (coho,

sockeye, chum, and pink) salmon in combination. Bag and possession limits for chinook salmon differ

depending on whether the catches are taken above or below Bluff Point near Homer. To the north of Bluff

Point, the daily bag and possession limit is one chinook salmon. To the south of Bluff Point, the daily bag

and possession limit is two chinook salmon. In addition, there is an annual catch limit of five chinook

salmon from Cook Inlet salt waters (north of a line between Cape Douglas and Point Adam, see Figure 2).

Recent sport catches of halibut from Cook Inlet, Prince William Sound, Resurrection Bay, Kodiak,

Yakutat, and adjacent portions of Gulf of Alaska (IPHC Area 3A) have exceeded 3,409 metric tons round

weight (Figure 4). During the past two decades, the share of halibut catch taken in the sport fishery has

grown from less than 2% to over 18% (Figure 5).

4D

4B 4A

4C

4E

3B

3A 2C

2B

2A

closed

ALASKA

WA

Canada
5

0

5

10

15

20

25

30

1977 1982 1987 1992 1997

thousand mt

Commercial Removals Bycatch Mortality Sportfishing

Figure 4. Commercial and sportfishing catches and bycatch mortality of

Pacific halibut in IPHC area 3A [IPHC 1999 and Howe et al. 1999].

0%

25%

50%

75%

100%

1977 1982 1987 1992 1997

Catch Share

Commercial Removals Bycatch Mortality Sportfishing

Figure 5. Percentages of Pacific halibut mortality due to commercial and

sportfishing catches and bycatch in IPHC area 3A [IPHC 1999

and Howe et al. 1999].

The growth of halibut sportfishing (Figures 4 and 5), combined with the adoption of individual fishing

quotas in the commercial fishery, and growth in the number of vessels licensed to offer charter services

(Figure 6) have led to proposals to cap sportfishing harvests of halibut.

1977 1982 1987 1992 1997

Commercial Removals Bycatch Mortality Sportfishing

Commercial Removals Bycatch Mortality Sportfishing
6

0

500

1,000

1,500

2,000

1984 1986 1988 1990 1992 1994 1996

Sport/Commercial

Sport

Figure 6. Numbers of vessels licensed by the IPHC for sport (charter) and

sport (charter)/commercial fishing off Alaska [NPFMC 2000].1

Lower and central Cook Inlet sportfishing catches of chinook salmon have grown from an average of less

than 25% of the total number caught in the early 1980s to a recent average of around 20,000 fish or nearly

50% of the combined commercial and sportfishing removals [Lafferty et al. 1998] (see Figures 7 and 8).

0

10,000

20,000

30,000

40,000

50,000

1980 1985 1990 1995

Upper Cook Inlet Commercial Lower Cook Inlet Commercial

Upper Cook Inlet Sport Lower Cook Inlet Sport

Figure 7. Commercial and sport catches (numbers) of chinook salmon from

Cook Inlet [Lafferty et al. 1998].

 

1

 The data series ends in 1996 when licensing functions were delegated to the Alaska Commercial Fishery Entry Commission.

Sport/Commercial

Sport

Upper Cook Inlet Commercial Lower Cook Inlet Commercial

Upper Cook Inlet Sport Lower Cook Inlet Sport
7

0%

25%

50%

75%

100%

1980 1985 1990 1995

Upper Cook Inlet Commercial Lower Cook Inlet Commercial

Upper Cook Inlet Sport Lower Cook Inlet Sport

Figure 8. Percentages of commercial and sport catches (numbers) of

chinook salmon from Cook Inlet [Lafferty et al. 1998].

Most of the commercial harvest of coho (98%) is taken in upper Cook Inlet. Two-thirds of the

sportfishing catches are taken in the Homer area with the remainder coming from waters along the eastern

shore of upper Cook Inlet. The share of Cook Inlet catches of coho taken in sport fisheries has grown

from about 15% (2,000 fish) in the early 1980s to more than 50% [Robert Clark, ADF&G Sportfish

Division, personal communication 1999] (see Figures 9 and 10).

0

10,000

20,000

30,000

40,000

50,000

60,000

1980 1985 1990 1995

Commercial Sport

Figure 9. Commercial and sport catches (numbers) of coho salmon from Cook

Inlet excluding sport catches from Seward and commercial catches

from the Lower Cook Inlet Eastern District [Robert Clark, ADF&G

Sportfish Division, personal communication 2000].

Upper Cook Inlet Commercial

Upper Cook Inlet Sport

Lower Cook Inlet Commercial

Lower Cook Inlet Sport

Commercial Sport
8

0%

25%

50%

75%

100%

1980 1985 1990 1995

Commercial Sport

Figure 10. Percentages of commercial and sport catches (numbers) of coho salmon

from Cook Inlet excluding sport catches from Seward and commercial

catches from the Lower Cook Inlet Eastern District [Robert Clark,

ADF&G Sportfish Division, personal communication 2000].

The economics of the commercial halibut fishery have been subject to considerable analysis, beginning

with Crutchfield and Zellner [1962]. The demographics of the commercial halibut fishery were examined

in the EA/RIR (environmental assessment / regulatory impact review) for the implementation of the

halibut/sablefish IFQ program [NPFMC 1991a, b]. Criddle [1994] describes the bioeconomics of the

commercial halibut fishery. The National Research Council’s (NRC) Committee to Review IFQs [1999]

includes an evaluation of the adoption of individual fishing quotas in the commercial halibut fishery.

Lin et al. [1988], Homans [1993], and Herrmann [1996, 1999, 2000] develop econometric models of the

exvessel demand for halibut. The economics of Alaska’s commercial salmon fishery have also been the

subject of rigorous study and are described in, for example, Herrmann [1994], Herrmann and Greenberg

[1994], and Herrmann [1993].

In contrast, until recently, there has been little formal analysis of Alaska’s marine recreational fisheries

for halibut or salmon. Coughenower [1986] provides a qualitative description of the halibut guide/charter

fishery. Jones and Stokes [1987] provide a small sample estimate of the consumer surplus associated

with Cook Inlet halibut and salmon sportfishing. Northern Economics [1990] provides an estimate of the

economic impact of the S.S. Glacier Bay oil spill that includes a qualitative discussion of sportfishing

benefits. Layman et al. [1996] provide a recent estimate of the economic benefits of chinook salmon

sportfishing on the Gulkana River, Alaska. Henderson et al. [1999] report estimates of the economic value

of subsistence and personal use salmon fisheries on the Copper River, Alaska. ISER (Institute for Social

and Economic Research) [1996], Berman et al. [1999], and Berman and Kim [1999] report on the

sportfishing related economic value of Kenai River sockeye salmon. Lee et al. [1999b] describe the

results of a survey that will be used to obtain estimates of the consumer surplus that accrues to

participants in lower Cook Inlet sport fisheries. The analysis relies extensively on the data developed in

Lee et al. [1999b].

There are two components to a comprehensive evaluation of marine sportfishing: estimation of the

net benefits that accrue to sport fishers and assessment of the economic impact generated by marine

Commercial Sport
9

sportfishing. Recreators fish because they anticipate that the expected benefits from the sportfishing and

associated activities will exceed their expected costs. While assessment of the net non-market benefits

that accrue to sport fishers is difficult, several estimation techniques have achieved broad acceptance. We

use a participation rate model to estimate changes in compensating variations given changes in expected

fishing behavior. (Lee et al. will undertake a more thorough examination of non-market benefits in

forthcoming research.)

The main focus of this study centers on the impact analysis. To estimate the changes in regional impacts

for possible environmental changes we develop a model of the relationship between trip attributes and

participation rates and an input–output model that estimates the economic impact of various levels of

participation. These model components are briefly discussed below and in greater detail in Sections 4 and

5, respectively.

Participation rates are estimated using an econometric model of the form:

(1) P( ) ( , , , ) T f cost catch size other i i ij ij =

where P (Ti) is the probability of taking trip i, costi is the cost of trip i, catchi j is the number of fish of

species j caught on trip i, sizei j is the average size of fish of species j caught on trip i, and other includes

qualitative variables to differentiate between the responses of resident and nonresident recreators, and to

account for other demographic characteristics. The data used for estimating the parameters of this model

are reported in Lee et al. [1999a].

Marine sportfishing can take place from shore, from private or rented boats, or from charter boats. The

expenditures associated with each of these choices contribute to regional economic activity, thus changes

in participation that arise from changes in trip attributes affect regional economic activity. Impact analysis

estimates the direct, indirect, and induced effects on output (production), income and employment by

industry and aggregated industries. Direct effects are production changes associated with immediate final

demand changes. Indirect effects are those associated with changes in inputs to the production process.

Induced effects are those caused by changes in household spending patterns due to changes in household

income generated by direct and indirect effects. Most economic activities generate secondary impacts

(indirect effects). That is, when goods or services are purchased, the seller in turn purchases other

goods and services. Secondary impacts are generated whether the initial activity involves commerce or

recreation. However, different activities generate different impacts. Moreover, the impact of alternative

activities depends on the scale considered. It is traditional to examine economic impacts at local, regional,

and national scales. Our focus on the Kenai Peninsula dictates a regional based impact assessment.

Input–output (I–O) is the most widely applied tool for assessing regional economic impacts.
10

Figure 11. Sportfishing for halibut from a charter vessel in Cook Inlet.

3. Baseline Expenditures

The baseline expenditures for residents fishing in the Cook Inlet marine sport fishery were calculated

for 1997 using information from the annual Alaska Department of Fish and Game sport fish survey2

[ADF&G unpublished data] and data reported from the University of Alaska Fairbanks (UAF) survey

undertaken for Alaska Sea Grant [Lee et al. 1999b]. Specifically, the ADF&G survey was used for effort

estimations while the UAF survey was used for all other computations (see Section 4 for survey

methodology).

Number of sportfishing-days

The annual ADF&G postal survey is used to estimate the total number of sport fishers and days fished

for all of the major sportfishing regions in Alaska. Vincent-Lang [1998] reports: “Mills and Howe [1992]

and Meyer [1994] have reviewed the postal survey and suggest that the estimates are sufficiently precise

and accurate for management of ‘large’ marine fisheries, such as those for halibut or rockfish.”

Based on responses to the 1997 ADF&G postal survey, ADF&G estimated that 140,905 residents

participated in Kenai Peninsula-area marine sport fisheries. The UAF survey estimate of 151,590 anglers

is slightly higher than ADF&G’s number, but that is most likely due to the fact that the UAF survey

emphasized the Kenai saltwater sports fishery and may have had a higher return from these participants

 

2

 The Alaska Department of Fish and Game pulled its reports for 1995–1997 that reported on the annual postal survey of effort

and fish harvest due to errors. The data used in this report is the updated data that was received from ADF&G that will eventually

be used in the new reports as the corrected data.
11

than from non-Kenai participants3

. ADF&G aggregate data estimates a total of 286,521 saltwater sport

days fished off the Kenai in 1997 4

. However, because the ADF&G survey incorporates data from all

marine sport fisheries off the Kenai Peninsula, and the UAF survey focused on lower and central Cook

Inlet sport fisheries, it was necessary to disaggregate the ADF&G data, exclude the Seward and “other

Gulf Coast East of Gore Point” reporting areas, and aggregate the remaining areas to permit comparison.

Members of the charterboat industry [Robert Ward, Homer Charter Association, personal communication

1999] indicated that the majority of the boats fishing the “Barren Islands” and “other Cook Inlet/Gulf

Coast west of Gore Point” regions are ported in Homer.

After these adjustments are made, the ADF&G estimate of angler-days fished off the Kenai Peninsula in

1997 is 197,556 days: 78,587 charter vessels days, 91,139 private vessel days, and 27,830 shore days (see

Table 1). We have grouped recreational fishers into nine categories: three residency categories and three

sportfishing modes. Residency is comprised of locals (Kenai Peninsula residents), Alaskans (Alaskan

residents who do not live in the Kenai Peninsula Borough), and nonresidents (U.S. residents from outside

Alaska). The sportfishing mode category is separated into charter (marine sportfishing from aboard

licensed charter boats), private (marine sportfishing from aboard privately owned or rented boats), and

shore (marine sportfishing from the shore). Although this level of detail is not available in the annual

ADF&G sportfishing survey reports, ADF&G was able to provide it to us. Tables 2 and 3 report our

estimates of recreational fishing days for various areas in Cook Inlet during 1997.

Table 1. Angler-days fished from lower and central Cook Inlet ports during 1997 [ADF&G

unpublished data].

Fishing Area Charter Private Shore Total

Anchor River, Whiskey Gulch, Deep Creek,

and Ninilchik River 30,693 48,841 1,132 80,666

Other Cook Inlet/Gulf Coast West of Gore Point 37,401 40,489 77,890

Other Cook Inlet North of Ninilchik River 769 339 1,108

Barren Islands 9,724 1,470 11,194

Seldovia Bay 1,642 1,642

Homer Spit (Kachemak Bay) 23,218 23,218

Shoreline – Other 1,838 1,838

Total 78,587 91,139 27,830 197,556

 

3

 The ADF&G unpublished data estimates of days fished include effort directed at species other than halibut and salmon while

the UAF survey focused exclusively on halibut and salmon effort.

4

 The UAF survey found a higher average number of days fished than did the ADF&G survey. This is despite the fact that the

survey results correspond closely in nearly all other respects. Both surveys estimated almost identical numbers of fishers in 1997.

However, the UAF survey estimated a higher amount of effort in the Kenai region than did the ADF&G survey. For example, for

sport fishers just taking halibut trips the UAF survey estimated an average of three days per sport fisher. This number was even

higher when the average sport fisher days were calculated for trips targeting salmon or halibut. Unable to resolve this single, but

important difference in survey results, we have adopted the ADF&G estimates because of their long and self-consistent estimates

of sportfishing-days per sport fisher.
12

Table 2. Person-days fished in lower and central Cook Inlet during 1997 by fishing area,

residency, and sportfishing mode [ADF&G unpublished data].

Fishing Area Local Alaskan

(non-local) Nonresident Total

Charter

Anchor River, Whiskey Gulch, Deep Creek,

and Ninilchik River 2,209 7,872 20,612 30,693

Other Cook Inlet/Gulf Coast West of Gore Point 3,350 9,590 24,461 37,401

Other Cook Inlet North of Ninilchik River 30 47 692 769

Barren Islands 1,929 2,389 5,406 9,724

Seldovia

Homer Spit (Kachemak Bay)

Shoreline – Other

Total 7,518 19,898 51,171 78,587

Private or bare-boat charters*

Anchor River, Whiskey Gulch, Deep Creek,

and Ninilchik River 14,575 20,150 14,116 48,841

Other Cook Inlet/Gulf Coast West of Gore Point 13,349 16,252 10,888 40,489

Other Cook Inlet North of Ninilchik River 47 164 128 339

Barren Islands 527 478 465 1,470

Seldovia

Homer Spit (Kachemak Bay)

Shoreline – Other

Total 28,498 37,044 25,597 91,139

Shore-based

Anchor River, Whiskey Gulch, Deep Creek,

and Ninilchik River 515 285 332 1,132

Other Cook Inlet/Gulf Coast West of Gore Point

Other Cook Inlet North of Ninilchik River

Barren Islands

Seldovia 1,070 99 473 1,642

Homer Spit (Kachemak Bay) 10,629 3,522 9,067 23,218

Shoreline – Other 647 861 330 1,838

Total 12,861 4,767 10,202 27,830

All sportfishing modes

Anchor River, Whiskey Gulch, Deep Creek,

and Ninilchik River 17,299 28,307 35,060 80,666

Other Cook Inlet/Gulf Coast West of Gore Point 16,699 25,842 35,349 77,890

Other Cook Inlet North of Ninilchik River 77 211 820 1,108

Barren Islands 2,456 2,867 5,871 11,194

Seldovia 1,070 99 473 1,642

Homer Spit (Kachemak Bay) 10,629 3,522 9,067 23,218

Shoreline – Other 647 861 330 1,838

Total 48,877 61,709 86,970 197,556

* Bare-boat charters are boats available for rent. Rentals may include fishing poles, bait, and advice, but do not

include an on-board guide or skipper. Consequently, bare-boat charters are not subject to the restrictions and

regulations that govern charter boats and they are treated as private vessels by the fisheries management agencies.

The effort estimates in Tables 1 and 2 are summarized in Table 3 and expressed as percentages in Table 4.
13

Table 3. Person-days fished by residency category and sportfishing mode [ADF&G

unpublished data].

Charter Private Shore Total

Local 7,518 28,498 12,861 48,877

Alaskan (non-local) 19,898 37,044 4,767 61,709

Nonresident 51,171 25,597 10,202 86,970

Total 78,587 91,139 27,830 197,556

Table 4. Effort distribution (%) by residency category and sportfishing mode [ADF&G

unpublished data].

Charter Private Shore Total

Local 3.8% 14.4% 6.5% 24.7%

Alaskan (non-local) 10.1% 18.8% 2.4% 31.2%

Nonresident 25.9% 13.0% 5.2% 44.0%

Total 39.8% 46.1% 14.1% 100.0%

Figure 12 shows the effort by sportfishing mode and residency.

100,000

75,000

50,000

25,000

0

Local Alaskan (non-local) Nonresident Total

Charter Private Shoreline

Figure 12. Person-days fished in lower and central Cook Inlet during

1997 by residency and sportfishing mode [Howe et al. 1999].

Tables 2, 3, and 4, and Figure 12 indicate that while most nonresident sportfishing effort is charter-based,

many Alaskans use private vessels and bare-boat charters. In total, 46% of the marine sportfishing effort

in 1997 was conducted from private boats or bare-boat charters. By comparison, 40% of the Cook Inlet

region marine sportfishing occurred on charter boats. Most, 83%, of the respondents who engaged

in shore-based marine sportfishing did so on the Homer spit, the locus of a tidal terminal fishery for

hatchery-reared salmon.
14

Figure 13. A young sport fisher hooked into a halibut

from aboard a charter boat in Cook Inlet.

Average daily fishing and non-fishing expenditures

I n th e UAF s u rv ey , r es po n de nt s wer e a sk ed t o p ro v id e de t ai le d i nf or mat io n r eg ar d in g e xp en se s i nc u rr ed o n 

t he ir mos t r ec en t s al mo n a nd h al ib ut f is hi ng t ri ps . The a ve ra ge d ai ly e xp en di t ur es f or t he f is he rs a re wei gh te d 

b y fi sh i ng d a ys f or th e f is hi ng ex pe n di tu re s a nd to ta l d ay s s pe nt o n t he tr ip f o r li v in g ex p en se s . Ta bl e 5 

r ep or ts th e a ve ra ge ex pe n se s in c ur re d b y re s po nd e nt s wh o s po r t fi sh e d in Co ok I n le t d ur in g 1 99 7 o r 19 98 .

Tab le 5 . Ave ra ge da il y e xp en d it ur e s fo r l ower an d ce n tr al Co ok I n le t s po rt fi s hi ng tr ip s, by r e si de nc y a nd 

s po rt fi s hi ng mo de ( $ /d ay ) [ Le e e t al . 1 99 9a ] .

L ocal* A lask an (n on -lo cal) N on resid en t

S ho re P rivate C harter S ho re P rivate C harter S ho re P rivate C harter

A uto or Truck F uel 7 .8 2 7 .8 2 7 .8 2 1 4.57 1 2.99 1 5.81 9 .3 4 7 .8 1 8 .0 8

A uto or RV R ental 0 .3 9 3 .9 7 2 8.91 2 .9 2 1 8.92 

A irfare 0 .3 5 5 .1 5 2 6.90 2 4.76 3 2.04 

O th er T ran sp o rtatio n 0 .7 0 0 .7 0 0 .7 0 1 .3 1 1 .8 3 0 .9 3 2 .3 0 2 .3 3

L od ging 3 .1 5 3 .1 5 3 .1 5 3 .8 6 6 .2 0 2 1.19 1 4.83 7 .8 3 2 2.94 

G ro ceries 8 .0 0 8 .0 0 8 .0 0 1 2.43 1 4.44 1 3.76 7 .4 7 1 0.72 9 .9 3

R estaurant an d Bar 1 0.74 1 0.74 1 0.74 3 .4 3 9 .5 8 1 3.95 1 0.20 6 .6 5 9 .6 3

T otal T ran sp o rtatio n and Lo dg in g 3 0.41 3 0.41 3 0.41 3 4.29 4 5.26 7 5.66 9 8.58 6 2.99 1 03 .8 7

C harter or G u id e 1 12 .8 6 1 16 .4 0 1 40 .7 5

F ishing Gear 2 .1 4 7 .1 2 2 .0 0 4 .5 0 5 .5 3 3 .5 8 2 0.00 1 7.12 1 5.50 

F ish Pro cessing 0 .9 2 1 0.50 2 .3 3 7 .1 4 9 .6 2 7 .8 7 3 2.72 

D erby 0 .3 6 1 1.70 0 .1 8 2 .1 3 0 .9 5 1 .6 5 1 .3 7

B oat Fu el an d R ep airs 1 5.89 3 1.53 1 5.76 

M oo rage or H aul O ut 8 .3 6 5 .4 8 9 .0 0

T otal F ish in g E xp en d itures 2 .1 4 3 2.65 1 37 .0 6 4 .5 0 4 5.05 1 29 .2 5 3 0.57 5 1.40 1 90 .3 4

T otal No n-F ishin g D ay Ex pen ditures 3 0.41 3 0.41 3 0.41 3 4.29 4 5.26 7 5.66 9 8.58 6 2.99 1 03 .8 7

T otal F ish in g D ay E x pend itu res* * 3 2.55 6 3.06 1 67 .4 7 3 8.79 9 0.31 2 04 .9 1 1 29 .1 5 1 14 .3 9 2 94 .2 1

* For “local” expenditures, the aggregate non-fishing expenditures for all types of fishing were used because of the low number

of total observations. For instance, the survey only had three observations of local residents’ expenditures for shore-based fishing.

** Total expenditures on days fished are the sum of the fishing expenditures and the living expenditures which were averaged

across the total days spent on a trip.
15

Mean transportation and living expenditures total $30.41 per day for local residents. Transportation

and living expenses for other Alaskans ranged between $34.29 and $75.66 per day, and from $62.99 to

$103.87 for nonresidents. Mean living expenditures were lower for nonresidents who fished off private

vessels than for those who fished from shore or from charter boats, due in part to the fact that the primary

trip purpose for many such respondents was to visit friends and family. However, not all of these base

expenditures are spent on the Kenai Peninsula or elsewhere in Alaska.

Mean local fishing expenditures ranged between of $2.14 and $137.06. Alaskan (non-local) and

nonresident fishing expenditure means varied from $4.50 to $129.25 and from $30.57 to $190.34,

respectively. These expenditures varied greatly with the different type of sportfishing mode (Table 6).

Table 6. Average (across residency categories) daily expenditures for lower and central

Cook Inlet sportfishing trips by sportfishing mode ($/day) [Lee et al. 1999a].

Shore Private Charter

Auto or Truck Fuel 11.87 9.82 11.27

Auto or RV Rental 14.74 1.65 11.26

Airfare 13.72 12.77 18.44

Other Transportation 1.78 1.71 1.93

Lodging 9.32 6.59 20.79

Groceries 11.39 12.05 11.13

Restaurant and Bar 10.10 7.56 11.88

Total Non-Fishing Day Expenditures 72.92 52.14 86.70

Charter or Guide 128.64

Fishing Gear 12.21 11.58 9.53

Fish Processing 4.91 5.04 20.48

Derby 0.48 0.95 2.55

Boat Fuel and Repairs 22.21

Moorage or Haul Out 7.52

Total Fishing Day Expenditures 17.60 47.29 161.19

Total Daily Expenditures* 90.52 99.43 247.89

* Total expenditures on days fished are the sum of the fishing expenditures and the living expenditures which

were averaged across the total days spent on a trip.

The mean fishing expenditure, across residents, for shore-based fishing was $17.60, for private boat

$47.29, and $161.19 for charter. Mean daily living expenditures were $72.92, $52.14, and $86.70 for

shore-based, private vessel-based, and charter-based recreators, respectively. Again, the relatively low

expenditure level for private boat-based sportfishing is most likely due to the fact that many such

individuals identified visiting Kenai Peninsula area friends or family as a primary trip purpose. The

largest expenditures are associated with customers of the charter industry. Figure 14 shows the expenses

for the charter industry by residency.
16

Figure 14. Mean daily expenditures, by residency category, for charter

fishing in lower and central Cook Inlet [Lee et al. 1999a].

The next step in estimating the baseline expenditures for sportfishing effort in Cook Inlet is to estimate

the distribution of these expenditures between the Kenai Peninsula, other Alaskan regions, and areas

outside of Alaska. This question was not directly addressed in the UAF survey. The UAF survey asked

about total expenditures, not a regional disaggregation of those expenditures. The following assumptions

were used to disaggregate trip expenditures to the level of detail required for the impact model:

• Auto and Truck Fuel. Allocated proportionally by the number of days spent in each area.

• Auto or RV Rental Fees. All vehicle rental fees were assumed to have been paid in Alaska, but

outside of the Kenai Peninsula (Anchorage or Fairbanks). This assumption may underestimate

expenditures made on the Kenai Peninsula, but probably not too much since there were no

reported rentals by Kenai Peninsula residents.

• Airfare. All airfare expenses were assumed to flow out-of-state. This assumption may

underestimate expenditures in the Alaska portion of the study.

• Lodging (trailer parks, campgrounds, hotels, motels, B&B, etc.). Allocated proportionally

by the number of days spent in each area.

• Food and Drink (Groceries) purchased at grocery or convenience stores. Allocated

proportionally by the number of days spent in each area.

• Food and Drink purchased at restaurants or bars. Allocated proportionally by the number

of days spent in each area.

• Guides or Charter Fees. Assumed to have been incurred on the Kenai Peninsula.

• Fishing Gear (bought only for this trip). We assumed that non-local Alaskans purchased

75% of reported fishing gear on the Kenai Peninsula and 25% elsewhere in Alaska and that

nonresidents and locals purchase 100% on the Kenai Peninsula. This is a pretty arbitrary

assumption. While it is likely that nonresidents purchase the majority of their gear on site,

some gear may be purchased in their home states or elsewhere in Alaska. Alaskans will

have a better idea of what gear is needed for sportfishing in Cook Inlet and may purchase

a substantial amount of gear before traveling to the Kenai Peninsula. Locals are assumed to

have purchased most of their gear for this particular trip on site. Because the gear purchase

questions were specific to the most recent trip, most larger purchases that may be made

o ut si de of Al as ka , l ik e f is hi ng ro ds , wil l h av e p re vi ou s ly b e en mad e a nd no t be re po r te d he r e.

$137.06

$30.41

Non-Fishing Fishing

$300

$200

$100

$0

Local Alaskan (non-local) Nonresident

$129.25

$75.66

$190.34

$103.87
17

• Fish Processing and Packing Fees. Assumed to have been made on the Kenai Peninsula.

• Fishing Derby Entry Fees. Assumed to have been incurred on the Kenai Peninsula.

• Boa t Fu e l, Lu br ic an t s, a n d Re pa i rs . Ag ai n, a so mewha t a rb it r ar y as s umpt i on t ha t a ny lo ca ls an d

o th er Al as ka n s bu y 7 5% o f t he ir bo at fu el o n t he Ken ai Pe ni ns u la a nd 25 % e ls ewhe r e in Al as ka .

• Moorage and Haul Out Fees. Assumed to have been made on the Kenai Peninsula.

• Other Transportation. (for example, cruises, bus tours, etc.). A relatively minor expense

that is assumed to flow out of Alaska.

Days spent on the Kenai and in Alaska

The UAF survey asked recreational fishers how many days they fished on their last trip, the number of

days of that trip spent on the Kenai Peninsula, and how many days were spent away from their principal

residence. Unfortunately, for nonresidents, the survey did not differentiate between the trip days spent

elsewhere in Alaska and those spent outside of Alaska. To estimate this we assumed that nonresidents

who flew spent all of their time in Alaska (flew directly to Alaska) while those that drove spent some of

their trip-days outside of Alaska. Table 7 shows the amount of time spent on the entire trip per fishing day

for the three different sportfishing modes for nonresidents who did and did not fly.

Table 7. Ratios of trip length to lower and central Cook Inlet fishing-day for nonresidents by

travel and sportfishing mode [Lee et al. 1999a].

Overall Mean Mean for Fliers Mean for Non-Flyers % that Flew

Shore 8.29 3.15 16.63 50%

Private 4.76 3.94 5.94 64%

Charter 7.63 4.89 11.56 63%

To estimate the amount of time spent in Alaska (Kenai and non-Kenai portions) for nonresidents we

assumed that the amount of time spent in Alaska per fishing day by tourists who drove to be the same as

that amount spent by tourists who flew. Therefore, we assume that whether a nonresident flew or not s/he

spent 3.15 days, on average, in Alaska for each shore-based fishing day (inclusive of the fishing day),

3.94 for those fishing in private boats, and 4.89 for those fishing on charters (Table 8).

Table 8. Ratios of total days spent on the Kenai and elsewhere in Alaska per lower and central Cook

Inlet fishing day [Lee et al. 1999a].

Shore Private Charter

Kenai Days/Fishing Day Local 1.29 1.00 1.00

Alaskan (non-local) 1.03 1.45 1.73

Nonresident 2.00 2.92 2.03

Other Alaska Days/Fishing Day Local 0.00 0.00 0.00

Alaskan (non-local) 0.06 0.00 0.52

Nonresident 1.15 1.02 2.86
18

So, for instance, the UAF survey reported nonresidents to have spent 2.03 days on the Kenai for each day

fished inclusive of the day fished. We assume that nonresident charter-based sport fishers spend 4.89 days

in Alaska per day fished so we calculate the time spent in Alaska outside of the Kenai to be the difference

of 2.86 days.

Total expenditures assuming that all trip expenditures are attributable to fishing

Baseline expenditure estimates were obtained by combining the estimated daily expenditures and the

estimated time spent per fishing day, and allocating these expenditures among regions. Tables 9, 10, and

11 report 1997 expenditure estimates for Kenai Peninsula residents who participated in Cook Inlet salmon

and halibut fisheries. Tables 12, 13, and 14 report estimated expenditures for Alaskans living outside the

Kenai Peninsula area that participated in Cook Inlet salmon and halibut fisheries. Tables 15, 16, and 17

include estimated expenditures by nonresidents. Table 18 summarizes the individual expenditures across

residency category. Table 19 summarizes the total expenditures by residency category and sportfishing

mode.

Table 9. Total expenditures by local residents fishing for halibut and salmon from the lower and central

Cook Inlet shore [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of Total Total $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.000 6.5% 12,861

Days spent on Kenai1 1.290 16,591

Days spent in Alaska2 0.000

Auto 7.82 129,739 129,739

RV

Lodge 3.15 52,261 52,261

Groceries 8.00 132,726 132,726

Restaurant & Bar 10.74 178,184 178,184

Charter

Gear 2.14 27,523 27,523

Processing

Derby

Boat Fuel

Haul

Total 27,523 492,909 520,432

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
19

Table 10. Total expenditures by local residents fishing for halibut and salmon from private boats in lower

and central Cook Inlet [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of Total Total $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.000 14.4% 28,498

Days spent on Kenai1 1.000 28,498

Days spent in Alaska2 0.000

Auto 7.82 222,854 222,854

RV

Lodge 3.15 89,769 89,769

Groceries 8.00 227,984 227,984

Restaurant & Bar 10.74 306,069 306,069

Charter

Gear 7.12 202,906 202,906

Processing 0.92 26,218 26,218

Derby 0.36 10,259 10,259

Boat Fuel 15.89 452,833 452,833

Haul 8.36 238,243 238,243

Total 930,460 846,676 1,777,135

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 11. Total expenditures by local residents fishing for halibut and salmon from charter boats in lower

and central Cook Inlet [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of Total Total $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.000 3.8% 7,518

Days spent on Kenai1 1.000 7,518

Days spent in Alaska2 0.000

Auto 7.82 58,791 58,791

RV

Lodge 3.15 23,682 23,682

Groceries 8.00 60,144 60,144

Restaurant & Bar 10.74 80,743 80,743

Charter 112.86 848,481 848,481

Gear 2.00 15,036 15,036

Processing 10.50 78,939 78,939

Derby 11.70 87,961 87,961

Boat Fuel

Haul

Total 1,030,417 223,360 1,253,777

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
20

Table 12. Total expenditures by Alaskan (non-local) residents fishing for halibut and salmon from the

lower and central Cook Inlet shore [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of Total Total $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 2.4% 4,767

Days spent on Kenai1 1.03 4,910

Days spent in Alaska2 0.06 286

Auto 14.57 71,539 4,167 75,706

RV

Lodge 3.86 18,953 1,104 20,057

Groceries 12.43 61,031 3,555 64,587

Restaurant & Bar 3.43 16,841 981 17,822

Charter

Gear 4.50 16,089 5,363 21,452

Processing

Derby

Boat Fuel

Haul

Total 16,089 168,364 5,363 9,808 199,623

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 13. Total expenditures by Alaskan (non-local) residents fishing for halibut and salmon from

private boats in lower and central Cook Inlet [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of Total Total $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 18.8% 37,044

Days spent on Kenai1 1.45 53,714

Days spent in Alaska2 0.00

Auto 12.99 697,742 697,742

RV 0.39 20,948 20,948

Lodge 6.20 333,026 333,026

Groceries 14.44 775,627 775,627

Restaurant & Bar 9.58 514,578 514,578

Charter

Gear 5.53 153,640 51,213 204,853

Processing 2.33 86,313 86,313

Derby 0.18 6,668 6,668

Boat Fuel 31.53 875,998 291,999 1,167,997

Haul 5.48 203,001 203,001

Total 1,325,620 2,320,973 343,213 20,948 4,010,754

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
21

Table 14. Total expenditures by Alaskan (non-local) residents fishing for halibut and salmon from

charter boats in lower and central Cook Inlet [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of Total Total $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 10.1% 19,898

Days spent on Kenai1 1.73 34,424

Days spent in Alaska2 0.52 10,347

Auto 15.81 544,236 163,585 707,822

RV 3.97 177,739 177,739

Lodge 21.19 729,435 219,252 948,687

Groceries 13.76 473,668 142,374 616,042

Restaurant & Bar 13.95 480,208 144,340 624,548

Charter 116.40 2,316,127 2,316,127

Gear 3.58 53,426 17,809 71,235

Processing 7.14 142,072 142,072

Derby 2.13 42,383 42,383

Boat Fuel

Haul

Total 2,554,008 2,227,547 17,809 847,291 5,646,654

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 15. Total expenditures by nonresidents fishing for halibut and salmon from the lower and central

Cook Inlet shore [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of Total Total $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 5.2% 10,202

Days spent on Kenai1 2.00 20,404

Days spent in Alaska2 1.15 11,732

Auto 9.34 190,573 109,580 300,153

RV 28.91 929,060 929,060

Lodge 14.83 302,591 173,990 476,581

Groceries 7.47 152,418 87,640 240,058

Restaurant & Bar 10.20 208,121 119,669 327,790

Charter

Gear 20.00 204,040 204,040

Processing 9.62 98,143 98,143

Derby 0.95 9,692 9,692

Boat Fuel

Haul

Total 311,875 853,703 1,419,940 2,585,518

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
22

Table 16. Total expenditures by nonresidents fishing for halibut and salmon from private boats in lower

and central Cook Inlet [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of Total Total $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 13.0% 25,597

Days spent on Kenai1 2.92 74,743

Days spent in Alaska2 1.02 26,109

Auto 7.81 583,745 203,911 787,656

RV 2.92 294,488 294,488

Lodge 7.83 585,240 204,433 789,673

Groceries 10.72 801,248 279,888 1,081,135

Restaurant & Bar 6.65 497,043 173,624 670,667

Charter

Gear 17.12 438,221 438,221

Processing 7.87 201,448 201,448

Derby 1.65 42,235 42,235

Boat Fuel 15.76 403,409 403,409

Haul 9.00 230,373 230,373

Total 1,315,686 2,467,274 1,156,344 4,939,305

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 17. Total expenditures by nonresidents fishing for halibut and salmon from charter boats in lower

and central Cook Inlet [Lee et al. 1999a].

Days Expenditures ($)

Ratio % of

Total Total $/Day Fishing(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 25.9% 51,171

Days spent on Kenai1 2.03 103,877

Days spent in Alaska2 2.86 146,349

Auto 8.08 839,327 1,182,500 2,021,828

RV 18.92 4,734,280 4,734,280

Lodge 22.94 2,382,941 3,357,247 5,740,189

Groceries 9.93 1,031,500 1,453,246 2,484,746

Restaurant & Bar 9.63 1,000,337 1,409,341 2,409,678

Charter 140.75 7,202,318 7,202,318

Gear 15.50 793,151 793,151

Processing 32.72 1,674,315 1,674,315

Derby 1.37 70,104 70,104

Boat Fuel

Haul

Total 9,739,888 5,254,105 12,136,615 27,130,608

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
23

Table 18. Total expenditures by all sport fishers fishing for halibut and salmon in lower and central Cook

Inlet [Lee et al. 1999a].

Days Expenditures ($)

Fishing (Kenai) Other (Kenai) Fishing

(Alaska) Other (Alaska) Total

Days Fished 197,556

Days spent on Kenai1 344,678

Days spent in Alaska2 194,823

Auto 3,338,547 1,663,744 5,002,291

RV 6,156,516 6,156,516

Lodge 4,517,896 3,956,027 8,473,923

Groceries 3,716,345 1,966,704 5,683,049

Restaurant & Bar 3,282,124 1,847,956 5,130,080

Charter 10,366,927 10,366,927

Gear 1,904,030 74,385 1,978,415

Processing 2,307,448 2,307,448

Derby 269,302 269,302

Boat Fuel 1,732,240 291,999 2,024,239

Haul 671,617 671,617

Total 17,251,564 14,854,913 366,384 15,590,946 48,063,807

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 19. Total expenditures by all sportfishers for halibut and salmon in lower and central

Cook Inlet, by residency and sportfishing mode [Lee et al. 1999a].

Fishing ($) Non-Fishing ($) Total ($)

Residency

Local 1,988,399 1,562,945 3,551,344

Alaska 4,262,100 5,594,931 9,857,032

Nonresident 11,367,449 23,287,982 34,655,431

Total 17,617,949 30,445,859 48,063,807

Fishing Mode

Shore 360,849 2,944,724 3,305,574

Private 3,914,978 6,812,216 10,727,194

Charter 13,342,122 20,688,918 34,031,040

Total 17,617,949 30,445,859 48,063,807

To this point, it has been assumed that all of the living and traveling expenditures (reported in Tables

9 –19) will have been incurred as a direct result of the respondents’ desire to fish for salmon and halibut

in lower and central Cook Inlet. Obviously, this is not the case. Some travelers would have taken the

Alaska and Kenai trips, and incurred some of the same expenditures, even if the sportfishing opportunities

had been unavailable or less attractive. For example, visitors on business trips may well have visited

Alaska whether or not they were planning to fish on the Kenai. It is fairly accurate to assume that fishing

expenses would not have been incurred if the respondents had not fished, but assumptions about whether

the trip would have been taken, and whether the other living and traveling expenses would have been

incurred, are less certain.
24

The baseline scenario incorporates the nine sets of total expenditures representing the three residency

categories and three sportfishing modes. These expenditures are totaled and summarized in Table 18.

Total expenditures for salmon and halibut sportfishing-related activities in lower and central Cook Inlet

are estimated to have been $48.1 million during 1997: $17.2 million in direct fishing expenditures and

$14.9 million in living and traveling expenditures on the Kenai Peninsula, and $0.5 million in fishing

expenditures and $15.6 million in living and traveling expenditures elsewhere in Alaska. That is, the

lower and central Cook Inlet sport fisheries resulted in a total of $32.2 million in direct expenditures

on the Kenai Peninsula and $16.0 million elsewhere in Alaska.

The largest category of direct fishing expenditures was Guide and Charter Fees, which totaled $10.4

million. Processing, Boat Fuel, and Gear accounted for $2.0 to $2.3 million each. The single largest

category of living expenses, Lodging, is estimated to have exceeded $8.5 million. All other expenses

ranged between $5.0 and $6.2 million.

Table 19 summarizes total expenditures by residency category and sportfishing mode. Nonresidents

are estimated to have spent $34.7 million (72.1%) of the total. Fishing and living expenditures attributable

to sportfishing activities supported by the charter industry were responsible for 70.8% of the total

expenditures ($34.0 million). Expenditures attributable to private boat and shore-based sportfishing were

estimated at $10.7 million and $3.3 million, respectively.

Total expenditures assuming that some trip expenditures are not attributable to fishing

There are many reasons for visiting the Kenai Peninsula. Respondents to the UAF survey [Lee et al.

1999b] who fished for halibut or salmon in lower and central Cook Inlet reported nine primary trip

purposes (Table 20).

Table 20. Primary purpose of trip to Alaska [Lee et al. 1999a].

