This paper points out that rational inattention is a nested regularized optimal transport problem. We use entropic optimal transport to establish the main results in Matejka and McKay (2015) and Caplin, Dean, and Leahy (2019) and extend them to arbitrary choice sets.

This paper proposes a positional poverty gap measure of multidimensional poverty within the Alkire-Foster counting framework. The measure captures the depth of deprivations even when indicators are ordinal, unlike the standard poverty gap, which requires cardinal variables. The proposed method draws on the fuzzy set literature and introduces a distribution-based measure of deprivation depth using the empirical cumulative distribution of each indicator, with the most deprived group as the benchmark. For each deprived individual, the method assigns a score based on the individual's relative position in the distribution. Depth is thus expressed as a difference in distributional positions, motivating the label positional poverty gap. The paper demonstrates that this measure preserves the identification and aggregation structure of the counting approach and satisfies its axiomatic properties when the reference distribution remains fixed over time. The framework remains flexible because it accommodates different identification rules, deprivation cutoffs, and variable types. Overall, it offers a simple, meaningful, and theoretically grounded way to incorporate depth into multidimensional poverty measurement with ordinal data.

This paper examines bilateral claims problems with lower and upper exclusion thresholds that determine whether an individual is excluded from initial gains or losses. We introduce the exclusion dilation operator, a method that transforms standard rules into extended rules incorporating exclusion thresholds. The operator first allocates gains and losses with respect to these thresholds and then distributes the remaining resources through a dilation transformation of an underlying standard rule. We axiomatically characterize this operator and examine which standard properties of the theory of fair allocation it preserves. While the operator maintains key properties such as homogeneity and monotonicity, it intentionally violates others, most notably order preservation, to reflect the asymmetries induced by exclusion thresholds. Our approach provides a formal methodology for resource allocation in contexts where symmetry is not appropriate due to legal and policy considerations.

Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs' understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.

We propose a model of whataboutism -- a rhetorical strategy that deflects criticism by citing similar misconduct that goes uncriticized on the critic's side -- and study its implications for social norms governing offensive speech. In an infinite-horizon psychological game with two rival camps, agents weigh the intrinsic benefit of offensive speech against the risk of condemnation. External criticism can be deflected via an equilibrium-based whataboutism rebuttal. We characterize the unique dynamically stable Psychological Subgame Perfect Equilibrium and show that the availability of whataboutism exacerbates offensive speech, to the extent that civility norms can break down entirely, especially in polarized societies.

This paper examines how loss aversion affects wages offered by employers and accepted by job seekers. I introduce a behavioral search model with monopsonistic firms making wage offers to job seekers who experience steeper disutility from pay cuts than utility from equivalent pay raises. Employers strategically reduce pay cuts to avoid offer rejections, and they exactly match offers to current salaries due to corner solutions. Loss aversion makes three predictions on the distribution of salary growth for job switchers, which I empirically test and confirm with administrative data in Korea. First, excess mass at zero wage growth is 8.5 times larger than what is expected without loss aversion. Second, the density immediately above zero is 8.8% larger than the density immediately below it. Third, the slope of the density below zero is 6.5 times steeper than the slope above it. When estimating model parameters with minimum distance on salary growth bins, incorporating loss aversion substantially improves model fit, and the marginal value of additional pay is 12% higher for pay cuts than pay raises in the primary specification. For a hypothetical hiring subsidy that raises the value of labor to employers by half of a standard deviation, incorporating loss aversion lowers its pass-through to wages by 18% (relative to a standard model) due to higher elasticity for pay cuts and salary matches that constrain subsidized wage offers. Somewhat surprisingly, salary history bans do not mitigate these effects as long as employers can imperfectly observe current salaries with noise.

In random matrix theory, the spectral distribution of the covariance matrix has been well studied under the large dimensional asymptotic regime when the dimensionality and the sample size tend to infinity at the same rate. However, most existing theories are built upon the assumption of independent and identically distributed samples, which may be violated in practice. For example, the observational data of continuous-time processes at discrete time points, namely, the high-frequency data. In this paper, we extend the classical spectral analysis for the covariance matrix in large dimensional random matrix to the spot volatility matrix by using the high-frequency data. We establish the first-order limiting spectral distribution and obtain a second-order result, that is, the central limit theorem for linear spectral statistics. Moreover, we apply the results to design some feasible tests for the spot volatility matrix, including the identity and sphericity tests. Simulation studies justify the finite sample performance of the test statistics and verify our established theory.

