Papers

I study treatment effect estimation when treatment events have persistent effects and can be experienced more than once. Natural disasters, job loss and health shocks are examples of such treatments. I show that the effect of a total treatment trajectory can be recovered under assumptions similar to those commonly invoked in single-event settings using suitably flexible TWFE models. Decomposing the total trajectory effect into portions attributable to distinct event occurrences, however, requires further assumptions. I propose an assumption similar to conditional parallel trends, imposing it on the growth of event-specific effects rather than on untreated outcomes. Combined with a linear-in-parameters model of effect growth, this assumption enables a sequential imputation estimator that consistently estimates the dynamic effects of each event occurrence and that can accommodate heterogeneity in effects according to observable event attributes, such as intensity. I demonstrate that several intuitive TWFE models fail to recover interpretable treatment effect parameters in the multi-event setting and illustrate the sequential imputation estimator's favourable performance using Monte Carlo simulations.

We study a matching mechanism where agents and objects are described by features rather than complete rankings. A single spectral projection reduces the problem to a one-dimensional sort, computable in O(N log N) time. We prove that on descaled features and preferences, our algorithm obtains the exact Nash Social Welfare (NSW) optimum within the projected space, with an unconditional utilitarian-welfare guarantee and a conditional NSW guarantee. The proposed mechanism is stable against exogenous noise but not strategy-proof; we provide an explicit profitable misreport. On an agentic AI shopping application, the diagnostics correctly anticipate both a success and a failure case. A 100-instance robustness study confirms the findings.

A production network can remain largely unidentified even when the economic decision it supports is identified. We characterize sufficient measurements for exposure-based decisions and compute sharp maximum regret over networks consistent with released totals. Using earlier and later vintages of Japan's interregional input-output accounts, we select measurements from the 1995 table and evaluate the frozen design against the 2005 benchmark. At roughly half the statistics required for full disclosure, the resulting monitoring set loses only 0.07 percentage points of average exposure relative to the benchmark optimum, yet its sharp maximum regret across compatible networks is 4.83 points. In U.S. coal deliveries surrounding a 2005 Wyoming rail disruption, additional shipment measurements identify the optimal set of plants to monitor for inventory risk, even though four monitored plants' exposures to the affected coal supply remain unidentified. The results distinguish good benchmark performance from a decision guarantee and show that decisions can be identified before individual exposures. They suggest evaluating network data by the economic decisions they support.

Studying heterogeneous treatment effects has become essential in experimental and observational studies. A critical assumption for obtaining reliable treatment effect estimates is overlap, which requires that treated and control units have sufficiently similar covariate distributions. Poor overlap may limit the effectiveness of estimators, especially those based on propensity scores, potentially leading to unreliable results. We investigate the effectiveness of kernel balancing (KBal) (Hazlett, 2020) as an alternative to propensity score methods for conditional average treatment effect (CATE) estimation, particularly in settings with overlap violations. Building on optimization-based balancing approaches, we integrate KBal weights into tree-based methods, specifically, causal forests (Athey et al., 2019) and the X-Learner (XRF) (Künzel et al., 2019), to assess their impact on bias reduction and estimation precision. Monte Carlo evidence shows that KBal achieves near-exact balance in a transformed feature space, thereby improving treatment effect estimation in cases where traditional reweighting methods struggle due to extreme weights, finite-sample bias, or insufficient removal of pre-existing confounding bias. We apply the proposed methods to the semi-synthetic IHDP benchmark dataset. Overall, the results indicate that KBal leads to performance improvements, especially in settings with nonlinear treatment effects and limited overlap, making it a useful alternative to propensity score methods.

