Papers

Artificial Intelligence (AI) is rapidly transforming economic systems by altering production processes, labor markets, and the structure of firms and industries. AI is not merely a technological innovation. It is fundamentally a major economic phenomenon and a new wave of innovation with important implications for productivity, employment, market structure, public policy, long-run economic growth, and collective welfare. This essay examines the economics of AI by analyzing the multiple channels through which AI influences economic activity and societal well-being. It argues that AI should be understood simultaneously as a form of capital, a form of synthetic labor, a general-purpose technology, as well as an economic infrastructure. The analysis shows that AI functions as a multi-channel engine of productivity growth through automation, augmentation, optimization, prediction, and innovation. It is argued that AI has the potential to increase output, reduce costs, stimulate entrepreneurship, and accelerate economic growth, while also reshaping labor markets through labor substitution, labor complementarity, and creative destruction. The essay further examines AI's effects on competition, market concentration, and entrepreneurship. Finally, it explores the welfare implications of AI for consumers, producers, workers, governments, and society as a whole, and concludes by outlining policy considerations aimed at maximizing the benefits of AI while mitigating its potential adverse consequences.

Market efficiency relies fundamentally on stable liquidity. Consequently, forecasting liquidity dynamics is a priority for both investors and regulators. We introduce a new tail-risk metric, Illiquidity-at-Risk (IlliQaR), designed to quantify the magnitude of extreme liquidity dry-ups. Relying upon the realized Amihud (a precise illiquidity measurement derived from high-frequency data as the ratio of realized volatility to trading volume) we assess the predictive power of various linear and non-linear econometric models, with a specific focus on the impact of discontinuous jump components. Accounting for these jumps is essential for achieving accurate probability coverage and better IlliQaR predictions during periods of systemic stress, where standard continuous models systematically underestimate the severity of liquidity evaporation. Our empirical analysis, encompassing the S&P 500 index and a cross-section of 25 large U.S. equities, demonstrates that incorporating jumps significantly improves forecasts of illiquidity. Our results suggest that individual stock IlliQaR violations often cluster during periods of S&P 500 liquidity stress. This indicates that Illiquidity at Risk is not just a localized concern but a systemic one, where the main index acts as a leading indicator for extreme dry-ups in individual stock liquidity.

This paper examines how outside options are incorporated into payoff distributions in games with coalition structures. We introduce and characterize the alpha-value, which "fully" incorporates outside options, and provide a new characterization of the Aumann-Dreze value as an allocation rule that does not incorporate outside options. We show that the chi-value (Casajus, 2009) is a component-wise convex combination of these two values and thus incorporates outside options in a discounted form.

In a network with ramp-limited generators and inaccurate net-demand forecasts, practical rolling-window dispatch can drive locational marginal prices (LMPs) below generators' bid-in offers. In such cases, out-of-market (OOM) settlements are used to compensate generators and maintain dispatch-following incentives, but OOM can have negative consequences, including nontransparent real-time price signals, discriminatory compensation, and incentives for untruthful bidding. This paper presents an optimal uniform pricing rule that minimizes demand payments, eliminates OOM make-whole payments, preserves LMP-based congestion charges, and ensures revenue adequacy. We derive the proposed pricing rule in closed form and relate it to existing pricing schemes. Numerical comparisons demonstrate favorable generator profits and reduced price volatility. However, higher generator profits are accompanied by increased demand payments, reflecting the in-market, uniform allocation of ramping costs while preserving the LMP-based congestion charges widely used in real-time market settlements. The numerical results also show that, under LMP with OOM settlement, a price-taking generator has an incentive to inflate its offer, whereas this incentive is absent under the proposed pricing rule within the tested bid range.

Limited attention forces organizations to decide not only how much to discuss, but also which issues merit discussion. We study strategic communication about a multidimensional decision when a receiver can respond only along a few endogenously chosen issues. Players agree on the ideal action but prioritize different errors. In equilibrium, communicated and omitted issues must be statistically unrelated and separable according to the sender's preferences. Thus, the sender's priorities determine the agenda; the receiver's do not. Synchronized priorities raise the receiver's best equilibrium payoff but lower his worst, so he may prefer a less synchronized sender.

