The COVID-19 pandemic disrupted socio-economic and healthcare systems in the Philippines, significantly affecting barangays. This study analyzes the cascading effects of the COVID-19 pandemic on key aspects of a barangay, namely mobility, accessibility of public services, economic and financial health, food security, educational engagement, and physical health. It focuses on data from 2,122 Filipino households collected during May to June 2021 as part of the World Bank COVID-19 Households Survey. A Bayesian network model was constructed to programmatically map the conditional dependencies among these variables, utilizing Python libraries. Survey responses were grouped into common variables based on shared characteristics and standardized through z-score normalization to serve as nodes in the Bayesian network. By extending the Bayesian network into an influence diagram, the results will help identify interventions to guide local government units (LGUs) and policymakers in crafting tailored recovery programs and strategies that address impacts on physical health, economic and financial health, food security, public service access, mobility, and educational engagement. These efforts ultimately aim to enhance barangay resilience and preparedness for future public health crises. The results indicate that interventions aimed at boosting food production, stabilizing market prices, and expanding income opportunities are the most effective in improving community outcomes. This highlights the vital role of targeted economic and food security measures in mitigating the socio-economic impacts of the pandemic and offers valuable insights for shaping future response and recovery efforts.

Over the last decade a wave of U.S. states began reimbursing doula services through Medicaid, hoping to improve infant health and narrow stark racial gaps in birth outcomes. I evaluate these mandates using the staggered 2021-2024 rollout, a panel of 32.1 million births from CDC WONDER (2016-2024), and a newly assembled measure of the state doula workforce drawn from the national provider registry. Identification comes from the policy's timing rather than from comparing doula users to non-users, addressing the selection problem that limits the existing observational literature. On average I find no detectable effect on low birth weight (LBW). Consistent with the heterogeneity emphasized by Peet (2022) and the maternal-health-disparities literature, however, the effect concentrates among the group at greatest risk: Black mothers, for whom LBW falls by roughly half a percentage point (about 5% of the baseline) in the states with the longest exposure, with flat pre-trends and a coherent upward shift in the birth-weight distribution. The estimate is marginal once I use inference valid for few treated clusters, and the binding constraint is statistical power: most mandates took effect in 2024-2025, at or beyond the end of the data. A two-stage least squares analysis shows that coverage roughly doubles the doula workforce (first-stage F approximately 21-35), and that the induced increase in doula supply is associated with lower Black LBW, though imprecisely. I read the results as credible early evidence that doula mandates work where they have had time to operate and where the need is greatest, rather than as a finished causal claim.

This paper studies the impact of geographic proximity to and utilization of publicly funded advisory services offered to US small businesses on firm take-up and performance. We leverage a novel administrative dataset from the Northern California Small Business Development Center (SBDC) Network covering all firm-center interactions from 2006-23. To address endogeneity in firm engagement with centers, we exploit exogenous variation in center-firm geographic proximity generated by center closures and openings. We instrument for paired center-firm consulting time with changes in distance resulting from these organizational shifts. A one standard deviation reduction in distance between a firm and corresponding center (20 miles) increases expected annual consulting time by 0.15 hours (7.5%); each additional consulting hour raises average firm annual revenue and employment by 3.6-5.2% and 1.6-2.9%, respectively. Back-of-the-envelope calculations suggest advisory services are cost-effective. This study provides novel causal evidence on take-up and effectiveness of small business advisory services in the US using quasi-experimental variation in geographic proximity. Our findings highlight the importance of both physical distance and localized expertise in shaping small business outcomes.

We develop an agent-based model in which inflation emerges from decentralized price-setting and credit-financed production in an endogenous-money economy. Firms operate under working-capital constraints, form market-based price expectations through heterogeneous adaptive learning, and set prices via cost-plus rules with endogenous mark-ups. Bank lending simultaneously creates deposits, while heterogeneous lending rates and credit rationing shape firms' financing costs and, through unit costs, their pricing decisions. The economy features interacting production and credit networks: intermediate-input linkages propagate cost shocks across supply chains, while bank--firm relationships transmit financial conditions across firms. The interaction of network-based pass-through, state-dependent pricing incentives, and evolving credit conditions generates inflationary regimes, including episodes driven by pricing cascades and feedback loops.

