Social Security Payments and Financialization: Lessons from the Greek Case
Bibliographic record
Abstract
This paper is founded on both the theoretical schemes of financialization, as a new regime of accumulation, and the shareholder value, the everyday finance, the structured finance, as well as the finance-led growth regime, whose special institutional forms concern the wage–labor nexus, the competition form, the monetary regime, the state–society relations, the insertion into the international regime, and the coherence and dynamic of the growth regime. It also aims to examine if the Greek social security system (the “system”) used financial logic in economic policy during the period of 2000q1–2021q3. It is econometrically approached through the short-run Granger causality tests but mainly the autoregressive distributed lag model in order to estimate the long-run relationships of the social contributions and benefits paid, with variables expressing the financialization either of the whole economy or particularly of one of the public sectors. So, these steady-state relationships proved statistically significant, and they are considered to be compatible with several mechanisms of the finance-led growth regime. Thus, the sustainability of the “system” should be insured by the policy makers in the economic progress and the creation of new jobs able to fund it. This article contributes to the literature by offering empirical evidence on the financialization and relevant compilation analysis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".