All Alaskans (non-local) Nonresidents

Fishing for halibut or salmon in Cook Inlet 63.5% 87.9% 43.0%

Visit/Vacation Alaska 14.3% 2.9% 24.4%

Kenai area freshwater fishing 8.7% 1.7% 12.0%

Visit relatives 7.0% 5.2% 11.2%

Business 2.5% 1.2% 3.7%

Saltwater/Freshwater fishing 1.6% 0.0% 2.5%

Visit friends 0.9% 1.2% 0.4%

Cruise ship 0.7% 1.2%

Hunting 0.9% 1.7%

The majority (63.5%) of respondents identified fishing for halibut or salmon in lower and central Cook

Inlet as the primary purpose of their most recent trip. This response was even more pronounced for

non-local Alaskans, 87.9% of whom listed fishing for halibut or salmon as the main reason for their trip.

However, less than half of the nonresidents (43%) identified fishing for halibut or salmon in lower and

central Cook Inlet as the primary motive of their trip. Another important reason (24.4%) for nonresident

trips was simply to visit and vacation in Alaska. Freshwater fishing and visiting relatives were also

important motives for nonresidents.
25

Figure 15. Salmon fishing on the Russian River. Freshwater fishing ranked

third highest among reasons for taking a trip that included fishing

for halibut or salmon in lower and central Cook Inlet.

Because there is not an exact correspondence between visits to the Kenai Peninsula and the desire

to fish for halibut or salmon, it was necessary to adjust the total expenditure estimates to reflect those

expenditures that are uniquely attributable to fishing in lower and central Cook Inlet. Consequently, we

adopted a set of assumptions regarding what respondents would do if the lower and central Cook Inlet

sportfishing portion of their trip were cancelled (Table 21).

Table 21. Assumed response to cancellation of the lower and central Cook Inlet sportfishing portion of

their trip.

Main Trip Purpose Alaskans (non-local) Nonresidents

Fishing for halibut or salmon in Cook Inlet Cancel entire trip Cancel entire trip

Visit/Vacation in Alaska (non-Kenai focus) Replace Kenai trip days with days

in other parts of Alaska

Replace Kenai trip days with days

in other parts of Alaska

Visit relatives Take full trip Take full trip

Freshwater fishing on Kenai Peninsula Reduce trip length by lost marine

fishing days

Reduce trip length by lost marine

fishing days

Business trip Take full trip Take full trip

Combined marine/freshwater fishing Reduce trip length by lost marine

fishing days

Reduce trip length by lost marine

fishing days

Visit friends Take full trip Take full trip

Cruise ship No observations Take full trip

Hunting No observations Take full trip
26

To estimate the amount of reduction in time spent on the Kenai and in Alaska for reduced fishing effort

we re-ran the scenarios in Table 19 for the number of days fished (instead of number of people), days

spent on the Kenai Peninsula, and days spent in Alaska. We then combined this data with the assumptions

in Table 21 to estimate the reduction in expenses associated with a reduction in lower and central Cook

Inlet sportfishing effort (Table 22).

Table 22. Reduction in fishing or visitation rates for a 100% reduction in fishing effort (days).

Locals Alaskans (non-local) Nonresidents

Fishing Reduction 100% 100% 100%

Kenai Living Expenses 100% 89.5% 64.0%

Alaska Living Expenses 100% 57.3% 23.8%

Although these are very broad assumptions and other scenarios such as substitute fishing trips are

plausible, we believe that estimates based on these assumptions are better than estimates that assume

that all trip expenditures are attributable to the lower and central Cook Inlet halibut and salmon-fishing

activity. These percentages can also be used to estimate the amount of the baseline expenditures

attributable to the fishing component of the trip assuming a dollar for dollar expenditure pattern with

days spent in Alaska5

. The calculations in Table 22 indicate that, for Alaskans, nearly 90% of the Kenai

Peninsula area living and transportation expenditures can be attributed to the fishing component of the

trips as can 57.3% of the living and transportation expenditure in Alaska. For nonresidents we estimate

that approximately 64.0% of the living and transportation expenditures taking place on the Kenai

Peninsula are a direct result of the fishing component of the saltwater fishing trip but that only 23.8%

of the total expenditures in Alaska are directly attributable to the fishing component of the trip.

Using the assumptions in Table 22 we revised the expenditure tables (Tables 9 –19) to reflect the

estimated actual expenditures that are strictly attributable to halibut and salmon sportfishing trips in lower

and central Cook Inlet. Only the recalculations of Tables 18 and 19 for the adjusted expenditures are

produced here (Tables 23 and 24). The calculations to derive the trip adjusted expenditures, as well as

a complete set of trip adjusted expenditure tables, can be found in the Appendix to Section 3.

 

5

 There is still the issue, for living expenditures, of whether Alaskans would have spent some portion of these amounts to live and

do other things on the lost fishing days. We assume that most of these living expenditures are trip specific but there is likely to be

some overlap with what they would have spent on living and doing an alternative activity. This issue will be discussed in Section 5.
27

Table 23. Total expenditures by all sport fishers fishing for halibut and salmon in lower and central

Cook Inlet that are directly attributable to the saltwater halibut and salmon fishing trip.

Expenditures ($) Days1

Fishing (Kenai) Other (Kenai) Fishing (Alaska) Other (Alaska) Total

Days Fished 197,556

Days spent on Kenai2 263,260

Days spent in Alaska3 49,930

Auto 2,619,715 452,168 3,071,883

RV 2,697,502 2,697,502

Lodge 3,226,870 1,015,354 4,242,224

Groceries 2,864,102 516,962 3,381,063

Restaurant & Bar 2,561,923 488,496 3,050,419

Charter 10,366,927 10,366,927

Gear 1,904,030 74,385 1,978,415

Processing 2,307,448 2,307,448

Derby 269,302 269,302

Boat Fuel 1,732,240 291,999 2,024,239

Haul 671,617 671,617

Total 17,251,564 11,272,610 366,384 5,170,482 34,061,041

1 Days are the number of days spent that are attributable to the saltwater fishing portion of the trip.

2

 Includes days fished on the Kenai Peninsula.

3

 Excludes days spent on the Kenai Peninsula.

Table 24. Expenditures attributable to sportfishing for halibut and salmon in lower and central

Cook Inlet, by residency and sportfishing mode [Lee et al. 1999a].

Fishing ($) Non-Fishing ($) Total ($)

Residency

Local 1,988,399 1,562,945 3,551,344

Alaska 4,262,100 4,775,483 9,037,583

Nonresident 11,367,499 10,104,664 21,472,113

Total 17,617,949 16,443,092 34,061,041

Fishing Mode

Shore 360,849 1,770,663 2,131,512

Private 3,914,978 4,884,698 8,799,675

Charter 13,342,122 9,787,732 23,129,853

Total 17,617,949 16,443,092 34,061,041

Using the estimate of living and transportation expenditures attributed directly to the lower and

central Cook Inlet halibut and salmon sportfishing trip reduced our estimate of total expenditures from

$48.1 million to $34.1 million. This $14.0 million reduction comes from the living and transportation

expenditure reductions of $3.6 million on the Kenai and $10.4 million from elsewhere in Alaska. Table

24 indicates that nonresidents continue to contribute the majority of the spending (63.4%) and that

participants in the charter industry are responsible for 68.3% of the total spending by sportfishing mode.
28

4. Participation Rate Model

Changes in fishery regulations, environmental quality, and resource abundance, as well as trip costs, can

affect the expected net benefit associated with sportfishing, and therefore the decision to participate in

(take) a sportfishing trip. Consequently, the ability to forecast changes in participation rates is important

in many policy settings. Previous studies have used demographic characteristics to explain changes

in the demand for recreational fishing [Holland and Ditton 1992; Aas 1995; Thunberg et al. 1999].

The disadvantage of these models is that the resulting forecasts are conditional on conjectures about

demographic change. That is, such modelssimply shift the focus from forecasting changes in participation

to predicting demographic change. Moreover, demography-based participation models are ill-suited for

predicting changes in the demand for recreational fishing that might arise in response to changes in trip

costs, fishing conditions, or management actions. Our approach avoids these problems by focusing

on explanatory variables that are subject directly to regulatory control and changing environmental

conditions. Consequently, our model is better suited for policy evaluation and for forecasting participation

rate responses to changes in trip costs and catch rates.

The modeling and results presented in this section are based on a stated preference survey described

in Lee et al. [1999b]. The survey was developed and administered following the Total Design Method

[Dillman 1978]. Surveys were mailed to a random sample of 4,000 sport fishers drawn from the

population of U.S. residents who purchased 1997 Alaska State sportfishing licenses. Alaskans comprised

49.3% of the sample, closely resembling their actual license sales proportion (49.7%). Sampled license

holders received one of nine versions of the survey during the first mailing, followed by a reminder card.

Non-respondents were sent a second survey 14 days after the initial survey was mailed. The first two

survey mailings and the reminder card were sent by first class mail. A third survey was sent by certified

mail to those who did not respond within 14 days after the second survey was mailed. All survey mailings

contained a cover letter, a prize entry card (to increase the response rate), a business reply envelope, and

survey. A total of 2,640 completed, or partially completed, surveys were returned for an overall response

rate of 70.1% based on delivered surveys.For more details and summary of the survey see Lee et al.

[1999b].

In addition to being asked to provide information about various economic and demographic variables and

actual trips taken, respondents were presented a set of hypothetical fishing trips, and asked to identify

which trips they would take. Each hypothetical trip was described in terms of six trip attributes and a cost

per day. The trip attributes were the average size and number of halibut, chinook, and coho caught. The

cost per day was identified as the sum of sportfishing related costs such as tackle and bait purchased

specifically for the trip, charter/guide fees, and trip specific transportation costs such as auto and boat

fuel.

Trip-by-trip preferences were elicited through a binary choice variable that indicated whether the

respondent would take a particular hypothetical trip. The advantage of this approach is that it is possible

to construct an experimental design that allows for substitution and complementary effects across

attributes, and for the possibility of nonlinear marginal utility. Substitution and complementary effects

refers to the catch of one species either diminishing or increasing the utility derived from the catch of

the other species. Nonlinear marginal utility refers to economic theory that would predict that the utility

derived from each additional catch diminishes. Therefore, the landing of the first halibut on a trip would

give more satisfaction than the landing of the second halibut all else (like the halibut weight) equal. While

these types of effects are predicted by economic theory, they are difficult to identify in empirical studies

of actual trips because attributes are often highly collinear or lack sufficient variation. Hypothetical trip

attributes were derived from historical means [Howe et al. 1998] and pretest discussions with recreational

fishers. Table 25 lists the attribute levels used in the Lee et al. [1999b] survey.
29

Table 25. Hypothetical trip attribute levels.

Attribute Level

Cost per day ($) 100 170 240

Halibut catch per day 0246

Average halibut weight (lbs.) 0 20 40 80

Chinook salmon catch per day 0 1 2

Average chinook salmon weight (lbs.) 0 15 25 50

Coho salmon catch per day 0246

Average coho salmon weight (lbs.) 0 7

These attribute levels combine to describe 4,608 unique trips6

. However, some of these hypothetical trips

are nonsensical or highly improbable and were dropped from consideration. For example, trips where no

fish are caught, but the average weight is positive are nonsensical. Similarly, trips with positive catches of

all three species during one day are implausible. The SAS Optex procedure was used to rank experimental

designs based on the D-optimality criterion. The D-optimality criterion maximizes the determinant of

the information matrix, X′X. Block designs with large D-optimality scores have a greater potential for

elucidating the effect of individual attributes. Block designs where at least one trip was dominated by at

least one other trip were dropped from further consideration.7

 While this approach has the advantage of

maximizing the information content of the stated preference observations, it does not allow testing of

the transitivity of preferences. Based on projected survey returns and the need to conserve degrees of

freedom, nine three-trip blocks (27 unique trips) were selected for use in the survey. Each of the 4,000

surveys was randomly assigned one of the nine blocks of three hypothetical trips. This design results in

a panel data set with multiple observations on each respondent’s participation decisions.

Econometric model

The participation decision was modeled using a random utility framework where the utility of individual

i associated with trip t is given by

(2) u fx z e it it i it = + ( ,,,) β γ .

The vector, xit, describes the attributes of the t-th trip taken by the i-th individual. Socioeconomic

and demographic variables for the i-th individual are included in the vector zi. β and γ are vectors of

parameters associated with the fishing trip attributes and socioeconomic variables, respectively. The

errors, eit, are normally distributed with an expected value of zero.

Respondents were asked whether they would take trip t, described by attributes xit. The i-th respondent

who takes trip t obtains a utility level of uit. Those who do not take trip t receive

(3) u fz e i0 0 i i = + (, , , ) 0 β γ

 

6

 The product of the number of categories of each attribute: 3 cost levels, 4 halibut catch levels, 4 halibut average sizes, 3 chinook

catch levels, 4 chinook average sizes, 4 coho catch levels, and 2 coho average sizes.

7

 In determining dominance, it was assumed that: catches of large fish are preferred to catches of small fish (within a species);

catches of more fish are preferred to catches of fewer fish (within a species); and that lower cost trips are preferred to higher cost

trips (when the trips are equal in all other characteristics).
30

the utility level associated with not taking the trip, which is also the opportunity cost of taking trip t.

Since the actual levels of utility are unobservable, the model is made operational by specifying a binary

indicator y* that denotes which choice was made, that is, yit

* = 1 if respondent i would take trip t and

yit

* = 0 otherwise. Assuming that individual i makes rational choices, yit

* = 1 implies that the expected

utility of taking trip t is greater than the expected utility of not taking the trip, that is, E (uit ≥ ui 0).

Conversely, yit

* = 0 implies that E (uit < ui 0).

A probabilistic choice model can then be formulated by noting that

(4)

P P

P

P

P

y xz u u

fx z e f z e

fx z f z e e

fx z f z

it i it i

it i it i i

it i i it i

it i i

* ,

( , , , ) (, , , )

( , , , ) (, , , )

( , , , ) (, ,

[ ] = = ≥ [ ]

= +≥ + [ ]

= − +− ≥ [ ]

= −

1

0

0 0

0

0

0

0

βγ βγ

βγ βγ

[ ] βγ β, ) γ ε + ≥ it 0

where εit=eit – ei 0.

Several econometric models are appropriate for binary choice panel data. The two most widely applied

are the fixed effects model [Chamberlain 1982] and the random effects model [Butler and Moffitt 1982].

The fixed effects model assumes that individual heterogeneity must be captured by individual specific

parametric shifts in the response function, thus it is appropriate for forecasting responses for those

particular individuals. In contrast, the random effects model assumes that individuals’ responses are

correlated with themselves. Consequently, the random effects framework is more appropriate when the

data are a random sample of individuals from a larger population of interest [Maddala 1987; Greene

1997]. Moreover, the random effects model allows inclusion of variables that do not vary across trips

(e.g., socioeconomic variables, zi), while the fixed effect model does not.

The random effects model assumes that the error term in Equation 4 is the sum of two independently

distributed components: one (μit) that varies across individuals and trips and another (vi) that only

varies across individuals. That is, εit=μit+vi, where each component is from an independent normal

distribution with zero mean and unit variance. The model is therefore called a random effects probit

model8

. The μit are usually assumed to have constant correlation across t, an assumption that greatly

reduces dimensionality of the problem, and requires the estimation of only one additional parameter,

ρ=Corr(εit,εir,), the correlation for an individual across trips. The presence of a statistically significant

random effect can be tested using the estimated t-ratio9

 for ρ. A Monte Carlo experiment by Guilkey and

Murphy [1993] has shown that use of the standard binomial probit model in cases where there is a random

effect can bias the estimates of the parameters’ standard errors.

Coefficient estimates

To ensure that the participation decisions were grounded in recent experience, coefficient estimation was

based on the 352 surveys returned by respondents who took at least one salmon or halibut sportfishing

trip in marine waters off the Kenai Peninsula during 1997. Each respondent answered questions regarding

three different hypothetical trips, yielding a total of 1,056 observations. The socioeconomic data provided

 

8

 A probit model is an abbreviation for “probability unit” model [Aldrich and Nelson 1984]. A binary probit model is used to

estimate the nonlinear functional form (nonlinear in the parameters) where the probability of choosing an event ranges between

0 and 1 and the cumulative distribution function is assumed standard normal.

9

 The number of standard errors away from zero.
31

by the respondents included household after-tax income, gender (a binary indicator variable equal to one

if the individual is male and zero otherwise), age (years), education (a binary indicator variable equal to

one if the individual is a college graduate), and a categorical variable to differentiate between Alaskan

residents and nonresidents. Summary statistics for these variables are presented in Table 26.

Table 26. Statistical summary of respondents’ socioeconomic characteristics [Lee et al. 1999a].

Alaskans N = 158 Nonresidents N = 194

Mean Std. Dev. Min Max Mean Std. Dev. Min Max

Per Capita Household Income $21,580 $12,660 $200 $70,000 $28,140 $17,020 $2,500 $110,000

Gender (1 = male) 0.734 0.443 0 1 0.753 0.433 0 1

Age 42.373 11.982 17 74 48.139 14.321 16 83

Education (1 = college graduate) 0.348 0.478 0 1 0.500 0.501 0 1

Days Fished 9.101 11.905 1 63 4.229 5.025 1 48

Utility was modeled as a hybrid quadratic function to allow for non-constant marginal utility and

substitution/complementarity effects across species, because it can readily accommodate socioeconomic

variables, and because polynomial models are linear in the parameters. Catch and average size were

combined to create an instrumental variable (w) representing the weight of the fish measured in pounds.

This allows for a more parsimonious model given the large number of parameters that need to be

estimated, allows for the identification of all quadratic terms, and can be modified to add separate

variables (species catch or species size) where appropriate. The fish weight variables, the products of

halibut, chinook salmon, and coho salmon catches and weights, are denoted whalibut, wchinook and wcoho,

respectively. The variables Halibut and Halibut 2

 (the catch and squared-catch of halibut, respectively)

were included to allow for additional variation that was due to catch alone.10 P denotes the cost of a

fishing trip. The model to be estimated, including the demographic variables is

(5) y w w w P Halibut Halibut z it s s it s s j s its itj l l P HC HC il * =+ + + + + + β β λ ππ π γ 0 ∑ ∑ , ∑ ∑2 2

where s and j index species (halibut, chinook, coho), and zl are categorical variables representing

Per Capita Household Income, Gender, Age, and Education and β , λ , and π are the associated parameters

to be estimated.

Equation 5 was estimated along with indicator variables to differentiate between Alaskans and

nonresidents.11 Because the same general study design was presented to each group, we only estimate

one random effect parameter.12

 

10 Variables for coho and chinook catch were not included because there was only one size category for coho salmon and only

two catch levels for chinook, consequently catch and size are highly collinear with weight.

11 The p-value on the null hypothesis that all parameters are the same across Alaskans and nonresidents is 0.18. Although not

statistically significant, we have chosen to model the Alaskans and nonresidents separately because many of the individual and

grouped parameters are statistically different from each other and the evaluation of certain policy considerations necessitates

separate estimates.

12 Furthermore, the p-value for the null hypothesis that ρAK = ρother US is 0.52 (χ2

= 0.4134 with 1 degree of freedom), indicating

that it is unlikely that they do not share a common random effect parameter (a χ2

= 3.85 would yield a p-value of 0.05 for

example).
32

The model was estimated with Limdep 7.0 for Windows [Greene 1998]. The estimated coefficients are

reported in Table 27. The random effect parameter ρ is statistically different from zero at the 99% level

(p =0.0057), supporting the presence of an identifiable random effect. A total of 35 parameters were

estimated. Fifteen of the parameters are significantly different from zero at the 1% level, ten additional

parameter estimates are significant at the 5% level and two are significant at the 10% level. The point

estimates of the parameters accord well with economic theory. The price coefficient is negative, as

expected, indicating a downward sloping demand curve for recreational fishing. The coefficients on

halibut, chinook salmon, and coho salmon weights and halibut catches are all positive, indicating that

each variable increases the demand for trips, all else equal. The weight squared terms and the cross terms

are all negative, implying that recreational fishers experience decreasing marginal utility and that catches

of each species are substitutes for catches of the others. The log likelihood at convergence is 542.503 and

the log likelihood was 731.047 when the parameters were set to zero. The McFadden R 2

 is 0.249. The

Veall and Zimmermann R 2

 is 0.442.

Table 27. Random effects probit model parameter estimates.

Alaskans (local and non-local) Nonresidents

Estimates t-ratios Estimates t-ratios

Intercept – 2.8415 3.03* – 1.4746 1.86*

Price – 0.0124 7.39* – 0.0094 6.96*

whalibut 0.0371 3.30* 0.0228 2.53*

wchinook 0.1037 4.32* 0.0732 3.56*

wcoho 0.1242 2.95* 0.1163 3.19*

whalibut2 – 0.0001 2.88* – 0.0001 1.33

wchinook2 – 0.0006 3.41* – 0.0004 2.52*

wcoho2 – 0.0008 1.13 – 0.0011 1.82*

whalibutwchinook – 0.0005 3.50* – 0.0004 3.20*

whalibutwcoho – 0.0007 2.84* – 0.0005 2.38*

wchinookwcoho – 0.0018 3.60* – 0.0010 2.26*

Halibut 1.1033 2.05* 0.9241 2.33*

Halibut2 – 0.1492 2.19* – 0.1297 2.52*

Per-Capita Household Income 0.0945 1.09 0.0021 0.04

Gender (1 = male) 0.3853 2.03* 0.0963 0.57

Age 0.0080 1.04 0.0003 0.05

Education ( 1 = college graduate) 0.2827 1.39 0.3853 2.49*

ρ 0.192 2.77* 0.192 2.77*

* Significantly greater (less) than zero at p 2 0.05 .13

 

13 The log-likelihood at convergence is the value of the log-likelihood function for this model evaluated at the parameter values

we report. These are the parameter values that maximize the log-likelihood function and were found by using a numerical

optimization algorithm. The McFadden R 2

 is 1 – LLm ⁄ LL 0, where LLm is the value of the log-likelihood function from the model,

and LL 0 is the value of the log-likelihood function with all of the slope coefficients set at zero. This is used as a measure of the

model’s fit. The Veall and Zimmermann measure is:

where N is the total number of observations [see Veall and Zimmermann 1996].

( )

( )( )

LL LL

LL LL N

LL

N LL

m

m

−

− +

−

−

0

0

0

0

2

2
33

Simulation model

Every change that affects expected trip attributes such as species mix, number, or size of fish caught will

affect the average sport fisher’s decision to participate, regardless of whether the attribute change is due

to natural population fluctuations, regulatory change, or environmental damage. For the purposes of the

impact assessment model we have grouped recreational fishers into categories according to residency

(locals, non-local Alaskans, and nonresidents) and sportfishing mode (charter, private, shore). Thus,

we model nine distinct expenditure patterns. For the participation model this type of disaggregation was

not possible because respondents to the stated preference questions were not segregated by sportfishing

mode. Consequently, the participation rate model forecasts do not differentiate between charter, private,

or shore sportfishing modes. Future studies may benefit from a finer level of disaggregation.

Answers to the UAF survey questions regarding respondents’ most recent lower and central Cook Inlet

halibut or salmon sportfishing trip were used to calculate the average trip attributes by residency category.

To ensure that the data were current, we only used observations from recreators who took a trip in 1997 or

1998. Our survey was administered in the middle of the fishing season in 1998. Of the survey respondents

who fished in 1997 or 1998, 73% of the respondents listed 1997 as their most recent trip and 27% listed

1998. The 1997 trips would tend to be near the end of the season while the 1998 trips represent early

season trips. This may skew the information for the fishery toward the end of the year somewhat,

especially for Alaskans who may take multiple trips. Figure 16 shows the seasonal distribution of most

recent trips.

0%

10%

20%

30%

40%

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Figure 16. Month in which survey respondents took their most recent

trip [Lee et al. 1999b].

Table 28 summarizes average fishing costs, mean catches, retained catches, and average weights for

halibut, chinook, and coho, derived from the Lee et al. [1999b] survey. For instance, the mean (across

sportfishing modes and target species) nonresident fishing trip for halibut in Cook Inlet resulted in a catch

of 2.43 halibut, of which 1.04 fish were retained and 1.40 released.

J an Feb Mar A pr May J un J ul A ug Sep O ct N ov D ec 
34

Table 28. Mean attributes of lower and central Cook Inlet sportfishing trips [Lee et al. 1999a].

A lask an s (lo cal and no n-local) N on resid en t

Fishing Cost $56.52 $130.71

Halibut Retained 0.72 1.04

Released 0.98 1.40

Total caught 1.71 2.43

Average weight (lbs.) 34.18 42.66

Chinook Retained 0.08 0.11

Released 0.11 0.04

Total caught 0.19 0.14

Average weight (lbs.) 28.34 30.87

Coho Retained 0.05 0.13

Released 0.01 0.18

Total caught 0.06 0.31

Average weight (lbs.) 10.60 9.60

The first row is the mean fishing cost per fishing-day. This is the cost calculated by combining the

expenditure data from the UAF survey (Table 5) with the percent of effort by sportfishing mode derived

from the ADF&G survey (Table 4). For example, the mean cost of a nonresident sportfishing-day

($130.71) is the product of the nonresident charter trip percentage (58.9%)14 and the mean cost of a

charter-based nonresident sportfishing-day ($190.34), plus the product of the nonresident private boat

sportfishing trip percentage (29.6%) and the mean cost of a private boat based nonresident sportfishingday ($51.40), plus the product of the shore-based trip percentage (11.8%) and the mean cost of a shorebased sportfishing-day ($30.57). Alaskan trips are 32.3% charter, 60.2% private, and 7.5% shore. The

smaller fishing costs for Alaskans ($56.52) reflects a lesser reliance on charter trips as well as lower trip

costs across sportfishing modes. Fishing expenditures are directly related to the sportfishing trip and

include the cost of the charter (including tipping), fishing gear, fish processing, derby fees, boat repairs,

moorage fees (and haul out) and miscellaneous expenditures, but do not include living expenses. Finally,

average weights are reported for each category. It is interesting to note nonresidents report halibut catches

that are, on average, 9 pounds heavier than those reported by residents.15

The data in Table 28 can be disaggregated by residency category and sportfishing model (Appendix to

Section 4) or at other levels of aggregation to facilitate comparisons with other surveys. For example, it

is interesting to compare the charter catch estimates from the UAF and ADF&G surveys, and the halibut

charter logbook program (Tables 29 and 30).

 

14 From Table 4, the percent of nonresident trips that are charter based is the percent of nonresident charter trips divided by the

percent of trips taken by nonresidents: 58.9% = 25.9% ⁄ 44.0%

15 This difference could reflect a greater reliance on charter services (larger boats able to fish deeper water and more experienced

skippers), or biased estimates of weight.
35

Table 29. Average daily charter halibut catch* for all Kenai Peninsula sport fishers

[Herrmann et al. 2001].

Charter 95% Confidence

Interval

UAF survey Retained 1.20 1.12 to 1.28

Released 1.71 1.32 to 2.10

Total catch 2.91 2.49 to 3.33

ADF&G survey Retained 1.24

Released 1.35

Total catch 2.59

*For trips where a variety of species are targeted.

Table 30. Average daily charter halibut catch* for all Kenai Peninsula sport fishers

to the west of Gore Point [Herrmann et al. 2001].

Charter 95% Confidence

Interval

UAF survey Retained 1.43 1.23 to 1.63

Released 2.08 1.48 to 2.68

Total catch 3.51 2.89 to 4.32

ADF&G survey Retained 1.85

Released 1.96

Total catch 3.81

*For halibut targeted trips only (also see footnote #4).

Side-by-side comparisons of the UAF and ADF&G estimates and the UAF and halibut charter logbook

estimates agree closely. The halibut charter logbook program [Dean and Howe 1999], first administered

in 1998, requires charter operators to maintain a daily catch log. Because the logbook program is new, the

first year results are preliminary and may not be precise. Nevertheless, when allowance is made for the

differences in methodology and time period sampled, there is surprisingly close agreement between the

logbook results for 1998 and the 1997–98 UAF results (Table 30).

The simulation model employs the sample enumeration method discussed in BenAkiva and Lerman

[1985]. The sample enumeration method takes into account differences in socioeconomic characteristics

and variability in the number of days fished per year by developing forecasts for each individual in the

sample. We use this information to weight the simulations by the number of days fished.16 The simulation

provides separate results for Alaskans and nonresidents.

The general formula for all forecasts is:

(6) % ˆ ˆ

ˆ

[ ][ ]

[ ]

() ()

( )

∆

Φ Φ

Φ Participation

u days u days

u days

ii i i i i

i i i

α = ∑ −∑

∑

1 0

0

 

16 In practice, weighting by the number of days fished has a very small effect of the simulations. This is because the demographic

variables are much less “important” than the fishing attribute variables in terms of making a forecast. Since the fishing attribute

variables are constant across all individuals with the Alaskan and nonresident designations, the effect of weighting by days fished

is very small.
36

where uˆij is the forecast of indirect utility for individual i with the fishing attributes j, j = 0 denotes the

initial or starting point fishing trip attributes and j = 1 denotes the new fishing trip attribute levels based on

an α percent change from the j = 0 levels, % Æ means percentage change, Φ ( • ) is the cumulative normal

distribution function, and daysi is the number of days individual i fished in marine waters off the Kenai

Peninsula in 1997.

The first set of simulations shows the responsiveness of participation rate to changes in the fishing

cost or price per sportfishing-day. Changes in the probability that an average sport fisher would take a

sportfishing trip are explored in simulations of three different costs per sportfishing-day, as each cost per

day is decreased and increased over a 30% interval. The results are represented in Figure 17 and Table 31.

(Note that changes in price are on the horizontal rather than vertical axis.) The price elasticity can be

determined by dividing the percentage change in the probability of taking a trip by the percent change in

the cost. As expected, elasticity is an increasing function of cost per day, for residents and nonresidents,

and the response of Alaskans is more elastic than that of nonresidents. It is interesting to note that price

is relatively inelastic for costs per day similar to those observed during actual fishing trips, $56.52 for

Alaskans and $130.71 for non-residents (see Table 28).

$200 per day

$125 per day

$50 per day

% Change in Cost

–30% –20% –10% 0% 10% 20% 30%

% Change in Participation

150%

100%

50%

0%

–50%

–100%

Nonresidents

–30% –20% –10% 0% 10% 20% 30%

% Change in Participation

150%

100%

50%

0%

–50%

–100%

$200 per day

$125 per day

$50 per day

% Change in Cost

Alaskans (local and non-local)

Figure 17. Percentage change in the probability that the average sport fisher will participate as a

function of changes in the cost per sportfishing-day. (All other trip attributes are set at the

survey mean levels reflected in Table 28.)

Table 31. Percentage changes in days fished in response to changes in the cost per sportfishing-day.

A lask an s (lo cal and no n-local) N on resid en ts

$200 per day $125 per day $50 per day $200 per day $125 per day $50 per day

Change in Cost % Æ in days fished

30% – 71 – 34 – 6 – 34 – 11 – 1

25% – 63 – 29 – 5 – 28 – 9 – 1

20% – 54 – 23 – 4 – 22 – 7 – 1

15% – 43 – 17 – 3 – 17 – 5 – 1

10% – 30 – 12 – 2 – 11 – 3 0

5% – 16 – 6 – 1 – 5 – 2 0

0% 0 0 0 0 0 0

– 5% 18 6 1 5 1 0

– 10% 37 12 2 10 3 0

– 15% 59 17 3 15 4 1

– 20% 81 23 4 19 5 1

– 25% 104 28 4 24 6 1

– 30% 129 34 5 27 7 1
37

We also modeled five changes in the expected catch (+20%, +10%, –10%, – 20%, and – 30%) using

Equation 6 and the trip attributes from Table 28. The averages are weighted by the number of days each

participant fished in 1997. Figure 18 depicts changes in the probability that an average sport fisher would

take a sportfishing trip as a function of percentage changes in the expected catch.

–20%

–10%

% Change in Expected Catch

–30% –20% –10% 0% 10% 20%

% Change in Participation

10%

20%

0%

–30%

–40%

Alaskans

Nonresidents

Figure 18. Percentage change in the probability that the average sport fisher will

participate as a function of changes in expected catch. (All other trip

attributes are set at the survey mean levels reflected in Table 28.)

Applying the probabilities represented in Figure 18 to the population of sport fishers provides estimates

of percentage changes in the number of sportfishing-days in the lower and central Cook Inlet salmon and

halibut sport fisheries (Table 32).

Table 32. Percentage changes in days fished in response to changes in catch, with ± 90% confidence

bounds.

Alaskans (local and non-local) Nonresidents

Mean – 90% +90% Mean – 90% +90%

Change in Catch % Æ in days fished

– 30% – 34.90 – 18.28 – 53.27 – 24.60 – 16.70 – 33.43

– 20% – 21.16 – 10.22 – 34.41 – 13.96 – 8.77 – 19.90

– 10% – 9.32 – 4.35 – 16.26 – 5.82 – 3.46 – 8.93

0% 0 0 0 0 0 0

+10% 6.97 2.92 13.56 3.99 1.97 7.08

+30% 11.97 4.86 23.91 6.64 2.97 12.64

Because the point estimates of percentage changes in the number of sportfishing-days are highly

nonlinear, the 90% confidence intervals were simulated following Krinsky and Robb [1986], using

10,000 Monte Carlo draws. These confidence intervals are reported in Table 32 and depicted in Figures

19 and 20 for Alaskan residents and nonresidents, respectively.
38

–25%

% Change in Expected Catch

–30% –20% –10% 0% 10% 20%

% Change in Participation

25%

0%

–50%

Alaskan Residents

Figure 19. The mean (solid line) and 90% confidence intervals (dotted lines)

on the % change in Alaskan resident sportfishing-days as a function

of % changes in expected catch. (All other trip attributes are set at

the survey mean levels reflected in Table 28.)

Nonresidents

–25%

% Change in Participation

25%

0%

–50%

% Change in Expected Catch

–30% –20% –10% 0% 10% 20%

Figure 20. The mean (solid line) and 90% confidence intervals (dotted lines)

on the % change in nonresident sportfishing-days as a function of

% changes in expected catch. (All other trip attributes are set at the

survey mean levels reflected in Table 28.)

These results suggest, for example, that a 10% reduction in expected catch will lead to a 9.32% reduction

in the probability that the average Alaskan who participated in the 1997 or 1998 lower and central Cook

Inlet salmon or halibut sport fishery would take a halibut or salmon sportfishing trip to lower and central

Cook Inlet and a 5.82% reduction in the corresponding probability for the average nonresident. When

these probabilities are applied to the population of sport fishers, they suggests that a 10% reduction in
39

catch can be expected to result in a 9.32% reduction in halibut and salmon sportfishing-days in lower and

central Cook Inlet by Alaskans and a 5.82% reduction in the number of nonresident sportfishing-days.

The 90% confidence intervals (an interval that should contain the true mean 90% of the time) suggest that

there is a 90% probability that a 10% reduction in expected catch will reduce the number of sportfishingdays by between 4.35% and 16.26% for Alaskans and between 3.46% and 8.93% for nonresidents (Table

32). If sportfishing catches are proportional to biomass, application of the probabilities represented in

Figure 18 to the population of sport fishers provides estimates of percentage changes in the number of

sportfishing-days in the lower and central Cook Inlet salmon and halibut sport fisheries (Table 32).

Moreover, Table 32 can be interpreted as representing the probable changes in participation that would

result from changes in the availability of fish.

Examination of Table 32, and Figures 19 and 20 highlights several important features of the model and

results. First, the model exhibits diminishing marginal returns. For example, a 10% increase in expected

catch will increase expected participation by 6.97% for Alaskan residents. Increasing expected catch from

110% of the 1997 mean to 120% of the 1997 mean would lead to a smaller incremental participation

increase of 5.00% (11.97% – 6.97%), etc. Second, nonresidents are less responsive to changes in expected

catch than residents. This result is consistent with the widely accepted belief that nonresidents are more

strongly motivated by the fishing experience than by the consumptive value of the catch. It is also

consistent with Alaskans having greater opportunities for substitution. Third, confidence intervals are

narrower for nonresidents than Alaskans, suggesting that nonresidents are more homogenous than

residents, and suggesting that greater confidence can be placed in forecasts of changes in nonresident

participation than in forecasts of resident participation.

The expected changes in sportfishing-days and 90% confidence intervals, relative to the 1997 fishery

conditions are shown for shore, private, and charter fishing modes in Tables 33, 34, and 35, respectively.

Table 36 sums the expected changes across fishing modes. Tables 37 and 38 sum the results across

residency categories for shore, private, and charter sportfishing modes, and in total. These disaggregations

are derived from the percentages reported in Table 32 and the estimates of 1997 days fished reported in

Table 3.

Table 33. Changes in days fished from shore in response to changes in catch, with ± 90% confidence

bounds.

Locals Alaskans (non-local) Nonresidents

Mean – 90% +90% Mean – 90% +90% Mean – 90% +90%

% Change in Catch Æ in days fished

– 30% – 4,488 – 2,351 – 6,851 – 1,664 – 871 – 2,539 – 2,510 – 1,704 – 3,411

– 20% – 2,721 – 1,314 – 4,425 – 1,009 – 487 – 1,640 – 1,424 – 895 – 2,030

– 10% – 1,199 – 559 – 2,091 – 444 – 207 – 775 – 594 – 353 – 911

0% 0 0 0 0 0 0 0 0 0

+10% 896 376 1,744 332 139 646 407 201 722

+20% 1,539 625 3,075 571 232 1,140 677 303 1,290

1997 Effort (Days) 12,861 4,767 10,202
40

Table 34. Changes in days fished from private boats in response to changes in catch, with ± 90%

confidence bounds.

Locals Alaskans (non– local) Nonresidents

Mean – 90% +90% Mean – 90% +90% Mean – 90% +90%

% Change in Catch Æ in days fished

– 30% – 9 ,9 46 – 5 ,2 09 – 1 5,18 1 – 1 2,92 8 – 6 ,7 72 – 1 9,73 3 – 6 ,2 97 – 4 ,2 75 – 8 ,5 57 

– 20% – 6 ,0 30 – 2 ,9 12 – 9 ,8 06 – 7 ,8 39 – 3 ,7 86 – 1 2,74 7 – 3 ,5 73 – 2 ,2 45 – 5 ,0 94 

– 10% – 2 ,6 56 – 1 ,2 40 – 4 ,6 34 – 3 ,4 53 – 1 ,6 11 – 6 ,0 23 – 1 ,4 90 – 8 86 – 2 ,2 86 

0% 0 0 0 0 0 0 0 0 0 

+10% 1 ,9 86 8 32 3 ,8 64 2 ,5 82 1 ,0 82 5 ,0 23 1 ,0 21 5 04 1 ,8 12 

+20% 3 ,4 11 1 ,3 85 6 ,8 14 4 ,4 34 1 ,8 00 8 ,8 57 1 ,7 00 7 60 3 ,2 35 

1997 Effort (Days) 28,498 37,044 25,597

Table 35. Changes in days fished from charter boats in response to changes in catch, with ± 90%

confidence bounds.

Locals Alaskans (non– local) Nonresidents

Mean – 90% +90% Mean – 90% +90% Mean – 90% +90%

% Change in Catch Æ in days fished

– 30% – 2,624 – 1,374 – 4,005 – 6,944 – 3,637 – 10,600 – 12,588 – 8,546 – 17,106

– 20% – 1,591 – 768 – 2,587 – 4,210 – 2,034 – 6,847 – 7,143 – 4,488 – 10,183

– 10% – 701 – 327 – 1,222 – 1,854 – 866 – 3,235 – 2,978 – 1,771 – 4,570

0% 0 0 0 0 0 0 0 0 0

+10% 524 220 1,019 1,387 581 2,698 2,042 1,008 3,623

+20% 900 365 1,798 2,382 867 4,758 3,398 1,520 6,468

1997 Effort (Days) 7,518 19,898 51,171

Table 36. Changes in days fished in response to changes in catch, with ± 90% confidence bounds.

Locals Alaskans (non– local) Nonresidents

Mean – 90% +90% Mean – 90% +90% Mean – 90% +90%

% Change in Catch Æ in days fished

– 30% – 17,058 – 8,935 – 26,037 – 21,536 – 11,280 – 32,872 – 21,395 – 14,524 – 29,074

– 20% – 10,342 – 4,995 – 16,819 – 13,058 – 6,307 – 21,234 – 12,141 – 7,627 – 17,307

– 10% – 4,555 – 2,126 – 7,947 – 5,751 – 2,684 – 10,034 – 5,062 – 3,009 – 7,766

0% 0 0 0 0 0 0 0 0 0

+10% 3,407 1,427 6,628 4,301 1,802 8,368 3,470 1,713 6,157

+20% 5,851 2,375 11,686 7,387 2,999 14,755 5,775 2,583 10,993

1997 Effort (Days) 48,877 61,709 86,970
41

Table 37. Changes in days fished in response to changes in catch by sportfishing mode.

