400 papers
Nonlinear dynamics of educational choices under social influence and endogenous returns
Decisions to pursue higher education are not fully explained by economic incentives, with social influence and peer effects playing a crucial, yet dynamically understudied, role. This paper develops a theoretical non-linear dynamics model analysing the interplay between economic returns and social pressure. We model a heterogeneous population of "Followers" who exhibit imitative behaviour, and "Positional Agents" who display counter-adaptive behaviour. Agents' preferences for education evolve endogenously, reacting to both aggregate enrolment and an endogenous wage premium that declines with the supply of educated workers. The aggregate dynamics are governed by a one-dimensional non-linear map. By assuming fixed population structure. we show that the social conflict between pro-cyclical imitative forces and counter-cyclical positional forces can destabilize the steady state, generating a period-doubling route to chaos. These complex, endogenous fluctuations in enrolment emerge only for intermediate, heterogeneous population mixes, while homogeneous populations remain stable. We argue that this instability represents a significant coordination failure, scrambling economic signals and hindering rational long-term planning for both students and institutions, making it a key policy concern. Finally, we also extend the result to the case where the population structure is endogenous.
2604.03102Apr 2026
View
Financial Intermediaries and Capital Centralization in Global FDI: A Network Approach to Tracing Transnational Corporate Control
Understanding how corporate control concentrates in modern ownership systems is crucial in an economy increasingly shaped by cross-border mergers and acquisitions. Rather than expanding productive capacity, these operations reorganize ownership and control over existing firms through complex transnational structures involving financial intermediaries, holding companies, and investment vehicles. As a result, corporate control may become highly concentrated even when formal ownership appears fragmented. This paper examines how foreign direct investments-related capital centralization reshapes firm-level governance by tracing how control converges on individual companies through multi-layered ownership networks. Focusing on two strategically relevant Italian firms, we show that control is rarely exercised solely by ultimate owners, but instead arises from the interaction of a small set of financially interconnected intermediaries operating along transnational ownership chains. The results show how small equity stakes translate into substantial governance power, highlighting the role of financial intermediation and raising implications for strategic autonomy and economic sovereignty in key sectors.
2604.02875Apr 2026
View
Hiring Discrimination and the Task Content of Jobs: Evidence from a Large-Scale Résumé Audit
We conducted a large-scale resume audit of 36,880 applications to 9,220 job advertisements for new college graduates across the United States. Firms express task preferences through job-advertisement text, which we link to occupation-level task measures from O*NET and the American Community Survey. We develop a model in which discrimination increases with evaluative discretion, defined as the share of hiring decisions driven by subjective rather than verifiable assessment. Callback gaps vary systematically with the task content of jobs. In management occupations, callbacks are 28 to 43 percent lower for Black men, Black women, White women, and Hispanic men than for otherwise identical White men. Broad occupation categories conceal important variation in task demands. When jobs are grouped by task intensity, discrimination concentrates in positions combining high analytical and interpersonal demands with low routine content. Decomposing task content into subjective-evaluation and objective-precision components, we find that subjective evaluation widens callback gaps while objective precision compresses them. Customer contact amplifies this divergence, widening gaps in non-routine jobs but not in routine jobs. Randomly assigned resume credentials that increase callbacks on average reduce gaps in low-discretion jobs but not in high-discretion jobs. Early-career exclusion from high-return task bundles may entrench long-run demographic gaps in employment outcomes.
2604.01933Apr 2026
View
Quantifying Inter-Annual Seasonal Drift in Tomato Prices Using Dynamic Time Warping: Evidence from Kolar Market
Tomato prices in Kolar market exhibit high volatility alongside recurring seasonal patterns, but the consistency of these patterns across years remains unclear. This study analysed weekly tomato prices and arrivals from 2010-2024 to quantify inter-annual variability using descriptive statistics, seasonal indices, and Dynamic Time Warping (DTW). Descriptive analysis confirmed extreme fluctuations (CV = 77% for prices, 102% for arrivals) with positive skewness and heavy tails, indicating frequent extreme events. Seasonal indices revealed recurring intra-year cycles, but year-to-year alignment varied substantially. DTW analysis for 2021-2024 quantified pattern similarity, showing that 2022-2023 had the highest alignment (DTW distance: 23,258) despite extreme price spikes, whereas 2021-2022 exhibited the weakest alignment (distance: 39,049), reflecting structural shifts in market dynamics. Path length metrics indicated minimal temporal warping in 2022-2023 (71 points) versus extensive alignment in 2021-2022 (83 points). These results demonstrate that while seasonal patterns recur, their temporal consistency is not fixed, highlighting the need for forecasting models that adapt to both magnitude volatility and temporal shifts. The study also illustrates the utility of DTW for agricultural price analysis and the limitations of relying solely on fixed seasonal patterns in volatile commodity markets.
