Match-fixing undermines the integrity of sport by eroding public trust and threatening the financial sustainability of clubs and leagues. The global expansion of sports betting markets has created new incentives and opportunities for manipulation, calling for rigorous, data-driven monitoring tools. Football, which accounts for the largest share of global betting turnover, remains particularly exposed: integrity reports continue to flag several suspicious matches, with past scandals in Italy and Turkey underlining the problem's persistence. This study uses high-frequency live-betting data from the Italian Serie B (2018/19-2020/21) to explore statistical approaches for detecting abnormal betting behaviour. A state-space modelling framework is employed to describe standard betting market dynamics and to predict expected betting volumes conditional on match characteristics. Deviations from these expectations can then be analysed using outlier detection techniques to identify potentially suspicious periods. The results demonstrate how statistical modelling can contribute to the early identification of irregular betting patterns, thereby supporting integrity assurance in live sports betting markets.

Less-salient taxes can ease the classic equality-efficiency trade-off by making people respond less to taxation. But deliberately obscuring taxes may be viewed as dishonest. This creates a three-way trade-off between equality, efficiency, and honesty. We analyze this trade-off in a simple setting with a linear income tax. We define and characterize the morally efficient frontier, trading off utilitarian welfare against honesty or transparency. Complete honesty is Pareto inefficient but not morally inefficient. More generally, any increase in honesty reduces utilitarian welfare. When utilitarian welfare is decomposed into equality and efficiency, the cost of honesty falls most robustly on equality: higher salience always reduces equality, while the effect on efficiency is ambiguous. This asymmetry is explained by the fact that salience increases the price of equality, which is the efficiency cost of a marginal increase in equality. Our approach could be applied to other settings in which utilitarian and procedural or deontological values conflict.

Do workers always work more for more? We investigate how intertemporal and uncompensated labor supply decisions change across observational and experimental windows, within the same workers. Combining a real-effort emoji-counting experiment on Prolific with observational data from platform administrative records, self-reported expectations and recalls, and smartphone-based screen-time logs, we find that expectations, and how easily accessible these are, play a central role in determining which kind of elasticities are observed. Uncompensated margins, in fact, diverge across windows and converge towards intertemporal elasticities in the observational window, where expectations lose power and income effects disappear. Similarly, intertemporal responses get loss-averse when expectations are more distant: wage losses retain an elastic effect while gains are rapidly discounted. Workers' behavior is thus simultaneously neoclassical and reference-dependent, as the type of response is largely determined by how wage changes are framed with reference to expectations or previous realizations.

We estimate long-run effects of Cuba's 1961 National Health Service and contemporaneous National Literacy Campaign using synthetic-control methods on newly assembled series for 21 former European colonies in the Americas, 1900--2022. Relative to synthetic Cuba, infant mortality falls 15--29 percent and average years of schooling rise 1.5--2 years; both effects are large, persistent, and robust to augmented SCM, synthetic difference-in-differences, interactive fixed effects, and matrix completion. Life-expectancy gains attenuate after 1990, consistent with the post-Soviet Special Period, suggesting that bundled health and literacy reforms permanently raise early-life survival and human capital, with smaller and less robust effects on adult longevity.

Social inflation, which is the rise in liability claim costs beyond general economic inflation, has become a major concern for insurers and reinsurers, yet it is difficult to quantify because litigation outcomes are heavy-tailed and the mix of cases reaching verdict versus settlement changes over time. Using a large database of US jury verdicts and settlements, we develop case-mix-adjusted social inflation measures through multiple channels that matter to reinsurers: plaintiff win rates (a frequency-type channel), settlement propensity (a frequency-type channel), and verdict/settlement severity. The approach combines rolling-window logistic regression for probabilities and quantile (value-at-risk) regression for severities, with uncertainty quantified via a random-weighted bootstrap. We find statistically significant relative increases in plaintiff win probability of approximately 20%-30% from 2009 to 2024, alongside a statistically significant relative decline in settlement probability of more than 10% over the same period. The dominant channel is verdict severity: Even after controlling for explanatory variables, verdict awards show a sharp rise after 2020, increasing by more than 100% from 2020 to 2024, whereas settlement amounts show limited and often statistically insignificant inflation. Therefore, inflation in total amounts payable to plaintiffs closely tracks verdict severity. Social inflation is more pronounced in corporate-defendant and uninsured-defendant cases and in states without tort caps or third-party litigation funding regulation. In addition, we find that social inflation has impacts not only on "nuclear verdicts" but also, in a similar manner, on moderate losses.

