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Winner’s curse in toll road concessions
Economics Letters 101 (2008) 172–174
Contents lists available at ScienceDirect
Economics Letters j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o n b a s e
Winner’s curse in toll road concessions Laure Athias a,b,⁎, Antonio Nuñez c a b c
ATOM, University of Paris Sorbonne, 112 bd. de l'Hôpital, 75013 Paris, France IDHEAP and SPAN, Rte de la Maladière 21, 1022 Chavannes-près-Renens, Switzerland LET, University of Lyon, 14, Avenue Berthelot, F-69363 Lyon, France
a r t i c l e
i n f o
Article history: Received 25 January 2007 Received in revised form 7 July 2008 Accepted 11 July 2008 Available online 25 July 2008
a b s t r a c t We empirically assess the effect of the winner’s curse in auctions for toll road concessions, taking into account, to our knowledge for the first time, problems of commitment and enforcement, using a unique dataset of 49 worldwide road concessions. © 2008 Elsevier B.V. All rights reserved.
Keywords: Auctions Common value Winner’s curse Incomplete contracts Concessions JEL classification: D44 H54 L51
1. Introduction Auctions for toll road concession contracts are common-value auctions in which an increase in the number of bidders has two counteracting effects on equilibrium bidding behaviour. First, increasing competition leads to more aggressive bidding, as each potential bidder tries to maximize her chances of winning against more rivals: this is the competitive effect. Second, the winner’s curse1 becomes more severe as the number of potential bidders increases, and rational bidders will bid less aggressively in response: this is the winner’s curse effect.2 If the winner’s curse effect is large enough, i.e. more than compensates for the increase in competition, prices could actually rise – in the context of procurement auctions – as the competition increases. As a result, governments should restrict entry, or favour negotiations over auctions (Bulow and Klemperer, 1996; Hong and Shum, 2002) when the winner’s curse is particularly strong. ⁎ Corresponding author. ATOM, University of Paris Sorbonne, 112 bd. de l'Hôpital, 75013 Paris, France. Tel.: +33 1 44 07 83 21; fax: +33 1 44 07 83 20. E-mail addresses: [email protected] (L. Athias), [email protected] (A. Nuñez). 1 The winner’s curse is an adverse-selection problem which arises because the winner tends to be the bidder with the most overly optimistic information concerning the value of the auction. 2 Thus, what is called winner’s curse effect in the rest of the paper is actually the internalization of the winner’s curse.
0165-1765/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2008.07.017
Nevertheless, these effects stand under the classical assumption that bidders are able to commit to bidding promises. One obstacle to these theoretical conclusions may be the realization by the intelligent bidder that the contract may later be subject to profitable renegotiation. This fact, highly highlighted in concession contracts (Guasch et al., 2003), affects bidding behaviour in subtle ways, and may strongly question the two theoretical effects highlighted above (Milgrom and Weber, 1982). In this paper, we consider the empirical importance of these considerations, using an original database of 49 worldwide toll road concession contracts, self collected. We show that bidders bid less aggressively in toll road concession auctions when they expect more competition (i.e. the winner’s curse effect is very strong), and that bidders will bid more strategically in weaker institutional frameworks, in which renegotiations are easier. 2. Auctions for road concessions In this paper we study bidding behaviour in first-price, sealed bid auctions, using data on road concessions. Concession contracts are awarded in two stages; in the first stage, private consortiums submit their technical qualifications, following the rules defined by the public authority. In the second stage, qualified consortiums are allowed to bid. Concessionaires are usually specific purpose companies, which shareholders are infrastructure construction companies and financial institutions, both either local or international.
L. Athias, A. Nuñez / Economics Letters 101 (2008) 172–174
173
Fig. 1. Distribution of TFD.
