pwc-archive/papers-with-abstracts huggingface dataset:
The Influence of Tourist Experience on Revisit Decisions with the Mediation of Tourist Satisfaction
Authors: Marsuni H. Muhammad, Ida Hidayanti, Sulfi Abdul Haji, Rahmat Sabuhari
Date: 2025-06-06 00:00:00
Tasks: Descriptive, Marketing

Nusliko Park Ecotourism Area is a combination of natural and artificial tourism with the background of Lake Nusliko, which can be developed as a leading tourist attraction. Marketing strategies can be formulated by analyzing the factors influencing the decision to revisit. The decision to revisit is a key indicator that shows the success of a tourism marketing strategy. This study aimed to test and analyze the relationship between tourist experience and tourist satisfaction with the decision to revisit the Nusliko Park Ecotourism area. This type of research is descriptive research. The population in this study was visitors to the Nusliko Park Ecotourism area. The sample criteria were visitors who had visited the Nusliko Park Ecotourism area at least twice and were domiciled in North Maluku. Data were collected through questionnaires, and 114 questionnaires were returned. The research method used non-probability sampling with purposive sampling techniques. These two techniques were used to make it easier for the author to obtain samples because the Nusliko Park Ecotourism area was closed when the research was conducted. After all, it was under repair. Data were processed and analyzed using SEM and through SmartPLS 4.0 software. The study results indicate that the tourist experience and satisfaction positively influence the decision to revisit. Through this study, the satisfaction variable was also found to mediate the relationship between the tourist experience and the decision to revisit
Improving choice model specification using reinforcement learning
Authors: Gabriel Nova, Sander van Cranenburgh, Stephane Hess
Date: 2025-06-06 00:00:00
Tasks: Deep Reinforcement Learning, model, reinforcement-learning, Reinforcement Learning

Discrete choice modelling is a theory-driven modelling framework for understanding and forecasting choice behaviour. To obtain behavioural insights, modellers test several competing model specifications in their attempts to discover the 'true' data generation process. This trial-and-error process requires expertise, is time-consuming, and relies on subjective theoretical assumptions. Although metaheuristics have been proposed to assist choice modellers, they treat model specification as a classic optimisation problem, relying on static strategies, applying predefined rules, and neglecting outcomes from previous estimated models. As a result, current metaheuristics struggle to prioritise promising search regions, adapt exploration dynamically, and transfer knowledge to other modelling tasks. To address these limitations, we introduce a deep reinforcement learning-based framework where an 'agent' specifies models by estimating them and receiving rewards based on goodness-of-fit and parsimony. Results demonstrate the agent dynamically adapts its strategies to identify promising specifications across data generation processes, showing robustness and potential transferability, without prior domain knowledge.
Failing Banks
Authors: Sergio Correia, Stephan Luck, Emil Verner
Date: 2025-06-06 00:00:00

Why do banks fail? We create a panel covering most commercial banks from 1863 through 2024 to study the history of failing banks in the United States. Failing banks are characterized by rising asset losses, deteriorating solvency, and an increasing reliance on expensive noncore funding. These commonalities imply that bank failures are highly predictable using simple accounting metrics from publicly available financial statements. Failures with runs were common before deposit insurance, but these failures are strongly related to weak fundamentals, casting doubt on the importance of non-fundamental runs. Furthermore, low recovery rates on failed banks' assets suggest that most failed banks were fundamentally insolvent, barring strong assumptions about the value destruction of receiverships. Altogether, our evidence suggests that the primary cause of bank failures and banking crises is almost always and everywhere a deterioration of bank fundamentals.
The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?
Authors: Fatih Kansoy
Date: 2025-06-04 00:00:00
Tasks: Diversity

This research presented an empirical investigation of the determinants of the net interest margin in Turkish Banking sector with a particular emphasis on the bank ownership structure. This study employed a unique bank-level dataset covering Turkey`s commercial banking sector for the 2001-2012. Our main results are as follows. Operation diversity, credit risk and operating costs are important determinants of margin in Turkey. More efficient banks exhibit lower margin and also price stability contributes to lower margin. The effect of principal determinants such as credit risk, bank size, market concentration and inflation vary across foreign-owned, state-controlled and private banks. At the same time, the impacts of implicit interest payment, operation diversity and operating cost are homogeneous across all banks
The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns
Authors: Matt Brigida
Date: 2025-06-03 00:00:00

