A Swing and a Hit: Optimal Disclosure Policy for Swing Pricing in Mutual Funds

Funded Research Proposal

What is the optimal disclosure policy for swing pricing by open-end mutual funds? Specifically, which disclosure rule, with respect to the swing factor and – thresholds, minimizes run risk in open-end mutual funds? Does the disclosure policy rule that is optimal from a financial stability perspective also preclude the possibility of front running by fund investors? Answering this question will likely also require a model of the optimal swing factor. By addressing this critical gap in the literature, my research aims to contribute to inform policy decisions regarding the proposed SEC rule.Read More

Strategies For Creating Value in Base of the Pyramid Markets: Evidence From a Field Experiment in India

Funded Research Proposal

In contexts with high poverty, impact-driven for-profit firms often take market-based approaches to drive inclusive growth while being financially sustainable. Even with the alignment between “doing well” and “doing good” that these firms supposedly seek to achieve, successful business strategies in these contexts have been associated with breadth of reach (reaching maximum number of beneficiaries) with little or no attention to depth of reach (repeated engagement with each beneficiary). However a focus on breadth alone may lead to tunnel vision when it comes to social impact. This paper draws the distinction between the two approaches and examines their implications for the outcomes for the different actors involved in BoP markets– the organization, its agents and the end beneficiaries. Read More

Private Equity and Productivity in US Healthcare

Funded Research Proposal

Private equity (PE) investment in US healthcare has increased dramatically in recent years. On one hand, widespread PE participation may be one avenue to stem rising costs and increase productivity, which have been elusive objects in US healthcare. Research from other sectors has shown that PE firms apply best-in-class management practices to improve productivity and firm value. On the other hand, high powered financial incentives for PE managers may create conflicts with the interests of consumers and of taxpayers, who finance about 45% of healthcare spending. This project aims to study how PE investment affects quality and productivity of healthcare delivery in the US, issues of first order importance for both policy and business. We have assembled the most comprehensive set of data resources used to study this question – confidential micro data on PE investments in healthcare firms, and national data on services, spending, and outcomes for about 30 million Medicare beneficiaries over 2000–17. We will deploy a differences-in differences research design, exploiting variation in the timing of PE acquisitions across firms over this long panel. We will examine effects on 1) growth 2) operating efficiency, and 3) quality of care delivered, over the short and long-run.Read More

Remanufacturing Consent: How Algorithmic Management Repurpose Workplace Consent

Funded Research Proposal

In recent years, the number of workers in the US who show up to work by turning on an app on their smartphone has dramatically increased. Dubbed on-demand or “gig” workers, these individuals log onto digital platforms and depend on algorithms, rather than managers, to set pay rate, segment work tasks, and supervise and evaluate their actions while on the job. Researchers have sounded alarms about the “encompassing, instantaneous, interactive, and opaque” (Kellogg et al., 2020) nature of such algorithmic management, concluding that it traps workers in an “invisible cage” (Rahman 2021). And yet, many on-demand workers report enjoying management by algorithms, enthusiastically noting the freedom that comes with this type of app-based work. Indeed, data from the Federal Reserve and Bureau of Labor from the past five years indicate a tight labor market alongside an increase in the number of workers in the on-demand economy (Kaplan et al., 2021; Katz & Krueger, 2016; 2019), suggesting that on-demand workers may not experience the work to be as oppressive as some scholars have suggested (Rosenblat, 2018; Ravenelle, 2019; Shapiro, 2018). Moving beyond money as the sole explanatory mechanism of why individuals work hard at their jobs, especially under less-than-ideal conditions, this study takes seriously how on-demand workers describe and value finding freedom and choice in their jobs. In doing so, I identify the limitations of the “carrots and sticks” metaphor that scholars have long used to describe the production of consent in the traditional workplace. Instead, I examine how algorithmic management, in conjunction with the new work arrangements of the gig economy, creates consent through the notion of workers having increased choices—a form of consent that, I argue, is more pervasive, but also more fragile.Read More