We develop a dynamic three-sector model in which Marx's aggregate equalities (total price equals total value and total profit equals total surplus value) are treated as strict conservation constraints throughout capital reallocation and technical diffusion. Three price-value coefficients are determined by the two aggregate equalities and a closure that sets one sector's price-based profit rate equal to the contemporaneous value-based average (the anchor). Before innovation, capital inflow expands the receiving sector's output and, under both anchors, lowers its unit price and excess profitability. This endogenous fall in price as output rises supports a two-timescale reading: demand-related price movements may precede slower adjustment through capital mobility. A labour-saving, capital-intensifying innovation generates temporary extra surplus value, revalues committed capital, and changes the economy-wide profit rate. At constant real wages, the final uniform rate rises, consistent with Okishio's theorem. When real wages rise sufficiently to keep aggregate exploitation approximately constant, the organic composition increases and the final rate falls. Strict aggregate conservation is computationally coherent and yields distinct transitional predictions under explicit closure and wage assumptions.

It is standard practice to include covariates in regression discontinuity designs (RDDs) and regression kink designs (RKDs), but the theoretical justification for doing so does not generally extend beyond linear estimands. This paper proposes a novel entropy balancing reweighting approach for covariate adjustment within a general framework of RDDs and RKDs. While conventional regression-based covariate adjustment methods generally fail to deliver consistent estimation for nonlinear estimands such as quantile treatment effects, our reweighting approach achieves consistency while improving efficiency. Moreover, even in settings where the regression-based covariate adjustment method already improves efficiency, our approach can deliver additional efficiency gains. Simulation studies corroborate these theoretical findings. We present an empirical application in which our covariate adjustment yields statistically significant results that would not be obtained without covariate adjustment.

In the last two decades, last-mile delivery (LMD) firms have seen immense growth fueled by the success of e-commerce, leading to faster and cheaper deliveries. Operating on thin margins, LMD firms strive for successful first-time deliveries to avoid the financial and reputational costs of reattempts. Delivery Agents (DAs) are integral to LMD efficiency, influencing customer experience, delivery success, and productivity. However, most LMD performance enhancement research focuses on process, technology, and incentives, which presume workers will conform to procedures and monitoring tools will function flawlessly. Nevertheless, in practice, DAs deviate from expected behaviors, i.e., indulge in misconduct, negatively affecting delivery efficiency, often resulting in returned parcels. One of the major misconducts is fake remarked deliveries, wherein DAs intentionally do not deliver the parcels and provide a fake reason for it. For instance, even without reaching a delivery address, a DA remarks 'customer unavailable' and records a delivery failure. In this study, we collaborated with a leading Indian LMD firm and, using instrumental variable regression, find that such misconduct leads to a spillover productivity loss. This effect reduces the next day's successful deliveries by 1.60% and first-time-right deliveries by 1.86%. We discuss misconduct's correlation with factors such as task complexity and offer novel insights into how opportunistic circumstances can influence worker behavior.

A decision maker may have several information sources available and choose which one to consult only after learning the decision problem she faces. When is one such set of sources uniformly more valuable than another? For unrestricted Bayesian decision problems, we show that the answer can be stated entirely in terms of Blackwell comparisons. Form a tagged mixture by drawing a source independently of the state and revealing both its identity and its signal. One source set is more valuable in every decision problem if and only if each tagged mixture of the second source set is Blackwell dominated by some tagged mixture of the first. The result applies to compact, possibly infinite source sets and general signal spaces. It also has an exact quantitative counterpart: the largest normalized value shortfall is the directed Le Cam deficiency between the source sets' tagged hulls. The analogous program for monotone decision problems reveals a boundary. We call the passage from problem-by-problem source-set superiority to a problem-independent pairwise dominance a lifting. The Blackwell lifting does not extend directly to the Lehmann order: mixing sources that individually satisfy the monotone likelihood ratio property (MLRP) need not preserve MLRP, and even when it does, no fixed Lehmann-dominating mixture need exist. Requiring one source to serve a finite bundle of monotone decision problems restores the equivalence.

