This paper investigates the conditions under which the Easterlin hypothesis holds within a neoclassical overlapping generations model with endogenous capital accumulation, wages, interest rates, and fertility. We develop a tractable analytical framework that maps economic transitions into utility space via a continuously differentiable first-order difference equation for cohort lifetime utilities. This reformulation allows for a transparent normative evaluation of non-steady-state paths without requiring explicit solutions to the underlying nonlinear system. Within this framework, we show that when fertility cycles emerge and children are normal goods, the utility of small cohorts strictly exceeds that of large cohorts. Crucially, this cohort-welfare asymmetry is driven by fertility preferences and is independent of the economy's position relative to the golden rule.

Information sharing among competing suppliers can improve decision-making under uncertainty, yet strategic concerns regarding rival exploitation often deter voluntary disclosure. We study information-sharing mechanisms in a Cournot oligopoly with uncertain demand, where a platform aggregates suppliers' signals through privacy-preserving channels and may also possess an exogenous external signal. The central challenge is to balance strategic safety with informational utility: privacy noise reduces the exposure of individual signals, but also lowers the value of the shared information pool. We first characterize a baseline setting in which access to aggregated information is contingent on participation. In a two-firm market without an external signal, firms refuse to share regardless of the privacy level. In an \(n\)-firm market, sharing may arise even without privacy safeguards because non-participating firms lose access to the aggregated signal. Building on this baseline, we show that privacy protection alone is insufficient to incentivize disclosure; it must be combined with a sufficiently informative external signal. We further show that firms with more accurate private signals require stronger privacy protection. Overall, our results characterize the sharing-feasible region and highlight the complementarity between privacy design and the external information environment.

We study a population-wide tournament in which agents, who care both about their absolute and relative wealth, experiment by searching over correlated objects. We explore the role of the agents' beliefs about the environment; namely, the stochastic processes corresponding to their experimentation. We find that although optimism leads to higher output, it also produces greater inequality. We connect these observations with empirical evidence suggesting a positive relationship between inequality and entrepreneurship.

This paper clarifies the identifying assumptions underlying causal inference based on treatment changes rather than treatment levels, and their relationship to conventional identification strategies. We characterize two distinct structural models, with non-nested identifying assumptions, under which treatment-change identification is valid conditional on observed covariates. We demonstrate that the identifying assumptions relying on treatment changes are generally not nested with those of methods relying on treatment levels, such as selection-on-observables strategies that control for past outcomes, treatments, and covariates, or difference-in-differences approaches that difference outcomes rather than treatments over time. We show, however, that under a random-walk restriction on the treatment process, conditioning on treatment changes is equivalent to conditioning on treatment levels given lagged treatment. This and other equivalence results motivate overidentification tests by jointly considering methods based on treatment levels and changes. Beyond these tests, the non-nesting results carry a structural double robustness implication: an estimator that differences both the outcome and the treatment over time, such as two-way fixed effects regression, remains consistent if either the treatment-change assumption or the parallel-trends assumption holds, without requiring both simultaneously. We characterize the causal models consistent with each method, investigate finite-sample behavior in a simulation study, and present an empirical application to cigarette demand.

Semiparametric discrete choice models are widely applied in economics, yet a fundamental tension arises when covariates are discrete as regression coefficients that are point identified under continuous regressors may become only partially identified. We show that this is not merely an identification problem but creates serious estimation pathologies. Classical estimators, including the maximum score estimator of Manski (1975), not only have population maximizers that are outer regions of the identified set (Komarova (2013)) but also converge to a random set drawn from a finite collection of deterministic regions that partition that outer region. To resolve this failure, we introduce the Random Set Quantile (RSQ) estimator which extracts the $τ$-quantile of the classical estimator for $τ\in (1/2,1)$. We prove this result for a class of widely used models, which includes binary/multinomial choice and discrete outcome panel data models. This construction is consistent and locally robust across the full parameter space, including precisely those configurations where classical estimators break down. A feasible implementation based on the $m$-out-of-$n$ bootstrap inherits both properties. We apply the methodology to the 2019 UK General Election, where the discrete support of Brexit-related covariates generates the partial identification our theory analyzes.

