ArXiv Paperboy (Econ.GN + Econ.TH)
LightNews LightNews
arxivecongnbot.bsky.social
ArXiv Paperboy (Econ.GN + Econ.TH)
@arxivecongnbot.bsky.social
110 followers 1 following 720 posts
posts updates from arXiv rss feeds for methodology papers in Economic Theory and General Economics.

maintainer: @paulgp.com

source code: https://github.com/paulgp/bsky_paperbot [forked from Apoorva Lal]
Posts Replies Media Videos
arxivecongnbot.bsky.social
arXiv📈🤖
Designing a Token Economy: Incentives, Governance, and Tokenomics
By Kivilo, Norta, Hattingh et al
In recent years, tokenomic systems, decentralized systems that use cryptographic tokens to represent value and rights, have evolved considerably. Growing complexity in incentive structures has expanded the applicability of blockchain beyond purely transactional use. Existing research predominantly examines token economies within specific use cases, proposes conceptual frameworks, or studies isolated aspects such as governance, incentive design, and tokenomics. However, the literature offers limited empirically grounded, end-to-end guidance that integrates these dimensions into a coherent, step-by-step design approach informed by concrete token-economy development efforts. To address this gap, this paper presents the Token Economy Design Method (TEDM), a design-science artifact that synthesizes stepwise design propositions for token-economy design across incentives, governance, and tokenomics. TEDM is derived through an iterative qualitative synthesis of prior contributions and refined through a co-designed case. The artifact is formatively evaluated via the Currynomics case study and additional expert interviews. Currynomics is an ecosystem that maintains the Redcurry stablecoin, using real estate as the underlying asset. TEDM is positioned as reusable design guidance that facilitates the analysis of foundational requirements of tokenized ecosystems. The specificity of the proposed approach lies in the focus on the socio-technical context of the system and early stages of its design.
February 11, 2026 at 4:44 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Incentive Pareto Efficiency in Monopoly Insurance Markets with Adverse Selection
By Andraos, Ghossoub
We study a monopolistic insurance market with hidden information, where the agent's type $\theta$ is private information that is unobservable to the insurer, and it is drawn from a continuum of types. The hidden type affects both the loss distribution and the risk attitude of the agent. Within this framework, we show that a menu of contracts is incentive efficient if and only if it maximizes social welfare, subject to incentive compatibility and individual rationality constraints. This equivalence holds for general concave utility functionals. In the special case of Yaari Dual Utility, we provide a semi-explicit characterization of optimal incentive-efficient menus of contracts. We do this under two different settings: (i) the first assumes that types are ordered in a way such that larger values of $\theta$ correspond to more risk-averse types who face stochastically larger losses; whereas (ii) the second assumes that larger values of $\theta$ correspond to less risk-averse types who face stochastically larger losses. In both settings, the structure of optimal incentive-efficient menus of contracts depends on the level of the social welfare weight. Moreover, at the optimum, higher types receive greater coverage in exchange for higher premia. Additionally, optimal menus leave the lowest type indifferent, with the insurer absorbing all surplus from the lowest type; and they exhibit efficiency at the top, that is, the highest type receives full coverage.
February 11, 2026 at 4:42 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Competitive Credit and Present Bias: A Stochastic Discounting Approach
By Chatterjee, Garrett
A prominent theme in behavioural contract theory is the study of present-biased agents represented through quasi-hyperbolic discounting. In a model of competitive credit provision, we study an alternative to this framework in which the agent has a private stochastic discount factor and may overestimate the likelihood of more patient values. Agent preferences, however, are timeconsistent. While a limiting case of our model corresponds to a "fully naive" agent in work on quasi-hyperbolic discounting, another case is where the agent has correct beliefs about future discounting. In equilibrium, the agent selects options with earlier consumption in case of less patient discount factor realisations, but is penalised by receiving worse terms. Our model thus accounts for an important feature of equilibrium contracts identified in Heidhues and K\H{o}szegi (2010). Unlike Heidhues and K\H{o}szegi, our framework often predicts excessively backloaded consumption, including when the agent holds correct beliefs about future discounting.
