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Draft socioeconomic overview of Northern and Central California counties' marine activities 2003
Prepared for Monterey Bay, Gulf of the Farallones and Cordell Bank National Marine Sanctuaries' Joint Management Plan Revision.
Authors: Rod Ehler, Vernon R. Leeworthy, and Peter C. Wiley
Joint Management Plan Revision (JMPR)
Monterey Bay National Marine Sanctuary (MBNMS)
Gulf of the Farallones National Marine Sanctuary (GFNMS)
Cordell Bank National Marine Sanctuary (CBNMS)
A Socioeconomic Overview
of the Northern and Central Coastal California Counties as They
Relate to Marine Related Industries and Activities
DRAFT April 2003
By
Rod Ehler, National Marine Sanctuary Program
Dr. Vernon R. (Bob) Leeworthy, Special Project Office
Peter C. Wiley, Special Projects Office
U.S. Department of Commerce
National Oceanic and Atmospheric Administration
National Ocean Service
National Marine Sanctuary Program (NMSP)
and
Special Projects Office (SPO)
Silver Spring, Maryland
Table of Contents
INTRODUCTION
Purpose
Background
Future Projects
DEMOGRAPHIC AND ECONOMIC PROFILE
Population
Population Density
Historical and Projected Population
Population Growth
Race
Age and Gender
Labor Force
Income and Employment
Income by Place of Residence
Income by Place of Work
Proprietors Income and Employment
Indicators of Economic Health and Wealth
Unemployment Rates
Per Capita Income
Income and Employment by Industry
Income and Employment: Additional Disaggregation
Commercial Fishing
Tourism and Recreation
VALUE OF MARINE RESEARCH
TOURISM AND RECREATION
California Travel Impacts by County
Marine Related Recreation
National Survey on Recreation and the Environment (NSRE)
Marine Recreational Fishing
Participation and Expenditures
Socio-economics
Pleasure Boating
Personal Watercraft
Kayaking
Whale and Other Wildlife Watching
Surfing
Beach Visitation
Scuba Diving
COMMERCIAL FISHING (CDFG)
INTRODUCTION
Purpose
The purpose of this document is to present the necessary background information on the local
social and economic (socio-economic) environment for which changes in management actions in
the JMPR study area can be analyzed in a socioeconomic impact analysis. The information
presented here is what we have found to date to be the “best available information”. In addition
to the socioeconomic characterization, we will provide some discussion on gaps in the data.
We will examine all direct uses potentially impacted; examples are 1) tourist/recreational use
(e.g., whale watching, kayaking, scuba diving) and 2) commercial industries (e.g., commercial
fishing, kelp harvesting). With respect to the local economies, these uses will have ripple or
multiplier effects as measured by market economic values (e.g., output/sales, income,
employment and tax revenues). In this report, we review available information to assess how
important these industries are to the local economies. We will also present what is known about
social and economic parameters that can be used in socioeconomic impact analyses.
Background
The MBNMS, GFNMS, and CBNMS are currently involved in a joint management plan revision
(JMPR), a process that is required by law to take place approximately every five years. The
management plans for the three northern and central California sanctuaries are between 9 and 20
years old. The National Marine Sanctuary Program (NMSP) is reviewing all three management
plans jointly. These sanctuaries are located adjacent to one another, managed by the same
program, and share many of the same resources and issues. In addition, all three sites share many
overlapping interest and user groups. It is also more cost-effective for the program to review the
three sites jointly rather than conducting three independent reviews. During the review, the
sanctuaries will evaluate management and operational strategies, regulations, and boundaries.
The review will look at whether the management programs at all three sanctuaries can be better
coordinated.
A sanctuary management plan is a site-specific planning and management document that
describes the objectives, policies, and activities for a sanctuary. Management plans generally
outline regulatory goals, describe boundaries, identify staffing and budget needs, set priorities
and performance measures for resource protection, research, and education programs. They also
guide the development of future management activities.
Any data gap identified as necessary to support the socioeconomic impact analysis will be
collected and compiled in a manner so as to capture both the temporal and spatial variation in
activities. The information will be linked with economic parameters from existing studies to
develop estimates of economic impacts as measured by changes in both market economic values
(e.g., sales/output, income and employment) and non-market economic values (e.g., consumer’s
surplus and economic rents). Socioeconomic profiles of those potentially impacted will be
compared against all users from a given user group and against the general population of the
local area (e.g., the coastal California counties).
To accomplish the above requires a review of the existing literature and databases available and
compiling this information in a manner that it can be used in the socioeconomic impact analyses.
In some cases, available information will not support certain aspects of the proposed analyses. In
2
addition, supplemental data collection and analysis may not be feasible with time and resources
available. What we are left with is what is commonly referred to as the “best available
information”.
Future Projects
There are currently 3 projects planned in support of the JMPR.
In early 2003, the National Marine Sanctuary Program and California Sea Grant will hold a
workshop to identify needed socio-economic studies associated with marine activities in the Joint
Management Plan Revision study area.
In October 2002, Dr. Caroline Pomeroy and Dr. Michael Dalton were awarded, through
California SeaGrant, $70k to conduct a study titled “Market Channels and Value Added to Fish
Landed at Monterey Bay Area Ports”.
In 2003, another study will be initiated that will investigate private household boat users. One of
the major gaps in information for all California Sanctuaries is the number of private household
boat users and amount of use, especially for non-consumptive users.
3
DEMOGRAPHIC AND ECONOMIC PROFILE
Population.
Population density and historical population estimates presented here are from the U.S.
Department of Commerce, Census Bureau (http://www.census.gov), while population
projections are from the University of California.
Population Density. The map below presents population density per square mile. Population is most
dense in the area reaching from San Francisco, down the eastern portion of San Mateo County to the San
Jose metropolitan area and continuing north through the western portion of Alameda County to the Oakland
metropolitan area. Pockets of dense coastal population also exist in the Santa Cruz and Monterey Peninsula
areas. Within the JMPR study area there are several inland areas of dense population, such as Salinas,
Vallejo, Concord, Walnut Creek, Napa, Santa Rosa, and Fairfield.
Figure 1. Population Density Per Square Mile
P o p u la t io n D e n s i t y
0 - 4 9 8. 81
4 9 8 .8 1 - 1 5 2 9 .5 2
1 5 2 9 .5 2 - 2 6 8 1 .5 2
2 6 8 1 .5 2 - 3 8 4 2 .7 1
3 8 4 2 .7 1 - 4 9 8 2 .2 6
4 9 8 2 .2 6 - 6 0 9 6 .6 4
6 0 9 6 .6 4 - 7 1 8 1 .5 4
7 1 8 1 .5 4 - 8 2 7 2 .6 5
8 2 7 2 .6 5 - 9 4 0 1 .0 5
9 4 0 1 .0 5 - 9 9 9 9 .9 9
4
Historical and Projected Population. The two largest counties in the study, in terms of
population, are Santa Clara (1.7 million) and Alameda (1.4 million). Combined, these two
counties account for almost 40 percent of the JMPR study area population. Santa Clara and
Alameda Counties saw growth very much in line with the overall JMPR study area rate of 12.5
percent over the period 1990 to 2000. The smallest county in terms of population, San Benito (53
thousand), has shown the highest rate of growth, 45 percent, over the period 1990 to 2000 and 113
percent over the period 1980 to 2000. All counties are expected to continue their growth, with the
exception of San Francisco, which is forecast to decline in population over the next few decades.
See Table 1a and 1b.
Table 1a. Population, Historical and Projected, for Coastal California
U.S. Census Bureau Actual University of California Forecast
1960 1970 1980 1990 2000 2000 2010 2020 2030 2040
CALIFORN IA 15,717,204 19,953,134 23,667,902 29,760,021 33,871,648 34,653,395 39,957,616 45,448,627 51,868,655 58,731,006
JMPR STUDY AREA 4,237,970 5,441,401 6,204,241 7,312,783 8,226,651 8,410,361 9,480,827 10,382,363 11,409,517 12,437,966
MEN DOCINO 51,059 51,101 66,738 80,345 86,265 90,442 105,225 118,804 133,440 149,731
SONOMA 147,375 204,885 299,681 388,222 458,614 459,258 544,513 614,173 684,311 753,729
MARIN 146,820 206,038 222,568 230,096 247,289 248,397 258,569 268,630 282,864 297,307
N APA 65,890 79,140 99,199 110,765 124,279 127,084 143,542 157,878 174,240 191,971
SOLANO 134,597 169,941 235,203 340,421 394,542 399,841 479,136 552,105 625,619 698,430
CON TRA COSTA 409,030 558,389 656,380 803,732 948,816 931,946 1,025,857 1,104,725 1,189,501 1,264,400
ALAMEDA 908,209 1,073,184 1,105,379 1,279,182 1,443,741 1,470,155 1,654,485 1,793,139 1,938,547 2,069,530
SAN FRANCISCO 740,316 715,674 678,974 723,959 776,733 792,049 782,469 750,904 724,863 681,924
SAN MATEO 444,387 556,234 587,329 649,623 707,161 747,061 815,532 855,506 907,423 953,089
SAN TA CRUZ 84,219 123,790 188,141 229,734 255,602 260,248 309,206 367,196 430,078 497,319
SAN TA CLARA 642,315 1,064,714 1,295,071 1,497,577 1,682,585 1,763,252 2,021,417 2,196,750 2,400,564 2,595,253
MON TEREY 198,351 250,071 290,444 355,660 401,762 401,886 479,638 575,102 700,064 855,213
SAN BENITO 15,396 18,226 25,005 36,697 53,234 51,853 68,040 82,276 97,941 114,922
SAN LUIS OBISPO 81,044 105,690 155,435 217,162 246,681 254,818 324,741 392,329 461,839 535,901
SAN TA BARBARA 168,962 264,324 298,694 369,608 399,347 412,071 468,457 552,846 658,223 779,247
Table 1b. Population Growth (% Change), Historical and Projected, for Coastal California
U.S. Census Bureau Actual University of California Forecast
1960 - 1970 1970 - 1980 1980 - 1990 1990 - 2000 2000 - 2010 2010 - 2020 2020 - 2030 2030 - 2040
27.0 18.6 25.7 13.8 15.3 13.7 14.1 13.2
CALIFORN IA
JMPR STUD Y AREA 28.4 14.0 17.9 12.5 12.7 9.5 9.9 9.0
MEN DOCIN O 0.1 30.6 20.4 7.4 16.3 12.9 12.3 12.2
SON OMA 39.0 46.3 29.5 18.1 18.6 12.8 11.4 10.1
MARIN 40.3 8.0 3.4 7.5 4.1 3.9 5.3 5.1
N APA 20.1 25.3 11.7 12.2 13.0 10.0 10.4 10.2
SOLAN O 26.3 38.4 44.7 15.9 19.8 15.2 13.3 11.6
CON TRA COSTA 36.5 17.5 22.4 18.1 10.1 7.7 7.7 6.3
ALAMEDA 18.2 3.0 15.7 12.9 12.5 8.4 8.1 6.8
SAN FRAN CISCO -3.3 -5.1 6.6 7.3 -1.2 -4.0 -3.5 -5.9
SAN MATEO 25.2 5.6 10.6 8.9 9.2 4.9 6.1 5.0
SAN TA CRUZ 47.0 52.0 22.1 11.3 18.8 18.8 17.1 15.6
SAN TA CLARA 65.8 21.6 15.6 12.4 14.6 8.7 9.3 8.1
MON TEREY 26.1 16.1 22.5 13.0 19.3 19.9 21.7 22.2
SAN BEN ITO 18.4 37.2 46.8 45.1 31.2 20.9 19.0 17.3
SAN LUIS OBISPO 30.4 47.1 39.7 13.6 27.4 20.8 17.7 16.0
SAN TA BARBARA 56.4 13.0 23.7 8.0 13.7 18.0 19.1 18.4
Sources: Population; U.S. Department of Commerce, Census Bureau (http://www.census.gov).
Population Projections; University of California
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Race. The demographic composition of the study area varies greatly. The four counties
(Mendocino, Sonoma, Marin, and Napa) that make up the northern section of the study are
predominately White (all at or above 80 percent) with less than average proportion of Blacks,
Asians, Hispanics and Latinos. It is important to point out that Mendocino County’s population
is almost 5 percent American Indian. The Bay Area counties of Solano, Contra Costa, Alameda,
San Francisco, San Mateo, and Santa Clara are the most diverse counties in the study area. The
White population of this area drops to 50 to 65 percent and the Black and Asian populations
increase dramatically to 10 to 30 percent. About one third of San Francisco’s population is Asian.
The remaining counties that comprise the Southern section of the study area are heavily
populated with Hispanics and Latinos, particularly in Monterey and San Benito Counties where
the Hispanic and Latino population stands at almost 50 percent.
Age and Gender. In terms of age, similar geographic variations do emerge. The Northern four
counties identified above are also the oldest, in terms of median age (34 to 41 years). The
proportion of people 45 and older is also greatest in these counties. With a few exceptions, the
remaining counties in the study area are quite similar in terms of age. San Francisco has the
highest proportion, 41 percent, of people 25 to 44 years and the lowest proportion, 15 percent, of
people under 18 years. The counties with the highest proportions at retirement age, 65 years and
older, are Napa and San Luis Obispo.
There are also variations in gender among the county populations. Three of the counties,
Monterey, San Luis Obispo, and San Francisco, have higher populations of males. Sonoma,
Contra Costa, Alameda, and San Mateo are more populated by females.
Table 2a. Demographic Profiles Coastal California Counties – Race, 2000
One race
Hispanic or
Native
Two or more
American
Total Pop. Latino (of
Black or Haw aiian races
Indian and Some other
any race)
One Race White African Asian and Other
Alaska race
American Pacific
Native
Islander
California 33,871,648 95.3 59.5 6.7 1.0 10.9 0.3 16.8 4.7 32.4
JMPR Stu d y Srea 8,226,651 95.2 60.3 6.6 0.8 16.4 0.5 10.6 4.8 21.7
86,265 96.1 80.8 0.6 4.8 1.2 0.1 8.6 3.9 16.5
Mend ocino Cou nty
458,614 95.9 81.6 1.4 1.2 3.1 0.2 8.4 4.1 17.3
Sonoma Cou nty
247,289 96.5 84.0 2.9 0.4 4.5 0.2 4.5 3.5 11.1
Marin Coun ty
124,279 96.3 80.0 1.3 0.8 3.0 0.2 10.9 3.7 23.7
N ap a Cou nty
394,542 93.6 56.4 14.9 0.8 12.7 0.8 8.0 6.4 17.6
Solan o Cou nty
948,816 94.9 65.5 9.4 0.6 11.0 0.4 8.1 5.1 17.7
Contra Costa County
1,443,741 94.4 48.8 14.9 0.6 20.4 0.6 8.9 5.6 19.0
Alam ed a Cou nty
776,733 95.7 49.7 7.8 0.4 30.8 0.5 6.5 4.3 14.1
San Francisco County
707,161 95.0 59.5 3.5 0.4 20.0 1.3 10.2 5.0 21.9
San Mateo Cou nty
255,602 95.6 75.1 1.0 1.0 3.4 0.1 15.0 4.4 26.8
Santa Cru z Cou nty
1,682,585 95.3 53.8 2.8 0.7 25.6 0.3 12.1 4.7 24.0
Santa Clara Cou nty
401,762 95.0 55.9 3.7 1.0 6.0 0.4 27.8 5.0 46.8
Monterey Cou nty
53,234 94.9 65.2 1.1 1.2 2.4 0.2 24.9 5.1 47.9
San Ben ito County
246,681 96.6 84.6 2.0 0.9 2.7 0.1 6.2 3.4 16.3
San Lu is Obisp o County
399,347 95.7 72.7 2.3 1.2 4.1 0.2 15.2 4.3 34.2
Santa Barbara County
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Sources: U.S. Department of Commerce, Census Bureau (http://www.census.gov).
Table 2b. Demographic Profiles Coastal California Counties – Age and Gender, 2000
Percent of total population Males per
Median 100 females
Total
Geographic area age
Population Under 18 to 25 to 45 to 65 All 18
18 24 44 64 years (years) ages years
years years years years and and
over over
33,871,648 27.3 9.9 31.6 20.5 10.6 33.3 99.3 97.1
California
COUN TY
86,265 25.5 8.1 25.6 27.1 13.6 38.9 98.9 97.1
Mend ocino County
458,614 24.5 8.8 29.2 24.9 12.6 37.5 97 94
Sonoma County
247,289 20.3 5.5 31 29.7 13.5 41.3 98.2 96.4
Marin County
N apa County 124,279 24.1 8.5 27.7 24.3 15.4 38.3 99.6 97.4
394,542 28.3 9.2 31.3 21.7 9.5 33.9 101.5 100.2
Solano County
948,816 26.5 7.7 30.6 23.9 11.3 36.4 95.4 92.2
Contra Costa County
1,443,741 24.6 9.6 33.9 21.7 10.2 34.5 96.6 94
Alam eda County
San Francisco County 776,733 14.5 9.1 40.5 22.3 13.7 36.5 103.4 103.1
707,161 22.9 7.9 33.2 23.5 12.5 36.8 97.8 95.6
San Mateo County
255,602 23.8 11.9 30.8 23.5 10 35 99.7 97.8
Santa Cruz County
1,682,585 24.7 9.3 35.4 21 9.5 34 102.8 101.9
Santa Clara County
401,762 28.4 10.9 31.4 19.3 10 31.7 107.3 107.7
Monterey County
53,234 32.2 8.8 31.5 19.3 8.1 31.4 102.5 99.6
San Benito County
246,681 21.7 13.6 27 23.3 14.5 37.3 105.6 105.2
San Luis Obispo County
399,347 24.9 13.3 29 20.1 12.7 33.4 100.1 98.1
Santa Barbara County
Sources: U.S. Department of Commerce, Census Bureau (http://www.census.gov).
Labor Force
Total labor force for the JMPR study area in 2001 was 4.5 million. As with population, the two
largest counties in terms of labor force for 2001 are Santa Clara (1.0 million) and Alameda (0.8
million) and the two smallest are San Benito (28.0 thousand) and Mendocino (43.0 thousand).
There has been a wide range of growth in labor force among study area counties. The period
1990 to 2001 has seen significant growth in San Benito (29 percent), Sonoma (28 percent), San Luis
Obispo (23 percent), and Solano (20 percent) Counties and slower than average growth in Santa
Barbara (5.4 percent), Santa Cruz (5.4 percent), Marin (5.8 percent) and San Francisco (7.8 percent)
Counties.
Unemployment in San Benito County has risen over the decade from 8.2 percent in 1990 to 11.7
percent in 2001, the highest in the study area. Monterey has the second highest unemployment
rate at 9.5 percent for 2001. Significantly lower than average unemployment rates are seen for
Marin (2.5 percent) and San Mateo (2.6 percent) Counties for 2001.
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Table 3. Labor Force, Labor Force Growth, and Unemployment
Labor Force
Unemployment Rate
Labor Force Growth
2001 2000 1995 1990 1990-1995 1995-2000 1990-2001 2001 2000 1995 1990
STATE TOTAL 17,362,300 17,090,800 15,412,200 15,193,400 1.4 10.9 14.3 5.3 4.9 7.8 5.8
JMPR STUDY AREA 4,522,890 4,485,360 4,032,640 3,954,280 2.0 11.2 14.4 4.3 3.0 6.1 4.3
MEN DOCIN O 42,970 42,540 41,330 37,560 10.0 2.9 14.4 6.6 6.6 9.6 7.8
SON OMA 262,600 259,100 225,300 205,300 9.7 15.0 27.9 2.9 2.6 5.5 3.9
MARIN 138,100 139,400 128,700 130,500 -1.4 8.3 5.8 2.5 1.7 4.3 2.5
N APA 66,600 65,200 57,700 57,400 0.5 13.0 16.0 3.3 3.2 6.2 4.1
SOLAN O 201,400 197,400 173,100 167,900 3.1 14.0 20.0 4.1 4.2 8.0 4.7
CON TRA COSTA 509,800 504,100 456,000 439,100 3.8 10.5 16.1 3.3 2.7 5.7 4.0
ALAMEDA 754,900 739,000 682,000 683,200 -0.2 8.4 10.5 4.5 3.0 5.8 4.0
SAN FRAN CISCO 436,900 434,300 398,200 405,300 -1.8 9.1 7.8 5.2 2.8 6.1 3.8
SAN MATEO 407,900 410,500 369,800 366,500 0.9 11.0 11.3 2.8 1.6 4.2 2.6
SAN TA CRUZ 143,900 142,100 139,800 136,500 2.4 1.6 5.4 6.1 5.6 9.3 7.1
SAN TA CLARA 1,012,700 1,008,100 867,000 840,600 3.1 16.3 20.5 4.5 2.0 4.9 4.0
MON TEREY 195,800 196,200 175,900 174,200 1.0 11.5 12.4 9.3 9.5 12.4 9.5
SAN BEN ITO 28,020 27,320 23,110 21,720 6.4 18.2 29.0 8.2 7.9 13.7 11.7
SAN LUIS OBISPO 118,600 116,000 101,600 96,200 5.6 14.2 23.3 2.8 3.0 6.6 4.8
SAN TA BARBARA 202,700 204,100 193,100 192,300 0.4 5.7 5.4 3.5 3.7 6.7 4.9
Source: U.S. Department of labor, Bureau of Labor Statistics, Division of Labor Force Statistic s
Income and Employment
Income is reported from two perspectives; 1) income by place of residence and 2) income by
place of work. Income and employment by place of work are further reported by industry.
