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Full-Text Articles in Business
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
School of Business: Faculty Publications and Other Works
Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, …
President Biden's Executive Order On Competition: An Antitrust Analysis, Herbert J. Hovenkamp
President Biden's Executive Order On Competition: An Antitrust Analysis, Herbert J. Hovenkamp
All Faculty Scholarship
In July, 2021, President Biden signed a far ranging Executive Order directed to promoting competition in the American economy. This paper analyzes issues covered by the Order that are most likely to affect the scope and enforcement of antitrust law. The only passage that the Executive Order quoted from a Supreme Court antitrust decision captures its antitrust ideology well – that the Sherman Act:
rests on the premise that the unrestrained interaction of competitive forces will yield the best allocation of our economic resources, the lowest prices, the highest quality and the greatest material progress, while at the same time …
The Remainder Effect: How Automation Complements Labor Quality, James Bessen, Erich Denk, Chen Meng
The Remainder Effect: How Automation Complements Labor Quality, James Bessen, Erich Denk, Chen Meng
Faculty Scholarship
This paper argues that automation both complements and replaces workers. Extending the Acemoglu-Restrepo model of automation to consider labor quality, we obtain a Remainder Effect: while automation displaces labor on some tasks, it raises the returns to skill on remaining tasks across skill groups. This effect increases between-firm pay inequality while labor displacement affects within-firm inequality. Using job ad data, we find firm adoption of information technologies leads to both greater demand for diverse skills and higher pay across skill groups. This accounts for most of the sorting of skills to high paying firms that is central to rising inequality.