A decision-maker (DM) repeatedly makes choices under uncertainty in a bandit environment, where only the realization of the chosen arm is observed. Another competing agent, the adviser (AD), repeatedly provides recommendations, but the realizations of these recommendations are unobserved unless they coincide with the DM's choice. Both agents possess partial information about the arms' realizations. The central question we focus on is whether, in the long run, an outside observer can identify which agent is more informed based solely on the observed decisions, recommendations, and arm realizations. A test selects one of the agents based on the observed data. We focus primarily on the class of scoring tests, which assign a numerical score to each observation and select the agent according to the average score. We study strategic agents whose objective is to be selected by the test. For simultaneous arm choices, we show that there exists a scoring test that successfully identifies the more-informed agent. For sequential arm choices, however, no such scoring test exists. Finally, we explore the tension between identifying the more-informed agent and maximizing welfare. A DM whose objective is to pass the test may not necessarily make welfare-maximizing decisions. In a binary-arm environment, we show that no scoring test can simultaneously identify the more informed agent and achieve more than half of the welfare attained by welfare-maximizing decisions.

It is now well known that estimators based on influence functions can be sub-optimal in terms of convergence rates in various settings. To address this issue, higher-order influence functions (HOIF) are developed, generalizing the classical semiparametric theory. However, most existing results in this regard focus on treatment effect parameters defined in explicit forms, such as average treatment effects (ATE). In applications, economists are often confronted with tasks of inferring more complex parameters, such as quantile treatment effects (QTE) or effects of complicated treatment regimes/policy. These more complex parameters can often only be implicitly defined as the solution to nonlinear estimating equations, which correspond to M/Z-estimation problems. Our current understanding of these problems is mainly limited to the classical semiparametric theory. Given the foundational role of HOIF for estimating explicit parameters such as ATE, a modest step toward enriching the statistical foundation of econometrics and causal inference is to develop the corresponding higher-order estimators for those more complex parameters. To this end, we consider parameters of a class of non-separable structural models in the econometrics literature and develop a class of higher-order estimators for the target parameters. Statistical properties of these higher-order estimators are derived using recent advances in U-processes theory. Our proposed higher-order estimators relax complexity-reducing assumptions, quantified by Holder smoothness, imposed on the nuisance parameters compared to existing alternative estimators for many important parameters in this class, including QTE and quantile dose-response functions, among others.

We study how election-night flash estimates shape voting in Peru's fragmented 2026 presidential election. We exploit a natural experiment: on April 12, 2026, 187 polling tables across 13 voting centers failed to install, and the \emph{Jurado Nacional de Elecciones} (JNE) extended voting for the affected $\approx\!55 000$ electors to Monday, April 13. These voters cast ballots after observing the Ipsos and Datum flash estimates; otherwise comparable Sunday voters did not. A Bayesian-updating model of multi-candidate plurality voting frames the analysis, yielding predictions about vote reallocation toward the three candidates the estimates rendered viable -- López Aliaga, Sánchez, and Nieto. We estimate treatment effects on candidate vote shares at both the \emph{acta} level and the acta-weighted polling-station level, comparing treated and control \emph{locales de votación} matched on pre-treatment covariates. How flash estimates reshape voting is of first-order importance for Peru, given its institutional instability and high political volatility over the past decade.

