3.3 Estimation results
3.3.2 Results for the indicators CEN T and COO
While the estimates for the coordination indicators are similar with the com-bined index, the relation of significance levels is ‘crossed’ for the pure
central-isation indicator, i.e. only intermediate bargaining levels bear a (marginally) significant impact. In face of the numerous problems with measurement of the centralisation indicators we do not try to create an intuitive explanation for this, but are satisfied with the observation that the signs and sizes of the effects remain stable after a change of the indicator.
3.4 Conclusion
Though our investigation leaves some uncertainties and question marks (like many other empirical studies), we have shown that centralisation and coor-dination of wage setting appear to have moderating effects on strike activity.
The findings are in line with several theories of strike activity and bargaining, ranging from screening models to the simple transaction cost model.
Variable Meaning
CENmed Dummy, = 1 for intermediate level of centralisation CENhi Dummy, = 1 for high level of centralisation
COOmed Dummy, = 1 for intermediate level of coordination COOhi Dummy, = 1 for high level of coordination
COMmed Dummy, = 1 if combined indicator takes on value 2 COMhi Dummy, = 1 if combined indicator takes on value 3
u Unemployment rate (source: OECD)
u2 Unemployment rate squared (source: OECD)
¯
u average of unemployment rate (over all countries in the sample)
i Inflation rate (source: IMF) i2 Inflation rate squared
GAP Output gap, computed as deviation of actual GDP from smoothed GDP. Smoothing is performed with the Hodrick-Prescott filter. value of the smoothing param-eter is 50 for all countries.
Prais-Winsten OLS estimation of the Prais-Winsten transformed re-gression equation
Obs. Number of observations in the sample
PV P-value
R2 Coefficient of Determination (R2); Pseudo-R2 in LAD estimation
ρ estimated coefficient of the AR(1)-Process represent-ing serial correlation of the residuals (used to build the Prais-Winsten regression
All dummy variables with subscriptmed(hi) take on value unity if the respective OECD indicators take on value2 (3), and zero otherwise. Ranges of all used OECD indicators are {1,2,3}. Consequently decentralised/uncoordinated countries form the (omitted) base category.
All estimations contain additionally a constant and 12 time dummies. The dummies are for each two years unity (and 0 otherwise); formally:
Dx−1/x=
1 ift=xort=x−1 0 otherwise
All coefficients of the time dummies (not reported in the tables) are negative (omitted base period is 1974–1974) and show a negative trend, but only some are individually significant.
F-Tests for common significance on all dummies always rejected the null hypothesis H0 : D75/76 = D77/78 = · · · = D97/98 = 0 in the OLS and Prais-Winsten regressions.
The two-year coding was chosen because perfect collinearity occurred otherwise in some regressions.
Table 3.2: Legend for tables 3.3 bis 3.5
Dependent variable: days lost per thousand employees (y)
Estimator Prais-Winsten Prais-Winsten Median-Regression DF BET AS <0.1
Coeff. coeff. t PV coeff. t PV coeff. z PV
COMmed -55.62 -1.16 0.24 -37.09 -1.50 0.13 -30.22 -1.65 0.05 COMhi -129.74 -2.49 0.01 -100.31 -3.43 0.00 -80.20 -4.60 0.00
u 66.54 3.15 0.00 51.47 4.63 0.00 33.11 3.71 0.00
u2 -3.33 -1.92 0.06 -2.36 -2.68 0.01 -1.45 -2.51 0.01
¯
u -33.42 -0.82 0.41 -4.60 -0.20 0.84 -0.15 0.06 0.48
i 31.88 2.82 0.01 7.42 1.13 0.26 5.02 0.78 0.22
i2 -0.43 -0.90 0.37 0.96 3.23 0.00 1.39 2.01 0.02
GAP 8.55 1.04 0.30 11.59 2.28 0.02 5.02 1.47 0.07
obs. 438 402 438
R2 0.24 0.42 0.25
ρ 0.36 0.37
Dependent variable: (yλ−1)/λ,λ = 0.12
Coeff. coeff. t PV coeff. t PV coeff. z PV
COMmed 0.05 0.18 0.85 -0.04 -0.17 0.86 -0.29 -1.10 0.14 COMhi -0.85 -2.37 0.02 -1.44 -4.51 0.00 -1.84 -4.33 0.00 GAP 0.20 2.90 0.00 0.23 3.42 0.00 0.21 3.11 0.00
obs. 438 404 438
R2 0.41 0.53 0.40
ρ 0.30 0.36
Dependent variable: ln(y)
Coeff. coeff. t PV coeff. t PV coeff. z PV
COMmed 0.07 0.38 0.70 0.06 0.35 0.72 -0.09 -0.55 0.29 COMhi -0.46 -1.90 0.06 -0.81 -3.61 0.00 -1.06 -3.43 0.00 GAP 0.14 2.99 0.00 0.15 3.46 0.00 0.17 3.09 0.00
obs. 438 406 438
R2 0.40 0.47 0.39
ρ 0.32 0.40
For definitions of variables and further explanations see table 3.2
Table 3.3: Regression results for the linear, Box-Cox and the log-linear speci-fication; indicator of centralisation: combined indicator of centralisation and coordination (OECD, 1997).
