Papers

We examine how macroeconomic and geopolitical developments in the United States, the euro area, and China affect the currency composition of central banks' foreign exchange reserves. Using an unbalanced panel of reserve shares for 53 countries over 1999-2023, we estimate a constrained system of equations that explicitly imposes the adding-up restriction on reserve shares. The results indicate substantial persistence in reserve holdings and significant cross-currency dependence, supporting a system-wide dynamics of reserve composition. In the country fixed-effects specification (1) issuer economic size, (2) uncertainty, (3) sanctions, (4) trade linkages, and (5) issuer credibility are significantly associated with reserve allocation across currencies. With the inclusion of year fixed effects, the persistence and cross-currency dependence remain, while trade linkages and sanctions emerge as the most important determinants of reserve composition. The results highlight the importance of accounting for the compositional nature and interdependence of reserve shares when examining the determinants of global reserve holdings.

We study auctions with costly entry in which bidders have incomplete information about their affiliated private valuations prior to entry. We examine whether indicative bidding–a mechanism requiring non-binding preliminary bids before entry–can stimulate participation and improve entrant selection, and we compare its performance with unrestricted and capped entry in a controlled laboratory experiment. When entry costs are high, indicative bidding generates significantly more revenue, primarily by increasing participation beyond theoretical predictions. When entry costs are low, its predicted revenue advantage is attenuated by higher-than-predicted selection inefficiency.

This non-technical survey introduces the topic of groundwater management to the mathematics and statistics communities. We focus on the emerging marketplaces for groundwater pumping rights, outlining probabilistic and statistical approaches to groundwater dynamics and game equilibria that underpin stakeholder decisions. After discussing rationing schemes and regulatory policies we list a range of open problems that are well suited to be tackled by the mathematical sciences researchers.

Artificial intelligence (AI) is transforming measurement in economics. AI models convert unstructured data, such as text and images, into structured variables at low cost, making previously prohibitive measurement feasible at scale. This shifts the bottleneck from finding any scalable measure of a phenomenon to choosing among many plausible ones, which may support different empirical conclusions. This review provides guidance for navigating that shift. We describe three stages at which AI enters the measurement pipeline—discovery, construct definition, and observation—and what each demands of researchers. We argue that credible inference with AI-generated variables requires appropriately designed validation: anchoring measurement to explicit criteria, rather than informal claims that a proxy is reasonable. We then examine how validation samples support valid inference even when AI predictions are arbitrarily biased, and what can be done when a random validation sample is unavailable.

Constitutions as the formal foundation of a country's legal and political system have important economic and political effects. Yet, we still know little about why constitutions set effective constraints on politicians in some societies, while being largely disregarded in others. Here, we ask if national culture matters for constitutional compliance. We study a cross-section of 115 countries, making use of novel indicators of constitutional compliance. We find that societies with a more individualistic population exhibit higher levels of compliance. These results are robust and extend to instrumental variables estimations. They imply a novel transmission channel from cultural traits to long-term economic development: individualistic national culture increases the credibility of constitutional self-commitments. Our analysis also supports the more general idea that the effects of formal institutions depend on the informal institutional environment in which they are embedded. Regarding religion, our results are consistent with past research that attributes the lack of development in the modern Muslim world to deficient institutional quality.

What happens to a party's vote share when it nominates a woman for executive office? The evidence on female candidates is dominated by legislative races and by designs that pool parties and periods into a single average effect, which is typically null. This paper builds a new county-level panel of US gubernatorial elections - 578 races in 49 states from 1980 to 2024, reconstructed and audited from primary sources - and estimates party-specific, time-varying nomination effects with a stacked difference-in-differences design around within-state transitions from male to female nominees. The average null conceals a sharp asymmetry. Republican transitions to a female nominee since 2016 are followed by a county-level vote-share decline of 6.5 percentage points, robust across cohort, weighting and inference checks. No comparable average differential is detected for Democratic nominees. Survey-ballot diagnostics are compatible with demand-side discrimination but cannot identify that channel.

