Selected work

Representative Papers

Who Owns the Online Media?

Aug 14, 2026

This study addresses the challenges of media ownership opacity and accountability by constructing ownership networks for thousands of European and American outlets to measure transnational market concentration. Employing a fixed-pair design, we examine the causal effect of ownership changes on content similarity. Results indicate that over half of media entities are controlled by single owners, with common ownership significantly driving reporting convergence, particularly in the United States. The primary contribution lies in establishing cross-nationally comparable quantitative metrics demonstrating that content homogenization is predominantly driven by ownership structures rather than audience demand. These findings provide robust causal evidence regarding the detrimental impact of media consolidation on information diversity, clarifying the structural determinants of news uniformity across Western media markets.

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Swimming in Dark Water: When Cartels Mimic Competition

Jun 29, 2026

This study uncovers a highly covert bid-rigging cartel in the road construction sector of Switzerland’s Canton Ticino between 1999 and 2005, which achieved effective collusion while evading detection. Drawing on extensive archival records, the research reconstructs how cartel members coordinated bids and allocated contracts through a formal “convention.” Combining regression analysis, machine learning, and double machine learning techniques, the study identifies behavioral patterns that mimicked competitive bidding. It reveals, for the first time, that a cost-based allocation mechanism—without side payments—can approximate optimal collusive outcomes. Moreover, the cartel systematically circumvented conventional econometric detection methods. Estimated overcharges average at least 45%, underscoring the substantial fiscal harm such collusion inflicts on public procurement.

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Migration-Driven Demographic Changes: effects on local communities in the canton of Fribourg

May 07, 2026

This study addresses the adaptive challenges that rapid immigration poses to local communities in housing, education, and social services by examining its demographic consequences. Leveraging a panel dataset covering 112 municipalities in the Swiss canton of Fribourg from 2010 to 2021, the paper applies for the first time the difference-in-differences estimator developed by De Chaisemartin and D’Haultfoeuille (2024), which accommodates non-binary, cumulative, and staggered treatment settings, to identify the causal effects of net immigration inflows on population aging, student composition, and household structure. The findings indicate that immigration significantly mitigates local population aging and induces persistent, albeit modest, shifts in educational and housing demographics, thereby offering rigorous empirical evidence to inform local policymaking.

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AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics

Apr 02, 2026

This study addresses the severe class imbalance in scientific multi-label text classification caused by the extreme long-tail distribution of domain-specific terms. To this end, the authors construct a large-scale corpus comprising 21,702 astrophysics paper abstracts annotated with 2,367 concepts from the Unified Astronomy Thesaurus. They propose a novel frequency-stratified evaluation strategy and systematically compare the performance of traditional machine learning models, neural networks, and lexically constrained large language models (LLMs). The findings reveal that lexically constrained LLMs achieve performance comparable to specialized models without domain-specific fine-tuning, while domain adaptation significantly improves classification of rare terms. The proposed evaluation framework effectively uncovers model robustness disparities across frequency strata, establishing a strong baseline and a new paradigm for tackling extreme imbalance in scientific text classification tasks.

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Recent publications

Latest Papers

Who Owns the Online Media?

Aug 14, 2026

This study addresses the challenges of media ownership opacity and accountability by constructing ownership networks for thousands of European and American outlets to measure transnational market concentration. Employing a fixed-pair design, we examine the causal effect of ownership changes on content similarity. Results indicate that over half of media entities are controlled by single owners, with common ownership significantly driving reporting convergence, particularly in the United States. The primary contribution lies in establishing cross-nationally comparable quantitative metrics demonstrating that content homogenization is predominantly driven by ownership structures rather than audience demand. These findings provide robust causal evidence regarding the detrimental impact of media consolidation on information diversity, clarifying the structural determinants of news uniformity across Western media markets.

0 citationsRead paper

Swimming in Dark Water: When Cartels Mimic Competition

Jun 29, 2026

This study uncovers a highly covert bid-rigging cartel in the road construction sector of Switzerland’s Canton Ticino between 1999 and 2005, which achieved effective collusion while evading detection. Drawing on extensive archival records, the research reconstructs how cartel members coordinated bids and allocated contracts through a formal “convention.” Combining regression analysis, machine learning, and double machine learning techniques, the study identifies behavioral patterns that mimicked competitive bidding. It reveals, for the first time, that a cost-based allocation mechanism—without side payments—can approximate optimal collusive outcomes. Moreover, the cartel systematically circumvented conventional econometric detection methods. Estimated overcharges average at least 45%, underscoring the substantial fiscal harm such collusion inflicts on public procurement.

0 citationsRead paper

Migration-Driven Demographic Changes: effects on local communities in the canton of Fribourg

May 07, 2026

This study addresses the adaptive challenges that rapid immigration poses to local communities in housing, education, and social services by examining its demographic consequences. Leveraging a panel dataset covering 112 municipalities in the Swiss canton of Fribourg from 2010 to 2021, the paper applies for the first time the difference-in-differences estimator developed by De Chaisemartin and D’Haultfoeuille (2024), which accommodates non-binary, cumulative, and staggered treatment settings, to identify the causal effects of net immigration inflows on population aging, student composition, and household structure. The findings indicate that immigration significantly mitigates local population aging and induces persistent, albeit modest, shifts in educational and housing demographics, thereby offering rigorous empirical evidence to inform local policymaking.

0 citationsRead paper

AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics

Apr 02, 2026

This study addresses the severe class imbalance in scientific multi-label text classification caused by the extreme long-tail distribution of domain-specific terms. To this end, the authors construct a large-scale corpus comprising 21,702 astrophysics paper abstracts annotated with 2,367 concepts from the Unified Astronomy Thesaurus. They propose a novel frequency-stratified evaluation strategy and systematically compare the performance of traditional machine learning models, neural networks, and lexically constrained large language models (LLMs). The findings reveal that lexically constrained LLMs achieve performance comparable to specialized models without domain-specific fine-tuning, while domain adaptation significantly improves classification of rare terms. The proposed evaluation framework effectively uncovers model robustness disparities across frequency strata, establishing a strong baseline and a new paradigm for tackling extreme imbalance in scientific text classification tasks.

0 citationsRead paper