Disclosure requirements can serve legitimate public purposes, but a blanket mandate risks negative economic ramifications. In a new paper, I examine how excessive mandatory disclosure could backfire, what makes an appropriate rule, and the l challenges of  evaluating policies on disclosure. 

The Problem

Information as public good can generate positive externality only when it is not so excessive that it becomes noise. Proponents of mandatory disclosure[1] argue that without such rules, information may be underprovided because producers cannot capture its full economic value. However, this overlooks the principle that social welfare is maximized when marginal benefits exceed marginal costs. Analysts are given incentives to produce high-quality research when marginal benefits such as commissions from additional research outweigh extra costs like time and resources. First-mover advantages, where analysts acquire undervalued stocks before publishing insights, further drive research without mandatory rules.

Proponents also warn of social waste from excessive and duplicative research, but this concern fails to account for market dynamics. In efficient markets with rapid information exchange, rational analysts would avoid conducting fruitless research, focusing instead on areas of expertise to minimize deadweight loss and enhance productivity. If duplicative research occurs, it implies that previous research needs refinement, and performing it again would improve information quality, addressing the free-rider problem inherent in non-rivalrous information. Incentives like reputational and financial rewards further mitigate underinvestment, as repeating analysis improves market signals, enhancing efficiency and creating positive externalities.

Excessive disclosure could waste resources, leading to deadweight loss. Certain  critiques of voluntary disclosure, citing investors’ bounded rationality and managerial manipulation, ignores evidence that abnormal stock returns before management buyouts (MBOs) or leveraged buyouts (LBOs)[2] reflect efficient incorporation of private information into prices, reducing arbitrage opportunities even when management could manipulate the market response for self-interest by withholding disclosure to trigger a fall in stock price. Securities laws like Rule 10b-5 and game theory also indicate managers, as repeat players, weigh litigation and reputational risks against manipulation gains. Stricter enforcement and public exposure could align incentives more effectively than mandatory disclosure.

Moreover, mandatory disclosure can generate negative externalities. Revenue disclosure rules under Financial Reporting Standards in the UK, designed for comparability, allow firms discretion in classifying complex revenue streams, reducing reliability. These rules mislead investors into trusting disclosures due to their mandatory nature, despite inconsistencies[3]. This obscures material information, weakens price discovery, and externalizes costs onto market participants.

While information as a public good can yield positive externalities, excessive mandatory disclosure risks information overload, diminishing marginal utility and impairing market efficiency.

Necessary Conditions for Mandatory Disclosure

Mandatory disclosure rules should prioritize standardized, objective data to reduce agency costs and information asymmetry without causing excessive compliance burdens. Easterbrook and Fischel[4] advocate for disclosures limited to historical, routinized financial data, enabling fair comparisons across companies while minimizing costs. This justifies mandatory financial reporting under IFRS but highlights flaws in ESG frameworks like the EU’s CSRD and the SEC’s Climate-Related Disclosure Rules. These require forward-looking, non-standardized data, such as information on carbon emissions or social metrics, which are difficult to quantify uniformly. For instance, multinational enterprises could face challenges consolidating inconsistent utility-usage data across jurisdictions, undermining comparability. Non-standardized ESG disclosures risk adverse selection, as investors may misinterpret or distrust the information, eroding market confidence. The high costs of ESG compliance disproportionately burden small and medium-sized enterprises (SMEs), acting as a regressive tax. Large firms can absorb these costs, but SMEs struggle with data collection, auditing, and reporting, distorting competition and stifling innovation. However, standardized information disclosure counters assumptions that extensive disclosures protect investors. Over-disclosure misallocates resources and reduces information’s marginal utility. Regulatory overreach, driven by public choice theory, sees regulators expand complex rules for rent-seeking, often without economic justification. Roe[5] notes populist support for stricter rules, even when benefits are unclear, exacerbating compliance costs and deadweight loss.

