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3.4 LEY DE ADQUISICIONES Y OBRA PÚBLICA
To start, we estimate the bivariate probit model of unionised employment and union membership defined by (4.15). The estimates are presented in Table 4.3. Column I gives the estimates of the reduced form model of unionised employment while column II gives the estimated parameters of the union membership equation. For comparison, column III reports the independent probit estimates of the determinants of union membership using the sub-sample of workers in unionised employment.
'^For example, it does not seem likely that job seekers make job choice decisions on the basis of whether they will be eligible for membership of a particular union or not. Presumably, what matters are the conditions of employment offered In different j o b s . Therefore, o n c e a worker has secured employment in a unionised location, the issue o f eligibility is taken as given: it is something over which the worker has no control.
Chapter 4
TABLE 4.3
Bivariate Probit Model of Unionised Employment and Union Membership
Bivariate Probit (1001 cases) I Unionised Employment P r ( U E = l ' ) B s.e. II Union Membership P r ( U = l ) 0 s.e. Independent Probit (680 cases)
m
Union Membership P r ( U = l | U E = l ) 0 s.e. Marginal Effect' Experience 0.031* 0.016 0.036 0.027 0 . 0 3 6 " 0.018 0.009 (Experience)^ 0.000 0.000 - o . o o r 0.000 - o . o o r 0.000 Education (years) 0.006 0.025 0.002 0.037 0.002 0.035 0.000 Tenure in current job - - 0.097"° 0.024 0.097"" 0.022(Tenure)^ - - -0.002"" 0.001 - 0 . 0 0 2 " • 0.001 0.024 Hours of work 0.020 0.032 0.020 0.031 • 0.005 (Hours)^ - - 0.000 0.000 0.000 0.000 Current occupation: Professional 0.09 0.18 -0.74"" 0.29 - 0 . 7 4 ' " 0.26 -0.25 Manager, administrator 0.01 0.20 -1.58"" 0.32 - 1 . 5 8 ' " 0.31 -0.57 Clerk -0.07 0.18 -0.96"" 0.25 - 0 . 9 6 " 0.25 -0.34 Salesperson -0.04 0.22 -0.26 0.34 -0.26 0.35 -0.08 Transport, -0.16 0.30 -0.15 0.41 -0.15 0.42 -0.04 communications worker
Service, sport and 0.15 0.22 -0.71" 0.34 - 0 . 7 1 " 0.30 -0.24 recreation worker Other occupations - 2 . 0 3 " 0.43 -1.43 2.54 -1.43 1.08 -0.52 Managerial status: Manager - - -0.39 0.24 -0.39' 0.22 -0.12 Supervisor - - 0.03 0.16 0.03 0.15 0.01 Sector of employment: Public sector 1.24"" 0.14 0.27 0.95 0.27 0.18 0.06 Industry of employment: Agriculture, mining 0.41 0.31 -0.23 0.61 -0.23 0.49 -0.07 Manufacturing 0.52''" 0.18 -0.83 0.56 - 0 . 8 3 " 0.27 -0.29 Construction 0.12 0.28 -0.53 0.57 -0.53 0.48 -0.17
156
Union Coverage and Membership
Wholesale and retail trade
-0.11 0.21 -0.71- 0.35 - o . 7 r 0.32 -0.24
Transport and storage 0.72" 0.28 0.28 0.46 0.28 0.29 0.06
Finance, property and business services -0.38*" 0.19 0.41 0.45 0.41 0.28 0.09 Recreational and personal services -0.15 0.21 -0.29 0.42 -0.29 0.35 -0.09 Personal characteristics Migrated < 18 years -0.22 0.14 -0.07 0.29 -0.07 0.21 -0.02 Migrated > 18 years -0.25' 0.15 -0.03 0.28 -0.03 0.19 -0.01 Female 0.10 0.11 -0.24 0.19 -0.24 0.15 -0.07 Married -0.01 0.14 -0.15 0.18 -0.15 0.17 -0.04 Separated, divorced, widowed -0.44" 0.21 -0.04 0.45 -0.04 0.27 -0.01 No. children -0.03 0.05 0.03 0.07 0.03 0.06 0.01 Political affiliation: Labor - - 0.23 0.15 0.23' 0.14 0.05 Coalition - - -0.46"" 0.18 -0.46"" 0.16 -0.15 Constant -0.39 0.39 0.77 1.83 0.77 0.84 - 0.07 (1.63) - Log-Likelihood -791.895 -289.68 2(lnL-lnLo) (x') 251.69-"+ 165.00™ Pseudo R^ (1-lnL/lnLo) 0.20+ 0.22 Notes:
Values of 0.000 or -0.000 reflect absolute values rounded to less than ± 0 . 0 0 1 'significant at 10% (two-tailed /-test for coefficients)
""significant at 5 % (two-tailed /-test for coefficients) •""significant at 1 % (two-tailed /-test for coefficients)
^Estimated from an independent probit model of unionised employment.