When shopping is delegated to AI agents, it is unclear whether the ranking advantage documented for humans persists. Across 7,000 sessions with five large language models (LLMs) and varying reasoning effort, we compare AI agents with human field data. AI agents search more extensively than human consumers. As with humans, lower-ranked listings are less likely to be inspected, although the effect is smaller for AI agents. Unlike humans, AI agents show a pattern consistent with the lost-in-the-middle effect, whereby middle listings have the lowest probability of inspection. At the choice stage, position effects are concentrated at lower reasoning effort, but higher effort reduces the middle penalty for every LLM that exhibits it. These findings suggest that, when search is delegated, displayed attributes may matter more than placement, and that exposure to position bias depends on how the AI agent is configured.

In 2025, bilateral foreign aid was reduced by 23%, affecting more than 130 aid recipient countries. We assess whether debt service relief or remittance increases can match the USD 26 billion in aid losses. Using a network-shock model calibrated to bilateral donors' individual cuts, we estimate recipient-country aid losses and evaluate compensation feasibility in terms of annual debt service payments that would need to be cancelled and remittance capacity (the headroom between flows and a theoretical maximum in which every working-age migrant sends funds) mobilised to financially offset them. We find that 18% external debt service relief and 10% of remittance mobilisation could compensate half of the affected countries. However, some countries remain locked out of either or both mechanisms. A fundamental trade-off in the global financial architecture emerged for large aid-cut losers: countries positioned to benefit from debt service relief lack large international diaspora networks (limiting their capacity to increase remittances), while those with established diaspora channels face structural exclusion of traditional debt markets, rendering debt service relief ineffective. These insights introduce nuance in how alternative finance sources can replace foreign aid.

In online product search, returned alternatives often look alike. We investigate when a product's visual separation from its closest look-alike in a returned set increases its choice probability. We introduce two occasion-level constructs: contextual visual distinctiveness (image distance from the nearest similar alternative in the consideration set) and relative fit (compatibility with the search query). We hypothesize that distinctiveness favors selection and is highly contextual, yields a premium that rises with relative fit, and matters most when text descriptions fail to differentiate options. Analyzing over 800,000 e-commerce search events using dense representations, we apply search-event and product fixed effects to evaluate the exact same product alongside varying visual neighbors. Results show a product is significantly more likely to be clicked when it lacks a close look-alike. This distinctiveness premium increases with relative fit and roughly doubles when competing descriptions are highly similar. Consistent with a model where distinctiveness aids in standing out pre-evaluation rather than increasing inherent utility, the extra clicks distinctiveness recruits convert 5-7% less often downstream. Ultimately, the evidence characterizes visual differentiation as a local attention allocation mechanism whose value depends on query fit and information from competing cues.

We study an experiment with 8.5 million users on Netflix's recommender system to measure how improvements in recommendation technology affect the set of products that get consumed. Improvements increase total consumption and users' reliance on recommendations while diffusing recommendations and consumption away from the most popular titles (“superstars") toward a larger number of moderately popular titles (“middle-tail"), with minimal effects on the most niche titles (“long-tail"). Our results challenge the notion that recommender systems polarize consumption – raising the consumption shares of the head and tail at the expense of the middle – and suggest that the returns to investing in middle-tail products grow as algorithms improve and platforms scale.