This paper explores policy learning from observational data, focusing on a nonlinear welfare criterion in a binary treatment setting. The nonlinear criterion is inspired by scenarios where policymakers prioritize specific population segments. We model this criterion using a utility function that encompasses potential outcomes and intermediate parameters, with the latter capturing higher moments of the outcome distributions. When formulated in the context of observational data, both the intermediate parameters and the welfare criterion depend on the propensity score, which we estimate using machine-learning techniques. To address bias in machine learning estimates, we introduce a novel reweighting-based debiasing approach that offers a promising alternative to traditional orthogonality-based methods. To tackle the complexities of infinite-dimensional policy spaces, we employ sieve approximations and $K$-fold cross-validation for model selection, thereby fully automating the policy-learning process. Despite these complexities, we demonstrate that both the welfare regret and the average welfare regret of our proposed policy learning method satisfy an oracle inequality, thereby providing theoretical guarantees on the performance of the estimated policy relative to the best possible policy. This finding extends the existing results from linear to nonlinear welfare criteria, from finite-dimensional to infinite-dimensional policy spaces, and from a known propensity score to a machine-learned one.

This paper develops a canonical-correlation-based method for detecting structural changes in high-dimensional transformed factor models. The proposed approach exploits the low-rank canonical-correlation structure induced by dynamically dependent common factors, while serially uncorrelated idiosyncratic components correspond to a noise subspace with zero canonical correlations. We construct an eigenvalue-ratio criterion that measures residual dynamic dependence in the estimated noise subspace and identifies the true change point under sufficient separation of the regime-specific loading spaces or dynamic canonical correlation structures. Since the change-point location and the regime-specific factor numbers are both unknown, we further propose an alternating iterative estimation procedure that updates them sequentially until convergence. Under suitable mixing and moment conditions, we establish asymptotic properties of the proposed estimators, with convergence rates depending explicitly on factor strength, cross-sectional dimension, and sample size. Monte Carlo experiments and empirical applications to intraday stock returns and U.S. temperature series demonstrate the finite-sample

We study optimal technology regulation when private learning occurs both through doing (scaling up the technology) and through waiting (as time passes). We show that an adaptive speed limit -- a cap on the rate at which the technology can increase per-unit time -- delivers optimal worst-case guarantees over all learning processes and/or preferences, and is the only time-consistent mechanism that does so.

We introduce and analyze Limit Continuous Poker, a variant of Von Neumann's Continuous Poker with variable but limited bet sizes. This simplified variant of poker captures aspects of information asymmetry, bluffing, balancing, and the impact of bet size limits while still being simple enough to solve analytically. We derive the Nash equilibrium strategy profile for this game, showing how the bettor's and caller's strategies depend on the bet size limits. We demonstrate that as the bet size limits approach extreme values, the strategy profile converges to those of other continuous poker variants. Finally, we connect these results to strategic implications of limited bet sizing in real-world poker.

We introduce a novel real-time dataset, Companies House Real-Time (CHRT), that captures daily firm creation and dissolution activity for the full population of UK-registered companies. CHRT provides a timely measure of business formation, becoming available months before official business demography statistics. We show that incorporation activity leads taxable business births and contains forward-looking information about employment and output growth. Consistent with this, a structural vector autoregression (SVAR) indicates that positive shocks to firm entry generate persistent increases in employment and output.

A single seller offers one or more goods to a single buyer. The buyer's values and the seller's costs are private information. Each player has a commonly known prior over the other player's value or cost, supported on a finite set. What is the optimal selling mechanism? We argue that, despite this question's importance and apparent simplicity, prior work offers no satisfactory answer. If the seller simply chooses an optimal menu given her realized costs, she fails to exploit her informational advantage. At the other extreme, the optimal trade mechanism that satisfies IC/IR constraints for both parties fails in practice, as it conditions prices on the seller's unknown costs in an unenforceable way. The seller's realistic capabilities lie somewhere in between: she may leverage private information but lacks unlimited commitment power. To bridge this gap, we consider a solution concept built on the realistic assumption that the seller can commit to prices but nothing more. Similar -- albeit technically distinct -- solution concepts have been studied in the context of auctions with multiple buyers. Our concept proves surprisingly rich even with a single buyer. In our model, the buyer and seller engage in multiple rounds of cheap talk before the seller posts a menu of priced bundles. The buyer then purchases. We measure value as profit for the seller and consumer surplus for the buyer. We prove that with a single good cheap talk cannot help either party, but show that it creates value in any extension of this canonical setting: multiple goods, multiple units, interdependent values, or repeated play. We also show that multiple rounds of communication can yield strictly higher expected profit than a single round. Finally, we discuss how realistic factors beyond our stripped-down model combine with cheap talk to enhance this value even further.

