Bracci Alberto, Boehnke Jörn, ElBahrawy Abeer, Perra Nicola, Teytelboym Alexander, Baronchelli Andrea
Department of Mathematics, City, University of London, London EC1V 0HB, UK.
Graduate School of Management, University of California Davis, 1 Shields Ave Davis, CA 95616, USA.
PNAS Nexus. 2022 Oct 6;1(4):pgac201. doi: 10.1093/pnasnexus/pgac201. eCollection 2022 Sep.
Online marketplaces are the main engines of legal and illegal e-commerce, yet their empirical properties are poorly understood due to the absence of large-scale data. We analyze two comprehensive datasets containing 245M transactions (16B USD) that took place on online marketplaces between 2010 and 2021, covering 28 dark web marketplaces, i.e. unregulated markets whose main currency is Bitcoin, and 144 product markets of one popular regulated e-commerce platform. We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology. Specifically, we find remarkable regularities in the distributions of transaction amounts, number of transactions, interevent times, and time between first and last transactions. We show that buyer behavior is affected by the memory of past interactions and use this insight to propose a model of network formation reproducing our main empirical observations. Our findings have implications for understanding market power on online marketplaces as well as intermarketplace competition, and provide empirical foundation for theoretical economic models of online marketplaces.
在线市场是合法和非法电子商务的主要引擎，但由于缺乏大规模数据，人们对其经验特征了解甚少。我们分析了两个包含2.45亿笔交易（160亿美元）的综合数据集，这些交易发生在2010年至2021年期间的在线市场上，涵盖28个暗网市场，即主要以比特币为货币的不受监管的市场，以及一个受欢迎的受监管电子商务平台的144个产品市场。我们发现，尽管在线市场在语言、存续期、产品、监管和技术方面存在显著差异，但其交易表现出惊人的相似模式。具体而言，我们在交易金额分布、交易数量、事件间隔时间以及首次和最后一次交易之间的时间方面发现了显著规律。我们表明，买家行为受过去互动记忆的影响，并利用这一见解提出了一个网络形成模型，该模型再现了我们的主要实证观察结果。我们的研究结果对于理解在线市场的市场力量以及市场间竞争具有重要意义，并为在线市场的理论经济模型提供了实证基础。