February 11, 2026 at 4:39 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Robust Trust
By Dworczak, Smolin
An agent chooses an action using her private information combined with recommendations from an informed but potentially misaligned adviser. With a known alignment probability, the adviser reports his signal truthfully; with remaining probability, the adviser can send an arbitrary message. We characterize the decision rule that maximizes the agent's worst-case expected payoff. Every optimal rule admits a trust region representation in belief space: advice is taken at face value when it induces a posterior within the trust region; otherwise, the agent acts as if the posterior were on the trust region's boundary. We derive thresholds on the alignment probability above which the adviser's presence strictly benefits the agent and fully characterize the solution in binary-state as well as binary-action environments.
February 11, 2026 at 4:35 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Selective Disclosure in Overlapping Generations
By Antic, Pei
We develop an overlapping generations model where each agent observes a verifiable private signal about the state and, with positive probability, also receives signals disclosed by his predecessor. The agent then takes an action and decides which signals to pass on. Each agent's action has a positive externality on his predecessor and his optimal action increases in his belief about the state. We show that as the communication friction vanishes, agents become increasingly selective in disclosing information. As the probability that messages reach the next generation approaches one, all signals except those with the highest likelihood ratio will be concealed in equilibrium.
February 11, 2026 at 4:33 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Behavioral Economics of AI: LLM Biases and Corrections
By Bini, Cong, Huang et al
Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigated? Drawing on the cognitive psychology and experimental economics literatures, we conduct the most comprehensive set of experiments to date$-$originally designed to document human biases$-$on prominent LLM families across model versions and scales. We document systematic patterns in LLM behavior. In preference-based tasks, responses become more human-like as models become more advanced or larger, while in belief-based tasks, advanced large-scale models frequently generate rational responses. Prompting LLMs to make rational decisions reduces biases.
February 11, 2026 at 4:32 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Sign-Dependent Spillovers of Global Monetary Policy
By Camara
This paper examines the sign-dependent international spillovers of Federal Reserve and European Central Bank monetary policy shocks. Using a consistent high-frequency identification of pure monetary policy shocks across 44 advanced and non-advanced economies and the methodology of Caravello and Martinez-Bruera, 2024, we document strong asymmetries in international transmission. Linear specifications mask these effects: contractionary shocks generate large and significant deteriorations in financial conditions, economic activity, and international trade abroad, while expansionary shocks yield little to no measurable improvement. Our results are robust across samples, identification strategies, and the framework proposed by Ben Zeev et al., 2023.
February 11, 2026 at 4:30 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Affirmative Action in India with Horizontal Reservations
By Ayg\"un, Turhan
India implements the world's most complex affirmative action program through vertical and horizontal reservations. Although applicants can belong to at most one vertical category, they can qualify for multiple horizontal reservation categories simultaneously. We examine resource allocation problems in India, where horizontal reservations follow a hierarchical structure within a one-to-all horizontal matching framework. We introduce the hierarchical choice rule and show that it selects the most meritorious set of applicants. We thoroughly analyze the properties of the aggregate choice rule, which comprises hierarchical choice rules across all vertical categories. We show that the generalized deferred acceptance mechanism, when coupled with this aggregate choice rule, is the unique stable and strategy-proof mechanism that eliminates justified envy.
February 11, 2026 at 4:26 PM
arxivecongnbot.bsky.social
arXiv📈🤖
Is there "Secret Sauce'' in Large Language Model Development?
By Mertens, Fischl-Lanzoni, Thompson
Do leading LLM developers possess a proprietary ``secret sauce'', or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, we estimate scaling-law regressions with release-date and developer fixed effects. We find clear evidence of developer-specific efficiency advantages, but their importance depends on where models lie in the performance distribution. At the frontier, 80-90% of performance differences are explained by higher training compute, implying that scale--not proprietary technology--drives frontier advances. Away from the frontier, however, proprietary techniques and shared algorithmic progress substantially reduce the compute required to reach fixed capability thresholds. Some companies can systematically produce smaller models more efficiently. Strikingly, we also find substantial variation of model efficiency within companies; a firm can train two models with more than 40x compute efficiency difference. We also discuss the implications for AI leadership and capability diffusion.
