The law determines the legal status of the worker
| Quick info | |
|---|---|
| Data type | Numeric |
| Scale | Metric |
| Value labels | not applicable |
| Technical name | labor_work_def |
| Category | Labour and labour market |
| Label | The law determines the legal status of the worker |
| Related indicators | |
"The law determines the legal status of the worker" measures the kind of legal relationship in the country's statutory or case law and relevant court decisions determining the worker's legal status. The variable is based on the CBR-LRI 1 indicator "The law, as opposed to the contracting parties, determines the legal status of the worker" (see Adams et al. 2017, 2023:14). To create a new scale for the variable "The law determines the legal status of the worker", the CBR-LRI (1)'s initial values were reversed. The variable now ranges from 0 to 1, where "1" means the parties are free to stipulate that the relationship is one of self-employment as opposed to employee status, and "0" means that the law requires employee status if specific requirements are met (such as the type of payment or the ratio of hiring).
Values in between describe, for example:
- 0.5 = the law allows the issue of status to be determined by the nature of the contract made by the parties
The original 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 with reversed scale result in the World of Labour Dataset (WoL) (for first version V1, 1970-2013 see Dingeldey et al. 2022).
Coding rules
The scale ranges from 0 to 1, where meeting specific criteria is required by law to qualify as an employee, represented by a score of "0" (Dingeldey et al. 2022). A missing value (-999) means the law has not been coded for this country year for multiple reasons or in the 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 Dingeldey et al. (2022, Online Supplement; Adams et al. 2023, on the variable 'The law, as opposed to the contracting parties, determines the legal status of the worker' see 2023:14). Dingeldey et al. (2022) describe the value of the variable "The law, as opposed to the contracting parties, determines the legal status of the worker" as "1= the parties are free to stipulate that the relationship is one of self-employment as opposed to employee status; 0.5=the law allows the issue of status to be determined by the nature of the contract made by the parties; 0=the law mandates employee status if certain criteria are met (such as form of payment, du-ration of hiring); further gradations between 0 and 1 reflect changes in the strength of law (reversed scale)" (2022, Online Supplement,https://academic.oup.com/ilj/article/51/3/560/6325574#supplementary-data).
Dingeldey et al. (2022) assess the scale level as “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 coded on a 0-1 scale, with 1- and/or 2-digit decimal. Users should use the WESIS scale level with caution and consult the coding template (algorithm) and values.
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:
- 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
- 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]]
- [1] Applies to the following countries: Namibia, Zimbabwe.
- [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.
- [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.
- [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.
Bibliographic info
Citation: Carlino, Marina, Irene Dingeldey, Heiner Fechner, Ulrich Mückenberger and Andrea Schäfer (2024). Compiled WoL and CBR Leximetric Datasets [Updated 2024]. University of Bremen
- 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
- 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
- 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
- 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
Misc
Project manager(s): 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
- 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
- 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
Revisions: No revisions yet
Sources
- 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
- 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)) -.
