Zhen Lian

Firms often experiment with new initiatives before scaling them. Because experimentation is costly, however, testing every proposal may not be economically viable. They therefore often use impact sizing: a low-cost preliminary assessment produced through managerial judgment, predictive models, or simulations. Although managers possess valuable project-specific information, they also have considerable discretion over the estimates they report. We study how a firm should jointly design its sizing-based and experiment-based policies when a manager privately observes the sizing signal, is biased toward implementation, and may exert costly effort to improve project quality. We develop a principal–agent model and establish three main findings. First, even when report manipulation is costless, impact sizing can strictly improve the firm’s payoff, but only within a wedge-shaped region of intermediate experimentation costs. When experimentation is inexpensive, the firm tests broadly and sizing is redundant; when it is too expensive, testing cannot profitably discipline exaggerated reports. Greater manipulation costs make truthful reporting easier to sustain and expand the conditions under which sizing creates value. Second, when manipulation costs alone cannot sustain the first-best allocation, truthful reporting may require policy distortions, including over-experimentation or adoption following unfavorable reports. Experimentation therefore both reveals project quality and disciplines reporting. Third, when the manager can improve project quality, experimentation also provides effort incentives. Sizing and effort can be complements when better screening raises the return to quality improvement, or substitutes when screening becomes an alternative to costly effort. Firms should therefore jointly design sizing governance, experimentation rules, and managerial effort incentives.

Gig economy platforms operate in dynamic and competitive labor markets in which workers can compare and choose among multiple job offers in real time. A central challenge for these platforms is to set worker compensation when individual workers’ reservation wages are heterogeneous, and when common market-wide factors such as competitor incentives shift workers’ willingness to accept offers. We study a profitmaximizing platform that must serve identical requests by sequentially making takeit-or-leave-it pay offers to a pool of workers, observing only acceptances and rejections. Each worker’s reservation wage consists of an individual component and a shared global factor that is initially unknown to the platform. We first characterize the optimal policy in a full-information benchmark where the global factor is known, and show that the problem can be solved efficiently via dynamic programming. The optimal policy leverages worker heterogeneity by initially offering pay below the myopic optimum, increasing pay following rejections and decreasing it following acceptances, and avoiding a set of dominated pay levels that are never optimal. We then analyze the realistic setting in which the global factor is uncertain and must be inferred from a small number of observations. While the exact belief-augmented solution is complex, we develop simple and interpretable heuristics with provable performance guarantees. In particular, we propose a direct-commit policy and a probe-and-commit policy that use little or no learning to adapt pay to market conditions. Our results provide actionable guidance for gig economy platforms seeking to set transparent, adaptive worker compensation in competitive spot labor markets.

The transparency and control of earnings are major concerns for gig economy workers across platforms such as ride-hailing and food delivery. While workers advocate for greater transparency, platforms selectively disclose information, shaping workers’ decision-making and earnings. Recently, the Federal Trade Commission (FTC) has highlighted lack of transparency as a key issue, and platforms have responded by introducing upfront pay quotes that provide per-trip compensation details for workers. Using a game-theoretic model, we analyze the strategic interactions between platforms and workers, incorporating tools from information design to examine how different transparency policies—specifically, flat commission rates versus upfront quote—shape equilibrium outcomes. We find that greater transparency can paradoxically increase platform control, as it allows platforms to fine-tune pay structures in ways that ultimately reduce worker autonomy. Moreover, while full information benefits the platform when it has flexibility in commission setting, it can backfire under commitment constraints, leading to lower profits than a no-information policy. Our findings highlight that transparency is not inherently beneficial for workers. Instead, its effects depend on how it interacts with pay policies. In particular, simple mechanisms, such as a fixed commission rate, can provide workers with more stability and bargaining power than per-trip transparency. These insights offer important guidance for policymakers and platform designers navigating the trade-offs of transparency in the gig economy.

