pwc-archive/papers-with-abstracts huggingface dataset:
Consumer lying in online reviews: recent evidence
Authors: Shawn Berry
Date: 2024-05-21 00:00:00
Tasks: Decision Making, Misinformation

The persistence of lying by some consumers in their online posts of experiences with businesses is problematic, and taints the global pool of information that is used for decision making by people that assume they are true accounts of experiences. This study is based on data from my dissertation about fake online Google reviews of restaurants (Berry, 2024), and leverages an instrument that quantifies the trust of people. The findings are based on a sample of n=351, and provide a general proxy for lying in online reviews, and sketch out the characteristics of a typical person that has the propensity to be untruthful. A predictive model of posting untrue online reviews is constructed. The findings have wider implications for the study and monitoring of deceptive behavior, including the propagation of misinformation, and a means of quantifying the potential for antisocial behavior as measured by the trust of people instrument in Berry (2024). Directions for future research and limitations are also discussed.
A 22 percent increase in the German minimum wage: nothing crazy!
Authors: Mario Bossler, Lars Chittka, Thorsten Schank
Date: 2024-05-21 00:00:00

We present the first empirical evidence on the 22 percent increase in the German minimum wage, implemented in 2022, raising it from Euro 9.82 to 10.45 in July and to Euro 12 in October. Leveraging the German Earnings Survey, a large and novel data source comprising around 8 million employee-level observations reported by employers each month, we apply a difference-in-difference-in-differences approach to analyze the policy's impact on hourly wages, monthly earnings, employment, and working hours. Our findings reveal significant positive effects on wages, affirming the policy's intended benefits for low-wage workers. Interestingly, we identify a negative effect on working hours, mainly driven by minijobbers. The hours effect results in an implied labor demand elasticity in terms of the employment volume of -0.2 which only partially offsets the monthly wage gains. We neither observe a negative effect on the individual's employment retention nor the regional employment levels.
Earthquakes and the wealth of nations: The cases of Chile and New Zealand
Authors: Diego Díaz, Pablo Paniagua, Cristián Larroulet
Date: 2024-05-20 00:00:00
Tasks: counterfactual

The consequences of natural disasters, such as earthquakes, are evident: death, coordination problems, destruction of infrastructure, and displacement of population. However, according to empirical research, the impact of a natural disaster on economic activity is mixed. Natural disasters could have significant economic effects, especially in developing economies. This is particularly important for highly seismic countries such as Chile and New Zealand. This paper contributes to the literature on natural disasters and economic development by analyzing the cases of two affected regions within these countries in the wake of major earthquakes experienced during 2010-2011: Maule (Chile) and Canterbury (New Zealand). We examine the impact of natural disasters on GDP per capita by applying the synthetic control method. Using the synthetic approach, we assess the effects of these two earthquakes by building counterfactuals to compare their recovery trajectories. We find that Chile and New Zealand experienced opposite economic effects. The Canterbury region grew 10% more in three years than its synthetic counterfactual without the earthquake, while the Maule region declined by 5%. We build synthetic controls at a regional and economic-sector level, looking at aggregated and sectoral effects. The difference in institutions, such as property rights and the large amount of government spending given for reconstruction after the New Zealand earthquake relative to Chile's, help to explain the difference in outcomes.
Impact Analysis of the Chesa Boudin Administration
Authors: Jordan G. Taqi-Eddin
Date: 2024-05-19 00:00:00
Tasks: regression

