Annual leave entitlements
| Quick info | |
|---|---|
| Data type | Numeric |
| Scale | Metric |
| Value labels |
normalised scale ranging from 0 to 1 |
| Technical name | labor_ann_lea_ent |
| Category | Labour and labour market |
| Label | Annual leave entitlements |
| Related indicators | |
This variable measures the normal length of annual paid leave guaranteed by (statutory) law or collective agreement. The same score is given for laws and for collective agreements which are de facto binding on most of the workforce (as in the case of systems which have extension legislation for collective agreements). Paid vacation is essential for employees to protect their health and participate in social and cultural life. Public holidays and entitlements based on seniority (length of service with the company) are not included.
Coding rules
The score is normalised on a 0-1 scale, with a leave entitlement of 30 days equivalent to a score of 1 (see Adams et al. 2023).
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There is a break in series due to coverage of legal text:
- 1880-1969 [1880-1979/89 [1]] + countries [2]: Values based on statutory law only.
- 1970-2022 [1980-2022 [3]]: Values - based on law or collective agreement - which have been extracted from CBR Leximetric Datasets (see Deakin et al 2023).
- Adams, Zoe, Bhumika Billa, Louise Bishop, Simon Deakin and Tvisha Shroff (2023). CBR Labour Regulation Index (Dataset of 117 Countries, 1970-2022) - Codes and Sources. Centre for Business Research, University of Cambridge. at: https://www.repository.cam.ac.uk/handle/1810/263766.2
- Deakin, Simon, Johna Armour and Mathias Siems (2023). CBR Leximetric Datasets [Updated 2023]. Apollo - University of Cambridge Repository. https://doi.org/10.17863/CAM.9130.2
- Responsible for data editing and entry: Andrea Schäfer, Jenny Hahs (2018-21), Jean-Yves Gerlitz (2018-20)
- Responsible for data coding: Heiner Fechner, Marina Carlino
- Principal Investigator: Irene Dingeldey, Ulrich Mückenberger
- student assistants (alphabetical ordering): Max Anders, Julia Bode, Jessica Bonn, Daniel Euler, Jan-Christopher Floren, Maxime Fischer, Jennifer Götte, Eliko Hagen, Désirée Hoppe, Irina Kyburz, Alexandra Kojnow, Tarek Mahmalat, Karolin Meyer, Oguz Mermut, Johanna Nold, Tanusha Pali, Gerrit Pantel, Johannes Ramsauer, Max Sudhoff, Kristina Walter, Caroline Zambiasi
- Version 0.002: Initial release
- The excel tabtamplatessources used for the WoL values can be found as a variable called 'source' when you download the data. The background information (legal excerpts, sources, etc.) on the WoL values can be found in the country templates in the Gesis data archive, at:
- Adams, Zoe, Bhumika Billa, Louise Bishop, Simon Deakin and Tvisha Shroff (2023). CBR Labour Regulation Index (Dataset of 117 Countries, 1970-2022) - Codes and Sources. Centre for Business Research, University of Cambridge. at: https://www.repository.cam.ac.uk/handle/1810/263766.2
The comparability of the values over time is therefore limited.
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[1] Applies to the following countries: Namibia, Zimbabwe.
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[2] Applies to all countries not covered by CBR Leximetric Datasets (see Deakin et al 2023): Albania, Benin, Bosnia/Herzegovina, Burkina Faso, Burundi, Central African Republic, Chad, Congo, El Salvador, Eritrea, Gambia, Guatemala, Guinea, Hong Kong, Haiti, Iraq, Jamaica, Kuwait, Laos, Lebanon, Liberia, Libya, Madagascar, Malawi, Mauritania, Mozambique, Nepal, Niger, North Korea, Papua New Guinea, Sierra Leone, Somalia, South Sudan, Tajikistan, Togo, Trinidad and Tobago, Turkmenistan and Uzbekistan for all times points available.
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[3] Applies to the following countries: (1991-2022) Armenia, Azerbaijan, Belarus, Croatia, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Macedonia, Moldova, Serbia, Slovenia, Ukraine; (1990-2022) Bulgaria, Czech Republic, Hungary, Latvia, Lithuania, Namibia, Poland, Romania, Vietnam; (1992/3/5-2022) Cambodia, Russia, Slovakia, Yemen; (1980/6-2022) China, United Arab Emirates, Zimbabwe; (2006-2022) Montenegro. St. Lucia and Malta are not included in the data.
Bibliographic info
Citation: Carlino, Marina, Irene Dingeldey, Heiner Fechner, Ulrich Mückenberger and Andrea Schäfer (2024). WoL Leximetric Datasets [Updated 2024]. University of Bremen.
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