We propose the first manipulation test designed for boundary discontinuity designs (BDDs) with general boundary shapes. A BDD is a multidimensional extension of the regression discontinuity design (RDD) in which treatment assignment is determined by whether the multidimensional running variable crosses a lower-dimensional boundary set. The test avoids multivariate density estimation and builds on the observation that, in the absence of manipulation, observations near the boundary should be approximately evenly split between treatment and control within arbitrary groups defined by their projections onto the boundary. We test this implication using a collection of binomial balance tests on observations near the boundary, with groups formed by k-means clustering. We establish the asymptotic validity of the test under suitable regularity conditions. We also evaluate finite-sample performance through Monte Carlo simulations and illustrate the test in three empirical applications.

Foundation models promise accurate forecasts with little or no task-specific training, but whether they can replace models designed specifically for electricity price forecasting remains unclear. We compare nine variants from five foundation model families, evaluated in zero-shot mode, with two state-of-the-art electricity price forecasting benchmarks in Germany, Poland, and Spain over 2021-2025. Their performance is assessed in terms of point and probabilistic forecasting accuracy, as well as economic value in battery energy storage arbitrage. Only the TabPFN models consistently and significantly outperform the benchmarks across all three markets and all statistical measures. However, this statistical dominance does not translate directly into economic dominance: TabPFN performs best under unlimited bids and riskier quantile-based strategies, whereas the Distributional Deep Neural Network benchmark is more profitable when risk tolerance is lower. Thus, foundation models cannot universally replace market-specific models, and their value depends on both model architecture and the decision problem.

We study identification and estimation of moments of random coefficients in short linear panels, allowing the number of heterogeneous coefficients to exceed the number of equations observed for each unit. Under moment homogeneity, different regressor histories impose restrictions on the same moment vector. We give necessary and sufficient conditions for these restrictions to identify moments of a given order, stated in terms of the row spaces generated by the regressor support. The results show that moments may be identified even when the coefficients cannot be recovered for any individual, and, for two full-row-rank histories, give the exact loss of independent restrictions caused by overlap of their row spaces. When the support condition fails, we establish nonidentification in the maintained model and characterize the sharp identified set implied by these conditional moments. At second order, the identified set is determined by positive-semidefinite covariance restrictions and is also sharp relative to the full joint distribution of outcomes and regressors. Under the support condition, weighted minimum-distance estimators are root-$N$ asymptotically normal; under conditional nondegeneracy, oracle generalized-inverse weighting attains the Chamberlain (1987) efficiency bound for the maintained conditional-moment model.

When do text embeddings work as inputs to empirical analysis? Their use rests on an assumption: that we can trade text for its low-dimensional embedding, and lose little in doing so. I make that assumption precise under a generative model in which documents are mixtures of latent topics. I study two uses—clustering units in embedding space and controlling for high-dimensional text. A cluster of embeddings is a set of documents with similar topic mixtures; controlling for the embedding is equivalent to controlling for the topic mixture, so validity reduces to whether that mixture captures the confounding. In an application to 363 U.S. metropolitan areas, embedding-based clusters of LLM-generated economic descriptions recover interpretable economic archetypes and separate local employment dynamics more sharply than clustering on model residuals, or on a curated set of industry and demographic covariates.

Tariff threats can move household beliefs before policy is enacted, yet their rapidly changing language is difficult to study with conventional surveys. We build a multi-agent system that turns 300 households from the Michigan Surveys of Consumers into persistent large-language-model agents exposed to social-media information over several simulated months. Calibrated agents reproduce some distributional and demographic patterns in human survey data collected after the announcement of Liberation Day tariffs. Simulated experiments indicate that immediacy, rate salience, semantic progression, message complexity, narrative, and sender identity jointly shape inflation and unemployment expectations and their dispersion. Open-ended responses trace these effects to attention, ambiguity, credibility, and causal narratives. A second experiment finds that central-bank explanations can coordinate beliefs, although their effects on average expectations depend on message content. The framework supports disciplined exploration of policy communication, subject to human validation rather than as a substitute for it.