Marketing Mix Models (MMMs) estimate the incremental sales effect of advertising from observational time series, yet they are rarely validated against ground truth, because ground truth is unobservable in real data. Synthetic data closes that gap in principle, but existing generators produce marketing spend exogenously - omitting the central difficulty of the estimation problem, since real budgets are planned around promotional calendars, seasons, and recent performance. This paper presents a parameterized generator, and a fixed reference instance, of a synthetic weekly retail dataset (156 weeks, three media channels) in which spend arises from four documented coordination mechanisms - quarterly budget feedback, anticipatory spending ahead of a promotional calendar, scheduled TV bursts, and algorithmic performance chasing - on a demand baseline with seasonal, quality, price, and unobserved sentiment components. Spend translates into incremental sales through two transformations, carryover and diminishing returns, instantiated here as geometric adstock and logistic saturation with known parameters; the true causal decomposition of every week's sales is recorded alongside the error-contaminated variables a practitioner would observe. Every mechanism is a parameter that can be varied or switched off, and a companion procedure simulates go-dark geo-experiments with exact treatment effects. The seeded generator and reference instance are publicly released with notebooks that reproduce every number in this paper.

Marketing Mix Models (MMMs) are widely used for marketing measurement and budget allocation, but face fundamental identification challenges: due to endogenous marketing spend decisions, MMM estimation on observational time-series data cannot recover the true causal effects of marketing. On the other hand, geo-experiments provide causal identification through randomization, but it is not clear how to use them efficiently to calibrate marketing mix models. We propose a novel structural estimation approach that recovers the complete set of MMM parameters - adstock decay ($\alpha$), saturation ($\lambda$), and effectiveness ($\beta$) - directly from geo-experimental time-series. By differencing outcomes between treatment and control regions, our method eliminates observed and unobserved confounding factors while preserving the temporal variation that identifies each parameter. We demonstrate on synthetic data that this approach recovers the true ROAS and the response curve over the range of spending covered by the experiments, together with credible estimates of the underlying parameters. Our framework enables efficient pooling across multiple experiments and provides a principled foundation for MMM calibration that fully utilizes the information experiments contain.

We study, theoretically and experimentally, how transparency about students' priorities and admission chances shapes their incentives to acquire information about their own preferences in school choice and college admissions. In the model, uninformed students choose schools based on a common prior. When they learn their own preferences, their choices become more heterogeneous, which frees up seats at popular schools. Students who know they have high priority have stronger incentives to learn because they can more readily act on what they learn, whereas students who know they have low priority are discouraged. Full priority disclosure concentrates learning among high-priority students. By pooling priorities, partial disclosure spreads learning incentives to pooled students and yields higher welfare. In the laboratory, however, full disclosure yields the highest welfare instead, followed by partial disclosure, and then no disclosure, because greater transparency improves subjects' understanding of the strategic environment, leading to fewer mistakes. These findings support full disclosure of priorities or admission chances to guide information acquisition. However, deviations in learning remain even under greater priority transparency, partly because subjects respond suboptimally to admission chances when these are provided directly rather than inferred. Students' ability to interpret and use them is therefore itself a policy concern.

The European Union has committed to reducing greenhouse gas emissions 55% below 1990 levels by 2030, but whether current trends are compatible with this ambition remains uncertain. We apply deep learning to high-resolution socioeconomic and sectoral data across EU27 member states till 2023 to project sectoral CO$_2$ trajectories under current trends, extrapolating observed sectoral momentum without assuming changes in the pace or effectiveness of the policy environment beyond what is already reflected in historical data. We project that EU27 emissions will exceed the 2030 target by 35% (620 Mt CO$_2$ shortfall), with only a small minority of countries on trajectories consistent with the bloc's commitments. While the Power sector achieves target-consistent reductions driven by the renewable transition, Mobility shows minimal progress and accounts for over a third of total emissions by 2030, reflecting a structural inertia across member states rather than geographically concentrated lag. Our findings indicate that substantial additional intervention is required to close Europe's ambition-implementation gap, and call for establishing up-to-date energy information in Europe.

