Fisheries management
and fisher discount rates
Louisa Coglan and Joel Lim
School of Economics and Finance, QUT
Sean Pascoe
CSIRO Marine and Atmospheric Research
CRICOS No. 00213J
Queensland University of Technology
Outline
• Aims of the study
• Conceptual model and functional forms
• Data
– Apparent relationships
• Model results
• Conclusions
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CRICOS No. 00213J
Purpose of this study
• to examine the relationship between fisher
discount rates and varying types of management
in Australian fisheries
• test the assumption that rights based
management instruments reduce uncertainty
around future catches
– lower discount rate
• Asche 2001; Alcock 2006
– increasing expectation of profits
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CRICOS No. 00213J
Conceptual model
ExprofitFEt  discountrate * K t
discountrate
ExprofitFEt
Lt  O 
 Kt
discountrate
ExprofitFEt
Lt  K t  O 
discountrate
Lt  O 
Licence value (L)
•
•
option value (O)
NPV of future expected full
equity economic profits
•
i.e. less op cost of capital (K)
discountrate  fn(it , it  n , managementt , managementt  n )
ExprofitFEt  fn( profitFEt , profitFEt  n , Boatst , Boatst  n ,
managementt , managementt  n )
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CRICOS No. 00213J
Functional form
• Assume non-linear function
– error correction form
– assumes management affects both expectations
about future profits and discount rate
– considers both long and short term dynamics
1
2
3
profitFE Boats ITE ITQ
( Lt  Kt )  (O ?) 
i 5  6 ITE  7 ITQ
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4
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CRICOS No. 00213J
Error correction model
 ln( Lt  Kt )  1 ln( profitFE )   2 Boats  3management
  4 i  5 imanagement
  L1 ln( Lt 1  Kt 1 )   L 2 ln( profitFEt 1 )   L 3 Boatst 1   L 4 managementt 1
  L 5it 1   L 6it 1management
• Red bit is the dynamic (short run) component
• Blue bit is the long run component
– Long run model:
 L3
L2
L4
ln( profitFEt 1 ) 
Boatst 1 
managementt 1
  L1
  L1
  L1
 L5
L6

it 1 
it 1management
  L1
  L1
ln( Lt 1  K t 1 ) 
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CRICOS No. 00213J
Other Functional forms considered
• Linear model
( Lt  Kt )   1   2 ITE  3 ITQ 
Option value
 profitFE 
(  4  5 ITE   6 ITQ) 

i

• Log-linear model
ln( Lt  K t )   1   2 ITE  3 ITQ 
Option value
 profitFE 
(  4  5 ITE   6 ITQ) ln 

i

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CRICOS No. 00213J
Data
• Economic survey data
– Fishery level (averages) – avoids issues relating to
heterogeneity between vessels
– Commonwealth fisheries data from ABARES annual reports
– SA fisheries data from Econsearch annual reports
– 1991/92 to 2010/11
• Not all fisheries in earlier years
• 233 observations
• 15 fisheries
–
–
–
–
–
5 Input control
2 ITE only
7 ITQ only
1 transition from input to ITE
1 transition from input to ITE to ITQ
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CRICOS No. 00213J
Data
Fishery
Commonwealth trawl sector
Eastern tuna and billfish fishery
Gillnet (shark boats)
Hook and trap (non trawl)
Northern Prawn Fishery
Torres Strait Prawn Fisheries
SA Abalone fishery
SA Blue Crab fishery
SA Gulf St Vincent Prawn Fishery
SA Lakes and Coorong Fishery
SA Northern Zone Rock lobster
SA Sardine fishery
SA Scalefish fishery
SA Southern Zone Rock lobster
SA Spencer Gulf and West Coast Prawn Fishery
Total
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Input controls
ITE
16
3
4
ITQ
20
15
15
17
17
15
15
15
10
1
5
9
11
15
15
15
75
43
115
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Apparent relationship
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Results
• Non-linear model
– dummy variables for each fishery (mostly significant not
displayed)
t
SE
coeff
0.046
0.246
0.325
0.072
0.053
0.542
0.117
1.722
1.527
0.652
0.594
0.096
-0.362
-0.065
-0.538
0.105
-0.331
-0.241
0.894
1.014
0.070
0.157
lnprofitD
lnInterestD
lnBoatNoD
lnTotalL
lnprofitL
lnInterestL
lnBoatNoL
ITE
ITQ
ITEI
ITQI
Adjusted R-squared:
LR coefficients
0.196
-0.617
-0.448
1.663
1.887
0.130
0.292
0.2307
F-statistic: 2.938 on 26 and 142 DF,
AIC
sig
Prob
0.040 *
2.072
0.144
-1.468
0.842
-0.199
0.000 ***
-7.479
0.050 *
1.981
0.542
-0.612
0.042 *
-2.054
0.604
0.519
0.508
0.664
0.915
0.107
0.792
0.264
p-value: 2.47e-05
138.1524
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CRICOS No. 00213J
Returns by management type
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Conclusions
• Higher profits lead to higher licence values
• Boat numbers also linked to higher licence
values
– expectations of higher profits
• ITQs and ITEs appear to be associated with
higher rates of return
– not fully capitalised into the licence value
– taken as higher “incomes”
• Unlikely that ITQs and ITEs affect discount rate
– but higher profits under ITQs lead to higher licence
values
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CRICOS No. 00213J