Responsibility for Cost Management Hinders Learning to Avoid the Winner s Curse

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1 Responsibility for Cost Management Hinders Learning to Avoid the Winner s Curse Robert Bloomfield S. C. Johnson Graduate School of Management Cornell University Ithaca, NY rjb9@cornell.edu Joan Luft Broad School of Business Michigan State University East Lansing, MI luftj@msu.edu Thanks to Tony Lednor and Christian Mastilak for research support, and the Johnson Graduate School of Management for funding. Helpful comments have been provided by Mark Nelson, Tom Dyckman and participants at Accounting Workshops at Cornell and Harvard Universities and the University of Arkansas.

2 Responsibility for Cost Management Hinders Learning to Avoid the Winner s Curse Abstract Errors in estimated product costs lead firms to win business that is unprofitable, because firms are more likely to win business when underestimated product costs lead them to bid below actual cost (Cooper et al. 1992; Hilton 2005). Feedback from repeated competitive bidding markets can teach people to bid well above estimated costs to avoid this winner s curse (Kagel 1995; Kagel and Levin 2002). We present experimental evidence that such learning is substantially hampered by managers sense of responsibility for the costs. This effect is consistent with psychological evidence that people tend to attribute bad outcomes to environmental factors out of their control, such as cost estimation errors, and attribute good outcomes to their own skills, such as their ability to choose effective cost management initiatives (Miller and Ross 1975; Zuckerman 1979). The results suggest that firms success in learning from market feedback might vary with organizational structure. 1

3 Responsibility for Cost Management Hinders Learning to Avoid the Winner s Curse I. INTRODUCTION Errors in estimated product costs lead firms to win business that is unprofitable, because firms are more likely to win business when underestimated product costs lead them to bid below actual cost (Cooper et al. 1992; Hilton 2005). 1 Feedback from repeated competitive bidding markets can teach people to bid well above estimated costs to avoid this winner s curse (Kagel 1995; Kagel and Levin 2002). We present experimental evidence that such learning is substantially hampered by managers sense of responsibility for the costs. This effect is consistent with psychological evidence that people tend to attribute bad outcomes to environmental factors out of their control, such as cost estimation errors, and attribute good outcomes to their own skills, such as their ability to choose effective cost management initiatives (Miller and Ross 1975; Zuckerman 1979). The results suggest that firms ability to respond to market feedback might vary with organizational structure. In our experiment, sellers bid on contracts for large complex projects, based on an estimate of project costs that includes error. Sellers firms undertake cost-management initiatives that are intended to increase their cost advantage compared to competitors. In one condition (responsibility for cost management), sellers choose among five initiatives, based on evidence of the initiatives past success. In the other condition (no responsibility), sellers receive the same information about the initiatives past success, but are assigned an initiative, as if the decision were made by another member of their firm. 1 One of the hardball competition tactics for which Stalk and Lachenauer (2004) provide anecdotal evidence is to encourage competitors into an unprofitable niche where they underestimate their costs. 2

4 Participants are not told the parameters governing the distributions of actual costs and estimation errors. Thus, they must rely on market feedback to learn optimal behavior over 14 periods in each of three replications of the task. In both settings, the effects of initiative on actual cost are small relative to noise in the cost estimates, making the auction an almost common-value auction (Klemperer 1998). In this setting, all equilibrium bids include a large amount of padding to avoid the winner s curse, but those who believe they have a cost advantage should pad less than those who believe they have a cost disadvantage. Our results show that the winner s curse is severe in early periods for participants in both conditions, who earn negative profits on average. Bidders without responsibility for cost management learn to avoid the curse, increasing the mean padding of their bids per period by over 60% between the first five and last five periods of the session. By the later periods of the third replication their losses are no longer statistically significant. In contrast, bidders with costmanagement responsibilities show no sustained increase in mean padding of bids, and their losses remain statistically significant throughout the session. Our results are consistent with the presence of attribution biases that lead sellers to attribute success to their own efforts or talents and failure to bad luck (Miller and Ross 1975). All sellers should attribute their initial losses to the bad luck of inaccurate cost estimates, for which they have no responsibility in our setting. How much they should pad bids to avoid these losses depends on the magnitude of their competitive cost advantage and cost-system estimation error. They must estimate these magnitudes based on experience, and attribution biases lead sellers with and without cost responsibility to interpret their experiences differently. A poor competitive cost position can be interpreted as bad luck for sellers without cost-management responsibility but as lack of talent or effort for sellers with responsibility. Attribution biases 3