February 11, 2026 at 4:43 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Analyzing Vaccine Manufacturing Supply Chain Disruptions for Pandemic Preparedness using Discrete-Event Simulation
By Kelchtermans, Stienen, Dietrich et al
The COVID-19 pandemic exposed critical vulnerabilities in vaccine supply chains, highlighting the need for robust manufacturing for rapid pandemic response to support CEPI's 100 Days Mission. We develop a discrete-event simulation model to analyze supply chain disruptions and enables policymakers and vaccine manufacturers to quantify disruptions and assess mitigation strategies. Unlike prior studies examining components in isolation, our approach integrates production processes, quality assurance and control (QA/QC) activities, and raw material procurement to capture system-wide dynamics. A detailed mRNA case study analyzes disruption scenarios for a facility targeting 50 million doses: facility shutdowns, workforce reductions, raw material shortages, infrastructure failures, extended procurement lead times, and increased QA/QC capacity. Three main insights emerge. First, QA/QC personnel are the primary bottleneck, with utilization reaching 84.5% under normal conditions while machine utilization remains below 33%. Doubling QA/QC capacity increases annual output by 79.1%, offering greater returns than equipment investments. Second, raw material disruptions are highly detrimental, with extended lead times reducing three-year output by 19.6% and causing stockouts during 51.8% of production time. Third, the model shows differential resilience: acute disruptions (workforce shortages, shutdowns, power outages) allow recovery within 6 to 9 weeks, whereas chronic disruptions (supply delays) cause prolonged performance degradation.
February 11, 2026 at 4:40 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Platform Design, Earnings Transparency and Minimum Wage Policies: Evidence from A Natural Experiment on Lyft
By Li, Liu, Sundararajan
We study the effects of a significant design and policy change at a major ridesharing platform that altered both provider earnings and platform transparency, examining how it affected outcomes for drivers, riders, and the platform, and providing managerial insights on balancing competing stakeholder interests while avoiding unintended consequences. In February 2024, Lyft introduced a policy guaranteeing drivers a minimum fraction of rider payments while increasing per-ride earnings transparency. The staggered rollout, first in major markets, created a natural experiment to examine how earnings guarantees and transparency affect ride availability and driver engagement. Using trip-level data from over 47 million rides across a major market and adjacent markets over six months, we apply dynamic staggered difference-in-differences models combined with a geographic border strategy to estimate causal effects on supply, demand, ride production, and platform performance. We find that the policy led to substantial increases in driver engagement, with distinct effects from the guarantee and transparency. Drivers increased working hours and utilization, resulting in more completed trips and higher per-hour and per-trip earnings, with stronger effects among drivers with lower pre-policy earnings and greater income uncertainty. Increased supply also generated positive spillovers on demand. We also find evidence that greater transparency may induce strategic driver behavior. In ongoing work, we develop a counterfactual simulation framework linking driver supply and rider intents to ride production, illustrating how small changes in driver choices could further amplify policy effects. Our study shows how platform-led interventions present an intriguing alternative to government-led minimum pay regulation and provide new strategic insights into managing platform change.
February 11, 2026 at 4:35 AM
arxivecongnbot.bsky.social
arXiv📈🤖
On the Inefficiency of Social Learning
By Brandl, Huang, Jain
We study whether a social planner can improve the efficiency of learning, measured by the expected total welfare loss, in a sequential decision-making environment. Agents arrive in order and each makes a binary action based on their private signal and the social information they observe. The planner can intervene by jointly designing the social information disclosed to agents and offering monetary transfers contingent on agents' actions. We show that, despite such flexibility, efficient learning cannot be restored with a finite budget: whenever learning is inefficient without intervention, no combination of information disclosure and transfers can achieve efficient learning while keeping total expected transfers finite.
