Prolonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island's RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by \$1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants' first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state \$2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment -- a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.

At the intersection of rising wealth inequality and intensifying environmental pressures, we investigate a reverse causal relationship that has received comparatively little attention: wealth inequality may not only be a consequence of environmental crises, but also act as a structural obstacle to the ecological transition itself. We develop a stylized agent-based model in which heterogeneous agents, whose initial wealth follows a Pareto distribution, allocate their income between either a Brown or a Green sector through a utility function. The function is designed to capture the trade-off between short-term returns and exposure to long-term systemic risks. A central ingredient is that wealthier agents perceive themselves as less vulnerable to environmental shocks, thereby reducing the amount of resources available for the transition. We show that, beyond inequality thresholds compatible with those observed in most developed countries, the economy remains locked in a Brown regime, even when a substantial share of agents is sensitive to externalities. We then assess a set of stylized fiscal policies (basic income, carbon taxation, Green incentives, and a combined scheme) and find that their effectiveness depends strongly on the inequality regime and on the regressivity embedded in the fiscal mechanism, revealing multidimensional trade-offs between transition speed, cumulative environmental destruction, growth, and fiscal pressure.

The R package bpvars was designed to forecast employment, unemployment, and labour market participation rates of 189 countries. However, it is generally applicable to dynamic panel data due to the flexibility of its modelling framework and robust coding. It includes a family of Bayesian hierarchical panel Vector Autoregressions (VARs) that are characterised by: (i) country-specific VAR models (ii) with their parameters' priors centred around their global counterparts, and (iii) featuring flexible multi-level hierarchical prior distributions (iv) with many variants of well-established in the literature benchmark choices, and (v) four alternative specifications including groupping of country-specific or global parameters. A~distinguishing feature is its implementation of missing observation treatment based on a model-coherent Bayesian approach. These models are accompanied by Bayesian prediction, offering a wide range of possible specifications that aim to increase forecasting precision and comply with various reporting standards. We also implement pseudo-out-of-sample recursive forecasting for evaluating point and density forecast performance. The package implements model specification, estimation, and forecasting routines, facilitating simple workflows and reproducibility, including estimation and forecasting results summaries and visualisations. It achieves extraordinary computational speed thanks to the employment of frontier econometric and numerical techniques, as well as algorithms written in C++.

We develop an estimation and inference framework for granular instrumental variables (GIVs) in models with latent aggregate shocks. Our key insight is that valid GIVs are characterized by the orthogonal complement of the factor-loading space. This characterization yields a feasible procedure for constructing GIVs when factor loadings are unknown and does not require a large cross-sectional dimension. We provide practical procedures for inference and specification testing, and apply the framework to estimate the aggregate equity market multiplier. Our empirical results reveal substantial heterogeneity in equity demand elasticities across investor sectors and may provide nuanced support for the inelastic-markets hypothesis.

Grid-scale batteries increasingly influence outcomes in wholesale electricity markets, but their observed bid patterns remain difficult to interpret. In particular, bids that appear to reflect strategic withholding may instead arise from rational operations under price uncertainty and risk management. We develop an asset-level model of a price-taking battery that submits stepwise buy and sell bid curves in the day-ahead market under a finite set of price scenarios. The battery chooses quantity--price pairs to maximize a mean--CVaR objective subject to physical and market constraints. A direct formulation is a mixed-integer linear program, but we show that its integer decisions can be removed, yielding an exact linear programming reformulation suitable for empirical analysis. Our empirical results deliver three insights. First, withholding behavior can arise even without market power, because scarce stored energy and uncertain future prices increase the value of holding energy. Second, the effect of uncertainty depends on the state of charge: when stored energy is scarce, greater uncertainty raises sell bid prices, whereas when stored energy is abundant it can lower them. Third, risk management reshapes bid curves into layered structures that secure profitable execution across a broad set of scenarios while preserving some exposure to rare but valuable price spikes.

Political scientists often interpret coefficient shrinkage under fixed effects as evidence that pooled associations are confounded. This paper shows why that inference is unreliable for slow-moving, mismeasured regressors. Fixed effects can remove much of the signal and identify coefficients from within-unit variation that is disproportionately measurement error, attenuating estimates toward zero. A lone fixed effects coefficient may therefore be unable to distinguish confounding from measurement error. I show that the attenuation depends on a regressor's empirical intraclass correlation and measurement reliability. I then propose a default workflow for panel regression. Researchers estimate reliability when possible, report pooled and fixed effects estimates with corrected within reliability, use partial identification bounds when the estimates share a sign, and report fixed effects as a within-unit estimate when they do not. For variables with no reliability estimate, I introduce an autocorrelation frontier that bounds the attenuation factor directly. I conclude by applying this workflow to several published results to show that the data often cannot distinguish attenuation from confounding, and the workflow makes clear which case the researcher faces.

