OCS Study 

MMS 2002-045 

 

Boating Uses, Economic Significance, 

and Information Inventory for 

North Carolina’s Offshore Area, 

“The Point” 

Volume II: Economic Analysis of “The Point” 

and Adjacent Counties – Baseline Information, 

Valuation, and Potential Impacts 

U.S. Department of the Interior

Minerals Management Service 

Gulf of Mexico OCS Region 
U.S. Department of the Interior 

Minerals Management Service 

Gulf of Mexico OCS Region 

OCS Study 

MMS 2002-045 

Boating Uses, Economic Significance, 

and Information Inventory for 

North Carolina’s Offshore Area, 

“The Point” 

Volume II: Economic Analysis of “The Point” 

and Adjacent Counties – Baseline Information, 

Valuation, and Potential Impacts 

Authors 

Raymond B. Palmquist 

North Carolina State University 

Department of Economics 

Raleigh, North Carolina 

Peter W. Schumann 

University of North Carolina 

Department of Economics and Finance 

Wilmington, North Carolina 

Jeffrey A. Michael 

Towson University 

Department of Economics 

Towson, Maryland 

Prepared under MMS Contract 

1435-01-98-CA-30949 

by 

North Carolina National Estuarine Research Reserve 

North Carolina Division of Coastal Management 

One Marvin Moss Lane 

Wilmington, North Carolina 28409 

Published by 

New Orleans

August 2002 
DISCLAIMER 

This report was prepared under contract between the Minerals Management Service (MMS) and 

the North Carolina Division of Coastal Management. This report has been technically reviewed

by the MMS, and it has been approved for publication. Approval does not signify that the 

contents necessarily reflect the views and policies of the MMS, nor does mention of trade names

or commercial products constitute endorsement or recommendation for use. It is, however, 

exempt from review and compliance with the MMS editorial standards. 

REPORT AVAILABILITY 

Extra copies of this model and user guide may be obtained from the Public Information Unit 

(Mail Stop 5034) at the following address: 

U.S. Department of the Interior 

Minerals Management Service 

Gulf of Mexico OCS Region 

Public Information Unit (MS 5034) 

1201 Elmwood Park Boulevard 

New Orleans, Louisiana 70123-2394 

Telephone: (504) 736-2519 or 

1-800-200-GULF 

CITATION 

Suggested citation: 

Palmquist, R.B., P.W. Schuhmann, and J.A. Michael. 2002. Boating Uses, Economic

Significance, and Information for North Carolina’s Offshore Area “The Point.” Volume II: 

Economic Analysis of “The Point” and Adjacent Counties: Baseline Information, Valuation, 

and Potential Impacts. U.S. Dept. of the Interior, Minerals Management Service, Gulf of 

Mexico OCS Region, New Orleans, LA. OCS Study MMS 2002-045. 29 pp. 

ABOUT THE COVER 

Cover photo courtesy of Dr. William Lang, Minerals Management Service. 

 
TABLE OF CONTENTS 

 Page 

List of Tables................................................................................................................................ vii

Introduction .....................................................................................................................................1 

Economic Analysis of Coastal Counties near “The Point” .............................................................1 

The Role of Tourism and Fishing in the Economy of the Outer Banks ................................. 1 

Economic Impacts of Notable U.S. Oil Spills........................................................................ 3 

Economic Impact Analysis: Techniques and Criticisms ....................................................... 4 

Economic Impact Analysis of Dare County ...........................................................................6 

Possible Oil Spill Scenarios and Their Predicted Economic Impact......................................7 

Commercial Fishing Scenarios............................................................................................. 10 

Tourism Scenarios ................................................................................................................ 10 

Conclusions on Potential Impacts.........................................................................................12 

The Economic Value of Recreational Fishing for Big Game Species at “The Point” ..................12 

Introduction........................................................................................................................... 13 

The Random Utility Model of Site Choice........................................................................... 13 

North Carolina Models and Data .......................................................................................... 14 

Modeling Expected Catch as a Poisson Process................................................................... 16 

Estimating the Random Utility Model..................................................................................19 

Deriving the Losses from Hypothetical Fishery Closures.................................................... 22 

Interpreting the Results.........................................................................................................24 

References.....................................................................................................................................25 

 
LIST OF TABLES 

Table Page 

Table 1. Selected Economic Statistics for Outer Banks, 1997 ..................................................... 2 

Table 2. Seasonal Statistics for Dare County Hotels, Motels, and Cottages, 1996-1997............. 3 

Table 3. Commercial Fishery Closures for Selected U.S. Oil Spills............................................ 4 

Table 4. Output, Employment and Personal Income Multipliers................................................. 6 

Table 5. Decrease in Output, Employment and Personal Income by Sector for Commercial 

Fishing ............................................................................................................................ 8 

Table 6. Decrease in Output, Employment and Personal Income by Sector for Tourism............ 9 

Table 7. Commercial Fishing Scenarios (reductions in dollar value of catch)........................... 10 

Table 8. Tourism Scenarios by Season (dollar value of tourist spending reductions)................ 11 

Table 9. Coastal North Carolina Angler Intercept Sites............................................................. 15 

Table 10. Species Groups Used in Random Utility Model........................................................... 16 

Table 11. Results of Poisson Regressions for Numbers of Fish Caught ...................................... 18 

Table 12. Random Utility Model Coefficients ............................................................................. 21 

Table 13. Welfare Estimates (per trip) for 100% and 50% Reductions in Big Game Fish 

Catch at “The Point” (1990 dollars) ............................................................................. 22 

Table 14. 1990 trips by Model and Wave..................................................................................... 23 

Table 15. Lost CV from Dare Trips (1990 dollars) 1

.................................................................... 23 

 
Introduction — 1 

INTRODUCTION 

The Chevron Corporation had planned to drill one or more exploratory wells in the Atlantic 

Ocean northeast of Cape Hatteras near an area known as “The Point.” The State of North 

Carolina and particularly many coastal North Carolina residents were concerned about negative 

environmental effects and the increased possibility of oil spills in the area due to both production 

and increased tanker traffic in the area. Tourism and commercial fishing are key industries in the 

local economy that could potentially be damaged by off-shore oil and gas production. There 

might also be significant impacts on recreational fishing. 

The purpose of this study is to generate baseline economic information for the counties that 

might be affected, explore the potential economic impacts of an oil spill off the Outer Banks, 

provide information on the value of recreational fishing at “The Point,” and estimate the 

potential losses to recreational fishing at “The Point” if there were an oil spill. This study also 

presents information on the effects of coastal oil releases elsewhere to guide the scenarios used 

here. First, the report describes the economies of affected coastal counties and the role of 

tourism and commercial fishing in these areas. Next, the report describes the impact of several 

significant oil spill cases in the U.S. that will be used to develop possible spill scenarios for 

North Carolina. Following that, the appropriateness of economic base and input-output analysis 

to this case is discussed, and the techniques are used to describe the baseline economies and to 

analyze the potential impacts on the coastal economy of an oil spill. 

The other major area studied in this research is recreational fishing. Using data on individual’s 

fishing, we model how decisions are made about where to fish. Such decisions are affected by 

the expected catch of the various species and by the cost of getting to the fishing site. Using 

statistical models, we are able to estimate the role that these and other factors play in the 

decisions. Using the results, we are able to run scenarios about the losses to recreational 

fishermen if fishing at “The Point” is closed for various periods. These scenarios are again based 

on experiences elsewhere around the country. 

ECONOMIC ANALYSIS OF COASTAL COUNTIES NEAR “THE POINT” 

THE ROLE OF TOURISM AND FISHING IN THE ECONOMY OF THE OUTER BANKS

The counties of the central and northern outer banks are most likely to be impacted by 

Chevron’s oil exploration activities. Hyde County, encompassing Ocracoke Island and a portion 

of the mainland across Pamlico Sound, is at the southern end of the area, but it is probably 

unlikely to be affected except through the fishing activities that may use “The Point.” Dare 

County, stretching along 85 miles of the outer banks from Hatteras to north of Kitty Hawk, is at 

the greatest risk from oil exploration. A short stretch of Currituck County, mostly encompassing 

the town of Corolla, completes the outer banks to the Virginia border, but it would also be less 

affected than Dare County. Fishing has been an economic staple of this region for generations, 

but tourism has been the source of most of the recent growth. Travel expenditures by tourists in 

Dare County increased from $232 million in 1990 to $395 million in 1997, an increase of 70% in 

seven years. These numbers have not been adjusted for inflation, but even after adjusting for 

inflation, travel expenditures grew by 42 percent over this period. Travel dependent employment 
2 — Information Inventory for North Carolina’s Offshore Area, “The Point”

in Dare County increased from 6,660 to 8,440 (27%) over the same period (Office of State 

Planning, State of North Carolina, 1997). 

