CHESAPEAKE BAY
COMMERCIAL FISHING
BENEFITS ANALYSIS
Steve Newbold
U.S. Environmental Protection Agency
National Center for Environmental Economics
October 2011
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OUTLINE
1. Key economic concepts for fishery benefits
estimation
– Consumer and producer surplus, common pool
resources, rent dissipation
2. Major Chesapeake Bay fisheries
3. Proposed modeling approach
– Bioeconomic framework: EwE/Atlantis + harvester
response functions for each stock
– Deacon et al. (2011), illustrative example
4. Data needs and modeling challenges
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KEY CONCEPTS
• Producers of commercially harvested fish will benefit
from the TMDL to the degree that the fish stocks
they target become more abundant and therefore
easier to catch.
• Consumers of commercially harvested fish will
benefit from the TMDL to the degree that the lower
harvesting costs are passed on in the form of lower
prices of fish at the market.
• To estimate benefits in the commercial fishing sector,
we need data on prices and quantities of harvested
fish with and without the TMDL.
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KEY CONCEPTS
• Economic benefits = WTP = consumer +
producer surplus change
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KEY CONCEPTS
• Fish stocks are common pool resources
• “Tragedy of the commons”: with no restrictions on
harvesting the stock will be over-exploited and all
rents dissipated (Gordon 1954, Scott 1955)
• Large literature on fishery economics that examine
alternative management approaches (Wilen 1999)
• Fisheries managed by catch shares less likely to
collapse (Costello et al. 2008)
• Slope of supply curve, and therefore benefits of
water quality improvements, will depend on the
nature of the management regime (Freeman 1991)
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MAJOR FISHERIES IN CHES. BAY
CHESAPEAKE BAY
Avg. landings and revenues 200-2009
"Fisheries Ecosystem
Planning for Chesapeake
Bay," 2006
Relative
Relative
abundance exploitation
MT
$/MT
Revenue
Cum. %
% of Atl.
rev.
Blue crab
24128
2085
50542512
49
54
low
over
Atlantic menhaden
178591
139
24824149
74
85
medium
full
Striped bass
1965
3849
7563285
81
56
high
limited
Atlantic croaker
5425
875
4746875
86
55
high
medium
Eastern oyster
367
11665
4281055
90
22
low
over
Summer flounder
1324
3077
4073948
94
17
medium
over
Blue crab (peelers)
650.7
4887.55
3387422
97
57
low
over
Black sea bass
299
5737
1715363
99
25
low
over
Weakfish
290
1808
524320
99
26
high
low
Horseshoe crab
238
967
230146
99
27
not est.
unknown
Bluefish
293
639
187227
100
7
low
over
Spanish mackerel
51
2029
103479
100
4
moderate
full
Blue crab (soft)
12
7980
100983
100
3
low
over
Black drum
32
2368
75776
100
56
not est.
unknown
American shad
39
1467
57213
100
7
low
low
Spotted sea trout
13
3155
41015
100
9
not est.
unknown
Tautog
7.1
3182
22592
100
4
low
over
Red drum
2.5
2747
6868
100
3
not est.
