Kaitlin Daniels

Bio: Kaitlin Daniels is an academic researcher from Washington University in St. Louis. The author has contributed to research in topics: Peak demand & Profit (economics). The author has an hindex of 4, co-authored 10 publications receiving 535 citations. Previous affiliations of Kaitlin Daniels include University of Pennsylvania.

The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Abstract: Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. We find that the optimal contract substantially increases the platform's profit relative to contracts that have a fixed price or fixed wage (or both) and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized in the press and has garnered the attention of regulators due to concerns for the welfare of providers and consumers. However, we find that providers and consumers are generally better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity.

The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Abstract: Recent platforms, like Uber and Lyft, offer service to consumers via "self-scheduling" providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a "surge pricing" policy, which pays providers a fixed commission of its dynamic price. With a stylized model that yields analytical and numerical results, we study several pricing schemes that could be implemented on a service platform, including surge pricing. We find that the optimal contract substantially increases the platform's profit relative to contracts that have a fixed price or fixed wage or both, and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized because of concerns for the welfare of providers and consumers. In our model, as labor becomes more expensive, providers and consumers are better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity. The e-companion is available at https://doi.org/10.1287/msom.2017.0618 .

The Impact of the Gig-Economy on Financial Hardship Among Low-Income Families

Abstract: Problem Definition: New work arrangements coordinated by gig-economy platforms offer workers discretion over their work schedules at the expense of traditional worker protections. We empirically measure the impact of expanding access to gigs on worker welfare, with a focus on low-income families. Academic/Practical Relevance: Understanding the welfare implication of access to gigs informs workers considering working gigs and regulators empowered to protect them. Additionally, firms who rely on this working arrangement may find themselves exposed to increased worker turnover and regulatory intervention if gigs negatively impact worker welfare. Methodology: We analyze a novel data set documenting the financial health of a sample of low-income families. We are interested in the likelihood that a family experiences hardship, meaning they fail to pay their bills on time. We leverage the sequential launch of Uber's UberX service across locations to identify the impact of the associated increase in access to gigs on hardship via a difference-in-differences design. The granularity of our data allows exploration of possible mechanisms for our results. Results: We find that UberX increases hardship among the low-income population, primarily by decreasing overall take-home pay (i.e. annual income less expenses). This is despite a corresponding reduction in income volatility, generally a boon to low-income families who have insufficient savings to weather unexpected dips in earnings. Managerial Implications: These results caution that gigs can be harmful to the most vulnerable members of society, bolstering the position of Uber drivers suing for employee status and governments seeking to regulate the gig economy. Our analysis of mechanisms driving this result offers guidance for effective mechanisms for improving worker financial health in the presence of gigs. Further, we find that gigs offer potential benefits to the low-income population through reduction in income volatility.

Trying and Failing: Biases in Donor Aversion to Rejection

Abstract: Non-profit organizations (NPOs) play a critical role in advancing the UN Sustainable Development Goals, directing resources from donors to aid recipients. To achieve this, they often rely on donations of physical goods, which can not only further the mission of the NPO, but also give new life to used goods destined for the landfill. However, not all donations are wanted or needed - the NPO may be space constrained or the donation may be of poor quality. Accepting unwanted donations imposes non-trivial operational costs on the NPO or its downstream partners. Nonetheless, NPOs often hesitate to reject unwanted items, fearing negative repercussions for future donations. To better understand these repercussions, we study how donors respond to rejection using a controlled experiment. Subjects repeatedly choose whether to complete a real-effort task that generates a donation, which is rejected with a fixed probability whose value is unknown to them. We measure subjects' donation decisions and beliefs about the probability that their donations are accepted. We compare these measures against a for-profit experimental condition wherein the subject, not an NPO, receives the payment generated by the real-effort task. Our results identify a mechanism by which rejections affect donations: via a reduction in donors' beliefs about future success. Moreover, we identify a novel instance of self-serving bias: when high effort is required to make a donation, subjects' beliefs respond significantly more negatively to rejection in the donation condition than in the for-profit condition. We propose ways in which NPOs can alleviate this bias.