2604.01792Apr 2026
View
Persistent geographical biases in global scientific collaboration and citations
Scientific knowledge flows enable cumulative progress by connecting researchers across disciplines, institutions, and countries. Yet it remains unclear how geography and national structures continue to shape these exchanges in an increasingly connected world. Using a large-scale bibliometric dataset from OpenAlex, which covers 39.35 million publications across 95 countries and 3,794 cities between 2000 and 2022, we examine global knowledge diffusion through two complementary channels: co-authorship and citation. We find that the constraining effect of geographic distance on collaboration has not diminished over time but has instead intensified, suggesting persistent structural or institutional barriers. Citation flows, by contrast, are less sensitive to spatial proximity, indicating that intellectual influence may diffuse more freely across borders. At the country level, research networks exhibit strong domestic preferences and a shared citation orientation toward the United States. China, while increasingly favored as a collaboration partner by other countries, continues to be systematically undercited within global citation flows. International mobility increases researchers' collaboration with scholars in their host country but has limited effects on citation flows. These results highlight the structural persistence of spatial and country biases in global science, with implications for equitable participation and recognition across regions.
2604.01602Apr 2026
View
Pay-Per-Crawl Pricing for AI: The LM-Tree Agent
As AI systems shift from directing users to content toward consuming it directly, publishers need a new revenue model: charging AI crawlers for content access. This model, called pay-per-crawl, must solve a problem of mechanism selection at scale: content is too heterogeneous for a fixed pricing framework. Different sub-types warrant not only different price levels but different pricing rules based on different unstructured features, and there are too many to enumerate or design by hand. We propose the LM Tree, an adaptive pricing agent that grows a segmentation tree over the content library, using LLMs to discover what distinguishes high-value from low-value items and apply those attributes at scale, from binary purchase feedback alone. We evaluate the LM Tree on real content from a major German technology publisher, using 8,939 articles and 80,451 buyer queries with willingness-to-pay calibrated from actual AI crawler traffic. The LM Tree achieves a 65% revenue gain over a single static price and a 47% gain over two-category pricing, outperforming even the publisher's own 8-segment editorial taxonomy by 40% -- recovering content distinctions the publisher's own categories miss.
2604.01416Apr 2026
View
From Automation to Augmentation: A Framework for Designing Human-Centric Work Environments in Society 5.0
Society 5.0 and Industry 5.0 call for human-centric technology integration, yet the concept lacks an operational definition that can be measured, optimized, or evaluated at the firm level. This paper addresses three gaps. First, existing models of human-AI complementarity treat the augmentation function phi(D) as exogenous -- dependent only on the stock of AI deployed -- ignoring that two firms with identical technology investments achieve radically different augmentation outcomes depending on how the workplace is organized around the human-AI interaction. Second, no multi-dimensional instrument exists linking workplace design choices to augmentation productivity. Third, the Society 5.0 literature proposes human-centricity as a normative aspiration but provides no formal criterion for when it is economically optimal. We make four contributions. (1) We endogenize the augmentation function as phi(D, W), where W is a five-dimensional workplace design vector -- AI interface design, decision authority allocation, task orchestration, learning loop architecture, and psychosocial work environment -- and prove that human-centric design is profit-maximizing when the workforce's augmentable cognitive capital exceeds a critical threshold. (2) We conduct a PRISMA-guided systematic review of 120 papers (screened from 6,096 records) to map the evidence base for each dimension. (3) We provide secondary empirical evidence from Colombia's EDIT manufacturing survey (N=6,799 firms) showing that management practice quality amplifies the return to technology investment (interaction coefficient 0.304, p<0.01). (4) We propose the Workplace Augmentation Design Index (WADI), a 36-item theory-grounded instrument for diagnosing human-centricity at the firm level. Decision authority allocation emerges as the binding constraint for Society 5.0 transitions, and task orchestration as the most under-researched dimension
