This article develops the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown and potentially infinite. The theory allows for varying degrees of persistence in the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two main contributions. First, we show that LPs can achieve semiparametric efficiency at a given horizon under classical assumptions on the data, provided that the controlled lag order diverges. Thus, the commonly perceived efficiency loss of LPs can become asymptotically negligible with many controls. Second, we propose LP-based inference procedures for (level and cumulated) impulse responses that possess robustness properties not shared by existing methods. Inference methods using two distinct standard errors are considered. The uniform validity for the first method depends on a zero fourth-order cumulant condition on shocks, while that of the second holds more generally for conditionally heteroskedastic martingale-difference shocks. We propose a bootstrap procedure that improves finite-sample performance and extend the standard error construction to structural responses.

This article views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation” (selecting from groups with proven track records) with “exploration” (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm’s existing practices. The same is not true for traditional supervised learning-based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

We study the role of public risk-sharing in markets where firms face substantial cost uncertainty, focusing on public reinsurance in health insurance. We develop a model where insurers internalize cost uncertainty through risk charges that raise effective marginal costs and create a role for reinsurance. Public reinsurance lowers both expected costs and cost volatility, particularly for smaller insurers, reducing prices and enhancing competition. Using an event study of staggered state-level reinsurance programs, we show that public reinsurance leads insurers to lower prices and private reinsurance purchases, benefiting financially constrained insurers the most. Structural estimates indicate that risk charges account for a substantial share of the premium-cost wedge and highlight public reinsurance’s comparative advantage over premium subsidies by providing risk protection and enhancing competition. Our results underscore the importance of accounting for firms’ risk exposure in policy design and provide a general framework for understanding public risk-sharing policies.

Firms are allowed to use aggregate data on market salaries to set pay, a practice known as salary benchmarking. Using national payroll data, we study firms that gain access to a tool that reveals market benchmarks for each job title. Using a difference-in-differences design, we find that the benchmark information reduces salary dispersion by 25%. Thus, salary dispersion must stem partly from aggregate uncertainty about the salaries offered by other firms. Our model formalizes how salary dispersion can arise even in competitive labour markets for identical workers when such uncertainty exists, and we discuss implications for an ongoing policy debate.

This paper studies the long-term effects of technology and know-how transfers on structural transformations. In the 1950s, the Soviet Union supported the construction of the 156 Projects, which were large-scale, capital-intensive industrial clusters in China. These projects included a technology transfer, consisting of state-of-the-art Soviet machinery and equipment, and a know-how transfer, via the training of Chinese engineers, production supervisors, and high-skilled technicians by Soviet experts. We use newly assembled data that follow steel plants for over four decades, and we exploit natural variation in the transfers they eventually received. We find that, while production advantages stemming from Soviet technology faded away if not complemented with training, the know-how transfer had a long-lasting impact on plant performance, stimulated technology upgrade when China was a closed economy, and increased exports to the Western world when China engaged in international trade. The know-how transfer also generated productivity and technology spillovers onto complementary establishments.

An agent samples projects over time, observing quality for each, while a principal can select at most one. The principal values quality, whereas the agent only wants a project chosen. Transfers are unavailable, but the principal can verify quality by paying a cost. We fully characterize the dynamics of verification by determining optimal mechanisms for this problem. With a low verification cost and a long horizon, the optimal mechanism involves a deterministic selection rule that initially discriminates on quality but chooses a project irrespective of its quality at a deadline. Verification occurs with an intermediate probability before the deadline, declining over time. We show how these conclusions change if the verification cost is high or the horizon is short, and under certain forms of imperfect commitment. Our analysis provides guidelines on how dynamics interact with the benefits of verification.

We explore the power that precise information about consumers’ preferences grants an intermediary in shaping competition. We think of an intermediary as an information designer who chooses what information to reveal to firms, which then compete à la Bertrand in a differentiated product market. We analyse the information designs that maximize consumer and producer surplus and uncover systematic differences in how information can be used to intensify or soften competition. Our analysis demonstrates the power that users’ data can endow intermediaries with and speaks directly to current regulatory debates regarding digital marketplaces.

We study identification and inference in first-price auctions with risk-averse bidders and selective entry, building on a flexible framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. Assuming exogenous variation in either the number of potential bidders (N) or a continuous instrument (z) shifting opportunity costs of entry, we provide a sharp characterization of the nonparametric restrictions implied by equilibrium bidding. This characterization implies that risk neutrality is nonparametrically testable. In addition, with sufficient variation in both N and z, the AS-RA model primitives are nonparametrically identified (up to a bounded constant) on their equilibrium domains. Finally, we explore new methods for inference in set-identified auction models based on Chen et al. (2018, Econometrica, vol. 86, 1965–2018), as well as novel and fast computational strategies using Mathematical Programming with Equilibrium Constraints. Simulation studies reveal the good finite-sample performance of our inference methods, which can readily be adapted to other set-identified flexible equilibrium models with parameter-dependent support.

We study collusion among firms against imperfectly monitored environmental regulation. Firms increase variable profits by violating regulation and reduce expected noncompliance penalties by violating jointly. We consider a case of three German automakers colluding to reduce the effectiveness of emissions control technology. By estimating a structural model of the European automobile industry from 2007 to 2018, we find that collusion lowers expected noncompliance penalties substantially and increases buyer and producer surplus. Due to increased pollution, welfare decreases by € 1.57–5.57 billion. We show how environmental policy design and antitrust play complementary roles in preventing noncompliance.