This paper analyzes first-price sealed-bid auctions of standing timber organized by the French forest service, Office National des Forêts (ONF). A feature of these auctions is that they are held with random reserve prices. We consider an auction model with a random reserve price within the independent-private-value paradigm. After establishing the identification of the model, we estimate the underlying bidders' private-value distribution by using a simple two-step nonparametric procedure. This procedure allows the computation of the winners' informational rents as well as the optimal reserve price. We then simulate a first-price sealed-bid auction with the optimal announced reserve price. Empirical results show that the optimal reserve price allows the ONF to extract more of bidders' willingnesses to pay. Moreover, our results show that, though sales do not vary much, profits for the ONF would significantly increase and less timber would be sold.

Motivated by several interesting features of the highway mowing auction data from the Texas Department of Transportation (TDoT), we study three competing procurement auction models with endogenous entry. Our entry and bidding models provide several interesting implications. For the first time, we show that even within an independent private value paradigm, as the number of potential bidders increases, bidders' equilibrium bidding behaviour can become less aggressive, and the expected procurement cost may rise because the “entry effect” is always positive and may dominate the negative “competition effect”. We then develop structural models of entry and bidding corresponding to the three models under consideration, controlling for unobserved auction heterogeneity, and use the recently developed semi-parametric Bayesian estimation method to analyse the data. We select the model that best fits the data, and use the corresponding structural estimates to quantify the “entry effect” and the “competition effect” with regard to the individual bids and the procurement cost.

This paper considers nonparametric identification of a two-stage entry and bidding game we call the Affiliated-Signal (AS) model. This model assumes that potential bidders have private values, observe signals of their values prior to entry, and then choose whether to undertake a costly entry process, but imposes only minimal structure on the relationship between signals and values. It thereby nests a wide range of entry processes, including in particular the Samuelson (1985) and Levin and Smith (1994) models as special cases. Working within the AS model, we map variation in factors affecting entry behavior (potential competition or entry costs) into identified bounds on model fundamentals. These bounds are constructive, collapse to point identification when available entry variation is continuous, and can readily be refined to produce the pointwise sharp identified set. We then extend our core results to accommodate nonseparable unobserved auction-level heterogeneity and potential endogeneity of entry shifters, thereby establishing a formal identification framework for structural analysis of auctions with selective entry.

During the early part of the COVID-19 pandemic, national and local governments introduced a number of policies to combat the spread of COVID-19. In this paper, we propose a new approach to bound the effects of such early-pandemic policies on COVID-19 cases and other outcomes while dealing with complications arising from (i) limited availability of COVID-19 tests, (ii) differential availability of COVID-19 tests across locations, and (iii) eligibility requirements for individuals to be tested. We use our approach study the effects of Tennessee’s expansion of COVID-19 testing early in the pandemic and find that the policy decreased COVID-19 cases.

This study experimentally examines how the leadership style of the audit committee (AC) chair (controlling or open) influences the amount of discretionary information that auditors intend to share with the AC, in the context of common meeting formats (i.e., AC meetings with versus without management present, or private meetings between the auditor and AC chair). Participants are highly experienced auditors, including partners and (senior) managers, from Big 4 accounting firms. We predict and find that an open AC chair mitigates the chilling effect of management's presence on the number of discretionary issues shared with the AC. Compared to those who attend AC meetings only, auditors who engage in private meetings with the AC chair before AC meetings plan to disclose fewer discretionary issues to the AC in subsequent AC meetings, but disclose more discretionary issues in total across meetings. AC chair leadership style has no impact on their discretionary information sharing in these private meetings. These results suggest that open AC chairs can mitigate the adverse effect of management's presence on auditors' discretionary information disclosure and have implications for regulators aiming to enhance corporate governance.

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 examine the information asymmetry between local and nonlocal investors with a large dataset of stock message board postings. We document that abnormal relative postings of a firm, that is, unusual changes in the volume of postings from local versus nonlocal investors, capture locals' information advantage. This measure positively predicts firms' short‐term stock returns as well as those of peer firms in the same city. Sentiment analysis shows that posting activities primarily reflect good news, potentially due to social transmission bias and short‐sales constraints. We identify the information driving return predictability through content‐based analysis. Abnormal relative postings also lead analysts' forecast revisions. Overall, investors' interactions on social media contain valuable geography‐based private information.