∆ in days fished

% Change in Catch Charter Private Shore Total

– 30% – 22,344 – 29,421 – 8,736 – 60,501

– 20% – 12,945 – 17,443 – 5,155 – 35,543

– 10% – 5,533 – 7,600 – 2,237 – 15,369

0% 0 0 0 0

+10% 3,955 5,588 1,636 11,180

+20% 6,681 9,545 2,788 19,013

1997 Effort (Days) 78,587 91,139 27,830 197,556

Table 38. Percentage changes in days fished in response to changes in catch by

sportfishing mode.

% ∆ in days fished

% Change in Catch Charter Private Shore Total

– 30% – 28.4% – 32.3% – 31.4% – 30.6%

– 20% – 16.5% – 19.1% – 18.5% – 18.0%

– 10% – 7.0% – 8.3% – 8.0% – 7.8%

0% 0.0% 0.0% 0.0% 0.0%

+10% 5.0% 6.1% 5.9% 5.7%

+20% 8.5% 10.5% 10.0% 9.6%

1997 Effort (Days) 78,587 91,139 27,830 197,556

The discussion of these participation-rate simulations has suggested that changes in expected catch

rates approximately reflect changes in biomass. While this characterization approximates the constant

exploitation yield strategy applied in halibut management, it is less representative of the constant

escapement strategy applied to salmon. Moreover, even where there is a close linkage between biomass

or abundance and target catches, the allocation among commercial, subsistence, and sport fishers is not

invariant. For example, policy makers could distribute all downside risk to the commercial sector, thereby

insulating the recreational and subsistence sectors from the effects of population fluctuations. Another

likely phenomenon in the long run is that recreational fishers will have increased access to alternative

fishing sites (e.g., Valdez, Whittier, Cordova, Kodiak, Dutch Harbor). (Given that Seward is a close

substitute for lower Cook Inlet, and that the participation rate model was also based on Seward sport

fishers, an implicit assumption is that changes in stock abundance are similar in areas fished from Seward

and lower and central Cook Inlet ports. If that is not the case, lower and central Cook Inlet halibut and

salmon sport fishers would be likely to substitute a Seward trip and there is likely to be an even larger

reduction in fishing participation.) Another assumption in this is that the news of the reduced expected

catches is instantaneous. In reality, it may take a while for reduced (increased) expected catches to be

realized by the public, delaying the onset of participation rate reductions (increases). Participation

reductions may be greater than indicated by the model for expected catch reductions occasioned by

anthropogenic environmental damages because perceptions of the impact of the environmental damage

may be greater than the actual impact. Finally, this model explicitly assumes a perfectly elastic supply

curve for shore, private, and charter trips. While this is true for shore and private trips it is not necessarily

true for charter trips. For example, as a short-run response to an environmental disaster that reduced the
42

desirability of lower and central Cook Inlet sportfishing trips, charter operators might choose to reduce

their prices (discounted trips). To the extent that this occurs, the model will overestimate changes in

participation.

Figures 21, 22 and 23 represent the effect of changes in expected catch of halibut, chinook, and coho on

Alaskan and nonresident participation decisions.

Figure 21. Percentage change in the probability that the average sport fisher will participate as a function

of changes in the expected catch of halibut. (Halibut size set at 35 pounds, fishing costs set at

$100, and the catch of all other species set to zero.)

Figure 22. Percentage change in the probability that the average sport fisher will participate as a function

of changes in the expected catch of chinook salmon. (Chinook size set at 30 pounds, fishing

costs set at $100, and the catch of all other species set to zero.)
43

Figure 23. Percentage change in the probability that the average sport fisher will participate as a function

of changes in the expected catch of coho salmon. (Coho size set at 7 pounds, fishing costs set

at $100, and the catch of all other species set to zero.)

Compensating variations

The expected level of economic welfare of a fishing trip with attributes x can be calculated from

our participation rate, econometric model by employing formulas similar to those presented in

Hanemann [1999]. let the expected maximum utility from each choice occasion be represented by

M = E{max(u 1, u 0)}, where u 1 = v 1 + e1 denotes the utility received by taking a fishing trip and

u0 = v0 + e 0 denotes the utility received from not taking a fishing trip. The economic welfare associated

with the choice occasion is therefore CV = – M / 1p , where CV represents the compensating variation

welfare measure and 1p is the marginal utility of income, and is equal to the coefficient estimate on the

price (cost of trip) variable. Since the marginal utility of income is constant in our econometric model,

this welfare measure is also the equivalent variation welfare measure.

Using standard probability theory, M is calculated by evaluating

(7) M v e e e de de v e e e de de v e v vev

= + ( ) ( ) + + ( ) ( ) −∞ −∞

+∞

−∞ −∞

+ + +∞ ∫ ∫ ∫ 0 0 01 1 0 1 1 01 0 1 ∫ 0 01 11 – – 0

φ φ , ,

where φ ( • ) is the bivariate normal probability density function.

If we normalize the utility of not taking a trip such that u0 = 0, the the trip will only be taken when

v1 + e1 3 0, and M can be evaluated by

(8) M v e e de v v v v = + ( ) ( ) = ( )+ ( ) −

+∞ ∫ 11 11 1 1 1 1

φ φ Φ .

Note that the lower limit of integration ensures that a trip will be taken if v1 + e1 3 0.

The individual’s estimated compensating variation is given by

(9) c M ˆ ˆ ij ij p = − π
44

where cˆ

ij

• is the compensating variation for person i and trip j. the weighted average compensating

variation across all individuals is

(10) ˆ

C cd u d u ˆ ˆ ˆ j ij ij ij ij ij

i

n

i

n • = ( ( )) ( ( )) = =

∑ ∑ Φ Φ

1 1

where di j is the individual’s total number of lower and central Cook Inlet salmon and halibut sportfishingdays fished as reported by ADF&G in 1997 (therefore di j Φ ( uˆij) is the individual’s days fished for a trip j

with corresponding attributes).

The estimated compensating variation is then

(11) ˆ

C c D Participation B BB

• = + ˆ • (1 %∆ α )

where DB is the total number of lower and central Cook Inlet salmon and halibut sportfishing-days taken

by all individuals, according to desired grouping, as reported by ADF&G for the baseline 1997 season.

Changes in compensating variations will then be calculated as

(12) ˆ ˆ – CCCˆ ∆ B S

• •• =

the estimated difference between the baseline trip Cˆ

B

•

, the compensating variation for all participants, and

the simulated trip Cˆ

S

•

 compensating variation for all participants.

Average estimated daily compensating variation with confidence intervals is reported in Table 39 and

totals are reported in Table 40 and shown in Figure 24.

Table 39. Compensating variations response to changes in catch, with ± 90% confidence bounds.*

Alaskans (local and non-local) Nonresidents

Change in Catch Mean – 90% +90% Mean – 90% +90%

– 30% $35.51 $16.45 $65.52 $64.56 $44.46 $89.74

– 20% $49.43 $25.27 $83.10 $83.62 $58.16 $114.03

– 10% $64.63 $35.46 $102.40 $102.08 $72.94 $137.23

0% $80.83 $45.75 $121.63 $118.88 $84.90 $157.22

+10% $95.86 $57.79 $140.50 $133.30 $98.40 $171.64

+20% $110.68 $68.97 $158.14 $144.85 $110.05 $183.76

*The 90% confidence intervals were simulated following Krinsky and Robb [1986], using 10,000 Monte Carlo draws.
45

Table 40. Changes in compensating variation in response to changes in expected catches.

Simulated Change to Expected Catch Mean CV per Day Sportfishing-days Total CV Change in Total CV

– 30% Locals $35.51 31,672 $1,124,503 – $2,802,007

Alaskans (non– local) $35.51 39,986 $1,419,726 – $3,537,637

Nonresidents $64.56 65,397 $4,221,858 – $6,116,949

Total 137,055 $6,766,087 – $12,456,593

– 20% Locals $49.43 38,534 $1,904,791 – $2,021,719

Alaskans (non– local) $49.43 48,651 $2,404,868 – $2,552,494

Nonresidents $83.62 74,828 $6,257,338 – $4,081,469

Total 162,013 $10,566,997 – $8,655,683

– 10% Locals $64.63 44,320 $2,864,537 – $1,061,973

Alaskans (non– local) $64.63 55,956 $3,616,583 – $1,340,780

Nonresidents $102.08 81,911 $8,361,115 – $1,977,692

Total 182,187 $14,842,235 – $4,380,445

0% Locals $80.83 48,877 $3,926,510 $0

Alaskans (non– local) $80.83 61,709 $4,957,363 $0

Nonresidents $118.88 86,970 $10,338,807 $0

Total 197,556 $19,222,680 $0

+10% Locals $95.86 52,281 $5,011,515 $1,085,005

Alaskans (non– local) $95.86 66,007 $6,327,221 $1,369,859

Nonresidents $133.30 90,447 $12,056,188 $1,717,382

Total 208,736 $23,394,925 $4,172,245

+20% Locals $110.68 54,727 $6,056,510 $2,130,000

Alaskans (non– local) $110.68 69,095 $7,646,565 $2,689,202

Nonresidents $144.85 92,748 $13,434,175 $3,095,369

Total 216,569 $27,137,250 $7,914,571

($ millions)

0

% Change in Expected Catch

–30% –20% –10% 0% 10% 20%

2

4

6

8

10

12

14

16 Alaskans

Nonresidents

Figure 24. The effect of increases/decreases in expected catch

on the magnitude of total compensating variations.
46

The estimated daily average compensating variations are $80.33 for Alaskans and $118.88 for

nonresidents. Total compensating variations are $19.2 million ($10.3 million for nonresidents and

$8.9 million for residents). To simulate the changes in compensating variation, when expected fishery

attributes change, the expected number of sport fishers predicted by Equation 6 were multiplied against

the simulated average compensating variation (Table 39).

For a 10% decrease in expected sportfishing catches of halibut and salmon the estimated industry

compensating variation for the lower and central Cook Inlet sports fishery declines from $19.2 to $14.8

million. At 20% and 30% declines in expected catch, compensating variations decline to $10.6 and

$6.8 million, respectively. These large decreases are due to a reduction in the number of participants

accompanied by a decrease in the compensating variations for the remaining participants, reflecting the

decrease in the quality of their trip. Percentage-wise, in all cases, the compensating variations decrease

faster for residents than nonresidents, reflecting differential access to substitute activities or differences in

the relative import of consumptive and non-consumptive aspects of sportfishing (Figure 25). For example,

for a 20% decrease in expected harvest, the compensating variations of Alaskan residents decrease by

51% while nonresident compensating variations drop only 39%.

–50%

–25%

% Change in CV

100%

75%

0%

–75%

–100%

% Change in Expected Catch

–30% –20% –10% 0% 10% 20%

Alaskans

Nonresidents

50%

25%

Figure 25. Percentage change in compensating variations as a

function of percentage changes in expected catch.

5. Input–Output Model

Economic impact analysis and input–output

In addition to developing a methodology for evaluating the effect of changes in sportfishing trip attributes

on participation rates, Section 4 provides estimates of the corresponding changes in the compensating

variation (net benefit) that accrues to sport fishers. Although changes in participation affect a multitude

of economic agents beyond the participants, these effects are not generally examined in net benefit

assessments because they are subsumed in the primary market demand, provided that secondary markets

are not distorted. Nevertheless, identification of the downstream monetary impacts helps reveal how

benefit changes are distributed among regions and economic sectors.
47

Economic impact analysis provides a snapshot of the economic interdependencies of various industries in

a regional economy, and therefore allows analysts to model the downstream effects of demand changes

for commodities or services. Since opportunity costs and willingness to pay do not enter into the impact

assessment framework, the results of an economic impact analysis should not be confused with statements

of value. It should be noted, however, that the results that yield the greatest value under a net benefit

analysis might at times imply very disproportional allocations among stakeholders. In contrast with net

benefit measures which do not identify the distributional or fairness consequences of alternative policies,

economic impact analyses track and identify impacts in revenue, income and employment terms. For a

more detailed discussion on the differences and appropriate uses of cost–benefit and economic impact

analyses, see Edwards [1994] and Steinback [1999].

Economic impact analyses are expenditure-based measures of impact assessment. That is, they use

changes in the monetary value of exchanges to measure the impact of alternative states (trip attributes,

policy settings, etc.). Economic impact modeling has taken several forms that vary in complexity

and degree of grounding in economic theory. Generally, there is a give and take between theoretical rigor

on the one hand and usefulness in real world applications on the other; the level of detail necessary for

policy related issues renders the more complex modeling processes prohibitively costly and cumbersome.

For this reason, input–output models (I–O) have emerged as the most commonly applied method for

measuring regional economic impacts in the damage assessment and policy arenas.

I–O models are attractive because prepared data and software are available and relatively low cost.

However, the ready availability of general models and standard data has occasionally led to hasty and

ill-formulated analyses that have been subject to deserved criticism. Archer [1984] provides examples

of the misuse of I–O results and the misleading policy implications that ensue. Caution must be exercised

in the interpretation of I–O model results. Consequently, it is important that the reader be familiar with

the assumptions and limitations of I–O modeling. Readers who are unfamiliar with I–O are directed to

the Appendix to Section 5 for a review of the theoretical underpinnings of I–O.

It cannot be overemphasized that economic impact analyses are not intended to elicit net benefits. They

are instead useful for delineating intra- and inter-regional economic linkages and for illustrating how

shocks to one or more economic sectors affect the output of commodities, services, employment, and

income. The nature of the impacts that are generated by I–O models comes from the persistent effect of

expenditures as money circulates through an economy. (See Appendix to Section 5 for a more detailed

discussion of the multiplier effect.)

Input–output model

The I–O framework is based on identifying sectors of regional economies through their usage of inputs in

the production process and the subsequent distribution of output throughout the economy. Relationships

are measured by the value of exchanges of goods and services among economic sectors within the region,

imports or exports from other regions, and final demand by households, government entities, and other

economic agents. The annual values of these exchanges are the data used in the I–O model (Figure 26).
48

To Purchasing Sectors Local Final Demand

From 1 . . . j . . . n H ou se ho lds 

Priva te 

I nv es tm e nt G ov er nm e nt Exp or ts 

Total G r os s

O utpu ts 

. . . . . . 

. . . . . . 

Producing Sectors

1

i

n

X11

•

•

•

Xi1

•

•

•

Xn1 . . . 

X1j

•

•

•

Xij

•

•

•

Xnj . . . 

X1n

•

•

•

Xin

•

•

•

Xnn

C1

•

•

•

Ci

•

•

•

Cn

I1

•

•

•

Ii

•

•

•

In

G1

•

•

•

Gi

•

•

•

Gn

E1

•

•

•

Ei

•

•

•

En

X1

•

•

•

Xi

•

•

•

Xn

Labor L1 . . . Lj . . . Ln LC LI LG LE L

Other Value

Added V1 . . . Vj . . . Vn VC VI VG VE V

Imports M1 . . . Mj . . . Mn MC MI MG M

Total Gross

Outlay X1 . . . Xj . . . Xn C I GEX

Figure 26. Sample input–output transactions table [Richardson 1972].

I–O models have been used for impact analyses of development projects and government policy changes.

For example, I–O models have been used to characterize the regional impact of changes in National

Forest harvest policies [Summers and Birss 1991], federal grazing policies [Geier and Holland 1991],

community development strategies [Geier et al. 1994], federal land use decisions [Fawson and Criddle

1994], and the impacts of guided sport fisheries off New England [Steinback 1999]. I–O models have also

been used to model the Alaska statewide economy [Logsdon et al. 1977; Weddelton 1986], to determine

management impacts of commercial fisheries on rural communities [Natcher 1996], and to describe the

economic significance of Alaska’s sport fisheries [NPFMC 1997; ISER 1999].

We selected IMPLAN [Olson and Lindall 1997] and the most commonly used I–O model as a base.

IMPLAN includes 21 economic and demographic variables for 528 industrial sectors for all U.S. counties

(and boroughs). The IMPLAN database is built from employment and income data sets including County

Business Patterns, ES 202, and the Regional Economic Information System. In cases where there are

disclosure problems, IMPLAN uses national averages as estimates for income and employment. IMPLAN

is recognized as the best source of U.S. secondary regional economic data. Nevertheless, although the

national level data are regularly updated, the regional data are infrequently updated. Moreover, regions

may have unique economic sectors or linkages that are not well represented in IMPLAN. Consequently,

in regions such as Alaska, with small numbers of firms (frequent disclosure problems), and a rapidly

evolving and heavily resource dependent economy, it is particularly essential that the transaction

coefficients be thoroughly updated and carefully groundtruthed with local data and expert knowledge.

Because the recreational fishing sector is not explicitly represented in IMPLAN, we have developed

a programming module that disaggregates IMPLAN sectors that include recreation related activities

to identify those activities generated by recreational fishing. This module utilizes IMPLAN generated

response coefficients and secondary regional economic data as inputs. The secondary model data is

augmented with data for the target sectors (e.g., sport/charter industry) supplied by primary data

collection. Thus this module, through its I–O framework, explicitly accounts for linkages between

various economic sectors, according to production and consumption patterns.

Individual sportfishing activities are accommodated differently from direct income generating activities

such as guiding, harvesting, and processing. This is because individual sportfishing activities are
49

accounted for by expenditure patterns in retail and service sectors, rather than treated as an identifiable

economic sector. The recreational fishing module allocates recreational expenditures among these sectors.

The sportfishing expenditure data were obtained from Lee et al. [1999b]. The operating cost data required

for modeling charter operations were drawn from NPFMC [1997] and updated in the groundtruthing

process.

Application of the IMPLAN database and model

In contrast to manufacturing sectors, which are well represented in IMPLAN, retail sectors are highly

aggregated. Because impacts associated with changes in sportfishing-related expenditures are primarily

retail, tracking them requires disaggregation of some of the IMPLAN sectors. Moreover, only 138 of

IMPLAN’s 528 sectors are represented in the Kenai Peninsula Borough.

While aggregating two or more I–O sectors into one is straightforward, there are many ways to

disaggregate one sector into two or more. For example, charter trip payments are included in IMPLAN’s

Amusement and Recreation Services sector. Without information describing the intermediate demand

components associated with charters, it is not possible to know how to correctly adjust the vector of

technical coefficients. While it might be tempting to represent the new “Charter” sector with a vector of

technical coefficients that is a simple fraction of the Amusement and Recreation sector, doing so would

render the technical coefficients matrix singular and prevent the model from finding a solution.

Bushnell and Hyle [1985], Wolsky [1984], and Gillen and Guccione [1990] suggest approaches that

directly modify the technical coefficients matrix. Jensen [1997] and Steinback [1999] note instead that

running an impact scenario in IMPLAN that mirrors that sector’s purchases can simulate the intermediate

demand effects of the aggregated sector. The former is technically preferable, but requires reprogramming

of IMPLAN’s social accounting matrices to reflect the characteristics of the disaggregated subsector. By

including the new sector within the model, the changes are noted within the use (absorption), byproducts,

and final demand matrices. Regional purchase coefficients and value-added features are likewise

constructed for the new sector. On the other hand, the impact scenario option is much less tedious. Using

IMPLAN’s front end, a demand shock is executed with components (events) that mirror the proportions

of the simulated sector’s production function. The resulting impacts can then be used to calculate

response coefficients (normal multipliers). (See Appendix to Section 5 for a technical discussion on

multipliers.) However, because the new subsector is not explicitly defined in the IMPLAN model, there

is no opportunity for it to play a role in the intermediate demand of other sectors within the model, thus

leading to possible underestimation of the actual multiplier effect. We used the first approach to represent

charter operations and the second to represent expenditures by sport fishers.

A model of the average charter operation’s purchasing pattern was constructed using data obtained by

NPFMC [1997, 2000] as well as discussions with local experts and members of industry during fieldwork

conducted for this study. Standard Industrial Classification codes for the corresponding inputs were

translated to the IMPLAN sectoral scheme (see Appendix to Section 5) and a production function was

estimated for the 1997 charter sector sales value of $10.4 million reported in Table 23. The estimated

average production function for the marine charter sector is reported in Table 41. These technical

coefficients were applied to the baseline charter sales data presented in Table 24. For a more detailed

accounting of the individual expense categories, corresponding Standard Industrial Classification codes

and translation to the IMPLAN sectoral scheme, the reader is referred to the Appendix to Section 5.
50

Table 41. Parameters values for the estimated average production function for the marine charter sector.

E xp en se Categ ory C oefficien t E xp en se Categ ory C oefficien t V alue A d ded C ateg ory C oefficien t

A dv ertisin g 0 .0 41 0 M ed ical 0 .0 01 5

B ait 0 .0 13 3 O ffice S up plies 0 .0 13 5 E mp lo yee C om p en satio n 0 .1 14 7

C om pu ter T otal 0 .0 06 6 P ro fession al Serv ices 0 .0 09 8 P ro prietor In co me 0 .1 94 9

C on tract S erv ices 0 .0 03 5 R ep air/M aint/T o ols/S up plies 0 .0 13 0 O PT I 0 .0 33 9

D ues 0 .0 13 9 S ub scrip tion s 0 .0 01 8 Ind irect B usiness T ax 0 .0 30 6

E lectro n ic S u pp lies 0 .0 00 4 T otal B o at M ain tenan ce 0 .0 13 2 (Sales T ax )

E ntertainm en t 0 .0 00 9 T otal B o ro ug h T ax 0 .0 36 9

F ed eral In co m e Tax 0 .0 41 6 T otal In su ran ce 0 .0 39 2

F uel & L ub ricatio n 0 .1 35 6 T otal L icenses 0 .0 24 3

G ear Rep lacem en t 0 .0 21 6 T otal T rav el 0 .0 18 1

G ro ceries 0 .0 00 8 T otal T ruck E xp en ses 0 .0 17 8

H ull Rep air 0 .0 05 4 T otal U tilities 0 .0 38 0

Interest P aid (Bo at) 0 .0 54 2 T rade S h ow s 0 .0 21 4

M oo rage & Bo at Storage 0 .0 18 2 W ork Gear/Clien t Su p plies 0 .0 20 2

T otal A b so rp tio n Co efficien t 0 .6 25 9 V alue A d ded C oefficien t 0 .3 74 1

For the other expenditure categories reported in Section 3, impact scenarios were run in IMPLAN to

generated response coefficients. These response coefficients and those developed for the charter operation

sector were included in $FISH, a stand-alone recreational module (see Appendix B [software manual] or

Hamel et al. [2001]). Where data limitations prevented construction of original production functions, the

model defaults to the values reported for input coefficients in Jensen [1997]. Table 42 lists the production

recipes used for each of the sportfishing expenditure categories in Section 3 that take place on the Kenai

Peninsula, and thus affect local economic impacts.

Table 42. Absorption sectors and coefficients for sportfishing expenditure categories.

Expenditure Category IMP LA N S ecto r # IMPLAN Sector Name Coefficient

Transportation, Food & Lodging

Auto or Truck Fuel 451 Automotive dealers & service stations 1.00

Groceries 450 Food stores 0.75

455 Miscellaneous retail 0.25

Lodging 463 Hotels and lodging places 1.00

Restaurant & Bar 454 Eating & drinking 1.00

Fishing Expenditures

Boat Fuel, Lubricants & Repairs 393 Boat building and repairing 0.10

448 Building materials & gardening 0.05

451 Automotive dealers & service stations 0.70

455 Miscellaneous retail 0.10

482 Miscellaneous repair shops 0.05

Charter & Guide Fees Table 41

Fish Processing or Packaging 98 Prepared fresh or frozen fish or seafood 1.00

Fishing Derby Entry Fees 503 Business associations 1.00

Fishing Gear 98 Prepared fresh or frozen fish or seafood 0.15

421 Sporting and athletic goods, n.e.c. 0.05

449 General merchandise stores 0.20

455 Miscellaneous retail 0.50

Haul Out & Moorage Fees 435 Motor freight transport and warehousing 0.10

436 Water transportation 0.45

451 Automotive dealers & service stations 0.10

473 Equipment rental and leasing 0.15

479 Automobile repair and services 0.20
51

To be useful, impact models should be linked to a demand model for the activity in question. While

an accurately groundtruthed impact model may appropriately predict the consequences of a specified

demand shock for recreational fishing, it will be of limited value without a quantified relationship

between the sportfishing-related demand for goods and services and variations in fishery conditions. It is

essential to know the set of circumstances that lead to a specific demand shock so that the impact model

can serve more as a tool for evaluating the effects of policies and/or simulated environmental change

instead of just as an academic exercise in assessing arbitrary levels of demand.

The participation rate model developed in Section 4 provides the linkage between the impact model,

$FISH, and variations in fishing conditions. $FISH generates estimates of economic activity on the

western Kenai Peninsula of increased/decreased angler spending and expresses these in terms of output

(sales), income, employment, and other value-added variables. Changes in these expenditures are driven

by changes in participation, which are determined by changes in trip attributes (e.g., fish catch and size,

and trip cost) modeled in Section 4. We apply percentage changes in effort level, by residency and fishing

mode, on a one-to-one basis to the baseline sportfishing-day expenditures reported in Table 25. Since the

participation rate model incorporates declining marginal utility and substitution effects, the resulting

economic impacts likewise reflect nonlinearities with respect to attribute levels. While the linear

assumptions underlying I–O still affect the model’s estimation of impacts, the driving variables exert a

nonlinear influence.

6. Impact Simulations

The simulations in Section 4 examine the influence of changes in trip attributes on the magnitude of net

benefits that accrue to recreational fishers. In addition, changes in the number of sportfishing trips taken

in lower and central Cook Inlet affect the level of economic activity on the Kenai Peninsula, within

Alaska, and ultimately the nation. This section combines the participation rate simulations of Section 4

with the regional economic model introduced in Section 5 to explore the economic impacts to the western

Kenai Peninsula from potential changes to the attributes of Cook Inlet salmon and halibut sportfishing

trips.

The impact simulations are based on trip attributes and potential changes to exploitable biomass examined

in Section 4. The simulations begin with the decision of the average sport fisher to participate in a fishery

when environmental conditions or regulatory changes are perceived to affect expected catch. The

input–output model includes nine categories of sport fishers: three residency categories (local,

non-local Alaskans, and nonresidents) and three sportfishing modes (charter, private, shore). These nine

sportfishing categories are represented by nine distinct expenditure patterns in the I–O model.

The baseline trip attributes are reported in Table 28, which summarizes the average fishing cost, average

catches, and average weights for Alaskan and nonresident fishing trips during 1997–98. The simulated

changes to participation are reported Tables 37 and 38.

Baseline expenditures

Economic impacts of the lower and central Cook Inlet salmon and halibut sport fisheries on the western

Kenai Peninsula depend on the portion of sportfishing expenditures spent in the region. Avid sport fishers

might base their decision to take a trip to the Kenai Peninsula Borough on the expected quality of the

lower and central Cook Inlet salmon or halibut sportfishing opportunity. Other visitors may be less

motivated by sportfishing and may choose to take a Kenai Peninsula trip, spending money locally on

food, lodging, and other recreational activities, regardless of the expected quality of the sportfishing

experience. Because this economic impact analysis is intended to isolate the monetary effects of changes
52

in participation in lower and central Cook Inlet marine sport fisheries, it is important that we only

use those expenditures directly attributable to sportfishing. The model applies the assumptions about

the effects of cancellation of the sportfishing component of the trip developed in Table 22 and the

corresponding estimates of 1997 expenditures attributed directly to saltwater fishing reported in Table 23.

Assessment of the regional economic impacts of marine sportfishing on the Kenai Peninsula Borough

begins with a baseline of expenditures that fluctuates as sport fisher behavior responds to changes in

fishing conditions. We begin by summing the totals spent on the “Fishing (Kenai)” and “Other (Kenai)”

categories from Table 23, to obtain an estimate of total spending, $28.5 million. This value can be

regarded as a measure of the economic magnitude of the marine sport fishery in terms of sales, or in the

lexicon of regional economic analysis, “output”. Economic significance is a description of the level of

economic activity associated with the activity or industry in question [ISER 1999]. However, this

measure is likely to be comprised of a significant amount of spending by local residents, which needs to

be netted out before consideration of impacts of changed spending patterns. It is assumed that local

residents will substitute spending on other regional recreational activities for their foregone sportfishing

expenditures; hence their contribution to economic significance is disregarded for purposes of impact

analysis (see discussion in Section 5).

Subtracting the spending of Kenai area residents (Tables 61, 62, and 63 in the Appendix to Section 3)

from the total expenditures attributable to the lower and central Cook Inlet sport fisheries leaves a

remainder of $25.0 million of “new” money to the region spent by non-local Alaskans and nonresidents

($15.3 million of fishing related expenses and $9.7 million of other expenses, see Table 43).

Table 43. Total Kenai Peninsula area expenditures by Alaskans (non-local)

and nonresidents that can be attributed directly to lower and central

Cook Inlet halibut or salmon sportfishing trips [Lee et al. 1999a].

Expenditures ($)

Fishing Expenditures Other Expenditures

Auto fuel 2,208,331

Auto/RV rentals

Lodge 3,061,159

Groceries 2,443,248

Restaurant & Bar 1,996,927

Charter 9,518,445

Gear 1,658,566

Processing 2,202,291

Derby 171,082

Boat Fuel 1,279,407

Haul/Moorage 433,374

Total 15,263,165 9,709,665

Increases in the amount of new money spent locally will stimulate economic activity whereas decreased

spending by non-locals leads to a reduction in economic activity. Variations in spending by non-locals

are driven by the changes in effort predicted by the participation rate model. For every percentage change

in effort measured by reduced or increased sportfishing-days, there is a proportional change in daily

expenditures across each of the nine combinations of residency and sportfishing mode. The changed

expenditures are summed and multiplied by the response coefficients developed in Section 5 to generate

estimates of the economic impact of regulatory or environmentally induced changes in fishing trip

attributes. The impacts can be examined in terms of output (direct, indirect, induced, total), employment,

employee earnings, proprietors’ income, personal income, other income, indirect taxes, and value added.
53

Simulations

We examined six scenarios: a continuation of the status quo; 10, 20, and 30% decreases in catch; and

10 and 20% increases in catch. The results are presented in Tables 44–53, below and in the Appendix to

Section 6 (Tables 76–80). These tables report estimated changes to the 10 aggregated direct expenditure

categories surveyed in Lee et al. [1999b]. The direct, indirect, induced, and total expenditure changes are

reported for output, personal income, and employment along with a final demand array of 26 local sectors

impacted. The 26 local sectors included in the final demand array represent aggregations of the 528

IMPLAN sectors, and their assignment to elements of the demand array is detailed in the Appendix to

Section 5. Total output effects (the sum of direct, indirect, and induced effects) for each of the nine

sportfishing categories are listed in the total local impact row. The Appendix to Section 6 includes

additional detail on the impacts on employee compensation, proprietor’s income, indirect business taxes,

other property type income, and total value added.

While the six scenarios that we modeled represent a broad range of changes in sportfishing opportunity,

evaluation of the risk and expected outcome of particular environmental damage scenarios may require

c on si de r at io n o f sp e ci fi c al ly t a il or e d sc en a ri os . The re f or e, we h av e d ev e lo pe d a s pr e ad sh ee t -b as e d p ro gr am

(and user manual) to accompany this report (see Appendix B [software manual] or Hamel et al. [2001]).

Ana ly st s wil l b e ab l e to us e th e p ro g ra m to ex ami ne d if f er en t s imul a ti on sc en ar i os o v er r es i de nc y c at eg o ri es 

and sportfishing modes. An example of the output from this software for an expected 10% decrease in

lower and central Cook Inlet sportfishing catches of salmon and halibut is shown in Figures 27–31.

The simulation model data input form is initialized with 1997 mean trip attributes (Table 29). Users can

vary sportfishing catch rates, average fish size and trip costs, and focus the analysis on charter, private

vessel, or shore-based sportfishing modes.

Figure 27. Example simulation model data input interface associated with a 10%

decrease in expected catch.
54

These user-selected trip attributes fuel the simulation’s estimates of economic impacts and compensating

variation. The screen represented in Figure 28 reconfirms the trip attributes specified by the user and

reports estimates of the changes in fishing effort relative to the 1997 fishery.

Figure 28. Example simulation screen showing changes in participation and sportfishing-days

associated with a 10% decrease in expected catch.

The simulation results suggest that if lower and central Cook Inlet halibut and salmon sportfishing catches

are reduced by 10% and average catch size remains constant, resident and nonresident participation will

be reduced by –9.32% and –5.82%, respectively (Table 32 and Figure 28). That is, the number of lower

and central Cook Inlet region sportfishing-days will diminish by 15,369 from the 1997 baseline of

197,556 days.
55

The screen represented in Figure 29 reports the direct, indirect, induced, and output impacts associated

with the user-specified scenario. Employment, income, and other value-added impacts can also be

displayed.

Figure 29. Example simulation screen showing regional economic impacts associated with

a 10% decrease in expected catch.

Direct output reflects the amount of increased or decreased spending of new money for each sportfishing

expense category. For example, reading across the line labeled “Auto or Truck Fuel”, there is a baseline

expenditure of $2.62 million spent in the shore, private, and charter sportfishing modes in 1997 that was

directly attributable to the lower and central Cook Inlet sport fisheries (Figure 29 and Table 44). The

reduction in sportfishing effort associated with a 10% decrease in sportfishing catches will lead to

decreases in fishing and non-fishing expenditures. For example, the direct effect on automotive fuel sales

is a $169,195 reduction (direct impact). As fuel sales decline, fuel retail outlets decrease their local

purchases of inputs from other sectors by $40,476 (the indirect effect), and households with members

employed by these sectors spend $40,973 less on local goods and services (the induced effect). Taken

together, the total impact on the Kenai Peninsula region economy that is attributable to the change in

automotive fuel sales that results from a 10% reduction in expected lower and central Cook Inlet marine

sportfishing catches is $250,162.
56

The screen represented in Figure 30 disaggregates the economic impacts into 26 final demand categories

for each of the impact classes (output, employment, personal income, etc).

Figure 30. Example simulation screen showing final demand category impacts associated

with a 10% decrease in expected catch.

While the direct effect represents a loss of $169,195 to the automotive fuel sector, the amounts of

the indirect and induced effects are distributed across the 528 IMPLAN industry sectors. We have

summarized the IMPLAN sectors into 26 categories of the final demand array. Note that sum of total

output impacts in Figure 30, –$2,483,646, equals the sum of the total output column in Table 45 as well

as the corresponding columns in Figure 29 and Table 44. The individual row entries under the output

column of Figure 30 (Table 45) show how each demand category is impacted by changes in sportfishing

expenditures. Results in the output column of Figure 30 indicate that businesses making up the Recreation

Activities category are hardest hit in terms of foregone sales revenues (–$386,555), followed by the

Other Local Purchases category (–$372,246), and the Motor Vehicles category (–$352,598). In terms of

employment, (Figure 30 column 3), the largest number of jobs are lost in Other Local Purchases category

(21), followed by Recreation Activities (25), and Food Processing (13). The largest losses in personal

income (Figure 31 column 6), arise in the Motor Vehicles category (–$175,891), with similar magnitude

losses in Recreation Activities (–$156,924), and Food Processing (–$155,692).
57

The screen represented by Figure 31 contrasts compensating variations under the user-specified scenario

with the 1997 baseline.

Figure 31. Example simulation screen showing compensating variations associated with a

10% decrease in expected catch.

Figure 31 (see also Table 39) suggests that with a 10% decrease in the expected sportfishing catch of

lower and central Cook Inlet halibut and salmon, the estimated compensating variation for the Cook Inlet

saltwater sport fishers declines from $19.2 to $14.8 million. This loss in consumer surplus comes from

a loss of $1.1 million for local fishermen, $1.3 million for other Alaskans, and $2.0 million for

nonresidents.

Tables 44 and 45 report the impacts of a 10% reduction in lower and central Cook Inlet halibut and

salmon sportfishing catches. Tables 46 through 53 respectively, report on the output (direct, indirect,

induced, total), employment, earnings, income, taxes, and value-added impacts of – 20%, – 30%, +10%,

and +20% changes in lower and central Cook Inlet halibut and salmon sportfishing catches.
58

Table 44. Regional economic impacts of a 10% decrease in lower and central Cook Inlet sportfishing

catches.

Response Coefficient B aselin e

E xp en ditures ($ )

D irect O utpu t

($)

Ind irect O utp ut

($)

Ind uced Ou tp u t

($)

T otal O u tp ut

($)

Industry Output

Auto or Truck Fuel 2,619,715 – 169,195 – 40,476 – 40,973 – 250,162

Groceries 2,864,102 – 182,725 – 23,492 – 49,744 – 255,444

Lodging 3,226,870 – 194,726 – 43,726 – 37,254 – 259,836

Restaurant & Bar 2,561,923 – 147,907 – 29,676 – 27,860 – 205,442

Boat Fuel, Lubricants & Repairs 1,732,240 – 104,879 – 22,469 – 24,180 – 151,272

Charter & Guide Fees 10,366,927 – 634,899 – 200,521 – 142,933 – 978,353

Fish Processing or Packaging 2,307,448 – 136,116 – 17,482 – 24,244 – 177,843

Fishing Derby Entry Fees 269,302 – 11,672 – 2,885 – 2,212 – 16,769

Fishing Gear 1,904,030 – 103,999 – 15,458 – 22,987 – 142,139

Haul Out & Moorage Fees 671,617 – 32,318 – 9,280 – 4,798 – 46,387

TOTAL 28,524,174 – 1,718,435 – 405,464 – 377,184 – 2,483,646

Personal Income

Auto or Truck Fuel 2,619,715 – 85,860 – 11,710 – 16,329 – 113,899

Groceries 2,864,102 – 111,829 – 6,628 – 19,825 – 138,282

Lodging 3,226,870 – 75,233 – 13,556 – 14,850 – 103,639

Restaurant & Bar 2,561,923 – 58,299 – 8,100 – 11,105 – 77,504

Boat Fuel, Lubricants & Repairs 1,732,240 – 51,048 – 6,532 – 9,637 – 67,217

Charter & Guide Fees 10,366,927 – 261,270 – 79,389 – 56,976 – 397,635

Fish Processing or Packaging 2,307,448 – 51,501 – 6,233 – 9,662 – 67,397

Fishing Derby Entry Fees 269,302 – 4,491 – 821 – 887 – 6,200

Fishing Gear 1,904,030 – 50,054 – 4,685 – 9,161 – 63,900

Haul Out & Moorage Fees 671,617 – 8,924 – 2,512 – 1,913 – 13,349

TOTAL 28,524,174 – 758,510 – 140,165 – 150,346 – 1,049,021

Employment

Auto or Truck Fuel 2,619,715 – 3 – 0 – 1 – 5

Groceries 2,864,102 – 5 – 0 – 1 – 6

Lodging 3,226,870 – 4 – 1 – 1 – 5

Restaurant & Bar 2,561,923 – 4 – 0 – 0 – 5

Boat Fuel, Lubricants & Repairs 1,732,240 – 2 – 0 – 0 – 3

Charter & Guide Fees 10,366,927 – 35 – 3 – 2 – 41

Fish Processing or Packaging 2,307,448 – 3 – 0 – 0 – 4

Fishing Derby Entry Fees 269,302 – 0 – 0 – 0 – 0

Fishing Gear 1,904,030 – 3 – 0 – 0 – 3

Haul Out & Moorage Fees 671,617 – 0 – 0 – 0 – 1

TOTAL 28,524,174 – 60 – 6 – 7 – 72
59

Table 45. Final demand impacts of a 10% decrease in lower and central Cook Inlet sportfishing catches.