Misinformation is widely reported to propagate faster on engagement-based platforms, yet prior work largely focused on empirical analysis, without identifying a specific algorithmic mechanism that results in this phenomenon. Thanks to the open-sourcing of X's recommendation algorithms, we conduct what is, to our knowledge, the first component-level study of the recommendation algorithm deployed by a social media platform, which examines how each of its components affects misinformation propagation. Specifically, we identify the engagement fungibility mechanism in the algorithm, where the final recommendation score is constructed as a weighted sum of all predicted user activities. As a result, a tweet can be repeatedly recommended simply because it is predicted to draw many instant reactions (e.g., likes and retweets), even when it is not expected to draw thoughtful responses (e.g., replies and quotes). Since misinformation typically draws a larger share of its engagement from instant reactions, this mechanism enables it to receive more recommendation exposure and to propagate faster. To empirically validate this mechanism, we re-implement X's recommendation algorithm on the USC X 2024 election corpus, and build a calibrated simulation study to analyze the impact of different scoring rules. We find that re-tuning the metric weights has little or even a negative impact on reducing the credibility exposure gap, while those scoring rules that set a precondition of thoughtful engagement for amplification would be able to alleviate the gap significantly, across 46 robustness checks. Our diagnosis, therefore, yields a simple and deployable fix, a reflective-threshold gate that withholds amplification until a tweet is predicted to draw thoughtful engagement, which we find to reallocate exposure away from low-credibility content at no cost to mainstream exposure and with no loss of engagement.

Panel conditioning, the causal effect of prior survey participation on responses, can vary with tenure. Under an additive model of cell means in period, entry cohort, and tenure, we characterize which features of the conditioning path a staggered panel identifies on its observed support, and how the unidentified component affects common panel estimators. The identified set of the path is an affine translate of the tenure projection of the cell design's kernel, and a linear functional of the path is identified exactly when it annihilates that projection. It always contains an affine direction and, when the entry cohorts share a stride, periodic directions, which exhaust it under a connectivity condition on observed increments; second differences at that stride are then identified, and ordinary ones generally are not when the stride exceeds one. Under a recruitment condition, an interrupted schedule such as the four-eight-four rotation of the Current Population Survey (CPS) distinguishes a constant increment per interview from one per calendar month, which no equally spaced schedule can. We give support conditions for recovery under a plateau, entry-wave negative controls, or bounded cohort drift. A second set of results links identification to regression: two-way fixed effects absorb every unidentified direction, so the remaining conditioning bias is normalization-invariant and itself identified, and a two-way regression with tenure indicators corrects it under a residual-rank condition. When event time is aligned with tenure, conditioning shifts event-study coefficients by a known linear functional of the path, producing pre-trends without anticipation; bounds on identified curvature bound those shifts. Simulations verify the identities, a 19-wave Japanese panel illustrates the support calculations, and published CPS month-in-sample indices give a descriptive, not identifying, example.

Access to past reports changes how consumers learn about competing information providers. A subscriber who has learned to interpret a familiar source may find an equally competent rival less useful. We study how opening the rival's archive changes renewal prices and the effect of outcome feedback. Better feedback strengthens the familiar source's advantage when rival reports are inaccessible, but can weaken it once some rival records are available. We derive the exact threshold in a Gaussian learning model with differentiated price competition. Opening further records narrows the renewal premium and raises consumer surplus. When the familiar source initially serves a sufficiently large share, however, partial opening can reduce aggregate forecasting quality and total surplus by drawing consumers toward a rival that remains less informative. Complete access improves both outcomes relative to every incomplete archive. These results concern a covered interior renewal market with inherited customer histories, before implementation costs. The results distinguish the quality of feedback from access to the records with which consumers combine it.

We study how learning about a forecasting method affects a seller's choice of evidence. More evidence improves the current forecast but makes the method's persistent component harder to identify. A monopolist may therefore collect less evidence initially to help customers interpret later forecasts. Because the seller captures only part of customers' gains from costly evidence, it switches to less initial evidence sooner than a welfare-maximizing regulator. A minimum evidence requirement improves welfare only for intermediate weights on future benefits. An independent assessment removes this learning trade-off but may be privately unprofitable despite being socially worthwhile.