According to the Office of State Planning, tourism is more important to the economy of Dare 

County than any other county in the state. As shown in Table 1, about half of the jobs in Dare 

County are directly dependent on tourist spending, and tourism expenditures were sixteen times 

greater than the value of the county’s commercial fishing landings in 1997. Hyde County’s 

economy is more balanced between fishing and tourism. Tourism dominates fishing in 

importance to the Currituck County economy, although the importance of both of these sectors is 

less than in Dare and Hyde counties, as most of Currituck County’s population reside on the 

mainland. 

Table 1. Selected Economic Statistics for Outer Banks, 1997 

Dare 

County 

Currituck 

County 

Hyde 

County 

Populationa 27,279 16,664 5,040 

Per Capita Income ($)a 19,271 17,873 14,976 

Total Employmenta 17,689 7,919 2,663 

Value of Commercial Fishing Landings ($)b 24,221,987 1,638,396 11,381,112 

Commercial Fishing Employment*c 1,594 43 225 

Tourism, Direct Expenditures ($)c 395,520,000 40,810,000 16,560,000 

Tourism Employment*c 8,440 650 310 

Gross Sales Hotels, Motels, Cottages ($)c 163,530,523 9,447,897 5,524,572 

Importance of Tourism (Rank of 100 NC counties)d First Sixth Tenth 

* Employment includes both full-time and part-time jobs. 

Sources: a

 Department of Sociology & Anthropology, North Carolina State University, 1998. 

b Diaby, S., 1998. c Department of Parks and Recreation, North Carolina State University, 1998. 

 d Office of State Planning, State of North Carolina, 1997. 

It is important to consider the seasonal nature of the tourism sector, because the economic 

impact of an oil spill in the busy summer season would be much greater than in the off season. 

The table below shows that nearly half of all lodging sales in Dare County occur during June, 

July and August, whereas only ten percent of lodging sales occur during the winter months. 

These statistics understate the seasonal nature of the tourist sector because lodging sales include 

a non-tourism component, which is more constant over the course of the year. Thus, the three 

summer months probably account for well over half of tourist related lodging sales. On the other 

hand, prices for other tourist purchases (food, gasoline, souvenirs, etc.) are probably higher 

during the summer, but are not likely to increase by the 70% margin for lodging. This price 

discrepancy would cause total lodging sales to vary more by the seasons than overall tourist 

spending. Due to these offsetting effects, it is impossible to determine whether total tourism 

expenditures are more or less seasonal than the lodging sales in Table 2. As lodging sales make 

up the largest portion of tourist expenditures, the figures in Table 2 can be thought of as a close 

approximation of the seasonal breakdown of overall tourist expenditures in the area. 
Economic Analysis of Coastal Counties near “The Point” — 3 

Table 2. Seasonal Statistics for Dare County Hotels, Motels, and Cottages, 1996-1997 

Season Occupancy rate (%) Avg. Daily Rate ($) Total sales ($) % of Annual Sales 

Winter 25.6 46.00 17,617,407.16 10.8 

Spring 43.7 56.67 37,985,358.12 23.2 

Summer 64.0 78.00 74,587,983.54 45.6 

Fall 39.7 54.67 33,339,774.19 20.4 

Winter includes December, January, and February, Spring includes March-May, Summer is June-August, and Fall is 

September-November. 

Source: Brothers and Mitchell, 1998.

The information presented in this section is used in developing the impact scenarios later in the 

report. 

ECONOMIC IMPACTS OF NOTABLE U.S. OIL SPILLS

Although North Carolina has never experienced a major oil spill, about 100 oil tankers dock in 

Wilmington and Morehead City every year. In a typical year, the Coast Guard reports one oil 

spill in North Carolina usually involving only a few gallons of oil (Perko, 1989). Offshore 

drilling would increase the risk of a release from the drilling rig and, depending on how and 

where the oil was transported, the risk of a spill due to more tanker traffic. Between 1978 and 

1996, the United States averaged 25 barge or tanker oil spills over 10,000 gallons, ranging from 

a high of 42 spills in 1990 to a low of 10 spills in 1996 (Etkin, 1996). In addition, there are 2-3 

spills (releases) per year from production wells or oil rigs like Chevron has proposed off the 

coast of North Carolina. Most of these spills are quickly contained and cause little harm to 

commercial fishing and tourism. This section reviews some selected spills for which there has 

been documented tourism and fishing losses. It is by no means a complete list. The purpose of 

discussing them is to spell out some plausible analysis scenarios for a significant spill near Cape 

Hatteras. These considerations were used to develop the range of scenarios used elsewhere in 

the report. 

The potential impacts of an oil spill on the coastal tourism industry has been the greatest 

concern of local residents opposed to offshore drilling in North Carolina (Raleigh News and 

Observer, 1998). However, other areas of the U.S. that have experienced oil spills have observed 

relatively small impacts on the overall tourism industry. Some individual sectors such as charter 

fishing boat operators are severely hurt by oil spills, whereas the largest tourism sectors, hotels 

and restaurants, are only mildly affected (Freeman et al., 1985). This is generally attributed to 

two effects. First, any reduction of tourists due to beach closings or other oil spill impacts is 

offset by the influx of individuals working on the spill cleanup who fill local hotels and 

restaurants during the period of beach closings. Second, many tourists still vacation at other 

nearby beaches and coastal areas. For example, an oil spill that closes Wrightsville Beach could 

be a windfall for hotel operators at Atlantic Beach. Thus, a spill may have minimal tourism 

impacts from a regional perspective. 

The timing of an oil spill is critical in determining its tourism impact. For example, the 1996 

Rhode Island oil spill occurred in January, the slowest time of year for the tourism industry. 

Hotel and restaurant operators in Rhode Island actually reported an increase in their usual winter 

business due to the cleanup effort. By spring, most lodging establishments were reporting their 

summer reservations ahead of the previous year’s pace, indicating that a winter oil spill had little 

impact on their summer business (Providence Journal, 1996). A winter spill off the coast of 
4 — Information Inventory for North Carolina’s Offshore Area, “The Point”

Massachusetts in 1976 also had no impact on the summer tourist season (Freeman et al., 1985). 

In January and February of 1969, a 3 million gallons of oil were released from an off shore well 

near Santa Barbara, California soiled beaches for weeks. However, expected hotel receipts in 

Santa Barbara only dropped 3% during the 1st quarter of the year, and there was no effect over 

the rest of the year (Mead and Sorenson, 1970). 

Oil spills are more costly during the tourist season. Oil from a Mexican spill washed up on 

Texas beaches for 4 weeks in August 1979 causing direct economic losses of $3.1 million and 

$3.8 million in recreation and tourism-related gross business receipts, respectively (Freeman et. 

al., 1985). 

For many U.S. oil spills, the losses to commercial fishing have been much greater than the 

losses to the local tourism industry. The table below shows the duration of closure for 

commercial fishing areas affected by a few significant U.S. oil spills. The two and half month 

closure of commercial fishing grounds near Block Island, Rhode Island in 1996 is the longest for 

any oil spill in the lower 48 states, and was widely criticized for being overly cautious 

(Providence Journal, 1996). The 1969 Santa Barbara spill was a blowout of an offshore oil well 

that produced a continuous flow of oil for weeks until crews were finally able to successfully cap 

it. A blowout of an offshore well produces a steady flow of oil for several days or weeks 

whereas tanker accidents can be very large but can be controlled more quickly. Commercial 

fishing is generally closed for only a few weeks after a major oil tanker spill (Freeman et. al. 

1985). 

Table 3. Commercial Fishery Closures for Selected U.S. Oil Spills 

Date and Location of Oil Spill Gallons Spilled Length of Fishery Closure 

1969 Santa Barbara, California 3,250,000 2 months 

1989 Wilmington, Delaware 306,000 1 week 

1990 Galveston, Texas 700,000 1 week 

1996 Block Island, Rhode Island 828,000 2 1⁄2 months 

In addition to the lost revenues during fishery closures, commercial fisherman can be damaged 

after fishing resumes through mortality to the fish stock and lower prices for their catch because 

some of concerns about contamination. The state of Rhode Island spent hundreds of thousands 

of dollars advertising that the beaches were open and the seafood was safe (Providence Journal, 

1996). The Governor of Maine found himself eating lots of lobster in public following a 1996 

spill in Portland Harbor that affected consumer confidence in the safety of Maine lobsters 

(Portland Press Herald, 1996). These effects are much more difficult to quantify and there have 

been few detailed studies of such effects with previous spills. A study of Alaskan Salmon prices 

after the Valdez spill found that prices were reduced by 50% for as long as three years after the 

spill (Mendelsohn, 1993). Price effects from most oil spills are probably much less dramatic 

than for the Valdez, the largest and most notorious oil spill in U.S. history. 

