pwc-archive/papers-with-abstracts huggingface dataset:
BayesSRW: Bayesian Sampling and Re-weighting approach for variance reduction
Authors: Carol Liu
Date: 2024-08-28 00:00:00

In this paper, we address the challenge of sampling in scenarios where limited resources prevent exhaustive measurement across all subjects. We consider a setting where samples are drawn from multiple groups, each following a distribution with unknown mean and variance parameters. We introduce a novel sampling strategy, motivated simply by Cauchy-Schwarz inequality, which minimizes the variance of the population mean estimator by allocating samples proportionally to both the group size and the standard deviation. This approach improves the efficiency of sampling by focusing resources on groups with greater variability, thereby enhancing the precision of the overall estimate. Additionally, we extend our method to a two-stage sampling procedure in a Bayes approach, named BayesSRW, where a preliminary stage is used to estimate the variance, which then informs the optimal allocation of the remaining sampling budget. Through simulation examples, we demonstrate the effectiveness of our approach in reducing estimation uncertainty and providing more reliable insights in applications ranging from user experience surveys to high-dimensional peptide array studies.
The effects of data preprocessing on probability of default model fairness
Authors: Di wu
Date: 2024-08-28 00:00:00
Tasks: Fairness

In the context of financial credit risk evaluation, the fairness of machine learning models has become a critical concern, especially given the potential for biased predictions that disproportionately affect certain demographic groups. This study investigates the impact of data preprocessing, with a specific focus on Truncated Singular Value Decomposition (SVD), on the fairness and performance of probability of default models. Using a comprehensive dataset sourced from Kaggle, various preprocessing techniques, including SVD, were applied to assess their effect on model accuracy, discriminatory power, and fairness.
Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables
Authors: Geonwoo Kim, Suyong Song
Date: 2024-08-26 00:00:00
Tasks: Econometrics, valid

We develop an estimator for treatment effects in high-dimensional settings with additive measurement error, a prevalent challenge in modern econometrics. We introduce the Double/Debiased Convex Conditioned LASSO (Double/Debiased CoCoLASSO), which extends the double/debiased machine learning framework to accommodate mismeasured covariates. Our principal contributions are threefold. (1) We construct a Neyman-orthogonal score function that remains valid under measurement error, incorporating a bias correction term to account for error-induced correlations. (2) We propose a method of moments estimator for the measurement error variance, enabling implementation without prior knowledge of the error covariance structure. (3) We establish the $\sqrt{N}$-consistency and asymptotic normality of our estimator under general conditions, allowing for both the number of covariates and the magnitude of measurement error to increase with the sample size. Our theoretical results demonstrate the estimator's efficiency within the class of regularized high-dimensional estimators accounting for measurement error. Monte Carlo simulations corroborate our asymptotic theory and illustrate the estimator's robust performance across various levels of measurement error. Notably, our covariance-oblivious approach nearly matches the efficiency of methods that assume known error variance.
Endogenous Treatment Models with Social Interactions: An Application to the Impact of Exercise on Self-Esteem
Authors: Zhongjian Lin, Francis Vella
Date: 2024-08-26 00:00:00

We address the estimation of endogenous treatment models with social interactions in both the treatment and outcome equations. We model the interactions between individuals in an internally consistent manner via a game theoretic approach based on discrete Bayesian games. This introduces a substantial computational burden in estimation which we address through a sequential version of the nested fixed point algorithm. We also provide some relevant treatment effects, and procedures for their estimation, which capture the impact on both the individual and the total sample. Our empirical application examines the impact of an individual's exercise frequency on her level of self-esteem. We find that an individual's exercise frequency is influenced by her expectation of her friends'. We also find that an individual's level of self-esteem is affected by her level of exercise and, at relatively lower levels of self-esteem, by the expectation of her friends' self-esteem.
Inference on Consensus Ranking of Distributions
Authors: David M. Kaplan
Date: 2024-08-25 00:00:00

Instead of testing for unanimous agreement, I propose learning how broad of a consensus favors one distribution over another (of earnings, productivity, asset returns, test scores, etc.). Specifically, given a sample from each of two distributions, I propose statistical inference methods to learn about the set of utility functions for which the first distribution has higher expected utility than the second distribution. With high probability, an "inner" confidence set is contained within this true set, while an "outer" confidence set contains the true set. Such confidence sets can be formed by inverting a proposed multiple testing procedure that controls the familywise error rate. Theoretical justification comes from empirical process results, given that very large classes of utility functions are generally Donsker (subject to finite moments). The theory additionally justifies a uniform (over utility functions) confidence band of expected utility differences, as well as tests with a utility-based "restricted stochastic dominance" as either the null or alternative hypothesis. Simulated and empirical examples illustrate the methodology.
Cross-sectional Dependence in Idiosyncratic Volatility
Authors: Ilze Kalnina, Kokouvi Tewou
Date: 2024-08-24 00:00:00

