pwc-archive/papers-with-abstracts huggingface dataset:
Fintech and MSEs Innovation: an Empirical Analysis
Authors: Siyu Chen, Qing Guo
Date: 2024-07-24 00:00:00

Employing a comprehensive survey of micro and small enterprises (MSEs) and the Digital Financial Inclusion Index in China, this study investigates the influence of fintech on MSE innovation empirically. Our findings indicate that fintech advancement substantially enhances the likelihood of MSEs engaging in innovative endeavors and boosts both the investment and outcomes of their innovation processes. The underlying mechanisms are attributed to fintech's role in fostering long-term strategic incentives and investment in human capital. This includes the use of promotions and stock options as rewards, rather than traditional perks like gifts or trips, the attraction of a greater number of university graduates, and the increase in both training expenses and the remuneration of technical staff. Our heterogeneity analysis reveals that fintech exerts a more pronounced effect on MSEs situated in economically developed areas, those that are five years old or younger, and businesses with limited assets and workforce. Additionally, we uncover that fintech stimulates the innovation of MSEs' independent research and development (R\&D) efforts. This paper contributes to the understanding of the nuanced ways in which fintech impacts MSE innovation and offers policy insights aimed at unleashing the full potential of MSEs' innovative capabilities.
Effect of Austerity Measures on Infant Mortality: Evidence from Greece
Authors: Robert J. Kolesar, Rok Spruk
Date: 2024-07-24 00:00:00
Tasks: counterfactual

This study examines the effect of fiscal austerity measures on infant mortality in Greece. Austerity measures were initiated by the tripartite committee and implemented between 2010 and 2017 to counteract deep fiscal deficit and large public debt. By comparing Greece with a plausible donor pool of OECD and Mediterranean member states in the period 1991-2020, we estimate a series of missing counterfactual scenarios to evaluate the infant mortality effects of large-scale reduction in spending on health care. A series of hybrid synthetic control and difference-in-differences estimates indicate a unique and pervasive increase in infant mortality after the implementation of austerity measures. Compared to a plausible OECD and Mediterranean counterfactual scenario, pro-cyclical austerity measures are associated with derailed and permanently increased infant mortality up to the present day. Our estimates suggest that compared to a plausible counterfactual scenario, the cumulative infant mortality cost of austerity policies exceeds 10,000 infant deaths or slightly less than 850 deaths for each year of the austerity policies. Notably, mortality increases are concentrated among boys. The estimated impacts survive a battery of rigorous robustness and placebo tests.
Artificial intelligence and financial crises
Authors: Jon Danielsson, Andreas Uthemann
Date: 2024-07-24 00:00:00

The rapid adoption of artificial intelligence (AI) poses new and poorly understood threats to financial stability. We use a game-theoretic model to analyse the stability impact of AI, finding it amplifies existing financial system vulnerabilities - leverage, liquidity stress and opacity - through superior information processing, common data, speed and strategic complementarities. The consequence is crises become faster and more severe, where the likelihood of a crisis is directly affected by how effectively the authorities engage with AI. In response, we propose the authorities develop their own AI systems and expertise, set up direct AI-to-AI communication, implement automated crisis facilities and monitor AI use.
Impacts of National Cultures on Managerial Decisions of Engaging in Core Earnings Management
Authors: Muhammad Rofiqul Islam, Abdullah Al Mehdi
Date: 2024-07-23 00:00:00
Tasks: Management

