pwc-archive/papers-with-abstracts huggingface dataset:
The Bones and Shapes of the Phillips Curve
Authors: Hanyuan Jiang
Date: 2025-06-16 00:00:00

The COVID-19 pandemic reignited debate on the U.S. Phillips curve. Using MSA-level panel data (2001-2024), we employ a Two-Stage Least Squares (2SLS) instrumental variable strategy with a shift-share instrument to estimate core non-tradable inflation's response to a v/u-based slack measure. We distinguish structural slope stability from state-dependent non-linearities via a threshold model. Our analysis addresses whether the slope of the Phillips Curve changed during and after the Pandemic in the United States by evaluating if recent inflation dynamics reflect an altered structural trade-off ("bones") or the activation of non-linear "shapes" in response to extreme labor market tightness. This distinction offers critical insights into the unemployment cost of disinflation.
The Anatomy of India's Industrial Interdependencies: 7-Digit Product-Level Supply-Use and Input-Output Tables from ASI Data (2016-2022) with a Case Study of the Mobile Phone Sector
Authors: Sourish Dutta
Date: 2025-06-16 00:00:00

Outlines the construction of high-resolution, 7-digit product-level Supply-Use Tables (SUTs) and symmetric Input-Output Tables (IOTs) for the Indian economy, leveraging microdata from the Annual Survey of Industries (ASI) for the period 2016-2022. We delineate a robust methodology encompassing the generation of detailed input and output flows, with a particular focus on the reconciliation of data from registered and unregistered manufacturing sectors through a meticulously developed NPCMS-NIC concordance. The critical transformation from the often-rectangular SUTs to square, symmetric product-by-product IOTs is explicated using the Industry Technology Assumption, a choice justified by its suitability for handling by-products prevalent in a diverse manufacturing landscape. The analytical prowess of this newly constructed high-resolution IOT framework is then demonstrated through its application to assess key economic impacts, specifically the Domestic Value Added (DVA) generated and the employment supported by production and export activities. A detailed case study of India's rapidly evolving mobile phone manufacturing sector (NPCMS 4722200) for the 2016-2022 period reveals profound structural shifts: significant output growth coupled with notable import substitution, a remarkable surge in exports, and a dynamic evolution in the DVA versus Foreign Value Added (FVA) shares, particularly in export-oriented production. The analysis further uncovers substantial employment growth, albeit with an increasing reliance on contractual labour and a heartening rise in female participation in the workforce. These meticulously constructed tables represent a significant methodological advancement and provide an invaluable empirical resource for nuanced analysis of sectoral interdependencies, the efficacy of industrial policy, and the complex dynamics of India's engagement with global value chains.
Life cycle assessment tools for road design: analysing linearity assumptions
Authors: Nikolaos Kalyviotis
Date: 2025-06-16 00:00:00

Road infrastructure significantly impacts how people move and live and the emissions associated with travel behaviour. The design of roads is crucial in mitigating emissions. This paper reviews existing transport life cycle assessment tools that have been developed by various entities and can be used for roads. The review focuses on data sources used in the analysis, methods of estimating carbon dioxide emissions, the underlying software that is used to make the estimates, and any limitations of the tools. A critical issue identified in life cycle assessment analysis is the erroneous assumption that relationships within the assessed systems are linear. The current tools focusing on transport infrastructure assessment were developed based on the linear assumptions and limitations of the life cycle assessment analysis. A significant research gap identified is that existing life cycle assessment tools are not integrated with the design process. The analysis is an add-on process to design and the results of an assessment are not then used iteratively to enhance the design. A case study on aggregate road design found that road area significantly correlates with emissions, slope adjustments reduce emissions, and soil type impacts emissions, suggesting future research should explore non-linear relationships for sustainable road design.
Varying reference-point salience
Authors: Alex Krumer, Felix Otto, Tim Pawlowski
Date: 2025-06-16 00:00:00

The salience of reference points and expectations may significantly influence the loss aversion mechanism in effort provision. We exploit a natural experiment where highly professional and incentivized individuals perform their task in a setting with exogenous variation of reference-point salience. While a relevant reference point is salient in some cases, where it influences individuals' expectations, it is obscured in others. This enables us to examine the interplay between reference-point salience and expectation-based loss aversion in shaping effort provision. Exploiting quasi-random variation around the reference point, our regression discontinuity analyses reveal that individuals with positive expectations outperform those with negative expectations only when the reference point is salient.
Large Language Models as 'Hidden Persuaders': Fake Product Reviews are Indistinguishable to Humans and Machines
Authors: Weiyao Meng, John Harvey, James Goulding, Chris James Carter, Evgeniya Lukinova, Andrew Smith, Paul Frobisher, Mina Forrest, Georgiana Nica-Avram
Date: 2025-06-16 00:00:00