The GDP of a country is modelled as the relative interaction between two agents - working hours, reflecting the social choice of a population, and Total Factor Productivity, reflecting the collective investment in productivity enhancers. It is shown that a Random Forest model can accu- rately predict the GDP from these two factors. The differences in the choices made by Germany and USA are analysed though Gini importance, SHAP plots and partial dependency. It is shown that the differences in the social structure of the countries are reflected in the relative contribution of working hours and productivity to the GDP.

Whether investment in digital health technology is associated with differences in hospital productivity is a question of substantial policy relevance, yet interpretation is constrained by challenges in causal identification and prior evidence is mixed. Technical efficiency in NHS acute hospital trusts in England is estimated using Bayesian stochastic frontier analysis. A four-input Cobb--Douglas production function incorporating clinical full-time equivalents, administrative full-time equivalents, non-labour expenditure, and physical capital derived from audited NHS accounts is fitted to 111 acute non-specialist trusts in 2024/25. Digital maturity, measured by the NHS Digital Maturity Assessment, is included in a trust-specific inefficiency equation alongside population deprivation, teaching status, and financial position controls. The composite digital maturity score is estimated to be negatively associated with technical inefficiency (\(\hatγ = -0.612\), 95\% credible interval \([-1.289, +0.005]\), \(P(γ< 0) = 0.974\)). Trusts in the highest digital maturity quartile are estimated to operate at 98.0\% of their production frontier compared with 93.2\% for the lowest quartile. This gap corresponds to approximately £20 million of additional cost-weighted activity per trust at mean output levels, or £1.1 billion in aggregate. Estimates are robust to functional form but are sensitive to the most conservative prior specification. Pillar-level analysis suggests that population health management and care pathway optimisation domains exhibit stronger associations with efficiency than other domains. Catchment deprivation is not estimated to have an independent association with efficiency after controlling for digital maturity.

This paper examines whether real-time GDP announcements can reliably identify business-cycle turning points. Using U.S. real-time GDP vintages from 1947 to 2021, we construct 4,356 recession indicators based on alternative smoothing methods and scaling variations. We then combine these indicators with alternative thresholds to generate 137,457 perfect recession classifiers. The selected classifiers identify all 12 historical recessions without generating false positives or false negatives. Restricting attention to the high-precision segment yields two classifiers with a standard deviation of detection errors below three months, while the selected ensemble signals recessions, on average, 3.04 months after their official onset. The framework accurately identifies recession episodes across vintages, suggesting that discrepancies in prior work may reflect limitations of traditional dating methods in addition to data revisions. Overall, the results indicate that real-time GDP announcements provide a practical proxy for NBER-style recession dating.

While risk pooling lowers the total cost of risk, efficiency alone does not make a pool viable. Participants need terms that ensure their participation, that are immune to subgroups breaking away, and that allow new members to join. Under cash-additive risk measures, the minimum cost of a coalition's risk determines the value created by that coalition, and deterministic side payments redistribute that value among participants. Institutional risk sharing is thus a transferable-utility cooperative game. We prove that the game is totally balanced whenever the risk measures are convex (agents are risk averse), so every coalition has a nonempty core and stable allocations always exist. We then analyze entry monotonicity through Population-Monotonic Allocation Schemes (Sprumont, 1990), a strong requirement that is notoriously difficult to construct and has received limited attention in risk sharing. We find several structural conditions that ensure that either the Arrow--Debreu pricing surplus allocation rule or the proportional-cost surplus allocation rule satisfies this entry-monotonicity property, the latter being a novel cooperative notion we propose. These verifiable structural conditions naturally arise in pooled (re)insurance and credit portfolios, providing pool designers with a practical toolkit for building risk pools that remain stable and attractive as they expand.

