Kolik nás může pracovat z domova? Výsledky pro Českou republiku [How Many of Us Can Work from Home? Evidence for the Czech Republic]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How well can a society and an economy face up to COVID-19 depends, among other factors, on how many jobs can be performed at home. Work from home has the potential to increase firms' productivity and quality of workers' lives regardless of COVID-19, but it can also create new challenges. In this paper, we estimate the share of Czech workers who could work from home, using detailed Czech labour force survey data and an internationally recognised occupational classification methodology. Overall, we apply in the Czech context a methodology developed by Dingel and Neiman and published by the Journal of Public Economics in 2020. Our results show that about one third of Czech workers can perform their jobs from home. This share is comparable with countries at similar per capita income levels and with the share of workers who worked from home in Czechia during COVID-19 in the spring of 2020. The ability to work from home is distributed unequally across sectors, regions and workers' education levels. Whereas around four fifths of workers in the financial or the information technology sectors can work from home, less than one in five workers in agriculture and culture can work from home. Most university-educated workers can work from home, but only one in ten workers with primary education can do so. About a half of the workers in Prague can work from home, while only about a quarter can do so in the rest of the Czech Republic.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it