Algorithmic Pricing and Transparency in the Gig Economy

Funded Research Proposal

Algorithms control pricing and match customers and workers in the gig economy. However, algorithms face several critiques: they lack transparency, can be biased, and can be inefficient. We empirically analyze these issues and show that algorithms lose efficiency from two sources: competition between platforms and misaligned worker incentives. We model workers’ strategic responses to variation in pricing and estimate counterfactuals on the effects of minimum wage and transparent pricing policies.Read More

Are Startups More Innovative than Incumbent Counterparts? Evidence from AI Companies

Funded Research Proposal

I plan to study whether AI startups are more innovative than their incumbent counterparts. Although the extant literature highlights startups’ initial innovation capacities that help them identify and leverage market and technology opportunities, startups’ innovation competency at the founding stage no longer properly explains differences in competitive advantages in the digital era. Compared to traditional industries, companies in the digital era, such as AI firms leveraging big data, do not require capital-intensive manufacturing facilities to commercialize their ideas because the end products and services are digitized. Hence, the source of entrepreneurial innovation built upon the foundation-based explanation becomes obscure in the digital era, while it remains unclear what types of innovation startups vs. incumbents are better at during the post-foundation stages of startups. I argue that AI startups have competitive advantages in innovation against their incumbent counterparts based on different types of tech-based strategies for algorithm innovation. Read More

Consumer Cryptocurrency Confidence Index (CCCI or C3i)

Funded Research Proposal

The Consumer Cryptoconfidence Index offers the first ongoing, consumer-centered data source specifically intended to capture sentiment related to this market exchange tool. Offering both a novel “snapshot” understanding of the typical consumer’s perception of and interaction with cryptocurrency as well as data that may be fruitfully connected to other marketplace changes, the C3i index will allow us to build our knowledge as the cryptocurrency market evolves. By developing this understanding, we hope to support decision-making among consumers, regulators, investors, and entrepreneurs, while also laying the groundwork for ongoing consumer-centered research in this domain.Read More

AI Decision Aids and Metacognition

Funded Research Proposal

The proposed research focuses on how people use and perceive innovative decision aids, for example, AI tools like ChatGPT. Individuals and organizations are increasingly outsourcing part of their labor or decision-making process to these tools. The proposed research aims to investigate the perception around using AI decision aids, both in terms of its impact on individuals’ metacognitive assessments and observers’ social judgments. Read More

Evolution of Internet Retailing and Buy vs. Make Decisions

Funded Research Proposal

We have obtained data from Digital Commerce 360 which publishes pertinent statistics on top 1000 internet retail sites. One interesting aspect of this data is detailed information regarding outsourcing/insourcing decisions including web hosting, search engine optimization services, product delivery etc. This is a pretty unique data spanning hundreds of companies and a last decade. We wish to use this data to analyze outsourcing decisions of internet retailers and how these choices affect their performance. The data shows variation across time and industries, and it is supplemented with many quantitative metrics for internet retailers including number of visitors, conversion rate and basket size etc. We believe this unique data will allow us to gain unique insights into make vs buy decisions by internet retailers.Read More

Risk in Discovery-Stage Biotech Innovation

Funded Research Proposal

The main goal of this project is to enhance our understanding of what drives firm decisions in discovery-stage drug development, when risk of failure and variance in outcomes are highest. Specifically, this project contributes to existing literature on biopharmaceutical innovation, by testing whether the theory that larger firms pursue novel drugs is valid in the earliest phase of the drug development pipeline. Using data on early-stage VC funding and FOIA’ed biopharmaceutical alliances across thirty years, I use data-driven methods to examine the relationship between the novelty of biotech innovation and investor decisions at the earliest stage of drug development and contextualize its magnitude against other innovation characteristics. In doing so, I resolve a potential discrepancy between academic findings and industry observations.Read More

Innovate or Excavate? A Raw Resource View of Induced Innovation

Funded Research Proposal

This project aims to quantify the effect of innovation on commodity price reversions. Commodity prices tend to be stationary with occasional deviations and then revert to their mean prices. There are two big forces behind this reversion. First, there are firms that invest in research that allows them to substitute away from the expensive commodity. Secondly, the commodity producers will expand extraction capacity in response to increased profitability. The former affects the demand side and the latter the supply side. The goal of this project is to estimate the magnitude of the effect of price reversion due to innovation.Read More

