pwc-archive/papers-with-abstracts huggingface dataset:
The Rise of Recommerce: Ownership and Sustainability with Overlapping Generations
Authors: Rubing Li, Arun Sundararajan
Date: 2024-05-15 00:00:00

The emergence of the branded recommerce channel - digitally enabled and branded marketplaces that facilitate purchasing pre-owned items directly from a manufacturer's e-commerce site - leads to new variants of classic IS and economic questions relating to secondary markets. Such branded recommerce is increasingly platform-enabled, creating opportunities for greater sustainability and stronger brand experience control but posing a greater risk of cannibalization of the sales of new items. We model the effects that the sales of pre-owned items have on market segmentation and product durability choices for a monopolist facing heterogeneous customers, contrasting outcomes when the trade of pre-owned goods takes place through a third-party marketplace with outcomes under branded recommerce. We show that the direct revenue benefits of branded recommerce are not their primary source of value to the monopolist, and rather, there are three indirect effects that alter profits and sustainability. Product durability increases, a seller finds it optimal to forgo marketplace fees altogether, and there are greater seller incentives to lower the quality uncertainty associated with pre-owned items. We establish these results for a simple two-period model as well as developing a new infinite horizon model with overlapping generations. Our paper sheds new insight into this emerging digital channel phenomenon, underscoring the importance of recommerce platforms in aligning seller profits with sustainability goals.
Rural Healthcare Access and Supply Constraints: A Causal Analysis
Authors: Vitor Melo, Liam Sigaud, Elijah Neilson, Markus Bjoerkheim
Date: 2024-05-13 00:00:00

Certificate-of-need (CON) laws require that healthcare providers receive approval from a state board before offering additional services in a given community. Proponents of CON laws claim that these laws are needed to prevent the oversupply of healthcare services in urban areas and to increase access in rural areas, which are predominantly underserved. Yet, the policy could lower rural access if used by incumbents to limit entry from competitors. We explore the repeal of these regulations in five U.S. states to offer the first estimate of the causal effects of CON laws on rural and urban healthcare access. We find that repealing CON laws causes a substantial increase in hospitals in both rural and urban areas. We also find that the repeal leads to fewer beds and smaller hospitals on average, suggesting an increase in entry and competition in both rural and urban areas.
The Causal Effect of Repealing Certificate-of-Need Laws for Ambulatory Surgical Centers: Does Access to Medical Services Increase?
Authors: Thomas Stratmann, Markus Bjoerkheim, Christopher Koopman
Date: 2024-05-13 00:00:00

In many states, certificate-of-need (CON) laws prevent ambulatory surgical centers (ASCs) from entering the market or expanding their services. This paper estimates the causal effects of state ASC-CON law repeal on the accessibility of medical services statewide, as well as for rural areas. Our findings show that CON law repeals increase ASCs per capita by 44-47% statewide and 92-112% in rural areas. Repealing ASC-CON laws causes a continuous increase in ASCs per capita, an effect which levels off ten years after repeal. Contrary to the 'cream-skimming' hypothesis, we find no evidence that CON repeal is associated with hospital closures in rural areas. Rather, some regression models show that repeal is associated with fewer medical service reductions.
Large increases in public R&D investment are needed to avoid declines of US agricultural productivity
Authors: Ariel Ortiz-Bobea, Robert G. Chambers, Yurou He, David B. Lobell
Date: 2024-05-13 00:00:00

Increasing agricultural productivity is a gradual process with significant time lags between research and development (R&D) investment and the resulting gains. We estimate the response of US agricultural Total Factor Productivity (TFP) to both R&D investment and weather, and quantify the public R&D spending required to offset the emerging impacts of climate change. We find that offsetting the climate-induced productivity slowdown by 2050 alone requires a sustained public R&D spending growth of 5.2-7.8% per year over 2021-2050. This amounts to an additional $208-$434B investment over this period. These are substantial requirements comparable to the public R&D spending growth that followed the two World Wars.
Trade Openness, Tariffs and Economic Growth: An Empirical Study from Countries of G-20
Authors: S M Toufiqul Huq Sowrov
Date: 2024-05-13 00:00:00
Tasks: regression

