pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Export-productivity Cutoff Contours with Profit Data: A Novel Threshold Estimation Approach
Authors: Peter H. Egger, Yulong Wang
Date: 2025-02-05 00:00:00

This paper develops a novel method to estimate firm-specific market-entry thresholds in international economics, allowing fixed costs to vary across firms alongside productivity. Our framework models market entry as an interaction between productivity and observable fixed-cost measures, extending traditional single-threshold models to ones with set-valued thresholds. Applying this approach to Chinese firm data, we estimate export-market entry thresholds as functions of domestic sales and surrogate variables for fixed costs. The results reveal substantial heterogeneity and threshold contours, challenging conventional single-threshold-point assumptions. These findings offer new insights into firm behavior and provide a foundation for further theoretical and empirical advancements in trade research.
Strategizing with AI: Insights from a Beauty Contest Experiment
Authors: Iuliia Alekseenko, Dmitry Dagaev, Sofia Paklina, Petr Parshakov
Date: 2025-02-05 00:00:00

A beauty contest is a wide class of games of guessing the most popular strategy among other players. In particular, guessing a fraction of a mean of numbers chosen by all players is a classic behavioral experiment designed to test iterative reasoning patterns among various groups of people. The previous literature reveals that the level of sophistication of the opponents is an important factor affecting the outcome of the game. Smarter decision makers choose strategies that are closer to theoretical Nash equilibrium and demonstrate faster convergence to equilibrium in iterated contests with information revelation. We replicate a series of classic experiments by running virtual experiments with modern large language models (LLMs) who play against various groups of virtual players. We test how advanced the LLMs' behavior is compared to the behavior of human players. We show that LLMs typically take into account the opponents' level of sophistication and adapt by changing the strategy. In various settings, most LLMs (with the exception of Llama) are more sophisticated and play lower numbers compared to human players. Our results suggest that LLMs (except Llama) are rather successful in identifying the underlying strategic environment and adopting the strategies to the changing set of parameters of the game in the same way that human players do. All LLMs still fail to play dominant strategies in a two-player game. Our results contribute to the discussion on the accuracy of modeling human economic agents by artificial intelligence.
A Framework to Monitor the Effects of External Shocks on Housing Markets
Authors: Anja Hahn, Sanela Omerovic, Sofie Waltl
Date: 2025-02-05 00:00:00

We develop a framework to holistically test for and monitor the impact of different types of events affecting a country's housing market, yet originating from housing-external sources. We classify events along three dimensions leading to testable hypotheses: prices versus quantities, supply versus demand, and immediate versus gradually evolving. These dimensions translate into guidance about which data type, statistical measure and testing strategy should be used. To perform such test suitable statistical models are needed which we implement as a hierarchical hedonic price model and a complementary count model. These models are amended by regime and contextual variables as suggested by our classification strategy. We apply this framework to the Austrian real estate market together with three disruptive events triggered by the COVID-19 pandemic, a policy tightening mortgage lending standards, as well as the cost-of-living crisis that came along with increased financing costs. The tests yield the expected results and, by that, some housing market puzzles are resolved. Deviating from the prior classification exercise means that some developments would have been undetected. Further, adopting our framework consistently when performing empirical research on residential real estate would lead to better comparable research results and, by that, would allow researchers to draw meta-conclusions from the bulk of studies available across time and space.
Cutting through Complexity: How Data Science Can Help Policymakers Understand the World
Authors: Arthur Turrell
Date: 2025-02-05 00:00:00

Economies are fundamentally complex and becoming more so, but the new discipline of data science-which combines programming, statistics, and domain knowledge-can help cut through that complexity, potentially with productivity benefits to boot. This chapter looks at examples of where innovations from data science are cutting through the complexities faced by policymakers in measurement, allocating resources, monitoring the natural world, making predictions, and more. These examples show the promise and potential of data science to aid policymakers, and point to where actions may be taken that would support further progress in this space.
The Resurgence of Trumponomics: Implications for the Future of ESG Investments in a Changing Political Landscape
Authors: Innocentus Alhamis
Date: 2025-02-04 00:00:00
Tasks: Navigate

Public policy shapes the economic landscape, influencing everything from corporate behavior to individual investment decisions. For Environmental, Social, and Governance (ESG) investors, these policy shifts can create opportunities and challenges as they navigate an ever-changing regulatory environment. The contrast between the Trump and Biden administrations offers a striking example of how differing political agendas can affect ESG investments. Trump's first term was marked by deregulation and policies favoring fossil fuels, which created an uncertain environment for sustainable investments. When Biden assumed office, his focus on climate action and clean energy reinvigorated the ESG sector, offering a more stable and supportive landscape for green investments. However, with Trump's return to power in his second term, these policies are being reversed again, leading to further volatility. This paper explores how such dramatic shifts in public policy influence economic strategies and directly impact ESG investors' decisions, forcing them to constantly reassess their portfolios in response to changing political climates.
Social Choice Rules with Responsibility for Individual Skills
Authors: Kensei Nakamura
Date: 2025-02-07 00:00:00

This paper examines normatively acceptable criteria for evaluating social states when individuals are responsible for their skills or productivity and these factors should be accounted for. We consider social choice rules over sets of feasible utility vectors \`a la Nash's (1950) bargaining problem. First, we identify necessary and sufficient conditions for choice rules to be rationalized by welfare orderings or functions over ability-normalized utility vectors. These general results provide a foundation for exploring novel choice rules with the normalization and providing their axiomatic foundations. By adding natural axioms, we propose and axiomatize a new class of choice rules, which can be viewed as combinations of three key principles: distribution according to individuals' abilities, utilitarianism, and egalitarianism. Furthermore, we show that at the axiomatic level, this class of choice rules is closely related to the classical bargaining solution introduced by Kalai and Smorodinsky (1975).
Impartial utilitarianism on infinite utility streams
Authors: Kensei Nakamura
Date: 2025-02-07 00:00:00
Tasks: Fairness

When evaluating policies that affect future generations, the most commonly used criterion is the discounted utilitarian rule. However, in terms of intergenerational fairness, it is difficult to justify prioritizing the current generation over future generations. This paper axiomatically examines impartial utilitarian rules over infinite-dimensional utility streams. We provide simple characterizations of the social welfare ordering evaluating utility streams by their long-run average in the domain where the average can be defined. Furthermore, we derive the necessary and sufficient conditions of the same axioms in a more general domain, the set of bounded streams. Some of these results are closely related to the Banach limits, a well-known generalization of the classical limit concept for streams. Thus, this paper can be seen as proposing an appealing subclass of the Banach limits by the axiomatic analysis.
Developing a Climate Litigation Framework: China's Contribution to International Environmental Law
Authors: Yedong Zhang
Date: 2025-02-06 00:00:00

Although "climate litigation" is not an indigenous term in China, localizing it is essential to support the development of an independent environmental legal knowledge system in China. Rooted in China's judicial tradition, which emphasizes substantive rationality, traditional legal theories have primarily focused on environmental law. However, the contemporary practices in the rule of law have created an unclear trajectory for climate litigation. Research in this area has long been trapped in a paradigm that relies on lawsuits for ecological environmental damage compensation and environmental public interest litigation, leading to a significant disconnect between theoretical frameworks and practical application. With the advancement of the "dual carbon" strategic goals-carbon peaking and carbon neutrality-it has become imperative to redefine the concept of climate litigation within the Chinese context. We need to establish a theoretical framework that aligns with the "dual carbon" objectives while providing theoretical and institutional support for climate litigation, ultimately contributing to the international discourse on climate justice. Additionally, Hong Kong's proactive climate governance and robust ESG (Environmental, Social, and Governance) practices provide valuable insights for developing comprehensive climate litigation mechanisms. Based on this analysis, we propose concrete plans for building a climate litigation system in China, establishing a preventive relief system and a multi-source legal framework at the substantive level and developing climate judicial mechanisms for mitigation and adaptation at the procedural level.
Exploring the Reform and Development Pathways of AIIB's Climate Accountability Mechanism in the Context of Global Climate Governance
Authors: Yedong Zhang
Date: 2025-02-06 00:00:00

After the Asian Infrastructure Investment Bank (AIIB) revised its Environmental and Social Framework, it has committed to certain climate-related objectives, yet an independent climate accountability mechanism has not been established. The absence of clear evaluation principles and procedural rules presents challenges in effectively addressing environmental investment disputes. This article reviews both domestic and international literature related to AIIB's climate accountability mechanism, identifying that the current Environmental and Social Framework's principled content and reliance on traditional approaches have resulted in issues of enforceability and the absence of an independent accountability institution. To enhance the effectiveness of AIIB's climate accountability mechanism, a reassessment of its development path is necessary. Future developments should transition from an additive path to a substitutive path, focusing on the application of international environmental and social standards, increasing stakeholder recognition and participation, promoting comprehensive reforms within AIIB, and establishing a coordinated independent accountability system. These measures aim to support the robust development of AIIB's climate accountability mechanism.
Good Data and Bad Data: The Welfare Effects of Price Discrimination
Authors: Maryam Farboodi, Nima Haghpanah, Ali Shourideh
Date: 2025-02-05 00:00:00

We ask when additional data collection by a monopolist to engage in price discrimination monotonically increases or decreases weighted surplus. To answer this question, we develop a model to study endogenous market segmentation subject to residual uncertainty. We give a complete characterization of when data collection is good or bad for surplus, which consists of a reduction of the problem to one with only two demand curves, and a condition for the two-demand-curves case that highlights three distinct effects of information on welfare. These results provide insights into when data collection and usage for price discrimination should be allowed.
Efficient Evaluation of Quantization-Effects in Neural Codecs
Authors: Wolfgang Mack, Ahmed Mustafa, Rafał Łaganowski, Samer Hijazy
Date: 2025-02-07 00:00:00
Tasks: Decoder, Quantization

Neural codecs, comprising an encoder, quantizer, and decoder, enable signal transmission at exceptionally low bitrates. Training these systems requires techniques like the straight-through estimator, soft-to-hard annealing, or statistical quantizer emulation to allow a non-zero gradient across the quantizer. Evaluating the effect of quantization in neural codecs, like the influence of gradient passing techniques on the whole system, is often costly and time-consuming due to training demands and the lack of affordable and reliable metrics. This paper proposes an efficient evaluation framework for neural codecs using simulated data with a defined number of bits and low-complexity neural encoders/decoders to emulate the non-linear behavior in larger networks. Our system is highly efficient in terms of training time and computational and hardware requirements, allowing us to uncover distinct behaviors in neural codecs. We propose a modification to stabilize training with the straight-through estimator based on our findings. We validate our findings against an internal neural audio codec and against the state-of-the-art descript-audio-codec.
Towards Explainable Spoofed Speech Attribution and Detection:a Probabilistic Approach for Characterizing Speech Synthesizer Components
Authors: Jagabandhu Mishra, Manasi Chhibber, Hye-jin Shim, Tomi H. Kinnunen
Date: 2025-02-06 00:00:00
Tasks: Attribute

We propose an explainable probabilistic framework for characterizing spoofed speech by decomposing it into probabilistic attribute embeddings. Unlike raw high-dimensional countermeasure embeddings, which lack interpretability, the proposed probabilistic attribute embeddings aim to detect specific speech synthesizer components, represented through high-level attributes and their corresponding values. We use these probabilistic embeddings with four classifier back-ends to address two downstream tasks: spoofing detection and spoofing attack attribution. The former is the well-known bonafide-spoof detection task, whereas the latter seeks to identify the source method (generator) of a spoofed utterance. We additionally use Shapley values, a widely used technique in machine learning, to quantify the relative contribution of each attribute value to the decision-making process in each task. Results on the ASVspoof2019 dataset demonstrate the substantial role of duration and conversion modeling in spoofing detection; and waveform generation and speaker modeling in spoofing attack attribution. In the detection task, the probabilistic attribute embeddings achieve $99.7\%$ balanced accuracy and $0.22\%$ equal error rate (EER), closely matching the performance of raw embeddings ($99.9\%$ balanced accuracy and $0.22\%$ EER). Similarly, in the attribution task, our embeddings achieve $90.23\%$ balanced accuracy and $2.07\%$ EER, compared to $90.16\%$ and $2.11\%$ with raw embeddings. These results demonstrate that the proposed framework is both inherently explainable by design and capable of achieving performance comparable to raw CM embeddings.
GenVC: Self-Supervised Zero-Shot Voice Conversion
Authors: Zexin Cai, Henry Li Xinyuan, Ashi Garg, Leibny Paola García-Perera, Kevin Duh, Sanjeev Khudanpur, Matthew Wiesner, Nicholas Andrews
Date: 2025-02-06 00:00:00
Tasks: Voice Conversion

Zero-shot voice conversion has recently made substantial progress, but many models still depend on external supervised systems to disentangle speaker identity and linguistic content. Furthermore, current methods often use parallel conversion, where the converted speech inherits the source utterance's temporal structure, restricting speaker similarity and privacy. To overcome these limitations, we introduce GenVC, a generative zero-shot voice conversion model. GenVC learns to disentangle linguistic content and speaker style in a self-supervised manner, eliminating the need for external models and enabling efficient training on large, unlabeled datasets. Experimental results show that GenVC achieves state-of-the-art speaker similarity while maintaining naturalness competitive with leading approaches. Its autoregressive generation also allows the converted speech to deviate from the source utterance's temporal structure. This feature makes GenVC highly effective for voice anonymization, as it minimizes the preservation of source prosody and speaker characteristics, enhancing privacy protection.
Blind Capon Beamformer Based on Independent Component Extraction: Single-Parameter Algorithm,
Authors: Zbyněk Koldovský, Jaroslav Čmejla, Stephen O'Regan
Date: 2025-02-06 00:00:00
Tasks: Direction of Arrival Estimation

We consider a phase-shift mixing model for linear sensor arrays in the context of blind source extraction. We derive a blind Capon beamformer that seeks the direction where the output is independent of the other signals in the mixture. The algorithm is based on Independent Component Extraction and imposes an orthogonal constraint, thanks to which it optimizes only one real-valued parameter related to the angle of arrival. The Cram\'er-Rao lower bound for the mean interference-to-signal ratio is derived. The algorithm and the bound are compared with conventional blind and direction-of-arrival estimation+beamforming methods, showing improvements in terms of extraction accuracy. An application is demonstrated in frequency-domain speaker extraction in a low-reverberation room.
Chest X-ray Foundation Model with Global and Local Representations Integration
Authors: Zefan Yang, Xuanang Xu, Jiajin Zhang, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
Date: 2025-02-07 00:00:00
Tasks: Mortality Prediction

Chest X-ray (CXR) is the most frequently ordered imaging test, supporting diverse clinical tasks from thoracic disease detection to postoperative monitoring. However, task-specific classification models are limited in scope, require costly labeled data, and lack generalizability to out-of-distribution datasets. To address these challenges, we introduce CheXFound, a self-supervised vision foundation model that learns robust CXR representations and generalizes effectively across a wide range of downstream tasks. We pretrain CheXFound on a curated CXR-1M dataset, comprising over one million unique CXRs from publicly available sources. We propose a Global and Local Representations Integration (GLoRI) module for downstream adaptations, by incorporating disease-specific local features with global image features for enhanced performance in multilabel classification. Our experimental results show that CheXFound outperforms state-of-the-art models in classifying 40 disease findings across different prevalence levels on the CXR-LT 24 dataset and exhibits superior label efficiency on downstream tasks with limited training data. Additionally, CheXFound achieved significant improvements on new tasks with out-of-distribution datasets, including opportunistic cardiovascular disease risk estimation and mortality prediction. These results highlight CheXFound's strong generalization capabilities, enabling diverse adaptations with improved label efficiency. The project source code is publicly available at https://github.com/RPIDIAL/CheXFound.
Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD
Authors: Aravind R. Krishnan, Yihao Liu, Kaiwen Xu, Michael E. Kim, Lucas W. Remedios, Gaurav Rudravaram, Adam M. Saunders, Bradley W. Richmond, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman, Lianrui Zuo
Date: 2025-02-07 00:00:00
Tasks: Generative Adversarial Network, Image Registration

Paired inspiratory-expiratory CT scans enable the quantification of gas trapping due to small airway disease and emphysema by analyzing lung tissue motion in COPD patients. Deformable image registration of these scans assesses regional lung volumetric changes. However, variations in reconstruction kernels between paired scans introduce errors in quantitative analysis. This work proposes a two-stage pipeline to harmonize reconstruction kernels and perform deformable image registration using data acquired from the COPDGene study. We use a cycle generative adversarial network (GAN) to harmonize inspiratory scans reconstructed with a hard kernel (BONE) to match expiratory scans reconstructed with a soft kernel (STANDARD). We then deformably register the expiratory scans to inspiratory scans. We validate harmonization by measuring emphysema using a publicly available segmentation algorithm before and after harmonization. Results show harmonization significantly reduces emphysema measurement inconsistencies, decreasing median emphysema scores from 10.479% to 3.039%, with a reference median score of 1.305% from the STANDARD kernel as the target. Registration accuracy is evaluated via Dice overlap between emphysema regions on inspiratory, expiratory, and deformed images. The Dice coefficient between inspiratory emphysema masks and deformably registered emphysema masks increases significantly across registration stages (p<0.001). Additionally, we demonstrate that deformable registration is robust to kernel variations.
Bridging Scales in Map Generation: A scale-aware cascaded generative mapping framework for seamless and consistent multi-scale cartographic representation
Authors: Chenxing Sun, Yongyang Xu, Xuwei Xu, Xixi Fan, Jing Bai, Xiechun Lu, Zhanlong Chen
Date: 2025-02-07 00:00:00
Tasks: Image Generation

Multi-scale tile maps are essential for geographic information services, serving as fundamental outcomes of surveying and cartographic workflows. While existing image generation networks can produce map-like outputs from remote sensing imagery, their emphasis on replicating texture rather than preserving geospatial features limits cartographic validity. Current approaches face two fundamental challenges: inadequate integration of cartographic generalization principles with dynamic multi-scale generation and spatial discontinuities arising from tile-wise generation. To address these limitations, we propose a scale-aware cartographic generation framework (SCGM) that leverages conditional guided diffusion and a multi-scale cascade architecture. The framework introduces three key innovations: a scale modality encoding mechanism to formalize map generalization relationships, a scale-driven conditional encoder for robust feature fusion, and a cascade reference mechanism ensuring cross-scale visual consistency. By hierarchically constraining large-scale map synthesis with small-scale structural priors, SCGM effectively mitigates edge artifacts while maintaining geographic fidelity. Comprehensive evaluations on cartographic benchmarks confirm the framework's ability to generate seamless multi-scale tile maps with enhanced spatial coherence and generalization-aware representation, demonstrating significant potential for emergency mapping and automated cartography applications.