Adverse selection

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Abstract

In this chapter, we discuss the concept of adverse selection, which is a problem that stems from the information asymmetry where a strategic behaviour by the more informed counterparty in a contract works against the interest of the less informed counterparties. We looked at the two ways in which management literature has suggested that it is used to manage this, both the accounting framework and the agency theory. We observe that in the agency theory, certain scholars have suggested that there should be penalties or incentives so that transparent behaviour is promoted to limit the agency problem. Our conclusion in this regard, is that the action whether to disclose material facts or not is still dependent on one economic agent expected to be unlikely in a situation where the disclosure of such information has a potential to reduce economic benefits to that particular economic agent. On the IAS Conceptual Framework, we observe that it appears to be appealing to the conscience or ethics that economic agents should do what is morally correct, which is clear from the use of the word “fair”. The challenge of this approach is whether the economic agent could be persuaded to do what is fair in the face of potential economic losses should they cede certain strategic advantages associated with having certain information that counterparties in the transaction do not have. The era of intense automation and digitization is likely going to push economic agents to some new forms of relationships. This could include sharing certain information that opens the opportunity for harvesting and storing big data that could be useful to economic agents. AI brings the possibility of building, linking and analysing this new big data sets that would otherwise be impossible for a human being, thus reducing the extent of adverse selection.

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APA

Moloi, T., & Marwala, T. (2020). Adverse selection. In Advanced Information and Knowledge Processing (pp. 71–79). Springer. https://doi.org/10.1007/978-3-030-42962-1_8

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