re-employment after retrenchment: evidence from the tcf industry study

The Australian Economic Review, vol. 32, no. 2, pp. 105–29

The University of Melbourne, Melbourne Institute of Applied Economic and Social Research 1999Published by Blackwell Publishers Ltd, 108 Cowley Road, Oxford OX4 1JF, UK and

350 Main Street, Malden, MA 02148, USA

* This research was conducted with the support of the Of-fice of Labour Market Adjustment (1993–94) and ARC in1995–97. Jeff Borland’s perceptive comments on an earlierversion of this paper and the helpful comments of two anon-ymous referees resulted in a more coherent presentation.

Abstract

Industry Commission inquiries into the passen-ger motor vehicle and textiles clothing andfootwear (TCF) industries have focused atten-tion on the employment prospects of workerswho are displaced as a result of structuralchange. The fact that older and less skilledworkers face considerable difficulty findingnew employment is now widely recognised. Inthis paper we examine the post-retrenchmentoutcomes for workers retrenched from jobs inthe TCF sector. The method of analysis—discrete-time event history analysis—improveson previous studies of post-retrenchment out-comes because labour market conditions areincorporated into the statistical model, re-dressing the over-emphasis on supply-side is-sues that characterise previous research. Theanalysis shows that local and national labourmarket conditions are important determinantsof employment outcomes. Personal character-istics, household circumstances and ascribedskill are also important as employers use theseattributes to filter potential recruits. The analy-sis suggests that the utility of retraining is vari-able, enhancing the employability of workerswith the best prospects (based on their per-sonal characteristics and skills) before takingup retraining but decreasing the employmentchances of those with poorer prospects.

1. Introduction

Recent debates on industry policy and the fu-ture of the textiles, clothing and footwear(TCF) sector have highlighted the importanceof labour adjustment issues associated withstructural change in the economy.

1

The govern-ment’s recent decision to slow the rate of de-cline in the TCF sector (Howard & Moore1997) demonstrates the political importance offinding equitable means to address labour ad-justment. This paper analyses both the processof labour adjustment among, and the role of la-bour adjustment assistance to, workers wholost jobs in the TCF sector prior to May 1993.

The paper is structured as follows. The firstsection introduces the TCF Industry Study andprovides an overview of the labour market out-comes among retrenched workers. It showsthat a high proportion of retrenched TCF work-ers did not return to employment in the fouryears after retrenchment. A brief review of thelimitations of previous retrenchment studies isthen followed by an introduction to the methodof analysis used in this paper, an approachknown as ‘discrete-time event history’ analy-sis. This method improves on previous studiesof retrenchment outcomes because it incorpo-rates labour demand conditions and employ-ment history. Changing demand conditions anddifferent retrenched worker profiles indicatedifferent likelihoods of re-employment. Con-sistent with previous research, these resultsshow that younger and more highly skilledworkers have far better chances in the labourmarket than older and less skilled workers.

Re-employment after Retrenchment: Evidence from the TCF Industry Study

Sally Weller and Michael Webber*Department of Geography and Environmental StudiesThe University of Melbourne

106 The Australian Economic Review June 1999

The University of Melbourne, Melbourne Institute of Applied Economic and Social Research

It is possible to view retrenchment as a minordisruption to an individual’s career, in whichthe cost to the individual is only the loss ofwages from retrenchment to the start of a newjob. Alternately, retrenchment from a job in thedeclining manufacturing sector might beviewed as marking the starting point of a sus-tained and long-term decline in an individual’scareer. The evidence from this research sug-gests that the latter view is closer to reality. Theanalysis suggests that retraining will assist onlya subset of retrenched workers, indicating thatalternative forms of adjustment compensationare needed to achieve equitable outcomes.

2. Background

From the early 1980s to 1991 industry policyencouraged an orderly contraction of the previ-ously highly protected TCF sector. TCF joblosses continued at a steady pace throughoutthe 1980s as imported goods steadily increasedtheir share of the stagnant domestic market andtechnical change raised productivity (IndustryCommission 1997). The March 1991 Eco-nomic Statement altered the arrangements ne-gotiated under the sectoral ‘Button Plan’ in1986–88, accelerating the pace of reduction ofthe protective tariffs and quotas that had re-stricted TCF import penetration (Button 1991).By 1991 many employers had already begunrestructuring their internal operations to meetinternational productivity standards, and manycould not imagine how further productivity im-provements alone would enable them to com-pete against lower cost imported goods. Theupshot was a dramatic acceleration of the de-cline of the TCF sector in the years 1991 to1993. Some employers closed their operationsentirely, others accelerated plans to source tex-tile and apparel overseas rather than manufac-ture locally, while others sought to competewith imports by accessing low cost domesticmanufacture through the network of subcon-tractors producing garments sewn by home-based outworkers. Whichever option firmschose, the prospects for factory-based TCFemployees were poor and factory-based TCFemployment contracted dramatically. TCF em-ployment fell by 22 per cent or 26000 jobs in

the four years 1989 to 1993 (Webber et al.1995).

The Button Plan recognised that many of theworkers at risk of losing TCF industry jobs hadlimited prospects in the labour market. Theylived, for the most part, either in declining partsof metropolitan regions where few new jobswere being created, or in country towns wherethe textile mill or clothing factory was themajor source of manufacturing employment,especially for women. A high proportion ofthose who lost TCF jobs had been recruited asmigrants, many were in the older age groups,and most had been working in the same job formany years. More than 70 per cent werewomen. Few had any formal qualifications andmany had problems with English language lit-eracy. In fact, literacy problems were commonto workers of both English speaking and non-English speaking backgrounds. Their TCF jobswere amongst the lowest paid in manufac-turing, and workers’ long service in the TCFsector suggested that few had alternative em-ployment options. The Button Plan includedgenerous labour adjustment arrangements thatencouraged retrenched workers to participatein retraining (Button 1987; Office of LabourMarket Adjustment 1991).

3. The TCF Industry Study

Under the TCF Plan, employers and unions hadagreed to ‘notify’ the Commonwealth Employ-ment Service (CES) and Office of Labour Mar-ket Adjustment of all retrenchments. The CESretained a database of all TCF retrenchedworkers and the sample of retrenched workerson which this study is based was drawn fromthat record. Under the Labour AdjustmentPackage, the CES attended workplaces to pro-vide information to workers and assist themwith CES registration procedures, resulting in ahigh rate of registration for larger urban clo-sures and closures in rural areas, where over 90per cent of retrenched employees are known tohave registered.

2

In urban areas, though, CESregistration rates could not be estimated accu-rately. If retrenchments in small urban non-union workplaces were not notified to the CES,workers from these firms (who tended to be

Weller & Webber: Re-employment after Retrenchment 107


1 6 11 16 21 26 31 36 41 46

050

100

150200250

300350400

450500550600

650

Any work

Training

Unemployed

Not in labour force

Retired (60 Women, 65 Men)

No contact

migrant women with poor re-employmentprospects) may not have registered and maytherefore be under-represented in the sample.Still, large numbers of urban women did regis-ter in response to information provided at com-munity service agencies and Migrant ResourceCentres. CES counter staff also identifiedformer TCF workers and referred them to theTCF Labour Adjustment benefits. Because theTCF Labour Adjustment Package training al-lowance was generous and not means tested, itprovided a strong incentive for workers to reg-ister. Nevertheless, this process may have ex-cluded some workers affected by plant closure:for example, workers who left ailing work-places prior to closure; workers who had otheremployment options to take up; and others whodid not seek or require CES help, such as olderworkers who opted to retire. Thus, the samplemay exclude those retrenched workers with thebest and those with the worst re-employmentprospects. Overall, the population of CES-registered retrenched workers is representativeof the group of retrenched TCF workers whoare the legitimate subjects of public policy in-tervention.

The TCF Industry Study selected 11 loca-tions (CES offices) across three states and sam-

pled from the stock of TCF retrenched workersregistered at those offices.

3

From a populationof 1640 workers registered at those offices, thestudy drew a sample of 605 former TCF work-ers. The sample was stratified by gender, eth-nicity and location (Appendix Table A1).Workers were approached for interview onfour occasions—June 1993, August 1994, Au-gust 1995 and April 1997—with over 80 percent of the original group remaining in contactfor the duration of the study. At interviews, in-formation about each person’s labour marketparticipation was recorded on a monthly basis.The survey interviews also collected extensivedescriptions of every job held, every period ofunemployment or retraining, and informationabout personal and household characteristics.At the conclusion of the study this informationwas consolidated into charts of monthly labourmarket flows. The general result of how re-trenched workers fared in the four years fol-lowing retrenchment is shown in Figure 1,which relies on ABS definitions of employ-ment, unemployment and not in the labourforce. Only a little over half of the originalsample of 605 people had found another jobafter four years, despite their willingness toparticipate in the retraining provided for them

Figure 1 Labour Force Status by Month since Retrenchment

Number of cases

Month since retrenchment



Table 1 Post-Retrenchment Transition Matrix

a

Transition to

b

Transition from

b

Employment Unemployment TrainingNot in

labour force Total

Employment 136

c

224 64 1 425

Unemployment 417 56

c

505 17 995

Training 144 374 169

c

1 688

Not in labour force 0 0 0 1 1

Total 697 654 738 20 2109

Note

s: (a) This table was generated from monthly flow data files that exclude concurrent activities.(b) Definitions as in ABS

Labour Force Survey

. (c) Transitions from one state to the same state involve a change of status within the state—for example unemployed seekingwork to unemployed and not looking for work; change of jobs or change of training course.

under labour adjustment arrangements. Thenumber of people who found work rose sharplyin the first three to six months after retrench-ment, but thereafter the improvement slowed(the ‘Any work’ category in Figure 1). Afterabout three years, there appears to be no furtherimprovement in overall employment out-comes. More than half of the retrenched work-ers in the study participated in retraining,which frequently extended over a relativelylong period (the ‘Training’ category in Figure1). The proportion of workers categorised as‘Unemployed’ falls quickly in the six monthsafter retrenchment, and after about 24 monthsthe number in that category is stable. The num-ber ‘Not in the labour force’ increases in thefirst year but then also remains fairly stable.Figure 1 also shows the number of people whowere excluded either by reaching the (then) re-tirement age (65 for men, 60 for women) or byhaving lost contact with the study.