Claims of soft-handed prosecutorial policies and increases in crime were precipitating factors in the removal of Chesa Boudin as district attorney of the city and county of San Francisco. However, little research has been conducted to empirically investigate the veracity of these indictments on the former district attorney. Using regression discontinuity design (RDD), I find that the Boudin administration led to a 36\% and 21\% reduction in monthly prosecutions and convictions respectively for all crimes. Moreover, his tenure increased monthly successful case diversions by 58\%. When only looking at violent crimes during this period, the SFDA's office saw a 36\% decrease, 7\% decrease, and 47\% increase in monthly prosecutions, convictions, and successful case diversions respectively. Although, the decrease in monthly convictions was not statistically significant for the violent crimes subset. Additionally, I did identify a potentially causal relationship between lower numbers of prosecutions and higher levels of criminal activity, however, such findings did not meet the standard for statistical significance. Finally, I conclude that using machine learning algorithms, such as neural networks and K-nearest neighbors, in place of ordinary least squares regression for the estimation of the reduced form equation possibly may decrease the size of the standard errors of the parameters in the structural equation. However, future research needs to be conducted in this space to corroborate these initially promising findings.
Road to perdition? The effect of illicit drug use on labour market outcomes of prime-age men in Mexico
Authors: José-Ignacio Antón, Juan Ponce, Rafael Muñoz de Bustillo
Date: 2024-05-17 00:00:00

This study addresses the impact of illicit drug use on the labour market outcomes of men in Mexico. We leverage statistical information from three waves of a comparable national survey and make use of Lewbel's heteroskedasticity-based instrumental variable strategy to deal with the endogeneity of drug consumption. Our results suggests that drug consumption has quite negative effects in the Mexican context: It reduces employment, occupational attainment and formality and raises unemployment of local men. These effects seem larger than those estimated for high-income economies
Bottom-up approach to assess carbon emissions of battery electric vehicle operations in China
Authors: Hong Yuan, Minda Ma
Date: 2024-05-17 00:00:00

The transportation sector is the third-largest global energy consumer and emitter, making it a focal point in the transition toward the net-zero future. To accelerate the decarbonization of passenger cars, this work is the first to propose a bottom-up charging demand model to estimate the operational electricity use and associated carbon emissions of best-selling battery electric vehicles (BEVs) in various climate zones in China during the 2020s. The findings reveal that (1) the operational energy demand of the top-20 selling BEV models in China, such as Tesla, Wuling Hongguang, and BYD, increased from 601 to 3054 giga-watt hours (GWh) during 2020-2022, with BEVs in South China contributing more than half of the total electricity demand; (2) from 2020 to 2022, the energy and carbon intensities of the best-selling models decreased from 1364 to 1095 kilowatt-hour per vehicle and from 797 to 621 kilograms of carbon dioxide (CO2) per vehicle, respectively, with North China experiencing the highest intensity decline compared to that in other regions; and (3) the operational energy demand of BEV stocks in China increased from 4774 to 12,048 GWh during 2020-2022, while the carbon emissions of BEV stocks rose to 6.8 mega-tons of CO2 in 2022, reflecting an annual growth rate of ~50%. In summary, this work delves into the examination and contrast of benchmark data on a nation-regional scale, as well as performance metrics related to BEV chargings. The primary aim is to support nationwide efforts in decarbonization, aiming for carbon mitigation and facilitating the swift evolution of passenger cars toward a carbon-neutral future.
A Deep Learning Approach to Heterogeneous Consumer Aesthetics in Retail Fashion
Authors: Pranjal Rawat
Date: 2024-05-17 00:00:00
Tasks: Deep Learning, Descriptive

In some markets, the visual appearance of a product matters a lot. This paper investigates consumer transactions from a major fashion retailer, focusing on consumer aesthetics. Pretrained multimodal models convert images and text descriptions into high-dimensional embeddings. The value of these embeddings is verified both empirically and by their ability to segment the product space. A discrete choice model is used to decompose the distinct drivers of consumer choice: price, visual aesthetics, descriptive details, and seasonal variations. Consumers are allowed to differ in their preferences over these factors, both through observed variation in demographics and allowing for unobserved types. Estimation and inference employ automatic differentiation and GPUs, making it scalable and portable. The model reveals significant differences in price sensitivity and aesthetic preferences across consumers. The model is validated by its ability to predict the relative success of new designs and purchase patterns.
Estimating Idea Production: A Methodological Survey
Authors: Ege Erdil, Tamay Besiroglu, Anson Ho
Date: 2024-05-17 00:00:00
Tasks: Bayesian Inference, Survey

