pwc-archive/papers-with-abstracts huggingface dataset:
Self-selection of Information and Belief Update: An Experiment on COVID-19 Vaccine Information Acquisition
Authors: ChienHsun Lin, Hans H. Tung
Date: 2025-06-23 00:00:00

Rational information acquisition theory predicts that people select the more informative information. Thus, people's beliefs will be more persuaded by the information they select. We test the prediction in a critical real-world context -- information about COVID-19 vaccines. We conducted an online experiment in Taiwan where the subjects selected information about COVID-19 vaccines, and then the subjects updated their beliefs about vaccine effectiveness and references to vaccines. As our design distinguishes different stages of the rational acquisition framework, it allows us to diagnose the underlying mechanism of the theory. Our empirical findings demonstrate evidence that people's information acquisition generally coheres with the rational theory framework predictions; that is, people choose information when the information is more likely to alter their decisions. We show that our subjects' beliefs change more when they see the information they select. We also find evidence of change in vaccine preferences and choices after they receive the information they select, which further suggests that the subjects follow the rational information acquisition framework.
The Theory of Economic Complexity
Authors: César A. Hidalgo, Viktor Stojkoski
Date: 2025-06-23 00:00:00

Economic complexity estimates rely on eigenvectors derived from matrices of specialization to explain differences in economic growth, inequality, and sustainability. Yet, despite their widespread use, we still lack a principled theory that can deduce these eigenvectors from first principles and place them in the context of a mechanistic model. Here, we calculate these eigenvectors analytically for a model where the output of an economy in an activity increases with the probability the economy is endowed with the factors required by the activity. We show that the eigenvector known as the Economic Complexity Index or ECI is a monotonic function of the probability that an economy is endowed with a factor, and that in a multi-factor model, it is an estimate of the average endowment across all factors. We then generalize this result to other production functions and to a short-run equilibrium framework with prices, wages, and consumption. We find that our main result does not depend on the introduction of prices or wages, and that the derived wage function is consistent with the convergence of economies with a similar level of complexity. Finally, we use this model to explain the shape of networks of related activities, such as the product space and the research space. These findings solve long standing theoretical puzzles in the economic complexity literature and validate the idea that metrics of economic complexity are estimates of an economy being endowed with multiple factors.
An AI-powered Tool for Central Bank Business Liaisons: Quantitative Indicators and On-demand Insights from Firms
Authors: Nicholas Gray, Finn Lattimore, Kate McLoughlin, Callan Windsor
Date: 2025-06-23 00:00:00

In a world of increasing policy uncertainty, central banks are relying more on soft information sources to complement traditional economic statistics and model-based forecasts. One valuable source of soft information comes from intelligence gathered through central bank liaison programs -- structured programs in which central bank staff regularly talk with firms to gather insights. This paper introduces a new text analytics and retrieval tool that efficiently processes, organises, and analyses liaison intelligence gathered from firms using modern natural language processing techniques. The textual dataset spans 25 years, integrates new information as soon as it becomes available, and covers a wide range of business sizes and industries. The tool uses both traditional text analysis techniques and powerful language models to provide analysts and researchers with three key capabilities: (1) quickly querying the entire history of business liaison meeting notes; (2) zooming in on particular topics to examine their frequency (topic exposure) and analysing the associated tone and uncertainty of the discussion; and (3) extracting precise numerical values from the text, such as firms' reported figures for wages and prices growth. We demonstrate how these capabilities are useful for assessing economic conditions by generating text-based indicators of wages growth and incorporating them into a nowcasting model. We find that adding these text-based features to current best-in-class predictive models, combined with the use of machine learning methods designed to handle many predictors, significantly improves the performance of nowcasts for wages growth. Predictive gains are driven by a small number of features, indicating a sparse signal in contrast to other predictive problems in macroeconomics, where the signal is typically dense.
Experiential marketing strategy and tourism demand in the contribution of the positioning of the floating islands Los Uros, Puno
Authors: Guina Flores Montalico
Date: 2025-06-22 00:00:00
Tasks: Descriptive, Marketing

