pwc-archive/papers-with-abstracts huggingface dataset:
How does Bike Absence Influence Mode Shifts Among Dockless Bike-Sharing Users? Evidence From Nanjing, China
Authors: Hongjun Cui, Zhixiao Ren, Xinwei Ma, Minqing Zhu
Date: 2025-03-18 00:00:00

Dockless bike-sharing (DBS) users often encounter difficulties in finding available bikes at their preferred times and locations. This study examines the determinants of the users' mode shifts in the context of bike absence, using survey data from Nanjing, China. An integrated choice and latent variable based on multinomial logit was employed to investigate the impact of socio-demographic, trip characteristics, and psychological factors on travel mode choices. Mode choice models were estimated with seven mode alternatives, including bike-sharing related choices (waiting in place, picking up bikes on the way, and picking up bikes on a detour), bus, taxi, riding hailing, and walk. The findings show that under shared-bike unavailability, users prefer to pick up bikes on the way rather than take detours, with buses and walking as favored alternatives to shared bikes. Lower-educated users tend to wait in place, showing greater concern for waiting time compared to riding time. Lower-income users, commuters, and females prefer picking up bikes on the way, while non-commuters and males opt for detours. The insights gained in this study can provide ideas for solving the problems of demand estimation, parking area siting, and multi-modal synergies of bike sharing to enhance utilization and user satisfaction.
A Note on the Asymptotic Properties of the GLS Estimator in Multivariate Regression with Heteroskedastic and Autocorrelated Errors
Authors: Koichiro Moriya, Akihiko Noda
Date: 2025-03-18 00:00:00
Tasks: regression

We study the asymptotic properties of the GLS estimator in multivariate regression with heteroskedastic and autocorrelated errors. We derive Wald statistics for linear restrictions and assess their performance. The statistics remains robust to heteroskedasticity and autocorrelation.
Treatment Effect Heterogeneity in Regression Discontinuity Designs
Authors: Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell, Filippo Palomba, Rocio Titiunik
Date: 2025-03-17 00:00:00
Tasks: regression

Empirical studies using Regression Discontinuity (RD) designs often explore heterogeneous treatment effects based on pretreatment covariates. However, the lack of formal statistical methods has led to the widespread use of ad hoc approaches in applications. Motivated by common empirical practice, we develop a unified, theoretically grounded framework for RD heterogeneity analysis. We show that a fully interacted local linear (in functional parameters) model effectively captures heterogeneity while still being tractable and interpretable in applications. The model structure holds without loss of generality for discrete covariates, while for continuous covariates our proposed (local functional linear-in-parameters) model can be potentially restrictive, but it nonetheless naturally matches standard empirical practice and offers a causal interpretation for RD applications. We establish principled bandwidth selection and robust bias-corrected inference methods to analyze heterogeneous treatment effects and test group differences. We provide companion software to facilitate implementation of our results. An empirical application illustrates the practical relevance of our methods.
Difference-in-Differences Designs: A Practitioner's Guide
Authors: Andrew Baker, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, Pedro H. C. Sant'Anna
Date: 2025-03-17 00:00:00

Difference-in-differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well-understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well.
Tracking the Hidden Forces Behind Laos' 2022 Exchange Rate Crisis and Balance of Payments Instability
Authors: Mariza Cooray, Rolando Gonzales Martinez
Date: 2025-03-17 00:00:00

This working paper uses a Dynamic Factor Model ('the model') to identify underlying factors contributing to the debt-induced economic crisis in the People's Democratic Republic of Laos ('Laos'). The analysis aims to use the latent macroeconomic insights to propose ways forward for forecasting. We focus on Laos's historic structural weaknesses to identify when a balance of payments crisis with either a persistent current account imbalance or rapid capital outflows would occur. By extracting latent economic factors from macroeconomic indicators, the model provides a starting point for analyzing the structural vulnerabilities leading to the value of the kip in USD terms dropping and contributing to inflation in the country. This findings of this working paper contribute to the broader literature on exchange rate instability and external sector vulnerabilities in emerging economies, offering insights on what constitutes as 'signals' as opposed to plain 'noise' from a macroeconomic forecasting standpoint.
SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement
Authors: Brian Cho, Ana-Roxana Pop, Ariel Evnine, Nathan Kallus
Date: 2025-03-17 00:00:00