ECONOMIC IMPACT ANALYSIS: TECHNIQUES AND CRITICISMS

Determining the economic impact of changes to industries such as commercial fishing and 

tourism begins with estimating the change in final demand for the industry’s output. For 
Economic Analysis of Coastal Counties near “The Point” — 5 

tourism, estimates of final demand are complex because it does not occur within the framework 

of a single industrial sector, nor does it encompass all of the output of any one sector. This 

report uses county level tourism expenditures estimated by the Department of Parks and 

Recreation Management at North Carolina State University with data from the U.S. Travel Data 

Center and the State of North Carolina. The total (direct, indirect and induced) economic 

impacts of the changes in final demand are estimated with input-output analysis (Fletcher, 1989; 

Johnson and Moore, 1993). The projected changes in final fishing and tourism sales would be 

the direct impacts of an oil spill. A decrease in final demand in one sector causes the demand for 

other sectors’ output to increase, because of purchases between the sectors. These secondary 

effects in the local economy determine the indirect impact. The induced impact is calculated by 

treating the household sector as one of the productive sectors in the input-output framework. 

The induced impact is the decrease in economic activity resulting from the decrease in household 

incomes generated by the direct and indirect impacts. The total economic impact may be 

summarized by presenting multipliers for output, income, or employment. 

Type I multipliers give the direct and indirect effects only — that is, the original expenditures 

resulting from the impacts plus the indirect effects of industries buying from industries. 

Household expenditures effects (induced effects) are not estimated. Type II multipliers are the 

direct, indirect, and induced effects where the induced effect is based on income. In Type III 

multipliers the induced effect is based on population, driven by an assumption that there is a 

linear relationship between per capita expenditures and the number of jobs. Most researchers are 

more comfortable with the assumption that there is a linear relationship between income and 

household expenditures than the number of jobs and household expenditures. Thus, only Type I 

and Type II multipliers are reported here. 

Input-output analysis has several well-known weaknesses (Isard, 1975; Armstrong and Taylor, 

1985; Nijkamp et al., 1986). First, it assumes that production in each sector is characterized by 

fixed, constant returns to scale technology. This is a significant restriction if one is analyzing 

impacts over very long periods of time. A restriction for short-run analysis is y the use of 

average cost data for firms when economic theory suggests that forms’ decisions are based on 

marginal costs. Also, input-output analysis requires very detailed data that is costly and difficult 

to obtain for local economies such as coastal North Carolina. Frequently, national data are used 

to model industry linkages which may not be a very a good approximation of the local economy. 

Finally, input-output models ignore the existence of supply constraints, so that all economic 

adjustments are made through changes in quantities rather than prices. Rapid expansion of 

output in many sectors may not be possible because of input shortages. Thus short-run inputoutput analysis is probably more accurate for decreases in final demand when capacity 

constraints are not an issue. Many economists believe that prices are “sticky downward” in the 

short-run, thus the pure quantity adjustment assumptions of input-output analysis may be less 

problematic for the analysis of short-run, negative impacts. 

The economic impact of an oil spill has several advantages for input-output analysis. An oil 

spill is a short-run negative impact on the local economy. Thus, the problematic assumptions of 

fixed, constant returns technology and pure quantity adjustments (no capacity constraints) are 

less damaging to the input-output results. Nevertheless, multipliers representing the indirect and 

induced impacts of the commercial fishing and tourism sectors should still be considered only as 

rough approximations of the true impacts. 
6 — Information Inventory for North Carolina’s Offshore Area, “The Point”

ECONOMIC IMPACT ANALYSIS OF DARE COUNTY

Multipliers for commercial fishing and tourism sectors in Dare County were generated with 

the IMPLAN input-output model. IMPLAN was developed for the USDA Forest Service as a 

tool for analyzing economic impacts and is widely used in studies of this type (Alward et al., 

1989). IMPLAN has over 500 sectors in the model. Commercial fishing is one of these sectors. 

For tourism there are a number of sectors which form this aggregate. The tourism sector consists 

of the following: automobile (451-gas stations, 477-car rental, 478-car wash, parking, 479-auto 

repairs & service), eating and drinking (454), lodging (463), tourist-related retail (449 general 

merchandise, 450 food stores, 452 apparel stores, 455 miscellaneous retail). Pooling them 

implicitly assumes that tourist expenditures are distributed between these categories in 

approximately the same proportions as total expenditures. This assumption is not likely to be too 

damaging for two reasons. The tourism industry dominates Dare County, so the tourist 

expenditures are actually a large part of the total expenditure data. Also, the multipliers in these 

individual sectors are very similar. Output multipliers range from 1.65 to 1.7, with only the auto 

sector being an outlier at about 1.5. 

Output, personal income, and employment multipliers are reported in Table 4. Employment 

multipliers are not particularly useful here because of the temporary nature of an oil spill event. 

It is doubtful that employment would fully adjust for a temporary negative shock in the way it 

would for a permanent decrease. The same critique can be leveled against income multipliers 

but a large portion of income in these sectors are tip and proprietor’s income that are more 

closely linked to total sales than employment. Output multipliers are likely to be the most 

accurate and appropriate in this case. 

Table 4. Output, Employment and Personal Income Multipliers 

Output Multipliers: 

Sector Type I Multiplier Type II Multiplier 

Commercial Fishing 1.26278 1.81927 

Tourism Sectors 1.25268 1.66580 

Employment Multipliers: 

Sector Type I Multiplier Type II Multiplier 

Commercial Fishing 1.027512 1.094780 

Tourism Sectors 1.153266 1.435373 

Personal Income Multipliers: 

Sector Type I Multiplier Type II Multiplier 

Commercial Fishing 1.15555 1.48450 

Tourism Sectors 1.20589 1.55265 

The Type II output multiplier is somewhat higher for commercial fishing than for tourism. 

Fishing has a higher induced effect. This is because the wages, salaries, and proprietors’ income 

form a larger fraction of the costs in the industry. Most of the value of the catch is retained as 

income within the county. In contrast, the tourism sector imports many of its inputs from other 

regions (e.g. retail merchandise, gasoline) and pays comparatively less of the revenues as 

employee compensation. Other county level studies estimate tourism income multipliers 
Economic Analysis of Coastal Counties near “The Point” — 7 

between 0.44 and 0.78 with most studies estimating an income multiplier around .60 (Fletcher, 

1989). Such results compare very well with the multipliers for tourist related sectors in Dare 

County shown above. 

One can also generate information about the sectors most likely to be affected by the indirect 

and induced effects generated by losses in commercial fishing and tourism. The many sectors in 

IMPLAN can be aggregated to form ten sectors using 1-digit SIC codes. The names of these 

sectors are descriptive of the types of firms they contain with exception of two of them. TCPU 

represents the transportation, communication, and public utilities sectors, and FIRE is the 

finance, insurance, and real estate sectors. The examples given here are for a $1,000,000 impact 

in the commercial fishing sector (Tables 5) and tourism (Tables 6). As before, the output tables 

should provide the best representation of the impacts. 

POSSIBLE OIL SPILL SCENARIOS AND THEIR PREDICTED ECONOMIC IMPACT

Based on the review of previous U.S. oil spills, an oil spill off Cape Hatteras might close 

beaches and/or the fishery for various durations up to two months. The effect of an oil spill will 

depend upon its location and size, weather conditions, effectiveness of the clean up effort, and 

the season of the year. A likely scenario would be that a spill that would not come very close to 

the shoreline due to the northeast flow of the gulfstream and local weather conditions. A spill of 

this nature could close fishing grounds for some time but would have only minimal impacts on 

the tourist economy (charter fishing boats excepted) if the oil stays out to sea. The tourism 

impacts of an oil spill will vary dramatically by season. To keep the number of scenarios to a 

reasonable number, the paper will look at plausible fishing and tourism impacts separately, and 

the impacts can be combined in various ways depending on the characteristics of a particular 

overall scenario. 

There are many possible long-run impacts on the commercial fishing sector beyond the shortterm loss in sales resulting from the closing of fishing grounds. Lower prices due to consumer 

perceptions of a tainted product could potentially last long after the fishery reopens. In addition, 

the oil spill could kill a portion of the fish stock and cause long-term changes in the fish 

population and catch rates. Alternatively, the temporary closure of the area to fishing might 

result in a larger stock of fish because of the reduction in commercial harvests. Additional 

considerations include differential effects by species (e.g. shellfish vs. finfish), the partial closure 

of certain fishing grounds causing increased fishing in the areas that remain open, and the effect 

of the mosaic of fishing regulations such as annual quotas on the catch of various species. 