While Figure 1 gives an overview of out-comes, it conceals the considerable movementsin and out of spells of employment, unemploy-ment or training. Some individuals experi-enced multiple transitions over the four yearsfrom retrenchment. Table 1 shows that a totalof 2109 post-retrenchment labour market tran-sitions were recorded for the 605 cases.

There were 654 transitions into spells of un-employment, a figure that excludes the 605transitions at retrenchment. The 697 transi-tions into employment consist of 136 occa-sions of workers changing jobs (moving fromemployment to employment), 417 moving

from unemployment to employment and 144from training to employment. This paper con-cerns transitions into the first spell of post-retrenchment employment from any of theseorigin states.

4. Conceptualising Labour Market Outcomes

Wooden (1988) and more recently Webber andCampbell (1997) review post-retrenchmentstudies in Australia. As Wooden observes,most are case studies of specific plant closures.The analysis of labour market outcomes in casestudies has been restricted to assessing the ef-fects of supply-side factors, most frequentlyexamining how personal characteristics andpre-retrenchment employment history affectre-employment outcomes. Different findingsreflect the unique characteristics of each partic-ular closure and the labour demand conditionspertaining to it. Previous retrenchment re-search has also been preoccupied with the re-trenchment experiences of blue-collar maleworkers, and few studies in Australia haveconsidered the impact of retrenchment onwomen’s careers.

Australian retrenchment studies have gener-ally relied on cross-sectional analyses of em-ployment outcomes at a specific point in time;usually the time at which a follow-up survey ofretrenched workers was carried out. In suchstudies the proportion of people employed orunemployed at any particular point after re-trenchment depends on the amount of time



elapsed since retrenchment. To demonstrate,Table 2 shows the proportion of retrenchedTCF workers who had held any post-retrenchment job (by ABS definitions) at 6, 12,24, 36 and 48 months after retrenchment.

Among younger men from English speakingbackgrounds,

4

only 29 per cent had found a jobwithin six months, but more than 80 per centhad returned to at least some work within fouryears. Among men from non-English speakingbackgrounds who were over 45 years of age(older NESB men), only 11 per cent had re-joined the workforce six months after retrench-ment, rising to a paltry 31 per cent after fouryears. Clearly the results of cross-sectionalstudies will vary widely depending on theamount of time between retrenchment and thefollow-up survey. Table 2 accentuates the widevariations in outcomes for different groups ofretrenched workers. Examining these varia-tions is the objective of the analyses presentedin later sections.

The way in which labour market history be-tween retrenchment and re-employment is con-ceptualised has considerable impact on theoutcomes of analysis (Tuma 1994). Overseas,studies of post-retrenchment careers have rec-ognised that a considerable ‘grey area’ existsbetween the states of unemployment and full-time work. Individuals often move between anumber of different possible labour marketstates including withdrawal from the labourforce, participation in retraining courses orshort hours part-time work. In their study of re-trenchment experiences in Sweden, for exam-

ple, Gonas and Westin (1993) identifypermanent full-time work as the only valid re-employment outcome and group short spells ofunemployment, training and part-time or casualwork together as states of ‘permanent tempo-rariness’. The range of states and large numberof transitions reported in Table 1 underscoresthe need for sensitivity to the multiple (and re-peatable) possibilities in post-retrenchment ca-reers. Women’s labour force participation afterretrenchment in particular involves a variety ofstates other than unemployment (Davies & Es-seveld 1989; Swaim & Podgursky 1994) whichneed to be taken into account in the conceptu-alisation of the analysis.

The quantitative analyses in the remainder ofthis paper therefore diverge from ABS defini-tions, opting to conceptualise the labour markethistories of retrenched TCF workers as fol-lows.

(i) The outcome of interest is the timing ofthe transition into the first spell of post-retrenchment employment

in a full-timejob or a part-time job of 20 hours perweek or more

. Jobs of less than 20 hoursduration have not been included as theoutcome, but are included as one of thepredictor variables. The policy issue rele-vant to jobs of a few hours per week iswhether or not they are associated with asubsequent transition to more substantialemployment—an assessment that re-quires them to be thought of as predictorsrather than outcomes. Furthermore, since

Table 2 Any Paid Work since Retrenchment

a

(per cent)

Subsample group At 6 months At 12 months At 24 months At 36 months At 48 months

Younger ESB men 29.1 41.9 67.4 81.4 83.7

Younger ESB women 19.2 30.3 60.6 70.7 76.8

Younger NESB men 20.7 29.3 48.3 69.0 79.3

Younger NESB women 16.0 21.7 42.5 53.8 62.3

Older ESB men 29.4 45.1 49.0 52.9 52.9

Older ESB women 17.0 36.2 53.2 59.6 63.8

Older NESB men 11.3 17.7 27.4 29.0 30.7

Older NESB women 11.5 16.7 24.0 31.3 38.5

Note

: (a) Any paid work includes all jobs: full-time and part-time of 1 hour or more duration, as per ABS definition.



the objective is to assess the change in cir-cumstances compared to the full-timework held prior to retrenchment, we haverestricted the definition of ‘success’ to anapproximation of pre-retrenchment con-ditions of employment.

(ii) There are also practical reasons for treat-ing short (less than 20 hours per week)part-time jobs as a separate state leadingto re-employment in a more substantialjob. Because these jobs often involvelow-paid menial work of less than 10hours per week, they do not limit theworker’s availability for other work. Infact, many such jobs were held concur-rently with full-time work, training orspells of unemployment, supplementinglow incomes or social security payments.

(iii) For women in particular, the distinctionbetween unemployment and withdrawalfrom the labour force is problematic. Cal-lender (1987) has shown that women’sjob search behaviour is less active thanmen’s and more reliant on personal net-works. Although women may state thatthey have withdrawn from the labourforce, they nevertheless seek workthrough their own networks and acceptwork when an opportunity arises. To dealwith this, a possible strategy might be totreat unemployment and labour forcewithdrawal as identical states for women.But that would lead to treating workerswho had unequivocally left the labourforce as unemployed, which in turnwould have the effect of extending the ap-parent duration of unemployment spells.Instead, months coded ‘Not in the labourforce’ were treated as equivalent to unem-ployment if the individual had any subse-quent active labour market participation(including training). This categorisationeffectively merges the ABS categories of‘Unemployed’ and ‘Not in the labourforce’ into one category for spells that fallbetween spells of employment or train-ing. Individuals with months coded ‘Notin the labour force’ who continued in that

state to the last observation in the data setwere considered to have left the labourforce permanently. These case-monthsare then excluded from the analysis be-cause individual histories are censored atthe completion of the last active labourmarket state.

(iv) Prevocational training and vocationaltraining are treated as separate states.

(v) The structure of the analysis demands thateach person’s record for each month becoded in only one state. Where individu-als participated in two activities concur-rently, the main activity was allocatedhierarchically taking full-time or 20 hoursor more part-time work first, then retrain-ing, short hours part-time work and fi-nally unemployment.

(vi) The observations relating to any individ-ual are censored after they make the tran-sition to a first post-retrenchment spell offull-time or part-time (20 hours or more)employment; or at the point of permanentwithdrawal from the labour force; or atthe last observation made for that individ-ual, to a maximum of 48 months after re-trenchment.

This conceptualisation focuses on the transi-tion to the first job after retrenchment. Webberand Campbell (1997), however, are critical ofstudies that are fixated on this first transition atthe exclusion of others. Webber and Campbellprefer to view labour market destinations as aflow or pattern of outcomes, where the differ-ent spells of employment, unemployment andtraining are defined as overall classes of desti-nation states, such as ‘stable work’ or ‘casual/intermittent work’. This is the strategy used,for example, by Harris and Redundancy andUnemployment Research Group (1987) in theirstudy of redundancy in Wales. Studies such asHarris et al. stress the need to be sensitive to theidea that retrenchment can be the catalyst todifferent, less secure forms of workforce at-tachment; that is, it can mark an individual’spoint of transition to ‘casualised’ employment.