2604.01364Apr 2026
View
Distributive Politics, Representation, and Redistricting
We develop a theory of distributive competition under redistricting that explains both electoral outcomes and the equilibrium allocation of policy benefits by endogenizing voter pivotality. In a multi-district model with primaries, general elections, and group-targeted transfers, districting shapes political influence through two channels: a selection channel for descriptive representation (who wins office) and a competition channel for substantive representation (who receives policy benefits). District composition alters candidate matchups, shifting voter responsiveness and political leverage, and each channel alone yields distinct predictions about whether packing or cracking voters is optimal. For minority voters, the welfare effects of districting depend on electoral leverage, preferences over descriptive versus partisan representation, primary rules, and competitiveness. The channels align on packing when minorities are electorally weak and value descriptive representation, and align on cracking when minorities are electorally pivotal and prioritize partisan outcomes. When the channels diverge, or when endogenous feedback reshapes electoral leverage, minority welfare can be nonmonotonic in voter concentration. Our results identify when majority-minority districts enhance minority welfare and when dispersion strengthens political influence.
2604.01340Apr 2026
View
Augmented Human Capital: A Unified Theory and LLM-Based Measurement Framework for Cognitive Factor Decomposition in AI-Augmented Economies
This paper proposes a decomposition of human capital into three orthogonal components -- physical-manual (H^P), routine-cognitive (H^C), and augmentable-cognitive (H^A) -- and develops a production function in which AI capital interacts asymmetrically with these components: substituting for routine cognitive work while complementing augmentable cognitive work through an amplification function phi(D). I derive a corrected Mincerian wage equation and show that the standard specification is misspecified in AI-augmented economies. Using LLM-generated measures of occupational augmentability for 18,796 O*NET task statements mapped to 440 Colombian occupations, merged with household survey microdata (N = 105,517 workers), I estimate the augmented Mincer equation. The wage return to H^A increases with AI adoption in the formal sector (beta_2 = +0.051, p < 0.001), while informal workers cannot capture augmentation rents (beta_2 = -0.044). A triple interaction confirms formality as the binding mechanism (beta_{AHC x D x Formal} = +0.272, p < 0.001). The augmentation premium is strongest for experienced workers (ages 46-65) and in health and education sectors. These results provide the first developing-country evidence of cognitive factor decomposition in AI-augmented labor markets and demonstrate that the binding constraint on human-AI complementarity in the Global South is not technology access but labor market institutions.
2604.01066Apr 2026
View
From Pluralistic Ignorance to Common Knowledge with Social Assurance Contracts
Societies and organizations often fail to surface latent consensus because individuals fear social censure. A manager might suspect a silent majority would offer a criticism, support a change, report a risk, or endorse a policy -- if only it were safe. Likewise, individuals with beliefs they think are rare and controversial might stay quiet for fear of consequences at work or an online mob. In both cases pluralistic ignorance produces a public discourse misaligned with privately-held beliefs. Social assurance contracts unlock latent consensus, making the public discussion more accurately reflect the underlying distribution of actual beliefs. They are akin to an open letter that publishes only when a stated threshold number of private signatures is reached. If it is not reached, nothing is revealed and no one is exposed. Whereas a single hand raised in dissent might get cut off, a thousand can be raised safely together. I build a formal model and derive rules for choosing the threshold. The mechanism (i) induces participation from those willing to speak if assured of company, resolving the core coordination problem in pluralistic ignorance; (ii) makes the threshold a transparent policy lever -- sponsors can maximize success, maximize public-coalition revelation, or hit a desired success probability; and (iii) turns success into information: meeting the threshold publicly reveals hidden agreement and can widen the range of views that can be expressed in public. I consider robustness to mistrust, organized opposition, and network structure, and outline low-trust implementations like cryptographic escrow. Applications include employee voice, safety and compliance, whistleblowing, and civic expression.