Sequential intraday electricity trading allows photovoltaic (PV) operators to reduce imbalance settlement costs as forecasts improve throughout the day. Yet deployable trading policies must jointly handle forecast uncertainty, intraday prices, liquidity, and the asymmetric economics of PV imbalance exposure. This paper proposes a feature-driven reinforcement learning (FDRL) framework for intraday PV trading in the Nordic market. Its main methodological contribution is a corrected reward that evaluates performance relative to a no-trade baseline, removing policy-independent noise that can otherwise push reinforcement learning toward inactive policies in high-price regimes. The framework combines this objective with a predominantly linear policy and a closed-form execution surrogate for efficient, interpretable training. In a strict walk-forward evaluation over 2021-2024 across four Nordic bidding zones (DK1, DK2, SE3, SE4), the method delivers statistically significant profit improvements over the spot-only baseline in every zone. Portfolio experiments show that a pooled cross-zone policy can match zone-specific models, while transfer-learning results indicate a two-cluster market structure and effective deployment in new zones with limited local data. The proposed framework offers an interpretable and computationally practical way to reduce imbalance costs, while the transfer results provide guidance for scaling strategies across bidding zones with different market designs.

We introduce Delphos, a deep reinforcement learning framework for assisting the discrete choice model specification process. Delphos aims to support the modeller by providing automated, data-driven suggestions for utility specifications, thereby reducing the effort required to develop and refine utility functions. Delphos conceptualises model specification as a sequential decision-making problem, inspired by the way human choice modellers iteratively construct models through a series of reasoned specification decisions. In this setting, an agent learns to specify high-performing candidate models by choosing a sequence of modelling actions, such as selecting variables, accommodating both generic and alternative-specific taste parameters, applying non-linear transformations, and including interactions with covariates, while interacting with a modelling environment that estimates each candidate and returns a reward signal. Specifically, Delphos uses a Deep Q-Network that receives delayed rewards based on modelling outcomes (e.g., log-likelihood) and behavioural expectations (e.g., parameter signs), and distributes this signal across the sequence of actions to learn which modelling decisions lead to well-performing candidates. We evaluate Delphos on both simulated and empirical datasets using multiple reward settings. In simulated cases, learning curves, Q-value patterns, and performance metrics show that the agent learns to adaptively explore strategies to propose well-performing models across search spaces, while covering only a small fraction of the feasible modelling space. We further apply the framework to two empirical datasets to demonstrate its practical use. These experiments illustrate the ability of Delphos to generate competitive, behaviourally plausible models and highlight the potential of this adaptive, learning-based framework to assist the model specification process.

How labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan's spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2\% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.

We show how to identify the distributions of the latent components in the two-way dyadic model for bipartite networks $y_{i,\ell}= α_i+η_{\ell}+\varepsilon_{i,\ell}$. This is achieved by a repeated application of the extension of the classical lemma of Kotlarski (1967) in Evdokimov and White (2012). We provide two separate sets of assumptions under which all the latent distributions are identified. Both rely on some of the latent components being identically distributed.

We provide and axiomatize a representation for preferences over lotteries that generalizes the expected utility model. Since the representation uses different utility functions to evaluate different lotteries, the preferences can be interpreted as coming from individuals that have context-dependent attitudes toward risks. The model enables generating various violations of the independence axiom that are not compatible with some of the most prominent models of non-expected utility. Depending on the specification chosen, the model can range from being very flexible with many different utility functions to being parsimonious with few or just one utility function.

Machine-learning (ML) methods now routinely generate regressors used in subsequent econometric analyses, for example, estimated propensity scores, control-function residuals, imputed covariates, learned proxies, or low-dimensional embeddings of high-dimensional data. As these ML-generated regressors become ubiquitous, the lack of general inference methods for models that use them has become a critical limitation. Standard plug-in and Double ML procedures ignore how generated regressors enter later stages, leading to large biases and invalid inference. We develop a three-step locally robust GMM framework for inference with ML generated regressors. A key new insight is downstream local robustness: by a functional chain rule, moment functions that are constructed to be orthogonal to the second step eliminate the complicated indirect (conditioning) effects from the ML-generated regressors. We show how to implement this automatically by estimating the associated Riesz representers through cross-fitted auxiliary regressions, allowing for generic non-Donsker ML in both early steps. In leading treatment-effect and counterfactual settings, simulations demonstrate severe bias in existing methods and reductions of 85-95% using our procedures.