While accepting the theoretical justification fortreating post-retrenchment outcomes as ca-reers, this analysis considers only the first jobtransition for two reasons. The first job aworker finds after retrenchment is importantbecause it sets the trajectory for subsequentjobs, and has a major impact on long-term ca-reer prospects (Gershuny & Marsh 1994). Thenature of the first post-retrenchment job mightsignal the beginning of a new and no less pres-tigious career, or could begin the decline to-ward a reduced and casualised workforceattachment. Whichever is the case, the natureof the first post-retrenchment job is crucial.The second issue is a practical one. Althoughretrenched TCF workers were observed for atleast four years after retrenchment, this was notsufficient time for clear patterns of career out-comes to emerge, due primarily to the fact thatmany retrenched workers spent quite long peri-ods (up to three years in some cases) in retrain-ing.

Separate analyses are presented for men andwomen throughout this paper. Men and womenheld different positions in the occupationalstructure of the TCF sector; they faced differ-ent pressures after retrenchment, and encoun-tered different re-employment opportunities.Local labour markets are segmented by differ-ences in labour supply, labour demand andstate intervention (Rubery & Wilkinson 1981;Peck 1989), which affect men’s and women’slabour market participation differently. On thesupply side, well-documented differences inthe division of labour in the household, thestigmatisation of social and cultural groups,and occupational socialisation that define‘men’s’ and ‘women’s’ work serve to reinforcegender segregation. The demand for labour isbecoming more specialised and labour marketsincreasingly differentiated with rising speciali-sation in the product market and decentralisa-tion of the industrial relations system, changesthat further undermine women’s position(Probert 1995; Charlesworth 1996; NSWWorking Women’s Centre 1997). The activi-ties of the state, through welfare provision andeligibility, taxation, and support for training,affect men and women differently (Cass 1995).The structure of the TCF Labour Adjustment

Package, through its non-means tested trainingallowance, created a strong incentive forwomen to maximise their training participa-tion. These considerations make it necessary totreat men and women as operating in differentlabour market contexts.

5. Discrete-Time Event History Analysis

Previous studies of retrenchment have at-tempted to analyse the impact of differentpersonal characteristics or retrenchment cir-cumstances on the labour market outcomes ofretrenched workers by using multivariate sta-tistical methods, such as multiple regressionanalysis (Deery et al. 1986) or logistic regres-sion approaches (Weller 1997). These methodsshare the limitation of simple cross-sectionalapproaches in that their findings depend on thelength of time workers are observed after re-trenchment. They also ignore the amount oftime it takes different individuals to find work,effectively treating a person who is re-employed after two weeks as having the sameoutcome as someone taking two months or twoyears to find a job. Perhaps more importantly,these methods are not able to take account ofcensored cases; that is, they fail to include thepeople who have not yet found work in the ob-servation period but could have found a job atsome time after the completion of data collec-tion. Furthermore, these analyses are unable toinclude time-varying predictor variables. Web-ber and Campbell (1997) draw attention to thefailure of retrenchment research to take the va-garies of labour demand into account, which,they argue, leads to an over-emphasis on thesupply side and too great a focus on the skill orpersonal deficits of retrenched workers. Theystress demand-side factors—the characteristicsof labour markets, cyclical economic condi-tions, and employer preferences—but sincethese vary with time, they cannot be incorpo-rated into conventional methods of analysingpost-retrenchment careers.

Adequate analysis of the labour market des-tinations of retrenched workers requires amethodological approach that can accommo-date both censored cases and time-varying pre-dictor variables. Event history analysis (Tuma,



Hannan & Groeneveld 1979; Allison 1984;Blossfeld, Hamerle & Mayer 1989; Tuma1994; Petersen 1995) provides a method foranalysing labour market history data that takescase censoring into account. A form of eventhistory analysis known as discrete-time eventhistory (Allison 1982) is the most appropriatemethod when data are collected in discrete timesegments, and it has the advantage of beingable to accommodate multiple time-varyingvariables. The discrete-time formulation doesnot require the assumption of proportional haz-ards (Cox & Oakes 1984) which is not appro-priate when the impact of different predictorsmight change over time, as in labour marketstudies. It has the additional advantage thatanalyses can be performed using standardstatistical software on a personal computer.Discrete-time event history has been used in avariety of contexts, and Rosenthal (1994) hasapplied the method to the analysis of labourmarket outcomes following retrenchment. Itsmain drawback is that data manipulation iscumbersome and data entry expensive.

The brief introduction to discrete-time eventhistory presented here draws on Allison (1982)and also Singer and Willett (1993), who pro-vide a readable introduction to the method.The outcome in discrete-time event history fo-cuses on when (rather than whether) an eventhas occurred. The ‘event’ of interest is not re-employment itself, but the probability that a re-trenched TCF worker will make a transition tore-employment in any time interval. The prob-ability of making a transition is the ‘hazard’ ofre-employment. An event occurring after time

t

1

but before time

t

2

is classified as having oc-curred within the interval (

t

1

,

t

2

). The discrete-time hazard,

h

i

, is defined as the conditionalprobability that an individual

i

will experiencethe event (re-employment) in time period

j

,given that the event was not experienced inprevious time periods. Thus:

h

j

= Pr(

T

=

j

|

T

≥

j

) (1)

where

T

is a discrete random variable. The setof discrete-time hazard probability parameters,

h

j

, over time intervals

j

, is known as thediscrete-time hazard function. Estimating the

conditional probabilities of the hazard is funda-mental to discrete-time event history analysis.Extending the basic framework, discrete-timeevent history enables various predictor vari-ables to be introduced. The vector of values ofpredictor variables

Z

P

(

Z

1

,

Z

2

,

Z

3

. . .

Z

P

) foreach individual

i

and time interval

j

is a vector(

z

1

ij

,

z

2

ij

,

z

3

ij

…

z

Pij

). The hazard equation (1)can then be extended to express the probabilityof individual

i

experiencing the event in timeperiod

j

, given that he or she had not previouslydone so, as:

h

ij

= Pr(

T

i

=

j

|

T

i

≥

j

,

Z

1

ij

=

z

1

ij

,

Z

2

ij

=

z

2

ij

. . .

Z

p

ij

=

z

pij

) (2)

Equation (2) suggests that the hazard for anyindividual depends on the values of a vector ofpredictor variables. The next step is to specifythe way in which the hazard depends on time.Because

h

ij

are probabilities, they are generallytransformed into the logistic form:

logit

h

ij

= log

e

h

ij

/(1 –

h

ij

) (3)

When this is done, the hazard is interpretedas the conditional log odds of an event occur-ring, which is a function of the predictor vari-ables and a baseline profile representing time,that is:

log

e

(

h

ij

/1 –

h

ij

) = (

α

D

1

ij

,

α

D

2

ij

. . .

α

D

pij

) + (

β

z

1

ij

,

β

z

2

ij

,

β

z

3

ij

. . .

β

z

pij

) (4)

Here the conditional log odds of an event (atransition into employment) occurring is a lin-ear function of a set of baseline constants(

α

D

1

ij

,

αD2ij . . . αDpij), one for each time inter-val, and a set of predictor variables (β z1ij, βz2ij,βz3ij . . . βzpij) that relate to personal or labourmarket characteristics. The constants (α1, α2. . . αp) represent the baseline logit hazard func-tion and act as multiple intercepts in logistic re-gression analysis, while the slope parameters βdescribe the effects of different predictor vari-ables. As will be shown below, the set of pre-dictor variables in discrete-time event historycan accept different values for each individual,and values can vary with time or place or othercharacteristics.



The maximum likelihood function is a prod-uct of censored and uncensored observations.The derivation of the maximum likelihoodfunction (see Singer & Willett 1993) yields thefunction:

where yij are event history indicators. Allison(1982) shows that the discrete-time hazardfunction is identical to the likelihood functionin a sequence of independent Bernoulli trials. Itis this characteristic that allows discrete-timehazard models to treat N dichotomous ob-served values of yij as the outcome variable in alogistic regression analysis.

6. Data Construction

The first step in estimating the hazard probabil-ities is to transform the original data, collectedfor 605 individuals over 48 monthly intervals,into a data set where each case is a ‘person-month’. For the TCF data, this yields a data setof over 29000 cases. After filtering to accordwith the conceptualisation of the data describedin Section 4 this results in a risk set of 5353person-months for men and 8794 person-months for women. Table 3 employs the LifeTable method to calculate the hazard rate forthe TCF Industry Study data. The 48 months ofobservation are grouped into 16 three-monthintervals (column 1).