Public support for early adoption can affect what later users learn from observed choices. An adopter may have favourable private information, or may have received a rebate. We study a government that randomly assigns eligibility for a fixed rebate and chooses the program's coverage and reporting rule. We characterize when disclosing individual eligibility changes optimal coverage from zero to a positive rate under general convex program costs. This requires both a favourable marginal return to a disclosed program and a bound on the direct benefit available from a large opaque intervention. In the benchmark, disclosure allows the government to increase early adoption and welfare while leaving expected total adoption unchanged. We then allow imperfect compliance and private information for the late user. An eligibility label can be strictly informative without changing anyone's decision, in which case disclosure has no welfare value. The policy reversal survives when both extensions are present. The results identify when an eligibility record is a useful part of the design of an adoption program.

Matrix time series is a series of matrix data observed over time. Analytical tools for such time series is needed in many applications in finance, economics, engineering and many other fields. To avoid the use of vectorization of the matrices which loses the column and row information, and the vector autoregression framework in traditional time series analysis, \cite{chen2021autoregressive} proposed the Matrix Autoregressive (MAR) Model. The model maintains and utilizes the matrix structure, leading to a substantial dimensional reduction and admitting explicit interpretations, comparing with the vector autoregressive model on the vectorized data. However, the MAR model still encounters difficulties in dealing with large dimensional matrix time series as the coefficient matrices in MAR models are also large. In this paper we propose to achieve further dimension reduction through reduced-rank constraints of the coefficient matrices in the MAR model. Estimation and rank determination procedures are studied. Theoretical investigation and empirical examples show that the reduced-rank constraint can achieve higher statistical efficiency than the MAR model.

Generative AI makes a paradox newly visible: learning from a common source can improve what each firm does today while narrowing the variety available for tomorrow's discoveries. The tension extends beyond AI. Fields turn recurring features of successful organizations into portable practices, such as just-in-time production, freemium business models, or mixture-of-experts architectures. These collective abstractions are partial templates others can adapt, not designs or universal prescriptions. We study how they shape discovery in a model of firms searching interdependent landscapes. A collective repertoire extracts practices from patterns shared by leading configurations; firms vary in how they evaluate those practices locally. The model reveals a generativity chain: population diversity supplies heterogeneous experience, collective abstraction turns it into reusable options, and situated judgment matches options to local needs. Collective learning is not inherently homogenizing. Copying leaders consumes diversity quickly, whereas repertoires paired with local judgment preserve generative capacity. Environmental disruption exposes the consequences: it devalues accumulated knowledge but reopens local search, helping homogeneous, exhausted industries while harming diverse industries whose repertoire remains useful, even though diverse industries still perform better overall. Recurring practices may also outlive their usefulness, making popularity a poor test of validity after change. These findings matter for organizations using generative AI, best-practice communities, and research fields converging on dominant strands. Generativity requires governing the diversity feeding knowledge and the judgment applied to it. Learning systems should be judged not only by the answers they spread today, but by whether they preserve the differences from which tomorrow's answers can be made.

This article develops an identification framework for heterogeneous policy effects when individual-level data are available only in the treated jurisdiction. The framework combines outcome changes from repeated cross-sections within that jurisdiction with a population-average effect identified from aggregate panel data. Under conditional relative parallel trends and overlap, this information identifies subgroup effects standardized to the treated jurisdiction's overall post-treatment covariate distribution, but generally not effects averaged over each subgroup's own distribution. I establish how these subgroup-average effects can nevertheless be recovered using covariate-specific aggregate effects or additional pretreatment observations under assumptions about the evolution of untreated trends. Simulations and an application to the Massachusetts health reform illustrate the framework.