Demand forecasts for infrastructure projects include many uncertainties. Thus, each bidder’s traffic appraisal represents an estimate, subject to error. No bidder knows what future traffic will be and each one realizes that the other bidders may possess information or analyses he would find useful for his own forecast. As a result, in toll road concession auctions, the winning bidder may be the one who most overestimates future traffic. Bidders who would fail to take this selection bias into account at the bidding stage would be subject to the winner’s curse. Reasonably sophisticated bidders should then bid cautiously. These considerations lead us to the following proposition: Proposition 1. The greater the number of bidders, the more likely bidders will be conservative to correct for traffic overestimation, i.e. the greater the effects of the winner’s curse. However, imperfect enforcement leading to renegotiations is a major characteristic of concession contracts (Guasch et al., 2003). Thus, when bidders expect a high likelihood of renegotiation that renders it possible to avoid any losses, they have strong incentives to submit bids containing promises difficult to satisfy, with the sole purpose of being awarded the tender. Uncertainty in forecasts is then used in a strategic way by bidders, which is exacerbated by information asymmetries in concession projects. This feature leads to the following proposition, which has, to our knowledge, never been tested: Proposition 2. The lower the likelihood of contract renegotiation, the more likely bidders will be conservative as the number of bidders increases, i.e. the greater the effects of the winner’s curse. To test this double prediction, we now turn to the empirical part of the paper.
competition increases. A good measure for this correction is the relative discrepancy between the forecast and the actual traffic. We have data on the traffic forecasts included in the bids submitted by the winning bidders, and on actual traffic coming from traffic counts. The average ratio between them is called Traffic Forecast Deviation (TFD), defined as: TFD ¼
1 t0 þn−1 forecastt ∑ n t¼t0 actualt
ð1Þ
where n is the number of years for which we could compute this deviation. One aspect of the contractual record draws immediate attention: the prevalence of traffic overestimation, as highlighted by the existing literature (Flyvbjerg et al., 2003), since the average overestimation is 25%. Fig. 1 gives the distribution of this variable in the sample.
Table 1 Data definitions and descriptive statistics Variable
Obs Mean
TFD
49
1.253
Std. dev. Min Max 0.453 0.8
NUMBER OF BIDDERS 49 (NB)
3.918
1.891
CIVIL LAW
49
0.735
0.446 0
HIGH INCOME COUNTRY 49 (HIC)
0.531
0.504 0
GOVERNMENT LEARNING
49
2.531
3.056 0
LENGTH
49
1
3. Data on road concession contract auctions We have constructed a unique dataset of 49 worldwide toll road concession contract auctions (highways, bridges and tunnels). Most of the data included in the database was provided by concessionaires and by regulators. Some others come from scientific and professional press. As explained above, bidders need to correct for traffic overestimation and increase their correction on their estimate when
107.089 112.997 0.5
Definition
3.399 Ratio forecast traffic/actual traffic 9 Number of bidders for the contract, after the prequalification stage 1 1 if the country in question is under civil law regime; 0 otherwise 1 1 if the country in question is a high income country; 0 otherwise (source: World Bank) 10 Number of concessions the public authority has awarded before the present project 510 Length of the facility in kilometres
174
L. Athias, A. Nuñez / Economics Letters 101 (2008) 172–174
Table 2 Estimation results
NUMBER OF BIDDERS (NB) GOVLEARN ⁎ NB
Model 1
Model 2
Model 3
Model 4
−0.220⁎⁎⁎ (−2.87)
− 0.169⁎⁎ (− 1.97) − 0.010 (− 1.13)
−0.132⁎ (−2.72)
−0.347⁎⁎⁎ (−3.28)
HIC ⁎ NB
−0.136⁎⁎⁎ (−2.44)
CIVIL LAW ⁎ NB LENGTH Constant R2 Adjusted R2 N
0.452⁎⁎⁎ (4.37) 0.149 0.131 49
− 0.056⁎⁎ (− 2.44) 0.641⁎⁎⁎ (4.73) 0.276 0.228 49
−0.075⁎⁎⁎ (−3.30) 0.730⁎⁎⁎ (5.74) 0.342 0.298 49
0.119⁎ (1.71) −0.070⁎⁎⁎ (−2.97) 0.768⁎⁎⁎ (5.45) 0.340 0.297 49
Significance levels: +0.15 ⁎ 0.10 ⁎⁎ 0.05 ⁎⁎⁎ 0.01. t-stat are in parenthesis. The dummy variables are not taken as logarithms in the model.
