A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Foundation models improve wage gap decomposition by capturing omitted career history factors.
We consider the role of unobservables, such as differences in search frictions, reservation wages, and productivities for the explanation of wage differentials between migrants and natives. We disentangle these by estimating an empirical general equilibrium search model with on-the-job search due to Bontemps, Robin, an…
Audit fees change based on company and economic factors during auditor switching.
Study finds short-term wage increases due to COVID-19, contrary to expectations.
Study finds optimal retirement timing in uncertain wage scenarios.
This paper presents a model of the dynamics of the wage income distribution.
The expression "wage transition" refers to the fact that over the past two or three decades in all developed economies wage increases have levelled off. There has been a widening divergence and decoupling between wages on the one hand and GDP per capita on the other hand. Yet, in China wages and GDP per capita climbed …
This article aims to present an elementary analytical solution to the question of the formation of a structure of differentiation of rates of return in a classical gravitation model and in a model of the dynamics of price-wage spirals.
In 2016, the majority of full-time employed women in the U.S. earned significantly less than comparable men. The extent to which women were affected by gender inequality in earnings, however, depended greatly on socio-economic characteristics, such as marital status or educational attainment. In this paper, we analyzed…
The paper covers the new model of wage distribution in typical group of people. The model provides the opportunity to reparameterize applicable income distribution model: Pareto, logarithmically normal, logarithmically logistic, Dagum etc. The model ensures the graduation of Gini index values by polynomial degree of wa…
Two sets of high quality income data are analysed in detail, one set from the UK, one from the USA. It is firstly demonstrated that both a log-normal distribution and a Boltzmann distribution can give very accurate fits to both these data sets. The absence of a power tail in the US data set is then discussed. Taken in …
Extends Demographic Parity for fairer wage predictions with expert knowledge.
Introduces a new quantile regression method for financial and wage data analysis.
The high pay packages of U.S. CEOs have raised serious concerns about what would constitute a fair pay.
We review the production function and the hypothesis of equilibrium in the neoclassical framework. We notify that in a soup of sectors in economy, while capital and labor resemble extensive variables, wage and rate of return on capital act as intensive variables. As a result, Baumol and Bowen's statement of equal wages…
Model uses Preisach hysteresis to predict gig worker acceptance, reducing costs and improving fill rates.
We discuss an optimal investment, consumption and insurance problem of a wage earner under inflation. Assume a wage earner investing in a real money account and three asset prices, namely: a real zero coupon bond, the inflation-linked real money account and a risky share described by jump-diffusion processes. Using the…
The paper analyzes optimal retirement strategies in a market with habit persistence and jump diffusion, finding discontinuous investment strategies.
Method selects valid IVs from a large set using clustering and test of overidentifying restrictions.
The paper studies and mitigates accuracy disparity in regression models.
Develops methods for fair classification under linear disparity constraints.
A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate of MIAs against diff…
Unified framework for causal inference under sample selection.
Following related work in law and policy, two notions of disparity have come to shape the study of fairness in algorithmic decision-making. Algorithms exhibit treatment disparity if they formally treat members of protected subgroups differently; algorithms exhibit impact disparity when outcomes differ across subgroups,…
Study decomposes racial healthcare disparities via shifts in mediator distributions.
In this paper, we study a stochastic optimal control problem with stochastic volatility. We prove the sufficient and necessary maximum principle for the proposed problem. Then we apply the results to solve an investment, consumption and life insurance problem with stochastic volatility, that is, we consider a wage earn…
Study shows explanation disparities in machine learning models are influenced by data and model properties.
Bayesian model identifies health disparities in disease progression.
Study shows label errors impact model disparity metrics, proposing mitigation methods.
Proposes a method to quantify and decompose disparity in ML models, separating exempt and non-exempt components.
End-to-end framework learns precise disparity for activity recognition.
Paper explores fair classification with bounded disparity using finite datasets.
Paper analyzes AI's impact on job tasks, predicting future demands.
We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning. We aim at promoting the use of confidence intervals when testing the so-called group disparate impact. We illustrate on some examples the importance of using confidence int…
Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the e…
The paper examines how adversarial robustness affects accuracy disparity across different classes.
We note a simple mechanism that may at least partially resolve several outstanding economic puzzles, including why the cyclically adjusted price to earnings ratio of the S&P 500 index has been oddly high for the past two decades, why gains to capital have outpaced gains to wages, and the persistence of the equity premi…
Paper tackles fairness in CCA by minimizing correlation disparity error.
New method evaluates multiple social disparities using machine learning.
Study highlights fairness issues in travel behavior prediction models.
Conformal prediction sets can lead to unfair outcomes.
When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms of the probability distributions of the input and output variables over each group. In this paper, we exploit this fact to reduce the dispa…
We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence cl…
We introduce an extension to Merton's famous continuous time model of optimal consumption and investment, in the spirit of previous works by Pliska and Ye, to allow for a wage earner to have a random lifetime and to use a portion of the income to purchase life insurance in order to provide for his estate, while investi…
Develops DML for nonlinear panel data models with fixed effects.
The paper analyzes how deep neural networks handle noisy labels and finds disparate impacts.
DCEM algorithm reduces bias in machine learning models trained on selective labels.
The paper introduces return parity for fairness in MDPs, addressing delayed and adverse effects.