These represent the number of months sinceretrenchment, rather than calendar time. Thesecond column shows the number of cases en-tering each three-month time interval, and thethird the number of censored cases5 that with-draw during that time interval. From these, thenumber of cases exposed to the risk of findinga job is calculated as the total cases entering theinterval less the cases that withdraw at that in-terval, where each censored case is assumed tocontribute 0.5 to the interval; that is, (a) – (b)/2= (c). This is known as the ‘risk set’ for the in-terval. The number of transitions into employ-ment in each interval is then given in column(d). Broadly speaking, the number of transi-tions into employment declines with time, al-

though there are additional transitions at about24 months for men and 30 months for womenas retrenched workers exit retraining and findemployment. The hazard rate, or the proportionof those at risk of finding a job who did actuallyfind work in that interval, is then given by (e) =(d)/(c).6

Assuming independent censoring, the mag-nitude of the hazard indicates the ‘risk’ of find-ing work in a particular month. The hazard ratedoes not necessarily decline with time, nordoes the hazard rate directly correspond to thenumber of transitions into employment. InTable 3, the interval 4–6 months for men in-cludes 19 transitions, with a hazard rate of0.0014, while at 28–30 months there are only 5transitions but the hazard rate remains 0.0014.The pattern of hazard probabilities over time isreferred to as the hazard function, given by thevalues in column (e), which indicate a declin-ing likelihood of finding work in the first fourintervals (the first year after retrenchment), butthereafter the pattern is by no means clear. Thehazard rate increases in the final two intervalsas the small number of cases remaining in therisk set produces large standard errors.

The outcome or dependent variable in thediscrete-time event history analysis is the tran-sition into work (d), which is coded 0 for eachperson-month when no transition into work (ina job of more than 20 hours per week) occursand coded 1 for each person-month in which atransition occurs. The descriptive informationpresented in Table 1 demonstrated the widevariations in outcomes between differentgroups of retrenched TCF workers. In the ex-panded file of person-months, variables de-scribing personal characteristics hold the samevalue for each individual in each person-month.7

Variables that change over time, such asmonths in training, take different values at dif-ferent times for each individual. Alternatively,predictors can vary with both time and individ-ual case. Since the sample included workerswho were retrenched at different calendardates, the value for the national unemploymentrate one month after retrenchment differs be-tween individuals, as well as by gender and bytime. A simplified version of the expanded data

h hij

yij

j 1=

j i

∏i 1=

n

∏= 1 hij–( ) 1 yij–( )



set is shown in Figure 2 to demonstrate the dataconstruction framework. For case A26, obser-vations cover six months from retrenchment tothe transition into re-employment, Job(1), atthe end of month six. During those six person-months the personal characteristic variables are

constant, while both unemployment rate andpost-retrenchment history vary with time. CaseA26 was unemployed in months 1 and 2, par-ticipated in vocational training in months 3 and4 then experienced a further two months ofunemployment before finding a first job. The

Table 3 Life Tables for Men and Women

Men

Interval

Number entering

(a)

Number withdrawn

(b)

Number exposed to risk

(c)

Number transitions to work

(d)

Hazard rate (e)

1–3 months 5453.0 683.0 5111.5 34.0 0.0022

4–6 months 4736.0 589.0 4441.5 19.0 0.0014

7–9 months 4128.0 537.0 3859.5 11.0 0.0010

10–12 months 3580.0 486.0 3337.0 13.0 0.0013

13–15 months 3081.0 442.0 2860.0 12.0 0.0014

16–18 months 2627.0 386.0 2434.0 9.0 0.0012

19–21 months 2232.0 359.0 2052.5 5.0 0.0008

22–24 months 1868.0 307.0 1714.5 10.0 0.0019

25–27 months 1551.0 265.0 1418.5 10.0 0.0024

28–30 months 1276.0 235.0 1158.5 5.0 0.0014

31–33 months 1036.0 213.0 929.5 5.0 0.0018

34–36 months 818.0 193.0 721.5 4.0 0.0019

37–39 months 621.0 180.0 531.0 3.0 0.0019

40–42 months 438.0 158.0 359.0 1.0 0.0009

43–45 months 279.0 143.0 207.5 3.0 0.0049

46–48 months 133.0 130.0 68.0 3.0 0.0150

Women

1–3 months 8794.0 945.0 8321.5 30.0 0.0012

4–6 months 7819.0 860.0 7389.0 21.0 0.0009

7–9 months 6938.0 810.0 6533.0 11.0 0.0006

10–12 months 6117.0 753.0 5740.5 11.0 0.0006

13–15 months 5353.0 706.0 5000.0 11.0 0.0007

16–18 months 4636.0 649.0 4311.5 17.0 0.0013

19–21 months 3970.0 587.0 3676.5 15.0 0.0014

22–24 months 3368.0 522.0 3107.0 6.0 0.0006

25–27 months 2840.0 484.0 2598.0 5.0 0.0006

28–30 months 2351.0 457.0 2122.5 3.0 0.0005

31–33 months 1891.0 406.0 1688.0 12.0 0.0024

34–36 months 1473.0 341.0 1302.5 12.0 0.0031

37–39 months 1120.0 301.0 969.5 5.0 0.0017

40–42 months 814.0 286.0 671.0 1.0 0.0005

43–45 months 527.0 268.0 393.0 7.0 0.0060

46–48 months 252.0 249.0 127.5 3.0 0.0079



values for months of unemployment andmonths of training are cumulative. IndividualA27 was unemployed for three months beforewithdrawing permanently from the labourforce, so the outcome variable Job(1) has a zerovalue for each observed month, with observa-tions censored at the end of the third month.Notice that the values for the national unem-ployment rate are different for A26 and A27because they were retrenched at differenttimes.

7. Modelling Labour Market Outcomes

The expanded data set can be analysed usingstandard logistic regression software (SPSSx inthis instance). The outcome is the transitioninto employment, Job(1) in Figure 2, that re-expresses the ‘Number of transitions to work’of Table 3 in ‘person-month’ format. Table 4lists and defines the predictor variables in-cluded in the analysis. They include variousmeasures of labour market conditions, the per-sonal characteristics and skills of retrenchedworkers, and their post-retrenchment experi-ences.

In carrying out the analysis, the predictorvariables were grouped, beginning with factorsbeyond the immediate control of the workersand building to the specific intervention of re-training. This is a strategy proposed by Ger-shuny and Marsh (1994) whose study of labourmarket careers sought to isolate the importanceof unemployment duration given personal andlabour market characteristics. In the discrete-

time event history logistic regression modelsconsidered below, each group of predictor vari-ables was added to the model in turn followingthe causal sequence. National labour forcecharacteristics are entered first; then the locallabour market; the personal characteristics ofworkers; key aspects of their household situa-tion; skills at retrenchment; post-retrenchmentexperiences; and finally retraining. The overallresults of this model-building exercise areshown in Table 5. The column labelled ‘–2LL’, the –2 log likelihood, is a measure of howwell each model fits the data, with values rep-resenting the probability of the observed re-sults under a specific hypothesis. A goodmodel is one with a high likelihood of obtain-ing the observed results, so better models areassociated with lower values of –2 times thelog likelihood. In Table 5, the –2 LL declines inmagnitude as each block of variables is addedin successive models, indicating that at eachstage the inclusion of additional variables im-proves the model’s fit. The column labelled‘Improvement’ gives the magnitude of that im-provement, providing a first estimate of therelative importance of different variables in ex-plaining the likelihood of re-employment. Thegoodness-of-fit statistic, which compares theobserved probabilities to those predicted by themodel, also measures how well the model fitsthe data.8

The first variables entered into the model arethe 16 time intervals, which act as a set of in-tercepts or ‘constant’ terms of logistic regres-sion. Next is the block of predictor variables

Figure 2 Data Transformation

Case MonthUnemployment

rate GenderMonths

unemployedMonths

vocational Job(1)

A26 1 7.7 F 1 0 0

A26 2 7.8 F 2 0 0

A26 3 8.1 F 2 1 0

A26 4 8.0 F 2 2 0

A26 5 8.0 F 3 2 0

A26 6 8.1 F 4 2 1

A27 1 8.3 M 1 0 0

A27 2 8.4 M 2 0 0

A27 3 8.4 M 3 0 0



Table 4 Predictor Variables for Logistic Regression

16 time interval intercepts 3-month time intervals from retrenchment

Employment conditions

National labour force participation rate Time varying, by gender. ABS Cat. no. 6203.0

National unemployment rate Time varying, by gender. ABS Cat. no. 6203.0

Local unemployment rate Time varying, based on DEET Small Area Labour Markets

Local labour market Location in 10 categories based on sample areas

Personal characteristics

Personal characteristic group 8 groups by gender, ethnicity and age (2 × 2 × 2)

Household characteristics

Dependent children Dummy (at 1993 interview)

Spouse employed Dummy (at 1993 interview)

Skills

Literacy Dummy. Self-report (at 1993 interview)

Qualifications Dummy. Formal accredited (at retrenchment)

