Expansion of e-commerce presents new opportunities for small and medium enterprises (SMEs) to enter broader market at lower costs, but the SMEs face barriers to growth after entry. To facilitate new entrants to overcome these barriers, we implement a training program as a randomized controlled experiment with over two million new sellers on a large e-commerce platform. The training focuses on practical skills specific to online business operations. Treated new sellers with access to the training earn higher revenues. These sellers improve marketing skills and attract more consumers to their online stores. Leveraging detailed consumer-seller matched search and browsing data, we find that consumers have higher purchase probability when they encounter new sellers. When consumers make purchases, they choose treated new sellers over incumbents; moreover doing so does not lower the quality of their purchases. We use a structural model to characterize consumer demand and recover sellers' underlying quality. Both treated and control new sellers have higher quality compared to incumbents. The training increases new sellers' likelihood of being encountered by consumers, which improves the matching quality between consumers and sellers. The counterfactual exercise shows that training leads to higher consumer surplus and sellers' total revenues due to market expansion. The platform could benefit in both short and long run because of the training.
This paper investigates online retailers' decision to acquire information and the impact of data access on their business strategy and on revenue growth. We take advantage of proprietary data from a large e-commerce platform that sells data analytics products to virtual stores operating on it. The product provides detailed information on customer sources and characteristics, aggregate demand, and competitor strategies. Our empirical investigation relies on several high-frequency panel datasets and makes use of back-end changes in the pricing, variety, and bundling of the data analytics products. Focusing on several consumer electronics and peripherals markets, we find three main results. (i) Data acquisition facilitates growth, but small retailers are very sensitive to the cost of data. (ii) Retailers take marketing and product actions with the data collected but leave prices largely unchanged. (iii) A counterfactual simulation shows that a uniform reduction in the cost of data raises overall platform sales while reducing market concentration on the margin. To further investigate the relationship between data acquistion, strategies and performance, we conduct a high-stake experiment among non-adopting stores and found evidence of information friction despite low take-up.
School closure during the COVID-19 outbreak could cause disruptions to parents’ labor supply. We use data from a unique survey on 1,354 junior high school students and their parents from Shaanxi province, China, to address this question. We find that this temporary shock that increased the needs for family-provided childcare significantly reduced the probability of parents returning to work when workplaces were already reopened, but schools were still closed. We document inequality both within and across households due to parents’ heterogeneous responses. Mothers, migrant workers, and children from low-income families are the most vulnerable group. Since parents needed to spend more time supervising their children when classes moved online, such additional childcare needs further increased parents’ burden of school closure.