This paper estimates heterogeneous strategic responses to nominal property tax rates set by contiguous counties in large U.S. cities between 2010 and 2019. Horizontal fiscal competition has been understudied in the United States due to the difficulty resolving endogeneity. This investigation adds to the empirical literature with three descriptive findings about metropolitan counties. First, suburban counties mimic property tax rates more than urban counties. Second, suburban counties are more likely to mimic regardless of where the influence comes from. Third, more competition lowers property tax rates, but suburban counties still mimic their neighbors more than urban counties.
Although research highlights the discipline-enhancing effect of fiscal rules at the national level, little is known about their impact on local budgets. We employ a dynamic fiscal reaction function within an LSDVC framework to explore the relevance of different local fiscal rules in the EU using a panel dataset of 26 European countries over a 26-year period (1997-2022). Our findings suggest that debt rules and expenditure rules have a positive relation with budget discipline on the local level. Thus, the mere existence of a DR is less relevant compared to its strength. Consequently, rule design should receive greater attention.
Fiscal transparency, a key element of good governance, is shaped by auditing, which compels bureaucracies to disclose information for accountability. However, the mechanisms through which auditing impacts transparency remain unclear. This study examines how auditing enhances fiscal transparency through various accountability pressures. Using a global panel dataset, it finds that auditing significantly improves fiscal transparency, though its impact varies in countries at different economic development levels, highlighting the importance of having mature auditing institutions. The findings offer a comparative perspective on auditing′s role in fiscal transparency and provide practical insights for policymakers to strengthen transparency and accountability.
This study systematically analyzes 69 peer-reviewed works comparing budget forecasting methods. It explores methodological evolution, geographic distribution, and evaluation trends. Four phases of development are identified: statistical methods, diversification, machine learning, and deep learning. A division emerges between traditional and next-generation techniques. Geographically, 43% of studies focus on the United States, while developing economies remain underrepresented. In evaluation, MAPE, RMSE, and MAE dominate, with directional errors largely neglected. Findings show that optimal method choice depends on context, supporting a pluralistic, context-sensitive approach rather than universal reliance on a single forecasting method.
This paper introduces a system dynamics (SD) model for analyzing public sector cost growth, where costs are tied to indices. The SD model isolates the effects of automatic indexation, providing probabilistic projections of expenditure growth. It enables testing of alternative indexation strategies and cost-reduction measures. Findings show how automatic indexation can accelerate spending during inflation spikes, emphasizing the need for risk- and uncertainty-aware fiscal analysis. Using the Finnish welfare system as an illustrative case, the model output is examined in terms of its value for economic policy planning, providing insights for academics and policymakers.
