Correcting Information Asymmetry via Deep Consumer Information; Compelling Companies to Let the Sunshine In
in CONSUMER LAW AND ECONOMICS (Klaus Mathis and Avishalom Tor, eds., Cham, Switzerland: Springer 2020) 151-176.
23 Pages Posted: 30 Apr 2019 Last revised: 2 Jan 2021
Date Written: March 15, 2019
Consumers that want to make ethical purchasing decisions and governments that want to make policy decisions to stimulate ethical manufacturing, are left in the dark. Many products are composed of several constituting parts, with or without negative externalities, manufactured by often separate producers, which increases the general perplexity about their degree of ethicality.
In ‘More Than You Wanted to Know: The Failure of Mandated Disclosure’, Professors Ben-Shahar and Schneider have exposed systemic challenges to mandated disclosurite systems. Building upon their work, and applying their lessons, this paper explores the possibility of a disclosurite system, “Deep Consumer Information”, which does not mandate, but nevertheless compels companies, due to market forces, to disclose information about the ethicality of their products. Combining Neoclassical Economics (giving consumers the opportunity to make rational choices about the relative weights they want to give to certain ethical issues, for example via intuitive sliders on an app) and Behavioral Economics (notifying the aggregate ethicality ranking of the constituting parts of a product, that can be displayed on the screen of a phone or at digital supermarket shelves), Deep Consumer Information is trying to correct the asymmetry between on the one hand; company and consumer, and on the other; company and government.
Company and Consumer
Certification organizations can rank the manufacturing of the constituting parts and the end-product in regard to their ethical degree. The lnternet of Things (IoT) makes it possible that each constituting part of a product will be assigned extra information about its ethicality and which data can be harvested by a ranking algorithm. The ranking of ethical manufacturing can be looked at from different or aggregate dimensions. If companies do not want to disclose information about the ethicality of their products, Deep Consumer Information will make assumptions, so that their ranking will be lower than companies that do cooperate. Ethicality rankings can enable consumers to compare between similar products and make more informed purchasing decisions based on intuitive one-number rankings that take negative externalities with their hidden costs into account.
Company and Government
Deep Consumer Information provides societies the possibility to create a level playing field for ethically manufactured products with those products that cause negative externalities. Governments can tax the unethically manufactured products by adding the hidden costs of the negative externalities, to reduce or remove the unfair price difference and cure this market imperfection in the process. This can lead to a virtuous loop of ever higher levels of ethical manufacturing.
The problem of information asymmetry is not unique to the People’s Republic of China. However, the Chinese government has experimented since 2015 with comprehensive methods to rank companies but also citizens to nudge and sanction them into model participants of society in regard to China’s specific standard of trustworthiness. In 2020, this Social Credit system must be implemented. Although some commentators expect this to lead to a dystopian future, ranking systems applied to the ethicality of products could have beneficial implications for society.
After the introduction (Section 1), this paper describes how Deep Consumer Information could tackle some problems of mandatory disclosurite systems, correcting the information asymmetry between company and consumer (Section 2), company and government (Section 3), but is facing other daunting challenges about what is an ethical product and who certifies the certifications (Section 4). The Concluding Section will provide an outlook into possible solutions (Section 5).
Keywords: Deep Consumer Information, law and economics, disclosurite system, information asymmetry, nudge, ranking products, certifications, trademark, PDO, PGI, geographical indications, standards, source of origin, algorithmic ethics
JEL Classification: K00, K19, K32, K39, I10, O13
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