Societal goals need to shift from over-reliance on gross domestic product (GDP) to broader aspects of sustainable and inclusive wellbeing (SIW). However, defining SIW and eventually measuring it with a single number is problematic because it involves many subjective and objective contributors that combine in complex, non-linear ways. Conventional approaches either use linear weighted averages or reduce SIW to subjective wellbeing alone. Neither is sufficient. This paper reviews aggregation methods for SIW against nine conditions derived from needs theory and strong sustainability: limited substitutability, penalisation of imbalances, non-linear transformations, respect for environmental ceilings, respect for lower limits, a formative measurement model, no correlation requirement, distributional sensitivity, cross-border spillovers, and intertemporal aggregation. We compare 13 methods, from simple arithmetic means to penalty-based indices, outranking multicriteria, data envelopment analysis, and insights from ecology, neuroscience, and machine learning. Our illustrative example shows that aggregation choices change significantly country rankings. Compensatory methods create similar rankings. No single method satisfies all nine conditions. We conclude that a future SIW composite indicator will require combining methods across levels: non-linear normalisation, non-compensatory aggregation, and measurement-level choices for inclusiveness and spillovers. This paper provides a step towards the headline aggregated indicator advocated by the UN High-Level Expert Group on Beyond GDP.

Forward-looking volatility forecasts are central inputs to derivatives pricing, market making, risk management, and volatility-linked trading strategies, with ARCH and GARCH models serving as the canonical workhorses. Such models are natural in standard asset markets, where prices are positive-valued stochastic processes and volatility is typically inferred from return dynamics. Prediction markets have a different structure: prices are bounded probabilities, payoffs are binary, and contracts resolve at known deadlines. We develop and estimate a volatility model tailored to binary prediction markets. The model combines two economic mechanisms: a Wright-Fisher deadline-resolution component, capturing how remaining binary uncertainty is forced to resolve over time, and a Glosten-Milgrom order-flow component, capturing volatility from informed trading as reflected in spreads and volume. Using a large panel of Kalshi contracts, we show that these structural variables carry substantial forecasting power. Plain ARCH/GARCH benchmarks are dominated by structural specifications; combining the structural model with residual GARCH dynamics gives the best overall forecasts. The model also provides an interpretable measurement framework: volatility is highest near fifty-fifty prices, rises near resolution, and varies across categories with the timing and discreteness of information arrival. Economics contracts are closer to smooth deadline-resolution dynamics, while sports contracts exhibit more event-concentrated, jump-like behavior. Across major categories, category-specific fitting does not systematically improve out-of-sample performance, suggesting that the structural specification transfers beyond the pooled headline result.

We study sequential decision making under evolving uncertainty in high-frequency financial markets, where changing market dynamics continually challenge static decision policies. We show that robustness has two economically meaningful dimensions: uncertainty tolerance, which determines how much uncertainty the decision maker allows, and action robustness, which governs how conservatively decisions respond. Robustness is not merely protection against model misspecification, but a state-dependent mechanism that reshapes sequential decision behaviors. Simulation and empirical evidence show that action robustness has a substantially larger impact than uncertainty tolerance. Moreover, excessive robustness may reduce profitability in illiquid markets by limiting execution opportunities.

This paper proposes a stochastic discount factor (SDF) scaled by time-varying volatility. By utilizing prices and market data implied solely from S\&P 500 options, the proposed framework recovers a stable, non-monotonic SDF that captures the pure forward-looking expectations of market participants while mitigating observation noise. Our empirical analysis reveals that the SDF exhibits a distinctive hump on the shallow put side, which transitions into a more clearly defined W-shape as the time to maturity increases, identifying maturity as a key factor influencing the intensity of the central hump. We show that this structural feature can be theoretically rationalized by stochastic volatility dynamics under a constant market price of risk. The equity premium derived from the time-varying volatility scaled SDF demonstrates superior out-of-sample predictive performance relative to existing benchmarks, such as the Martin bounds.

