Knowledge that Transforms

Decisions are typically made by drawing on multiple sources of information, yet the correlation among these sources is often hard to assess. We study robustly optimal strategies—those that maximize the worst-case payoff guarantee across all correlation structures. With two states and two actions, the robustly optimal strategy is strikingly simple: use only the best information source and ignore the rest. With more actions, a robustly optimal strategy decomposes the problem into local comparisons and assigns each comparison to the source that is best at that comparison. With more states, the same specialization logic survives through a concavification characterization: the decision problem is decomposed into payoff-relevant components, and each component is assigned to its best source. These results offer a new rationale for why ignoring information can be optimal and highlight the benefit of using specialized information sources when correlations are ambiguous.

In this paper I revisit the interpretation of the linear instrumental variables (IV) estimand as a weighted average of conditional local average treatment effects (LATEs). I focus on a situation in which additional covariates are required for identification while the reduced-form and first-stage regressions may be misspecified due to an implicit homogeneity restriction on the effects of the instrument. I show that the weights on some conditional LATEs are negative and the IV estimand is no longer interpretable as a causal effect under a weaker version of monotonicity, i.e. when there are compliers but no defiers at some covariate values and defiers but no compliers elsewhere. The problem of negative weights disappears in the interacted specification of Angrist and Imbens (I1995), which avoids misspecification and seems to be underused in applied work. I illustrate my findings in an application to the causal effects of pretrial detention on case outcomes. In this setting, I reject the stronger version of monotonicity, demonstrate that the interacted instruments are sufficiently strong for consistent estimation using the jackknife methodology, and present several estimates that are economically and statistically different, depending on whether the interacted instruments are used.

We demonstrate how private equity (PE) promotes nonbank lending by substituting for bank monitoring. Relative to non-PE-backed loans, PE-backed loans exhibit lower bank monitoring, smaller loan shares retained by lead banks, and larger shares held by nonbanks. The substitution is strongest when sponsors have greater incentives to engage with portfolio firms, is not associated with weaker loan performance, and persists during borrower distress. Our findings suggest that PE sponsors’ active engagement—through monitoring and support in distress—reduces banks’ need for monitoring and information production, enabling them to retain less skin in the game and sell loans to nonbanks.

We examine how different forms of government intervention - explicit deposit insurance, implicit guarantees from nationalization, and assurance effects from re-privatization - shape depositor behavior during a banking crisis. Using proprietary monthly account-level data, we show that depositors respond to formal insurance limits even in an environment of broad state support, but adjust only partially. Stabilization is markedly stronger after the takeover by a financially strong private bank than during state ownership, despite unchanged statutory coverage. These patterns are inconsistent with a strong bailout-expectations view in which explicit insurance is largely irrelevant and instead point to credibility as a key determinant of depositor behavior. We further show that political trust amplifies the effectiveness of government guarantees. Overall, our findings highlight that the form and credibility of government backstops, rather than coverage levels alone, are central to retail deposit funding stability during crises.