| Abstrakt: |
In the aftermath of the COVID-19 pandemic, the U.S. Congress transferred nearly $1 trillion USD to state and local governments between April 2020 and March 2021 to support vaccination efforts, keep schools open, and sustain economic recovery. As of March 2023, much of this money remained unspent, raising questions about the underlying process of determining the size and distribution of aid. This paper explores how Google search data and machine learning models can work in real-time to assist policy makers in evaluating fiscal policy proposals. These results are among the first pieces of evidence that economic models can feasibly integrate alternative sources of data to provide real-time estimates of economic activity at the state level. The author's models provide reliable and accurate estimates of state and local fiscal need and indicate the states that need relief the most months ahead of official estimates. The more tailored models presented in this paper could lead to more equitable and effective outcomes at a fraction of the cost to taxpayers when used to inform emergency fiscal stimulus distribution in the future. Data and code is available at https://github.com/johnkearns617/Fintech-Trends-and-State-and-Local-Finance(Link is external). [ABSTRACT FROM AUTHOR] |