2604.00874Apr 2026
View
Green Subsidies and Local Transitions: Evidence from Energy Communities
This paper studies the effectiveness and incidence of the renewable energy Production Tax Credit (PTC) and Investment Tax Credit (ITC). I leverage new geographical variation in the 2023 PTC and ITC to test whether renewable energy credits had real economic impacts. Communities with greater tax credits accumulated 32% more renewable energy capital and produced 28% more renewable energy compared to similar counties. These renewable investments had local economic spillovers, increasing county level construction wages by 7%. However, local increases in investment and wages from renewable projects did not improve political support for renewable energy, but rather increased opposition to congressional action on climate change by 2%.
2604.00582Apr 2026
View
When AI Improves Answers but Slows Knowledge Creation: Matching and Dynamic Knowledge Creation in Digital Public Goods
Generative AI helps users solve problems more efficiently, but without leaving a public trace. Fewer discussions and solutions reach public platforms, and the archives that future problem-solvers depend on can shrink. We build a dynamic model of public good provision where agents contribute by solving problems that other agents posted on a public platform, and the accumulated solutions form a depreciating public archive. AI reduces archive creation through two margins that require different instruments. The flow margin: the posted volume of knowledge-enhancing queries declines as AI resolves more problems privately before they reach the platform. The resolution margin: the probability that posted queries are resolved declines as AI raises contributors' outside options, thinning the contributor pool and creating congestion on the platform. The two margins interact through a self-undermining feedback that can generate low-archive traps. The decomposition yields a diagnostic prediction: in the congested regime, a joint decline in posted volume and conditional resolution requires that supply-side pool thinning is quantitatively present, whereas volume decline with stable or rising resolution indicates that private diversion alone is the dominant force. Encouraging public sharing of AI-assisted solutions offsets the decline associated with private diversion but cannot repair participation-driven deterioration in conditional resolution, which requires maintaining contributor engagement directly.
2604.00468Apr 2026
View
Should I State or Should I Show? Aligning AI with Human Preferences
As AI agents become more autonomous, properly aligning their objectives with human preferences becomes increasingly important. We study how effectively an AI agent learns a human principal's preference in choice under risk via stated versus revealed preferences. We conduct an online experiment in which subjects state their preferences through written instructions ("prompts") and reveal them through choices in a series of binary lottery questions ("data"). We find that on average, an AI agent given revealed-preference data predicts subjects' choices more accurately than an AI agent given stated-preference prompts. Further analysis suggests that the gap is driven by subjects' difficulty in translating their own preferences into written instructions. When given a choice between which information source to give to an AI agent, a large portion of subjects fail to select the more informative one. Moreover, when predictions from the two sources conflict, we find that the AI agent aligns more frequently with the prompt, despite its lower accuracy. Overall, these results highlight the revealed preference approach as a powerful mechanism for communicating human preferences to AI agents, but its success depends on careful implementation.
2603.29317Mar 2026
View
The Inflation of Resetting Workers
The standard wage Phillips curve aggregates away from which workers reset wages when. I show this aggregation omits a first-order term: the covariance between workers' cost-push exposure and their reset frequency. I introduce two sufficient statistics and embed them in a multi-country HANK model calibrated to six euro-area economies. The omitted term generates 7 percent more cumulative core inflation in the baseline and 10--26 percent more when monetary policy is delayed. Two economies with identical openness can differ by 6.6 percentage-point-quarters solely from within-country composition. Targeted essentials subsidies reduce welfare loss by 32 percent relative to aggressive tightening. Out of sample, the model correctly predicts the persistence ranking across the UK, the US, and Japan.
2603.29154Mar 2026
View
Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?
This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the supply side, we estimate an AI production function via scaling-law experiments linking performance to data, compute, and model size. Because AI systems exhibit predictable but diminishing returns to these inputs, the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly. Full automation is therefore often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium. On the demand side, we introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase.
2603.29121Mar 2026
View
Mental Models of Causal Structure in Economics and Psychology
A burgeoning literature in economics studies how people form beliefs about the causal structures linking economic variables, and what happens when those beliefs are mistaken. We survey this research and connect it to a rich literature in cognitive science. After providing an accessible introduction to causal Directed Acyclic Graphs, the dominant modeling approach, we review theory and evidence addressing three nested questions: how individuals reason within a fully parameterized causal structure, how they estimate its parameters, and how they learn such structures to begin with. We then discuss methodological challenges and review applications in microeconomics, macroeconomics, political economy, and business.