Stablecoins, typically pegged to fiat currencies, cannot achieve true stability because they inherit fluctuations in the underlying unit of account. To overcome this limitation, we introduce a stablecoin pegged to the Maximum Likelihood Value (MLV), a newly defined unit of account derived as the most probable configuration of latent real-value movements that explains observed nominal-value (price) changes. Grounded in inferential statistics and modern portfolio theory, MLV represents the most stable unit of account, as it enforces a zero real return on the minimum-variance portfolio. Empirical results confirm the operational viability of an MLV-pegged stablecoin: MLV can be computed in real time from 500 asset price series and improves annualized returns and Sharpe ratios while substantially reducing turnover in portfolio optimization.

This paper introduces and studies the Multi-Objective Enterprise Green Supply Chain Network Design problem (MOEDGSCND) an extension of the well known Multi-Objective Supply Chain Network Design (MO-SCND), in this case a three-objective combinatorial optimisation problem that simultaneously minimises total cost f1, carbon dioxide emissions f2, and customer dissatisfaction f3 over binary decisions on which distribution centres to open and how to assign customer zones to open facilities. The problem generalises capacitated facility location and is known to be NP-hard. Furthermore this paper lays out a formal specification of decision variables, objective functions, all constraints and then investigates and compares three solution approaches using a single numerical instance: (A) exact weighted-sum integer linear programming solved via PuLP, (B) three Fully Polynomial-Time Approximation Schemes (FPTAS) providing (.94 + $\epsilon$)-guarantee approximations for $\epsilon$ := 0.15, and (C) two baseline heuristics for comparison. On an eight-customer, five-candidate-DC instance the exact solver certifies the optimal Pareto front across eight weight combinations, and all three FPTAS variants achieve their approximation guarantees, finishing within milliseconds compared to centiseconds for the exact ILP using the PuLP solver on this scale.

Posted prices for AI inference have fallen steadily since 2024, yet the measured speed of that fall depends almost entirely on the method of measurement. This paper constructs quality-adjusted price indices for the AI inference market from public data. The panel assembles 21,024 posted-price observations across 3,208 models and 86 providers and joins them to 4,605 benchmark scores through a latent quality index estimated from benchmark response patterns, so the quality ladder of the hedonic tradition is built here from evaluations in place of product characteristics. Measured by the matched-model methods that statistical agencies apply to software, inference prices fell at 0.10 log points a year. The quality-adjusted index fell at 0.73, so 87% of the decline is invisible to current methods, with direct consequences for measured competition, concentration and productivity in this market. Counted per completed task, moreover, the buyer's price stopped falling. Reasoning models raised token consumption faster than token prices fell, and the seller's and buyer's prices accordingly diverged. A pre-registered validity audit disciplines the quality measure and yields the sharpest result. Excluding contamination-flagged benchmarks leaves model rankings intact at 0.998 yet moves the index by 0.49 log points a year, so the leaderboard-stability arguments standard in AI evaluation offer no defence of economic statistics built on benchmarks. Prices, quality and the audit are fully reproducible from public sources at zero cost.

We develop a computational framework for Bayesian decision-making. We show that as long as no action is optimal in every state, Bayes-optimal choice is intractable. This hardness need not arise from large action, state, or signal spaces, nor from a complicated represented utility function: extracting enough information from a hard-to-interpret signal to act optimally can itself be computationally hard. We also characterize tractability across approximation notions and identify their sources of difficulty. Under the probably approximately correct criterion, sample-based Bayesian learning is tractable if and only if the signal support is bounded. Our results provide justifications for bounded rationality, costly Bayesian inference, and sample-based Bayesian learning.

