Professor Jeffery Zhang from Michigan Law will be speaking at our February blue bag lunch talk on Wednesday, February 1 at 12pm. The talk will be virtual on Zoom. Please register here by January 31.
Professor Nejat Seyhun will discuss a new paper on "insider giving," as a potent substitute for insider trading due to lax reporting requirements and legal restrictions.
More than a decade after the 2008 financial crisis, U.S. policymakers still have not adequately addressed one of the primary causes of the crash: foreign banks.
Many statutes now permit bounties for whistleblowers who provide enforcement relevant information to the authorities. The growth in such bounties has been quite rapid in recent years generating substantial scholarly, policy and practical interest. However, much of the scholarship does not address a critical feature of corporate liability in the US – there is considerable uncertainty about both the scope and definition of wrongdoing. This talk examines the effects of this uncertainty on the desirable structure and incidence of bounty regimes. Some key findings are that the greater this uncertainty the harder it will be to gather information about wrongdoing both within a firm and more generally because individuals will likely be reluctant to share information that might be relevant to enforcement. This has numerous effects. First, as gathering and sharing of information becomes more difficult it will become harder to deter and prevent wrongdoing, which in part depends on gathering and sharing information. Second, weaker gathering and sharing of information within the firm will hamper the ability of employees to work together cohesively. This not only worsens firm performance (which has its own costs), but also is likely to increase wrongdoing because poor firm performance is a key predicator of corporate wrongdoing. The analysis thus counsels caution in extending whistleblower bounties to areas where the underlying law is uncertain, provides insights on how one might design a bounty system in light of this uncertainty (e.g., differentiating between internal and external whistleblowers, varying bounties by firm size), and lays out certain steps that might be taken to ameliorate some of the identified effects of uncertainty.
The Data Privacy and Portability in Financial Technology Symposium celebrates the Michigan Technology Law Review’s 25th Anniversary by hosting an event dedicated to cutting-edge scholarship at the intersection of technology and the law. Specifically, this symposium is designed to examine the inherent tensions between securing privacy rights and the ease at which transactions occur, facilitated by new innovative technologies.
This will be a presentation of two large-scale field experiments designed to test the hypothesis that group membership can increase participation and pro-social lending for an online crowdlending community, Kiva. The first experiment uses variations on a simple email manipulation to encourage Kiva members to join a lending team, testing which types of team recommendation emails are most likely to get members to join teams as well as the subsequent impact on lending. We find that emails do increase the likelihood that a lender joins a team, and that joining a team increases lending in a short window following our intervention. The impact on lending is large relative to median lender lifetime loans. We also find that lenders are more likely to join teams recommended based on location similarity rather than team status. Our results suggest team recommendations can be an effective behavioral mechanism to increase pro-social lending. In a second field experiment, we manipulate forum messages to explore the underlying mechanisms for teams to be effective.
Historically, public infrastructure systems such as roads, water utilities, and schools are financed using a combination of tax revenue, government and revenue-backed bonds. This system has repeatedly fallen short due to insufficient tax revenue and political aversion towards funding “social infrastructure”. Especially for schools, the access to quality infrastructure is highly correlated (in the US) to poverty, stemming from property values, credit worthiness and other factors. A recent bill (not passed) required a 1:6 leverage of federal with state and private finance, compared to 1:12 in Europe and 1:30 proposed under the Climate accords. Either infrastructure has not been built or upgraded, or private capital has stepped in the breach. At the Center for Smart Infrastructure Finance, we're asking whether data-driven models can close the gap by taking advantage of the internet of things (IoT): smart sensors that deliver information which can be monetized. This seminar will explore how private financing models that leverage digital data supply chains to attract 'efficient capital' (e.g. insurance, options trades, debt securities, variable interest rate bonds) can be adapted to financing public infrastructure while limiting recourse to the citizens that use it, and leveling the economic disparities of access.