International trade has been in the forefront of economic development and growth debates. Trade openness, its definition, scope, and impacts have also been studied numerously. Tariff has been dubbed as negative influencer of economic growth as per conventional wisdom and most empirical studies. This paper empirically examines relationships among trade openness as trade share to GDP, import tariff rate and economic growth. Panel dataset of 11 G-20 member countries were selected for the study. Results found a positively significant correlation between trade openness and economic growth. Tariff has negatively significant correlation with economic growth in lagged model. OLS and panel data fixed-effects regression were employed to carry out the regression analysis. To deal with endogeneity in trade openness variable, a 1-year lag regression technique was conducted. Results are robust and significant. Policy recommendation suggests country specific trade opening and tariff relaxation.
Packing Peanuts: The Role Synthetic Data Can Play in Enhancing Conventional Economic Prediction Models
Authors: Vansh Murad Kalia
Date: 2024-05-13 00:00:00

Packing peanuts, as defined by Wikipedia, is a common loose-fill packaging and cushioning material that helps prevent damage to fragile items. In this paper, I propose that synthetic data, akin to packing peanuts, can serve as a valuable asset for economic prediction models, enhancing their performance and robustness when integrated with real data. This hybrid approach proves particularly beneficial in scenarios where data is either missing or limited in availability. Through the utilization of Affinity credit card spending and Womply small business datasets, this study demonstrates the substantial performance improvements achieved by employing a hybrid data approach, surpassing the capabilities of traditional economic modeling techniques.
Counting Clinical Trials: New Evidence on Pharmaceutical Sector Productivity
Authors: Maya M. Durvasula, Sabri Eyuboglu, David M. Ritzwoller
Date: 2024-05-12 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

We develop a method for assigning high-quality labels to unstructured text. This method is based on fine-tuning an efficient, open-source language model with data extracted from a large, proprietary language model. We apply this method to construct a census of published clinical trials. With these data, we revisit a literature that contends that pharmaceutical sector productivity is declining. Central to this conclusion are measurements of substantial increases in the quantity of clinical trials over time, unmatched by trends in measures of output. In our data, the quantity, quality, and composition of clinical trials are stable since 2010. We show that previous measurements are an artifact of biases introduced by shifts in the composition of other forms of research.
China's plug-in hybrid electric vehicle transition: an operational carbon perspective
Authors: Yanqiao Deng, Minda Ma, Nan Zhou, Zhili Ma, Ran Yan, Xin Ma
Date: 2024-05-12 00:00:00

Assessing the emissions of plug-in hybrid electric vehicle (PHEV) operations is crucial for accelerating the carbon-neutral transition in the passenger car sector. This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China's top twenty selling PHEV models across different regions from 2020 to 2022. The results indicate that (1) the actual electricity intensity of the best-selling PHEV models (20.2-38.2 kWh/100 km) was 30-40% higher than the New European Driving Cycle values, and the actual gasoline intensity (4.7-23.5 L/100 km) was 3-6 times greater than the New European Driving Cycle values. (2) The overall energy use of the best-selling models varied among different regions, and the energy use from 2020 to 2022 in Southern China was double that Northern China and the Yangtze River Middle Reach. (3) The top-selling models emitted 4.7 megatons of carbon dioxide nationwide from 2020 to 2022, with 1.9 megatons released by electricity consumption and 2.8 megatons released by gasoline combustion. Furthermore, targeted policy implications for expediting the carbon-neutral transition within the passenger car sector are proposed. In essence, this study explores and compares benchmark data at both the national and regional levels, along with performance metrics associated with PHEV operations. The main objective is to aid nationwide decarbonization efforts, focusing on carbon reduction and promoting the rapid transition of road transportation toward a net-zero carbon future.
Fight like a Woman: Domestic Violence and Female Judges in Brazil
Authors: Helena Laneuville, Vitor Possebom
Date: 2024-05-12 00:00:00