Renewables now dominate Great Britain's generation mix but rarely occupy the marginal price-setting position, which raises the question of which flexible technologies translate a renewable-rich system into real-time price formation. This study reconstructs the price-ranked edge of the eligible bid or offer stack in the GB Balancing Mechanism across 50,684 half-hourly Settlement Periods from 2023 to 2025 and attributes it to individual Balancing Mechanism Units, separating long-system bid-active from short-system offer-active conditions and retaining co-marginal ties. Batteries rose from 0.8% to 36.2% of bid-active and from 3.2% to 26.9% of offer-active marginality, displacing combined-cycle gas on the bid side and pumped storage on the offer side. Capacity-normalised marginal capture rose on both sides, and unit-quarter fixed effects show that an additional 100 MW of Balancing Mechanism active capacity was associated with a 0.55 and 0.48 percentage-point increase in quarterly bid- and offer-side marginal share. By 2025, battery actions were around {\pounds}9 to 10/MWh more favourable than volume-matched non-battery alternatives drawn from the same ladder, yet batteries were under-represented by 12.9 percentage points in the highest-priced 5% of short-system periods. Individual batteries remain consistent with price-taking, while the fleet has become endogenous to routine balancing price formation.

In centralized school choice, a designer who announces a mechanism may deviate from it to favor some students without being detected. A mechanism is credible if it admits no such deviation. We study this credibility problem when students do not know others' reports and may observe only part of the assignment. Credibility is demanding: among common school choice mechanisms, only deferred acceptance is credible, and only when students observe enough of the assignment. We therefore rank mechanisms by their credibility. Deferred acceptance is more credible than any other stable mechanism and strictly more credible than the Boston mechanism, regardless of how much of the assignment students observe. Its ranking relative to top trading cycles and efficiency-adjusted deferred acceptance depends on disclosure: deferred acceptance is strictly more credible when students observe the entire assignment, whereas the ranking reverses when students observe only their own assignment.

We introduce a behavioral notion of domain-specific risk aversion that separates attitudes toward risk from deterministic utility: an agent is more pure risk averse in one domain than in another if, for prizes that are indifferent under certainty, he is more averse to risk in the former domain than in the latter. We develop a model that goes beyond expected utility by allowing risk attitudes to vary across domains, while preserving expected utility within each domain. The domains are subjective and need not be specified in advance; they are identified from choice behavior. We establish uniqueness of the model's parameters and provide an axiomatic characterization.

Concave consumption functions imply a marginal propensity to consume that falls with wealth. I characterize the utility functions that guarantee this property in finite-horizon optimal saving problems with stochastic discounting, returns, income, and borrowing limits. Under conditional impatience—the conditional expected discounted gross return does not exceed one—consumption functions are always concave if and only if inverse absolute prudence, $-u''/u'''$, is concave. When no conditional-impatience restriction is imposed, hyperbolic absolute risk aversion (HARA) is necessary and sufficient for uniform concavity. Thus conditional impatience permits declining marginal propensities to consume for a preference class strictly larger than HARA.

Industrial recommenders give new content initial views through budgeted exploration, then use early performance to decide further delivery. On many short-video platforms, exploration is the primary way new videos reach viewers. Viewer-side tests measure consumption; the published budget objectives we review omit creator response. We analyze four experiments on a major short-video platform. An eight-month creator ablation finds production exploration raises videos posted per creator by 8.55% and creators posting at least once by 7.10% relative to a minimal floor. A budget-matched reallocation raises creator participation with no detectable short-run viewer-side change. A year-long viewer ablation finds 1.74% more video views but 2.13% less view time. A delivered view creates immediate feed value, can trigger organic take-up, and can induce creator supply. Take-up and supply replenish a shared corpus, creating two measurement limits. Viewer-side A/B tests cancel the corpus effect when both arms consume the same corpus. Giving each arm its own corpus avoids cancellation, but turnover still controls the horizon. If the corpus turns over at rate w per posting cycle, a t-cycle experiment expresses at most wt of the eventual corpus effect. More users reduce noise but do not speed turnover. Before the corpus path visibly bends, data cannot distinguish a modest fast effect from an arbitrarily large slow one, so a valid confidence interval may lack a finite upper endpoint. As predicted, the three-week co-diverted experiment cannot determine the sign of the eventual corpus effect. Within the window, it identifies the direct feed effect, and an exploratory cohort analysis detects organic lift after exploration ends. The experiments establish a positive creator response, measure the gross corpus flow visible within three weeks, and show the design and duration needed to identify total value.