5 therefore lead sellers without cost-management responsibilities to be relatively more likely to attribute their losses to poor competitive position (high actual costs), which implies they should pad their bids more than their higher-performing competitors. In contrast, sellers with costmanagement responsibilities are likely to attribute their losses less to poor competitive position and more to cost-estimation errors. Consistent with these predictions, sellers in the responsibility condition believe they have more competitive actual production costs and less accurate cost systems than sellers in the noresponsibility condition. An analysis of responses to feedback also shows that sellers with costmanagement responsibilities increase padding less in response to their own losses than do sellers without such responsibilities. However, both groups of sellers respond similarly after observing that another bidder in their market lost money. Our results shed new light on the conditions under which managers are likely to make appropriate adjustments to noisy estimated costs. Involvement in managing production is often expected to provide knowledge that aids individuals in responding to market feedback and adjusting production and pricing decisions to allow for cost-system error (Gupta and King 1997; Briers et al. 1999). Although involvement in production can produce beneficial knowledge, our results suggest that it can also have negative effects on individuals cost-related judgments by inducing biases that hamper learning from experience. Managers subjective adjustments to reported costs are often less than optimal and vary considerably across settings (Cooper and Kaplan 1987; Gupta and King 1997; Briers et al. 1999; Joshi, Krishnan and Lave 2001). Our results suggest responsibility assignment as a new reason that contributes to this variation. Pricing and cost-management responsibilities are often sharply separated in firms organized by function (e.g., marketing versus production functions); but product- or region-based 4

6 organization, or emphasis on cross-functional projects like target costing and activity-based management, creates combined responsibility for cost management and pricing 2 that can have negative effects on judgment if it is accompanied with high levels of cost-estimation error. Our results also point out important limitations to the common argument that accountingrelated decision errors will be eliminated through market discipline. Reduction of the winner s curse through market discipline is limited in our setting, as in most product markets, because an unbiased seller has no opportunity for arbitrage by buying an underpriced good from a competitor; as a result, marginal prices are set by sellers who underbid the most. 3 As a result, discipline must occur either through the bankruptcy of low bidders or through bidders responses to feedback. Bankruptcy is likely to have a weak disciplinary effect on pricing decisions among multi-product companies in environments beset by many random influences. Thus learning from feedback is the primary source of pricing-decision improvement. Even though our feedback is designed to be more informative than would be expected in most product markets (all bidders in our market observe the exact profit or loss of the low bidder), self-serving biases interfere with learning and allow pricing errors to persist. Finally, we extend understanding of self-serving attribution biases in business settings. Recent research in finance has shown how self-serving attribution biases can lead financialmarket traders to become overconfident with experience, and how overconfidence generates excess trading and patterns of over- and under-reaction to information (Daniel, Hirshleifer, and Subrahmanyam, 1998; Gervais and Odean 2001; and Barber and Odean 2002). Our study 2 Examples include involvement of production personnel in bid preparation (Cooper and Kaplan 1999, p. 113), and the creation of product-manager positions with profit and loss responsibility, connecting marketing, sales, engineering, and manufacturing functions (Cooper and Kaplan 1999, p. 521). 3 Ganguly, Kagel and Moser (1994) show how short-selling restrictions can cause similar problems in financial markets. 5

7 examines effects of similar biases in an organizational setting, showing how the biases can be exacerbated or mitigated by job design choices within the organization in this case, by separating versus combining the tasks of cost management and bidding. The remainder of the paper is organized as follows. Section II develops the hypothesis. Section III describes the experiment, Section IV presents the results, and Section V concludes the paper. II. THEORY AND HYPOTHESIS The Winner s Curse Textbooks, cases, and practitioner literature on product costing contain numerous examples of the winner s curse, in which firms win market share but lose money on products or services that are priced too low because their cost is underestimated (Cooper 1989; Cooper et al. 1992; Turney and Ittner 1993; Hilton 2005). The winner s curse arises when sellers fail to recognize that the bidder with the lowest cost estimate is likely to have the largest cost underestimate; hence if they bid aggressively based on low cost estimates, they will win bids but lose money when actual costs exceed the estimates. This information is easiest to understand in a common value auction in which all sellers in a market face identical costs, and all use the same noisy but unbiased cost estimation method. If the ranking of bids is the same as the ranking of cost estimates, the seller who bids the least can conclude that her cost estimate is likely to be too low, because every other bidder has a higher cost estimate. In equilibrium, all bidders must therefore pad their bids by bidding well above estimated cost, with the equilibrium level of padding increasing in the cost estimation error relative to variation in actual costs (Milgrom and Weber 1982). However, a large body of research shows that sellers fail to 6

