思维领导

We study whether access to local pollution information causes investors to make greener portfolio allocations, exploiting the rollout of air quality monitoring stations in India. Using a triple-differences framework on the trading records of 19 million investors, we show that retail investors’ holdings in “brown” stocks become more negatively related to local pollution after a nearby station appears. This effect is more pronounced on “alert” dates when air quality is reported to be harmful. The effect is strongest among tech-savvy investors likely “treated” by real-time pollution data, and younger investors, who may be more sensitive to environmental concerns.

We document a prominent yet underappreciated feature of the post-2000 Chinese economy: a vertical structure in which key upstream industries are dominated by state-owned enterprises (SOEs), while downstream industries are largely open to private competition. We develop a general-equilibrium model to analyze how this vertical structure—interacting with industrialization, globalization, and labor abundance—has shaped the Chinese economy. The framework offers new insights into SOE profitability, structural change, resource misallocation, and economic reform. First, upstream SOEs become more profitable as downstream private firms raise their productivity and face greater external demand during industrialization and globalization, helping explain the unprecedented prosperity of SOEs between 2002 and 2007. Second, reducing upstream market power would facilitate labor reallocation from agriculture to non-agricultural sectors, raising GDP and aggregate welfare. Third, preferential credit subsidies to SOEs can improve welfare by alleviating upstream under-supply; consequently, removing these subsidies without dismantling SOE monopoly power could reduce welfare. Quantitative analyses using firm-level data support the theoretical findings. We further show that this vertical structure can arise endogenously as an equilibrium outcome.

A platform matches a unit mass of sellers, each owning a single product of heterogeneous quality, to a unit mass of buyers with differing valuations for unit-quality. After matching, sellers make take-it-or-leave-it price-offers to buyers. Initially, valuations of buyers are only known to them and the platform, but sellers make inferences from the matching algorithm. The efficient matching is positive assortative, but buyer-optimal matchings are stochastically negative assortative when there are few low-value buyers (i.e., compared to lower-quality sellers, high-quality ones are matched to buyers with lower expected valuation). Although everyone trades, generating rents for the side lacking bargaining power results in inefficient matching.