We investigate the impact of judges' gender on the outcome of domestic violence cases. Using data from S\~ao Paulo, Brazil, between 2010 and 2019, we compare conviction rates by judge's gender and find that a domestic violence case assigned to a female judge is 31\% (10 p.p.) more likely to result in conviction than a case assigned to a male judge with similar career characteristics. To show that this decision gap rises due to different gender perspectives about domestic violence and not because female judges are stricter than their male counterparts in all rulings, we compare it against the gender conviction-rate gap in similar types of crime. We find that the gender conviction-rate gap for domestic violence cases is significantly larger than the same gap for other misdemeanor cases (3 p.p. larger) and for other physical assault cases (8 p.p. larger). Furthermore, we find evidence that at least two channels explain this gender conviction-rate gap for domestic violence cases: gender-based differences in evidence interpretation and gender-based sentencing criteria. Lastly, we find that this gender conviction rate has no significant impact on the probability of appeals, ruling reversals or recidivism.
Breaking open the black box of the production function: an agent-based model accounting for time in production processes
Authors: Jack Birner, Marco Mazzoli, Eleonora Priori, Pietro Terna
Date: 2024-05-11 00:00:00

Traditional notions of production function do not consider the time dimension, appearing thus timeless and instantaneous. We propose an agent-based model accounting for the whole production side of the economy to unfold the production process from its very beginning, when firms receive production orders, to the delivery of the products to the market. In the model we analyze with a high-degree of details how heterogeneous firms, having labor and capital as productive factors, behave along all the realization processes of their outputs. The main focus covers: i) the heterogeneous duration of firms' production processes, ii) the adaptive strategies they implement to adjust their choices, and iii) the possible failures which may occur due to the duration of the production. Our agent-based model is a controlled experiment: we use a virtual central planner mechanism, which acts as the demand side of the economy, to observe which firm individual behaviors and aggregate macroeconomic outcomes emerge as a reply to its different behaviors in a ceteris paribus environment. Our applied goal, then, is to discuss the role of industrial policy by modeling production processes in detail.
How does ClinicalTrials.gov Impact Company Innovation?
Authors: Yazhou Niu
Date: 2024-05-02 00:00:00

Pharmaceutical companies may have incentives to exaggerate the therapeutic effects of their developed products during the clinical stage, which endangers the health of patients. To increase transparency in clinical practice, the NIH established ClinicalTrials.gov in 2000, which indicates a significant impact on medicine. However, little evidence shows how ClinicalTrials.gov affects medical enterprises innovation. By identifying the patent application activities through USPTO, Pubmed, and Compustat, we used coherent DID to prove the impact of ClinicalTrials.gov on innovation. We found that the emergence of ClinicalTrials.gov reduced the number of patent applications and led to a shift in RD directions. This effect can also be moderated depending on firm size, probably because small companies are more incentivized to manipulate data. Hence, we suggest agencies could consider wide-ranging influences when formulating open science policies.
Goodness-of-fit and utility estimation: what's possible and what's not
Authors: Joshua Lanier, John K. -H. Quah
Date: 2024-05-14 00:00:00

A goodness-of-fit index measures the consistency of consumption data with a given model of utility-maximization. We show that for the class of well-behaved (i.e., continuous and increasing) utility functions there is no goodness-of-fit index that is continuous and accurate, where the latter means that a perfect score is obtained if and only if a dataset can be rationalized by a well-behaved utility function. While many standard goodness-of-fit indices are inaccurate we show that these indices are (in a sense we make precise) essentially accurate. Goodness-of-fit indices are typically generated by loss functions and we find that standard loss functions usually do not yield a best-fitting utility function when they are minimized. Nonetheless, welfare comparisons can be made by working out a robust preference relation from the data.
Revealed preference and revealed preference cycles: a survey
Authors: Paweł Dziewulski, Joshua Lanier, John K. -H. Quah
Date: 2024-05-14 00:00:00
Tasks: Survey

