Previous research indicates that zero tillage technology offers a profitable alternative to crop residue burning, with significant potential to reduce agricultural emissions and contribute to improvements in air quality and public health. Yet, empirical evidence on the link between zero tillage adoption and residue burning remains scarce, adding to the difficulties policy makers face in this context. This study addresses this gap by integrating high-resolution satellite imagery with household survey data from India to examine the empirical relationship between zero tillage and residue burning. We compare different methods for constructing burn indicators from remote-sensing data and assess their predictive power against survey-based measures. Our findings reveal a robust negative association between zero tillage and crop residue burning, with reductions in the incidence of burning of 50% or more across both survey data and satellite-derived indicators. By providing insights into optimal geospatial data integration methods, our study also makes a methodological contribution that can inform future research and support evidence-based policy interventions for more sustainable agricultural practices.

We study information disclosure in competitive markets with adverse selection. Sellers privately observe product quality, with higher quality entailing higher production costs, while buyers trade at the market-clearing price after observing a public signal. Because sellers' participation in trade conveys information about quality, the designer faces endogenous constraints in the set of posteriors that she can induce. We reformulate the designer's problem as a martingale optimal transport exercise with an additional condition that rules out further information transmission through sellers' participation decisions, and characterize the optimal signals. When the designer maximizes trade volume, the solution features negative-assortative matching of inefficient and efficient sellers. When the objective is a weighted combination of price and surplus, optimal signals preserve this structure as long as the weight on the price is high enough, otherwise they fully reveal low-quality types while pooling middle types with high-quality sellers.

We revisit tail-index regressions. For linear specifications, we find that the usual full-rank condition can fail because conditioning on extreme outcomes causes regressors to degenerate to constants. More generally, the conditional distribution of the covariates in the tails concentrates on the values at which the tail index is minimized. Away from those points, the conditional density tends to zero. For local nonparametric tail index regression, the convergence rate can be very slow. We conclude with practical suggestions for applied work.

Equity markets have long been regarded as unpredictable, with intraday price movements treated as stochastic noise. This study challenges that view by introducing the Extended Samuelson Model (ESM), a natural science-based framework that captures the dynamic, causal processes underlying market behavior. ESM identifies peaks, troughs, and turning points across multiple timescales and demonstrates temporal compatibility: finer timeframes contain all signals of broader ones while offering sharper directional guidance. Beyond theory, ESM translates into practical trading strategies. During intraday sessions, it reliably anticipates short-term reversals and longer-term trends, even under the influence of breaking news. Its eight market states and six directional signals provide actionable guardrails for traders, enabling consistent profit opportunities. Notably, even during calm periods, ESM can capture 10-point swings in the S&P 500, equivalent to $500 per E-mini futures contract. These findings resonate with the state-based approaches attributed to Renaissance Technologies' Medallion Fund, which delivered extraordinary returns through systematic intraday trading. By bridging normal conditions with crisis dynamics, ESM not only advances the scientific understanding of market evolution but also provides a robust, actionable roadmap for profitable trading.

This paper examines the impact of temperature shocks on European Parliament elections. We combine high-resolution climate data with results from parliamentary elections between 1989 and 2019, aggregated at the NUTS-2 regional level. Exploiting exogenous variation in unusually warm and hot days during the months preceding elections, we identify the effect of short-run temperature shocks on voting behaviour. We find that temperature shocks reduce ideological polarisation and increase vote concentration, as voters consolidate around larger, more moderate parties. This aggregated pattern is explained by a gain in support of liberal and, to a lesser extent, social democratic parties, while right-wing parties lose vote share. Consistent with a salience mechanism, complementary analysis of party manifestos shows greater emphasis on climate-related issues in warmer pre-electoral contexts. Overall, our findings indicate that climate shocks can shift party systems toward the centre and weaken political extremes.

