Measuring Transaction Costs in Public Sector Contracting Through Machine Learning and Contract Text

• 成果类型: Article; Early Access
• 署名作者: Potoski, Matthew; Lund-Sorensen, Bjarke; Petersen, Ole Helby
• 署名单位: University of California System; University of California Santa Barbara; Roskilde University
• 刊物名称: PUBLIC ADMINISTRATION REVIEW
• ISSN/ISSBN: 0033-3352
• DOI: 10.1111/puar.13947
• 发表日期: 2025
• 关键词: service delivery arrangements; management capacity; GOVERNMENT; performance; governance; ECONOMICS; MARKET

摘要

Transaction cost (TC) theoretical constructs are central to research throughout the social sciences, yet key concepts, such as measurability and asset specificity, often defy systematic empirical measurement. In government contracting research, empirical measurements of key TC theoretical constructs are limited to the International City/County Management Association's surveys of US municipal and county governments. We present a preregistered method using machine learning algorithms to generate product-level TC measures from contract text data and a government contract manager survey. We verify the algorithms' out-of-sample performance and use them to generate TC measures for additional products from corresponding contract text data. The result is a publicly available database of new TC measures for 176 diverse products and services covered in the European Union's Common Procurement Directives. These new measures facilitate the application of the TC framework across public management, including research on government contracting, collaboration, networks, and governance.