Understanding strategic bidding in multi-unit auctions: a case study of the Texas electricity spot market.

Explanations of deviations from static profit maximization, other than scale economies, do not have strong empirical support. Explanations based on risk preferences are ruled out by the fact that the first-order condition of optimality (equation(2)) does not depend on the curvature of the utility function. Collusion appears to be unlikely because the heterogeneity in bid patterns is consistent with a collusive coalition that includes the small but not the large players, a possibility we believe to be unlikely. Engineering-based explanations such as unmeasured adjustment costs or transmission constraints also appear implausible due to the periods we choose to analyze; for a more detailed discussion, see our working article (Hortacsu and Puller, 2005).

[] Learning. One might also expect bidders to gradually learn the rules of competition in this market, and improve their bidding behavior over time. To explore this hypothesis further, we examine whether there are any time trends in bidder performance.

Table 5 reports the results of a regression of individual bidder's daily performance (measured by the percentage of ex post profit achieved for that day) on a time trend and controls for seasonality and the bidder's expost optimal generation in that period. The specification includes bidder fixed effects to account for firm-level sources of variation. Notice that, for the entire sample of firms, the estimated coefficient for the time trend is positive, and indicates a 3 percentage point improvement in performance for every 100 days (or roughly 10% over a year). It is interesting to note that this time trend is not significant (though of the same estimated magnitude) for the top six bidders. For the rest of the firms, the time trend in performance is strongly significant.

Thus, our data support a learning hypothesis, although the rate of learning in this market strikes us as being rather slow (especially compared to rates of learning reported in laboratory experiments). (32) Firms in this market face a considerable amount of uncertainty, which may slow bidders' ability to infer optimal decision rules from their experiences. Moreover, several bidders have told us that they do not have the resources to perform detailed "ex post" analyses that will enable them to assess how successful their bidding has been, and that they view our exercise as providing useful information in this regard.

6. Quantifying efficiency losses

* Inefficiencies arise in electricity markets when the bids do not lead to least-cost production. (33) In a balancing market, firms bidding above marginal cost on the INC side may not be called upon to produce despite the fact they have low-cost generators. Similarly, firms bidding below marginal cost on the DEC side may not be called to reduce production even if they have high-cost plants operating.

We measure the cost of production in the balancing market implied by actual bids and compare those costs to the production costs under various counterfactual bidding behaviors. Our benchmark for efficiency is competitive bidding, which is an equilibrium under an alternative market design--the multi-unit Vickrey auction. We calculate the production costs if firms instead were to bid their marginal cost functions. These calculations suffer from one data limitation--the generation data are incomplete for a few of the smaller firms. This does not affect our ability to analyze bidding behavior for the remaining firms but prevents us from calculating efficient dispatch for many days in our sample. Therefore, these measures of efficiency loss should be interpreted with caution for they only represent a fraction of our sample period.

Results are shown in Table 6. The average hourly dispatch costs in the balancing market are $29,874 under actual bidding and $23,571 under marginal cost bidding, implying that actual production costs are 27% higher than least-cost production.

We can decompose the welfare losses into the two major sources of inefficiency. The first source of inefficiency is the strategic exercise of market power--large firms face steeper residual demand functions and thus have incentives to bid steeper than marginal cost. This may result in inefficient production when low-cost units are withheld from the market while higher-cost units are called to generate. (34)

A second source of inefficiency is the behavior of small generators. As we have noted, many of the smaller participants submit extremely steep bid functions--even though static profit maximization suggests that they should bid very close to marginal cost. As a result, they are often not called to produce despite the fact that it would be efficient to do so. This source of inefficient production can arise from a variety of sources as we discuss above (e.g., fixed cost of establishing a sophisticated trading operation). To the extent that the inefficiencies result from scale economies, the fixed cost of setting up trading operations or outsourcing to power marketers should be included in a complete welfare analysis.

To decompose the total amount of production inefficiency into these two components, we separate the firms into two groups: "strategic" bidders who exercise market power optimally, and "nonstrategic" bidders who bid excessively steep schedules that effectively minimize their participation in the market. We first compute counterfactual generation costs in this market under the assumption that strategic firms ignore their market power and bid competitively, that is, we calculate what the total generation cost would have been if the strategic firms were bidding their marginal costs. In this counterfactual, we assume that the nonstrategic firms continue to bid what they are observed to bid, that is, that they do not respond to the (counterfactual) change in the behavior of their strategic competitors. (35)

We assume that the three largest firms--Reliant, TXU, and Calpine--as well as the other three firms in the top six in Table 1 (Brownsville, Bryan, and Tenaska) are strategic. We find counterfactual dispatch costs when these six firms bid marginal cost and all other finns submit their actual bid schedules. The difference in dispatch costs between this counterfactual bidding strategy and the actual bids can be interpreted as the "efficiency loss due to market power." The remaining efficiency loss is due to nonstrategic firms that bid so as to not participate in the market.

Table 6 decomposes the efficiency losses. Again, the total efficiency loss due to misrepresentation of marginal costs is on average $6303 per hour, or 27% of the total cost of efficient generation in the balancing market. If the strategic firms were to bid their marginal costs, the total efficiency loss would have been only $1203 less than the actual efficiency loss. This means that most (81%) of the observed efficiency loss is due to the steep bid schedules submitted by the nonstrategic bidders.

We should note, once again, that the above calculation relies on relatively few intervals (62 out of a total of 220 periods--the latter periods are especially prone to the missing data problem). This calculation is largely based upon the first six months of the market's operation. However, this calculation points out that the observed deviations from static profit maximization are not without economic consequence. In fact, they cause larger efficiency losses than the "near-optimal" exercise of market power by the strategic firms. Submitting bid functions that are too steep not only sacrifices producer surplus but also prevents technologically efficient firms from supplying energy to the balancing market.

One would expect that these efficiency losses due to the nonstrategic firms' behavior would dissipate over time. Unfortunately, as we noted above, we face data limitations that prevent us from conducting a long time series analysis, especially in the later part of the sample. However, market forces are at play to reduce the inefficiencies. To the extent there are scale effects, the small firms may outsource bidding decisions or consolidate bidding activities across plants. It is noteworthy that there are a variety of power marketers and large energy trading firms that offer energy asset management services to generators in ERCOT. Such outsourcing can increase participation by nonstrategic firms and reduce inefficiencies without substantially increasing the fixed cost of bidding expertise.

7. Conclusion

Our analysis of the ERCOT balancing market yields the following conclusions. The first conclusion, we believe, is a comforting one for economic theory. In a marketplace that is marked by considerable uncertainty and institutional complexity, two factors that may pose analytical challenges for both the firms competing in the market and the economists who are observing (and, in some cases, advising) them, firms with large stakes in the market behave close to theoretical predictions of a strategic model of oligopolistic interaction. Indeed, as two empirical industrial organization researchers who have previously utilized such models to infer supply and demand parameters in other markets, we interpret the behavioral pattern displayed by Reliant as good news for previous and future empirical work on oligopolistic markets. More specifically, the confirmation of the basic predictions of the uniform-price share auction model is important, as one could use this model to forecast bidder behavior in restructured electricity markets that are being put into operation in many different parts of the world.