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Everything is sex: theoretical extensions and empirical tests of the Maestripieri hypothesis for the beauty premium
No full textPurpose I test three different theories of the “beauty premium,” why more physically attractive workers earned more than less physically attractive workers. Design/methodology/approach I analyze two prospectively longitudinal datasets with nationally representative samples: National Longitudinal Study of Adolescent to Adult Health in the US (Study 1) and National Child Development Study in the UK (Study 2). Findings Analyses support the evolutionary psychological hypothesis that the beauty premium stems from individuals’ desire for sexual contact. Originality/value This is the first attempt to put three different hypotheses for the beauty premium with prospectively longitudinal data with large, nationally representative samples
Polarization of opportunity
No full textWe introduce the concept of polarization of opportunities (POp) to explore how various circumstances shape unfair inequalities. While conventional measures of inequality of opportunity (IOp) focus on outcome disparities linked to factors such as race or gender, they do not account for how these circumstances group individuals into relatively uniform clusters. POp fills this gap by examining both the influence of circumstances and their role in clustering individuals into distinct poles. Using U.S. data, our analysis shows that while income polarization and IOp have risen over time, POp has decreased
Climate change and urban-agrarian solidarities
No full textAs climate change has heightened the significance of urban-agrarian material entanglements and intellectual and political oppositions, the need to speak across them has become more urgent. Scholarship in agrarian and urban studies is concerned with specific social processes and political demands in agrarian and urban contexts, respectively. These processes and demands are often articulated in opposition to one another, despite the fact that the places and people each studies are materially and politically connected. This paper argues that scholars in these fields should not only work to understand how these dynamics across space are materially interconnected, but also where and how their politics converge, in order to move from a position of opposition to one of solidarity. We trace entanglements across urban and agrarian studies and spaces through the lenses of food, energy, and water to identify: (1) relational material and political dynamics and (2) through the lens of social reproduction, shared demands across differently articulated political claims. We conclude by describing work of translation, commensuration, and imagination that scholars might engage in to facilitate understanding and coalition-building across urban and agrarian studies and movements
Misallocating misallocation?
No full textThe macrodevelopment literature on misallocation quantifies aggregate productivity losses resulting from microeconomic distortions, relative to a first-best benchmark. However, sources of these distortions are often insufficiently explored. The microdevelopment literature in contrast provides evidence of distortions resulting from market failures owing to asymmetric information, missing markets, transaction costs, and limited state capacity, often without examining the resulting macrolevel consequences. If distortions result from such market failures rather than policies, second-best welfare-improving policies may aggravate productive misallocation. We illustrate these points in the context of manufacturing, agriculture, and rural-urban allocation of labor and land. Hence future research should devote more effort to identifying the source of distortions and using appropriate benchmarks for welfare going beyond productivity
Innovative novel regularized memory graph attention capsule network for financial fraud detection
No full textFinancial fraud detection (FFD) is crucial for ensuring the safety and efficiency of financial transactions. This article presents the Regularised Memory Graph Attention Capsule Network (RMGACNet), an original architecture aiming at improving fraud detection using Bidirectional Long Short-Term Memory (BiLSTM) networks combined with advanced feature extraction and classification algorithms. The model is tested on two reliable datasets: the European Cardholder (ECH) transactions dataset, which contains 284,807 transactions and 492 fraud instances, and the IEEE-CIS dataset, which has more than 1 million transactions. Our approach enhances comparison to existing methods of feature selection and classification accuracy. On the ECH dataset, RMGACNet achieves an accuracy of 0.9772, a precision of 0.9768, and an F1 score of 0.9770 measures; on the IEEE-CIS dataset, it achieves an accuracy of 0.9882, a precision of 0.9876 and an F1 score of 0.9879. The findings indicate that RMGACNet routinely surpasses existing models’ efficiency and accuracy while ensuring strong execution time performance, especially when handling large-scale datasets. The suggested model demonstrates scalability and stability, making it suitable for real-time financial systems
Hexagon-net: heterogeneous cross-view aligned graph attention networks for implied volatility surface prediction
No full textImplied Volatility Surface (IVS) prediction is critical for options hedging, portfolio management, and risk control. These applications encounter significant challenges, including imbalanced data distributions and inherent uncertainties in forecasting. Recent advances relying on deep learning have led to significant progress in addressing these issues. However, several key problems have not yet been solved: (i) Moneyness and maturities of traded options change over time. Therefore, proper spatio-temporal alignment is an essential prerequisite for the downstream forecasting exercise. (ii) Different regions of the IVS are unevenly informed because of liquidity constraints and therefore should not be modeled uniformly. (iii) The complex interconnections among data points in the IVS from various perspectives-such as the well-known 'smirk' patterns across dimensions-are neither explicitly addressed nor effectively captured by existing models. To address these issues, we propose a novel end-to-end heterogeneous cross(x)-view aligned graph attention network (Hexagon-Net), which aligns historical IVS data, learns distinctive IVS patterns, propagates predictive information, and forecasts future IVS movements simultaneously. Extensive experiments on stock index options datasets demonstrate that Hexagon-Net significantly and consistently outperforms the previous approaches in IVS modeling and deep learning. Additionally, we present further experiments-such as ablation studies, sensitivity analyses, and alternative configurations-to explore the reasons behind its superior performance
Two sides of the same coin - the consequences of Trump's tariffs for the currency markets
No full textProfessor Stephanie Rickard examines the impact of Trump’s tariffs on the US Dollar and Chinese Yuan and the possible consequences for their respective economies
How will US tariffs affect the UK economy?
No full textIn this interview with Alexis Papazoglou, Thomas Sampson explains what the US tariffs mean for the UK economy and answers questions on the effects of Brexit on the UK’s ability to negotiate the tariffs, as well as on what the UK Government can do to ease the effect of the tariffs. This conversation took place on Wednesday April 9, hours before Donald Trump temporarily paused all tariffs above 10 per cent, with the exception of China
Unraveling the interplay between carryover effects and reward autocorrelations in switchback experiments
No full textA/B testing has become the gold standard for policy evaluation in modern technological industries. Motivated by the widespread use of switchback experiments in A/B testing, this paper conducts a comprehensive comparative analysis of various switchback designs in Markovian environments. Unlike many existing works which derive the optimal design based on specific and relatively simple estimators, our analysis covers a range of state-ofthe- art estimators developed in the reinforcement learning (RL) literature. It reveals that the effectiveness of different switchback designs depends crucially on (i) the size of the carryover effect and (ii) the auto-correlations among reward errors over time. Meanwhile, these findings are estimator-agnostic, i.e., they apply to most RL estimators. Based on these insights, we provide a workflow to offer guidelines for practitioners on designing switchback experiments in A/B testing
The opportunities and challenges of developing and implementing local climate adaptation targets
No full textClimate adaptation policies have been developed at global, national and local levels, however, significant implementation gaps persist. Adaptation targets – achieved through metrics to assess the effectiveness of an adaptation action or policy – offer a potential solution to improve implementation. If adaptation actions can be compared and tracked, it should be possible to identify which actions are most effective, where more support is needed, the extent to which vulnerabilities are addressed, and evidence what progress is made. Despite this potential, the development and delivery of adaptation targets has been challenging because: (1) adaptation is context-specific - a target in one place may not be suitable in another; and (2) there is often a lack of clarity over how metrics should be designed. We aim to stimulate debate in this area through development of guiding principles for creating climate adaptation targets. These principles aim to increase the robustness of targets through the lens of equity and vulnerability as well as highlighting some key challenges and limitations in the development and implementation of adaptation targets at the local level