The convolution of a Gaussian and a Cauchy distribution, known as the Voigt distribution, is widely used in spectroscopy and provides a natural framework for modeling heavy-tailed measurement noise. We derive analytical expressions for its density, score, Hessian, Fisher information, and conditional moments using the scaled complementary error function, enabling stable maximum likelihood estimation without numerical convolution, finite-difference derivatives, or pseudo-Voigt approximations. The conditional expectation of the latent Gaussian component is governed by a redescending location score, so extreme observations are automatically discounted rather than propagated. This structure leads to the Gauss-Cauchy Convolution (GCC) filter for state-space models with Gaussian latent dynamics and Voigt measurement errors, where the Masreliez Gaussian prediction approximation preserves a Voigt prediction-error density. In an application to log realized volatility for the Technology Select Sector SPDR Fund, the GCC filter separates persistent latent variation from transient measurement noise and attains the highest implemented prediction-error criterion among the Gaussian, Student-$t$, Huber, and related filtering specifications considered.

We study economies where consumers interact independently with many monopolists. When consumer valuations over goods are correlated, correlation can distort the induced distribution of consumer surplus (information rents). We identify which shifts in the correlation structure over values make the induced distribution more or less fair, in the sense of second order stochastic dominance. We then investigate the role taxation can have on information rents, and show the tax authority never benefits from randomizing the allocation of goods. We characterize the set of mechanisms that are on the fairness-efficiency frontier under regularity conditions on the distribution of types. Furthermore, under these conditions all allocations on the fairness-efficiency frontier ration the good more than an unregulated monopolist. Finally, we discuss implications of our model for luxury commodity taxation.

Behavioral theories rest on parsimony: a small number of mechanisms organizing many decisions. We define a Maximum Rule Concentration Index that measures how parsimoniously a dataset of risky choices can be organized through a library of simple, parameter-free decision rules drawn from canonical behavioral theories: salience, regret, disappointment, modal-payoff focusing, extreme-outcome screening, and limited attention. Applied to three lottery-choice datasets, the data exhibit detectable parsimony: for a majority of subjects, observed concentration exceeds what standard utility models generate on the same menus. The concentration organizes around salience thinking, modal-payoff focusing, and regret.

This paper develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighted convex-in-parameter function over observation pairs with similar covariates, where the similarity is governed by a localization (bandwidth) parameter. While classical results establish asymptotic normality under restrictive bandwidth conditions, we show that valid Gaussian and bootstrap-based inference remains possible under substantially weaker assumptions. First, we extend the theory of small bandwidth asymptotics to convex pairwise difference estimation settings, deriving robust Gaussian approximations even when a smaller than standard bandwidth is used. Second, we employ a debiasing procedure based on generalized jackknifing to enable inference with larger bandwidths, while preserving convexity of the objective function. Third, we construct a novel bootstrap method that adjusts for bandwidth-induced variance distortions, yielding valid inference across a wide range of bandwidth choices. Our proposed inference method enjoys demonstrably greater robustness, while retaining the practical appeal of convex pairwise difference estimators.

Boundary Discontinuity (BD) designs are used in empirical research to learn about causal treatment effects along a continuous assignment boundary defined by a bivariate score. These designs are also known as multi-score regression discontinuity (RD) designs, and include geographic RD designs as a prominent example. This article introduces \pkg{rd2d}, a statistical software package for \proglang{R}, \proglang{Python}, and \proglang{Stata} that implements local polynomial estimation and inference for BD designs using either the bivariate score or a univariate signed distance-to-boundary score. The software covers sharp and fuzzy BD designs, providing automatic bandwidth selection, robust bias-corrected pointwise inference, uniform confidence bands, cluster-robust inference with joint or separate fitting conventions, covariate-adjusted efficiency improvements, mass-point checks, and covariance regularization, among other features. We illustrate the package with an empirical application to Opportunity Zones, where eligibility has a strong first-stage effect on designation but no significant effects on early workplace-job growth.

