Tanga Mohr and John Whitehead [1]
Department of Economics
Appalachian State University
Introduction
The Regional Greenhouse Gas Initiative (RGGI) is a cap-and-trade program that covers the electric power sector in more than 10 northeastern states. The cap-and-trade program creates markets for a limited number CO2 allowances, reducing greenhouse gases. Laboratory experiments were used to inform RGGI about the most efficient design for the primary auction and the secondary markets (e.g., Shobe et al. 2010). These experiments were single unit auctions but RGGI conducts multi-unit auctions. The purpose of this research is to explore the efficiency of multi-unit auction designs in the RGGI context.
Auctions
In first price auctions, bidders pay their bid. Theory predicts that bidders in first price auctions of a single unit will shade their bids. In second price auctions, all winning bidders pay the same market clearing bid. Theory predicts that bids will be equal to value in second price auctions of a single unit. Theory is not so clear in first and second price multi-unit auctions (Khezr and Cumpston 2022).
Real and Hypothetical Auctions
Real auctions are incentivized; i.e., subject earnings are real and depend on bidding behavior. Hypothetical auctions are not incentivized; i.e., subject earnings are fixed and do not depend on bidding behavior. We expect incentivized subjects to make bids closer to theoretical predictions (noting that theoretical predictions are not sharp in multi-unit auctions) (see Mohr and Whitehead 2023).
Methods
We conducted multi-unit induced value auctions using the VECONLAB platform. In induced value auctions, subjects are told how much an item is worth and then make a bid for that item. Each subject has demand for three units and the induced value for each unit differs in each round and over 18 rounds of bidding. We have 74 subjects in four treatments:
Real, 1st price auction
Hypothetical, 1st price auction
Real, 2nd price auction
Hypothetical, 2nd price auction
We use latent class regression models to explore various bidding strategies that were used by subjects.
Results
Using naive models (assuming that all subjects behave in the same way), we find no differences in bidding behavior in real and hypothetical experimental sessions.
Using latent class models we identify two different types of bidding behavior for both auctions. In the first price auctions one class suggests that subjects in the hypothetical session bid their value and shade their bids by 85% in the real sessions. In the other class, all subjects shade their bids by 68%.
In the second price auctions one class suggests that hypothetical and real subjects shade their bids by 88% and 84%, respectively. In the other class, hypothetical and real subjects shade their bids by 53% and 72% respectively.
Conclusions
We find some evidence that real auctions yield results closer to theory. Latent class models can lend additional insights to experimental auction behavior. We plan to conduct more incentivized first and second price auctions in the future. [2]
References
Khezr, Peyman, and Anne Cumpston. “A review of multiunit auctions with homogeneous goods.” Journal of Economic Surveys 36, no. 4 (2022): 1225-1247.
Mohr, Tanga, and John C. Whitehead. External Validity of Inferred Attribute NonAttendance: Evidence from a Laboratory Experiment with Real and Hypothetical Payoffs. Department of Economics Working Paper No. 23-05. 2023.
Shobe, William, Karen Palmer, Erica Myers, Charles Holt, Jacob Goeree, and Dallas Burtraw. “An experimental analysis of auctioning emission allowances under a loose cap.” Agricultural and Resource Economics Review 39, no. 2 (2010): 162-175.
Note
[1] This study was funded by the Walker College of Business Dean’s Club. It was conducted with a student at Appalachian State University who was going to use it for an Honors Thesis. The student ghosted on us and we’re left with the responsibility for producing a poster for the Dean’s Club poster session (a requirement for securing Dean’s Club funding).
[2] More detail to come over the next 6 days …
