Signaling Resilience: A Computational Assessment of Narratives in Local Government Budgets

• 成果类型: Article
• 署名作者: Greer, Robert A.; Moldogaziev, Tima T.; Scott, Ryan P.; Scott, Tyler A.
• 署名单位: Texas A&M University System; Texas A&M University College Station; Bush School of Government & Public Service; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Colorado State University System; Colorado State University Fort Collins; University of California System; University of California Davis
• 刊物名称: JOURNAL OF PUBLIC ADMINISTRATION RESEARCH AND THEORY
• ISSN/ISSBN: 1053-1858
• DOI: 10.1093/jopart/muad001
• 发表日期: 2023
• 页码: 688-700
• 关键词: credit ratings; management; INFORMATION; IMPACT; RISK; strategies; security; state; debt; ORGANIZATION

摘要

Local governments consider a wide range of policies to increase resilience in the face of myriad risks and employ a variety of tactics to communicate about these policies to external actors. An important platform to signal resilience as a policy priority is through the budget process wherein local communities decide who gets what, when, and how. Using computational text mining techniques, we assess how county governments in California signal efforts toward resilience in their budgets during the 2012-2017 fiscal years, as well as whether and how those signals are received by the capital market. Comparable budget documents are available for 38 counties across the state for a total of 161 county-year observations. To test the relationship between local government resilience signals and capital market outcomes, we focus on county underlying credit ratings issued by counties. Empirical results show that county underlying credit ratings are insensitive to resilience signals in local government budgets. By examining the efficacy of resilience signals and their effects on the capital market, we offer evidence on the link between policy signaling and financial outcomes at the local government level.