Thinking Structurally: How Structural Attributions Impact Support For Solutions and Willingness to Take Collective Action

Funded Research Proposal

Though nearly everyone recognizes the importance of addressing issues like climate change, gender bias, and police brutality, we face bitter and debilitating conflict with respect to the causes of these challenges. In this project, we study the consequences of “structural attributions” for social problems — that is, believing a problem was caused by policies, infrastructure, and/or institutions.Read More

Silence Helps Men but Hurts Women: The Differential Gender Effects of Silence in Negotiation

Funded Research Proposal

Current literature consistently shows a gender gap in people’s performance in negotiation. In this research, we aim to use a GPT Negotiation Bot to understand whether men and women may be perceived differently when they use silence — a common and important conversation strategy — in negotiation.Read More

Upward Digitization and Downward Novelty in Book Industry

Funded Research Proposal

Digitization has fundamentally changed how content is produced, distributed, and consumed in cultural industries where novelty is an essential and necessary ingredient for success. Using the book publishing industry as a setting, we investigate whether and how digitization impacts content novelty.Read More

The Prevalence and Consequences of Algorithms in Hiring: A Field Experiment

Funded Research Proposal

We conduct an audit study to measure the prevalence and impact of AI hiring applications on job applicants. We apply to thousands of jobs, treating half of our application resumes by embedding the job posting within the resume such that machines have access to the job posting text when screening candidates, but human evaluators would not. We can then evaluate if algorithmic resume screening is more likely to select resumes that are a closer match to postings. By also varying applicant race and gender, we can determine if these algorithmic selection algorithms have a differential impact on candidates from underrepresented backgrounds.Read More

Instant Payment Systems and Competition for Deposits

Funded Research Proposal

How do instant payment technologies impact financial intermediation? I use municipality-level data on the development of Pix in Brazil and combine it with branch-level banking data to provide evidence that instant payments positively impact deposit market competition—Pix usage increases checking, saving, and time deposits of small banks relative to large banks.Read More

Pivoting and Experimentation in Lean Startups

Funded Research Proposal

While recent scholarly and practitioner research has stressed the importance of learning in entrepreneurial settings, little scholarly work has formally examined the mechanisms through which learning drives behavior in those settings. Specifically, we investigate how an active learning process based on evaluating performance relative to an aspiration drives the pivoting and experimentation behavior of startups in environments with high degrees of uncertainty and potentially intense selection pressures.Read More

Motivating Goal Pursuit by Normalizing Difficulty

Funded Research Proposal

In this research, we test a simple messaging intervention to motivate individuals struggling in their goal pursuit. Specifically, we study the impact of setting a norm about the experience of goal pursuit. We suggest that informing consumers that it is normal to struggle during goal pursuit – a “difficult goal pursuit” norm – is an effective approach to motivating struggling consumers.Read More

Can Patients Differentiate Between Chatbots and Physicians? Using Conversational AI to Facilitate Provider to Patient Messaging

Funded Research Proposal

Hummy Song, Operations, Information and Decisions The Wharton School; Christian Terwiesch, Operations, Information and Decisions, The Wharton School; Hessam Bavafa, Wisconsin School of Business; David Asch, Health Care Management, The Wharton School; Xufei Liu, PhD in Operations, Information and Decisions, The Wharton School Abstract: Since ChatGPT was introduced, it has passedRead More

Competitive Procurement in Health Insurance Market – Evidence from Medicaid Managed Care

Funded Research Proposal

Competitive procurement is commonly used when government contracts with private firms to provide public services, such as road and bridge construction. However, competitive procurement in health insurance marketing is rarely studied. The nature of the selection market makes the impact of competitive procurement ambiguous ex-ante because the value of winning the bid is determined by downstream competition with self-selection consumers. This project will study the welfare impact of competitive procurement in the selection market in the context of the Medicaid Managed Care market.Read More