We study risk-neutral control in Markov decision processes with an absorbing catastrophic state. Even though rewards are linear and the agent has no utility curvature, probability weighting, or framing dependence, standard Bellman optimality produces three prospect-theory-like signatures: an S-shaped value-function profile (convex near catastrophe, concave in the far field), an endogenous loss-sensitivity coefficient $λ^*(S) > 1$, and a reflection-effect policy reversal. Across 495 configurations, the optimal policy plays safe near catastrophe in positive-drift (growth) regimes despite the risky action's higher immediate expected value, and plays risky near catastrophe in negative-drift (decline) regimes despite the safe action's lower immediate expected loss. We derive a closed-form expression for the asymptotic loss-aversion plateau $\barλ$ that depends only on win probability $p$, payoff asymmetry $r = |Δ_\ell/Δ_w|$, and discount factor $β$, and matches numerical solutions to $R^2 = 0.999$. The mechanism does not require asymmetric payoffs. Across a sweep of $(p,β)$ at three asymmetry levels, the asymmetry share of $\barλ$ above unity has median 4.6% at $r = 1.25$ and rises to 13.9% at $r = 2$, with the boundary contribution exceeding the asymmetry contribution in every cell tested. The phenomena persist under tabular Q-learning (a model-free agent reproduces $V^*$ at correlation 0.98 in growth and 1.00 in decline) and under stochastic transitions with Gaussian, heavy-tailed Student-$t_3$, and asymmetric skew-normal noise up to 50% of the step size, where the asymptotic plateau tracks the closed-form prediction within 0.41% for safe-channel noise and within 9.6% for risky-channel or both-channel noise. These results identify absorbing failure states as a sufficient structural mechanism for prospect-theory-like behavior under optimal control.

Recession indicators are often viewed as U.S. specific, raising the question of whether labor market-based rules such as the Sahm Rule and the Michez Rule can reliably detect recessions in other countries. To answer this, we evaluate whether such rules can be adapted to Japan by calibrating thresholds and smoothing parameters to Japanese labor market data. We construct a large set of 95,832 recession indicators combining unemployment and vacancy data. The selected classifiers are statistically perfect as they identify all 11 historical recessions in the 1970-2021 training period without generating any false positives. Among these, 193 classifiers lie on the anticipation-precision frontier. Restricting attention to the high-precision segment yields six classifiers with a standard deviation of detection errors below 3 months. The selected classifier ensemble signals recessions, on average, 0.06 months after their true onset. Overall, these findings suggest that slack-based labor market rules provide a general framework for improving real-time recession detection across countries.

Data centers now account for 4.4% of United States electricity demand, yet the grid-level effectiveness of the renewable energy certificates (RECs) and power purchase agreements (PPAs) hyperscalers use to claim carbon neutrality remains unclear. We develop a game-theoretic model in which a data center operator chooses among RECs, PPAs, and behind-the-meter colocation while generators make entry decisions under endogenous financing costs. The model identifies a timing wedge -- the mismatch between consumption and credited renewable generation -- as a central mechanism through which AI demand degrades reliability, raises prices, and increases emissions even when RECs cover 100% of annual consumption. Colocation with storage addresses this wedge directly and induces the greatest renewable entry by eliminating generator revenue risk. We test these predictions by exploiting the staggered release of large language models as a natural experiment, using difference-in-differences on a novel dataset linking AI activity to local grid outcomes. AI demand significantly increases fossil generation, wholesale prices (up to 25% in treated PJM zones), and outage frequency (0.5--1 additional outages per year) near data centers, with impacts scaling in model size. Data centers with on-site generation exhibit a sign reversal in power-quality effects, consistent with the model's prediction that behind-the-meter capacity absorbs demand spikes. Counterfactual analyses show that edge inference, spatial reallocation, and colocated storage each substantially mitigate grid impacts, while REC-only strategies do not. Together, our results demonstrate that the externalities of AI to the grid are tightly coupled to procurement design and the spatial organization of data center infrastructure.