Final Demand Category O utpu t ($) Job s E mp lo yee

E arning s ($)

P ro prietors

Incom e ($)

P erso nal

Incom e ($)

O th er

Incom e ($)

Ind irect

T ax es ($ )

V alue

A dd ed ($ )

Banking/Credit Services – 31,880 – 0 – 6,7 22 – 288 – 7,0 10 – 5,0 05 – 4,0 69 – 16,084

Business/Labor Assoc – 23,175 – 0 – 10,087 — – 10,087 — – 264 – 10,352

Civic/Religious Assoc – 3,1 95 – 0 – 1,7 92 — – 1,7 92 — – 0 – 1,7 92

Communications – 29,334 – 0 – 6,1 96 – 716 – 6,9 12 – 4,7 84 – 999 – 12,695

Eating & Drinking Places – 184 ,141 – 5 – 58,315 – 14,266 – 72,581 – 8,8 56 – 4,9 46 – 86,383

Education – 3,1 83 – 0 – 1,2 85 – 195 – 1,4 80 — — – 1,4 80

Fabrics/Apparel – 4,0 24 – 0 – 1,6 18 – 218 – 1,8 37 – 643 – 478 – 2,9 57

Food Processing – 324 ,195 – 7 – 133 ,624 – 22,068 – 155 ,692 – 23,687 – 15,125 – 194 ,504

Health Care – 51,766 – 1 – 22,706 – 9,9 27 – 32,633 – 4,6 34 – 654 – 37,921

Hotels & Lodging – 190 ,341 – 4 – 59,374 – 20,689 – 80,063 – 18,159 – 8,6 95 – 106 ,917

Household Furnishings – 3,2 24 – 0 – 1,3 16 – 580 – 1,8 96 – 323 – 336 – 2,5 55

Household Industry – 10 – 0 – 10 — – 10 — — – 10

Housing – 72,985 – 1 – 2,8 50 284 – 2,5 65 – 16,599 – 13,539 – 32,704

Insurance – 5,9 62 – 0 – 1,9 29 – 263 – 2,1 92 – 149 – 668 – 3,0 09

Motor Vehicles – 352 ,598 – 7 – 150 ,583 – 25,308 – 175 ,891 – 26,491 – 22,084 – 224 ,466

Other Local Purchases – 372 ,246 – 21 – 51,619 – 92,388 – 144 ,007 – 44,318 – 4,3 98 – 192 ,723

Personal Services – 54,127 – 1 – 15,210 – 11,597 – 26,807 – 4,9 20 – 459 – 32,186

Petroleum Products – 16,919 – 0 – 1,2 36 – 157 – 1,3 93 – 744 – 2,1 34 – 4,2 71

Publications/Paper – 14,307 – 0 – 5,1 41 – 402 – 5,5 43 – 4,3 47 – 68 – 9,9 57

Recreation Activities – 386 ,555 – 16 – 36,618 – 120 ,305 – 156 ,924 – 41,445 – 3,7 58 – 202 ,127

Retail Trade – 187 ,403 – 6 – 79,768 – 30,034 – 109 ,802 – 20,027 – 15,536 – 145 ,365

State/Local Services – 19,431 – 0 – 7,0 74 — – 7,0 74 – 3,1 44 – 8 – 10,226

Transportation Services – 55,998 – 1 – 9,4 12 – 1,7 09 – 11,121 – 2,7 06 – 1,9 81 – 15,808

U.S. Postal Service – 14,161 – 0 – 9,5 31 — – 9,5 31 1,4 71 — – 8,0 60

Utilities – 48,382 – 0 – 9,0 74 – 916 – 9,9 90 – 13,755 – 1,9 01 – 25,647

Wholesale Trade – 34,103 – 0 – 13,816 – 373 – 14,189 – 2,1 76 – 2,3 24 – 18,689

Total Local Impacts – 2,4 83,646 – 72 – 696 ,906 – 352 ,115 – 1,0 49,021 – 245 ,440 – 104 ,426 – 1,3 98,887 
60

Table 46. Regional economic impacts of a 20% decrease in lower and central Cook Inlet sportfishing

catches.

Response Coefficient B aselin e

E xp en ditures ($ )

D irect O utpu t

($)

Ind irect O utp ut

($)

Ind uced Ou tp u t

($)

T otal O u tp ut

($)

Industry Output

Auto or Truck Fuel 2,619,715 – 391,826 – 93,735 – 94,886 – 579,332

Groceries 2,864,102 – 424,332 – 54,553 – 115,518 – 593,203

Lodging 3,226,870 – 456,540 – 102,516 – 87,342 – 609,192

Restaurant & Bar 2,561,923 – 343,976 – 69,015 – 64,791 – 477,782

Boat Fuel, Lubricants & Repairs 1,732,240 – 241,095 – 51,652 – 55,584 – 347,741

Charter & Guide Fees 10,366,927 – 1,495,608 – 472,361 – 336,702 – 2,304,671

Fish Processing or Packaging 2,307,448 – 323,900 – 41,601 – 57,692 – 423,192

Fishing Derby Entry Fees 269,302 – 27,416 – 6,777 – 5,197 – 39,390

Fishing Gear 1,904,030 – 246,891 – 36,696 – 54,570 – 337,435

Haul Out & Moorage Fees 671,617 – 75,097 – 21,564 – 11,150 – 107,789

TOTAL 28,524,174 – 4,026,681 – 950,469 – 883,431 – 5,819,726

Personal Income

Auto or Truck Fuel 2,619,715 – 198,837 – 27,118 – 37,816 – 263,771

Groceries 2,864,102 – 259,695 – 15,391 – 46,039 – 321,125

Lodging 3,226,870 – 176,385 – 31,782 – 34,816 – 242,983

Restaurant & Bar 2,561,923 – 135,582 – 18,838 – 25,827 – 180,246

Boat Fuel, Lubricants & Repairs 1,732,240 – 117,349 – 15,016 – 22,153 – 154,518

Charter & Guide Fees 10,366,927 – 615,464 – 187,014 – 134,215 – 936,693

Fish Processing or Packaging 2,307,448 – 122,552 – 14,832 – 22,993 – 160,376

Fishing Derby Entry Fees 269,302 – 10,549 – 1,929 – 2,084 – 14,563

Fishing Gear 1,904,030 – 118,827 – 11,121 – 21,748 – 151,697

Haul Out & Moorage Fees 671,617 – 20,737 – 5,837 – 4,445 – 31,019

TOTAL 28,524,174 – 1,775,977 – 328,877 – 352,136 – 2,456,990

Employment

Auto or Truck Fuel 2,619,715 – 8 – 1 – 2 – 11

Groceries 2,864,102 – 12 – 1 – 2 – 14

Lodging 3,226,870 – 8 – 1 – 2 – 11

Restaurant & Bar 2,561,923 – 9 – 1 – 1 – 11

Boat Fuel, Lubricants & Repairs 1,732,240 – 5 – 1 – 1 – 7

Charter & Guide Fees 10,366,927 – 82 – 8 – 6 – 96

Fish Processing or Packaging 2,307,448 – 8 – 0 – 1 – 9

Fishing Derby Entry Fees 269,302 – 0 – 0 – 0 – 1

Fishing Gear 1,904,030 – 7 – 0 – 1 – 8

Haul Out & Moorage Fees 671,617 – 1 – 0 – 0 – 1

TOTAL 28,524,174 – 140 – 13 – 15 – 168
61

Table 47. Final demand impacts of a 20% decrease in lower and central Cook Inlet sportfishing catches.

Final Demand Category O utpu t ($) Job s E mp lo yee

E arning s ($)

P ro prietors

Incom e ($)

P erso nal

Incom e ($)

O th er

Incom e ($)

Ind irect

T ax es ($ )

V alue

A dd ed ($ )

Banking/Credit Services – 74,735 – 1 – 15,756 – 674 – 16,430 – 11,736 – 9,539 – 37,706

Business/Labor Assoc – 54,391 – 1 – 23,668 — – 23,668 — – 619 – 24,288

Civic/Religious Assoc – 7,482 – 0 – 4,195 — – 4,195 — – 0 – 4,196

Communications – 68,657 – 0 – 14,504 – 1,676 – 16,179 – 11,210 – 2,342 – 29,731

Eating & Drinking Places – 428,819 – 11 – 135,801 – 33,222 – 169,023 – 20,623 – 11,518 – 201,165

Education – 7,455 – 0 – 3,010 – 457 – 3,466 — — – 3,466

Fabrics/Apparel – 9,449 – 0 – 3,801 – 513 – 4,314 – 1,510 – 1,122 – 6,946

Food Processing – 762,723 – 17 – 313,359 – 51,796 – 365,155 – 55,442 – 35,304 – 455,901

Health Care – 121,247 – 2 – 53,182 – 23,252 – 76,433 – 10,854 – 1,531 – 88,819

Hotels & Lodging – 446,209 – 9 – 139,188 – 48,501 – 187,689 – 42,569 – 20,384 – 250,642

Household Furnishings – 7,557 – 0 – 3,086 – 1,359 – 4,444 – 758 – 788 – 5,990

Household Industry – 23 – 0 – 23 — – 23 — — – 23

Housing – 170,550 – 1 – 6,659 665 – 5,995 – 38,789 – 31,639 – 76,422

Insurance – 13,992 – 0 – 4,527 – 617 – 5,145 – 349 – 1,567 – 7,061

Motor Vehicles – 818,837 – 16 – 349,615 – 58,796 – 408,412 – 61,563 – 51,270 – 521,244

Other Local Purchases – 875,074 – 49 – 121,125 – 217,542 – 338,668 – 104,118 – 10,337 – 453,122

Personal Services – 126,530 – 3 – 35,550 – 27,113 – 62,663 – 11,503 – 1,073 – 75,239

Petroleum Products – 39,630 – 0 – 2,896 – 367 – 3,263 – 1,743 – 4,998 – 10,004

Publications/Paper – 33,430 – 1 – 12,004 – 937 – 12,941 – 10,147 – 158 – 23,245

Recreation Activities – 909,969 – 38 – 86,101 – 283,388 – 369,489 – 97,623 – 8,853 – 475,964

Retail Trade – 439,959 – 15 – 187,309 – 70,444 – 257,753 – 47,012 – 36,477 – 341,243

State/Local Services – 45,476 – 1 – 16,560 — – 16,560 – 7,356 – 18 – 23,933

Transportation Services – 131,076 – 1 – 22,028 – 3,999 – 26,027 – 6,331 – 4,638 – 36,996

U.S. Postal Service – 33,257 – 0 – 22,383 — – 22,383 3,455 — – 18,928

Utilities – 113,148 – 0 – 21,221 – 2,143 – 23,364 – 32,159 – 4,445 – 59,968

Wholesale Trade – 80,052 – 1 – 32,432 – 876 – 33,307 – 5,108 – 5,456 – 43,871

Total Local Impacts – 5,819,726 – 168 – 1,629,983 – 827,008 – 2,456,990 – 575,046 – 244,077 – 3,276,113
62

Table 48. Regional economic impacts of a 30% decrease in lower and central Cook Inlet sportfishing

catches.

Response Coefficient B aselin e

E xp en ditures ($ )

D irect O utpu t

($)

Ind irect O utp ut

($)

Ind uced Ou tp u t

($)

T otal O u tp ut

($)

Industry Output

Auto or Truck Fuel 2,619,715 – 668,091 – 159,824 – 161,787 – 987,801

Groceries 2,864,102 – 725,914 – 93,325 – 197,619 – 1,014,806

Lodging 3,226,870 – 789,860 – 177,363 – 151,111 – 1,053,963

Restaurant & Bar 2,561,923 – 589,468 – 118,270 – 111,031 – 818,770

Boat Fuel, Lubricants & Repairs 1,732,240 – 407,429 – 87,287 – 93,932 – 587,650

Charter & Guide Fees 10,366,927 – 2,601,838 – 821,743 – 585,744 – 4,009,325

Fish Processing or Packaging 2,307,448 – 570,020 – 73,212 – 101,530 – 744,761

Fishing Derby Entry Fees 269,302 – 47,536 – 11,750 – 9,011 – 68,298

Fishing Gear 1,904,030 – 433,335 – 64,408 – 95,779 – 592,254

Haul Out & Moorage Fees 671,617 – 128,567 – 36,917 – 19,089 – 184,536

TOTAL 28,524,174 – 6,962,057 – 1,644,100 – 1,526,632 – 10,062,164

Personal Income

Auto or Truck Fuel 2,619,715 – 339,031 – 46,237 – 64,479 – 449,747

Groceries 2,864,102 – 444,266 – 26,329 – 78,760 – 549,355

Lodging 3,226,870 – 305,163 – 54,987 – 60,235 – 420,385

Restaurant & Bar 2,561,923 – 232,345 – 32,282 – 44,259 – 308,886

Boat Fuel, Lubricants & Repairs 1,732,240 – 198,310 – 25,375 – 37,436 – 261,121

Charter & Guide Fees 10,366,927 – 1,070,693 – 325,340 – 233,488 – 1,629,521

Fish Processing or Packaging 2,307,448 – 215,674 – 26,102 – 40,464 – 282,240

Fishing Derby Entry Fees 269,302 – 18,291 – 3,345 – 3,614 – 25,250

Fishing Gear 1,904,030 – 208,562 – 19,519 – 38,172 – 266,253

Haul Out & Moorage Fees 671,617 – 35,502 – 9,993 – 7,609 – 53,104

TOTAL 28,524,174 – 3,067,837 – 569,509 – 608,517 – 4,245,863

Employment

Auto or Truck Fuel 2,619,715 – 14 – 2 – 3 – 18

Groceries 2,864,102 – 20 – 1 – 3 – 24

Lodging 3,226,870 – 14 – 2 – 3 – 19

Restaurant & Bar 2,561,923 – 15 – 1 – 2 – 19

Boat Fuel, Lubricants & Repairs 1,732,240 – 8 – 1 – 2 – 11

Charter & Guide Fees 10,366,927 – 143 – 13 – 10 – 167

Fish Processing or Packaging 2,307,448 – 13 – 1 – 2 – 16

Fishing Derby Entry Fees 269,302 – 1 – 0 – 0 – 1

Fishing Gear 1,904,030 – 12 – 1 – 2 – 14

Haul Out & Moorage Fees 671,617 – 1 – 0 – 0 – 2

TOTAL 28,524,174 – 242 – 23 – 27 – 292
63

Table 49. Final demand impacts of a 30% decrease in Cook Inlet sportfishing catches.

Final Demand Category O utpu t ($) Job s E mp lo yee

E arning s ($)

P ro prietors

Incom e ($)

P erso nal

Incom e ($)

O th er

Incom e ($)

Ind irect

T ax es ($ )

V alue

A dd ed ($ )

Banking/Credit Services – 129,280 – 1 – 27,251 – 1,166 – 28,417 – 20,310 – 16,502 – 65,229

Business/Labor Assoc – 94,216 – 2 – 40,987 — – 40,987 — – 1,071 – 42,058

Civic/Religious Assoc – 12,925 – 0 – 7,248 — – 7,248 — – 1 – 7,248

Communications – 118,545 – 1 – 25,049 – 2,893 – 27,942 – 19,383 – 4,049 – 51,373

Eating & Drinking Places – 736,034 – 19 – 233,092 – 57,023 – 290,115 – 35,398 – 19,770 – 345,284

Education – 12,879 – 0 – 5,199 – 789 – 5,988 — — – 5,988

Fabrics/Apparel – 16,376 – 1 – 6,588 – 889 – 7,478 – 2,618 – 1,946 – 12,042

Food Processing – 1,324,922 – 30 – 542,294 – 89,727 – 632,021 – 95,734 – 60,760 – 788,515

Health Care – 209,530 – 4 – 91,905 – 40,181 – 132,086 – 18,757 – 2,646 – 153,490

Hotels & Lodging – 771,884 – 15 – 240,777 – 83,901 – 324,678 – 73,638 – 35,262 – 433,577

Household Furnishings – 13,073 – 0 – 5,338 – 2,350 – 7,688 – 1,311 – 1,364 – 10,362

Household Industry – 39 – 0 – 39 — – 39 — — – 39

Housing – 293,928 – 2 – 11,477 1,146 – 10,331 – 66,849 – 54,526 – 131,706

Insurance – 24,235 – 0 – 7,842 – 1,069 – 8,911 – 604 – 2,715 – 12,230

Motor Vehicles – 1,400,837 – 28 – 597,943 – 100,633 – 698,577 – 105,407 – 87,679 – 891,662

Other Local Purchases – 1,518,683 – 85 – 209,766 – 378,262 – 588,028 – 180,564 – 17,936 – 786,528

Personal Services – 218,158 – 5 – 61,283 – 46,753 – 108,036 – 19,840 – 1,848 – 129,724

Petroleum Products – 68,490 – 0 – 5,004 – 634 – 5,639 – 3,012 – 8,638 – 17,289

Publications/Paper – 57,609 – 1 – 20,666 – 1,613 – 22,280 – 17,464 – 271 – 40,015

Recreation Activities – 1,581,775 – 65 – 149,463 – 492,977 – 642,440 – 169,812 – 15,399 – 827,651

Retail Trade – 762,361 – 26 – 324,651 – 121,934 – 446,585 – 81,457 – 63,215 – 591,256

State/Local Services – 78,513 – 1 – 28,599 — – 28,599 – 12,693 – 30 – 41,322

Transportation Services – 226,345 – 2 – 38,035 – 6,903 – 44,937 – 10,926 – 8,011 – 63,875

U.S. Postal Service – 57,649 – 1 – 38,801 — – 38,801 5,990 — – 32,811

Utilities – 195,183 – 1 – 36,609 – 3,696 – 40,305 – 55,455 – 7,667 – 103,426

Wholesale Trade – 138,695 – 1 – 56,190 – 1,517 – 57,707 – 8,850 – 9,453 – 76,010

Total Local Impacts – 10,062,164 – 292 – 2,812,096 – 1,433,767 – 4,245,863 – 994,091 – 420,758 – 5,660,712
64

Table 50. Regional economic impacts of a 10% increase in Cook Inlet sportfishing catches.

Response Coefficient B aselin e

E xp en ditures ($ )

D irect O utpu t

($)

Ind irect O utp ut

($)

Ind uced Ou tp u t

($)

T otal O u tp ut

($)

Industry Output

Auto or Truck Fuel 2,619,715 122,820 29,382 29,743 181,595

Groceries 2,864,102 132,104 16,984 35,963 184,677

Lodging 3,226,870 138,790 31,165 26,552 185,196

Restaurant & Bar 2,561,923 106,702 21,409 20,098 148,209

Boat Fuel, Lubricants & Repairs 1,732,240 76,953 16,486 17,742 110,993

Charter & Guide Fees 10,366,927 449,274 141,895 101,144 692,312

Fish Processing or Packaging 2,307,448 94,825 12,179 16,890 123,894

Fishing Derby Entry Fees 269,302 8,295 2,051 1,572 11,918

Fishing Gear 1,904,030 72,718 10,808 16,073 99,387

Haul Out & Moorage Fees 671,617 23,343 6,703 3,466 33,505

TOTAL 28,524,174 1,225,825 289,061 269,242 1,771,687

Personal Income

Auto or Truck Fuel 2,619,715 62,327 8,500 11,854 82,681

Groceries 2,864,102 80,849 4,791 14,333 99,973

Lodging 3,226,870 53,621 9,662 10,584 73,868

Restaurant & Bar 2,561,923 42,058 5,843 8,012 55,913

Boat Fuel, Lubricants & Repairs 1,732,240 37,456 4,793 7,071 49,319

Charter & Guide Fees 10,366,927 184,882 56,178 40,318 281,378

Fish Processing or Packaging 2,307,448 35,878 4,342 6,731 46,952

Fishing Derby Entry Fees 269,302 3,192 584 631 4,406

Fishing Gear 1,904,030 34,999 3,276 6,406 44,680

Haul Out & Moorage Fees 671,617 6,446 1,814 1,382 9,642

TOTAL 28,524,174 541,708 99,784 107,320 748,812

Employment

Auto or Truck Fuel 2,619,715 2 0 1 3

Groceries 2,864,102 4 0 1 4

Lodging 3,226,870 3 0 0 3

Restaurant & Bar 2,561,923 3 0 0 3

Boat Fuel, Lubricants & Repairs 1,732,240 2 0 0 2

Charter & Guide Fees 10,366,927 25 2 2 29

Fish Processing or Packaging 2,307,448 2 0 0 3

Fishing Derby Entry Fees 269,302 0 0 0 0

Fishing Gear 1,904,030 2 0 0 2

Haul Out & Moorage Fees 671,617 0 0 0 0

TOTAL 28,524,174 42 4 5 51
65

Table 51. Final demand impacts of a 10% increase in lower and central Cook Inlet sportfishing catches.

Final Demand Category O utpu t ($) Job s E mp lo yee

E arning s ($)

P ro prietors

Incom e ($)

P erso nal

Incom e ($)

O th er

Incom e ($)

Ind irect

T ax es ($ )

V alue

A dd ed ($ )

Banking/Credit Services 22,727 0 4,793 205 4,998 3,566 2,901 11,465

Business/Labor Assoc 16,492 0 7,181 — 7,181 — 189 7,369

Civic/Religious Assoc 2,282 0 1,279 — 1,279 — 0 1,280

Communications 20,962 0 4,426 512 4,938 3,412 713 9,063

Eating & Drinking Places 132,578 3 41,986 10,271 52,257 6,376 3,561 62,194

Education 2,273 0 918 139 1,057 — — 1,057

Fabrics/Apparel 2,862 0 1,151 155 1,306 457 340 2,102

Food Processing 229,856 5 95,205 15,703 110,908 16,925 10,853 138,685

Health Care 36,950 1 16,207 7,086 23,293 3,308 467 27,068

Hotels & Lodging 135,688 3 42,326 14,749 57,074 12,945 6,199 76,218

Household Furnishings 2,298 0 938 413 1,351 230 240 1,822

Household Industry 7 0 7 — 7 — — 7

Housing 52,279 0 2,041 (204) 1,838 11,890 9,698 23,426

Insurance 4,243 0 1,373 187 1,560 106 475 2,141

Motor Vehicles 254,909 5 108,900 18,286 127,186 19,132 15,972 162,290

Other Local Purchases 264,244 15 36,744 65,419 102,163 31,490 3,123 136,775

Personal Services 38,750 1 10,891 8,301 19,192 3,520 329 23,042

Petroleum Products 12,076 0 882 112 994 531 1,523 3,048

Publications/Paper 10,249 0 3,687 288 3,975 3,119 48 7,143

Recreation Activities 273,825 11 25,986 85,136 111,122 29,332 2,660 143,114

Retail Trade 133,300 4 56,720 21,393 78,114 14,246 11,049 103,410

State/Local Services 13,887 0 5,054 — 5,054 2,249 5 7,308

Transportation Services 40,010 0 6,726 1,221 7,947 1,935 1,415 11,297

U.S. Postal Service 10,068 0 6,776 — 6,776 (1,046) — 5,730

Utilities 34,614 0 6,492 656 7,147 9,846 1,360 18,353

Wholesale Trade 24,262 0 9,829 265 10,095 1,548 1,654 13,296

Total Local Impacts 1,771,687 51 498,518 250,294 748,812 175,116 74,774 998,702
66

Table 52. Regional economic impacts of a 20% increase in lower and central Cook Inlet sportfishing

catches.

Response Coefficient B aselin e

E xp en ditures ($ )

D irect O utpu t

($)

Ind irect O utp ut

($)

Ind uced Ou tp u t

($)

T otal O u tp ut

($)

Industry Output

Auto or Truck Fuel 2,619,715 208,716 49,930 50,543 308,596

Groceries 2,864,102 224,131 28,815 61,016 313,328

Lodging 3,226,870 234,132 52,574 44,793 312,418

Restaurant & Bar 2,561,923 180,879 36,291 34,070 251,240

Boat Fuel, Lubricants & Repairs 1,732,240 131,323 28,134 30,276 189,411

Charter & Guide Fees 10,366,927 755,685 238,670 170,125 1,164,480

Fish Processing or Packaging 2,307,448 158,468 20,353 28,226 207,047

Fishing Derby Entry Fees 269,302 13,978 3,455 2,650 20,082

Fishing Gear 1,904,030 121,711 18,090 26,901 166,347

Haul Out & Moorage Fees 671,617 39,590 11,368 5,878 56,824

TOTAL 28,524,174 2,068,612 487,681 454,478 2,989,775

Personal Income

Auto or Truck Fuel 2,619,715 105,916 14,445 20,144 140,504

Groceries 2,864,102 137,170 8,129 24,318 169,617

Lodging 3,226,870 90,457 16,299 17,855 124,611

Restaurant & Bar 2,561,923 71,295 9,906 13,581 94,782

Boat Fuel, Lubricants & Repairs 1,732,240 63,919 8,179 12,066 84,164

Charter & Guide Fees 10,366,927 310,975 94,493 67,815 473,283

Fish Processing or Packaging 2,307,448 59,958 7,257 11,249 78,464

Fishing Derby Entry Fees 269,302 5,378 984 1,063 7,425

Fishing Gear 1,904,030 58,579 5,482 10,721 74,783

Haul Out & Moorage Fees 671,617 10,932 3,077 2,343 16,352

TOTAL 28,524,174 914,580 168,250 181,155 1,263,986

Employment

Auto or Truck Fuel 2,619,715 4 1 1 6

Groceries 2,864,102 6 0 1 7

Lodging 3,226,870 4 1 1 6

Restaurant & Bar 2,561,923 5 0 1 6

Boat Fuel, Lubricants & Repairs 1,732,240 3 0 1 4

Charter & Guide Fees 10,366,927 42 4 3 48

Fish Processing or Packaging 2,307,448 4 0 0 4

Fishing Derby Entry Fees 269,302 0 0 0 0

Fishing Gear 1,904,030 3 0 0 4

Haul Out & Moorage Fees 671,617 0 0 0 1

TOTAL 28,524,174 71 7 8 86
67

Table 53. Final demand impacts of a 20% increase in lower and central Cook Inlet sportfishing catches.

Final Demand Category O utpu t ($) Job s E mp lo yee

E arning s ($)

P ro prietors

Incom e ($)

P erso nal

Incom e ($)

O th er

Incom e ($)

Ind irect

T ax es ($ )

V alue

A dd ed ($ )

Banking/Credit Services 38,342 0 8,086 346 8,432 6,015 4,894 19,341

Business/Labor Assoc 27,803 1 12,108 — 12,108 — 318 12,426

Civic/Religious Assoc 3,852 0 2,160 — 2,160 — 0 2,160

Communications 35,399 0 7,473 864 8,337 5,758 1,203 15,299

Eating & Drinking Places 224,564 6 71,116 17,398 88,514 10,800 6,032 105,346

Education 3,837 0 1,550 235 1,785 — — 1,785

Fabrics/Apparel 4,824 0 1,939 261 2,200 770 572 3,542

Food Processing 386,928 9 160,583 26,472 187,055 28,580 18,358 233,993

Health Care 62,371 1 27,357 11,961 39,318 5,584 788 45,689

Hotels & Lodging 228,915 5 71,406 24,882 96,289 21,839 10,457 128,585

Household Furnishings 3,877 0 1,583 697 2,280 389 404 3,073

Household Industry 12 0 12 — 12 — — 12

Housing 88,369 1 3,450 (344) 3,106 20,098 16,393 39,597

Insurance 7,154 0 2,315 316 2,630 178 801 3,610

Motor Vehicles 432,480 9 184,786 31,017 215,803 32,446 27,104 275,352

Other Local Purchases 445,035 25 61,954 110,065 172,018 53,055 5,260 230,333

Personal Services 65,486 2 18,407 14,027 32,435 5,948 557 38,940

Petroleum Products 20,383 0 1,489 189 1,678 896 2,571 5,145

Publications/Paper 17,324 0 6,236 487 6,723 5,276 82 12,081

Recreation Activities 460,775 19 43,759 143,204 186,963 49,339 4,474 240,776

Retail Trade 224,686 8 95,594 36,081 131,675 24,014 18,623 174,312

State/Local Services 23,452 0 8,533 — 8,533 3,799 9 12,341

Transportation Services 67,561 1 11,358 2,063 13,421 3,268 2,389 19,078

U.S. Postal Service 16,967 0 11,420 — 11,420 (1,763) — 9,657

Utilities 58,482 0 10,967 1,108 12,075 16,638 2,299 31,012

Wholesale Trade 40,898 0 16,569 447 17,016 2,610 2,787 22,413

Total Local Impacts 2,989,775 86 842,210 421,776 1,263,986 295,537 126,376 1,685,900

In considering these impacts, the analyst must take into account the assumptions and caveats discussed in

Section 5 underlying the use of input–output analysis. These include, but are not limited to, the potential

for upwardly biased estimates for induced effects and employment impacts, because of the assumed linear

relationship between production and labor inputs. This assumption is less likely to be true for labor than it

is for other factors of production, and it assumes that resources are available to satisfy increased demand

without input price increases or input substitution. Kenai Peninsula employment patterns are subject

to a substantial seasonal effect typical throughout coastal Alaska, with a large seasonal influx of migrant

labor. The induced multiplier effect for certain expenditures will tend to be smaller the greater the

proportion of imported labor, since itinerant workers consume local goods and services only while

inhabiting the region. The analyst needs to take this unaccounted for leakage into consideration when

commenting on the economic impact of alternatives.

Summary of results

The simulation model results can be summarized graphically to illustrate the relationship between

changes in lower and central Cook Inlet sportfishing catches of halibut and salmon and regional economic

impacts. Figure 32 illustrates the relationship between changes in sportfishing catches and total output.
68

Figure 32. Industry output impacts from changes in expected catches of salmon

and halibut in the lower and central Cook Inlet sport fisheries.

Figure 33 provides a corresponding representation of the relationship between changes in catch and

changes in personal income.

–2

–1

–4

Millions of Dollars

–3

0

–5

1

% Change in Expected Catch

–30% –20% –10% 0% 10% 20%

2

Figure 33. Personal income impacts from changes in expected catch of salmon

and halibut in the lower and central Cook Inlet sport fisheries.

The expected changes to employment for given changes to expected sportfishing catch of salmon and

halibut are represented in Figure 34.

–4

–2

4

–8

–6

Millions of Dollars

0

–10

–12

% Change in Expected Catch

–30% –20% –10% 0% 10% 20%

2
69

–150

–100

–250

–200

Jobs

0

–300

–350

% Change in Expected Catch

–30% –20% –10% 0% 10% 20% –50

50

100

150

Figure 34. Employment impacts from changes in expected catch of salmon

and halibut in the lower and central Cook Inlet sport fisheries.

It is evident in each of these figures that the impact is nonlinear and that the marginal impact declines

as catch increases. That is, there is a larger decline in expenditures and jobs when moving from a 20%

decrease to a 30% decrease in expected catch than when moving from a 20% increase to a 10% increase.

This result is consistent with the principle of declining marginal utility where utility, and therefore

participation, increases at a decreasing rate as the average individual sport fisher becomes more

successful.

7. Conclusions

The Cook Inlet Planning Area includes and abuts productive commercial, subsistence, and sport fishing

grounds. OCS exploration, development and production activities could affect the productivity of these

fisheries, the quality of recreation opportunities, and the demand for tourism-related services. Sportfishing

provides non-monetary benefits to participants and monetary benefits to tourism-related businesses. This

study and a companion study, funded in part by Alaska Sea Grant, develop estimates of the net economic

benefits that accrue to participants in the lower and central Cook Inlet salmon and halibut sport fisheries,

the relationship between catch, size of catch, and the number of sportfishing-days, and the regional (Kenai

Peninsula area) economic impact of changes in the annual total number of person-days fished.

Results from a survey of Kenai Peninsula area marine recreational fishers [Lee et al. 1999b] were used to

develop a predictive model of participation rate changes and, in conjunction with a regional input–output

model, to measure the net benefits (compensating variations) to sport fishers and the regional economic

impact of marine sportfishing on the Kenai Peninsula economy. The baseline trip, patterned on the 1997

mean trip, is reported along with five sample levels of changes in expected harvests that may result

from natural stock dynamic processes, changes in allocation between commercial, subsistence, and

sport fishers, changes in catch limits, or environmental damage resulting from minerals exploration,

development, production, or transportation activities. A computer simulation model, the Cook Inlet

Region Marine Sportfishing Economic Assessment, and an accompanying user manual have been

developed as part of this project as a tool for MMS and other resource managers (see Appendix B

[software manual] or Hamel et al. [2001]).
70

Participation Rate Model

(Random Effects Probit

Parameter Model)

1997 Survey of Cook Inlet

Saltwater Sports Fishermen

Consumer Surplus

(Compensating Variations)

Alaska Sea Grant Funding

Economic Sportfishing Analysis

(Final Reports and Software)

Regional Impact Analysis

(IMPLAN and Production Functions)

Minerals Management Service/

Coastal Marine Institute

Funding

Figure 35. Extramural funding sources and their relationship to components of the

lower and central Cook Inlet sportfishing analyses.

Figure 35 provides a schematic representation of the relationship between extramural funding sources

used to support this study, major components of the analysis, and this report. The Alaska Sea Grant

project funded a 1998 survey of 4,000 (2,640 completed) individuals who purchased Alaska fishing

licenses in 1997 [Lee et al. 1999b]. Data generated by that survey were used to estimate a model of the

relationship between the probability that a typical lower or central Cook Inlet sport fisher will take a trip

given trip cost, and catch (species, size, number). The resulting random utility theory model, estimated

using a binary probit estimation technique, allowed for declining marginal utility as well as the

interactions between salmon and halibut catches. The participation rate model is used specifically to

predict changes in participation given changes in expected catch that may result from changes in biomass

(abundance) or changes in catch limits.

The estimated change in the probability of the mean sport fisher taking a trip is transformed into a

prediction of changes in total sportfishing effort measured in fishing days. These changes in fishing effort

simulate the response of recreators to changes in expected catches and are used to predict changes to the

net benefits of sportfishing as well as regional economic impacts.
71

Net benefits to recreational fishers are measured by compensating variations. That is, the value of a

sportfishing trip is measured as the amount of money that could be added to the price of the trip until

the sport fisher would be indifferent to taking the trip. Consequently, the compensating variation is a

measure of the consumer surplus occasioned by sportfishing. Reductions in expected catch reduce the

compensating variation in two ways. First, the marginal sport fisher will drop out of the fishery as the

expected benefits (in terms of catch) decrease, thereby decreasing the total net benefits of the fishery.

Second, the net benefit of taking a trip is also reduced for all the sport fishers who continue to participate

because the trip produces less net benefit when the catch rate declines.

Unlike the net benefits, which are a measure of economic efficiency, impact analysis is a measure of

distribution. Changes in compensating variations only affect regional economic activity when they lead

to changes in the total number of sportfishing-days. Moreover, the net impact is limited to those recreators

who do not substitute other types of expenditures on the Kenai Peninsula in lieu of expenditures that

they would have made if they had gone fishing. However, even within the local economy, changes in

spending patterns redistribute wealth. These two measures, net benefits (efficiency) and impact analysis

(distribution), make up the economic analysis of sportfishing that was undertaken in this study.

Figures 36 and 37 provide a graphical representation of our analytic approach and the implicit

assumptions behind the model.

Supply (S)

Willingness to Pay

(WTP)

Total Expenditures

(TE)

Consumer

Surplus

(CS)

Q Days Fished

P

Fishing

Price

Figure 36. Willingness to pay, consumer surplus, and total expenditures.
72

Days Fished

P

Fishing

Price

Q1 Q0

CSB

CSA

TEB TEA

S

WTP0

WTP1

Figure 37. Effect of a decrease in willingness to pay on consumer surplus and total

expenditures.

Figure 36 shows a demand curve for fishing trips before the change. The vertical axis measures the

price of the fishing trip and quantity is in days fished. The total expenditures are measured as PQ or the

rectangle labeled Total Expenditures. The consumer surplus is the area below the willingness to pay

curve (WTP) and above the price of the trip (P). Figure 37 shows an example of an inward shift in a trip

possibly caused by the reduction in expected catch. Here, for any given price, the number of trips taken is

reduced.

When the demand curve for fishing trips shifts inward (from WTP0 to WTP1), the total expenditures are

reduced from TE = TEA + TEB to TEB. As to their local impact this will depend on what is being measured.

There will be redistribution away from industries most benefiting from the sport fishing, especially as

fishing participation, which was bringing in new money into the region, decreases. The total impact, as

far as expenditures go, to the Kenai Peninsula will depend on the change in participation for changes in

environmental conditions that affect expected catches as well as the amount of new money that was

coming into the region due to the marine sportfishing effort.

A simplistic view of consumer surplus is that it will decrease from CS = CSA + CSB to CSB. This is

caused by both a reduction in total trips (Q0 to Q1) and a reduction in the surplus of those remaining

in the fishery. In our measurements of consumer surplus, we net out the income effect, and use the

compensating variation measurement of consumer surplus as shown in Figure 38.
73

Y

Days Fished

E2

E0

E1

I1

I0

b1 b2 b0

Figure 38. Compensating variation.

For example, in Figure 38 the price of a fishing day has been decreased vis-à-vis the price of all other

goods (Y). The budget line rotates back from b0 to b1. The consumer is made worse off as her utility

decreases from I0 to I1. The expenditure needed to bring the consumer back to the original indifference

curve (I0) is E2 – E1. This is the compensating variation and is measured as the amount of compensation

required to leave the sport fisher indifferent as to the original bundle of goods at the old price and the new

bundle of goods at the new price. (It should be noted that Figures 36 and 37 depict perfectly elastic supply

curves. The implication of this assumption is discussed at greater length below.)

We have presented the results of five simulated changes to the fishery conditions from the mean 1997

fishing trip. The expected catch was increased by 10 and 20% of the baseline fishery averages and

decreased by 10, 20, and 30% of the baseline averages. The resulting changes in compensating variations

and total expenditure impacts to the Kenai are reported in Section 6 and summarized in Table 54, where

they are aggregated across residency category and sportfishing mode.
74

Table 54. Changes in compensating variations (CV) and regional economic impacts in response to

changes in catch.

 % Change in

Catch

% Change in

Participation

Change in Mean

Total CV ($)

Change in Output

(Sales) ($)

Change in Personal

Income ($)

Change in

Employment (Jobs)

– 30% – 31.30% – 12,456,593 – 10,062,164 – 4,245,863 – 292

– 20% – 21.16% – 8,655,683 – 5,819,726 – 2,456,990 – 168

– 10% – 8.01% – 4,380,445 – 2,483,646 – 1,049,021 – 72

0% 0 0 0 0 0

+10% 5.85% 4,172,245 1,771,687 748,812 51

+20% 9.97% 7,914,571 2,989,775 1,263,986 86

The results indicate, for example, that for a 10% decrease in expected salmon and halibut landings, net

benefits to sport fishers will decrease by $4.4 million. The regional impacts, for the selected measures,

indicate that there will be a $2.5 million decrease of direct, indirect, and induced output expenditures in

the Kenai Peninsula region, which will result in a decrease of $1.0 million in personal expenditures and

a loss of 72 jobs. In the case of all impacts, the higher the catch rates the smaller the marginal change

in impact. This is due to the fact that there is a declining marginal value of additional fish catches and,

therefore, the change in participation rates diminishes as the catch rates increase and vice versa.

Limitations of the results and the need for future research

In any large-scale economic study, there is a trade-off between economic realism and cost in terms of

money and time. However, no study can completely capture all of the economic values as the studies are

limited by their explicit and implicit economic assumptions and data limitations. In this study, where

there was very little precedence for applied analysis (although plenty of theoretical work), much of the

applied work was new territory. Looking back over the project, some things worked out very well and

others could have been improved. This section is written to help the reader to understand the limitations

of this study and to help future studies improve on our approach and analysis.

In the survey, we asked about the respondents’ expenditure patterns on the fishing trip. However, if we

had to do the survey over again, we would have asked a question(s) to ascertain if the travelers would

have made the trip (or the same length trip) to the Kenai had they not been fishing. In the Appendix to

Section 3 we use a question that asked the sport fisher’s primary purpose of the trip along with a set of

assumptions (Tables 58 and 59) to estimate the amount of expenditures that were solely due to the fishing

aspect of the trip, that is, Kenai Peninsula area expenditures that would have been made if the fishing

portion of the trip were cancelled. However, had we asked this question directly, we would not have

needed to rely on assumptions to model respondents’ behavior.

In the participation model, when estimating the changes in the probability that individual fishers would

take a trip, given varying trip attributes, it is assumed that the price of the trip will remain constant at P

(see Figure 36). In other words, we assume that supply is perfectly inelastic. While this assumption is

appropriate for shore and private trips, it is probably incorrect for charter trips. To the extent that charter

trips make up a sizeable portion of sportfishing effort, and to the extent that charter trips do not exhibit

perfectly elastic supply curves, there may be price adjustment especially in the short-run. For example,

charter operators might respond to a short-run change in expected catches by lowering their prices and

keeping their customer base rather than holding prices constant and losing customers as assumed in our

model. While our assumption is valid in the long run, it may be unrealistic in the short run. If there is an

upward sloping supply curve for charters then there would still be a loss inefficiency for the charter
75

industry when there is an environmental change; however, it would come from producer surplus not from

consumer surplus. Additionally, if price were lowered to maintain the current level of participation, there

would be little regional impact outside of fish processing. Therefore, for the charter industry, our results

more closely reflect long-run results than short-run results especially with respect to income distribution.

For shore and private vessels this is not a factor.

Finally, a complete examination of economic efficiency would estimate producer surplus at all levels.

This was outside of the scope of this study and therefore losses to consumers in terms of net benefits may

underestimate total losses. Future work may want to include an estimation of the producer surpluses.

8. Acknowledgements

This report is the result of research supported in part by Minerals Management Service through the

University of Alaska Coastal Marine Institute project 12-35-0001-30661 task order 14196. The University

of Alaska Fairbanks School of Management provided matching funds. Although not used as formal

“match”, considerable additional support for this project has been provided in the form of release time

by: the Alaska Fisheries Science Center, National Marine Fisheries Service; the North Pacific Fishery

Management Council; and Utah State University.

We are also grateful for information provided by the Kenai, Seward, and Homer Chambers of Commerce,

Becky Hultberg and Craig Layman (Kenai Peninsula Borough Economic Development District), Sheri

Hobbs (City of Homer), John Williams (former Kenai Peninsula Borough mayor), Tim Evers (Deep

Creek Charter Association), Frank Libal (Anchor Point Charter Association), Robert Ward (Homer

Charter Association), Karl Kircher (Kenai Peninsula Fishermen’s Association), Theo Matthews (United

Cook Inlet Drifters Associations), Al Howe (Alaska Department of Fish and Game), Nancy and John

Hillstrand (Coal Point Trading Co.), Kurt Eriksson (National Bank of Alaska, Soldotna), Doug

Coughenower (Alaska Sea Grant Marine Advisory Program), and Emmett Trimble, Simone Klutts,

Vicki Stik, and Tom Boedeker.