We study the repeated allocation of a single indivisible resource among $n$ strategic players. Each player $i$ has a privately known value distribution $D_i$, and values are drawn independently across players and periods. The goal is to find fair and efficient mechanisms. We apply the repeated first-price auction with equal initial endowments of virtual money. We show that each player can asymptotically secure the same fair-floor guarantee $f(D_i)$ as in Csóka 2026; consequently, the mechanism is $1.283$-optimal. This provides a simpler and more robust alternative mechanism for this special case and may also help derive sharper upper bounds on the price of anarchy.

We study a setting in which an agent receives private information before choosing from a menu and anticipates hindsight scrutiny. Such scrutiny creates a motive for conservatism toward menu expansions. Our key axiom, conservatism, is a direct weakening of preference for flexibility: Adding an option is weakly beneficial whenever it leaves the menu's hindsight benchmark unchanged. Together with standard axioms, conservatism characterizes a scrutiny representation of preferences over menus in which the agent behaves as if she evaluates each menu by subtracting anticipated scrutiny from the material value of informed choice. Menu preference identifies a unique minimum pair of private information and scrutiny intensity, while incorporating subsequent stochastic-choice data pins down the actual pair. Applications to medical liability and corporate innovation illustrate the implications of our model for accountability design.

The VCG family and the AGV mechanism are two classical approaches to efficient implementation in the static social choice problem. In 2024, Csóka et al. showed that AGV has critical weaknesses. In contrast, the transferable-utility Guaranteed Utility Mechanism (TU-GUM) retains all the standard desirable properties of AGV while adding further ones, including collusion-proofness, because it implements efficiency in Guaranteed Utility Equilibrium. TU-GUM also applies to a more general dynamic setting with multiple extensions. Moreover, TU-GUM is a special case of an even more general and robust mechanism that combines contingent first-price tendering with the coordinated execution of dynamic stochastic multi-agent projects through a surprisingly simple rule. This paper summarizes and connects existing results from a different perspective, with some minor new observations.

In this paper, we study causal non-causal state space models to model time series characterised by a local explosive increase followed by a sharp decrease such as stock prices. To motivate the use of causal non-causal state space models, we show that the causal non-causal convolution autoregressive model introduced by Gourieroux and Zakoian (2017) can be consistent with the rational expectations stock price model. As in a causal state space model, a central question is how to perform state and parameter inference in the causal non-causal state space model, which we discuss in the paper. We also study the causal non-causal convolution autoregressive model in more detail, providing some new results for the model. To illustrate the usefulness of causal non-causal state space models, we use the causal non-causal convolution autoregressive model to estimate the size of the dot-com bubble in both real time and a posteriori with the stable non-causal autoregressive model considered also by Gourieroux and Zakoian (2017) as a benchmark.

We present a suite of R packages for macroeconomic forecasting that leverages advanced Bayesian, structural, multivariate, dynamic, hierarchical, non-linear, and non-Gaussian models. The suite enables both structural and predictive analyses, and is adapted to time series data across various types, dimensions, and sampling frequencies. Each additional feature increases computational complexity. To address this challenge, our software design incorporates a carefully curated selection of models, efficient algorithms implemented in C++, advanced econometric and numerical methods, robust handling of complex input and output objects, and standardised workflows. This approach combines the computational efficiency of C++ with the convenience of working with data in R. We demonstrate that our packages facilitate original research contributions in forecasting, as illustrated by our example in which vector autoregressions with non-centred stochastic volatility enhance density and point predictions relative to models with centred stochastic volatility.

South Korea has the world's lowest fertility and an intense education race. Families devote nine percent of lifetime income to education, mostly to private tutoring with near-zero measured returns. I show that competition for coveted careers generates an assignment externality: when all families spend more, the admission bar rises and children become costly. In a quantitative model calibrated to this mechanism, replacing score-based assignment with a capacity-preserving lottery raises completed fertility by 0.24 children per couple. Pronatal transfers deliver one eighth of that increase. Education taxes deliver almost none. Preference shifts, not a fiercer race, explain the decline across cohorts.

