Knowledge that Transforms

We propose new confidence sets (CSs) for the regression discontinuity parameter in fuzzy designs. Our CSs are based on local linear regression, and are bias‐aware, in the sense that they take possible bias explicitly into account. Their construction shares similarities with that of Anderson–Rubin CSs in exactly identified instrumental variable models, and thereby avoids issues with “delta method” approximations that underlie most commonly used existing inference methods for fuzzy regression discontinuity analysis. Our CSs are asymptotically equivalent to existing procedures in canonical settings with strong identification and a continuous running variable. However, they are also valid under a wide range of other empirically relevant conditions, such as setups with discrete running variables, donut designs, and weak identification.

We provide a unified approach to stochastic dynamic programming with recursive utility based on an elementary application of Tarski's fixed point theorem. We establish that the exclusive source of multiple values is the presence of multiple recursive utilities consistent with the given aggregator, each yielding a legitimate value of the recursive program. We also present sufficient conditions ensuring a unique value of the recursive program in some circumstances. Overall, acknowledging the unavoidable failure of uniqueness in general, we argue that the greatest fixed point of the Bellman operator should have a privileged position.

We investigate optimal project management in a setting plagued by an indefinite number of setbacks that are discovered en route to project completion. The contractor can cover up delays in progress due to shirking either by making false claims of setbacks or by postponing the reports of real ones. The sponsor optimally induces work and honest reporting via a soft deadline and a reward for completion that specifies a bonus for early delivery. Late‐stage setbacks trigger randomization between minimally feasible project extension and (inefficient) cancellation. Because extensions may be granted repeatedly, arbitrarily large overruns in schedule and budget are possible after which the project may still be canceled.

A burgeoning literature in labor economics is focused on modeling employer labor market power, generally finding nontrivial estimates of monopsony power. A smaller literature also simultaneously incorporates product market power. Deb, Eeckhout, Patel, and Warren (2024) is an example of applying an oligopoly‐oligopsony model to the U.S. labor market, arguing for important effects on wage levels and inequality from rising market power. I support combining IO and labor as a fruitful way of studying wages and business dynamism, but argue for looking more broadly at (i) differential degrees of employer power in labor and product markets; (ii) investigating the dynamic sources of markups (e.g., through innovation), and (iii) considering wage bargaining models, not just wage posting models, which have some starkly different implications for wage setting.

Supply chain disturbances can lead to substantial increases in production costs. To mitigate these risks, firms may take steps to reduce their reliance on volatile suppliers. We construct a model of endogenous network formation to investigate how these decisions affect the structure of the production network and the level and volatility of macroeconomic aggregates. When uncertainty increases in the model, producers prefer to purchase from more stable suppliers, even though they might sell at higher prices. The resulting reorganization of the network tends to reduce macroeconomic volatility, but at the cost of a decline in aggregate output. The model also predicts that more productive and stable firms have higher Domar weights—a measure of their importance as suppliers—in the equilibrium network. We provide a basic calibration of the model using U.S. data to evaluate the importance of these mechanisms.

We study the effect of interest rates on wealth inequality. While lower rates decrease the growth rate of rentiers, they also increase the growth rate of entrepreneurs by making it cheaper to raise capital. To understand which effect dominates, we derive a sufficient statistic for the effect of interest rates on the Pareto exponent of the wealth distribution: it depends on the lifetime equity and debt issuance rate of individuals in the right tail of the wealth distribution. We estimate this sufficient statistic using new data on the trajectory of top fortunes in the U.S. Overall, we find that the secular decline in interest rates (or more generally of required rates of returns) can account for about 40% of the rise in Pareto inequality; that is, the degree to which the super rich pulled ahead relative to the rich.

This paper studies the role of fiscal capacity in European state consolidation. Our analysis is organized around novel data on the territories and cities of the Holy Roman Empire in the early modern period. Territories implementing an early fiscal reform were more likely to survive, increased in size, and achieved a more compact extent. We provide evidence for the causal interpretation of these results and show key mechanisms: revenues, military investments, and marriage success. The imposition of Imperial taxes, quasi‐random in timing and size, increased the benefits of an efficient tax administration on the side of rulers, driving the implementation of fiscal centralization. Within territories, Chambers became the dominant administrative institution, tilting the consolidating states toward absolutism.

This paper studies how social media affects the dynamics of protests and strikes in China during 2009–2017. Based on 13.2 billion microblog posts, we use tweets and retweets to measure social media communication across cities and exploit its rapid expansion for identification. We find that, despite strict government censorship, Chinese social media has a sizeable effect on the geographical spread of protests and strikes. Furthermore, social media communication considerably expands the scope of protests by spreading events across different causes (e.g., from anticorruption protests to environmental protests) and dramatically increases the probability of far‐reaching protest waves with simultaneous events occurring in many cities. These effects arise even though Chinese social media barely circulates content that explicitly helps organize protests.

The maximum‐likelihood estimator of nonlinear panel data models with fixed effects is asymptotically biased under rectangular‐array asymptotics. The literature has devoted substantial effort to devising methods that correct for this bias as a means to salvage standard inferential procedures. The chief purpose of this paper is to show that the (recursive, parametric) bootstrap replicates the asymptotic distribution of the (uncorrected) maximum‐likelihood estimator and of the likelihood‐ratio statistic. This justifies the use of confidence sets and decision rules for hypothesis testing constructed via conventional bootstrap methods. No modification for the presence of bias needs to be made.