Because of the difficulty in forecasting these complicating factors, the scenarios will mainly 

examine what is likely the most important effect, short-run closure of fishing grounds. 
8 — Information Inventory for North Carolina’s Offshore Area, “The Point”

Table 5. Decrease in Output, Employment and Personal Income by Sector for Commercial 

Fishing 

Decrease in Output by Sector ($) 

Sector Direct Impact Indirect Impact Induced Impact Total Impact 

Commercial Fishing 1,000,000 804 101 1,000,905 

Agriculture 0 2,477 3,910 6,387 

Mining 0 103 105 208 

Construction 0 87,605 10,913 98,518 

Manufacturing 0 10,630 13,952 24,582 

TCPU 0 48,926 56,522 105,448 

Trade 0 45,904 148,999 194,903 

FIRE 0 15,572 125,625 141,197 

Services 0 44,366 182,593 226,959 

Government 0 6,390 13,773 20,163 

ALL SECTORS 1,000,000 262,777 556,493 1,819,270 

Decrease in Employment by Sector (# of jobs) 

Sector Direct Impact Indirect Impact Induced Impact Total Impact 

Commercial Fishing 157.3 0.1 0.0 157.4 

Agriculture 0.0 0.0 0.0 0.0 

Mining 0.0 0.0 0.0 0.0 

Construction 0.0 1.1 0.1 1.2 

Manufacturing 0.0 0.0 0.0 0.0 

TCPU 0.0 0.3 0.4 0.7 

Trade 0.0 1.2 4.0 5.2 

FIRE 0.0 0.1 0.9 1.0 

Services 0.0 1.1 4.5 5.6 

Government 0.0 0.2 0.4 0.6 

ALL SECTORS 157.3 4.1 10.3 171.7 

Decrease in Personal Income by Sector ($) 

Sector Direct Impact Indirect Impact Induced Impact Total Impact 

Commercial Fishing 567,843 457 57 568,357 

Agriculture 0 437 690 1,127 

Mining 0 40 41 81 

Construction 0 28,563 3,558 32,121 

Manufacturing 0 2,823 3,705 6,528 

TCPU 0 10,372 11,983 22,355 

Trade 0 19,879 64,523 84,402 

FIRE 0 1,393 11,235 12,628 

Services 0 19,628 80,780 100,408 

Government 0 4,741 10,218 14,959 

ALL SECTORS 567,843 88,333 186,790 842,966 
Economic Analysis of Coastal Counties near “The Point” — 9 

Table 6. Decrease in Output, Employment and Personal Income by Sector for Tourism 

Decrease in Output by Sector ($) 

Sector Direct Impact Indirect Impact Induced Impact Total Impact

Tourism 1,000,000 21,038 92,229 1,113,267 

Agriculture 0 3,229 3,004 6,233 

Mining 0 78 80 158 

Construction 0 15,416 8,221 23,637 

Manufacturing 0 10,395 10,732 21,127 

TCPU 0 40,895 43,063 83,958 

Trade 0 25,324 34,858 60,182 

FIRE 0 52,227 93,782 146,009 

Services 0 74,231 119,642 193,873 

Government 0 9,846 10,212 20,058 

ALL SECTORS 1,000,000 252,679 415,823 1,668,502 

Decrease in Employment by Sector (# of jobs) 

Sector Direct Impact Indirect Impact Induced Impact Total Impact

Tourism 27.8 0.6 2.6 31.0 

Agriculture 0.0 0.0 0.0 0.0 

Mining 0.0 0.0 0.0 0.0 

Construction 0.0 0.0 0.0 0.0 

Manufacturing 0.0 0.0 0.0 0.0 

TCPU 0.0 0.3 0.3 0.6 

Trade 0.0 0.4 0.6 1.0 

FIRE 0.0 0.4 0.7 1.1 

Services 0.0 2.0 3.2 5.2 

Government 0.0 0.3 0.3 0.6 

ALL SECTORS 27.8 4.0 7.7 39.5 

Decrease in Personal Income by Sector ($) 

Sector Direct Impact Indirect Impact Induced Impact Total Impact

Tourism 411,833 8,664 37,983 458,480 

Agriculture 0 587 546 1,133 

Mining 0 30 31 61 

Construction 0 5,026 2,680 7,706 

Manufacturing 0 2,760 2,850 5,610 

TCPU 0 8,670 9,129 17,799 

Trade 0 10,251 14,111 24,362 

FIRE 0 4,671 8,387 13,058 

Services 0 36,827 59,356 96,183 

Government 0 7,305 7,576 14,881 

ALL SECTORS 411,833 84,791 142,649 639,273 
10 — Information Inventory for North Carolina’s Offshore Area, “The Point”

COMMERCIAL FISHING SCENARIOS

A range of possible scenarios if there were a significant release of oil was used. The total value 

of commercial fishing landings in 1997 was $24,221,987. For commercial fishing these 

scenarios assumed closure of the fishing ground for two weeks, one month, or two months. A 

scenario was also run with a 50% reduction in the value of the catch for six months because of 

consumer concerns. The scenarios for species group one assumed that the catch of billfish 

(marlin, sailfish, etc.), dolphin, tuna, wahoo, and bonito by Dare County based fishing boats 

would be eliminated for the three durations. These are the fish that live in the warm waters of 

the Gulf Stream where the effects would be most likely to occur. Scenarios for species group 

two assumed that in addition to the temporary elimination of the above species, there would be a 

50% reduction of the catch of sharks, mackerel, sea bass, grouper, bluefish, and tilefish. The 

latter effect would be less likely to result directly from the oil but the value of the catch might be 

reduced because of consumer concerns. The final set of scenarios assumed that all fish would be 

tainted in actuality or is consumers’ minds for the various durations. The results for these 

scenarios are reported in Table 7. 

Table 7. Commercial Fishing Scenarios (reductions in dollar value of catch) 

Duration of Closure Species Group 11 Species Group 22 All Fish Tainted 

2 weeks $76,392 $152,784 $931,230 

1 month $165,517 $331,034 $2,018,499 

2 months $331,034 $662,068 $4,036,998 

1⁄2 of catch for six months 

after closure 

$496,551 $993,101 $6,055,497 

1

 Group 1 includes bill fish (marlin, sailfish, etc.), dolphin, tuna, wahoo, and bonito. 

2

 Group 2 includes the species in group 1 plus 50% of the catch of sharks, mackeral, sea bass, grouper, bluefish, 

and tilefish.

TOURISM SCENARIOS

Tourism impacts will vary by season. Winter oil spills have been shown to have no impact on 

summer tourist visitation. Furthermore, the spending from the clean-up effort should offset a 

small decrease in winter visitation. Thus, negative economic impacts are expected to be minor 

for a winter oil spill of any size. In constructing the summer scenarios, it is important to consider 

that although the beaches may be closed due to oil pollution, most of the hotels and cottages 

would remain open. It is unlikely that the islands would be evacuated as they would be during a 

hurricane. Not all of the popular recreational activities in the area would be precluded by the oil 

spill (e.g. hang gliding, aquariums, golf, visiting historic sites, shopping, dining out, etc.), 

although the beach certainly is central to the decision to visit an area. People who are unable to 

visit the beach may spend their time in ways that cause them to spend more money than they 

would at the beach. Many people who have already placed deposits on lodging may or may not 

be allowed to get a refund on their reservations on short notice. It is difficult to predict how 

many tourists would choose to stay home. 

Nevertheless, some illustrative scenarios are presented below. Representative losses vary by 

season due to the variation in total expenditures by season. The duration of the effects is given 

for two weeks, one month, and two months, as it was for the commercial fishing impacts. Also a 

residual impact for an additional six months at one-half the original reduction is provided to 
Economic Analysis of Coastal Counties near “The Point” — 11 

allow for negative perceptions after the spill is corrected. Visitation reductions of 25%, 50%, 

and 100% are presented. Obviously, the losses would be greatest if the spill occurred in the 

summer. The spring and fall only differ significantly when the residual effects are included, 

since the residual after the spring is in the summer while the residual after the fall is in the 

winter. As discussed above, the visitation loss in the winter may be relatively less than in the 

summer, so this should be considered in interpreting the tables. In 1997 total direct tourism 

expenditures were $395,520,000. This is broken down by season in Table 8. 