The propositions to be tested formulated above suggest two main factors that are likely to influence the bidding behaviour and then TFD: the number of bidders and the likelihood of contract renegotiation. The actual number of bidders, after the prequalification stage, accounts for the level of competition. The hypothesis is that bidders will be more conservative the larger is the number of bidders, i.e. we expect a negative impact of the NUMBER OF BIDDERS variable on our TFD variable. Regarding the likelihood of contractual renegotiation, we used three variables. The first one, the variable GOVLEARN, reflects the experience of the procuring authority in awarding concession contracts. As a large number of prior concessions should decrease the probability of renegotiation (Guasch et al., 2003), we expect a negative impact of this variable interacted with the number of bidders variable on our dependent TFD variable. The second one is the indicator “high income country” (HIC) developed by the World Bank in 2006. As in Laffont (2005), the intuition behind is that wealthier countries are more able to invest in institutions than poorer countries, so that the probability of renegotiation is smaller and then the winner’s curse effect will be stronger. We expect therefore a negative impact of the crossed variable HIC ⁎ NUMBER OF BIDDERS on our TFD dependent variable. The third one, CIVIL LAW reflects the legal system of the country in which the project takes place. The intuition is that institutional features that traditionally characterize a common law regime make it more difficult to renegotiate under such a legal regime than under a civil law system (La Porta and Shleifer 2003). We expect therefore a lower winner’s curse effect in civil law countries. In addition, we include the physical LENGTH of the infrastructure as a control variable since it captures the heterogeneity in the sample (as the length is highly correlated with the forecast errors3) and renormalize the regression errors (See Table 1 for descriptive statistics). 4. Econometric results To test our double prediction, we first analyse the overall impact of the number of bidders on bidding behaviour (Model 1). We then
3 Regressing the squared errors on LENGTH and the other control variables, we find a very significant (t-test = −3.05) negative correlation.
identify in Models 2 to 4, if the theoretical effects still hold when we account for the possibility for bidders to renegotiate the contract. Results are reported in Table 2. The first striking result is that the number of bidders is clearly an important variable, driving the value of bidders’ tenders. Model 1 shows a negative impact of a fiercer competition on the traffic forecast deviation variable. It means that, overall, bidders are more conservative the more bidders there are, i.e. the effect of the winner’s curse in toll road concession contract auctions is strong. Results of models 2 to 4 show that this phenomenon is significantly stronger when bidders expect a lower likelihood of renegotiation. In particular, Model 2 indicates that the effect of the variable GOVLEARN interacted with the number of bidders is negative, though almost not significant, on the TFD variable. In addition, as predicted, the effect of the variable HIC interacted with the number of bidders is significantly negative on the traffic forecast deviation, meaning that bidders are more cognizant of the winner’s curse in HIC. Also, we find as predicted that the variable CIVIL LAW interacted with the number of bidders is positive on the traffic forecast deviation, implying that bidders less internalize the winner’s curse when bidding in civil law countries. Endogeneity of bidders is a concern for empirical studies on auctions. However, it is not a major issue here because, first, our dependent variable is not the bid itself but traffic forecast deviation, so that the potentiality of unobservable determinants of traffic forecast deviation is weak and, second, bidder endogeneity tends to mitigate the winner’s curse yet we find statistical evidence of the winner’s curse. 5. Conclusion and policy implications Using an original database, we show that the winner’s curse effect is particularly strong in toll road concession contract auctions. More precisely, the winner’s curse effect prevails on the competitive effect so that bidders bid less aggressively when they expect more competition. We also observe that the effect of the winner’s curse is weaker when the likelihood of renegotiation is higher, so that bidders will bid more strategically in weaker institutional frameworks. These original results point out the necessity to improve on the current theoretical framework by taking into account as a primary concern the impact of the perspective of later profitable renegotiation on equilibrium bidding behaviour. References Bulow, J., Klemperer, P., 1996. Auctions versus negotiations. American Economic Review, 86, 180–194. Flyvbjerg, B., Bruzelius, N., Rothengatter, W., 2003. Megaprojects and Risk — An Anatomy of Ambition. Cambridge University Press. Guasch, J.L., Laffont, J.J. and Straub, S., 2003, Renegotiation of Concession Contracts in Latin America. Mimeo. Hong, H., Shum, M., 2002. Increasing competition and the winner’s curse: evidence from procurement. Review of Economic Studies 69 (4), 871–898. Laffont, J.J., 2005. Regulation and development. Collection Frederico Caffe Lectures. Cambridge University Press. La Porta, R., Shleifer, A., 2003. Courts. Quarterly Journal of Economics 118. Milgrom, P., Weber, R., 1982. A theory of auctions and competitive bidding. Econometrica 50 (5), 1089–1122.