This study investigates the impact of Hofstede's cultural dimensions on abnormal core earnings management in multiple national cultural contexts. We employ an Ordinary Least Squares (OLS) regression model with abnormal core earnings as the dependent variable. The independent variables analyzed include Hofstede's dimensions: Power Distance Index (PDI), Individualism (IDV), Masculinity (MAS), and Uncertainty Avoidance Index (UAI). Our findings reveal that individualism is positively associated with abnormal core earnings, suggesting that cultures characterized by high individualism may encourage practices that inflate earnings due to the prominence of personal achievement and rewards. In contrast, masculinity negatively correlates with abnormal core earnings, indicating that the risk-taking attributes associated with masculine cultures may deter earnings management. Interestingly, uncertainty avoidance is positively linked to abnormal core earnings, supporting the notion that managers tend to engage more in earnings management to minimize fluctuations in financial reports in cultures with high uncertainty avoidance. The relationship between power distance and abnormal core earnings is found to be non-significant, indicating no substantial effect in this context. These findings contribute to the literature on cultural influences in financial reporting, providing valuable insights for policymakers and multinational firms concerning the cultural contexts within which financial decisions and reporting occur.
Bridging Climate Awareness and Sustainable Entrepreneurship: A Conceptual Framework Based on the Theory of Planned Behavior
Authors: Muhammad Rofiqul Islam, Abdullah Al Mehdi
Date: 2024-07-23 00:00:00

Many studies have examined the connection between the intention to start a business and environmental values. However, there still needs to be more knowledge in the extant literature about how climate change campaigns influence sustainable entrepreneurial intention. This study uses the Theory of Planned Behavior (TPB) to develop a theoretical framework to explain how climate change campaigns affect the intention to start a sustainable business. This interdisciplinary conceptual research model bridges the gap between climate awareness, sustainable values, and entrepreneurial intentions, offering a robust framework for understanding and fostering sustainable entrepreneurial behaviors. Our study lays the groundwork for future empirical studies and real-world interventions to advance sustainability through entrepreneurship.
Stock-driven Household Attention
Authors: Hie Joo Ahn, Shihan Xie
Date: 2024-07-23 00:00:00

We investigate the effects of stockholding on households' attention to the macroeconomy. Households' attentiveness is measured by their accuracy of inflation expectations and perceptions. Relative to non-stockholders, stockholders produce more accurate inflation forecasts and backcasts, disagree less about future inflation, and adjust their outlook more responsively to news, suggesting that stock-market participation raises households' attention. Frequent changes in stock prices incentivize stockholders to closely monitor financial markets for optimal trading, given the low cost of acquiring information. Consequently, paying attention to the macroeconomy helps hedge the risks associated with holding stocks. Therefore, attention heterogeneity driven by stockholdings can be a channel through which the distributional consequences of monetary policy are created.
How does the national new area impact the local economy? -- An empirical analysis from Zhoushan
Authors: Yi Zheng
Date: 2024-07-22 00:00:00
Tasks: counterfactual

To empirically study the policy impact of a National New Area on the local economy, this paper evaluates the effect of the Zhoushan Archipelago New Area on local GDP growth rate and economic efficiency. By collecting input and output data from 20 prefectural-level cities in Jiangsu, Zhejiang, and Anhui provinces from 1995 to 2015, we estimate the economic efficiency of these cities using data envelopment analysis. Subsequently, we construct counterfactuals for Zhoushan by selecting comparable cities from the dataset, excluding Zhoushan, and applying a panel data approach. The difference between the actual and counterfactual values for GDP growth rate and economic efficiency in Zhoushan is analyzed to determine the treatment effect of the National New Area policy. The research reveals that in the initial four years, the New Area policy enhanced Zhoushan's economic efficiency but negatively affected its GDP growth rate. This influence gradually disappeared after four years. Further analysis suggests that the policy's effect on GDP growth rate varies with the level of economic development in different regions, having a more substantial impact in less developed areas. Therefore, we conclude that establishing a New Area in relatively undeveloped zones is more advantageous.
Collective Attention in Human-AI Teams
Authors: Josie Zvelebilova, Saiph Savage, Christoph Riedl
Date: 2024-07-03 00:00:00