Skilled occupation in TCF job Dummy. Skilled = ASCO 4 or higher

TCF job

TCF occupation specific to TCF Dummy. Based on ASCO classification

Years service in the TCF Dummy. 10+ years in TCF

Post-retrenchment experience

Months unemployed Cumulative, time varying

Months short part-time work Cumulative, time varying

Training participation

Months prevocational training Cumulative, time varying

Months vocational training Cumulative, time varying

representing general labour market conditions.These improve the model for both men andwomen. Men’s re-employment chances appearto be more sensitive to the economic climatethan women’s chances, although the magni-tude of improvement is not large for men or forwomen. It appears that characteristics uniqueto local labour markets exert a much greater in-fluence on outcomes than general trends in thelabour market. Although economic conditionsare important, personal and household charac-teristic have an even greater impact on thechances of re-employment, and the effect islarger for men compared to women. House-hold responsibilities are the most important de-terminants of re-employment for men in thisestimation, but apparently have little impact on

the time it takes women to find work. Skills atretrenchment are important, but have less im-pact than labour market conditions or personalcharacteristics. This result suggests that skilltraining cannot provide a panacea for unem-ployment so long as jobs are scarce andworker’s personal attributes (gender, age andethnicity) do not match those employers’ de-mand. The characteristics of the TCF job heldprior to retrenchment have little impact on thelikelihood of re-employment for men orwomen, providing compelling evidence thatTCF specific occupational skills are effec-tively rendered obsolete by retrenchment andthe diminishing opportunities in that sector.Post-retrenchment history—months since re-trenchment spent either not working or in



shorter hours part-time work—is important,but still not as important as local labour marketconditions and personal and household charac-teristics. The final variables represent themonths in retraining. For both men andwomen, the model is greatly improved by theaddition of retraining variables, with trainingparticipation the second most important factor(after household structure) in the timing of thetransition to employment for men, and the sec-ond most important, after local employmentconditions, for women. This is not at all sur-prising given the long duration of retraining formany retrenched TCF workers. The impact ofretraining varies between different groups ofworkers (as shown by the interaction term),which reflects differences in the type and dura-tion of training undertaken by different groupsof workers. The Labour Adjustment Packagewas more generous for workers with languageor literacy problems, and the local CES admin-istration had a major influence in determiningwhich retrenched workers could access retrain-ing (Webber, Weller & O’Neill 1996). BothCES policy toward retraining and the enthusi-asm of retrenched workers for retrainingcourses were related to perceptions of the locallabour market: retraining participation washigher where expectations of re-employmentwere lower.

For both men and women, the final modelincluding all predictor variables is the best ofthe models examined. However, other mea-sures of the fit of models give cause for con-cern. While each model successfully predictedthe cases (person-months) that failed to makethe transition into employment (over 95 percent successful), the success at predicting atransition into employment was poor. This ispartly due to the low probability of re-employment in any time interval. Examinationof residuals and diagnostic plots revealedmany cases with large residual values.9 Thislimitation needs to be considered when evalu-ating the parameter estimates described in thefollowing section.

The models described here do not includejob search strategies, earnings or potentialearnings of retrenched workers, factors thatwould have been fundamental to a study in-formed by a search theoretic approach. Infor-mation about job search methods wascollected, but responses simply listed the usualmethods: visiting CES, looking at newspaperadvertisements, door-to-door canvassing, theuse of personal networks, and so on. These donot appear to have been the relevant determi-nant of re-employment, since most post-retrenchment jobs came through personal con-tacts regardless of the nominated job search

Table 5 Logistic Regression Models: The Transition to First Post-Retrenchment Job

Groups of variables added

Models for men (n = 5393)a

Models for women (n = 8704)a

Goodness of fit –2 LL Improvement Goodness of fit –2 LL Improvement

Base model (no variables) 7477.67 12045.51

16 time interval intercepts 5236.02 875.60 6602.07 6460.01 697.73 11347.78

Employment conditions 5055.01 856.91 18.69 6385.80 686.61 11.11

Local labour market 4862.25 819.84 37.08 6236.02 645.50 41.12

Personal characteristics 4251.31 788.82 31.02 4835.60 619.70 25.80

Household characteristics 3784.77 726.42 62.40 4662.27 611.52 8.18

Skills 3318.40 706.86 19.56 5807.63 588.64 22.88

TCF job 3412.57 701.56 5.30 4679.75 584.03 4.60

Post retrenchment 2826.97 671.68 29.88 3405.62 557.47 26.56

Training participation 2102.12 614.74 56.94 2867.20 517.85 39.62

Interactionsb 1942.35 598.55 16.19 2118.65 501.59 16.26

Notes: (a) Cases with standardised residuals > 5 excluded (men 59, women 105). (b) Interactions are training with personal characteristics.



methods.10 Recent research in the United King-dom (Rubery & Wilkinson 1994) suggests em-ployer strategies in the labour market, ratherthan workers’ individual approaches to job

search, are the crucial determinants of labourmarket outcomes. These, unfortunately, cannotbe incorporated into a structure based on indi-vidual employment histories.

Table 6 Logistic Regression Parameters: The Transition to First Post-Retrenchment Job

Men WomenVariables β Standard error β Standard error

Time intercepts (not shown)

National labour force participation rate (M or F) –0.2593 0.26 0.7313** 0.24

National unemployment rate (M or F) –0.3566** 0.13 –0.1386 0.18

Local unemployment rate (all) –0.2478* 0.10 0.1926* 0.09

Local labour market

Adelaide –0.8523 0.43 2.2097** 0.38

Bankstown 0.6683 0.48 2.1901** 0.49

Country Vic 0.4382 0.36 2.0464** 0.56

Geelong –0.9337 0.48 –2.1097** 0.67

Hunter Valley –1.3419** 0.48 –0.0007 0.73

Mount Gambier 1.3760* 0.62 –1.0660 0.88

Newcastle –2.0851** 0.68 –0.9821 0.85

Petersham 3.0434** 0.80 0.3775 0.89

Preston –0.4453 0.65 –1.5967* 0.65

Springvale 0.1325 –1.0684 0.78

Personal characteristics

Younger ESB 1.6950** 0.30 1.1934** 0.38

Older ESB 0.6679* 0.32 –0.2062 0.47

Younger NESB –1.1522** 0.41 0.3656 0.40

Older NESB –1.2108** 0.14 –1.3528 0.54

Dependents (children) 1.1058** 0.19 –0.5855** 0.18

Spouse job (job) –0.2031 0.19 0.1003 0.20

Literacy (lower literacy) 0.3889* 0.17 –0.3711* 0.18

Qualified (quals) 0.5064** 0.17 0.4585 0.23

TCF skill (skilled) 0.4144** 0.16 0.9577** 0.21

TCF occup. (TCF) –0.4292** 0.15 0.1882 0.18

Years TCF (> 10 years) 0.2142 0.16 –0.3441 0.18

Months unemployed (UE) –0.7112** 0.09 –0.5445** 0.10

Months work < 20 hrs (PPT) –1.1026 1.11 –0.6554** 0.19

MthsUE*time 0.0035 0.00 0.0104** 0.00

MthsPPT*time 0.0053 0.03 0.0100* 0.00

Prevocational training –1.0419 0.98 –0.3707** 0.07

Vocational training –0.4671** 0.07 –0.1459** 0.05

Prevoc*personal ns *

Voc*personal ns ns

Note: * significant at the 0.05 level; ** significant at the 0.01 level; ns not significant.



8. Analysis of Re-employment Prospects

This section discusses the parameters of thefinal model in Table 5—the model that in-cludes all predictor variables. The informationpresented in Table 6 summarises the full tableof parameters (Appendix Tables A2 and A3,which show that as each set of variables isadded the parameter values remain relativelystable). The column labelled ‘β’ reports thelogistic regression parameters, which are inter-preted as the change in the log odds of the eventoccurring (making a transition into Job 1)given a one unit change in the predictor vari-able. If women’s national labour force partici-pation rate increases by one point, for example,the positive value for the logistic regression pa-rameter (β) indicates that the log odds of a tran-sition into employment occurring increases by0.73. For dichotomous variables, the contrastgives the deviation from the average effect. Formen, having dependent children increases thelog odds of making the transition by 1.11,while for women the opposite is the case: hav-ing children decreases the log odds of makinga transition into employment by 0.59. Table 6also shows the standard error of logistic regres-sion parameters (in the column labelled ‘Stan-dard error’) indicating the significance of theWald statistic.11

The log odds of making a transition to em-ployment can be re-expressed as transitionprobabilities by reference to equation (3),which gave:

logit hij = loge hij /(1 – hij) (3)

Remembering that hij represents the hazardprobability, then:

Pr(job) = hj =

where z is the set of intercepts (α) and parame-ters (β) given by equation (4). In the exampleabove, the log odds of a man with dependentchildren finding work is 1.11, suggesting thathaving dependent children increases the condi-tional probability of making a transition intoemployment by 0.57. For women, having de-

pendent children decreases the log odds offinding work by 0.59, and decreases the proba-bility of finding work by 0.36. The impact ofeach independent variable is discussed belowin order of entry into the final model.

8.1 Labour Demand Conditions

The national labour force participation rate var-ies with both time and gender. For women themagnitude of this parameter is large and itspositive sign suggests that as the labour marketparticipation rate increases, so does the likeli-hood (log odds) of making a transition intowork. Since the labour force participation rateincreases as the economy improves, this isstrong evidence that the likelihood of a re-trenched woman being re-employed is in-fluenced by general economic conditions.Women are less likely than men to be recogn-ised as unemployed in official statistics, and asa result their labour force participation rates area better predictor of outcomes than their unem-ployment rates (Gregory 1991). For men, onthe other hand, unemployment rates rather thanlabour force participation rates are statisticallysignificant. As expected, an increase in the na-tional unemployment rate is associated with adecreasing likelihood of re-employment.

