Public budgeting and finance is a discipline that encompasses communities of research and practice. Too often, however, these communities fail to engage each other, instead choosing to operate independently. The result is that the research being conducted fails to address the questions of the day and our governments’ challenges. In this article, we come together as a community of academics and practitioners to establish an agenda for where future research should be conducted. This agenda aims to align the research being undertaken within the academic community with the needs of those working in the community of practice. After establishing ten areas where research is needed, we followed a ranked-choice voting process to establish a prioritization for them. Based on the outcome of this process, the two primary areas where research is currently needed most are the fiscal health of our governments and the implementation of social equity budgeting.

Revenue forecasting accuracy is critical to governmental operations. This paper addresses the question: What is the best technique for forecasting sales tax revenue? Prior studies in this area have focused on the differences between machine learning techniques and traditional approaches and neglected to consider how differences in pre-processing steps for the data before the forecasting model is applied are important. Here, we show that machine learning techniques do not always provide increased forecasting accuracy. Instead, the modeling choices matter, but less than the prior literature and practice suggested. Rather, pre-processing makes the most significant difference in forecasting accuracy, and forecasters need to understand the unique characteristics of time series data to improve forecasting performance. The immediate implications of these findings are that the focus of practitioners of in sales tax revenue forecasting should shift from prioritizing model choice towards data pre-processing.

As nations work to respond to COVID-19, trust in government is critical to achieving health outcomes. Studies argue that greater trust in government is associated with increased compliance with COVID-19 public health policies. This analysis investigated predictors of public trust during COVID-19 in 16 countries grouped in four regions. The data used (n = 47,000) are taken from the Worldwide COVID-19 Attitudes and Beliefs dataset. Five hypotheses test the effects of stringency, geographic location, age, gender, income, and education levels on public trust. Findings reveal that increased stringency measures and education levels are positively associated with trust.

Public Administration ReviewVolume 83, Issue 3 p. 710-711 BOOK REVIEW Teaching Public Budgeting and Finance: A Practical Guide By Bruce D. McDonald, Meagan M. Jordan (Ed.), New York: Routledge. 2022. pp. 285. $44.95 (Pbk). ISBN: 978-1-032-14668-3 Sarah E. Larson, Corresponding Author Sarah E. Larson [email protected] orcid.org/0000-0002-9644-2019 University of Central Florida, Orlando, Florida, USA Correspondence Sarah E. Larson, University of Central Florida, Orlando, Florida, USA. Email: [email protected]Search for more papers by this author Sarah E. Larson, Corresponding Author Sarah E. Larson [email protected] orcid.org/0000-0002-9644-2019 University of Central Florida, Orlando, Florida, USA Correspondence Sarah E. Larson, University of Central Florida, Orlando, Florida, USA. Email: [email protected]Search for more papers by this author First published: 29 March 2023 https://doi.org/10.1111/puar.13624Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. REFERENCES Farrell, Catherine, William Hatcher, and John Diamond. 2022. "Reflecting on over 100 Years of Public Administration Education." Public Administration 100(1): 116–28. 10.1111/padm.12808 Web of Science®Google Scholar Knox, Claire Connolly. 2013. "Teaching Grammar and Editing in Public Administration: Lessons Learned from Early Offerings of an Undergraduate Administrative Writing Course." Journal of Public Affairs Education 19(3): 515–36. 10.1080/15236803.2013.12001749 Google Scholar Mallinson, Daniel J. 2018. "Teaching Public Budgeting in the Age of Austerity Using Simulations." Teaching Public Administration 36(2): 110–25. 10.1177/0144739418769406 Web of Science®Google Scholar Volume83, Issue3May/June 2023Pages 710-711 ReferencesRelatedInformation

As saliency of the tax burden increases, the preference for a lower burden increases, but most counties are restricted by the state from adopting new taxes or changing the existing rates. Some states allow counties to adopt a charter, freeing them from state control. Using a panel of Florida counties from 1980 to 2017, we explore whether citizens act to reduce their property tax once a charter is passed. Citizens act against their preferences not by lowering burden but rather by increasing it in the case of debt service, suggesting citizens are maximizing their optimal tax burden in exchange for services.