Income and employment by place of work is also reported for wage and salary workers versus
proprietors (business owners). Differences in these measurements often reveal important
differences about the nature of the local economies that are important for socioeconomic impact
analyses. For example, a large difference between income by place of residence and income by
place of work might reveal that the economy of the area under study is largely driven by income
earned from sources unrelated to work in the area and this will dampen the impacts of
management changes that impact local work related income and employment. A large number
of proprietors indicate the prevalence of small businesses that receive special treatment under
Federal Regulatory Impact Reviews.
Income by Place of Residence versus Income by Place of Work. A wide variation is seen in the
study area when comparing income by place of residence and place of work. In 1990, net income
(the difference between income by place of residence and place of work) as a percent of income
by place of work in the study area was 34.9 percent of the income by place of work. In 2000, this
proportion has dropped to only 24.7 percent. In 2000, this ratio was negative for two of the study
area counties, San Francisco (-9.4%) and Santa Clara (-2.6%).
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Table 4. Personal Income by Place of Residence and by Place of Work For California
1990 2000
A B A-B=C D C/B D /B A B A-B=C D C/B
Adjustment
Income by N et Income as Income by N et Income as
Income by Adjustment for Residence Income by Adjustment
Place of % of Income Place of % of Income
Place of Work Net Income* for as % of Place of Work Net Income* for
Residence by Place of Residence by Place of
($000's) Residence** Income by ($000's) Residence**
($000's) Work ($000's) Work
Place of Work
California 655,567,167 482,925,921 172,641,246 -75,934 35.7 0.0 1,093,065,244 825,224,182 267,841,062 121,446 32.5
JMPR Stu dy Area 186,542,551 138,283,627 48,258,924 -1,697,072 34.9 -1.2 363,936,984 291,743,151 72,193,833 -3,620,072 24.7
Mendocino 1,357,933 826,068 531,865 3,514 64.4 0.4 2,146,557 1,286,730 859,827 18,266 66.8
Sonoma 8,875,485 4,838,019 4,037,466 1,274,648 83.5 26.3 16,046,410 9,834,626 6,211,784 1,833,287 63.2
Marin 8,249,379 3,898,749 4,350,630 1,667,415 111.6 42.8 15,003,372 7,300,898 7,702,474 3,338,923 105.5
N apa 2,606,253 1,396,070 1,210,183 351,517 86.7 25.2 4,729,986 2,907,793 1,822,193 467,688 62.7
Solano 6,723,681 3,777,645 2,946,036 1,482,811 78.0 39.3 10,866,704 5,419,529 5,447,175 3,020,738 100.5
Contra Costa 21,769,539 11,492,645 10,276,894 4,399,175 89.4 38.3 39,194,448 20,729,218 18,465,230 9,187,760 89.1
Alameda 29,944,932 22,178,340 7,766,592 220,194 35.0 1.0 55,972,377 41,084,692 14,887,685 3,373,599 36.2
San Francisco 22,564,471 25,700,858 -3,136,387 -9,483,245 -12.2 -36.9 42,910,077 47,381,499 -4,471,422 -12,970,485 -9.4
San Mateo 19,708,771 12,503,307 7,205,464 1,535,803 57.6 12.3 41,512,033 33,242,279 8,269,754 77,797 24.9
Santa Cruz 5,061,315 2,809,424 2,251,891 754,967 80.2 26.9 9,610,039 5,294,057 4,315,982 2,072,654 81.5
Santa Clara 39,217,410 35,253,151 3,964,259 -4,022,888 11.2 -11.4 92,879,526 95,335,504 -2,455,978 -14,515,058 -2.6
Monterey 7,406,878 5,188,051 2,218,827 21,119 42.8 0.4 11,969,747 8,392,940 3,576,807 176,972 42.6
San Benito 654,107 344,368 309,739 121,555 89.9 35.3 1,341,148 743,924 597,224 287,779 80.3
San Luis Obispo 3,890,698 2,341,009 1,549,689 112,049 66.2 4.8 6,669,227 4,174,320 2,494,907 152,359 59.8
Santa Barbara 8,511,699 5,735,923 2,775,776 -135,706 48.4 -2.4 13,085,333 8,615,142 4,470,191 -142,351 51.9
* Net Income: There are several sources of income unrelated to work in a county that are recorded and they are generally referred to as transfer payments and property income. Social security and pensions are two of the most
important transfer payments and dividends, interest and rent are the most important sources of property income. Social Security and Medicare deductions from current workers are recorded as a deduction in income by place of work
in deriving income by place of residence. Adjustment for residence is also included in net income.
** Ad ju stment for Resid ence: The other d ifference between income by place of w ork and resid ence is called the resid ence ad justment. The resid ence ad ju stment is the net flow of incom e to a county that results
from some resid ents that work outsid e the county of resid ence and bring income into the cou nty (inflow of incom e) versu s resid ents from other counties that work inside the county but take their incomes hom e
to their cou nties of residence (ou tflow of income).
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic
Information System (REIS).
There are several sources of income unrelated to work in a county that are recorded and they are
generally referred to as transfer payments and property income. Social security and pensions are
two of the most important transfer payments and dividends, interest and rent are the most
important sources of property income. Social Security and Medicare deductions from current
workers are recorded as a deduction in income by place of work in deriving income by place of
residence. The other difference between income by place of work and residence is called the
residence adjustment. The residence adjustment is the net flow of income to a county that results
from some residents that work outside the county of residence and bring income into the county
(inflow of income) versus residents from other counties that work inside the county but take their
incomes home to their counties of residence (outflow of income).
In 1990, a total of $1.7 billion of the income in the JMPR study area was earned in counties
outside of the place of work. By 2000, this adjustment grew to $3.6 billion.
Proprietors Income and Employment. Proprietors (small businesses) account for a significant
proportion of both income and employment in study area counties. In 1990, proprietors in the
JMPR study area accounted for 9.1% of income and 14.2% of employment. In the 1990s, the
relative importance of proprietors increased. By 2000, proprietors accounted for 9.8% of the
income and 18.9% of the employment. These proportions were slightly lower than that for the
entire State of California. This is a fairly good indicator that small businesses are very important
in the study area. See Table 5.
As with other economic indicators we have summarized, there is wide variation among the
individual counties in the study area. In several of the counties in the southern section of the
study area (Monterey, San Benito, San Luis Obispo, and Santa Barbara), proprietors account for a
substantially higher amount of income and employment. Several of the counties show a
significantly lower proportions of proprietors income/total income as compared to proprietors
employment/total employment. Mendocino County’s proprietors income is only 2.0 percent of
9
total income as compared to proprietors employment which is 19.5 percent of total employment.
Other counties with similar scenarios are Solano and Alameda.
Table 5. Proprietors Income and Employment
1990 2000
Proprietors % of Total Proprietors Proprietors % of Total Proprietors
Income Personal Employment % of Total Income Personal Employment % of Total
($000's) Income ($000's) Employment ($000's) Income ($000's) Employment
California 62,148,804 9.5 2,852,772 16.8 120,226,020 11.0 3,830,282 19.5
JMPR Stu d y Area 16,889,884 9.1 779,007 360.4 35,757,023 9.8 1,032,751 1215.1
Mend ocino 179,230 2.0 11,738 16.8 323,938 2.0 14,147 19.5
Sonoma 873,075 9.8 50,195 24.4 1,859,063 11.6 65,618 24.2
Marin 820,613 9.9 44,389 29.7 1,708,962 11.4 56,043 31.6
N ap a 236,157 9.1 12,774 21.3 581,449 12.3 18,654 22.4
Solano 468,445 7.0 22,437 16.3 622,863 5.7 27,165 17.0
Contra Costa 1,660,360 7.6 84,000 21.0 3,955,517 10.1 110,789 23.4
Alam ed a 2,112,047 7.1 114,688 15.1 4,306,712 7.7 153,069 17.0
San Francisco 3,561,713 15.8 89,429 12.6 6,116,714 14.3 116,914 15.1
San Mateo 1,638,198 8.3 72,670 18.2 3,824,705 9.2 99,268 19.6
Santa Cru z 482,714 9.5 26,763 21.2 1,047,858 10.9 38,712 25.9
Santa Clara 2,295,244 5.9 145,677 13.9 6,198,826 6.7 190,713 14.8
Monterey 942,285 12.7 30,850 15.2 2,322,076 19.4 42,444 19.0
San Benito 88,965 13.6 3,756 24.0 238,064 17.8 5,416 25.1
San Lu is Obisp o 458,857 11.8 26,888 25.1 1,020,870 15.3 38,117 27.1
Santa Barbara 1,071,981 12.6 42,753 19.8 1,629,406 12.5 55,682 22.1
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic
Information System (REIS).
Indicators of Economic Health and Wealth
Unemployment rates and Per Capita Income. Unemployment rates and per capita incomes are
probably the two most popular measures used as indicators of the health and wealth of
communities, states or nations. Through the 1990s both unemployment and real per capita
income (per capita income in 2001 dollars i.e., adjusted for inflation using the Consumer Price
Index) moved in the same directions for most counties in the study area. Unemployment
throughout the study area rose during the first half of the decade and dropped significantly
during the second half. Monterey and San Benito Counties have historically had the highest
unemployment rates. Marin and San Mateo Counties have historically had the lowest
unemployment rates.
Real per capita income remained fairly level during the 1990 to 1995 period, with the counties in
the study area reporting slight increases or slight declines. It was the period 1995 to 2000 that
had sharp increases in real per capita income. The four counties with the highest real per capita
income in 2000, Marin ($62,331), San Mateo ($60,301), San Francisco ($56,834), and Santa Clara
($56,716) also had the highest increases from 1995 to 2000 in the study area. Mendocino ($25,554)
and San Benito ($25,586) had the lowest real per capita income in 2000. Monterey County had the
smallest increase from 1995 to 2000 in real per capita income in the study area
10
Table 6. Unemployment Rates and Per Capita Incomes
Unemployment Rate (%) Per Capita Income Per Capita Income (2001 $)
1990 1995 2000 1990 1995 2000 1990 1995 2000
California 5.8 7.8 4.9 21,882 24,339 32,149 29,653 28,280 33,058
Mend ocino 7.6 9.6 6.6 16,794 19,374 24,852 22,758 22,511 25,554
Sonom a 3.9 5.5 2.6 22,729 25,569 34,863 30,801 29,709 35,848
Marin 2.5 4.3 1.7 35,786 43,340 60,618 48,494 50,358 62,331
N ap a 4.1 6.2 3.2 23,420 27,568 37,928 31,737 32,032 39,000
Solano 4.8 8.0 4.2 19,576 20,867 27,354 26,528 24,246 28,127
Contra Costa 4.0 5.7 2.7 26,899 31,065 41,110 36,451 36,095 42,272
Alamed a 4.0 5.8 3.0 22,926 27,212 38,624 31,068 31,618 39,716
San Francisco 3.8 6.1 2.8 31,188 35,992 55,272 42,264 41,820 56,834
San Mateo 2.6 4.2 1.6 30,313 36,064 58,644 41,078 41,904 60,301
Santa Cru z 7.1 9.3 5.6 22,043 26,117 37,567 29,871 30,346 38,629
Santa Clara 3.9 4.9 2.0 26,174 32,488 55,157 35,469 37,749 56,716
Monterey 9.6 12.4 9.5 20,717 24,832 29,695 28,074 28,853 30,534
San Benito 12.1 13.7 7.9 17,758 19,687 24,883 24,064 22,875 25,586
San Lu is Obisp o 4.6 6.6 3.0 17,825 20,594 26,932 24,155 23,929 27,693
Santa Barbara 4.9 6.7 3.7 22,970 25,467 32,734 31,127 29,591 33,659
Source: Income: U.S. Department of Commerce, Bureau of Economic Analysis, Regional
Economic Information System (REIS).
Unemployment rate: U.S. Department of Labor, Bureau of Labor Statistics, Division of Labor
Force Statistics
Income and Employment by Industry. For purposes of economic impact analyses, in terms of
income and employment impacts, income and employment by industry is critical because it
provides the necessary control totals in the economic accounting system. A limitation of this
accounting system is that it is still based on the old industrial economy and generally is not
designed to yield direct insights into how the use of natural resources and the environment are
connected to the economy. Linking the economy and the environment is the very heart of the
Socioeconomic Team’s task. We need to be able to answer the question, if the use of the natural
resources of the JMPR Study Area are changed, what will be the impact on the income and
employment in the local economies? To answer this question requires supplemental information
organized so that it maps directly into the current system of accounting. In some cases, the
income and employment by industry statistics can give us upper bound estimates of the direct
portion of impact (i.e., not counting multiplier impacts) for particular uses. Our approach here is
to first look at the most aggregated information, then proceed to evaluate information collected
by other institutions and how it maps into the more aggregated statistics. Each step along the
way our objective is to see how close we can get to linking the economy with the environment
and assessing the relative importance to the economy of natural resource base uses.
Tables 7 and 8 show the values and percentages of income and employment by industry
to counties in the study area. At this very aggregated level, the distributions for both income and
employment by industry are very similar for most of the counties. The counties in the study area
are driven by the services sector.
11
Table 7, Personal Income by Industry
Personal Income By Industry ($000s), 2000
Ag. Services, Governm ent
Transpor- Finance,
Forestry, Manufactu rin Wholesale and
Total Farm Mining Constru ction tation and Retail Trad e Insurance, and Services
Fishing, & g Trad e Governm ent
Public Utilities Real Estate
Other Enterp rises
California 825,224,182 8,424,649 7,943,257 2,851,715 47,012,923 128,467,273 49,823,365 47,115,376 71,496,822 71,830,864 271,009,369 119,248,569
Stud y Area 291,743,151 3,018,746 2,042,716 934,675 16,166,414 59,886,105 14,794,266 15,037,837 22,250,049 25,216,023 101,689,185 30,288,137
Mendocino 1,286,730 25,863 41,009 (D) 103,509 210,441 64,176 (D) 178,114 50,536 345,782 233,640
Sonoma 9,834,626 178,115 120,951 76,092 1,112,460 1,969,874 389,684 365,396 1,006,663 710,265 2,670,638 1,234,488
Marin 7,300,898 (833) (D) (D) 607,793 242,514 203,739 291,487 812,576 1,045,498 3,330,911 661,473
N apa 2,907,793 115,764 70,345 (D) 246,501 622,755 120,846 (D) 286,533 180,987 782,277 390,449
Solano 5,419,529 24,315 44,744 24,727 571,423 601,996 253,821 205,811 647,217 235,418 1,282,427 1,527,630
Contra Costa 20,729,218 60,334 164,980 365,513 1,876,810 2,079,544 1,595,809 853,299 1,975,171 2,396,625 7,068,915 2,292,218
Alam ed a 41,084,692 (119) 186,215 51,243 2,780,983 6,883,531 2,596,816 3,428,926 3,492,682 2,005,942 13,077,290 6,581,183
San Francisco 47,381,499 - 126,426 79,519 1,480,390 1,750,359 3,589,434 1,474,814 3,703,088 10,727,986 18,730,070 5,719,413
San Mateo 33,242,279 102,958 (D) (D) 1,751,030 4,428,802 2,789,664 1,524,252 2,605,707 2,900,905 15,353,673 1,637,553
Santa Cruz 5,294,057 221,624 75,315 5,204 377,375 922,955 169,562 256,572 563,451 298,412 1,668,896 734,691
Santa Clara 95,335,504 211,521 297,463 225,922 3,805,161 38,327,098 2,130,155 5,711,362 4,705,760 3,322,790 31,531,680 5,066,592
Monterey 8,392,940 1,387,752 628,427 9,550 437,838 499,764 284,149 313,453 794,580 473,230 1,893,698 1,670,499
San Benito 743,924 118,750 26,672 (D) 72,983 96,512 (D) 51,363 70,241 33,409 112,627 120,840
San Luis Obispo 4,174,320 151,587 93,602 12,500 418,977 334,179 322,879 107,693 529,648 251,528 1,101,806 849,921
Santa Barbara 8,615,142 421,115 166,567 84,405 523,181 915,781 283,532 453,409 878,618 582,492 2,738,495 1,567,547
Personal Income By Industry (% of Total), 2000
1.0 1.0 0.3 5.7 15.6 6.0 5.7 8.7 8.7 32.8 14.5
California
1.0 0.7 0.3 5.5 20.5 5.1 5.2 7.6 8.6 34.9 10.4
Stud y Area
Mendocino 2.0 3.2 8.0 16.4 5.0 13.8 3.9 26.9 18.2
Sonoma 1.8 1.2 0.8 11.3 20.0 4.0 3.7 10.2 7.2 27.2 12.6
0.0 8.3 3.3 2.8 4.0 11.1 14.3 45.6 9.1
Marin
4.0 2.4 8.5 21.4 4.2 9.9 6.2 26.9 13.4
N apa
0.4 0.8 0.5 10.5 11.1 4.7 3.8 11.9 4.3 23.7 28.2
Solano
Contra Costa 0.3 0.8 1.8 9.1 10.0 7.7 4.1 9.5 11.6 34.1 11.1
0.0 0.5 0.1 6.8 16.8 6.3 8.3 8.5 4.9 31.8 16.0
Alam ed a
0.0 0.3 0.2 3.1 3.7 7.6 3.1 7.8 22.6 39.5 12.1
San Francisco
San Mateo 0.3 5.3 13.3 8.4 4.6 7.8 8.7 46.2 4.9
Santa Cruz 4.2 1.4 0.1 7.1 17.4 3.2 4.8 10.6 5.6 31.5 13.9
0.2 0.3 0.2 4.0 40.2 2.2 6.0 4.9 3.5 33.1 5.3
Santa Clara
16.5 7.5 0.1 5.2 6.0 3.4 3.7 9.5 5.6 22.6 19.9
Monterey
San Benito 16.0 3.6 9.8 13.0 6.9 9.4 4.5 15.1 16.2
San Luis Obispo 3.6 2.2 0.3 10.0 8.0 7.7 2.6 12.7 6.0 26.4 20.4
4.9 1.9 1.0 6.1 10.6 3.3 5.3 10.2 6.8 31.8 18.2
Santa Barbara
Table 8, Employment by Industry
Employment By Industry (number of jobs), 2000
Ag. Services, Transpor- Finance, Governm ent
Forestry, Manu factu rin tation and Wholesale Insu rance, and
Total Farm Mining Constru ction Retail Trad e Services
Fishing, & g Pu blic Trad e and Real Governm ent
Other Utilities Estate Enterp rises
California 19,654,877 328,861 408,406 38,870 1,040,795 2,047,587 879,014 912,202 3,006,849 1,696,230 6,759,116 2,536,947
Stud y Area 5,476,530 81,482 88,267 7,457 301,249 595,826 246,627 232,547 813,704 478,845 2,009,938 607,067
Mend ocino 49,818 3,163 2,012 (D) 3,139 6,128 1,425 (D) 8,768 2,930 14,662 6,437
Sonom a 271,593 9,475 6,167 533 20,665 34,060 8,269 8,581 44,113 23,514 86,505 29,711
Marin 177,605 843 (D) (D) 12,179 5,646 4,437 5,717 29,750 23,498 77,433 14,410
N ap a 83,401 5,350 2,703 (D) 5,183 11,227 1,977 (D) 12,941 5,947 26,396 9,468
Solano 159,852 2,597 2,346 535 12,524 11,066 5,179 5,108 30,569 10,758 45,904 33,266
Contra Costa 473,822 2,920 7,314 2,308 35,875 28,015 24,829 15,107 77,652 58,440 173,520 47,842
Alamed a 902,712 1,155 7,953 710 51,011 103,259 50,453 62,191 128,300 60,754 312,288 124,638
San Francisco 773,679 - 2,990 587 26,111 32,222 43,684 23,879 107,614 103,642 335,359 97,591
San Mateo 506,154 3,449 (D) (D) 27,773 39,328 46,863 23,409 71,099 49,874 206,770 31,770
Santa Cruz 149,630 8,949 2,995 132 8,878 11,980 3,813 5,708 26,456 11,247 50,902 18,570
Santa Clara 1,290,679 5,295 12,236 861 63,005 271,595 37,638 63,107 168,551 79,712 489,782 98,897
Monterey 223,754 18,710 26,197 281 9,967 11,062 6,182 6,768 34,662 14,996 60,034 34,895
San Benito 21,573 2,079 1,098 (D) 1,713 2,628 (D) 1,380 3,474 1,363 4,295 2,896
San Lu is Obisp o 140,869 5,050 5,177 323 10,325 8,838 5,647 3,886 27,359 12,519 41,096 20,649
Santa Barbara 251,389 12,447 9,079 1,187 12,901 18,772 6,231 7,706 42,396 19,651 84,992 36,027
Employment By Industry (% of jobs), 2000
California 1.7 2.1 0.2 5.3 10.4 4.5 4.6 15.3 8.6 34.4 12.9
Stud y Area 1.5 1.6 0.1 5.5 10.9 4.5 4.2 14.9 8.7 36.7 11.1
Mend ocino 6.3 4.0 6.3 12.3 2.9 17.6 5.9 29.4 12.9
Sonom a 3.5 2.3 0.2 7.6 12.5 3.0 3.2 16.2 8.7 31.9 10.9
Marin 0.5 6.9 3.2 2.5 3.2 16.8 13.2 43.6 8.1
N ap a 6.4 3.2 6.2 13.5 2.4 15.5 7.1 31.6 11.4
Solano 1.6 1.5 0.3 7.8 6.9 3.2 3.2 19.1 6.7 28.7 20.8
Contra Costa 0.6 1.5 0.5 7.6 5.9 5.2 3.2 16.4 12.3 36.6 10.1
Alamed a 0.1 0.9 0.1 5.7 11.4 5.6 6.9 14.2 6.7 34.6 13.8
San Francisco 0.0 0.4 0.1 3.4 4.2 5.6 3.1 13.9 13.4 43.3 12.6
San Mateo 0.7 5.5 7.8 9.3 4.6 14.0 9.9 40.9 6.3
Santa Cruz 6.0 2.0 0.1 5.9 8.0 2.5 3.8 17.7 7.5 34.0 12.4
Santa Clara 0.4 0.9 0.1 4.9 21.0 2.9 4.9 13.1 6.2 37.9 7.7
Monterey 8.4 11.7 0.1 4.5 4.9 2.8 3.0 15.5 6.7 26.8 15.6
San Benito 9.6 5.1 7.9 12.2 6.4 16.1 6.3 19.9 13.4
San Lu is Obisp o 3.6 3.7 0.2 7.3 6.3 4.0 2.8 19.4 8.9 29.2 14.7
Santa Barbara 5.0 3.6 0.5 5.1 7.5 2.5 3.1 16.9 7.8 33.8 14.3
(D) Not shown to avoid disclosure of confidential information, but the estimates are included in the totals.