Reading and evaluating product reviews is central to how most people decide what to buy and consume online. However, the recent emergence of Large Language Models and Generative Artificial Intelligence now means writing fraudulent or fake reviews is potentially easier than ever. Through three studies we demonstrate that (1) humans are no longer able to distinguish between real and fake product reviews generated by machines, averaging only 50.8% accuracy overall - essentially the same that would be expected by chance alone; (2) that LLMs are likewise unable to distinguish between fake and real reviews and perform equivalently bad or even worse than humans; and (3) that humans and LLMs pursue different strategies for evaluating authenticity which lead to equivalently bad accuracy, but different precision, recall and F1 scores - indicating they perform worse at different aspects of judgment. The results reveal that review systems everywhere are now susceptible to mechanised fraud if they do not depend on trustworthy purchase verification to guarantee the authenticity of reviewers. Furthermore, the results provide insight into the consumer psychology of how humans judge authenticity, demonstrating there is an inherent 'scepticism bias' towards positive reviews and a special vulnerability to misjudge the authenticity of fake negative reviews. Additionally, results provide a first insight into the 'machine psychology' of judging fake reviews, revealing that the strategies LLMs take to evaluate authenticity radically differ from humans, in ways that are equally wrong in terms of accuracy, but different in their misjudgments.
Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning
Authors: Stella C. Dong, James R. Finlay
Date: 2025-06-16 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

This paper develops a novel multi-agent reinforcement learning (MARL) framework for reinsurance treaty bidding, addressing long-standing inefficiencies in traditional broker-mediated placement processes. We pose the core research question: Can autonomous, learning-based bidding systems improve risk transfer efficiency and outperform conventional pricing approaches in reinsurance markets? In our model, each reinsurer is represented by an adaptive agent that iteratively refines its bidding strategy within a competitive, partially observable environment. The simulation explicitly incorporates institutional frictions including broker intermediation, incumbent advantages, last-look privileges, and asymmetric access to underwriting information. Empirical analysis demonstrates that MARL agents achieve up to 15% higher underwriting profit, 20% lower tail risk (CVaR), and over 25% improvement in Sharpe ratios relative to actuarial and heuristic baselines. Sensitivity tests confirm robustness across hyperparameter settings, and stress testing reveals strong resilience under simulated catastrophe shocks and capital constraints. These findings suggest that MARL offers a viable path toward more transparent, adaptive, and risk-sensitive reinsurance markets. The proposed framework contributes to emerging literature at the intersection of algorithmic market design, strategic bidding, and AI-enabled financial decision-making.
Inequality's Economic and Social Roots: the Role of Social Networks and Homophily
Authors: Matthew O. Jackson
Date: 2025-06-16 00:00:00

I discuss economic and social sources of inequality and elaborate on the role of social networks in inequality, economic immobility, and economic inefficiencies. The lens of social networks clarifies how the entanglement of people's information, opportunities, and behaviors with those of their friends and family leads to persistent differences across communities, resulting in inequality in education, employment, income, health, and wealth. The key role of homophily in separating groups within the network is highlighted. A network perspective's policy implications differ substantially from a narrower economic perspective that ignores social structure. I discuss the importance of ``policy cocktails'' that include aspects that are aimed at both the economic and social forces driving inequality.
Parenthood Penalty in Russia: Evidence from Exogenous Variation in Family Size
Authors: Vadim Ustyuzhanin
Date: 2025-06-13 00:00:00

The present study aimed to improve upon the existing correlational literature on the parenthood penalty in Russia. An instrumental variables approach based on sibling sex composition and multiple births was employed alongside difference-in-differences designs to analyze rich census and longitudinal datasets. To the best of the authors' knowledge, this is the first study to provide causal estimates of the effect of fertility decisions on subsequent labor market outcomes for mothers and fathers in contemporary Russia. The study's primary finding is that, in contrast to the approximately 10 percent long-term motherhood penalty observed in developed countries, the causal impact of childbearing on women's employment in Russia is most significant in the first year after birth, reducing employment by around 15 percent. This penalty then rapidly declines to a modest 3 percent once children reach school age. The analysis indicates an absence of a systematic fatherhood penalty in terms of employment, although a modest increase in labor supply is observed.
Digital payment policy impact analysis on the intention to use QRIS (quick response code Indonesian standard) during COVID-19 pandemic
Authors: Wishnu Badrawani
Date: 2025-06-13 00:00:00