Cryptoassets are increasingly entangled with the traditional financial system, and how this activity integrates into national economies and behaves under stress bears on financial stability and the design of public digital money. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirect proxies to infer and locate market participants. Here we use a regulatory registry that directly identifies the on-chain addresses of all crypto-asset service providers (CASPs) registered in Austria, reconstructing their on-chain transaction activity across Bitcoin, Ether, USDC, and USDT through May 2025, and separating retail-like from institutionally mediated flows. We find that Austrian CASPs intermediate roughly USD 30 billion with external counterparties and are integrated globally rather than domestically. In value, this activity is dominated by a few institutional counterparties; in number, by retail-like ones. Around three major shocks, the Terra-Luna collapse, the FTX bankruptcy, and the Silicon Valley Bank failure, the two groups respond through different mechanisms, and stablecoins do not act as a uniform safe haven. The clearest case is SVB, where retail-like deposits and institutional withdrawals are consistent with USDC's two-tiered redemption mechanism. These patterns are invisible in aggregate data. Registry-based, transaction-level measurement thus offers a reproducible, cross-jurisdictional basis for monitoring how cryptoasset markets transmit risk.

Assuming that the asset price $X$ follows a constant elasticity of variance process, this paper studies the optimal prediction problem $\inf_{0\leq \tau\leq T}\mathbb{E}|X_\tau-\ell|$, where the infimum is taken over stopping times $\tau$ of $X$ and $\ell$ is a hidden aspiration level independent of $X$. Adopting the aspiration level hypothesis, we show that a class of admissible laws of $\ell$ leads to optimal trading boundaries which are located relative to the median interval of $\ell$ and serve as predictors of the resistance and support levels. The existence of these boundaries is proved and nonlinear integral equations are derived to characterise them uniquely. In the positive drift case the stopping set is bounded by two curves, while in the negative drift case the stopping set is described by a single boundary.

The digital age has facilitated the sharing of underutilized assets. This paper focuses on privately owned autonomous vehicles (AVs), a unique class of robots that can move independently and provide transportation services. When not in personal use, private AV owners can lease their vehicles to a platform that operates an on-demand mobility service (MoD). We refer to this service as AV crowdsourcing, and develop a time-expanded network flow model that captures temporal and spatial heterogeneity in AV usage of both owners and passengers while preserving analytical tractability. We analyze the conditions under which AV crowdsourcing reduces MoD operating costs and identify their key factors, namely, the complementarity of the mobility pattern between AV owners and MoD passengers, the slack time reserved by vehicle owners, and the vehicle repositioning distance. A case study of Chicago further reveals substantial spatiotemporal heterogeneity in optimal prices and service quality. The results demonstrate how centralized dispatching can simultaneously fulfill the high demand in downtown areas while maintaining relatively high service quality in peripheral regions. Our findings provide insights into how supply heterogeneity and market conditions jointly shape the performance of AV crowdsourcing systems that leverage the underutilized private robotic assets.

Generative AI is fast becoming the first place people turn for expert advice. The advice it provides can be directional rather than neutral, shaped in part by the choices of its designers and regulators. When clients consult AI before meeting an expert, they carry this directional advice into a relationship that once rested on the expert's judgment alone. We study its consequences in healthcare through a large-scale preregistered field experiment at a Chinese hospital, where we randomize patients' access to an AI chatbot before their outpatient visit. Examination of the conversation logs shows that the chatbot routinely cautions against the use of medications, especially Traditional Chinese Medicine and antibiotics, while issuing clean recommendations for diagnostic testing, consistent with the liability-driven guardrails encoded in AI training. This directionality propagates into clinical practice. Prescription rates decline among treated patients while diagnostic testing increases, and these effects are more pronounced among physicians who are receptive to patient input and those with more intensive prescribing styles. Beyond shifting healthcare utilization, survey results show that AI access reduces patient compliance and satisfaction, shifting the balance of authority between patients and physicians.