How does the presence of an AI assistant affect the collective attention of a team? We study 20 human teams of 3-4 individuals paired with one voice-only AI assistant during a challenging puzzle task. Teams are randomly assigned to an AI assistant with a human- or robotic-sounding voice that provides either helpful or misleading information about the task. Treating each individual AI interjection as a treatment intervention, we identify the causal effects of the AI on dynamic group processes involving language use. Our findings demonstrate that the AI significantly affects what teams discuss, how they discuss it, and the alignment of their mental models. Teams adopt AI-introduced language for both terms directly related to the task and for peripheral terms, even when they (a) recognize the unhelpful nature of the AI, (b) do not consider the AI a genuine team member, and (c) do not trust the AI. The process of language adaptation appears to be automatic, despite doubts about the AI's competence. The presence of an AI assistant significantly impacts team collective attention by modulating various aspects of shared cognition. This study contributes to human-AI teaming research by highlighting collective attention as a central mechanism through which AI systems in team settings influence team performance. Understanding this mechanism will help CSCW researchers design AI systems that enhance team collective intelligence by optimizing collective attention.
Generalization of Zhou fixed point theorem
Authors: Lu Yu
Date: 2024-07-25 00:00:00

We give two generalizations of the Zhou fixed point theorem. They weaken the subcompleteness condition of values, and relax the ascending condition of the correspondence. As an application, we derive a generalization of Topkis's theorem on the existence and order structure of the set of Nash equilibria of supermodular games.
Diversity in Choice as Majorization
Authors: Federico Echenique, Teddy Mekonnen, M. Bumin Yenmez
Date: 2024-07-24 00:00:00
Tasks: Diversity

We use majorization to model comparative diversity in school choice. A population of agents is more diverse than another population of agents if its distribution over groups is less concentrated: being less concentrated takes a specific mathematical meaning borrowed from the theory of majorization. We adapt the standard notion of majorization in order to favor arbitrary distributional objectives, such as population-level distributions over race/ethnicity or socioeconomic status. With school admissions in mind, we axiomatically characterize choice rules that are consistent with modified majorization, and constitute a principled method for admitting a diverse population of students into a school. Two important advantages of our approach is that majorization provides a natural notion of diversity, and that our axioms are independent of any exogenous priority ordering. We compare our choice rule to the leading proposal in the literature, ``reserves and quotas,'' and find ours to be more flexible.
Robust Comparative Statics with Misspecified Bayesian Learning
Authors: Aniruddha Ghosh
Date: 2024-07-24 00:00:00

We present novel monotone comparative statics results for steady-state behavior in a dynamic optimization environment with misspecified Bayesian learning. Building on \cite{ep21a}, we analyze a Bayesian learner whose prior is over parameterized transition models but is misspecified in the sense that the true process does not belong to this set. We characterize conditions that ensure monotonicity in the steady-state distribution over states, actions, and inferred models. Additionally, we provide a new monotonicity-based proof of steady-state existence, derive an upper bound on the cost of misspecification, and illustrate the applicability of our results to several environments of general interest.
Quantity Limits on Addictive Goods
Authors: Eric Gao
Date: 2024-07-24 00:00:00

Addiction is a major societal issue leading to billions in healthcare losses per year. Policy makers often introduce ad hoc quantity limits-limits on the consumption or possession of a substance-something which current economic models of addiction have failed to address. This paper enriches Bernheim and Rangel (2004)'s model of addiction driven by cue-triggered decisions by incorporating endogenous choice of how much of the addictive good to consume, instead of just whether or not consumption happens. Stricter quality limits improve welfare as long as they do not preclude the myopically optimal level of consumption.
Dynamic Signals
Authors: Mark Whitmeyer, Cole Williams
Date: 2024-07-23 00:00:00

In this paper, we reveal that the signal representation of information introduced by Gentzkow and Kamenica (2017) can be applied profitably to dynamic decision problems. We use this to characterize when one dynamic information structure is more valuable to an agent than another, irrespective of what other dynamic sources of information the agent may possess. Notably, this robust dominance is equivalent to an intuitive dynamic version of Brooks, Frankel, and Kamenica (2022)'s reveal-or-refine condition.
Colonel Blotto Game: An Analysis and Extension to Networks
Authors: Sidarth Erat
Date: 2024-07-21 00:00:00