February 11, 2026 at 4:34 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Effectiveness of Rent Controls: Evidence from Spain
By Garcia
Growing concerns about housing affordability have prompted the adoption of rent control policies and renewed debates over their effectiveness. This paper provides the first empirical evaluation of the 2024 rent control policy implemented in Catalonia under Spain's new national housing law. To identify the causal effect of the policy on the rental market, I use municipality-level administrative data and implement several difference-in-differences strategies and event study designs. The results point to a reduction in tenancy agreements and a less robust decrease in rental price growth. While the findings highlight important short-term consequences of rent control, they also underscore the need for caution due to data limitations and limited robustness in some estimates.
February 11, 2026 at 4:31 AM
arxivecongnbot.bsky.social
arXiv📈🤖
On- and off-chain demand and supply drivers of Bitcoin price
By Ciaian, Kancs, Rajcaniova
Around three quarters of Bitcoin transactions take place off-chain. Despite their significance, the vast majority of the empirical literature on cryptocurrencies focuses on on-chain transactions. This paper presents one of the first analysis of both on- and off-chain demand- and supply-side factors. Two hypotheses relating on-chain and off-chain demand and supply drivers to the Bitcoin price are tested in an ARDL model with daily data from 2019 to 2024. Our estimates document the differential contributions of on-chain and off-chain drivers on the Bitcoin price. Off-chain demand pressures have a significant impact on the Bitcoin price in the long-run. In the short-run, both demand and supply drivers significantly affect the Bitcoin price. Regarding transactions on the blockchain, only on-chain demand pressures are statistically significant - both in the long- and short-run. These findings confirm the dual nature of the Bitcoin price dynamics, where also market fundamentals affect the Bitcoin price in addition to speculative drivers. Bitcoin whale trading has less significant impact on price in the long-run, while is more pronounced contemporaneously and one-period lag.
February 11, 2026 at 4:29 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Competitive Sequential Screening
By Ball, Kattwinkel, Knoepfle
Two horizontally differentiated firms compete for consumers who are partially informed about their future preferences. The firms screen consumers by offering menus of option contracts. Each consumer enters contracts with both firms. Subsequently, each consumer learns his preferences and purchases only one product. We find the unique equilibrium. Relative to spot pricing, consumption is distorted because each consumer is endogenously locked into one firm. If contracting is sufficiently early, so that consumers are less informed and hence less differentiated, consumers benefit; this reverses the conclusion in the monopoly case. Exclusive contracting further benefits consumers by intensifying competition.
February 11, 2026 at 4:25 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Double Disadvantage: How Gender and Residential Location Shape Hiring Outcomes in Pakistan's IT Sector
By Khalil
This paper examines how gender and residential socioeconomic status shape hiring outcomes in the information technology sector using a field experiment from the city of Karachi, Pakistan. Employers in Pakistan can openly state preferences regarding gender, residential location, and other characteristics, but the majority in the information technology sector choose not to do so. This creates an opportunity to examine whether discrimination persists when such biases are not explicitly stated. An analysis of explicitly gender-targeted job ads shows that men are preferred over women across most occupations, even in traditionally pink-collar roles. Moreover, results from a resume audit experiment, submitting 2,032 applications to 508 full-time job openings, show that men receive more callbacks for job interviews than women, even in the absence of explicit gender preferences in job ads. The study also indicates a significant premium favoring candidates from high-income areas, who receive 45 percent more callbacks than applicants from low-income neighborhoods. This advantage remains robust even after controlling for commuting distance. Qualitative interviews with human resource officials suggest that employers associate productivity with both gender and neighborhood socioeconomic status. Residential address acts as a proxy for class background and signals education, skills, and perceived "fit" in professional settings. These perceptions may reinforce stereotypes, disadvantaging women and candidates from low-income backgrounds.
February 11, 2026 at 4:23 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Continuation-Performance Decomposition in Dynamic Games with Irreversible Failure
By Kirk
Once failure is irreversible, continuation payoffs cannot be meaningfully aggregated across strategies that differ in their survival properties. Standard scalar evaluation sidesteps this by arbitrarily completing payoffs beyond termination, but such completions are extrinsic to the game form. This paper introduces continuation-performance decomposition (CPD), proving that any evaluation satisfying natural regularity conditions, such as failure-completion invariance, survival locality, and local expected-utility coherence -- must separate continuation from performance lexicographically. Continuation priority thus emerges as a consequence of well-posed evaluation, not as a behavioral assumption. We establish equivalence between CPD and the limit of games with diverging failure penalties, show that viability is a game-form invariant independent of payoffs, and apply the framework to bank runs: preemptive withdrawals reflect rational viability vetoes rather than coordination failure when continuation is distributively asymmetric. CPD resolves a representational problem, not a preference problem.
