LOCAL REACTIONS TO THE FINANCIAL CRISIS: WHAT INFLUENCE OF NATIONAL CONTEXT VS INDIVIDUAL STRATEGIES?
Bibliographic record
Abstract
The 2008 crisis has damaged or weakened most European Local Governments (LGs)' financial situation. The shock has been more or less intense depending on the national context and policies, and on individual situations and strategies. After the crisis, different and successive types of recovery plans, austerity measures, and institutional reforms have been implemented by States, with several diverse effects on LGs' situation. The previous situation of LGs in terms of financial autonomy, State protection or local responsibilities and actions also influenced their post crisis situation, not to mention the provision of bankruptcy in certain nations. At the individual level, depending on their size and capacities, LGs followed different strategies to cope with the crisis and the decrease in public resources. Taking a short or longer term perspective, LGs have had various options, from brutal cost cuts to more elaborated restructuring of their actions and missions even by outsourcing, from basic fiscal leverage to new strategies for enhancing revenues. In order to study the influence of both national and individual characteristics, this paper proposes a quantitative comparative study, based on a unique database of French and Italian LGs financial data, from 2006 to 2014, where financial data have been reclassified according to recent developments in the field (CEFG Group, 2015). It provides some descriptive statistics and mean comparisons of the evolution of key financial indicators from 2006 to 2014: global revenues, operating balance, part of tax resources in global revenues, level and cost of debt, level of operating expenses and personnel costs, level of investment expenses. The study focus on municipalities between 50 000 and 1 million inhabitants (122 municipalities in France, 145 in Italy).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".