The Colonel Blotto game, introduced by Borel in the 1920s, is often used for modeling various real-life settings, such as elections, lobbying, etc. The game is based on the allocation of limited resources by players to a set of fields. Each field is ``won'' and a corresponding field-specific value is obtained by the player who sends the most resources. In this paper, we formulate a discrete Blotto game played on a general \textit{accessibility network} (i.e., the bipartite graph made of players and the fields they can allocate resources to). The primary goal is to find how the topology of the accessibility network controls the existence and uniqueness of equilibrium allocations, and how it affects the fraction of fields that are entered and the average payoff of players at equilibrium. We establish that, in a 2-regular topology, when the values of fields are close enough and the number of players is not a multiple of 4, then there is a unique equilbrium. We also prove that players are better off and fields are more likely to be entered in a regular topology than a random topology. We find numerically that dispersion of field weights negatively affects average player payoff. The main contribution is a framework for analyzing contests where players are permitted access to some (but not necessarily all) venues of competition.
Causality: a decision theoretic approach
Authors: Pablo Schenone
Date: 2018-12-18 00:00:00

We propose a decision theoretic framework that allows a decision maker to express its causal model of the world. We extend the model of Savage (1972) by allowing the decision maker (DM) to choose policy interventions prior to choosing acts over the nonintervened variables. We define what it means for the DM's choices to express the DM's belief that the relation between some variables is causal. We provide axioms characterizing when the DM's causal model, as expressed through the DM's choices, is represented as a directed acyclic graph. A final axiom characterizes when the DM's causal model has a representation like the one in Pearl (1995). Consequently, under this additional axiom one can apply Pearl's results to identify the DM's causal model from the DM's probabilistic model.
Reshape Dimensions Network for Speaker Recognition
Authors: Ivan Yakovlev, Rostislav Makarov, Andrei Balykin, Pavel Malov, Anton Okhotnikov, Nikita Torgashov
Date: 2024-07-25 00:00:00
Tasks: Speaker Recognition

In this paper, we present Reshape Dimensions Network (ReDimNet), a novel neural network architecture for extracting utterance-level speaker representations. Our approach leverages dimensionality reshaping of 2D feature maps to 1D signal representation and vice versa, enabling the joint usage of 1D and 2D blocks. We propose an original network topology that preserves the volume of channel-timestep-frequency outputs of 1D and 2D blocks, facilitating efficient residual feature maps aggregation. Moreover, ReDimNet is efficiently scalable, and we introduce a range of model sizes, varying from 1 to 15 M parameters and from 0.5 to 20 GMACs. Our experimental results demonstrate that ReDimNet achieves state-of-the-art performance in speaker recognition while reducing computational complexity and the number of model parameters.
Detection of manatee vocalisations using the Audio Spectrogram Transformer
Authors: Stefano Schiappacasse, Taco de Wolff, Yann Henaut, Regina Cervera, Aviva Charles, Felipe Tobar
Date: 2024-07-25 00:00:00
Tasks: Denoising, Transfer Learning

The Antillean manatee (\emph{Trichechus manatus}) is an endangered herbivorous aquatic mammal whose role as an ecological balancer and umbrella species underscores the importance of its conservation. An innovative approach to monitor manatee populations is passive acoustic monitoring (PAM), where vocalisations are extracted from submarine audio. We propose a novel end-to-end approach to detect manatee vocalisations building on the Audio Spectrogram Transformer (AST). In a transfer learning spirit, we fine-tune AST to detect manatee calls by redesigning its filterbanks and adapting a real-world dataset containing partial positive labels. Our experimental evaluation reveals the two key features of the proposed model: i) it performs on par with the state of the art without requiring hand-tuned denoising or detection stages, and ii) it can successfully identify missed vocalisations in the training dataset, thus reducing the workload of expert bioacoustic labellers. This work is a preliminary relevant step to develop novel, user-friendly tools for the conservation of the different species of manatees.
Multi-Stage Face-Voice Association Learning with Keynote Speaker Diarization
Authors: Ruijie Tao, Zhan Shi, Yidi Jiang, Duc-Tuan Truong, Eng-Siong Chng, Massimo Alioto, Haizhou Li
Date: 2024-07-25 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speaker Verification