We study a public good game with N citizens and a Governor who allocates resources from a common fund. Citizens may voluntarily contribute or be compelled to do so if audited, in which case shirkers face a penalty. The Governor decides how much of the fund to devote to public good provision, with the remainder embezzled. Crucially, the Governor's utility combines material payoffs from embezzlement with belief-dependent reputational concerns. We fully characterize the symmetric subgame perfect equilibria (SSPE) of the game. The model always admits at least one pure-strategy equilibrium, ranging from universal free-riding with complete embezzlement to full contribution with efficient provision. Mixed-strategy equilibria exist only in a narrow region of parameter values and may involve multiple equilibria. Our analysis highlights the roles of penalties, audits, and reputational incentives in sustaining contribution and provision, thereby linking public good provision with the broader literature on corruption, embezzlement, and psychological game theory.

Decision makers often opt for the deferral outside option when they find it difficult to make an active choice. Contrary to existing logit models with an outside option where the latter is assigned a fixed value exogenously, this paper introduces and studies a class of logit models where that option's value is menu-dependent, may be determined endogenously, and could be interpreted as proxying the varying degree of decision difficulty at different menus. We focus on the *power logit* special class of these models. We show that these explain some observed choice-deferral effects that are caused by hard decisions, including non-monotonic "roller-coaster" choice-overload phenomena that are regulated by the presence or absence of a clearly dominant feasible alternative. We illustrate the usability, novel insights and explanatory gains of the proposed framework for duopolistic modelling and empirical discrete choice analysis.

This paper introduces a new factor contributing to the decline in marriage and fertility: the growth of leisure technology. Over recent decades, high-income countries have experienced two notable shifts in household and family dynamics. First, there has been a significant decline in marriage rates and fertility. Second, time has increasingly been allocated to leisure activities. This paper presents a unified model of marriage and fertility, incorporating intra-household bargaining dynamics. The model, calibrated using data from Japan between 2019 and 2023, is employed to assess the impact of leisure technology growth on marriage and fertility during 2005-2009. The findings highlight that leisure technology growth makes single life relatively more appealing compared to marriage and parenthood. The model explains 21.1% of the decline in marriage and 73.1% of the decrease in fertility.

We study a spatiotemporal service matching problem in which demand, heterogeneous in location and time sensitivity/preference, is to be assigned to service stations. The planner seeks to maximize social welfare, defined as total service reward minus spatial and temporal costs, by optimally scheduling demand to stations and service time under processing capacity constraints. We formulate the problem as an optimal transport (OT) model that allows for both demand-capacity imbalance and endogenously unserved demand when service costs exceed rewards. Leveraging a barycenter-style decomposition, we reformulate the problem as a finite-dimensional convex optimization problem that generalizes semi-discrete OT and enables scalable computation. We characterize the geometry of optimal assignments, showing that spatial partitions correspond to generalized Laguerre cells. Temporally, we show that the structure of the optimal schedule depends on demand heterogeneity: when demand differs only in temporal cost sensitivity, higher-sensitivity demand is assigned service times closer to the common ideal time; when demand differs only in preferred times, the assignment is order-preserving with respect to preferred times. We further propose an envy-free, individually rational implementation of the optimal schedule using time-dependent pricing and a finite-slot mechanism with explicit bounds depending on the number of required slots. To illustrate the framework, we extend the classic Hotelling linear-city model on a line segment by incorporating a continuum of waiting-cost sensitivities, demonstrating how optimal spatial partitions vary with changes in sensitivity heterogeneity and reward.

Childhood socioeconomic disadvantage is a well established determinant of health in later life. Less is known about how early-life deprivation unfolds when individuals experience major institutional transformation and migration in adulthood. Cohorts socialized under Soviet institutions provide a useful setting to examine life-course divergence under systemic change. This study uses harmonized data from the Survey of Health, Ageing and Retirement in Europe (SHARE) on older adults residing in Estonia, Latvia, and Israel to examine the association between retrospectively reported childhood deprivation and multiple health outcomes in later life, including poor self-rated health, chronic disease burden, functional limitation, depression, and a composite multifrailty indicator. Logistic regression models and predicted probabilities assess whether childhood deprivation predicts late-life health across different adult institutional contexts and whether associations vary by linguistic affiliation. Higher levels of childhood deprivation are consistently associated with poorer health outcomes across all three countries. Individuals in the highest deprivation quintile show substantially higher odds of adverse health outcomes, including multifrailty. Stratified analyses for Estonia and Latvia indicate broadly similar deprivation-health gradients among national-language and Russian-speaking populations. These findings highlight the persistence of childhood disadvantage and the importance of early-life conditions in shaping health inequalities in ageing populations exposed to systemic transformation.

