Experiments in the Lobbying Activity of Fishers with

Heterogeneous Preferences

Matthew FreemanLouisiana State University/LA Sea Grant

Chris AndersonUniversity of Rhode Island

Why lobbying activity?

• In ’09 NMFS report, 16% of 251 stocks with a “known” overfishing status are subject to overfishing, and 23% of 199 stocks with a “known” overfished status are overfished.

• To further improve the status of fish stocks, existing regulations need to be evaluated and improved.

• However, fishery management can be impacted by regulatory capture.

Research Question

• Motivating Question– Does lobbying activity influence plans of fishery

(and other CPR) regulation, and if so, to what extent?

• Use experimental economics & game theory• Test market mechanisms in controlled

environment• Guide creation of policy & expose flaws in

management design

Experimental Design

• The experiment is modeled upon a common pool fishery harvested by 10 firms, which are heterogeneous in externality cost.

• 8 subjects comprise group A, which faces a higher externality cost (1.45), than the 2 subjects comprising group B, which faces a lower externality cost (1.4).

Experimental Parameters

2( )i i iB x ax bx

(3 1.45( ))

(3 1.4( ))iA iA iA

iB iB iB

x x X

x x X

( , ) ( )i i iC x X X x

2200 0.035i ix x

Treatment Descriptions

• 1st treatment (15 rounds):– each subject chooses a level of resource

extraction.– his profit is a function of both his individual choice

& the summed choices of the other subjects’.

Treatment Descriptions

• 2nd treatment (6 rounds, 6 distinct caps): – proposed cap (limits level of resource extraction). – each subject may lobby to increase or to decrease the

proposed cap, or take no action. Lobbying expenditures deducted from profit at end of round.

– final cap based on the proposed cap & the summation of lobbying to increase & decrease the cap.

– each subject may then choose a level of extraction (bound by final cap).

Question 1

• Do individuals allocate effort in the unregulated CPR consistent with the Nash Equilibrium predictions?– Censored Tobit model• Dependent variable: effort levels• 3 independent variables: round, dummy variable for

low-cost group (Group B), interactive term for effect of round from low-cost group

– Group A converges quickly on NE; Group B doesn’t converge quickly but still shows upward trend

Unregulated Effort Levels

Censored Tobit ModelBase treatment Group A individuals N=1800

Alternate treatment Group B individuals Standard Error

Constant 10.74* 0.29

Dummy (for alternate

treatment)

1.28 0.64

Round 0.07 0.03

Interactive term on Round

(for alternate treatment)

0.06 0.07

* Denotes p values of <0.0001.

Indicates Group B has higher initial effort level

Indicates Group B retains higher effort level

Question 2

• Do individuals lobby to change a proposed regulation on effort in a manner consistent with their group type and the Nash Equilibrium predictions of non-cooperative behavior?– Use t-test to see if observed levels are equal to

non-cooperative & cooperative model predictions– Group B performs more consistently with N-C

model than does Group A. Free-riding is prevalent within Group A.

Lobbying Expenditures

Bounds for Profitable Cooperative & Non-cooperative Lobbying

Cooperative Cooperative Non-cooperative Non-cooperative

A B A B

Upper bound 7.46 9.89 12.21 12.72

Lower bound 6.09 4.17 1.27 1.32

Social optimum 6.78 7.02 6.78 7.02

t-test of Observed Lobbying ExpendituresInitial Cap Group A

Cooperative

DF = 95

Group A Non-

cooperative

DF = 95

Group B

DF = 23

2 Reject null

(-5.02)

Reject null

(5.15)

Cannot reject null

(1.98)

7 Cannot reject null

(1.75)

Cannot reject null

(1.75)

Cannot reject null

(0.00)

9 Reject null

(-3.36)

Reject null

(2.97)

Cannot reject null

(2.00)

11 Reject null

(-4.98)

Reject null

(3.05)

Reject null

(2.45)

13 Reject null

(-8.66)

Reject null

(2.45)

Cannot reject null

(2.00)

16 Reject null

(-7.63)

Cannot reject null

(-0.84)

Reject null

(3.81)

* t-values are in parentheses. Tests are at the 95% confidence interval.

Deviation

Total from both groups > Group A’s coop model prediction

Total from both groups falls between Group A’s coop & N-C model predictions

Lobbying Expenditures

Question 3

• Do individuals allocate effort in a regulated CPR, post-lobbying, consistent with the Nash Equilibrium predictions?– Use t-tests of the observed effort levels, with the

Nash equilibrium predictions as a benchmark– At final caps 2-5, can’t reject that observed effort

levels are equal to predicted levels– At finals cap > 5, some deviation occurs

Regulated Effort Levels

Due to lobbying efforts toward the preferred regulation, fewer final caps occurred at levels higher than thirteen.

Comments on Regulated Levels

• While perhaps not meaningful per individual, small deviations could be economically meaningful when considering the cumulative effort for a large industry.

• Future research -hold several rounds after a final regulation is imposed to see if an upward trend in effort level occurs (as w/unregulated treatment).

Concluding Remarks• Homogeneity within groups denotes similar

interests & might encourage cooperative actions (Poteete and Ostrom, 2004).

• Our experiments show that, due to the relation between group size & cost of lobbying, one group may have no incentive to lobby, if the optimal interests are close to one another. – This concept adds another dimension to the role of

group size, beyond the effect it has on determining the impact of one individual’s lobbying contributions (Poteete and Ostrom, 2004; Olson, 1965).

Concluding Remarks

• These experimental results support an idea of strong collective action against regulation that is below the optimal level and of weaker collective action against regulation that is above the optimal level.

• Despite being unable to reach the predicted levels of cooperative lobbying, the individuals in our experiments did show some restraint in resource appropriation after the final cap was imposed.

Questions?

We gratefully acknowledge the support for this research provided by a NOAA/Sea Grant Joint Fellowship in Marine

Resource Economics and by RI Sea Grant.