Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Lessons from contract and auction
theory
Uwe Latacz-Lohmann
Department of Agricultural Economics,
University of Kiel
and
School of Agricultural and Resource Economics,
The University of Western Australia
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Incentive theory
Principal-agent theory
Mechanims design theory
design of proper institutions for successful economic exchange between a principal and an agent
Contract theory
Auction theory
Delegation of a task
Procurement of a good
Maximize social welfare
Maximize value-for-money
Asymmetric information
Conflict of interest between principal and agent
Hierarchical relationship
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Payments for Environmental Services
implemented through
Conservation contracts
Conservation auctions
Management prescriptions
Compensation payment
Contract allocation mechanism
Alternative contract allocation mechanisms:
First-come, first-serve (the common model in EU AEP)
Individual contract negotiation (SSSI management agreements UK)
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Outline
1.
2.
3.
4.
5.
6.
The nature of agri-environmental contracting
Contract design to address adverse selection
Contract design to address moral hazard
Conservation auction theory - bidding models
Auction performance estimates
Conclusions
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
The nature of agri-environmental
contracting
1.
Uncertainty about the traded good


2.
3.
4.
5.
Measurement problems
Contracting on effort rather than output
Noise in relationship between effort and env. output
Non-separabilities in benefit functions
Uncertainty about the value of the traded good
Information asymmetry
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Information asymmetry
Agri-environmental contracting as a game with asymmetric information
time
N chooses
the type of L
(’bad’ or ‘good’)
A designs
the contract
L accepts
(or rejects)
and receives
payment
L supplies
effort
(‘high’ or ‘low’)
N chooses
the state of
nature
Only observed
by L
(‘hidden information’)
not perfectly
observed by A
(‘hidden action’)
resulting in
‘adverse selection’
resulting in
‘moral hazard’
observed by
A and L ex post,
but not verifiable
by a third party
(‘non-verifiability’)
implying
non-contractability
of environ. output
L = landholder; A = environmental agency;
N = nature
Environmental
output
(public good)
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Contract theory

Information asymmetry has fundamental implications
for the design of (conservation) contracts

Contracts must elicit information from agent(s)
… at the expense of some information rent

Contract theory concerned with design of second-best
contracts under asymmetric information
Hidden information: trade-off between efficiency and rent
extraction
Hidden action: trade-off btw. efficiency and insurance
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
The first-best, full-info contract
t
� *= V(q) - t
𝑊
�*= t- θ�q = 0
𝑈
A*
*
t
𝑡̅
*
W* = V(q) - t
First-best production level: equating
principal‘s marginal value and agent‘s
marginal cost.
First-best payment level = agent‘s
individual cost
B*
𝑞�*
U*= t- θq = 0
q*
Source: Adapted from Laffont and Martimort (2002)
q
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Contract design to address adverse
selection: self-selection contracts

Challenge: devise a menu of contacts{(t, q); (𝑡̅, 𝑞� )}
such that (t, q) is preferred to (𝑡̅, 𝑞� ) by the low-cost
agent and (𝑡̅, 𝑞� ) is preferred to (t, q) by the high-cost
agent.
� ) - 𝒕𝒕̅) (1)
Max p(V(q) – t) + (1 – p)(V(𝒒𝒒
� )}
{ (t, q); (𝒕𝒕̅, 𝒒𝒒
subject to
t – θq ≥ 0
Participation constrain low-cost agent
𝒕𝒕̅ – �
𝛉𝛉�
𝒒𝒒 ≥ 0
Participation constrain high-cost agent
� θ Self-selection constraint low-cost agent
t – θq ≥ 𝒕𝒕̅ – 𝒒𝒒
� Self-selection constraint low-cost agent
� ≥ t – q𝛉𝛉
𝒕𝒕̅ – �
𝛉𝛉𝒒𝒒
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Solution to the self-selection problem
� – θ) 𝑞𝑞�SB,q*); (θ
� 𝑞𝑞�SB, 𝑞𝑞�SB< 𝑞𝑞�*)}
̅ , 𝑞𝑞�SB)} = {(θq* + (θ
{(tSB,qSB); (𝑡𝑡SB
In English:
Second-best contract menu under hidden information:


for the low-cost agent: the first-best, full-information
production level and overcompensation of costs;
for the high-cost agent: reduced output level with a
payment equal to costs
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Self-selection contracts
Wu and Babcock (1996)
•
Model setup:
 Maximise net welfare gain from the contract subject to
self-selection constraint and participation constraint
 Contract variables = mgt. prescriptions and payment rate
 Continuum of agent types
•
Key findings:
 Offer a uniform payment to all farmers equal to the
highest compliance costs
 Less restrictive management prescriptions than under
perfect information
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Self-selection contracts
Moxey, White, Ozanne (1999)

A model of two farmer ‘types’ (high and low productivity)

Model setup:
 Maximise ‘net social welfare of a contract’ subject to two
self-selection constraints and two participation constraints
(one for each farmer type)

Key findings:
 Overcompensate the low-productivity farmer
 Demand less input reduction from the high-productivity
farmer than under perfect information
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Contract design to address moral hazard:
overview
 Asymmetric information about agents' effort level
 Standard contract theory:
 Observe output as a proxy of effort and link
payments to output (high/low).
 Trade-off between incentive provision and insurance
 Conservation contracting:
 Output not observable/verifiable/contractable
 Principal must monitor effort directly and penalize
low effort
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Contract design to address moral hazard:
models
 Principal’s objective: cost minimisation or social welfare
maximisation
 Contract variables: Monitoring rate, level of fine,
payment level, management prescriptions (effort level)
 Risk-neutral and risk-averse agents
 Honest or dishonest farmers
 Single agent or multiple agents
 One, two or continuum of farmer types (costs)
 Static (one-shot) or dynamic enforcement situation
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Contract design to address moral hazard:
some insights and policy conclusions
 Set fine as high as possible
 Overcompensate agents to encourage compliance
 Harness risk aversion of agents: mean-penality
preserving shift in compliance instruments
 Target compliance monitoring on high-cost agents
 Target compliance monitoring on those who have
cheated in the past
 Reduce stringency of management prescriptions
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Auction theory:
why auctions?
 Alternative mechanism to address adverse selection
 Rely on competition rather than self-selection
 Price discovery: harness information held privately by
bidders in determining prices for public goods
 Cost revelation: agents reveal their costs with the bids
 Cost-effectiveness: Auctions reduce information rents
and enhance cost-effectiveness
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Characteristics of conservation auctions
 Sealed-bid, multiple-unit procurement auctions
 Trading a heterogeneous good
 Repeated auctions: multiple bidding rounds
 Budget-constrained versus target-constrained auctions
 Discriminatory-price versus uniform-price auctions
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Auction theory for budget-constrained auctions
(Latacz-Lohmann and Van der Hamsvoort, 1997)
 bidding strategies predicated on the belief of a
maximum acceptable bid, or bid cap, β.
 β = implicit reserve price
 Bidders form expectations about β
 Probability that a bid b is accepted
 Optimal bid: Max
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Auction theory for budget-constrained auctions
(Latacz-Lohmann and Van der Hamsvoort, 1997)
 Optimal bid formula for risk-neutral agent
 Overbidding  info rent
 Imperfect cost revelation
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Auction theory for target-constrained auctions
(Hailu, Schilizzi and Thoyer, 2005)

model of the Nash equilibrium risk-neutral bid function in a
multi-unit procurement auction (Harris and Raviv, 1981)

Optimal bid formula for risk-neutral agent
v = bidder’s private value (cost) uniformly distributed on [0, 1];
n = number of bidders
m = number of units demanded by auctioneer (i.e. target)
u = integrand for values between v and 1

Overbidding  info rent

Imperfect cost revelation
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Auction performance estimates:
overview
 Data source:
 From the field or from the lab
 Counterfactual:
 fixed-rate payment
 first-best contract
 self-selection contract
 Alternative auction designs:
 Payment format: discriminatory-price versus uniform-price
 Auction format: budget-constrained versus targetconstrained
 Information policy: information revealed or concealed
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Auction performance estimates:
auction versus fixed-rate payments

BushTender pilot auction (2001): Same amount of biodiversity
benefits would have cost seven times as much!!? (Stoneham
et al., 2003)

Auction for Landscape Recovery: efficiency gains between 200
and 315% (White and Burton, 2005)

