Abstract Biomedical innovation suffers from a “funding gap” between the needs of drug development firms and the availability of funds. The requirement of large investments for drug development projects and the high pipeline risk associated with FDA approval causes this funding gap in part. In this paper, we propose a new financial instrument—the “FDA hedge”—that pays off upon FDA approval failure. We develop a theory to show that the FDA hedge can help eliminate the funding gap. Using novel project-level data, we establish empirically that FDA hedge risk is idiosyncratic, and show how better sharing this risk can spur welfare-enhancing R&D. (JEL G11, G13, G22, I11, L65, O32 Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

We construct a new measure of deposit insurance generosity for many countries, empirically model the exogenous international influences on the adoption and generosity of deposit insurance and use a novel econometric method to explore the causal chain from the expansion of deposit insurance generosity to increased overall lending, increased lending to households, increased banking system leverage, and more severe and frequent banking crises. Greater deposit insurance generosity robustly produces greater overall lending relative to bank assets and more lending to households relative to both bank assets and GDP, and results in higher banking system leverage. Our estimates, however, are not conclusive regarding whether greater deposit insurance generosity resulted in greater total loans relative to GDP or in more frequent or severe banking crises.

How should sovereign bailouts take account of the effects bailouts have on policy reforms? Conflicted recipient governments complicate bailout choices because some reforms that spur growth reduce rents that benefit government decision makers. Our model takes account of whether bailout generosity and policy reforms are strategic substitutes, strategic complements or both, and each case implies a different optimal bailout contract, which generally cannot achieve First Best. Conditional forgiveness of some loan payments when economic outcomes are sufficiently favorable can achieve outcomes closer to First Best, and this is so for a small ex ante amount of the bailout subsidy.

An expansion in mortgage credit to subprime borrowers is widely believed to have been a principal driver of the 2002-2006 U.S. house price boom. By contrast, this paper documents a robust, negative correlation between the growth in the share of purchase mortgages to subprime borrowers and house price appreciation at the county-level during this time. Using two different instrumental variables approaches, we also establish causal evidence that house price appreciation lowered the share of purchase loans to subprime borrowers. Further analysis using micro-level credit bureau data shows that higher house price appreciation reduced the transition rate into first-time homeownership for subprime individuals. Finally, the paper documents that subprime borrowers did not play a significant role in the increased speculative activity and underwriting fraud that the literature has linked directly to the housing boom. Taken together, these results are more consistent with subprime borrowers being priced out of housing boom markets rather than inflating prices in those markets.

Different share classes on the same firms provide a natural experiment to explore how investor clienteles affect momentum and short-term reversals. Domestic retail investors have a greater presence in Chinese A shares and foreign institutions are relatively more prevalent in B shares. These differences result from currency conversion restrictions and mandated investment quotas. We find that only B shares exhibit momentum and earnings drift and only A shares exhibit monthly reversals. Institutional ownership strengthens momentum in B shares. These patterns accord with a setting where short-term reversals (which represent inventory risk premia) prevail in a market dominated by noise traders and momentum prevails in markets where noise traders are less prevalent relative to informed investors who underreact to fundamental signals. Overall, our findings confirm that clienteles matter in generating stock return predictability from past returns.

This essay describes the evolution and recent convergence of two methodological approaches to causal inference. The first one, in statistics, started with the analysis and design of randomized experiments. The second, in econometrics, focused on settings with economic agents making optimal choices. I argue that the local average treatment effects framework facilitated the recent convergence by making key assumptions transparent and intelligible to scholars in many fields. Looking ahead, I discuss recent developments in causal inference that combine the same transparency and relevance.

A joint model for the Value at Risk (VaR) and expected shortfall (ES) can be estimated using a joint scoring function. Previous work has modelled the ES as the product of the VaR and a constant factor. However, this implies the same dynamics for the ES and the VaR. We propose a time-varying multiplicative factor. The ES has been expressed as the product of an expectile and a constant factor that depends on the expectile level. We rewrite this as the product of a quantile and a function of a time-varying expectile level. The expectile level is a function of the Omega ratio, which is the ratio of the expected gain to the expected loss. This leads us to model the ES as the product of the VaR and a factor that is a function of a time-varying Omega ratio. We provide empirical support using stock indices and individual stocks.

ABSTRACT We use experimental markets to examine how pushing investment information and the value relevance of that information interact to influence investors’ value estimate accuracy and market price efficiency. Developments in technology allow information to be pushed to investors anytime and anywhere. However, in addition to value‐relevant information, pushed information often includes information that is irrelevant for assessing firm value. Drawing on psychology theory, we find that pushing information has divergent effects depending on the value relevance of the information. Pushing only value‐relevant information increases investors’ processing of the information and leads to more accurate value estimates and market prices than when not pushed. In contrast, pushing a mix of value‐relevant and value‐irrelevant information reduces investors’ processing of value‐relevant information, leading to less accurate value estimates and market prices due to poorer acquisition and integration of information than when not pushed or when only value‐relevant information is pushed. Collectively, our results reveal a dark side to push technologies, particularly with the growing presence of value‐irrelevant information.

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.