Accurately modeling the production of new ideas is crucial for innovation theory and endogenous growth models. This paper provides a comprehensive methodological survey of strategies for estimating idea production functions. We explore various methods, including naive approaches, linear regression, maximum likelihood estimation, and Bayesian inference, each suited to different data availability settings. Through case studies ranging from total factor productivity to software R&D, we show how to apply these methodologies in practice. Our synthesis provides researchers with guidance on strategies for characterizing idea production functions and highlights obstacles that must be addressed through further empirical validation.
Mode of sustainable economic development
Authors: N. S. Gonchar
Date: 2024-05-16 00:00:00

To implement the previously formulated principles of sustainable economic development, all non-negative solutions of the linear system of equations and inequalities, which are satisfied by the vector of real consumption, are completely described. It is established that the vector of real consumption with the minimum level of excess supply is determined by the solution of some quadratic programming problem. The necessary and sufficient conditions are established under which the economic system, described by the "input-output" production model, functions in the mode of sustainable development. A complete description of the equilibrium states for which markets are partially cleared in the economy model of production "input-output" is given, on the basis that all solutions of system of linear equations and inequalities are completely described. The existence of a family of taxation vectors in the "input-output" model of production, under which the economic system is able to function in the mode of sustainable development, is proved. Restrictions were found for the vector of taxation in the economic system, under which the economic system is able to function in the mode of sustainable development. The axioms of the aggregated description of the economy is proposed.
The Machiavellian frontier of stable mechanisms
Authors: Qiufu Chen, Yuanmei Li, Xiaopeng Yin, Luosai Zhang, Siyi Zhou
Date: 2024-05-21 00:00:00
Tasks: Position

The impossibility theorem in Roth (1982) states that no stable mechanism satisfies strategy-proofness. This paper explores the Machiavellian frontier of stable mechanisms by weakening strategy-proofness. For a fixed mechanism $\varphi$ and a true preference profile $\succ$, a $(\varphi,\succ)$-boost mispresentation of agent i is a preference of i that is obtained by (i) raising the ranking of the truth-telling assignment $\varphi_i(\succ)$, and (ii) keeping rankings unchanged above the new position of this truth-telling assignment. We require a matching mechanism $\varphi$ neither punish nor reward any such misrepresentation, and define such axiom as $\varphi$-boost-invariance. This is strictly weaker than requiring strategy-proofness. We show that no stable mechanism $\varphi$ satisfies $\varphi$-boost-invariance. Our negative result strengthens the Roth Impossibility Theorem.
Random Attention Span
Authors: Dazhuo Wei
Date: 2024-05-19 00:00:00

In this paper, I introduce a random attention span model (RAS) which uses stopping time to identify decision-makers' behavior under limited attention. Unlike many limited attention models, the RAS identifies preferences using time variation without any need for menu variation. In addition, the RAS allows the consideration set to be correlated with the preference. I also use the revealed preference theory that provides testable implications for observable choice probabilities. Then, I test the model and estimate the preference distribution using data from M-Turk experiments on choice behaviors that involve lotteries; there is general alignment with the distribution results from logit attention model.
A simple proof of the representation theorem for betweenness preferences
Authors: Yutaro Akita
Date: 2024-05-18 00:00:00

This paper presents a simple proof of Dekel (1986)'s representation theorem for betweenness preferences. The proof is based on the separation theorem.
Selling Correlated Information Products
Authors: Klajdi Hoxha
Date: 2024-05-18 00:00:00
Tasks: Decision Making

