Research

Managers often face substantial challenges in assigning the right workers to the right tasks, in part because they have limited information about workers’ relative skills and preferences. We conduct a randomized field experiment in three large garment manufacturing plants in China to study whether providing managers with information about workers improves task allocation and firm outcomes. We provide randomly selected team managers with reports on workers’ relative efficiency across tasks, including overall efficiency, learning speed, efficiency variability by task complexity, and performance in multi-worker versus solo tasks; for a subset of teams, the reports additionally include workers’ task preferences. We find that providing information increases team efficiency by approximately 10 percent and reduces worker turnover within four months by 34–47 percent. Exploring the underlying mechanisms, we find that the intervention substantially improves managers’ knowledge of workers. However, using detailed task-level administrative data, we find that managers incorporate only workers’ relative overall efficiency into their assignment decisions, with no corresponding evidence that they adjust assignments based on the other dimensions of worker performance or preferences. The results show that information provision can improve worker-task matching and organizational performance, while highlighting a gap between managers’ ability to acquire information and their ability to apply multiple dimensions of that information in complex assignment decisions.

This paper studies the impact of a large-scale poverty alleviation program in China, which aims at promoting economic development in designated National Poor Counties, on the entry of polluting firms. Using rich data on firm registration and performance, I use both difference-in-differences and triple difference models to estimate the policy impact. The results show that poverty alleviation attracts both polluting firms and non-polluting firms. However, the policy attracts fewer polluting firms than non-polluting firms—compared with the entry of non-polluting firms, the policy decreases the average number of new polluting firms by 16% annually. Moreover, the estimation of dynamic effects suggests that the negative policy impact on the entry of polluting firms increases over time and persists in the long run. Exploring mechanisms driving the impact, I show that first, the poverty alleviation policy increases the exit of polluting firms by 23%, indicating a more difficult industrial environment for polluting firms to survive. Second, it increases regional income that could lead to an increase in people’s marginal willingness to pay for environmental quality improvement. Lastly, National Poor Counties tend to provide smaller government support to polluting firms relative to non-polluting firms.

Small and medium-sized enterprises (SMEs) face persistent barriers to formal credit, and while expanding loan access improves firm performance on average, less is known about which firms benefit most. This paper provides new evidence from Bangladesh, in collaboration with a commercial bank, combining administrative records on the universe of SME loan applicants in 2021–2022 with original survey data on roughly 2,000 firms. The identification strategy compares accepted and rejected applicants who passed an initial screening, using coarsened exact matching. On average, receiving a loan increased sales by 18 percent, profits by 20 percent, and employment by 8 percent. But the employment gains are uneven: the average effect is driven entirely by firms with at least five employees, which saw a 14-percent increase in employment. Expanding credit to smaller firms can raise their owners’ incomes, but only larger SMEs create jobs for others.

This project is currently in progress. More details will be added soon.