An institution that cannot compel disclosure can still allocate exposure. We study two opposed advocates who select verifiable observations and a receiver who averages the selected record without correcting for selection. The institution commits exposure before the state and evidence are known; each withheld item's assigned weight expires. With commonly known pools and strictly opposed monotone preferences, all finite-game Nash equilibria produce one action and common cutoff. In large pools with a vanishing anchor, directional tail moments determine the implementing exposure share. Under common evidence laws, equal exposure implements every state. The institutional restriction matters: full within-camp reallocation induces extreme selection and, on a fixed support, eliminates state responsiveness. In a finite-state common-law benchmark, small reallocation separates disclosure cutoffs and changes implementing shares according to favorable-tail frequencies. Variation in these frequencies across states creates a positive local welfare cost even after exposure is optimized. Ex ante results bound the cost of commitment and identify information sufficient for limiting implementation. Separately, under linear payoffs, uniform item weights and continuous evidence laws on a compact state space, all Bayesian equilibria converge to the baseline outcome when advocates observe only their own samples and learn the state. Finite-pool results characterize a precision cost under common laws, balanced exposure, uniform weights and equal limiting camp sizes, and first corrections to exact risk-minimizing shares under additional regularity.

To achieve carbon neutrality, electrifying vehicles in a cleaner electricity grid has become a key pathway for global cities. While extensive research has examined the environmental benefits of electric vehicles (EVs), their financial impacts remain underexplored. Using Singapore-a smart city-state - as a social laboratory, we combine high-resolution data on the geographic distribution of EV chargers with nearly 1 million individual-level monthly expenditure records from a major commercial bank and about 65,000 housing transaction records, spanning January 2019 to March 2021. Our results show that proximity to charging stations during the initial phase of EV infrastructure rollout significantly increases household consumption by 1-3%. This effect is driven primarily by public housing residents, who are generally more financially constrained than their private housing counterparts. We identify three channels underlying this increase in consumption: appreciation in housing wealth, catalysts for commercial activity and discretionary spending around EV infrastructure, and budget reallocation from fuel expenses to other consumption categories. Notably, hybrid car owners exhibit the strongest trade-offs-reducing fuel spending while increasing expenditure on entertainment or necessity goods among public housing residents. Overall, our findings demonstrate that green infrastructure can shape economic behavior and enhance consumer welfare, extending its impact beyond environmental objectives.

In this chapter, we provide a synthesis of empirical work on the intergenerational mobility in China, examining how much a child's success depends on their parents' success. We start with an overview of data sources and measure of intergenerational mobility in China's context, followed by discussion on the empirical findings in five categories of intergenerational mobility: income, education, social class, wealth, and health. Major channels of transmission of socioeconomic status across generations are investigated, including human and social capital, fertility and migration decision, housing price and trade liberalization in the market transition, and belief. Finally, we provide policy implications to promote upward mobility for children born to less advantaged parents in China's context.

Using the October 2021 announcement of Hong Kong's Northern Metropolis Plan as a quasi-natural experiment, we examine its impacts on households, firms, and demographics. Leveraging 100,576 housing transactions, 4.7 million consumption records, and district-level demographic data, difference-in-differences estimates show housing prices in the treated region rose by 3.9% within one year, alongside a 4.1% increase in consumption. Firm entry expanded, with inflows of higher-income, higher-educated households. We find a mild, lagged spillover to Shenzhen's housing market but no significant firm response. While the policy narrows cross-region inequality, it increases within-region inequality and raises affordability concerns for lower-SES households.

We study the properties of the cumulative offer mechanism (COM) when institutions' choice functions satisfy strong observable substitutability (strong OS). First, we show that each of the following properties of the COM characterizes strong OS: IR monotonicity, weak Maskin monotonicity, and dropping monotonicity. Dropping monotonicity is a new condition weaker than weak Maskin monotonicity, and it is interpretable as a weakening of strategy-proofness and non-bossiness. Second, we show that when choice functions are strongly OS, weak group strategy-proofness of the COM reduces to individual strategy-proofness. However, strong OS is neither necessary nor "almost necessary" for this reduction.