Bernd Brandl University of Vienna Department of Industrial Sociology

1. The Determinants of Trade Union Density in Cross-Country Comparisons:Theortical Opulance and Empirical Destitution Bernd Brandl University of Vienna Department of Industrial Sociology • 1st TURI network Conference: • The future of trade union structures and strategies

2. Introductory remarks and motivation • Researcher offer a number of determinants which possibly explain differences in the level of trade union density across countries and shifts over time • From a macro-perspective (socio-)economic, political and institutional factors are used to explain differences in trade union density across countries and over time • Empirical studies (usually) use aggregate pooled time series to investigate the empirical relevance of determinants on basis of different models • Results of empirical studies are ‘mixed’ regarding the relevance of specific determinants 1st TURI network Conference

3. Introductory remarks and motivation • The heterogeneity in empirical results is unsatisfactory for researchers and ‘policy makers’ as there is uncertainty about what determinants are ‘really’ important • The aim of this paper is to investigate systematically the empirical ‘relevance’ of determinants in explaining variations of trade union density across countries and over time using a Bayesian Model Averaging approach • The work allows the identificationof variables that are robust in explaining trade union density, i.e. provide explanatory power independent of what (specific) theory or model is used! 1st TURI network Conference

4. Contents • Determinants of trade union density • The empirical relevance of determinants and model uncertainty • Bayesian Model Averaging and robust determinants • Summary and conclusions 1st TURI network Conference

5. Determinants of trade union density

6. I. Determinants of trade union density • Trade union density: A country comparison • Averages from 1970 to 2000. • Source: OECD 1st TURI network Conference

7. I. Determinants of trade union density • Trade union density: A country comparison • Shifts over time from 1970 to 2000. • Source: OECD 1st TURI network Conference

8. I. Determinants of trade union density • Literature is rich in offering theories and models that explain trade union density (across countries and in time) • Literature is also reach in specifying determinants (factors, variables) that explain differences in trade union membership across countries and changes over time: • Institutional determinants (number of union trade unions, organization of trade unions ...) • Economic determinants (unemployment rate, business cycle …) • Socio-economic determinants (demographic characteristics, structure of the economy, education of population, religion …) • Political determinants (Corporatism, political orientation of government …) 1st TURI network Conference

9. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

10. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

11. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

12. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

13. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

14. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

15. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

16. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

17. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

18. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

19. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

20. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

21. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

22. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

23. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

24. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

25. I. Determinants of trade union density • Analyzed variables in cross-country studies 1st TURI network Conference

26. I. Determinants of trade union density • Analyzed variables in cross-country studies • 76 Variables • + 67 Interaction terms (variables) • = 143 Regressors • There are much more other variables which are reasonable! • Further lags • Further interactions 1st TURI network Conference

27. II. The empirical relevance of determinants and model uncertainty

28. II. The empirical relevance of determinants and model uncertainty • (Usually) theoretical literature offers testable predictions and can used as a basis for empirical studies: • Unfortunately, not only predictions of theories are differing, but also the results of empirical studies are ‘heterogeneous’. • One reason for the heterogeneity: Estimation of different models! • Depending on what combination of regressors the investigator chooses to put into his regression different significant determinants of union density are achieved! 1st TURI network Conference

29. II. The empirical relevance of determinants and model uncertainty • Examples: 1st TURI network Conference

30. II. The empirical relevance of determinants and model uncertainty • A ‘phenomenon’ that can be ‘observed’ in empirical studies: • One study (based on a specific theoretical model) concludes that a specific variable has no significant influence • Another study (based on a slightly different specification) concludes that the same variable has a significant positive influence • Another study (based again on a slightly different specification) concludes that the same variable has a significant negative influence • Reasons for estimating different models or for the existence of ‘model uncertainty’ • Theories are different (and competing) • Theory is not precise enough in offering the ‘true’ model so that empirical researchers have to ‘check’ alternative specifications • Small sample size (number of observations limited) so that a selection has to be made 1st TURI network Conference

31. II. The empirical relevance of determinants and model uncertainty • Comments on model uncertainty: • Model uncertainty is pervasive in social science • There is (almost) no theory in social science that is strong enough to ‘dictate’ a single model specification • “All models are wrong, some are useful” Box (1979) • The typical case is one in which a number of variables are plausible predictors • The problem is how to decide which specification to use • Bayesian model averaging is (at least) an interesting approach in this context 1st TURI network Conference

32. III. Bayesian Model Averaging and robust determinants

33. III. Bayesian Model Averaging and robust determinants • Bayesian Model Averaging (BMA) • Bayesian statisticians reject idea of a single, true estimate • Instead: each parameter has a distribution! • It is not ‘believed’ that any of the models is actually correct – all models are used as proxies for some unknown underlying model • BMA provides a coherent mechanism for accounting for model uncertainty as probabilities to different possible models are attached • The idea of BMA is to average across several models instead of selecting one model • It takes the K variables and runs a regression on all 2K subsets of the Kvariables, before averaging over all these models 1st TURI network Conference

