Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

We investigate the impact of information sharing between rivals in a dynamic auction with asymmetric information. Firms bid in sequential auctions to obtain inputs. Their inventory of inputs, determined by the results of past auctions, are privately known state variables that determine bidding incentives. The model is analyzed numerically under different information sharing rules. The analysis uses the restricted experience based equilibrium concept of Fershtman and Pakes (2012) which we refine to mitigate multiplicity issues. We find that increased information about competitors' states increases participation and inventories, as the firms are more able to avoid the intense competition in low inventory states. While average bids are lower, social welfare is unchanged and output is increased. Implications for the posture of antitrust regulation toward information sharing agreements are discussed.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Index funds are one of the most common ways investors access financial markets and are perceived to be a transparent and low-cost alternative to active investment management. Despite these purported virtues of index fund investing and the introduction of new products and competitors, many funds remain expensive and fund managers appear to exercise substantial market power. Why do index funds have market power? We develop a novel quantitative dynamic model of demand for and supply of index funds. In the model, investors are subject to inertia, search frictions, and have heterogeneous preferences. These frictions on the demand side create market power for index fund managers, which fund managers can further exploit by price discriminating and charging higher expense ratios to retail investors. Our results suggest that the average expense ratios paid by retail investors are roughly 45% higher as a result of search frictions and are 40% higher as a result of inertia compared to the friction-less baseline. In our counterfactuals, we find an interaction between search frictions and inertia--inertia imposes higher (lower) costs on investors when search frictions are low (high).