Type of employment law
| Quick info | |
|---|---|
| Data type | Numeric |
| Scale | Multinomial |
| Value labels |
|
| Technical name | labor_emplaw_typ |
| Category | Labour and labour market |
| Label | Type of employment law |
| Related indicators | |
"Type of employment law" provides information on the specific individual employment legislation in place in a country during a particular year. The categorization of countries according to their "Type of employment law" is recreated from the SPE-typology (see Dingeldey et al. 2020), which distinguishes the standard-setting (S), privileged (P) and compensatory (E) functions of labor law.
The corresponding classification for the variable "Type of employment law" distinguishes between 8 ideal types:
- "laissez-faire" (spe)
- "elitist" (sPe)
- "market egalitarian" (spE)
- "individualising" (sPE)
- "proto-socialist" (Spe)
- "paternalist" (SPe)
- "universalist" (SpE)
- "ordre public social" (SPE)
Uppercase letters represent strong legislation, lowercase letters indicate weak legislation.
The variable is available for 115 countries from 1970 to 2013, using 12 World of Labour (WoL) variables and 23 CBR-LRI variables (see Dingeldey et al. 2020, 2022). The data from 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 12 World of Labour (WoL) variables were coded using statutory law, only. The combined 35 variables result in the World of Labour Dataset (WoL) (for first version V1, 1970-2013 see Dingeldey et al. 2020, 2022). The different coding basis (text for the coding of legal norms) leads to a different mapping of the scope of law between the CBR and WoL variables and may have an impact on the classification of countries according to the SPE-typology of Dingeldey et al. (2020) as presented in this variable.
Coding rules
The calculations reproduce the SPE typology from Dingeldey et al. 2020, 2022 with the exact same data (in consultation with the persons responsible for the calculations and exchange of the STATA-do files). 35 variables from the Worlds of Labor (WoL) data set, which combines 23 CBR-LRI variables (Adams et al. 2017) and 12 self-coded variables for 115 countries for the years 1970 to 2013, were used for this purpose. The 23 CBR-LRI variables used are based on the coding of provisions of law and relevant court decisions (see Adams et al. 2017, 2023) and the 12 WoL variables are based on the coding of statutory law, only (see Dingeldey et al. 2020, 2022). This means that the 35 variables combined into an additive index have a different range of textual bases that they cover.
The calculations corresponded to the procedure of Dingeldey et al. 2020, 2022:
- First, all missing values were excluded from the analysis, missing values for Belarus for the year 1999 were replaced with 1 and for Khazakhstan for 1994 with zero and the variables CBR-LRI 1 and 18 were reversed in their scale (i.e. calculated 1- the respective value).
- Second, the mean value of all variables was then calculated for a single aspect (for the difference between aspects and dimensions, see Dingeldey et al. 2020, 2022), i.e. the values of the variables within an aspect were added up and divided by their number. The same was done with the aspects within a dimension and dimensions within a function. Thus, all variables were included equally in the calculations, regardless of their text basis and scale level. Some of the variables, such as 'equal right for equal pay', are based on a binary scale level.
- Third, adjustments were made exactly as in Dingeldey et al. 2020, 2022: Since the generated indices for the dimensions working time, protection against dismissal and selectivity, in contrast to the other dimensions, showed a maximum below '1' across all years (working time: 0.87; protection against dismissal: 0.95 and selectivity: 0.72), their scales were normalized, i.e. in each case the value of the index was divided by its maximum value. These stops resulted in three additive indices for the functions “protection” (s/S), “privilege” (p/P) and “equality” (e/E).
- Fourth, the mean as threshold value based on anchor points were introduced (the years 1980 for S and P and 2006 for E were selected as anchor points).
This was then used to create 8 types [1]"spe" [2]"sPe" [3]"spE" [4]"sPE" [5]"Spe" [6]"SPe" [7]"SpE" [8]"SPE".
If the country was assigned the SPE-type “sPe”, this means that:
- 1. the index value for s (protection) for the country is below the mean value across all countries for the year 1980
- 2. the index value for P (privilege) for the country is above the mean value across all countries for the year 1980 and
- 3. the index value for e (equalizing) for the country is below the mean value across all countries for the year 2006.
Depending on how many countries are part of the analysis or which years are selected, the values should vary. The analysis, calculation and description of the variables was carried out by Andrea Schäfer.
Bibliographic info
Citation: Schäfer, Andrea, Marina Carlino, Irene Dingeldey, Heiner Fechner, Ulrich Mückenberger and (2024). Type of employment law data - short version [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 (2020). Measuring Legal Segmentation in Labour Law. SOCIUM SFB 1342 Working Papers No. 5, Bremen: SOCIUM, University of Bremen. https://www.socialpolicydynamics.de/f/90e3891ffd.pdf
- 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), p.560–597
Misc
Project manager(s): Responsible for data editing, calculation, description (WESIS) and entry: Andrea Schäfer (2021-2025); Responsible for data coding: Heiner Fechner (2018-2025), Marina Carlino (2022-2025); Principal Investigator: Irene Dingeldey, Ulrich Mückenberger; Student assistants for coding (alphabetical ordering): 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, Johannes Ramsauer, Max Sudhoff, Kristina Walter, Caroline Zambiasi </ul>
- 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) + 12 variables from WoL, V1
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 not available (for more information on sources pls contact the person responsible for data coding – see entry: Project manager(s)) -.