The impact of the local unemployment rate(which due to data limitations varies by timeand place but not gender) is significant butgives opposite effects for men and women. Formen, the result is similar to that of the nationalrate, with higher local unemployment rates as-sociated with lower chances of finding work.For women, the model suggests that the oppo-site is the case: women are more likely to findwork when local unemployment rates arehigher. This outcome is counterintuitive, prob-ably resulting from the fact that figures forlocal labour markets were not obtainable bygender. While local labour market data werematched to retrenched workers by residentialpostcode, it is also possible that the figures donot accurately represent the scope of the labourmarkets in which retrenched workers conducttheir job search. If the geographical scope ofwomen’s job search activity is limited to asmall neighbourhood area (Hanson & Pratt

1

1 e z–+----------------



1991), then local labour market data, as de-fined, may be of limited relevance.12

8.2 Local Labour Market Conditions

Local labour markets evolve to both reflect andshape the characteristics of local employmentopportunities. As people with particular skillsare attracted by existing employment opportu-nities in a particular place, the composition ofthe local labour force develops to reflect thelocal employment mix. At the same time, thecomposition of the local labour force (or poten-tial labour force) attracts firms seeking particu-lar skills or qualities in their workforce. Localemployment services and training providersalso develop distinctive local identities as theytoo respond to and interact with local supplyand demand characteristics. Over time, locallabour markets develop unique practices andtraditions, which are often shaped by industrialrelations issues. For some of the locations in-cluded in this study, such as Camperdown inVictoria, the closure of the local clothing plantalso signalled the exit of the TCF industriesfrom the locality. In other areas, such as ininner Western Sydney, the TCF sector re-mained a major employer despite numerousclosures. Variations in the number of opportu-nities for employment in the TCF and relatedindustries in each location, and differences inthe number and timing of TCF retrenchmentsin the same areas, are important to the re-employment prospects of retrenched workers.Because of their similar personal charac-teristics, skills and employment histories,retrenched TCF workers are essentially com-peting with each other for a small number ofaccessible jobs in a local labour market.

For the ten local labour markets, the logisticregression parameters in Table 6 compare theeffect for each location to the overall locationeffect. As expected, the impact of local labourmarkets is significant, with large variations be-tween places. For men, the log odds of makinga transition into employment are poorer in theHunter Valley, Newcastle and to a lesser extentin Geelong and Adelaide. Consistent with ex-pectations, these are areas where both generalemployment opportunities and TCF sector op-

portunities were bleak. Men fared significantlybetter in Mount Gambier, where general em-ployment opportunities were relatively betterand where the timber industry offered numer-ous lower skill vacancies. The relatively buoy-ant economy in Mount Gambier made it easierfor men with trade or technical skills to findwork or to set themselves up in small businessventures. Men living in Petersham in innerWestern Sydney, where the TCF sector sur-vives and where the inner urban location pro-vides retrenched workers with a wide field ofjob vacancies, also enjoyed relatively betteroutcomes. Women’s prospects were better inAdelaide, Bankstown and Country Victoria(Camperdown) and poorer in Geelong and Pre-ston areas. In Bankstown work in the TCFsector was still available and Sydney also of-fered more prospects for TCF-related employ-ment in importing or retail sectors. Preston andGeelong areas both continued to have activeTCF firms, but in both areas the policies of sur-viving TCF employers focused on streamliningoperations and reducing employment.

Very few retrenched TCF workers relocatedin search of work despite the relocation assis-tance provided under the TCF Labour Adjust-ment Package. Those who did relocate tendedto be younger people with no dependents whowere at the higher end of the skill profile of theretrenched worker group. Most retrenchedworkers had no desire to sever their hometownlinks, but those who did consider moving foundthe costs of moving too great, given that amove to an area with better employment pros-pects almost always implies a move to moreexpensive housing. As Hoover (1971) ob-serves, relocation assistance programs work toencourage the most able workers to leave de-pressed areas, and so increase regional disad-vantage.

8.3 Personal and Household Characteristics

Personal characteristics are highly significantpredictors of the likelihood of making a transi-tion into employment after retrenchment. Theanalysis considered the prospects of eightgroups of retrenched workers, classified bygender, age and ethnicity. For both men and



women, younger workers from English speak-ing backgrounds were most likely to havefound work. The strong positive parameter in-dicates that younger ESB workers have muchbetter outcomes than other groups, even afterskills and post-retrenchment experiences aretaken into account. Age and ethnicity seem tobe of greater importance to the re-employmentchances of men than women. These outcomesare directly related to employer preferences,and to employers’ often stereotypical views ofthe characteristics of the appropriate worker fora particular vacancy (Weller, Cussen & Web-ber 1999). Older NESB women often foundwork in health, community services and hospi-tality services sectors where employers valuetheir language and nurturing skills. Yet overall,retrenched TCF workers from non-Englishspeaking backgrounds have poorer prospectsthan those born in English speaking countries.Employers use English language proficiency tofilter job applicants, recruiting only the mostliterate of the pool of applicants. Thus, to finda job a worker does not just have to be literate,but has to be more literate than other applicantscompeting for the same vacancy. Non-Englishspeaking background workers are also disad-vantaged by their reliance on word-of-mouthrecruitment, their limited familiarity with em-ployment beyond the TCF sector, and theirminimal formal job search skills.

Household circumstances clearly have amajor impact on post-retrenchment labourmarket participation. Men with dependent chil-dren were more likely to have found a job, butfor women the opposite is true: having depen-dent children decreases the likelihood of find-ing work. A man whose wife did not work wasmore likely to make the transition into work,but for women, those with a husband who isemployed were more likely to work. (Or put inreverse, a woman whose husband did not workwas also likely not to work, although the effectis not statistically significant.13) Neither resultis surprising. For both men and women, house-hold structures and domestic responsibilitiesshape labour force participation (Morris 1991;Walby 1991). However, since everybody inthis study worked full-time prior to retrench-ment, it is difficult to attribute differences in

the likelihood of finding work to lifestyle pref-erences or constraints on participation such aslack of child-care.

8.4 Skill Characteristics

The idea of skill encompasses both technicalcompetencies and socially constructed percep-tions of what ‘skilled’ work is. The concept ofskill is especially problematic in the TCF sec-tor where gender-specific definitions surviveas a remnant from the era of craft-based pro-duction by male tailors and female dressmak-ers. Women’s skill has been systematicallyundervalued in this sector: sewing skills learntin the domestic sphere and transferred to therealm of paid employment have never been ad-equately recognised (O’Donnell 1984). Fivedifferent measures of skill have been built intothe model of outcomes. They are self-reportedliteracy; formal qualifications; former TCF oc-cupation level (skilled or unskilled); formerTCF occupation type (TCF specific or not);and years of experience in the TCF sector.

Women who reported higher literacy stan-dards were much more likely to have made thetransition into employment. Surprisingly, menwho by their own self-report had higher liter-acy skill appear less likely to have found work.However, only a little more than 40 per cent ofmen who held a formal qualification assessedthemselves as having high literacy. Men hold-ing a formal qualification were more likely tohave re-entered work, with the log odds ofmaking a transition into work increasing by0.51 for qualified men. Many men from skilledTCF occupations held a relatively portablemetals trade credential which enabled theirqualifications and skill to be applied in otherindustry sectors. For women, formal qualifica-tions are less important to re-employment, butthose who held skilled jobs in the TCF sectorare significantly more likely to have foundwork. Men often held qualifications and askilled TCF job, while women in skilled TCFemployment often lacked formal accreditation.For women, work experience has a stronger in-fluence than qualifications; while for men,both qualifications and work history are im-portant. Men whose job in the TCF sector was



not in a TCF specific occupation (such as astoreman or accountant) were more likely tofind work, but the reverse was true for women:those who had worked in jobs not specific tothe TCF industries were less likely to havefound work. This is explained by skill differ-ences: men in non-TCF specific jobs tended tobe specialists with valued skills, whereaswomen in non-TCF specific jobs generally didlow skill work (for example, packing) that re-quired less skill than TCF specific machiningjobs.

A conventional finding in redundancy re-search is that workers with many years of ser-vice in one industry or firm have poor re-employment outcomes. Yet in this model theeffect of length of service is opposite for menand women. Among women, those whoworked in the TCF sector for a longer time areless likely to have found work, consistent withexpectations. But this is not true for men, whoappear to do slightly better with longer TCFservice (although the result is not statisticallysignificant). There is no obvious explanationfor this unexpected result, although many yearsof stable TCF employment might impress pro-spective employers, especially for men withspecific skills to offer. In the context of the newmanagement styles of the 1990s that focusedon marketing, demand-driven production, andenterprise-based industrial relations, therewere TCF sector employers who actively dis-criminated against the recruitment of formerTCF workers whose attitudes, they believed,were embedded in ‘old-style’ work practices.In the TCF sector after 1993, then, industrialexperience within the TCF sector often becamean impediment to re-employment, except forthose with a specific skill in demand. The TCFsector’s public reputation as inefficient and un-competitive could only diminish the chances ofretrenched workers finding work in other sec-tors.