9. Study Products

Publications

Criddle, K.R., J.A. Greenberg, H. Geier, C. Hamel, M. Herrmann, S.T. Lee and C.E. Lewis. 1998. An

economic assessment of the marine sport fisheries in lower Cook Inlet, p. 5–12. In University of Alaska

Coastal Marine Institute Annual Report No. 4. OCS Study MMS 98–0062, University of Alaska

Fairbanks.

Hamel, C. 2001. Economic impact of marine recreational fisheries on the Kenai Peninsula Borough. Master’s

thesis, University of Alaska Fairbanks. In preparation.

Hamel, C., M. Herrmann, S.T. Lee and K.R. Criddle. 2001. $FISH: An economic assessment of Lower Cook

Inlet sport fisheries. Software program and manual.

Herrmann, M., S.T. Lee, C. Hamel, K.R. Criddle, H.T. Geier, J.A. Greenberg and C.E. Lewis. 2001. An

economic assessment of the sport fisheries for halibut, and chinook and coho salmon in lower and

central Cook Inlet. Final Report. OCS Study MMS 2000-062, University of Alaska Coastal Marine

Institute, University of Alaska Fairbanks, 135 p.
76

Herrmann, M., S.T. Lee, C. Hamel, K.R. Criddle, J.A. Greenberg, H. Geier and C. Lewis. 2000. An economic

assessment of the marine sport fisheries in lower Cook Inlet, p. 9–40. In University of Alaska Coastal

Marine Institute Annual Report No. 6. OCS Study MMS 2000-046, University of Alaska Fairbanks.

NPFMC. 1999. Public review draft environmental assessment, regulatory impact review, initial regulatory

flexibility analysis for a regulatory amendment to implement management measures under a guideline

harvest level and/or moratorium for halibut in areas 2C And 3A: Chapter 4.0 – Economic tools and

analytical framework. NPFMC, Anchorage.

NPFMC. 2000. Draft environmental assessment, regulatory impact review, initial regulatory flexibility

analysis for a regulatory amendment to implement management measures under a guideline harvest level

and/or moratorium for halibut in areas 2C And 3A: Chapter 4.0 – Economic tools and analytical

framework. NPFMC, Anchorage.

Working papers

Hamel, C., M. Herrmann, T.S. Lee, K.R. Criddle and H. Geier. Linking sportfishing trip attributes,

participation decisions, and regional economic impacts in Lower and Central Cook Inlet, Alaska.

Working paper, University of Alaska Fairbanks. Submitted to The Annuals of Regional Science.

Herrmann, M., K.R. Criddle and C. Hamel. An economic evaluation of the demand for marine sport fisheries

in the Lower Cook Inlet. Working Paper. Being prepared for Marine Resource Economics.

Herrmann, M., T.S. Lee, K.R. Criddle and C. Hamel. Results of a survey of participants in the Lower and

Central Cook Inlet halibut and salmon sport fishery. Working paper, University of Alaska Fairbanks.

Alaska Fisheries Research Bulletin (under review).

Lee, T.S. M. Herrmann, K. Criddle and Charles Hamel. The effect of fishery attributes on participation rates

and economic welfare. Working paper, National Marine Fisheries Service. Being prepared for Journal of

Agriculture and Resource Economics.

Presentations

Hamel, C., M. Herrmann, S.T. Lee, K.R. Criddle and Hans Geier. 2000. Linking sportfishing trip attributes,

participation decisions, and regional economic impacts in Lower and Central Cook Inlet, Alaska.

Presentation at the 2000 Alaska Chapter American Fisheries Society Meeting, Fairbanks, 13–16

November 2000.

Hamel, C., M. Herrmann, T.S. Lee, C. Hamel and K.R. Criddle. 2000. An economic discussion of the marine

sport fisheries in Lower Cook Inlet. Presented at the tenth meeting of the International Institute of

Fisheries Economics and Trade, Corvallis, Oregon, 10–24 July 2000.

Herrmann, M., K.R. Criddle, S.T. Lee and Charles Hamel. 2000. An economic evaluation of the demand for

marine sport fisheries in the Lower Cook Inlet. Presentation at the 2000 Alaska Chapter American

Fisheries Society Meeting, Fairbanks, 13–16 November 2000.

Herrmann, M., S.T. Lee, K.R. Criddle and C. Hamel. 2000. Results of a survey of participants in the Lower

and Central Cook Inlet halibut and salmon sport fishery. Presentation at the 2000 Alaska Chapter

American Fisheries Society Meeting, Fairbanks, 13–16 November 2000.

Herrmann, M., S.T. Lee, C. Hamel, K.R. Criddle, H. Geier, J. Greenberg and C. Lewis. 1998. An economic

assessment of the sport fisheries for halibut, and chinook and coho salmon in lower Cook Inlet.

University of Alaska Coastal Marine Institute Annual Research Review, February 1998, Fairbanks.

Herrmann, M., S.T. Lee, C. Hamel, K.R. Criddle, H. Geier, J. Greenberg and C. Lewis. 1999. An economic

assessment of the marine sport fisheries, p. 41 (abstract). In Proceedings Seventh MMS Information

Transfer Meeting. Alaska OCS Region, Minerals Management Service, January 1999, Anchorage.

(http://www.mms.gov/Alaska/ess/itm/itm99/itm.htm)
77

Herrmann, M., S.T. Lee, C. Hamel, K.R. Criddle, H. Geier, J. Greenberg and C. Lewis. 1999. An economic

assessment of the sport fisheries for halibut, and chinook and coho salmon in lower Cook Inlet. CMI

University of Alaska Coastal Marine Institute Annual Research Review, February 1999, Fairbanks.

Herrmann, M., S.T. Lee, C. Hamel, K.R. Criddle, H. Geier, J. Greenberg and C. Lewis. 2000. An economic

assessment of the sport fisheries for halibut, and chinook and coho salmon in lower Cook Inlet.

University of Alaska Coastal Marine Institute Annual Research Review, March 2000, Fairbanks.

Lee, S.T., M. Herrmann, K.R. Criddle and C. Hamel. 2000. The effect of fishery attributes on participation

rates: the Kenai Peninsula marine sport fishery. Western Agricultural Economics Association,

Vancouver, Canada.

Lee, S.T. 1999. Evaluating the benefits of recreational fisheries. Fisheries Centre, University of British

Columbia, Vancouver, Canada.

Lee, S.T. 2000. Evaluating the benefits of recreational fisheries. National Marine Fisheries Service

Socioeconomic Workshop, La Jolla, CA.

Lee, S.T., M. Herrmann, K.R. Criddle and C. Hamel. 2000. The effect of fishery attributes on participation

rates: The Kenai Peninsula marine sport fishery. Western Agricultural Economics Association,

Vancouver, Canada.

Lee T.S., M. Herrmann, K.R. Criddle and C. Hamel. 2000. Modeling the economic effect of fishery attributes

on participation rates: The Kenai Peninsula marine sport fishery. Presentation at the 2000 Alaska

Chapter American Fisheries Society Meeting, Fairbanks, 13–16 November 2000.

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82
83

APPENDIX A

Appendix to Section 3. Total Expenditures

Adjustments made to total expenditure estimates for multipurpose trips

Total expenditures assuming that some trip expenditures cannot be attributed to fishing

Appendix to Section 4. Estimated Trip Attributes by Sportfishing Mode,

Residency, Target Species, and Region

Appendix to Section 5. Input–Output

Input–output theory

The transactions table

Multipliers

Ratio multipliers and response coefficients

Assumptions and limitations

Specific changes to the baseline model

The Kenai–Nikiski model

The Homer model

The Anchor Point and Ninilchik model

Soldotna and balance of northern Kenai Peninsula model

IMPLAN baseline data

Appendix to Section 6. Impact Analysis
84
85

Appendix to Section 3. Total Expenditures

Adjustments made to total expenditure estimates for multipurpose trips

Section 3 reports results based on calculations adjusted to account for the fact that there are many reasons that a

visitor may visit the Kenai Peninsula. Because some trips are multipurpose, changes in fishing opportunity will

not result in one-for-one changes in the number of visitor days to the Kenai Peninsula. This appendix presents the

assumptions and calculations used to adjust the estimated response of visitor days to changes in the number of days

s pent f ish in g in Co o k In let. Th e p rim ar y reaso ns th at a K en ai s altwater f is h in g trip was tak en ar e r ep or ted in Tab le 55 .

Table 55. Primary purpose of visit to the Kenai Peninsula for lower and central Cook Inlet

halibut and salmon sport fishers (by trip) [Lee et al. 1999a].

n = 408 Alaskans Nonresidents

Fishing for halibut or salmon in Cook Inlet 87.9% 43.0%

Visit/Vacation Alaska 2.9% 24.4%

Kenai area freshwater fishing 1.7% 12.0%

Visit relatives 5.2% 11.2%

Business 1.2% 3.7%

Saltwater/Freshwater fishing 0.0% 2.5%

Visit friends 1.2% 0.4%

Cruise Ship 1.2%

Hunting 1.7%

However, there is a difference between the total amount of trips identified by trip purpose and the total amount of

trip fishing days identified by trip purpose. Table 56 summarizes the percent of total trip fishing days attributable to

the primary purpose of the trip.

Table 56. Primary purpose of visit to the Kenai Peninsula for lower and central Cook Inlet

halibut and salmon sport fishers (by fishing days) [Lee et al. 1999a].

n = 408 Alaskans (less locals) Nonresidents

Fishing for halibut or salmon in Cook Inlet 89.5% 51.0%

Visit/Vacation Alaska 2.6% 23.2%

Kenai area freshwater fishing 0.9% 8.8%

Visit relatives 4.9% 10.1%

Business 1.4% 2.9%

Saltwater/Freshwater fishing 0.0% 2.2%

Visit friends 0.7% 0.5%

Cruise Ship 0.7%

Hunting 0.7%

It is assumed that if the fishing trip is cancelled that all of the Kenai fishing days would be cancelled. More pertinent

to the calculations of total trip days spent on the Kenai are the days spent on the Kenai by trip purpose. These values

are reported in Table 57.
86

Table 57. Primary purpose of visit to the Kenai Peninsula for lower and central Cook Inlet

halibut and salmon sport fishers (by fishing days) [Lee et al. 1999a].

n = 408 Alaskans (less locals) Nonresidents

Fishing for halibut or salmon in Cook Inlet 80.3% 34.1%

Visit/Vacation Alaska 5.9% 25.6%

Kenai area freshwater fishing 1.2% 14.5%

Visit relatives 7.2% 19.7%

Business 1.9% 2.6%

Saltwater/Freshwater fishing 0.0% 1.9%

Visit friends 3.4% 0.6%

Cruise Ship 0.0% 0.4%

Hunting 0.0% 0.5%

Table 58 summarizes the assumptions made in the main report (Table 22) for adjustments to the percent of the

days spent on the Kenai, and in other parts of Alaska, due to changes in Cook Inlet fishing trip days. For example,

nonresidents whose main trip purpose was to visit relatives made up 11.2% of the total trips but 19.7% of the total

days spent on the Kenai. This is presumably due to longer visits to the Kenai for nonresidents visiting relatives than

for those making a trip just to fish.

Table 58. Assumed effects of the cancellation of the saltwater fishing portion of

the Kenai Peninsula trip.

Main Purpose of Trip Alaskans (non-local) and other U.S.

Fishing for halibut or salmon in Cook Inlet Cancel entire trip

Visit/Vacation Alaska Cancel Kenai trip replace these days

with days in other parts of Alaska

Kenai area freshwater fishing Still take full trip

Visit relatives Reduce days spent in Kenai and Alaska

by amount of days lost saltwater fishing

Business Still take full trip

Saltwater/Freshwater fishing Reduce days spent in Kenai and Alaska

by amount of days lost saltwater fishing

Visit friends Still take full trip

Cruise Ship No observations

Hunting No observations

The assumptions in Table 58 result in the following percentages of the trip due to the fishing component as reported

in Table 59.

Table 59. Assumed net effect of the cancellation of the saltwater fishing portion of the Kenai

Peninsula saltwater fishing trip on all days applied to the Appendix to Section 3.

Main Purpose of Trip Kenai Portion Alaska Portion (net Kenai)

Fishing for halibut or salmon in Cook Inlet – 100% – 100%

Visit/Vacation Alaska – 100% + Kenai Portion

Kenai area freshwater fishing 0% 0%

Visit relatives – Fishing Days – Fishing Days

Business 0% 0%

Saltwater/Freshwater fishing – Fishing Days – Fishing Days

Visit friends 0% 0%

Cruise Ship 0% NA

Hunting 0% NA
87

Table 60 is a reproduction of Table 22.

Table 60. Estimated reduction in visitation rates for a 100% reduction in fishing effort (days)

[Lee et al. 1999a].

Locals Alaskans Nonresidents

Fishing Reduction 100% 100% 100%

Kenai Living Expense Reduction 100% 89.5% 64.0%

Alaska Living Reduction (net Kenai) 100% 57.3% 23.8%

For illustration we use the nonresident Kenai living reduction values. The 64.0% number is interpreted as 64.0%

of the total days spent on the Kenai by nonresidents having been directly related to the Kenai saltwater fishing

component of the trip (i.e., if the trip had not been taken these days would not have been spent on the Kenai).

Therefore, when a fishing trip is reduced 1 day, 0.64 days spent by nonresidents on the Kenai Peninsula14 will be

lost.

Sample Calculation: reduction in nonresident expenditures on Kenai region living expenses is equal to

34.1% + 25.6% + 10.1% (557/1587) + 2.2% (557/1587) = 64.0 %, where 557 is the number of nonresident days

reported in the survey spent fishing, 1,587 is the number of days reported living on the Kenai, and 557 is the

number of nonresident days reported living on the Kenai.

Total expenditures assuming that some trip expenditures cannot be attributed to fishing

Table 61. Total expenditures by local residents fishing for halibut and salmon from the lower and central Cook

Inlet shore that are attributed directly to the saltwater halibut and salmon fishing trip [Lee et al. 1999a].

Days Expenditures ($)

n = 34 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.000 6.5% 12,861 12,861

Days spent on Kenai1 1.290 16,591 16,591

Days spent in Alaska2 0.000

Auto 7.82 129,739 129,739

RV

Lodge 3.15 52,261 52,261

Groceries 8.00 132,726 132,726

Restaurant & Bar 10.74 178,184 178,184

Charter

Gear 2.14 27,523 27,523

Processing

Derby

Boat Fuel

Haul

Total 27,523 492,909 520,432

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

 

14 It is possible that the saltwater trips on the Cook Inlet side could be replaced by saltwater trips out of Seward thus

redistributing the Kenai Peninsula expenditures from west to east.
88

Table 62. Total expenditures by local residents fishing for halibut and salmon from private boats in lower and

central Cook Inlet that are attributed directly to the saltwater halibut and salmon fishing trip [Lee et al.

1999a].

Days Expenditures ($)

n = 34 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.000 14.4% 28,498 28,498

Days spent on Kenai1 1.000 28,498 28,498

Days spent in Alaska2 0.000

Auto 7.82 222,854 222,854

RV

Lodge 3.15 89,769 89,769

Groceries 8.00 227,984 227,984

Restaurant & Bar 10.74 306,069 306,069

Charter

Gear 7.12 202,906 202,906

Processing 0.92 26,218 26,218

Derby 0.36 10,259 10,259

Boat Fuel 15.89 452,833 452,833

Haul 8.36 238,243 238,243

Total 930,460 846,676 1,777,135

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 63. Total expenditures by local residents fishing for halibut and salmon from charter boats in lower and

central Cook Inlet that are attributed directly to the saltwater halibut and salmon fishing trip [Lee et al.

1999a].

Days Expenditures ($)

n = 34 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.000 3.8% 7,518 7,518

Days spent on Kenai1 1.000 7,518 7,518

Days spent in Alaska2 0.000

Auto 7.82 58,791 58,791

RV

Lodge 3.15 23,682 23,682

Groceries 8.00 60,144 60,144

Restaurant & Bar 10.74 80,743 80,743

Charter 112.86 848,481 848,481

Gear 2.00 15,036 15,036

Processing 10.50 78,939 78,939

Derby 11.70 87,961 87,961

Boat Fuel

Haul

Total 1,030,417 223,360 1,253,777

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
89

Table 64. Total expenditures by Alaskans (non-local) fishing for halibut and salmon from the lower and central

Cook Inlet shore that are attributed directly to the saltwater halibut and salmon fishing trip [Lee et al.

1999a].

Days Expenditures ($)

n = 7 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 2.4% 4,767 4,767

Days spent on Kenai1 1.03 4,910 4,394

Days spent in Alaska2 0.06 286 164

Auto 14.57 64,027 2,388 66,415

RV

Lodge 3.86 16,963 633 17,595

Groceries 12.43 54,623 2,037 56,660

Restaurant & Bar 3.43 15,073 562 15,635

Charter

Gear 4.50 16,089 5,363 21,452

Processing

Derby

Boat Fuel

Haul

Total 16,089 150,686 5,363 5,620 177,757

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 65. Total expenditures by Alaskans (non-local) fishing for halibut and salmon from private boats in lower

and central Cook Inlet that are attributed directly to the saltwater halibut and salmon fishing trip

[Lee et al. 1999a].

Days Expenditures ($)

n = 73 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 18.8% 37,044 37,044

Days spent on Kenai1 1.45 53,714 48,074

Days spent in Alaska2 0.00

Auto 12.99 624,479 624,479

RV 0.39 18,749 18,749

Lodge 6.20 298,058 298,058

Groceries 14.44 694,186 694,186

Restaurant & Bar 9.58 460,547 460,547

Charter

Gear 5.53 153,640 51,213 204,853

Processing 2.33 86,313 86,313

Derby 0.18 6,668 6,668

Boat Fuel 31.53 875,998 291,999 1,167,997

Haul 5.48 203,001 203,001

Total 1,325,620 2,077,271 343,213 18,749 3,764,852

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
90

Table 66. Total expenditures by Alaskans (non-local) fishing for halibut and salmon from charter boats in lower

and central Cook Inlet that are attributed directly to the saltwater halibut and salmon fishing trip

[Lee et al. 1999a].

Days Expenditures ($)

n = 85 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 10.1% 19,898 19,898

Days spent on Kenai1 1.73 34,424 30,809

Days spent in Alaska2 0.52 10,347 5,929

Auto 15.81 487,091 93,734 580,826

RV 3.97 145,849 145,849

Lodge 21.19 652,844 125,631 778,476

Groceries 13.76 423,933 81,580 505,513

Restaurant & Bar 13.95 429,787 82,707 512,493

Charter 116.40 2,316,127 2,316,127

Gear 3.58 53,426 17,809 71,235

Processing 7.14 142,072 142,072

Derby 2.13 42,383 42,383

Boat Fuel

Haul

Total 2,554,008 1,993,655 17,809 529,503 5,094,974

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 67. Total expenditures by nonresidents fishing for halibut and salmon from the lower and central Cook

Inlet shore that are attributed directly to the saltwater halibut and salmon fishing trip [Lee et al. 1999a].

Days Expenditures ($)

n = 8 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 5.2% 10,202 10,202

Days spent on Kenai1 2.00 20,404 13,059

Days spent in Alaska2 1.15 11,732 2,792

Auto 9.34 121,967 26,080 148,047

RV 28.91 458,248 458,248

Lodge 14.83 193,658 41,410 235,068

Groceries 7.47 97,547 20,858 118,406

Restaurant & Bar 10.20 133,197 28,481 161,679

Charter

Gear 20.00 204,040 204,040

Processing 9.62 98,143 98,143

Derby 0.95 9,692 9,692

Boat Fuel

Haul

Total 311,875 546,370 575,077 1,433,323

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
91

Table 68. Total expenditures by nonresidents fishing for halibut and salmon from private boats in lower and

central Cook Inlet that are attributed directly to the saltwater halibut and salmon fishing trip [Lee et al.

1999a].

Days Expenditures ($)

n = 28 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 13.0% 25,597 25,597

Days spent on Kenai1 2.92 74,743 47,836

Days spent in Alaska2 1.02 26,109 6,214

Auto 7.81 373,597 48,531 422,127

RV 2.92 157,825 157,825

Lodge 7.83 374,553 48,655 423,208

Groceries 10.72 512,798 66,613 579,412

Restaurant & Bar 6.65 318,107 41,323 359,430

Charter

Gear 17.12 438,221 438,221

Processing 7.87 201,448 201,448

Derby 1.65 42,235 42,235

Boat Fuel 15.76 403,409 403,409

Haul 9.00 230,373 230,373

Total 1,315,686 1,579,056 362,947 3,257,688

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.

Table 69. Total expenditures by nonresidents fishing for halibut and salmon from charter boats in lower and

central Cook Inlet that are attributed directly to the saltwater halibut and salmon fishing trip [Lee et al.

1999a].

Days Expenditures ($)

n = 173 Ratio % of

Total

Total

Days

Adjusted

Days $/Day Fishing

(Kenai)

Other

(Kenai)

Fishing

(Alaska)

Other

(Alaska) Total

Days Fished 1.00 25.9% 51,171 51,171

Days spent on Kenai1 2.03 103,877 66,481

Days spent in Alaska2 2.86 146,349 34,831

Auto 8.08 537,169 281,435 818,605

RV 18.92 1,916,831 1,916,831

Lodge 22.94 1,525,082 799,025 2,324,107

Groceries 9.93 660,160 345,873 1,006,033

Restaurant & Bar 9.63 640,216 335,423 975,639

Charter 140.75 7,202,318 7,202,318

Gear 15.50 793,151 793,151

Processing 32.72 1,674,315 1,674,315

Derby 1.37 70,104 70,104

Boat Fuel

Haul

Total 9,739,888 3,362,627 3,678,587 16,781,103

1

 Includes days fished.

2

 Excludes days spent on the Kenai Peninsula.
92

Appendix to Section 4. Estimated Trip Attributes by Sportfishing Mode,

Residency, Target Species, and Region

Table 70. Kenai Peninsula trip attributes [Lee et al. 1999a].

Including Seward-based Trips Excluding Seward-based Trips

Charter and Private Charter only Charter and Private Charter only

All Halibut All Halibut All Halibut All Halibut

Alaskans n = 328 n = 154 n = 141 n = 76 n = 223 n = 121 n = 100 n = 56

Halibut Retained 0.66 1.20 1.13 1.40 0.72 1.22 1.22 1.38

Released 0.75 1.42 1.36 1.78 0.98 1.72 1.79 2.22

Total Caught 1.40 2.62 2.49 3.18 1.71 2.94 3.01 3.61

Average Weight (lbs.) 33.57 33.13 33.63 33.52 34.18 33.38 33.78 33.54

Chinooks Retained 0.07 0.11 0.08 0.13

Released 0.08 0.07 0.11 0.11

Total Caught 0.15 0.18 0.19 0.24

Average Weight (lbs.) 29.00 27.50 28.34 26.00

Coho Retained 0.18 0.24 0.05 0.11

Released 0.05 0.05 0.01 0.01

Total Caught 0.23 0.29 0.06 0.12

Average Weight (lbs.) 9.84 9.54 10.60 10.00

Nonresidents n = 381 n = 164 n = 263 n = 133 n = 271 n = 124 n = 192 n = 98

Halibut Retained 0.92 1.28 1.11 1.37 1.04 1.40 1.19 1.45

Released 1.15 1.62 1.44 1.72 1.40 1.92 1.66 2.01

Total Caught 2.07 2.90 2.55 3.09 2.43 3.33 2.85 3.45

Average Weight (lbs.) 42.07 41.82 43.08 43.98 42.66 41.41 43.02 43.41

Chinooks Retained 0.09 0.10 0.11 0.12

Released 0.06 0.08 0.04 0.04

Total Caught 0.16 0.19 0.14 0.15

Average Weight (lbs.) 29.42 29.93 30.87 31.41

Coho Retained 0.21 0.21 0.13 0.12

Released 0.17 0.14 0.18 0.18

Total Caught 0.38 0.34 0.31 0.29

Average Weight (lbs.) 9.22 9.68 9.60 10.31
93

Appendix to Section 5. Input–Output

Input–output theory15

The transactions table

An examination of the structure and foundation of input–output begins with the transactions table, a matrix of

identified industrial sectors for the economy under consideration (e.g., Figure 39 [duplicates Figure 26 in body of

report]). The transactions table depicts the amount of goods and services required from each producing sector to

fulfill final demand as well as the value of the inputs each sector purchases from all others toward the production of

a unit of output for a specified period of time. In their production of goods or services, firms must purchase inputs

from other producing sectors, and these purchases make up intermediate demand. Final demand includes household

consumption, private investment requirements, public (government) demand, and net exports. The production by

firms and the supply of labor by households fulfill the “requirements” of both final and intermediate demand, and

the exchanges reveal the reliance of one industry upon the rest. Mapping these inter-industry relationships forms the

basis for the transactions table and provides static description of the economy. Subject to some arguably limiting

assumptions, hypothetical changes in demand can be fed into the model to predict impacts.

To Purchasing Sectors Local Final Demand

From 1 . . . j . . . n H ou se ho lds 

Priva te 

I nv es tm e nt G ov er nm e nt Exp or ts 

Total G r os s

O utpu ts 

. . . . . . 

. . . . . . 

Producing Sectors

1

i

n

X11

•

•

•

Xi1

•

•

•

Xn1 . . . 

X1j

•

•

•

Xij

•

•

•

Xnj . . . 

X1n

•

•

•

Xin

•

•

•

Xnn

C1

•

•

•

Ci

•

•

•

Cn

I1

•

•

•

Ii

•

•

•

In

G1

•

•

•

Gi

•

•

•

Gn

E1

•

•

•

Ei

•

•

•

En

X1

•

•

•

Xi

•

•

•

Xn

Labor L1 . . . Lj . . . Ln LC LI LG LE L

Other Value

Added V1 . . . Vj . . . Vn VC VI VG VE V

Imports M1 . . . Mj . . . Mn MC MI MG M

Total Gross

Outlay X1 . . . Xj . . . Xn C I GEX

Figure 39. Sample input–output transactions table [Richardson 1972].

The components of Figure 39 can be expressed as a system of linear equations:

(13)

X x x x x CIGE

X x x x x CIGE

X x x x x CIGE

Xx x

j n

j n

i i i ij in i i i i

nn n

1 11 12 1 1 1 1 1 1

2 21 22 2 2 2 2 2 2

1 2

1 2

= + ++ ++ + ++ +

= + ++ ++ + ++ +

•

•

= + + + + + ++ +

•

•

= +

, ,

, ,

K K

K K

K K

++ ++ + ++ + K K x x CIGE nj nn n n n n

 

15 This section draws on Archer [1977], Miller and Blair [1985], and Richardson [1972].
94

which can be compactly written as:

(14) X x CIGE i ij

i

n

= + ++ + ii i i

=

∑ ( )

1

for all i, where Xi is the gross output of sector i, xi j is the i-th industrial demand by the j-th industry, Ci is

consumption, or household demand for the i-th output, Ii is private investment in the i-th output, Gi is government

purchases of the i-th output, and Ej is the level of net exports of the i-th output.

Reading across the first row of Figure 39, purchasing sectors S1 through Sn each require some portion of producing

sector S1’s output, x1,1 through x1n respectively, as inputs for their own production process. The model assumes

that final demand provides the impetus for initial changes in production, and that changes in intermediate (or

intersectoral) demand follow from the additional inputs required to fulfill S1’s initial output changes. The sum of

these final and intermediate demand requirements, X1, is the total output from sector S1.

Because the entire economy’s industrial sectors can be considered in either their producing or purchasing roles, the

input requirements for S1 from all other industrial sectors can be found in the first column of Figure 39. Inputs which

cannot be satisfied locally within the region are treated as imports, and these, along with the other inputs described

above, sum to sector S1’s total gross outlay, X1.

The transactions table not only depicts the trade flows for a selected economy, but also provides the means for

identifying regional trade imbalances, and ultimately allows for the calculation of expressions of potential impact

arising from changes in demand. Consider the ratio formed by dividing any of the xi j entries by its corresponding

total gross outlays, Xj. The resulting input coefficient is the proportion that xi j comprises of all of that particular

sector’s purchases. Taking the sum of all column elements of the local demand components (Ci, Ii, or Gi) except for

imports, and dividing by the total outlay for each category can form a similar measure for local demand, the regional

purchase coefficient. This ratio can be interpreted as the proportion of local demand purchased locally [Olson and

Lindall 1997].

Given the deterministic nature of the relationships represented by the transactions table, a change in demand for

one sector will have a predictable, industry-wide effect on changes in output. Drawing from a numerical example

will further clarify this point and serve to introduce the following section on multipliers. Table 71 represents a

transactions table for a two-sector economy, industries S1 and S 2. Of the value-added components of final demand,

only household consumption is included to facilitate the illustration.

Table 71. Simplified transactions table for a hypothetical regional economy [adapted from Archer 1977].

Purchasing Sectors

Industry 1 Industry 2 Local Household

Expenditure

Exports Gross Output

Producing Industry 1 100 320 200 380 1,000

Sectors Industry 2 300 640 300 360 1,600

Value Added 200 160 100 540 1,000

Imports 400 480 400 1,280

Gross Outlay 1,000 1,600 1,000 1,280 4,880

In order to produce $1,000 worth of output, Industry 1 purchases $100, $300, $200, and $400 worth of goods and

services from itself, sector S 2, the labor market, and imports respectively, for a total outlay of $1,000. Note that

the balance of final outlays and output implies zero accounting profits in this example. However, in a complete

input–output model such as IMPLAN, accounting profits form a sectoral component within the value-added

category, and the equivalence between outlays and output is maintained [Olson and Lindall 1997]. Forming the
95

ratio for each of the four inter-industry input requirements to their corresponding gross outlays produces a 2 × 2

matrix of input coefficients, A:

(15) A . .

. . = =











 



 





100

1000 320

1600

300

1000

340

1600

01 02

03 04

The Direct Requirements (A) matrix describes a “production recipe” for each industry [Hastings and Brucker 1993].

In other words, the ai j entries specify the proportion of each input, relative to all other inputs, needed for a

purchasing sector to produce a unit of output. Hence, each column of A describes a linear production function for

one of the two purchasing sectors in this example.

To further simplify the exercise, we can reduce household expenditures and exports into the general heading of final

demand by rewriting Equation 13 so that total output X and final demand Y are 2 × 1 column vectors and A is the

2 × 2 Direct Requirements matrix. That is,

(16) X = A X + Y

For the example, Equation 16 becomes

(17)

x

x

. .

. .

x

x

. .

. .

x

x

1

2

01 02

03 04

200 380

300 360

01 02

03 04

580

660

1

2

1

2





 



 



 









 



 



 



 



 









 



 



 

 = += + 

Solving for final demand, Y, and simplifying through use of the identity matrix (I) results in

IX – A X = Y

(18)..

or

1 0

0 1

01 02

03 06

580

660

1

2

1

2





 









 



 



 









 



 



 

 − =  x

x

. .

. .

x

x

which can be rewritten as

(I – A) X = Y

(19)..

or

09 02

03 06

580

660

1

2

. .

. .

x

x

−

− = 



 









 



 



 





Solving for X by inverting (I – A) produces the multiplier form of the original system:

X = (I – A)

–1Y

(20) ..

x

x

. .

. .

. .

. .

1

2

1 09 02

03 06

580

660

1 25 0 42

0 62 1 87

580

660

1000

1600





 



 



 



 



 



 



 









 



 



 

 =  −

− = =

−
96

where (I – A)

–1, the Leontieff inverse or Total Requirements matrix, can be interpreted as the proportion of any

change in final demand by which output would have to respond to fulfill such a change. In other words, given

the relationships gleaned from the transactions table, an exogenous monetary infusion (diffusion) modeled as an

increase (decrease) in final demand will have the following successive effect on production:

(21) ∆ X = (I – A)

–1 ∆ Y

The multipliers contained in the Total Requirements matrix gauge the potential productivity associated with

potential demand changes. To clarify this, solving for the Total Requirements matrix and substituting into the form

of Equation 21 gives

(22)

∆

∆

∆

∆

X

X

. .

. .

Y

Y

1

2

1

2

1 25 0 42

0 62 1 87





 



 



 









 

 = 

Suppose that household expenditures for product 1 increased by 15 units while non-local demand for product 2

diminishes so that 5 units less were exported. The Total Requirements matrix offers a simultaneous solution to the

above system and we see that output of X1 would increase by 16.65 units while production of X 2 would drop by

0.05 units:

(23)

∆

∆

X

X

. .

. .

.

.

1

2

1 25 0 42

0 62 1 87

15

5

16 65

0 05





 



 



 









 



 



 

 =  − = −

The impact of other combinations of changes in the demand for one or both goods can be explored, subject to

limitations and assumptions discussed later.

Multipliers

Output Multiplier—The above example outlined a procedure for deriving the basic output multiplier, one of several

categories of indices that serve to gauge a regional economy’s potential response to exogenous changes. Because

the ai j entries of the Leontieff inverse matrix, (I – A)

–1, encompass the degree of inter-industry linkage among all

sectors as well as the initial intra-sectoral response to demand changes, their values can be thought of as comprising

two separate effects. The first, or direct effect, regards the unitary response with which a sector’s output rises or

falls to fulfill a new demand requirement. In other words, as demand for Xi increases by one unit, sector Si responds

by producing one more unit of output. That is, the direct effect is the “production changes associated with the

immediate effects of final demand changes” [Olson and Lindall 1997]. However, in producing that one additional

unit, sector Si must increase its purchase of inputs, thus prompting a rise in intermediate demand for the goods

and services of other industries through its incurred outlays. As these other industries respond by mounting their

respective production levels, they propagate another round of intermediate demand associated with their incremental

purchases of factors. To the extent that some of these factors happen to be units of Xi, a new series of endogenously

determined demand for sector Si’s output is touched off, etc. These incremental additions to the original and unitary

multiplier constitute the indirect effect. That is, the indirect effects are “the changes in inter-industry purchases as

they respond to the new demands of the directly affected industries” [Olson and Lindall 1997].

Returning to the example, again consider the aij elements of the Total Requirements matrix from Equation 21

(24)

∆

∆

∆

∆

X

X

. .

. .

Y

Y

1

2

1

2

1 25 0 42

0 62 1 87





 



 



 









 

 = 
97

While the example was expressed in physical units of goods or services in the previous section, it will now be

discussed in monetary values, as this is typically the fashion in which model results are reported. Let demand for

good X1 increase by one dollar while holding the demand for X 2 constant so that ∆Y1 = 1 and ∆Y2 = 0. Equation 22

becomes:

(25)

∆

∆

X

X

. .

. .

.

.

1

2

1 25 0 42

0 62 1 87

1

0

1 25

0 62





 



 



 









 



 



 

 = = 

Remembering that the first entry (a1,1 = 1.25) signifies that a unit change in the demand for good X1 (or ∆Y1 = 1)

will affect $1.25 worth of good X1 from sector S1, and also keeping in mind that the direct effect is unitary, we can

subtract 1 from 1.25 to back out the indirect effect. In this example 0.25 dollars worth of X1 are produced to satisfy

the various rounds of indirect demands associated with the initial change in final demand for X1. Even though final

demand for good X 2 remained constant, the inter-industry dependence between sectors S1 and S 2 necessitates the

additional production of good X 2 to fulfill sector S1’s input requirements. The value of good X 2’s production

necessary to fulfill the requirements of the $1 demand change for good X1 is 0.62, the a 2,1 element. This amount is

made up entirely of indirect (inter- and intra-sectoral) effects as no exogenous change in demand for X 2 occurred, so

that summing it with the 0.25 dollars worth of indirect effect from X1 gives us a total indirect effect of 0.87. At this

point it should be apparent that the total (direct plus indirect) output multiplier effect for sector S1 can be obtained

simply by summing the a n1 entries of the (I – A)

–1 matrix. Similarly, if we wanted to determine the dollar value of

the total effects attributable to a dollar’s worth of increased final demand for good X 2, then we would sum the a n2

elements. Generally then, the output multiplier for good Xj can be stated as:

(26) Oj ij

i

n

=

=

∑α

1

where n is the number of industrial sectors with intermediate demand requirements.

While the simple output multiplier captures inter-industry (indirect) interactions, it reveals nothing about the

relationship between production changes and household income and consumption expenditures. In fact, as long

as labor is a factor for the production processes modeled above, then additional payments to households would,

in theory, stimulate increased spending. Similar to the indirect effects discussed above, increased consumption

translates into increased demand for all other goods and services. The addition this component makes to the

output multiplier is called an induced effect. By augmenting the A matrix in Equation 15 to include the Value

Added/Household Expenditure row and column of Table 71, consumer spending can be treated endogenously as

another industrial sector for deriving output multipliers. Input coefficients for households are arrived at in the same

way as they were for sectors S1 and S 2, by dividing each input requirement by its respective gross outlay, thereby

extending the production functions by the household requirements:

(27) A* =

















01 02 02

03 04 03

02 01 01

...

...

...

Inversion of A* yields the following Total Requirements matrix:

(28) I A* ( ) − =

















−1

1 40 0 55 0 50

0 91 2 12 0 91

0 41 0 36 1 32

...

...

...
98

By simulating a change in demand of one dollar’s worth of good X1 as we did earlier with the closed model, we can

derive an output multiplier which includes the direct, indirect, and induced effects. ∆Y * will now be a 3 × 1 vector,

with ∆Y1 = 1 and all other elements equal to zero. As before, pre-multiplying the demand vector by the Leontieff

inverse gives the value of the total output resulting from a one-dollar change in final demand for X1, which in this

case is 2.72. Alternatively, summing the elements of the first column of the (I – A*)

–1 matrix yields the same results,

revealing a general form for deriving the total effects multiplier parallel to that of Equation 26 for the direct and

indirect effects:

(29) Oj ij i

n * * = ∑

=

+

α

1

1

The total output multiplier represented above contains all three effects of industrial production. Simply

subtracting from the total the sums of the direct and indirect effects can isolate the induced values of

output produced in response to inter- and intra-industrial payments to households. That is, the induced

effect is O *1 – O 1. For the above example, O 1 = 1.87 (from the closed model) and O *1 = 2.72, then we can

see that 0.85 dollars worth of output effected across both sectors by a one dollar increase in demand for X1

is attributable to the household receipts and expenditures.

The two other principal categories of multipliers predict income and employment responses to exogenous change.

There are many variants of the income and employment multipliers that will be presented below. Moreover, these

multipliers can be stated in ratio form to emphasize their relative magnitude. The purpose and usefulness of these

ratio multipliers has been debated contentiously in the literature, and will be briefly described towards the end of this

section.

Income Multiplier—Output multipliers describe the “structural interdependence between each sector and the rest of

the economy”; however, policymakers and constituents who look to economic impact analyses for policy direction

are usually more concerned with the effects on income and/or employment [Richardson 1972]. Income multipliers

“translate” output responses triggered by demand changes into measures of new income created [Miller and Blair

1985]. Based on the labor input coefficients derived from augmenting the A matrix with respect to households

(i.e. treating households endogenously within the Direct Requirements matrix), we can estimate the amount of value

from additional output realized as income. The initial effect of a change in demand upon household income can also

be derived from each industry’s production function (i.e. columns of the A* matrix). Consider that each dollar’s

worth of exogenous spending for good Xi is met with a dollar’s worth of direct production from sector S1, and recall

that the input coefficients within the production functions describe the proportion of each dollar’s worth of outlays

disbursed to factors.

Reading down the first column of the A* matrix, for each dollar’s worth of X1 produced, sector S1 makes 0.20

dollar’s worth of payments to household income. Hence, 0.2 can be interpreted as the direct multiplier effect for

income associated with demand changes for good X1, or more generally speaking, a*n+1j is the direct income effect

for any Xj sector.

(30) A* =

















01 02 02

03 04 03

02 01 01

...

...

...

Of course, identifying a direct income effect by itself does not suffice the rigors of even a simple impact analysis,

for the exhaustive derivation of the output multipliers above informed us that inter-industry demands spawn indirect

income effects as well. Naturally, any indirect effects from backward linked industries will involve payments to

labor. To obtain an estimate for the combined direct and indirect income effects, we need to transform respective

output values into values paid out as income. Since both output effects fall under the values of each entry in the

(I – A)

–1 matrix, summing over the products of each household coefficient, a*n+1j, multiplied by its respective output

multiplier effect, aij, produces the direct and indirect income multipliers.
99

For example, to compute the direct and indirect multiplier effect on income of a dollar’s worth of additional demand

for good X1, we make a column vector out of the column elements of the (I – A)

–1 matrix corresponding to sector S1,

and pre-multiply by a row vector of the first two n + 1 labor input coefficients from A* to obtain:

(31) Ha a . . .