Online marketplaces frequently run pricing experiments in environments where users choose from a list of items. In these settings, items compete for users' limited attention and demand, creating interference among items within a list: Changing prices for any item can affect the demand for others, biasing estimates from item-level A/B tests. Besides, a key consideration in pricing experiments is preserving platform coherency across prices and item availability. This requirement rules out experimental designs such as user-level A/B tests as they violate platform coherency. We propose Two-Sided Prioritized Ranking (TSPR) to estimate the total average treatment effect of price changes in such settings. TSPR exploits position bias in ranked search results to create variation in treatment exposure without compromising coherency. TSPR randomizes both users and items and reorders ranked lists, prioritizing treated items for one group of users and untreated items for the other. All users see the same items at consistent prices, but differ in exposure to treatment as they pay disproportionate attention across ranks. In semi-synthetic simulations based on Expedia hotel search data, TSPR outperforms baseline coherency-preserving experiment designs by reducing estimation bias and providing sufficient statistical power.

We study competitive equilibria in exchange economies when a continuum of goods is conflated into a finite set of commodities. The design of conflation choices affects the allocation of scarce resources among agents, by constraining trading opportunities and shifting competitive pressures. We demonstrate the consequences on relative prices, trading positions, and welfare.

We study statistical inference on unit roots and cointegration for time series in a Hilbert space. We develop statistical inference on the number of common stochastic trends embedded in the time series, i.e., the dimension of the nonstationary subspace. We also consider tests of hypotheses on the nonstationary and stationary subspaces themselves. The Hilbert space can be of an arbitrarily large dimension, and our methods remain asymptotically valid even when the time series of interest takes values in a subspace of possibly unknown dimension. This has wide applicability in practice; for example, to cointegrated vector time series that are either high-dimensional or of finite dimension, to high-dimensional factor models that include a finite number of nonstationary factors, to cointegrated curve-valued (or function-valued) time series, and to nonstationary dynamic functional factor models. To illustrate our methods, we include two empirical examples.

Using rich Swedish administrative data, we apply causal machine learning methods to study how earnings losses after job displacement vary with observable characteristics that may be relevant for targeting policy interventions for workers. Heterogeneity in effects is as large within as across worker groups defined by age and schooling, and as large within as across establishments. A substantial portion of cross-establishment heterogeneity can be explained by industry and local labor market characteristics, suggesting a role for place- and industry-based targeting. The largest losses are concentrated among already vulnerable workers, indicating that well-designed targeting policies can improve both efficiency and equity.

Non-survey methods have been developed and applied for estimating regional input-output tables. However, there is an ongoing debate about the assumptions necessary for these methods and their accuracy. To address these issues, this study presents a deep learning method for estimating regional input-output tables. First, the quantitative economic data for regions is augmented by linear combinations. Then, deep learning is performed on each item in the input-output table, treating these items as target variables. Finally, regional input-output tables are estimated through matrix balancing to the predicted values from the trained model. The estimation accuracy of this method is verified using the 2015 input-output table for Japan as a benchmark. Compared to matrix balancing under the ideal assumption of known row and column sums, our method generally demonstrates higher estimation accuracy. Thus, this method is anticipated to provide a foundation for deriving more precise estimates of regional input-output tables.

The long-term relationship between radiative forcing and surface temperature is imperative for predicting the impacts of climate change. This study employs multicointegration to characterize this relationship and uses Transformed and Augmented Ordinary Least Squares (TAOLS) to estimate the model. The main goal is to estimate the Equilibrium Climate Sensitivity (ECS), defined as the global mean surface air temperature increase following a doubling of atmospheric carbon dioxide. Our results show that the ECS lies between $2.12^{\circ}$C and $2.49^{\circ}$C, which is lower than the existing maximum likelihood estimate of $2.8^{\circ}$C. TAOLS offers a more robust and accessible tool for climate research, providing novel insights for ongoing debates about Earth's warming trajectory.

