Difference between revisions of "Annual leave entitlements"
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<li>[[Maximum daily working time]]</li> | <li>[[Maximum daily working time]]</li> | ||
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|description = | |description = | ||
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. | 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. | ||
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|codingrules = | |codingrules = | ||
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). | 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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[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. | [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. | ||
</ul> | </ul> | ||
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|Important note = | |Important note = | ||
The values of the variables are based on values stored in extensive Excel tables. In addition to the values, the legal excerpts and sources of these excerpts are also documented there. The values in the Excel tables differ (depending on the version) from the values in the WESIS database. This applies in particular to the values of variables that are based on the original CBR-LRI data. The reason for this difference is a different range of data material used for coding (see break in series) and different legal interpretations by the coders. | The values of the variables are based on values stored in extensive Excel tables. In addition to the values, the legal excerpts and sources of these excerpts are also documented there. The values in the Excel tables differ (depending on the version) from the values in the WESIS database. This applies in particular to the values of variables that are based on the original CBR-LRI data. The reason for this difference is a different range of data material used for coding (see break in series) and different legal interpretations by the coders. | ||
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|citation = | |citation = | ||
Carlino, Marina, Dingeldey, Irene, Fechner, Heiner, Mückenberger, Ulrich & Schäfer, Andrea (2024). WoL Leximetric Datasets [Updated 2024]. University of Bremen. | Carlino, Marina, Dingeldey, Irene, Fechner, Heiner, Mückenberger, Ulrich & Schäfer, Andrea (2024). WoL Leximetric Datasets [Updated 2024]. University of Bremen. | ||
|relatedpublications = | |relatedpublications = | ||
<ul> | <ul> | ||
| − | + | Adams, Z., Billa, B., Bishop, L., Deakin, S. & Shroff, T. (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, Armour, John, & Siems, Mathias (2023). CBR Leximetric Datasets [Updated 2023]. Apollo - University of Cambridge Repository. https://doi.org/10.17863/CAM.9130.2 | |
</ul> | </ul> | ||
|projectmanagers = | |projectmanagers = | ||
<ul> | <ul> | ||
<li> Responsible for data editing and entry: Andrea Schäfer, Jenny Hahs (2018-21), Jean-Yves Gerlitz (2018-20) | <li> Responsible for data editing and entry: Andrea Schäfer, Jenny Hahs (2018-21), Jean-Yves Gerlitz (2018-20) | ||
| − | <li> Responsible for data coding: Marina Carlino | + | <li> Responsible for data coding: Heiner Fechner, Marina Carlino |
<li> Principal Investigator: Irene Dingeldey, Ulrich Mückenberger | <li> Principal Investigator: Irene Dingeldey, Ulrich Mückenberger | ||
<li> 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 | <li> 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 | ||
Revision as of 14:01, 6 September 2024
| 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 the CBR-LRI leximetric dataset (see Deakin et al 2023).
- 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 sources 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, Z., Billa, B., Bishop, L., Deakin, S. & Shroff, T. (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-LRI leximetric dataset (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, Dingeldey, Irene, Fechner, Heiner, Mückenberger, Ulrich & Schäfer, Andrea (2024). WoL Leximetric Datasets [Updated 2024]. University of Bremen.
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Adams, Z., Billa, B., Bishop, L., Deakin, S. & Shroff, T. (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, Armour, John, & Siems, Mathias (2023). CBR Leximetric Datasets [Updated 2023]. Apollo - University of Cambridge Repository. https://doi.org/10.17863/CAM.9130.2
Misc
Project manager(s):
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