Generative artificial intelligence illustrates a broader organizational puzzle: young, resource-constrained firms can build on knowledge produced across a wider field to address problems that exceed their internal experience. We theorize one mechanism as social imitation: a distributed and recursive process through which communities observe practices and outcomes across organizations, distill recurrent elements into portable strategies, circulate them, and revise the collective repertoire as adoption produces new evidence. This perspective endogenizes the object of imitation. Organizations do not merely copy practices; communities collectively produce what becomes available to copy. We distinguish the source of a candidate (directly observed organizations or a collective) from its mode of adoption (blind or informed), and examine four forms of imitation in an agent-based model of organizational search. Collective sourcing alone does not improve performance. Its value depends on situated evaluation: assessing whether a candidate fits the adopter's configuration. As interdependence increases, informed social imitation improves population performance and the best solution discovered. As problems grow, it becomes more likely to outperform informed imitation from one or several directly observed peers because collective distillation sustains a broader candidate repertoire. Most gains arise from evaluating a small candidate set, and population performance can improve when only a minority of organizations possesses evaluative capability. Social imitation thus reveals a division of cognitive labor: communities expand the strategies organizations can consider, while organizations create value by determining which strategies fit. It explains how firms can mobilize knowledge beyond their boundaries, and why widespread access to collective knowledge does not produce equal benefits.

Appointing more women to corporate boards is widely expected to also raise firms' environmental, social, and governance (ESG) performance. We provide the first meta-analysis of this relationship, drawing on 533 estimates from 106 studies that measure ESG performance with Bloomberg or LSEG ratings. The average reported effect of a one-percentage-point increase in board gender diversity is about 0.28 ESG points, but much of it does not survive scrutiny. Correcting for publication bias with a battery of linear and non-linear methods lowers the effect to between roughly 0.08 and 0.17 points. A best-practice estimate that also imposes sound study design puts it near 0.12 for most of the world, markedly higher for the Middle East, and near -0.11 for the Southeast Asian markets that dominate the Asian evidence. The differences that remain across studies are systematic, driven mainly by geography and by the choice of estimation method rather than by the ESG-rating provider or the controls a study includes. Board gender diversity may be well worth pursuing on its own merits, but the evidence that it reliably raises ESG scores is weaker than the published record suggests.

Cursed players understand the distribution of opponents' actions and the distribution of the payoff-relevant state, conditional on their own information, but respond as if the two were independent. Cursed equilibrium additionally requires statistically correct beliefs about the distribution of play. We propose a solution concept that separates the bias from equilibrium: cursed rationalizability iteratively deletes every action that is not a cursed response to a conjecture about opponents' surviving play. The cursed-rationalizable set is the interim correlated rationalizable set of a virtual game, in which payoffs are averaged over the state. Under strategic complements, cursed rationalizability yields the same bounds on behavior as cursed equilibrium. A cautious refinement, the analog of admissibility, delivers sharp predictions in the laboratory games that documented the winner's curse.

Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using "Tool-Lab," an adaptation of information-board process tracing that places product attributes behind costly tool calls, we examine how marketing pricing cues (i.e., just-below pricing and promotional framing) influence AI shopping agents. Across eight commercially deployed LLMs from three providers, we trace pre-choice information acquisition. Under zero cost, pricing cues rarely mislead. Imposing acquisition costs under a vague goal prompt leads LLMs to omit diagnostic attributes required to compute unit price and choose suboptimal choices resembling human heuristics. Relative to a specific goal prompt that mainly preserves diagnostic search and choice optimality, a vague goal prompt under constraints creates a search-mediated vulnerability. This research demonstrates that marketing heuristics in delegated AI shopping are governed by storefront information architecture, not necessarily immutable LLM flaws.

We study a continuous-time game in which two firms choose how quickly to advance their capabilities while an exogenous safety threshold advances at a fixed rate. When the capability frontier (max of firms' capabilities) exceeds the safety threshold, all firms are exposed to common disaster that arrives at a hazard rate increasing in the capability-safety difference. We characterize Markov perfect equilibria in terms of the disaster risk, how quickly safety advances, and flow payoffs: when they are low, only racing is an equilibrium; when they are intermediate, racing and pacing coexist; when they are high, only pacing survives. Across all subgame perfect equilibria, low risk induces perpetual racing while high risk rules it out and we bound the probability of disaster across all SPE. If internal capabilities are hidden with a fixed lag until deployment, it is harder to sustain pacing which highlights the importance of transparency about internal capabilities.