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic
Information System (REIS).
12
Commercial fisheries would be included under the category “Agricultural Services, Forestry,
Fishing and Other”. In 2000, this category accounted for only 0.7% of income and 1.6% of
employment by place of work in the study area. Several of the counties (Monterey, San Benito,
Mendocino, and Napa) did have higher proportions than the average. This serves as a first step
upper bound on the proportion of income by place of work for the direct impacts of the
harvesting portion (not including multiplier impacts) of commercial fishing. Other direct
impacts of commercial fishing would include some portion of Wholesale Trade (e.g., fish houses
and buyers) and some portion of Manufacturing (fish processing).
The Retail Trade and Services sectors are where the direct impacts of tourism/recreation would
be included. However, these categories are too broad to yield any useful bounds for estimation
of the direct impacts for tourism/recreation. The accounts, as stated above, were simply not
designed for this purpose. In any case, the first step of linking the three natural resource use
activities to the economy yielded only limited insights.
Income and Employment: Additional Disaggregation
The accounts reviewed above are what are called two-digit SIC (Standard Industrial
Classification) level of aggregations. The SIC system of accounting can actually go down to four
and six digit levels, which contain more specificity about the activity. However, because of
nondisclosure rules to protect the privacy of business information, the four digit level is the best
available for large counties and even here there are many categories for which information is not
reported due to nondisclosure. In this step, we will explore how much detail we can glean about
the three sectors that are our primary interest. Only income is reported at the lower levels of
disaggregation.
Commercial Fishing Industry. In 1995, fishing income was a little over $117 million in the State
of California. This represents less than one percent (0.02%) of income by place of work. Two of
the counties (Mendocino, 0.66% and Monterey, 0.32 percent) do have higher proportions of
fishing income, however, they remain under one percent of total income by place of work. The
year 1995 was chosen for analysis because it was the last year that a significant number of
counties were able to release data. Again, this would be the income received by harvesters or
commercial fishermen including crews and proprietors of the harvesting operations. It would
not include buyers and fish houses or processors of commercial fish products.
Table 9. Direct Income to Commercial Fishing Harvesting Sector ($000s)
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
California 170,671 140,424 129,910 133,414 120,338 117,640 109,820 106,752 94,532 103,807 103,391
Mendocino 6,043 5,386 4,975 5,545 6,079 6,009 5,608 6,148 5,207 6,241 6,085
Sonom a 2,547 2,065 1,946 1,286 1,593 1,327 1,399 796 709 770 824
Marin (D) 1,274 1,246 1,452 1,702 1,394 (D) (D) (D) (D) (D)
Napa (D) 123 126 149 207 (L) (L) 52 50 (D) 60
Solano 400 204 140 154 236 127 135 154 145 164 (D)
Contra Costa (D) 1,115 1,052 1,157 1,526 1,034 917 687 (D) (D) (D)
Alamed a 2,764 2,279 1,783 1,570 1,410 1,549 (D) (D) (D) (D) (D)
San Francisco (D) 631 540 323 421 546 1,773 652 (D) 859 (D)
San Mateo (D) 4,375 3,276 3,644 3,860 2,707 (D) (D) 3,015 3,597 (D)
Santa Cruz 1,113 917 649 639 739 563 630 764 (D) (D) (D)
Santa Clara 677 644 572 545 578 433 472 469 364 453 463
Monterey (D) 21,500 23,929 24,002 13,994 18,898 13,126 11,682 (D) (D) (D)
San Benito (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L)
San Luis Obispo (D) 4,328 3,905 4,851 4,895 (D) (D) (D) (D) 4,173 (D)
Santa Barbara (D) 3,797 3,261 3,206 3,292 2,909 2,970 2,148 (D) (D) (D)
(D) Not shown to avoid disclosure of confidential information, but the estimates are included in the totals.
(L) Less that $50,000, but the estimates for this item are included in the totals.
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic
Information System (REIS).
13
Tourism and Recreation. Tourism/recreation has been a notoriously difficult activity to
document because the expenditures made while undertaking the activities are spread across so
many sectors. Few that really capture the industry. Three commonly used are “Eating and
Drinking Places” (within Retail Trade), “Hotels and Other Lodging Places”, and “Amusement
and Recreation Services” (within Services). A fourth is sometimes included “Museums, Botanical
and Zoological Gardens” (within Services). The first three indicators of tourism/recreation are
commonly used by the United Nations Environmental Programme when profiling third world
countries for economic development programs. Unfortunately, these three sectors tell us very
little about tourism/recreation. They are not good discriminators across areas in a single point in
time, nor are they good indicators of the trends of tourism/recreation over time in a given place.
Life style changes have resulted in high proportions of the local population eating out. Business
related travel is a major portion of hotel and motel business and some communities may have
extensive numbers of hotel and motels with very little in the way of tourism/recreation. In
highly diverse economies like the U.S., measurements from these three industries yield nothing
of use to get us close to linking natural resource uses with the economy. We must look elsewhere
for supplemental information to get us closer to our goal.
Income and Employment: Supplemental Information. In step 2, we were able to narrow in on commercial
fishing contributions to the local economies at the first stage of direct impacts. The industry accounts did
not support any additional insights for tourism/recreation. In this step, we seek out additional sources of
information and to see what they might reveal about the activities and their income and employment
impacts.
Commercial Fishing Industry. For the commercial fisheries, we will first go to information compiled by
the Pacific Fisheries Management Council (PFMC). The PFMC maintains a data base called PacFin which
reports commercial fish landings by port, county and species. The PFMC also has developed a regional
economic impact model to translate ex vessel value (i.e., the dollar amounts received by harvesters for their
catch) to total income generated within the county where landed. This amount will include full
multiplier impacts.
VALUE OF MARINE SCIENTIFIC RESEARCH IN THE STUDY AREA
Data gap for possible further investigation.
TOURISM AND RECREATION
Below we present the information and our preliminary assessment of the range of relative
importance of tourism/recreation to the JMPR study area economy. Marine recreation uses in
the JMPR study area would be some sub-set of these estimates.
California Travel Direct Impacts by County - Method
A study, California Travel Impacts by County, 1992-2000, prepared by Dean Runyan Associates for
the California Travel and Tourism Commission and the Division of Tourism of the Technology,
Trade and Commerce Agency was completed in March 2002. As stated in the introduction, the
report describes the economic impacts of travel to and through the state of California over the
time period 1992 to 2000. These estimates of the direct impacts associated with traveler spending
in California were produced using the Regional Travel Impact Model (RTIM) developed by De an
Runyan Associates. The input data used to detail the economic impacts of the California travel
14
industry were derived from various local, state and federal sources. For accuracy, the following
explanation of analysis methods is from the report.
Types Of Travel Impacts Included. Most of the travel that occurs in California is included in the
scope of this analysis. All trips to California by U.S. residents and foreign visitors are included.
The travel of California residents to other destinations within California is included, provided
that it is neither commuting nor other routine travel. Travel to non-California destinations by
California residents is not included as a component of destination spending. Outbound air travel
impacts are included in the air transportation category. The impacts associated with both
overnight and day travel are included if the travelers remain at the destination overnight or the
destination is over 50 miles, one-way, from the traveler's home. These definitions are used to
screen and, if necessary, to interpret and adjust local data used for travel impact measurements.
The most conservative interpretation is employed where data limitations cause deviations from
the above definition. The terms “traveler” and “visitor” are used interchangeably in this report.
Both represent a person who is traveling in the state of California, away from his or her home, on
a trip as defined above. The purpose of such travel can be for business, pleasure, shopping, to
attend meetings, or for personal, medical or educational
purposes.
Air Transportation And Travel Arrangement. This analysis focuses on travel and tourism as a
component of local and statewide economies, and therefore focuses on destination-specific
impacts. However, some impacts associated with non-destination-specific spending and
employment are included. These non-destination-specific industries are air transportation and
travel arrangement (travel agents and tour operators). These industries are classified as
nondestination-specific because they provide services for travel to, through and from specific
destinations. It is important to note, however, that the impacts of these industries (e.g.,
employment) occur within specific geographic areas, primarily those with commercial airport
facilities.
Thirty-three counties in California had scheduled passenger air transportation in 2000. The
associated employment impacts are allocated in this report to the county in which the
employment is based. The associated spending impacts are also allocated to that county as non-
destination spending.1 However, it is important to recognize that the benefits from air travel also
extend to those counties that do not provide air transportation. This might include, for example,
an overnight visitor in Mendocino County who traveled by air from Chicago to Oakland.
Because air transportation facilities provide travel services that benefit businesses throughout the
state, it is appropriate to include air transportation as a component of the travel industry. But
because of the regional character of air travel, it is sometimes useful to exclude this sector when
analyzing local economic impacts. These considerations are, of course, most relevant with respect
to those counties with the largest air transportation impacts.
Direct Versus Indirect Impacts Or “Multipliers”. Economic impact measurements reported herein
represent only direct economic impacts. Direct economic impacts include only the spending by
travelers and the employment generated by that spending. Indirect or “multiplier” effects, which
refer to the additional spending of businesses and employees induced by travel spending, are not
included.
San Francisco and San Mateo counties are the only exception. The employment associated with
1
air transportation employment in San Mateo County is allocated to San Mateo, whereas most of
the air transporation travel spending is allocated to San Francisco.
15
Impact Categories. The specific categories of travel impacts included in this analysis are as follows:
Expenditures: Purchases by travelers during their trip, including lodging taxes and other
applicable local and state taxes, paid by the traveler at the point of sale.
Total Earnings: The earnings (wage and salary disbursements, earned benefits and
proprietor income) of employees of businesses that receive travel expenditures. Only the
earnings attributable to travel expenditures are included; this typically is only a portion
of all business receipts.
Employment: Employment associated with the above earnings; this includes both full-
and part-time positions of wage and salary workers as well as proprietors.
Local Tax Receipts: Tax receipts collected by counties and municipalities, as levied on
applicable travel-related purchases.
State Tax Receipts: State taxes, such as sales and gasoline taxes, attributable to travel
expenditures and business taxes as levied on travel industry firms and employees.
Visitor Categories. Travelers are classified according to the type of accommodation in which they
stay. The types of visitors are as follows:
Hotel/Motel/B&B Guest: Travelers staying in hotels, motels, resorts, bed & breakfast
establishments, and other commercial accommodations, excluding campgrounds, where
a transient lodging tax is collected.
Private Camper: Travelers staying in a privately owned (i.e., commercial) campground.
Public Camper: Travelers staying in a publicly managed campground such as those
managed by the California State Parks and Recreation Commission, the U.S. Forest
Service or the National Park Service.
Private Home Visitor: Travelers staying as guests with friends or relatives.
Vacation Home Visitor: Travelers using their own vacation home or timeshare and those
borrowing or renting a vacation home where transient lodging tax is not collected.
Day Visitor: Both in-state and out-of-state residents whose trip does not include an
overnight stay at a destination in California.
The “travel industry” as described in this report refers to a collection of businesses that provide
goods and services to the traveling public. These types of businesses are coded according to the
U.S. Office of Management and Budget's Standard Industrial Classifications (SIC).
Local taxes refer to all city and county taxes. These include local sales taxes and room taxes.
Property taxes are not included. State taxes include the state sales tax, the state gasoline fuel tax,
and income taxes on travel industry firms and employees.
16
Interpretation Of Impact Estimates. Users of this information should be aware of several issues
regarding the interpretation of the impact estimates contained herein:
When comparing the impact estimates associated with different locations or different
time periods, it is more appropriate to focus on destination spending (which excludes air
transportation) rather than total travel spending.
The estimates in this report are expressed in current dollars. There is no adjustment for
inflation.
The employment and business service categories found in the impact tables do not
perfectly correspond to the industry categories used in various state and federal
government publications. The spending and employment categories used in this report
refer to a particular type of service, as opposed to an industry classification. For example,
the accommodations category in this report includes only that spending or employment
attributable to paid accommodations. It does not include spending on eating and
drinking in a hotel restaurant or recreational services provided at a resort. In addition,
government employees are not distinguished from the employees of commercial
enterprises, as is often the case in other data series published by government agencies.
In the detailed table for each county, the first breakout, Travel Spending by Type of Traveler
Accommodation, shows the travel spending by each type of traveler in the county. The second
breakout, Travel Spending by Type of Business Service, indicates the amount of expenditures for
different goods and services (e.g., accommodations, recreation) by all traveler types. Destination
spending refers to all travel-related spending in the county except air transportation and travel
arrangement.
California Travel Impacts by County – Results
Total travel spending in the JMPR study area was estimated by Dean Runyan at $25.2 billion in
2000. This accounts for 1/3 of the $75.4 billion that travelers to California contributed to the state
economy. Four billion was spent on air transportation in the study area in 2000. Total
destination spending, total spending excluding air transportation and travel arrangement, was
estimated to be $21.0 billion.
Employment in the study area generated by travel spending was estimated to be approximately
250 thousand. While San Francisco County accounts for approximately $5.6 billion, or about ¼,
of the travel destination spending in the study area, it accounts for a disproportionately small
amount of the employment generated by travel spending.
Spending on recreation related travel activities was estimated at $3.5 billion. Recreation travel
spending, the sector we are most interested in, is largely driven by five counties. San Francisco
($1.0 billion), Santa Clara ($484 million), San Mateo ($355 million), Monterey ($300 million), and
Alameda ($290 million) Counties together account for 69.8 percent of the recreational spending in
the study area.
In the study area, an estimated 47,793 jobs are generated by the recreation component of travel
spending. Recreational travel employment is driven by the same counties, with the exception of
San Francisco, which was found to employ a very small number of people (15).
17
Total earnings generated by travel spending in the study area was estimated to be $8.5 billion in
2000. Again, the five counties previously mentioned, San Francisco ($2.1 billion), Santa Clara ($1.2
billion), San Mateo ($1.7 billion), Monterey ($629 million), and Alameda ($807 million) account
for 76.3 percent of the earnings generated by travel spending in the study area.
Total tax revenues generated by travel spending in the study area were $1.6 billion in 2000. Of
this, $676 million were local taxes and $942 million were state taxes. Local taxes refer to sales and
use taxes, and transient occupancy taxes collected by cities and counties. Property taxes and
business license taxes are not included. State taxes include the state sales tax, the state gasoline
fuel tax, corporate income taxes and personal income taxes.
Table 10. Travel Impacts, 2000
Study Contra Mendo- Mon- San San San Luis San Santa Santa Santa
CA Alameda Marin N apa Solano Sonoma
Area Costa cino terey Benito Francisco Obispo Mateo Barbara Clara Cruz
Travel Spending by Type of Traveler Accommodation ($Million)
D estination Spending 66,000 20,977 2,008 896 516 66 1,853 628 74 5,592 961 2,178 1,151 3,192 514 430 918
Hotel, Motel, B&B 34,500 13,580 1,315 422 239 35 1,256 386 12 4,355 471 1,408 684 2,206 247 140 405
Private Campground 2,500 425 3 29 38 3 12 19 19 - 72 20 23 83 37 40 28
500 101 - 6 4 1 15 2 1 - 20 9 16 2 12 1 14
Public Campground
Private Home 7,100 1,788 245 179 81 7 107 20 21 245 68 254 112 257 36 76 81
Vacation Home 3,600 451 11 26 25 4 44 16 2 28 76 15 27 16 68 7 87
D ay Travel 17,700 4,631 433 235 130 18 419 185 20 964 254 471 290 627 114 167 304
8,800 4,053 504 - 5 9 25 4 0 2,853 7 457 14 165 - - 10
Air Transportation
Travel Arrangement 600 58 1 9 11 1 7 1 0 5 2 2 4 6 5 1 5
Total Spending 75,400 25,236 2,531 905 533 75 1,885 633 75 8,502 970 2,659 1,169 3,419 518 430 933
Travel Spending by Type of Business Service ($Million)
D estination Spending 66,000 20,977 2,008 896 516 66 1,853 628 74 5,592 961 2,178 1,151 3,192 514 430 918
12,900 4,963 434 142 97 13 461 139 9 1,603 196 467 242 818 135 50 158
Accommodations
Eating, D rinking 16,000 5,072 442 180 130 16 500 153 22 1,401 259 491 304 749 123 94 209
Food Stores 2,200 599 51 28 22 2 50 19 7 123 44 55 36 90 23 19 30
Ground Transport 8,800 2,372 423 266 56 9 71 26 4 290 62 380 92 447 46 86 116
Recreation 12,100 3,487 290 119 92 12 300 137 14 1,003 147 355 182 484 79 84 188
Retail Sales 13,900 4,484 367 162 119 14 471 154 18 1,173 254 430 295 604 108 98 217
Air Transportation 8,800 4,053 504 - 5 9 25 4 0 2,853 7 457 14 165 - - 10
Travel Arrangement 600 58 1 9 11 1 7 1 0 5 2 2 4 6 5 1 5
Total Spending 75,400 25,236 2,531 905 533 75 1,885 633 75 8,502 970 2,659 1,169 3,419 518 430 933
Earnings Generated by Travel Spending ($Million)
Total Earnings 24,900 8,458 807 253 191 25 629 221 22 2,111 306 1,705 372 1,204 178 127 309
Employment Generated by Travel Spending (Jobs)
Accommodations 201,000 66,881 6,690 2,300 1,390 201 6,100 1,820 170 16,700 3,810 5,820 4,100 11,560 2,480 1,040 2,700
398,000 109,458 10,500 4,470 2,870 398 10,390 3,150 640 23,500 7,740 9,460 8,030 16,370 3,440 2,990 5,510
Eating, D rinking
Food Stores 12,000 2,812 260 150 100 12 220 80 40 400 280 220 200 420 140 120 170
Ground Transport 47,000 11,857 2,480 1,040 230 47 300 150 20 1,500 330 1,740 500 2,130 180 510 700
Recreation 248,000 62,278 5,910 2,550 1,730 248 4,590 2,390 210 14,500 3,250 5,870 3,570 9,070 1,890 2,280 4,220
Retail Sales 114,000 32,124 2,870 1,330 870 114 3,220 1,040 170 6,500 2,500 2,720 2,560 4,340 990 1,030 1,870
52,000 22,792 3,110 - 40 52 290 20 - 1,600 60 16,050 130 1,360 - - 80
Air Transportation
Travel Arrangement 28,000 8,899 900 490 520 28 180 60 1 2,600 140 840 250 2,220 300 60 310
Total Employment 1,100,000 317,100 32,710 12,330 7,760 1,100 25,280 8,710 1,250 67,300 18,120 42,710 19,330 47,480 9,430 8,020 15,570
Tax Revenues Generated by Travel Spending ($Million)
1,700 676 58 22 11 2 53 17 1 258 21 61 32 101 14 7 19
Local Taxes
State Taxes 3,100 942 99 56 25 3 76 25 3 211 43 115 52 145 22 24 43
Total Taxes 4,800 1,618 157 78 35 5 130 42 5 469 64 177 83 246 36 31 62
Table 11. Total Recreation Travel Spending by County, 1992-2000 ($Millions)
18
Average
1992 1993 1994 1995 1996 1997 1998 1999 2000 Annual
Change
State Total 7,400 7,600 7,900 8,300 9,100 10,000 10,700 11,500 12,100 6.4
JMPR Study Area 1,975 2,066 2,169 2,334 2,591 2,869 3,080 3,386 3,536 7.6
Alam ed a 138 144 148 160 179 197 215 254 290 9.8
Contra Costa 70 73 76 81 87 97 106 113 119 6.9
Marin 49 55 58 61 67 73 78 86 92 8.3
Mend ocino 43 43 45 48 49 51 54 57 61 4.5
Monterey 186 193 199 212 236 254 266 295 300 6.2
N apa 76 79 88 98 106 117 125 128 137 7.6
San Benito 9 9 9 10 11 12 12 13 14 6.0
San Francisco 536 566 602 649 730 813 872 992 1,003 8.2
San Lu is Obisp o 100 105 101 102 112 119 127 136 147 5.0
San Mateo 206 213 228 250 278 310 330 346 355 7.1
Santa Barbara 119 123 129 135 143 153 163 174 182 5.5
Santa Clara 221 233 250 281 328 382 423 456 484 10.4
Santa Cruz 50 52 52 55 60 66 69 78 79 6.0
Solano 53 55 57 58 61 67 70 76 84 5.9
Sonom a 119 123 127 134 145 158 170 181 188 5.9
Source: The California Travel and Tourism Commission, The California Technology, Trade, and
Commerce Agency, and Dean Runyan Associates.