Tailored, objective disclosures that mitigate agency costs are essential to balance transparency with efficiency, avoiding information overload and regulatory ambiguity that undermine market confidence. This is why mandatory disclosures of related-party transactions, management emoluments, and internal share dealings are preferred, as these are idiosyncratic data unavailable to markets otherwise. Such disclosures counter managerial opportunism, intensified by globalized, complex corporate structures. Mahoney’s agency cost model[6] highlights managers’ incentives to exploit compensation or transactions, necessitating mandatory reporting to reduce fraud risks and enhance transparency.

Challenges and Opportunities in Evaluating Policy

To assess mandatory disclosure regimes, principal component regression analysis can deconstruct policy rationales, testing their correlation with objectives like public or special interest theories. This method, enhanced by parametric bootstrapping in a regulatory sandbox, addresses data endogeneity and noise, ensuring robust, [IS THAT WHAT YOU MEAN? – falsifiable] results and advancing empirical research in political science and econometrics.

Natural experiments offer another approach to evaluate disclosure rules by addressing endogeneity. For instance, to test if ESG disclosures (treatment) improve firm performance (outcome), comparing a jurisdiction with a new disclosure rule with a similar jurisdiction without it can serve as an instrumental variable (IV). This IV, being relevant and exogenous, isolates causal effects by eliminating self-selection biases, enabling credible inferences about disclosure’s impact on performance.

However, reflexivity poses a methodological challenge. Researchers, as market participants, influence outcomes through their observations and expectations, creating feedback loops. For example, investor reactions to anticipated disclosure rules may alter market prices, complicating causal inferences. This dynamic, akin to Schrödinger’s cat, challenges the assumption of independent data observation. To address reflexivity, researchers must adopt sophisticated designs and interpret results cautiously, acknowledging the co-created nature of policy outcomes. These methods, combined with awareness of reflexivity, enhance the rigor of policy evaluation, ensuring more accurate assessments of mandatory disclosure’s economic and regulatory impacts.

Conclusion

Policymakers must balance transparency benefits with economic costs like information overload and resource misallocation, which can reduce social welfare. Targeted mandatory disclosures focusing on financial data, related-party transactions, and management remuneration curb self-dealing and agency costs but require careful design. Overbroad rules risk regulatory overreach, fostering rent-seeking and deadweight losses. Policy researchers can use statistical modelling and natural experiments to assess how disclosures reduce information asymmetry and enhance market efficiency.

However, data limitations, model assumptions, and researcher bias pose challenges. A pragmatic, probabilistic approach to policy evaluation is essential, embracing uncertainty and refining policies based on evolving evidence. This ensures disclosure regulations remain effective, balancing transparency with economic efficiency while minimizing unintended consequences and promoting social welfare.

ENDNOTES

[1] John C Coffee, “Market Failure and the Economic Case for a Mandatory Disclosure System” (1984) 70 Va L Rev 717.

[2] Maria R Borges & Ricardo Gairifo, “Abnormal Returns Before Acquisition Announcements: Evidence from Europe” (2013) 45:26 Applied Economics 3723.

[3] Kamran Malikov, Stuart Manson & Jerry Coakley, “Earnings Management Using Classification Shifting of Revenue” (2018) 50:3 The British Accounting Review 291.

[4] Frank H Easterbrook & Daniel R Fischel, “Mandatory Disclosure and the Protection of Investors” (1984) 70 Va L Rev 669.

[5] Mark J Roe, “A Political Theory of American Corporate Finance” (1991) 91 Columbia Law Review 10.

[6] Paul G Mahoney, “Mandatory Disclosure as a Solution to Agency Problems” (1995) 62:3 U Chi L Rev 1047.

This post comes to us from Hing Nin Kwok at York University’s Osgoode Hall Law School. It is based on his recent article, “The Perils of Excessive Mandatory Disclosure: Balancing Transparency, Agency Costs, and Economic Efficiency in Corporate Law,” available here.