The rise of the information economy has highlighted how data may be an input into production (Veldkamp and Chung, forthcoming). Since firms conversely accumulate data through production, large firms may be able to accumulate data more quickly, giving them an advantage. The advantage is especially stark in retail, where a key dimension of productivity is the ability to forecast demand for individual products (Samaniego de la Parra and Shenoy, 2023). Walmart came to dominate U.S. retail in part using the data generated by its own sales to predict demand and guide logistics (LeCavalier, 2010). However, accumulating data through scale is challenging in developing countries because retail is fragmented across thousands of small single-establishment shops (Lagakos, 2016). This study aims to answer two research questions:
1. Does a lack of information about potential demand constrain the growth of small shops in developing countries?
2. Can a scalable technology that pools data generated by individual shops overcome the informational barrier to growth?
The project will answer these questions by designing and testing a user-friendly mobile phone app with shop owners in Lusaka, Zambia. Shop owners will input data on their own products and sales and receive recommendations on new products that might be profitable to stock. The recommendations will be made using information aggregated from other users of the app. By aggregating data across shops owned by different entrepreneurs, the app will deliver scale economies in data collection like those enjoyed by a large retailer in a developed country.