Table 12. Direct Recreation Travel-Generated Employment by County, 1992-2000 (Jobs)
Average
1992 1993 1994 1995 1996 1997 1998 1999 2000 Annual
Change
State Total 195,000 194,000 206,000 210,000 222,000 241,000 236,000 248,000 248,000 3.1
JMPR Stud y Area 45,480 46,120 49,740 51,790 55,190 60,170 60,460 64,930 63,010 4.2
Alam ed a 3,580 3,630 3,840 4,000 4,310 4,680 4,810 5,650 5,910 6.6
Contra Costa 1,970 1,980 2,130 2,190 2,270 2,510 2,520 2,630 2,550 3.4
Marin 1,150 1,260 1,360 1,400 1,460 1,590 1,610 1,740 1,730 5.3
Mend ocino 890 860 930 960 940 970 920 960 980 1.3
Monterey 3,570 3,600 3,800 3,940 4,210 4,460 4,420 4,820 4,590 3.3
N apa 1,860 1,880 2,140 2,300 2,410 2,610 2,590 2,490 2,390 3.4
San Benito 170 180 180 180 200 210 200 210 210 2.8
San Francisco 9,800 10,000 11,000 11,500 12,400 13,600 13,800 15,500 14,500 5.2
San Lu is Obisp o 2,790 2,850 2,820 2,750 2,900 3,050 2,970 3,150 3,250 2.0
San Mateo 4,400 4,420 4,860 5,160 5,530 6,060 6,050 6,210 5,870 3.8
Santa Barbara 2,780 2,790 3,000 3,050 3,110 3,280 3,440 3,570 3,570 3.2
Santa Clara 5,470 5,600 6,210 6,750 7,580 8,700 8,850 9,410 9,070 6.7
Santa Cruz 1,570 1,580 1,640 1,690 1,760 1,900 1,890 2,010 1,890 2.4
Solano 1,890 1,900 2,010 2,000 2,030 2,180 2,080 2,210 2,280 2.4
Sonom a 3,590 3,590 3,820 3,920 4,080 4,370 4,310 4,370 4,220 2.1
Source: The California Travel and Tourism Commission, The California Technology, Trade, and
Commerce Agency, and Dean Runyan Associates.
Our next task is to identify how much of the tourism/recreation currently relates to marine
resource uses.
Marine Related Recreation.
Generally, we know that recreational fishing, scuba diving (both consumptive and non
consumptive), pleasure boating, whale and other wildlife watching, surfing, kayaking, personal
19
watercraft use, and beach visitation take place in the three JMPR sanctuaries. Quantitative
estimates of the amount of activity in the study area or in the general area off the coast of
Northern California are few in number and often incomplete. More is known about recreational
fishing than for the other activities.
National Survey on Recreation and the Environment (NSRE) 2000. For the NSRE, "marine
recreation" was defined as participation in at least one of 19 activities/settings, including beach
visitation, visitation to watersides besides beaches for outdoor recreation, swimming, snorkeling,
scuba diving, surfing, wind surfing, fishing, motor-boating, sailing, personal watercraft use,
rowing, canoeing, kayaking, hunting for waterfowl in a water-based surrounding, viewing or
photographing birds in a water-based surrounding, viewing or photographing other wildlife in a
water-based surrounding, and viewing or photographing scenery in a water-based surrounding.
For activities, "marine" was defined as activities in oceans, sounds, and in mixed fresh-saltwater
in tidal portions of rivers and bays. For settings (e.g., beaches, watersides, water-based
surroundings, etc.) "marine" was defined as saltwater or saltwater surroundings such as oceans,
sounds, and mixed fresh-saltwater in tidal portions of rivers and bays. (Leeworthy and Wiley,
2000)
The results below are for the State of California. Activities in the JMPR study area would be a
subset of the state total.
In 2000, beach visitation was the most popular marine related activity in California. 12.6 million
people visited the beach for a total of over 150 million days. Viewing or photographing Scenery
was second in terms of total days with 4.2 million people and 108 million days. Swimming was
the activity with the third highest participation rate with 8.4 million people spending almost 95
million days swimming. Other popular activities were bird watching, viewing other wildlife,
surfing, visiting watersides besides beaches, and fishing.
Table 13. California Marine Recreation
20
By Place of
By Place of Activity
Resid ence
Activity N u mber of N umber of N umber of
Particip ation
Particip ants Days Participants
Rate (%)
(millions) (millions) (millions)
Beach Visitation 6.1 12.6 151.4 9.1
Visiting Watersid es Besid es Beaches 0.7 1.5 20.7 1.1
Sw imming 4.1 8.4 94.6 6.1
Snorkeling 0.3 0.7 3.8 1.3
Scu ba Diving 0.1 0.3 1.4 0.4
Su rfing 0.5 1.1 22.6 0.7
Wind su rfing 0.0 0.1 0.1
Fishing 1.3 2.7 20.3 2.5
Motorboating 0.8 1.5 11.6 1.5
Sailing 0.5 1.1 6.8 1.0
Personal Watercraft Use 0.3 0.7 2.9 0.7
Canoeing 0.1 0.2 0.2
Kayaking 0.2 0.4 0.5
Row ing 0.1 0.3 0.2
Water-skiing 0.1 0.3 3.3 0.2
Bird Watching 1.3 2.6 65.8 1.9
View ing Other Wild life 1.2 2.6 38.6 4.4
View ing or Photograp hing Scenery 2.0 4.2 107.9 2.9
H unting Waterfow l 0.1 0.1 0.1
Source: National Survey on Recreation and the Environment (NSRE) 2000.
Marine Recreational Fishing.
Marine Angler Expenditures in the Pacific Coast Region, 2000. Approximately 440 thousand
saltwater anglers fished 2.2 million days in the Northern California region in 2000. In addition to
the leisure benefits these anglers received from participating in saltwater fishing, their
expenditures generated monetary benefits in the form of sales, income, and employment
throughout the Pacific Coast. A variety of goods and services were purchased from sporting
goods stores, specialty stores, bait and tackle shops, guide services, marinas, grocery stores,
automobile service stations, and restaurants. The economic impacts of these purchases rippled
throughout the Pacific Coast’s economy and provided income and jobs in manufacturing,
transportation industries, and service sectors (NMFS, 2001)
The majority of saltwater anglers, 388 thousand, were residents. Most of the resident mode of
fishing was private/rental boats and shore. A much higher proportion of the 51 thousand non-
resident anglers fished from party/charter boats.
Average per person trip expenditures in 2000 were highest for charter/party boats for both
residents ($112) and non-residents ($328). Average party/charter fees for residents were $56 and
$52 for non-residents. Average per person annual expenditures was $1,588.
Saltwater anglers in Northern California spent a total of $761 million in 2000. Anglers on
party/charter boats spent $35 million; on private/rental boats spent $46 million; and on shore
spent $48 million. Of this, residents spent $741 million and non-residents spent $21 million.
21
Taken as a whole, the expenditure estimates provide an indication of the importance of marine
recreational fishing to the economies of the coastal counties in Northern California.
Figure 2. The Northern California Region, NMFS
Table 14. Estimated Number of Days Fished and Participants in Northern California by Mode
and Resident Status, 2000
Resident N on-Resident Total
Total D ays 2,074,628 92,377 2,167,005
198,267 39,429 237,696
Party/Charter Boat D ays
963,959 30,961 994,920
Private/Rental Boat D ays
912,402 21,987 934,389
Shore D ays
387,927 51,221 439,148
Total Participants
Average D ays per Participant 5.3 1.8 4.9
Table 15. Northern California Average Per Person Expenditures by Mode and Resident Status
22
Resident N on-Resident
Trip Expenditures
Party/Charter Boat 112.03 327.73
Private/Rental Boat 43.91 125.46
Shore 48.48 173.80
Annual Expenditures 1,587.84
Table 16. Northern California Total Expenditures by Mode and Resident Status ($000s)
Resident N on-Resident Total
Trip Expenditures
Party/Charter Boat 22,212 12,922 35,134
Private/Rental Boat 42,322 3,884 46,206
Shore 44,229 3,821 48,050
Annual Expenditures 631,993 631,993
Total Resident Expenditures 740,758 740,758
Total Expenditures 740,758 20,628 761,385
Source: National Marine Fisheries Service, Marine Angler Expenditures in the Pacific Coast
Region, 2000
23
Table 17. Northern California Average Per Person Expenditures by Mode and Resident Status
Party/Charter Private/Rental Shore
Non- Non- Non-
Residents Residents Residents
Residents Residents Residents
Trip Expenditures
Private Transportation 20.45 72.00 13.53 64.24 18.50 66.19
Food 16.49 22.86 8.96 23.38 13.00 29.27
Lodging 8.58 45.04 3.66 10.21 9.90 30.41
Public Transportation 1.83 114.98 0.13 2.97 0.77 36.92
Boat Fuel 9.71 11.94
Party/Charter Fees 56.11 51.62
Access/Boat Launching 0.84 1.24 1.22 3.02 0.96 0.15
Equipment Rental 5.13 18.76 0.67 1.37 1.45 4.62
Bait & Ice 2.60 1.22 6.03 8.33 3.89 6.24
Total Trip Expenditures 112.03 327.72 43.91 125.46 48.47 173.80
Annual Expenditures All
Rods & Reels 69.66
Other Tackle 49.26
Gear 14.49
Camping Equipment 7.89
Binoculars 1.76
Clothing 13.34
Magazines 2.09
Club Dues 2.08
License Fees 33.96
Boat Accessories 125.52
Boat Purchase 407.72
Boat Maintenance 105.44
Fishing Vehicle 582.53
Fishing Vehicle Maintenance 149.72
Vacation Home 16.53
Vacation Home Maintenance 5.86
Total Annual Expenditures 1,587.85
Source: National Marine Fisheries Service, Marine Angler Expenditures in the Pacific Coast
Region, 2000
24
Table 18. Northern California Total Expenditures by Mode and Resident Status ($000s)
Party/Charter Private/Rental Shore
Non- Non- Non-
Residents Residents Residents
Residents Residents Residents
Trip Expenditures
Private Transportation 4,055 2,839 13,044 1,989 16,879 1,455
Food 3,269 902 8,634 724 11,866 644
Lodging 1,701 1,776 3,525 316 9,033 669
Public Transportation 363 4,533 122 92 698 812
Boat Fuel 9,358 370
Party/Charter Fees 11,126 2,036
Access/Boat Launching 166 49 1,176 93 877 3
Equipment Rental 1,017 740 646 43 1,327 101
Bait & Ice 515 48 5,816 258 3,548 137
Total Trip Expenditures 22,212 12,923 42,321 3,885 44,228 3,821
Annual Expenditures All
Rods & Reels 27,023
Other Tackle 19,111
Gear 5,621
Camping Equipment 3,059
Binoculars 683
Clothing 5,174
Magazines 811
Club Dues 807
License Fees 13,172
Boat Accessories 50,137
Boat Purchase 162,855
Boat Maintenance 42,116
Fishing Vehicle 232,680
Fishing Vehicle Maintenance 59,801
Vacation Home 6,604
Vacation Home Maintenance 2,339
Total Annual Expenditures 631,993
Total Resident Expenditures 740,758
Total Expenditures 761,385
Source: National Marine Fisheries Service, Marine Angler Expenditures in the Pacific Coast
Region, 2000
25
Pacific Socio-Economics Fishing Survey – Northern California, 1998. In 1998,NMFS completed
the Pacific Socio-economics Fishing Survey. This survey had a Northern California component.
The following are highlights from the survey results.
About 35% of the Northern California anglers surveyed own a boat used for recreational
saltwater fishing.
The anglers surveyed on a party/charter or rental boat spent on average $34 per day on boat fees,
bait, and fishing licenses. Anglers fishing from shore spent on average $9 per day on parking
fees, bait, and fishing licenses.
Anglers interviewed on multi-day trips spent an average of 5 nights away from home
and spent $171 on lodging expenses.
About 13% of anglers surveyed who were employed gave up some income by taking a
day of fishing. The average income “missed” was around $436 per trip.
The anglers surveyed who live in-state have been fishing an average of 20 years.
Figure 3. Recreational Fishing Socioeconomic Survey Results
26
Recreational Activities Possibly Requiring Additional Data Collection
Pleasure Boating
Personal Watercraft Use
Kayaking
Whale and Other Wildlife Watching
Surfing
Beach Visitation
Scuba Diving
COMMERCIAL FISHING IN THE JMPR STUDY AREA
The California Department of Fish and Game (CDFG) collects information on the pounds and ex
vessel value of the commercial catch by species and by 10 by 10 mile block where caught. We
obtained that information for 348 CDFG blocks that run from Point Conception to the Oregon
Boarder. The JMPR Study Area and the three sanctuaries are a subset of these blocks. These are
historical data from 1988 to 2000. The data fields are:
Year
Month
Block Number
Port Landed
Species
Gear
Value
Pounds
The first step was to define each of the Sanctuaries involved in the JMPR in terms of these CDFG
blocks. That is, the CDFG blocks that “best” defines each Sanctuary. 10 by 10 minute resolution
is pretty rough and will most likely understate or overstate what is caught in each sanctuary.
With this in mind, we have historically (Channel Islands) used the centroid method to determine
whether or not a block should be included in the analysis. In other words, if the center of the
block lies within the Sanctuary, it would be included. However, this method is subject to
local/expert judgment. If a block’s center is located outside a Sanctuary boundary, but is
identified as vital to the analysis, it can be included.
We have defined preliminary study areas for each of the three Sanctuaries. It is important to
keep in mind that where two Sanctuaries share a common boundary, a block can be assigned to
only one of the Sanctuaries. In other words, we don’t want to double count a block in the
analysis. Also, blocks cannot be split. It’s either all or none of the block.
27
Any primary data collection efforts for the study area will attempt to bring the spatial resolution
down to 1 by 1-mile blocks.
Preliminary analysis is presented for the sum of ex vessel value of all commercial fisheries species
for the period 1991 to 2000. The ArcView map presented below shows the spatial distribution of
the value. The block with the highest historical value is located directly west of Santa Cruz and
just outside MBNMS. The map also identifies several other “hotspots” in terms of value.
Figure 4.
Commercial Fisheries - All Species - CDFG
Sum of Ex Vessel Value - 1991 to 2000
Value of Catch
5698 - 94248
94248 - 192249
192249 - 341146
341146 - 657275
657275 - 1045697
1045697 - 1638091
1638091 - 2966310
2966310 - 4490971
4490971 - 8922178
8922178 - 26050441
N
W E
80 0 80 160 Miles
S
Analysis presented here is the first step. Additional analysis could include:
Cross Tabulation of Where Fish Caught and Where Fish Landed
For estimating economic impacts on the local economies, we can establish cross tabulations of
catch by study area and by port landed for each species group.
Monthly Data
So far, we have done nothing with the monthly data. It could be useful in looking at the
seasonality of the different fisheries. Production of graphs over the past few years for each
species group could be informative.
28
Gear Type
Cross tabulations and maps of gear and species types could be run. This, combined with the
monthly patterns might define certain fishery fleets (squid/wetfish in the Channel Islands NMS
used purse seine gear and the fishermen that fished these species fished them during different
seasons of the year.
MBNMS has historically had the highest total value of commercial fishing in the study area. In
MBNMS in 2000, 33.5 million pounds of fish were caught with a total ex vessel value of $7.1
million dollars. GFNMS in 2000 had 0.5 million pounds of fish caught valued at $1.1 million. 440
thousand pounds of fish were caught in CBNMS in 2000 with an ex vessel value of $0.4 million.
Commercial fishing catch increased dramatically from the early 1990s through the mid 1990s.
Table 19. Commercial Fisheries, All Species, CDFG
Pounds and Ex Vessel Value, 1990 to 2000
JMPR Sanctuaries
Monterey Bay Gulf of the Farallones Cord ell Bank
Year
Pou nd s Value ($) Pound s Valu e ($) Pou nd s Value ($)
1990 7,771,627 475,445 182,376 184,574 65,206 98,122
1991 3,315,382 449,514 338,188 319,370 35,206 34,666
1992 6,621,627 806,724 1,571,305 1,355,780 368,737 211,516
1993 12,342,390 2,188,186 1,297,596 1,113,075 327,952 184,211
1994 25,795,188 6,494,288 2,353,857 2,163,109 597,838 548,659
1995 12,046,810 7,518,315 1,619,440 1,954,280 136,591 127,945
1996 21,748,731 7,141,664 1,677,245 2,355,415 129,019 145,111
1997 42,812,366 9,557,799 1,296,882 1,729,326 181,319 171,776
1998 19,612,520 5,870,207 891,705 1,581,974 417,874 377,206
1999 27,693,714 6,400,464 822,971 1,162,465 440,447 368,834
2000 33,513,661 7,128,238 533,710 1,130,798 138,634 255,133
For the three sanctuaries combined, 1997 was, economically, the most productive year for the
commercial fisheries. 44.3 million pounds of fish were caught with an ex vessel value of $11.4
million. The most recent year for which we have data, 2000, was also a highly productive year,
with 34.2 million pounds caught within the three sanctuaries and 70.3 million pounds caught in
the entire Point Sal to Point Arena study area.
Table 20. Commercial Fisheries, All Species, CDFG
Pounds and Ex Vessel Value, 1990 to 2000
Three Sanctuaries Combined and Entire Study Area
29
Total JMPR Sanctuaries Point Sal to Point Arena
Year
Pou nd s Value ($) Pou nd s Value ($)
1990 8,019,209 758,141 9,798,425 1,707,832
1991 3,688,777 803,550 5,813,341 2,824,082
1992 8,561,668 2,374,020 12,158,685 5,071,224
1993 13,967,938 3,485,472 19,617,885 6,789,243
1994 28,746,883 9,206,056 42,231,653 16,895,747
1995 13,802,842 9,600,540 32,845,328 21,298,184
1996 23,554,995 9,642,191 37,584,762 17,381,430
1997 44,290,567 11,458,900 61,719,033 24,085,211
1998 20,922,099 7,829,388 32,147,973 14,897,034
1999 28,957,132 7,931,762 56,526,999 16,821,007
2000 34,186,005 8,514,169 70,274,840 19,186,580
In 2000, the highest ex-vessel value species group in the three-sanctuary area was salmon at over
$2.1 million and just under a million pounds. In 1990, only 31 thousand pounds of salmon was
caught with an ex-vessel value of $85 thousand. In 2000, the next 4 top-ranked species in terms of
ex-vessel value were squid ($1.7 million), rockfishes ($1.2 million), crab ($0.9 million), and flatfish
($0.8 million). In terms of overall significance to the commercial fishery, several of the species
groups have increased from 1990 to 2000, including salmon, rockfishes, anchovy and sardines,
roundfish, and tuna. The economic importance of mackerel has decreased from $93 thousand in
1990 to $25 thousand in 2000. Additionally, wild abalone, once a $45 thousand fishery and
ranked #5 in 1990, has been banned. In 1998, the California Department of Fish and Game
(CDFG) closed the whole commercial industry of wild abalone.
