pwc-archive/papers-with-abstracts huggingface dataset:
Live-cell imaging powered by computation
Authors: Hari Shroff, Ilaria Testa, Florian Jug, Suliana Manley
Date: 2024-01-02 00:00:00
Tasks: Navigate

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. We focus here on computational methods that promise to boost live-cell fluorescence microscopy, where intra-cellular dynamics and cell viability constrain measurements considerably. Considering the tradeoffs between signal-to-noise ratio (SNR), spatial resolution, temporal resolution, and multi-color and multi-channel capacity, we review computational methods that can be layered on top of commonly used existing microscopies, as well as hybrid methods that integrate computation and microscope hardware.
Machine-learning-based particle identification with missing data
Authors: Miłosz Kasak, Kamil Deja, Maja Karwowska, Monika Jakubowska, Łukasz Graczykowski, Małgorzata Janik
Date: 2023-12-21 00:00:00
Tasks: Missing Values

In this work, we introduce a novel method for Particle Identification (PID) within the scope of the ALICE experiment at the Large Hadron Collider at CERN. Identifying products of ultrarelativisitc collisions delivered by the LHC is one of the crucial objectives of ALICE. Typically employed PID methods rely on hand-crafted selections, which compare experimental data to theoretical simulations. To improve the performance of the baseline methods, novel approaches use machine learning models that learn the proper assignment in a classification task. However, because of the various detection techniques used by different subdetectors, as well as the limited detector efficiency and acceptance, produced particles do not always yield signals in all of the ALICE components. This results in data with missing values. Machine learning techniques cannot be trained with such examples, so a significant part of the data is skipped during training. In this work, we propose the first method for PID that can be trained with all of the available data examples, including incomplete ones. Our approach improves the PID purity and efficiency of the selected sample for all investigated particle species.
Evolutionary Dynamics with Randomly Distributed Benevolent Individuals
Authors: Yuxin Geng, Xingru Chen
Date: 2023-12-30 00:00:00

Understanding the evolution of cooperation is pivotal in biology and social science. Public resources sharing is a common scenario in the real world. In our study, we explore the evolutionary dynamics of cooperation on a regular graph with degree $k$, introducing the presence of a third strategy, namely the benevolence, who does not evolve over time, but provides a fixed benefit to all its neighbors. We find that the presence of the benevolence can foster the development of cooperative behavior and it follows a simple rule: $b/c > k - p_S(k-1)$. Our results provide new insights into the evolution of cooperation in structured populations.
The Anatomy Spread of Online Opinion Polarization: The Pivotal Role of Super-Spreaders in Social Networks
Authors: Yasuko Kawahata
Date: 2023-11-27 00:00:00
Tasks: Anatomy

The study investigates the role of 'superspreaders' in shaping opinions within networks, distinguishing three types: A, B, and C. Type A has a significant influence in shaping opinions, Type B acts as a counterbalance to A, and Type C functions like media, providing an objective viewpoint and potentially regulating A and B's influence. The research uses a confidence coefficient and z-score to survey superspreaders' behaviors, with a focus on the conditions affecting group dynamics and opinion formation, including environmental factors and forgetfulness over time. The findings offer insights for improving online communication security and understanding social influence. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.
Changes-in-Changes for Ordered Choice Models: Too Many "False Zeros"?
Authors: Daniel Gutknecht, Cenchen Liu
Date: 2024-01-01 00:00:00

In this paper, we develop a Difference-in-Differences model for discrete, ordered outcomes, building upon elements from a continuous Changes-in-Changes model. We focus on outcomes derived from self-reported survey data eliciting socially undesirable, illegal, or stigmatized behaviors like tax evasion or substance abuse, where too many "false zeros", or more broadly, underreporting are likely. We start by providing a characterization for parallel trends within a general threshold-crossing model. We then propose a partial and point identification framework for different distributional treatment effects when the outcome is subject to underreporting. Applying our methodology, we investigate the impact of recreational marijuana legalization for adults in several U.S. states on the short-term consumption behavior of 8th-grade high-school students. The results indicate small, but significant increases in consumption probabilities at each level. These effects are further amplified upon accounting for misreporting.
Identification of Nonlinear Dynamic Panels under Partial Stationarity
Authors: Wayne Yuan Gao, Rui Wang
Date: 2023-12-30 00:00:00