This work outlines the modeling steps for developing a tool aimed at supporting policymakers in guiding policies toward more sustainable wheat production. In the agricultural sector,policies affect a highly diverse set of farms, which differ across several dimensions such as size,land composition, local climate, and irrigation availability. To address this significant heterogeneity, we construct an Agent-Based Model (ABM). The model is initialized using a representative survey of Italian farms, which captures their heterogeneity. The ABM is then scaled to include a number of farms comparable to those operating nationwide. To capture broader dynamics, the ABM is integrated with two additional components:a global model of international wheat markets and a tool for assessing the environmental impacts of wheat production. This integrated framework enables us to account for the feedback loop between global prices and local production while evaluating the environmental implications of policy measures.

In a multi-follower Bayesian Stackelberg game, a leader plays a mixed strategy over $L$ actions to which $n\ge 1$ followers, each having one of $K$ possible private types, best respond. The leader's optimal strategy depends on the distribution of the followers' private types. We study an online learning version of this problem: a leader interacts for $T$ rounds with $n$ followers with types sampled from an unknown distribution every round. The leader's goal is to minimize regret, defined as the difference between the cumulative utility of the optimal strategy and that of the actually chosen strategies. We design learning algorithms for the leader under different feedback settings. Under type feedback, where the leader observes the followers' types after each round, we design algorithms that achieve $\mathcal O\big(\sqrt{\min\{L\log(nKA T), nK \} \cdot T} \big)$ regret for independent type distributions and $\mathcal O\big(\sqrt{\min\{L\log(nKA T), K^n \} \cdot T} \big)$ regret for general type distributions. Interestingly, those bounds do not grow with $n$ at a polynomial rate. Under action feedback, where the leader only observes the followers' actions, we design algorithms with $\mathcal O( \min\{\sqrt{ n^L K^L A^{2L} L T \log T}, K^n\sqrt{ T } \log T \} )$ regret. We also provide a lower bound of $\Omega(\sqrt{\min\{L, nK\}T})$, almost matching the type-feedback upper bounds.

When parameters are weakly identified, bounds on the parameters may provide a valuable source of information. Existing weak identification estimation and inference results are unable to combine weak identification with bounds. Within a class of minimum distance models, this paper proposes identification-robust inference that incorporates information from bounds when parameters are weakly identified. This paper demonstrates the value of the bounds and identification-robust inference in a simple latent factor model and a simple GARCH model. This paper also demonstrates the identification-robust inference in an empirical application, a factor model for parental investments in children.

The normality assumption for random errors is fundamental in the analysis of variance (ANOVA) models, yet it is seldom subjected to formal testing in practice. In this paper, we develop Neyman's smooth tests for assessing normality in a broad class of ANOVA models. The proposed test statistics are constructed via the Gaussian probability integral transformation of ANOVA residuals and are shown to follow an asymptotic Chi-square distribution under the null hypothesis, with degrees of freedom determined by the dimension of the smooth model. We further propose a data-driven selection of the model dimension based on a modified Schwarz's criterion. Monte Carlo simulations demonstrate that the tests maintain the nominal size and achieve high power against a wide range of alternatives. Our framework thus provides a systematic and effective tool for formally validating the normality assumption in ANOVA models.

We consider the problem of aggregating individual preferences over alternatives into a social ranking. A key feature of the problems that we consider - and the one that allows us to obtain positive results, in contrast to negative results such as Arrow's Impossibililty Theorem - is that the alternatives to be ranked are outcomes of a competitive process. Examples include rankings of colleges or academic journals. The foundation of our ranking method is that alternatives that agents rank higher than the one they receive (and thus have been rejected by) should also be ranked higher in the aggregate ranking. We introduce axioms to formalize this idea, and call any ranking that satisfies our axioms a desirable ranking. We show that as the market grows large, any desirable ranking coincides with the true underlying ranking of colleges by quality. Last, we provide an algorithm for constructing desirable rankings, and show that the outcome of this algorithm is the unique ranking of the colleges that satisfy our axioms.