34. III. Bayesian Model Averaging and robust determinants • Bayesian Averaging of Classical Estimates (BACE) • Sala-i-Martin, Doppelhofer and Miller, AER, 2004 • The BACE approach constructs estimates by averaging weighted OLS coefficients across models • The weights given to individual regressions have a Bayesian justification similar to the BIC • Advantages of the BACE approach: • In contrast to a standard Bayesian approach that requires the specification of a prior distribution for all parameters, BACE requires the specification (assumption) of only one prior hyper-parameter: the expected model size (= 7). • The interpretation of estimates is straightforward: • the weights applied to different models are proportional to the logarithm of the likelihood function corrected for degrees of freedom. 1st TURI network Conference

35. III. Bayesian Model Averaging and robust determinants • Determinants of trade union density – BACE results • Dependent variable(s): • Share of trade union members in relation to the total number of employees (i.e. trade union density) • Yearly percentage change • (Source and definition: OECD) • Cross-country panel data set (balanced): • OECD countries (Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, UK, USA) • Time: 1970 to 2000 • (missing values!) • 55 (theoretically grounded) variables: • Number of trade unions; strike activity; Ghent-System; participation of unions in socio-economic policy making; centralization of wage bargaining, unemployment rate, trade openness, … 1st TURI network Conference

36. III. Bayesian Model Averaging and robust determinants • Determinants of differences in the level across countries • Robust determinants - Level(# 15): • Number of union confederations (-) • Ghent-System (+) • Inflation (+) • Union activities in policy-making (+) • Business activities in policy-making (-) • Centralisation of bargaining level (-) • Yearly change of wage and salary earner; Extension practice; Closed/Union shop practice, Bargaining governability, Change of compensation per employee, change of wage rates, change of unit labour costs, change in productivity 1st TURI network Conference

37. III. Bayesian Model Averaging and robust determinants • Determinants of trade union density • Robust Determinants - Change(#10): • Ghent-System (+) • Left parties in government (+) • GDP growth (-) • Trade openness (-) • Change in total labour force (-) • Change in unemployment rate (+) • Change in productivity (+); Population growth (-), Change employment share (-), one period lagged change in union density (+) 1st TURI network Conference

38. Summary and conclusions

39. IV. Summary and conclusions • What we already knew! • Trade union density is different in different countries • Trade union density is declining in many countries • There are a lot of theories and studiesavailable which aimed to explain differences in the level of trade union density and the decline over time • We also knew that sometheories and studies are saying this and some are saying that • I knew that that this is not satisfactory • at least I have the impression that the current state of research is unsatisfactory because we do not know what to do after reading all these studies 1st TURI network Conference

40. IV. Summary and conclusions • What do we know now? • We still do not knowwhich theory is the ‘true’ one • But we know that some theories are explaining trade union density better than other theories • And there are variables which arerobustly and highly correlated with trade union density and there are variables that are not correlated with trade union density • These variables are of GENERAL relevance (not only specifical)! • For the problem at hand the paper showed that: • Only 15 variables (out of 55) are robustly ‘correlated’ with the level of union density • Only 10 variables (out of 55) are robustly ‘correlated’ with the change 1st TURI network Conference

41. IV. Summary and conclusions • What do we know now? • We still do not knowwhich theory is the ‘true’ one • But we know that some theories are explaining trade union density better than other theories • And there are variables which arerobustly and highly correlated with trade union density and there are variables that are not correlated with trade union density • These variables are of GENERAL relevance (not only specifical)! • For the problem at hand the paper showed that: • Only 15 variables (out of 55) are robustly ‘correlated’ with the level of union density • Only 10 variables (out of 55) are robustly ‘correlated’ with the change • In fact: Only few determinants are able to explain trade union density • BUT: These few determinants are able to provide us an “instrument” for changing the situation, i.e. ‘policy makers’ (for example trade unions) may use these few variables to increase the number of members in trade unions! • The general relevance allows a very high degree of certainty that something can be changed! 1st TURI network Conference

42. IV. Summary and conclusions • The relevance of these (few) variables for trade unions • Trade union density (memberships) varies with the business cycle • Variables: in particular unemployment, inflation and economic growth • hard to ‘use’ as a policy instrument for trade unions • Trade union density depends on country specific ‘traditions’ (i.e. Ghent system) and on ‘global economic trends’ • ‘traditions’ and ‘global trends’ are also hard to use a • policy tool (instrument) 1st TURI network Conference

43. IV. Summary and conclusions • The relevance of these (few) variables for trade unions • There are two determinants that might be considered by trade unions to increase their memberships: • Centralisation of collective bargaining • Trade unions should bargain collective agreements on the ‘right’ level, i.e. not too central and not too decentralized • There is an ‘optimal’ level of bargaining in between • Number of union confederations • The more ‘united’ and ‘integrated’ unions are the more members they have! • It is not easy to unify different trade unions because of different traditions • but it is possible because trade unionists are the ones who are able to change the situation 1st TURI network Conference

44. IV. Summary and conclusions • FINAL REMARKS • THERE ARE DEFENITELY NO EASY WAYS (NO EASY INSTRUMENTS) FOR TRADE UNIONS TO INCREASE THEIR MEMBER SHARES • BUT THERE ARE WAYS! • The analysis identified ‘ways’ and ‘instruments’ that can be used and which are (extremely) empirically relevant. • There might be other ‘ways’ (country specific ways) but the two ways identified by this work will work with a high probability! 1st TURI network Conference