A common assumption in cryptocurrency markets is a positive relationship between total-value-locked (TVL) and cryptocurrency returns. To test this hypothesis we examine whether the returns of TVL-sorted portfolios can be explained by common cryptocurrency factors. We find evidence that portfolios formed on TVL exhibit returns that are linear functions of aggregate crypto market returns, that is they can be replicated with appropriate weights on the crypto market portfolio. Thus, strategies based on TVL can be priced with standard asset pricing tools. This result holds true both for total TVL and a simple TVL measure that removes a number of ways TVL may be overstated.
Balancing Profit and Fairness in Risk-Based Pricing Markets
Authors: Jesse Thibodeau, Hadi Nekoei, Afaf Taïk, Janarthanan Rajendran, Golnoosh Farnadi
Date: 2025-05-30 00:00:00
Tasks: Fairness, Reinforcement Learning (RL)

Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and consumer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's \emph{local} demographic gap implicitly bounds the \emph{global} opt-out disparity, motivating firm-level penalties. Building on this insight we introduce \texttt{MarketSim} -- an open-source, scalable simulator of heterogeneous consumers and profit-maximizing firms -- and train a reinforcement learning (RL) social planner (SP) that selects a bracketed fairness-tax while remaining close to a simple linear prior via an $\mathcal{L}_1$ regularizer. The learned policy is thus both transparent and easily interpretable. In two empirically calibrated markets, i.e., U.S. health-insurance and consumer-credit, our planner simultaneously raises demand-fairness by up to $16\%$ relative to unregulated Free Market while outperforming a fixed linear schedule in terms of social welfare without explicit coordination. These results illustrate how AI-assisted regulation can convert a competitive social dilemma into a win-win equilibrium, providing a principled and practical framework for fairness-aware market oversight.
Nexus of Team Collaboration Stability on Mega Construction Project Success in Electric Vehicle Manufacturing Enterprises: The Moderating Role of Human-AI Integration
Authors: Jun Cui
Date: 2025-06-04 00:00:00
Tasks: Management

This study investigates how team collaboration stability influences the success of mega construction projects in electric vehicle manufacturing enterprises, with human-AI integration as a moderating variable. Using structural equation modeling (SEM) with data from 187 project teams across China's electric vehicle sector, results indicate that team collaboration stability significantly enhances project success. The moderating effect of human-AI integration strengthens this relationship, suggesting that enterprises implementing advanced human-AI collaborative systems achieve superior project outcomes when team stability is maintained. These findings contribute to both team collaboration theory and provide practical implications for mega project management in the rapidly evolving electric vehicle industry.
Welfare Reform: Consequences for the Children
Authors: Marianne Simonsen, Lars Skipper, Jeffrey A. Smith
Date: 2025-06-04 00:00:00

This paper uses register-based data to analyze the consequences of a recent major Danish welfare reform for children's academic performance and well-being. In addition to work requirements, the reform brought about considerable reductions in welfare transfers. We implement a comparative event study that contrasts outcomes for individuals on welfare at the time of reform announcement before and after the implementation of the reform with the parallel development in outcomes for an uncontaminated comparison group, namely those on welfare exactly one year prior. Our analysis documents that mothers' propensity to receive welfare decreased somewhat as a consequence of the reform, just as we observe a small increase in hours worked. At the same time, we do not detect negative effects on short-run child academic performance. We do find small negative effects on children's self-reported school well-being and document substantial upticks in reports to child protective services for children exposed to the reform.
Market power abuse in wholesale electricity markets
Authors: Alice Lixuan Xu, Jorge Sánchez Canales, Chiara Fusar Bassini, Lynn H. Kaack, Lion Hirth
Date: 2025-06-04 00:00:00
Tasks: Specificity

In wholesale electricity markets, prices fluctuate widely from hour to hour and electricity generators price-hedge their output using longer-term contracts, such as monthly base futures. Consequently, the incentives they face to drive up the power prices by reducing supply has a high hourly specificity, and because of hedging, they regularly also face an incentive to depress prices by inflating supply. In this study, we explain the dynamics between hedging and market power abuse in wholesale electricity markets and use this framework to identify market power abuse in real markets. We estimate the hourly economic incentives to deviate from competitive behavior and examine the empirical association between such incentives and observed generation patterns. Exploiting hourly variation also controls for potential estimation bias that do not correlate with economic incentives at the hourly level, such as unobserved cost factors. Using data of individual generation units in Germany in a six-year period 2019-2024, we find that in hours where it is more profitable to inflate prices, companies indeed tend to withhold capacity. We find that the probability of a generation unit being withheld increases by about 1 % per euro increase in the net profit from withholding one megawatt of capacity. The opposite is also true for hours in which companies benefit financially from lower prices, where we find units being more likely to be pushed into the market by 0.3 % per euro increase in the net profit from capacity push-in. We interpret the result as empirical evidence of systematic market power abuse.
Impact of COVID-19 on The Bullwhip Effect Across U.S. Industries
Authors: Alper Saricioglu, Mujde Erol Genevois, Michele Cedolin
Date: 2025-06-04 00:00:00
Tasks: Management