This study aims to evaluate the adoption of Bank Indonesia's QRIS (Quick Response code Indonesian Standard) payment system policy. The evaluation is hindered by the contemporaneous emergence of the COVID-19 pandemic, which acts as a confounding factor in adopting the new payment instrument. To disentangle the impact of central bank policy from the pandemic, a novel variation of the model of Unified Theory of Acceptance and Use of Technology (UTAUT) is proposed and is estimated using purposive sampling from an online survey with 572 respondents during the pandemic. The result of the study successfully disentangles the policy effect from the pandemic effect, and also separate the risk of pandemic with common risks (PR) and other technology adoption determinants. The results indicate that perceived central bank policy and pandemic risk are the most influential variables affecting the intention to use QRIS. The findings suggest that this measurement approach can be appropriately used as a complementary tool to examine the effectiveness of the central bank's policy in influencing people's behavior.
EconGym: A Scalable AI Testbed with Diverse Economic Tasks
Authors: Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, Haifeng Zhang
Date: 2025-06-13 00:00:00
Tasks: Benchmarking

Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation-yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multi-government coordination, and large-scale agent interactions. To address this gap, we introduce EconGym, a scalable and modular testbed that connects diverse economic tasks with AI algorithms. Grounded in rigorous economic modeling, EconGym implements 11 heterogeneous role types (e.g., households, firms, banks, governments), their interaction mechanisms, and agent models with well-defined observations, actions, and rewards. Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories across 25+ economic tasks for AI-driven policy learning and analysis. Experiments show that EconGym supports diverse and cross-domain tasks-such as coordinating fiscal, pension, and monetary policies-and enables benchmarking across AI, economic methods, and hybrids. Results indicate that richer task composition and algorithm diversity expand the policy space, while AI agents guided by classical economic methods perform best in complex settings. EconGym also scales to 10k agents with high realism and efficiency.
Experimenting with Networks
Authors: Arun G. Chandrasekhar, Matthew O. Jackson
Date: 2025-06-12 00:00:00

We provide an overview of methods for designing and implementing experiments (field, lab, hybrid, and natural) when there are networks of interactions between subjects.
Modern approaches to building effective interpretable models of the property market using machine learning
Authors: Irina G. Tanashkina, Alexey S. Tanashkin, Alexander S. Maksimchuik, Anna Yu. Poshivailo
Date: 2025-06-05 00:00:00

In this article, we review modern approaches to building interpretable models of property markets using machine learning on the base of mass valuation of property in the Primorye region, Russia. The researcher, lacking expertise in this topic, encounters numerous difficulties in the effort to build a good model. The main source of this is the huge difference between noisy real market data and ideal data which is very common in all types of tutorials on machine learning. This paper covers all stages of modeling: the collection of initial data, identification of outliers, the search and analysis of patterns in data, the formation and final choice of price factors, the building of the model, and the evaluation of its efficiency. For each stage, we highlight potential issues and describe sound methods for overcoming emerging difficulties on actual examples. We show that the combination of classical linear regression with interpolation methods of geostatistics allows to build an effective model for land parcels. For flats, when many objects are attributed to one spatial point the application of geostatistical methods is difficult. Therefore we suggest linear regression with automatic generation and selection of additional rules on the base of decision trees, so called the RuleFit method. Thus we show, that despite the strong restriction as the requirement of interpretability which is important in practical aspects, for example, legal matters, it is still possible to build effective models of real property markets.
Non-Discriminatory Personalized Pricing
Authors: Philipp Strack, Kai Hao Yang
Date: 2025-06-26 00:00:00

A monopolist offers personalized prices to consumers with unit demand, heterogeneous values, and idiosyncratic costs, who differ in a protected characteristic, such as race or gender. The seller is subject to a non-discrimination constraint: consumers with the same cost, but different characteristics must face identical prices. Such constraints arise in regulated markets like credit or insurance. The setting reduces to an optimal transport, and we characterize the optimal pricing rule. Under this rule, consumers may retain surplus, and either group may benefit. Strengthening the constraint to cover transaction prices redistributes surplus, harming the low-value group and benefiting the high-value group.
Reasoning about Bounded Reasoning
Authors: Shuige Liu, Gabriel Ziegler
Date: 2025-06-24 00:00:00

Interactive decision-making relies on strategic reasoning. Two prominent frameworks are (1) models of bounded reasoning, exemplified by level-$k$ models, which keep reasoning implicit, and (2) epistemic game theory, which makes reasoning explicit. We connect these approaches by "lifting" static complete-information games into incomplete-information settings where payoff types reflect players' reasoning depths as in level-$k$ models. We introduce downward rationalizability, defined via minimal belief restrictions capturing the basic idea common to level-$k$ models, to provide robust yet well-founded predictions in games where bounded reasoning matters. We then refine these belief restrictions to analyze the foundations of two seminal models of bounded reasoning: the classic level-$k$ model and the cognitive hierarchy model. Our findings shed light on the distinction between hard cognitive bounds on reasoning and beliefs about co-players' types. Furthermore, they offer insights into robustness issues relevant for market design. Thus, our approach unifies key level-$k$ models building on clear foundations of strategic reasoning stemming from epistemic game theory.
Asymptotic Equilibrium Analysis of the Boston Mechanism
Authors: Josue Ortega
Date: 2025-06-24 00:00:00

We analyze the performance of the Boston mechanism under equilibrium play in uniform random markets. We provide two results. First, while the share of students assigned to their first preference is 63% under truthfulness, this fraction becomes vanishingly small in any Nash equilibrium of the preference revelation game induced by the Boston mechanism. Second, we show that there is a Nash equilibrium of the corresponding preference revelation game where the average student is assigned to a highly undesirable school-dramatically worse ranked than the logarithmic rank achieved under truthfulness.
Visibly Fair Mechanisms
Authors: Inácio Bó, Gian Caspari, Manshu Khanna
Date: 2025-06-23 00:00:00
Tasks: Fairness

Priority-based allocation of individuals to positions are pervasive, and elimination of justified envy is often, an absolute requirement. This leaves serial dictatorship (SD) as the only rule that avoids justified envy under standard direct mechanisms. What if SD outcomes are undesirable from a designer's perspective? We propose visible fairness, which demands fairness relative to the (potentially purposefully incomplete) preference information the mechanism elicits. Visibly fair mechanisms generalize SD; we fully characterize them and provide necessary and sufficient conditions for strategy-proofness. We show how to apply these results to design strategy-proof visibly fair rules that satisfy a broad class of distributional objectives. Visible fairness, however, results in a new information-efficiency trade-off: meeting distributional goals leads to the avoidance of eliciation of information about preferences that could prevent inefficiencies.
Disaster Risk Financing through Taxation: A Framework for Regional Participation in Collective Risk-Sharing
Authors: Fallou Niakh, Arthur Charpentier, Caroline Hillairet, Philipp Ratz
Date: 2025-06-23 00:00:00

We consider an economy composed of different risk profile regions wishing to be hedged against a disaster risk using multi-region catastrophe insurance. Such catastrophic events inherently have a systemic component; we consider situations where the insurer faces a non-zero probability of insolvency. To protect the regions against the risk of the insurer's default, we introduce a public-private partnership between the government and the insurer. When a disaster generates losses exceeding the total capital of the insurer, the central government intervenes by implementing a taxation system to share the residual claims. In this study, we propose a theoretical framework for regional participation in collective risk-sharing through tax revenues by accounting for their disaster risk profiles and their economic status.
Broad Validity of the First-Order Approach in Moral Hazard
Authors: Eduardo Azevedo, Ilan Wolff
Date: 2025-06-23 00:00:00
Tasks: valid

The first-order approach (FOA) is the main tool for the moral hazard principal-agent problem. Although many existing results rely on the FOA, its validity has been established only under relatively restrictive assumptions. We demonstrate in examples that the FOA frequently fails when the agent's reservation utility is low (such as in principal-optimal contracts). However, the FOA broadly holds when the agent's reservation utility is at least moderately high (such as in competitive settings where agents receive high rents). Our main theorem formalizes this point. The theorem shows that the FOA is valid in a standard limited liability model when the agent's reservation utility is sufficiently high. The theorem also establishes existence and uniqueness of the optimal contract. We use the theorem to derive tractable optimal contracts across several settings. Under log utility, option contracts are optimal for numerous common output distributions (including Gaussian, exponential, binomial, Gamma, and Laplace).
Interim correlated rationalizability in large games
Authors: Lukasz Balbus, Michael Greinecker, Kevin Reffett, Lukasz Wozny
Date: 2025-06-23 00:00:00

We provide general theoretical foundations for modeling strategic uncertainty in large distributional Bayesian games with general type spaces, using a version of interim correlated rationalizability. We then focus on the case in which payoff functions are supermodular in actions, as is common in the literature on global games. This structure allows us to identify extremal interim correlated rationalizable solutions with extremal interim Bayes-Nash equilibria. Notably, no order structure on types is assumed. We illustrate our framework and results using the large versions of the electronic mail game and a global game.
An Axiomatization of the Random Priority Rule
Authors: Christian Basteck
Date: 2025-06-22 00:00:00
Tasks: Fairness

We study the problem of assigning indivisible objects to agents where each is to receive at most one. To ensure fairness in the absence of monetary compensation, we consider random assignments. Random Priority, also known as Random Serial Dictatorship, is characterized by equal-treatment-of-equals, ex-post efficiency and probabilistic (Maskin) monotonicity -- whenever preferences change so that a given deterministic assignment is ranked weakly higher by all agents, the probability of that assignment arising should be weakly larger. Probabilistic monotonicity implies strategy-proofness (in a stochastic dominance sense) for random assignment problems and is equivalent to it on the universal domain of strict preferences; for deterministic rules it coincides with Maskin monotonicity.