2603.29070Mar 2026
View
Business cycle synchronization between the EU and Western Balkan candidate economies: A Wavelet Analysis
Business cycle synchronization between EU and Western Balkan candidate economies is usually modeled with aggregate time-domain correlations that mix short-run and long-run dynamics. This paper addresses that limitation by combining wavelet-based time-frequency decomposition with Bayesian zero-inflated beta regression. Using annual dyad-year data for 2001--2021, we estimate synchronization separately at shorter (1.5--4.5 years) and longer (4.5--8.5 years) horizons and relate each horizon to its correlates. The results show that EU--WB dyads are less synchronized than EU--EU dyads in the short run, and that trade deepening over time is more positively associated with short-run synchronization in EU--WB pairs. At longer horizons, the positive association between shared EU/EMU membership and synchronization weakens or reverses when the same country pair moves into deeper institutional integration, while differences across country pairs in average EU/EMU status become negligible. Over the same horizon, trade deepening within a pair is more consistently associated with synchronization, and more persistent structural dissimilarity is associated with lower synchronization. EU--WB dyads are no longer clearly less synchronized at these frequencies, and the remaining convergence pattern is more consistent with sectoral differences narrowing over time than with trade. These findings indicate that synchronization channels are horizon-dependent and that conclusions based on single-horizon correlation measures can obscure the distinction between short-term coupling and structural convergence.
2603.28951Mar 2026
View
Similarity of Information in Games
Algorithmic content targeting homogenizes information, with implications for strategic interactions. For example, this increased homogenization was arguably responsible for the run on the Silicon Valley Bank. We argue that existing measures of similarity are inappropriate for studying games -- especially coordination games -- because they do not discipline agents' conditional beliefs. We propose a class of stochastic orders, Concentration Along the Diagonal (CAD), built on agents' conditional beliefs. In canonical binary-action coordination games, greater CAD-similarity is both necessary and sufficient for strategic similarity -- agents adopt the same strategy. We further demonstrate CAD's applicability in congestion games, collective action, and second-price auctions.
2603.28190Mar 2026
View
Carbon Regulation and Competition in the European Airline Industry
The European Union Emissions Trading System is set to substantially increase the effective carbon price faced by airlines. To quantify the impact of this carbon regulation on the European airline industry, we estimate a two-stage model of airline competition with endogenous route entry, flight frequencies, and pricing using European data on market shares and prices. Counterfactual simulations reveal that the impacts of carbon pricing are highly asymmetric across carrier types and market segments. Consumer surplus declines by up to 25% overall, with medium-haul markets bearing the brunt at up to 90%, while short-haul markets experience positive net welfare gains (including carbon revenue and the social value of avoided emissions) as airlines reallocate capacity toward shorter routes. We find that airline profits decline by 8-45% across scenarios, while carbon tax revenue of $0.9-3.1 billion and a social value of avoided CO2 emissions of $0.5-1.4 billion partially offset the welfare losses. We also show that a hypothetical Wizz Air-Ryanair merger primarily benefits firm profits through network expansion synergies.
2603.27724Mar 2026
View
A Market Design Proposal for Decoupling Carbon and Electricity Prices
In European day-ahead electricity markets, carbon allowance costs passed through by marginal fossil plants raise consumer expenditure and generate inframarginal rents for non-emitting generators. We propose a settlement modification: when the zonal day-ahead price exceeds a threshold, non-emitting generation is remunerated at the clearing price minus a fixed CO2 proxy deduction, while all other units continue to receive the uniform price. The mechanism thus reallocates a part of the inframarginal rents to consumers. Using hourly data we estimate static average expenditure reductions of about 8.5% in Austria and 4.7% in Germany in 2025. We discuss bidding incentives around the threshold, interactions with Contracts for Difference, implementation in coupled bidding zones, and a gas-cost variant for the 2022 energy crisis.
2603.25874Mar 2026
ViewPage 1 of 20