The war of attrition with two-sided asymmetric information is a foundational model in political economy, yet it generically admits a continuum of perfect Bayesian equilibria. This paper characterizes the sources of equilibrium multiplicity. We identify conditions on the type distribution that determine which form of multiplicity arises: when the lower limit of the hazard potential -- the integral of the hazard rate normalized by type -- diverges, the free parameter is the relative aggressiveness of strategies; when that limit is finite, the free parameter is the mass of types conceding immediately. We prove that the Amann-Leininger payoff perturbation and the introduction of behavioral types -- two seemingly distinct refinements -- are mathematically equivalent and succeed in selecting a unique equilibrium if and only if the type support is bounded. For unbounded supports, multiplicity persists. These results provide guidance for applied theorists: choosing distributions with bounded support ensures existing refinements deliver unique predictions.

Instrumental variable (IV) methods rely critically on the exclusion restriction, which is untestable in exactly-identified models under standard assumptions. We propose a framework combining IV analysis with the LiNGAM method to test this restriction by exploiting non-Gaussianity in the data. Under non-Gaussian structural errors, the exclusion violation parameter is point-identified without additional instruments. Five complementary tests (bootstrap percentile, asymptotic normal, permutation, likelihood ratio, and independence-based) are introduced to assess the restriction under varying data conditions. Monte Carlo simulations and an empirical application to the Card (1995) dataset demonstrate controlled Type I error rates and reasonable power against economically relevant violations.

Despite the scale of capital being deployed toward AI initiatives, no empirical framework currently exists for benchmarking where a firm stands relative to competitors in AI readiness and deployment, or for translating that position into auditable financial outcomes. In practice, private equity deal teams, management consultants, and corporate strategists have relied on qualitative judgment and ad-hoc maturity labels; tools that are neither comparable across industries nor grounded in observable economic data. This paper introduces the AI Transformation Gap Index (AITG), a composite empirical framework that measures the distance between a firm's current AI deployment and a time varying, industry constrained capability frontier, then maps that distance to dollar denominated value creation, execution feasibility under uncertainty, and competitive disruption risk. Five linked modules address this gap: cross industry normalization (IASS), a dynamic capability ceiling that evolves with frontier capabilities (AFC), trajectory based firm scoring with integrated execution risk (IFS), a CES bottleneck value decomposition mapping gap scores to enterprise value (VCB), and a competitive hazard measure for inaction (ADRI). I calibrate the framework for 22 industry verticals and apply it to 14 public companies using public filings. A retrospective construct validity exercise correlating AITG scores with observed EBITDA margin expansion yields Spearman rho_s = 0.818 (n = 10), directionally consistent with predictions though insufficient for causal identification. A counterintuitive result emerges: the largest AI transformation gaps do not produce the highest value density, because implementation friction, CES bottlenecks, and timing lags erode the theoretical upside of wide gaps.

This paper develops a simple model of the world supply chain to estimate the effects of sanctions that restrict the flow of inputs from one country to another. Such restrictions operate through changes in the weights of the global production network: the sanctioning country ceases supplying certain inputs to the target country and reallocates its production to other destinations. Using the OECD Inter-Country Input--Output tables, we calibrate the model to assess the vulnerability of the Indian economy. We consider two classes of counterfactuals: restrictions on a single sector of a foreign country supplying India, and restrictions on all sectors of a foreign country supplying India. We then rank foreign countries and foreign country-sectors by the risk that their supply restrictions pose to economic activity in India. Our results show that India's greatest country-level vulnerability is to China, followed by the United Arab Emirates, the United States, Saudi Arabi and Russia, with the vulnerability to China being twice as much that to the UAE.

We develop a deep learning algorithm for constructing globally accurate approximations to functional rational expectations equilibria of dynamic stochastic economies in the sequence space. We use deep neural networks to parameterize key equilibrium objects, such as policies or prices, as functions of truncated histories of exogenous shocks. We train the neural networks to satisfy equilibrium conditions along simulated paths of the economy. We illustrate the performance of our method in three environments: (i) a high-dimensional overlapping generations economy with multiple sources of aggregate risk; (ii) an economy with heterogeneous households and firms facing uninsurable idiosyncratic risk and large shocks to idiosyncratic and aggregate volatility; and (iii) a stochastic life-cycle economy with a continuous asset choice and a discrete early-retirement choice that induces local convexities in the continuation values of working-age cohorts. We also propose practical neural policy architectures that guarantee monotonicity of predicted policies, enabling the endogenous grid method to simplify parts of the algorithm. We achieve high precision throughout, with the mean error in equilibrium conditions below $0.2\%$.