Table 8. Tourism Scenarios by Season (dollar value of tourist spending reductions) 

Winter Season: Total expenditures = $40,343,040 

Duration of Impact 25% Visitation Loss 50% Visitation Loss Total Loss 

2 weeks $1,551,655 $3,103,311 $6,206,622 

1 month $3,361,920 $6,723,840 $13,447,680 

2 months $6,723,840 $13,447,680 $26,895,360 

1⁄2 of reduction for 6 

months after spill 

$22,940,160 $45,880,320 $91,760,640 

Spring Season: Total Expenditures = $91,760,640 

Duration of Impact 25% Visitation Loss 50% Visitation Loss Total Loss 

2 weeks $3,529,255 $7,058,511 $14,117,022 

1 month $7,646,720 $15,293,440 $30,586,880 

2 months $15,293,440 $30,586,880 $61,173,760 

1⁄2 of reduction for 6 

months after spill 

$45,089,280 $90,178,560 $180,357,120 

Summer Season: Total Expenditures = $180,357,120 

Duration of Impact 25% Visitation Loss 50% Visitation Loss Total Loss 

2 weeks $6,936,812 $13,873,624 $27,747,249 

1 month $15,029,760 $30,059,520 $60,119,040 

2 months $30,059,520 $60,119,040 $120,238,080 

1⁄2 of reduction for 6 

months after spill 

$20,171,520 $40,343,040 $80,686,080 

Fall Season: Total Expenditures = $80,686,080 

Duration of Impact 25% Visitation Loss 50% Visitation Loss Total Loss 

2 weeks $3,103,311 $6,206,622 $12,413,243 

1 month $6,723,840 $13,447,680 $26,895,360 

2 months $13,447,680 $26,895,360 $53,790,720 

1⁄2 of reduction for 6 

months after spill 

$10,085,760 $20,171,520 $40,343,040 
12 — Information Inventory for North Carolina’s Offshore Area, “The Point”

 

CONCLUSIONS ON POTENTIAL IMPACTS

This section has examined the potential economic impacts of an oil spill resulting from 

Chevron’s proposed oil exploration about forty miles off shore from Cape Hatteras. Tourism 

dominates the local economy and the impacts to this sector are potentially large. As a 

comparison, the tourism losses from a hurricane are much greater. Hurricane Fran in September 

1996 was estimated to reduce direct tourism expenditures in coastal North Carolina by $211 

million, much more than the estimated tourism losses from a large, summer oil spill (Brothers, 

1998). However, the effects of the oil spill would be more concentrated than the effects of a 

major hurricane. 

Although it has a smaller role in the economy, the impacts to the commercial fishing sector 

may be just as important for two reasons. First, the northeast flowing gulfstream currents and the 

distance from shore make it very likely that an oil spill would not reach the shore before being 

contained. In this case, the tourism impacts would be minimal while the commercial fishing 

impacts could still be substantial. Second, the multiplier effects of a dollar loss in fishing sales 

are higher than those in the tourism sector. The larger multipliers are because most fishing sales 

are retained as personal income, and relatively few inputs are imported from outside the region. 

While this study has concentrated on Dare County, the potential fishing impacts are particularly 

important to tiny Hyde County, where the local economy is equally based in fishing and tourism. 

Tourists who avoid the Outer Banks because of an oil spill are likely to choose alternate 

locations rather than forgo their vacations altogether. An oil spill near Cape Hatteras would 

probably be good for the tourism business in Wrightsville Beach or Myrtle Beach. Thus, as one 

broadens the region of analysis, the economic impact of tourism losses diminishes although there 

may be substantial redistribution between areas. 

THE ECONOMIC VALUE OF RECREATIONAL FISHING FOR BIG GAME 

SPECIES AT “THE POINT” 

The primary goal of this portion of the study is to derive the economic losses to recreational 

anglers that might result from a closure of the large game fisheries at “The Point” off the coast of 

North Carolina.1 The dollar value of these losses to recreational anglers will be measured using 

the concept of “consumer surplus” or more precisely “compensating surplus.” This represents 

the amount of money that would be necessary to compensate the angler for reductions in the 

expected catch or the temporary loss of a fishing site. In the current case, this concept can also 

be referred to as “willingness to accept,” the amount of money that the angler would have to 

receive to be willing to accept the reduction in the expected catch or the temporary loss of a 

fishing site. 

This portion of the study involves three steps. First, we use historical creel survey data from 

the Marine Recreational Fisheries Statistics Survey (MRFSS) to estimate the expected catch of 

each recreational angler. Second, we use a random utility model of recreation demand to 

1

 “The Point” is an area of approximately 35 square miles of open ocean located approximately 

30 miles east of Salvo, North Carolina. The region is characterized by dynamic oceanographic 

and biological factors mixed with geological contours that serve to attract a wide variety of 

marine life, including several popular sport fish species (Currin and Ross, 1999). 
The Economic Value of Recreational Fishing for Big Game Species at “The Point” — 13 

estimate the effect of expected catch on the recreationists’ site choice decisions. Finally, we 

derive measures of the willingness to accept compensation for decreases in expected catch rates. 

INTRODUCTION

The extensive literature on the valuation of recreational experiences was developed principally 

as a means of valuing the reduction in services to user groups that resulted from damages to 

natural resources.2

 Recreation demand models are based on the idea that the price of a 

recreational experience is represented at least in part by the costs incurred in accessing the 

recreation site. The random utility model is one type of model contained within the larger 

recreation demand framework, and views the choice of a recreation site as a function of the 

satisfaction or utility derived from each of the available sites. The recreationist chooses the site 

that provides the most satisfaction or utility. A function describing how trip utility is related to 

site attributes is estimated using data on individual trips and site characteristics. The results can 

be used to measure the compensating surplus from a change in one of the site attributes. As 

recreation site decisions are made prior to the realization of quality, the relevant site 

characteristics in a random utility model are expected quality measures. For recreational fishing 

trips, a characteristic likely to influence site choice is the expected catch rate of different species. 

As there are dozens of different species that potentially can be caught on a given fishing trip, so 

having the expected catch of each species as a site characteristic is not a realistic option for 

deriving species-specific measures. However, we can value the benefits of an improvement in 

individual species catch by separating the relevant species from the other aggregates. 

We estimate the lost benefits from decreasing the recreational catch of three species and one 

species aggregate commonly targeted and caught at “The Point” offshore North Carolina. These 

are the dolphin, tuna, wahoo and a bill fish aggregate, which is composed of marlin, sailfish, 

swordfish and spearfish3

. The expected catches of these species and of other species treated in 

aggregate form are modeled as following a Poisson process. Poisson regressions are a statistical 

technique appropriate for data taking non-negative, integer values. A random utility model of 

site choice is then estimated as a function of these expected catch rates, travel costs, and other 

site characteristics. 

THE RANDOM UTILITY MODEL OF SITE CHOICE

Discrete choice or random utility models of recreation behavior are well established in the 

literature.4 Their primary focus is on the choice among alternative mutually exclusive sites for a 

given recreational trip. The site choice decision is assumed to be a function of the utility derived 

from the alternative sites, where the site that is chosen yields the maximum utility to the 

 

2

 For example, the losses incurred by recreationists following oil spills. 

3

 These are species that are found in the warm waters of the Gulf Stream. Boats leaving from 

Dare County are usually heading for the vicinity of “The Point” if these fish are targeted. (The 

one exception would be white marlin generally caught north of “The Point”, but this species 

was not in the data set.) If these species were targeted and/or caught and the boat launch was 

in Dare County, it was assumed the fishing was at “The Point”. This decision was based on 

conversations with representatives of the Division of Marine Fisheries, the Oregon Inlet 

Fishing Center, charter boat captains, and a recreational fishing instructor. 

4

 See Bockstael et al. (1987and 1989) and Kaoru et al. (1995) for good examples 
14 — Information Inventory for North Carolina’s Offshore Area, “The Point”

 

individual. Some of the site and individual characteristics that influence the utility and hence the 

site decision are observable while others are known only to the individual, so that from the 

perspective of the researcher, there is a random component to utility. Hanemann (1999) has 

shown that by assuming that utility is linear, and that the error term is distributed as a type 1 

extreme value random variable, the utility function can be estimated using a simple logit model. 

Given an estimate of the indirect utility function, the benefits or losses from a change in the 

quality of one of the site characteristics can be estimated as the per trip compensating surplus: 

[1] CSk = (1/β)(ln ∑i exp(Vik

0

) — ln ∑i exp(Vik

1

)) 

where β is the coefficient on access price in the indirect utility function, and V0 and V1 

represent expected utility before and after the quality change respectively. Because of the 

structure of the model, β estimates the marginal utility of income, so 1/β converts the difference 

in expected utility into a dollar measure, compensating surplus. 

NORTH CAROLINA MODELS AND DATA

This portion of the study uses data from the Marine Recreational Fisheries Statistics Survey 

(MRFSS) intercept data for North Carolina for 1990. Recreational anglers were intercepted and 

interviewed at 261 different public access locations across 11 coastal counties.5 Information was 

collected about aspects of the trip such as mode of fishing, species targeted, and quantity and 

type of fish caught. Angler characteristics such as county of residence, age, sex, and fishing 

experience were also collected. For this study, intercept points are aggregated to the county level. 

Two pairs of counties (Tyrell and Dare, and Craven and Pamlico) are aggregated together due to 

limited data availability for one site in each pair.6

 We thus have nine “sites” over which choice 

will be modeled (Table 9). 

In order to estimate the model outlined above, we must specify a form for the indirect utility 

function. This requires identifying observable variables that are likely to influence site choice, 

such as access costs and measures of site quality. Access costs can be measured by the sum of 

direct travel costs and the opportunity cost of travel time. The quality of a each site can be a 

composed of an estimate of the expected catch rate of different species groups, as well as other 

site-specific characteristics which are likely to influence choice. 

The geography of the North Carolina coast is such that different types of fishing opportunities 

are available in different counties. Counties that include the barrier island chain known as the 

Outer Banks (Dare, Hyde, and Carteret) offer both ocean fishing and sound fishing opportunities. 