In our context-free model of a commons, the function$\mathcal{W}$ transforms the profile of the agents' types $(x_{1},..,x_{n})$ to a freely transferable output $\mathcal{W}(x_{1},..,x_{n})$ that they must share fairly. We expand the ubiquitous concept of \textit{endogenous fair shares} to include both a lower and an upper bound on agent $i$'s share at the interim stage where $i$ only knows its own type $x_{i}$. Two functions $(g^{-},g^{+})$ form a pair of tight guarantees if 1) they satisfy the system of inequalities $% \sum_{1}^{n}g^{-}(x_{i})\leq \mathcal{W}(x)\leq \sum_{1}^{n}g^{+}(x_{i})$ for all profiles, and 2) the interval $[g^{-}(x_{i}),g^{+}(x_{i})]$ is inclusion minimal across all types. For super (resp sub) modular functions 1) the \textit{Unanimity }share% \textit{\ }$\frac{1}{n}\mathcal{W}(x_{i},x_{i},..,x_{i})$ is the unique tight upper (resp lower) guarantee, 2) two \textit{Stand Alone} shares $% g(x_{i})=\mathcal{W}(x_{i},\overbrace{x_{0},..,x_{0}})-\frac{n-1}{n}\mathcal{% W}(\overbrace{x_{0},..,x_{0}})$ (where $x_{0}$ is the smallest or largest type) bracket all tight guarantees on the other side of Unanimity, 3) serial cost sharing implements the Unanimity and Stand Alone guarantees. In applications to specific microeconomic models, tight guarantees vindicate or dismiss familiar deterministic sharing rules and suggest new ones with a clear normative interpretation. Our examples include joint production with substitute or complementary inputs, allocating an indivisible good and cash transfers, sharing the cost (or benefit) of the variance or the spread of types, the waiting cost in a queue, and more.

We study moment-based estimation with two sequentially collected variables subject to non-monotone missingness. The commonly used Missing at Random (MAR) assumption requiring all missingness mechanisms to depend on the same fully observed covariates often fails in such cases. We introduce a sequential MAR assumption that allows asymmetric missingness mechanisms across stages. Based on this assumption, we construct an Augmented Inverse-Probability-Weighted GMM (AIPW-GMM) estimator. The estimator features an asymmetric structure for the augmentation term, guarantees double robustness, and achieves the closed-form semiparametric efficiency bound. An application to two-period survey data from the Oregon Health Insurance Experiment supports the observable implications of the new assumption. The proposed approach reduces the standard errors by more than 50% for the estimated effects of the Oregon Health Plan among older adults, "driving" previously statistically insignificant estimates significant.

We examine how the empirical content of revealed preference theory depends on the dimensionality of the choice environment. While higher-dimensional choice problems may appear more demanding, we show that revealed preference restrictions become less informative. Using Selten's Area measure, we establish that for any fixed number of observations, the empirical content of GARP converges to zero exponentially fast in the number of goods. We provide complementary proofs based on revealed preference graphs and the Afriat inequalities, and show in simulations calibrated to scanner data that the effect is quantitatively large. We also evaluate potential responses in observational and experimental settings and find that, while these can slow the rate, they do not eliminate this loss of empirical content.

Residual-based goodness-of-fit tests for parametric time-series models are often complicated by parameter-estimation effects, which can alter the limiting behavior of diagnostic statistics. We propose a sample-splitting generalized spectral test (in the spirit of Escanciano(2006)) for assessing conditional mean specification in linear and nonlinear time-series models. The procedure estimates the model parameter on a fitting subsample and constructs a generalized spectral Cramer-von Mises statistic from residuals computed on a checking/testing subsample. The statistic aggregates pairwise conditional mean restrictions over all lags and is therefore bandwidth-free and free of truncation-lag selection. Under mild regularity conditions and a score-alignment condition, the residual-based process has the same limiting null distribution as the infeasible oracle process based on the true errors. Although the resulting limiting law is still non-pivotal, it can be consistently approximated by a simple multiplier bootstrap that does not require generating bootstrap time series or re-estimating parameters. Such an oracle-equivalence property is in sharp contrast to the original full-sample test, for which parameter estimation contributes an additional first-order term to the limiting process, and requires re-estimating parameters in each bootstrapped sample. We further establish consistency of the proposed test against fixed alternatives and nontrivial power against local alternatives. Extensive simulations and real data analyses show that the proposed test controls size well, has comparable power, and delivers substantial computational savings in models where repeated estimation is costly.