Dependent variable: days lost per thousand employees (y)
Estimator Prais-Winsten Prais-Winsten Median-Regression DF BET AS <0.1
Coeff. coeff. t PV coeff. t PV coeff. z PV
CENmed -86.68 -1.92 0.06 -57.90 -2.11 0.04 -63.06 -4.10 0.00 CENhi -103.01 -1.36 0.17 -97.23 -2.40 0.02 -98.14 -2.67 0.00 u 0.65 2.99 0.00 0.54 5.33 0.00 0.30 3.31 0.00
GAP 9.11 1.08 0.28 10.62 2.08 0.04 4.83 1.36 0.09
obs. 438 398 438
R2 0.23 0.42 0.24
ρ 0.37 0.42
Dependent variable: (yλ−1)/λ,λ = 0.12
Coeff. coeff. t PV coeff. t PV coeff. z PV
CENmed -0.43 -1.31 0.19 -0.62 -2.10 0.04 -0.90 -3.16 0.00 CENhi -0.32 -0.66 0.51 -0.73 -1.59 0.11 -0.53 -1.30 0.10 GAP 0.21 2.95 0.00 0.17 2.64 0.01 0.17 2.73 0.00
obs. 438 401 438
R2 0.40 0.51 0.39
ρ 0.30 0.37
Dependent variable: ln(y)
Coeff. coeff. t PV coeff. t PV coeff. z PV
CENmed -0.24 -1.09 0.28 -0.33 -1.63 0.10 -0.47 -2.78 0.00 CENhi -0.08 -0.26 0.80 -0.37 -1.20 0.23 -0.19 -0.88 0.19 GAP 0.15 3.06 0.00 0.13 2.93 0.00 0.13 3.04 0.00
obs. 438 406 438
R2 0.39 0.47 0.38
ρ 0.31 0.38
For definitions of variables and further explanations see table 3.2
Table 3.4: Regression results for the linear, Box-Cox and the log-linear speci-fication; indicator of centralisation: indicator of centralisation (OECD, 1997).
Dependent variable: days lost per 1000 employees(y)
Estimator Prais-Winsten Prais-Winsten Median-Regression DF BET AS <0.1
Coeff. coeff. t PV coeff. t PV coeff. z PV
COOmed 32.71 0.76 0.45 -23.71 -0.96 0.34 -37.28 -0.96 0.17 COOhi -106.90 -2.33 0.02 -107.78 -3.70 0.00 -90.92 -3.86 0.00 u 0.65 3.06 0.00 0.50 4.54 0.00 0.30 3.30 0.00
GAP 8.59 1.06 0.29 10.01 2.10 0.04 5.05 1.43 0.08
Beob. 438 400 438
R2 0.24 0.42 0.25
ρ 0.35 0.36
Dependent variable: (yλ−1)/λ,λ = 0.12
Coeff. coeff. t PV coeff. t PV coeff. z PV
COOmed -0.22 -0.68 0.50 -0.33 -1.31 0.19 -0.64 -1.58 0.06 COOhi -1.14 -3.22 0.00 -1.78 -5.59 0.00 -1.84 -4.30 0.00 GAP 0.20 2.96 0.00 0.22 3.64 0.00 0.23 2.75 0.00
Beob. 438 391 438
R2 0.41 0.60 0.40
ρ 0.31 0.34
Dependent variable: ln(y)
Coeff. coeff. t PV coeff. t PV coeff. z PV
COOmed -0.17 -0.80 0.42 -0.23 -1.39 0.17 -0.37 -1.54 0.06 COOhi -0.65 -2.74 0.01 -1.12 -5.26 0.00 -1.16 -4.16 0.00 GAP 0.14 3.04 0.00 0.14 3.55 0.00 0.13 2.60 0.00
Beob. 438 395 438
0.40 0.53 0.39 0.39
ρ 0.32 0.39
For definitions of variables and further explanations see table 3.2
Table 3.5: Regression results for the linear, Box-Cox and the log-linear speci-fication; indicator of centralisation: indicator of coordination (OECD, 1997).
Chapter 4
Two Centralisation Models with Heterogenous Firms
4.1 Introduction
As mentioned in the introduction of the book, pleas against centralised bar-gaining are often backed up by the argumentation that wages have to re-flect heterogeneity of firms, regions and industries in order to obtain efficient markte outcomes, and that decentralised bargaining matches these flexibility requirements.
In this chapter we construct two models capturing certain aspects of firm heterogeneity and show that they do not yield unambiguous results against centralisation of wage bargaining. The first model is a straightforward ap-plication of Robinson’s (1933) work on monopolistic discrimination. Though the results can be derived in a quite general form, it is difficult to provide an intuitive interpretation of the conditions required for positive (or negative effects of centralisation. Among other problems, the model suffers from the fact that the aggregation problem is solved by assuming it away (this will become clear below).
The second model tries to address this problem by deriving the centralised union objective using the median voter approach. Though things appear more involved at the outset, it is easier to derive intuitive conditions for positive (or negative) employment effects of centralisation in this modelling framework.
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