How do consultants price expertise? This paper studies a problem of selling information products (expertise) to a buyer (client) who faces decision-making problem under uncertainty. The client is privately informed about the type of expertise she needs and her willingness to pay (WTP) for additional information. A monopolist seller (consultant) designs and sells information products as Blackwell experiments over the underlying states associated with each client-specific desired expertise. Because there is correlation across states, a client with high WTP may find it profitable to purchase information about a low type's state, whenever correlation is sufficiently high. I find that the consultant can extract full (socially efficient) surplus whenever such (marginal) gains do not exceed the (marginal) costs of buying cheaper, but noisier information. Otherwise, unlike typical results in mechanism design, I find that buyers with low and sufficiently high value for information get no information rents, and only the "middle" types enjoy positive surplus. Common pricing structures observed in practice, like flat/hourly rates or value-based fees, are obtained as optimal contracts if correlation across states is sufficiently high or low, respectively.
Source Localization by Multidimensional Steered Response Power Mapping with Sparse Bayesian Learning
Authors: Wei-Ting Lai, Lachlan Birnie, Xingyu Chen, Amy Bastine, Thushara D. Abhayapala, Prasanga N. Samarasinghe
Date: 2024-05-20 00:00:00

We propose an advance Steered Response Power (SRP) method for localizing multiple sources. While conventional SRP performs well in adverse conditions, it remains to struggle in scenarios with closely neighboring sources, resulting in ambiguous SRP maps. We address this issue by applying sparsity optimization in SRP to obtain high-resolution maps. Our approach represents SRP maps as multidimensional matrices to preserve time-frequency information and further improve performance in unfavorable conditions. We use multi-dictionary Sparse Bayesian Learning to localize sources without needing prior knowledge of their quantity. We validate our method through practical experiments with a 16-channel planar microphone array and compare against three other SRP and sparsity-based methods. Our multidimensional SRP approach outperforms conventional SRP and the current state-of-the-art sparse SRP methods for localizing closely spaced sources in a reverberant room.
Speech-dependent Data Augmentation for Own Voice Reconstruction with Hearable Microphones in Noisy Environments
Authors: Mattes Ohlenbusch, Christian Rollwage, Simon Doclo
Date: 2024-05-19 00:00:00
Tasks: Data Augmentation

Own voice pickup for hearables in noisy environments benefits from using both an outer and an in-ear microphone outside and inside the occluded ear. Due to environmental noise recorded at both microphones, and amplification of the own voice at low frequencies and band-limitation at the in-ear microphone, an own voice reconstruction system is needed to enable communication. A large amount of own voice signals is required to train a supervised deep learning-based own voice reconstruction system. Training data can either be obtained by recording a large amount of own voice signals of different talkers with a specific device, which is costly, or through augmentation of available speech data. Own voice signals can be simulated by assuming a linear time-invariant relative transfer function between hearable microphones for each phoneme, referred to as own voice transfer characteristics. In this paper, we propose data augmentation techniques for training an own voice reconstruction system based on speech-dependent models of own voice transfer characteristics between hearable microphones. The proposed techniques use few recorded own voice signals to estimate transfer characteristics and can then be used to simulate a large amount of own voice signals based on single-channel speech signals. Experimental results show that the proposed speech-dependent individual data augmentation technique leads to better performance compared to other data augmentation techniques or compared to training only on the available recorded own voice signals, and additional fine-tuning on the available recorded signals can improve performance further.
Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge System
Authors: Vimal Manohar, Szu-Jui Chen, Zhiqi Wang, Yusuke Fujita, Shinji Watanabe, Sanjeev Khudanpur
Date: 2024-05-17 00:00:00
Tasks: Data Augmentation, Speech Dereverberation, speech-recognition, Speech Recognition

