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We present results of several experiments that deal with endogenous entry in auctions and auction valuation. One observation that is constant across all the experiments we report is that laboratory subjects have a difficult time evaluating potential gains from auctions. Even after they are given some experience with particular auctions, the uncertainty inherent in the auctions (the probability of winning as well as the potential gains from winning) makes it difficult for subjects to compare different auction mechanisms. This highlights the need for new experimental procedures to be used for testing theories that involve endogenous auction entry in the laboratory.

The purpose of this paper is to examine the underpricing effect in Treasury auctions.

The paper compares two winner's curse models using a dataset on multi‐unit auctions. The dataset is from Swedish Treasury auctions, which is under a discriminatory auction mechanism. One model is a single‐unit equilibrium model assuming that each bidder bids for 100 percent of the auctioned securities, which is described by Wilson and solved by Levin and Smith. The other model is a multi‐unit model calibrated by Goldreich using the US Treasury auctions data and assumes that each bidder bids for one unit of the auctioned securities.

The empirical results show that, although both models work well in predicting the bid‐shading, the multi‐unit model fits the Swedish Treasury auctions data better than the single‐unit model.

The evidence implies that bidders rationally adjust their bids due to the winner's curse/champion's plague.

This study provides close quantitative predictions of the amount of bid‐shading using both single‐unit model of Wilson and multi‐unit model of Goldreich, and indicates that winner's curse or champion's plague worries bidders in countries other than the USA.

Local public officials rely on tax and non-tax incentive packages to develop their economies. No conclusive evidence supports the economic improvement incentives afford. We investigate, with an experimental approach, the political reasons public officials use tax incentives. The experiment uses simulation gaming to model local economic development as an auction, in that way permitting us to compare the impact that motives, goals, and contexts have on outcomes. Our findings suggest that the majority of economic development competitors fall victim to the “winner’s curse”-overestimating and overbidding the potential payoff for business development.

This research aims to contrast bid competitiveness with respect to the average bid auction (ABA) and the non-ABA bidding formats used by the Public Works Department (PWD) of Malaysia.

The research uses the ordinary least square regression and the Monte–Carlo simulation to point out significant predictors which affect the bid ratio and fitting probability distributions to bidding data, respectively.

This research shows that the bidding strategy adopted is dependent on the different formats used. In the ABA format, bidders are more likely to submit identical bid prices. In the non-ABA format, they bid according to the first-price auction strategy, which suggests greater variation between bid prices as a winning strategy and the reduction in the bid price to an estimated price ratio when more bidders bid.

Bidders lose more money when the distance between the project location and a firm’s operational office is greater. Best-fit probability density functions follow a gamma distribution for the ABA format and a Weibull distribution for the non-ABA format. The location and number of bidders affect bidders’ strategy to win.

This research presents empirical insights concerning the comparisons of different type of bidding formats practiced by PWD of Malaysia and its implications on the construction companies’ bidding behaviors especially when it comes to its economic consequences. The significant factors that affect the different auction mechanisms used can serve as a basis for improving the present methods employed by PWD and in other countries.

This paper aims to assess all the Brazilian electric power transmission line auctions occurred between 1999 and 2017.

A copula-based Roy/endogenous switching regression model is used. The suitability of this model is twofold: it takes into account the selection bias problem involving auctions data and it allows more flexibility in modeling the joint distribution between the unobserved components of the selection and outcome equations; thus, normal distribution assumptions are not needed.

The main results suggest that stated-owned companies have the highest probability of winning an auction, and there is a non-competitive behavior among the players in the auction. The results also suggest some departure from joint normality in the data.

The copula-based sample selection approach used in this paper is consistent under non-normality and allows one to address different types of nonlinearities in the data such as asymmetry and heavy tails.

The aim of this case study is to review the in-depth (and successful) investigation carried out in 2016 by the Italian Competition Authority [Autorità Garante della Concorrenza e del Mercato (AGCM)] on a nation-wide (multi-lot) framework agreement for consulting services. We also critically assess the tender design and emphasize which dimensions may have facilitated the uncovered anticompetitive agreement.

The case study borrows from the official Antitrust Authorities’ findings and from the tender documents to paint a comprehensive picture of the cartel’s strategy.

The case study emphasizes that AGCM’s the “conjectured logic” of the cartel’s behaviour (endogenous evidence) did coincide with those pieces of evidence seized by police forces for criminal crimes at the cartel members’ premises (exogeneous evidence). This infrequent feature of bidding rings investigations underlines the importance of theoretical as well as practical analyses of cartels’ behaviour in public procurement markets.

As the antitrust investigation was triggered by a confidential report sent by the awarding authority (Consip, the Italian national central purchasing body), the case study also emphasizes the importance of informal as well as formal co-operation between awarding authorities, especially central purchasing bodies, and competition authorities.

The case study belongs to a small set of applied research papers attempting at building a bridge between public procurement design, particularly of sizeable framework agreements, and the mechanisms devised by cartels to “game” procurement procedures. All this is accomplished by looking at all design dimensions that were exploited by cartel’s members.

When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator can severely over-reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not too small, they can either over-reject or under-reject seriously when it is. In a previous paper, we showed that procedures based on randomization inference (RI) can work well in such cases. However, RI can be impractical when the number of possible randomizations is small. We propose a bootstrap-based alternative to RI, which mitigates the discrete nature of RI p values in the few-clusters case. We also compare it to two other procedures. None of them works perfectly when the number of clusters is very small, but they can work surprisingly well.

This paper aims to empirically test the effect of list price and bidding strategies in ascending auctions of residential real estate.

Three regression models are estimated, using a unique data set from 629 condominium apartments in the inner-city of Stockholm, Sweden, sold between January 2010 and December 2011.

The results show that jump bidding has the predicted effect of reducing competition by scaring off bidders. However, a higher average bid increment leads to a higher selling price. Furthermore, results show that a fast auction in terms of average time between bids acts to increase the probability of so-called auction fever as both the number of bidders and the selling price are positively correlated with the speed of the auction. While the average behavior of all auction participants, in terms of jump bidding and time between bids, significantly affects auction outcomes, differences in strategies applied by winners and losers show mixed results. The results of this study with respect to sellers’ list price strategy show that underpricing is an ineffective strategy in terms of enticing more bidders to participate in the auction. Furthermore, underpricing is not sufficient to have a positive effect on the selling price.

This paper is one of the first papers to empirically analyze how different bidding strategies affect the outcome of residential real estate auctions in terms of competition and the final selling price.

This paper aims to study aggressive bidding strategies in real estate auctions – a structural equation modelling (SEM) approach.

The authors use two data sets to study aggressive bidding strategies. First, the results from a survey with 1,803 participants examining real estate auctions are used to identify bidding strategies and related motivations. Second, the authors apply SEM by using data from 1,078 exclusive auction journals from real estate sales in Norway to study both the direct and indirect price effects of the bidding strategies.

The authors define four aggressive bidding strategies: high opening bid, high bid increase (jump bids), short acceptance deadline and short response time. The authors find that all four strategies yield a higher sales price. Bidders can actively influence the behaviour of the other participants and cool the potential auction fever, thus reducing the final price premium.

This paper gives households, investors and policymakers a better understanding of how bidding strategies affect real estate auctions and the final price.