We formulate a complete-information two-player strategic timing game that we refer to as the reverse war of attrition (RWoA). Waiting yields a strictly positive current rent, and the player that outwaits its rival may earn an even higher flow. However, remaining in the waiting state when the rival has moved triggers a discrete late-mover penalty. The distinctive primitive is a player-owned productive buffer which enables the rent-generating activity while waiting, but is degraded by this activity. A rival's move can raise the waiting rent and accelerate depletion. We characterize a unique symmetric equilibrium. We apply RWoA to Bertrand competition in a homogeneous-good market, where vintage inventory serves as the buffer clock for timing repricing. We also discuss competitive maintenance. As an extension, we replicate the bilateral game across a continuum of disjoint heterogeneous local markets and, via the exact law of large numbers, derive deterministic aggregate state shares.

Matching theory has largely evolved around two canonical rules: deferred acceptance (DA) in the marriage problem and top trading cycles (TTC) in the object reallocation problem. Although the two rules operate through different procedures, we show that they rest on a common axiomatic foundation. In the marriage problem, stability decomposes into individual rationality and pair rationality, and a classic result characterizes DA by these two axioms together with strategy-proofness for the proposing side. We show that individual rationality, pair rationality, and strategy-proofness likewise characterize TTC. More strongly, three weak rationality axioms concerning individuals and pairs, together with strategy-proofness, uniquely determine TTC. Even weak rationality requirements therefore rule out strategy-proof implementation when there is a cap on the size of exchange cycles.

This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.

A Central Bank Digital Currency (CBDC) is central-bank money in digital form, held by the public. Leading designs have two limitations: conversion from bank deposits into CBDC can accelerate deposit flight, requiring safeguards, and the CBDC stays outside credit creation and monetary-policy operations. Here we present a CBDC architecture based on grassroots currencies that overcomes these limitations. The architecture has three components: (1) Money: sovereign grassroots coins, which are digital debts of one unit of fiat currency issued by the central bank, constituting a direct CBDC; (2) Credit and Liquidity: non-sovereign grassroots coins, which are digital debts of one unit of the same fiat currency, redeemable at par, that can be issued by any person, natural or legal, thus adding credit; and (3) Interest: grassroots bonds, sovereign and non-sovereign, adding maturity and thus interest, standard banking instruments, and the central bank's instruments of monetary policy. The central bank can therefore lend, absorb liquidity, set its rates and buy and sell securities in the coins and bonds the public holds, choosing the counterparties and terms of its credit operations, and without converting bank deposits into newly issued central bank money on demand. We prove that the arbitrage-free price of any non-sovereign grassroots coin whose issuer redeems it on demand is one unit of the fiat currency. The central bank can choose to deal with any counterparty, not just banks, and we argue that the central bank's interest rates on lending and bonds bound from above and below the corresponding interest rates of its counterparties. Sovereign and non-sovereign grassroots coins and bonds have been implemented and tested on a small scale.

We propose a nonparametric test for unobserved treatment effect heterogeneity in regression discontinuity designs. Under the null of no unobserved heterogeneity, a transformed outcome that imputes treated potential outcomes for untreated units must have a continuous conditional distribution at the cutoff. We convert this implication into an integrated conditional-moment restriction using characteristic functions, thereby allowing the conditional local average treatment effect to be an unrestricted function of covariates. We derive the asymptotic distribution of the test statistics via a $U$-process and establish the validity of a multiplier bootstrap procedure for calculating critical values. Monte Carlo experiments show well-controlled size and increasing power. Two empirical applications illustrate how the test distinguishes between heterogeneity explained by observables and that explained by unobserved factors.

Researchers increasingly use generative AI, particularly large language models (LLMs), to automate tasks across the research pipeline. We study the reliability of these tools at the reading, classification, and synthesis of large bodies of academic literature. We frame LLM-assisted literature reviews as a measurement problem, treating models as measurement systems and tracing how their errors affect downstream conclusions. As a test case, we use three different implementations of ChatGPT to identify and extract metadata from economics papers that use rainfall as an instrumental variable. We benchmark each implementation against a subset of human-labeled evaluation data, and then deploy those implementations to extract metadata from the full corpus. The LLMs perform well on binary classification, but performance deteriorates as tasks demand greater contextual interpretation. More importantly, how much researchers can rely on model outputs depends not only on the complexity of the reading task but also on the type of claims the data is asked to support. The same amount of measurement error substantially affects paper-level claims while having little effect on broader claims about the literature. Measurement error in LLM-generated data is thus most consequential at precisely the level of detail that constitutes an LLM's principal value added over human reviewers. We conclude that standard model performance metrics are informative about the quality of generated data but do not by themselves establish the credibility of downstream inference. Researchers must also evaluate whether substantive claims are robust to the measurement system used to generate the underlying data.

