Suggested Retail Prices Under Uncertainty: My Research Experience

1. Suggested Retail Prices Under Uncertainty: My Research Experience Erica Leavitt RFF Intern Summer 2010

2. 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 )

3. 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

4. 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.

5. 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)?

6. 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

7. 5 possible welfare effects of SRPs MCE MC0 MCA=MCe MCA SRP pM pM SRP MPB MPB MPB MR MR Qm Q*=Qsrp Qm=Qsrp Q* Condition 2) Gross overestimation. SRP non-binding. No DWL created or averted. Condition 1) Correct estimation, optimal SRP Green DWL averted

8. MCE MCA MCA MCE pM pM SRP SRP MC0 MC0 MPB MPB MR MR Qm Qsrp Q* Qm Qsrp Q* Condition 4) Moderate Underestimation. SRP binding. Green DWL averted. Condition 3) Moderate Overestimation. SRP binding. Green DWL averted.

9. MCA pM MCE MC0 SRP MPB MR Qsrp Qm Q* Condition 5) Gross underestimation. Red DWL created.

10. 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.

11. DWLpolicy-DWLnp versus xC Parameters: mB=mC=1 (yP=100, qOpt=50) Symmetric except for boundary conditions

12. DWLpolicy-DWLnp versus xC mB=1, mC=4 Asymmetry: More room for error if costs UNDERESTIMATED (Boundary conditions work in opposite direction)

13. DWLpolicy-DWLnp versus xC mB=1, mC=1/4 Asymmetry: More room for error if costs OVERESTIMATED Boundary conditions work in the same direction

14. Overestimation and underestimation limits to avert DWL MCA Overestimation limit Overestimation limit Underestimation limit pM MCA pM Underestimation limit MC0 MC0 MPB MPB MR MR Qm Q* Qm Q* mC>mB More room for error if underestimation mB>mC More room for error if overestimation

15. 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)

16. Part II: Comparing Subsidy to SRP • Optimally-set subsidy always outperforms SRP in this model because is superior on both fronts: • Can correct for social externality, while SRP cannot. • Performs better at correcting for market power.

17. 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.

18. Acknowledgements • Carolyn Fischer • Ramanan Laxminarayan • Health Grand Challenges program