The Bullwhip Effect, describing the amplification of demand variability up the supply chain, poses significant challenges in Supply Chain Management. This study examines how the COVID-19 pandemic intensified the Bullwhip Effect across U.S. industries, using extensive industry-level data. By focusing on the manufacturing, retailer, and wholesaler sectors, the research explores how external shocks exacerbate this phenomenon. Employing both traditional and advanced empirical techniques, the analysis reveals that COVID-19 significantly amplified the Bullwhip Effect, with industries displaying varied responses to the same external shock. These differences suggest that supply chain structures play a critical role in either mitigating or intensifying the effect. By analyzing the dynamics during the pandemic, this study provides valuable insights into managing supply chains under global disruptions and highlights the importance of tailoring strategies to industry-specific characteristics.
My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions
Authors: Cathy, Yang, Kevin Bauer, Xitong Li, Oliver Hinz
Date: 2025-06-04 00:00:00

Amid ongoing policy and managerial debates on keeping humans in the loop of AI decision-making, we investigate whether human involvement in AI-based service production benefits downstream consumers. Partnering with a large savings bank in Europe, we produced pure AI and human-AI collaborative investment advice, passed it to customers, and examined their advice-taking in a field experiment. On the production side, contrary to concerns that humans might inefficiently override AI output, we find that giving a human banker the final say over AI-generated financial advice does not compromise its quality. More importantly, on the consumption side, customers are more likely to follow investment advice from the human-AI collaboration compared to pure AI, especially when facing riskier decisions. In our setting, this increased reliance leads to higher material welfare for consumers. Additional analyses from the field experiment and an online experiment show that the persuasive power of human-AI advice cannot be explained by consumers' beliefs about enhanced advice quality due to human-AI complementarities. Instead, the benefit stems from human involvement acting as a peripheral cue that increases the advice's affective appeal. Our findings suggest that regulations and guidelines should adopt a consumer-centered approach by fostering service environments in which humans and AI systems can collaborate to improve consumer outcomes. These insights are relevant for managers designing AI-based services and for policymakers advocating for human oversight in AI systems.
Disentangling Barriers to Welfare Program Participation with Semiparametric and Mixed Effect Approaches
Authors: Lei Bill Wang, Sooa Ahn
Date: 2025-06-03 00:00:00

This paper examines why eligible households do not participate in welfare programs. Under the assumption that there exist some observed fully attentive groups, we model take-up as a two-stage process: attention followed by choice. We do so with two novel approaches. Drawing inspiration from the demand estimation for stochastically attentive consumers literature, Approach I is semiparametric with a nonparametric attention function and a parametric choice function. It uses fully attentive households to identify choice utility parameters and then uses the entire population to identify the attention probabilities. By augmenting Approach I with a random effect that simultaneously affects the attention and choice stages, Approach II allows household-level unobserved heterogeneity and dependence between attention and choice even after conditioning on observed covariates. Applied to NLSY panel data for WIC participation, both approaches consistently point to two empirical findings with regard to heterogeneous policy targeting. (1) As an infant ages towards 12 months and beyond, attention probability drops dramatically while choice probability steadily decreases. Finding (1) suggests that exit-prevention is the key for increasing the take-up rate because once a household exits the program when the infant ages close to 12 months old, it is unlikely to rejoin due to low attention. A value-increasing solution is predicted to be effective in promoting take-up by reducing exit probability. In contrast, an attention-raising solution is predicted to be ineffective. (2) Higher educated households are less attentive but more likely to enroll if attentive. Finding (2) suggests that running informational campaigns with parenting student groups at higher education institutions could be an effective strategy for boosting take-up.
Green Shields: The Role of ESG in Uncertain Time
Authors: Fatih Kansoy, Dominykas Stasiulaitis
Date: 2025-06-02 00:00:00

The rapid growth of sustainable investing, now exceeding 35 trillion USD globally, has transformed financial markets, yet the implications for monetary policy transmission remain underexplored. While existing literature documents heterogeneous firm responses to monetary policy through traditional channels such as size and leverage, it remains unknown whether environmental, social, and governance (ESG) characteristics create distinct transmission mechanisms. Using high-frequency identification around 160 Federal Reserve announcements from 2005 to 2025, we uncover an asymmetric pattern: high-ESG firms gain 1.6 basis points of protection from contractionary target surprises, yet suffer 2.6 basis points greater sensitivity to forward guidance shocks. This asymmetry persists within industries and intensifies with investor climate awareness. Remarkably, the Paris Agreement inverted these relationships: before December 2015, high-ESG firms were more vulnerable to contractionary policy within industries; afterward, they gained protection, representing a 186 basis point reversal. We develop a two-period model featuring heterogeneous investors with sustainability preferences that quantitatively matches these patterns. The model reveals how ESG investors' non-pecuniary utility creates differential demand elasticities, simultaneously protecting green firms from immediate rate changes while amplifying forward guidance vulnerability through their longer investment horizons. These findings establish environmental characteristics as a new dimension of monetary policy non-neutrality, with important implications as sustainable finance continues expanding.
Pricing the Right to Renege in Search Markets: Evidence from Trucking
Authors: Richard Faltings
Date: 2025-06-02 00:00:00
Tasks: counterfactual