'Effect of a one-unit change in the independent variable on the probability of union membership with all other variables set at their mean values.
Base categories for dummy variables: Occupation - Tradesperson, process worker, labourer; Managerial status - non-manager, non-supervisor; Sector - Private sector; Industry - Public administration and defence, public utilities, and community services industries; Political affiliation - no party affiliation or minor party affiliation; Migrant status - Australian bom; Marital status - Never married.
Chapter 4
The parameter estimates in column I determine the individual's estimated probability of unionised employment.'^ Several of the estimates are worthy of comment.
First, with the exception of the other occupations' coefficient'^ none of the occupational coefficients are statistically significant. This indicates, for example, that a manager or administrator does not have a significantly lower estimated probability of unionised employment than a tradesperson or a process worker (the base occupations). Moreover, it implies that the significant differences in the probabilities of union membership across occupations
reported in Chapter 3 are not the product of strong differences in the availability of union membership across occupations. Rather, it would seem that workers in different occupations have different propensities for union membership.
There is some evidence that workers with longer experience are more likely to be employed in unionised establishments (in particular, the estimated coefficient indicates that an extra year's experience increases an individual's probability of being employed in a unionised workplace by approximately 1 percentage point). This suggests that part of the reason why younger workers
' t e n u r e , hours of work and managerial status are excluded from the unionised employment equation. These variables are excluded for two reasons. First, tenure, hours of work and managerial status are assumed to be independent of the individual's decision to seek unionised employment (and also to be independent of the unionised employer's decision to offer such employment). The second reason is that the bivariate probit model is not identified if the unionised employment and union membership equations contain the same variables.
'^Recall that the other occupations dummy variable is for agricultural workers, mine workers and members of the armed services. Less than 3.5% of all respondents are in this occupational category.
Union Coverage and Membership
are less likely to be union members than older workers is because they are less
likely to be employed in unionised establishments. Interestingly, researchers
investigating youth membership in Britain also find that young employees are
less likely to be employed in unionised workplaces than older employees
(Spilsbury et. al. 1987; Payne, 1989).
One of the strongest determinants of unionised employment is public
sector employment. In particular, the estimated coefficient indicates that the
probability of unionised employment is approximately 30 percentage points
higher for a public sector employee than it is for a similar private sector
employee.'^
The covariance between the unobserved determinants of unionised
employment and union membership, a,2, is positive (0.07) but statistically
insignificant (indeed, the standard error is more than 20 times larger than the
estimated covariance).
Note that the estimated covariance provides a test of sample selection
bias in the union membership equation. Because the covariance is
insignificant, we cannot reject the null hypothesis that there is no sample
selection bias. However, this result must be treated cautiously. First, the test
'^As in Chapter 3, w e evaluate the marginal effect at the sample means of all of the independent variables.
'^If the estimate were significant, a positive covariance would imply that individuals with unmeasured characteristics that indicate that they are more likely to be in unionised employment are also more likely to join unions.
^"In addition to the bivariate probit model of unionised employment and union membership reported in Table 4 . 3 w e also estimated a bivariate probit model of eligible unionised employment and union membership. H o w e v e r , the estimated covariance was also insignificant ( f f i 2 = - 0 . 4 5 , s . e = 0 . 8 1 ) .