8.5 Post-Retrenchment Experiences

Post-retrenchment experiences are importantto the likelihood of making the transition toemployment. As the number of months in un-employment increases, the likelihood of mak-

ing a transition into employment decreases.Every additional month of unemployment de-creases the log odds of making a transition intowork by 0.71 for men and 0.54 for women.14 Itshould be kept in mind though, that the uniqueopportunities for retraining provided for thisgroup of workers resulted in fewer aggregatemonths in a state of ‘unemployment’ thanmight have been expected if the labour adjust-ment assistance had not been available. The pa-rameter for months of unemployment displaysa relatively high partial correlation (R = –0.29for men and –0.24 for women): the number ofmonths of unemployment is related to othervariables in the model. Engaging in part-timework also decreases the likelihood of transitioninto employment, but the effect is significantonly for women. It appears the part-time (low-paid) jobs are not a stepping stone to more sta-ble employment, but rather a trap that limits ca-reer prospects (Burgess & Campbell 1998).This finding validates our definition of theseshort hours part-time jobs as supplementary toother states rather than as a successful post-retrenchment employment outcome.15

The intervention, retraining under the TCFLabour Adjustment Package, shows large andsignificant effects for both men and women.Each additional month in prevocational train-ing (including English language and literacytraining) is associated with a decrease in thelikelihood of finding a job, with the log odds offinding work decreasing by 1.04 for men and0.37 for women. Vocational training similarlygives poor outcomes, with the log odds param-eters of –0.47 for men and –0.15 for women.For both men and women, prevocational train-ing comprised English language classes orbasic literacy and numeracy tuition. Women’sparticipation in vocational courses more oftensignalled a complete career change (to train foroffice or community service occupations),while men in vocational training more oftenbuilt on prior knowledge in shorter skill spe-cific courses (for example, Certificate in Com-puter Control). For men vocational training hasa relatively high partial correlation with othervariables (R = –0.24) while for women it is pre-vocational training that correlates highly withother characteristics (R = –0.23).



Since participation in retraining was selec-tive, training participation cannot be said tocause the poorer outcomes: rather, those withthe poorest employment prospects enrolled inretraining courses for the longest times. Also,the length of time spent in training depended onthe type of training undertaken. For vocationalcourses (but not prevocational training), courseduration is a function of depth of skill. Workerswith a recognised qualification could enrol inshort and specific skill update courses thatquickly enhanced their employment prospects.Enrolling in a longer vocational course impliedundertaking retraining for a new occupation—vocationally starting over. Retrenched workerswith lower literacy levels were unable to accesshigher level vocational training at all, but spendlong periods in prevocational courses buildingtheir basic skills toward that end. The successof both prevocational and vocational study inemployment terms is then inversely related tothe duration of retraining. Pre-existing charac-teristics and skills limited the types of trainingavailable to different individuals and shapedthe type and duration of training undertaken.Appendix Tables A2 and A3 show the way inwhich the parameters of all variables are al-tered by the addition of training to the model.These figures seem to indicate that trainingserved to accentuate the importance of otherfactors, further increasing positive parametersfor some and further decreasing negative re-sults for others. This we interpret as showingthat training enhanced the prospects of thoseworkers with relatively good re-employmentchances, but merely added to the disadvantageof those worse off in the labour market byweakening their attachment to the workforce.Nevertheless, these models do not take the dif-ferent propensities to enter training into ac-count.16

9. Conclusion

This paper began by demonstrating that work-ers retrenched from the TCF industries facedconsiderable difficulty re-entering the labourforce. It showed that although the proportion ofretrenched workers who found work rises overtime, even workers who did find a job experi-

ence a considerable time lag between retrench-ment and re-employment. The outcome wasrarely a smooth transition to a new job in an-other sector. A large proportion of the workerswho participated in this study has borne a sus-tained and long-term personal cost arising fromtrade liberalisation and the accompanyingchanges in the TCF sector.

Using a discrete-time event history approachto the multivariate analysis of outcomes, it wasshown that the characteristics of local labourmarkets and the personal profiles of retrenchedworkers are important determinants of labourmarket prospects. Clearly, the various factorsthat influence labour market outcomes arecomplex and interrelated. However, this analy-sis shows that older workers, workers with lowskills at retrenchment and those in depressedregions have very poor re-employment pros-pects. The likelihood of re-employment for re-trenched workers is closely related to employerrecruitment preferences, and many retrenchedTCF workers did not find work because theylacked the personal attributes that employersdesire. Skills at retrenchment are less signifi-cant predictors of re-employment prospectsthan the characteristics of the workers and theirlocal labour markets. But skills are an impor-tant determinant of outcomes because they in-fluence the range of options for retrainingavailable to each retrenched worker. While thisanalysis shows that employment prospects de-cline with additional months in training, furtheranalysis taking the selectivity of training par-ticipation into account is needed before anyfirm conclusion can be drawn on the utility oftraining. A tentative conclusion, however, isthat retrenched workers who gained most fromretraining were those with higher skill levels atretrenchment, who could build their skill reper-toire to adapt to new contexts. Given the waythat recruitment-filtering mechanisms operate,it is difficult to see how retraining alone couldhave altered the re-employment chances ofthose who are not at all competitive in de-pressed local labour markets.

Policies promoting the ‘structural adjust-ment’ of the economy, which promoted theplanned decline of the TCF sector, assumedthat the labour adjustment problem could be



managed through retraining and job matchingassistance to retrenched workers. The TCF La-bour Adjustment Package provided the mostcomprehensive retraining effort that has beenseen in this country. But the outcomes for TCFretrenched workers show that the adjustmentproblem is intractable and cannot be resolvedthrough the types of adjustment assistance pro-vided in the early 1990s. Furthermore, wewould argue that the outcome of the latest(1997) Industry Commission inquiry into theTCF industry demonstrates that after the expe-riences of training programs in the early 1990s,the wider community now believes that theproblem cannot be resolved through training orother micro-level ‘adjustment’ policies alone.A politically acceptable solution to labour ad-justment needs to acknowledge that manyolder, lower skilled workers will be forced towithdraw from the labour market, probably forthe remainder of their working lives. Adjust-ment policies then need to open the way forconstructive retraining programs to enhancethe skills of workers, focusing on quality-accredited training that has the potential to re-juvenate individual careers. But policies mustalso provide adequate compensation for thosewith little realistic prospect of re-employment.

First version received April 1998;final version accepted January 1999 (Eds).

Appendix 1

Table A1 Characteristics of TCF Industry Study Sample

CES- registered population

TCF Industry

Study sample

Men

Younger ESB 169 86

Younger NESB 115 58

Older ESB 85 51

Older NESB 81 62

Women

Younger ESB 406 99

Younger NESB 390 106

Older ESB 130 47

Older NESB 264 96

Total 1640 605

Locations

Adelaide (metro, SA) 144 77

Bankstown (metro, NSW) 167 84

Country Victoria (Vic.) 104 39

Geelong (Vic.) 193 43

Hunter Valley (NSW) 212 41

Mount Gambier (SA) 74 35

Newcastle (NSW) 106 47

Petersham (metro, NSW) 167 59

Preston (metro, Vic.) 281 88

Springvale (metro, Vic.) 199 92

Total 1640 605

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Table A2 Men, Logistic Regression Models: First Job after Retrenchment

Variables entered

ParametersTime intercepts

(β)Employment

conditions (β)Local labour market (β)

Personal attributes (β) Household (β) Skill (β) TCF job (β)

Post retrenchment

(β) Training (β)

Interaction training bypersonal (β)

Time intercepts (not shown)National participation rate (men) –0.4564* –0.3047 –0.2910 –0.1592 –0.1736 –0.1252 –0.1444 –0.2402 –0.2593National unemployment rate (men) –0.1448 –0.0875 –0.1331 –0.2264* –0.2578* –0.2747* –0.2119 –0.3504** –0.3566**Local unemployment rate –0.1456** –0.2522** –0.2319** –0.2876** –0.2470** –0.2448** –0.2824** –0.2350* –0.2478*Adelaide 0.0255 –0.0659 –0.0769 –0.3062 –0.4667 –0.3536 –0.9065* –0.8523Bankstown –0.4636 –0.0884 –0.2330 –0.4536 –0.2863 0.1208 0.6478 0.6683Country Victoria 0.8851** 0.3519 0.5778 0.7385* 0.7459* 0.5373 0.5566 0.4382Geelong –0.1364 –0.2497 –0.9311* –0.8922* –0.9088* –0.8341 –0.8954 –0.9337Hunter 0.0174 –0.3416 –0.5986 –0.4935 –0.4509 –0.7291 –1.1480* –1.3419**Mount Gambier 0.2116 0.4090 0.6732 0.7648 0.8706 0.5181 1.0359 1.3760*Newcastle –1.1057* –1.4330** –1.6370** –1.7924** –1.8190** –2.2802** –2.1866** –2.0851**Petersham 1.9200** 2.3277** 3.3013** 3.0572** 3.0618** 3.3275** 3.0063** 3.0434**Preston –1.0646 –0.8604 –0.8040 –0.6602 –0.7658 –0.4277 –0.4454 –0.4453Springvale –0.2893 –0.0496 –0.2718 0.0376 0.0191 0.1209 0.3353 0.1325Younger ESB 1.0086** 1.2522** 1.3871** 1.4200** 1.4580** 1.7591** 1.6950**Younger NESB 0.2924 0.6536 0.5027 0.3884 0.5344 0.4911 0.6679*Older ESB –0.0670 –0.5209* –0.5191 –0.4192 –0.7733* –0.9324** –1.1522**Older NESB –1.2340** –1.3849** –1.3707** –1.3892** –1.2191** –1.3178** –1.2108**Children 1.2243** 1.0661** 1.1140** 1.1323** 1.1792** 1.1058**Spouse working –0.4059** –0.2832 –0.3397* –0.3612* –0.2819 –0.2031Lower literacy 0.3393* 0.3945** 0.3537* 0.3890 0.3889*Qualifications –0.3108* –0.2893 0.4273** 0.4948** 0.5064**TCF skilled occupation 0.4382** 0.4642** 0.4321** 0.4860** 0.4144**TCF specific occupation –0.3146* –0.3531* –0.4327** –0.4292**TCF 10+ years 0.0613 0.0109 0.1239 0.2142Duration unemployment –0.1814** –0.6833** –0.7112**Duration work (< 20 hrs) –0.5128 –0.9431 –1.1026Unemployment by time 0.0028 0.0027 0.0035Work (< 20 hrs) by time 0.0078 –0.0012 0.0053Vocational training –0.5108** –1.0419Prevocational training –0.5043** –0.4671**Prevocational by personal nsVocational by personal nsGoodness of fit 5236.02 5055.01 4862.25 4251.31 3784.77 3318.40 3412.57 2826.97 2102.12 1942.35–2 log likelihood 875.60 856.91 819.84 788.82 726.42 706.86 701.56 671.68 614.74 598.55Improvement 6602.07 18.69 37.08 31.02 62.40 19.56 5.30 29.88 56.94 16.19Degrees of freedom 16 3 9 3 2 3 2 4 2 6