This research is motivated by a change in Florida that affected assisted living facilities (ALFs) by removing the property tax exemptions for those operated by nonprofit organizations. As such, this paper addresses two research questions: (1) Are there systematic differences between Florida's for-profit- and nonprofit-designated ALFs in terms of operations and service provision? (2) What was the impact, if any, of losing and regaining their property tax exemptions on the operations of Florida's nonprofit ALFs? We use data from the Florida Agency for Health Care Administration to conduct difference of means testing for-profit versus nonprofit ALFs, as well as annual financial data of each ALF property owners' IRS Form 990 from the National Center for Charitable Statistics Core Files and parcel-level property data from the state of Florida to conduct regression analysis using three different dependent variables measuring various components of nonprofit ALF operations. We find that nonprofit ALFs have greater legitimacy in service delivery than for-profits, as nonprofit ALFs have higher service capacity, quality, and variety than for-profit ALFs in Florida. In addition, property tax exemptions for nonprofit ALFs in Florida decrease total expenses and increase officer compensation.

Implemented in over 200 North American local governments, priority-based budgeting (PBB) reflects a contemporary attempt to systematically determine and implement desired budgetary reallocation. The study utilizes a lagged dependent variable alternative to difference in difference analysis to examine reallocation patterns for 32 early-adopting US cities before and after PBB implementation. The findings suggest that PBB fulfills its promise, as low-priority departmental budgetary allocations shrink by 2%-3% following PBB implementation. These PBB early adopters offer evidence that PBB can effectively transcend the marginal, transactional nature of incremental budgeting practices; however, its effectiveness must be weighed against potentially substantial costs of implementation. Applications For Practice What level of reallocation is necessary to justify a significant and collective organizational effort to alter budgetary and accounting practices? If 4% or above, a wise practitioner should think twice about a head-first leap into priority-based budgeting, absent more compelling evidence. The study provides evidence that mere signaling of a move toward priority-based budgeting implementation can psychologically affect budgetary behavior prior to actual implementation of the approach. The findings also indicate that budgetary reallocation toward higher-priority departments begins to wane by the third year after priority-based budgeting is implemented, potentially limiting its long-term effectiveness.

The purpose of this study is to investigate how cities are developing and implementing social marketing practices. Driven by a 2017 survey administered to a sample of Florida's public officials, the research design of this study employs an exploratory data analysis including cluster analysis, univariate visualization, and bivariate visualization to information regarding public social marketing utilization. The findings of this study suggest possible confusion surrounding what social marketing actually is and how the practice can best be utilized within a strategic management context. This research adds value by contributing to a relatively nascent literature on social marketing within an American public administration context.

The growth and expansion of “Big Data” is fundamentally changing public service delivery. Big Data is getting “bigger,” and public organizations will have new opportunities to cultivate and challenges to address. To understand the effects of the growth of data on public organizations, we introduce the Public Data Primacy (PDP) theoretical framework, which builds on existing scholarship through four propositions about data, technology, and its use in the public sector. The framework posits that public sector work will become increasingly data-centric as data continues to get “bigger.” Ultimately, the PDP leads to two predictions about the public sector. First, we predict that the primacy of data in the delivery of public services is inevitable. Second, this forthcoming reality will require public servants to adopt new models of public service oriented around data. The PDP theoretical framework provides a systematic lens in which public administration scholarship can evaluate the future of data growth and its impacts upon public service delivery.

When COVID-19 hit the U.S. in early 2020, individuals aged 65+ were identified as a higher risk population. In Florida, Governor DeSantis issued Emergency Order 20-006 to prohibit visitation to facilities housing groups of high-risk people, including assisted living facilities (ALFs). Regardless, 672 ALFs of the 3,019 in Florida had reported at least one positive case of COVID-19 by a resident or staff member as of June 30, 2020. Prior research has highlighted the differences in service delivery between nonprofit and for-profit health care providers. This manuscript fills the void of minimal research on quality of care differences among ALFs based on ownership status. We find that nonprofit ALFs have experienced fewer positive cases of COVID-19 among their residents, but less evidence of a difference between nonprofit and for-profit ALFs in terms of resident deaths. We also find evidence that the type of nursing services matters for protecting ALF residents from COVID-19.