Craven/Pamlico and Beaufort counties offer only sound fishing opportunities, and the remaining 

four sites (Onslow, Pender, New Hanover, and Brunswick counties) offer only ocean fishing 

opportunities. To account for these differences, we will employ two dummy variables in the site 

choice model, so that the utility derived from a site will be a function of the type of fishing 

opportunities available at that site. To account for differences in size of sites, we follow the 

suggestion made by Ben-Akiva and Lerman (1985) and use the log of the number of intercept 

5

 Anglers originating from private points of access, such as personally owned docks, were not 

included in the survey. 

6

 Tyrell County and Craven County each had less than 5 observations. The counties aggregated 

together border one another, and offer similar fishing opportunities. 
The Economic Value of Recreational Fishing for Big Game Species at “The Point” — 15 

points as an additional quality variable. As we are dealing with boat fishing, this is the number 

of launch points identified in the NMFS data. 

Table 9. Coastal North Carolina Angler Intercept Sites 

Site Number Location 

1 Dare County and Tyrell County 

2 Hyde County 

3 Beaufort County 

4 Pamlico County and Craven County 

5 Carteret County

6 Onslow County 

7 Pender County 

8 New Hanover County 

9 Brunswick County 

In addition to these variables, we hypothesize that anglers make site choice decisions based on 

expectations. Anglers are likely to be concerned with total numbers of fish expected to be caught 

or, in the case of larger fish that are caught infrequently, the probability that at least one fish will 

be caught. By including these quality measures in our specification for utility, we have a means 

for deriving the value of changes in stocks. We assume that each angler forms an expectation of 

the catch of different types of fish when making the decision of where to go fishing. We wish to 

ascertain the value attributable to different game species. Because there are dozens of species 

that are regularly caught by recreational anglers in North Carolina, employing a model with such 

a large number of catch rates as choice variables would be very difficult to successfully model 

and is probably an unrealistic view of the way site choice decisions are made. It may be more 

realistic to assume that anglers view potential catch in broad categories of fish according to 

where, when, and how they can be caught. We will therefore aggregate most species into two 

broad species groups and consider the species of interest for policy analysis (the big game 

species available at “The Point”) separately (Table 10). 
16 — Information Inventory for North Carolina’s Offshore Area, “The Point”

Table 10. Species Groups Used in Random Utility Model 

Species Group Species Included 

Bill Fish Sailfish, Blue Marlin, White Marlin, Longbill Spearfish, Sailfish, 

Swordfish 

Dolphin Dolphinfish 

Tuna Skipjack Tuna, Little Tunny, Albacore, Bluefin Tuna, Yellowfin Tuna, 

Blackfin Tuna, Bigeye Tuna 

Wahoo Wahoo 

Bonito Atlantic Bonito, Striped Bonito 

Other Surface fish Bluefish, Barracuda, Cobia, , Mackerel Family (excluding tuna), 

Tarpon Family 

Other Bottom Fish Flounder Family, Cod Family, Snappers, Groupers, Jacks, Grunts, 

Seabass, Porgy, Wreckfish, Croakers, Chubbyu, Drum Family, Sharks, 

Skates, Rays, Dogfish 

MODELING EXPECTED CATCH AS A POISSON PROCESS

Since anglers are intercepted after their trip has taken place, information about the catch 

expected prior to the trip is not obtained. As actual or realized catch is endogenous, and may not 

bear any relation to expected catch (Bockstael et al., 1989), we must form a proxy for expected 

catch. We will assume that different anglers will have different expectations about the catch of 

different species. It is also likely that individual characteristics such as fishing experience, age, 

familiarity with the site, choice of target, and gear used will influence expected catch. To 

incorporate these assumptions into a model of catch expectations, we model catch to be an 

expectation formed by a Poisson process (see McConnell et al., 1995; Smith et al., 1993; Kaoru 

et al., 1995; and Schuhmann, 1998). 

The catch rate of a specific species or species group is likely to be influenced by factors such 

as the age and experience of the angler, type of gear and bait, season, species being targeted, and 

availability of stock. By modeling actual catch as a function of these variables, we will form a 

reasonable proxy for expected catch. Actual catch on a given trip must take on integer values 

greater than or equal to zero, hence modeling catch per trip with a Poisson process will allow for 

a better fit of the data than continuous specifications such as OLS. 

Given observations on the catch per trip of different species, and variables that will influence 

catch, we can estimate the Poisson model via maximum likelihood. For each species group, the 

equation we will estimate is: 

[2] Qa

 = exp [β0 + β1(bill fish target) + β2(dolphin target) + β3(tuna target) + β4(wahoo 

target) + β5(other surface fish target) + β6(other bottom fish target) + β7(site 

1) + β8(site 2) + β9(site 3) + β10(site 4) + β11(site 5) + β12(site 6) + β13(site 7) 

+ β14(site 8) + β15(number of NC fishing trips in past 12 months) + 

β16(private boat) + β17(male) + β18(age) + β19(age2

) + β20(predicted hours 

fished) ] 

where Qa

ik = the number of fish of species group a caught by angler k at site i, target variables = 

1 if the angler is targeting that species and = 0 otherwise, site variables = 1 if the angler is fishing 

at that site and = 0 otherwise, private boat = 1 if the angler is fishing from a privatly owned boat 
The Economic Value of Recreational Fishing for Big Game Species at “The Point” — 17 

(as opposed to a charter boat) and 0 otherewise, male = 1 if the angler is a male and = 0 

otherwise, and predicted hours fished = length of trip in hours as predicted by an OLS regression 

using these same independent variables plus a dummy variable for whether or not the individual 

was employed.7

The results of these regressions are reported in Table 11, and can be interpreted as logarithmic 

elasticities. That is, each coefficient indicates the percentage change in expected catch per trip 

given a one-unit change in the independent variable. The signs of the estimated coefficients in 

the 12 equations are mostly as expected. Notice that the site dummy variables are generally 

significant. The signs of the target dummy variables conform with expectations — the sign for a 

particular dummy is always positive and significant in the equation for that species — indicating 

that anglers targeting a particular species are much more likely to catch that species. It appears 

that anglers fishing from private boats can expect to catch fewer fish than anglers in charter 

boats, all other things equal. An unexpected result is the generally negative signs on the 

“predicted hours” variable. This may be an indication of fishing quality on particular days 

combined with the presence of explicit or implicit limits on catch. If quality of fishing is good 

on a particular day, an angler may need only a short trip to realize his or her allowable catch. 

Alternatively, the additional satisfaction derived from catch an additional fish probably 

diminishes as more fish are caught, so a successful trip may be shortened. In either case, short 

trips and good fishing quality may be positively correlated. 

 

7

 Because of the potential for endogeneity between hours fished and catch per trip, we use 

predicted hours fished as an instrument for the hours fished for each angler. 
Table 11. Results of Poisson Regressions for Numbers of Fish Caught

Bill fish Dolphin 

Catch Catch 

Tuna 

Catch 

Wahoo 

Catch 

Bonito 

Catch 

Other Surfac

Fish Catch 

Coefficient Coefficient 

(Std. Error) (Std. Error) 

Coefficient 

(Std. Error) 

Coefficient 

(Std. Error) 

Coefficient 

(Std. Error) 

Coefficient 

(Std. Error) 

Bill target 4.50 (1.32) -2.49 (0.59) 2.70 (0.65) -1.75 (7242.35) -16.38 (7669.47) -3.11 (0.7Dolphin Target -15.57 (7164.93) 1.80 (0.13) 1.56 (0.74) 1.35 (0.86) -15.54 (4730.73) -2.05 (0.0Tuna Target 2.69 (1.44) -1.31 (0.20) 2.05 (0.58) -2.11 (4105.39) 0.76 (1.61) -0.86 (0.1Wahoo Target 4.06 (51386.26) 4.57 (1.07) -15.79 (18893.51) 6.80 (2.74) -16.27 (31150.13) 1.19 (0.7Other Surface Fish Target 

-14.06 (1852.80) 0.27 (0.19) -15.72 (745.05) 1.00 (0.91) 0.23 (1.21) 1.01 (0.0

Other Bottom Fish Target 

-14.84 (1969.43) -14.82 (167.80) -15.58 (779.69) -14.18 (1117.32) -15.71 (1291.91) 0.25 (0.0