To design effective digital interventions, experimenters face the challenge of learning decision policies that balance multiple objectives using offline data. Often, they aim to develop policies that maximize goal outcomes, while ensuring there are no undesirable changes in guardrail outcomes. To provide credible recommendations, experimenters must not only identify policies that satisfy the desired changes in goal and guardrail outcomes, but also offer probabilistic guarantees about the changes these policies induce. In practice, however, policy classes are often large, and digital experiments tend to produce datasets with small effect sizes relative to noise. In this setting, standard approaches such as data splitting or multiple testing often result in unstable policy selection and/or insufficient statistical power. In this paper, we provide safe noisy policy learning (SNPL), a novel approach that leverages the concept of algorithmic stability to address these challenges. Our method enables policy learning while simultaneously providing high-confidence guarantees using the entire dataset, avoiding the need for data-splitting. We present finite-sample and asymptotic versions of our algorithm that ensure the recommended policy satisfies high-probability guarantees for avoiding guardrail regressions and/or achieving goal outcome improvements. We test both variants of our approach approach empirically on a real-world application of personalizing SMS delivery. Our results on real-world data suggest that our approach offers dramatic improvements in settings with large policy classes and low signal-to-noise across both finite-sample and asymptotic safety guarantees, offering up to 300\% improvements in detection rates and 150\% improvements in policy gains at significantly smaller sample sizes.
Functional Factor Regression with an Application to Electricity Price Curve Modeling
Authors: Sven Otto, Luis Winter
Date: 2025-03-16 00:00:00
Tasks: regression, valid

We propose a function-on-function linear regression model for time-dependent curve data that is consistently estimated by imposing factor structures on the regressors. An integral operator based on cross-covariances identifies two components for each functional regressor: a predictive low-dimensional component, along with associated factors that are guaranteed to be correlated with the dependent variable, and an infinite-dimensional component that has no predictive power. In order to consistently estimate the correct number of factors for each regressor, we introduce a functional eigenvalue difference test. Our setting allows us to construct a novel central limit theorem for the regression parameters in a fully functional model, making it possible to construct confidence bands and conduct statistical inference. The model is applied to forecast electricity price curves in three different energy markets. Its prediction accuracy is found to be comparable to popular machine learning approaches, while providing statistically valid inference and interpretable insights into the conditional correlation structures of electricity prices.
Identification and estimation of structural vector autoregressive models via LU decomposition
Authors: Masato Shimokawa, Kou Fujimori
Date: 2025-03-16 00:00:00

Structural vector autoregressive (SVAR) models are widely used to analyze the simultaneous relationships between multiple time-dependent data. Various statistical inference methods have been studied to overcome the identification problems of SVAR models. However, most of these methods impose strong assumptions for innovation processes such as the uncorrelation of components. In this study, we relax the assumptions for innovation processes and propose an identification method for SVAR models under the zero-restrictions on the coefficient matrices, which correspond to sufficient conditions for LU decomposition of the coefficient matrices of the reduced form of the SVAR models. Moreover, we establish asymptotically normal estimators for the coefficient matrices and impulse responses, which enable us to construct test statistics for the simultaneous relationships of time-dependent data. The finite-sample performance of the proposed method is elucidated by numerical simulations. We also present an example of an empirical study that analyzes the impact of policy rates on unemployment and prices.
Towards a Formal Framework of the Ethereum Staking Market
Authors: Noé Arnold, Juan Beccuti, Thunj Chantramonklasri, Matthias Hafner, Nicolas Oderbolz
Date: 2025-03-18 00:00:00

