Digital labor platforms are increasingly used to procure human input, ranging from annotating data and red-teaming AI models, to ride-sharing and food delivery. A central concern in such markets is the ability of platforms to suppress wages by exploiting the abundance of low-cost labor. To study this exploitation pattern, we introduce a novel posted-price procurement model with coverage objectives. A platform seeks to complete M tasks by posting prices to sequentially arriving workers, each of whom accepts a task if it exceeds their private cost. First, we show that under natural assumptions on the workers' estimated cost, there exists a simple pricing strategy for the platform to cover all M tasks with wait time O(M), while paying only a O(log(M)/M) fraction of the total cost of labor. This result highlights how platforms can exploit workers' uncertainty about the cost of labor to effectively suppress wages. Then, we study collective action as a lever to increase wages and promote welfare in digital labor markets. In particular, we show how a small coalition of targeted low-cost workers who commit to a price floor forces the platform's total spending from logarithmic to linear in M. In contrast, a randomly sampled coalition of equal size remains largely ineffective. We complement our theory with synthetic experiments, showcasing the benefits of collective action across different market regimes.

翻译：数字劳动平台正日益被用于获取人力输入，涵盖从数据标注、AI模型红队测试到共享出行和外卖配送等领域。此类市场的核心问题在于，平台能否利用低成本劳动力的充裕性来压低工资。为研究这一剥削模式，我们提出了一种带有覆盖目标的新型定价采购模型。平台通过向顺序到达的工人发布定价来尝试完成M项任务，每位工人仅当任务报价超过其私人成本时才会接受。首先，我们证明，在工人预估成本的合理假设下，存在一种简单定价策略，能使平台在等待时间O(M)内覆盖全部M项任务，而支付的薪资仅占劳动力总成本的O(log(M)/M)比例。这一结果揭示了平台如何利用工人对劳动力成本的不确定性来有效压低工资。进一步地，我们研究了集体行动作为提升数字劳动力市场工资与福利的杠杆。具体而言，我们展示：由低成本工人组成的小规模目标联盟通过承诺设置最低价格，能迫使平台的总支出从对数增长转变为线性增长（相对于M），而同等规模的随机采样联盟则几乎无效。我们通过合成实验补充了理论分析，展示了不同市场机制下集体行动的优势。