An empirical study of scoring auctions and quality manipulation corruption

Huang, Yangguang

doi:10.1016/j.euroecorev.2019.103322

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…Huang and Xia's research (2019) on quality manipulation corruption finds that buyers would overstate their preference in response to corruption deterrence and quality distortion. In Huang's related empirical study (Huang, 2019), he shows that the quality testing agents may magnify the score of a favored firm in exchange for a bribe, according to data of Chinese server room procurement auctions. In a complex procurement auction model of community, the existence of competition provides a more efficient approach to deter favoritism and decrease corruption (Caillaud & Lambert-Mogiliansky, 2021).…”

Section: Related Literaturementioning

confidence: 99%

Does surround‐bidding corruption hurt procurers?

Yin

Zhang

2023

The Manchester School

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We consider a model of corruption in the form of surround-bidding in a first-price procurement auction in which bidders' private cost follows uniform distribution.We find that the briber's high-price bidding function is less aggressive than honest suppliers' while his low-price one is more aggressive. As the bribery cost increases, both the briber's low-price and high-price bidding functions become less aggressive. The winning probability and expected profit of the briber increase, while the winning probability and expected profit of honest suppliers decrease. Surprisingly, although the briber's high-price bid may be the winning bid, which is harmful to the procurer, the procurer's expected payment decreases, that is, the procurer benefits from surround-bidding corruption, because the benefit due to more intense competition outweighs the harm caused by the briber's high-price bid.

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Section: Related Literaturementioning

confidence: 99%

Does surround‐bidding corruption hurt procurers?

Yin

Zhang

2023

The Manchester School

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“…1. Institutional quality broadly captures the quality of governance of a country, including measures of voice and accountability, rule of law, governance effectiveness, regulatory quality, political stability and absence of violence/terrorism, and control of corruption (Huang, 2019). Given the close relationship between the legal system and these factors, in this study, we argue that legal origin serves as an indicator of institutional quality.…”

Section: Notesmentioning

confidence: 99%

The influence of corporate governance on the performance of family-controlled firms: exploring the effects of legal jurisdiction

Yang

Shan

et al. 2022

IJMF

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PurposeThis study investigates effects of corporate governance on the financial performance of family-controlled firms and how these effects differ between common law and civil law jurisdictions.Design/methodology/approachThis study applies a number of corporate governance measures to the largest 243 publicly listed family-controlled businesses worldwide from 2009 to 2018. The corporate governance measures include board independence, board gender diversity, corporate governance index (CGI) and the percentage of family ownership.FindingsThe empirical evidence indicates that board independence improves financial performance; this positive effect is more pronounced in common law than civil law jurisdictions. Board gender diversity has a negative impact on financial performance under common law but a positive impact in civil law jurisdictions. Moreover, the CGI and family ownership structure are positively associated with financial performance, and no difference is found between the two jurisdiction types. In addition, family ownership negatively moderates CGI in civil law countries only.Originality/valueThis study provides new insight on the relevance of considering jurisdictional differences when examining the effect of corporate governance on performance. The study also addresses important concerns in family business research relating to unobserved heterogeneity and endogeneity. Implications of these for research and practice are discussed in the paper.

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“…Corruption is a form of dishonesty undertaken by a person or organisation with the authority to acquire illicit benefit. ere are empirical studies to detect corruption by analysing public tenders in many countries, for example, in China [55], Russia [56], the Czech Republic [57], and Hungary [58]. e application of algorithms by governments or enterprises to detect collusion or corruption [59], especially using machine learning methods [60][61][62], has become an almost inevitable topic and the subject of numerous studies.…”

Section: Complexitymentioning

confidence: 99%

Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain

et al. 2020

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Recommending the identity of bidders in public procurement auctions (tenders) has a significant impact in many areas of public procurement, but it has not yet been studied in depth. A bidders recommender would be a very beneficial tool because a supplier (company) can search appropriate tenders and, vice versa, a public procurement agency can discover automatically unknown companies which are suitable for its tender. This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest classifier. The bidders recommender is described theoretically, so it can be implemented or adapted to any particular situation. It has been successfully validated with a case study: an actual Spanish tender dataset (free public information) which has 102,087 tenders from 2014 to 2020 and a company dataset (nonfree public information) which has 1,353,213 Spanish companies. Quantitative, graphical, and statistical descriptions of both datasets are presented. The results of the case study were satisfactory: the winning bidding company is within the recommended companies group, from 24% to 38% of the tenders, according to different test conditions and scenarios.

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An empirical study of scoring auctions and quality manipulation corruption

Cited by 8 publications

References 29 publications

Does surround‐bidding corruption hurt procurers?

Does surround‐bidding corruption hurt procurers?

The influence of corporate governance on the performance of family-controlled firms: exploring the effects of legal jurisdiction

Bidders Recommender for Public Procurement Auctions Using Machine Learning: Data Analysis, Algorithm, and Case Study with Tenders from Spain

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