We propose a generalization of the synthetic control and interventions methods to the setting with dynamic treatment effects. We consider the estimation of unit-specific treatment effects from panel data collected under a general treatment sequence. Here, each unit receives multiple treatments sequentially, according to an adaptive policy that depends on a latent, endogenously time-varying confounding state. Under a low-rank latent factor model assumption, we develop an identification strategy for any unit-specific mean outcome under any sequence of interventions. The latent factor model we propose admits linear time-varying and time-invariant dynamical systems as special cases. Our approach can be viewed as an identification strategy for structural nested mean models -- a widely used framework for dynamic treatment effects -- under a low-rank latent factor assumption on the blip effects. Unlike these models, however, it is more permissive in observational settings, thereby broadening its applicability. Our method, which we term synthetic blip effects, is a backwards induction process in which the blip effect of a treatment at each period and for a target unit is recursively expressed as a linear combination of the blip effects of a group of other units that received the designated treatment. This strategy avoids the combinatorial explosion in the number of units that would otherwise be required by a naive application of prior synthetic control and intervention methods in dynamic treatment settings. We provide estimation algorithms that are easy to implement in practice and yield estimators with desirable properties. Using unique Korean firm-level panel data, we demonstrate how the proposed framework can be used to estimate individualized dynamic treatment effects and to derive optimal treatment allocation rules in the context of financial support for exporting firms.

I study how to regulate firms' access to consumer data when a regulator faces non-Bayesian uncertainty about how firms will exploit the consumer's information to segment the market and set prices. I fully characterize all worst-case optimal policies when the regulator maximizes consumer surplus: the regulator allows a firm to access data only if the firm cannot use the database to identify a small group of consumers.

What role do non-elected bureaucrats play when elections provide imperfect accountability and create incentives for pandering? We develop a model where politicians and bureaucrats interact to implement policy. Both can either be good, sharing the voters' preferences over policies, or bad, intent on enacting policies that favor special interests. Our analysis identifies the conditions under which good bureaucrats choose to support, oppose, or force pandering. When bureaucrats wield significant influence over policy decisions, good politicians lose their incentives to pander, a shift that ultimately benefits voters. An intermediate level of bureaucratic influence over policymaking can be voter-optimal: large enough to prevent pandering but small enough to avoid granting excessive influence to potentially bad bureaucrats.

We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails -- unlike the common centred parameterization. This feature makes it well suited for assessing partial identification of any shock of interest. Second, Monte Carlo experiments on small and large systems indicate that the non-centred setup estimates structural parameters more precisely and normalizes conditional variances efficiently. Third, revisiting prominent fiscal structural vector autoregressions, we show how the non-centred approach identifies tax shocks that are consistent with estimates reported in the literature.

This paper proposes a ridgeless kernel method for solving infinite-horizon, deterministic, continuous-time models in economic dynamics, formulated as systems of differential-algebraic equations with asymptotic boundary conditions (e.g., transversality). Traditional shooting methods enforce the asymptotic boundary conditions by targeting a known steady state -- which is numerically unstable, hard to tune, and unable to address cases with steady-state multiplicity. Instead, our approach solves the underdetermined problem without imposing the asymptotic boundary condition, using regularization to select the unique solution fulfilling transversality among admissible trajectories. In particular, ridgeless kernel methods recover this path by selecting the minimum norm solution, coinciding with the non-explosive trajectory. We provide theoretical guarantees showing that kernel solutions satisfy asymptotic boundary conditions without imposing them directly, and we establish a consistency result ensuring convergence within the solution concept of differential-algebraic equations. Finally, we illustrate the method in canonical models and demonstrate its ability to handle problems with multiple steady states.

This paper introduces the Voting with Random Proposers (VRP) procedure to address the challenges of agenda manipulation in voting. In each round of VRP, a randomly selected proposer suggests an alternative that is voted on against the previous round's winner. In a framework with single-peaked preferences, we show that the VRP procedure guarantees that the Condorcet winner is implemented in a few rounds with truthful voting, and in just two rounds under sufficiently symmetric preference distributions or if status quo positions are not extreme. The results have applications for committee decisions, legislative decision-making, and the organization of citizens' assemblies and decentralized autonomous organizations.

