Pension funds, national savings and financial development: unpacking conditional effects using U.S. time series data
Bibliographic record
Abstract
The relationship between pension funds and national savings (NS) varies depending on the existing level of savings within an economy. Similarly, the structural impacts of pension funds on financial development are influenced by these savings levels. This study examines both the direct effects of pension funds on NS and the interactive effects between savings and pension funds on NS and financial market development. Quarterly time series data from the USA, covering the period from the first quarter of 1960 to the third quarter of 2024, are utilized for the analysis. The dataset includes indicators such as total pension funds (TPF), private pension funds’ (PPF) investments, NS, personal savings (PS) and various measures of financial development. The autoregressive distributed lag (ARDL) approach is employed to capture both short-run dynamics and long-run relationships among these variables. The findings indicate that the effect of pension funds on NS is conditional upon the level of existing NS. When NS levels are high, the positive impact of pension funds on further boosting these savings is more pronounced. Furthermore, the interaction between NS and TPF demonstrates a positive and significant influence on financial development in both the short and long term.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".