Notes: Cases = 5453, exclude 59 cases with standardised residuals greater than 5; 5394 cases available for analysis.* significant at the 0.05 level; ** significant at the 0.01 level; ns not significant.

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Table A3 Women, Logistic Regression Models: First Job after Retrenchment

Variables entered

ParametersTime intercepts

(β)Employment

conditions (β)Local labour market (β)

Personal attributes (β) Household (β) Skill (β) TCF job (β)

Post retrenchment

(β) Training (β)

Interaction training bypersonal (β)

Time intercepts (not shown)National participation rate (women) 0.6102** 0.5256* 0.5379* 0.5077* 0.4181 0.4363* 0.4920* 0.6522** 0.7313**National unemployment rate (women) 0.1302 0.1294 0.1222 0.1472 0.1701 0.1714 0.0743 –0.0260 –0.1386Local unemployment rate –0.0156 0.1217 0.1431* 0.1439 0.1295 0.1353 0.1222 0.1608 0.1926*Adelaide 1.2754** 1.2622** 1.3799** 1.5022** 1.4446** 1.5921** 2.0276** 2.2097**Bankstown 0.7310* 1.2237** 1.2352* 1.5348** 1.5149** 1.5229** 1.9323** 2.1901**Country Victoria 1.3892** 0.9663* 0.8601 0.8484 0.8446 1.5197** 1.8319** 2.0464**Geelong –0.2425 –0.4824 –0.4978 –1.1674* –1.2895* –1.5621* –1.9782** –2.1097**Hunter –0.3569 –0.7571 –0.7808 –1.3027 –0.9431 –1.3496 –0.7109 –0.0007Mount Gambier 0.8664 0.4467 0.5693 0.9737 1.0316 0.6949 –0.0437 –1.0660Newcastle –0.8730 –0.7416 –0.6713 –0.5627 –0.6351 –0.7256 –0.6006 –0.9821Petersham –0.6498 –0.0345 0.0002 0.4433 0.5940 0.5073 0.0659 0.3775Preston –1.2150* –1.2656* –1.3891* –1.4472* –1.6096** –1.4623* –1.4295* –1.5967*Springvale –0.9247 –0.6177 –0.7058 –0.8225 –0.9524 –0.7373 –1.0949 –1.0684Younger ESB 0.9564** 0.8862** 0.9716** 0.8060** 0.9312** 1.0192** 1.1934**Younger NESB 0.1164 –0.0260 –0.3205 –0.1398 –0.1946 –0.3975 –0.2062Older ESB 0.2124 0.4383 0.5149 0.4108 0.4066 0.4071 0.3656Older NESB –1.2852** –1.2985** –1.1660** –1.0770** –1.1432 –1.0288** –1.3528Children –0.4035** –0.3819* –0.3799* –0.4192** –0.5120** –0.5855**Spouse working 0.1149 0.1415 0.0513 0.0546 –0.0100 0.1003Lower literacy –0.3716* –0.3780* –0.3332* –0.2527 –0.3711*Qualifications 0.2760 0.3175 0.3687 0.4002 0.4585TCF skilled occupation 0.7563** 0.6932** 0.7883** 0.9459** 0.9577**TCF specific occupation –0.0045 –0.0367 0.1286 0.1882TCF 10+ years –0.3468 –0.2904 –0.2782 –0.3441Duration unemployment –0.2892** –0.5303** –0.5445**Duration work (< 20 hrs) –0.3303 –0.5920** –0.6554**Unemployment by time 0.0087** 0.0104** 0.0104**Work (< 20 hrs) by time 0.0071 0.0090 0.0100*Vocational training –0.1365** –0.1459**Prevocational training –0.3518** –0.3707**Prevocational by personal *Vocational by personal nsGoodness of fit 6460.01 6385.80 6236.02 4835.60 4662.27 5807.63 4679.75 3405.62 2867.20 2118.65–2 log likelihood 697.73 686.61 645.50 619.70 611.52 588.64 584.03 557.47 517.85 501.59Improvement 11347.78 11.11 41.12 25.80 8.18 22.88 4.608 26.56 39.62 16.26Degrees of freedom 16 3 9 3 2 3 2 4 2 6

Note: Cases = 8794, exclude 105 cases with standardised residuals greater than 5; 8689 cases remain for analysis.* significant at the 0.05 level; ** significant at the 0.01 level; ns not significant.



Endnotes

1. See Industry Commission (1997), and in par-ticular the dissenting comments by AssociateCommissioner Mr Phillip Brass concerning la-bour adjustment.

2. For large closures CES registrations werecompared to employee lists held by the Tex-tiles Clothing and Footwear Union of Australiato verify registration rates.

3. The eleven locations were selected on theOffice of Labour Market Adjustment’s advice:each rural location was the site of a large clo-sure and urban locations were in areas withhigh densities of TCF employment. Details ofworkers eligible for labour adjustment assis-tance were retained in the CES database afterthey became ‘inactive’, so the CES-registeredpopulation represents a stock of ‘notified’ re-trenched workers.

4. This classification is used throughout thispaper. It adapts and adds an age dimension tothe four-way segmentation of the Australian la-bour force proposed by Collins (1984). Theclassification separates first by gender and thenby ethnic background, where the two catego-ries English speaking background (ESB) andnon-English speaking background (NESB) arebased on country of birth. The sample is thendivided again by age, into two groups split atage 45 on retrenchment, following Herron(1985) and exploratory data analysis.

5. In Section 4, a case was defined as censoredat employment in the first job after retrench-ment, permanent labour force withdrawal or atthe end of the observations for that individual,up to 48 months.

6. The actuarial Life Tables formula for estimat-ing the hazard gives a slightly different result tothe presentation by Allison (1982), where thehazard rate is simply given by the ratio of thenumber of transitions to the number at risk inany time period. The Life Table method of cal-culation is preferable given the relatively longtime intervals used in this analysis.

7. Worker’s ages are not entered as time vary-ing. Whether an individual was 48 or 51 at re-trenchment seemed of little importance to theiroutcomes over the 48-month follow-up timespan.

8. The goodness-of-fit statistic is defined as

9. The final models exclude cases with standar-dised residuals of ≥ 5. A stricter rule wouldhave excluded most transitions from the analy-sis.

10. Workers were also asked, for each spell ofunemployment, if they had been offered anywork that was not accepted, which revealedthat these workers certainly did not have multi-ple employment options. High training partici-pation suggests that the Labour Adjustmenttraining allowance of $171.00 per week (abouthalf a clothing machinist’s earnings) wasgreater than the hypothetical reservation wage.

11. The Wald statistic tests if the parameter issignificantly different from zero and is thesquare of the ratio of the coefficient to its stan-dard error. Because the TCF study data arebased on a stratified sample, the significance ofparameters should be interpreted conserva-tively.

12. A ‘local labour market’ is a large area whenmobility is restricted by lack of transport orchild-care responsibilities. Child-care andschooling arrangements impact on job searchscope because workers with child-care respon-sibilities must finish work and have time toreach the child-care centre or school before itsclosing time.

13. See Morris (1995) for a discussion of theimplications of household unemployment.

14. For women there is also an interaction ef-fect between the number of months unem-ployed and time, but its magnitude is verysmall.

Z2 residuali2

Pi 1 Pi–( )------------------------=



15. Alternately, it may be that women and oldermen who find a part-time job are satisfied withreduced hours of work and the modest lifestylethat implies.

16. See Webber et al. (1996) for a discussionof regional differences in program administra-tion and the selectivity of training participa-tion.

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re-employment after retrenchment: evidence from the tcf industry study

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