We introduce a novel monthly county-level consumption dataset constructed from spending data on over 350 million credit cards in the Federal Reserve's Y-14M reports, covering over 3,000 U.S. counties since 2014. We first show that the data closely approximate traditional consumption measures, explaining 92 percent of the variation in monthly adjusted personal consumption expenditures (PCE) growth at the national level and capturing meaningful cross-sectional variation in annual adjusted PCE growth at the state level. As a proof of concept, we use the county-month panel to estimate heterogeneous consumption responses to monetary policy shocks across the county-level income distribution, an analysis infeasible with traditional consumption data. We find that low-income counties exhibit larger spending declines than high-income counties, consistent with heterogeneous agent New Keynesian models. Finally, we provide practical guidance for researchers working with similar data, discussing coverage, sample composition, and the approximation of credit card spending from credit bureau data.

Form 8-K filings are the primary channel through which U.S. public companies disclose material events, but the SEC item codes attached to them are coarse: a single item spans routine administrative changes and chief executive departures, and many of the most market-moving disclosures fall into a catch-all item. Large language models make fine-grained labelling feasible at corpus scale, but only if the labels can be traced to the source text and shown to be reliable. We present a two-stage system that tags 8-K disclosures against a three-tier taxonomy of 119 event types. The first stage constrains output to valid taxonomy entries and anchors every tag to a verbatim quote via fuzzy n-gram validation; the second re-grades each cited quote against the category definition to produce a quality score. Applying the system to 292,984 filings from 2022 to 2026 yields 601,088 grounded event tags, which we release. Over 5,125 stratified tags, an LLM judge finds precision rises monotonically with the quality score, from 12% to 96%, while unsupported tags fall from 8% to near zero. Ablation shows the score is calibrated only when assigned in a dedicated second pass. An event study on unsigned abnormal returns confirms, without any language model, that the taxonomy separates economically distinct events sharing an item code.

In this paper we apply second-order stochastic dominance (SSD) to the problem of enhanced indexation with asset subset (sector) constraints. The problem we consider is how to construct a portfolio that is designed to outperform a given market index whilst having regard to the proportion of the portfolio invested in distinct market sectors. In our approach, subset SSD, the portfolio associated with each sector is treated in a SSD manner. In other words in subset SSD we actively try to find sector portfolios that SSD dominate their respective sector indices. However the proportion of the overall portfolio invested in each sector is not pre-specified, rather it is decided via optimisation. Our subset SSD approach involves the numeric solution of a multivariate second-order stochastic dominance problem. Computational results are given for our approach as applied to the S&P500 over the period 3rd October 2018 to 29th December 2023. This period, over 5 years, includes the Covid pandemic, which had a significant effect on stock prices. The S&P500 data that we have used is made publicly available for the benefit of future researchers. Our computational results indicate that the scaled version of our subset SSD approach outperforms the S&P500. Our approach also outperforms the standard SSD based approach to the problem. Our results show, that for the S&P500 data considered, including sector constraints improves out-of-sample performance, irrespective of the SSD approach adopted. Results are also given for Fama-French data involving 49 industry portfolios and these confirm the effectiveness of our subset SSD approach.

