Estimation of Theoretical
Relationships
WP 5
Workshop
WP5: Estimation of the theoretical relationships
Objective: The estimation of the theoretical enforcement
model for use in the computer model.
 It has started at the beginning of 2008.
 Partners: All but CEFAS and JRC.
 Deliverable by month 24 (beginning of 2009).
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WP5: Estimation of the theoretical relationships
Role in the project:
WP2+WP4
WP5
Data
Estimation
WP4 will give WP5
the limits of the
availability of data,
by CS and overall.
or
Model
Depending on how
we proceed:
If a existing model is
used WP6 gives the
limits to WP5
If a new model is
designed, WP5 is
giving the limits to
WP6.
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WP6
WP5: Estimation of the theoretical relationships
Role as itself
D5 month 24
Report including:
How?
Estimation of the enforcement
relationships:
Estimating a benefit function
• For each case study.
• Comparison of (if) different
characteristics.
Estimating a enforcement cost
function
A probability of penalty function
Using:
Models: WP1
Software: WP6
Data: WP2+WP4
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WP5: Estimation of the theoretical relationships
Role as itself
A fisheries benefit function.
This is basically a standard bioeconomic model involving a fisheries profit function and a fish stock growth
function (this can be a biomass model or an age-structured relationship) and the link between the two. Note
that starting from scratch, it is generally a substantial amount of work to construct this kind of a bio-economic
model. However, in the case studies selected either this kind of a model already exists (and only needs to be
updated) or considerable amount of groundwork has already been conducted.
A fisheries enforcement cost function.
This function simply relates enforcement effort (along its various dimensions) to costs of enforcement. To
estimate this function properly requires data on enforcement costs and enforcement effort. This should be
provided in WP-4.
A probability of penalty function
This function relates enforcement effort to the probability that a violation will entail a sanction. To estimate
this function properly requires data on enforcement effort and the above probability of sanctions if a violation
occurs. These latter data, while of course fundamental, are difficult to obtain. A major task of this project (WP-3
and WP-4) is to discover ways to obtain measures of this kind. In the absence of good numerical measures
approximations to this function, based on qualitative and technical data will have to be employed.
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WP5: Estimation of the theoretical relationships
Workshop
Objective of the workshop:
To have a discussion (think-tank) of what are our needs from the
coordination point of view of this WP.
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WP5: Estimation of the theoretical relationships
A fisheries benefit function.
Case study
Lead partner
Enforcement issue
Northern hake
AZTI
minimum mesh sizes
The Bay of Saint-Brieuc Scallops
CEDEM
input restrictions
CCAMLR South Georgia/
Kerguelen
JRC
IUU
Ligurian and Northern Tyrrhenian
Sea bottom trawling fishery
IREPA
net size, seasonal and area closures, mixed fishery
Norwegian fisheries
NHH
technical restrictions, TAC-restrictions, individual quotas
Icelandic cod fishery
IoES
time/area closures, minimum fish size restrictions, no discarding rules,
effort restrictions and individual transferable quota
Dutch beam trawl
LEI
quotas, input restrictions, technical measures
Kattegat & Skagerrak nephrops
fishery
FOI
undersized lobsters and illegal bycatch
(Western) Channel Fisheries
CEMARE
gear and access restrictions
Do we have a bioeconomic model?
Control variables?
λ estimation-approximation.
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WP5: Estimation of the theoretical relationships
Data needed
A fisheries enforcement cost function.
Corresponding cost of enforcement as disaggregated by enforcement categories
as possible.
From the data of N.H CS, we have found that there is not a clear strategy for
enforcement in terms of:
-
Budget for control activities is always the same proportion of the overall
amount.
-
From the data that we have, linear relationship between effort and cost is
found.
We don’t have data for doing so, how can be deal with it?
Do we have a simple function in mind?; assumptions?...
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WP5: Estimation of the theoretical relationships
Penalty function
A probability of penalty function
This function relates enforcement effort to the probability that a violation will
entail a sanction. To estimate this function properly requires data on
enforcement effort and the above probability of sanctions if a violation
occurs. These latter data, while of course fundamental, are difficult to obtain.
A major task of this project (WP-3 and WP-4) is to discover ways to obtain
measures of this kind. In the absence of good numerical measures
approximations to this function, based on qualitative and technical data will
have to be employed.
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WP5: Estimation of the theoretical relationships
What is needed?
• I’d like to know what type of assistance you need.
• Software you will use, questions you will answer.
• Some coordination will be needed between WP5 and WP6.
• In terms of assistance we can implement all you want in R. Even if
estimation are not restricted to this software.
• Estimation of which enforcement methods?, single?, aggregated?
(Take into account that we will have short time series for
estimation).
• Any standard estimation procedure?
We (AZTI) are working on the specific characteristics of
the penalty function, based on land based enforcement.
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Penalty Functions
WP 5
Workshop
WP5: Estimation of the theoretical relationships
Penalty function
World of fishermen
Probability of being sanctioned
It should be the ratio between sanctions and inspections
conditioned to violate the rules…
But..
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WP5: Estimation of the theoretical relationships
Penalty function
World of inspectors
Selectivity:
the perceived increased risk of inspection and
detection of a contravention resulting from selecting
the businesses, persons, actions or areas to be
inspected
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WP5: Estimation of the theoretical relationships
Penalty function
Starting point The 'Table of Eleven'
The 'Table of Eleven' is a behaviour-analysis model allowing
legislators, policy makers and enforcers to get a picture of the
motives for compliance or non-compliance of a specific rule in a
specific target group.
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WP5: Estimation of the theoretical relationships
Table of Eleven
Aspects of spontaneous compliance:
1. knowledge of the regulation
2. cost / benefit ratio
3. degree of acceptance of the regulation
4. loyalty and obedience of the regulatee
5. informal monitoring
Aspects of monitoring:
6. informal report probability
7. monitoring probability
8. detection probability
9. selectivity of the inspector
Aspects of sanctions:
10. chance of sanctions
11. severity of sanctions
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Real approach