The human brain has the capability to associate the unknown person's voice and face by leveraging their general relationship, referred to as ``cross-modal speaker verification''. This task poses significant challenges due to the complex relationship between the modalities. In this paper, we propose a ``Multi-stage Face-voice Association Learning with Keynote Speaker Diarization''~(MFV-KSD) framework. MFV-KSD contains a keynote speaker diarization front-end to effectively address the noisy speech inputs issue. To balance and enhance the intra-modal feature learning and inter-modal correlation understanding, MFV-KSD utilizes a novel three-stage training strategy. Our experimental results demonstrated robust performance, achieving the first rank in the 2024 Face-voice Association in Multilingual Environments (FAME) challenge with an overall Equal Error Rate (EER) of 19.9%. Details can be found in https://github.com/TaoRuijie/MFV-KSD.
A Comprehensive Review and Taxonomy of Audio-Visual Synchronization Techniques for Realistic Speech Animation
Authors: Jose Geraldo Fernandes, Sinval Nascimento, Daniel Dominguete, André Oliveira, Lucas Rotsen, Gabriel Souza, David Brochero, Luiz Facury, Mateus Vilela, Hebert Costa, Frederico Coelho, Antônio P. Braga
Date: 2024-07-24 00:00:00
Tasks: Audio-Visual Synchronization

In many applications, synchronizing audio with visuals is crucial, such as in creating graphic animations for films or games, translating movie audio into different languages, and developing metaverse applications. This review explores various methodologies for achieving realistic facial animations from audio inputs, highlighting generative and adaptive models. Addressing challenges like model training costs, dataset availability, and silent moment distributions in audio data, it presents innovative solutions to enhance performance and realism. The research also introduces a new taxonomy to categorize audio-visual synchronization methods based on logistical aspects, advancing the capabilities of virtual assistants, gaming, and interactive digital media.
Schrödinger Bridge for Generative Speech Enhancement
Authors: Ante Jukić, Roman Korostik, Jagadeesh Balam, Boris Ginsburg
Date: 2024-07-22 00:00:00
Tasks: Denoising, Speech Denoising, Speech Dereverberation, Speech Enhancement

This paper proposes a generative speech enhancement model based on Schr\"odinger bridge (SB). The proposed model is employing a tractable SB to formulate a data-to-data process between the clean speech distribution and the observed noisy speech distribution. The model is trained with a data prediction loss, aiming to recover the complex-valued clean speech coefficients, and an auxiliary time-domain loss is used to improve training of the model. The effectiveness of the proposed SB-based model is evaluated in two different speech enhancement tasks: speech denoising and speech dereverberation. The experimental results demonstrate that the proposed SB-based outperforms diffusion-based models in terms of speech quality metrics and ASR performance, e.g., resulting in relative word error rate reduction of 20% for denoising and 6% for dereverberation compared to the best baseline model. The proposed model also demonstrates improved efficiency, achieving better quality than the baselines for the same number of sampling steps and with a reduced computational cost.
Enhanced Depth Estimation and 3D Geometry Reconstruction using Bayesian Helmholtz Stereopsis with Belief Propagation
Authors: Razieh Azizi, Hamidreza Amindavar, Hassan Aghaeinia
Date: 2024-07-25 00:00:00
Tasks: 3D geometry, Depth Estimation

Helmholtz stereopsis is one the versatile techniques for 3D geometry reconstruction from 2D images of objects with unknown and arbitrary reflectance surfaces. HS eliminates the need for surface reflectance, a challenging parameter to measure, based on the Helmholtz reciprocity principle. Its Bayesian formulation using maximum a posteriori (MAP) probability approach has significantly improved reconstruction accuracy of HS method. This framework enables the inclusion of smoothness priors which enforces observations and neighborhood information in the formulation. We used Markov Random Fields (MRF) which is a powerful tool to integrate diverse prior contextual information and solved the MAP-MRF using belief propagation algorithm. We propose a new smoothness function utilizing the normal field integration method for refined depth estimation within the Bayesian framework. Utilizing three pairs of images with different viewpoints, our approach demonstrates superior depth label accuracy compared to conventional Bayesian methods. Experimental results indicate that our proposed method yields a better depth map with reduced RMS error, showcasing its efficacy in improving depth estimation within Helmholtz stereopsis.