Trust can attract fraud when it delays verification. We develop a two-market signaling model in which generative AI lowers fabrication, verification, and targeting costs. When fabrication becomes profitable before verification, claim credibility first falls and later recovers. Across markets, higher prior quality can delay verification, creating an interval in which only the lower-quality market checks. If targeting becomes profitable in this interval, deceptive sellers enter the higher-quality but less vigilant market, and their entry can initially reverse its reliability advantage. The inflow also triggers verification and deters further entry. We call this self-limiting mechanism trust arbitrage. In the age of generative AI, trust can thus create an endogenous but temporary protection gap that redirects deception across markets.

The design of credible auctions—mechanisms where a revenue-maximizing auctioneer has no incentive to deviate from the protocol—faces a fundamental cryptographic barrier when the auctioneer controls shill bidders. While a natural approach is to use Secure Multi-Party Computation (MPC) to remove the trusted auctioneer, the impossibility of fair coin flipping of Cleve (1986) implies that monolithic MPC protocols grant the auctioneer a "free option": they can learn the auction's outcome and unilaterally abort if the revenue is unsatisfactory. Cryptographic commitments with ex-ante penalties mitigate this abort asymmetry, but no finite penalty suffices for heavy-tailed distributions. We circumvent this barrier by introducing the MPC Decomposition Principle. Rather than encrypting the entire mechanism, we use MPC strictly as an information-restriction tool. We isolate the winner determination problem into a minimal MPC gadget that computes and reveals the winner's identity but no payment information. This qualitative restriction mathematically bounds the information leaked upon an abort. By combining this gadget with sequential revelation and finite economic penalties, we design the Sequential Revelation Auction (SRA). We prove that bounding the information leakage strictly bounds the value of the free option, showing that a penalty of $k \geq \sum_{i=1}^n Rev(F_i)$ is sufficient for credibility, and tight: for equal-revenue distributions, every smaller penalty admits a profitable deviation. Using constant-round MPC, the SRA resolves an open question of Akbarpour and Li (2020) and Ferreira and Weinberg (2020) by providing a constant-round, incentive-compatible, revenue-optimal credible auction for all product distributions with vanishing revenue tails

Matching mechanisms differ in how much of an agent's preference ranking must be determined and reported to obtain a particular object. A mechanism is singleton-attainable (SA) if every object that an agent can obtain through some report can also be obtained by reporting only that object as acceptable. With an SA mechanism, once an attainable object has been identified, the agent need not rank or report any other object. Singleton-attainability identifies a distinct dimension in matching theory and market design: transparent access to attainable outcomes, separate from incentives, stability, welfare or equity. We establish general conditions for SA and derive its strategic implications. Top-lift invariance and truncation invariance together imply SA, while strategyproofness and stability each imply SA. By contrast, no Pareto improvement over a strategyproof, individually rational, and non-wasteful mechanism is SA. In particular, every Pareto improvement over Deferred Acceptance violates SA. We introduce report width, which measures how many acceptable objects may have to be reported to obtain an object. SA mechanisms have report width one. Report width is unbounded for a large class of efficient mechanisms that Pareto-improve Deferred Acceptance. Stable selection with report-induced priorities has width one when priorities are monotone and maximal width under reverse priority dominance. Rank-welfare maximization has width one when the outside-option rank is fixed and maximal width when it is report-dependent. These results reveal a structural divide, which we call the width dichotomy: across all mechanisms and families in our classification and all structural classes we study, report width is either one or unbounded.