. . n, n,

* * 1 11 12

1 1

2 1

02 01

1 25

0 62 = == 0 312 ( ) + +





 



 ( ) 



 





α

α

,

,

where H1 is the direct and indirect household income effect resulting from a change in demand for good X1. In more

general terms then, the household income multiplier can be expressed as:

(32) H a j nj i

n

ij * * = ∑ + = ( ) 1 ( ) 1

α

The justifications for having derived induced effects for output multipliers in the previous section are founded on

the theoretical notion that increased payments made to suppliers of labor continue to circulate through the economy

as households make additional expenditures. Analogously, induced effects for income multipliers can be derived

from an augmentation of the Leontieff inverse, (I – A*)

–1, in which household consumption and labor supply

are endogenously treated. Now instead of taking the products of the an+1i entries with the corresponding column

multipliers for sector Sj from the open (I – A)

–1 matrix, we multiply the former by the same sector’s multipliers in

the closed model. The total income multiplier inclusive of induced effects is denoted as:

(33) H a j ni i

n

ij * * * = ∑ + = ( )( ) 1

1

α

There are variations of the income multiplier that have been designed to adjust for overstatements resulting from the

structural form above. Essentially, the assumption that consumer spending rises both linearly and homogeneously

with increased income inflates estimates of the induced effect. The manner in which more sophisticated multipliers

compensate for the overstatement will be discussed later.

Employment Multipliers—Policymakers are often interested in considering the distributional effects of income

increases as they pertain to population changes and employment patterns in general. An analogous measure to the

income multiplier can be described in terms of the physical employment necessary to fuel the labor requirements

prompted by exogenous demand changes. This presumption is acceptable so long as a consistent relationship is

assumed between the “value of output of a sector and [physical] employment in that sector” [Miller and Blair 1985].

If we know the number of persons employed in each industry, then input coefficients expressed as units of

employment can be determined and then substituted for the labor input coefficients in the A* matrix. Caution is

warranted here as it is for each of the previous multipliers. The interpretation of this labor/output relationship is that

it describes the number of jobs necessary to fulfill the labor component of the production mix for a dollar’s worth

of output. For our continuing example, assume that sector S1 employs six workers while S 2 utilizes four and

households employ two (as domestic services). Using the notation ei to denote number of workers per i-th sector,

then e1 = 6, e2 = 4, and e3 = 2. To convert these values into employment coefficients, we divide each by its

corresponding outlays, denoting the operation as w*n+1i = ei /Xi . Substituting the resulting w*n+1i values for the a*n+1i

entries, our A* matrix becomes:

(34) Aw =

















01 02 02

03 04 03

0 006 0 0025 0 001

...

...

.. .
100

Multiplying the first two w*n+1i entries by their corresponding ai j elements of the closed (I – A)

–1 matrix gives us the

direct and indirect multiplicative effects for employment (Ej ):

(35) Ej ni i

n

= ∑ w + ij = ( ) 1 ( ) 1

* α

As with the income multipliers, the direct and indirect components can be isolated from the total by recalling that

the direct effects are revealed in the row vector culled from the (n + 1) row of the A* matrix. To see this we can

simulate a unitary demand change for good X 2 and compute the employment multiplier:

(36) Eww . . .

. . * * 2 31 32

1 2

2 2

0 006 0 003

0 4

1 87 == = ( ) 0 008 



 



 ( ) 



 

 , ,  ,

,

α

α

This means that every dollar’s worth of output of good X 2 creates 0.008 jobs, or by extension, that a job will be

created somewhere in the economy as a consequence of each $125 worth of output from sector S 2. Subtracting the

direct effect from the combined direct and indirect effects of E 2 gives 0.008 – 0.0024 = 0.0056, the indirect

component.

In order to capture any induced effects from household spending, we would repeat the exercise with the closed

(I – A*)

–1, and multiply all three of the converted employment coefficients by the a*ij entries instead of the α i j

elements. Doing so generates multipliers inclusive of household spending for output and income. The operation is

represented as:

(37) Ej ni i

n

ij a * * * = ∑ + = ( )( ) 1

1

α

Each of the categories of multipliers provides predictive measures of impact for any combination of final

demand changes for goods and services. By manipulating the components of intermediate (intersectoral) demand,

assumptions can be imposed on the role of household earnings and expenditures throughout the regional economy.

As we have seen repeatedly, the effects of increased household incomes associated with direct and indirect

production responses can be omitted from the calculation of multipliers or included as an induced effect by simply

augmenting the Leontieff inverse with the household’s technical coefficients. While the open model understates the

total impacts due to the omission, the augmented model probably overstates induced effects because of the assumed

homogeneity in consumption. Although demarcating the range of possible impacts attributed to household spending

by comparing results from both procedures is one possible solution to this limitation, it is an unimaginative way of

bypassing the inherent theoretical problems underlying the model’s structure. Refinements in the methodology have

been developed to solve for overstatement of the induced effects that stem from the unrealistic construction of

homogeneous consumption functions, thus imparting a more representative effect more in line with consumer choice

theories. These adjustment techniques are described below.

Compensated Induced Effects—It was pointed out in the discussion on assumptions and caveats that the very

traits which render input–output so appealing also frustrate its ability to adhere to some of the more fundamental

principles of economics. Restricting production and consumption functions to linear forms is imposing enough

without the added assumption of homogeneity. A function is homogeneous (of degree one) when it can be shown

that a doubling of inputs results in a doubling of outputs. This is demonstrated with the following production

example where quantity q, is a function of inputs Xn:
101

(38) q fX X X = n (, , ) 1 2 K

and we multiply all inputs by a constant λ, so that

(39) f X X X fX X Xn ( , ,, ) (, ,, ) λλ λ 1 2 3 12 K K =

then for any linear specification, k will be equal to 1. In other words, outputs increase in the same proportion as

inputs, bearing on the constant returns to scale characteristic introduced earlier [Nicholson 1995]. Though limiting in

its treatment of real world production scenarios where diminishing returns and input substitution are likely to exist,

these assumptions may be relaxed for short-term predictive purposes. On the other hand, it is difficult to envision a

case where a doubling of income leads to a doubling of consumption for all goods and services, yet this is exactly

what induced multiplier effects convey, leading to inevitable overestimation.

Moore and Petersen [1955] developed an approach to approximate induced effects by estimating linear communitywide consumption functions for each sector, of the form Ci=a+bYi. In this case, Yi is the direct and indirect income

associated with final demand for Xi. Therefore, ∆Ci will equal ∆Yi. Instead, Miernyk et al. [1967] estimate aggregate

linear consumption functions for several income classes, and then combine these to simulate a non-linear function

through a stepwise functional form. The slope of this function declines over larger values of income and thus

behaves in a more theoretically appealing manner than does the linear, homogeneous approach. However, both

approaches are costly in terms of data requirements. Not only are income levels and consumption patterns necessary

for the “cost center” sources (i.e. sport fishers in this study), they are also needed for all consumers in the study

region. Data collection efforts for I–O models are typically exhausted by the mere solicitation of consumer

expenditures and production features for the sectors of primary interest when secondary sources of data are

unavailable.

Ratio multipliers and response coefficients

A great deal of confusion has emerged around the interpretation and misinterpretation of the multipliers developed

in the previous section, and much of the I–O literature has been devoted to treatment of these issues. The multipliers

discussed above were derived for predicting the effects of exogenous changes in demand for output, income, and

employment. This is the form of the multiplier used for estimating impacts, and depending on the source cited,

can be referred to as the “normal” multiplier [Archer 1977], “Keynesian” multiplier [Archer 1984], or “response

coefficient” [Bushnell and Hyle 1985; Rose et al. 1981; Jensen 1997]. The “ratio multiplier” is a very different but

widely applied concept. While the normal multiplier describes the total effects of a dollar change in demand, the

ratio multiplier is an expression that relates the degree of internal linkage for the sector in question by establishing

the proportion by which total effects exceed direct effects. The ratio multiplier, also referred to as the “conventional”

or “traditional” multiplier [Bushnell and Hyle 1985], is formulated simply by dividing the total effects of the normal

multiplier by the direct effect. For a Type I ratio multiplier, the total effects are comprised of the sum of the direct

and indirect effects, and for Type II, the induced component is also included.

A simple example will help to illustrate how the ratio multiplier is calculated and to shed light on its significance

as a policy tool. Recall the income multiplier developed earlier and that the value for the direct and indirect effects

was 0.312 dollars (Equation 31). To obtain the Type I ratio multiplier, we divide the sum of the direct and indirect

effects by the direct effect: 0.312 4 0.20 = 1.56. Confusion among inexperienced practitioners of input–output has

probably arisen from the misuse of the term “multiplier” to describe what is in fact an index of internal linkage

or economic self-sufficiency. It is not intended as a multiplicative factor for predictive impact modeling, but its

widespread erroneous use in this manner has sparked much controversy over the use of impact modeling in general

[Archer 1977]. Instead, the ratio multiplier describes the degree of internal linkage and relative magnitude of the

indirect effect, which is useful for comparisons among sectors in conjunction with other information. Even if one

sector’s ratio multiplier is substantially greater than another’s, the analyst needs to consider the circumstances

that could lead to misleading interpretations. For example, a large ratio multiplier might be more a function of a

particularly small direct effect (denominator for the ratio multiplier) than the overall significance of the direct and

induced effects as compared with those for other sectors. In other words, sectors with the highest ratio multipliers
102

do not necessarily yield the highest impacts. It is important to assess the amount of exogenous sales (demand

change), which are required to generate an initial unit of direct income [Archer 1977; Bushnell and Hyle 1985],

and the normal multipliers take this standardization into account, making cross-sectoral comparisons possible.

Ratio multipliers have been dismissed as meaningless on their own for predictive impact purposes and are instead

used to gauge degrees of self-sufficiency among distinct economies of a given area. The impacts derived for this

study rely on the induced normal multipliers, and are referred to as response coefficients in the accompanying

software package.

Assumptions and limitations

Input–output models have gained favor among regional economists and other analysts because of the relative

operational simplicity and low cost associated with available pre-packaged programs. Even when considerable work

is involved in building an original model, linear algebra provides simpler mathematical framework than is available

for sophisticated alternatives. However, these conveniences come at the expense of certain limitations inherent in the

restrictive assumptions underlying the model’s design. Despite these limitations, an understanding of the caveats can

help the analyst overcome the weaknesses. The following discussion is adapted from Archer [1977] and Olson and

Lindall [1997].

Unlike actual economies, input–output models are static in form because: 1) constant returns to scale are assumed;

2) there is implied supply elasticity in all sectors and absence of supply constraints; 3) relative prices are constant

and commodity input structure is fixed; 4) sector output is homogenous; and 5) industry technology is fixed.

Because of the linear production specification for all industrial sectors, increased production requires the purchase

of inputs in exactly the same proportions regardless of magnitude. I–O also assumes that the only limiting factor

on production is the level of demand, implying that marginal costs are constant and that firms have unlimited access

to inputs. Therefore, returns to scale are constant over the entire range of production possibilities. Since relative

prices are fixed, I–O models operate under the assumption that resources have no opportunity costs so that changes

in demand affect production without altering the mix of commodities and services. Finally, the fixed technology

assumption states that the same technology is used to produce all of an industry’s products, and that technology is

static.

These limitations become increasingly binding the greater the simulated change in the impact analysis. Although

real world production relationships are most probably non-linear, it is not unreasonable to approximate these with

linear specifications for small changes from the starting point. On the other hand, simulations that involve drastic

changes from the means are likely to have poor predictive abilities. Determining acceptable levels for demand

shocks to I–O models relies on intuition and qualitative assessments of the industries in question. As a general rule,

practitioners of I–O should have less confidence in results the further the impact scenario deviates from the baseline

conditions.

Specific changes to the baseline model

The regional economic modeling system IMPLAN has not been used as extensively in Alaska as it has in other

U.S. regions. One reason is that Alaska is large and sparsely populated. In addition, most of the population is

concentrated in Anchorage (Southcentral), Fairbanks (Interior), Juneau (Southeast), and the Kenai Peninsula. In the

rest of Alaska, populations are very small, isolated from each other, and may have more direct links to distant urban

centers such as Seattle than they do to proximate communities. Many of Alaska’s industries (oil, fisheries, tourism)

operate with little connection to local economies. In fact, much of the labor force is imported, residing out-of-state

for much of each year. Thus, Alaska’s economy differs from the U.S. economy in general.

Before IMPLAN is used for impact analysis in a community in Alaska, it must be modified to accurately reflect the

local economy. Economically small regions are difficult to model because of the problem of data masked to ensure

confidentiality [Geier et al. 1994]. While REIS data is often used elsewhere, disclosure problems and a lack of self-
103

employment data in REIS renders it unsuitable for many Alaskan industries. Consequently, community research is

the most efficient and accurate method to gather the data to regionalize the IMPLAN model.

The first step is to identify the study area. We combined Seward with other areas of the Kenai Peninsula Borough

that also did not participate materially in the lower Cook Inlet sport fisheries and identified four other sub-regions

of the Kenai Peninsula Borough that had their own regional identities. We defined these five areas by zip code and

ordered IMPLAN databases for each area. The five areas had populations ranging from 2,755 to 17,212. Four of the

zip code areas were to be included in the model for impact analysis.

To structure our interviews, we used the Output, Value Added, and Employment report (OVE). We targeted persons

who were identified to us by the Kenai Peninsula Borough Economic Development District as being familiar with

their local communities. They included mayors, borough- and city-council persons, real estate agents, Chamber of

Commerce directors, and bankers. We interviewed at least three local informed experts in each community to

“groundtruth” a line-by-line (sector-by-sector) review of the OVE report.

The five regional economies were very different from each other. The northern part of the Kenai Peninsula Borough

was dominated by the oil industry, and included most of the wholesale and retail sales activity. Commercial

and sport fishing activities were distributed throughout the borough, with regional differences in operation by

commercial guiding industries, and also among the commercial fishers by gear type. Logging and related industries

were also unevenly distributed, with very real regional differences in operation. Sand and gravel mining industries

were concentrated in the south central part of the borough. The use of zip codes to define discrete subareas

contributed to the accuracy of our final model, as it should in any area where the IMPLAN databases need to be

corrected.

Individual sectors of the five IMPLAN models were adjusted to reflect information provided by the informed

experts. In addition, the models were adjusted to reflect Alaska Department of Labor ES202 data. However, even

this data did not provide sufficient detail for commercial fishing, guided/charter sportfishing, or bed and breakfasts.

Because these industries may have a significant amount of unreported income and employment, we conducted a

second round of interviews with actual participants.

The ES202 data was particularly helpful for fish processing and large-scale tourism oriented industries. The ES202

data was also useful for examining the seasonality of different industries. We used the data to construct a peak

seasonal employment model. While the peak seasonal model employment figures exceeded the original IMPLAN

numbers, we felt that a peak seasonal employment model would provide a more accurate measure of the effect of

most impact scenarios.

The mean Alaska unemployment rate (1990–1997) has shown a 3.7% seasonal variation between the months of

January/February (lowest) to August (highest). The Kenai Peninsula Borough seasonal variation has exceeded 10%

during the same time period, due to its reliance on recreation and the commercial fishing. However, what was not

captured by the unemployment rate is the seasonal variation of the size of the labor force. The average low labor

force occurs in December/January, and the high generally falls in July. The average (1990–1997) high is 23,842,

while the average low is 19,159; an average difference is approximately 24%. This shows the seasonality of the

labor force much more clearly than the unemployment rate. However, we must be mindful that this does not reflect

proprietors and non-unemployment insurance covered employees, like the commercial fishing industry, and the

sportfishing guide/charter industry. So, the employment numbers should be even higher than they are because of the

large amount of summer employment that does not appear in State data. Much of this employment is nonresidential.

Commercial fishing, fish processing, guiding, and seasonal industries tied to tourism and natural resources account

for much of the nonresidential employment.

Nonresidential employment, of course, leads to different household consumption patterns. Many nonresident

employees on the Kenai Peninsula Borough are college students recruited from in state and from out of state. They

tend to minimize expenditures and transfer a large part of their incomes to locations outside the Kenai Peninsula

Borough.

We attempted to remedy employment data shortcomings in the commercial fishing sector through the use

of ADF&G data for crew licenses and commercial fishing permits, and through our interviews with industry
104

representatives. Thus, we were able to estimate and verify the accuracy of our estimates for employment in the

commercial fishing and guided/charter sportfishing industries. We also used data from several representative guides

to construct production functions for the three sectors that we assigned to those guides: Homer, Deep Creek, and

Kenai River. We further estimated employment and constructed another production function for the sport fishprocessing sector using informed interviewees.

Another sector that was underestimated by IMPLAN models for the Kenai Peninsula Borough was the Hotels and

Lodging sector. Bed and breakfast inns are a recent addition to this sector in the Kenai Peninsula Borough, and most

are seasonally operated and do not appear in employment statistics because of the structure of the firms in the sector.

Thus, we had to estimate an increase in this sector also. This was done solely through the first round of interviews

with community experts. It was not possible to estimate a new production function, but the amount of proprietors

and other property income should be adjusted upwards from the existing rations to reflect the higher number of

owner/operators in these businesses relative to other business in the Hotels and Lodgings sector.

Commercial fishing, Hotels and Lodging, and other existing sectors were changed before the zip codes were

aggregated. Thus, to construct the overall Kenai Peninsula Borough model for analysis of the lower Cook Inlet sport

fisheries, four models were constructed and their data was corrected using the IMPLAN editor for the sectors where

there were only employment number changes. After correction, the four zip code models were aggregated using

Microsoft Access and a spreadsheet. The SA Industry Data tables for the four zip code models were imported into

the spreadsheet for aggregation. After aggregation, the revised table was imported back into Access for use in

IMPLAN.

The Kenai–Nikiski model

The first model is for the Kenai–Nikiski area. Expert interviews were held with John Williams, former Borough

Mayor, Becky Hultberg from the Borough Economic Development Office, and Rick Ross and Laura Measles of the

Kenai Chamber of Commerce. The model had 92 sectors from IMPLAN. The expert interviews and correlation of

the model with other data sources including the agricultural statistics publication from USDA, the Kenai Peninsula

Borough School District employment roster, and other miscellaneous sources yielded the following changes.

Agriculture and Agricultural Services—Sector 3, Ranch Fed Cattle, was doubled for employment and all income

components. This was indicated by 3 out of 4 experts, and validated by Agricultural Statistics. The other agricultural

sectors, 6, 9, 13, 18, and 23, were not changed. Sector 27, Landscape and Horticultural Services, was increased from

6 to 24 because of the ES202 numbers with a like increase in all other components.

Mining—Sectors 31 and 37 were zeroed-out as there was no evidence of either gold or coal mining in the

Kenai–Nikiski area. Sector 38, Natural Gas and Crude Petroleum, was considered to be fully disclosed in the ES202

report, and was adjusted to 307.

Construction—Only two construction sectors 48 and 49 were adjusted according to ES202 numbers and expert

opinion. The other sectors were accepted as IMPLAN presented them by the experts and were not significantly

different from the ES202 numbers.

Manufacturing—Sector 133, Logging Camps and Contractors, was expanded according to expert opinion from 3

to 15 employees. Sector 174, Newspapers, was adjusted down from 127 to 100 due to ES202 data. Sector 202,

Nitrogenous and Phosphoric Fertilizers, was adjusted from 320 to 351 employees. Sector 210, Petroleum Refining,

was acceptable. Sector 244, Ready-Mixed Concrete, was also okay. Sector 282, Fabricated Structural Metal,

deserves some comment. The APC module facility was left out of this model as the date of most data was prior to

its opening. Sector 354, Industrial Machines Not Elsewhere Classified, was specified in the ES202 numbers. Sector

393, Boat Building and Repair, was added with 5 employees.

Transportation and Public Utilities—Sector 434, Local, Interurban Passenger Transit, was added with 10 employees

according to expert interviews. Sector 435, Motor Freight Transport and Warehousing, was cut from 91 employees

to 60. This was calculated from both ES202 numbers and also expert interviews. Sector 436, Water Transportation,

was seen by experts as a seasonal industry, and it was not represented accurately by IMPLAN or ES202, so it was
105

doubled from 11 to 22 employees. Sector 437, Air Transportation, was increased to 124, the seasonal high from the

ES202 files. Experts agreed that it should be higher than IMPLAN’s 70, but were unable to pin an exact number.

Sector 438, Pipelines except Natural Gas, was increased from 0 to 4 employees according to ES202 files. Sector

439, Arrangement of Passenger Transportation, was adjusted from 12 to 15 according to expert interviews as well as

collaborating evidence from ES202. Sector 443, Electric Services, was adjusted from 98 to 67 in accordance with

the ES202 files. Sector 446 was adjusted down from 39 to 34.

Wholesale and Retail Trade—Wholesale and retail trade sectors were adjusted according to the ratios in ES202 files

and then individually corrected according to expert opinion. Sector 447, Wholesale Trade, was adjusted from 180

to 350 according to ES202. Sector 448, Building Materials and Gardening, retail, was adjusted down from 64 to 25

with deference to ES202 and experts. Sector 449, General Merchandise Stores, was reduced from 327 to 250 with

reference to ES202 and expert opinion. Sector 451, Automotive Dealers & Service Stations, was adjusted from 119

to 200 according to expert opinion. Sector 453, Furniture and Home Furnishings Stores, was adjusted from 12 to 8

according to ES202 and expert opinion.

Finance, Insurance, and Real Estate—Sector 456, Banking, was adjusted from 68 to 57 according to ES202. Sector

457, Credit Agencies, was adjusted from 70 to 18 according to ES202 and expert opinion. Sector 459, Insurance

Carriers, was adjusted from 1 to 10 according to ES202. Sector 462, Real Estate, was adjusted from 74 to 45

according to expert opinion.

Services—Sector 471, Photo-Finishing, Commercial Photography, was adjusted from 30 to 10 according to expert

opinion. Sector 472, Services to Buildings, was adjusted from 144 to 100 according to expert opinion. Sector 479,

Automobile Repair and Services, was adjusted from 64 to 50 according to expert opinion. Sector 480, Electrical

Repair Shops, was adjusted from 1 to 12 according to ES202 and expert opinion. Sector 483, Motion Pictures, was

adjusted from 59 to 45 according to ES202 and expert opinion. Sector 484, Theatrical Producers, Bands, Etc., was

adjusted from 1 to 10 according to expert opinion. Sector 485, Bowling Alleys and Pool Halls, was adjusted from

19 to 10 according to expert opinion. Sector 498, Job Training and Related Services, was adjusted from 5 to 10

according to expert opinion and ES202. Sector 503, Business Associations, was adjusted from 5 to 8 according to

ES202. Sector 504, Labor and Civic Organizations, was adjusted from 212 to 76 according to ES202. Sector 505,

Religious Organizations, was adjusted from 7 to 140, according to ES202. Sector 506, Engineering, Architectural

Services, was adjusted from 52 to 26 according to expert opinion. Sector 508, Management and Consulting Services,

was adjusted from 25 to 20 according to expert opinion.

Government—Sector 511, State and Local Electric Utilities, was adjusted from 2 to 0 according to expert opinion

and ES202. Sector 513, U.S. Postal Service, was adjusted from 25 to 33 according to ES202. Sector 519, Federal

Government – Military, was adjusted from 121 to 2 according to ES202. Sector 520, Federal Government – NonMilitary, was adjusted from 102 to 137 according to ES202. Sector 522, State & Local Government – Education,

was adjusted from 370 to 379 according to the Kenai Borough School District Employment numbers. Sector 523,

State & Local Government – Non-Education, was adjusted from 485 to 806 according to ES202.

The Homer model

The sector-by-sector correction of the Homer/Seldovia model was completed with input from Derotha Ferraro,

Homer Chamber of Commerce, and Shari Hobbs, Homer Mayor’s office. Also taken into account were the ES202

files from Alaska Department of Labor. When there was not agreement between interview subjects, their answers

were averaged. In sectors where ES202 data was relatively complete, these data were used. Absent any conflicting

information, IMPLAN database numbers were accepted as the best estimates.

Construction Sectors—Sector 48, New Residential Structures, was adjusted from 16 employees to 76, averaged

according to expert opinions. Sector 50, New Utility Structures, was increased from 9 to 30 employees according to

expert opinion. Sector 54, New Government Facilities, was increased from 9 to 30, also according to expert opinion.

Sector 55, Maintenance and Repair Residential, average expert opinion increased employment from 11 to 61. Sector

56, Maintenance and Repair of Other Facilities, was decreased from 91 to 45 according to expert opinion.
106

Manufacturing Sectors—Sector 67, Canned Fruits and Vegetables, was averaged according to expert opinion from

1 to 26. Sector 98, Prepared Fresh and Frozen Seafoods was adjusted from 345 to 246 according to ES202 seasonal

high. Sector 128, Canvas Products, was increased from 6 employees to 26, on advice from expert (D). Sector 133,

Logging Camps and Logging Contractors, was increased from 44 to 100 according to expert opinion. Sector 144,

Prefabricated Wood Buildings, was increased from 1 to 4 employees according to expert opinion. Sector 174,

Newspapers, was decreased from 29 to 20 according to expert opinion. Sector 176, Book Publishing, was increased

from 1 employee to 10 according to average expert opinion. Sector 179, Commercial Printing, increased from

4 to 9 according to average expert opinion. These numbers also cross-referenced closely with ES202 data. Sector

241, Pottery Products, was added with 2 employees according to expert opinion and ES202 data. Sector 311,

Construction Machinery and Equipment, was added because it was indicated in the ES202 files. Sector 354,

Industrial Machines, was decreased from 39 to 3 according to ES202 files. Sector 393, Boat Building and Repair,

was increased from 1 to 50 by average expert opinion. Sector 415, Jewelry, Precious Metal, was increased from 12

to 25 because of ES202 data and an allowance for workers not covered by unemployment insurance.

Transportation and Communication Service Sectors—The service sectors exhibited the greatest variation from

IMPLAN numbers because the industries contain lots of proprietor’s income and thus non-UI reported employment.

Sector 434, Local, Interurban Transport, was increased from 6 employees to 16 according to expert opinion. Sector

436, Water Transportation was increased from 124 to 134 because of expert opinion and ES202 evidence of higher

employment. Sector 437, Air Transportation, was increased from 44 to 75 due to expert opinion. Sector 439,

Arrangement of Passenger Transportation, was adjusted from 16 to 26 according to expert opinion. Sector 441,

Communications, Except Radio and TV, was adjusted from 3 to 4 according to ES202. Sector 442, Radio and TV

Broadcasting, was adjusted from 30 to 18 according to ES202. Sector 443, Electric Services, was adjusted from 92

to 75 according to ES202.

Retail Trade—Sector 448, Building Materials and Gardening Supplies, was adjusted from 43 to 63 according to

ES202 and allowing for proprietors. Sector 451, Automotive Dealers and Service Stations, was adjusted from 50 to

56 according to ES202. Sector 452, Apparel and Accessory Stores, was adjusted from 6 to 12 according to expert

opinion. Sector 453, Furniture and Home Furnishings Store, was adjusted from 11 to 14 according to ES202 files.

Sector 454, Eating and Drinking Establishments, employment was adjusted from 281 to 500 according to ES202,

plus an added approximation of proprietors.

Finance, Insurance, and Real Estate—Sector 456, Banking, was adjusted from 49 to 36 according to expert opinion.

Sector 460, Insurance Agents and Brokers, was adjusted from 43 to 16 according to expert opinion and ES202.

Sector 463, Hotels and Lodging Places, was adjusted from 151 to 250 according to expert opinion, ES202, and

allowing for operators.

Services—Sector 464, Laundry, Cleaning, and Shoe Repair, was adjusted from 38 to 20 according to expert

opinion. Sector 466, Beauty and Barber Shops, was adjusted from 9 to 25 according to expert opinion. Sector 468,

Miscellaneous Personal Services, was adjusted from 8 to 20 according to average expert opinion. Sector 470,

Other Business Services, was adjusted from 22 to 20 according to expert opinion. Sector 471, Photo-Finishing,

Commercial Photography, was adjusted from 60 to 35 according to average expert opinion. Sector 472, Servicesto

Buildings, was adjusted from 30 to 12 according to estimates from ES202. Sector 479, Automobile Repair and

Services, was adjusted from 53 to 31 according to ES202. Sector 482, Miscellaneous Repair Shops, was adjusted

from 2 to 10 according to expert opinion. Sector 484, Theatrical Producers, Bands, Etc., was adjusted from 1 to 30

according to average expert opinion. Sector 488, Amusement and Recreation Services, was adjusted from 159 to 200

by consensus expert opinion. Sector 489, Membership Sports and Recreation Clubs, was adjusted from 54 to 15

according to expert opinion. Sector 492, Hospitals, was adjusted from 190 to 219 according to ES202. Sector 493,

Other Medical and Health Services, was adjusted from 42 to 15 according to expert opinion. Sector 494, Legal

Services, was adjusted from 16 to 24 according to expert opinion. Sector 498, Job Training and Related Services,

was adjusted from 2 to 4 according to ES202. Sector 499, Child Day Care Services, was adjusted from 13 to 20

according to consensus expert opinion. Sector 500, Social Services, was adjusted from 54 to 94 according to ES202

and expert opinion. Sector 501, Residential Care, was adjusted from 5 to 15 according to ES202 and expert opinion.

Sector 502, Other Nonprofit Organizations, was adjusted from 4 to 28 according to ES202. Sector 503, Business

Associations, was adjusted from 3 to 11 according to ES202. Sector 504, Labor and Civic Organizations, was

adjusted from 42 to 56 according to ES202. Sector 505, Religious Organizations, was adjusted from 12 to 20
107

according to expert opinion. Sector 506, Engineering and Architectural Services, was adjusted from 16 to 8

according to ES202.

Government—Sector 513, U.S. Postal Service, was adjusted from 19 to 2. Sector 522, State and Local Government

– Education, was adjusted from 279 to 278 according to the Kenai Peninsula Borough School District Employment

catalog. Sector 523, State and Local Government – Non-Education, was adjusted from 366 to 118 according to

ES202.

The Anchor Point and Ninilchik model

The documentation for the communities of Ninilchik and Anchor Point relied mainly upon the expert testimony

of three people recommended by the Kenai Peninsula Borough Economic Development District: one who was

considered expert for both communities; one considered expert in Anchor Point; and, one for Ninilchik. They were,

respectively, real estate agent Emmett Trimble, business owner Simone Klutts, and business owner Vicki Stik.

They were presented with the value added and employment report from the IMPLAN zip code model and asked to

determine the accuracy of all sectors. Following is a discussion of the sectors that were changed from the original

IMPLAN zip code model for Anchor Point and Ninilchik.

Agriculture—Sector 3, Ranch Fed Cattle, was changed from 0 to 3 according to expert opinion with additional data

from the Alaska Agricultural Statistics. Sector 7, Hogs, Pigs, and Swine, was adjusted from 0 to 1 according to

expert opinion and additional documentation from the Alaska Agricultural Statistics. Sector 8, Other Meat Animal

Products, was adjusted from 0 to 7 according to expert opinion and additional documentation from the Alaska

Agricultural Statistics. Sector 9, Miscellaneous Livestock, was adjusted from 2 to 10 according to expert opinion

and additional documentation from the Alaska Agricultural Statistics. Sector 13, Hay and Pasture, was adjusted from

2 to 13 according to expert opinion and additional documentation from the Alaska Agricultural Statistics. Sector 23,

Greenhouse and Nursery Products, was adjusted from 1 to 7 according to expert opinion.

Mining—Sector 41, Sand and Gravel, was adjusted from 2 to 50 according to expert opinion.

Construction—Sector 48, New Residential Structures, was adjusted from 7 to 21 according to expert opinion. Sector

54, New Government Facilities, was adjusted from 4 to 20 according to expert opinion. Sector 55, Maintenance and

Repair, Residential, was adjusted from 2 to 15 according to expert opinion. Sector 56, Maintenance and Repair

Other Facilities, was adjusted from 19 to 11 according to expert opinion.

Manufacturing—Sector 133, Logging Camps and Logging Contractors, was adjusted from 37 to 50 according to

expert opinion. Sector 134, Sawmills and Planing Mills, General, was adjusted from 0 to 4 according to expert

opinion. Sector 144, Prefabricated Wood Buildings, was adjusted from 1 to 3. Sector 244, Ready-Mixed Concrete,

was adjusted from 0 to 5 according to expert opinion. Sector 275, Cutlery, was adjusted from 1 to 0 according to

expert opinion. Sector 393, Fabricated Structural Metal, was adjusted from Boat Building and Repair, was adjusted

from 4 to 13 according to expert opinion.

Transportation and Public Utilities—Sector 435, Motor Freight Transport and Warehousing, was adjusted from

2 to 6 according to expert opinion. Sector 436, Water Transportation, was adjusted from 7 to 10 according to expert

opinion. Sector 433, Electric Services, was adjusted from 0 to 65 according to ES202. Sector 445, Water Supply and

Sewerage Systems, was adjusted from 0 to 12 according to expert opinion.

Wholesale and Retail Trade—Sector 447, Wholesale Trade, was adjusted from 8 to 16 according to expert opinion

and ES202. Sector 449, General Merchandise Stores, was adjusted from 5 to 25 according to expert opinion and

ES202. Sector 450, Food Stores, was adjusted from 8 to 72 according to ES202. Sector 451, Automotive Dealers

& Service Stations, was adjusted from 8 to 20 according to expert opinion. Sector 454, Eating & Drinking, was

adjusted from 64 to 100 according to expert opinion. Sector 455, Miscellaneous Retail, was adjusted from 10 to 26

according to expert opinion. Sector 463, Hotel and Lodging Places, was adjusted from 23 to 55 according to expert

opinion.
108

Services—Sector 472, Services to Buildings, was adjusted from 0 to 3 according to expert opinion. Sector 473,

Equipment Rental and Leasing, was adjusted from 0 to 2 according to expert opinion. Sector 479, Automobile

Repair and Services, was adjusted from 6 to 12 according to expert opinion. Sector 482, Miscellaneous Repair

Shops, was adjusted from 2 to 6 according to expert opinion. Sector 499, Child Day Care Services, was adjusted

from 3 to 12 according to expert opinion. Sector 504, Labor and Civic Organizations, was adjusted from 9 to 20

according to ES202. Sector 505, Religious Organizations, was adjusted from 0 to 10 according to expert opinion.

Government—Sector 513, U.S. Postal Service, was adjusted from 6 to 12 according to expert opinion. Sector 520,

Federal Government – Non-Military, was adjusted from 24 to 48 according to expert opinion and ES202. Sector

522, State and Local Government – Education, was adjusted from 87 to 55 according to the Kenai Peninsula School

District Personnel List. Sector 523, State & Local Government – Non-Education, was adjusted from 114 to 28

according to ES202. Sector 525, Domestic Services, was adjusted from 8 to 13 according to expert opinion.

Soldotna and balance of northern Kenai Peninsula model

The documentation for these communities relies mainly upon the expert testimony of Kurt Eriksson, National Bank

of Alaska Soldotna, and Tom Boedeker. They were presented with the value added and employment report from the

IMPLAN zip code model and asked to determine the accuracy of all sectors. Following is a discussion of the sectors

that were changed from the original IMPLAN zip code model for Soldotna.

Agriculture—Sector 3, Ranch Fed Cattle, was adjusted from 1 to 2 according to Alaska Agricultural Statistics.

Sector 7, Hogs, Pigs, and Swine, was adjusted from 1 to 3 according to Alaska Agricultural Statistics and expert

opinion. Sector 22, Forest Products, was adjusted from 1 to 6 according to expert opinion. Sector 23, Greenhouse

and Nursery Products, was adjusted from 11 to 13 according to expert opinion. Sector 27, Landscape and

Horticultural Services, was adjusted from 8 to 11 according to expert opinion.

Construction—Sector 48, New Residential Structures, was adjusted from 51 to 204 according to expert opinion.

Sector 49, New Industrial and Commercial Buildings, was adjusted from 194 to 291 according to expert opinion.

Sector 50, New Utility Structures, was adjusted from 29 to 20 according to expert opinion and ES202. Sector 51,

New Highways and Streets, was adjusted from 103 to 130 according to expert opinion. Sector 53, New Mineral

Extraction Facilities, was adjusted from 197 to 25 according to expert opinion. Sector 54, New Government

Facilities, was adjusted from 31 to 25 according to expert opinion. Sector 55, Maintenance and Repair, Residential,

was adjusted from 12 to 25 according to expert opinion.

Manufacturing—Sector 67, Canned Fruits and Vegetables, was adjusted from 1 to 0. Sector 79, Bread, Cake,

and Related Products, was adjusted from 0 to 5 according to ES202. Sector 133, Logging Camps and Logging

Contractors, was adjusted from 1 to 60 according to expert opinion and ES202. Sector 140, Structural Wood

Members, was adjusted from 0 to 9 according to ES202. Sector 144, Prefabricated Wood Buildings, was adjusted

from 0 to 30 according to ES202 and expert opinion. Sector 242, Concrete Block and Brick, was adjusted from 3 to

30 according to ES202 and expert opinion. Sector 244, Ready-Mixed Concrete, was adjusted from 0 to 23 according

to ES202. Sector 275, Cutlery, was adjusted from 2 to 3 according to expert opinion. Sector 337, Industrial Furnaces

and Ovens, was adjusted from 0 to 7 according to ES202. Sector 392, Shipbuilding and Repair, was adjusted from

0 to 3 according to ES202. Sector 393, Boat Building and Repair, was adjusted from 1 to 5 according to ES202 and

expert opinion.

Transportation and Public Utilities—Sector 435, Motor Freight Transport and Warehousing, was adjusted from

15 to 50 according to ES202 and expert opinion. Sector 436, Water Transportation, was adjusted from 0 to 50

according to ES202. Sector 437, Air Transportation, was adjusted from 18 to 36 according to ES202 and expert

opinion. Sector 439, Arrangement of Passenger Transportation, was adjusted from 12 to 19 according to ES202 and

expert opinion. Sector 440, Transportation Services, was 2 to 15 according to expert opinion and ES202. Sector

441, Communications, Except Radio and TV, was adjusted from 51 to 54 according to ES202. Sector 443, Electric

Services, was adjusted from 3 to 5 according to expert opinion and ES202. Sector 444, Gas Production and

Distribution, was adjusted from 10 to 22 according to ES202. Sector 446, Sanitary Services and Steam Supply,

was adjusted from 21 to 26 according to expert opinion.
109

Wholesale and Retail Trade—Sector 447, Wholesale Trade, was adjusted from 109 to 130 according to expert

opinion. Sector 448, Building Materials & Gardening, was adjusted from 69 to 90 according to ES202 and expert

opinion. Sector 449, General Merchandise Stores, was adjusted from 64 to 213 according to ES202 averaged with

Sector 450. Sector 450, Food Stores, was adjusted according to ES202 averaged with Sector 449. Sector 451,

Automotive Dealers & Service Stations, was adjusted from 207 to 278 according to ES202 and expert opinion.

Sector 452, Apparel & Accessory Stores, was adjusted from 62 to 31 according to ES202 and expert opinion. Sector

453, Furniture & Home Furnishings Store, was adjusted from 51 to 45 according to expert opinion. Sector 454,

Eating & Drinking, was adjusted from 416 to 430 according to ES202 and expert opinion.

Finance, Insurance, and Real Estate—Sector 456, Banking, was adjusted from 95 to 60 according to expert opinion

and ES202. Sector 457, Credit Agencies, was adjusted from 70 to 35 according to expert opinion. Sector 462, Real

Estate, was adjusted from 190 to 350 according to expert opinion. Sector 463, Hotels and Lodging Places, was

adjusted from 439 to 470 according to expert opinion.

Services—Sector 464, Laundry, Cleaning, and Shoe Repair, was adjusted from 53 to 60 according to expert opinion.

Sector 468, Miscellaneous Personal Services, was adjusted from 18 to 36 according to expert opinion. Sector 470,

Other Business Services, was adjusted from 15 to 25 according to ES202 and expert opinion. Sector 472, Services

to Buildings, was adjusted from 120 to 125 according to expert opinion. Sector 473, Equipment Rental and Leasing,

was adjusted from 42 to 47 according to expert opinion. Sector 476, Detective and Protective Services, was adjusted

from 11 to 25 according to ES202. Sector 477, Automobile Renting and Leasing, was adjusted from 5 to 10

according to expert opinion. Sector 478, Automobile Parking and Car Wash, was adjusted from 7 to 9 according to

expert opinion. Sector 480, Electrical Repair Service, was adjusted from 13 to 25 according to expert opinion. Sector

481, Watch, Clock, Jewelry, and Furniture Repair, was adjusted from 8 to 15 according to expert opinion. Sector

482, Miscellaneous Repair Shops, was adjusted from 14 to 25 according to expert opinion. Sector 483, Motion

Pictures, was adjusted from 19 to 40 according to expert opinion and ES202. Sector 484, Theatrical Producers,

Bands, etc., was adjusted from 2 to 20 according to expert opinion. Sector 489, Membership Sports and Recreation

Clubs, was adjusted from 0 to 7 according to ES202. Sector 490, Doctors and Dentists, was adjusted from 246 to

200 according to ES202 and expert opinion. Sector 492, Hospitals, was adjusted from 407 to 376 according to

ES202. Sector 494, Legal Services, was adjusted from 19 to 35 according to expert opinion. Sector 495, Elementary

and Secondary Schools, was adjusted from 76 to 28 according to ES202. Sector 497, Other Educational Services,

was adjusted from 14 to 20 according to expert opinion. Sector 499, Child Day Care Services, was adjusted from

71 to 80 according to expert opinion. Sector 500, Social Services, was adjusted from 29 to 60 according to ES202.

