Knowledge that Transforms

Since the 2000s, U.S. public pension funds have actively shifted their risky investments away from public equities and toward alternative assets like private equity and hedge funds—some much more than others. We explore a range of possible explanations for these trends and argue that beliefs about the risk-adjusted return of alternatives have played a central role. Pension beliefs are shaped by investment consultants, peers, and experience in the 1990s. Other demand-side channels related to risk-seeking motives, spending needs, and fund governance have weaker empirical support. We also consider supply-side factors and agency interpretations of the data.

We develop a model of data-driven competition in which product quality depends on users’ data contributions and firms’ investments in data infrastructure. Users’ inability to internalize the value generated by their data, together with firms’ strategic investment incentives, can lead to data underinvestment—limiting quality—or overinvestment—concentrating market power. We introduce a data union that coordinates users’ contributions and can be implemented through a data trust or intermediary that aggregates user data. By shaping data compensation, the union enables users to capture and internalize more of their data’s value, enhancing user welfare, especially when combined with data sharing among firms. We apply these insights to financial data, digital payments, and Open Finance. Our analysis shows that data portability can strengthen competition but, without coordination, may leave data investment too low or excessive.

We argue that models coming from a variety of fields, such as matching models and discrete choice models among others, share a common structure that we call matching function equilibria with partial assignment. This structure includes an aggregate matching function and a system of nonlinear equations. We provide a proof of existence and uniqueness of an equilibrium and propose an efficient algorithm to compute it. For a subclass of matching models, we also develop a new parameter-free approach for constructing the counterfactual matching equilibrium. It has the advantage of not requiring parametric estimation when computing counterfactuals. We use our procedure to analyze the impact of the elimination of the Social Security Student Benefit Program in 1982 on the marriage market in the United States. We estimate several candidate models from our general class of matching functions and select the best fitting model using information based criterion.

We derive bounds on the rational variation in option‐implied beliefs about market returns. While risk preferences distort option‐implied (or risk‐neutral ) beliefs away from subjective beliefs, one can nonetheless bound risk‐neutral belief movement under a general assumption on the stochastic discount factor. The resulting test requires no knowledge of the objective distribution and allows significantly more flexibility in discount rates than standard volatility tests. Implementing our test empirically using index options, we find that there is so much movement in risk‐neutral beliefs that the bounds are routinely violated. Our results imply significant excess index‐price volatility.

Online experiments have surged in popularity, but how credible are their published results? We examine the universe of hypotheses tested on Amazon Mechanical Turk and reported in leading journals between 2010 and 2021. We find evidence of widespread p-hacking, publication bias, and under-powered studies. The extent of the problems varies substantially across fields, with marketing particularly badly afflicted and little evidence of distortion in economics. We explore correlates of increased credibility and compare the online results to laboratory results from the same outlets, finding similar distortions in both, with bunching just above the significance threshold more pronounced on MTurk.

Using online job postings data, we explore whether auditors adjust their human capital in response to clients' cyber breach incidents. We find that audit offices increase their demand for cybersecurity specialists after their clients' cyber breaches, implying that cyber breaches change not only client risk but also their auditor's production function. Consistent with expectations, we document that this impact is larger when breaches involve their own clients (rather than those of peer offices within the same audit firm) and when breaches directly involve financial information. Our evidence holds for both cyber‐focused and non‐cyber‐focused offices, highlighting the broad impact of cybersecurity risks on audit practices. Additionally, we find that audit offices with relatively high investment in cybersecurity human capital are better able to preserve their market share among clients in high‐cyber‐risk industries after a client's cyber breach.

We identify a new and important property of affine term structure models: at long maturities, the slope of the term structure of risk premia becomes “almost observable”. Exploiting this property by fitting models to long-maturity data dramatically improves the precision of estimates of risk premia and expected short rates across the term structure. In a Monte Carlo experiment we show that using rates with maturities up to 25 years, rather than 10 years as is standard in the literature, reduces the standard deviation of estimation errors in risk premia, expected short rates and term premia by over 70%. Applying this approach to US Treasury data, we find that variation in expected short rates accounts for a much larger share of variation in yields (and variation in term premia a correspondingly smaller fraction) than in the widely used estimates reported by the Federal Reserve. Accounting for time-variation in yield volatility is key to successful extraction of the risk premium information in long rates. Our approach therefore departs from much of the empirical literature in two important respects: it incorporates long-maturity yields and allows for time-varying yield volatility, whereas most existing studies ignore long-term yields and rely on constant volatility models.

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.

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.