This research expands the existing literature on Bitcoin (BTC) price misalignments by incorporating transaction-level data from a peer-to-peer (P2P) exchange, this http URL (LB). It examines how broader economic and regulatory factors influence cryptocurrency markets and highlights the role of cryptocurrencies in facilitating international capital movements. By constructing shadow exchange rates (SERs) for national currencies against the US dollar based on BTC prices, we calculate discrepancies between these SERs and their official exchange rates (OERs), referred to as BTC premiums. We analyze various factors driving the BTC premiums on LB, including those sourced from the BTC blockchain, mainstream centralized BTC exchanges, and international capital transfer channels. Unlike in centralized markets, our results indicate that the microstructure of the BTC blockchain does not correlate with BTC premiums in the P2P market. Regarding frictions from international capital transfers, we interpret remittance costs as indicators of inefficiencies in traditional capital transfer systems. For constrained currencies subject to severe capital controls and managed exchange rate regimes, increased transaction costs in conventional currency exchange channels almost entirely translate into higher BTC premiums. Additionally, our analysis suggests that BTC premiums can serve as short-term predictors of future exchange rate depreciation for unconstrained currencies.

This paper analyzes optimal insurance design when the insurer internalizes the effect of coverage on third-party service prices. A monopolistic insurer contracts with risk-averse agents who have sequential two-dimensional private information and preferences represented by Yaari's dual utility. Insurance contracts shape service demand and, through a market-clearing condition, determine equilibrium third-party prices. We characterize the structure of optimal contracts and show they take simple forms: either full coverage after a deductible is paid or limited coverage with an out-of-pocket maximum, closely mirroring real-world insurance plans. Technically, we formulate the problem as a sequential screening model and solve it using tools from optimal transport theory.

I study dynamic contracting where a principal (Sender) privately observes a Markovian state and seeks to motivate an agent (Receiver) who takes actions. Sender can both use payments to augment ex-post payoffs or persuasion to alter the informational environment as ways to provide incentives. For any stage-game payoffs, cost of transfers, rate of future discounting, and Markov transition rule, optimal transfers are backloaded-payments occur only when Sender commits to reveal the state at all continuation histories. In a rideshare example, the optimal contract is a loyalty program: drivers receive the static optimal information structure until a random promotion time, after which the state is fully revealed and only payments are used to motivate the driver.

A novel approach to Forecast Error Variance Decompositions (FEVD) in nonlinear Structural Vector Autoregressive models with Gaussian innovations is proposed, called the Hermite FEVD (HFEVD). This method employs a Hermite polynomial expansion to approximate the future trajectory of a nonlinear process. The orthogonality of Hermite polynomials under the Gaussian density facilitates the construction of the decomposition, providing a separation of shock effects by time horizon, by components of the structural innovation and by degree of nonlinearity. A link between the HFEVD and nonlinear Impulse Response Functions is established and distinguishes between marginal and interaction contributions of shocks. Simulation results from standard nonlinear models are provided as illustrations and an application to fiscal policy shocks is examined.

In this paper, we develop a unified approach to study partial identification of a finite-dimensional parameter defined by a general moment model with incomplete data. We establish a novel characterization of the identified set for the true parameter in terms of a continuum of inequalities defined by conditional optimal transport. For the special case of an affine moment model, we show that the identified set is convex and that its support function can be easily computed by solving a conditional optimal transport problem. For parameters that may not satisfy the moment model, we propose a two-step procedure to construct its identified set. Finally, we demonstrate the generality and effectiveness of our approach through several running examples.

