Knowledge that Transforms

This paper studies how the accuracy of a short‐term performance measure affects its value when a principal decides whether to retain or fire an agent, who can manipulate that measure. Compared with ignoring performance and never firing the agent, the performance measure is valuable when the savings in the expected cost of the retention error is more than the expected cost of the firing error. A more accurate performance measure can have a non‐monotonic effect on its value: Greater accuracy improves the principal's ability to correctly identify the agent, which increases efficient firing, but it causes the incompetent agent to increase manipulation to avoid being fired, which increases inefficient retention. With a less accurate performance measure, the incompetent agent has weaker incentives for manipulation, and an increase in accuracy increases its value due to the identification effect. With a more accurate performance measure, the manipulation effect dominates, and an increase in accuracy decreases its value. The results imply that the principal prefers an imperfect performance measure when the agent can choose the level of manipulation.

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.