Table 21. Commercial Fisheries, All Species Groups, CDFG
Three JMPR Sanctuaries Combined
Ranked by Value
Pounds and Ex Vessel Value, 1990 and 2000
30
2000 1990
Sp ecies Grou p Pou nd s Valu e ($) Sp ecies Grou p Pou nd s Valu e ($)
Salm on 991,194 2,078,047 Squ id 3,766,616 259,735
Squ id 13,939,345 1,677,840 Crab 61,511 118,117
Rockfishes 647,124 1,181,384 Mackerel 3,568,344 93,247
Crab 369,445 901,990 Salm on 31,258 84,917
Flatfish 1,498,816 831,224 Abalone 9,659 44,944
Anchovy & Sard ines 15,984,661 713,081 Flatfish 111,429 40,268
Praw n 70,553 618,401 Rockfishes 82,879 35,075
Rou nd fish 128,367 159,997 Sw ord fish 5,223 20,243
Tu na 110,073 114,500 Anchovy & Sard ines 249,522 15,931
Scu lp in & Bass 24,667 46,369 Rou nd fish 25,150 14,244
Shrim p 67,964 44,534 Urchins 59,711 11,862
Sword fish 12,262 42,915 Other 36,997 11,374
Mackerel 159,097 25,537 Sharks 4,226 5,306
Sharks 31,437 20,715 Rays & Skates 5,698 1,540
Urchins 21,331 16,813 Su rf Perch 395 518
Rays & Skates 70,004 13,708 Sp iny Lobster 79 455
Other 20,131 12,868 Tu na 463 321
Grenad iers 30,299 5,554 Octop u s 49 47
Su rf Perch 2,369 2,800
Smelts & Gru nion 3,957 2,560
Sp iny Lobster 291 1,852
CA Sheep shead 260 761
H erring & Roe 1,843 461
Octop u s 349 158
Sea Cu cu mbers 138 90
Mu ssels, Snails, Clam s, Oysters 28 14
31
Monterey Bay National Marine Sanctuary (MBNMS)
Gulf of the Farallones National Marine Sanctuary (GFNMS)
Cordell Bank National Marine Sanctuary (CBNMS)
A Socioeconomic Overview
of the Northern and Central Coastal California Counties as They
Relate to Marine Related Industries and Activities
DRAFT April 2003
By
Rod Ehler, National Marine Sanctuary Program
Dr. Vernon R. (Bob) Leeworthy, Special Project Office
Peter C. Wiley, Special Projects Office
U.S. Department of Commerce
National Oceanic and Atmospheric Administration
National Ocean Service
National Marine Sanctuary Program (NMSP)
and
Special Projects Office (SPO)
Silver Spring, Maryland
Table of Contents
INTRODUCTION
Purpose
Background
Future Projects
DEMOGRAPHIC AND ECONOMIC PROFILE
Population
Population Density
Historical and Projected Population
Population Growth
Race
Age and Gender
Labor Force
Income and Employment
Income by Place of Residence
Income by Place of Work
Proprietors Income and Employment
Indicators of Economic Health and Wealth
Unemployment Rates
Per Capita Income
Income and Employment by Industry
Income and Employment: Additional Disaggregation
Commercial Fishing
Tourism and Recreation
VALUE OF MARINE RESEARCH
TOURISM AND RECREATION
California Travel Impacts by County
Marine Related Recreation
National Survey on Recreation and the Environment (NSRE)
Marine Recreational Fishing
Participation and Expenditures
Socio-economics
Pleasure Boating
Personal Watercraft
Kayaking
Whale and Other Wildlife Watching
Surfing
Beach Visitation
Scuba Diving
COMMERCIAL FISHING (CDFG)
INTRODUCTION
Purpose
The purpose of this document is to present the necessary background information on the local
social and economic (socio-economic) environment for which changes in management actions in
the JMPR study area can be analyzed in a socioeconomic impact analysis. The information
presented here is what we have found to date to be the “best available information”. In addition
to the socioeconomic characterization, we will provide some discussion on gaps in the data.
We will examine all direct uses potentially impacted; examples are 1) tourist/recreational use
(e.g., whale watching, kayaking, scuba diving) and 2) commercial industries (e.g., commercial
fishing, kelp harvesting). With respect to the local economies, these uses will have ripple or
multiplier effects as measured by market economic values (e.g., output/sales, income,
employment and tax revenues). In this report, we review available information to assess how
important these industries are to the local economies. We will also present what is known about
social and economic parameters that can be used in socioeconomic impact analyses.
Background
The MBNMS, GFNMS, and CBNMS are currently involved in a joint management plan revision
(JMPR), a process that is required by law to take place approximately every five years. The
management plans for the three northern and central California sanctuaries are between 9 and 20
years old. The National Marine Sanctuary Program (NMSP) is reviewing all three management
plans jointly. These sanctuaries are located adjacent to one another, managed by the same
program, and share many of the same resources and issues. In addition, all three sites share many
overlapping interest and user groups. It is also more cost-effective for the program to review the
three sites jointly rather than conducting three independent reviews. During the review, the
sanctuaries will evaluate management and operational strategies, regulations, and boundaries.
The review will look at whether the management programs at all three sanctuaries can be better
coordinated.
A sanctuary management plan is a site-specific planning and management document that
describes the objectives, policies, and activities for a sanctuary. Management plans generally
outline regulatory goals, describe boundaries, identify staffing and budget needs, set priorities
and performance measures for resource protection, research, and education programs. They also
guide the development of future management activities.
Any data gap identified as necessary to support the socioeconomic impact analysis will be
collected and compiled in a manner so as to capture both the temporal and spatial variation in
activities. The information will be linked with economic parameters from existing studies to
develop estimates of economic impacts as measured by changes in both market economic values
(e.g., sales/output, income and employment) and non-market economic values (e.g., consumer’s
surplus and economic rents). Socioeconomic profiles of those potentially impacted will be
compared against all users from a given user group and against the general population of the
local area (e.g., the coastal California counties).
To accomplish the above requires a review of the existing literature and databases available and
compiling this information in a manner that it can be used in the socioeconomic impact analyses.
In some cases, available information will not support certain aspects of the proposed analyses. In
2
addition, supplemental data collection and analysis may not be feasible with time and resources
available. What we are left with is what is commonly referred to as the “best available
information”.
Future Projects
There are currently 3 projects planned in support of the JMPR.
In early 2003, the National Marine Sanctuary Program and California Sea Grant will hold a
workshop to identify needed socio-economic studies associated with marine activities in the Joint
Management Plan Revision study area.
In October 2002, Dr. Caroline Pomeroy and Dr. Michael Dalton were awarded, through
California SeaGrant, $70k to conduct a study titled “Market Channels and Value Added to Fish
Landed at Monterey Bay Area Ports”.
In 2003, another study will be initiated that will investigate private household boat users. One of
the major gaps in information for all California Sanctuaries is the number of private household
boat users and amount of use, especially for non-consumptive users.
3
DEMOGRAPHIC AND ECONOMIC PROFILE
Population.
Population density and historical population estimates presented here are from the U.S.
Department of Commerce, Census Bureau (http://www.census.gov), while population
projections are from the University of California.
Population Density. The map below presents population density per square mile. Population is most
dense in the area reaching from San Francisco, down the eastern portion of San Mateo County to the San
Jose metropolitan area and continuing north through the western portion of Alameda County to the Oakland
metropolitan area. Pockets of dense coastal population also exist in the Santa Cruz and Monterey Peninsula
areas. Within the JMPR study area there are several inland areas of dense population, such as Salinas,
Vallejo, Concord, Walnut Creek, Napa, Santa Rosa, and Fairfield.
Figure 1. Population Density Per Square Mile
P o p u la t io n D e n s i t y
0 - 4 9 8. 81
4 9 8 .8 1 - 1 5 2 9 .5 2
1 5 2 9 .5 2 - 2 6 8 1 .5 2
2 6 8 1 .5 2 - 3 8 4 2 .7 1
3 8 4 2 .7 1 - 4 9 8 2 .2 6
4 9 8 2 .2 6 - 6 0 9 6 .6 4
6 0 9 6 .6 4 - 7 1 8 1 .5 4
7 1 8 1 .5 4 - 8 2 7 2 .6 5
8 2 7 2 .6 5 - 9 4 0 1 .0 5
9 4 0 1 .0 5 - 9 9 9 9 .9 9
4
Historical and Projected Population. The two largest counties in the study, in terms of
population, are Santa Clara (1.7 million) and Alameda (1.4 million). Combined, these two
counties account for almost 40 percent of the JMPR study area population. Santa Clara and
Alameda Counties saw growth very much in line with the overall JMPR study area rate of 12.5
percent over the period 1990 to 2000. The smallest county in terms of population, San Benito (53
thousand), has shown the highest rate of growth, 45 percent, over the period 1990 to 2000 and 113
percent over the period 1980 to 2000. All counties are expected to continue their growth, with the
exception of San Francisco, which is forecast to decline in population over the next few decades.
See Table 1a and 1b.
Table 1a. Population, Historical and Projected, for Coastal California
U.S. Census Bureau Actual University of California Forecast
1960 1970 1980 1990 2000 2000 2010 2020 2030 2040
CALIFORN IA 15,717,204 19,953,134 23,667,902 29,760,021 33,871,648 34,653,395 39,957,616 45,448,627 51,868,655 58,731,006
JMPR STUDY AREA 4,237,970 5,441,401 6,204,241 7,312,783 8,226,651 8,410,361 9,480,827 10,382,363 11,409,517 12,437,966
MEN DOCINO 51,059 51,101 66,738 80,345 86,265 90,442 105,225 118,804 133,440 149,731
SONOMA 147,375 204,885 299,681 388,222 458,614 459,258 544,513 614,173 684,311 753,729
MARIN 146,820 206,038 222,568 230,096 247,289 248,397 258,569 268,630 282,864 297,307
N APA 65,890 79,140 99,199 110,765 124,279 127,084 143,542 157,878 174,240 191,971
SOLANO 134,597 169,941 235,203 340,421 394,542 399,841 479,136 552,105 625,619 698,430
CON TRA COSTA 409,030 558,389 656,380 803,732 948,816 931,946 1,025,857 1,104,725 1,189,501 1,264,400
ALAMEDA 908,209 1,073,184 1,105,379 1,279,182 1,443,741 1,470,155 1,654,485 1,793,139 1,938,547 2,069,530
SAN FRANCISCO 740,316 715,674 678,974 723,959 776,733 792,049 782,469 750,904 724,863 681,924
SAN MATEO 444,387 556,234 587,329 649,623 707,161 747,061 815,532 855,506 907,423 953,089
SAN TA CRUZ 84,219 123,790 188,141 229,734 255,602 260,248 309,206 367,196 430,078 497,319
SAN TA CLARA 642,315 1,064,714 1,295,071 1,497,577 1,682,585 1,763,252 2,021,417 2,196,750 2,400,564 2,595,253
MON TEREY 198,351 250,071 290,444 355,660 401,762 401,886 479,638 575,102 700,064 855,213
SAN BENITO 15,396 18,226 25,005 36,697 53,234 51,853 68,040 82,276 97,941 114,922
SAN LUIS OBISPO 81,044 105,690 155,435 217,162 246,681 254,818 324,741 392,329 461,839 535,901
SAN TA BARBARA 168,962 264,324 298,694 369,608 399,347 412,071 468,457 552,846 658,223 779,247
Table 1b. Population Growth (% Change), Historical and Projected, for Coastal California
U.S. Census Bureau Actual University of California Forecast
1960 - 1970 1970 - 1980 1980 - 1990 1990 - 2000 2000 - 2010 2010 - 2020 2020 - 2030 2030 - 2040
27.0 18.6 25.7 13.8 15.3 13.7 14.1 13.2
CALIFORN IA
JMPR STUD Y AREA 28.4 14.0 17.9 12.5 12.7 9.5 9.9 9.0
MEN DOCIN O 0.1 30.6 20.4 7.4 16.3 12.9 12.3 12.2
SON OMA 39.0 46.3 29.5 18.1 18.6 12.8 11.4 10.1
MARIN 40.3 8.0 3.4 7.5 4.1 3.9 5.3 5.1
N APA 20.1 25.3 11.7 12.2 13.0 10.0 10.4 10.2
SOLAN O 26.3 38.4 44.7 15.9 19.8 15.2 13.3 11.6
CON TRA COSTA 36.5 17.5 22.4 18.1 10.1 7.7 7.7 6.3
ALAMEDA 18.2 3.0 15.7 12.9 12.5 8.4 8.1 6.8
SAN FRAN CISCO -3.3 -5.1 6.6 7.3 -1.2 -4.0 -3.5 -5.9
SAN MATEO 25.2 5.6 10.6 8.9 9.2 4.9 6.1 5.0
SAN TA CRUZ 47.0 52.0 22.1 11.3 18.8 18.8 17.1 15.6
SAN TA CLARA 65.8 21.6 15.6 12.4 14.6 8.7 9.3 8.1
MON TEREY 26.1 16.1 22.5 13.0 19.3 19.9 21.7 22.2
SAN BEN ITO 18.4 37.2 46.8 45.1 31.2 20.9 19.0 17.3
SAN LUIS OBISPO 30.4 47.1 39.7 13.6 27.4 20.8 17.7 16.0
SAN TA BARBARA 56.4 13.0 23.7 8.0 13.7 18.0 19.1 18.4
Sources: Population; U.S. Department of Commerce, Census Bureau (http://www.census.gov).
Population Projections; University of California
5
Race. The demographic composition of the study area varies greatly. The four counties
(Mendocino, Sonoma, Marin, and Napa) that make up the northern section of the study are
predominately White (all at or above 80 percent) with less than average proportion of Blacks,
Asians, Hispanics and Latinos. It is important to point out that Mendocino County’s population
is almost 5 percent American Indian. The Bay Area counties of Solano, Contra Costa, Alameda,
San Francisco, San Mateo, and Santa Clara are the most diverse counties in the study area. The
White population of this area drops to 50 to 65 percent and the Black and Asian populations
increase dramatically to 10 to 30 percent. About one third of San Francisco’s population is Asian.
The remaining counties that comprise the Southern section of the study area are heavily
populated with Hispanics and Latinos, particularly in Monterey and San Benito Counties where
the Hispanic and Latino population stands at almost 50 percent.
Age and Gender. In terms of age, similar geographic variations do emerge. The Northern four
counties identified above are also the oldest, in terms of median age (34 to 41 years). The
proportion of people 45 and older is also greatest in these counties. With a few exceptions, the
remaining counties in the study area are quite similar in terms of age. San Francisco has the
highest proportion, 41 percent, of people 25 to 44 years and the lowest proportion, 15 percent, of
people under 18 years. The counties with the highest proportions at retirement age, 65 years and
older, are Napa and San Luis Obispo.
There are also variations in gender among the county populations. Three of the counties,
Monterey, San Luis Obispo, and San Francisco, have higher populations of males. Sonoma,
Contra Costa, Alameda, and San Mateo are more populated by females.
Table 2a. Demographic Profiles Coastal California Counties – Race, 2000
One race
Hispanic or
Native
Two or more
American
Total Pop. Latino (of
Black or Haw aiian races
Indian and Some other
any race)
One Race White African Asian and Other
Alaska race
American Pacific
Native
Islander
California 33,871,648 95.3 59.5 6.7 1.0 10.9 0.3 16.8 4.7 32.4
JMPR Stu d y Srea 8,226,651 95.2 60.3 6.6 0.8 16.4 0.5 10.6 4.8 21.7
86,265 96.1 80.8 0.6 4.8 1.2 0.1 8.6 3.9 16.5
Mend ocino Cou nty
458,614 95.9 81.6 1.4 1.2 3.1 0.2 8.4 4.1 17.3
Sonoma Cou nty
247,289 96.5 84.0 2.9 0.4 4.5 0.2 4.5 3.5 11.1
Marin Coun ty
124,279 96.3 80.0 1.3 0.8 3.0 0.2 10.9 3.7 23.7
N ap a Cou nty
394,542 93.6 56.4 14.9 0.8 12.7 0.8 8.0 6.4 17.6
Solan o Cou nty
948,816 94.9 65.5 9.4 0.6 11.0 0.4 8.1 5.1 17.7
Contra Costa County
1,443,741 94.4 48.8 14.9 0.6 20.4 0.6 8.9 5.6 19.0
Alam ed a Cou nty
776,733 95.7 49.7 7.8 0.4 30.8 0.5 6.5 4.3 14.1
San Francisco County
707,161 95.0 59.5 3.5 0.4 20.0 1.3 10.2 5.0 21.9
San Mateo Cou nty
255,602 95.6 75.1 1.0 1.0 3.4 0.1 15.0 4.4 26.8
Santa Cru z Cou nty
1,682,585 95.3 53.8 2.8 0.7 25.6 0.3 12.1 4.7 24.0
Santa Clara Cou nty
401,762 95.0 55.9 3.7 1.0 6.0 0.4 27.8 5.0 46.8
Monterey Cou nty
53,234 94.9 65.2 1.1 1.2 2.4 0.2 24.9 5.1 47.9
San Ben ito County
246,681 96.6 84.6 2.0 0.9 2.7 0.1 6.2 3.4 16.3
San Lu is Obisp o County
399,347 95.7 72.7 2.3 1.2 4.1 0.2 15.2 4.3 34.2
Santa Barbara County
6
Sources: U.S. Department of Commerce, Census Bureau (http://www.census.gov).
Table 2b. Demographic Profiles Coastal California Counties – Age and Gender, 2000
Percent of total population Males per
Median 100 females
Total
Geographic area age
Population Under 18 to 25 to 45 to 65 All 18
18 24 44 64 years (years) ages years
years years years years and and
over over
33,871,648 27.3 9.9 31.6 20.5 10.6 33.3 99.3 97.1
California
COUN TY
86,265 25.5 8.1 25.6 27.1 13.6 38.9 98.9 97.1
Mend ocino County
458,614 24.5 8.8 29.2 24.9 12.6 37.5 97 94
Sonoma County
247,289 20.3 5.5 31 29.7 13.5 41.3 98.2 96.4
Marin County
N apa County 124,279 24.1 8.5 27.7 24.3 15.4 38.3 99.6 97.4
394,542 28.3 9.2 31.3 21.7 9.5 33.9 101.5 100.2
Solano County
948,816 26.5 7.7 30.6 23.9 11.3 36.4 95.4 92.2
Contra Costa County
1,443,741 24.6 9.6 33.9 21.7 10.2 34.5 96.6 94
Alam eda County
San Francisco County 776,733 14.5 9.1 40.5 22.3 13.7 36.5 103.4 103.1
707,161 22.9 7.9 33.2 23.5 12.5 36.8 97.8 95.6
San Mateo County
255,602 23.8 11.9 30.8 23.5 10 35 99.7 97.8
Santa Cruz County
1,682,585 24.7 9.3 35.4 21 9.5 34 102.8 101.9
Santa Clara County
401,762 28.4 10.9 31.4 19.3 10 31.7 107.3 107.7
Monterey County
53,234 32.2 8.8 31.5 19.3 8.1 31.4 102.5 99.6
San Benito County
246,681 21.7 13.6 27 23.3 14.5 37.3 105.6 105.2
San Luis Obispo County
399,347 24.9 13.3 29 20.1 12.7 33.4 100.1 98.1
Santa Barbara County
Sources: U.S. Department of Commerce, Census Bureau (http://www.census.gov).
Labor Force
Total labor force for the JMPR study area in 2001 was 4.5 million. As with population, the two
largest counties in terms of labor force for 2001 are Santa Clara (1.0 million) and Alameda (0.8
million) and the two smallest are San Benito (28.0 thousand) and Mendocino (43.0 thousand).
There has been a wide range of growth in labor force among study area counties. The period
1990 to 2001 has seen significant growth in San Benito (29 percent), Sonoma (28 percent), San Luis
Obispo (23 percent), and Solano (20 percent) Counties and slower than average growth in Santa
Barbara (5.4 percent), Santa Cruz (5.4 percent), Marin (5.8 percent) and San Francisco (7.8 percent)
Counties.
Unemployment in San Benito County has risen over the decade from 8.2 percent in 1990 to 11.7
percent in 2001, the highest in the study area. Monterey has the second highest unemployment
rate at 9.5 percent for 2001. Significantly lower than average unemployment rates are seen for
Marin (2.5 percent) and San Mateo (2.6 percent) Counties for 2001.
7
Table 3. Labor Force, Labor Force Growth, and Unemployment
Labor Force
Unemployment Rate
Labor Force Growth
2001 2000 1995 1990 1990-1995 1995-2000 1990-2001 2001 2000 1995 1990
STATE TOTAL 17,362,300 17,090,800 15,412,200 15,193,400 1.4 10.9 14.3 5.3 4.9 7.8 5.8
JMPR STUDY AREA 4,522,890 4,485,360 4,032,640 3,954,280 2.0 11.2 14.4 4.3 3.0 6.1 4.3
MEN DOCIN O 42,970 42,540 41,330 37,560 10.0 2.9 14.4 6.6 6.6 9.6 7.8
SON OMA 262,600 259,100 225,300 205,300 9.7 15.0 27.9 2.9 2.6 5.5 3.9
MARIN 138,100 139,400 128,700 130,500 -1.4 8.3 5.8 2.5 1.7 4.3 2.5
N APA 66,600 65,200 57,700 57,400 0.5 13.0 16.0 3.3 3.2 6.2 4.1
SOLAN O 201,400 197,400 173,100 167,900 3.1 14.0 20.0 4.1 4.2 8.0 4.7
CON TRA COSTA 509,800 504,100 456,000 439,100 3.8 10.5 16.1 3.3 2.7 5.7 4.0
ALAMEDA 754,900 739,000 682,000 683,200 -0.2 8.4 10.5 4.5 3.0 5.8 4.0
SAN FRAN CISCO 436,900 434,300 398,200 405,300 -1.8 9.1 7.8 5.2 2.8 6.1 3.8
SAN MATEO 407,900 410,500 369,800 366,500 0.9 11.0 11.3 2.8 1.6 4.2 2.6
SAN TA CRUZ 143,900 142,100 139,800 136,500 2.4 1.6 5.4 6.1 5.6 9.3 7.1
SAN TA CLARA 1,012,700 1,008,100 867,000 840,600 3.1 16.3 20.5 4.5 2.0 4.9 4.0
MON TEREY 195,800 196,200 175,900 174,200 1.0 11.5 12.4 9.3 9.5 12.4 9.5
SAN BEN ITO 28,020 27,320 23,110 21,720 6.4 18.2 29.0 8.2 7.9 13.7 11.7
SAN LUIS OBISPO 118,600 116,000 101,600 96,200 5.6 14.2 23.3 2.8 3.0 6.6 4.8
SAN TA BARBARA 202,700 204,100 193,100 192,300 0.4 5.7 5.4 3.5 3.7 6.7 4.9
Source: U.S. Department of labor, Bureau of Labor Statistics, Division of Labor Force Statistic s
Income and Employment
Income is reported from two perspectives; 1) income by place of residence and 2) income by
place of work. Income and employment by place of work are further reported by industry.
