pwc-archive/papers-with-abstracts huggingface dataset:
Who is More Bayesian: Humans or ChatGPT?
Authors: Tianshi Mu, Pranjal Rawat, John Rust, Chengjun Zhang, Qixuan Zhong
Date: 2025-04-14 00:00:00
Tasks: Binary Classification

We compare the performance of human and artificially intelligent (AI) decision makers in simple binary classification tasks where the optimal decision rule is given by Bayes Rule. We reanalyze choices of human subjects gathered from laboratory experiments conducted by El-Gamal and Grether and Holt and Smith. We confirm that while overall, Bayes Rule represents the single best model for predicting human choices, subjects are heterogeneous and a significant share of them make suboptimal choices that reflect judgement biases described by Kahneman and Tversky that include the ``representativeness heuristic'' (excessive weight on the evidence from the sample relative to the prior) and ``conservatism'' (excessive weight on the prior relative to the sample). We compare the performance of AI subjects gathered from recent versions of large language models (LLMs) including several versions of ChatGPT. These general-purpose generative AI chatbots are not specifically trained to do well in narrow decision making tasks, but are trained instead as ``language predictors'' using a large corpus of textual data from the web. We show that ChatGPT is also subject to biases that result in suboptimal decisions. However we document a rapid evolution in the performance of ChatGPT from sub-human performance for early versions (ChatGPT 3.5) to superhuman and nearly perfect Bayesian classifications in the latest versions (ChatGPT 4o).
Position Uncertainty in a Prisoner's Dilemma Game : An Experiment
Authors: Chowdhury Mohammad Sakib Anwar, Konstantinos Georgalos, Sonali SenGupta
Date: 2025-04-14 00:00:00
Tasks: Descriptive, Position

Gallice and Monzon (2019) present a natural environment that sustains full cooperation in one-shot social dilemmas among a finite number of self-interested agents. They demonstrate that in a sequential public goods game, where agents lack knowledge of their position in the sequence but can observe some predecessors' actions, full contribution emerges in equilibrium due to agents' incentive to induce potential successors to follow suit. Furthermore, they show that this principle extends to a number of social dilemmas, with the prominent example that of the prisoner's dilemma. In this study, we experimentally test the theoretical predictions of this model in a multi- player prisoner's dilemma environment, where subjects are not aware of their position in the sequence and receive only partial information on past cooperating actions. We test the predictions of the model, and through rigorous structural econometric analysis, we test the descriptive capacity of the model against alternative behavioural strategies, such as conditional cooperation, altruistic play and free-riding behaviour. We find that the majority resorts to free-riding behaviour, around 30% is classified as Gallice and Monzon (2019) types, followed by those with social preference considerations and the unconditional altruists.
Public Health Insurance of Children and Maternal Labor Market Outcomes
Authors: Konstantin Kunze
Date: 2025-04-14 00:00:00

This paper exploits variation resulting from a series of federal and state Medicaid expansions between 1977 and 2017 to estimate the effects of children's increased access to public health insurance on the labor market outcomes of their mothers. The results imply that the extended Medicaid eligibility of children leads to positive labor supply responses of single mothers and to negative labor supply responses of married mothers. The analysis of mechanisms suggests that extended children's Medicaid eligibility positively affects take-up of Medicaid and health of children.
Short-Term Effects of COVID-19 on Wages: Empirical Evidence and Underlying Mechanisms
Authors: Bo Wu
Date: 2025-04-14 00:00:00

This study investigates the causal relationship between the COVID-19 pandemic and wage levels, aiming to provide a quantified assessment of the impact. While no significant evidence is found for long-term effects, the analysis reveals a statistically significant positive influence on wages in the short term, particularly within a one-year horizon. Contrary to common expectations, the results suggest that COVID-19 may have led to short-run wage increases. Several potential mechanisms are proposed to explain this counterintuitive outcome. The findings remain robust when controlling for other macroeconomic indicators such as GDP, considered here as a proxy for aggregate demand. The paper also addresses issues of external validity in the concluding section.
Predicting Children's Travel Modes for School Journeys in Switzerland: A Machine Learning Approach Using National Census Data
Authors: Hannes Wallimann, Noah Balthasar
Date: 2025-04-14 00:00:00

Children's travel behavior plays a critical role in shaping long-term mobility habits and public health outcomes. Despite growing global interest, little is known about the factors influencing travel mode choice of children for school journeys in Switzerland. This study addresses this gap by applying a random forest classifier - a machine learning algorithm - to data from the Swiss Mobility and Transport Microcensus, in order to identify key predictors of children's travel mode choice for school journeys. Distance consistently emerges as the most important predictor across all models, for instance when distinguishing between active vs. non-active travel or car vs. non-car usage. The models show relatively high performance, with overall classification accuracy of 87.27% (active vs. non-active) and 78.97% (car vs. non-car), respectively. The study offers empirically grounded insights that can support school mobility policies and demonstrates the potential of machine learning in uncovering behavioral patterns in complex transport datasets.
To Buy an Electric Vehicle or Not? A Bayesian Analysis of Consumer Intent in the United States
Authors: Nafisa Lohawala, Mohammad Arshad Rahman
Date: 2025-04-14 00:00:00

The adoption of electric vehicles (EVs) is considered critical to achieving climate goals, yet it hinges on consumer interest. This study explores how public intent to purchase EVs relates to four unexamined factors: exposure to EV information, perceptions of EVs' environmental benefits, views on government climate policy, and confidence in future EV infrastructure; while controlling for prior EV ownership, political affiliation, and demographic characteristics (e.g., age, gender, education, and geographic location). We utilize data from three nationally representative opinion polls conducted by the Pew Research Center between 2021 and 2023, and employ Bayesian techniques to estimate the ordinal probit and ordinal quantile models. Results from ordinal probit show that respondents who are well-informed about EVs, perceive them as environmentally beneficial, or are confident in development of charging stations are more likely to express strong interest in buying an EV, with covariate effects--a metric rarely reported in EV research--of 10.2, 15.5, and 19.1 percentage points, respectively. In contrast, those skeptical of government climate initiatives are more likely to express no interest, by more than 10 percentage points. Prior EV ownership exhibits the highest covariate effect (ranging from 19.0 to 23.1 percentage points), and the impact of most demographic variables is consistent with existing studies. The ordinal quantile models demonstrate significant variation in covariate effects across the distribution of EV purchase intent, offering insights beyond the ordinal probit model. This article is the first to use quantile modeling to reveal how covariate effects differ significantly throughout the spectrum of EV purchase intent.
Agentic Workflows for Economic Research: Design and Implementation
Authors: Herbert Dawid, Philipp Harting, Hankui Wang, Zhongli Wang, Jiachen Yi
Date: 2025-04-13 00:00:00

This paper introduces a methodology based on agentic workflows for economic research that leverages Large Language Models (LLMs) and multimodal AI to enhance research efficiency and reproducibility. Our approach features autonomous and iterative processes covering the entire research lifecycle--from ideation and literature review to economic modeling and data processing, empirical analysis and result interpretation--with strategic human oversight. The workflow architecture comprises specialized agents with clearly defined roles, structured inter-agent communication protocols, systematic error escalation pathways, and adaptive mechanisms that respond to changing research demand. Human-in-the-loop (HITL) checkpoints are strategically integrated to ensure methodological validity and ethical compliance. We demonstrate the practical implementation of our framework using Microsoft's open-source platform, AutoGen, presenting experimental examples that highlight both the current capabilities and future potential of agentic workflows in improving economic research.
Communication, Awareness and Acceptance of Digital Banking Amidst Cash Crunch in Southeast and South-South, Nigeria
Authors: Okechukwu Christopher Onuegbu, Bettina Oboakore Agbamu, Belinda Uju Anyakoha, Ogonna Wilson Anunike
Date: 2025-04-13 00:00:00
Tasks: Survey

Digital banking is among the technological innovations currently reverberating the cyber wave. this study seeks to assess communication, awareness and acceptance of it among the residents of south-east and south-south, nigeria. the survey objectives were to ascertain awareness level of the south-east and south-south residents towards digital banking during the cash crunch, determine the acceptance level of digital banking among the south-east and south-south residents, find out the role of communication in awareness and acceptance of digital banking during the cash crunch in south-east and south-south nigeria, and assess the usage of digital banking amidst cash crunch in south-east and south nigeria. the study methodology is a sample survey which allowed researchers to administer questionnaires on 385 respondents out of the 50,166,807 study population. the findings showed that awareness level of digital banking was good (36%) in south-east and south-south nigeria during the cash crunch but it level of acceptance and usage improved more (37%) after the cash crunch. the study also ascertained that communication contribute significantly (59%) towards the usage and acceptance of digital banking in the two zones. it further showed that usage of digital banking in south-east and south-south has improved due to significant contributions of communication.
What should the encroaching supplier do in markets with some loyal customers? A Stackelberg Game Approach
Authors: Gurkirat Wadhwa, Veeraruna Kavitha
Date: 2025-04-13 00:00:00
Tasks: Friction

Considering a supply chain with partial vertical integration, we attempt to seek answers to several questions related to the cooperation competition based friction, abundant in such networks. Such an SC can represent a supplier with an inhouse production unit that attempts to control an outhouse production unit via the said friction. The two production units can have different sets of loyal customer bases and the aim of the manufacturer supplier duo would be to get the best out of the two customer bases. Our analysis shows that under certain market conditions, an optimal strategy might be to allow both units to earn positive profits particularly when they hold similar market power and when customer loyalty is high. In cases of weaker customer loyalty, however, the optimal approach may involve pressurizing the outhouse unit to operate at minimal profits. Even more intriguing is the scenario where the outhouse unit has a greater market power and customer loyalty remains strong here, it may be optimal for the inhouse unit to operate at a loss just enough to dismantle the downstream monopoly.
Structural robustness of the international food supply network under external shocks and its determinants
Authors: Han-Yu Zhu, Yin-Ting Zhang, Wen-Jie Xie, Wei-Xing Zhou
Date: 2025-04-11 00:00:00

The stability of the global food supply network is critical for ensuring food security. This study constructs an aggregated international food supply network based on the trade data of four staple crops and evaluates its structural robustness through network integrity under accumulating external shocks. Network integrity is typically quantified in network science by the relative size of the largest connected component, and we propose a new robustness metric that incorporates both the broadness p and severity q of external shocks. Our findings reveal that the robustness of the network has gradually increased over the past decades, punctuated by temporary declines that can be explained by major historical events. While the aggregated network remains robust under moderate disruptions, extreme shocks targeting key suppliers such as the United States and India can trigger systemic collapse. When the shock broadness p is less than about 0.3 and the shock severity q is close to 1, the structural robustness curves S(p,q) decrease linearly with respect to the shock broadness p, suggesting that the most critical economies have relatively even influence on network integrity. Comparing the robustness curves of the four individual staple foods, we find that the soybean supply network is the least robust. Furthermore, regression and machine learning analyses show that increaseing food (particularly rice and soybean) production enhances network robustness, while rising food prices significantly weaken it.
What is patent quality?
Authors: Gaetan de Rassenfosse
Date: 2025-04-05 00:00:00

Patent quality is a critical yet complex concept of interest to inventors, legal experts, and policymakers alike. This article demystifies patent quality by examining its various dimensions and definitions. It begins by discussing invention quality, emphasizing that the notion of 'technological superiority' is often too abstract to capture practical impact. The work distinguishes between incremental and radical innovations, highlighting that inventions can be evaluated by improvements over previous technologies and their economic potential. Four definitions of patent quality are explored: one based on invention quality, another linking quality with economic value, a third focusing on drafting clarity, and a fourth measuring compliance with legal standards. The article explains that high-quality patents may not necessarily protect valuable inventions and that weak patents can hold significant economic value due to legal advantages. This accessible review provides a comprehensive framework that benefits a broad audience interested in innovation policy and intellectual property.
Mensuração da Transferência de Riqueza em Planos de Contribuição Definida com a Marcação de Ativos na Curva
Authors: Eduardo Fraga L. de Melo, Rodrigo S. Targino
Date: 2025-04-05 00:00:00

The methodology for measuring financial assets in defined contribution (DC) pension plans has significant implications whether wealth transfers will occur among participants. In December 2024, a regulatory act was issued for Closed Pension Entities, allowing the use of the hold-to-maturity (HTM) measurement method of treasury bonds in DC plans. This article quantifies the financial impact on participants of adopting HTM valuation in these plans, using real data from the term structure of the real interest rates to assess the resulting wealth transfers. The analysis highlights how HTM valuation creates asymmetries in financial outcomes, benefiting some participants at the expense of others. Wealth transfers occur both during any withdrawal of funds and at the time of contributions, including portfolio reallocations that involve buying or selling bonds. Partial use of HTM or attempts to immunize outflows do not completely eliminate wealth transfers. The results reinforce that the use of mark-to-market (MTM) valuation of assets in DC plans prevents wealth transfers and, consequently, financial losses for participants. O m\'etodo de mensura\c{c}\~ao de ativos financeiros em planos de previd\^encia na modalidade de contribui\c{c}\~ao definida (CD, ou contribui\c{c}\~ao vari\'avel CV, na fase de acumula\c{c}\~ao) tem implica\c{c}\~oes significativas se haver\'a transfer\^encia de riqueza entre os participantes. Em Dez/2024 foi publicada norma para as Entidades Fechadas de Previd\^encia Complementar possibilitando o uso da marca\c{c}\~ao na curva de t\'itulos p\'ublicos federais nos planos CD e CV na fase de acumula\c{c}\~ao. Este artigo quantifica o impacto financeiro nos participantes da ado\c{c}\~ao da marca\c{c}\~ao na curva (HTM {\it Hold to Maturity}) nestes planos, utilizando dados reais da estrutura a termo da taxa de juros de cupom de IPCA para avaliar as transfer\^encias de riqueza resultantes dessa ado\c{c}\~ao. A an\'alise evidencia como a marca\c{c}\~ao na curva gera assimetrias nos resultados financeiros, beneficiando alguns participantes em detrimento de outros. As transfer\^encias de riqueza ocorrem tanto em qualquer retirada de recursos quanto tamb\'em na entrada (contribui\c{c}\~oes), inclusive realoca\c{c}\~oes da carteira que impliquem venda ou compra de t\'itulos. O uso do HTM de forma parcial ou a tentativa de imuniza\c{c}\~ao de sa\'idas n\~ao eliminam por completo transfer\^encias de riqueza. Os resultados refor\c{c}am que, para fins de cotiza\c{c}\~ao, o uso da marca\c{c}\~ao a mercado (MTM {\it Mark to Market}) de ativos em planos CD (e CV na fase de diferimento) evita transfer\^encias de riqueza e, por consequ\^encia, preju\'izos financeiros aos seus participantes.
A Unified Theory of School Choice
Authors: Peter Doe
Date: 2025-04-12 00:00:00

In school choice, policymakers consolidate a district's objectives for a school into a priority ordering over students. They then face a trade-off between respecting these priorities and assigning students to more-preferred schools. However, because priorities are the amalgamation of multiple policy goals, some may be more flexible than others. This paper introduces a model that distinguishes between two types of priority: a between-group priority that ranks groups of students and must be respected, and a within-group priority for efficiently allocating seats within each group. The solution I introduce, the unified core, integrates both types. I provide a two-stage algorithm, the DA-TTC, that implements the unified core and generalizes both the Deferred Acceptance and Top Trading Cycles algorithms. This approach provides a method for improving efficiency in school choice while honoring policymakers' objectives.
Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning
Authors: Davoud Shariat Panah, Alessandro N Franciosi, Cormac McCarthy, Andrew Hines
Date: 2025-04-15 00:00:00
Tasks: Classification, Self-Supervised Learning, Sound Classification

Asthma is a chronic respiratory condition that affects millions of people worldwide. While this condition can be managed by administering controller medications through handheld inhalers, clinical studies have shown low adherence to the correct inhaler usage technique. Consequently, many patients may not receive the full benefit of their medication. Automated classification of inhaler sounds has recently been studied to assess medication adherence. However, the existing classification models were typically trained using data from specific inhaler types, and their ability to generalize to sounds from different inhalers remains unexplored. In this study, we adapted the wav2vec 2.0 self-supervised learning model for inhaler sound classification by pre-training and fine-tuning this model on inhaler sounds. The proposed model shows a balanced accuracy of 98% on a dataset collected using a dry powder inhaler and smartwatch device. The results also demonstrate that re-finetuning this model on minimal data from a target inhaler is a promising approach to adapting a generic inhaler sound classification model to a different inhaler device and audio capture hardware. This is the first study in the field to demonstrate the potential of smartwatches as assistive technologies for the personalized monitoring of inhaler adherence using machine learning models.
Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis
Authors: Yifan Yang, Shujie Liu, Jinyu Li, Yuxuan Hu, Haibin Wu, Hui Wang, Jianwei Yu, Lingwei Meng, Haiyang Sun, Yanqing Liu, Yan Lu, Kai Yu, Xie Chen
Date: 2025-04-14 00:00:00
Tasks: Language Modeling, Language Modelling, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Recent zero-shot text-to-speech (TTS) systems face a common dilemma: autoregressive (AR) models suffer from slow generation and lack duration controllability, while non-autoregressive (NAR) models lack temporal modeling and typically require complex designs. In this paper, we introduce a novel pseudo-autoregressive (PAR) codec language modeling approach that unifies AR and NAR modeling. Combining explicit temporal modeling from AR with parallel generation from NAR, PAR generates dynamic-length spans at fixed time steps. Building on PAR, we propose PALLE, a two-stage TTS system that leverages PAR for initial generation followed by NAR refinement. In the first stage, PAR progressively generates speech tokens along the time dimension, with each step predicting all positions in parallel but only retaining the left-most span. In the second stage, low-confidence tokens are iteratively refined in parallel, leveraging the global contextual information. Experiments demonstrate that PALLE, trained on LibriTTS, outperforms state-of-the-art systems trained on large-scale data, including F5-TTS, E2-TTS, and MaskGCT, on the LibriSpeech test-clean set in terms of speech quality, speaker similarity, and intelligibility, while achieving up to ten times faster inference speed. Audio samples are available at https://anonymous-palle.github.io.
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning
Authors: Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish, Arunasish Sen, Jian Xie, Grant P. Strimel, Andreas Schwarz
Date: 2025-04-12 00:00:00
Tasks: Instruction Following

We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50M is built from publicly available speech corpora, which collectively contain 14K hours of speech, and leverages LLMs along with off-the-shelf expert models. The dataset spans five languages, encompassing a diverse range of speech understanding as well as controllable speech generation instructions. Using SIFT-50M, we train SIFT-LLM, which outperforms existing speech-text LLMs on instruction-following benchmarks while achieving competitive performance on foundational speech tasks. To support further research, we also introduce EvalSIFT, a benchmark dataset specifically designed to evaluate the instruction-following capabilities of speech-text LLMs.