8 pad bids enough (and buyers fail to shave bids enough) to eliminate the winner s curse completely (Kagel 1995). Most product markets are better described by almost common value auctions in which sellers costs are similar but not identical (Klemperer 1998). In this case, the appropriate level of padding depends on bidders beliefs about their competitive position. Those who believe they have the worst competitive position (the highest costs) should pad bids the most. If they win the bidding, it is likely to be because they have underestimated costs more than their competitors, not because they are actually the low-cost producers. In order not to lose money on winning bids they must therefore pad enough to compensate for particularly large cost underestimates. In contrast, those with the best competitive position (the lowest costs) should pad bids the least. If they win the bidding it is likely to be because they are actually the low-cost producers, not because they have severely underestimated their costs; therefore, they do not need to add as much padding to their cost estimates in order to make profits on winning bids. Managers can sometimes use market feedback to identify and adjust for the winner s curse. Cooper and Kaplan (1987, 211) describe a firm in which managers learned to adjust their prices to allow for cost-estimation error by observing the pricing behavior of market competitors with no apparent economic or technical advantage (i.e., with similar costs). In consequence, they used a rule-of-thumb for pricing that set the desired profit margin higher for products that were expected to have a large negative cost-estimation error. Market feedback is not always successful in eliminating or even reducing the winner s curse, however, and the factors that support or impede learning are not fully understood. Little learning to avoid the winner s curse occurs in two-party acquisition problems, as long as participants do not switch between buyer and seller roles (Ball, Bazerman, and Carroll 1991; 7

9 Foreman and Murnighan 1996). Significant learning often occurs in auctions (Lind and Plott 1991; Garvin and Kagel 1994; Foreman and Murnighan 1996), although it tends not to transfer well across different auctions (e.g., auctions with different numbers of participants, Kagel and Levin 1986). The winner s curse has been shown to persist with professional bidders from the commercial construction industry as well as with student subjects (Dyer, Kagel and Levin 1989), and pricing consistent with the winner s curse has been identified in the art market (Goetzmann and Spiegel 1995) and in government contracting (Bilginsoy 2000). Attribution Biases Research in psychology has extensively documented self-serving attribution bias, i.e., individuals tendency to take credit for success and attribute failure to factors out of their control (Miller and Ross 1975; Zuckerman 1979). In most winner s-curse experiments, participants have no influence over or responsibility for the value of the object for which they are bidding or the accuracy of the information given to them. Such experimental settings are analogous to firms in which production, accounting and marketing responsibilities are separated, and the manager who sets product prices has no responsibility for managing or estimating production costs. In the present study, we contrast this setting with one in which the same manager has some responsibility for both managing production costs and setting prices, but still has no responsibility or control over the magnitude of error in cost estimates. Based on attribution theory, we predict that this combination of decision responsibilities will limit individuals learning from market feedback to adjust for the error in reported product costs. In our setting, success and failure are determined by three factors: competitive cost advantage, error in the cost estimate, and bidding strategy. No managers in this setting are responsible for error in their cost estimate; cost errors therefore become sources of profit or loss 8

10 that lie outside the managers control, and according to attribution theory will be blamed for unprofitable outcomes. Managers who are not responsible for cost management will also tend to blame unprofitable outcomes on high actual costs (poor competitive advantage in costs). Even among such managers we predict that attribution biases can interfere with learning, as they may be slow to respond to feedback indicating that their past decisions about how much to pad bids have been wrong. We predict attribution biases will be more severe among managers who also have responsibility for cost management, however, because such managers will be less willing to attribute losses to high actual costs. Instead, they will believe that their selection of a costmanagement initiative was successful in reducing their costs, but that uncontrollable errors in cost estimation are driving their losses. Because cost-estimation error should lead to less bidpadding when sellers have a greater cost advantage over their competitors, and because sellers in the responsibility condition are more likely to believe they have such a cost advantage, they are less ready to conclude that cost-estimation error requires them to increase their bid padding substantially. H. Compared to managers who are responsible for pricing decisions only, managers who are responsible for both pricing and cost-management decisions learn less well from market feedback to avoid the winner s curse. III. METHOD Overview and Experimental Design The experiment is conducted using computerized laboratory markets in which 64 MBA students participate as bidders in auctions for production jobs. A cohort refers to a group of four bidders who compete to win contracts for large, complex production jobs. All costs and prices 9

11 are denominated in laboratory dollars that are ultimately converted into cash. Each bidder s firm has a cost-management initiative that may affect production costs. We manipulate participants sense of responsibility for their production costs by allowing participants in the eight cohorts randomly assigned to a responsibility setting to choose their own cost-management initiatives based on information about the initiatives past performance. Participants in the remaining eight cohorts are in a no-responsibility setting and are assigned cost-management initiatives by the experimenter but also see the same information about the initiatives past performance. We manipulate experience by allowing participants to bid in three regimes of 14 periods each. Each regime has a new set of five cost-management initiatives. The experiment has a 2 x 2 x 3 x 14 mixed design, with cost-management responsibility (present or absent) and order of regime 4 manipulated between cohorts and regime (1, 2 and 3) and period (1-14) manipulated within cohorts. Order of regime had no significant effect in any of our analyses, and is not discussed further. Instructions to participants are provided in Appendix A. Actual Project Costs Participants were told that their actual production cost each period included two components: A base cost, which was the same for all bidders in each period but differed from period to period for a variety of reasons, such as the size and location of the project, changes in input prices, and so on. A cost reduction due to the cost management initiative chosen by or assigned to the seller. This reduction remained the same through the 14 periods of each regime, and 4 A different set of cost-management initiatives was presented to participants in each regime. All cohorts received the set shown as Regime 1 in Figure 1 first. Half of the cohorts in each responsibility condition received the set shown as Regime 2 in Figure 1 next, followed by the Regime 3 set, while the other half received these two sets of initiatives in the reverse order. 10