The rapid rise of e-commerce has transformed consumer behavior, prompting questions about how online adoption influences offline shopping. We examine whether consumers who adopt a retailer's online shopping channels become more price-sensitive in their subsequent offline purchases with that retailer. Using transaction-level data from a large Brazilian pet supplies retailer operating both online and offline, we compare "adopters" -- customers who began shopping online after a period of offline-only purchasing -- with "non-adopters" who remained offline-only. We estimate a discrete choice logit model with individual-level heterogeneity, based on an algorithm that can handle both high-dimensional fixed effects and price endogeneity. We then apply a staggered difference-in-differences approach to the estimated price elasticities and obtain the Average Treatment Effect on the Treated (ATT). We find that offline price sensitivity increases significantly after online adoption in three out of four product categories, particularly in items with low switching costs, such as pet hygiene. These results underscore the importance of recognizing cross-channel effects in consumer behavior and contribute to the literature on pricing and multichannel retailing by identifying online adoption as a key driver of offline price sensitivity.

This paper studies the partial identification of treatment effects in Instrumental Variables (IV) settings with binary outcomes under violations of independence. I derive the identified sets for the treatment parameters of interest in the setting, as well as breakdown values for conclusions regarding the true treatment effects. I derive $\sqrt{N}$-consistent nonparametric estimators for the bounds of treatment effects and for breakdown values. These results can be used to assess the robustness of empirical conclusions obtained under the assumption that the instrument is independent from potential quantities, which is a pervasive concern in studies that use IV methods with observational data. In the empirical application, I show that the conclusions regarding the effects of family size on female unemployment using same-sex siblings as the instrument are highly sensitive to violations of independence.

We build on theoretical results from the mechanism design literature to analyze empirical models of second-degree price discrimination (2PD). We show that for a random-coefficients discrete choice ("BLP") model to be suitable for studying 2PD, it must capture the covariance between two key random effects: (i) the "baseline" willingness to pay (affecting all product versions), and (ii) the perceived differentiation between versions. We then develop an experimental design that, among other features, identifies this covariance under common data constraints in 2PD environments. We implement this experiment in the field in collaboration with an international airline. Estimating the theoretically motivated empirical model on the experimental data, we demonstrate its applicability to 2PD decisions. We also show that test statistics from our design can enable qualitative inference on optimal 2PD policy even before estimating a demand model. Our methodology applies broadly across second-degree price discrimination settings.

Ensuring efficiency and envy-freeness in allocating indivisible goods without money often requires randomization. However, existing combinatorial assignment mechanisms (for applications such as course allocation, food banks, and refugee resettlement) guarantee these properties either ex ante or ex post, but not both. We propose a new class of mechanisms based on Competitive Equilibrium from Random Incomes (CERI): Agents receive random token budgets and select optimal lotteries at competitive prices that clear markets in expectation. Our main insight is to let the CERI price vector guide all ex-post allocations. We show that all ordinally efficient allocations are CERI allocations, which can be implemented as lotteries over near-feasible Pareto-efficient outcomes. With identical budget distributions, CERI allocations are ordinally envy-free; with budget distributions on small supports, ex-post allocations are envy-free up to one good. Moreover, we design an asymptotically efficient implementation of CERI that satisfies a strong new non-manipulability property in large markets.

With the growth of artificial skills, organizations are increasingly confronting the problem of optimizing skill policy decisions guided by economic principles. This paper addresses the underlying complexity of this challenge by developing an in-silico framework based on Monte Carlo simulations grounded in empirical realism to analyze the economic impact of human and machine skills, individually or jointly deployed, in the execution of tasks presenting varying levels of complexity. Our results provide quantitative support for the established notions that automation tends to be the most economically-effective strategy for tasks characterized by low-to-medium generalization difficulty, while automation may struggle to match the economic utility of human skills in more complex scenarios. Critically, our simulations highlight that, when a high level of generalization is required and the cost of errors is high, combining human and machine skills can be the most effective strategy, but only if genuine augmentation is achieved. In contrast, when failing to realize this synergy, the human-machine policy is severely penalized by the inherent costs of its dual skill structure, causing it to destroy value and become the worst choice from an economic perspective. The takeaway for decision-makers is unambiguous: in complex and critical contexts, simply allocating human and machine skills to a task may be insufficient, and a human-machine skill policy is neither a silver-bullet solution nor a low-risk compromise. Rather, it is a critical opportunity to boost competitiveness that demands a strong organizational commitment to enabling augmentation. Also, our findings show that improving the cost-effectiveness of machine skills over time, while useful, does not replace the fundamental need to focus on achieving augmentation.