In collaboration with the San Francisco Unified School District (SFUSD), this paper introduces an interactive optimization framework to tackle complex school scheduling challenges. The choice of school start and end times is an optimization challenge, as schedules influence the district’s transportation system, and limiting the associated costs is a computationally difficult combinatorial problem. However, it is also a policy challenge, as transportation costs are far from the only consequence of school schedule changes. Policymakers need time and knowledge to balance these considerations and reach a consensus carefully; past implementations have failed because of policy issues, despite state-of-the-art optimization approaches. We first motivate our approach with a microfoundation model of the interplay between policymakers and researchers, arguing that limiting their dependency is key. Building on these insights, we propose a framework that includes (1) a fast algorithm capable of solving the school schedule problem that compares favorably to the literature and (2) an interactive optimization approach that leverages this speed to allow policymakers to explore a variety of solutions in a transparent and efficient way, facilitating the policy decision-making process. The framework led to the first optimization-driven school start time changes in the United States, updating the schedule of all 133 schools in SFUSD in 2021, with annual transportation savings exceeding $5 million. A comprehensive survey of approximately 27,000 parents and staff in 2022 provides evidence of the approach’s effectiveness.

We propose a theory of gig economies in which workers participate in a shared labor pool utilized by multiple firms. Since firms share the same pool of workers, they face a trade-off in setting pay rates; high pay rates are necessary to maintain a large worker pool and thus reduce the likelihood of lost demand, but they also lower a firm’s profit margin. We prove that larger firms pay more than smaller firms in the resulting pay equilibrium. These diseconomies of scale are strong too; firms smaller than a critical size pay the minimal rate possible (the workers’ reservation wage), while all firms larger than the critical size earn the same total profit regardless of size. This scale disadvantage in labor costs contradicts the conventional wisdom that gig companies enjoy strong network effects and suggests that small firms have significant incentives to join an existing gig economy, implying gig markets are highly contestable. Yet we also show that the formation of a gig economy requires the existence of a large firm, in the sense that an equilibrium without any firms participating only exists when no single firm has enough demand to form a gig economy on its own. The findings are consistent with stylized facts about the evolution of gig markets such as ride sharing.

We develop an economic model of autonomous vehicle (AV) ride-hailing markets in which uncertain aggregate demand is served with a combination of a fixed fleet of AVs and an unlimited potential supply of human drivers (HVs). We analyze market outcomes under two dispatch platform structures (common platform vs. independent platforms) and two levels of supply competition (monopoly AV vs. competitive AV). A key result of our analysis is that the lower cost of AVs does not necessarily translate into lower prices; the price impact of AVs is ambiguous and depends critically on both the dispatch platform structure and the level of AV supply competition. In the extreme case, we show if AVs and HVs operate on independent dispatch platforms and there is a monopoly AV supplier, then prices are even higher than in a pure HV market. When AVs are introduced on a common dispatch platform, we show that whether the equilibrium price is reduced depends on the level of AV competition. If AVs are owned by a monopoly firm, then the equilibrium price is the same as in a pure HV market. In fact, the only market structure that leads to unambiguously lower prices in all demand scenarios is when AVs and HVs operate on a common dispatch platform and the AV supply is competitive. Our results illustrate the critical role dispatch platform and market structure play in realizing potential welfare gains from AVs.

We develop a theoretical model of optimal growth in two-sided markets. The model posits that market output (number of transactions) is a function of the stock of supply and demand. This market output is modeled using a homogeneous production function, which can have increasing or decreasing returns to scale. The supply and demand stock levels follow a growth model in which the rate of growth at each point in time is a function of both the surplus each side of the market receives and the attrition of supply and demand (supply and demand lifetimes). The surplus can be apportioned between the two sides of the market by changing the price paid to sellers and the price charged to buyers, which we assume the platform controls. Through these price levers, the platform can pay subsidies to one or both sides of the market. We investigate the behavior of optimal market growth, including the point at which the market becomes self-sustaining and the long-run optimal size of the market. We characterize the optimal balance between supply and demand as the market size grows and determine optimal subsidy policies that maximize discounted total profit. For the case of both increasing and decreasing returns without price constraints, we show the optimal policy is to grow using an impulse of subsidy spending (a subsidy shock) to move the market immediately to its optimal long-run size. This result is consistent with the “race to growth” observed in many two-sided markets like ride-sharing.