12 was an absolute amount (e.g., $50) rather than a percentage of the base cost. When a new regime began, sellers chose or were assigned a new cost management initiative that might result in a different amount of cost reduction. Sellers did not know which cost management initiative other sellers in their cohort had chosen (or been assigned), but they knew it was possible for more than one seller to have the same initiative. Participants did not know the parameters of the distributions from which these cost components were drawn but had to learn them from experience. Both distributions were uniform, with the base cost ranging over the interval [400, 1000] and the cost reduction ranging over the interval [45, 55]. Estimated Project Costs Before bidding in each round, participants were given an estimate of their product cost. Participants were told that the estimate was just as likely to be above actual cost as below it, and that the estimation error would vary randomly across periods and bidders. Participants were told that no bidder would have error-free estimates of the actual cost of his or her projects, and that the errors in bidders cost estimates were likely (though not certain) to differ from each other. Participants were not told the actual range of cost estimation errors, which was over the interval [-200, 200]. Our setting differs from prior experimental studies of the winner s curse in two ways. First, most studies of the winner s curse have people bid for an asset with a common cost or value (Lind and Plott 1991; Foreman and Murnighan 1996). In such a setting, winning the auction provides a strong signal that one s estimate of cost or value is too optimistic. In contrast, some of the variation in bids in our setting (as in many natural settings) is due to variations in actual cost. This reduces the information about cost-estimation error conveyed by winning the 11

13 auction, because the winning bidder may have a true competitive advantage. However, the high variation in cost estimates, relative to variation in actual costs, makes the winner s curse almost as strong as in common-value auctions. The second difference from prior studies is that we do not inform participants of the parameters of the setting; rather, they must learn those parameters from experience. In particular, they must attribute past bids and earnings outcomes to either noise in cost estimates or variation in competitive advantages or both. Because participants cannot know in advance that variation in cost errors is high relative to variation in competitive cost advantage, the winner s curse is quite likely to arise in early periods. Our focus is on how a sense of responsibility for the competitive cost advantage alters participants ability to learn from feedback and avoid the winner s curse in later periods. Cost Initiatives For each regime, each bidder has a cost-reduction initiative that is either assigned (in the no-responsibility setting) or selected by them (in the responsibility setting). To make the importance of the initiative more salient, and provide a reasonable basis for a decision, participants were given fictitious data regarding each of the initiatives available for each regime. Specifically, they received statistics on five characteristics, described as follows in the experimental instructions: (i) Mean cost reduction achieved through this initiative at other firms. Other things equal, greater cost reduction is better. (ii) Similarity between your firm and others that have used this initiative, rated on a 1 4 scale. A higher rating means more similarity. Success in cost reduction in a very different industry or different size of firm may not be predictive of cost success for your firm. 12

14 (iii) (iv) (v) Percent of firms at which this initiative was judged as successful. A high mean cost reduction could be driven by one or two firms only, while other firms had no success at all. Total number of firms who have used this initiative. Even if 100% of users of the initiative were successful in reducing costs, this is not very informative if only one or two firms have used the initiative. User ratings of difficulty and cost of implementation, rated on a 1 7 scale. A higher rating means an easier and less costly implementation. Figure 1 presents the information on each initiative in each regime. We made no statements as to the source or validity of the statistics. The statistics were intended to allow sellers in both conditions the opportunity to believe they could assess the likelihood of having a competitive advantage. In the responsibility setting it also allowed sellers the opportunity to believe they had chosen the best initiative because they knew which dimensions of information were most important. The choice or assignment of cost management initiatives did not change the distribution from which individual participants cost reductions were drawn. Participants were told that costs of bidders with different initiatives might or might not differ. In fact, the choice of cost initiative had no effect on actual costs, which were based on predetermined random variables. (Thus, some bidders had a competitive cost advantage over others, but the advantage did not depend on which cost management initiative they had chosen.) While participants probably expected cost initiatives to have larger and more consistent effects on costs, such beliefs should have been constant across both the responsibility and no-responsibility treatments; thus, deviation of actual from expected parameters could not account for any differences across those treatments. 13

15 Bidding and Feedback In each period, participants learned their own estimated costs and entered bids from $0 to $1500 using the interface shown in Figure 2. The production contract was assigned to the lowest bidder at the price they bid (ties were broken randomly). After each round of trading, participants learned all four bids, whether they personally entered the winning bid, and the earnings of the winning bidder, as shown in Figure 3. By providing participants with vague prior knowledge about the distributions of costs and cost estimates, but providing extremely informative feedback, we maximize the extent of learning over the course of the session. Payments When all market sessions were completed, laboratory dollars were converted to U.S. dollars and investors received their cash payments. The conversion from laboratory to U.S. currency was based on the formula: US$ Payment = (Net Earnings in Laboratory $ + Adjustment Factor) x Exchange Rate Participants were not told in advance what the exchange rate or the adjustment factor would be. However, they were told that the exchange rate was positive, meaning that the more laboratory dollars they won, or the fewer they lost, the more $US they would take home. They were also told that minimum payment was $5 per hour, and that average payments would be approximately $20. Concealing the adjustment factor and exchange rate was intended to avoid risk-seeking behavior among traders who might otherwise know they would receive the minimum payment. 14

16 IV. RESULTS Preliminaries Before testing our hypothesis, we conduct some preliminary analyses to verify that our goals in experimental design were achieved. Two key goals were to make the winner s curse widespread in early rounds, and to allow opportunities for participants to learn to avoid it. Table 1 demonstrates that the winner s curse is prevalent. We compute mean earnings at the cohort level (in laboratory dollars) by computing the mean earnings per period over all 42 periods for each of the 16 cohorts. Mean cohort-level earnings per period over all three regimes are -48.6, which is significantly less than zero (p < 0.001, t-test using the 16 cohort means as the units of analysis). The result is not driven by only a few cohorts: 15 of the 16 cohorts have negative mean earnings. The result is also not driven by a small number of individual participants: 57 of 64 (89%) of participants have negative mean earnings over the 42 market periods. These data demonstrate that the winner s curse is severe and widespread in our setting, as expected. The table also demonstrates that participants have sufficient opportunities to learn to avoid the winner s curse. From regime 1 to regime 3, mean cohort-level losses drop from to While 95% of participants experienced mean losses in regime 1, only 66% did so in regime 3. Another goal was to provide broad opportunities for feedback and learning, and not to have winning bids come from a small proportion of our participant pool. Within each regime, over 90% of participants won at least 3 bids, while no participant won more than 18 bids out of the 42 rounds. Thus, feedback opportunities were well distributed. We also test whether participants believed (as indicated by the instructions) that their cost system provided unbiased estimates of cost across all projects, although it might overestimate 15

17 costs of some projects while underestimating the costs of others. To test our success, we asked participants Considering only the last set of 14 rounds, what was the average bias in your estimated costs? Participants in the no-responsibility setting believed estimated costs were negligibly below actual costs (mean = -3.75, std. dev = 74.0), while participants in the responsibility setting believed that estimated costs were negligibly above actual costs (mean = 0.63, s.d. = 68.1). The means are not significantly different from one another, nor are they significantly different from zero (t > 0.3 for all comparisons). Thus, on average, participants in both conditions believed that their costs were unbiased: in both conditions they had the same understanding of the pricing task as adjustment for errors that could either under- or overestimate product costs. Analysis of Padding We hypothesize that participants will learn to avoid the winner s curse, but that learning will be less effective in the responsibility setting. To test this hypothesis, we examine the extent to which participants pad their bids by bidding above the estimated cost provided by their cost accounting system. Padding, defined as bid minus estimated cost, is our most direct measure of participants strategies, and is unaffected by the variables that can influence earnings (including other participants strategies and realizations of actual cost). 5 In the course of the experiment, mean padding never became so large that a further increase would have been suboptimal (mean earnings never became positive); thus, padding increases can be interpreted as positive learning throughout the experiment. 5 We do not attempt to determine optimal padding. Optimal bids depend on individual conjectures about noise and competitive positions. These conjectures change in response to feedback, and should result in an evolution of padding toward the level that generates non-negative returns. 16

18 For our statistical analysis of padding, we divide each regime into three segments: early (periods 1-5), middle (periods 6-9) and late (periods 10-14) and calculate mean padding per period in each segment. Table 2 and Figure 4 show evidence that participants learn to increase their padding and do so more effectively in the no-responsibility setting. Panel A of Table 2 and Figure 4 report results for the complete set of data. Panel B in Figure 4 and one set of tests in Panel B of Table 2 omit a single cohort in the responsibility condition ( cohort 15 ), which behaves significantly differently from the other cohorts in that condition. The mean padding per period in cohort 15 is 173.7, which is 4.7 standard deviations from the mean of the other seven cohorts in the responsibility treatment (81.1). The only three participants who earned positive profits in the responsibility condition were from this cohort. 6 To assess statistical significance, we estimate repeated-measures ANOVAs with factors for regime (1-3), segment (early, middle, late), and responsibility treatment (responsibility, noresponsibility), using cohort means as the unit of analysis (n = 16). Our hypothesis predicts an interaction between responsibility and experience. Because learning effects may be different for different kinds of experience (across segments with the same cost-management initiative and across regimes with different cost-management initiatives) we test separately for interactions of responsibility with regime and segment. We also test for an effect of responsibility condition on the change in padding between the first five and last five periods of the experiment, as a test for the effect of participants experience in all 42 periods of bidding and feedback. Table 2, Panel B reports the key results of these ANOVAs. 6 Moreover, three of the four participants in this cohort made identical cost-management initiative choices in each regime, whereas in all other cohorts, each of the four participants chose a different set of costmanagement initiatives across the three regimes. 17

19 We find that the change in padding across regimes varies systematically with responsibility condition (regime x responsibility F = 4.23, p = 0.04, all cohorts; F = 11.29, p =.002 without cohort 15). This interaction reflects the increase in average padding from 90.0 in regime 1 to in regime 3 in the no-responsibility condition, compared to a negligible change from 89.3 to 90.7 in the responsibility condition (all cohorts). The effect of segment (early, middle, and late periods within each regime) varies with responsibility condition, although this variation is statistically significant only when the outlier cohort is omitted (F = 1.09, p =.37, all cohorts; F = 3.35, p =.04 without cohort 15). This interaction reflects the increase in padding from 96.1 in early segments to in late segments in the no-responsibility condition, compared to a smaller change from 91.4 to 98.2 in the responsibility condition (all cohorts). 7 Finally, we compare only the early segment of regime 1 with the late segment of regime 3, to test the total effect of experience both across and within regimes. This analysis also shows a strong interaction between experience and responsibility, with an overall increase of padding from 75.4 to for the no-responsibility group, compared to an increase from 82.2 to 85.1 for the responsibility group (F = 5.26, p =.04, all cohorts; F = 13.50, p =.003 without cohort 15). Thus, analysis of padding strongly supports our prediction that participants will find it more difficult to learn to avoid the winner s curse when they feel responsibility for cost management. Analysis of Earnings Many prior studies have shown that individual behavior in laboratory markets need not translate into market-level effects. For example, Kachelmeier (1996) finds that bidding and asking behavior in a double auction is influenced by historical cost information (the sunk cost 7 There are no significant (p <.10) responsibility x regime x segment interactions in any of these analyses. 18

20 fallacy), which should be irrelevant. However, he finds no influence of sunk costs on transaction prices, suggesting that the market mechanism is able to keep the psychological bias from affecting market-level prices and earnings. In contrast, we expect individual overbidding to cause market-level effects because of the way specific elements of the market structure interact with judgment biases created by cost-management responsibility. The competitive structure of our market, like many product markets, is one-sided: while there is competition among the sellers to provide the lowest asking price, buyers have no opportunity to compete by bidding more. As a result, prices are set by the seller who asks the least. In our setting, sellers with more biased judgment ask lower prices (unlike Kachelmeier 1996, where sellers with more biased judgment ask higher prices). Thus, biased sellers are more likely to win bids and influence mean earnings measures. Mean earnings per period by responsibility condition, regime, and segment are shown in Table 3 and Figure 5. To test whether earnings by winning bidders are influenced by the interaction of experience and responsibility condition, we simply repeat the analyses described above, using mean cohort earnings per period instead of mean cohort padding per period in each segment as our dependent variable. There is some evidence of interaction between regime and responsibility: with data from all cohorts included, losses in the no-responsibility setting decrease from 69.3 in regime 1 to 21.4 in regime 3; the corresponding decrease in the responsibility setting is from 68.6 to 47.9, about half as large. The omnibus F-test for regime x responsibility interaction is significant at conventional levels only if the outlier cohort is excluded (F = 1.73, p =.22 with all cohorts, F = 4.90 p =.03 without cohort 15). A directional contrast test for linear trend in the interaction provides a more powerful test, however (t = 1.79, p =.05 with all cohorts; t = 3.04, p =.01 without cohort 15; t-tests are one-tailed). 19

21 We find no evidence of an interaction between time segment and responsibility. Mean losses in the no-responsibility setting decline from 37.2 in early periods to 31.9 in late periods; the corresponding decrease in the responsibility setting is from 49.2 to The interaction is not statistically significant (p s >.6). Finally we examine the change in earnings from the early segment of regime 1 to the late segment of regime 3. Earnings increase from to in the no-responsibility setting, compared to an increase from 67.0 to in the responsibility setting, but the interaction is not significant. Interactions between responsibility and experience are less strong for earnings than for padding, but we do not believe this can be attributed with confidence to the effect of market discipline. Our power to detect learning effects is lower for earnings than for padding, for two reasons. First, earnings are influenced by a random variable with a large variance (actual cost), while padding is not subject to this random influence. Second, while padding in each period is the simple average of four participants decisions, earnings are heavily influenced by the decision of the low bidder. The identity of the low bidder differs from period to period: thus the low bid includes variation from period to period due to individual effects that are averaged out in the padding variable. The ratio of mean to variance for padding and earnings support this interpretation: as a proportion of the mean, variance is about twice as large for earnings as for padding. Analysis of Learning from Feedback To provide more direct evidence of how responsibility for cost management affects participants learning, we examine how changes in padding are associated with prior profits in the cohort. Reasonable bidders would increase their padding more after observing that the low bidder in their market experienced a larger loss in the prior round. They would respond even 20

