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An econometric identification of abnormally low bids in the procurement market: discriminant analysis

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Abstract

In the public construction procurement market, ‘abnormally low bids (ALB)’ are prevalent and they cause many social and economic problems. Also, when the procurement bids are colluded, ALB make the competitive price systematically underestimated. As many countries regulate ALB, their criteria to identify ALB are not homogenous. Most of the criteria are based on construction cost, which is usually inaccurate, vulnerable to accounting manipulation, and limited to the supply side information of the market. We propose an econometric identification process of ALB using a discriminant analysis. It is based on a general mixture model and easily estimable by MLE. We apply our method to Korean public construction bidding data from 2007 to 2016. The estimation results identify the determinants of the bid prices, along with the determinants of ALB, and presents a more accurate assessment of the collusion damage.

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Data availability

The data used in this paper are available from the authors upon request.

Notes

  1. Carpineti et al. (2006). Although the U.S. Federal Acquisition Regulation does not define ALB explicitly, it emphasizes that all prices during the procurement procedure should be fair and reasonable. The footnote 27 in Carpineti et al. (2006) explains what ‘fair and reasonable prices’ are.

  2. See Harrower (1999) for a detailed explanation.

  3. OECD(2016), Ibrahimi(2017)

  4. We agree with an anonymous referee that there exist collusions other than bid manipulation, and such a collusion does not necessarily raise the bid price. As De Leverano (2019) shows, non-price collusions such as market sharing agreements may not increase the bid price. It would not be successful at all to ‘detect’ collusion using bid price in such cases. As the referee points out, there have been developed a number of alternative collusion detection methods, even machine learing techniques as in Palshikar and Apte (2008) or Allahbakhsh et al. (2013). However, what we focus here is not collusion detection but damage assessment. Even in non-price collusion cases, ALB can mislead the damage assessment. As ALB lowers the estimated competitive price, the collusion effect based on the lowered competitive price would be ‘relatively’ overestimated, even though the collusion did not actually increase the bid price.

  5. For example, McAfee and McMillan (1987).

  6. As Ellison (1994) deals with time-series data, he uses a Markov structure for the logit function. We employ a contemporary logit structure, as we apply our method to cross-section data.

  7. Sometimes, the participating firms in an auction may offer ALB as a strategic ‘collusive’ behavior, to manipulate the tendering in favor of other bidders. This interesting behavior is important to analyze the relations between ALB and collusion. Ideally, if the data for all the participating bids are available, \(Z_{t}\) may include some variables on the collusive behaviors of the participating firms to investigate toward this end. Unfortunately, however, we only have the winner’s bids in our data set. Thus, in our model, \(\lambda_{t}\) is the probability of a ‘winning’ bid being an ALB, and cannot include the variables affecting ‘losing’ bids. We are grateful to an anonymous referee for drawing our attention to this important issue.

  8. We use GAUSS and R for the numerical maximization.

  9. Lee and Porter(1984), pp 400–401.

  10. For the structure and regulations in Korean public construction procurement market, see Jeong and Lee (2018).

  11. We use only the bids in which the number of bidders are less than 21. The reasons are: first, any bid with more than 20 bidders is regulated by a different Pre-Qualification standard, second, those bids with more than 20 bidders are usually very small construction project.

  12. There exists lots disaggregation in our collected data. Although lots disaggregation can make difference in the relations between the number of bidders and collusion, we could not control for lots disaggregation due to lack of information.

  13. Thus, there exists a possibility of under-detection for the collusion dummy.

  14. We are grateful to an anonymous referee for pointing this out.

  15. The maximum bid rate is 1 as the bid was exactly the same as the preliminary estimate in a contract (Juam Dam water conveyance tunnel stabilization project).

  16. Based on the discussion of the reasons motivating ALB in Sect. 2, it is natural to include some company level characteristics such as financial problems, legal liability limit, or liquidity status of the company in the regression model. We discovered, unfortunately, that such information is highly confidential in reality. Instead, we have tried an alternative model with the winner company heterogeneity as additional explanatory variables. We have included dummies for the 11 frequent bid winners (which won more than 20 bids in our data) in the model, to incorporate the company characteristics. The estimation results are almost identical to the one we present in Tables 3 and 4, and the winner heterogeneity turns out statistically insignificant all but one exception. The results are available from the authors upon request.

  17. ‘p.p.’ stands for ‘percentage point.’.

  18. As an anonymous referee points out, the collusion dummy variable could be endogenous. In our mixture regression model, however, a simultaneous equation system is not easy to implement. We note this limitation, and leave the collusion endogeneity analysis as a future work.

  19. We are grateful to an anonymous referee for pointing this out. Of course, though, as we do not have all bidding data except the winner’s, we should be cautious in making any assertive conclusion.

  20. As an anonymous referee suggests, it would be ideal if we could follow-up these 82 ALB contracts to see what happens after the construction is complete. Our inability to do so due to our limited resources is noted.

  21. This is somewhat counter-intuitive, considering a lowest price auction would naturally attract more price-ALB. We conjecture the reasons are as follows. First, in Korean procurement market, the weight on construction plan score is quite lower than price score. Choi and Lee (2012) explains that Korean government prefers low weight on construction plan score because such a subjective evaluation may attract a dispute on fairness. Second, ALB could occur to make up the bidder’s low construction plan score in a turn-key type bidding. As we only detect price-ALB, such a drastic reduction of price in turn-key type bidding is identified as ALB, regardless of the quality score. As we mentioned in Introduction, this is a limitation of our approach.

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Acknowledgements

We are grateful to Jin S. Cho, Yun J. Choi, Jiyoung Lee, the participants of Yonsei School of Economics Wednesday Seminar, and two anonymous referees for their helpful advice and comments. We also thank Construction Association of Korea (CAK) for the valuable data. This study was partially funded by the National Research Foundation of Korea (Grant Number NRF-2016S1A3A2923769). The authors declare that they have no conflict of interest.

Funding

This study was partially funded by the National Research Foundation of Korea (Grant Number NRF-2016S1A3A2923769).

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  1. School of Economics, Yonsei University, Seoul, Korea

    Jinook Jeong & Hyunwoo Lee

  2. Department of Economics, Emory University, Atlanta, USA

    Jung Jae Kim

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  1. Jinook Jeong
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Correspondence to Jinook Jeong.

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Appendix

Appendix

See Tables 6 and 7.

Table 6 Comparison of competitive bids and colluded bids
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Table 7 Comparison by estimated posterior probability of ALB
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Jeong, J., Lee, H. & Kim, J.J. An econometric identification of abnormally low bids in the procurement market: discriminant analysis. J. Ind. Bus. Econ. 51, 211–234 (2024). https://doi.org/10.1007/s40812-023-00257-1

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