February 11, 2026 at 4:19 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Distributional Preferences for Market Design
By Echenique, Mekonnen, Yenmez
We develop a general framework for incorporating distributional preferences in market design. We identify the structural properties of these preferences that guarantee the path independence of choice rules. In decentralized settings, a greedy rule uniquely maximizes these preferences; in centralized markets, the associated deferred-acceptance mechanism uniquely implements them. This framework subsumes canonical models, such as reserves and matroids, while accommodating complex objectives involving intersectional identities that lie beyond the scope of existing approaches. Our analysis provides unified axiomatic foundations and comparative statics for a broad class of distributional policies.
February 11, 2026 at 4:15 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Droughts and Deluges: Effects of Climate Extremes on the Gender Gap in Labor Supply
By Sarkar
Over the past three decades, extreme climate events have caused losses of worth USD 4.5 trillion. Using a panel of 151 countries (1995-2019), I examine how extreme climate conditions shape gender gap in labor force participation. Key results show that the gender gap in paid labor exhibits a U-shaped relationship with droughts and an inverted U-shaped relationship with extreme wet conditions. The drought pattern is primarily driven by gender gap in employment while wetness affects gender gap in participation through unemployment. These relationships vary with country characteristics. Countries with high disaster-displacement risk exhibit declining gender gaps in participation during excess wetness while moderate-risk economies experience expanded gaps during droughts. Furthermore, the drought U-shape is most pronounced in countries with low to moderate empowerment while the nonlinear wet responses is concentrated only in moderately empowered countries. Lastly, both droughts and excess wetness expands gender gap in countries with weak net resilience to climate shocks.
February 11, 2026 at 4:13 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Model Restrictiveness in Functional and Structural Settings
By Fudenberg, Gao, You
We generalize the notion of model restrictiveness in Fudenberg, Gao and Liang (2026) to a wider range of economic models with semi/non-parametric and structural ingredients. We show how restrictiveness can be defined and computed in infinite-dimensional settings using Gaussian process priors (including with shape restrictions) and other alternativess in Bayesian nonparametrics. We also extend the restrictiveness framework to structural models with endogeneity, instrumental variables, multiple equilibria, and nonparametric nuisance components. We discuss the importance of the user-specific choice of discrepancy functions in the context of Rademacher complexity and GMM criterion function, and relate restrictiveness to the limit of the average-case learning curve in machine learning. We consider applications to: (1) preferences under risk, (2) exogenous multinomial choice, and (3) multinomial choice with endogenous prices: for (1), we obtain results consistent with those in Fudenberg, Gao and Liang (2026); for (2) and (3), our findings show that nested logit and mixed logit exhibit similar restrictiveness under standard parametric specifications, and that IV exogeneity conditions substantially increase overall restrictiveness while altering model rankings.
February 11, 2026 at 4:08 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Partially Identified Ambiguity
By Lim
This paper develops a theory of learning under ambiguity induced by the decision maker's beliefs about the collection of data correlated with the true state of the world. Within our framework, two classical results on Bayesian learning extend to the setting with ambiguity: experiments are equivalent to distributions over posterior beliefs, and Blackwell's more informative and more valuable orders coincide. When applied to the setting of robust Bayesian analysis, our results clarify the source of time inconsistency in the Gamma-minimax problem and provide an argument in favor of the conditional Gamma-minimax criterion. We also apply our results to a persuasion game to illustrate that our model provides a natural benchmark for communication under ambiguity.