We introduce the first version of the AI Productivity Index (APEX), a benchmark for assessing whether frontier AI models can perform knowledge work with high economic value. APEX addresses one of the largest inefficiencies in AI research: outside of coding, benchmarks often fail to test economically relevant capabilities. APEX-v1.0 contains 200 test cases and covers four domains: investment banking, management consulting, law, and primary medical care. It was built in three steps. First, we sourced experts with top-tier experience e.g., investment bankers from Goldman Sachs. Second, experts created prompts that reflect high-value tasks in their day-to-day work. Third, experts created rubrics for evaluating model responses. We evaluate 23 frontier models on APEX-v1.0 using an LM judge. GPT 5 (Thinking = High) achieves the highest mean score (64.2%), followed by Grok 4 (61.3%) and Gemini 2.5 Flash (Thinking = On) (60.4%). Qwen 3 235B is the best performing open-source model and seventh best overall. There is a large gap between the performance of even the best models and human experts, highlighting the need for better measurement of models' ability to produce economically valuable work.

Lying at the interface between Network Science and Machine Learning, node embedding algorithms take a graph as input and encode its structure onto output vectors that represent nodes in an abstract geometric space, enabling various vector-based downstream tasks such as network modelling, data compression, link prediction, and community detection. Two apparently unrelated limitations affect these algorithms. On one hand, it is not clear what the basic operation defining vector spaces, i.e. the vector sum, corresponds to in terms of the original nodes in the network. On the other hand, while the same input network can be represented at multiple levels of resolution by coarse-graining the constituent nodes into arbitrary block-nodes, the relationship between node embeddings obtained at different hierarchical levels is not understood. Here, building on recent results in network renormalization theory, we address these two limitations at once and define a multiscale node embedding method that, upon arbitrary coarse-grainings, ensures statistical consistency of the embedding vector of a block-node with the sum of the embedding vectors of its constituent nodes. We illustrate the power of this approach on two economic networks that can be naturally represented at multiple resolution levels: namely, the international trade between (sets of) countries and the input-output flows among (sets of) industries in the Netherlands. We confirm the statistical consistency between networks retrieved from coarse-grained node vectors and networks retrieved from sums of fine-grained node vectors, a result that cannot be achieved by alternative methods. Several key network properties, including a large number of triangles, are successfully replicated already from embeddings of very low dimensionality, allowing for the generation of faithful replicas of the original networks at arbitrary resolution levels.

Policymakers often use recursive binary split rules to partition populations based on binary outcomes and target subpopulations whose probability of the binary event exceeds a threshold. We call such problems Latent Probability Classification (LPC). Practitioners typically employ Classification and Regression Trees (CART) for LPC. We prove that in the context of LPC, classic CART and the knowledge distillation method, whose student model is a CART (referred to as KD-CART), are suboptimal. We propose Maximizing Distance Final Split (MDFS), which generates split rules that strictly dominate CART/KD-CART under the unique intersect assumption. MDFS identifies the unique best split rule, is consistent, and targets more vulnerable subpopulations than CART/KD-CART. To relax the unique intersect assumption, we additionally propose Penalized Final Split (PFS) and weighted Empirical risk Final Split (wEFS). Through extensive simulation studies, we demonstrate that the proposed methods predominantly outperform CART/KD-CART. When applied to real-world datasets, MDFS generates policies that target more vulnerable subpopulations than the CART/KD-CART.