Daniel Hellerstein (ERS) and Sean Sylvia (AREC/UMD)

1. Conservation program enrollment mechanisms using auctions: what can laboratory experiments tell us about the use of imprecise cost information? Daniel Hellerstein (ERS) and Sean Sylvia (AREC/UMD) AERE Summer Workshop, Seattle WA, June 8-10, 2011. The views expressed are the authors and should not be attributed to the Economic Research Service or the USDA

2. Motivation Conservation programs need some means of choosing which applicants to accept … to wit… an enrollment mechanism • Goals of an enrollment mechanism: • Minimizing program expenditures/ maximizing benefits • Encouraging broad participation • Inducing adoption of enhanced environmental practices • Minimizing impacts on production

3. Example: the CRP’s EBI The Conservation Reserve Program (CRP) is a ~31 million acre, $1.5 billion/year program established in 1986. Objectives include erosion control, water quality protection, and providing wildlife habitat • The CRP’s enrollment mechanism • Offers are ranked using an Environmental Benefits Index (EBI) that incorporates environmental impacts and the bid. • Each parcel’s bid can not exceed a bid cap (a maximum bid).

4. Landowner costs are heterogeneous. If a single price were paid to all offers, owners of low cost parcels could earn substantial rents A precise bid cap (equal to a parcel’s opportunity cost) could deliver substantial savings to program administrators. However, a poorly chosen bid cap can increase total expenditures Cost heterogeneity

5. Example: unbiased bid caps can stink 10 parcels with heterogeneous cost (but otherwise the same) Goal: accept 5 of 10 parcels, whose cost range from 1 to 10 • Two bid cap measures: • accurate/unbiased • less accurate/upwardly biased

6. Prior findings: Stringent bid caps lead to higher acquisition costs • Max bids were varied in stringency, from 80% (of a tickets maximum possible cost) to 120% • 80% yielded the highest acquisition costs • 120% yielded acquisition costs similar to 80% • Costs were minimized at 90%

7. Goals of this study • In auction setting with asymmetric bidders and noisy assessments: • What are the performance characteristics of several different auction mechanisms? • We examined: • Quotas • Target bids • Endogenous target bids

8. Experimental Design Participant enters bids (BID) on zero, one or both tickets Participant can also purchase “points” (q) on zero, one or both tickets Choices Simple case: SCORE=BID- q Accept the 12 lowest scoring tickets Earnings: EARN= BID – ticketCost - (0.5 x q) Ranking Design Instrument

9. Asymmetric costsTickets belong to one of 4 types Each participant receives: an (a) or a (b) ticket, and a (c ) or a (d) ticket

10. Three treatments (that complement the “basic” treatment)

11. Sample screen: basic treatment

12. Sample screen: targetBid treatment

13. Sample screen: quota treatment

14. Sample screen: endog targetBid

15. Example: session 5, round 6(standard treatment) Type A Type B Type C Type D

16. Example: session 5, round 28(targetBid treatment)

17. Analysis

18. Prior findings: Quota auctions can reduce acquisition costs • Using two ticket types, imposing a quota reduced acquisition costs by an average of 8%. • Cost reduction due to reduced bids by “low costs” tickets was greater than cost increases due to accepting higher cost tickets

19. Endog target Standard targetBid quota Profit rate

20. Some aggregate dependent variables… Mean (sd) A = # tickets accepted, T = # tickets offered sCost= sorted ticket costs ,low to high (t=1..T) Accept: 0/1 dummy, 1 if accepted (t=1..T)

21. Basic regressions

22. Panel regressions (panel=round number)

23. Difference models

24. Conclusions • Use of an alternative auction mechanism can decrease program expenditures • Bid targets seem to be somewhat more effective than endogenous bid targets or quotas • Cost savings vary around 10% • There is an increase in social costs (as more expensive “lands” are enrolled), that range around 3% (and that are highest in endogenous bid treatments)

