Quantifying the Relationship Between Team Dysfunctions and Performance in Capstone Projects
本文通过量化团队失调与工程顶点项目表现之间的关系，基于Lencioni的模型评估团队动态，揭示了两者间存在弱至中等程度的关联。
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Financial Technologies, Labor Markets, and Wage Inequality: Evidence from Instant Payment Systems
This study investigates the impact of instant payment systems on labor markets and wage inequality. Leveraging Brazilian Pix data linked to matched employer-employee records, we employ a triple-difference design and a calibrated monopsony model for causal identification. Results indicate that instant payments significantly increase demand for low-income jobs and raise wages by alleviating payment frictions for small firms, thereby effectively narrowing the wage gap. These effects are particularly pronounced in regions characterized by low-skilled labor scarcity. By elucidating the micro-mechanisms through which fintech reduces rather than exacerbates inequality, this research provides novel evidence supporting the role of digital finance in promoting distributive equity within labor markets.
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Enhancing Capstone Program Workflow: A Case Study on a Platform for Managing Academic-Industry Projects
This study addresses the multi-stage coordination challenges in managing capstone course projects at higher education institutions, particularly concerning student interest alignment, external project solicitation, and team formation. The authors propose and implement a web-based management platform designed to facilitate academic–industry collaboration, marking the first application of a systematic digital tool across the entire capstone project lifecycle. The platform emphasizes automated project solicitation and algorithm-driven intelligent team formation, integrating student profiles, project data, and matching logic to support multi-stakeholder collaboration. Deployed successfully at Insper, the system has significantly improved both the efficiency of project allocation and the quality of team composition, offering a reproducible operational model and foundational dataset for similar educational contexts.
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Nota de Pol\'itica P\'ublica: Quanto de produtividade precisamos para reduzir a jornada de trabalho?
This study quantifies the increase in total factor productivity (TFP) required to maintain GDP unchanged following a reduction in the statutory weekly working hours from 44 to 36, under the short-run constraint of fixed capital stock. By constructing a short-term macroeconomic structural model and combining TFP calibration with counterfactual simulations, the paper establishes—for the first time under a fixed-capital assumption—a precise multiplier relationship between reduced working hours and necessary productivity gains. The analysis reveals that an 18.2% reduction in working hours necessitates an immediate TFP increase of approximately 8.5% to fully offset the associated output loss. These findings provide a clear, actionable economic threshold for policymakers considering labor time regulations, offering direct and quantifiable guidance for evaluating the feasibility and implications of such reforms.
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Question-Driven Analysis and Synthesis: Building Interpretable Thematic Trees with LLMs for Text Clustering and Controllable Generation
Traditional unsupervised text analysis suffers from poor interpretability and weak semantic coherence in data-scarce domains. To address this, we propose Recursive Topic Partitioning (RTP), the first framework that deeply integrates problem-driven binary semantic tree construction with large language models (LLMs), explicitly encoding clustering logic and transforming analytical pathways into structured prompts—thereby enabling interpretable clustering and controllable generation in a closed loop. RTP synergizes recursive semantic segmentation, contextual reasoning, and prompt engineering to jointly support topic discovery and data synthesis. Experiments demonstrate that RTP-generated semantic trees significantly outperform keyword-based methods (e.g., BERTopic) in structural quality and coherence; it achieves substantial gains in few-shot classification tasks; and it enables precise, semantics-guided controllable text generation aligned with user-specified semantic features.
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Quantifying the Relationship Between Team Dysfunctions and Performance in Capstone Projects
本文通过量化团队失调与工程顶点项目表现之间的关系，基于Lencioni的模型评估团队动态，揭示了两者间存在弱至中等程度的关联。
0 citationsRead paper
Financial Technologies, Labor Markets, and Wage Inequality: Evidence from Instant Payment Systems
This study investigates the impact of instant payment systems on labor markets and wage inequality. Leveraging Brazilian Pix data linked to matched employer-employee records, we employ a triple-difference design and a calibrated monopsony model for causal identification. Results indicate that instant payments significantly increase demand for low-income jobs and raise wages by alleviating payment frictions for small firms, thereby effectively narrowing the wage gap. These effects are particularly pronounced in regions characterized by low-skilled labor scarcity. By elucidating the micro-mechanisms through which fintech reduces rather than exacerbates inequality, this research provides novel evidence supporting the role of digital finance in promoting distributive equity within labor markets.
0 citationsRead paper
Enhancing Capstone Program Workflow: A Case Study on a Platform for Managing Academic-Industry Projects
This study addresses the multi-stage coordination challenges in managing capstone course projects at higher education institutions, particularly concerning student interest alignment, external project solicitation, and team formation. The authors propose and implement a web-based management platform designed to facilitate academic–industry collaboration, marking the first application of a systematic digital tool across the entire capstone project lifecycle. The platform emphasizes automated project solicitation and algorithm-driven intelligent team formation, integrating student profiles, project data, and matching logic to support multi-stakeholder collaboration. Deployed successfully at Insper, the system has significantly improved both the efficiency of project allocation and the quality of team composition, offering a reproducible operational model and foundational dataset for similar educational contexts.
0 citationsRead paper
Nota de Pol\'itica P\'ublica: Quanto de produtividade precisamos para reduzir a jornada de trabalho?
This study quantifies the increase in total factor productivity (TFP) required to maintain GDP unchanged following a reduction in the statutory weekly working hours from 44 to 36, under the short-run constraint of fixed capital stock. By constructing a short-term macroeconomic structural model and combining TFP calibration with counterfactual simulations, the paper establishes—for the first time under a fixed-capital assumption—a precise multiplier relationship between reduced working hours and necessary productivity gains. The analysis reveals that an 18.2% reduction in working hours necessitates an immediate TFP increase of approximately 8.5% to fully offset the associated output loss. These findings provide a clear, actionable economic threshold for policymakers considering labor time regulations, offering direct and quantifiable guidance for evaluating the feasibility and implications of such reforms.
0 citationsRead paper
Question-Driven Analysis and Synthesis: Building Interpretable Thematic Trees with LLMs for Text Clustering and Controllable Generation
Traditional unsupervised text analysis suffers from poor interpretability and weak semantic coherence in data-scarce domains. To address this, we propose Recursive Topic Partitioning (RTP), the first framework that deeply integrates problem-driven binary semantic tree construction with large language models (LLMs), explicitly encoding clustering logic and transforming analytical pathways into structured prompts—thereby enabling interpretable clustering and controllable generation in a closed loop. RTP synergizes recursive semantic segmentation, contextual reasoning, and prompt engineering to jointly support topic discovery and data synthesis. Experiments demonstrate that RTP-generated semantic trees significantly outperform keyword-based methods (e.g., BERTopic) in structural quality and coherence; it achieves substantial gains in few-shot classification tasks; and it enables precise, semantics-guided controllable text generation aligned with user-specified semantic features.
0 citationsRead paper