We study the finite-sample behavior of the Generalized Fisher Transformation (GFT), the parametrization of a correlation matrix $C$ by $γ(C)=\operatorname{vecl}\log C$. The GFT coordinates extend Fisher's transformation to dimension $n>2$: for elliptical data their finite-sample distributions are close to Gaussian. More strikingly, the coordinates are nearly uncorrelated and their covariance is largely invariant to $C$. This approximate orthogonality and invariance make GFT-based inference far better behaved in finite samples than inference based on sample correlations or element-wise Fisher transformed correlations, yielding estimation errors that are approximately Gaussian, weakly dependent, and nearly pivotal.

Optimal public policy requires a social welfare function defined over individual utilities. While there is substantial research on income-based social welfare functions, no published study has directly elicited public preferences over utility when measured by subjective wellbeing. Using a novel survey instrument with a representative UK sample (N=2,068), we estimate the public's social welfare function for life satisfaction. We find significant aversion to wellbeing inequality, with a median isoelastic parameter $α$=0.48. This implies a social welfare function approximately equal to the sum of square roots of individual utilities. The median respondent values improving the wellbeing of the least satisfied by one unit roughly twice as much as improving the most satisfied by one unit. Our findings provide ethically grounded distributional weights for wellbeing policy evaluation and cost-benefit analysis.

We consider the random assignment problem with abstract continuous and convex preferences. In particular, we admit preference relations that are not constrained by independence or transitivity. By extending the Hylland--Zeckhauser pseudo-market mechanism, we show that weakly efficient and envy-free random assignments always exist. For preferences that can be represented via skew-symmetric bilinear (SSB) utility functions -- which generalize linear expected utility functions -- we prove the existence of efficient and approximately envy-free random assignments. Efficient and envy-free random assignments exist under a mild additional assumption on preferences. These findings have notable implications for ordinal random assignment, where ordinal preferences are extended to preferences over lotteries via the pairwise comparison (PC) extension. While the probabilistic serial rule and popular random assignments frequently and significantly violate PC-efficiency and PC-envy-freeness, respectively, random assignments that satisfy both conditions do exist.

We estimate the causal price elasticity of gas demand on Ethereum mainnet (L1) and Arbitrum One (L2), a quantity necessary for calibrating fee mechanism simulations, evaluating resource pricing reforms, and explaining observed usage patterns. A two-way fixed effects panel regression instrumented by each wallet's own lagged base fee removes the congestion-driven endogeneity that causes naive regressions to substantially underestimate demand sensitivity. On Ethereum mainnet (full year 2025), the pooled IV elasticity is -0.006***, near-inelastic: a 10% fee increase reduces total gas demand by approximately 0.06%. On Arbitrum One (October 2025--April 2026), the pooled IV elasticity is -0.036**. Both chains are inelastic in the aggregate, with L2 measurably more responsive than L1. A per-resource decomposition of L2 demand reveals elasticities ranging from modestly elastic computation (-0.027*) to -0.27*** for refunds, with storage growth (-0.15***) and calldata (-0.06*) in between. Behavioral clustering identifies always-on protocol wallets as near-inelastic and high-volume operators as substantially more responsive, with cluster-level elasticities up to roughly 6x the pooled estimate. These results establish an empirical foundation for downstream simulations and for evaluating fee mechanism designs.

