AI@CBS

U.S. universities have experienced a shift in research funding away from federal and towards private industry sources. This paper evaluates whether the source of funding -- federal or private industry -- is relevant for commercialization of research outputs. We link person-level grant data from 22 universities to patent and career outcomes (including IRS W-2 records). To identify a causal effect, we exploit individual-level variation in exposure to narrow federal R&D programs stemming from pre-existing field specialization.

Using US Census employer-employee matched data, I show that employer financial distress accelerates the exit of employees to found start-ups. This effect is particularly evident when distressed firms are less able to enforce contracts restricting employee mobility into competing firms. Entrepreneurs exiting financially distressed employers earn higher wages prior to the exit and after founding start-ups, compared to entrepreneurs exiting non-distressed firms. Consistent with distressed firms losing higher-quality workers, their start-ups have higher average employment and payroll growth.

Current accounting practice expenses many investments in intangible assets to the income statement, confusing earnings from current revenues with investments to gain future revenues. This has led to increasing calls to book those investments to the balance sheet. Drawing on the relevant research, this paper proposes solutions for the accounting for intangible assets that contrast with balance sheet recognition, and compares them to current practice and the IFRS standards that dictate practice.

We recover valuations of time using detailed data from a large ride-hail platform, where drivers bid on trips and consumers choose between a set of rides with different prices and waiting times. We estimate demand as a function of prices and waiting times and find that price elasticities are substantially higher than waiting-time elasticities. We show how these estimates can be mapped into values of time that vary by place, person, and time of day. We find that the value of time during non-work hours is 16% lower than during work hours.

Using a novel dataset of over 3,500 public and private firms, we construct the network of firm connections through executives and directors on the eve of the 1929 financial market crash. We find that more connected firms have 17% higher 10-year survival rates on average. Consistent with a role in facilitating access to working capital, the results are particularly strong for small firms, private firms, cash-poor firms, and firms located in counties with high bank suspension rates during the crisis.

New technologies such as Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), virtual assistants, chatbots, and robots, which are typically powered by Artificial Intelligence (AI), are dramatically transforming the customer experience. In this paper, we offer a fresh typology of new technologies powered by AI and propose a new framework for understanding the role of new technologies on the customer/shopper journey.

How does access to public equity markets affect real outcomes? We examine the human capital of IPO-filing firms and how going public affects their labor force. While IPO-filing ?rms have high average wages and limited industrial diversification, a successful IPO increases departures of high-wage employees to startups and triggers industrial diversification through employment growth in non-core industries. Surprisingly, IPOs do not significantly affect earnings growth of pre-IPO workers. Instead, post-IPO hires receive larger earnings increases upon joining.

How do ethical arguments affect AI adoption in business? We randomly expose business decision-makers to arguments used in AI fairness activism. Arguments emphasizing the inescapability of algorithmic bias lead managers to abandon AI for manual review by humans and report greater expectations about lawsuits and negative PR. These effects persist even when AI lowers gender and racial disparities, and when engineering investments to address AI fairness are feasible. Emphasis on status quo comparisons yields opposite effects.