This paper studies how different categories of Ethereum stakers respond to changes in the consensus issuance schedule and how such changes may impact the composition of the staking market. Our findings show that solo stakers are more sensitive to variations in staking rewards compared to ETH holders using centralized exchanges or liquid staking providers. This heightened sensitivity is not explained by the assumptions on cost structure, which would suggest the opposite, but is instead driven by market dynamics. Factors such as superior MEV access and DeFi yields available to other staking methods, as well as the potential presence of inattentive stakers, contribute to this effect. Consequently, a reduction in issuance is likely to crowd out solo stakers while increasing the market share of centralized entities, further concentrating validator power.
Techno-Feudalism and the Rise of AGI: A Future Without Economic Rights?
Authors: Pascal Stiefenhofer
Date: 2025-03-18 00:00:00

The rise of Artificial General Intelligence (AGI) marks an existential rupture in economic and political order, dissolving the historic boundaries between labor and capital. Unlike past technological advancements, AGI is both a worker and an owner, producing economic value while concentrating power in those who control its infrastructure. Left unchecked, this shift risks exacerbating inequality, eroding democratic agency, and entrenching techno-feudalism. The classical Social Contract-rooted in human labor as the foundation of economic participation-must be renegotiated to prevent mass disenfranchisement. This paper calls for a redefined economic framework that ensures AGI-driven prosperity is equitably distributed through mechanisms such as universal AI dividends, progressive taxation, and decentralized governance. The time for intervention is now-before intelligence itself becomes the most exclusive form of capital.
Climate Risk and Corporate Value: Evidence from Temperature Bins and Panel Regression
Authors: Bo Wu
Date: 2025-03-18 00:00:00

In empirical research, this article uses daily climate data provided by the National Oceanic and Atmospheric Administration (NOAA) of the United States to construct a temperature box with a range of 5\( ^\circ\text{C} \), focusing on analyzing the impact of extreme high temperatures (\( >30^\circ\text{C} \)) and extreme low temperatures (\( \le -10^\circ\text{C} \)) on the asset value of enterprises. The results based on a panel regression model show that extreme high and low temperatures can significantly reduce the asset value of enterprises. In the robustness test, this article used lagged climate data for testing, and the results still showed that extreme high temperatures had a significant negative impact on the asset value of enterprises, verifying the reliability of the benchmark regression results. In-depth heterogeneity analysis shows that in the process of addressing climate risks, companies exhibit significant differentiation based on different ownership types and industry characteristics. According to research, state-owned enterprises are relatively less affected by extreme weather due to their resource advantages and policy preferences. In addition, foreign-funded enterprises demonstrate a high level of risk resistance due to their strong management efficiency and supply chain control capabilities. In the manufacturing sector, heavy industries such as steel and communication manufacturing are particularly affected by the negative effects of extreme weather, which fully demonstrates that these industries face more severe challenges when dealing with extreme weather conditions.
Is the distribution of resolvable uncertainty Type I extreme value? A Test for Random Coefficient Models using Choice Probabilities
Authors: Romuald Meango
Date: 2025-03-18 00:00:00

Stated choice probabilities are increasingly used in conjunction with the random-coefficient model (RCM) to describe individual preferences. They allow survey respondents to express uncertainty about the future or the incompleteness of a hypothetical scenario: the resolvable uncertainty. Parametric assumptions such as a Type I extreme value (EV1) distribution are almost always imposed on this uncertainty to identify and estimate the associated RCM. This paper proposes the first test for these parametric assumptions, based on a nonparametric identification result for the population distribution of the interquantile range of the resolvable uncertainty. In all four empirical applications considered, the test finds strong evidence against the EV1 assumption.
Investing in nature: Stakeholder's willingness to pay for Tunisian forest services
Authors: Islem Saadaoui
Date: 2025-03-17 00:00:00