This paper studies identification for a wide range of nonlinear panel data models, including binary choice, ordered response, and other types of limited dependent variable models. Our approach accommodates dynamic models with any number of lagged dependent variables as well as other types of (potentially contemporary) endogeneity. Our identification strategy relies on a partial stationarity condition, which not only allows for an unknown distribution of errors but also for temporal dependencies in errors. We derive partial identification results under flexible model specifications and provide additional support conditions for point identification. We demonstrate the robust finite-sample performance of our approach using Monte Carlo simulations, and apply the approach to analyze the empirical application of income categories using various ordered choice models.
Robust Inference in Panel Data Models: Some Effects of Heteroskedasticity and Leveraged Data in Small Samples
Authors: Annalivia Polselli
Date: 2023-12-29 00:00:00

With the violation of the assumption of homoskedasticity, least squares estimators of the variance become inefficient and statistical inference conducted with invalid standard errors leads to misleading rejection rates. Despite a vast cross-sectional literature on the downward bias of robust standard errors, the problem is not extensively covered in the panel data framework. We investigate the consequences of the simultaneous presence of small sample size, heteroskedasticity and data points that exhibit extreme values in the covariates ('good leverage points') on the statistical inference. Focusing on one-way linear panel data models, we examine asymptotic and finite sample properties of a battery of heteroskedasticity-consistent estimators using Monte Carlo simulations. We also propose a hybrid estimator of the variance-covariance matrix. Results show that conventional standard errors are always dominated by more conservative estimators of the variance, especially in small samples. In addition, all types of HC standard errors have excellent performances in terms of size and power tests under homoskedasticity.
Decision Theory for Treatment Choice Problems with Partial Identification
Authors: José Luis Montiel Olea, Chen Qiu, Jörg Stoye
Date: 2023-12-29 00:00:00
Tasks: All

We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely characterize the minimax-regret optimal rule that least frequently randomizes, and show that, in some cases, it can outperform other minimax-regret optimal rules in terms of what we term profiled regret. We analyze the implications of our results in the aggregation of experimental estimates for policy adoption, extrapolation of Local Average Treatment Effects, and policy making in the presence of omitted variable bias.
Game Mining: How to Make Money from those about to Play a Game
Authors: James W. Bono, David H. Wolpert
Date: 2024-01-04 00:00:00

It is known that a player in a noncooperative game can benefit by publicly restricting his possible moves before play begins. We show that, more generally, a player may benefit by publicly committing to pay an external party an amount that is contingent on the game's outcome. We explore what happens when external parties -- who we call ``game miners'' -- discover this fact and seek to profit from it by entering an outcome-contingent contract with the players. We analyze various structured bargaining games between miners and players for determining such an outcome-contingent contract. These bargaining games include playing the players against one another, as well as allowing the players to pay the miner(s) for exclusivity and first-mover advantage. We establish restrictions on the strategic settings in which a game miner can profit and bounds on the game miner's profit. We also find that game miners can lead to both efficient and inefficient equilibria.
Airport service quality perception and flight delays: examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys
Authors: Alessandro V. M. Oliveira, Bruno F. Oliveira, Moises D. Vassallo
Date: 2024-01-04 00:00:00

The service quality of a passenger transport operator can be measured through face-to-face surveys at the terminals or on board. However, the resulting responses may suffer from the influence of the intrinsic aspects of the respondent's personality and emotional context at the time of the interview. This study proposes a methodology to generate and select control variables for these latent psychosituational traits, thus mitigating the risk of omitted variable bias. We developed an econometric model of the determinants of passenger satisfaction in a survey conducted at the largest airport in Latin America, S\~ao Paulo GRU Airport. Our focus was on the role of flight delays in the perception of quality. The results of this study confirm the existence of a relationship between flight delays and the global satisfaction of passengers with airports. In addition, favorable evaluations regarding airports' food/beverage concessions and Wi-Fi services, but not their retail options, have a relevant moderating effect on that relationship. Furthermore, dissatisfaction arising from passengers' interaction with the airline can have negative spillover effects on their satisfaction with the airport. We also found evidence of blame-attribution behavior, in which only delays of internal origin, such as failures in flight management, are significant, indicating that passengers overlook weather-related flight delays. Finally, the results suggest that an empirical specification that does not consider the latent psychosituational traits of passengers produces a relevant overestimation of the absolute effect of flight delays on passenger satisfaction.
Female Entrepreneur on Board:Assessing the Effect of Gender on Corporate Financial Constraints
Authors: Ruiying Xiao
Date: 2024-01-04 00:00:00
Tasks: Management

This study investigates the impact of female leadership on the financial constraints of firms, which are publicly listed entrepreneurial enterprises in China. Utilizing data from 938 companies on the China Growth Enterprise Market (GEM) over a period of 2013-2022, this paper explores how the female presence in CEO positions, senior management, and board membership influences a firm's ability to manage financial constraints. Our analysis employs the Kaplan-Zingales (KZ) Index to measure these constraints, encompassing some key financial factors such as cash flow, dividends, and leverage. The findings reveal that companies with female CEOs or a higher proportion of women in top management are associated with reduced financial constraints. However, the influence of female board members is less clear-cut. Our study also delves into the variances of these effects between high-tech and low-tech industry sectors, emphasizing how internal gender biases in high-tech industries may impede the alleviation of financing constraints on firms. This research contributes to a nuanced understanding of the role of gender dynamics in corporate financial management, especially in the context of China's evolving economic landscape. It underscores the importance of promoting female leadership not only for gender equity but also for enhancing corporate financial resilience.
Impact of Green Marketing Strategy on Brand Awareness: Business, Management, and Human Resources Aspects
Authors: Mahdi Nohekhan, MohammadMahdi Barzegar
Date: 2024-01-04 00:00:00
Tasks: Management, Marketing

Given the move towards industrialization in societies, the increase in dynamism and competition among companies to capture market share, raising concerns about the environment, government, and international regulations and obligations, increased consumer awareness, pressure from nature-loving groups, etc., organizations have become more attentive to issues related to environmental management. Over time, concepts such as green marketing have permeated marketing literature, making environmental considerations one of the most important activities of companies. To this end, this research examines the impact of green marketing strategy on brand awareness (case study: food exporting companies). The population of this research consists of 345 employees and managers of companies like Kalleh, Solico, Pemina, Sorbon, Mac, Pol, and Castle, from which 182 individuals were randomly selected as the sample using Cochran's formula. This research is practical, and the required data have been collected through a survey and a questionnaire. The research results indicate that (1) green marketing strategy significantly affects brand awareness. (2) Green products have a significant positive effect on brand awareness. (3) Green promotions have a significant positive effect on brand awareness. (4) Green distribution has a significant positive effect on brand awareness. (5) Green pricing has a significant positive effect on brand awareness.
Global analysis reveals persistent shortfalls and regional differences in availability of foods needed for health
Authors: Leah Costlow, Anna Herforth, Timothy B. Sulser, Nicola Cenacchi, William A. Masters
Date: 2024-01-02 00:00:00

Most people around the world still lack access to sufficient quantities of all food groups needed for an active and healthy life. This study traces historical and projected changes in global food systems toward alignment with the new Healthy Diet Basket (HDB) used by UN agencies and the World Bank to monitor the cost and affordability of healthy diets worldwide. Using the HDB as a standard to measure adequacy of national, regional and global supply-demand balances, we find substantial but inconsistent progress toward closer alignment with dietary guidelines, with large global shortfalls in fruits, vegetables, and legumes, nuts, and seeds, and large disparities among regions in use of animal source foods. Projections show that additional investments aimed at reducing chronic hunger would modestly accelerate improvements in adequacy where shortfalls are greatest, revealing the need for complementary investments to increase access to under-consumed food groups especially in low-income countries.
Urban and non-urban contributions to the social cost of carbon
Authors: Francisco Estrada, Veronica Lupi, Wouter Botzen, Richard S. J. Tol
Date: 2024-01-01 00:00:00

The social cost of carbon (SCC) serves as a concise gauge of climate change's economic impact, often reported at the global and country level. SCC values are disproportionately high for less-developed, populous countries. Assessing the contributions of urban and non-urban areas to the SCC can provide additional insights for climate policy. Cities are essential for defining global emissions, influencing warming levels and associated damages. High exposure and concurrent socioenvironmental problems exacerbate climate change risks in cities. Using a spatially explicit integrated assessment model, the SCC is estimated at USD$137-USD$579/tCO2, rising to USD$262-USD$1,075/tCO2 when including urban heat island (UHI) warming. Urban SCC dominates, with both urban exposure and the UHI contributing significantly. A permanent 1% reduction of the UHI in urban areas yields net present benefits of USD$484-USD$1,562 per urban dweller. Global cities have significant leverage and incentives for a swift transition to a low-carbon economy, and for reducing local warming.
Actualised and future changes in regional economic growth through sea level rise
Authors: Theodoros Chatzivasileiadis, Ignasi Cortes Arbues, Jochen Hinkel, Daniel Lincke, Richard S. J. Tol
Date: 2023-12-31 00:00:00

This study investigates the long-term economic impact of sea-level rise (SLR) on coastal regions in Europe, focusing on Gross Domestic Product (GDP). Using a novel dataset covering regional SLR and economic growth from 1900 to 2020, we quantify the relationships between SLR and regional GDP per capita across 79 coastal EU & UK regions. Our results reveal that the current SLR has already negatively influenced GDP of coastal regions, leading to a cumulative 4.7% loss at 39 cm of SLR. Over the 120 year period studied, the actualised impact of SLR on the annual growth rate is between -0.02% and 0.04%. Extrapolating these findings to future climate and socio-economic scenarios, we show that in the absence of additional adaptation measures, GDP losses by 2100 could range between -6.3% and -20.8% under the most extreme SLR scenario (SSP5-RCP8.5 High-end Ice, or -4.0% to -14.1% in SSP5-RCP8.5 High Ice). This statistical analysis utilising a century-long dataset, provides an empirical foundation for designing region-specific climate adaptation strategies to mitigate economic damages caused by SLR. Our evidence supports the argument for strategically relocating assets and establishing coastal setback zones when it is economically preferable and socially agreeable, given that protection investments have an economic impact.
Minimalist Market Design: A Framework for Economists with Policy Aspirations
Authors: Tayfun Sönmez
Date: 2023-12-30 00:00:00
Tasks: Philosophy

In this monograph, I describe the philosophy, evolution, and successful applications of minimalist market design while also presenting the advancements in matching theory achieved through these applications. In doing so, I provide a paradigm for economists who wish to influence policy and shape institutions through their research.
Does the World Bank's Ease of Doing Business Index Matter for FDI? Findings from Africa
Authors: Bhaso Ndzendze
Date: 2023-12-30 00:00:00

This paper investigates whether foreign investment (FDI) into Africa is at least partially responsive to World Bank-measured market friendliness. Specifically, I conducted analyses of four countries between 2009 and 2017, using cases that represent two of the highest scorers on the bank's Doing Business index as of 2008 (Mauritius and South Africa) and the two lowest scorers (DRC and CAR), and subsequently traced all four for growths or declines in FDI in relation to their scores in the index. The findings show that there is a moderate association between decreased costs of starting a business and growth of FDI. Mauritius, South Africa and the DRC reduced their total cost of starting a business by 71.7%, 143.7% and 122.9% for the entire period, and saw inward FDI increases of 167.6%, 79.8% and 152.21%, respectively. The CAR increased the cost of starting businesses but still saw increases in FDI. However, the country also saw the least amount of growth in FDI at only 13.3%.
Theoretical Steps to Optimize Transportation in the Cubic Networks and the Congestion Paradox
Authors: Joonkyung Yoo
Date: 2024-01-01 00:00:00
Tasks: All

Given a player is guaranteed the same payoff for each delivery path in a single-cube delivery network, the player's best response is to randomly divide all goods and deliver them to all other nodes, and the best response satisfies the Kuhn-Tucker condition. The state of the delivery network is randomly complete. If congestion costs are introduced to the player's maximization problem in a multi-cubic delivery network, the congestion paradox arises where all coordinates become congested as long as the previous assumptions about payoffs are maintained.
Community Enforcement with Endogenous Records
Authors: Harry Pei
Date: 2024-01-01 00:00:00

I study repeated games with anonymous random matching where players endogenously decide whether to disclose signals about their past actions. I establish an-anti folk theorem, that when players are sufficiently long-lived, they will almost always play their dominant actions and will almost never cooperate. When players' expected lifespans are intermediate, they can sustain some cooperation if their actions are substitutes but cannot sustain any cooperation if their actions are complements. Therefore, the maximal level of cooperation a community can sustain is not monotone with respect to its members' expected lifespans and the complementarity of players' actions can undermine their abilities to sustain cooperation.
Equilibrium existence in a discrete-time endogenous growth model with physical and human capital
Authors: Luis Alcala
Date: 2023-12-30 00:00:00

This paper studies a discrete-time version of the Lucas-Uzawa endogenous growth model with physical and human capital in the presence of externalities. Existence of an optimal equilibrium is proved using tools from dynamic programming with bounded or unbounded returns. The proofs also rely on properties of isoelastic utility and homogeneous production functions and apply well-known inequalities in real analysis, seldom used in the literature, which significantly simplify the task of verifying certain assumptions that are rather technical in nature. Some advantages of adopting a parametric family of isoelastic utility functions, instead of the ad hoc formulation typically used, are also discussed.