We examine competition between two-sided platforms in a sharing economy. In sharing economies, workers self-schedule their supply based on the wage they receive. The platforms compete for workers as well as consumers. To attract workers, platforms use diverse wage schemes, including fixed commission rate, dynamic commission rate, and fixed wage. We develop a model to examine the impacts of the self-scheduled nature of the supply on competing platforms and the role of the wage scheme in the platform competition. We find that the price competition between platforms is more intense in a sharing economy compared with an economy with a fixed supply of workers if and only if the platforms serve more consumers and workers in the sharing economy than in the traditional economy, regardless of the wage scheme employed by the platforms. Further, any of the three wage schemes can be the best for the platforms and the worst for consumers and workers, depending on the market characteristics. In markets where the competition is more fierce on the demand side than on the supply side, the fixed-wage scheme results in the highest profits for the platforms and lowest surpluses for consumers and workers. In contrast, in markets where the competition on the supply side is more competitive, when the supply is highly (mildly) more competitive, the fixed-commission-rate (dynamic-commission-rate) scheme generates the highest profits for platforms, leading to the lowest surpluses for consumers and workers and the lowest social welfare. The differential impacts of the wage scheme on the price (demand side) and quantity (supply side) competition explain our findings. This paper was accepted by Kartik Hosanagar, information systems. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mnsc.2022.4302 .

ABSTRACT: We argue that reductions in shareholder taxes should lower the cost of equity capital more for financially constrained firms than for other companies. Consistent with this prediction, we find that, following the 1997 (TRA) and the 2003 (JGTRRA) cuts in U.S. individual shareholder taxes, financially constrained firms enjoyed larger reductions in their cost of equity capital than did other firms. The results are consistent with the incidence of the tax reductions falling mostly on firms with both pressing needs for capital and disproportionate ownership by individuals, the only shareholders who benefited from the legislations. The paper provides a partial explanation for the seemingly puzzling finding that, following the unprecedented 2003 reduction in dividend tax rates, non-dividend-paying firms outperformed dividend-paying firms. The results suggest that it was not dividend status that mattered, but financial constraint, a common attribute of non-dividend-paying companies. Data Availability: Data are available from public sources identified in the study.

Cold start describes a commonly recognized challenge for online advertising platforms: with limited data, the machine learning system cannot accurately estimate the click-through rates (CTR) of new ads and, in turn, cannot efficiently price these new ads or match them with platform users. Traditional cold start algorithms often focus on improving the learning rates of CTR for new ads to improve short-term revenue, but unsuccessful cold start can prompt advertisers to leave the platform, decreasing the thickness of the ad marketplace. To address these issues, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness on the platform. Based on duality theory and bandit algorithms, we develop the shadow bidding with learning (SBL) algorithms with a provable regret upper bound of [Formula: see text], where K is the number of ads and d captures the error magnitude of the underlying machine learning oracle for predicting CTR. Our proposed algorithms can be implemented in a real online advertising system with minimal adjustments. To demonstrate this practicality, we have collaborated with a large-scale video-sharing platform, conducting a novel, two-sided randomized field experiment to examine the effectiveness of our SBL algorithm. Our results show that the algorithm increased the cold start success rate by 61.62% while compromising short-term revenue by only 0.717%. Our algorithm has also boosted the platform’s overall market thickness by 3.13% and its long-term advertising revenue by (at least) 5.35%. Our study bridges the gap between the theory of bandit algorithms and the practice of cold start in online advertising, highlighting the value of well-designed cold start algorithms for online advertising platforms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mnsc.2022.4550 .