Site 1 16.73 (4124.54) 16.28 (333.32) 15.95 (1542.95) -0.58 (2728.19) 16.12 (2684.53) 0.57 (0.0Site 2 2.36 (5883.54) 13.38 (333.32) -0.12 (2193.15) 2.02 (3566.45) 0.46 (3713.29) -0.44 (0.1Site 3 11.29 (19230.74) 14.43 (1594.89) 14.44 (7226.45) 16.04 (11929.72) 15.54 (11647.96) -1.69 (0.7Site 4 -0.43 (6715.88) 0.57 (579.24) 0.16 (2542.68) 1.96 (4215.27) 0.29 (4298.99) -0.88 (0.1Site 5 -0.27 (4463.91) 14.06 (333.32) 15.20 (1542.95) 16.68 (2447.40) 15.91 (2684.53) -0.28 (0.0Site 6 0.11 (4810.15) 12.21 (333.32) -0.20 (1803.08) -0.12 (2860.92) 0.21 (3118.38) -0.52 (0.1Site 7 0.45 (7483.15) 0.52 (663.17) 0.71 (2823.37) 1.52 (4813.49) 0.07 (4903.12) 0.10 (0.1Site 8 -0.62 (5086.57) 12.05 (333.32) 0.47 (1891.90) -0.19 (2927.31) 15.77 (2684.53) -0.28 (0.1Trips in Past 12 Months -0.02 (0.03) 0.00 (0.00) 0.00 (0.00) 0.01 (0.01) 0.01 (0.00) 0.00 (0.0Private Boat Dummy -9.64 (6.01) -4.25 (0.65) -2.22 (2.13) -5.07 (4.55) -0.07 (5.57) -1.50 (0.2Male Dummy 18.01 (2313.88) 0.34 (0.20) 0.58 (0.67) 0.15 (1.34) 15.68 (1588.20) 0.36 (0.0Age 0.14 (0.16) -0.06 (0.01) 0.16 (0.09) 0.19 (0.13) 0.15 (0.21) 0.07 (0.0Age Squared 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.0Predicted Hours Fished -3.18 (1.95) -0.35 (0.21) -0.07 (0.73) -0.09 (1.51) -0.09 (1.87) -0.15 (0.0Constant -18.37 (4729.27) -11.50 (333.32) -22.79 (1542.96) -21.97 (2447.42) -38.77 (3119.17) 0.12 (0.5Log Likelihood -23.50 -127.00 -15.00 -7.00 -7.00 -644.Pseudo R2 0.50 0.52 0.52 0.45 0.45 0.
The Economic Value of Recreational Fishing for Big Game Species at “The Point” — 19 

ESTIMATING THE RANDOM UTILITY MODEL

Using the expected catch results we estimate the following indirect utility function for 1990 

boat mode recreational fishing trips: 

[3] Vik = α1(travel costik) + α2(expected catch Bill fishik) + α3(expected catch Dolphinik) + 

α4 (expected catch Tunaik) + α5 ( expected catch Wahooik) + α6 ( expected catch 

Bonitoik) + α7 ( expected catch other surface fishik) + α8 (expected catch other 

bottom fishik ) + α9 (outerbanks dummyi) + α10 (sound site dummyi) + α11 (log 

of intercept pointsi)

where: 

travel costik = (.41) (round-trip distance in miles to site i by angler k ) 

+ (.66)(hourly wage)(hours driving time),8

outerbanks dummyi = 1 for Dare, Hyde, and Carteret counties (sites 1,2, and 5) 

 = 0 otherwise, 

sound site dummyi = 1 for Craven/Pamlico and Beaufort counties (sites 3 and 4) 

 = 0 otherwise, 

log of intercept pointsi = log of the number of intercept points at site i as reported in the 

MRFSS data set. 

The coefficients in Table 11 are used to generate proxies for expected catch, and the expected 

probability that catch will be greater than one fish for each of the species groups in Table 10 for 

each angler at each site for all single-day fishing trips in our sample. These values, along with 

the other site quality characteristics, were used to estimate the per-trip indirect utility function 

detailed in equation [3]. 

Preliminary estimates of equation [3] were problematic for two reasons. First, the coefficients 

on the catch rates of billfish and dolphin in equation [3] were not significantly different than 

zero. This is likely due to a lack of significant variation in the (generally low) catch rates of these 

species across the sites in our model. Hence, this result should not be interpreted as meaning that 

catch of billfish and dolphin do not enhance the satisfaction realized on a recreational fishing 

trip. Rather, we should assume that because these species are specifically targeted, the catch of 

these species does contribute to trip utility, but lack of variation for species that are infrequently 

caught may prevent the site choice model from revealing their contribution to trip utility. 

Second, the coefficient on bonito was negative and significant at the 5 percent level. This would 

seem to indicate that increasing the catch of bonito would detract from trip utility. Despite the 

 

8

 The explicit travel costs were estimated at $0.41 per mile, which was the cost of operating a 

motor vehicle in 1990 (Bureau of the Census, 1991). Driving time was calculated assuming 45 

miles per hour average speed. County level income data were used to estimate the hourly 

wage (Bureau of the Census, 1991). Two-thirds of the wage is used as an approximation of the 

opportunity cost of time. We lack information on boat related travel costs such as those 

associated with accessing fishing grounds far offshore. Because the RUM uses travel costs as 

the “price” of a recreation opportunity, omitting these other costs may bias the welfare results 

downwards. 
20 — Information Inventory for North Carolina’s Offshore Area, “The Point” 

fact that no anglers in our sample were specifically targeting bonito, we find this unlikely to be 

the case, as bonito are generally regarded as an enjoyable fish to catch. 

To attempt to address both of these problems, in the utility model we replace the expected 

catch with the expected probability that catch will be greater than one fish for the five species of 

interest. This specification can be justified by assuming that since these species are caught so 

infrequently, anglers do not make site choice decisions based on the number of fish they expect 

to catch, but rather on whether or not they expect to catch at least one fish. This model resulted 

in insignificant coefficients for billfish and bonito. Because the bonito did not perform well in 

either model, and because no anglers in our sample were targeting bonito, we added the expected 

catch of bonito to the expected catch of other surface fish to yield a new estimate of the expected 

catch of other species. This sum was then entered as a quality characteristic in both forms of the 

site choice model. By estimating this new model we are imposing the restriction that the 

coefficient on bonito, α6 , is equal to the coefficient on other surface fish, α7 , in the indirect 

utility function given by equation [2]. Using a likelihood ratio test for the restricted and 

unrestricted indirect utility functions, we cannot reject the hypothesis that the bonito and other 

surface fish coefficients are equal at the one-percent level for both the catch model and the 

success model. We therefore will use the restricted models and thus have six expected catch 

rates as quality variables in addition to the two dummy variables and the log of the number of 

intercept points. Despite the insignificance of the bill fish coefficient, we decided to leave it as a 

separate variable in the site choice models, reasoning that it was far less likely that these larger 

fish would have the same contribution to utility as any other species in the model. We therefore 

proceed with two models of the per trip indirect utility. Model 1 contains the expected catch in 

numbers of billfish, dolphin, tuna, wahoo, other surface fish (including bonito) and other bottom 

fish. Model 2 contains the expected probability that at least one fish will be caught for billfish, 

dolphin, tuna, and wahoo, plus the expected catch in numbers of other surface fish (including 

bonito) and other bottom fish. 

The results for both site choice models are given in Table 12. These estimates were used to 

estimate the compensating surplus (CS) or per-trip willingness-to-accept for several different 

catch changes by estimating the compensating variation measure reported in equation [1]. As we 

are interested in the losses that might be realized by anglers not being able to fish at “The Point,” 

we value 50 and 100 percent decreases in catch of the four big game species for trips originating 

from site 1 (Dare/Tyrell). The mean per-angler, per-trip compensating surplus measure was 

calculated for each angler. The means and standard deviations in 1990 dollars are reported in 

Table 13. Notice that Model 1 produces losses that are slightly higher than those found with 

Model 2. In both cases, because expected catch and hence losses depend on angler-specific 

variables, there is large variation in the estimates across the sample. These values can be 

interpreted as the mean per angler loss from a one-trip closure of the big game fisheries for boat 

trips launching from Dare County. 
The Economic Value of Recreational Fishing for Big Game Species at “The Point” — 21 

Table 12. Random Utility Model Coefficients 

 Model 1 

Catch Only 

Model 2 

Catch and Success 

Variable 

Travel Cost -0.0639** 

(0.0019) 

-0.0636** 

(0.0019) 

Bill fish 

Expected Catch 

55.5493 

(57.0970) 

Dolphin 

Expected Catch 

0.0522 

(0.0656) 

Tuna 

Expected Catch 

18.9363** 

 (5.2294) 

Wahoo 

Expected Catch

3.2207** 

 (0.9905) 

Other Surface 

Expected Catch 1

0.6610** 

 (0.1051) 

0.6130** 

(0.1125) 

Other Bottom 

Expected Catch 

0.5778** 

 (0.0713) 

0.5885** 

(0.0715) 

Bill fish 

Expected Success 

53.1466 

(58.2419) 

Dolphin 

Expected Success 

1.4940+

(0.8624) 

Tuna 

Expected Success 

15.1976* 

(5.9610) 

Wahoo 

Expected Success 

4.5529* 

(2.2532) 

Outerbanks 

Dummy 

5.018** 

(0.2279) 

5.0101** 

(0.2289) 

Sound 

Dummy 

-1.7987** 

 (0.1558) 

-1.8324** 

(0.1573) 

Log of Number of Launch Points 0.2134* 

(0.1074) 

0.1890+

(0.1082) 

Log likelihood function -1121.768 -1119.629 +

 Significant at the 10% level 

* Significant at the 5% level 

** Significant at the 1% level 

1

 The expected catch of other surface fish is found as the sum of the expected catch of a surface fish 

aggregate and the expected catch of bonito. Hence, this model represents a restricted form of a 

model where the expected catch of Bonito was entered as a separate argument in the utility function. 