This study explores the economic value of Aleppo pine forests, a unique and threatened ecosystem in the border region of central Tunisia. These forests play a vital role in supporting small rural communities, but face increasing pressures and restrictions on their use. This research aims to assign a monetary value to forest conservation, considering the region's specific socio-economic context. Strategies for empowering local residents as key actors in developing sustainable cross-border initiatives are further investigated. Employing the contingent valuation method, a survey of 350 local residents and international users was conducted to assess their willigness to pay fo forest conservation efforts. Logistic regression analysis revealed that sociodemographic factors, such as monthly income and preferred payment method, significantly influence both and the likehood of participation. These findingd highlight the feasibility and importance of reconciling economic development with ecological sustainability in this critical region.
Is Crime Displacement Inevitable? Evidence from Police Crackdowns in Fortaleza, Brazil
Authors: José Raimundo Carvalho, Marcelino Guerra
Date: 2025-03-17 00:00:00

We evaluated one of the most common policing strategies in Brazil: the allocation of police blitzes. This place-based focused deterrence intervention has well-defined assignments, and 3,423 interventions were precisely recorded in Fortaleza-CE, Brazil, between 2012 and 2013. Our analysis takes advantage of the high spatiotemporal daily data resolution coming from an unprecedented longitudinal micro-Big Data (GPS and PING records) to make comparisons of small intervention areas, while controlling for common daily trends, deterrence (spatial and temporal), and diffusion; to show that an average police crackdown causes a 35% decrease in violent crime occurrences. There are diminishing returns of public safety to hours spent by police in a single area, corroborating what police officers know well from their own experience and discretionary behavior. Although crime increases by 6% immediately after the end of a blitz, we observe lasting deterrent effects (diffusion) after 2-3 days. The residual deterrence cancels the relocation of the crime, and the intervention does not generate significant temporal displacement. In addition, we do not find spatial displacement from crime in blocks up to 1.5 km from a blitz. This type of micropolicing tactics generates deterrence by being highly visible in a street segment for a short period and intermittently quasirandom in space-time, which produces uncertainty that might be crucial in minimizing the temporal and spatial displacement of crime. Of public policy interest, we show that the allocation of blitzes passes in an initial cost-benefit analysis.
The impact of artificial intelligence technology on cross-border trade in Southeast Asia: A meta-analytic approach
Authors: Jun Cui
Date: 2025-03-15 00:00:00

This study investigates the impact of artificial intelligence (AI) technology on cross-border trade using a qualitative content analysis approach. By synthesizing existing empirical studies, we aim to quantify the overall effect of AI on trade flows and identify the key moderating and mediating variables. Besides, our results show that AI adoption significantly increases trade volumes in Southeast Asia. Likewise, these effects are stronger in regions with advanced technological infrastructure and favorable regulatory frameworks. In addition, Trade firm size partially mediates the relationship between AI technology and trade performance. Furthermore, this study draws on several key theoretical frameworks that provide a comprehensive understanding of the mechanisms through which AI technology is affecting cross-border trade in Southeast Asia. The primary theories used in this research include the technology, organization, and environment (TOE) framework, the diffuse innovation (DOI) theory, Dynamic Capabilities Theory, Comparative Advantage Theory, Network theory, Transaction Cost Economics (TCE), the resource-based view, and the institution theory. Consequently, this study contributes to the existing literature by providing a comprehensive analysis of the role of AI in international trade and highlighting the importance of contextual factors in maximizing the benefits of AI. Thus, our findings underscore the need for favorable policies and robust infrastructure to facilitate AI-driven trade growth. A discussion of limitations and future research directions will also be part of the report in Southeast Asia Trade.
My Boss is a Narcissist Bully: A Game Theoretic Approach to Stop Bullies
Authors: Pascal Stiefenhofer, Cafer Deniz, Liangxun Xie, Jing Qian
Date: 2025-03-18 00:00:00

This paper investigates effective strategies for dealing with workplace bullying perpetrated by a narcissistic boss. Adopting a game-theoretic framework, we propose a three-stage sequential game with a simultaneous form game, incorporating a war of attrition in the final stage. Our findings demonstrate that victims of bullying should consistently choose to signal to escalate the situation and report bulling rather than ignore and tolerate the abusive behavior. Additionally, we explore how leveraging the narcissist's inherent fears can empower the victim in selecting the most advantageous equilibrium solution. By employing this comprehensive approach, individuals facing a narcissistic boss bullying can effectively address and mitigate these challenging circumstances.
Correlation uncertainty: a decision-theoretic approach
Authors: Gerrit Bauch, Lorenz Hartmann
Date: 2025-03-17 00:00:00

We provide a decision-theoretic foundation for uncertainty about the correlation structure on a Cartesian product of probability spaces. Our contribution is two-fold: we first provide a full characterization of the set of possible correlations between subspaces as a convex polytope. Its extreme points are identified as the local maxima of mutual information and as maximally zero probability measures. Second, we derive an axiomatic characterization of preferences narrowing down the set of correlations a decision maker considers, making behavior about correlation testable. Thereby, she may regard collections of subspaces as independent from one another. We illustrate our model and results in simple examples on climate change, insurance and portfolio choice.
Local and Global Optima: Sheaves and Polynomial Approximations
Authors: Fernando Tohmé
Date: 2025-03-16 00:00:00

In this paper, we analyze how global optima of an agent's preferences can be reconstructed from the solutions found for local problems. A sheaf-theoretic analysis provides an abstract characterization of the global solution, and polynomial approximations are obtained when only a few local instances are available.
A Note on Obvious Manipulations of Quantile Stable Mechanisms
Authors: R. Pablo Arribillaga, Eliana Pepa-Risma
Date: 2025-03-14 00:00:00

In two-sided matching markets with contracts, quantile (or generalized median) stable mechanisms represent an interesting class that produces stable allocations which can be viewed as compromises between both sides of the market. These mechanisms balance the competing priorities of the parties while maintaining stability. This paper explores obvious manipulations of quantile stable mechanisms. Unfortunately, we get that any quantile stable mechanism different to the doctor-proposal DA is obviously manipulable. Our findings highlight the trade-offs between robustness to manipulation and other desirable properties, such as equity, in the design of stable matching mechanisms.
FNSE-SBGAN: Far-field Speech Enhancement with Schrodinger Bridge and Generative Adversarial Networks
Authors: Tong Lei, Qinwen Hu, Ziyao Lin, Andong Li, Rilin Chen, Meng Yu, Dong Yu, Jing Lu
Date: 2025-03-17 00:00:00
Tasks: Speech Enhancement

The prevailing method for neural speech enhancement predominantly utilizes fully-supervised deep learning with simulated pairs of far-field noisy-reverberant speech and clean speech. Nonetheless, these models frequently demonstrate restricted generalizability to mixtures recorded in real-world conditions. To address this issue, this study investigates training enhancement models directly on real mixtures. Specifically, we revisit the single-channel far-field to near-field speech enhancement (FNSE) task, focusing on real-world data characterized by low signal-to-noise ratio (SNR), high reverberation, and mid-to-high frequency attenuation. We propose FNSE-SBGAN, a framework that integrates a Schrodinger Bridge (SB)-based diffusion model with generative adversarial networks (GANs). Our approach achieves state-of-the-art performance across various metrics and subjective evaluations, significantly reducing the character error rate (CER) by up to 14.58% compared to far-field signals. Experimental results demonstrate that FNSE-SBGAN preserves superior subjective quality and establishes a new benchmark for real-world far-field speech enhancement. Additionally, we introduce an evaluation framework leveraging matrix rank analysis in the time-frequency domain, providing systematic insights into model performance and revealing the strengths and weaknesses of different generative methods.