The rapid diffusion of agentic AI has created a new coverage problem for commercial insurance: some AI-mediated losses are now affirmatively insured, some create silent-AI exposure under legacy cyber, technology errors-and-omissions (E&O), directors-and-officers (D&O), employment practices liability (EPLI), crime, and media policies, and others are being actively excluded. This paper maps that emerging boundary by coding 55 AI threat classes against 26 insurance products, endorsements, and exclusion regimes using public carrier materials and OWASP/MITRE threat catalogs. We identify a four-tier insurability frontier: affirmatively insured perils, silent-AI exposures, actively excluded perils, and perils outside conventional private insurance structures. Our coding measures publicly claimed positioning rather than executed contract wording; the headline statistics describe what carriers publicly state about coverage, not what would be paid in any specific claim. Three patterns emerge. First, affirmative AI coverage is beginning to differentiate by primary risk emphasis: public materials often position Munich Re around model performance and drift, Armilla and parts of the Lloyd's market around hallucination and broader AI liability, Tokio Marine Kiln and CFC around IP and technology E&O concerns, Apollo ibott around emerging autonomous system liability, and Coalition around deepfake and AI-enabled cyber response. Second, legacy lines retain silent-AI exposure where AI is an instrumentality rather than the legal cause of loss. Third, foundation model concentration is the clearest genuinely novel insurability frontier because upstream model failure can correlate losses across many cedents at once; the relevant market design question is which insurability constraint each candidate structure relaxes, not merely which systemic risk template exists.

This paper studies general multi-unit probabilistic assignment problems involving indivisible objects, with a particular focus on achieving the fairness notion of equal treatment of equals (ETE) and satisfying various efficiency criteria. We extend the definition of ETE so that it accommodates a wide range of constraints and applications. We introduce the ETE reassignment procedure, which transforms any assignment into one that satisfies ETE, and examine whether the efficiency properties satisfied by the original assignment -- namely, ex-post efficiency, ordinal efficiency, and rank-minimizing efficiency -- are preserved under the ETE reassignment. We show that, while the ETE reassignment of an ex-post efficient assignment remains ex-post efficient, it may fail to preserve ordinal efficiency in general settings. However, since the ETE reassignment of a rank-minimizing assignment preserves rank-minimizing efficiency, there must exist an assignment satisfying both ETE and ordinal efficiency. Furthermore, we propose a computationally efficient method for constructing assignments that satisfy both ETE and ordinal efficiency under general upper bound constraints by combining the serial dictatorship rule with appropriately specified priority lists and the ETE reassignment procedure.

I study a decentralized marriage market with search frictions, costly pre-marital skill investments, and non-transferable utility. Despite a fully symmetric environment, asymmetric equilibria -- in which one gender systematically invests more in skills than the other -- can arise. The match payoffs are microfounded through a non-cooperative household game in which spouses allocate time between labor-market work and domestic production. An asymmetric equilibrium becomes available precisely as the high-skill wage rises. Further, the symmetric equilibria can be fragile while the asymmetric ones are not. Thus, rising skill premia may amplify rather than narrow gender gaps in skill acquisition.

Exchange rate forecasting remains a challenging problem, particularly for emerging economies, where the observed time series exhibit pronounced long-memory dependence, nonlinear dynamics, and sensitivity to macro-financial drivers. Classical models such as ARFIMA capture long-range persistence but fail to adequately represent nonlinear relationships, while modern machine learning approaches often neglect the underlying long-memory structure in macroeconomic series. To address this gap, we propose a Neural AutoRegressive Fractionally Integrated Moving Average (NARFIMA) model that integrates ARFIMA-based long-memory modeling with neural networks for nonlinear function approximation, while incorporating exogenous macroeconomic and uncertainty indicators. The framework provides a unified approach for capturing persistence, nonlinear dynamics, and external shocks. We establish asymptotic stationarity of the NARFIMA process and develop conformal prediction intervals for distribution-free uncertainty quantification. Empirical results for BRIC exchange rates show that NARFIMA consistently outperforms a broad range of forecasting benchmarks across multiple horizons, underscoring the importance of explicitly modeling long-memory dependence in exchange rate dynamics. The `narfima' R package provides an implementation of our approach.

In high-risk environments, traditional indemnity insurance is often unaffordable or ineffective, despite its well-known optimality under expected utility. We compare excess-of-loss indemnity insurance with parametric insurance within a common mean-variance framework, allowing for fixed costs, heterogeneous premium loadings, and binding budget constraints. Motivated by the disaster insurance and risk-sharing literature, we show that, once these realistic frictions are introduced, parametric insurance can yield higher welfare for risk-averse individuals, even under the same utility objective and without relying on behavioral assumptions. The welfare advantage arises precisely when indemnity insurance becomes impractical (particularly when households face binding premium budgets), and disappears once both contracts are unconstrained. Our results help reconcile classical insurance theory with the growing use of parametric risk transfer in high-risk settings, and rationalize the interest in hybrid designs that combine both indemnity and parametric elements.