This paper summarizes our acoustic modeling efforts in the Johns Hopkins University speech recognition system for the CHiME-5 challenge to recognize highly-overlapped dinner party speech recorded by multiple microphone arrays. We explore data augmentation approaches, neural network architectures, front-end speech dereverberation, beamforming and robust i-vector extraction with comparisons of our in-house implementations and publicly available tools. We finally achieved a word error rate of 69.4% on the development set, which is a 11.7% absolute improvement over the previous baseline of 81.1%, and release this improved baseline with refined techniques/tools as an advanced CHiME-5 recipe.
Distinctive and Natural Speaker Anonymization via Singular Value Transformation-assisted Matrix
Authors: Jixun Yao, Qing Wang, Pengcheng Guo, Ziqian Ning, Lei Xie
Date: 2024-05-17 00:00:00
Tasks: Speaker anonymization, Speaker Verification

Speaker anonymization is an effective privacy protection solution that aims to conceal the speaker's identity while preserving the naturalness and distinctiveness of the original speech. Mainstream approaches use an utterance-level vector from a pre-trained automatic speaker verification (ASV) model to represent speaker identity, which is then averaged or modified for anonymization. However, these systems suffer from deterioration in the naturalness of anonymized speech, degradation in speaker distinctiveness, and severe privacy leakage against powerful attackers. To address these issues and especially generate more natural and distinctive anonymized speech, we propose a novel speaker anonymization approach that models a matrix related to speaker identity and transforms it into an anonymized singular value transformation-assisted matrix to conceal the original speaker identity. Our approach extracts frame-level speaker vectors from a pre-trained ASV model and employs an attention mechanism to create a speaker-score matrix and speaker-related tokens. Notably, the speaker-score matrix acts as the weight for the corresponding speaker-related token, representing the speaker's identity. The singular value transformation-assisted matrix is generated by recomposing the decomposed orthonormal eigenvectors matrix and non-linear transformed singular through Singular Value Decomposition (SVD). Experiments on VoicePrivacy Challenge datasets demonstrate the effectiveness of our approach in protecting speaker privacy under all attack scenarios while maintaining speech naturalness and distinctiveness.
Implementation of the Feedforward Multichannel Virtual Sensing Active Noise Control (MVANC) by Using MATLAB
Authors: Boxiang Wang
Date: 2024-05-17 00:00:00

The multichannel virtual sensing active noise control (MVANC) methodology is an advanced approach that may provide a wide area of silence at specific virtual positions that are distant from the physical error microphones. Currently, there is a scarcity of open-source programs available for the MVANC algorithm. This work presents a MATLAB code for the MVANC approach, utilizing the multichannel filtered-x least mean square (MCFxLMS) algorithm. The code is designed to be applicable to systems with any number of channels. The code can be found on GitHub.
Efficient Learned Wavelet Image and Video Coding
Authors: Anna Meyer, Srivatsa Prativadibhayankaram, André Kaup
Date: 2024-05-21 00:00:00
Tasks: Data Compression, Video Compression

Learned wavelet image and video coding approaches provide an explainable framework with a latent space corresponding to a wavelet decomposition. The wavelet image coder iWave++ achieves state-of-the-art performance and has been employed for various compression tasks, including lossy as well as lossless image, video, and medical data compression. However, the approaches suffer from slow decoding speed due to the autoregressive context model used in iWave++. In this paper, we show how a parallelized context model can be integrated into the iWave++ framework. Our experimental results demonstrate a speedup factor of over 350 and 240 for image and video compression, respectively. At the same time, the rate-distortion performance in terms of Bj{\o}ntegaard delta bitrate is slightly worse by 1.5\% for image coding and 1\% for video coding. In addition, we analyze the learned wavelet decomposition by visualizing its subband impulse responses.
Hybrid Digital-Analog Semantic Communications
Authors: Huiqiang Xie, Zhijin Qin, Zhu Han, Khaled B. Letaief
Date: 2024-05-21 00:00:00
Tasks: Denoising, Semantic Communication

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digital-analog approach for multimedia transmission. This is achieved through the introduction of digital-analog allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality.