Suggested Retail Prices Under
Uncertainty: My Research
Experience
Erica Leavitt
RFF Intern
Summer 2010
Internship Details
• Resources for the Future: Think-tank in
Washington, D.C.
– Specific division: Center for Disease Dynamics,
Economics, and Policy (CDDEP)
– Advisor: Ramanan Laxminarayan
• Internship goal:
– To pursue an independent research project that falls
within the mission statement of RFF (research on
environmental, energy, natural resource and public
health issues rooted primarily in economics and other
social sciences )
Initial Research Question
• Initial motivation: Analyze a dataset from a
pilot study in Tanzania where anti-malarials
(ACTs) were heavily subsidized.
• What were the effects of implementing suggested
retail prices in one of the intervention districts?
• Would have been an empirical project
Research Turning Point
• We realized that there were theoretical
questions regarding SRPs that had not been
answered.
• I transformed the research project from a
specific empirical analysis to a broader
theoretical one.
New Questions
– 1) Governments, unlike manufacturers, have
imperfect information about the costs of
supplying a product. Thus, how should SRPs be set
in a context of uncertain costs?
– 2) To what extent can SRPs be used to address
spillover benefits, and how do they compare to
other policy alternatives (subsidy)?
I. How should SRPs be set under
uncertainty?
Assumptions:
• Linear costs, linear MPB
(1)MCa=yC+mC q
(2)MCe=yC+xC+mC q
(3) MPB=yP-mB q
• No externality but a monopolistic supplier: the
policy-planner intervenes to address the market
failure due to imperfect competition.
• Policy-planner sets SRP where MPB=MCe
5 possible welfare effects of SRPs
MCE
MC0
MCA=MCe
MCA
SRP
pM
pM
SRP
MPB
MR
MPB
Qm Q*=Qsrp
Condition 1) Correct estimation, optimal SRP
Green DWL averted
MR
MPB
Qm=Qsrp Q*
Condition 2) Gross overestimation. SRP nonbinding. No DWL created or averted.
MCE
MCA
MCA
MCE
pM
pM
SRP
SRP
MC0
MC0
MR
Qm Qsrp Q*
MPB
MR
MPB
Qm Qsrp Q*
Condition 3) Moderate Overestimation. SRP binding. Condition 4) Moderate Underestimation. SRP binding.
Green DWL averted.
Green DWL averted.
MCA
pM
MCE
MC0
SRP
MR
Qsrp Qm
MPB
Q*
Condition 5) Gross underestimation. Red
DWL created.
Policy Question
How can we set SRPs to end up at condition 3 or
4, rather than condition 1 or 5?
These differences in social deadweight loss can
profoundly impact people’s lives.
DWLpolicy-DWLnp versus xC
Parameters: mB=mC=1 (yP=100, qOpt=50)
Symmetric except for boundary conditions
DWLpolicy-DWLnp versus xC
mB=1, mC=4
Asymmetry: More room for error if costs
UNDERESTIMATED
(Boundary conditions work in opposite direction)
DWLpolicy-DWLnp versus xC
mB=1, mC=1/4
Asymmetry: More room for error if costs OVERESTIMATED
Boundary conditions work in the same direction
Overestimation and underestimation
limits to avert DWL
MCA
Overestimation
limit
Overestimation
limit
Underestimation
limit
pM
MCA
pM
Underestimation
limit
MC0
MC0
MR
MPB
Qm Q*
mC>mB
More room for error if underestimation
MR
Qm
Q*
mB>mC
More room for error if
overestimation
MPB
Part I. Summary
• Novel finding: The policy-planner’s optimal
estimation strategy should be adjusted based
on the costs and demand slope parameters.
• If mC>mB: may want to purposefully
underestimate.
• If mC<mB: may want to purposefully
overestimate. (More absolute condition)
Part II: Comparing Subsidy to SRP
• Optimally-set subsidy always outperforms SRP
in this model because is superior on both
fronts:
1) Can correct for social externality, while SRP
cannot.
2) Performs better at correcting for market power.
Conclusion
• Hope to expand research into a senior thesis
• Learned how a research process can evolve
(sharp transformation from empirical to
theoretical)
• Hope to produce a research paper that will
have substantial policy effects.
– A correct use of SRPs can improve social welfare,
an incorrect use can prevent people from
purchasing a beneficial good such as drugs.
Acknowledgements
• Carolyn Fischer
• Ramanan Laxminarayan
• Health Grand Challenges program