Income and employment by place of work is also reported for wage and salary workers versus
proprietors (business owners). Differences in these measurements often reveal important
differences about the nature of the local economies that are important for socioeconomic impact
analyses. For example, a large difference between income by place of residence and income by
place of work might reveal that the economy of the area under study is largely driven by income
earned from sources unrelated to work in the area and this will dampen the impacts of
management changes that impact local work related income and employment. A large number
of proprietors indicate the prevalence of small businesses that receive special treatment under
Federal Regulatory Impact Reviews.
Income by Place of Residence versus Income by Place of Work. A wide variation is seen in the
study area when comparing income by place of residence and place of work. In 1990, net income
(the difference between income by place of residence and place of work) as a percent of income
by place of work in the study area was 34.9 percent of the income by place of work. In 2000, this
proportion has dropped to only 24.7 percent. In 2000, this ratio was negative for two of the study
area counties, San Francisco (-9.4%) and Santa Clara (-2.6%).
8
Table 4. Personal Income by Place of Residence and by Place of Work For California
1990 2000
A B A-B=C D C/B D /B A B A-B=C D C/B
Adjustment
Income by N et Income as Income by N et Income as
Income by Adjustment for Residence Income by Adjustment
Place of % of Income Place of % of Income
Place of Work Net Income* for as % of Place of Work Net Income* for
Residence by Place of Residence by Place of
($000's) Residence** Income by ($000's) Residence**
($000's) Work ($000's) Work
Place of Work
California 655,567,167 482,925,921 172,641,246 -75,934 35.7 0.0 1,093,065,244 825,224,182 267,841,062 121,446 32.5
JMPR Stu dy Area 186,542,551 138,283,627 48,258,924 -1,697,072 34.9 -1.2 363,936,984 291,743,151 72,193,833 -3,620,072 24.7
Mendocino 1,357,933 826,068 531,865 3,514 64.4 0.4 2,146,557 1,286,730 859,827 18,266 66.8
Sonoma 8,875,485 4,838,019 4,037,466 1,274,648 83.5 26.3 16,046,410 9,834,626 6,211,784 1,833,287 63.2
Marin 8,249,379 3,898,749 4,350,630 1,667,415 111.6 42.8 15,003,372 7,300,898 7,702,474 3,338,923 105.5
N apa 2,606,253 1,396,070 1,210,183 351,517 86.7 25.2 4,729,986 2,907,793 1,822,193 467,688 62.7
Solano 6,723,681 3,777,645 2,946,036 1,482,811 78.0 39.3 10,866,704 5,419,529 5,447,175 3,020,738 100.5
Contra Costa 21,769,539 11,492,645 10,276,894 4,399,175 89.4 38.3 39,194,448 20,729,218 18,465,230 9,187,760 89.1
Alameda 29,944,932 22,178,340 7,766,592 220,194 35.0 1.0 55,972,377 41,084,692 14,887,685 3,373,599 36.2
San Francisco 22,564,471 25,700,858 -3,136,387 -9,483,245 -12.2 -36.9 42,910,077 47,381,499 -4,471,422 -12,970,485 -9.4
San Mateo 19,708,771 12,503,307 7,205,464 1,535,803 57.6 12.3 41,512,033 33,242,279 8,269,754 77,797 24.9
Santa Cruz 5,061,315 2,809,424 2,251,891 754,967 80.2 26.9 9,610,039 5,294,057 4,315,982 2,072,654 81.5
Santa Clara 39,217,410 35,253,151 3,964,259 -4,022,888 11.2 -11.4 92,879,526 95,335,504 -2,455,978 -14,515,058 -2.6
Monterey 7,406,878 5,188,051 2,218,827 21,119 42.8 0.4 11,969,747 8,392,940 3,576,807 176,972 42.6
San Benito 654,107 344,368 309,739 121,555 89.9 35.3 1,341,148 743,924 597,224 287,779 80.3
San Luis Obispo 3,890,698 2,341,009 1,549,689 112,049 66.2 4.8 6,669,227 4,174,320 2,494,907 152,359 59.8
Santa Barbara 8,511,699 5,735,923 2,775,776 -135,706 48.4 -2.4 13,085,333 8,615,142 4,470,191 -142,351 51.9
* Net Income: There are several sources of income unrelated to work in a county that are recorded and they are generally referred to as transfer payments and property income. Social security and pensions are two of the most
important transfer payments and dividends, interest and rent are the most important sources of property income. Social Security and Medicare deductions from current workers are recorded as a deduction in income by place of work
in deriving income by place of residence. Adjustment for residence is also included in net income.
** Ad ju stment for Resid ence: The other d ifference between income by place of w ork and resid ence is called the resid ence ad justment. The resid ence ad ju stment is the net flow of incom e to a county that results
from some resid ents that work outsid e the county of resid ence and bring income into the cou nty (inflow of incom e) versu s resid ents from other counties that work inside the county but take their incomes hom e
to their cou nties of residence (ou tflow of income).
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic
Information System (REIS).
There are several sources of income unrelated to work in a county that are recorded and they are
generally referred to as transfer payments and property income. Social security and pensions are
two of the most important transfer payments and dividends, interest and rent are the most
important sources of property income. Social Security and Medicare deductions from current
workers are recorded as a deduction in income by place of work in deriving income by place of
residence. The other difference between income by place of work and residence is called the
residence adjustment. The residence adjustment is the net flow of income to a county that results
from some residents that work outside the county of residence and bring income into the county
(inflow of income) versus residents from other counties that work inside the county but take their
incomes home to their counties of residence (outflow of income).
In 1990, a total of $1.7 billion of the income in the JMPR study area was earned in counties
outside of the place of work. By 2000, this adjustment grew to $3.6 billion.
Proprietors Income and Employment. Proprietors (small businesses) account for a significant
proportion of both income and employment in study area counties. In 1990, proprietors in the
JMPR study area accounted for 9.1% of income and 14.2% of employment. In the 1990s, the
relative importance of proprietors increased. By 2000, proprietors accounted for 9.8% of the
income and 18.9% of the employment. These proportions were slightly lower than that for the
entire State of California. This is a fairly good indicator that small businesses are very important
in the study area. See Table 5.
As with other economic indicators we have summarized, there is wide variation among the
individual counties in the study area. In several of the counties in the southern section of the
study area (Monterey, San Benito, San Luis Obispo, and Santa Barbara), proprietors account for a
substantially higher amount of income and employment. Several of the counties show a
significantly lower proportions of proprietors income/total income as compared to proprietors
employment/total employment. Mendocino County’s proprietors income is only 2.0 percent of
9
total income as compared to proprietors employment which is 19.5 percent of total employment.
Other counties with similar scenarios are Solano and Alameda.
Table 5. Proprietors Income and Employment
1990 2000
Proprietors % of Total Proprietors Proprietors % of Total Proprietors
Income Personal Employment % of Total Income Personal Employment % of Total
($000's) Income ($000's) Employment ($000's) Income ($000's) Employment
California 62,148,804 9.5 2,852,772 16.8 120,226,020 11.0 3,830,282 19.5
JMPR Stu d y Area 16,889,884 9.1 779,007 360.4 35,757,023 9.8 1,032,751 1215.1
Mend ocino 179,230 2.0 11,738 16.8 323,938 2.0 14,147 19.5
Sonoma 873,075 9.8 50,195 24.4 1,859,063 11.6 65,618 24.2
Marin 820,613 9.9 44,389 29.7 1,708,962 11.4 56,043 31.6
N ap a 236,157 9.1 12,774 21.3 581,449 12.3 18,654 22.4
Solano 468,445 7.0 22,437 16.3 622,863 5.7 27,165 17.0
Contra Costa 1,660,360 7.6 84,000 21.0 3,955,517 10.1 110,789 23.4
Alam ed a 2,112,047 7.1 114,688 15.1 4,306,712 7.7 153,069 17.0
San Francisco 3,561,713 15.8 89,429 12.6 6,116,714 14.3 116,914 15.1
San Mateo 1,638,198 8.3 72,670 18.2 3,824,705 9.2 99,268 19.6
Santa Cru z 482,714 9.5 26,763 21.2 1,047,858 10.9 38,712 25.9
Santa Clara 2,295,244 5.9 145,677 13.9 6,198,826 6.7 190,713 14.8
Monterey 942,285 12.7 30,850 15.2 2,322,076 19.4 42,444 19.0
San Benito 88,965 13.6 3,756 24.0 238,064 17.8 5,416 25.1
San Lu is Obisp o 458,857 11.8 26,888 25.1 1,020,870 15.3 38,117 27.1
Santa Barbara 1,071,981 12.6 42,753 19.8 1,629,406 12.5 55,682 22.1
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic
Information System (REIS).
Indicators of Economic Health and Wealth
Unemployment rates and Per Capita Income. Unemployment rates and per capita incomes are
probably the two most popular measures used as indicators of the health and wealth of
communities, states or nations. Through the 1990s both unemployment and real per capita
income (per capita income in 2001 dollars i.e., adjusted for inflation using the Consumer Price
Index) moved in the same directions for most counties in the study area. Unemployment
throughout the study area rose during the first half of the decade and dropped significantly
during the second half. Monterey and San Benito Counties have historically had the highest
unemployment rates. Marin and San Mateo Counties have historically had the lowest
unemployment rates.
Real per capita income remained fairly level during the 1990 to 1995 period, with the counties in
the study area reporting slight increases or slight declines. It was the period 1995 to 2000 that
had sharp increases in real per capita income. The four counties with the highest real per capita
income in 2000, Marin ($62,331), San Mateo ($60,301), San Francisco ($56,834), and Santa Clara
($56,716) also had the highest increases from 1995 to 2000 in the study area. Mendocino ($25,554)
and San Benito ($25,586) had the lowest real per capita income in 2000. Monterey County had the
smallest increase from 1995 to 2000 in real per capita income in the study area
10
Table 6. Unemployment Rates and Per Capita Incomes
Unemployment Rate (%) Per Capita Income Per Capita Income (2001 $)
1990 1995 2000 1990 1995 2000 1990 1995 2000
California 5.8 7.8 4.9 21,882 24,339 32,149 29,653 28,280 33,058
Mend ocino 7.6 9.6 6.6 16,794 19,374 24,852 22,758 22,511 25,554
Sonom a 3.9 5.5 2.6 22,729 25,569 34,863 30,801 29,709 35,848
Marin 2.5 4.3 1.7 35,786 43,340 60,618 48,494 50,358 62,331
N ap a 4.1 6.2 3.2 23,420 27,568 37,928 31,737 32,032 39,000
Solano 4.8 8.0 4.2 19,576 20,867 27,354 26,528 24,246 28,127
Contra Costa 4.0 5.7 2.7 26,899 31,065 41,110 36,451 36,095 42,272
Alamed a 4.0 5.8 3.0 22,926 27,212 38,624 31,068 31,618 39,716
San Francisco 3.8 6.1 2.8 31,188 35,992 55,272 42,264 41,820 56,834
San Mateo 2.6 4.2 1.6 30,313 36,064 58,644 41,078 41,904 60,301
Santa Cru z 7.1 9.3 5.6 22,043 26,117 37,567 29,871 30,346 38,629
Santa Clara 3.9 4.9 2.0 26,174 32,488 55,157 35,469 37,749 56,716
Monterey 9.6 12.4 9.5 20,717 24,832 29,695 28,074 28,853 30,534
San Benito 12.1 13.7 7.9 17,758 19,687 24,883 24,064 22,875 25,586
San Lu is Obisp o 4.6 6.6 3.0 17,825 20,594 26,932 24,155 23,929 27,693
Santa Barbara 4.9 6.7 3.7 22,970 25,467 32,734 31,127 29,591 33,659
Source: Income: U.S. Department of Commerce, Bureau of Economic Analysis, Regional
Economic Information System (REIS).
Unemployment rate: U.S. Department of Labor, Bureau of Labor Statistics, Division of Labor
Force Statistics
Income and Employment by Industry. For purposes of economic impact analyses, in terms of
income and employment impacts, income and employment by industry is critical because it
provides the necessary control totals in the economic accounting system. A limitation of this
accounting system is that it is still based on the old industrial economy and generally is not
designed to yield direct insights into how the use of natural resources and the environment are
connected to the economy. Linking the economy and the environment is the very heart of the
Socioeconomic Team’s task. We need to be able to answer the question, if the use of the natural
resources of the JMPR Study Area are changed, what will be the impact on the income and
employment in the local economies? To answer this question requires supplemental information
organized so that it maps directly into the current system of accounting. In some cases, the
income and employment by industry statistics can give us upper bound estimates of the direct
portion of impact (i.e., not counting multiplier impacts) for particular uses. Our approach here is
to first look at the most aggregated information, then proceed to evaluate information collected
by other institutions and how it maps into the more aggregated statistics. Each step along the
way our objective is to see how close we can get to linking the economy with the environment
and assessing the relative importance to the economy of natural resource base uses.
Tables 7 and 8 show the values and percentages of income and employment by industry
to counties in the study area. At this very aggregated level, the distributions for both income and
employment by industry are very similar for most of the counties. The counties in the study area
are driven by the services sector.
11
Table 7, Personal Income by Industry
Personal Income By Industry ($000s), 2000
Ag. Services, Governm ent
Transpor- Finance,
Forestry, Manufactu rin Wholesale and
Total Farm Mining Constru ction tation and Retail Trad e Insurance, and Services
Fishing, & g Trad e Governm ent
Public Utilities Real Estate
Other Enterp rises
California 825,224,182 8,424,649 7,943,257 2,851,715 47,012,923 128,467,273 49,823,365 47,115,376 71,496,822 71,830,864 271,009,369 119,248,569
Stud y Area 291,743,151 3,018,746 2,042,716 934,675 16,166,414 59,886,105 14,794,266 15,037,837 22,250,049 25,216,023 101,689,185 30,288,137
Mendocino 1,286,730 25,863 41,009 (D) 103,509 210,441 64,176 (D) 178,114 50,536 345,782 233,640
Sonoma 9,834,626 178,115 120,951 76,092 1,112,460 1,969,874 389,684 365,396 1,006,663 710,265 2,670,638 1,234,488
Marin 7,300,898 (833) (D) (D) 607,793 242,514 203,739 291,487 812,576 1,045,498 3,330,911 661,473
N apa 2,907,793 115,764 70,345 (D) 246,501 622,755 120,846 (D) 286,533 180,987 782,277 390,449
Solano 5,419,529 24,315 44,744 24,727 571,423 601,996 253,821 205,811 647,217 235,418 1,282,427 1,527,630
Contra Costa 20,729,218 60,334 164,980 365,513 1,876,810 2,079,544 1,595,809 853,299 1,975,171 2,396,625 7,068,915 2,292,218
Alam ed a 41,084,692 (119) 186,215 51,243 2,780,983 6,883,531 2,596,816 3,428,926 3,492,682 2,005,942 13,077,290 6,581,183
San Francisco 47,381,499 - 126,426 79,519 1,480,390 1,750,359 3,589,434 1,474,814 3,703,088 10,727,986 18,730,070 5,719,413
San Mateo 33,242,279 102,958 (D) (D) 1,751,030 4,428,802 2,789,664 1,524,252 2,605,707 2,900,905 15,353,673 1,637,553
Santa Cruz 5,294,057 221,624 75,315 5,204 377,375 922,955 169,562 256,572 563,451 298,412 1,668,896 734,691
Santa Clara 95,335,504 211,521 297,463 225,922 3,805,161 38,327,098 2,130,155 5,711,362 4,705,760 3,322,790 31,531,680 5,066,592
Monterey 8,392,940 1,387,752 628,427 9,550 437,838 499,764 284,149 313,453 794,580 473,230 1,893,698 1,670,499
San Benito 743,924 118,750 26,672 (D) 72,983 96,512 (D) 51,363 70,241 33,409 112,627 120,840
San Luis Obispo 4,174,320 151,587 93,602 12,500 418,977 334,179 322,879 107,693 529,648 251,528 1,101,806 849,921
Santa Barbara 8,615,142 421,115 166,567 84,405 523,181 915,781 283,532 453,409 878,618 582,492 2,738,495 1,567,547
Personal Income By Industry (% of Total), 2000
1.0 1.0 0.3 5.7 15.6 6.0 5.7 8.7 8.7 32.8 14.5
California
1.0 0.7 0.3 5.5 20.5 5.1 5.2 7.6 8.6 34.9 10.4
Stud y Area
Mendocino 2.0 3.2 8.0 16.4 5.0 13.8 3.9 26.9 18.2
Sonoma 1.8 1.2 0.8 11.3 20.0 4.0 3.7 10.2 7.2 27.2 12.6
0.0 8.3 3.3 2.8 4.0 11.1 14.3 45.6 9.1
Marin
4.0 2.4 8.5 21.4 4.2 9.9 6.2 26.9 13.4
N apa
0.4 0.8 0.5 10.5 11.1 4.7 3.8 11.9 4.3 23.7 28.2
Solano
Contra Costa 0.3 0.8 1.8 9.1 10.0 7.7 4.1 9.5 11.6 34.1 11.1
0.0 0.5 0.1 6.8 16.8 6.3 8.3 8.5 4.9 31.8 16.0
Alam ed a
0.0 0.3 0.2 3.1 3.7 7.6 3.1 7.8 22.6 39.5 12.1
San Francisco
San Mateo 0.3 5.3 13.3 8.4 4.6 7.8 8.7 46.2 4.9
Santa Cruz 4.2 1.4 0.1 7.1 17.4 3.2 4.8 10.6 5.6 31.5 13.9
0.2 0.3 0.2 4.0 40.2 2.2 6.0 4.9 3.5 33.1 5.3
Santa Clara
16.5 7.5 0.1 5.2 6.0 3.4 3.7 9.5 5.6 22.6 19.9
Monterey
San Benito 16.0 3.6 9.8 13.0 6.9 9.4 4.5 15.1 16.2
San Luis Obispo 3.6 2.2 0.3 10.0 8.0 7.7 2.6 12.7 6.0 26.4 20.4
4.9 1.9 1.0 6.1 10.6 3.3 5.3 10.2 6.8 31.8 18.2
Santa Barbara
Table 8, Employment by Industry
Employment By Industry (number of jobs), 2000
Ag. Services, Transpor- Finance, Governm ent
Forestry, Manu factu rin tation and Wholesale Insu rance, and
Total Farm Mining Constru ction Retail Trad e Services
Fishing, & g Pu blic Trad e and Real Governm ent
Other Utilities Estate Enterp rises
California 19,654,877 328,861 408,406 38,870 1,040,795 2,047,587 879,014 912,202 3,006,849 1,696,230 6,759,116 2,536,947
Stud y Area 5,476,530 81,482 88,267 7,457 301,249 595,826 246,627 232,547 813,704 478,845 2,009,938 607,067
Mend ocino 49,818 3,163 2,012 (D) 3,139 6,128 1,425 (D) 8,768 2,930 14,662 6,437
Sonom a 271,593 9,475 6,167 533 20,665 34,060 8,269 8,581 44,113 23,514 86,505 29,711
Marin 177,605 843 (D) (D) 12,179 5,646 4,437 5,717 29,750 23,498 77,433 14,410
N ap a 83,401 5,350 2,703 (D) 5,183 11,227 1,977 (D) 12,941 5,947 26,396 9,468
Solano 159,852 2,597 2,346 535 12,524 11,066 5,179 5,108 30,569 10,758 45,904 33,266
Contra Costa 473,822 2,920 7,314 2,308 35,875 28,015 24,829 15,107 77,652 58,440 173,520 47,842
Alamed a 902,712 1,155 7,953 710 51,011 103,259 50,453 62,191 128,300 60,754 312,288 124,638
San Francisco 773,679 - 2,990 587 26,111 32,222 43,684 23,879 107,614 103,642 335,359 97,591
San Mateo 506,154 3,449 (D) (D) 27,773 39,328 46,863 23,409 71,099 49,874 206,770 31,770
Santa Cruz 149,630 8,949 2,995 132 8,878 11,980 3,813 5,708 26,456 11,247 50,902 18,570
Santa Clara 1,290,679 5,295 12,236 861 63,005 271,595 37,638 63,107 168,551 79,712 489,782 98,897
Monterey 223,754 18,710 26,197 281 9,967 11,062 6,182 6,768 34,662 14,996 60,034 34,895
San Benito 21,573 2,079 1,098 (D) 1,713 2,628 (D) 1,380 3,474 1,363 4,295 2,896
San Lu is Obisp o 140,869 5,050 5,177 323 10,325 8,838 5,647 3,886 27,359 12,519 41,096 20,649
Santa Barbara 251,389 12,447 9,079 1,187 12,901 18,772 6,231 7,706 42,396 19,651 84,992 36,027
Employment By Industry (% of jobs), 2000
California 1.7 2.1 0.2 5.3 10.4 4.5 4.6 15.3 8.6 34.4 12.9
Stud y Area 1.5 1.6 0.1 5.5 10.9 4.5 4.2 14.9 8.7 36.7 11.1
Mend ocino 6.3 4.0 6.3 12.3 2.9 17.6 5.9 29.4 12.9
Sonom a 3.5 2.3 0.2 7.6 12.5 3.0 3.2 16.2 8.7 31.9 10.9
Marin 0.5 6.9 3.2 2.5 3.2 16.8 13.2 43.6 8.1
N ap a 6.4 3.2 6.2 13.5 2.4 15.5 7.1 31.6 11.4
Solano 1.6 1.5 0.3 7.8 6.9 3.2 3.2 19.1 6.7 28.7 20.8
Contra Costa 0.6 1.5 0.5 7.6 5.9 5.2 3.2 16.4 12.3 36.6 10.1
Alamed a 0.1 0.9 0.1 5.7 11.4 5.6 6.9 14.2 6.7 34.6 13.8
San Francisco 0.0 0.4 0.1 3.4 4.2 5.6 3.1 13.9 13.4 43.3 12.6
San Mateo 0.7 5.5 7.8 9.3 4.6 14.0 9.9 40.9 6.3
Santa Cruz 6.0 2.0 0.1 5.9 8.0 2.5 3.8 17.7 7.5 34.0 12.4
Santa Clara 0.4 0.9 0.1 4.9 21.0 2.9 4.9 13.1 6.2 37.9 7.7
Monterey 8.4 11.7 0.1 4.5 4.9 2.8 3.0 15.5 6.7 26.8 15.6
San Benito 9.6 5.1 7.9 12.2 6.4 16.1 6.3 19.9 13.4
San Lu is Obisp o 3.6 3.7 0.2 7.3 6.3 4.0 2.8 19.4 8.9 29.2 14.7
Santa Barbara 5.0 3.6 0.5 5.1 7.5 2.5 3.1 16.9 7.8 33.8 14.3
(D) Not shown to avoid disclosure of confidential information, but the estimates are included in the totals.