Intra-household joint home-based tours - trips in which household members depart together, engage in shared activities, and return together - represent a significant share of daily travel, yet are systematically ignored in transport simulations. Conflating joint and solo tours within a single mode choice framework introduces bias in preference parameter estimates. This paper proposes a three-step methodology to integrate joint tours in agent-based transport models: a Random Forest classifier to identify joint tours, a Multinomial Logit model estimating mode choice specific to joint tours, and a Penalized Logistic Regression for driver/passenger assignment. Applied to the Paris region using household travel survey data, the methodology successfully replicates observed joint tour shares and mode distributions in a synthetic population. The proposed framework enables more reliable evaluation of policies whose impacts differ between joint and solo travel, such as HOV lanes or family transit fare discounts.

Most LLM benchmarks rank models on their ability to automate work tasks. In practice, however, models are often used to assist other (human or LLM) agents. The question that drives model selection is therefore not only which model produces the best output, but which model most improves the work of another (weaker) agent. We introduce a unified framework that evaluates the capability of models to automate and augment another agent's performance. Across seven economically grounded real-world tasks, an assistant model writes assistance text for a standardized lower-capacity worker model, which produces the deliverable. In automation mode, the assistant produces the output directly. Outputs are scored through blind pairwise comparisons by an LLM judge panel with task-specific rubrics, replicated across ten runs. Rankings across the two regimes are only modestly correlated, and the automation winner loses augmentation on five of seven tasks. Assistance is not reliably positive. The unaided worker outranks every assisted condition on three tasks, and only one model's guidance beats no guidance on average. These results suggest that automation ability is an incomplete proxy for assistance quality, motivating benchmarks that evaluate models according to the roles they play in human-AI and multi-agent systems.

This paper develops a revolving-door model in which a regulator designs policy signals that affect a regulated firm's capital structure while holding an implicit career option to leave public service for the regulated firm or its advisers. The model has three linked mechanisms. First, regulatory signals alter the firm's pricing, leverage, and risk choices by changing the tradeoff between consumer surplus and firm profit. Second, because the regulator's outside payoff is convex in regulated-firm value, the career option rises with firm value, leverage, and signal-induced volatility. Third, when signal-induced volatility raises the option vega of firm equity, the same mechanism can increase tail risk and reduce distance to default. A proof-of-concept calibration illustrates leverage-vega warning regions and shows how a regulator-induced signal can move a firm from a go-leverage region into an early-warning or crash-risk region. The calibration includes maturity comparisons, sensitivity grids over compensation convexity and initial leverage, a bank-like parameter anchor, stochastic signal simulations, and a stopping-horizon diagnostic. Evidence from federal payroll data on post-employment restrictions supports the career-option primitive by showing that limits on revolving-door opportunities affect post-agency pay. The exercises also organize historical regulatory failures: Lehman Brothers as a regulatory-vacuum case, Silicon Valley Bank as a distorted-signal and weak-supervision case, and Signature Bank as a converted-signal case involving post-public-service incentives, lighter oversight, rapid growth, uninsured-deposit exposure, and tail risk. The model does not predict collapse dates; it identifies conditions under which regulatory forbearance, weak debt penalties, and career-option incentives can delay oversight until firms cross a leverage-vega warning threshold.

How level-0 players behave and how they are perceived by higher-level players are central questions in the literature on level-k models of boundedly rational strategic reasoning. To study these twin questions, we apply the intra-team communication method developed by Burchardi and Penczynski (2014) to identify level-0 actions and beliefs in the canonical beauty contest game and in a variant of the 11-20 game of Goeree et al. (2018), in which behavior appears inconsistent with the standard level-k model. In the beauty contest game, we replicate Burchardi and Penczynski (2014)'s finding that elicited level-0 beliefs align with observed level-0 actions. In the variant of the 11-20 game, however, elicited level-0 beliefs and observed level-0 actions diverge, and both depart from the standard level-0 assumption, suggesting a complementary explanation for the behavioral pattern documented by Goeree et al. (2018).