This paper introduces an econometric framework for analyzing cross-sectional dependence in the idiosyncratic volatilities of assets using high frequency data. We first consider the estimation of standard measures of dependence in the idiosyncratic volatilities such as covariances and correlations. Naive estimators of these measures are biased due to the use of the error-laden estimates of idiosyncratic volatilities. We provide bias-corrected estimators and the relevant asymptotic theory. Next, we introduce an idiosyncratic volatility factor model, in which we decompose the variation in idiosyncratic volatilities into two parts: the variation related to the systematic factors such as the market volatility, and the residual variation. Again, naive estimators of the decomposition are biased, and we provide bias-corrected estimators. We also provide the asymptotic theory that allows us to test whether the residual (non-systematic) components of the idiosyncratic volatilities exhibit cross-sectional dependence. We apply our methodology to the S&P 100 index constituents, and document strong cross-sectional dependence in their idiosyncratic volatilities. We consider two different sets of idiosyncratic volatility factors, and find that neither can fully account for the cross-sectional dependence in idiosyncratic volatilities. For each model, we map out the network of dependencies in residual (non-systematic) idiosyncratic volatilities across all stocks.
The Turing Valley: How AI Capabilities Shape Labor Income
Authors: Enrique Ide, Eduard Talamàs
Date: 2024-08-29 00:00:00

Do improvements in Artificial Intelligence (AI) benefit workers? We study how AI capabilities influence labor income in a competitive economy where production requires multidimensional knowledge, and firms organize production by matching humans and AI-powered machines in hierarchies designed to use knowledge efficiently. We show that advancements in AI in dimensions where machines underperform humans decrease total labor income, while advancements in dimensions where machines outperform humans increase it. Hence, if AI initially underperforms humans in all dimensions and improves gradually, total labor income initially declines before rising. We also characterize the AI that maximizes labor income. When humans are sufficiently weak in all knowledge dimensions, labor income is maximized when AI is as good as possible in all dimensions. Otherwise, labor income is maximized when AI simultaneously performs as poorly as possible in the dimensions where humans are relatively strong and as well as possible in the dimensions where humans are relatively weak. Our results suggest that choosing the direction of AI development can create significant divisions between the interests of labor and capital.
The Green Peace Dividend: the Effects of Militarization on Emissions and the Green Transition
Authors: Balázs Markó
Date: 2024-08-29 00:00:00

This paper argues that military buildups lead to a significant rise in greenhouse gas emissions and can disrupt the green transition. Identifying military spending shocks, I use local projections to show that a percentage point rise in the military spending share leads to a 0.9-2% rise in total emissions, as well as a 1% rise in emission intensity, depending on the economy's overall emission intensity. Using a dynamic production network model calibrated for the US, I find that a permanent shock of the same size would increase total emissions by between 0.36% and 1.81%, and emission intensity by between 0.22% and 1.5%. The empirical analysis indicates that green patenting is reduced by 10-25% following such a shock, and the model suggests that investment in renewables could be crowded out by defence spending under certain circumstances, hindering the energy transition. These effects can significantly raise climate damages and temperatures. Depending on the social cost of carbon and the composition of a military spending shock, I estimate that doubling the military spending share in the US in 2017 (equal to 3.3% of GDP) would have led to climate damages equivalent to between 0.07-2.6% of GDP per year.
Time is Knowledge: What Response Times Reveal
Authors: Jean-Michel Benkert, Shuo Liu, Nick Netzer
Date: 2024-08-27 00:00:00

Response times contain information about economically relevant but unobserved variables like willingness to pay, preference intensity, quality, or happiness. Here, we provide a general characterization of the properties of latent variables that can be detected using response time data. Our characterization generalizes various results in the literature, helps to solve identification problems of binary response models, and paves the way for many new applications. We apply the result to test the hypothesis that marginal happiness is decreasing in income, a principle that is commonly accepted but so far not established empirically.
The Climate Cost of Climate Investment: A Two-Period Perspective
Authors: Shaunak Kulkarni, Rohan Ajay Dubey
Date: 2024-08-26 00:00:00

A one-size-fits-all paradigm that only adapts the scale and immediate outcome of climate investment to economic circumstances will provide a short-lived, economically inadequate response to climate issues; given the limited resources allocated to green finance, it stands to reason that the shortcomings of this will be exacerbated by the fact that it comes at the cost of long-term, self-perpetuating, systemic solutions. Financial commitments that do not consider the capital structure of green finance in an economy will cumulatively dis-aggregate the economic cost of climate investment, to erode the competitive advantage of the most innovative economies, while simultaneously imposing the greatest financial burden on economies that are most vulnerable to the impact of climate change; such disaggregation will also leave 'middle' economies in a state of flux - honouring similar financial commitments to vulnerable or highly developed peers, but unable to generate comparable return, yet sufficiently insulated from the impact of extreme climate phenomena to not organically develop solutions. In the face of these changing realities, green innovation needs to expand beyond technology and address systemic inefficiencies - lack of clear responsibility, ambiguously defined commitments, and inadequate checks & balances to name a few. Clever application of financial engineering demonstrates promise, and simple measures like carbon-credit exchanges have been effective in mitigating imperfections at the grassroots level. We believe that information- and incentive-centric systemic advancements can usher a fresh wave of green innovation that stands on the shoulders of giants to ensure effective implementation of technological breakthroughs; economic development that will create an international community equipped with a robust framework to deal with long-term crises in a strategic manner.
Insuring Long-Term Care in Developing Countries: The Interaction between Formal and Informal Insurance
Authors: Jiayi Wen, Xiaoqing Yu
Date: 2024-08-26 00:00:00

Does public insurance reduce uninsured long-term care (LTC) risks in developing countries, where informal insurance predominates? This paper exploits the rollout of LTC insurance in China around 2016 to examine the impact of public LTC insurance on healthy workers' labor supply, a critical self-insurance channel. We find that workers eligible for public LTC insurance were less likely to engage in labor work and worked fewer weeks annually following the policy change, suggesting a mitigation of uninsured risks. However, these impacts were insignificant among those with strong informal insurance coverage. Parallel changes in anticipated formal care use corroborate these findings. While our results reveal that public LTC insurance provides limited additional risk-sharing when informal insurance predominates, they also underscore its growing importance.
From catch-up to frontier: The utility model as a learning device to escape the middle-income trap
Authors: Su Jung Jee, Kerstin Hötte
Date: 2024-08-26 00:00:00
Tasks: Active Learning

The second-tier patent system, known as utility models (UMs), has been considered a tool for supporting technological learning in catching-up economies. UMs often impose lower inventive step requirements than patents, making them accessible to inventors with limited capacity. However, the mechanisms by which the UM system supports the catch-up process and the system's long-term impact in the post-catch-up period remain underexplored. We address this gap by empirically examining how and under which conditions the UM system can create synergies with catch-up strategies and influence the emerging industrial structure in the post-catch-up phase. Drawing on the literature on catch-up and intellectual property rights (IPR), we argue that catch-up strategies that prioritize imitative and adaptive learning in short-cycle technologies can be complemented by the UM system, supporting the development of indigenous capabilities to produce high-impact frontier technologies and internalize their value-added. Using South Korea as a case study, we present two key findings: First, the country's post-catch-up frontier technologies (US patents) are more impactful when they build on active learning experiences encoded in domestic UMs. Second, UM-reliant frontier technologies contribute to value internalization (local value-added relative to foreign value-added). Our findings extend the literature on the role of different types of IPR in various stages of development and inform policy discussions on how UMs can be used to support capability building in developing countries.
Import competition and domestic vertical integration: Theory and Evidence from Chinese firms
Authors: Xin Du, Xiaoxia Shi
Date: 2024-08-25 00:00:00
Tasks: Specificity

What impact does import competition have on firms' production organizational choices? Existing literature has predominantly focused on the relationship between import competition and firms' global production networks, with less attention given to domestic. We first develop a Nash-bargaining model to guide our empirical analysis, then utilize tariff changes as an exogenous shock to test our theoretical hypotheses using a database of Chinese listed firms from 2000 to 2023. Our findings indicate that a decrease in downstream tariffs lead to an increase in vertical integration. In our mechanism tests, we discover that a reduction in upstream tariffs also enhances this effect. Moreover, the impact of tariff reductions on vertical integration is primarily observed in industries with high asset specificity, indicating that asset-specificity is a crucial mechanism. We further explore whether import competition encourages vertical integration for technological acquisition purpose, the effect is found only among high-tech firms, while it's absent in non-high-tech firms. Our research provides new perspectives and evidence on how firms optimize their production organization in the process of globalization.
The Future of Work: Inequality, Artificial Intelligence, and What Can Be Done About It. A Literature Review
Authors: Caleb Peppiatt
Date: 2024-08-23 00:00:00

Generative Artificial Intelligence constitutes a new wave of automation. There is broad agreement among economists that humanity is potentially entering into a period of profound change. However, significant uncertainties and disagreements exist concerning a variety of overlapping topics: the share of jobs in which human labour is displaced and/or reinstated through automation; the effects on income inequality; the effects on job satisfaction; and, finally, what policy changes ought to be pursued to reduce potential negative impacts. This literature review seeks to clarify this landscape by mapping out key disagreements between positions, and to identify the critical elements upon which such disagreements rest. By surveying the current literature, the effects of AI on the future of work will be clarified.
A new approach to the theory of optimal income tax
Authors: Vassili N. Kolokoltsov, Egor M. Dranov, Denis E. Piskun
Date: 2024-08-10 00:00:00
Tasks: Portfolio Optimization

The Nobel-price winning Mirrlees' theory of optimal taxation inspired a long sequence of research on its refinement and enhancement. However, an issue of concern has been always the fact that, as was shown in many publications, the optimal schedule in Mirrlees' paradigm of maximising the total utility (constructed from individually optimised individual ones) usually did not lead to progressive taxation (contradicting the ethically supported practice in developed economies), and often even assigned minimal tax rates to the higher paid strata of society. The first objective of this paper is to support this conclusion by proving a theorem on optimal tax schedule in (practically most exploited) piecewise-linear environment under a simplest natural utility function. The second objective is to suggest a new paradigm for optimal taxation, where instead of just total average utility maximization one introduces a standard deviation of utility as a second parameter (in some analogy with Marcowitz portfolio optimization). We show that this approach leads to transparent and easy interpreted optimality criteria for income tax.
Monetizing digital content with network effects: A mechanism-design approach
Authors: Vincent Meisner, Pascal Pillath
Date: 2024-08-27 00:00:00

We design profit-maximizing mechanisms to sell an excludable and non-rival good with positive and/or negative network effects. Buyers have heterogeneous private values that depend on how many others also consume the good. In optimum, an endogenous number of the highest types consume the good, and we can implement this allocation in dominant strategies. We apply our insights to digital content creation, and we are able to rationalize features seen in monetization schemes in this industry such as voluntary contributions, community subsidies, and exclusivity bids.
Integrating Continuous and Binary Relevances in Audio-Text Relevance Learning
Authors: Huang Xie, Khazar Khorrami, Okko Räsänen, Tuomas Virtanen
Date: 2024-08-27 00:00:00
Tasks: Contrastive Learning

Audio-text relevance learning refers to learning the shared semantic properties of audio samples and textual descriptions. The standard approach uses binary relevances derived from pairs of audio samples and their human-provided captions, categorizing each pair as either positive or negative. This may result in suboptimal systems due to varying levels of relevance between audio samples and captions. In contrast, a recent study used human-assigned relevance ratings, i.e., continuous relevances, for these pairs but did not obtain performance gains in audio-text relevance learning. This work introduces a relevance learning method that utilizes both human-assigned continuous relevance ratings and binary relevances using a combination of a listwise ranking objective and a contrastive learning objective. Experimental results demonstrate the effectiveness of the proposed method, showing improvements in language-based audio retrieval, a downstream task in audio-text relevance learning. In addition, we analyze how properties of the captions or audio clips contribute to the continuous audio-text relevances provided by humans or learned by the machine.
Similarity Metrics For Late Reverberation
Authors: Gloria Dal Santo, Karolina Prawda, Sebastian J. Schlecht, Vesa Välimäki
Date: 2024-08-27 00:00:00

Automatic tuning of reverberation algorithms relies on the optimization of a cost function. While general audio similarity metrics are useful, they are not optimized for the specific statistical properties of reverberation in rooms. This paper presents two novel metrics for assessing the similarity of late reverberation in room impulse responses. These metrics are differentiable and can be utilized within a machine-learning framework. We compare the performance of these metrics to two popular audio metrics using a large dataset of room impulse responses encompassing various room configurations and microphone positions. The results indicate that the proposed functions based on averaged power and frequency-band energy decay outperform the baselines with the former exhibiting the most suitable profile towards the minimum. The proposed work holds promise as an improvement to the design and evaluation of reverberation similarity metrics.
Impact of Noisy Labels on Sound Event Detection: Deletion Errors Are More Detrimental Than Insertion Errors
Authors: Yuliang Zhang, Roberto Togneri, Defeng, Huang
Date: 2024-08-27 00:00:00
Tasks: Event Detection, Sound Event Detection, Temporal Localization

This study explores the critical but underexamined impact of label noise on Sound Event Detection (SED), which requires both sound identification and precise temporal localization. We categorize label noise into deletion, insertion, substitution, and subjective types and systematically evaluate their effects on SED using synthetic and real-life datasets. Our analysis shows that deletion noise significantly degrades performance, while insertion noise is relatively benign. Moreover, loss functions effective against classification noise do not perform well for SED due to intra-class imbalance between foreground sound events and background sounds. We demonstrate that loss functions designed to address data imbalance in SED can effectively reduce the impact of noisy labels on system performance. For instance, halving the weight of background sounds in a synthetic dataset improved macro-F1 and micro-F1 scores by approximately $9\%$ with minimal Error Rate increase, with consistent results in real-life datasets. This research highlights the nuanced effects of noisy labels on SED systems and provides practical strategies to enhance model robustness, which are pivotal for both constructing new SED datasets and improving model performance, including efficient utilization of soft and crowdsourced labels.