22 more strongly to losses that they personally experienced, because such losses are more informative about their own costs. However, attribution biases should hinder bidders from learning from their own losses, which they would tend to attribute to circumstances beyond their control, particularly in the responsibility condition. We test responses to feedback within the responsibility and no-responsibility settings by estimating for each group the regression: PadChange i,t = a+b 1 *CohortProfit t-1 + b 2 *WinDum i,t-1 + b 3 *(CohortProfit t-1 *WinDum i,t-1 ) + ε i,t, where: PadChange i,t = Change in padding of bidder i from period t-1 to t CohortProfit t-1 = Profit of low bidder in cohort in period t-1 WinDum i,t-1 = a dummy variable with value 1 if trader i was the low bidder We conduct a pooled regression for each responsibility setting that includes one observation for every bidder-period for which we have the necessary data (8 cohorts x 4 bidders/cohort x 13 rounds/regime x 3 regimes = 1248). We find that bidders in both settings decrease their padding after failing to win the prior round (a = -15.1, p <.001, in the no-responsibility setting; a = -21.3, p <.001, in the responsibility setting) and increase their padding after winning the prior round (b 2 = 41.5, p <.001, no responsibility; b 2 = 47.5, p <.001, responsibility). These changes in padding may reflect mean reversion, if there is a random element in padding and individuals won bids when their padding was particularly low. They may also reflect the attractiveness of winning as such: bidders who did not win in period t 1 might be willing to reduce their padding in the hopes of winning in period t, although large losses suffered by the t 1 winner should make them more cautious about this reduction. 21

23 Lower profits (or greater losses) to the low bidder in the prior period are associated with larger increases (or smaller decreases) in padding in the current period (b 1 = in the noresponsibility setting, b 1 = in the responsibility setting, p < for both). This indicates that both groups learn to increase padding in response to the public feedback that the prior low bidder suffered the winner s curse. The key difference between the two settings lies in the winner s responses to their own profit, after controlling for the overall response to the profit of the low bidder. The coefficient b 3 is -.32 in the no-responsibility setting, indicating that bidders in that setting respond to their own profits significantly more strongly than they respond to others profits (p < 0.001). In contrast, the coefficient b 3 is 0.13 in the responsibility setting (p < 0.13). Thus, winners with costmanagement responsibility are not more responsive to their own profits than they are to others profits. Compared to winners without cost-management responsibility, they appear to be less willing to interpret their losses as strong indications that low cost estimates are unreliable (i.e., that they do not have a significant competitive cost advantage) and padding should increase. A combined regression with dummy variables to capture the main effects of responsibility and its interactions with the variables in the regression above indicate that the coefficient on b 3 is the only one that differs significantly across the two settings (p = 0.051, 1-tailed). 8 Overall, our analysis of learning is consistent with our hypothesis that bidders who feel responsibility for their competitive advantage will have difficulty learning from feedback that potentially reflects on that advantage. 8 Parameter estimates are similar when excluding cohort 15, but the coefficient on b 3 is not statistically significant (p = 0.12, 1-tailed). 22

24 Supplementary Analyses Our data indicate that participants in both of our treatments succumb to the winner s curse, but that only the participants in the no-responsibility setting act as though they are able to learn from feedback to pad bids and avoid losses. We interpret this data as evidence of an attribution bias among those with responsibility for both cost management and bidding. Further analysis of the data and post-experiment questions provides support for this explanation and allows us to reject some alternative explanations of the results. First, failure to learn in the responsibility condition is not due to lower levels of attention and comprehension in that condition. In fact, after they have chosen a cost-management initiative, participants in the responsibility condition take significantly longer to choose their bids than those in the no-responsibility condition (means of 20 seconds vs. 15 seconds per bid, respectively; t = 2.83, p =.01), suggesting that participants with cost-management responsibility pay more attention to the bidding task than those without responsibility. Moreover, participants with cost-management responsibility have at least as accurate an understanding of their market performance as sellers without responsibility. A post-experiment question asked participants to estimate their relative profit standing (the percentage of participants in other cohorts with earnings lower than their own). The Pearson correlation between individuals relative-profit estimates and actual profits was significant both for sellers with cost-management responsibility (r =.66, p =.00) and sellers without responsibility (r =.46, p =.01). 9 Second, differences between the responsibility and no-responsibility conditions are not driven by differences in the cost-management initiatives. The information on initiatives shown in Figure 1 was constructed with the intention of making the initiatives about equally attractive 9 Correlations without cohort 15 are similar: r =.69 (p =.00) for sellers with cost-management responsibility and r =.46 (p =.01) for sellers without responsibility. 23