Delegating cognitive operations to generative artificial intelligence redistributes execution and raises a governance problem: where human direction of the task remains. We distinguish two routes. Specified delegation places that direction before execution, through instructions, constraints, or criteria that delimit the task. Iterative coproduction places it during production, through interventions that correct or redirect provisional outputs. To examine both routes, we use aggregate monthly cells from the Anthropic Economic Index for April and May 2026. The AEI distinguishes two modes of use: 1P API, which corresponds to direct traffic through Anthropic's API, and Claude.ai, which combines activity from Chat and Cowork. On this basis, we test whether a stronger work-related orientation of human-AI interaction is associated with more specified delegation within each mode and whether the increase in the iterative profile is greater in Claude.ai than in 1P API. The main analysis uses level-0 O*NET tasks and estimates how both profiles change when an eligible record reallocates ten percentage points from personal use to work-related use. The iterative comparison is restricted to 1,411 node-month pairs observed and eligible in both modes. Specified delegation increases by 2.76 points in 1P API (95% CI: [2.30, 3.22]) and by 1.45 in Claude.ai (95% CI: [0.93, 1.97]). On the common support, iterative coproduction changes by-0.30 points in 1P API and by 0.15 in Claude.ai, yielding a between-mode difference of 0.45 points (95% CI: [0.15, 0.75]). These findings show that work-related orien tation is associated with stronger traces of prior human direction and that the observable iterative response varies across modes of use. The article shifts attention from how much the AI executes to when human direction leaves observable traces.

Trust in local institutions matters for trade, public goods provision, conflict resolution, and democratic consolidation. Using individual data from rounds 6 and 7 of the Afrobarometer surveys, I document that respondents in former British colonies are substantially more likely to trust traditional leaders than respondents in former French colonies. Cross-country comparisons of this kind are confounded by unobserved heterogeneity in pre- and post-colonial histories. To make progress on identification, I focus on Cameroon, which contains regions colonized by Britain and regions colonized by France within a single modern state, and I exploit the former colonial partition line as a geographic discontinuity, comparing respondents who live close to either side of the anglophone-francophone boundary. The theoretical argument is that the two colonial powers differed systematically in how they treated customary authority: British indirect rule preserved the governance functions of chiefs, while French direct rule subordinated chiefs to the administration and assigned them its most coercive tasks, taxation and forced labor above all, and these divergent experiences shaped the legitimacy of chieftaincy in ways that persist through institutional continuity and socialization. Consistent with this argument, respondents on the formerly British side are 22 to 26 percentage points more likely to trust traditional leaders than their neighbors on the formerly French side, and they are far more likely to have contacted a traditional leader in the previous year.

Reputation and institutional certification are the two main trust mechanisms under information asymmetry, yet their interaction remains poorly understood. Here we analyze nearly one million eBay listings of Pokemon trading cards. Each listing pairs the seller's reputation with their signal choice, that is, whether they used costly third-party certification, their own costless claim (self-grading), or neither to convey the card's quality. First, we find that certification and self-grading partition the market. Self-grading prevails where reputation is high and value low, whereas certification prevails in the market's opposite corner. Furthermore, seller reputation raises the price premium of self-grading but not of certification. We then show that both empirical patterns follow from a signaling model in which false self-grading damages reputation, whereas certification requires an up-front fee. These results suggest that, even within a single market, trust rests not on a choice between reputation and institutions but on a division of labor.

Platforms summarize providers' past achievements into credentials that buyers use to judge quality. Generative AI can now produce much of the work those achievements certify, raising fears that those quality signals are worthless. We audit how well such credentials stay informative in Kaggle's 2010-2026 archive, where medals are won on predictions scored against withheld answers and two evaluation formats ran side by side. Across 444,698 participations, a medal's power to predict performance sits almost entirely in its first year, in both formats and eras. Fresh medals kept most of their value through the AI transition. About half of the collapse in the informativeness of one format's medals is institutional: the platform had been phasing out that format before AI, and its medal stock aged out on schedule. Old medals look more informative only in isolation. The platform's official lifetime-tier display discards up to a sixth of the medals' predictive power. A recency-weighted index fit before the AI era explains AI-era performance about 13% better than the tiers and selects entrants who perform better on average, though the tiers still identify extreme top performers better. Displaying a recent-performance summary alongside the lifetime tiers would recover the discarded information for buyers.