This paper proposes a generalized Mundlak estimator based on graph neural networks (GME-GNN). The estimator is designed to mitigate bias arising from group-level heterogeneity and to accommodate within-group dependence among individuals. Traditional fixed-effects models handle group heterogeneity via group-specific intercepts, but require overly strict linear additivity and intra-group independence assumptions, and are confined to within-group comparisons. Rather than relying on intercepts, GME-GNN uses aggregated group-level balancing statistics to fully control between-group confounding, enabling valid cross-group comparisons and relaxing linearity constraints. It further employs graph neural network message-passing to adaptively learn nonlinear representations and capture intra-group interaction effects. Theoretical analysis shows that the estimator satisfies double robustness and is asymptotically normal. Simulation and empirical studies confirm its performance.

Artificial intelligence feels omnipresent, yet the disruption many expect has not fully arrived. The main reason is not model capability, nor even the tools built to harness those models. Rather, most organizations are still using AI to accelerate workflows designed for a pre-AI world. We offer a three-stage lens: Augmentation, Automation, and Reconstruction, and argue that the most consequential disruption resides in the third stage where workflows and markets are rebuilt around delegation, machine-to-machine interaction, continuous monitoring, and auditable constraints. Achieving this system-level transformation takes time: it requires trust and accountability infrastructure, machine-legible and interoperable data and interfaces, the design and adoption of these new workflows, and economic incentives that favor reconstruction rather than local optimization: the complementary investments that produce the familiar "productivity J-curve" of general-purpose technologies. We illustrate this transition through examples in consumer markets, education, news, and coding. Finally, we emphasize a normative point: the agentic future is not predetermined. Leaders must both skate to where the puck is going and actively steer it toward a good place, ensuring innovation delivers welfare gains felt by businesses and consumers around the world.

Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt. We define Agentic Technical Debt as the accumulated liability created when prompts, memory, tool schemas, orchestration graphs, control policies, and observability routines are patched together faster than they can be validated, standardized, and governed. We define Stochastic Tax as the recurring operating burden of keeping probabilistic agent behavior within acceptable bounds. The distinction matters: debt is a stock of design and governance liability, while the tax is a flow of operating cost that arises because stochastic agents act through tools and workflows. We outline how managers can make both visible through lightweight dashboards and governance controls.

We study markets where firms compete for consumer attention by subsidizing costly product inspection. These subsidies do not change product quality, but they alter the order in which consumers search by lowering inspection costs. We establish a subsidy-sorting principle: in any equilibrium, higher-quality firms provide weakly larger subsidies, leading consumers to search in descending subsidy order. A unique equilibrium survives forward-induction reasoning in the spirit of the Intuitive Criterion: low-quality firms are never inspected, intermediate-quality firms separate with strictly increasing subsidies, and high-quality firms pool at the full subsidy. This equilibrium maximizes information revelation among all possible outcomes and ensures efficient inspection. We then extend the analysis to AI-mediated platforms that can create and price inspection tokens. The platform's optimal linear pricing leads to excessive inspection relative to the social optimum. While this distortion does not reduce consumer welfare, it reallocates surplus from sellers to the platform and consumers.

The 2022 rise in U.S. mortgage rates increased relocation costs for homeowners with low-rate mortgages. This cost varies across destinations because each draws workers from a different mix of labor markets. We build an in-migration mortgage-payment wedge from HMDA loans and pre-shock IRS migration networks. From 2017 to 2024, higher wedges reduce college-educated homeowner in-migration, leave renters unaffected, and raise H-1B sponsorship requests. The implied offset is 14 H-1B sponsorship requests per 100 deterred college-educated domestic in-migrants. We show that mortgage lock-in operates as a destination-side labor-market shock that shifts part of firms' adjustment toward employer-sponsored immigration.

I prove that it is irrational for agents with even slightly private preferences to condition their strategy on private information that is payoff-irrelevant to them, contrary to powerful techniques for analyzing communication and repeated games. In repeated games with public+private monitoring, this means all pure equilibria are perfect public equilibria, and non-trivial belief free equilibria do not exist. In a wide class of communication games (up to allowing receiver commitment), this means persuasion is impossible with state-independent preferences. Nevertheless, these analytic techniques may be made compatible with private preferences through perturbation approaches: considering either payoff-relevance of the private information or correlation between parties' information. An example of the latter occurs by introducing `atonement' to repeated games equilibria.