In many markets, advance interim contracts include an explicit right to renege, granting one party the option to switch to more efficient matches that emerge later in the search process. This paper studies the formation and welfare implications of such interim contracts, leveraging novel data from a brokerage firm in the trucking industry. The broker allocates advance shipment contracts to carriers through a dynamic auction mechanism and penalizes cancellations through a reputational mechanism. I develop a theoretical model linking the carrier's bidding problem to the firm's cancellation penalties through a dynamic job-search problem and structurally estimate the model from rich data on bids and cancellations. In counterfactual simulations, I show that the firm is incentivized to lower cancellation penalties as the option value of the right to renege is priced into carrier bids. The results rationalize the large degree of contractual flexibility observed in the trucking industry as an efficient market outcome rather than one constrained by limited enforcement.
Effect of Insecurity on Agricultural Output in Benue State, Nigeria
Authors: Victor Ushahemba Ijirshar, Isaiah Iortyom Udaah, Bridget Ngodoo Mile, Joyce Seember Vershima, Abba Adaudu
Date: 2025-06-02 00:00:00
Tasks: Descriptive

This study examined the effect of insecurity on agricultural output in Benue state. A descriptive survey design was employed, and 400 respondents were purposively selected from insecurity-prone local government areas, namely, Guma LGA, Agatu LGA, Gwer LGA, Gwer-West LGA, Katsina-Ala LGA, Logo LGA, Ukum LGA and Kwande LGA. The data were collected through the administration of a questionnaire and were analysed using t tests and structural equation modelling (SEM). The t-test was used to compare farmers' incomes before and after the insecurity in the study area to assess if the differences were statistically significant, while Structural Equation Modelling analysed the complex relationships among multiple variables, employing regression and factor analysis to model both direct and indirect effects. The results revealed that the monetary value of crop and livestock output decreased during periods of insecurity. Furthermore, the study showed that insecurity has an adverse effect on crop and livestock output. This means that a one percent increase in insecurity leads to a 0.211% and 0.311% decrease in crop and livestock output respectively. The study concluded that insecurity reduced agricultural output in Benue state. Based on the findings, it was recommended that the government deploy more security personnel, establish community policing initiatives, and employ modern surveillance technologies to deter criminal activities in insecure areas. Additionally, for places experiencing farmer-herder conflict, the government should provide grazing reserves for herdsmen and further strengthen the state law on open grazing prohibition and the establishment of ranch law.
Spending Behavior and Economic Impacts of Urban Digital Consumption Vouchers
Authors: Ming-Huan Liou, Shou-Yung Yin, Hsiang-Wen Mao
Date: 2025-06-02 00:00:00

This paper evaluates the Taipei Bear Vouchers 2.0 program using verified user-level survey data and a regional input-output model to assess the effectiveness of consumption vouchers as a fiscal stimulus tool. We focus on three key behavioral mechanisms: expenditure substitution, induced consumption, and the intensity of treatment through varying voucher face values. Our findings show that voucher effectiveness differs by type. Accommodation vouchers stimulate the most additional spending due to low expenditure substitution and high induced consumption effects, while sports vouchers often replace existing consumption. Increases in voucher value further enhance marginal consumption, especially when this change is a part of unexpected policy. Taking these behavioral responses into account, we find that the output multiplier of the program rises significantly, and indirect benefits extend to untargeted sectors through inter-industry linkages. These results highlight the critical role of consumer behavior in shaping policy outcomes and offer practical guidance for designing more effective and targeted consumption voucher programs.
An Empirical Analysis of Tiff's Impact on American Business Formation
Authors: Ruiming Min
Date: 2025-06-01 00:00:00

This study examines whether the tariff policies delivered on promises to revitalize American manufacturing and create jobs. Using county-level business application data from 2018-2025, we analyze the relationship between tariff implementation and new business formation through linear regression analysis. Our findings reveal a statistically significant positive association between US tariffs on China and American business applications. However, when Chinese retaliatory tariffs are included in the analysis, their negative coefficient substantially exceeds the positive US tariff effect, suggesting that retaliatory measures largely offset the benefits of protectionist policies. Control variables including inflation rate, federal funds rate, and government spending show significant positive effects on business formation. These results indicate that while protectionist trade policies may stimulate domestic business formation, their effectiveness is significantly diminished by retaliatory responses from trading partners. The study provides evidence that unilateral tariff measures without diplomatic coordination produce limited net benefits, confirming that trade wars create scenarios where potential gains are neutralized by counteractions.