over
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PREVIOUS STUDIES - GENERAL
• Clark (1990)—“Bible” of bioeconomic modeling
• Homans and Wilen (1997)—estimated a model of a
regulated open access fishery
• Lipton and Hicks (2003)—DO and striped bass
recreational fishery in the Chesapeake Bay
• Massey et al. (2006)—water quality and summer
flounder recreational fishery in MD coastal bays
• Finnoff and Tschirhart (2008)—general equilibrium
bioeconomic model of an 8-species ecosystem
• Smith and Crowder (2011)—transitional rents from N
reductions in open access blue crab fishery
• Deacon et al. (2011)—calibrated model of capacity
constrained fishery
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PREVIOUS STUDIES – CHES. BAY
• Kahn and Kemp (1985)—fishery support by SAV in
Chesapeake Bay
• Anderson (1989)—seagrass and blue crabs in VA
• Lipton and Hicks – DO and recreational catch of
striped bass in the Chesapeake Bay
• Mistiaen et al. (2003)—low DO and blue crabs in
tributaries of Chesapeake Bay
• Sanchirico et al. (2006)—ecosystem management of
Chesapeake Bay fisheries
• Kar and Chakraborty (2009)—bioeconomic model of
Chesapeake Bay oyster fishery
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OUR TASK
• We want to develop a general but relatively simple
framework that can be applied to multiple species
parameterized using readily available fishery
statistics and results from previous studies
• Must represent management constraints and the
incentive structure these constraints provide to the
harvesters
• Integrate harvester and manager response functions
with biological production functions to form a multispecies bioeconomic model
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PROPOSED APPROACH
Estimate (as in Homans and Wilen 1997) or calibrate (as
in Deacon et al. (2011) a fishing effort production
function for each stock for integration with EwE and/or
Atlantis:
• Fishing mortality rate:
1 b
F = q éêaL + (1 - a )K ù
ú
ë
û
b
b
F
é1 - e - (F + M )T ù
ú
û
F + M êë
• Harvest:
H= A
• Profits (rents):
P = pH - wLT - rK
• Biological dynamics:
At + 1 = g ( A t - H t , Q )
( EwE / Atlantis )
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PROPOSED APPROACH
Can represent a range of management regimes:
• Open access: K increases until profits = 0
• Regulated open access (e.g., TAC): regulator closes
season ( limits T ) to ensure H £ Hˆ , but with no limit
on entry K still increases until profits = 0.
• Capacity constrained: K restricted to ensure
sH £ Hˆ . Other inputs may expand but unless they are
perfect substitutes for K then profits > 0
• Optimal management: K and L chosen to maximize
profits
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DEMAND MODEL
• Multistage Demand Model
– Allocates household income to expenditure categories
– Captures substitution between different commodities and
harvests from different estuaries
• Changes in consumer welfare
– Demand is a function of
•
•
•
•
Income (total expenditures)
Prices of Chesapeake harvest
Prices for harvest from other regions
Price indices for other commodities
– Expenditure function is used with projections of income and
prices to estimate changes in consumer surplus
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ILLUSTRATIVE EXAMPLE
Consider the mythological fish “Atlantic henmaden”
(similar to Atlantic menhaden, but not quite the same)
• 2 life stages, B-H stock-recruitment function
• Take α and M from pervious fisheries biology studies
• Set H, F, p to match recent levels for Atl. menhaden
• Calibrate β assuming steady-state conditions
• Assume w =$40K /yr, K = 20, L = 1000, assume open
access to calibrate r, guess b = -2, and calibrate a
assuming cost minimization, price elasticity ¶¶ Hp Hp =
-0.3
• Suppose TMDL will increase α and β by 10%,
decrease M by 5% (increase eq. A by 27%)
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ILLUSTRATIVE EXAMPLE
Benefits depend on the management regime:
Open
Access
Optimal
management
Regulated open
access
Capacity
constrained
Base
TMDL
Base
TMDL
Base
TMDL
Base
TMDL
A [109 fish]
2.78
2.99
4.21
4.94
4.32
5.06
3.80
4.29
K [vessels]
28
36
11
13
24
35
14
18
L [laborers]
1196
1466
536
654
1568
2373
783
1017
F [yr-1]
0.74
0.94
0.30
0.36
0.70
1.02
0.39
0.51
1
1
1
1
0.39
0.33
1
1
H [10- fish]
11.9
15.1
8.7
12.2
9.4
13.5
10.0
14.0
P [107 $/yr]
0
0
7.9
12.8
0
0
7.3
11.7
T [yr/yr]
PS [107 $/yr]
0
4.94
0
4.33
CS [107 $/yr]
2.4
2.7
3.2
3.1
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≈ $25-75 million / yr
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THE MOST IMPORTANT SLIDE
Data needs &
modeling challenges
1. Data on H and p from NOAA’s comm. fishery stats
2. K and L (& w?) from vessel observer programs ( ? )
3. w from BLS (by state, possibly county, probably not
by stock)
4. How to obtain data on r? (calibrate if open access)
5. How to estimate, calibrate, or transfer b?
6. How to characterize existing and future
management regimes in each fishery?
7. How to handle spillover effects due to fish
migrations? (Massey et al. 2006)
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