25 ex ante. However, if some initiatives seemed much more attractive to participants than others, and if most participants in the no-responsibility condition were assigned to initiatives they did not prefer, the less aggressive bidding in the no-responsibility condition might be explained by plausible beliefs that the assigned cost-management initiative was unlikely to be successful and actual costs were unlikely to be as low as the lower cost estimates. In fact, participants preferences over initiatives were fairly diverse: the modal pattern was for the four individuals in a cohort to choose three different initiatives. The initiatives were not exactly equally attractive, however: participants displayed some preference for the initiative that had been used at firms most similar to the participants own. We therefore tested whether sellers bidding behavior in the no-responsibility condition differed, depending on which initiative was assigned to them (e.g., whether sellers who were assigned the initiatives more favored in the responsibility condition bid more aggressively). ANOVAs with mean padding by individual in each regime as the dependent variable and initiative assigned as the independent variable showed some effect of initiative in the first regime (F = 3.34, p =.05), but not in the second and third regimes (F =.08, p =.97, and F = 1.18, p =.32, respectively). Therefore it appears that the less aggressive bidding in the no-responsibility condition was not driven by individuals who received a less-preferred initiative. In addition to rejecting the alternative explanations above, we also analyze debriefing data to test some predictions that would be implied by attribution biases. We expected that participants with cost-management responsibility would be less likely than no-responsibility participants to attribute losses to cost disadvantages (which they controlled) and more likely to attribute them to errors in cost estimates (which they did not control). To test these conjectures, we asked all participants Considering only the LAST SET OF 14 ROUNDS, how do you think 24

26 your actual costs compared with your competitors costs, on average? (The response scale indicated dollar estimates of the amount by which their costs were lower or higher than their competitors.) By the end of the experiment, participants had a negative view of their performance: on average, they believed they had a cost disadvantage compared to their competitors. However, participants with cost-management responsibility condition believed this disadvantage was smaller than participants without responsibility. A simple comparison of means yields a marginally significant difference between the groups (mean estimates of $12.82 versus $20.31; one-tailed p =.09 without cohort 15, p =.13, all cohorts). Including a covariate for stated beliefs about the precision of cost estimates clarifies the difference (p = 0.04 for difference between groups in estimate of cost advantage, all cohorts). We also asked participants Considering only the LAST SET OF 14 ROUNDS, what was the average [absolute] difference between your costs and your cost estimates? Participants in the no-responsibility group estimated an average absolute error of 84.69, while those in the responsibility setting estimated an absolute error of The difference is statistically significant (1-tailed p = 0.045, all cohorts; p =.066 without cohort 15). Estimates of competitive advantage affect the extent of the winner s curse because those who believe they have a greater competitive advantage should pad bids less. Consistent with this conjecture, we find that differences in beliefs about competitive advantage significantly affected padding. Participants who believed they had less cost disadvantage in the last 14 periods (regime 3) padded their bids less in these periods (Pearson correlation between individuals mean regime 3 padding and their beliefs about their competitive cost advantage, r =.29, p =.02, all cohorts; r =.34, p =.01 without cohort 15). 25

27 Overall, analysis of debriefing data are consistent with our prediction that responsibility for cost management leads bidders to attribute losses to errors in cost estimates, rather than to competitive disadvantages, and that the resulting misperception of competitive position leads to persistently aggressive bidding, even in light of repeated feedback indicating that less aggressive bids would reduce losses. V. DISCUSSION Prior research has shown that individuals can learn from market feedback to adjust appropriately for erroneous or irrelevant information included in cost estimates (Briers et al. 1999; Gupta and King 1997; Kachelmeier1996; Waller, Shapiro and Sevcik 1999). In these prior studies, participants make only one type of decision, usually pricing. Our study contrasts the performance of individuals with responsibility for pricing decisions only and the performance of individuals with responsibility for cost-management decisions as well as pricing. In a setting where cost-estimation error initially induces a severe winner s curse, market feedback eliminates the curse for participants who make pricing decisions only, but fails to do so for participants who make both cost-management and pricing decisions. Although participants with cost-management responsibility spend longer on the pricing decisions than participants with pricing responsibility only, they are less successful in improving their pricing decisions. They overestimate their competitive cost advantage (or underestimate their disadvantage), compared to participants with pricing responsibility only; and in consequence, they add smaller pads to their cost estimates when bidding. When they lose money on a winning bid because actual costs are higher than estimated, participants in both experimental conditions pad their next bid more; but this learning effect is smaller for participants with cost-management responsibility than for those without. We suggest that this 26