Using a likelihood ratio test, we cannot reject the null hypothesis that the coefficients are equal at the 

1percent level.
22 — Information Inventory for North Carolina’s Offshore Area, “The Point” 

Table 13. Welfare Estimates (per trip) for 100% and 50% 

Reductions in Big Game Fish Catch at “The Point” 

(1990 dollars) 

Model 1 Model 2 

Mean Std Dev Mean Std Dev 

100% -9.60 59.21 -7.29 36.82 

50% -5.04 31.36 -3.06 15.05 

DERIVING THE LOSSES FROM HYPOTHETICAL FISHERY CLOSURES

The random utility model presented above allows us to derive the per-angler, per-trip 

willingness-to-accept for decreases in trip quality. In order to value longer fishery closures — 

such as those that would be caused by a spill of oil or other hazardous substance — we can 

expand these pre-trip values using estimates of total trips during a particular period of time. 

In conjunction with the on-site creel survey, each year the National Marine Fisheries Service 

estimates annual recreational fishery participation in numbers of anglers and total trips for each 

coastal state. The trip estimates are categorized by wave and mode, where a wave is a twomonth interval.9

 Modes are divided into man-made structure, beach/bank ocean, beach/bank 

inland, private/rental boat inland, charter boat inland, private/rental boat ocean ≤ 3 miles from 

shore, charter boat ocean ≤ 3 miles from shore, private/rental boat ocean > 3 miles from shore, 

and charter boat ocean > 3 miles from shore. The last two of these are of interest for this study, 

as “The Point” is far offshore. Using these data, we construct estimates of total trips by 

private/rental boat to ocean > 3 miles from shore from Dare county by multiplying the sample 

percentage of offshore trips that are from Dare (0.2734) from our intercept sample by the NMFS 

estimate of total 1990 trips > 3 miles. These values, along with the NMFS estimates of total trips 

by mode and wave, are reported in Table 14. 

In order to derive the losses to recreational anglers from closure of the big game fisheries at 

“The Point,” we combine the estimates of total trips reported in Table 6, with the per trip 

compensating variation measures reported in Table 5. We use these values to examine the 

following 6 closure scenarios: complete closure of Dare big game for 2 weeks, complete closure 

of Dare big game for 1 month, complete closure of Dare big game for 2 months, complete 

closure of Dare big game for 2 weeks and then a 50% reduction in the probability of success for 

the remainder of a 6 month period, complete closure of Dare big game for 1 month and then a 

50% reduction in the probability of success for the remainder of a 6 month period, and complete 

closure of Dare big game for 2 months and then a 50% reduction in the probability of success for 

the remainder of a 6 month period. We use both a January 1 and a July 1 starting point for each 

of these scenarios, hence, we examine a total of 12 closure scenarios. The losses in dollars are 

reported in Table 15. 

 

9

 For all years except 1989, there were no on-site MRFSS surveys during wave 1 (JanuaryFebruary) hence the corresponding estimate of total trips is zero. NOAA personnel indicate 

that either no anglers were intercepted who were fishing offshore, or due to low expected 

fishing pressure, no interviews were conducted during wave 1. We therefore construct an 

estimate of trips during wave 1 in 1990 by applying the proportion of 1989 total trips taken 

during wave 1 to the estimate of 1990 total trips. 
The Economic Value of Recreational Fishing for Big Game Species at “The Point” — 23 

Table 14. 1990 trips by Model and Wave 

MONTHS Private Boat Trips 

> 3 Miles 

Charter BoatTrips 

> 3 Miles 

Total Trips 

> 3 Miles 

Estimate of Dare 

Trips > 3 Miles 2

JAN-FEB1 2099 0 2099 574 

MARCH-APRIL 64208 7133 71341 19505 

MAY-JUNE 53384 21211 74595 20394 

JULY-AUG 104912 38720 143632 39269 

SEPT-OCT 43294 9661 52955 14478 

NOV-DEC 9558 3586 13144 3594 

TOTAL 277455 80311 357766 97813 

1 There were no interviews during wave 1 in 1990. This value is an estimate constructed by applying the proportion 

of 1989 total trips taken during wave 1 to the estimate of 1990 total trips. 

2

 Found by multiplying sample percentage of Dare trips > 3 miles (0.2734) by total trips > 3 miles.

Table 15. Lost CV from Dare Trips (1990 dollars) 1 

Start time = January 1 Start time = July 1 

Scenario Model 1 Model 2 Model 1 Model 2 

1 -1,377 -1,046 -94,246 -71,568 

2 -2,755 -2,092 -188,491 -143,135 

3 -5,509 -4,183 -376,982 -286,271 

4 -204,637 -124,454 -333,763 -216,989 

5 -205,291 -125,060 -378,529 -258,516 

6 -206,600 -126,274 -468,062 -341,570 1

 Found by multiplying Dare trips by per trip CV measures over the length of the scenario. 

Closure Scenarios: 

1 = Complete closure of Dare big game for 2 weeks. 

2 = Complete closure of Dare big game for 1 month. 

3 = Complete closure of Dare big game for 2 months. 

4 = Complete closure of Dare big game for 2 weeks and then a 50% reduction in the probability 

of success for the remainder of a 6-month period. 

5 = Complete closure of Dare big game for 1 month and then a 50% reduction in the probability of 

success for the remainder of a 6-month period. 

6 = Complete closure of Dare big game for 2 months and then a 50% reduction in the probability 

of success for the remainder of a 6-month period. 

Estimated losses for closures during wave 1 are relatively low due to the low estimate for 

offshore fishing during January and February. Closure scenarios that begin in January and 

extend beyond wave 1 (scenarios 4, 5, and 6) affect fishing during more popular months and 

result in significantly larger losses. Because wave 4 is the most popular in terms of numbers of 

offshore trips, closures that include July and August have the largest effect on recreational angler 

welfare. As a result, the estimates of losses for all scenarios beginning in January are 

significantly lower that those beginning in July. 
24 — Information Inventory for North Carolina’s Offshore Area, “The Point” 

INTERPRETING THE RESULTS

In addition to generating baseline data, this study has presented the results for various 

scenarios in the unlikely event of the release of oil from the drilling operation. The various 

scenarios used for the impacts on Dare County and the impacts on recreational fishermen are 

almost identical. However, the nature of the numbers generated from the input-output model is 

quite different from the values estimated for the recreational fishing model. It is important to 

interpret them carefully. 

One important difference is that the losses estimated for recreational fishing take into account 

the fact that anglers will substitute other locations if fishing around “The Point” is restricted. 

The losses estimated for recreational fishing are a net losses after all adjustments have been 

made. In a benefit-cost study, they are legitimate costs. On the other hand, the earlier section of 

this report concentrates on impacts on Dare County and does not consider effects in other 

counties that are potentially partially offsetting. The input-output analysis also does not consider 

the various substitutions and reactions that will take place in the event of a spill. Thus, the dollar 

values for the tourism and commercial fishing scenarios cannot be directly compared to those for 

recreational fishing. They will include impacts that would not be considered as losses in a 

benefit-cost study. This is not to say that the impact analysis results are not useful. 

Distributional questions are important. It is just that the numbers in the two parts of the study 

should not be directly compared. 

It is also important to note the differences in the scenarios for commercial fishing and tourism. 

Both are impact analyses. However, the commercial fishing scenarios for species group 1 

assume that the physical effects of the spill are only occurring in the vicinity of “The Point.” 

The tourism impacts require that some of the spill makes landfall, news of the spill reduces visits 

even without physical effects, or at least part of the tourists are intending to fish at “The Point.” 

Any or all of these may be true, but direct comparison of commercial fishing and tourism 

impacts must be approached with caution, and the assumptions must be made explicitly. 

With these caveats, the results in this report have generated baseline information about the 

economies of the coastal counties and the value of recreational fishing. By examining the 

experiences elsewhere with offshore releases, we have generated a range of scenarios and 

estimated the effects on the coastal counties and recreational fishing should there be a release off 

“The Point.” 
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The Department of the Interior Mission

As the Nation's principal conservation agency, the Department of the Interior has responsibility 

for most of our nationally owned public lands and natural resources. This includes fostering 

sound use of our land and water resources; protecting our fish, wildlife, and biological diversity; 

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interests of all our people by encouraging stewardship and citizen participation in their care. 

The Department also has a major responsibility for American Indian reservation communities 

and for people who live in island territories under U.S. administration. 

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