Financial Potential of Czech Employees from the Perspective of Gender Statistics
Bibliographic record
Abstract
In the third quarter of 2022, the average gross monthly nominal wage rose by 6.1 percent compared to last year, but in real terms it fell by 9.8 percent due to inflation. The decline is the same as in the previous quarter. Inflation and a real drop in average wages have already forced three quarters of employees significantly to reduce some expenses. Considering the current situation, the biggest savings relate to holidays, eating out in restaurants, culture or sports activities. In general, people save by limiting purchases of better or better quality products or services, as well as branded products. The aim of this paper is to capture the situation regarding the development of the wages of Czech men and women since the last financial and subsequently economic crisis, through a period of significant economic conjuncture, which was followed by the coronavirus crisis, ensued by the current energy crisis, which is largely related, among other things, to the war conflict in Ukraine. For this purpose, not only statistics measuring the level of wages in the individual years 2009–2021 were calculated, but for this purpose models of the entire wage distribution were constructed and their development in the monitored period was captured. The three-parameter lognormal curves became the basis of these models, the parameters of which were estimated by the maximum likelihood method ensuring the minimum variance of the obtained estimates. Predictions of the entire wage distributions of men and women were constructed for the period 2022–2026 in order to specify the expected development of wage distributions. As part of these predictions, exponential smoothing of time series was applied, which assigns the highest weight to the most recent observations, and the weights of individual observations decrease exponentially towards the past.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".