Merging Romania's local administrative units: a simulation-based assessment of fiscal self-sufficiency and tools for policymakers

This dataset accompanies the study "Merging Romania's local administrative units: a simulation-based assessment of fiscal self-sufficiency and tools for policymakers" by Paul-Vasile Zai and Iannis-Patriciu Ilișuan. It contains the normalized inputs, scenario outputs, and scoring results of 34452 simulated mergers of Romanian local administrative units (LAUs), designed to assess whether territorial amalgamation can restore local fiscal self-sufficiency, defined as the ratio of a unit's own revenue to its necessary expenditure. The simulations cover all 3181 non-county Romanian LAUs over the period 2014–2024 and are organized under two contrasting merger logics: a population-centric approach, reflecting a top-down policy that imposes population thresholds, and a self-sufficiency-centric approach, reflecting a bottom-up, organic process driven by fiscal performance. Each approach comprises several methods and a range of parameter values (population thresholds or percentage thresholds). Scenario files. The archive follows a consistent hierarchy: approach / method / (percent_xx | threshold_xx). Each scenario folder contains five files: LAU_definition.csv holds definitions and attributes of local administrative units: identifier (SIRUTA code), name, settlement type, county, region, centroid, 2011 and 2021 population, surface area, and 2011/2021 density. bundle_definition.csv holds the composition of merged unit groupings and their member LAUs. budget_data.csv holds annual municipal budget data by unit, including own revenue, personnel costs, goods and services costs, financial expenses, necessary expenses, and EU funds. LAU_borders.gpkg holds the post-merger administrative boundaries in GeoPackage format. LAU_colored.png is a rendered map of the boundaries in LAU_borders.gpkg, visualizing the merged configuration for that scenario. Every tabular file carries four leading metadata columns (approach, method, modifier, year) so each record can be traced to its originating scenario. All column headers are provided in English. The scenario data are aggregated and scored to evaluate and rank the merger configurations, yielding two CSVs: scores.csv contains one row per scenario × incentive × incentive-multiplier combination, identified by approach, method, modifier, and year; each row records the merger's mean self-sufficiency and the original (pre-merger) mean, the relative self-sufficiency increase, necessary expenses and their pre-merger baseline, the administrative reduction ratio, the share of resulting units that are self-financing (success rate), the applied incentive (tax reduction or expanded EU-fund access) with its cost base and multiplier, the resulting merger expenses, average surface area, average unit consumption, and a composite score combining these factors. max_scores.csv contains aggregated scores at three levels (method, method × incentive, and modifier × incentive), each decoded into approach, method, modifier, and incentive, with the average score and the share of scenarios exceeding self-financing thresholds for the merger process. The underlying data are publicly available from Romanian public institutions: the aggregated local budget database (identifiable by SIRUTA code) from the Ministry of Development, Public Works and Administration; population and surface-area data from the National Institute of Statistics; and geographic boundary data from the National Agency of Cadastre and Land Registration.