Auction experiment Kiel/Perth: Cost savings in the range of 30
to 60%, quickly eroding with repetition (Schilizzi and LataczLohmann, 2007)

Simulated cost savings between 16 and 29% (Latacz-Lohmann
and Van der Hamsvoort, 1997)
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Auction performance estimates:
auction versus self-selection contracts
Cost-effectiveness of auction versus self-selection contract as a function of
bidders‘ expectation about the maximum acceptable bid
Source: Glebe (2008, ERAE)
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Conclusions
 Contract models have clarified the principles, but
 Assumptions too restrictive (e.g. two farmer types,
knowing their costs, rational behaviour)
 Often (incomprehensible) corner solutions
 Recommendations often out of step with the intuition of
policy administrators
 Models not adapted to the complex regulatory process
of conservation contracting
 Not really useful to inform practical contract design, but
papers publish well in high-impact journals
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Conclusions
 Auction theory has had a significant impact on policy, but
 Bidding models too simple to cater for the complexity of
agri-environmental contracting (uncertainty)
 Little scope for extending auction theory (except
combinatorial auctions, … )
 Economic experiments more promising than theory
development, but
 Evidence of performance advantages inconclusive,
preference for cooperative approaches, equal treatment
mentality
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Alternatives?
Contract
attributes (z)
CONTRACT 1
CONTRACT 2
CONTRACT 3
No
contract
Permitted fertilizers
Organic fertilizer
No fertilizer
Organic and
mineral fertilizers
No mowing before
22 June
1 June
22 June
Max. stocking rate
4 LU/ha
2 LU/ha
4 LU/ha
Contract duration
1 year
10 years
5 years
Annual payment
€450/ha
€350/ha
€250/ha
I would choose none of the
contracts
I would choose …
O
O
O
O
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Cost estimates for individual farms
𝑾𝑾𝑾𝒊 = 𝑪𝑪𝑪𝑪𝒊
= 𝟏𝟏𝟏, 𝟎𝟎 ∗ 𝒛𝟏 + 𝟐𝟐𝟐, 𝟖𝟖 ∗ 𝒛𝟐 − 𝟒, 𝟏𝟏 ∗ 𝒛𝟑 + 𝟏𝟏𝟏, 𝟔𝟔 ∗ 𝒛𝟒
− 𝟏𝟏. 𝟑𝟑 ∗ 𝒛𝟓 + 𝟏𝟏𝟏, 𝟏𝟏 ∗ 𝒔𝒊𝒊 + 𝟔𝟔, 𝟒𝟒 ∗ 𝒔𝒊𝒊 − 𝟑𝟑, 𝟓𝟓 ∗ 𝒔𝒊𝒊𝒊
+ 𝟐, 𝟎𝟎 ∗ 𝒔𝒊𝒊𝒊 − 𝟏, 𝟔𝟔 ∗ 𝒔𝒊𝒊𝒊 + 𝟏, 𝟒𝟒 ∗ 𝒔𝒊𝒊 − 𝟎, 𝟑𝟑 ∗ 𝒔𝒊𝒊 − 𝟐, 𝟒𝟒
∗ 𝒔𝒊𝒊 − 𝟎, 𝟗𝟗 ∗ 𝒔𝒊𝒊𝒊 + 𝟎, 𝟕𝟕 ∗ 𝒔𝒊𝒊𝒊 − 𝟖𝟖, 𝟐𝟐 ∗ 𝒔𝒊𝒊𝒊 + 𝟑, 𝟕𝟕 ∗ 𝒔𝒊𝒊𝒊
− 𝟑, 𝟐𝟐 ∗ 𝒔𝒊𝒊𝒊 ≤ 𝒂
Z variables = contract attributes
S variables = farm / farmer characteristics
Estimate of average participation cost for contract 1 = €323/ha
Lowest cost in sample = €0/ha
Highest cost in sample = €795/ha
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Supply curve for area under contract
Conservation Tenders in Developed and Developing Countries Status Quo, Challenges and Prospects
Lessons from contract and auction
theory
Uwe Latacz-Lohmann
Department of Agricultural Economics,
University of Kiel
and
School of Agricultural and Resource Economics,
The University of Western Australia