The Impact of the Nonprofit Sector on Employment and Unemployment in Developed Economies: Dynamic Panel Data Analysis
Bibliographic record
Abstract
In the last quarter of the last century, the reshaping of the state apparatus under the domination of liberal economics and globalization led to a rapid and radical transformation of the nonprofit sector. With growing incomes, expenditures, staff and volunteers, these organizations have become major economic actors in many countries today. Indeed, economic research and technical reports on the sector have started to attract attention in the last few decades. When we look at these studies, we can say that the sector has a very active presence in labor markets, especially in developed countries. However, these studies do not econometrically evaluate the direction and extent to which the sector actually affects employment in countries. Therefore, the subject of this study was to investigate the data set of the 16 developed countries for the period of 2008 to 2018 using the least squares dummy variable corrected estimator. It was observed that there is a positive relationship between the employment rate and the gross value added of nonprofit organizations, and there is no statistically significant relationship between the employment rate and the world giving index. In addition, a negative relationship was found between the unemployment rate and the gross value added of nonprofit organizations and the world giving index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".