Panel data models with unobserved heterogeneity in the form of interactive effects standardly assume that the time effects -- or ``common factors'' -- enter linearly. This assumption is restrictive because it concerns an unobserved component of the model, for which a particular functional form is rarely justified. By contrast, linearity in the observable regressors can often be motivated by economic theory or empirical convention. Linearity in the factors has mainly persisted because it is convenient and improves on standard fixed effects. This paper relaxes that assumption by combining the common correlated effects (CCE) approach with sieve methods. The resulting estimator -- abbreviated ``SCCE'' -- preserves key advantages of CCE, including computational simplicity and good small-sample and asymptotic properties, while allowing for a broader class of factor structures that nests the linear case. This makes it suitable for a wide range of empirical applications.

Early evaluations of NYC's congestion pricing program indicate overall improvements in vehicle speed and transit ridership. However, its distributional impacts remain understudied, as does the design of compensatory transit strategies needed to mitigate potential welfare losses. This study identifies population segments and regions most affected by congestion pricing, and evaluates how those welfare losses can be compensated through transit improvements funded by the toll revenues. We estimate joint mode and destination models using aggregated synthetic trips in the NY-NJ-CT-PA Combined Statistical Area (CSA) and calibrate toll-related parameters using post-toll changes reported by MTA. Compensatory transit strategies are evaluated by quantifying the reductions in transit wait time and fare discounts required to offset the CS losses. The results show that the program leads to an accessibility-related CS loss of $397.23 million per year, while generating net passenger toll revenue of $523.44 million per year estimated based on the MTA's report--indicating a net welfare gain. However, these gains in benefits conceal significant disparities. Achieving a general compensation requires modest investment--a 0.63-minute (13%) reduction in wait time or $165.15 million in annual fare subsidies for NYC residents, and a 2.12-minute (28%) reduction or $171.42 million for New Jersey residents. However, ensuring that no population group and county unit is made worse off is substantially more costly and infeasible through transit improvements alone. These findings underscore the need for differentiated compensation strategies: uniform fare discounts lead to overcompensation for some groups, whereas segment-specific discounts, origin-based fare reductions, or commuter pass bundles can achieve equitable accessibility restoration at lower fiscal cost.

We provide a mechanistic foundation for economic complexity methods. In our model, an economy's ability to produce an activity depends on the joint presence of required capabilities. We analytically derive the Economic Complexity Index (ECI) for this model and show that it is a monotonic function of the stock of capabilities in an economy. We then explore the family of functions and conditions that are compatible with economic complexity estimates and show that multiplicatively separable production functions are incompatible with economic complexity estimates. By contrast, additive non-separable functions are sufficient regardless of whether they are log super-modular. We also show that this model explains differences in the shape of networks of related activities, such as the product space or research space. These findings solve long standing puzzles in the literature on economic complexity.

This paper studies decision problems where the decision maker's choice of action affects the probability distribution of a payoff relevant random variable. We establish sufficient conditions for the existence of an expected utility maximizing action in such settings. The main requirement is a mild continuity condition on the family of possible distributions. We also show that this condition is a minimal requirement. Our result does not require common assumptions such as the monotone likelihood ratio property (MLRP) or the convexity of distribution functions condition (CDFC). It can therefore be used to prove the existence of an optimal action in many settings where existing results do not apply, including an important class of problems where the support of the random variable depends on the decision maker's choice and the density functions are not pointwise continuous.

This paper asks whether regional wage differences can identify the effects of a national minimum wage. I study two common exposure-based approaches: effective-minimum-wage designs, which compare the minimum wage to contemporaneous local wages, and fraction-affected/gap designs, which measure pre-reform exposure to the new minimum. Using theory, simulations, and evidence from Brazil, I show when these approaches can mislead and how their performance depends on specification choices. The results lead to practical recommendations for applied researchers, including when to avoid these designs, how to test their assumptions, which specifications are more reliable, and how similar concerns may apply to other settings.

A principal hires an agent to work on a long-term project that culminates in a breakthrough or a breakdown. At each time, the agent privately chooses to work or shirk. Working increases the arrival rate of breakthroughs and decreases the arrival rate of breakdowns. To motivate the agent to work, the principal conducts costly inspections. She fires the agent if shirking is detected. We characterize the principal's optimal inspection policy. Predictable inspections are optimal if work primarily generates breakthroughs. Random inspections are optimal if work primarily prevents breakdowns. Crucially, the agent's actions affect the survival rate of the project, which determines his risk attitude over the timing of planned inspections.