February 11, 2026 at 4:05 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Prophet Inequalities via Linear Programming
By Bayrak, P{\i}nar, Vohra
Prophet inequalities bound the expected reward that can be obtained in a stopping problem by the optimal reward of its corresponding off-line version. We propose a systematic technique for deriving prophet inequalities for stopping problems associated with selecting a point in a polyhedron. It utilizes a reduced-form linear programming representation of the stopping problem. We illustrate the technique to derive a number of known results as well as some new ones. For instance, we prove a $\frac{1}{2}$-prophet inequality when the underlying polyhedron is an on-line polymatroid; one whose underlying submodular function depends upon the realized rewards. We also demonstrate a composition by the Minkowski sum property. If an $r-$ prophet inequality holds for polyhedra $P^1$ and $P^2$, it also holds for their Minkowski sum.
February 11, 2026 at 4:01 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Bank Failures: The Roles of Solvency and Liquidity
By Correia, Luck, Verner
Bank failures can stem from runs on otherwise solvent banks or from losses that render banks insolvent, regardless of withdrawals. Disentangling the relative importance of liquidity and solvency in explaining bank failures is central to understanding financial crises and designing effective financial stability policies. This paper reviews evidence on the causes of bank failures. Bank failures -- both with and without runs -- are almost always related to poor fundamentals. Low recovery rates in failure suggest that most failed banks that experienced runs were likely fundamentally insolvent. Examiners' postmortem assessments also emphasize the primacy of poor asset quality and solvency problems. Before deposit insurance, runs commonly triggered the failure of insolvent banks. However, runs rarely caused the failure of strong banks, as such runs were typically resolved through other mechanisms, including interbank cooperation, equity injections, public signals of strength, or suspension of convertibility. We discuss the policy implications of these findings and outline directions for future research.
February 11, 2026 at 3:59 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Is there "Secret Sauce'' in Large Language Model Development?
By Mertens, Fischl-Lanzoni, Thompson
Do leading LLM developers possess a proprietary ``secret sauce'', or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, we estimate scaling-law regressions with release-date and developer fixed effects. We find clear evidence of developer-specific efficiency advantages, but their importance depends on where models lie in the performance distribution. At the frontier, 80-90% of performance differences are explained by higher training compute, implying that scale--not proprietary technology--drives frontier advances. Away from the frontier, however, proprietary techniques and shared algorithmic progress substantially reduce the compute required to reach fixed capability thresholds. Some companies can systematically produce smaller models more efficiently. Strikingly, we also find substantial variation of model efficiency within companies; a firm can train two models with more than 40x compute efficiency difference. We also discuss the implications for AI leadership and capability diffusion.
February 11, 2026 at 2:15 AM
arxivecongnbot.bsky.social
arXiv📈🤖
Analyzing Vaccine Manufacturing Supply Chain Disruptions for Pandemic Preparedness using Discrete-Event Simulation
By Kelchtermans, Stienen, Dietrich et al
The COVID-19 pandemic exposed critical vulnerabilities in vaccine supply chains, highlighting the need for robust manufacturing for rapid pandemic response to support CEPI's 100 Days Mission. We develop a discrete-event simulation model to analyze supply chain disruptions and enables policymakers and vaccine manufacturers to quantify disruptions and assess mitigation strategies. Unlike prior studies examining components in isolation, our approach integrates production processes, quality assurance and control (QA/QC) activities, and raw material procurement to capture system-wide dynamics. A detailed mRNA case study analyzes disruption scenarios for a facility targeting 50 million doses: facility shutdowns, workforce reductions, raw material shortages, infrastructure failures, extended procurement lead times, and increased QA/QC capacity. Three main insights emerge. First, QA/QC personnel are the primary bottleneck, with utilization reaching 84.5% under normal conditions while machine utilization remains below 33%. Doubling QA/QC capacity increases annual output by 79.1%, offering greater returns than equipment investments. Second, raw material disruptions are highly detrimental, with extended lead times reducing three-year output by 19.6% and causing stockouts during 51.8% of production time. Third, the model shows differential resilience: acute disruptions (workforce shortages, shutdowns, power outages) allow recovery within 6 to 9 weeks, whereas chronic disruptions (supply delays) cause prolonged performance degradation.
February 11, 2026 at 2:12 AM