Publications

Incomplete vaccine uptake and limited vaccine availability for some segments of the population could lead policymakers to consider re-imposing restrictions to help reduce fatalities. Early in the pandemic, full business shutdowns were commonplace. Given this response, much of the literature on policy effectiveness has focused on full closures and their impact. But were complete closures necessary? Using a hand-collected database of partial business closures for all U.S. counties from March through December 2020, we examine the impact of capacity restrictions on COVID-19 fatality growth. For the restaurant and bar sector, we find that several combinations of partial capacity restrictions are as effective as full shutdowns. For example, point estimates indicate that, for the average county, limiting restaurants and bars to 25% of capacity reduces the fatality growth rate six weeks ahead by approximately 43%, while completely closing them reduces fatality growth by about 16%. The evidence is more mixed for the other sectors that we study. We find that full gym closures reduce the COVID-19 fatality growth rate, while partial closures may be counterproductive relative to leaving capacity unrestricted. Retail closures are ineffective, but 50% capacity limits reduce fatality growth. We find that restricting salons, other personal services and movie theaters is either ineffective or counterproductive.

Visual design characteristics of products play an important role in consumer preferences for many categories. However, characterization of quantification of visual design is a challenging problem. We provide a method to automatically discover and quantify visual characteristics (attributes) from image data using a disentanglement-based approach. While the deep learning literature has shown that supervision is required to obtain unique disentangled representations, ground truth visual characteristics are typically unknown in real world applications. Our method does not require such supervision, and instead uses readily available structured product characteristics as supervisory signals to enable disentanglement. No prior knowledge on design characteristics is required, yet we are able to discover human interpretable and statistically independent characteristics. We apply this method to automatically discover visual product characteristics of watches, and discover 6 human interpretable visual characteristics providing a disentangled representation. We conduct visual conjoint analysis to obtain consumer preferences over visual characteristics. Our generative method is also able to create novel visual designs that correspond to ideal points of different consumer segments.

One of the most widely discussed issues in banking regulation and research is transparency. Yet, whether depositors – banks’ most important claimholders – are affected by transparency, is an empirical open question. Analyzing US commercial banks from 1994 to 2019, we show that uninsured deposit flows are more sensitive to information about bank performance when banks are more transparent. We also link transparency to deposit rates, banks’ investment funding patterns, and profitability. In addition, we find consistent evidence from a differences-in-difference analysis using the Sarbanes-Oxley Act of 2002 as a shock to transparency. Overall, our findings demonstrate that transparency is important in shaping depositors’ behavior and highlight its potential costs.

We study how innovative firms manage their innovation portfolios after filing for Chapter 11 reorganization using three decades of data. We find that they sell off core (i.e., technologically critical and valuable), rather than peripheral, patents in bankruptcy. The selling pattern is driven almost entirely by firms with greater use of secured debt, and the mechanism is secured creditors exercising their control rights on collateralized patents. Creditor-driven patent sales in bankruptcy have implications for technology diffusion—the sold patents diffuse more slowly under new ownership and are more likely to be purchased by patent trolls.

This paper studies competition between Örms when consumers observe a pri- vate signal of their preferences over products. Within the class of signal structures which induce pure-strategy pricing equilibria, we derive signal structures which are optimal for Örms and those which are optimal for consumers. The Örm-optimal policy ampliÖes underlying product di§erentiation, thereby relaxing competition, while ensuring consumers purchase their preferred product, thereby maximizing total welfare. The consumer-optimal policy dampens di§erentiation, which inten- siÖes competition, but induces some consumers to buy their less-preferred prod- uct. Our analysis sheds light on the limits to competition when the information possessed by consumers can be designed áexibly.

The Black Lives Matter (BLM) movement, which sought to combat systemic racism and brutality against Black individuals, saw significant corporate engagement following George Floyd's murder. This corporate response was unprecedented in both scale and visibility. Companies issued public statements, with some pledging financial support for racial equity initiatives and committing to internal reviews and policy changes. CEOs like Satya Nadella of Microsoft and Tim Cook of Apple publicly denounced racial injustices. However, these actions revealed several dilemmas. Critics from both sides labeled the corporations' efforts as "woke capitalism" or mere "virtue signaling," questioning their authenticity given historical practices contradictory to their current stances. Furthermore, internal disparities became evident as some employees felt companies' public support did not translate into equitable workplace policies. For instance, claims of superficial diversity efforts and slow progression in meaningful hiring practices persisted. Additionally, companies faced the challenge of balancing stakeholder expectations, particularly younger, socially conscious employees, with broader customer bases and political pressures. These complex dynamics underscored the intricate nature of corporate engagement in social justice issues and raised questions about the substantive impact of such support.

The New York Federal Reserve (New York Fed) played a critical role during the COVID-19 crisis by implementing most of the Federal Reserve's emergency lending facilities. These facilities aimed to stabilize financial markets by providing liquidity and preventing further economic collapse. Utilizing large financial institutions as counterparties, the New York Fed could quickly revive previous programs and establish new ones. However, as the COVID-19 pandemic unfolded, there was a recognized need to diversify the pool of counterparties beyond the traditionally narrow set of large financial institutions. This diversification effort aimed to include smaller financial institutions, including those owned by veterans, women, and minorities, that were typically excluded but were essential in providing a broader market participation. The challenge for the New York Fed was to define parameters and bring on new counterparties while efficiently operating multiple new facilities and programs.

For the future of its diversity program, the New York Fed needs to address several critical questions. It must consider what the goals of its diversity efforts should be and determine how to effectively reach new counterparties. Moreover, the New York Fed must establish criteria for involving smaller and less-established financial entities to ensure the effectiveness of the programs remains uncompromised.