Most digital job-search assistance encourages unemployed workers to broaden their search toward related occupations, targeting one important source of search inefficiency: insufficient occupational diversification. Our analysis suggests that the relevant margin of adjustment depends on the underlying search problem. Building on a detailed analysis of job seekers' beliefs and search behavior, we identify a large group of pessimistic workers for whom the main constraints are low search effort and low aspirations, rather than insufficient occupational diversification. This diagnosis points to an unexpected intervention: rather than encouraging these workers to search in new occupations, we encourage them to search more intensively and apply for better-paying jobs within the occupations they already consider. We evaluate this diagnosis-based intervention, alongside a standard occupational recommendation, in a large-scale randomized experiment conducted with the French Public Employment Service. The motivational intervention increases search effort, raises reservation wages, and improves reemployment outcomes along the predicted margins. Occupational recommendations, by contrast, primarily benefit workers whose search problem lies in the allocation of attention across occupations and operate by activating existing perceptions rather than correcting beliefs. More broadly, our findings show how digital platforms can combine subjective expectations, behavioral data, targeted interventions, and randomized experimentation to diagnose job seekers' needs and iteratively improve intervention design.

Job seekers' expectations about reemployment are increasingly used to study job search, but what their biases reveal about underlying beliefs and preferences is ambiguous. We combine new survey data, structural modeling, and machine learning to uncover the informational content of these expectations and show how they can be used to improve the targeting of employment support. Using a new panel of French job seekers' subjective expectations linked to administrative records, we show that reemployment expectation biases are strongly associated with, and summarize, biases in beliefs about the two fundamentals of search, job offer arrival rates and the wage distribution. These underlying biases are heterogeneous but strongly positively correlated, so their effects on search compound. We then estimate a structural job search model with multiple sources of biased beliefs and show that correcting them helps pessimistic job seekers but can demotivate and hurt optimistic ones, providing a rationale for targeting. Finally, we develop a machine-learning stratification that recovers policy-relevant groups, with distinct patterns of biased beliefs and behaviors, from easily elicited reemployment expectations alone. This gives employment services a simple tool to target informational interventions.

Research activity generates highly localized positive spillovers, yet in the U.S. it has become increasingly spatially misaligned with population and economic activity as people moved away from legacy cities where major research institutions remain anchored. Reallocating researchers toward population centers could broaden local access to knowledge but may reduce knowledge production if legacy institutions or large research clusters raise researcher output. The spatial reallocation of researchers therefore entails a trade-off between gains in knowledge access and losses in knowledge production. To quantify the knowledge production loss from reallocation, we use bibliographic data to separate individual effects from location effects and show that location effects account for substantial differences in research output across locations. Our instrumental-variables estimates provide robust evidence of own-institution agglomeration effects but weaker evidence of external-cluster effects. We then incorporate these estimates into counterfactual reallocations and show that marginally reallocating researchers toward large metropolitan areas with relatively little research activity can plausibly raise aggregate output because the associated knowledge production losses are modest and can be offset by small access gains.

The terminal crisis of capitalism has been predicted regularly since 1848. It has not occurred. We treat this 175-year forecasting record as a stylized fact in need of a model, and we supply one, unifying results from adaptive systems, mechanism design and computational complexity, behavioral decision theory, global games, and the political economy of redistribution. Ten assumptions deliver twelve formal results and one theorem. Recurring shock classes have geometrically decaying collapse hazards: within the banking class alone, the lag from systemic failure to a system-wide deposit backstop fell from 816 days (1930-33) to 18 days (2008) to 2 days (2023). A centralized replacement of the price mechanism fails on at least one of two horns, computation or truthful input, and we are explicit about which horn survives modern algorithms. We derive Davies's J-curve from cumulative prospect theory together with a new result: the revolutionary window closes endogenously, with length inversely proportional to the speed of hedonic adaptation, roughly four to nine years at calibrated parameters, and with its perceptibility threshold derived from the participation constraint rather than assumed. Personalized media shift protest games, in the model, into the unique no-attack equilibrium of a global game. Redistribution is a dial, not an identity: countries with market Ginis of 0.505-0.512 sustain disposable Ginis from 0.273 to 0.395. We state seven testable predictions and six falsification conditions, and map which assumption each rival school must deny.

We design a method for measuring the risk preferences of agents in the deep past. The method combines a structural model of crop choice as a portfolio allocation with machine-learning prediction of expected crop returns, using historic agronomic and climate data. We estimate county-level risk preferences for the United States and farmer-level preferences in Kansas from 1889 to 1929. More risk averse farmers leveraged less, were less likely to purchase novel WWI Liberty Bonds, and were more likely to participate in local risk-sharing institutions. We show that higher risk aversion predicts slower tractor adoption and farm mechanization during the 1920s.

Neurofinance shows that financial valuation depends on evolving neural states, while temporal experience is itself state dependent. Yet intertemporal models typically treat time as exogenous and ask how delay affects valuation. This paper examines the converse question: can valuation-related neural dynamics generate subjective financial time? We develop a continuous-time model on a joint financial–neuro-evaluative state space in which subjective financial time is accumulated valuation along dynamically admissible histories. We characterise realised temporal-rate dispersion and show that, under matched financial dynamics, distinct neuro-evaluative trajectories can generate different subjective financial times. Financial equivalence therefore need not imply temporal equivalence. Valuation curvature further determines local path dependence. A proof-of-concept behavioural–fMRI analysis uses \(1{,}183\) observations, \(1{,}126\) valuation transitions, and \(798\) financially matched pairs. Using a medial-prefrontal valuation-state coordinate based on vmPFC and dmPFC responses, the strongest participant exhibits a positive association between neural-state separation and subsequent valuation divergence (\(\rho=0.253,\ p_{\mathrm{perm}}=0.001\)), robust to residual financial distance (\(\rho=0.251\)) and tighter matching (\(\rho=0.207\)). Participant effects are heterogeneous, although combined evidence gives \(p=0.0216\). The data support the neural state-separation premise rather than directly identifying subjective financial time. The framework establishes a theoretical and empirically grounded basis for endogenous subjective time in neurofinance. \keywords{Neurofinance $\cdot$ Subjective financial time $\cdot$ Neural valuation $\cdot$ Relative subjective value $\cdot$ Intertemporal choice $\cdot$ Temporal dispersion $\cdot$ Path dependence

Economics journals ration access to peer review with time and money. A July 2026 census of 500 RePEc-ranked economics journals accepting unsolicited submissions finds that 113 (22.6 per cent) charge a fee, with incidence falling from 68 per cent in the top 50 to 2 per cent in the bottom 50. A sequential-submission model shows that waiting pays part of the capacity-clearing price, leaving the fee as a residual. Because rejected manuscripts move down the ranking, a fee reduces system-wide referee demand only when authors stop, shorten their submission chains or switch to journals requiring fewer reports.

Academic freedom has come under growing strain worldwide. To study whether and how academics respond to political pressure, we exploit a natural experiment: the publication in early 2025 of a "blacklist" of words flagged by the U.S. government. We find that the release of this list led to a sharp reduction in the use of these flagged words among economists at universities that rely heavily on federal funding, relative to scholars from institutions that are less dependent on federal funding or based in the UK. The drop is driven by content related to gender, race, and environment. We show that changes are not simply semantic but reflect actual paper content and that neither the individual funding status nor time-invariant author characteristics are driving the effects. We also document interesting heterogeneous effects by department quality and author gender and ethnicity. Our findings are consistent with the idea that scholars respond strongly to political pressure.