26. Future work

27. References: Higgins, Nathaniel*, Michael Roberts*, and Daniel Hellerstein, 2011, Using Quotas to Enhance Competition in Asymmetric Auctions: A Comparison of Theoretical and Experimental Outcomes, for submission to Games and Economic Behavior Hellerstein, Daniel* and Nathaniel Higgins*, 2010, “The Effective Use of Limited Information: Do Bid Maximums Reduce Procurement Costs in Asymmetric Auctions?”Agricultural and Resource Economics Review, 39 (2, April):288-304 Higgins, Nathaniel. "Computational and Experimental Market Design." PhD Dissertation, University of Maryland, College Park, 2010. Hellerstein, Daniel* and Nathaniel Higgins, 2009, “The Effective Use of Limited Information: Do Bid Maximums Reduce Procurement Costs in Asymmetric Auctions?”, Presentation at Northeastern Agricultural and Resource Economics Association conference, Burlington VT, June Higgins, Nathaniel*, Michael Roberts*, and Daniel Hellerstein, 2008, “Cost Saving Procurement Auctions for Environmental Services”, Poster presentation at AAEA Summer Meetings, Denver CO, July

28. Appendix: miscellaneous tidbits

29. The CRP Current enrollment (April 2011): 31.2 million acres • Current acreage is a 5.6 million acre drop from the 2007 peak (36.8 million) • Current acreage includes 5.0 million acres of continuous signup • Average cost per acre: $46 for general, $102 for continuous Source: ERS using FSA CRP contract data as of October 2009

30. Optimal: offer actual cost to everyone Total Expenditure: 15 Offered, and rejected Offered, and accepted parcel

31. Examples of too stringent maximums Total Expenditure: 30 Expenditures, when using a single price, is twice actual cost Offer 6 to everyone Not offered Offered & accepted parcel

32. Total Expenditure: 18.7 A moderately upwardly biased cap is almost as good as the optimal Offer the less accurate, and upwardly biased, cost Offered & rejected Offered & accepted parcel

33. Total Expenditure: 31.5 An unbiased, and accurate, bid cap can be significantly worse than a biased/inaccurate cap, and can be worse than single price Offered & accepted Not offered Offer the more accurate, and unbiased, cost

34. How well are quality points used?

35. What influences efficient use of q points?

36. Models Basic: Where: s,r = session, and round within session T = vector of treatment dummies (target, endogTarget, quota), only one of which is non-zero X = round specific variables (such as maxPrior and qSmart) Z = session descriptor (such as exper) C = round specific costs (such as vickCost and vickRatio) – these have the same (or nearly the same) values in round r, regardless of session, eps = error components: session specific (s), round specific ( r), and observation specific (rs) Y = average profit, ratio of actual expenditures to optimal (full information) cost, or ratio of actual costs (of enrolled parcels) expenditures to optimal cost.

37. Panel (within round): Notes: Variables are demeaned , using round-specific means. The eps_r “round specific” error component is conditioned out by the FE or RE estimator. (the demeaning or quasi-demeaning). The C variables, due to changes in # of participants, are not completely removed. However, their variance is reduced, hence one expects they will have reduced influence in the regression.

38. Difference (within session): • Notes: • This model uses all “pairs” or rounds that share a session, and that have at least one of the T variables differ. • Thus, it is a panel model, where each panel use observations within a single session. • Note that the first differencing uses all “interesting” pairs within a session (it is not a simple “adjacent round” first difference). • Definition of “first differencing” for a pair of observations in panel s: • x_s,r12 = x_s,r2 – x_s,r1 • (where r1 and r2 are rounds). • The eps_s “session specific” error component is conditioned out by the first differencing. • The Z variables are also conditioned out. • Note that delta T can be negative, which means a treatment was no longer used.

39. Difference of differences: • Notes: • This model compares “pairs” of rounds between sessions s1 and s2. • Each comparison uses pairs (S2, S2) that have the same first (r1) and second round (r2). • S1 must have a round that is identical (in terms of T) to a round in S2 • S1 must have a round that is different than a round in S2 • Thus, the difference within a pair is compared to a difference within another pair. • Definition of “difference in differencing” for a pair of observations spanning rounds r1 and r2, in sessions s1 and s2: • x_s12r1r2 = (x_s1,r2 – x_s1,r1) - (x_s2,r2 – x_s2,r1) • Where the r2 treatments are the same, and the r1 treatments are different. • Note that the difference in differencing : • Controls for Z (the first difference removes session specific variables) • Controls for changes in eps_r (the within pair changes are the same) • Controls for changes in cost structure (since r1 and r2 have the very similar cost structure across all sessions), hence C is essentially conditioned out.