pwc-archive/papers-with-abstracts huggingface dataset:
Quantum-Inspired Cournot Model
Authors: Amarendra Sharma
Date: 2025-04-28 00:00:00
Tasks: Decision Making, model

This paper introduces economists to quantum-inspired approaches for modeling firm behavior in a Cournot Duopoly, designed for accessibility and pedagogical use. We present key quantum concepts -- superposition, entanglement, and quantum search algorithms (Grover's and D\"{u}rr-H\o{}yer's) -- in an intuitive manner, tailored for those without a quantum physics background. These concepts are applied to represent uncertainty, interdependence, and optimization in novel ways, offering fresh perspectives on firm decision-making. By incorporating numerical examples, we illustrate how quantum-inspired models differ from classical Cournot outcomes, highlighting potential advantages in capturing complex strategic interactions. The paper aims to bridge quantum computing and economic theory, providing a foundation for teaching and exploring advanced modeling techniques.
Bridging Short- and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting
Authors: Tiantian Tu
Date: 2025-04-27 00:00:00
Tasks: Decision Making, Time Series, Time Series Forecasting

Time series forecasting is crucial for decision-making across various domains, particularly in financial markets where stock prices exhibit complex and non-linear behaviors. Accurately predicting future price movements is challenging due to the difficulty of capturing both short-term fluctuations and long-term dependencies in the data. Convolutional Neural Networks (CNNs) are well-suited for modeling localized, short-term patterns but struggle with long-range dependencies due to their limited receptive field. In contrast, Transformers are highly effective at capturing global temporal relationships and modeling long-term trends. In this paper, we propose a hybrid architecture that combines CNNs and Transformers to effectively model both short- and long-term dependencies in financial time series data. We apply this approach to forecast stock price movements for S\&P 500 constituents and demonstrate that our model outperforms traditional statistical models and popular deep learning methods in intraday stock price forecasting, providing a robust framework for financial prediction.
Effects of Price Regulations on Service Utilization and Public Insurance Costs: Evidence from Telehealth Parity Laws
Authors: Piyush Akimitsu
Date: 2025-04-21 00:00:00
Tasks: Experimental Design

This study explores Telehealth Parity Laws (TPLs) and their heterogeneous treatment effects by policy type on outpatient utilization and Medicare costs, considering broadband and licensure infrastructure. State-specific legislative framings create varied Price (physician reimbursement) and Cost (consumer expense) control combinations within a quasi-experimental design. Partial equilibrium causal estimates reveal negligible impacts on Medicare enrollment, indicating that Medicare cost shifts stem purely from outpatient utilization changes. Broadband access correlates with increased preventable hospital stays but lower Medicare costs and enrollment. Additionally, the Interstate Licensure Compact increases enrollment among the aged and disabled, possibly addressing previously unmet demand for healthcare services.
The Japanese Personnel Inquiry Records (PIR): Elite, Adoption, Family Composition in the 1900s
Authors: Hiroshi Kumanomido, Yutaro Takayasu, Suguru Otani
Date: 2025-04-26 00:00:00

We construct a novel elite-level dataset on prewar Japanese elites using the Personnel Inquiry Records (PIR), a historical biographical source covering 1903 to 1939. The PIR contains rich information on family structure, education, occupation, and spatial mobility, capturing elite formation during Japan's transition from a feudal to modern society. By reconstructing intergenerational links and family networks, we document the persistence and transformation of elite status. The dataset provides a foundational resource for quantitative historical research on elite reproduction, social stratification, and institutional development.
Skills Beget Skills: Evidence from Historical School Reforms Targeting Health and Further Education
Authors: Volha Lazuka, Peter Sandholt Jensen
Date: 2025-04-24 00:00:00

This paper investigates the dynamic complementarity between school health and education reforms implemented in Denmark between 1940 and 1965: the introduction of school doctors and the expansion of secondary education. Using a staggered difference-in-differences approach for multiple treatments, we study the reform effects on individuals' outcomes in the ages 55-64. We find that each reform leads to significant improvements in health and education outcomes, including reduced mortality, fewer hospitalizations, and higher educational attainment. The singular impact of each reform is doubled when both reforms are implemented together, resulting in a 9 percent increase in earnings. These findings underscore high societal returns to complementary school investments in the long term.
The Effects of Trade Openness on CO2 Emission in Vietnam
Authors: Le Thi Thanh Mai, Hoang-Anh Le, Kim Taegi
Date: 2025-04-24 00:00:00

This paper investigates the relationship between trade openness and CO2 emissions in Vietnam using the data from 1986 to 2014. We examine the consistency of the environmental Kuznets curve hypothesis (EKC) and the pollution heaven hypothesis (PHH) in Vietnam case. In 1986 Vietnam government began to launch free-market economic reforms. Since then, Vietnam economy experienced the breakthrough innovation in trade openness. On the other hand, Vietnam witness a growing level of CO2 emission. The annual growth rate of CO2 emission during the period is 7.26%, and that of trade volume is 16.11%. The empirical results show that the relationship between CO2 emissions and income per capita is an inverted U-shaped, consistent with to EKC hypothesis. We also find that the pollution heaven hypothesis is supported in that energy use and international trade contribute to air pollution, but becoming a full member of WTO brings positive effect to Vietnamese environment.
International Comparisons: Multilateral Indices and Nonparametric Welfare Bounds
Authors: Hubert Wu
Date: 2025-04-23 00:00:00

Multilateral index numbers, such as those used to make international comparisons of prices and income, are fundamental objects in economics. However, these numbers are often challenging to interpret in terms of economic welfare, as a data-dependent 'taste bias' can arise for these indices that distorts measurement of price and income levels and dispersion. To study this problem, I develop a means to appraise indices' economic interpretability using non-parametric bounds on a true cost-of-living index. These bounds improve upon their classical counterparts, define a new class of indices that generalises the 'star' system, and show how existing indices can be corrected for preference misspecification. In my main application I appraise existing international comparison methods. I find that taste bias generally leads to overestimates (underestimates) of price (income) levels. Superlative indices, such as the Fisher-GEKS index, lie within the permitted bounds more frequently than non-superlative methods, but the mean size of this bias is modest in all examined cases. My results can thus be interpreted as supporting the economic validity of the myriad multilateral methods which are in practical use.
Complementarity Between Paid and Organic Installs in Mobile App Advertising
Authors: Harang Ju, Michael Zhao, Sinan Aral
Date: 2025-04-22 00:00:00
Tasks: Marketing

Prior spending shutoff experiments in search advertising have found that paid ads cannibalize organic traffic. But it is unclear whether the same is true for other high volume advertising channels like mobile display advertising. We therefore analyzed a large-scale spending shutoff experiment by a US-based mobile game developer, GameSpace. Contrary to previous findings, we found that paid advertising boosts organic installs rather than cannibalizing them. Specifically, every $100 spent on ads is associated with 37 paid and 3 organic installs. The complementarity between paid ads and organic installs is corroborated by evidence of temporal and cross-platform spillover effects: ad spending today is associated with additional paid and organic installs tomorrow and impressions on one platform lead to clicks on other platforms. Our findings demonstrate that mobile app install advertising is about 7.5% more effective than indicated by paid install metrics alone due to spillover effects, suggesting that mobile app developers are under-investing in marketing.
FinTextSim: Enhancing Financial Text Analysis with BERTopic
Authors: Simon Jehnen, Joaquín Ordieres-Meré, Javier Villalba-Díez
Date: 2025-04-22 00:00:00
Tasks: Sentence, Stock Price Prediction

Recent advancements in information availability and computational capabilities have transformed the analysis of annual reports, integrating traditional financial metrics with insights from textual data. To extract valuable insights from this wealth of textual data, automated review processes, such as topic modeling, are crucial. This study examines the effectiveness of BERTopic, a state-of-the-art topic model relying on contextual embeddings, for analyzing Item 7 and Item 7A of 10-K filings from S&P 500 companies (2016-2022). Moreover, we introduce FinTextSim, a finetuned sentence-transformer model optimized for clustering and semantic search in financial contexts. Compared to all-MiniLM-L6-v2, the most widely used sentence-transformer, FinTextSim increases intratopic similarity by 81% and reduces intertopic similarity by 100%, significantly enhancing organizational clarity. We assess BERTopic's performance using embeddings from both FinTextSim and all-MiniLM-L6-v2. Our findings reveal that BERTopic only forms clear and distinct economic topic clusters when paired with FinTextSim's embeddings. Without FinTextSim, BERTopic struggles with misclassification and overlapping topics. Thus, FinTextSim is pivotal for advancing financial text analysis. FinTextSim's enhanced contextual embeddings, tailored for the financial domain, elevate the quality of future research and financial information. This improved quality of financial information will enable stakeholders to gain a competitive advantage, streamlining resource allocation and decision-making processes. Moreover, the improved insights have the potential to leverage business valuation and stock price prediction models.
Visualizing Public Opinion on X: A Real-Time Sentiment Dashboard Using VADER and DistilBERT
Authors: Yanampally Abhiram Reddy, Siddhi Agarwal, Vikram Parashar, Arshiya Arora
Date: 2025-04-21 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification, Text Normalization

In the age of social media, understanding public sentiment toward major corporations is crucial for investors, policymakers, and researchers. This paper presents a comprehensive sentiment analysis system tailored for corporate reputation monitoring, combining Natural Language Processing (NLP) and machine learning techniques to accurately interpret public opinion in real time. The methodology integrates a hybrid sentiment detection framework leveraging both rule-based models (VADER) and transformer-based deep learning models (DistilBERT), applied to social media data from multiple platforms. The system begins with robust preprocessing involving noise removal and text normalization, followed by sentiment classification using an ensemble approach to ensure both interpretability and contextual accuracy. Results are visualized through sentiment distribution plots, comparative analyses, and temporal sentiment trends for enhanced interpretability. Our analysis reveals significant disparities in public sentiment across major corporations, with companies like Amazon (81.2) and Samsung (45.8) receiving excellent sentiment scores, while Microsoft (21.7) and Walmart (21.9) exhibit poor sentiment profiles. These findings demonstrate the utility of our multi-source sentiment framework in providing actionable insights regarding corporate public perception, enabling stakeholders to make informed strategic decisions based on comprehensive sentiment analysis.
Evidence of Donor Bias in Chicago Police Stops
Authors: Angela Zorro Medina, David Hackett, Devin Green, Robert Vargas
Date: 2025-04-21 00:00:00

This study provides the first empirical evidence that private donations to police departments can influence officer behavior. Drawing on the psychology of reciprocity bias, we theorize that public donations create social debts that shape discretionary enforcement. Using quasi-experimental data from Chicago, we find that after 7-Eleven sponsored a police foundation gala, investigatory stops, particularly of Black pedestrians, increased around its stores. These findings reveal a racialized pattern of donor bias in policing and call into question the consequences of private donations to public law enforcement.
Agricultural Economics and Innovation in the Inca Empire
Authors: Luis-Felipe Arizmendi
Date: 2025-04-21 00:00:00
Tasks: Diversity

By studying the Inca Empire's agricultural accomplishments, we learn how ancient civilizations adapted to their circumstances, used natural resources effectively, and sustained agriculture for millennia. This understanding affects global food production systems as we face land degradation, climate change, and sustainable farming. Inca terrace farming is a sustainable and innovative food production method still relevant today. By studying the past and applying its ideas to modern agriculture, we can make global food production more sustainable and resilient. Keywords: Inca Empire, Terrace farming, Agricultural innovation, Food production, ancient civilizations, Crop diversity
Identity Dilemma in Immigrant Consumers: a discourse analysis of diaspora marketing
Authors: Mohammad Saleh Torkestani, Zohreh Dehdashti Shahrokh, Kobra Bakhshizadeh Borj, Pedram Jahedi
Date: 2025-04-21 00:00:00
Tasks: Marketing

Purpose - Diasporas of a country of origin are groups interested in country-of-origin scattered in host countries and familiar with markets of host countries. This paper aims to discourse analysis of diaspora marketing researches. Methodology - In judgmental sampling using selected keywords 24 researches in two eras before and after 2010 are selected for discourse analysis. Faircloughs critical discourse analysis model is used to analyze research data. Findings- Two different discourses can be distinguished in diaspora marketing researches. The diaspora niche marketing discourse is focused on penetrating into diaspora niche market. The diaspora marketing strategy discourse is focused on developing into international market using diaspora leverage. Research limitations and implications - Judgmental sampling and lived experience of researchers in a developing country which is usually considered as a diasporas country of origin rather than a diasporas host country limit the generalizability of research results. Practical implications - Understanding the extensions of diaspora marketing concept and diaspora marketing discourses is helpful for designing appropriate strategies for entering and developing in the international market. Originality - this paper investigates extensions of diaspora concept in previous research identifies diaspora marketing discourses and compares dominant diaspora marketing discourse with global marketing discourse.
The Influence of Establishing Belt and Road Node Cities on the Development of Digital Inclusive Finance Across Chinese Provinces
Authors: Bo Wu
Date: 2025-04-20 00:00:00
Tasks: Descriptive

Domestic and foreign scholars have conducted many studies on the influencing factors of digital inclusive finance, but few studies on the impact of the "Belt and Road" and the establishment of node cities on digital inclusive finance. Therefore, it is necessary to study the impact of the establishment of node cities in the "Belt and Road" on the development of digital inclusive finance. This paper mainly uses descriptive analysis and literature analysis to summarize and analyze the development status of China's digital inclusive finance and relevant theoretical research on the development of the "Belt and Road" Initiative, and analyzes and collates the background knowledge needed for the demonstration from the aspects of the development status of China's digital inclusive finance and the impact of digital inclusive finance on the economy. In this process, referring to the relevant economic theories, the theoretical model of this paper is proposed and the influence machine analysis is carried out. Empirically, this paper selects the development level of digital inclusive finance in 31 provinces in China from 2011 to 2020 as the explained variable, takes the establishment of "Belt and Road" node cities as a quasi-natural experiment, and verifies the promoting effect of the establishment of "Belt and Road" node cities on the development of digital inclusive finance in provinces through the differential differential method. And verify whether the level of Internet development is a mediating variable. The empirical results show that the establishment of node cities in the "Belt and Road" does promote the development of digital inclusive finance in provinces with the level of Internet development as an intermediary variable.
Human aversion? Do AI Agents Judge Identity More Harshly Than Performance
Authors: Yuanjun Feng, Vivek Chodhary, Yash Raj Shrestha
Date: 2025-03-31 00:00:00
Tasks: AI Agent

This study examines the understudied role of algorithmic evaluation of human judgment in hybrid decision-making systems, a critical gap in management research. While extant literature focuses on human reluctance to follow algorithmic advice, we reverse the perspective by investigating how AI agents based on large language models (LLMs) assess and integrate human input. Our work addresses a pressing managerial constraint: firms barred from deploying LLMs directly due to privacy concerns can still leverage them as mediating tools (for instance, anonymized outputs or decision pipelines) to guide high-stakes choices like pricing or discounts without exposing proprietary data. Through a controlled prediction task, we analyze how an LLM-based AI agent weights human versus algorithmic predictions. We find that the AI system systematically discounts human advice, penalizing human errors more severely than algorithmic errors--a bias exacerbated when the agent's identity (human vs AI) is disclosed and the human is positioned second. These results reveal a disconnect between AI-generated trust metrics and the actual influence of human judgment, challenging assumptions about equitable human-AI collaboration. Our findings offer three key contributions. First, we identify a reverse algorithm aversion phenomenon, where AI agents undervalue human input despite comparable error rates. Second, we demonstrate how disclosure and positional bias interact to amplify this effect, with implications for system design. Third, we provide a framework for indirect LLM deployment that balances predictive power with data privacy. For practitioners, this research emphasize the need to audit AI weighting mechanisms, calibrate trust dynamics, and strategically design decision sequences in human-AI systems.
Collective decisions under uncertainty: efficiency, ex-ante fairness, and normalization
Authors: Leo Kurata, Kensei Nakamura
Date: 2025-05-06 00:00:00
Tasks: Fairness

This paper studies preference aggregation under uncertainty in the multi-profile framework introduced by Sprumont (2018, 2019) and characterizes a new class of aggregation rules that can address classical concerns about Harsanyi's (1955) utilitarian rules. Our class of aggregation rules, which we call relative fair aggregation rules, is grounded in three key ideas: utilitarianism, egalitarianism, and the 0--1 normalization. These rules are parameterized by a set of weights over individuals. Each ambiguous alternative is evaluated by computing the minimum weighted sum of the 0--1 normalized utility levels within that weight set. For the characterization, we propose two novel key axioms -- weak preference for mixing and restricted certainty independence -- developed using a new method of objectively randomizing outcomes even within the fully uncertain Savagean framework. Furthermore, we show that relative utilitarian aggregation rules can be identified from the above class by imposing an axiom stronger than restricted certainty independence, and that the Rawlsian maximin version can be derived by considering strong preference for mixing instead.
Numerical Representation of Preferences over Random Availability Functions
Authors: Somdeb Lahiri
Date: 2025-04-26 00:00:00
Tasks: Relation

We interpret a fuzzy set as a random availability function and provide sufficient conditions under which a preference relation over the set of all random availability functions can be represented by a utility function.
Impact of random monetary shock: a Keynesian case
Authors: Paramahansa Pramanik, Lambert Dong
Date: 2025-05-01 00:00:00

This study investigates the optimal strategy for a firm operating in a dynamic Keynesian market setting. The firm's objective function is optimized using the percent deviations from the symmetric equilibrium of both its own price and the aggregate consumer price index (CPI) as state variables, with the strategy in response to random monetary shocks acting as the control variable. Building on the Calvo framework, we adopt a mean field approach to derive an analytic expression for the firm's optimal strategy. Our theoretical results show that greater volatility leads to a decrease in the optimal strategy. To asses the practical relevance of our model, we apply it to four leading consumer goods firms. Empirical analysis suggests that the observed decline in strategies under uncertainty is significantly steeper than what the model predicts, underscoring the substantial influence of market volatility.
Optimal Platform Design
Authors: Cole Wittbrodt
Date: 2025-04-30 00:00:00

Search and matching increasingly takes place on online platforms. These platforms have elements of centralized and decentralized matching; platforms can alter the search process for its users, but are unable to eliminate search frictions entirely. I study a model where platforms can change the distribution of potential partners that an agent searches over and characterize search equilibria on platforms. When agents possess private information about their match characteristics and the platform designer acts as a profit maximizing monopolist, I characterize the optimal platform. If match characteristics are complementary and utility is transferable, I show that the only possible source of inefficiency in the optimal platform is exclusion, unlike standard non-linear pricing problems. That is, the optimal platform is efficient conditional on inclusion. Matching on the optimal platform is perfectly assortative -- there is no equilibrium mismatch.
On spatial systems of cities
Authors: Gianandrea Lanzara, Matteo Santacesaria
Date: 2025-04-30 00:00:00

Are there multiple equilibria in the spatial economy? This paper develops a unified framework that integrates systems of cities and regional models to address this question within a general geographic space. A key feature is the endogenous formation of commuting areas linking a continuum of residential locations to a finite set of potential business districts. Using tools from computational geometry and shape optimization, we derive sufficient conditions for the existence and uniqueness of spatial equilibria. For plausible parameter values, urban location is indeterminate, but, conditional on an urban system, city sizes are uniquely determined. The framework reconciles seemingly conflicting empirical findings on the role of geography and scale economies in shaping the spatial economy.