1. The CBR-LRI Dataset: Methods, Properties and Potential of Leximetric Coding of Labour Laws
Simon Deakin
CBR, University of Cambridge
Labour Legislation: Its Role in the Contemporary Economy
Friedrich Ebert Stiftung and Higher School of Economics, Moscow,

2. The CBR Labour Regulation Index
117 countries, 44 years ( )
40 indicators
5 sub-indices: different forms of employment, working time, dismissal, employee representation, industrial action
Dataset publicly available for downloading and use:
Adams, Z., Bishop, L. and Deakin, S. (2016) ‘CBR Labour Regulation Index (Dataset of 117 Countries)’, in J. Armour, S. Deakin and M. Siems (eds.) CBR Leximetric Datasets (Cambridge: University of Cambridge Data Repository).

3. Leximetrics Quantitative analysis of legal systems
Data coding using content analysis of legal texts

4. Research questions
‘Laws created to protect workers often hurt them’ (World Bank, Doing Business, 2008)
‘Employment regulations are unquestionably necessary not just to protect workers from arbitrary or unfair treatment but to ensure efficient contracting between employers and workers’ (World Bank, Doing Business, 2015)

5. Evidence-based policy in question
Britain’s greatest enemy… the experts’ ( 16/06/10/michael-goves-guide-to- britains-greatest-enemy-the-experts/)
‘Our view is that current modeling practices, in their development and use, are a significant threat to the legitimacy and the utility of science in contested policy environments. A commitment to transparency and parsimony will encourage modelers themselves to focus on parameters, inputs, assumptions, and relationships that are well constrained and understood. Further, the assumption- laden aspects of the system should be clearly spelled out’ (Saltelli and Funtowicz, 2014, 2/andrea/)

6. Principles for constructing leximetric datasets
Theoretical priors should be spelled out
Choices on identification and definition of indicators need to be justified
Weighting and aggregation issues should be addressed
Primary sources should be fully sourced
The means by which values were derived from primary sources should be transparent

7. Steps in data coding
(i) identification of a general phenomenon of interest (‘labour law’)
(ii) development of a conceptual construct (‘regulation’ of labour market relations, both individual and collective) 
(iii) identification of indicators or variables which, singly or together, express the construct in numerical terms 
(iv) development of a coding algorithm which sets out a series of steps to be taken in assigning numerical values to the primary source material 
(v) identification of a measurement scale which is embedded in the algorithm 
(vi) allocation of weights, where necessary or relevant, to the individual variables or indicators
(vii) aggregation of the individual indicators in an index which provides a composite measure of the phenomenon of interest

8. Trends by area of law

9. Trends by region

10. Trends by legal origin

11. Selected OECD countries vs. selected BRICS

12. CBR-LRI vs. OECD EPI

13. Use in econometric analysis
No presumption for or against a particular theory of labour law’s impact on the economy
Suitable for panel data and time series analysis
Should be used in conjunction with other institutional datasets (e.g. World Bank, Freedom House data on ‘rule of law’
‘Cambridge equation’ (pooled mean group regression model) most appropriate for dynamic panel data analysis capable of distinguishing between short-run and long-run effects of labour regulation (Pesaran, Shin and Smith)

14. Econometric results
Increases in DFE and EPL correlated with higher rate of labour force participation
Increase in DFE correlated with reduced self-employment (but EPL increases it)
Increases in DFE and EPL correlated with higher labour share
Increases in DFE and EPL correlated with lower unemployment
Effects on productivity unclear in this sample (but related work on the OECD systems shows that increases in DFE and EPL are correlated with higher productivity per worker employed)

15. Pooled mean group estimation with different forms of employment
Labour force
participation
Employment to population
Self-employment
Productivity per worker
Labour share
Unemployment rate
Long run
DFE
0.0120**
0.2393***
***
0.0274***
***
GDP growth
0.0020***
0.0399***
***
0.6572***
***
***
Population
0.0003***
0.0006**
***
0.0089***
***
***
Freedom House ** **
***
Short run
Error correction
***
***
***
***
***
***
Δ DFE **
0.0592
0.0162
0.0210**
Δ GDP growth
***
***
0.0005**
0.0003
0.0008***
Δ Population
0.0626 **
0.0176
Δ Freedom House
0.0008
0.0016*
0.0006 *
0.0004
Constant
0.0841***
0.0150***
0.1136***
0.0949***
0.2003***
0.0321***
Observations
2386
1336

16. Pooled mean group estimation with employment protection legislation
Labour force
participation
Employment to population
Self-employment
Productivity per worker
Labour share
Unemployment rate
Long run
EPL
0.0572***
0.3468***
0.0349***
0.3733
0.0374***
***
GDP growth
0.0020***
0.0195***
***
0.5588***
***
***
Population
0.0003***
0.0076***
***
0.0080***
***
***
Freedom House *
0.0091*
0.0056**
***
Short run
Error correction
***
***
***
***
***
***
Δ EPL
0.0402
0.1156
0.0032
0.0405
Δ GDP growth
0.0004
0.0002
0.0007***
Δ Population
0.1364 **
Δ Freedom House
0.0008
0.001 *
0.0015
Constant
0.0812***
0.0166***
0.0964***
0.1068***
0.1861***
0.0283***
Observations
2386
1336

17. Conclusion
The genie of quantification cannot be put back in the bottle, nor should it
But there needs to be a debate about the methods involved in leximetric analysis
Data coding methods can be improved
With better data we may should get better answers to the policy issues posed by labour regulation