Subsidies and Economic and Financial Performance of Enterprises
Bibliographic record
Abstract
The aim of this article is to analyze the economic and financial performance of Slovenian enterprises, as a European Union (EU) member state case study. A favorable economic and financial performance is crucial for long-term sustainable enterprise growth and survival. Eight economic and financial performance indicators are used to evaluate the sustainability in the growth of enterprises: seven of them are financial indicators—assets, revenues from sales, equity, net profits, operating efficiency, return on equity, and value added per employee—while the eighth variable is the economic indicator for the number of employees. A distinction is made between enterprises that did and that did not receive subsidies from national and EU funds. Three enterprise-level data sources are combined in the empirical analysis: balance sheet data from enterprise accounts, own surveys data, and government data on public subsidies to enterprises. The mean values and standard deviations of economic and financial indicators based on balance sheet data for the years in two financial periods are estimated. The summary statistics for economic and financial indicators and correlation analysis are conducted and the results of the economic and financial indicators are compared using the parametric paired sample two-tailed t-test that allows comparison between the enterprises in the two financial periods. An increase in the economic and financial indicators is investigated by comparing the enterprises that did receive subsidies with the enterprises that did not receive subsidies in the two financial periods. The empirical results confirm that the value added per employee is the only financial indicator where a positive link is found between the financial indicator and subsidies. The results suggest that subsidies can be important for cash flow into enterprises, but entrepreneurial activities are crucial for favorable economic and financial performance and long-term sustainable growth in a competitive market environment.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".