V
NV ∩ NPO
PO
V ∩ NPO
V ∩ PO
NV ∩ PO
C
 (e) ??
FisheryActions
Illegal
Potential
Control
Actions
actions
offenders
under control
Simplification

V
NV ∩ NPO
PO
V ∩ NPO
V ∩ PO
NV ∩ PO
C
 (e) ??
FisheryActions
Illegal
Potential
Control
Actions
actions
offenders
under control
WP5: Estimation of the theoretical relationships
Penalty function (simplified approach)
π(e)  p( S | V )  p(C | V)  p(S | V  C)
S: Sanction
V: Violate
C: Control
π(e)  p( S | V )  p(C | V)
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WP5: Estimation of the theoretical relationships
Penalty function (simplified approach)
p (C  V )
π( e) 
 ... 
p(V )

PV    e / N

π( e) 


1   (  1) 
  PV

Intensity of controlling PO
 Proportion of PO
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WP5: Estimation of the theoretical relationships
Penalty function (simplified approach)
 (e)
 0;
e
 (e)
 0;
n
 
1
e
 ( e)   ;
 n
 0
 (e)  0;
 1
e
 ( e)  ;
n
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WP5: Estimation of the theoretical relationships
Penalty function (simplified approach)
 (e)
 0;
e
 (e)
 0;
n
  PV
 0

e
 ( e) 
 ;
1   (  1) n
e
 ( e)  ;
n
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WP5: Estimation of the theoretical relationships
Penalty function (Real approach)
p (C  V )
π( e) 
 ... 
p (V )

PV    e / N

π( e) 


1   (  1) 
  PV

Intensity of controlling PO
 Proportion of PO
 Proportion of PO that are V
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Real approach
WP5: Estimation of the theoretical relationships
Penalty function (Real approach)
gamma = 1
alpha
alpha
1.0
0.8
0.8
0.6
0.6
piE
piE
0.8
0.4
0.4
0.2
0.2
0.0
5000
4000
3000
2000
e
1000
0.0
5000
4000
3000
2000
e
1000
100
80
60
40
beta
20
0 0
100
80
60
40
0.6
beta
20
0 0
alpha
alpha
0.4
0.8
0.8
0.6
0.6
piE
piE
0.4
0.4
0.2
0.2
0.0
5000
4000
3000
2000
e
1000
100
80
60
40
20
0 0
beta
0.2
0.0
5000
4000
3000
2000
e
1000
100
80
60
40
20
0 0
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beta
0.0
Real approach
WP5: Estimation of the theoretical relationships
Penalty function (Real approach)
gamma = 0.8
alpha
alpha
0.8
0.7
0.6
0.6
piE 0.4
piE 0.4
0.2
0.6
0.2
0.0
5000
4000
3000
2000
e
1000
100
80
60
40
beta
20
0.0
5000
4000
3000
2000
e
1000
0 0
100
80
60
0.5
40
beta
20
0 0
alpha
0.4
alpha
0.3
0.6
0.6
0.2
piE 0.4
piE 0.4
0.2
0.2
0.0
5000
4000
3000
2000
e
1000
100
80
60
40
20
beta
0.0
5000
4000
3000
2000
e
1000
0 0
0.1
100
80
60
40
20
0 0
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beta
0.0
WP5: Estimation of the theoretical relationships
Time for discussing these issues
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