The damage to transportation infrastructure caused by disasters can indirectly lead to economic damage through economic interdependence, even in areas that are not directly affected. However, even when transportation routes are interrupted by a disaster, the damage can be mitigated if alternative routes are secured. Rural areas with low-density transportation networks are more vulnerable to traffic disruptions in a disaster. This study develops a method for evaluating the effectiveness of redundant transportation networks in mitigating economic vulnerability in the event of a disaster. Our methodology combines inter-regional road network connectivity with a spatial computable general equilibrium (SCGE) model. We apply the method to road disruption scenarios in the Chugoku region of Japan, which has a system of parallel highways. The affected areas are in close geographical proximity to many rural areas and have strong economic interdependencies with them. Several counterfactual simulations depicted the situation without the alternative road and the disaster. We evaluate the transportation impacts, measured by changes in travel time, and the economic impacts, measured by negative benefits, respectively. The results suggest that the economic vulnerability reduction effect is more far-reaching than the transportation impacts.

Bitcoin price prediction has attracted hundreds of academic papers and continuous social media debate, yet the field lacks consensus on even basic questions: can any model beat a naive "today's price" baseline at horizons of one to six months? We survey the peer-reviewed landscape, categorize papers by evaluation methodology, and contrast academic findings with informal but substantive discourse on X/Twitter. The picture that emerges is sobering. At short-to-medium horizons, no peer-reviewed study has shown robust superiority over the naive baseline across multiple market regimes. Daily predictability is real but does not extend to hourly or monthly horizons, and may not survive transaction costs. The stock-to-flow model has failed formal out-of-sample testing, and Metcalfe's Law valuations have been challenged as spurious. The Bitcoin price power law, while empirically compelling, has not been subjected to formal distributional tests. Meanwhile, social media practitioners raise valid statistical critiques -- ordinary least squares (OLS) violations, backtest overfitting, spurious regressions -- that the academic literature has not formalized. We identify open research directions and propose concrete methodological standards for future work -- walk-forward evaluation, multi-regime holdout windows, naive baseline comparison, inclusion of zero in hyperparameter grids, and Diebold-Mariano significance testing -- arguing that the field's primary need is not more models but better evaluation.

Like physical products, new technologies are developed using globally sourced inputs. Yet while the supply chains behind physical goods are well understood, we know far less about the international supply chain of scientific knowledge that powers U.S. innovation, or how vulnerable it may be to disruption. Here, I uncover this supply chain by tracing multi-generational citation paths connecting NSF-funded research to downstream patents, and stress-test it by simulating barriers to scientific knowledge flows across the U.S. border. The U.S. knowledge supply chain extends globally, and frictions impeding the movement of ideas across the U.S. border reduce its connectivity, extend its length, and lower innovation productivity. These impacts extend to technology areas deemed critical to national priorities by U.S. Congress, including Semiconductors, Quantum Science, and AI.

Consumer heterogeneity in revealed-preference data is larger than bilateral rationality tests can reveal. We construct a continuous nonparametric metric of this hidden heterogeneity by repeatedly subsampling choices, partitioning agents into groups whose pooled data are jointly rationalisable under a chosen consistency criterion and recording how often each pair is co-classified. The resulting kernel is positive semi-definite, embeds the population in a Hilbert space, and induces a metric with the triangle inequality. Under a necessary-and-sufficient contrast-rank condition, its spectral structure recovers latent preference types. Inference on demographic correlates proceeds via a Monte-Carlo-conditional test and a finite-sample-valid permutation test. Applied to US grocery scanner data, the construction reveals a joint-rationality gap of 0.62 between near-saturated pairwise compatibility and population-level co-typing; binary lottery data yield a comparable gap of 0.38. Standard demographics organise only a modest part of the scanner kernel structure.

