arXiv subjects

W. Benedikt Schmal

Publications and source records attributed to W. Benedikt Schmal.

5 recordsLinked to original sources

arXiv2024

La Révolution Dévore ses Enfants: Pricing Implications of Transformative Agreements

With the widespread dissemination of the internet, academia envisioned free availability and rapid dissemination of new knowledge. However, most researchers continued publishing in established journals instead of switching to fully open-access alternatives. That preserved the market power of the large commercial publishing houses owning thousands of journals behind paywalls. To turn these portfolios into open access, research institutions around the globe have been negotiating `transformative agreements:' Papers are published fully open access, and universities pay only for the publication but not for subscriptions any longer. In this paper, I demonstrate that publishers controlling a large stock of paywalled publications can use them as leverage to ensure high revenues even with decreasing publication numbers. By that, this industrial policy may harm competitors that only publish under open access. This could harm competition and perpetuate the position of the incumbent players.

econ.GN↗

arXiv2024

Trapped in Transformative Agreements? A Multifaceted Analysis of >1,000 Contracts

Transformative agreements between academic publishers and research institutions are ubiquitous. The 'Efficiency and Standards for Article Charges' (ESAC) Initiative lists more than 1,000 contracts in its database. We make use of this unique dataset by web-scraping the details of every contract to substantially expand the overview spreadsheet provided by the ESAC Initiative. Based on that hitherto unused data source, we combine qualitative and quantitative methods to conduct an in-depth analysis of the contract characteristics and the TA landscape. Our analysis demonstrates that research institutions seem to be 'trapped' in transformative agreements. Instead of being a bridge towards a fully Open Access world, academia is stuck in the hybrid system. This endows the legacy (non-Open Access) publishing houses with substantial market power. It raises entry barriers, lowers competition, and increases costs for libraries and universities.

cs.DL↗

arXiv2024

Academic Knowledge: Does it Reflect the Combinatorial Growth of Technology?

I explore the concept of growth being rooted in the recombination of existing technology as an explanation for the remarkable growth witnessed during the Industrial Revolution as it was recently proposed by Koppl et al.(2023). I adapt their combinatorial growth theory to assess its applicability in generating academic knowledge within universities and research institutions, particularly in the field of economics. The central question is whether significant combinatorial growth can also be anticipated in academia. The current career structures discourage the recombination of ideas, theories, or methods, making it more advantageous for early career researchers to stick to the status quo. I employ machine-learning-based natural language analysis of the top 5 journals in economics. The analysis reveals limited correlations between topics over the past three decades, suggesting the presence of isolated topic islands rather than productive recombination. This confirms the theoretical considerations beforehand. Overall, the institutional order of academia makes combinatorial growth at the research frontier unlikely.

econ.GN↗

arXiv2024

Quantitative Tools for Time Series Analysis in Natural Language Processing: A Practitioners Guide

Natural language processing tools have become frequently used in social sciences such as economics, political science, and sociology. Many publications apply topic modeling to elicit latent topics in text corpora and their development over time. Here, most publications rely on visual inspections and draw inference on changes, structural breaks, and developments over time. We suggest using univariate time series econometrics to introduce more quantitative rigor that can strengthen the analyses. In particular, we discuss the econometric topics of non-stationarity as well as structural breaks. This paper serves as a comprehensive practitioners guide to provide researchers in the social and life sciences as well as the humanities with concise advice on how to implement econometric time series methods to thoroughly investigate topic prevalences over time. We provide coding advice for the statistical software R throughout the paper. The application of the discussed tools to a sample dataset completes the analysis.

econ.GN↗

arXiv2022

From Rules to Regs: A Structural Topic Model of Collusion Research

Collusive practices of firms continue to be a major threat to competition and consumer welfare. Academic research on this topic aims at understanding the economic drivers and behavioral patterns of cartels, among others, to guide competition authorities on how to tackle them. Utilizing topical machine learning techniques in the domain of natural language processing enables me to analyze the publications on this issue over more than 20 years in a novel way. Coming from a stylized oligopoly-game theory focus, researchers recently turned toward empirical case studies of bygone cartels. Uni- and multivariate time series analyses reveal that the latter did not supersede the former but filled a gap the decline in rule-based reasoning has left. Together with a tendency towards monocultures in topics covered and an endogenous constriction of the topic variety, the course of cartel research has changed notably: The variety of subjects included has grown, but the pluralism in economic questions addressed is in descent. It remains to be seen whether this will benefit or harm the cartel detection capabilities of authorities in the future.

econ.GN↗