Firm-to-firm production networks matter for aggregate propagation, but they are rarely observed. This paper reconstructs national-scale, weighted firm-to-firm networks from two public objects: a sectoral input--output table and the distribution of firm sizes by sector. The algorithm first draws a binary buyer-seller backbone from a sector-aware gravity model and then assigns weights by a minimum-energy program. A Markov closure makes the reconstructed network primitive, so it has a unique stationary distribution. The weighting program keeps one-step firm balances and sectoral flows close to the data; the stationary money vector is then checked ex post and remains close in aggregate. For the United States we reconstruct a network with about 6.5 million firms and 340 million links in roughly four hours on a single workstation. We also reconstruct the networks of Japan, the United Kingdom, Australia, Finland, and Denmark. The Japanese reconstruction, built without any link data, reproduces the heavy-tailed degree regime documented in the country's observed production network. The reconstructed networks exhibit customer tails heavier than supplier tails, though the algorithm treats the two sides symmetrically. We also run computational experiments on the reconstructed networks to assess the systemic risk posed by the failure of individual firms. These experiments show that neither firm size nor degree nor sectoral position is a good proxy for the aggregate losses generated by a firm's failure. For such questions, there is no good substitute for the complete weighted buyer-seller network that we reconstruct. We release the reconstruction code, the generated networks, a Python library, and a graphical

Sparse index tracking is often evaluated through rolling reconstruction: a sparse portfolio is fitted on an in-sample window, held over the next period, and rebuilt when the window rolls forward. This can achieve low realised tracking error, but it treats rebalancing primarily as repeated construction and can generate large turnover and frequent substitutions in the selected constituents. We propose a new workflow that separates sparse-tracker construction from sparse-tracker maintenance. A hybrid optimisation-plus-sampling framework provides the metrics operating at the decision level for both layers. The initial tracker is built from a calibrated shrinkage model and uncertainty-aware posterior support screening. Subsequent rebalance dates are handled in the self-financing change variable $\Delta w$. The default action is to preserve the existing tracker; local repairs are implemented only when realised tracking deterioration and posterior directional evidence jointly suggest intervention. In a 2020-2025 S&P 500-style case study, we show that the proposed tracker occupies a distinct low-turnover operating region. Moreover, we demonstrate that the proposed $\Delta w$ maintenance layer can be attached to externally constructed trackers, where it gives consistent improvements over simply holding the initial tracker. Additional diagnostics, sensitivity experiments, and computational details are reported in the companion Supplementary Material. Replication code and logs of several experiments are available at \href{this https URL}{this https URL}.

This paper suggests methods for estimation of the $\tau$-quantile, $\tau \in (0,1)$, as a parameter along with the other finite-dimensional parameters identified by general conditional quantile restrictions. We employ a generalized method of moments framework allowing for non-linearities and dependent data, where moment functions are smoothed to aid both computation and tractability. Consistency and asymptotic normality of the estimators are established under weak assumptions. Simulations illustrate the finite-sample properties of the methods. An empirical application using a quantile intertemporal consumption model with multiple assets estimates the risk attitude, which is captured by $\tau$, together with the elasticity of intertemporal substitution.

Centralized hydrothermal planning models determine generation schedules and electricity spot prices based on inflow forecasts in audited-cost power systems, such as those prevalent in Latin America, and provide operational benchmarks and decision support in hydro-dominated competitive electricity markets. Consequently, biased forecasts can propagate directly into both operational decisions and market outcomes. This paper studies how persistent optimistic inflow-forecast bias propagates through the Brazilian hydrothermal power system and market. For a stylized hydrothermal model, we show analytically that optimistic bias weakly reduces water values and weakly increases first-stage hydro discharge relative to the unbiased optimum, thereby lowering reservoir storage and postponing thermal commitment. Using official Brazilian planning and operational data, we provide empirical evidence consistent with this mechanism. We then conduct a controlled SDDP experiment to compare policies trained under biased and bias-corrected inflow-forecast processes, evaluating both under the same bias-corrected inflow scenarios. The policy trained under biased forecasts produces lower reservoir levels, delayed dry-season thermal dispatch, sharper spot-price peaks, higher reliability risk, and higher expected operating costs. Finally, we show that these distortions increase the price-quantity risk for hydropower producers and reduce their willingness to contract. The results indicate that inflow-forecast bias is not merely a statistical forecasting problem, but can be a source of operational inefficiency, reliability risk, and distorted market incentives in hydro-dominated power systems. We argue that the insights and policy implications drawn in this paper may be relevant beyond Brazil to other hydro-dominated systems and electricity markets that are increasingly reliant on energy storage.

