Systematic Review Dataset of Financial Distress Prediction Models for Municipalities
Description
This dataset was compiled as part of the systematic review process. The dataset includes information extracted from 24 peer-reviewed studies published between 2000 and 2024. The purpose of the dataset was to evaluate and compare the methodologies, indicators, predictive performance, and theoretical underpinnings of various financial distress prediction (FDP) models applied to municipal contexts.
Files
Steps to reproduce
Step 1: Develop eligibility criteria: Define inclusion and exclusion criteria targeting peer-reviewed studies (2000–2024) that introduce financial distress prediction (FDP) models for municipalities. Step 2: Conduct literature search: Conduct a systematic search using Scopus, Web of Science, Google Scholar, and EBSCOhost with predefined keywords such as ("Municipalities" OR "local governments" OR "councils") AND ("Financial distress" OR "Fiscal strain" OR "Financial condition”) AND ("Prediction" OR "Modelling" OR "Measurement"). Adjust for each database. Each set of keywords used for each database is included in the dataset. Step 3: Screen the literature: Import search results into Covidence software. Conduct title/abstract and full-text screening using PRISMA guidelines. Step 4: Perform data extraction: Extract data using a structured form within Covidence, covering study metadata, model type, indicators used, evaluation metrics, and contextual relevance. Step 5: Conduct data validation & Structuring: Clean, standardise, and organise the extracted data into thematic categories. Final dataset saved in Excel format.
Institutions
- University of KwaZulu-Natal - Westville Campus