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

Conservation programs need some means of choosing which applicants to accept …

to wit… an enrollment mechanism

Motivation

Goals of an enrollment mechanism:

•Minimizing program expenditures/ maximizing benefits•Encouraging broad participation•Inducing adoption of enhanced environmental practices•Minimizing impacts on production

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).

•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

Example: unbiased bid caps can stink

Two bid cap measures: 1. accurate/unbiased2. less accurate/upwardly

biased

10 parcels with heterogeneous cost (but otherwise the same)

Goal: accept 5 of 10 parcels, whose cost range

from 1 to 10 Type Total cost

Actual 15

Single price (6th highest cost)

30

Less accurate cap

18.7

Accurate & unbiased cap

31.5

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%

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

Goals of this study

Experimental Design Twelve 1.5 hour sessions On average, 10 participants per

session

5 or 6 treatments per session 8 auctions per treatment

Participant’s receive two “tickets” per auction

Tickets have a randomly generated cost, and a fairly large bid cap

Des

ign

Simple case: SCORE=BID- q

Accept the 12 lowest scoring tickets

Earnings:

EARN= BID – ticketCost - (0.5 x q)

Ran

kin

g

Participant enters bids (BID) on zero, one or both tickets

Participant can also purchase “points” (q) on zero, one or both ticketsCh

oic

esIn

stru

men

t

Asymmetric costs Tickets belong to one of 4 types

Description of type Cost range Bid cap Target bid

(a) Low cost with low variance

30 -45 90 39

(b) Low cost with medium variance

20-65 130 45

(c) Medium cost with medium variance

35 – 95 190 71

(d) High cost with high variance

40- 150 300 94

Each participant receives:1.an (a) or a (b) ticket, and 2. a (c ) or a (d) ticket

Three treatments (that complement the “basic” treatment)

Description Score calculation (lower scores are better)

Quota A two stage acceptance procedure:

1. Within each type, the highest scoring ticket is dropped

2. The survivors are pooled, with A (A=12 usually) lowest scoring tickets accepted

BID- q

(q=quality points purchased)

target Bid

A target bid is assigned to each ticket type. • Bids below the targetBid: score is reduced• Bids above the targetBid: score is increased

BID - q +

((BID-0.5q)–targetBid) )

Endog

targetBid

• Target is announced after all bids are received• Target is the lowest bid received for this ticket type• Thus, scores will be increased for all tickets

BID - q +

((BID-0.5q)–lowest_t) )

lowest_t= lowest bid received for type t tickets

Sample screen: basic treatment

Sample screen: targetBid treatment

Sample screen: quota treatment

Sample screen: endog targetBid

Example: session 5, round 6

(standard treatment)

-30

30

90

150

0 5 10 15 20

$ a

nd

po

ints

Tickets, sorted by ticket-type (small to large bid cap)

offer

cost

points

Type A Type B Type C Type D

Example: session 5, round 28

(targetBid treatment)

Analysis

We focus on aggregate results

Several linear regressions are used to discern the impacts of the several treatments.

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

Profit rate

Standard targetBid quota Endog target

optCost “optimal” cost of acquisition

524.3 (93.4)

expend Expenditures (bids of accepted

tickets)

875.7 (151.9)

sCost Social cost (cost of accepted tickets)

569.1 (98.4)

expEff

socEff

Efficiency expenditures, or forgone production, over optimal cost:

expend/optCost

sCost/optCost

1.68 (0.17)

1.09 (0.06)

avgProfit Average profit (of accepted offers)

21.3 (6.0)

Some aggregate dependent variables…

T

ttt

A

ii AcceptqsCost *5.0

)5.0(* tt

T

tt qBidAccept

)))5.0((*

A

qCostBidAcceptT

ttttt

tt

T

tt qCostAccept 5.0

A = # tickets accepted, T = # tickets offeredsCost= sorted ticket costs ,low to high (t=1..T)Accept: 0/1 dummy, 1 if accepted (t=1..T)

Mean (sd)

Basic regressions

N=206, (t-stat) expEff avgProfit socEff

intercept 1.19 (9.9) 5.7 (4.8) 1.1 (13.5)

target -0.18 (-8.9) -10.5 (-12.9) 0.030 (2.5)

endogTarget -0.13 (-6.0) -7.3 (-9.3) 0.039 (3.1)

quota -0.14 (-5.81) -7.7 (- 8.3) 0.023 (1.7)

maxPrior 0.0054 (6.8) 0.21 (7.1) 0.00064 (1.4)

vickCost -0.018 (-13.6) -0.33 (-6.2) -0.0023 (-2.7)

vickRatio 0.73 (7.3) 12.5 (3.2) 0.09 (1.4)

Exper -0.0041 (-0.9) 0.034 (.21) -0.004 (-1.9)

qSmart 0.32 (5.2) 7.5 (3.1) 0.016 (0.4)

R-square [ f-stat ] 0.66 [< 0.001] 0.58 [< 0.001] 0.18 [< 0.001]