Experiential focused on creating memorable and meaningful experiences for consumers, has emerged as a key strategy in promoting tourist destinations. particularly in destinations seeking to highlight their unique cultural characteristics. In this study, the influence of experiential marketing on the tourism demand of the floating islands Los Uros, Puno, an emblematic destination in Peru, is analyzed. The objective of the research was to evaluate how experiential marketing strategies impact tourism demand in this destination, focusing on the sensorial, affective and thought experiences that tourists perceive. The methodology adopted a quantitative approach, with a non experimental cross-sectional design, descriptive scope and a correlational analysis. The study population consisted of 158 tourists. To collect the data, a questionnaire based on a 5-point Likert scale was used, with a total of 21 items. Among the results, a significant positive correlation was found (r = 0.809, p < 0.001) between experiential marketing and tourism demand. Sensory (r = 0.780, p < 0.001), affective (r = 0.772, p < 0.001) and thought (r = 0.797, p < 0.001) were described as determining factors in tourists' decisions when visiting Los Uros. In addition, the multiple regression analysis indicated that experiential marketing explains 83% of the variability in tourism demand. In conclusion, experiential marketing strategies have a significant impact on tourism demand. It is recommended that marketing managers of Los Uros Island implement tactics that promote memorable experiences, prioritizing strategies that involve experiences that stimulate the thinking and emotions of tourists.
Social Group Bias in AI Finance
Authors: Thomas R. Cook, Sophia Kazinnik
Date: 2025-06-20 00:00:00
Tasks: counterfactual, Decision Making

Financial institutions increasingly rely on large language models (LLMs) for high-stakes decision-making. However, these models risk perpetuating harmful biases if deployed without careful oversight. This paper investigates racial bias in LLMs specifically through the lens of credit decision-making tasks, operating on the premise that biases identified here are indicative of broader concerns across financial applications. We introduce a reproducible, counterfactual testing framework that evaluates how models respond to simulated mortgage applicants identical in all attributes except race. Our results reveal significant race-based discrepancies, exceeding historically observed bias levels. Leveraging layer-wise analysis, we track the propagation of sensitive attributes through internal model representations. Building on this, we deploy a control-vector intervention that effectively reduces racial disparities by up to 70% (33% on average) without impairing overall model performance. Our approach provides a transparent and practical toolkit for the identification and mitigation of bias in financial LLM deployments.
A new equilibrium: COVID-19 lockdowns and WFH persistence
Authors: Laura Ketter, Todd Morris, Lizi Yu
Date: 2025-06-20 00:00:00

This paper documents a robust link between COVID-19 lockdowns and the uptake and persistence of working from home (WFH) practices. Exploiting rich longitudinal data, we use a difference-in-differences strategy to compare office workers in three heavily locked-down Australian states to similar workers in less affected states. Locked-down workers sustain 43% higher WFH levels through 2023 - 0.5 days per week - with a monotonic dose-response relationship. Persistence is driven by adjustments on both sides of the labor market: employers downsize office space and open remote or hybrid positions, while employees relocate away from city centers and invest in home offices and technology.
Optimal Regulation and Investment Incentives in Financial Networks
Authors: Matthew O. Jackson, Agathe Pernoud
Date: 2025-06-19 00:00:00

We examine optimal regulation of financial networks with debt interdependencies between financial firms. We first characterize when it is firms have an incentive to choose excessively risky portfolios and overly correlate their portfolios with those of their counterparties. We then characterize how optimal regulation depends on a firm's financial centrality and its available investment opportunities. In standard core-periphery networks, optimal regulation depends non-monotonically on the correlation of banks' investments, with maximal restrictions for intermediate levels of correlation. Moreover, it can be uniquely optimal to treat banks asymmetrically: restricting the investments of one core bank while allowing an otherwise identical core bank (in all aspects, including network centrality) to invest freely.
Social Media Can Reduce Misinformation When Public Scrutiny is High
Authors: Gavin Wang, Haofei Qin, Xiao Tang, Lynn Wu
Date: 2025-06-19 00:00:00
Tasks: Misinformation

Misinformation poses a growing global threat to institutional trust, democratic stability, and public decision-making. While prior research has often portrayed social media as a channel for spreading falsehoods, less is known about the conditions under which it may instead constrain misinformation by enhancing transparency and accountability. Here we show this dual potential in the context of local governments' GDP reporting in China, where data falsifications are widespread. Analyzing official reports from 2011 to 2019, we find that local governments have overstated GDP on average. However, after adopting social media for public communications, the extent of misreporting declines significantly but only in regions where the public scrutiny over political matters is high. In such regions, social media increases the cost of misinformation by facilitating greater information disclosure and bottom-up monitoring. In contrast, in regions with low public scrutiny, adopting social media can exacerbate data manipulation. These findings challenge the prevailing view that social media primarily amplifies misinformation and instead highlight the importance of civic engagement as a moderating force. Our findings show a boundary condition for the spread of misinformation and offer insights for platform design and public policy aimed at promoting accuracy and institutional accountability.
Artificial Intelligence, Lean Startup Method, and Product Innovations
Authors: Gavin Wang, Lynn Wu
Date: 2025-06-19 00:00:00

Although AI has the potential to drive significant business innovation, many firms struggle to realize its benefits. We examine how the Lean Startup Method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping focused on developing minimum viable products, and controlled experimentation, focused on rigorous testing such as AB testing. We find that LSM complements discovery oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using AB testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high quality product in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct, as different AI capabilities require distinct organizational processes to achieve optimal outcomes.
Long Coalition Leads to Shrink? The Roles of Tipping and Technology-Sharing in Climate Clubs
Authors: Lei Zhu, Zhihao Yan, Hongbo Duan, Yongyang Cai, Xiaobing Zhang
Date: 2025-06-19 00:00:00

Global cooperation is posited as a pivotal solution to address climate change, yet significant barriers, like free-riding, hinder its realization. This paper develops a dynamic game-theoretic model to analyze the stability of coalitions under multiple stochastic climate tippings, and a technology-sharing mechanism is designed in the model to combat free-ridings. Our results reveal that coalitions tend to shrink over time as temperatures rise, owing to potential free-ridings, despite a large size of initial coalition. The threat of climate tipping reduces the size of stable coalitions compared to the case where tipping is ignored. However, at post-tipping period, coalitions temporarily expand as regions respond to the shock, though this cooperation is short-lived and followed by further shrink. Notably, technology-sharing generates greater collective benefits than sanctions, suggesting that the proposed dynamic technology-sharing pathway bolsters coalition resilience against free-riding while limiting the global warming. This framework highlights the critical role of technology-sharing in fostering long-term climate cooperation under climate tipping uncertainties.
The Economic Value of Depth
Authors: Pedro Afonso Fernandes
Date: 2025-06-18 00:00:00

The main goal of this article is to introduce an economic perspective in the social logic of space. Firstly, we describe the economic model of a linear city to show how depth can generate value by creating local monopolies in less integrated spaces. Then, a new syntactic measure, the d-value, is proposed to capture the relation between the depth of some space from outside and the mean depth of all spaces from outside. An application to a public housing estate suggests that economic activities and services may be located in spaces with a d-value close to one. The article is complemented by a Prolog programme with a special predicate to compute the d-value.
How does online shopping affect offline price sensitivity?
Authors: Shirsho Biswas, Hema Yoganarasimhan, Haonan Zhang
Date: 2025-06-18 00:00:00
Tasks: counterfactual, Sensitivity

The rapid rise of e-commerce has transformed consumer behavior, prompting questions about how online adoption influences offline shopping. We examine whether consumers who adopt online shopping with a retailer become more price-sensitive in their subsequent offline purchases with that retailer. Using transaction-level data from a large Brazilian pet supplies retailer operating both online and offline, we compare ''adopters'' - customers who began shopping online after a period of offline-only purchasing - with ''non-adopters'' who remained offline-only. We estimate a discrete choice logit model with individual-level heterogeneity, using a novel algorithm to handle high-dimensional fixed effects and address price endogeneity. We then apply a staggered difference-in-differences approach to the estimated price elasticities and obtain the Average Treatment Effect on the Treated (ATT). We find that offline price sensitivity increases significantly after online adoption in three out of four product categories, particularly in items with low switching costs, such as pet hygiene. Counterfactual pricing simulations show that incorporating these behavioral spillovers into pricing strategies can increase firm profits by up to 4.1%. These results underscore the importance of recognizing cross-channel effects in consumer behavior and contribute to the literature on pricing and multichannel retailing by identifying online adoption as a key driver of offline price sensitivity.
Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models
Authors: Tobias Schmidt, Kai-Robin Lange, Matthias Reccius, Henrik Müller, Michael Roos, Carsten Jentsch
Date: 2025-06-18 00:00:00
Tasks: Articles, Semantic Role Labeling, valid

As interest in economic narratives has grown in recent years, so has the number of pipelines dedicated to extracting such narratives from texts. Pipelines often employ a mix of state-of-the-art natural language processing techniques, such as BERT, to tackle this task. While effective on foundational linguistic operations essential for narrative extraction, such models lack the deeper semantic understanding required to distinguish extracting economic narratives from merely conducting classic tasks like Semantic Role Labeling. Instead of relying on complex model pipelines, we evaluate the benefits of Large Language Models (LLMs) by analyzing a corpus of Wall Street Journal and New York Times newspaper articles about inflation. We apply a rigorous narrative definition and compare GPT-4o outputs to gold-standard narratives produced by expert annotators. Our results suggests that GPT-4o is capable of extracting valid economic narratives in a structured format, but still falls short of expert-level performance when handling complex documents and narratives. Given the novelty of LLMs in economic research, we also provide guidance for future work in economics and the social sciences that employs LLMs to pursue similar objectives.
An advanced reliability reserve incentivizes flexibility investments while safeguarding the electricity market
Authors: Franziska Klaucke, Karsten Neuhoff, Alexander Roth, Wolf-Peter Schill, Leon Stolle
Date: 2025-06-17 00:00:00

To ensure security of supply in the power sector, many countries are already using or discussing the introduction of capacity mechanisms. Two main types of such mechanisms include capacity markets and capacity reserves. Simultaneously, the expansion of variable renewable energy sources increases the need for power sector flexibility, for which there are promising yet often under-utilized options on the demand side. In this paper, we analyze how a centralized capacity market and an advanced reliability reserve with a moderately high activation price affect investments in demand-side flexibility technologies. We do so for a German case study of 2030, using an open-source capacity expansion model and incorporating detailed demand-side flexibility potentials across industry, process heat, and district heating. We show that a centralized capacity market effectively caps peak prices in the wholesale electricity market and thus reduces incentives for investments in demand-side flexibility options. The advanced reliability reserve induces substantially higher flexibility investments while leading to similar overall electricity supply costs and ensuring a similar level of security of supply. The advanced reliability reserve could thus create a learning environment for flexibility technologies to support the transition to climate neutral energy systems. Additionally, an advanced reliability reserve could be introduced faster and is more flexible than a centralized capacity market. While concrete design parameters are yet to be specified, we argue that policymakers should consider the reliability reserve concept in upcoming decision on capacity mechanisms in Germany and beyond.
Numerical evaluation of deliberative discussions of the UK food system: stimuli, demographics, and opinion reversion
Authors: John Buckell, Thomas Hancock
Date: 2025-06-17 00:00:00

There is increasing acknowledgement - including from the UK government - of the benefit of employing deliberative processes (deliberative fora, citizens' juries, etc.). Evidence suggests that the qualitative reporting of deliberative fora are often unclear or imprecise. If this is the case, their value to policymakers could be diminished. In this study we develop numerical methods of deliberative processes to document people's preferences, as a complement to qualitative analysis. Data are taken from the Food Conversation, a nationwide public consultation on reformations of the food system comprising 345 members of the general public. Each participant attended 5 workshops, each with differing stimuli covering subtopics of the food system. In each workshop, individuals twice reported responsibility, from 0-10, for changing the food system for 5 stakeholders (governments, the food industry, supermarkets, farmers, individuals). Analyses examined individuals' perceptions of food system change responsibility. Governments were most responsible and farmers least so. We assessed variation by workshop content, and by demographics. Reported responsibility changed most for individuals, and changed least for the food industry. We devise a model to document a reversion effect, where shifts in perceptions on responsibility that occurred during workshops waned over time; this was strongest among those who intended to vote (rather than not to). These results can support qualitative analyses and inform food system policy development. These methods are readily adopted for any such deliberative process, allowing for statistical evaluation of whether they can induce opinion change.
The Pharmaceutical Price Regulation Crisis: Implications on Antidepressant Access for Low-Income Americans
Authors: Nicole Hodrosky, Gabriel Cacho, Faiza Ahmed, Rohana Mudireddy, Yapin Wen, Kymora Nembhard, Michael Yan
Date: 2025-06-16 00:00:00
Tasks: Articles

Depression affects more than 280 million people worldwide, with poorer communities having disproportionate burden as well as barriers to treatment. This study examines the role of pharmacy pricing caps in access to antidepressants among poorer Americans through bibliometric analysis of the 100 most cited articles on antidepressant pricing and access in the Web of Science Core Collection. We used tools like Bibliometrix and VOSviewer to visualize publication trends, dominant contributors, thematic clusters, and citation networks in the literature. Findings highlight intransigent inequalities in access to antidepressants based on astronomically high drug pricing as well as systemic inequalities against racial and ethnic minorities in particular. Branded antidepressant high prices are associated with low initiation of therapy as well as regimen compliance, heightened mental illness outcomes, as well as increased health utilization. This work uncovers critical gaps in the literature and demands immediate policy action to make antidepressants affordable as well as appropriately accessible to marginalized communities.