Sector 502, Other Nonprofit Organizations, was adjusted from 1 to 50 according to expert opinion. Sector 503,

Business Associations, was adjusted from 32 to 20, according to ES202 and expert opinion. Sector 504, Labor and

Civic Organizations, was adjusted from 42 to 15 according to ES202. Sector 505, Religious Organizations, was

adjusted from 38 to 22 according to ES202 and expert opinion. Sector 506, Engineering, Architectural Services, was

adjusted from 32 to 44 according to ES202 and expert opinion. Sector 507, Accounting, Auditing and Bookkeeping,

was adjusted from 65 to 75 according to expert opinion. Sector 508, Management and Consultant Services, was

adjusted from 11 to 37 according to ES202.

Government—Sector 511, State and Local Utilities, was adjusted from 4 to 0 according to expert opinion. Sector

513, U.S. Postal Service, was adjusted from 37 to 31 according to ES202. Sector 522, State & Local Government

– Education, was adjusted from 542 to 491 according to the Kenai Peninsula Borough School Employment Roster.

Sector 523, State & Local Government – Non-Education was adjusted from 711 to 370 according to ES202 and

expert opinion. Sector 525, Domestic Services, was adjusted from 53 to 150 according to expert opinion.
110

IMPLAN baseline data

Table 72. General IMPLAN model information: household numbers and mean household income by zip code

based subarea.

Household Income

< 5K 5 –10K 10 –15K 15 –20K 20 –30K 30 – 40K 40 –50K 50 –70K 70K+ Total

Kenai – Nikiski:

2,730 sq miles, 11,753 residents

Number of households 1 40 2 25 3 00 2 65 5 66 5 56 4 89 1 ,0 76 9 00 4 ,5 17 

Soldotna:

4,645 sq miles, 17,212 residents

Number of households 2 08 3 34 4 45 3 92 8 36 8 22 7 22 1 ,5 90 1 ,3 30 6 ,6 78 

Anchor Point – Ninilchik:

570 sq miles, 2,755 residents

Number of households 3 3 5 3 7 1 6 3 1 34 1 31 1 15 2 54 2 12 1 ,0 65 

Homer – Seldovia:

15,275 sq miles, 8,864 residents

Number of households 1 11 1 79 2 38 2 09 4 46 4 38 3 85 8 48 7 09 3 ,5 63 

Total: 23,220 sq miles, 40,584 residents

Total household income 9 57 ,6 60 ,98 4

Number of households 1 5,82 3

Mean household income 6 0,52 3

Table 73. Output, value added, and employment in the study area. (All variables except employment are in millions

of dollars.)

IMPLAN

Sectors IMPLAN Sector Names Industry

Output Employment Employee

Compensation

Proprietor

Income

Other Property

Income

Indirect

Business Tax

1 Dairy Farm Products 0.187944 2 0.008873 0.068579 0.016712 0.000997

2 Poultry and Eggs 0.091530 1 0.003750 0.000000 0.000000 0.000000

3 Ranch Fed Cattle 0.275775 9 0.011071 0.021687 0.009379 0.001856

4 Range Fed Cattle 0.053206 2 0.005412 0.014569 0.006493 0.001084

6 Sheep, Lambs and Goats 0.037252 6 0.002066 0.010833 0.003794 0.000840

7 Hogs, Pigs and Swine 0.224615 5 0.008297 0.010028 0.006429 0.001436

8 Other Meat Animal Products 0.276773 7 0.072758 0.000000 0.000000 0.000000

9 Miscellaneous Livestock 0.959207 79 0.116454 0.195820 0.184563 0.007455

13 Hay and Pasture 0.548297 56 0.070981 0.115848 0.173175 0.018186

16 Fruits 0.027914 1 0.006775 0.017497 0.017774 0.001851

18 Vegetables 0.323406 5 0.034240 0.085726 0.183228 0.004812

22 Forest Products 0.172417 7 0.139528 0.004870 0.000587 0.001594

23 Greenhouse and Nursery Prod 2.405635 62 0.717508 0.476588 0.723521 0.011568

24 Forestry Products 11.577130 10 4.865414 2.558976 – 0.007530 0.000000

25 Commercial Fishing 42.452000 328 1.229000 23.900000 4.244000 2.251000

26 Ag, Forestry, Fishery Services 41.077011 1,773 1.727404 26.952858 0.003782 0.000000

27 Landscape and Horticulture 1.004093 37 0.422339 0.105908 – 0.000282 0.000000

31 Gold Ores 0.000000 0 0.000000 0.660145 0.240856 0.544865

37 Coal Mining 3.718044 12 0.951615 0.299479 0.539516 0.212318

38 Natural Gas & Crude Petroleum 152.822403 307 22.286860 0.137410 56.489685 14.959482

41 Sand and Gravel 3.212210 50 1.268353 – 0.007175 0.043174 0.010711

48 New Residential Structures 29.888811 357 8.537996 0.429226 1.863657 0.070746

49 New Commercial Building 38.944309 526 18.838739 2.503038 3.644666 0.294197

50 New Utility Structures 6.216228 67 2.441026 0.380190 1.392274 0.052855

51 New Highways and Streets 26.082237 220 7.688272 1.319647 4.484946 0.171331
111

Table 73. (continued)

IMPLAN

Sectors IMPLAN Sector Names Industry

Output Employment Employee

Compensation

Proprietor

Income

Other Property

Income

Indirect

Business Tax

53 New Mineral Extract Facilities 12.994324 197 7.598982 3.217901 5.365077 0.006775

54 New Government Facilities 68.016945 89 4.361479 0.538459 7.899123 0.304791

55 Maint and Repair, Residential 13.310281 140 4.298064 0.377638 1.156787 0.044066

56 Maint and Repair Other 52.856743 487 17.167292 3.559559 9.788436 0.407676

57 Maint and Repair Oil and Gas 90.262932 1,001 61.859791 0.350939 14.335698 0.000000

67 Canned Fruits and Vegetables 3.871938 29 0.614613 0.003751 0.137180 0.001938

71 Custom Processors 2.000000 70 0.800000 0.200000 0.100000 0.060000

79 Bread, Cake, and Related Prod 0.873450 5 0.231930 0.000000 0.000000 0.000000

97 Canned and Cured Sea Foods 19.029049 187 6.166262 0.003635 0.048804 0.000452

98 Prep Fresh or Frozen Seafood 150.986221 2,210 67.485901 1.278959 11.340320 0.133431

128 Canvas Products 0.973867 26 0.235391 0.005108 0.013897 0.001569

133 Log Camps and Log Contractors 40.383743 185 5.902300 0.394521 2.350718 0.402403

134 Sawmills and Planing Mills, General 0.610864 4 0.115252 0.000000 0.000000 0.000000

140 Struct Wood Members, N.E.C 0.943434 9 0.223362 0.000000 0.000000 0.000000

144 Prefabricated Wood Buildings 4.747231 37 0.880868 0.005821 0.021417 0.002836

174 Newspapers 6.180384 120 2.589937 0.213493 2.385698 0.032019

176 Book Publishing 0.742320 11 0.080241 0.001241 0.025490 0.000502

179 Commercial Printing 1.354768 19 0.318166 0.018505 0.072990 0.008036

202 Nitrogenous/Phosphatic Fert 173.064163 351 30.661814 0.370143 – 27.045559 4.449693

210 Petroleum Refining 214.702698 162 15.687890 1.988441 9.442089 27.079660

241 Pottery Products, N.E.C 0.088042 2 0.044894 0.000000 0.000000 0.000000

242 Concrete Block and Brick 4.743502 30 1.334093 0.010046 0.074518 0.017359

244 Ready-Mixed Concrete 9.934810 72 2.756462 0.143848 0.782699 0.127204

275 Cutlery 0.159396 4 0.036744 0.010349 0.031830 0.001638

282 Fabricated Structural Metal 3.203821 29 0.432957 0.172147 0.152269 0.018704

311 Constr Machinery and Equip 1.113725 5 0.261470 0.000000 0.000000 0.000000

337 Industrial Furnaces and Ovens 0.893648 7 0.298417 0.000000 0.000000 0.000000

354 Industrial Machines N.E.C. 2.171923 23 0.608892 0.555358 0.240587 0.027481

392 Ship Building and Repairing 3.376394 33 1.574052 0.213788 0.243417 0.046895

393 Boat Building and Repairing 7.051503 73 1.857990 0.012647 0.064194 0.006132

415 Jewelry, Precious Metal 1.419027 25 0.156639 0.079401 0.050946 0.001048

429 Signs and Advertising Displays 0.778604 12 0.113154 0.112161 0.027182 0.002605

434 Local Passenger Transit 7.893198 245 2.428647 1.284860 0.878143 0.166940

435 Motor Freight and Warehousing 11.232040 125 2.570351 0.512887 1.415114 0.256128

436 Water Transportation 44.816757 216 6.179594 0.374730 1.280492 1.664185

437 Air Transportation 31.529009 235 5.796894 1.192769 1.202868 1.634800

438 Pipe Lines, Except Natural Gas 3.689872 4 0.314088 0.000000 0.000000 0.000000

439 Arrange Passenger Transport 2.861649 60 0.597810 0.474597 0.154130 0.042740

440 Transportation Services 2.031888 19 0.406091 0.137666 0.060420 0.002804

441 Comm, Except Radio and TV 16.760351 58 3.746234 0.394488 3.636073 0.741086

442 Radio and TV Broadcasting 4.405313 39 0.817810 0.115523 0.225780 0.057319

443 Electric Services 82.985374 212 15.059435 1.683339 29.392427 3.654519

444 Gas Production and Distribution 11.740775 22 1.774055 0.064826 0.990468 0.197956

445 Water Supply and Sewerage Sys 3.337002 20 1.010868 0.053242 -0.092416 0.095043

446 Sanitary Svcs and Steam Supply 10.752735 66 2.273008 0.232119 -0.069645 0.237382

447 Wholesale Trade 65.866585 594 26.684801 0.720415 4.202782 4.489123

448 Building Materials & Gardening 9.605376 178 4.622252 1.275499 0.969955 0.784773

449 General Merchandise Stores 16.499325 570 8.622360 0.431554 1.629006 1.468855

450 Food Stores 28.710424 709 15.445184 2.403192 3.076915 2.332783

451 Auto Dealers & Service Stations 27.294859 554 12.063550 1.839984 1.827780 1.786495

452 Apparel & Accessory Stores 1.703195 57 0.711751 0.100570 0.296371 0.221779

453 Furniture & Furnishings Stores 2.577920 67 1.052588 0.463501 0.258434 0.268881

454 Eating & Drinking 49.705368 1,303 15.741036 3.850855 2.390498 1.335099

455 Miscellaneous Retail 26.310511 916 10.429534 5.154718 2.879325 2.144669

456 Banking 22.788244 153 4.248957 0.170559 4.769254 2.970170

457 Credit Agencies 1.397088 56 0.826004 0.050820 -0.930938 0.172233

458 Security/Commodity Brokers 0.692355 6 0.189772 0.001964 0.031038 0.003271

459 Insurance Carriers 2.993351 19 0.897057 0.000000 -0.048568 0.385362

460 Insurance Agents and Brokers 5.077574 121 2.170830 1.198725 1.035741 0.199595

461 Owner-occupied Dwellings 63.469902 0 0.000000 0.000000 49.113533 14.439820

462 Real Estate 59.470222 484 2.322092 – 0.231793 13.525408 11.032242

463 Hotels and Lodging Places 48.233215 949 15.045561 5.242788 4.601468 2.203413

464 Laundry, Clean and Shoe Repair 2.383940 118 0.616307 0.877730 0.101164 0.033621

466 Beauty and Barber Shops 6.076117 221 1.488017 1.784216 0.454235 0.026075

467 Funeral Service and Crematories 0.173871 4 0.044814 0.060830 0.024539 0.001844
112

Table 73. (continued)

IMPLAN

Sectors IMPLAN Sector Names Industry

Output Employment Employee

Compensation

Proprietor

Income

Other Property

Income

Indirect

Business Tax

468 Miscellaneous Personal Services 2.507464 64 0.165503 0.112196 0.040140 0.010961

470 Other Business Services 6.096404 118 1.435767 1.469217 1.366750 0.050853

471 Commercial Photography 1.440177 45 0.411984 0.832109 0.423853 0.032656

472 Services To Buildings 5.721159 240 1.695210 2.317784 0.468151 0.063720

473 Equipment Rental and Leasing 6.029505 79 1.180776 1.139670 0.931684 0.115538

475 Comp and Data Processing Svcs 0.777072 14 0.199764 0.227711 0.064139 0.005909

476 Detective and Prot Services 1.363565 37 0.496449 0.386730 0.000055 0.000005

477 Automobile Rental and Leasing 2.150700 31 0.332096 0.156094 0.139188 0.052038

478 Auto Parking and Car Wash 1.494013 29 0.187042 0.103354 0.040793 0.019383

479 Auto Repair and Services 11.391490 164 2.533370 1.695754 2.555129 0.246880

480 Electrical Repair Service 1.668478 39 0.360512 0.060229 0.123321 0.003369

481 Watch, Jewelry and Furn Rep 0.757056 15 0.057039 0.017758 0.006380 0.002910

482 Miscellaneous Repair Shops 4.034085 75 0.835904 0.399239 0.631081 0.032158

483 Motion Pictures 7.606745 98 0.773160 0.513697 0.193725 0.027192

484 Theatrical Producers, Bands Etc. 3.349400 60 0.476570 0.059400 0.013738 0.001119

485 Bowling Alleys and Pool Halls 1.074287 29 0.222815 0.510751 0.037004 0.027859

486 Marine Charter Boats 13.566000 571 1.220000 4.341000 1.492000 0.135000

488 Amusement and Recreation Svcs 37.284000 582 7.336200 4.201100 1.377000 0.182500

489 Sports and Recreation Clubs 0.496839 22 0.137164 0.524819 0.015350 0.013740

490 Doctors and Dentists 22.750988 448 9.543023 4.384151 2.974609 0.367821

491 Nursing and Protective Care 4.694432 109 2.410834 0.961331 0.143430 0.111398

492 Hospitals 46.337612 595 21.923016 9.284808 2.040382 0.267628

493 Other Medical and Health Svcs 14.756278 379 4.474514 1.885687 1.634996 0.107569

494 Legal Services 5.425562 109 2.364611 1.397490 0.636196 0.059121

495 Elem and Secondary Schools 2.231139 61 0.691391 0.251690 0.000000 0.000000

497 Other Educational Services 1.512934 44 0.446580 0.054566 0.000000 0.000000

498 Job Trainings & Related Svcs 0.671499 14 0.461095 0.000000 0.000000 0.000000

499 Child Day Care Services 3.681965 145 1.259075 0.000000 0.425105 0.000000

500 Social Services, N.E.C. 8.426696 264 4.564272 0.000000 0.000000 0.000000

501 Residential Care 1.526296 86 0.997407 0.000000 0.000000 0.000000

502 Other Nonprofit Organizations 3.477053 78 2.093038 0.000000 0.000000 0.000250

503 Business Associations 2.312967 39 0.889984 0.000000 0.000000 0.003417

504 Labor and Civic Organizations 5.971159 167 3.499366 0.000000 0.000000 0.245290

505 Religious Organizations 5.718175 192 2.804364 0.000000 0.000000 0.000000

506 Engineering, Architectural Svcs 3.995126 78 1.518801 0.504536 0.322567 0.011529

507 Acct, Auditing and Bookkeeping 7.432057 166 1.836944 1.207063 0.327323 0.004840

508 Mgm and Consulting Services 5.862024 73 1.901073 0.358412 0.101125 0.026223

509 Res, Devel & Testing Servic 1.401402 32 0.644275 0.153373 0.036986 0.010083

511 State and Local Electric Utilities 0.905821 3 0.201323 0.000000 0.881153 0.000000

512 Oth State and Local Govt Enter 12.811302 58 3.156869 0.000000 3.265979 0.007829

513 U.S. Postal Service 5.594696 78 3.765522 0.000000 – 0.581284 0.000000

519 Federal Government – Military 3.733826 270 3.733826 0.000000 0.000000 0.000000

520 Federal Govt – Non-Military 19.982292 412 19.982292 0.000000 0.000000 0.000000

522 State & Local Govt – Education 41.479939 1,203 41.479939 0.000000 0.000000 0.000000

523 State & Local Govt – Non-Educ 60.458088 1,322 60.458088 0.000000 0.000000 0.000000

525 Domestic Services 1.487472 226 1.487472 0.000000 0.000000 0.000000

526 Kenai River Guides 7.167000 259 0.322520 2.293440 0.788370 0.071670

528 Inventory Valuation Adjustment – 1.567368 0 0.000000 0.000000 – 2.214494 0.000000

Total 2,339.530478 27,205 693.376219 147.821938 263.353927 109.173561
113

Table 74. Charterboat expenditure categories, SIC classification, and IMPLAN sectoral translation.

Charter Operation

Expenses

Technical

Coefficients

SIC

Code SIC Name SIC Subcategory IMPLAN

Sectors IMPLAN Sector Names

Bait 0.0192 5941 Sporting Goods/Bike Shops Bait & Tackle Shops– Retail 455 Misc Retail

Boat Fuel 0.1922 5541 Gasoline Gas Stations Marine Gas Stations 451 Auto Dealers & Gas Stat

Boat Ins 0.0483 6331 Fire, Marine, & Cas Ins Ins Carriers 459 Ins Carriers

Boat Oil 0.0040 5542 Gasoline Stations Marine Gas Stations 213 Lub Oils & Greases

Booking Agents 0.0540 7999 Amusement & Recreation Ticket Sales Offices 488 Amusement & Recreation

Brochures 0.0583 2731 Books: Pub/Printing Pamphlets 176 Book Pub

Cellular Svc 0.0150 4812 Radiotelephone Comm Cellular Telephone Svcs 441 Comm, Except Radio/TV

Cleaning Supplies 0.0094 5251 Hardware Stores Hardware Stores – Retail 448 Bldg Mat & Garden Suppl

Comp Hardware 0.0026 5734 Comp/Software Stores 453 Home Furnishing Stores

Comp Software 0.0026 5734 Comp/Software Stores 453 Furnishing Stores

Dues 0.0201 8611 Business Assoc Trade Assoc 503 Business Assoc

Electronic Supplies 0.0006 5551 Boat Dealers Marine Supply – Retail 455 Misc Retail

Engine Maint 0.0075 5251 Hardware Stores Hardware Stores – Retail 451 Auto & Gas Stations

Engine Repair 0.0065 7699 Misc Repair Engine Repair, Except Auto 482 Misc Repair

Entertainment 0.0013 5812 Eating Places 454 Eating & Drinking

Gear Replacement 0.0313 5551 Boat Dealers Marine Supply – Retail 455 Misc Retail

Groceries 0.0011 5411 Grocery Stores Supermarkets, Grocery – Retail 450 Food Stores

Health Ins 0.0031 6321 Accident & Health Ins Health Ins 459 Ins Carriers

Hull Maint 0.0213 3732 Boat Building & Repairing Fishing Boats, Small 393 Boat Building & Repairing

Hull Repair 0.0078 3732 Boat Building & Repairing Fishing Boats, Small 393 Boat Building & Repairing

Interest Paid (Boat) 0.0785 6159 Misc Bus Credit Inst Credit Institutions, Agricultural 456 Banking

Internet Access 0.0096 4812 Radiotelephone Comms Cellular Telephone Svcs 441 Comms, Except Radio/TV

Licenses – Other 0.0165 9651 Comm Lic, & Inspection Prof Occupation Lic & Permit 523 State/Local Govt – Non-Ed

Medical 0.0022 8062 Hospitals 492 Hospitals

Moorage/Boat Storage 0.0523 4493 Marinas Marinas 436 Water Transportation

Office Supplies 0.0195 5943 Stationary Stores Pen & Pencil Stores – Retail 455 Misc Retail

Periodicals/Other 0.0005 2741 Misc Pub Business Svcs Newsletters 178 Misc Pub

Permits 0.0186 9651 Comm Lic & Inspection Prof Occupation Licensing 523 State/Local Govt – Non-Ed

Postage 0.0112 4311 US Post 513 US Post

Rental Units 0.0131 5610 Real Estate Operators 462 Real Estate

Shop Electric 0.0062 4911 Electric Svcs 511 State/Local Electric Util

Shop Heat Oil 0.0085 5983 Fuel Oil Dealers – Retail 455 Misc Retail

Shop Maint 0.0036 5251 Hardware Stores Hardware Stores – Retail 448 Bldg Mat & Garden Suppl

Shop Repair 0.0049 5251 Hardware Stores Hardware Stores – Retail 448 Bldg Mat & Garden Suppl

Maint/Small Tools 0.0030 5251 Hardware Stores Hardware Stores – Retail 448 Bldg Mat & Garden Suppl

Small Tools 0.0074 5251 Hardware Stores Hardware Stores – Retail 448 Bldg Mat & Garden Suppl

Subscriptions 0.0035 5963 Direct Sales Magazine Subscription Sales 455 Misc Retail

Telephone Svc 0.0281 4813 Telephone Comm Local / Long Distance 441 Comm, Except Radio/TV

Trade Shows 0.0310 7389 Business Trade Show Arrangement 470 Other Business

Truck Fuel 0.0221 5541 Gasoline Stations Filling Stations – Retail 451 Auto Dealers & Gas Stat

Truck Ins 0.0052 6321 Accident & Health Ins 459 Ins Carriers

Truck Maint 0.0056 5531 Auto & Home Supply Auto Parts Dealers – Retail 451 Auto Dealers & Gas Stat

Truck Repair 0.0187 7538 General Auto Repair Shops Engine Repair, Truck 479 Auto Repair

Visitor Guides 0.0571 2741 Misc Pub Guides 178 Misc Pub

Client Supplies 0.0292 5251 Hardware Stores Hardware Stores – Retail 452 Accessory Stores

Workers Comp 0.0052 6331 Fire, Marine, & Cas Ins Worker’s Compensation Ins 459 Ins Carriers

Yellow Pages 0.0321 2741 Misc Pub Telephone Directories 178 Misc Pub
114

Table 75. Final demand categories, IMPLAN sector descriptions, and sector numbers [Jensen 1997].

• Banking/Credit Services: Banking (456), Credit Agencies (457), and Security and Commodity Brokers (458).

• Business/Labor Associations: Business Associations (503), and Labor and Civic organizations (504).

• Civic/Religious Associations: Other Nonprofit organizations (502), and Religious organizations (505).

• Communications: Radio and TV Receiving Sets (370), Phonograph Records and Tape (371), Telephone and Telegraph Apparatus (372),

Radio and TV Communication Equipment (373), Communications Equipment Not Elsewhere Classified (374), Electron Tubes (375),

Printed Circuit Boards (376), Semiconductors and Related Devices (377), Electronic Components Not Elsewhere Classified (378),

Communications, Except Radio and TV (441), and Radio and TV Broadcasting (442).

• Eating and Drinking Places: Eating and Drinking (454).

• Education: Elementary and Secondary Schools (495), Colleges, Universities, Schools (496), Other Educational Services (497), Job

Trainings and Related Services (498), and State and Local Government – Education (522).

• Fabrics/Apparel: Broad-woven Fabric Mills and Finishing (108), Narrow Fabric Mills (109), Women’s Hosiery, Except Socks (110)

Hosiery Not Elsewhere Classified (111), Knit Outerwear Mills (112), Knit Underwear Mills (113), Knit Fabric Mills (114), Knitting Mills,

Not Elsewhere Classified (115), Yarn Mills and Finishing of Textiles, Not Elsewhere Classified (116), Carpets and Rugs (117), Thread

Mills (118), Coated Fabrics, Not Rubberized (119), Tire Cord and Fabric (120), Non-woven Fabrics (121), Cordage and Twine (122),

Textile Goods, Not Elsewhere Classified (123), Apparel Made From Purchased Materials (124), Curtains and Draperies (125), House

furnishings, Not Elsewhere Classified (126), Textile Bags (127), Canvas Products (128), Pleating and Stitching (129), Automotive and

Apparel Trimmings (130), Schiffi Machine Embroideries (131), Fabricated Textile Products, Not Elsewhere Classified (132), Leather

Tanning and Finishing (221), Footwear Cut Stock (222), House Slippers (223), Shoes, Except Rubber (224), Leather Gloves and Mittens

(225), Luggage (226), Women’s Handbags and Purses (227), Personal Leather Goods (228), Leather Goods, Not Elsewhere Classified

(229), and Apparel and Accessory Stores (452).

• Food Processing: Dairy Farm Products (1), Poultry and Eggs (2), Ranch Fed Cattle (3), Range Fed Cattle (4), Cattle Feedlots (5), Sheep,

Lambs and Goats (6), Hogs, Pigs and Swine (7), Other Meat Animal Products (8), Miscellaneous Livestock (9), Food Grains (11), Fruits

(16), Tree Nuts (17), Vegetables (18), Sugar Crops (19), Miscellaneous Crops (20), Oil Bearing Crops (21), Meat Packing Plants (58),

Sausages and Other Prepared Meats (59), Poultry Processing (60), Creamery Butter (61), Cheese, Natural and Processed (62), Condensed

and Evaporated Milk (63), Ice Cream and Frozen Desserts (64), Fluid Milk (65), Canned Specialties (66), Canned Fruits and Vegetables

(67), Dehydrated Food Products (68), Pickles, Sauces, and Salad Dressings (69), Frozen Fruits, Juices and Vegetables (70), Custom

Processors (71), Flour and Other Grain Mill Products (72), Cereal Preparations (73), Rice Milling (74), Blended and Prepared Flour (75),

Wet Corn Milling, (76), Bread, Cake, and Related Products (79), Cookies and Crackers (80), Sugar (81), Confectionery Products (82),

Chocolate and Cocoa Products (83), Chewing Gum (84), Salted and Roasted Nuts and Seeds (85), Cottonseed Oil Mills (86), Soybean Oil

Mills (87), Vegetable Oil Mills, Not Elsewhere Classified (88), Animal and Marine Fats and Oils (89), Shortening and Cooking Oils (90),

Malt Beverages (91), Malt (92), Wines, Brandy, and Brandy Spirits (93), Distilled Liquor, Except Brandy (94), Bottled and Canned Soft

Drinks and Water (95), Flavoring Extracts and Syrups, Not Elsewhere Classified (96), Canned and Cured Sea Foods (97), Prepared Fresh or

Frozen Fish or Seafood (98), Roasted Coffee (99), Potato Chips and Similar Snacks (100), Manufactured Ice (101), Macaroni and Spaghetti

(102), Food Preparations Not Elsewhere Classified (103), and Food Stores (450).

• Health Care: Drugs (195), Surgical and Medical Instrument (407), Surgical Appliances and Supplies (408), Dental Equipment and Supplies

(409), Doctors and Dentists (490), Nursing and Protective Care (491), Hospitals (492), and Other Medical and Health Services (493).

• Hotels and Lodging: Hotels and Lodging Places (463), Household Furnishings, Wood Household Furniture (148), Upholstered Household

Furniture (149), Metal Household Furniture (150), Mattresses and Bedsprings (151), Wood TV and Radio Cabinets (152), Household

Furniture, Not Elsewhere Classified, (153), Wood Office Furniture (154), Metal Office Furniture (155), Public Building Furniture (156),

Wood Partitions and Fixtures (157), Metal Partitions and Fixtures (158), Blinds, Shades, and Drapery Hardware (159), Furniture and

Fixtures, Not Elsewhere Classified (160), Vitreous China Food Utensils (238), Fine Earthenware Food Utensils (239), and Furniture and

Home Furnishings Stores (453).

• Household Industry: Domestic Services (525).

• Housing: New Residential Structures (48), Mobile Homes (143), Owner-occupied Dwellings (461), and Real Estate (462).

• Insurance: Insurance Carriers (459), Insurance Agents and Brokers (460).

• Motor Vehicles: Carburetors, Pistons, Rings, Valves (350), Storage Batteries (379), Electrical Equipment, Not Elsewhere Classified (383),

Motor Vehicles (384), Truck and Bus Bodies (385), Motor Vehicle Parts and Accessories (386), Truck Trailers (387), Motorcycles,

Bicycles, and Parts (395), Automotive Dealers and Service Stations (451), Automobile Rental and Leasing (477), Automobile Parking and

Car Wash (478), and Automobile Repair and Services (479).

• Other Local Purchases: Cotton (10), Feed Grains (12), Hay and Pasture (13), Grass Seeds (14), Tobacco (15), Forest Products (22),

Greenhouse and Nursery Products (23), Forestry Products (24), Commercial Fishing (25), Iron ores (28), Copper ores (29), Lead and Zinc

ores (30), Gold ores (31), Silver ores (32), Ferroalloy ores, Except Vanadium (33), Metal Mining Services (34), Uranium-radium-vanadium

ores (35), Metal ores, Not Elsewhere Classified (36), Coal Mining (37), Natural Gas and Crude Petroleum (38), Natural Gas Liquids (39),

Dimension Stone (40), Sand and Gravel (41), Clay, Ceramic, Refractory Minerals, Not Elsewhere Classified (42), Potash, Soda, and Borate

Minerals (43), Phosphate Rock (44), Chemical, Fertilizer Mineral Mining, Not Elsewhere Classified (45), Nonmetallic Minerals (Except

Fuels) Service (46), Misc. Nonmetallic Minerals, Not Elsewhere Classified (47), New Industrial and Commercial Buildings (49), New

Utility Structures (50), New Highways and Streets (51), New Farm Structures (52), New Mineral Extraction Facilities (53), New

Government Facilities (54), Maintenance and Repair, Residential (55), Maintenance and Repair Other Facilities (56), Maintenance and

Repair Oil and Gas Wells (57), Dog, Cat, and Other Pet Food (77), Prepared Feeds, Not Elsewhere Classified (78), Cigarettes (104), Cigars

(105), Chewing and Smoking Tobacco (106), Tobacco Stemming and Redrying (107), Logging Camps and Logging Contractors (133),

Sawmills and Planing Mills, General (134), Hardwood Dimension and Flooring Mills (135), Special Product Sawmills, Not Elsewhere

Classified (136), Millwork (137), Wood Kitchen Cabinets (138), Veneer and Plywood (139), Structural Wood Members, Not Elsewhere
115

Table 75. (continued)

Classified (140), Wood Containers (141), Wood Pallets and Skids (142), Prefabricated Wood Buildings (144), Wood Preserving (145),

Reconstituted Wood Products (146), Wood Products, Not Elsewhere Classified (147), Alkalies and Chlorine (186), Industrial Gases (187),

Inorganic Pigments (188), Inorganic Chemicals Not Elsewhere Classified (189), Cyclic Crudes, Intermediate and Industrial organic

Chemicals (190), Plastics Materials and Resins (191), Synthetic Rubber (192), Cellulosic Man-made Fibers (193), organic Fibers,

Noncellulosic (194), Soap and Other Detergents (196), Polishes and Sanitation Goods (197), Surface Active Agents (198), Toilet

Preparations (199), Paints and Allied Products (200), Gum and Wood Chemicals (201), Nitrogenous and Phosphatic Fertilizers (202),

Fertilizers, Mixing Only (203), Agricultural Chemicals, Not Elsewhere Classified (204), Adhesives and Sealants (205), Explosives (206),

Printing Ink (207), Carbon Black (208), Chemical Preparations, Not Elsewhere Classified (209), Paving Mixtures and Blocks (211), Asphalt

Felts and Coatings (212), Tires and Inner Tubes (215), Rubber and Plastics Footwear (216), Rubber and Plastics Hose and Belting (217),

Gaskets, Packing and Sealing Devices (218), Fabricated Rubber Products, Not Elsewhere Classified (219), Miscellaneous Plastics Products

(220), Glass and Glass Products, Excluding Containers (230), Glass Containers (231), Cement, Hydraulic (232), Brick and Structural Clay

Tile (233), Ceramic Wall and Floor Tile (234), Clay Refractories (235), Structural Clay Products, Not Elsewhere Classified (236), Vitreous

Plumbing Fixtures (237), Porcelain Electrical Supplies (240), Pottery Products, Not Elsewhere Classified (241), Concrete Block and Brick

(242), Concrete Products, Not Elsewhere Classified (243), Ready-mixed Concrete (244), Lime (245), Gypsum Products (246), Cut Stone

and Stone Products (247), Abrasive Products (248), Asbestos Products (249), Minerals, Ground or Treated (250), Mineral Wool (251), Nonclay Refractories (252), Nonmetallic Mineral Products, Not Elsewhere Classified (253), Blast Furnaces and Steel Mills (254),

Electrometallurgical Products (255), Steel Wire and Related Products (256), Cold Finishing Of Steel Shapes (257), Steel Pipe and Tubes

(258), Iron and Steel Foundries (259), Primary Copper (260), Primary Aluminum (261), Primary Nonferrous Metals, Not Elsewhere

Classified (262), Secondary Nonferrous Metals (263), Copper Rolling and Drawing (264), Aluminum Rolling and Drawing (265),

Nonferrous Rolling and Drawing, Not Elsewhere Classified (266), Nonferrous Wire Drawing and Insulating (267), Aluminum Foundries

(268), Brass, Bronze, and Copper Foundries (269), Nonferrous Castings, Not Elsewhere Classified (270), Metal Heat Treating (271),

Primary Metal Products, Not Elsewhere Classified (272), Metal Cans (273), Metal Barrels, Drums and Pails (274), Cutlery (275), Hand and

Edge Tools, Not Elsewhere Classified (276), Hand Saws and Saw Blades (277), Hardware, Not Elsewhere Classified (278), Metal Sanitary

Ware (279), Plumbing Fixture Fittings and Trim (280), Heating Equipment, Except Electric (281), Fabricated Structural Metal (282), Metal

Doors, Sash, and Trim (283), Fabricated Plate Work (Boiler Shops) (284), Sheet Metal Work (285), Architectural Metal Work (286),

Prefabricated Metal Buildings (287), Miscellaneous Metal Work (288), Screw Machine Products and Bolts, Etc. (289), Iron and Steel

Forgings (290), Nonferrous Forgings (291), Automotive Stampings (292), Crowns and Closures (293), Metal Stampings, Not Elsewhere

Classified (294), Plating and Polishing (295), Metal Coating and Allied Services (296), Small Arms Ammunition (297), Ammunition,

Except For Small Arms, Not Elsewhere Classified (298), Small Arms (299), Other ordnance and Accessories (300), Industrial and Fluid

Valves (301), Steel Springs, Except Wire (302), Pipe, Valves, and Pipe Fittings (303), Miscellaneous Fabricated Wire Products (304), Metal

Foil and Leaf (305), Fabricated Metal Products, Not Elsewhere Classified (306), Steam Engines and Turbines (307), Internal Combustion

Engines, Not Elsewhere Classified (308), Farm Machinery and Equipment (309), Lawn and Garden Equipment (310), Construction

Machinery and Equipment (311), Mining Machinery, Except Oil Field (312), Oil Field Machinery (313), Elevators and Moving Stairways

(314), Conveyors and Conveying Equipment (315), Hoists, Cranes, and Monorails (316), Industrial Trucks and Tractors (317), Machine

Tools, Metal Cutting Types (318), Machine Tools, Metal Forming Types (319), Industrial Patterns (320), Special Dies and Tools and

Accessories (321), Power Driven Hand Tools (322), Rolling Mill Machinery (323), Welding Apparatus (324), Metalworking Machinery,

Not Elsewhere Classified (325), Textile Machinery (326), Woodworking Machinery (327), Paper Industries Machinery (328), Printing

Trades Machinery (329), Food Products Machinery (330), Special Industry Machinery Not Elsewhere Classified (331), Pumps and

Compressors (332), Ball and Roller Bearings (333), Blowers and Fans (334), Packaging Machinery (335), Power Transmission Equipment

(336), Industrial Furnaces and Ovens (337), General Industrial Machinery, Not Elsewhere Classified (338), Electronic Computers (339),

Computer Storage Devices (340), Computer Terminals (341), Computer Peripheral Equipment, (342), Calculating and Accounting

Machines (343), Typewriters and Office Machines Not Elsewhere Classified (344), Automatic Merchandising Machine (345), Commercial

Laundry Equipment (346), Refrigeration and Heating Equipment (347), Measuring and Dispensing Pumps (348), Service Industry

Machines, Not Elsewhere Classified (349), Fluid Power Cylinders and Actuators (351), Fluid Power Pumps and Motors (352), Scales and

Balances (353), Industrial Machines Not Elsewhere Classified (354), Transformers (355), Switchgear and Switchboard Apparatus (356),

Motors and Generators (357), Carbon and Graphite Products (358), Relays and Industrial Controls (359), Electrical Industrial Apparatus,

Not Elsewhere Classified (360), Household Cooking Equipment (361), Household Refrigerators and Freezers (362), Household Laundry

Equipment (363), Electric House wares and Fans (364), Household Vacuum Cleaners (365), Household Appliances, Not Elsewhere

Classified (366), Electric Lamps (367), Wiring Devices (368), Lighting Fixtures and Equipment (369), Primary Batteries, Dry and Wet

(380), Engine Electrical Equipment (381), Magnetic and Optical Recording Media (382), Aircraft (389), Aircraft and Missile Engines and

Parts (390), Aircraft and Missile Equipment, (391), Railroad Equipment (394), Complete Guided Missiles (396), Tanks and Tank

Components (398), Search and Navigation Equipment (400), Laboratory Apparatus and Furniture (401), Automatic Temperature Controls

(402), Mechanical Measuring Devices (403), Instruments To Measure Electricity (404), Analytical Instruments (405), Optical Instruments

and Lenses (406), X-Ray Apparatus (410), Electro-medical Apparatus (411), Ophthalmic Goods (412), Watches, Clocks, and Parts (414),

Jewelry, Precious Metal (415), Silverware and Plated Ware (416), Jewelers Materials and Lapidary Work (417), Pens and Mechanical

Pencils (422), Lead Pencils and Art Goods (423), Marking Devices (424), Carbon Paper and Inked Ribbons (425), Costume Jewelry (426),

Fasteners, Buttons, Needles, Pins (427), Brooms and Brushes (428), Signs and Advertising Displays (429), Burial Caskets and Vaults (430),

Hard Surface Floor Coverings (431), Manufacturing Industries, Not Elsewhere Classified (432), Transportation Services (440), Electrical

Repair Service (480), Watch, Clock, Jewelry and Furniture Repair (481), Miscellaneous Repair Shops (482), Motion Pictures (483),

Theatrical Producers, Bands Etc. (484), Bowling Alleys and Pool Halls (485), Racing and Track Operation (487), Amusement and

Recreation Services, Not Elsewhere Classified (488), Membership Sports and Recreation Clubs (489), Child Day Care Services (499),

Research, Development and Testing Services (509), Federal Electric Utilities (514), Non-comparable Imports (516), Used and Secondhand

Goods (518), Federal Government – Military (519), Federal Government – Non-Military (520), Commodity Credit Corporation (521), State

and Local Government – Non-Education (523), Rest Of The World Industry (524), Dummy (527), Inventory Valuation Adjustment (528).
116

Table 75. (continued)

• Personal Services, Agricultural, Forestry, Fishery Services (26), Landscape and Horticultural Services (27), Laundry, Cleaning and Shoe

Repair (464), Portrait and Photographic Studios (465), Beauty and Barber Shops (466), Funeral Service and Crematories (467),

Miscellaneous Personal Services (468), Advertising (469), Other Business Services (470), Photo-finishing, Commercial Photography (471),

Services To Buildings (472), Equipment Rental and Leasing (473), Personnel Supply Services (474), Computer and Data Processing

Services (475), Detective and Protective Services (476), Legal Services (494), Engineering, Architectural Services (506), Accounting,

Auditing and Bookkeeping (507), Management and Consulting Services (508).

• Pe tr o l e u m P r o d u c ts , Pe tr o l e u m R e f i n i n g ( 2 1 0 ) , Lu b r ic a ti n g O i ls a n d G r e a s e s ( 2 1 3 ) , Pe tr o l e u m a n d Co a l Pr o d u c t s , N o t El s e w h e r e 

Cl a s s i f ie d ( 2 1 4 ) 

• Publications/Paper, Pulp Mills (161), Paper Mills, Except Building Paper (162), Paperboard Mills (163), Paperboard Containers and Boxes

(164), Paper Coated and Laminated Packaging (165), Paper Coated and Laminated Not Elsewhere Classified (166), Bags, Plastic (167),

Bags, Paper (168), Die-cut Paper and Board (169), Sanitary Paper Products (170), Envelopes (171), Stationery Products (172), Converted

Paper Products, Not Elsewhere Classified (173), Newspapers (174), Periodicals (175), Book Publishing (176), Book Printing (177),

Miscellaneous Publishing (178), Commercial Printing (179), Manifold Business Forms (180), Greeting Card Publishing (181), Blank books

and Loose-leaf Binder (182), Bookbinding and Related (183), Typesetting (184), Plate Making (185).