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic
Information System (REIS).
12
Commercial fisheries would be included under the category “Agricultural Services, Forestry,
Fishing and Other”. In 2000, this category accounted for only 0.7% of income and 1.6% of
employment by place of work in the study area. Several of the counties (Monterey, San Benito,
Mendocino, and Napa) did have higher proportions than the average. This serves as a first step
upper bound on the proportion of income by place of work for the direct impacts of the
harvesting portion (not including multiplier impacts) of commercial fishing. Other direct
impacts of commercial fishing would include some portion of Wholesale Trade (e.g., fish houses
and buyers) and some portion of Manufacturing (fish processing).
The Retail Trade and Services sectors are where the direct impacts of tourism/recreation would
be included. However, these categories are too broad to yield any useful bounds for estimation
of the direct impacts for tourism/recreation. The accounts, as stated above, were simply not
designed for this purpose. In any case, the first step of linking the three natural resource use
activities to the economy yielded only limited insights.
Income and Employment: Additional Disaggregation
The accounts reviewed above are what are called two-digit SIC (Standard Industrial
Classification) level of aggregations. The SIC system of accounting can actually go down to four
and six digit levels, which contain more specificity about the activity. However, because of
nondisclosure rules to protect the privacy of business information, the four digit level is the best
available for large counties and even here there are many categories for which information is not
reported due to nondisclosure. In this step, we will explore how much detail we can glean about
the three sectors that are our primary interest. Only income is reported at the lower levels of
disaggregation.
Commercial Fishing Industry. In 1995, fishing income was a little over $117 million in the State
of California. This represents less than one percent (0.02%) of income by place of work. Two of
the counties (Mendocino, 0.66% and Monterey, 0.32 percent) do have higher proportions of
fishing income, however, they remain under one percent of total income by place of work. The
year 1995 was chosen for analysis because it was the last year that a significant number of
counties were able to release data. Again, this would be the income received by harvesters or
commercial fishermen including crews and proprietors of the harvesting operations. It would
not include buyers and fish houses or processors of commercial fish products.
Table 9. Direct Income to Commercial Fishing Harvesting Sector ($000s)
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
California 170,671 140,424 129,910 133,414 120,338 117,640 109,820 106,752 94,532 103,807 103,391
Mendocino 6,043 5,386 4,975 5,545 6,079 6,009 5,608 6,148 5,207 6,241 6,085
Sonom a 2,547 2,065 1,946 1,286 1,593 1,327 1,399 796 709 770 824
Marin (D) 1,274 1,246 1,452 1,702 1,394 (D) (D) (D) (D) (D)
Napa (D) 123 126 149 207 (L) (L) 52 50 (D) 60
Solano 400 204 140 154 236 127 135 154 145 164 (D)
Contra Costa (D) 1,115 1,052 1,157 1,526 1,034 917 687 (D) (D) (D)
Alamed a 2,764 2,279 1,783 1,570 1,410 1,549 (D) (D) (D) (D) (D)
San Francisco (D) 631 540 323 421 546 1,773 652 (D) 859 (D)
San Mateo (D) 4,375 3,276 3,644 3,860 2,707 (D) (D) 3,015 3,597 (D)
Santa Cruz 1,113 917 649 639 739 563 630 764 (D) (D) (D)
Santa Clara 677 644 572 545 578 433 472 469 364 453 463
Monterey (D) 21,500 23,929 24,002 13,994 18,898 13,126 11,682 (D) (D) (D)
San Benito (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L)
San Luis Obispo (D) 4,328 3,905 4,851 4,895 (D) (D) (D) (D) 4,173 (D)
Santa Barbara (D) 3,797 3,261 3,206 3,292 2,909 2,970 2,148 (D) (D) (D)
(D) Not shown to avoid disclosure of confidential information, but the estimates are included in the totals.
(L) Less that $50,000, but the estimates for this item are included in the totals.
Source: U.S. Department of Commerce, Bureau of Economic Analysis, Regional Economic
Information System (REIS).
13
Tourism and Recreation. Tourism/recreation has been a notoriously difficult activity to
document because the expenditures made while undertaking the activities are spread across so
many sectors. Few that really capture the industry. Three commonly used are “Eating and
Drinking Places” (within Retail Trade), “Hotels and Other Lodging Places”, and “Amusement
and Recreation Services” (within Services). A fourth is sometimes included “Museums, Botanical
and Zoological Gardens” (within Services). The first three indicators of tourism/recreation are
commonly used by the United Nations Environmental Programme when profiling third world
countries for economic development programs. Unfortunately, these three sectors tell us very
little about tourism/recreation. They are not good discriminators across areas in a single point in
time, nor are they good indicators of the trends of tourism/recreation over time in a given place.
Life style changes have resulted in high proportions of the local population eating out. Business
related travel is a major portion of hotel and motel business and some communities may have
extensive numbers of hotel and motels with very little in the way of tourism/recreation. In
highly diverse economies like the U.S., measurements from these three industries yield nothing
of use to get us close to linking natural resource uses with the economy. We must look elsewhere
for supplemental information to get us closer to our goal.
Income and Employment: Supplemental Information. In step 2, we were able to narrow in on commercial
fishing contributions to the local economies at the first stage of direct impacts. The industry accounts did
not support any additional insights for tourism/recreation. In this step, we seek out additional sources of
information and to see what they might reveal about the activities and their income and employment
impacts.
Commercial Fishing Industry. For the commercial fisheries, we will first go to information compiled by
the Pacific Fisheries Management Council (PFMC). The PFMC maintains a data base called PacFin which
reports commercial fish landings by port, county and species. The PFMC also has developed a regional
economic impact model to translate ex vessel value (i.e., the dollar amounts received by harvesters for their
catch) to total income generated within the county where landed. This amount will include full
multiplier impacts.
VALUE OF MARINE SCIENTIFIC RESEARCH IN THE STUDY AREA
Data gap for possible further investigation.
TOURISM AND RECREATION
Below we present the information and our preliminary assessment of the range of relative
importance of tourism/recreation to the JMPR study area economy. Marine recreation uses in
the JMPR study area would be some sub-set of these estimates.
California Travel Direct Impacts by County - Method
A study, California Travel Impacts by County, 1992-2000, prepared by Dean Runyan Associates for
the California Travel and Tourism Commission and the Division of Tourism of the Technology,
Trade and Commerce Agency was completed in March 2002. As stated in the introduction, the
report describes the economic impacts of travel to and through the state of California over the
time period 1992 to 2000. These estimates of the direct impacts associated with traveler spending
in California were produced using the Regional Travel Impact Model (RTIM) developed by De an
Runyan Associates. The input data used to detail the economic impacts of the California travel
14
industry were derived from various local, state and federal sources. For accuracy, the following
explanation of analysis methods is from the report.
Types Of Travel Impacts Included. Most of the travel that occurs in California is included in the
scope of this analysis. All trips to California by U.S. residents and foreign visitors are included.
The travel of California residents to other destinations within California is included, provided
that it is neither commuting nor other routine travel. Travel to non-California destinations by
California residents is not included as a component of destination spending. Outbound air travel
impacts are included in the air transportation category. The impacts associated with both
overnight and day travel are included if the travelers remain at the destination overnight or the
destination is over 50 miles, one-way, from the traveler's home. These definitions are used to
screen and, if necessary, to interpret and adjust local data used for travel impact measurements.
The most conservative interpretation is employed where data limitations cause deviations from
the above definition. The terms “traveler” and “visitor” are used interchangeably in this report.
Both represent a person who is traveling in the state of California, away from his or her home, on
a trip as defined above. The purpose of such travel can be for business, pleasure, shopping, to
attend meetings, or for personal, medical or educational
purposes.
Air Transportation And Travel Arrangement. This analysis focuses on travel and tourism as a
component of local and statewide economies, and therefore focuses on destination-specific
impacts. However, some impacts associated with non-destination-specific spending and
employment are included. These non-destination-specific industries are air transportation and
travel arrangement (travel agents and tour operators). These industries are classified as
nondestination-specific because they provide services for travel to, through and from specific
destinations. It is important to note, however, that the impacts of these industries (e.g.,
employment) occur within specific geographic areas, primarily those with commercial airport
facilities.
Thirty-three counties in California had scheduled passenger air transportation in 2000. The
associated employment impacts are allocated in this report to the county in which the
employment is based. The associated spending impacts are also allocated to that county as non-
destination spending.1 However, it is important to recognize that the benefits from air travel also
extend to those counties that do not provide air transportation. This might include, for example,
an overnight visitor in Mendocino County who traveled by air from Chicago to Oakland.
Because air transportation facilities provide travel services that benefit businesses throughout the
state, it is appropriate to include air transportation as a component of the travel industry. But
because of the regional character of air travel, it is sometimes useful to exclude this sector when
analyzing local economic impacts. These considerations are, of course, most relevant with respect
to those counties with the largest air transportation impacts.
Direct Versus Indirect Impacts Or “Multipliers”. Economic impact measurements reported herein
represent only direct economic impacts. Direct economic impacts include only the spending by
travelers and the employment generated by that spending. Indirect or “multiplier” effects, which
refer to the additional spending of businesses and employees induced by travel spending, are not
included.
San Francisco and San Mateo counties are the only exception. The employment associated with
1
air transportation employment in San Mateo County is allocated to San Mateo, whereas most of
the air transporation travel spending is allocated to San Francisco.
15
Impact Categories. The specific categories of travel impacts included in this analysis are as follows:
Expenditures: Purchases by travelers during their trip, including lodging taxes and other
applicable local and state taxes, paid by the traveler at the point of sale.
Total Earnings: The earnings (wage and salary disbursements, earned benefits and
proprietor income) of employees of businesses that receive travel expenditures. Only the
earnings attributable to travel expenditures are included; this typically is only a portion
of all business receipts.
Employment: Employment associated with the above earnings; this includes both full-
and part-time positions of wage and salary workers as well as proprietors.
Local Tax Receipts: Tax receipts collected by counties and municipalities, as levied on
applicable travel-related purchases.
State Tax Receipts: State taxes, such as sales and gasoline taxes, attributable to travel
expenditures and business taxes as levied on travel industry firms and employees.
Visitor Categories. Travelers are classified according to the type of accommodation in which they
stay. The types of visitors are as follows:
Hotel/Motel/B&B Guest: Travelers staying in hotels, motels, resorts, bed & breakfast
establishments, and other commercial accommodations, excluding campgrounds, where
a transient lodging tax is collected.
Private Camper: Travelers staying in a privately owned (i.e., commercial) campground.
Public Camper: Travelers staying in a publicly managed campground such as those
managed by the California State Parks and Recreation Commission, the U.S. Forest
Service or the National Park Service.
Private Home Visitor: Travelers staying as guests with friends or relatives.
Vacation Home Visitor: Travelers using their own vacation home or timeshare and those
borrowing or renting a vacation home where transient lodging tax is not collected.
Day Visitor: Both in-state and out-of-state residents whose trip does not include an
overnight stay at a destination in California.
The “travel industry” as described in this report refers to a collection of businesses that provide
goods and services to the traveling public. These types of businesses are coded according to the
U.S. Office of Management and Budget's Standard Industrial Classifications (SIC).
Local taxes refer to all city and county taxes. These include local sales taxes and room taxes.
Property taxes are not included. State taxes include the state sales tax, the state gasoline fuel tax,
and income taxes on travel industry firms and employees.
16
Interpretation Of Impact Estimates. Users of this information should be aware of several issues
regarding the interpretation of the impact estimates contained herein:
When comparing the impact estimates associated with different locations or different
time periods, it is more appropriate to focus on destination spending (which excludes air
transportation) rather than total travel spending.
The estimates in this report are expressed in current dollars. There is no adjustment for
inflation.
The employment and business service categories found in the impact tables do not
perfectly correspond to the industry categories used in various state and federal
government publications. The spending and employment categories used in this report
refer to a particular type of service, as opposed to an industry classification. For example,
the accommodations category in this report includes only that spending or employment
attributable to paid accommodations. It does not include spending on eating and
drinking in a hotel restaurant or recreational services provided at a resort. In addition,
government employees are not distinguished from the employees of commercial
enterprises, as is often the case in other data series published by government agencies.
In the detailed table for each county, the first breakout, Travel Spending by Type of Traveler
Accommodation, shows the travel spending by each type of traveler in the county. The second
breakout, Travel Spending by Type of Business Service, indicates the amount of expenditures for
different goods and services (e.g., accommodations, recreation) by all traveler types. Destination
spending refers to all travel-related spending in the county except air transportation and travel
arrangement.
California Travel Impacts by County – Results
Total travel spending in the JMPR study area was estimated by Dean Runyan at $25.2 billion in
2000. This accounts for 1/3 of the $75.4 billion that travelers to California contributed to the state
economy. Four billion was spent on air transportation in the study area in 2000. Total
destination spending, total spending excluding air transportation and travel arrangement, was
estimated to be $21.0 billion.
Employment in the study area generated by travel spending was estimated to be approximately
250 thousand. While San Francisco County accounts for approximately $5.6 billion, or about ¼,
of the travel destination spending in the study area, it accounts for a disproportionately small
amount of the employment generated by travel spending.
Spending on recreation related travel activities was estimated at $3.5 billion. Recreation travel
spending, the sector we are most interested in, is largely driven by five counties. San Francisco
($1.0 billion), Santa Clara ($484 million), San Mateo ($355 million), Monterey ($300 million), and
Alameda ($290 million) Counties together account for 69.8 percent of the recreational spending in
the study area.
In the study area, an estimated 47,793 jobs are generated by the recreation component of travel
spending. Recreational travel employment is driven by the same counties, with the exception of
San Francisco, which was found to employ a very small number of people (15).
17
Total earnings generated by travel spending in the study area was estimated to be $8.5 billion in
2000. Again, the five counties previously mentioned, San Francisco ($2.1 billion), Santa Clara ($1.2
billion), San Mateo ($1.7 billion), Monterey ($629 million), and Alameda ($807 million) account
for 76.3 percent of the earnings generated by travel spending in the study area.
Total tax revenues generated by travel spending in the study area were $1.6 billion in 2000. Of
this, $676 million were local taxes and $942 million were state taxes. Local taxes refer to sales and
use taxes, and transient occupancy taxes collected by cities and counties. Property taxes and
business license taxes are not included. State taxes include the state sales tax, the state gasoline
fuel tax, corporate income taxes and personal income taxes.
Table 10. Travel Impacts, 2000
Study Contra Mendo- Mon- San San San Luis San Santa Santa Santa
CA Alameda Marin N apa Solano Sonoma
Area Costa cino terey Benito Francisco Obispo Mateo Barbara Clara Cruz
Travel Spending by Type of Traveler Accommodation ($Million)
D estination Spending 66,000 20,977 2,008 896 516 66 1,853 628 74 5,592 961 2,178 1,151 3,192 514 430 918
Hotel, Motel, B&B 34,500 13,580 1,315 422 239 35 1,256 386 12 4,355 471 1,408 684 2,206 247 140 405
Private Campground 2,500 425 3 29 38 3 12 19 19 - 72 20 23 83 37 40 28
500 101 - 6 4 1 15 2 1 - 20 9 16 2 12 1 14
Public Campground
Private Home 7,100 1,788 245 179 81 7 107 20 21 245 68 254 112 257 36 76 81
Vacation Home 3,600 451 11 26 25 4 44 16 2 28 76 15 27 16 68 7 87
D ay Travel 17,700 4,631 433 235 130 18 419 185 20 964 254 471 290 627 114 167 304
8,800 4,053 504 - 5 9 25 4 0 2,853 7 457 14 165 - - 10
Air Transportation
Travel Arrangement 600 58 1 9 11 1 7 1 0 5 2 2 4 6 5 1 5
Total Spending 75,400 25,236 2,531 905 533 75 1,885 633 75 8,502 970 2,659 1,169 3,419 518 430 933
Travel Spending by Type of Business Service ($Million)
D estination Spending 66,000 20,977 2,008 896 516 66 1,853 628 74 5,592 961 2,178 1,151 3,192 514 430 918
12,900 4,963 434 142 97 13 461 139 9 1,603 196 467 242 818 135 50 158
Accommodations
Eating, D rinking 16,000 5,072 442 180 130 16 500 153 22 1,401 259 491 304 749 123 94 209
Food Stores 2,200 599 51 28 22 2 50 19 7 123 44 55 36 90 23 19 30
Ground Transport 8,800 2,372 423 266 56 9 71 26 4 290 62 380 92 447 46 86 116
Recreation 12,100 3,487 290 119 92 12 300 137 14 1,003 147 355 182 484 79 84 188
Retail Sales 13,900 4,484 367 162 119 14 471 154 18 1,173 254 430 295 604 108 98 217
Air Transportation 8,800 4,053 504 - 5 9 25 4 0 2,853 7 457 14 165 - - 10
Travel Arrangement 600 58 1 9 11 1 7 1 0 5 2 2 4 6 5 1 5
Total Spending 75,400 25,236 2,531 905 533 75 1,885 633 75 8,502 970 2,659 1,169 3,419 518 430 933
Earnings Generated by Travel Spending ($Million)
Total Earnings 24,900 8,458 807 253 191 25 629 221 22 2,111 306 1,705 372 1,204 178 127 309
Employment Generated by Travel Spending (Jobs)
Accommodations 201,000 66,881 6,690 2,300 1,390 201 6,100 1,820 170 16,700 3,810 5,820 4,100 11,560 2,480 1,040 2,700
398,000 109,458 10,500 4,470 2,870 398 10,390 3,150 640 23,500 7,740 9,460 8,030 16,370 3,440 2,990 5,510
Eating, D rinking
Food Stores 12,000 2,812 260 150 100 12 220 80 40 400 280 220 200 420 140 120 170
Ground Transport 47,000 11,857 2,480 1,040 230 47 300 150 20 1,500 330 1,740 500 2,130 180 510 700
Recreation 248,000 62,278 5,910 2,550 1,730 248 4,590 2,390 210 14,500 3,250 5,870 3,570 9,070 1,890 2,280 4,220
Retail Sales 114,000 32,124 2,870 1,330 870 114 3,220 1,040 170 6,500 2,500 2,720 2,560 4,340 990 1,030 1,870
52,000 22,792 3,110 - 40 52 290 20 - 1,600 60 16,050 130 1,360 - - 80
Air Transportation
Travel Arrangement 28,000 8,899 900 490 520 28 180 60 1 2,600 140 840 250 2,220 300 60 310
Total Employment 1,100,000 317,100 32,710 12,330 7,760 1,100 25,280 8,710 1,250 67,300 18,120 42,710 19,330 47,480 9,430 8,020 15,570
Tax Revenues Generated by Travel Spending ($Million)
1,700 676 58 22 11 2 53 17 1 258 21 61 32 101 14 7 19
Local Taxes
State Taxes 3,100 942 99 56 25 3 76 25 3 211 43 115 52 145 22 24 43
Total Taxes 4,800 1,618 157 78 35 5 130 42 5 469 64 177 83 246 36 31 62
Table 11. Total Recreation Travel Spending by County, 1992-2000 ($Millions)
18
Average
1992 1993 1994 1995 1996 1997 1998 1999 2000 Annual
Change
State Total 7,400 7,600 7,900 8,300 9,100 10,000 10,700 11,500 12,100 6.4
JMPR Study Area 1,975 2,066 2,169 2,334 2,591 2,869 3,080 3,386 3,536 7.6
Alam ed a 138 144 148 160 179 197 215 254 290 9.8
Contra Costa 70 73 76 81 87 97 106 113 119 6.9
Marin 49 55 58 61 67 73 78 86 92 8.3
Mend ocino 43 43 45 48 49 51 54 57 61 4.5
Monterey 186 193 199 212 236 254 266 295 300 6.2
N apa 76 79 88 98 106 117 125 128 137 7.6
San Benito 9 9 9 10 11 12 12 13 14 6.0
San Francisco 536 566 602 649 730 813 872 992 1,003 8.2
San Lu is Obisp o 100 105 101 102 112 119 127 136 147 5.0
San Mateo 206 213 228 250 278 310 330 346 355 7.1
Santa Barbara 119 123 129 135 143 153 163 174 182 5.5
Santa Clara 221 233 250 281 328 382 423 456 484 10.4
Santa Cruz 50 52 52 55 60 66 69 78 79 6.0
Solano 53 55 57 58 61 67 70 76 84 5.9
Sonom a 119 123 127 134 145 158 170 181 188 5.9
Source: The California Travel and Tourism Commission, The California Technology, Trade, and
Commerce Agency, and Dean Runyan Associates.