The release of SR 26-2 marks a significant modernization of U.S. model risk management by replacing SR 11-7 with a more risk-based and materiality-sensitive supervisory framework. However, generative and agentic AI are excluded, creating an important governance challenge for banking organizations and other financial institutions. Although generative AI may not directly estimate credit risk or make underwriting decisions, its outputs can materially affect the surrounding control environment through monitoring interpretation, policy analysis, or adverse-action language drafting. These uses may influence how regulated financial decisions are explained, challenged, documented, and governed. This paper proposes the Generative AI Control Framework (GAICF), an SR 26-2-compatible governance framework for generative AI-enabled financial workflows. The framework translates core model risk management principles into a layered control structure for generative AI applications that operate outside the formal model boundary but remain embedded within regulated banking processes. GAICF provides a practical approach for financial institutions seeking to align emerging generative AI governance practices with the risk-based supervisory expectations reflected in SR 26-2.

We derive an operational-time variance kernel for a latent-order-book reaction boundary and use it to separate three objects usually collapsed in calendar-time volatility models: a structural boundary cumulant, a clock projection, and a pricing-measure choice. The reaction boundary is the zero of a bid--ask imbalance field. For a locally linear book, signed order-flow perturbations displace this zero through a damped Abel response kernel, so the variance of boundary increments is obtained as a finite-scale Green-function cumulant rather than introduced as a primitive diffusion coefficient. For long-memory forcing with exponent $0<\gamma<1$, the operational variance has a closed asymptotic form involving effective signed-forcing intensity, liquidity slope, resilience, memory, and operational coarse-graining scale. A deterministic activity clock gives the benchmark local-volatility projection. More general, non-unique clocks generate candidate calendar-time pricing systems. We argue that such projections are admissible only when the induced forward density operator and backward valuation operator remain adjoint on the same state space. Adjoint consistency is therefore a reality constraint on operational-to-calendar time projection: it disciplines non-unique time and identifies where incompleteness enters.

This paper extends the cap-axis integral diagnostic to general characteristic axes, measuring factor-model pricing errors as bridge-alpha curves. A predetermined characteristic order generates prefix portfolios; subtracting equal-exposure aggregate portfolios yields zero-investment bridges indexed by cutoff p. The null is a zero-curve restriction on the subspace generated by the order, not a pointwise decile test. In 1967-2024 CRSP data, adding a counterpart factor flips the curve's sign on every axis, but only HML and CMA overcorrect enough to be rejected, whereas RMW and UMD flatten their axes. A size-split reconstruction traces the overcorrection to 2x3 factor construction rather than the premium. Axis pricing errors are largely unrelated to maximum-Sharpe gains.

Large Language Models (LLMs) increasingly power search engines and AI assistants that retrieve and summarize content from many sources. By serving answers directly, these systems obscure the original content creators' contributions, threatening the compensation that sustains a healthy content ecosystem. We frame this as a problem of fair document valuation and compensation, and propose a framework based on the Shapley value. Because exact Shapley computation is prohibitively expensive at scale, we develop Cluster Shapley, an approximation that groups semantically similar documents via LLM embeddings and computes Shapley values at the cluster level, with formal bounds on both the approximation error and the induced revenue-attribution error. On Amazon product review data, off-the-shelf approximations such as Monte Carlo sampling and Kernel SHAP perform suboptimally in LLM settings, whereas Cluster Shapley substantially improves the efficiency--accuracy frontier. Simple attribution heuristics (e.g., equal or relevance-based allocation), though computationally cheap, yield highly unfair outcomes. Our approach is agnostic to the exact LLM used, the summarization process used, and the evaluation procedure, which makes it broadly applicable to a variety of summarization settings.