• Recreation Activities, Motor Homes (388), Ship Building and Repairing (392), Boat Building and Repairing (393), Travel Trailers and

Camper (397), Photographic Equipment and Supplies (413), Musical Instruments (418), Dolls (419), Games, Toys, and Children’s Vehicles

(420), Sporting and Athletic Goods, Not Elsewhere Classified (421), Marine Charter Boats (486), Kenai River Guides (526).

• Retail Trade, Building Materials and Gardening (448), General Merchandise Stores (449), Miscellaneous Retail (455).

• State/Local Services, Social Services, Not Elsewhere Classified (500), Residential Care (501), Other State and Local Government

Enterprises (512), Other Federal Government Enterprises (515).

• Transportation Services, Transportation Equipment, Not Elsewhere Classified (399), Railroads and Related Services (433), Local,

Interurban Passenger Transit (434), Motor Freight Transport and Warehousing (435), Water Transportation (436), Air Transportation (437),

Pipe Lines, Except Natural Gas (438), Arrangement Of Passenger Transportation (439), Local Government Passenger Transit (510).

• U.S. Postal Service, U.S. Postal Service (513).

• Utilities, Electric Services (443), Gas Production and Distribution (444), Water Supply and Sewerage Systems (445), Sanitary Services and

Steam Supply (446), State and Local Electric Utilities (511), Scrap (517).

• Wholesale Trade, Wholesale Trade (447).
117

Appendix to Section 6. Impact analysis

Table 76. Regional economic impacts of a 10% decrease in lower and central Cook Inlet sportfishing catches.

Response Coefficient Bas elin e 

e xp en ditur es ($ ) D ir ec t O utpu t ( $) I nd ir ec t O utp ut ( $) I nd uc ed Ou tp u t ($ ) Total O u tp ut ($ )

Employment Compensation

Auto or Truck Fuel 2,619,715 – 74,507 – 9,415 – 13,087 – 97,009

Groceries 2,864,102 – 91,501 – 5,290 – 15,888 – 112,680

Lodging 3,226,870 – 55,791 – 11,027 – 11,900 – 78,719

Restaurant & Bar 2,561,923 – 46,840 – 6,827 – 8,899 – 62,566

Boat Fuel, Lubricants & Repairs 1,732,240 – 42,840 – 5,300 – 7,723 – 55,863

Charter & Guide Fees 10,366,927 – 58,071 – 65,235 – 45,659 – 168,965

Fish Processing or Packaging 2,307,448 – 43,139 – 4,538 – 7,744 – 55,420

Fishing Derby Entry Fees 269,302 – 4,491 – 685 – 712 – 5,888

Fishing Gear 1,904,030 – 38,655 – 3,634 – 7,342 – 49,632

Haul Out & Moorage Fees 671,617 – 6,556 – 2,075 – 1,533 – 10,164

TOTAL 28,524,174 – 462,392 – 114,026 – 120,488 – 696,906

Proprietors Income

Auto or Truck Fuel 2,619,715 – 11,353 – 2,295 – 3,243 – 16,890

Groceries 2,864,102 – 20,329 – 1,337 – 3,937 – 25,602

Lodging 3,226,870 – 19,441 – 2,529 – 2,950 – 24,920

Restaurant & Bar 2,561,923 – 11,459 – 1,273 – 2,206 – 14,938

Boat Fuel, Lubricants & Repairs 1,732,240 – 8,209 – 1,232 – 1,914 – 11,355

Charter & Guide Fees 10,366,927 – 203,199 – 14,154 – 11,317 – 228,670

Fish Processing or Packaging 2,307,448 – 8,362 – 1,695 – 1,919 – 11,976

Fishing Derby Entry Fees 269,302 0 – 137 – 175 – 311

Fishing Gear 1,904,030 – 11,399 – 1,050 – 1,819 – 14,268

Haul Out & Moorage Fees 671,617 – 2,368 – 437 – 380 – 3,185

TOTAL 28,524,174 – 296,118 – 26,139 – 29,858 – 352,115

Indirect Business Taxes

Auto or Truck Fuel 2,619,715 – 11,023 – 2,595 – 2,160 – 15,777

Groceries 2,864,102 – 14,793 – 1,508 – 2,622 – 18,923

Lodging 3,226,870 – 8,171 – 2,110 – 1,963 – 12,243

Restaurant & Bar 2,561,923 – 3,973 – 1,967 – 1,468 – 7,408

Boat Fuel, Lubricants & Repairs 1,732,240 – 6,112 – 1,424 – 1,275 – 8,811

Charter & Guide Fees 10,366,927 – 6,333 – 12,177 – 7,530 – 26,040

Fish Processing or Packaging 2,307,448 – 2,470 – 943 – 1,278 – 4,691

Fishing Derby Entry Fees 269,302 – 17 – 136 – 116 – 269

Fishing Gear 1,904,030 – 6,257 – 945 – 1,212 – 8,414

Haul Out & Moorage Fees 671,617 – 1,056 – 540 – 253 – 1,849

TOTAL 28,524,174 – 60,205 – 24,344 – 19,876 – 104,426

Other Property Type Income

Auto or Truck Fuel 2,619,715 – 11,355 – 7,086 – 4,660 – 23,100

Groceries 2,864,102 – 19,674 – 4,227 – 5,657 – 29,558

Lodging 3,226,870 – 17,063 – 6,358 – 4,236 – 27,657

Restaurant & Bar 2,561,923 – 7,113 – 4,901 – 3,168 – 15,182

Boat Fuel, Lubricants & Repairs 1,732,240 – 7,521 – 3,854 – 2,750 – 14,125

Charter & Guide Fees 10,366,927 – 69,849 – 19,817 – 16,252 – 105,917

Fish Processing or Packaging 2,307,448 – 5,840 – 1,909 – 2,757 – 10,507

Fishing Derby Entry Fees 269,302 0 – 361 – 251 – 612

Fishing Gear 1,904,030 – 8,853 – 2,460 – 2,614 – 13,927

Haul Out & Moorage Fees 671,617 – 3,235 – 1,074 – 546 – 4,855

TOTAL 28,524,174 – 150,503 – 52,048 – 42,890 – 245,440

Total Value Added

Auto or Truck Fuel 2,619,715 – 108,238 – 21,390 – 23,149 – 152,777

Groceries 2,864,102 – 146,296 – 12,363 – 28,104 – 186,763

Lodging 3,226,870 – 100,466 – 22,024 – 21,048 – 143,539

Restaurant & Bar 2,561,923 – 69,385 – 14,969 – 15,741 – 100,095

Boat Fuel, Lubricants & Repairs 1,732,240 – 64,681 – 11,810 – 13,661 – 90,152

Charter & Guide Fees 10,366,927 – 337,452 – 111,382 – 80,758 – 529,592

Fish Processing or Packaging 2,307,448 – 59,812 – 9,085 – 13,698 – 82,594

Fishing Derby Entry Fees 269,302 – 4,508 – 1,318 – 1,255 – 7,081

Fishing Gear 1,904,030 – 65,164 – 8,090 – 12,987 – 86,241

Haul Out & Moorage Fees 671,617 – 13,215 – 4,126 – 2,711 – 20,053

TOTAL 28,524,174 – 969,217 – 216,558 – 213,112 – 1,398,887
118

Table 77. Regional economic impacts of a 20% decrease in lower and central Cook Inlet sportfishing catches.

Response Coefficient Bas elin e 

e xp en ditur es ($ ) D ir ec t O utpu t ( $) I nd ir ec t O utp ut ( $) I nd uc ed Ou tp u t ($ ) Total O u tp ut ($ )

Employment Compensation

Auto or Truck Fuel 2,619,715 – 172,546 – 21,803 – 30,307 – 224,656

Groceries 2,864,102 – 212,487 – 12,286 – 36,897 – 261,670

Lodging 3,226,870 – 130,804 – 25,853 – 27,901 – 184,558

Restaurant & Bar 2,561,923 – 108,933 – 15,876 – 20,697 – 145,506

Boat Fuel, Lubricants & Repairs 1,732,240 – 98,479 – 12,183 – 17,754 – 128,416

Charter & Guide Fees 10,366,927 – 136,795 – 153,672 – 107,556 – 398,024

Fish Processing or Packaging 2,307,448 – 102,653 – 10,798 – 18,427 – 131,878

Fishing Derby Entry Fees 269,302 – 10,549 – 1,608 – 1,673 – 13,831

Fishing Gear 1,904,030 – 91,767 – 8,628 – 17,430 – 117,825

Haul Out & Moorage Fees 671,617 – 15,234 – 4,823 – 3,562 – 23,618

TOTAL 28,524,174 – 1,080,249 – 267,531 – 282,203 – 1,629,983

Proprietors Income

Auto or Truck Fuel 2,619,715 – 26,291 – 5,314 – 7,509 – 39,114

Groceries 2,864,102 – 47,208 – 3,105 – 9,142 – 59,455

Lodging 3,226,870 – 45,580 – 5,929 – 6,916 – 58,425

Restaurant & Bar 2,561,923 – 26,649 – 2,961 – 5,130 – 34,740

Boat Fuel, Lubricants & Repairs 1,732,240 – 18,870 – 2,833 – 4,399 – 26,102

Charter & Guide Fees 10,366,927 – 478,669 – 33,342 – 26,659 – 538,669

Fish Processing or Packaging 2,307,448 – 19,898 – 4,034 – 4,566 – 28,498

Fishing Derby Entry Fees 269,302 0 – 321 – 411 – 732

Fishing Gear 1,904,030 – 27,060 – 2,493 – 4,319 – 33,872

Haul Out & Moorage Fees 671,617 – 5,503 – 1,015 – 883 – 7,400

TOTAL 28,524,174 – 695,728 – 61,346 – 69,933 – 827,008

Indirect Business Taxes

Auto or Truck Fuel 2,619,715 – 25,527 – 6,009 – 5,002 – 36,537

Groceries 2,864,102 – 34,353 – 3,502 – 6,089 – 43,945

Lodging 3,226,870 – 19,156 – 4,946 – 4,602 – 28,704

Restaurant & Bar 2,561,923 – 9,239 – 4,576 – 3,414 – 17,228

Boat Fuel, Lubricants & Repairs 1,732,240 – 14,051 – 3,273 – 2,930 – 20,254

Charter & Guide Fees 10,366,927 – 14,918 – 28,684 – 17,739 – 61,341

Fish Processing or Packaging 2,307,448 – 5,877 – 2,244 – 3,041 – 11,162

Fishing Derby Entry Fees 269,302 – 41 – 319 – 273 – 633

Fishing Gear 1,904,030 – 14,855 – 2,244 – 2,877 – 19,976

Haul Out & Moorage Fees 671,617 – 2,454 – 1,255 – 587 – 4,297

TOTAL 28,524,174 – 140,472 – 57,052 – 46,554 – 244,077

Other Property Type Income

Auto or Truck Fuel 2,619,715 – 26,296 – 16,410 – 10,791 – 53,496

Groceries 2,864,102 – 45,687 – 9,817 – 13,137 – 68,641

Lodging 3,226,870 – 40,005 – 14,907 – 9,931 – 64,843

Restaurant & Bar 2,561,923 – 16,543 – 11,398 – 7,367 – 35,308

Boat Fuel, Lubricants & Repairs 1,732,240 – 17,288 – 8,860 – 6,321 – 32,470

Charter & Guide Fees 10,366,927 – 164,541 – 46,681 – 38,283 – 249,505

Fish Processing or Packaging 2,307,448 – 13,898 – 4,543 – 6,561 – 25,002

Fishing Derby Entry Fees 269,302 0 – 849 – 590 – 1,438

Fishing Gear 1,904,030 – 21,017 – 5,839 – 6,206 – 33,062

Haul Out & Moorage Fees 671,617 – 7,517 – 2,496 – 1,268 – 11,281

TOTAL 28,524,174 – 352,791 – 121,801 – 100,455 – 575,046

Total Value Added

Auto or Truck Fuel 2,619,715 – 250,660 – 49,536 – 53,609 – 353,805

Groceries 2,864,102 – 339,735 – 28,710 – 65,265 – 433,711

Lodging 3,226,870 – 235,545 – 51,636 – 49,349 – 336,530

Restaurant & Bar 2,561,923 – 161,364 – 34,812 – 36,607 – 232,783

Boat Fuel, Lubricants & Repairs 1,732,240 – 148,688 – 27,149 – 31,404 – 207,241

Charter & Guide Fees 10,366,927 – 794,923 – 262,379 – 190,238 – 1,247,540

Fish Processing or Packaging 2,307,448 – 142,327 – 21,619 – 32,595 – 196,540

Fishing Derby Entry Fees 269,302 – 10,590 – 3,096 – 2,947 – 16,633

Fishing Gear 1,904,030 – 154,699 – 19,204 – 30,831 – 204,734

Haul Out & Moorage Fees 671,617 – 30,708 – 9,589 – 6,300 – 46,596

TOTAL 28,524,174 – 2,269,239 – 507,729 – 499,144 – 3,276,113
119

Table 78. Regional economic impacts of a 30% decrease in lower and central Cook Inlet sportfishing catches.

Response Coefficient Bas elin e 

e xp en ditur es ($ ) D ir ec t O utpu t ( $) I nd ir ec t O utp ut ( $) I nd uc ed Ou tp u t ($ ) Total O u tp ut ($ )

Employment Compensation

Auto or Truck Fuel 2,619,715 – 294,203 – 37,176 – 51,675 – 383,055

Groceries 2,864,102 – 363,507 – 21,018 – 63,120 – 447,645

Lodging 3,226,870 – 226,305 – 44,729 – 48,271 – 319,305

Restaurant & Bar 2,561,923 – 186,677 – 27,207 – 35,468 – 249,352

Boat Fuel, Lubricants & Repairs 1,732,240 – 166,421 – 20,588 – 30,002 – 217,011

Charter & Guide Fees 10,366,927 – 237,976 – 267,337 – 187,111 – 692,423

Fish Processing or Packaging 2,307,448 – 180,656 – 19,002 – 32,429 – 232,087

Fishing Derby Entry Fees 269,302 – 18,291 – 2,789 – 2,901 – 23,981

Fishing Gear 1,904,030 – 161,067 – 15,143 – 30,592 – 206,802

Haul Out & Moorage Fees 671,617 – 26,081 – 8,256 – 6,098 – 40,435

TOTAL 28,524,174 – 1,861,183 – 463,245 – 487,668 – 2,812,096

Proprietors Income

Auto or Truck Fuel 2,619,715 – 44,828 – 9,061 – 12,804 – 66,693

Groceries 2,864,102 – 80,759 – 5,312 – 15,639 – 101,711

Lodging 3,226,870 – 78,858 – 10,258 – 11,965 – 101,081

Restaurant & Bar 2,561,923 – 45,668 – 5,075 – 8,791 – 59,534

Boat Fuel, Lubricants & Repairs 1,732,240 – 31,889 – 4,787 – 7,434 – 44,109

Charter & Guide Fees 10,366,927 – 832,717 – 58,003 – 46,378 – 937,097

Fish Processing or Packaging 2,307,448 – 35,019 – 7,100 – 8,035 – 50,153

Fishing Derby Entry Fees 269,302 0 – 556 – 713 – 1,269

Fishing Gear 1,904,030 – 47,495 – 4,376 – 7,580 – 59,451

Haul Out & Moorage Fees 671,617 – 9,421 – 1,737 – 1,511 – 12,669

TOTAL 28,524,174 – 1,206,654 – 106,264 – 120,849 – 1,433,767

Indirect Business Taxes

Auto or Truck Fuel 2,619,715 – 43,526 – 10,245 – 8,528 – 62,299

Groceries 2,864,102 – 58,769 – 5,991 – 10,417 – 75,177

Lodging 3,226,870 – 33,142 – 8,558 – 7,961 – 49,661

Restaurant & Bar 2,561,923 – 15,833 – 7,841 – 5,850 – 29,524

Boat Fuel, Lubricants & Repairs 1,732,240 – 23,744 – 5,531 – 4,951 – 34,227

Charter & Guide Fees 10,366,927 – 25,953 – 49,900 – 30,860 – 106,713

Fish Processing or Packaging 2,307,448 – 10,343 – 3,949 – 5,352 – 19,644

Fishing Derby Entry Fees 269,302 – 70 – 552 – 474 – 1,097

Fishing Gear 1,904,030 – 26,072 – 3,939 – 5,049 – 35,060

Haul Out & Moorage Fees 671,617 – 4,202 – 2,148 – 1,006 – 7,356

TOTAL 28,524,174 – 241,655 – 98,656 – 80,448 – 420,758

Other Property Type Income

Auto or Truck Fuel 2,619,715 – 44,836 – 27,980 – 18,399 – 91,215

Groceries 2,864,102 – 78,157 – 16,794 – 22,474 – 117,425

Lodging 3,226,870 – 69,212 – 25,791 – 17,181 – 112,185

Restaurant & Bar 2,561,923 – 28,349 – 19,533 – 12,624 – 60,507

Boat Fuel, Lubricants & Repairs 1,732,240 – 29,215 – 14,973 – 10,682 – 54,871

Charter & Guide Fees 10,366,927 – 286,244 – 81,209 – 66,599 – 434,052

Fish Processing or Packaging 2,307,448 – 24,458 – 7,995 – 11,547 – 44,000

Fishing Derby Entry Fees 269,302 0 – 1,471 – 1,022 – 2,494

Fishing Gear 1,904,030 – 36,888 – 10,249 – 10,892 – 58,029

Haul Out & Moorage Fees 671,617 – 12,869 – 4,274 – 2,170 – 19,313

TOTAL 28,524,174 – 610,229 – 210,269 – 173,593 – 994,091

Total Value Added

Auto or Truck Fuel 2,619,715 – 427,393 – 84,462 – 91,407 – 603,262

Groceries 2,864,102 – 581,193 – 49,114 – 111,651 – 741,958

Lodging 3,226,870 – 407,517 – 89,336 – 85,378 – 582,231

Restaurant & Bar 2,561,923 – 276,528 – 59,656 – 62,733 – 398,917

Boat Fuel, Lubricants & Repairs 1,732,240 – 251,270 – 45,879 – 53,070 – 350,218

Charter & Guide Fees 10,366,927 – 1,382,889 – 456,449 – 330,947 – 2,170,286

Fish Processing or Packaging 2,307,448 – 250,476 – 38,046 – 57,362 – 345,885

Fishing Derby Entry Fees 269,302 – 18,361 – 5,369 – 5,110 – 28,840

Fishing Gear 1,904,030 – 271,522 – 33,707 – 54,113 – 359,342

Haul Out & Moorage Fees 671,617 – 52,572 – 16,416 – 10,785 – 79,773

TOTAL 28,524,174 – 3,919,721 – 878,433 – 862,558 – 5,660,712
120

Table 79. Regional economic impacts of a 10% increase in lower and central Cook Inlet sportfishing catches.

Response Coefficient Bas elin e 

e xp en ditur es ($ ) D ir ec t O utpu t ( $) I nd ir ec t O utp ut ( $) I nd uc ed Ou tp u t ($ ) Total O u tp ut ($ )

Employment Compensation

Auto or Truck Fuel 2,619,715 54,086 6,834 9,500 70,420

Groceries 2,864,102 66,152 3,825 11,487 81,464

Lodging 3,226,870 39,765 7,860 8,482 56,106

Restaurant & Bar 2,561,923 33,791 4,925 6,420 45,136

Boat Fuel, Lubricants & Repairs 1,732,240 31,433 3,889 5,667 40,988

Charter & Guide Fees 10,366,927 41,093 46,162 32,309 119,565

Fish Processing or Packaging 2,307,448 30,053 3,161 5,395 38,609

Fishing Derby Entry Fees 269,302 3,192 487 506 4,185

Fishing Gear 1,904,030 27,029 2,541 5,134 34,704

Haul Out & Moorage Fees 671,617 4,735 1,499 1,107 7,341

TOTAL 28,524,174 331,328 81,183 86,007 498,518

Proprietors Income

Auto or Truck Fuel 2,619,715 8,241 1,666 2,354 12,261

Groceries 2,864,102 14,697 967 2,846 18,510

Lodging 3,226,870 13,857 1,802 2,102 17,761

Restaurant & Bar 2,561,923 8,267 919 1,591 10,777

Boat Fuel, Lubricants & Repairs 1,732,240 6,023 904 1,404 8,331

Charter & Guide Fees 10,366,927 143,790 10,016 8,008 161,814

Fish Processing or Packaging 2,307,448 5,825 1,181 1,337 8,343

Fishing Derby Entry Fees 269,302 0 97 124 221

Fishing Gear 1,904,030 7,970 734 1,272 9,976

Haul Out & Moorage Fees 671,617 1,711 315 274 2,300

TOTAL 28,524,174 210,380 18,601 21,313 250,294

Indirect Business Taxes

Auto or Truck Fuel 2,619,715 8,002 1,883 1,568 11,453

Groceries 2,864,102 10,695 1,090 1,896 13,681

Lodging 3,226,870 5,824 1,504 1,399 8,726

Restaurant & Bar 2,561,923 2,866 1,419 1,059 5,344

Boat Fuel, Lubricants & Repairs 1,732,240 4,485 1,045 935 6,465

Charter & Guide Fees 10,366,927 4,481 8,617 5,329 18,427

Fish Processing or Packaging 2,307,448 1,721 657 890 3,268

Fishing Derby Entry Fees 269,302 12 96 83 191

Fishing Gear 1,904,030 4,375 661 847 5,884

Haul Out & Moorage Fees 671,617 763 390 183 1,336

TOTAL 28,524,174 43,223 17,363 14,188 74,774

Other Property Type Income

Auto or Truck Fuel 2,619,715 8,243 5,144 3,382 16,769

Groceries 2,864,102 14,223 3,056 4,090 21,369

Lodging 3,226,870 12,162 4,532 3,019 19,712

Restaurant & Bar 2,561,923 5,132 3,536 2,285 10,953

Boat Fuel, Lubricants & Repairs 1,732,240 5,518 2,828 2,018 10,364

Charter & Guide Fees 10,366,927 49,427 14,023 11,500 74,950

Fish Processing or Packaging 2,307,448 4,069 1,330 1,921 7,320

Fishing Derby Entry Fees 269,302 0 257 178 435

Fishing Gear 1,904,030 6,190 1,720 1,828 9,738

Haul Out & Moorage Fees 671,617 2,337 776 394 3,507

TOTAL 28,524,174 107,300 37,201 30,615 175,116

Total Value Added

Auto or Truck Fuel 2,619,715 78,571 15,527 16,804 110,902

Groceries 2,864,102 105,767 8,938 20,319 135,024

Lodging 3,226,870 71,606 15,698 15,002 102,306

Restaurant & Bar 2,561,923 50,055 10,799 11,356 72,210

Boat Fuel, Lubricants & Repairs 1,732,240 47,459 8,665 10,024 66,148

Charter & Guide Fees 10,366,927 238,791 78,818 57,147 374,755

Fish Processing or Packaging 2,307,448 41,668 6,329 9,543 57,539

Fishing Derby Entry Fees 269,302 3,204 937 892 5,033

Fishing Gear 1,904,030 45,564 5,656 9,081 60,302

Haul Out & Moorage Fees 671,617 9,545 2,981 1,958 14,484

TOTAL 28,524,174 692,231 154,347 152,124 998,702
121

Table 80. Regional economic impacts of a 20% increase in lower and central Cook Inlet sportfishing catches.

Response Coefficient Bas elin e 

e xp en ditur es ($ ) D ir ec t O utpu t ( $) I nd ir ec t O utp ut ( $) I nd uc ed Ou tp u t ($ ) Total O u tp ut ($ )

Employment Compensation

Auto or Truck Fuel 2,619,715 91,911 11,614 16,144 119,669

Groceries 2,864,102 112,235 6,489 19,489 138,213

Lodging 3,226,870 67,082 13,259 14,309 94,649

Restaurant & Bar 2,561,923 57,282 8,349 10,883 76,514

Boat Fuel, Lubricants & Repairs 1,732,240 53,641 6,636 9,670 69,947

Charter & Guide Fees 10,366,927 69,119 77,646 54,345 201,109

Fish Processing or Packaging 2,307,448 50,223 5,283 9,015 64,521

Fishing Derby Entry Fees 269,302 5,378 820 853 7,052

Fishing Gear 1,904,030 45,239 4,253 8,592 58,085

Haul Out & Moorage Fees 671,617 8,031 2,542 1,878 12,451

TOTAL 28,524,174 560,141 136,891 145,178 842,210

Proprietors Income

Auto or Truck Fuel 2,619,715 14,005 2,831 4,000 20,835

Groceries 2,864,102 24,935 1,640 4,829 31,404

Lodging 3,226,870 23,375 3,041 3,547 29,962

Restaurant & Bar 2,561,923 14,013 1,557 2,698 18,268

Boat Fuel, Lubricants & Repairs 1,732,240 10,278 1,543 2,396 14,217

Charter & Guide Fees 10,366,927 241,857 16,847 13,470 272,173

Fish Processing or Packaging 2,307,448 9,735 1,974 2,234 13,943

Fishing Derby Entry Fees 269,302 0 164 210 373

Fishing Gear 1,904,030 13,340 1,229 2,129 16,698

Haul Out & Moorage Fees 671,617 2,901 535 465 3,901

TOTAL 28,524,174 354,440 31,359 35,977 421,776

Indirect Business Taxes

Auto or Truck Fuel 2,619,715 13,598 3,201 2,664 19,463

Groceries 2,864,102 18,145 1,850 3,216 23,211

Lodging 3,226,870 9,824 2,537 2,360 14,721

Restaurant & Bar 2,561,923 4,858 2,406 1,795 9,060

Boat Fuel, Lubricants & Repairs 1,732,240 7,653 1,783 1,596 11,032

Charter & Guide Fees 10,366,927 7,538 14,493 8,963 30,994

Fish Processing or Packaging 2,307,448 2,875 1,098 1,488 5,461

Fishing Derby Entry Fees 269,302 21 162 139 322

Fishing Gear 1,904,030 7,323 1,106 1,418 9,847

Haul Out & Moorage Fees 671,617 1,294 662 310 2,265

TOTAL 28,524,174 73,130 29,297 23,949 126,376

Other Property Type Income

Auto or Truck Fuel 2,619,715 14,007 8,741 5,748 28,496

Groceries 2,864,102 24,132 5,185 6,939 36,256

Lodging 3,226,870 20,516 7,645 5,093 33,254

Restaurant & Bar 2,561,923 8,699 5,994 3,874 18,567

Boat Fuel, Lubricants & Repairs 1,732,240 9,417 4,826 3,443 17,686

Charter & Guide Fees 10,366,927 83,137 23,587 19,343 126,067

Fish Processing or Packaging 2,307,448 6,800 2,223 3,210 12,232

Fishing Derby Entry Fees 269,302 0 433 301 733

Fishing Gear 1,904,030 10,361 2,879 3,059 16,299

Haul Out & Moorage Fees 671,617 3,963 1,316 668 5,947

TOTAL 28,524,174 181,031 62,828 51,679 295,537

Total Value Added

Auto or Truck Fuel 2,619,715 133,520 26,387 28,556 188,463

Groceries 2,864,102 179,447 15,164 34,473 229,085

Lodging 3,226,870 120,797 26,481 25,308 172,586

Restaurant & Bar 2,561,923 84,853 18,306 19,250 122,408

Boat Fuel, Lubricants & Repairs 1,732,240 80,989 14,788 17,106 112,882

Charter & Guide Fees 10,366,927 401,650 132,572 96,121 630,344

Fish Processing or Packaging 2,307,448 69,633 10,577 15,947 96,158

Fishing Derby Entry Fees 269,302 5,399 1,579 1,503 8,480

Fishing Gear 1,904,030 76,263 9,467 15,199 100,929

Haul Out & Moorage Fees 671,617 16,189 5,055 3,321 24,565

TOTAL 28,524,174 1,168,741 260,375 256,783 1,685,900
122
123

APPENDIX B

The following is the manual that accompanies the software

package $FISH and is a reproduction of Hamel et al. [2001]
124
125

$FISH

An economic assessment of lower and central Cook Inlet

sport fisheries

SOFTWARE MANUAL

April 2001

Charles Hamel1

, Mark Herrmann2, S. Todd Lee3, and Keith R. Criddle4

The authors are with: 1) North Pacific Fishery Management Council; 2) Department of Economics, University of Alaska Fairbanks; 3)

Alaska Fisheries Science Center, National Marine Fisheries Service; and, 4) Department of Economics, Utah State University. Any

views expressed are solely those of the authors, and should not be construed as representing those of the institutions by which they

are employed. Correspondence should be addressed to Charles Hamel (chuck.hamel@noaa.gov) or Mark Herrmann

(ffmlh@uaf.edu). 1. Introduction to $FISH
126

1. Introduction to $FISH

$FISH is an interactive Microsoft Excel based computer program developed to accompany An Economic

Assessment of the Sport Fisheries for Halibut, and Chinook and Coho Salmon in Lower and Central Cook

Inlet [Herrmann et al. 2001]. This program is designed to explore the economic effects of changes in the

attributes of lower and central Cook Inlet sport fisheries for halibut or salmon that might arise from

changes in abundance, or regulatory change. The program derives compensating variations (net benefits

to sport fishers) and regional economic impacts to the western Kenai Peninsula associated with the

perceived angler reaction to modeled changes.

System requirements

$FISH.xls is designed to run in Microsoft Excel for Office 97 or Office 2000 under the Windows 9x,

Windows 2000, or Windows NT operating system. A Pentium class PC with at least 64 MB of RAM is

recommended. The program is memory and processor intensive and will run best on PCs with a large

amount of memory and a fast processor.

Installation

$FISH.xls is distributed as a compressed Microsoft Excel file. Upon extraction, save $FISH.xls to a

preferred directory and launch with the standard file open sequence in Microsoft Excel. Depending on

options selected during the installation of Microsoft Excel, it may be necessary to copy some drivers into

system directories.

2. Operating $FISH

Initial View

When $FISH.xls is opened, the user may be warned it contains macros. These macros are required for

program operation. To run the program, select the Enable Macros button. $FISH.xls is set to open as a

password-protected, read-only workbook. Please select the Read only button to continue to launch the

application. Additionally, a message box may appear prompting the user to update any information linked

to other workbooks. If this message appears, select No .
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An opening splash screen will be displayed:

After the Begin button has been selected a form will appear prompting the user to enter: 1) mean catch

and weight attributes; 2) the percentage change to apply to the mean attributes; 3) sectors to include in the

analysis (i.e. charter, private boat, and shore-based fishing); 4) baseline year and the number of angler

days for the year modeled; and 5) an inflation factor to apply to the model’s nominal dollar values.

IMPORTANT: The user can select effort values that correspond to the 1996 through 1999 time period, or

supplant his or her own values under the “Other” option of the tab strip. However, the estimated angler

days expenditures are based on 1997 data, which is the default year for this model. (See Table 1 of

Herrmann et al. [2001] for a more detailed representation of Cook Inlet sportfishing effort.)
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The Alaska Department of Fish and Game (ADF&G) reported effort levels for 1996–1999 can be selected

by clicking on the appropriate years. Or, if “Other” is selected, the user can model for any effort desired

by entering new values. Note however, that unless the mean 1997 level attributes are changed, the

resulting monetary effects will be based on 1997 level average trip attributes even if the total figures are

based on alternative year total effort.

Selecting the Next > button will update the trip attributes and display a worksheet titled Baseline Data.

Changes in estimated participation are displayed by residency, and the expected participation change in

terms of angler days fished for each sector and residency category are also shown. If the baseline data

needs to be changed, the user form can be called back by selecting the Change Data button.

This and other worksheets within the model have been programmatically customized for viewing on a

17 inch monitor. If the user needs to make changes to these settings, they can be adjusted by calling the

Zoom feature under the View heading of Excel’s menu bar.

After entering new data values or accepting the default values, select the Next > button to move to the

output worksheets. The Next > button is always located in the bottom right-hand corner of the display.

To print the current worksheet the user can select the Print button. On the last page the reader can select

.Return to return to the Baseline Data sheet.
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Additional Worksheets

Upon clicking the Next > button from the Baseline Data worksheet, the model will generate economic

impacts and output them to a worksheet titled Economic Impacts. This sheet shows the regional

economic impacts resulting from changes in angler spending for the 10 expenditure categories. (See

Table 23 of Herrmann et al. [2001] for the corresponding baseline expenditures.) Impacts can be

evaluated for sales (output), employment, income, and other value added classes.

A worksheet titled Impacts by Final Demand Category shows the same economic impacts distributed

among 26 aggregated final demand categories. (See Herrmann et al. [2001] Appendix A.)

The fourth and last worksheet, titled Compensating Variations, reports changes to the consumer surplus

measurement described in Section 4 of Herrmann et al. [2001]. The baseline compensating variations for

the 1997 fishery can be found in Herrmann et al. [2001], Table 39.

Simulations

A variety of baseline simulations can be run using the default data for the lower and centralCook Inlet

1997 sport fishery. To perform a simulation click the Begin button from the initial view or the Change Data 

button on the Baseline Data worksheet to call up the Baseline Attributes (1997) user form. All

simulation changes are to be made in this display. Whenever changes to the data are to be simulated, click

the Next > button on the user form.

% Change

% Change is the most important user-modified input. Changes from the baseline mean expected catches

are simulated by selecting the % Change drop down list for the relevant attribute and selecting from the

range of percentage changes shown. Catch numbers and weight changes are available in 5% increments

over the range – 100% to +20%. % Change is bounded by an upper limit of +20% because it is driven by

a functional form chosen for damage assessment and reductions in angler activity stemming from policy

changes. The range for cost percentage changes is – 50% to +50%.

Estimated 1997 Angler Days

The estimated effort used in the Herrmann [2001] study was based on Alaska Department of Fish and

Game estimated effort for the Cook Inlet portion of the Kenai Peninsula for 1997. This effort is likely

to change over the years and can be altered by entering new effort figures in the lower effort box titled

.Estimated 1997 Angler Days . (Before making changes to the fishing attributes please read the Appendix

[note 1].)

Inflation

The baseline simulation defaults to a 1997-dollar base. To inflate these dollar figures to represent impacts

in current dollar terms, enter a percentage value in the Inflation Index text box. For example, if the analyst

believes that the dollar has inflated in value by 8.2% since 1997, s/he should enter 8.2% in the Inflation Index 

box.

Baseline Fishery Data

The baseline fishery data is displayed for the 1997 season aggregated across all fishing sectors. This

information can be changed to reflect current fishery conditions, data permitting. To change this data

click the relevant text box and enter new values. The Alaskan and nonresident values for fish catch,

weight, and trip cost need to be changed individually. (Before making changes to the fishing attributes

please read the Appendix [note 2].)
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Sectors

The initial baseline simulations are for data aggregated across all fishing sectors. If the analyst wishes

to evaluate sector specific effects, such as changes to the charter fishery only, s/he can choose the

appropriate checkbox. (Before making changes to the fishing attributes please the Appendix [note 2].)

3. An Example

The following example evaluates the economic effects of a simulated decline in expected catch of 10%

for both halibut and salmon. This demonstration recreates the scenario and results of values reported in

Herrmann et al. [2001]. From the Baseline Data window select the Change Data button to call up the

Baseline Attributes (1997) user form. Select – 10% from the drop down lists corresponding to the halibut

catch and each of the salmon rows, and make sure that all three sectors are included for analysis

(shoreline, private boat, and charter). The Baseline Attributes (1997) screen should look like:
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After clicking the Next > button, the resulting Baseline Data worksheet will look like:

The model predicts that if the expected catch of halibut and salmon decreases by 10%, resident and

nonresident fishing effort (in days) will decrease by 9.32 and 5.82 percent respectively. (See Table 32 in

Herrmann et al. [2001].) The effort in angler days is shown below the percentage changes. For the 10%

expected reduction in catch it is estimated that sportfishing days will diminish by 15,369 of the 1997

baseline (197,556), for a total of 182,187 days. Estimated changes are disaggregated to fishing sector and

residency category.
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Clicking the Next > button calls up the following view:

The economic impacts are discussed in greater detail in Herrmann et al. [2001]. The above table includes

economic impacts on output from changes in spending for the 10 expenditure sectors queried in the UAF

survey. The direct output reflects the amount of increased or decreased spending of new money for each

angler expense category. For example, reading across the line labeled “Auto or Truck Fuel”, we begin

with a total of $2.62 million based on the amount of money spent by sport fishers in the shore, private

boat, and charter fishery modes that were directly attributable to the saltwater fisheries in 1997. The effort

reductions for residents (9.32%) and nonresidents (5.82%) translate into equivalent reductions in angler

day expenditures for each group. This amounts to a decrease in sales of automotive fuels by $169,195, the

entry under the “Direct Output” column. As fuel sales decline, fuel retail outlets decrease their local

purchases of inputs from other sectors, causing these sectors to also decrease their inputs for a combined

indirect effect of $40,476, and households with members employed by these sectors spend less on local

goods and services for an induced effect of $40,973. Taken together, the total effect on regional sales

(output) caused by the anticipated decreases in sport caught fish and subsequent decline in fuel spending

is $250,162.
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Selecting the Next > button brings up the Impacts by Final Demand Category worksheet, which shows

the simulated impacts reported above in terms of 26 final demand categories by each of the impact classes

(output, employment, personal income, etc.).

Note that the output change of – $2,483,646 matches the “Total Output” figure under the Economic

Impacts view shown earlier. The decreased angler spending in the 10 expenditure categories and

consequent effects are apportioned across a list of 26 industrial sectors.17 For example, the decreased

angler spending causes a decrease in sales of “Recreational Activities” of $386,555. Consequently, 16

jobs are lost and personal income within this sector declines by $156,924.

 

17 These sector aggregations are developed in Jensen [1997].
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Selecting the Next > button one last time calls up the Compensating Variations worksheet:

Compensating variation is analogous to consumer surplus and represents the net benefits to anglers of

fishing. Simply stated, this reflects the difference between the costs anglers would have been willing to

pay and what they actually incurred to fish. For a 10% decrease in expected catch of halibut and salmon

the estimated compensating variation for the Cook Inlet saltwater sport fishers declines from $19.2 to

$14.8 million. This total loss in consumer surplus is estimated to come from a loss of $1,061,973 from

local fishermen, $1,340,780 from other Alaskans and $1,977,692 from nonresidents.

To model a new scenario, select the Return button to return to the Baseline Data worksheet.
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4. Appendix

NOTE 1. Rather than altering the baseline fishery data, changes in expected catch should be simulated by

modification of the % Change . The % Change category affects demand while changing the baseline fishery

data does not. % Change is used to simulate hypothetical changes to the fishery. The baseline fishery data

should reflect actual trip data.

NOTE 2. $FISH was designed based on a “generic” fishing trip using 1997 data. The participation-rate

model was not based on a particular fishing mode; that is, when the respondents stated their preferences

to whether or not they would have taken a presented trip they were not told that the trip was on board

a charter or private vessel or a shoreline trip. To estimate the changes to these three trip modes for

simulated changes in catch, weights, or trip price, the same percentage is applied across fishing modes.

If a researcher wants to simulate just the charter industry, for instance, a check of just the charter industry

will disaggregate these numbers using the “generic” trip attributes. An alternative way of modeling the

charter industry is to use fishery characteristics more often found with a charter trip, and then changing

the charter fishery attributes to reflect this. These two methods will give different results and need to be

discussed within the context of the underlying assumptions.

5. Acknowledgements

This manual is the result of research supported in part by the Minerals Management Service through

University of Alaska Coastal Marine Institute project 12-35-0001-30661 task order 14196 and by Alaska

Sea Grant with funds provided under grant 98-403 R14-17. The University of Alaska Fairbanks School of

Management provided matching funds. Although not used as formal “match”, considerable additional

support for this project was provided in the form of release time by: the Alaska Fisheries Science Center,

National Marine Fisheries Service; the North Pacific Fishery Management Council; and Utah State

University. In addition, the Department of Agricultural and Resource Economics at Oregon State

University made sabbatical office space and supplies available.

We are also grateful for information provided by the Kenai, Seward, and Homer Chambers of Commerce,

Becky Hultberg and Craig Layman (Kenai Peninsula Borough Economic Development District), Sheri

Hobbs (City of Homer), John Williams (former Kenai Peninsula Borough mayor), Tim Evers (Deep

Creek Charter Association), Frank Libal (Anchor Point Charter Association), Robert Ward (Homer

Charter Association), Karl Kircher (Kenai Peninsula Fishermen’s Association), Theo Matthews (United

Cook Inlet Drifters Associations), Al Howe (Alaska Department of Fish and Game), Nancy and John

Hillstrand (Coal Point Trading Co.), Kurt Eriksson (National Bank of Alaska, Soldotna), Doug

Coughenower (Alaska Sea Grant Marine Advisory Program), and Emmett Trimble, Simone Klutts, Vicki

Stik, and Tom Boedeker. Finally, we are indebted to Francine Davis for fund management.