Demand Response in Electricity Markets: Voluntary and Automated Curtailment Contracts

Abstract: Demand response firms that offer curtailment contracts provide incentives for consumers to reduce consumption during peak demand events. The foregone consumption is sold as virtual supply on the electricity market. We explore two curtailment contract types: automated and voluntary. The automated contract requires set curtailment from enrolled consumers, whereas the voluntary contract allows curtailment to vary with the consumer's opportunity cost. In this paper we study the optimal choice of contract type, first from the firm's point of view and then from a social planner's perspective. We show how either contract type can be optimal depending on market/customer conditions. More specifically, when the supply curve for the wholesale electricity market has a steep slope, the firm would prefer to rely on automated contracts. On the other hand, if the cost of curtailment for the customer is highly variable, the firm might prefer to offer a voluntary contract. While the firm's optimal choice is not always welfare maximizing, we find that it does maximize curtailment and so can be viewed as consistent with the environmentally optimal choice. We demonstrate our theoretical findings using a case study from a curtailment service provider, EnerNOC, and data from the PJM electricity market.

Coordinating Supply and Demand on an On-Demand Service Platform with Impatient Customers

Abstract: We consider an on-demand service platform using earning-sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price-sensitive customers...

Ridesourcing systems: A framework and review

Abstract: With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation industry. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensitive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform and many other factors. Diverse variables and factors in the system are strongly endogenous and interactively dependent. How to design and operate ridesourcing systems is vital—and challenging—for all stakeholders: passengers/users, drivers/service providers, platforms, policy makers, and the general public. In this paper, we propose a general framework to describe ridesourcing systems. This framework can aid understanding of the interactions between endogenous and exogenous variables, their changes in response to platforms’ operational strategies and decisions, multiple system objectives, and market equilibria in a dynamic manner. Under the proposed general framework, we summarize important research problems and the corresponding methodologies that have been and are being developed and implemented to address these problems. We conduct a comprehensive review of the literature on these problems in different areas from diverse perspectives, including (1) demand and pricing, (2) supply and incentives, (3) platform operations, and (4) competition, impacts, and regulations. The proposed framework and the review also suggest many avenues requiring future research.

Spatial Pricing in Ride-Sharing Networks

Abstract: Motivated by the prevalence of ride-sharing platforms, in “Spatial Pricing in Ride-Sharing Networks,” Bimpikis, Candogan, and Saban explore the impact of the demand pattern for rides across a netwo...

Peer-to-Peer Product Sharing: Implications for Ownership, Usage and Social Welfare in the Sharing Economy

Abstract: We describe an equilibrium model of peer-to-peer product sharing, or collaborative consumption, where individuals with varying usage levels make decisions about whether or not to own. Owners are able to generate income from renting their products to non-owners while non-owners are able to access these products through renting on as needed basis. We characterize equilibrium outcomes, including ownership and usage levels, consumer surplus, and social welfare. We compare each outcome in systems with and without collaborative consumption and examine the impact of various problem parameters including rental price, platform's commission fee, cost of ownership, owner's moral hazard cost, and renter's inconvenience cost. Our findings indicate that, depending on the rental price, collaborative consumption can result in either lower or higher ownership and usage levels, with higher ownership and usage levels more likely when the cost of ownership is high. We show that consumers always benefit from collaborative consumption, with individuals who, in the absence of collaborative consumption, are indifferent between owning and not owning benefiting the most. We also show that the platform's profit is not monotonic in the cost of ownership, implying that a platform is least profitable when the cost of ownership is either very high or very low.

Socially and Environmentally Responsible Value Chain Innovations: New Operations Management Research Opportunities

Abstract: By examining the state of operations management (OM) research from 1980 to 2015 and by considering three new industry trends, we propose new OM research directions in socially and environmentally responsible value chains that fundamentally expand existing OM research in three dimensions: (a) contexts (emerging and developing economies); (b) objectives (economic, environmental, and social responsibility); and (c) stakeholders (producers, consumers, shareholders, for-profit/nonprofit/social enterprises, governments, and nongovernmental organizations). In this paper, we describe some examples of this new research direction that are intended to stimulate more exciting OM research, to contribute to the economic and social well-being of both developing and developed economies. This paper was accepted by Teck-Hua Ho, operations management.