1835 papers
Using SHARE data from 16 European countries between 2015 and 2022, this study demonstrates that marriage and cohabitation significantly alleviate financial distress compared to being single, with relationship stability, income, and employment serving as key protective factors against economic hardship.
This study evaluates Pogi, a recursive permission controller for dynamic exposure in long-only portfolios, and concludes that it fails to demonstrate robust welfare superiority over various benchmarks, thereby highlighting the necessity of judging dynamic exposure rules against exposure-matched controls and external validation.
This study employs a comprehensive time-series econometric analysis of Indian data from 2001 to 2022 to explore the complex, non-causal relationship between rising crime and prison populations, declining recidivism, and increasing spiritual rehabilitation interventions, ultimately advocating for a robust national data architecture to better evaluate and support evidence-based correctional reforms.
This paper employs an AI-driven causal inference framework combining machine learning with quasi-experimental designs to demonstrate that New York City's housing policies, particularly the 2019 rent stabilization expansion, effectively reduce severe housing cost burden but exhibit significant heterogeneous effects across different neighborhood contexts, thereby enabling more precise, targeted policy interventions.
This qualitative case study demonstrates how small businesses in dynamic sectors like e-commerce and fintech can enhance project efficiency, flexibility, and customer satisfaction by adopting Agile methodologies such as Scrum and Kanban, as evidenced by the successful implementations at Shopify and Revolut, while also highlighting the cultural and strategic challenges inherent in such transitions.
This paper reviews the evolution and significance of intangible asset protection in international business, identifies gaps in current research regarding methodology and categorization, and proposes a structured future research agenda to deepen understanding of protection mechanisms and associated risks.
This paper demonstrates that machine learning models, enhanced by feature engineering and SHAP-based explainability, significantly outperform traditional econometric methods in predicting high-frequency market volatility, thereby advancing computational economics for improved risk analysis and investment planning.
This paper proposes a structured, multi-dimensional framework and presents a database of over 400 Dutch energy hub initiatives to systematically analyze their development, assess their maturity, and evaluate their role in the energy transition.
This paper develops an evolutionary model demonstrating how general-purpose platforms enable entrepreneurs to access productive capabilities without ownership, thereby altering commercialization regimes by shifting organizational selection thresholds, creating hysteresis in incumbent adaptation, and driving creative destruction through institutional governance and platform-mediated capability recombination.
Using panel data from Chinese listed firms (2011–2023), this study reveals that while executive environmental cognition generally promotes green innovation, responsibility-oriented cognition drives substantive innovation more effectively than pressure-oriented cognition, with these effects varying significantly across ownership types, industry intensities, and institutional contexts.