Public Finance and Budgeting: Examining the Leading Topics and Future Research Agendas
A groundbreaking study has shed light on the leading topics in the field of public finance and budgeting, revealing how they have evolved over the past four decades and identifying areas that warrant further investigation.
Led by Can Chen, along with his former doctoral students Shiyang Xiao and Boyuan Zhao, the study employed a cutting-edge machine-learning technique called structural topic modeling (STM) to analyze a vast collection of scholarly articles. By scrutinizing the titles and abstracts of over 1,000 publications, Chen and his team were able to identify 15 latent topics related to public budgeting, public finance, and public financial management.
Remarkably, the team discovered that many of these topics overlapped with the content covered by the Certified Public Finance Officers (CPFO) exams. However, there were certain areas that received less attention in academia, a fact that may hint at underexplored research agendas.
When asked about the inspiration behind the study, Chen highlighted two key motivators. Firstly, the team wanted to commemorate the 40th anniversary of a renowned journal in the field. Secondly, they saw an opportunity to apply the emerging field of machine learning to public finance research, in a bid to introduce a more sophisticated and data-driven approach.
The research also unveiled a significant trend – a decline in practitioners' engagement in publishing in the journal. This finding emphasizes the importance of establishing closer ties between practitioners and scholars, while also urging doctoral students to consider the perspectives and practical challenges faced by professionals in the field.
Chen believes that the implications of this analysis are far-reaching. This study will play a pivotal role in equipping students, scholars, and practitioners with a comprehensive understanding of the evolving landscape of public finance and budgeting. Furthermore, it will serve as a catalyst for identifying new research topics and addressing critical future challenges, such as health care, technology, and climate change, within the realm of public finance.
Ultimately, this study highlights the need to bridge the gap between academic research and real-world practice. By fostering a closer collaboration between scholars and practitioners, the findings of this research can be translated into practical solutions that have a tangible impact on government budgeting and finance.
Analyst comment
Neutral news. The study using machine learning to analyze trends in public finance and budgeting has revealed key findings, including the need for more practitioner engagement and addressing future challenges. This analysis will impact students, practitioners, and scholars in guiding future research and promoting practical application of findings. Overall, it will contribute to understanding the evolution and landscape of the field. Market impact: Minimal.