Afriat's Theorem (1967) states that a dataset can be thought of as being generated by a consumer maximizing a continuous and increasing utility function if and only if it is free of revealed preference cycles containing a strict relation. The latter property is often known by its acronym, GARP (for generalized axiom of revealed preference). This paper surveys extensions and applications of Afriat's seminal result. We focus on those results where the consistency of a dataset with the maximization of a utility function satisfying some property can be characterized by a suitably modified version of GARP.
The Perils of Overreaction
Authors: Konstantin von Beringe, Mark Whitmeyer
Date: 2024-05-13 00:00:00

In order to study updating rules, we consider the problem of a malevolent principal screening an imperfectly Bayesian agent. We uncover a fundamental dichotomy between underreaction and overreaction to information. If an agent's posterior is farther away from the prior than it should be under Bayes' law, she can always be exploited by the principal to an unfettered degree: the agent's ex ante expected loss can be made arbitrarily large. In stark contrast, an agent who underreacts (whose posterior is closer to the prior than the Bayesian posterior) cannot be exploited at all.
M$^4$oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Experts
Authors: Yufeng Jiang, Yiqing Shen
Date: 2024-05-15 00:00:00
Tasks: Image Segmentation, Mixture-of-Experts, Semantic Segmentation

Medical imaging data is inherently heterogeneous across different modalities and clinical centers, posing unique challenges for developing generalizable foundation models. Conventional entails training distinct models per dataset or using a shared encoder with modality-specific decoders. However, these approaches incur heavy computational overheads and suffer from poor scalability. To address these limitations, we propose the Medical Multimodal Mixture of Experts (M$^4$oE) framework, leveraging the SwinUNet architecture. Specifically, M$^4$oE comprises modality-specific experts; each separately initialized to learn features encoding domain knowledge. Subsequently, a gating network is integrated during fine-tuning to modulate each expert's contribution to the collective predictions dynamically. This enhances model interpretability and generalization ability while retaining expertise specialization. Simultaneously, the M$^4$oE architecture amplifies the model's parallel processing capabilities, and it also ensures the model's adaptation to new modalities with ease. Experiments across three modalities reveal that M$^4$oE can achieve 3.45% over STU-Net-L, 5.11% over MED3D, and 11.93% over SAM-Med2D across the MICCAI FLARE22, AMOS2022, and ATLAS2023 datasets. Moreover, M$^4$oE showcases a significant reduction in training duration with 7 hours less while maintaining a parameter count that is only 30% of its compared methods. The code is available at https://github.com/JefferyJiang-YF/M4oE.
Enhancing Image Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy
Authors: Weixuan Chen, Shunpu Tang, Qianqian Yang
Date: 2024-05-15 00:00:00
Tasks: Image Reconstruction, Semantic Communication

Semantic communication (SemCom) enhances transmission efficiency by sending only task-relevant information compared to traditional methods. However, transmitting semantic-rich data over insecure or public channels poses security and privacy risks. This paper addresses the privacy problem of transmitting images over wiretap channels and proposes a novel SemCom approach ensuring privacy through a differential privacy (DP)-based image protection and deprotection mechanism. The method utilizes the GAN inversion technique to extract disentangled semantic features and applies a DP mechanism to protect sensitive features within the extracted semantic information. To address the non-invertibility of DP, we introduce two neural networks to approximate the DP application and removal processes, offering a privacy protection level close to that by the original DP process. Simulation results validate the effectiveness of our method in preventing eavesdroppers from obtaining sensitive information while maintaining high-fidelity image reconstruction at the legitimate receiver.
The Developing Human Connectome Project: A Fast Deep Learning-based Pipeline for Neonatal Cortical Surface Reconstruction
Authors: Qiang Ma, Kaili Liang, Liu Li, Saga Masui, Yourong Guo, Chiara Nosarti, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert
Date: 2024-05-14 00:00:00
Tasks: GPU, Surface Reconstruction

The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain magnetic resonance (MR) images for the dHCP neonatal dataset. However, the current implementation of the pipeline requires more than 6.5 hours to process a single MRI scan, making it expensive for large-scale neuroimaging studies. In this paper, we propose a fast deep learning (DL) based pipeline for dHCP neonatal cortical surface reconstruction, incorporating DL-based brain extraction, cortical surface reconstruction and spherical projection, as well as GPU-accelerated cortical surface inflation and cortical feature estimation. We introduce a multiscale deformation network to learn diffeomorphic cortical surface reconstruction end-to-end from T2-weighted brain MRI. A fast unsupervised spherical mapping approach is integrated to minimize metric distortions between cortical surfaces and projected spheres. The entire workflow of our DL-based dHCP pipeline completes within only 24 seconds on a modern GPU, which is nearly 1000 times faster than the original dHCP pipeline. The qualitative assessment demonstrates that for 82.5% of the test samples, the cortical surfaces reconstructed by our DL-based pipeline achieve superior (54.2%) or equal (28.3%) surface quality compared to the original dHCP pipeline.
Parameter-Efficient Instance-Adaptive Neural Video Compression
Authors: Hyunmo Yang, Seungjun Oh, Eunbyung Park
Date: 2024-05-14 00:00:00
Tasks: parameter-efficient fine-tuning, Video Compression

Learning-based Neural Video Codecs (NVCs) have emerged as a compelling alternative to standard video codecs, demonstrating promising performance, and simple and easily maintainable pipelines. However, NVCs often fall short of compression performance and occasionally exhibit poor generalization capability due to inference-only compression scheme and their dependence on training data. The instance-adaptive video compression techniques have recently been suggested as a viable solution, fine-tuning the encoder or decoder networks for a particular test instance video. However, fine-tuning all the model parameters incurs high computational costs, increases the bitrates, and often leads to unstable training. In this work, we propose a parameter-efficient instance-adaptive video compression framework. Inspired by the remarkable success of parameter-efficient fine-tuning on large-scale neural network models, we propose to use a lightweight adapter module that can be easily attached to the pretrained NVCs and fine-tuned for test video sequences. The resulting algorithm significantly improves compression performance and reduces the encoding time compared to the existing instant-adaptive video compression algorithms. Furthermore, the suggested fine-tuning method enhances the robustness of the training process, allowing for the proposed method to be widely used in many practical settings. We conducted extensive experiments on various standard benchmark datasets, including UVG, MCL-JVC, and HEVC sequences, and the experimental results have shown a significant improvement in rate-distortion (RD) curves (up to 5 dB PSNR) and BD rates compared to the baselines NVC. Our code is available on https://github.com/ohsngjun/PEVC.
On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks
Authors: Chenhao Wu, Qingbo Wu, Haoran Wei, Shuai Chen, Lei Wang, King Ngi Ngan, Fanman Meng, Hongliang Li
Date: 2024-05-13 00:00:00
Tasks: Adversarial Robustness, Image Compression

Despite demonstrating superior rate-distortion (RD) performance, learning-based image compression (LIC) algorithms have been found to be vulnerable to malicious perturbations in recent studies. However, the adversarial attacks considered in existing literature remain divergent from real-world scenarios, both in terms of the attack direction and bitrate. Additionally, existing methods focus solely on empirical observations of the model vulnerability, neglecting to identify the origin of it. These limitations hinder the comprehensive investigation and in-depth understanding of the adversarial robustness of LIC algorithms. To address the aforementioned issues, this paper considers the arbitrary nature of the attack direction and the uncontrollable compression ratio faced by adversaries, and presents two practical rate-distortion attack paradigms, i.e., Specific-ratio Rate-Distortion Attack (SRDA) and Agnostic-ratio Rate-Distortion Attack (ARDA). Using the performance variations as indicators, we evaluate the adversarial robustness of eight predominant LIC algorithms against diverse attacks. Furthermore, we propose two novel analytical tools for in-depth analysis, i.e., Entropy Causal Intervention and Layer-wise Distance Magnify Ratio, and reveal that hyperprior significantly increases the bitrate and Inverse Generalized Divisive Normalization (IGDN) significantly amplifies input perturbations when under attack. Lastly, we examine the efficacy of adversarial training and introduce the use of online updating for defense. By comparing their advantages and disadvantages, we provide a reference for constructing more robust LIC algorithms against the rate-distortion attacks.