Deconstructing causal linkages between place attributes and disaster outcomes at coarse scales like zip codes and counties is difficult because heterogeneous socio-economic characteristics operating at finer scales are masked. However, capturing detailed disaster outcomes about individuals and households for large areas can be equally complicated. This dichotomy highlights the need for a more nuanced and empirically-driven approach to understanding financial disaster recovery support. This study assessed how social characteristics influenced federal disaster recovery support following the 2015 South Carolina floods. Ordinary linear and spatial regression models provided a mechanism for pinpointing statistically significant links between individual/compound vulnerabilities and resource distribution from four federal disaster response and recovery programmes. The study makes two unique contributions. First, exploration of how social characteristics influence recovery support is a critical, yet understudied path toward fair and equitable disaster recovery. Second, finer scale inquiry across a large impact area is rare in quantitative case studies of US disasters. While we found flood recovery assistance to be strongly associated with physical damage overall the relationship was more tenuous in places with higher social vulnerability. Results indicate that future disaster recovery programs focusing on both physical damage and social vulnerable would lead to a more equitable disaster recoveries. Findings provide new understanding of equity at the intersection of social vulnerability, impacts, and disaster recovery and showcase both best-practices and areas for programme improvements for future disasters.

The outbreak of COVID—19 has raised considerable alarm about public health and safety. The response to the outbreak, however, has also brought concern regarding its impact on local governments in the United States. Local governments have been a primary respondent in the fight against the COVID—19 disease, but the response has also reduced income from a key source of revenue, sales tax. Using North Carolina counties as a case study, we explore the shock to sales and use tax revenue faced by local governments from COVID—19; we, then, estimate its impact on county fiscal health. Our results show that while many local governments were financially struggling before the outbreak, the drop in sales tax revenue severely threatens their ability to provide continued response to the virus as well as their ability to remain solvent.

The impact of COVID-19 and the use of shelter in place orders upon county-level government revenues and the future fiscal health of these municipalities is an ongoing concern for county government officials. Florida presents a unique case as the state relies on the sales tax revenue driven by in-state sales for over 60% of state revenue. This study presents findings on two measures of fiscal health (operating ratio and revenue loss per capita) under various scenarios of loss to county revenues through declines in general sales, local option sales, and fuel taxes. Findings suggest that counties within the panhandle in Florida may have to tap into reserves to make up for revenue loss.

Purpose The purpose of this paper is to test the theory of optimal monitoring, which posits that more generous county homestead exemptions lower the incentive for residents to monitor school operations, thereby increasing inefficiency in service outcomes. Design/methodology/approach This research uses two-stage Simar and Wilson’s data envelopment analysis to assess county school districts’ efficiency in the state of Georgia for each year from 2007 to 2012. Findings Controlling for other factors known to be correlated with government efficiency, such as fiscal capacity and competition, this study finds evidence that higher property tax burdens resulting from lower county school district homestead exemptions, as a proxy of more intense citizens’ monitoring pressures, are associated with improved county school district performance efficiency. These results provide empirical support for the theory of optimal monitoring. Practical implications Increased government funding toward education is more likely to improve education outcomes if accompanied by efficiency control mechanisms. One such mechanism could be increased transparency of government operations and accountability of public officials. Originality/value This research uses a newer and more robust estimation of relative efficiency and analyzes a more common type of property tax exemption. This improves the internal validity and generalizability of the findings regarding the theory of optimal monitoring.

Many governments have adopted strategic plans to improve outcomes, but these efforts often fail because they do not take implementation into consideration as they plan. Past research indicates that the situational context of a strategic initiative influences implementation success. This study explores how context influences the relationship between implementation best practices and success by examining 155 strategic initiatives from 36 U.S. municipalities through a series of multiple regression analyses. The evidence indicates that context does alter the success rate of implementation best practices and should be taken into consideration during strategic planning in order to improve government effectiveness.

Online sales and use transactions are the subject of ongoing policy discussion. To collect revenues from the transactions, states have taken to passing individual legislation regarding the taxation of online transitions. These laws fall into three categories: economic nexus, affiliate nexus, and economic and affiliate nexus. Several federal policy solutions have been introduced in form of federal legislation. In the most recent congressional legislative sessions, several pieces of legislation have been proposed including Remote Transactions Parity Act of 2017 (RTPA) and the Remote Transactions Parity and Simplification Act (RTPSA). RTPA determines the sales tax rate and base by the physical location of the buyer of the good. RTPSA determines the sales tax rate based on the physical location of the buyer and the base from the definition of base determined by state of the seller. Comparing revenue estimates under the two policy proposals, RTPSA allows for the collection of additional revenue by states at a lower estimated annual expenditure cost.