Contents lists available at ScienceDirect
Economics Letters j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o n b a s e
Winner’s curse in toll road concessions Laure Athias a,b,⁎, Antonio Nuñez c a b c
ATOM, University of Paris Sorbonne, 112 bd. de l'Hôpital, 75013 Paris, France IDHEAP and SPAN, Rte de la Maladière 21, 1022 Chavannes-près-Renens, Switzerland LET, University of Lyon, 14, Avenue Berthelot, F-69363 Lyon, France
a r t i c l e
i n f o
Article history: Received 25 January 2007 Received in revised form 7 July 2008 Accepted 11 July 2008 Available online 25 July 2008
a b s t r a c t We empirically assess the effect of the winner’s curse in auctions for toll road concessions, taking into account, to our knowledge for the first time, problems of commitment and enforcement, using a unique dataset of 49 worldwide road concessions. © 2008 Elsevier B.V. All rights reserved.
Keywords: Auctions Common value Winner’s curse Incomplete contracts Concessions JEL classification: D44 H54 L51
1. Introduction Auctions for toll road concession contracts are common-value auctions in which an increase in the number of bidders has two counteracting effects on equilibrium bidding behaviour. First, increasing competition leads to more aggressive bidding, as each potential bidder tries to maximize her chances of winning against more rivals: this is the competitive effect. Second, the winner’s curse1 becomes more severe as the number of potential bidders increases, and rational bidders will bid less aggressively in response: this is the winner’s curse effect.2 If the winner’s curse effect is large enough, i.e. more than compensates for the increase in competition, prices could actually rise – in the context of procurement auctions – as the competition increases. As a result, governments should restrict entry, or favour negotiations over auctions (Bulow and Klemperer, 1996; Hong and Shum, 2002) when the winner’s curse is particularly strong. ⁎ Corresponding author. ATOM, University of Paris Sorbonne, 112 bd. de l'Hôpital, 75013 Paris, France. Tel.: +33 1 44 07 83 21; fax: +33 1 44 07 83 20. E-mail addresses: [email protected] (L. Athias), [email protected] (A. Nuñez). 1 The winner’s curse is an adverse-selection problem which arises because the winner tends to be the bidder with the most overly optimistic information concerning the value of the auction. 2 Thus, what is called winner’s curse effect in the rest of the paper is actually the internalization of the winner’s curse.
0165-1765/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2008.07.017
Nevertheless, these effects stand under the classical assumption that bidders are able to commit to bidding promises. One obstacle to these theoretical conclusions may be the realization by the intelligent bidder that the contract may later be subject to profitable renegotiation. This fact, highly highlighted in concession contracts (Guasch et al., 2003), affects bidding behaviour in subtle ways, and may strongly question the two theoretical effects highlighted above (Milgrom and Weber, 1982). In this paper, we consider the empirical importance of these considerations, using an original database of 49 worldwide toll road concession contracts, self collected. We show that bidders bid less aggressively in toll road concession auctions when they expect more competition (i.e. the winner’s curse effect is very strong), and that bidders will bid more strategically in weaker institutional frameworks, in which renegotiations are easier. 2. Auctions for road concessions In this paper we study bidding behaviour in first-price, sealed bid auctions, using data on road concessions. Concession contracts are awarded in two stages; in the first stage, private consortiums submit their technical qualifications, following the rules defined by the public authority. In the second stage, qualified consortiums are allowed to bid. Concessionaires are usually specific purpose companies, which shareholders are infrastructure construction companies and financial institutions, both either local or international.
L. Athias, A. Nuñez / Economics Letters 101 (2008) 172–174
173
Fig. 1. Distribution of TFD.
