Knowledge that Transforms

Retirement savings decisions are increasingly mediated by digital technologies that promise to help individuals plan adequately for their retirement. We exploit a natural experiment to show that introducing a digital pension application increases the probability of making a voluntary retirement contribution by 1.8 percentage points, from an average pretreatment contribution rate of 2.8%. Men and higher-income earners are more likely to respond to the app introduction. We then leverage a field experiment to show that using the app affects contribution behavior mainly through reducing the “hassle” costs of making contributions, rather than by providing information on the associated tax savings.

Modern asset pricing models combine recursive preferences with complex dynamics for the underlying consumption process. The existence of solutions is for many of these models an unsettled question. This paper introduces a novel technique to prove existence and nonexistence, as well as uniqueness for models with recursive preferences. The approach applies to many models of interest, including those with long-run consumption risks, with stochastic volatility and jumps, with time-varying consumption disasters, and with smooth ambiguity aversion and learning. Collectively, the proven results settle the existence question for many of today’s leading asset pricing models.

We find that disclosing bank-specific information reallocates systemic risk, but whether it mitigates systemic bank runs depends on the nature of information disclosed. Disclosure reveals banks’ resilience to adverse shocks and shifts systemic risk from weak to strong banks. Yet, only disclosure of banks’ exposure to systemic risk can mitigate systemic bank runs because it shifts systemic risk from more vulnerable banks to those less vulnerable. Disclosure of banks’ idiosyncratic shortfalls of funds does not differentiate such exposure, rendering the resultant reallocation of systemic risk ineffective in mitigating systemic runs.

We develop a dynamic model of costly stock short-selling and lending market and obtain implications that simultaneously support many empirical regularities related to short-selling. In our model, investors’ belief disagreement leads to shorting demand, whereby short-sellers pay shorting fees to borrow stocks from lenders. Our main novel results are as follows. Short interest is positively related to shorting fee and predicts stock returns negatively. Higher short-selling risk can be associated with lower stock returns and less short-selling activity. Stock volatility is increased under costly short-selling. An application to GameStop episode yields implications consistent with observed patterns.

The large divergence in the returns of top-performing star firms and the rest of the economy is substantially reduced when we account for the mismeasurement of intangible capital. Star firms produce and invest more per dollar in invested capital, have more valuable innovations as measured by the market value of patents, and are as exposed to competitive shocks as nonstars. Star firms have higher markups that are predicted early in their life cycle at a time when they are small. Overall, after we correct for the mismeasurement of intangibles, the evidence points to the superior ability of star firms.

How much of a loan should a lender retain, and how do loan sales affect loan performance? We address these questions in a model in which a lender originates loans that it can sell to investors. The lender reduces default risk through screening at origination and monitoring after origination, but is subject to moral hazard. The optimal lender-investor contract can be implemented by requiring the lender to initially retain a share of the loan that it gradually sells to investors, rationalizing loan sales after origination. The model generates novel predictions linking loan and lender characteristics to initial retention, sales dynamics, and loan performance.

We develop a quantitative model with imperfections in domestic and international financial markets that generates strong effects of U.S. monetary policy on emerging markets (EMs). Financial imperfections prevent arbitrage both between local EM lending and borrowing rates, and between local-currency and dollar borrowing rates. An adverse feedback effect between financial health and external conditions amplifies the domestic “financial accelerator,” leading to large cross-border spillovers of U.S. monetary policy shocks. The model implies a link between uncovered interest parity violations and local credit spreads, a prediction we show the data strongly supports.

This paper conducts a textual analysis of earnings call transcripts to quantify climate risk exposure at the firm level. We construct dictionaries that measure physical and transition climate risks separately and identify firms that proactively respond to climate risks. Our validation analysis shows that our measures capture firm-level variations in respective climate risk exposure. Firms facing high transition risk, especially those that do not proactively respond, have been valued at a discount in recent years as aggregate investor attention to climate-related issues has been increasing. We document differences in how firms respond through investment, green innovation, and employment when facing high climate risk exposure.

The real estate market is highly intermediated, with 90% of buyers and sellers hiring an agent. However, low barriers to entry and fixed commission rates result in large market share for inexperienced intermediaries. Using micro-level data on 8.5 million listings and a novel research design, we show that house listings by inexperienced agents have a lower probability of selling, and this effect is strongest during the housing bust. We estimate that 3.7% more listings would have been sold in a flexible commission equilibrium. Eighty percent of this improvement comes from competition and the remainder from commission variation across experience.

The classic view assumes banks prioritize immediate repayment by selling assets until default. We endogenize run frequency and study how general settlement rules trade off liquidity provision net of fire sale losses against induced run incentives. Panic runs are eliminated when all illiquid assets are sold under orderly resolution, but liquidity provision in a run is minimal. When suspension after some fire sales is followed by immediate liquidation, run frequency falls then rises in suspension delay. Thus, optimal suspension may require some sale of illiquid assets, in contrast to MMF norms. Ex post discretion induces excessive liquidation and more frequent runs.