View source for Legally mandated notice period (CBR-LRI)

{{IndicatorForm
|datatype=Numeric
|scale=Metric
|scale=Metric
|scale=Metric
|valuelabels=not applicable
|techname=labor_dis_not_per
|category=Labour and labour market
|label=Legally mandated notice period
|relatedindicators=<ul>
<li>[[Legally mandated redundancy compensation]]</li>
<li>[[Law imposes procedural constraints on dismissal]]</li>
<li>[[Law imposes substantive constraints on dismissal]]</li>
<li>[[Reinstatement normal remedy for unfair dismissal]]</li>
<li>[[Notification of dismissal]]</li>
</ul>
|description="Legally mandated notice period" measures the length of notice for all dismissals. The score is normalised on a 0-1 scale. 
This variable is taken from the CBR Labour Regulation Index Dataset (‘CBR-LRI’), which provides data on labour laws in 117 countries for the period from (in most cases) 1970 to 2022, except for post-socialist countries (see Adams et al. 2017, 2023). The existing CBR-LRI data points were supplemented with data points prior to 1970 or corresponding points in time for socialist countries (see coding rules). In addition, data points for a further 36 countries were added (see coding rules). The CBR Labour Regulation Index Dataset was coded using provisions of law and relevant court decisions, which are taken from secondary sources, national law databases, and ILO NATLEX data (see Adams et al. 2017, 2023). The supplemented data points were coded using statutory law, only. The combined data points result in the World of Labour Dataset (WoL) (for first version V1, 1970-2013 see Dingeldey et al. 2022).
|codingrules=The score is normalised on a 0-1 scale, "1" corresponds to 12 weeks length of notice and "0" to 0 weeks length of notice (see Adams et al. 2023, Dingeldey et al. 2022). A missing value (-999) means the law has not been coded for this country year for multiple reasons or in case of supplemented data points there is ‘no information available’. Statutory law is coded for the year in which it comes into force. Until the law was altered, the coding values were carried over for the ensuing years. As a result, the values between the years when the statutory legislation is in effect are an “estimate” of the legal standards/norms based on former law. The coding template (algorithm) with the definition of the variable and instructions for the coding process is described in Adams et al. (2017, 2023) and Dingeldey et al. (2022, Online Supplement). 
As Adams et al. (2023) describe, the value of the variable "Legally mandated notice period (all dismissals)" "Measures the length of notice, in weeks, that has to be given to a worker with 3 years’ employment. Normalise the score so that 0 weeks = 0 and 12 weeks = 1." (2023:17). 
Dingeldey et al. (2022) describe the value of the variable "Legally mandated notice period" as the "Length of notice in weeks that has to be given to a worker with 3 years’ employment; score normalised from 0 to 1; 1= 12; weeks 0= 0 weeks" (2022, Online Supplement,https://academic.oup.com/iljarticle/51/3/560/6325574#supplementary-data).
The coding template (algorithm) with the definition of the variable and instructions for the coding process is described in Adams et al. (2017, 2023) and Dingeldey et al. (2022, Online Supplement). Assessment of the scale level in Dingeldey et al. (2022) and Adams et al. (2017, 2023) and the description of the values in the template differ. As Adams et al. (2023) state “Some indicators use binary coding but most use non-binary or graduated scores. The template indicates the approach to scoring in each case. Some indicators are expressed as cardinal variables (for example, those relating to minimum qualifying periods of continuous employment) but most are expressed on an ordinal scale.” (p. 7) and Dingeldey et al. (2022) state “The nature of each indicator’s scale depended on the number of possible states that we could envisage for that variable; in the end, all indicators that we developed had either binary (true and false) or graduated scales, some of which, drawing on the CBR, were ordinal in nature, and some cardinal (in the case, for example, of certain working time standards).” (p. 582).  
The scale level for WESIS was set uniformly to metric for all CBR-LRI and supplemented data points in the 1st project phase of CRC 1342; this is retained because all variables are normalised on 0-1 scale. Users should use the WESIS scale level with caution and consult the coding template (algorithm) and values.<br>
There is a break in time series within countries (in most cases 1880 to 1969 and 1970 to 2022, except for post-socialist countries) and limited comparability between countries [see 2] due to coverage of legal text during coding:
<ul>
<li>Variable values are based on statutory law, only for the period 1880-1969 (see WoL data points) [1880-1979/89 [see 1]] + 36 additional countries [see 2] for all time points available </li>
<li>Variable values are based on provisions of law, relevant court decisions or collective agreement (see CBR Leximetric Datasets, Deakin et al. 2023) for the period 1970-2022 [1980-2022 [see 3]] </li>
</ul>
<br>
<ul>
<li>[1] Applies to the following countries: Namibia, Zimbabwe.</li>
<li>[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, Turkmenistan and Uzbekistan for all times points available.</li> 
<li>[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. </li>
<li>[4] Important note: The (supplemented) data points of the variable are stored in extensive Excel tables, in which the corresponding sources, i.e. legal extracts and sources of these extracts, are also documented as the basis for the coded data points. A publication of the version: 0.002 Excel tables in GESIS is planned.</li>
</ul>
|citation=Carlino, Marina, Irene Dingeldey, Heiner Fechner, Ulrich Mückenberger and Andrea Schäfer (2024). Compiled WoL and CBR Leximetric Datasets [Updated 2024]. University of Bremen
|relatedpublications=<ul>
<li>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://doi.org/10.17863/CAM.9130.2
<li>Carlino, Marina, Fechner, Heiner, and Schäfer, Andrea (2025, forthcoming). Using leximetrics for coding legal segmentation in employment law: The development and potential of the Worlds of Labour database. In I. Dingeldey, H. Fechner, & U. Mückenberger (Eds.), Constructing Worlds of Labour. Coverage and Generosity of Labour Law as Outcomes of Regulatory Social Policy. Palgrave Macmillan. p.53-83 
<li>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
<li>Dingeldey, Irene, Heiner Fechner, Jean-Yves Gerlitz, Jenny Hahs and  Ulrich Mückenberger (2022). Worlds of Labour: Introducing the Standard-Setting, Privileging and Equalising Typology as a Measure of Legal Segmentation in Labour Law, Industrial Law Journal, 51(3): 560–597, https://doi.org/10.1093/indlaw/dwab016
</ul>
|projectmanagers=Responsible for data editing, description (WESIS) and entry: Andrea Schäfer (2021-2025), Jenny Hahs (2018-21), Jean-Yves Gerlitz (2018-20); Responsible for data coding: Heiner Fechner (2018-2025), Marina Carlino (2022-2025); 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
|datarelease=<ul>
 <li>Version 0.001: Initial release with data from CBR-LRI published in April 2017 (data for the period from (in most cases) 1970 to 2013) + supplemented data points from WoL, V1</li>
 <li>Version 0.002: Updated with data from CBR-LRI 2023, V2* (data for the period from (in most cases) 1970 to 2022) + supplemented data points from WoL, V2</li>
</ul>
|revisions=No revisions yet
|sources=<ul>
<li>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</li>
<li>The sources used for coding the WoL-values are described in WESIS with “n. k.” for “not known”, information on sources can be found in the column 'source' in the Excel files (for more information on sources pls contact the person responsible for data coding – see entry: Project manager(s)) -. </li>
</ul>
}}