Demand forecasts for infrastructure projects include many uncertainties. Thus, each bidder’s traffic appraisal represents an estimate, subject to error. No bidder knows what future traffic will be and each one realizes that the other bidders may possess information or analyses he would find useful for his own forecast. As a result, in toll road concession auctions, the winning bidder may be the one who most overestimates future traffic. Bidders who would fail to take this selection bias into account at the bidding stage would be subject to the winner’s curse. Reasonably sophisticated bidders should then bid cautiously. These considerations lead us to the following proposition: Proposition 1. The greater the number of bidders, the more likely bidders will be conservative to correct for traffic overestimation, i.e. the greater the effects of the winner’s curse. However, imperfect enforcement leading to renegotiations is a major characteristic of concession contracts (Guasch et al., 2003). Thus, when bidders expect a high likelihood of renegotiation that renders it possible to avoid any losses, they have strong incentives to submit bids containing promises difficult to satisfy, with the sole purpose of being awarded the tender. Uncertainty in forecasts is then used in a strategic way by bidders, which is exacerbated by information asymmetries in concession projects. This feature leads to the following proposition, which has, to our knowledge, never been tested: Proposition 2. The lower the likelihood of contract renegotiation, the more likely bidders will be conservative as the number of bidders increases, i.e. the greater the effects of the winner’s curse. To test this double prediction, we now turn to the empirical part of the paper.
competition increases. A good measure for this correction is the relative discrepancy between the forecast and the actual traffic. We have data on the traffic forecasts included in the bids submitted by the winning bidders, and on actual traffic coming from traffic counts. The average ratio between them is called Traffic Forecast Deviation (TFD), defined as: TFD ¼
1 t0 þn−1 forecastt ∑ n t¼t0 actualt
ð1Þ
where n is the number of years for which we could compute this deviation. One aspect of the contractual record draws immediate attention: the prevalence of traffic overestimation, as highlighted by the existing literature (Flyvbjerg et al., 2003), since the average overestimation is 25%. Fig. 1 gives the distribution of this variable in the sample.
Table 1 Data definitions and descriptive statistics Variable
Obs Mean
TFD
49
1.253
Std. dev. Min Max 0.453 0.8
NUMBER OF BIDDERS 49 (NB)
3.918
1.891
CIVIL LAW
49
0.735
0.446 0
HIGH INCOME COUNTRY 49 (HIC)
0.531
0.504 0
GOVERNMENT LEARNING
49
2.531
3.056 0
LENGTH
49
1
3. Data on road concession contract auctions We have constructed a unique dataset of 49 worldwide toll road concession contract auctions (highways, bridges and tunnels). Most of the data included in the database was provided by concessionaires and by regulators. Some others come from scientific and professional press. As explained above, bidders need to correct for traffic overestimation and increase their correction on their estimate when
107.089 112.997 0.5
Definition
3.399 Ratio forecast traffic/actual traffic 9 Number of bidders for the contract, after the prequalification stage 1 1 if the country in question is under civil law regime; 0 otherwise 1 1 if the country in question is a high income country; 0 otherwise (source: World Bank) 10 Number of concessions the public authority has awarded before the present project 510 Length of the facility in kilometres
174
L. Athias, A. Nuñez / Economics Letters 101 (2008) 172–174
Table 2 Estimation results
NUMBER OF BIDDERS (NB) GOVLEARN ⁎ NB
Model 1
Model 2
Model 3
Model 4
−0.220⁎⁎⁎ (−2.87)
− 0.169⁎⁎ (− 1.97) − 0.010 (− 1.13)
−0.132⁎ (−2.72)
−0.347⁎⁎⁎ (−3.28)
HIC ⁎ NB
−0.136⁎⁎⁎ (−2.44)
CIVIL LAW ⁎ NB LENGTH Constant R2 Adjusted R2 N
0.452⁎⁎⁎ (4.37) 0.149 0.131 49
− 0.056⁎⁎ (− 2.44) 0.641⁎⁎⁎ (4.73) 0.276 0.228 49
−0.075⁎⁎⁎ (−3.30) 0.730⁎⁎⁎ (5.74) 0.342 0.298 49
0.119⁎ (1.71) −0.070⁎⁎⁎ (−2.97) 0.768⁎⁎⁎ (5.45) 0.340 0.297 49
Significance levels: +0.15 ⁎ 0.10 ⁎⁎ 0.05 ⁎⁎⁎ 0.01. t-stat are in parenthesis. The dummy variables are not taken as logarithms in the model.
