Kolik nás může pracovat z domova? Výsledky pro Českou republiku [How Many of Us Can Work from Home? Evidence for the Czech Republic]
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".