Table 12. Direct Recreation Travel-Generated Employment by County, 1992-2000 (Jobs)
Average
1992 1993 1994 1995 1996 1997 1998 1999 2000 Annual
Change
State Total 195,000 194,000 206,000 210,000 222,000 241,000 236,000 248,000 248,000 3.1
JMPR Stud y Area 45,480 46,120 49,740 51,790 55,190 60,170 60,460 64,930 63,010 4.2
Alam ed a 3,580 3,630 3,840 4,000 4,310 4,680 4,810 5,650 5,910 6.6
Contra Costa 1,970 1,980 2,130 2,190 2,270 2,510 2,520 2,630 2,550 3.4
Marin 1,150 1,260 1,360 1,400 1,460 1,590 1,610 1,740 1,730 5.3
Mend ocino 890 860 930 960 940 970 920 960 980 1.3
Monterey 3,570 3,600 3,800 3,940 4,210 4,460 4,420 4,820 4,590 3.3
N apa 1,860 1,880 2,140 2,300 2,410 2,610 2,590 2,490 2,390 3.4
San Benito 170 180 180 180 200 210 200 210 210 2.8
San Francisco 9,800 10,000 11,000 11,500 12,400 13,600 13,800 15,500 14,500 5.2
San Lu is Obisp o 2,790 2,850 2,820 2,750 2,900 3,050 2,970 3,150 3,250 2.0
San Mateo 4,400 4,420 4,860 5,160 5,530 6,060 6,050 6,210 5,870 3.8
Santa Barbara 2,780 2,790 3,000 3,050 3,110 3,280 3,440 3,570 3,570 3.2
Santa Clara 5,470 5,600 6,210 6,750 7,580 8,700 8,850 9,410 9,070 6.7
Santa Cruz 1,570 1,580 1,640 1,690 1,760 1,900 1,890 2,010 1,890 2.4
Solano 1,890 1,900 2,010 2,000 2,030 2,180 2,080 2,210 2,280 2.4
Sonom a 3,590 3,590 3,820 3,920 4,080 4,370 4,310 4,370 4,220 2.1
Source: The California Travel and Tourism Commission, The California Technology, Trade, and
Commerce Agency, and Dean Runyan Associates.
Our next task is to identify how much of the tourism/recreation currently relates to marine
resource uses.
Marine Related Recreation.
Generally, we know that recreational fishing, scuba diving (both consumptive and non
consumptive), pleasure boating, whale and other wildlife watching, surfing, kayaking, personal
19
watercraft use, and beach visitation take place in the three JMPR sanctuaries. Quantitative
estimates of the amount of activity in the study area or in the general area off the coast of
Northern California are few in number and often incomplete. More is known about recreational
fishing than for the other activities.
National Survey on Recreation and the Environment (NSRE) 2000. For the NSRE, "marine
recreation" was defined as participation in at least one of 19 activities/settings, including beach
visitation, visitation to watersides besides beaches for outdoor recreation, swimming, snorkeling,
scuba diving, surfing, wind surfing, fishing, motor-boating, sailing, personal watercraft use,
rowing, canoeing, kayaking, hunting for waterfowl in a water-based surrounding, viewing or
photographing birds in a water-based surrounding, viewing or photographing other wildlife in a
water-based surrounding, and viewing or photographing scenery in a water-based surrounding.
For activities, "marine" was defined as activities in oceans, sounds, and in mixed fresh-saltwater
in tidal portions of rivers and bays. For settings (e.g., beaches, watersides, water-based
surroundings, etc.) "marine" was defined as saltwater or saltwater surroundings such as oceans,
sounds, and mixed fresh-saltwater in tidal portions of rivers and bays. (Leeworthy and Wiley,
2000)
The results below are for the State of California. Activities in the JMPR study area would be a
subset of the state total.
In 2000, beach visitation was the most popular marine related activity in California. 12.6 million
people visited the beach for a total of over 150 million days. Viewing or photographing Scenery
was second in terms of total days with 4.2 million people and 108 million days. Swimming was
the activity with the third highest participation rate with 8.4 million people spending almost 95
million days swimming. Other popular activities were bird watching, viewing other wildlife,
surfing, visiting watersides besides beaches, and fishing.
Table 13. California Marine Recreation
20
By Place of
By Place of Activity
Resid ence
Activity N u mber of N umber of N umber of
Particip ation
Particip ants Days Participants
Rate (%)
(millions) (millions) (millions)
Beach Visitation 6.1 12.6 151.4 9.1
Visiting Watersid es Besid es Beaches 0.7 1.5 20.7 1.1
Sw imming 4.1 8.4 94.6 6.1
Snorkeling 0.3 0.7 3.8 1.3
Scu ba Diving 0.1 0.3 1.4 0.4
Su rfing 0.5 1.1 22.6 0.7
Wind su rfing 0.0 0.1 0.1
Fishing 1.3 2.7 20.3 2.5
Motorboating 0.8 1.5 11.6 1.5
Sailing 0.5 1.1 6.8 1.0
Personal Watercraft Use 0.3 0.7 2.9 0.7
Canoeing 0.1 0.2 0.2
Kayaking 0.2 0.4 0.5
Row ing 0.1 0.3 0.2
Water-skiing 0.1 0.3 3.3 0.2
Bird Watching 1.3 2.6 65.8 1.9
View ing Other Wild life 1.2 2.6 38.6 4.4
View ing or Photograp hing Scenery 2.0 4.2 107.9 2.9
H unting Waterfow l 0.1 0.1 0.1
Source: National Survey on Recreation and the Environment (NSRE) 2000.
Marine Recreational Fishing.
Marine Angler Expenditures in the Pacific Coast Region, 2000. Approximately 440 thousand
saltwater anglers fished 2.2 million days in the Northern California region in 2000. In addition to
the leisure benefits these anglers received from participating in saltwater fishing, their
expenditures generated monetary benefits in the form of sales, income, and employment
throughout the Pacific Coast. A variety of goods and services were purchased from sporting
goods stores, specialty stores, bait and tackle shops, guide services, marinas, grocery stores,
automobile service stations, and restaurants. The economic impacts of these purchases rippled
throughout the Pacific Coast’s economy and provided income and jobs in manufacturing,
transportation industries, and service sectors (NMFS, 2001)
The majority of saltwater anglers, 388 thousand, were residents. Most of the resident mode of
fishing was private/rental boats and shore. A much higher proportion of the 51 thousand non-
resident anglers fished from party/charter boats.
Average per person trip expenditures in 2000 were highest for charter/party boats for both
residents ($112) and non-residents ($328). Average party/charter fees for residents were $56 and
$52 for non-residents. Average per person annual expenditures was $1,588.
Saltwater anglers in Northern California spent a total of $761 million in 2000. Anglers on
party/charter boats spent $35 million; on private/rental boats spent $46 million; and on shore
spent $48 million. Of this, residents spent $741 million and non-residents spent $21 million.
21
Taken as a whole, the expenditure estimates provide an indication of the importance of marine
recreational fishing to the economies of the coastal counties in Northern California.
Figure 2. The Northern California Region, NMFS
Table 14. Estimated Number of Days Fished and Participants in Northern California by Mode
and Resident Status, 2000
Resident N on-Resident Total
Total D ays 2,074,628 92,377 2,167,005
198,267 39,429 237,696
Party/Charter Boat D ays
963,959 30,961 994,920
Private/Rental Boat D ays
912,402 21,987 934,389
Shore D ays
387,927 51,221 439,148
Total Participants
Average D ays per Participant 5.3 1.8 4.9
Table 15. Northern California Average Per Person Expenditures by Mode and Resident Status
22
Resident N on-Resident
Trip Expenditures
Party/Charter Boat 112.03 327.73
Private/Rental Boat 43.91 125.46
Shore 48.48 173.80
Annual Expenditures 1,587.84
Table 16. Northern California Total Expenditures by Mode and Resident Status ($000s)
Resident N on-Resident Total
Trip Expenditures
Party/Charter Boat 22,212 12,922 35,134
Private/Rental Boat 42,322 3,884 46,206
Shore 44,229 3,821 48,050
Annual Expenditures 631,993 631,993
Total Resident Expenditures 740,758 740,758
Total Expenditures 740,758 20,628 761,385
Source: National Marine Fisheries Service, Marine Angler Expenditures in the Pacific Coast
Region, 2000
23
Table 17. Northern California Average Per Person Expenditures by Mode and Resident Status
Party/Charter Private/Rental Shore
Non- Non- Non-
Residents Residents Residents
Residents Residents Residents
Trip Expenditures
Private Transportation 20.45 72.00 13.53 64.24 18.50 66.19
Food 16.49 22.86 8.96 23.38 13.00 29.27
Lodging 8.58 45.04 3.66 10.21 9.90 30.41
Public Transportation 1.83 114.98 0.13 2.97 0.77 36.92
Boat Fuel 9.71 11.94
Party/Charter Fees 56.11 51.62
Access/Boat Launching 0.84 1.24 1.22 3.02 0.96 0.15
Equipment Rental 5.13 18.76 0.67 1.37 1.45 4.62
Bait & Ice 2.60 1.22 6.03 8.33 3.89 6.24
Total Trip Expenditures 112.03 327.72 43.91 125.46 48.47 173.80
Annual Expenditures All
Rods & Reels 69.66
Other Tackle 49.26
Gear 14.49
Camping Equipment 7.89
Binoculars 1.76
Clothing 13.34
Magazines 2.09
Club Dues 2.08
License Fees 33.96
Boat Accessories 125.52
Boat Purchase 407.72
Boat Maintenance 105.44
Fishing Vehicle 582.53
Fishing Vehicle Maintenance 149.72
Vacation Home 16.53
Vacation Home Maintenance 5.86
Total Annual Expenditures 1,587.85
Source: National Marine Fisheries Service, Marine Angler Expenditures in the Pacific Coast
Region, 2000
24
Table 18. Northern California Total Expenditures by Mode and Resident Status ($000s)
Party/Charter Private/Rental Shore
Non- Non- Non-
Residents Residents Residents
Residents Residents Residents
Trip Expenditures
Private Transportation 4,055 2,839 13,044 1,989 16,879 1,455
Food 3,269 902 8,634 724 11,866 644
Lodging 1,701 1,776 3,525 316 9,033 669
Public Transportation 363 4,533 122 92 698 812
Boat Fuel 9,358 370
Party/Charter Fees 11,126 2,036
Access/Boat Launching 166 49 1,176 93 877 3
Equipment Rental 1,017 740 646 43 1,327 101
Bait & Ice 515 48 5,816 258 3,548 137
Total Trip Expenditures 22,212 12,923 42,321 3,885 44,228 3,821
Annual Expenditures All
Rods & Reels 27,023
Other Tackle 19,111
Gear 5,621
Camping Equipment 3,059
Binoculars 683
Clothing 5,174
Magazines 811
Club Dues 807
License Fees 13,172
Boat Accessories 50,137
Boat Purchase 162,855
Boat Maintenance 42,116
Fishing Vehicle 232,680
Fishing Vehicle Maintenance 59,801
Vacation Home 6,604
Vacation Home Maintenance 2,339
Total Annual Expenditures 631,993
Total Resident Expenditures 740,758
Total Expenditures 761,385
Source: National Marine Fisheries Service, Marine Angler Expenditures in the Pacific Coast
Region, 2000
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Pacific Socio-Economics Fishing Survey – Northern California, 1998. In 1998,NMFS completed
the Pacific Socio-economics Fishing Survey. This survey had a Northern California component.
The following are highlights from the survey results.
About 35% of the Northern California anglers surveyed own a boat used for recreational
saltwater fishing.
The anglers surveyed on a party/charter or rental boat spent on average $34 per day on boat fees,
bait, and fishing licenses. Anglers fishing from shore spent on average $9 per day on parking
fees, bait, and fishing licenses.
Anglers interviewed on multi-day trips spent an average of 5 nights away from home
and spent $171 on lodging expenses.
About 13% of anglers surveyed who were employed gave up some income by taking a
day of fishing. The average income “missed” was around $436 per trip.
The anglers surveyed who live in-state have been fishing an average of 20 years.
Figure 3. Recreational Fishing Socioeconomic Survey Results
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Recreational Activities Possibly Requiring Additional Data Collection
Pleasure Boating
Personal Watercraft Use
Kayaking
Whale and Other Wildlife Watching
Surfing
Beach Visitation
Scuba Diving
COMMERCIAL FISHING IN THE JMPR STUDY AREA
The California Department of Fish and Game (CDFG) collects information on the pounds and ex
vessel value of the commercial catch by species and by 10 by 10 mile block where caught. We
obtained that information for 348 CDFG blocks that run from Point Conception to the Oregon
Boarder. The JMPR Study Area and the three sanctuaries are a subset of these blocks. These are
historical data from 1988 to 2000. The data fields are:
Year
Month
Block Number
Port Landed
Species
Gear
Value
Pounds
The first step was to define each of the Sanctuaries involved in the JMPR in terms of these CDFG
blocks. That is, the CDFG blocks that “best” defines each Sanctuary. 10 by 10 minute resolution
is pretty rough and will most likely understate or overstate what is caught in each sanctuary.
With this in mind, we have historically (Channel Islands) used the centroid method to determine
whether or not a block should be included in the analysis. In other words, if the center of the
block lies within the Sanctuary, it would be included. However, this method is subject to
local/expert judgment. If a block’s center is located outside a Sanctuary boundary, but is
identified as vital to the analysis, it can be included.
We have defined preliminary study areas for each of the three Sanctuaries. It is important to
keep in mind that where two Sanctuaries share a common boundary, a block can be assigned to
only one of the Sanctuaries. In other words, we don’t want to double count a block in the
analysis. Also, blocks cannot be split. It’s either all or none of the block.
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Any primary data collection efforts for the study area will attempt to bring the spatial resolution
down to 1 by 1-mile blocks.
Preliminary analysis is presented for the sum of ex vessel value of all commercial fisheries species
for the period 1991 to 2000. The ArcView map presented below shows the spatial distribution of
the value. The block with the highest historical value is located directly west of Santa Cruz and
just outside MBNMS. The map also identifies several other “hotspots” in terms of value.
Figure 4.
Commercial Fisheries - All Species - CDFG
Sum of Ex Vessel Value - 1991 to 2000
Value of Catch
5698 - 94248
94248 - 192249
192249 - 341146
341146 - 657275
657275 - 1045697
1045697 - 1638091
1638091 - 2966310
2966310 - 4490971
4490971 - 8922178
8922178 - 26050441
N
W E
80 0 80 160 Miles
S
Analysis presented here is the first step. Additional analysis could include:
Cross Tabulation of Where Fish Caught and Where Fish Landed
For estimating economic impacts on the local economies, we can establish cross tabulations of
catch by study area and by port landed for each species group.
Monthly Data
So far, we have done nothing with the monthly data. It could be useful in looking at the
seasonality of the different fisheries. Production of graphs over the past few years for each
species group could be informative.
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Gear Type
Cross tabulations and maps of gear and species types could be run. This, combined with the
monthly patterns might define certain fishery fleets (squid/wetfish in the Channel Islands NMS
used purse seine gear and the fishermen that fished these species fished them during different
seasons of the year.
MBNMS has historically had the highest total value of commercial fishing in the study area. In
MBNMS in 2000, 33.5 million pounds of fish were caught with a total ex vessel value of $7.1
million dollars. GFNMS in 2000 had 0.5 million pounds of fish caught valued at $1.1 million. 440
thousand pounds of fish were caught in CBNMS in 2000 with an ex vessel value of $0.4 million.
Commercial fishing catch increased dramatically from the early 1990s through the mid 1990s.
Table 19. Commercial Fisheries, All Species, CDFG
Pounds and Ex Vessel Value, 1990 to 2000
JMPR Sanctuaries
Monterey Bay Gulf of the Farallones Cord ell Bank
Year
Pou nd s Value ($) Pound s Valu e ($) Pou nd s Value ($)
1990 7,771,627 475,445 182,376 184,574 65,206 98,122
1991 3,315,382 449,514 338,188 319,370 35,206 34,666
1992 6,621,627 806,724 1,571,305 1,355,780 368,737 211,516
1993 12,342,390 2,188,186 1,297,596 1,113,075 327,952 184,211
1994 25,795,188 6,494,288 2,353,857 2,163,109 597,838 548,659
1995 12,046,810 7,518,315 1,619,440 1,954,280 136,591 127,945
1996 21,748,731 7,141,664 1,677,245 2,355,415 129,019 145,111
1997 42,812,366 9,557,799 1,296,882 1,729,326 181,319 171,776
1998 19,612,520 5,870,207 891,705 1,581,974 417,874 377,206
1999 27,693,714 6,400,464 822,971 1,162,465 440,447 368,834
2000 33,513,661 7,128,238 533,710 1,130,798 138,634 255,133
For the three sanctuaries combined, 1997 was, economically, the most productive year for the
commercial fisheries. 44.3 million pounds of fish were caught with an ex vessel value of $11.4
million. The most recent year for which we have data, 2000, was also a highly productive year,
with 34.2 million pounds caught within the three sanctuaries and 70.3 million pounds caught in
the entire Point Sal to Point Arena study area.
Table 20. Commercial Fisheries, All Species, CDFG
Pounds and Ex Vessel Value, 1990 to 2000
Three Sanctuaries Combined and Entire Study Area
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Total JMPR Sanctuaries Point Sal to Point Arena
Year
Pou nd s Value ($) Pou nd s Value ($)
1990 8,019,209 758,141 9,798,425 1,707,832
1991 3,688,777 803,550 5,813,341 2,824,082
1992 8,561,668 2,374,020 12,158,685 5,071,224
1993 13,967,938 3,485,472 19,617,885 6,789,243
1994 28,746,883 9,206,056 42,231,653 16,895,747
1995 13,802,842 9,600,540 32,845,328 21,298,184
1996 23,554,995 9,642,191 37,584,762 17,381,430
1997 44,290,567 11,458,900 61,719,033 24,085,211
1998 20,922,099 7,829,388 32,147,973 14,897,034
1999 28,957,132 7,931,762 56,526,999 16,821,007
2000 34,186,005 8,514,169 70,274,840 19,186,580
In 2000, the highest ex-vessel value species group in the three-sanctuary area was salmon at over
$2.1 million and just under a million pounds. In 1990, only 31 thousand pounds of salmon was
caught with an ex-vessel value of $85 thousand. In 2000, the next 4 top-ranked species in terms of
ex-vessel value were squid ($1.7 million), rockfishes ($1.2 million), crab ($0.9 million), and flatfish
($0.8 million). In terms of overall significance to the commercial fishery, several of the species
groups have increased from 1990 to 2000, including salmon, rockfishes, anchovy and sardines,
roundfish, and tuna. The economic importance of mackerel has decreased from $93 thousand in
1990 to $25 thousand in 2000. Additionally, wild abalone, once a $45 thousand fishery and
ranked #5 in 1990, has been banned. In 1998, the California Department of Fish and Game
(CDFG) closed the whole commercial industry of wild abalone.
Table 21. Commercial Fisheries, All Species Groups, CDFG
Three JMPR Sanctuaries Combined
Ranked by Value
Pounds and Ex Vessel Value, 1990 and 2000
30
2000 1990
Sp ecies Grou p Pou nd s Valu e ($) Sp ecies Grou p Pou nd s Valu e ($)
Salm on 991,194 2,078,047 Squ id 3,766,616 259,735
Squ id 13,939,345 1,677,840 Crab 61,511 118,117
Rockfishes 647,124 1,181,384 Mackerel 3,568,344 93,247
Crab 369,445 901,990 Salm on 31,258 84,917
Flatfish 1,498,816 831,224 Abalone 9,659 44,944
Anchovy & Sard ines 15,984,661 713,081 Flatfish 111,429 40,268
Praw n 70,553 618,401 Rockfishes 82,879 35,075
Rou nd fish 128,367 159,997 Sw ord fish 5,223 20,243
Tu na 110,073 114,500 Anchovy & Sard ines 249,522 15,931
Scu lp in & Bass 24,667 46,369 Rou nd fish 25,150 14,244
Shrim p 67,964 44,534 Urchins 59,711 11,862
Sword fish 12,262 42,915 Other 36,997 11,374
Mackerel 159,097 25,537 Sharks 4,226 5,306
Sharks 31,437 20,715 Rays & Skates 5,698 1,540
Urchins 21,331 16,813 Su rf Perch 395 518
Rays & Skates 70,004 13,708 Sp iny Lobster 79 455
Other 20,131 12,868 Tu na 463 321
Grenad iers 30,299 5,554 Octop u s 49 47
Su rf Perch 2,369 2,800
Smelts & Gru nion 3,957 2,560
Sp iny Lobster 291 1,852
CA Sheep shead 260 761
H erring & Roe 1,843 461
Octop u s 349 158
Sea Cu cu mbers 138 90
Mu ssels, Snails, Clam s, Oysters 28 14
31