The price system is often said to economize on information. This paper asks how much information it saves. I develop a formal framework for measuring the informational burden that agents would have to bear if they had to reason individually over feasible coalitional alternatives. Taking the convergence theorem of Debreu and Scarf (1963) as the benchmark, I ask how much coalition-feasibility information must be held, and how it must be distributed, for individual acceptance decisions to recover the core. Agents' information is represented as finite sets of meaning-bearing sentences in a formal language, and reasoning is modeled as proof in Gentzen's sequent calculus. The main result characterizes the minimal informational structure under which unanimous acceptability coincides with the core for all transferable-utility games on a fixed player set: every coalition must be known to at least one of its members. Removing information about even a single coalition from all of its members suffices to break the equivalence for some game. Applying this result to the $k$-fold replica economy, I identify and count the load-bearing coalitions whose feasibility information must be distributed across the population. The associated average per-agent informational burden grows as $Θ(4^k/k^{3/2})$ -- exponentially in the size of the economy. This is, precisely and formally, what the price system saves.

We analyze a continuous-time preemption game with shared catastrophic externalities. When the cost of catastrophe is embedded in both players' payoffs, the risk term cancels out in the equilibrium indifference condition. This creates a "suicide region" where competitive pressures force rational agents to deploy despite negative risk-adjusted net present values. We apply this framework to the race for artificial general intelligence (AGI). We show that this suicide region widens as the cost of systemic ruin grows: higher catastrophic risk does not deter the race but instead enlarges the set of conditions under which rational actors deploy despite negative social value. We characterize the resulting welfare distortion against a social planner's benchmark and demonstrate how two complementary mechanisms - private liability and prize-sharing - can close the suicide region. Private liability raises the cost of unsafe deployment while prize-sharing reduces the strategic imperative to deploy first. "Warning shots" (sub-existential disasters) will fail to deter AGI acceleration, as the winner-takes-all nature of the race remains intact.

Computational reproducibility is central to scientific credibility, yet verifying published results at scale remains costly. We develop an AI-assisted workflow for automated full-paper replication -- retrieving materials, reconstructing environments, executing code, and matching outputs to point estimates reported in regression tables. We define a universe of all empirical and quantitative papers from the three top political science journals (2010--2025) and measure stated data availability using automated extraction. For a stratified sample of 384 studies, we apply the workflow to conduct full-paper replication, totaling 3,523 empirical models. We find that journal verification requirements, combined with data archiving mandates, drive reproducibility: the share of fully or largely reproducible papers rises from 20.8% before DA-RT adoption to 82.5% after, and conditional on accessible replication packages, 92.1% of papers are fully or largely reproducible (234/254). As a secondary application, we apply standardized IV diagnostics to 84 studies (597 IV specifications among 1,910 replicated models), illustrating how automated execution enables systematic reanalysis across heterogeneous empirical settings.

We study a monopolistic insurance market with hidden information, where the agent's type $θ$ is private information that is unobservable to the insurer, and it is drawn from a continuum of types. The hidden type affects both the loss distribution and the risk attitude of the agent. Within this framework, we show that a menu of contracts is incentive efficient if it maximizes social welfare function, subject to incentive compatibility and individual rationality constraints. This holds for general utility functionals. In the special case of Yaari Dual Utility, we provide two partial converse statements to this result, and we give a semi-explicit characterization of optimal solutions to the social welfare maximization problem. We do this under two different settings: (i) the first assumes that types are ordered in a way such that larger values of $θ$ correspond to more risk-averse types who face stochastically larger losses; whereas (ii) the second assumes that larger values of $θ$ correspond to less risk-averse types who face stochastically larger losses. In both settings, the structure of optimal menus of contracts depends on the level of the social welfare weight, and we examine several properties thereof.

We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that are rigorously justified, while also allowing for the assessment of existing privacy guarantees through game theory. We show that pure and probabilistic differential privacy are special cases of our framework, and provide new interpretations of the post-processing inequality in this setting. Further, we demonstrate that privacy guarantees can be established for deterministic algorithms, which are overlooked by current privacy standards.