The propositions to be tested formulated above suggest two main factors that are likely to influence the bidding behaviour and then TFD: the number of bidders and the likelihood of contract renegotiation. The actual number of bidders, after the prequalification stage, accounts for the level of competition. The hypothesis is that bidders will be more conservative the larger is the number of bidders, i.e. we expect a negative impact of the NUMBER OF BIDDERS variable on our TFD variable. Regarding the likelihood of contractual renegotiation, we used three variables. The first one, the variable GOVLEARN, reflects the experience of the procuring authority in awarding concession contracts. As a large number of prior concessions should decrease the probability of renegotiation (Guasch et al., 2003), we expect a negative impact of this variable interacted with the number of bidders variable on our dependent TFD variable. The second one is the indicator “high income country” (HIC) developed by the World Bank in 2006. As in Laffont (2005), the intuition behind is that wealthier countries are more able to invest in institutions than poorer countries, so that the probability of renegotiation is smaller and then the winner’s curse effect will be stronger. We expect therefore a negative impact of the crossed variable HIC ⁎ NUMBER OF BIDDERS on our TFD dependent variable. The third one, CIVIL LAW reflects the legal system of the country in which the project takes place. The intuition is that institutional features that traditionally characterize a common law regime make it more difficult to renegotiate under such a legal regime than under a civil law system (La Porta and Shleifer 2003). We expect therefore a lower winner’s curse effect in civil law countries. In addition, we include the physical LENGTH of the infrastructure as a control variable since it captures the heterogeneity in the sample (as the length is highly correlated with the forecast errors3) and renormalize the regression errors (See Table 1 for descriptive statistics). 4. Econometric results To test our double prediction, we first analyse the overall impact of the number of bidders on bidding behaviour (Model 1). We then
3 Regressing the squared errors on LENGTH and the other control variables, we find a very significant (t-test = −3.05) negative correlation.
identify in Models 2 to 4, if the theoretical effects still hold when we account for the possibility for bidders to renegotiate the contract. Results are reported in Table 2. The first striking result is that the number of bidders is clearly an important variable, driving the value of bidders’ tenders. Model 1 shows a negative impact of a fiercer competition on the traffic forecast deviation variable. It means that, overall, bidders are more conservative the more bidders there are, i.e. the effect of the winner’s curse in toll road concession contract auctions is strong. Results of models 2 to 4 show that this phenomenon is significantly stronger when bidders expect a lower likelihood of renegotiation. In particular, Model 2 indicates that the effect of the variable GOVLEARN interacted with the number of bidders is negative, though almost not significant, on the TFD variable. In addition, as predicted, the effect of the variable HIC interacted with the number of bidders is significantly negative on the traffic forecast deviation, meaning that bidders are more cognizant of the winner’s curse in HIC. Also, we find as predicted that the variable CIVIL LAW interacted with the number of bidders is positive on the traffic forecast deviation, implying that bidders less internalize the winner’s curse when bidding in civil law countries. Endogeneity of bidders is a concern for empirical studies on auctions. However, it is not a major issue here because, first, our dependent variable is not the bid itself but traffic forecast deviation, so that the potentiality of unobservable determinants of traffic forecast deviation is weak and, second, bidder endogeneity tends to mitigate the winner’s curse yet we find statistical evidence of the winner’s curse. 5. Conclusion and policy implications Using an original database, we show that the winner’s curse effect is particularly strong in toll road concession contract auctions. More precisely, the winner’s curse effect prevails on the competitive effect so that bidders bid less aggressively when they expect more competition. We also observe that the effect of the winner’s curse is weaker when the likelihood of renegotiation is higher, so that bidders will bid more strategically in weaker institutional frameworks. These original results point out the necessity to improve on the current theoretical framework by taking into account as a primary concern the impact of the perspective of later profitable renegotiation on equilibrium bidding behaviour. References Bulow, J., Klemperer, P., 1996. Auctions versus negotiations. American Economic Review, 86, 180–194. Flyvbjerg, B., Bruzelius, N., Rothengatter, W., 2003. Megaprojects and Risk — An Anatomy of Ambition. Cambridge University Press. Guasch, J.L., Laffont, J.J. and Straub, S., 2003, Renegotiation of Concession Contracts in Latin America. Mimeo. Hong, H., Shum, M., 2002. Increasing competition and the winner’s curse: evidence from procurement. Review of Economic Studies 69 (4), 871–898. Laffont, J.J., 2005. Regulation and development. Collection Frederico Caffe Lectures. Cambridge University Press. La Porta, R., Shleifer, A., 2003. Courts. Quarterly Journal of Economics 118. Milgrom, P., Weber, R., 1982. A theory of auctions and competitive bidding. Econometrica 50 (5), 1089–1122.