Panel regressions (panel=round number)

N=206 avgProfit (haus prob<0.81) expEff (haus prob<0.01)

RE FE RE FE

Intercept 5.7 (1.2) -2.1 (-0.3) 1.19 (9.9) 1.20 (6.4)

Target -10.5 (-12.9) -8.2 (-5.9) -0.18 (-8.9) -.014 (-4.1)

endogTarget

-7.9 (-9.3) -5.4 (-3.9) -0.12 (-6.0) -0.083 (-2.3)

Quota -7.7 (-8.4) -5.2 (-3.7) -0.13 (-5.8) -0.088 (-2.4)

maxPrior 0.21 (7.1) 0.21 (6.5) 0.0053 (6.8) 0.0051 (6.3)

vickCost -0.33 (-6.2) -0.37 (-2.4) -0.18 (-13.6) -0.015 (-3.8)

vickRatio 12.1 (3.2) 18.3 (2.1) 0.72 (7.33) 0.59 (2.7)

Exper 0.04 (0.2) 0.052 (-0.3) -0.004 (-0.9) -0.0069 (-1.6)

qSmart 7.5 (3.1) 9.9 (4.0) 0.32 (5.2) 0.37 (5.9)

R-sq [f-stat] 0.47 [<0.0001] 0.55 [<0.0001]

0.66 [<0.0001] 0.64 [<0.0001)

Y= avgProfit Difference Difference in difference

intercept 1.43 (109) 1.33 (7.8)

target Change in targetBid treatment

-9.7 (-37.2) -6.9 (-10.1)

endogTarget Change in endogTarg treatment

-7.49 (-27.3) -7.2 (-10.7)

Quota Change in quota treatment

-7.08 (-23.0) -5.5 (-8.1)

maxPrior Max accepted bid prior round

0.093 (8.1) 0.02 (1.9)

vickCost Total cost in a vickery auction cost

-0.39 (-25.7) n.a.

vickRatio vickCost/optcost 15.8 (14.1) n.a.

qSmart 1 if perfect q point useage

3.3 (3.1) -0.027 (-0.03)

R-square [f-stat] 0.54 [< 0.001] 0.06 [< 0.001]

N 1822 2565

Difference models

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)

…. Other conclusions

• Quality improvements, even when unambiguously beneficial, are often not undertaken

• Failure to utilize quality improvements seems to be related to

• treatment type (endogenous targetBid treatments had worse results),

• bidder competence (accepted offers use quality points more effectively)

Future work

• Examine other cost range distributions, and other allocations of quality points

• Devise a structural model that uses individual observations (ticket within an auction), to replace the convenient aggregate models.

• Farmers & agricultural landowners as experimental subjects

• etc etc etc

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

Appendix: miscellaneous tidbits

The CRP

Current enrollment (April 2011): 31.2 million acres

Source: ERS using FSA CRP contract data as of October 2009

0

5

10

15

20

25

30

35

40

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Year

Mil

lio

n A

cres

0

0.5

1

1.5

2

2.5

Bil

lio

n D

oll

ars

CRP Acres Continuous CRP Acres Yearly $ Outlay

• 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

Optimal: offer actual cost to everyone

Total Expenditure: 15

Offered, and accepted

Offered, and rejected

parcel

Offer 6 to everyone

Total Expenditure: 30Expenditures, when using a single price, is twice actual cost

Offered & accepted

Not offered

parcel

Examples of too stringent maximums

Offer the less accurate, and upwardly biased, cost

Total Expenditure: 18.7A moderately upwardly biased cap is almost as good as the optimal

Offered &accepted

Offered &rejected

parcel

Offer the more accurate, and unbiased, cost

Total Expenditure: 31.5An unbiased, and accurate, bid cap can be significantly worse than a

biased/inaccurate cap, and can be worse than single price

Offered & acceptedNot

offered

How well are quality points used?

N=6470 Coefficient (tstat)

Mean (stddev) Min / Max

Intercept 0.51 (12.4)

Round -0.0041 (-5.6) 18 (9.7) 2 / 38

cheapTalk 0.27 (0.42) 0.02 0 / 1 dummy

compQuests 0.07 (9.0) 4.6 (0.7) 2 / 5

Cost -0.002 (-11.3) 60.4 (30.3) 20 / 150

Target -0.15 (-8.9) 0.32 0 / 1 dummy

endogTarget -0.16 (-8.6) 0.21 0 / 1 dummy

Quota -0.085 (-4.2) 0.17 0 / 1 dummy

R-square [f-test prob]

0.071 [<0.0001]

qSmart (dependent variable)

0.57 (0.46) 0 / 1

What influences efficient use of q points?

Basic:

Models

rsrszscrXsrTsrsr ZCXTY

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.

Panel (within round):

rssscrXrTrr ZCXTY

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.

12,1212,12,12,12, rsrcrsXrsTrsrs CXTY

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.

Difference (within session):

12,122

12,122

12,122

12,122

rsXrsTrsrs XTY

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.

Difference of differences: