financial stress and the long-term outcomes of job loss
TRANSCRIPT
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Citation: Weller, S.A. (2012) Financial stress and the long‐term
outcomes of job loss. Work Employment and Society, 26: 10–25.
Financial Stress and the Long‐Term Outcomes of Job Loss
Sally A. Weller
Centre for Strategic Economic Studies, Victoria University, Melbourne, Australia
ABSTRACT
This paper examines the longer term effects of job loss for middle income households
in Australia. Specifically, it analyses the experiences of workers who lost their jobs in
the 2001 collapse of an Australian airline, Ansett Airlines. Since Ansett employees’
savings were tied up in the Ansett corporate structure, its workers faced the double
jeopardy of losing both their careers and their savings. The paper illuminates the role
of financial losses in overall outcomes and argues that an adequate understanding of
post‐redundancy experiences must incorporate employment, wellbeing and financial
effects. The paper concludes that employment policies pay insufficient attention to
the financial risks that accompany job loss. To reduce the adverse impacts of job loss
for middle income households, institutional frameworks need to address the
interactions among labour markets, financial markets and housing markets.
KEYWORDS
job loss/ financial stress/ long‐term outcomes/ Ansett Airlines
Introduction
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In contemporary advanced economies, flexible labour markets that enable labour to
move fluidly between regions, industries, sectors and occupations are thought to
contribute positively to productivity growth (OECD, 2009, p. 120). In this context,
involuntary job loss has come to be seen as a routine event, a “fact of life for
everybody” (Schmid, 1998, p. 6). Policies promoting labour market flexibility have
exhorted workers to accept risks and to learn to manage careers punctuated by
multiple job changes. Yet the precise nature and magnitude of the risks that workers
face when their employment ends unexpectedly are seldom explored.
The high incidence of job loss during the Global Financial Crisis has
highlighted the relationship between employment, household finances and housing.
Yet there has been surprisingly little research on the financial impacts of job loss in
contemporary economies or on the relationships among different types of
outcomes. To address this gap in knowledge, this paper analyses the long‐term
outcomes experienced by workers who lost their jobs in the 2001 collapse of an
Australian airline, Ansett Airlines. Former Ansett workers—a workforce emblematic
of Pusey’s (2003) middle Australia—faced the double jeopardy of losing both their
careers and their savings.1
The paper is structured as follows. The next section traces the theoretical
reorientation of redundancy research from its 1980s focus on labour market
outcomes to its recent emphasis on workers’ emotional reactions and adaptive
capacities. Both approaches, it is argued, underplay the importance of redundancy’s
financial impacts. The third section uses the experiences of former Ansett Airlines
employees to explore the relationships among the career, financial and personal
impacts of job loss. It shows that, for men but not for women, financial hardships
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contributed significantly to adverse long‐term outcomes. The penultimate section
considers the policy implications of this case, while the conclusion argues for the
extension of institutional frameworks to address the interdependencies among the
employment, personal, financial and housing repercussions of job loss.
The Shifting Focus of Job‐Loss Research
The role of financial stress in redundancy outcomes needs to be positioned
theoretically in relation to wider developments in job loss research. This section
contends that over the last twenty years, the dominant approaches to job loss
impacts have shifted from a primary focus on post‐redundancy employment
outcomes to now emphasise redundancy’s personal, emotional or psychological
effects. Paradoxically, this reorientation has occurred at the same time as changes in
social protection and household finances have increased the material impacts of
involuntary job loss.
In the 1980s, most job loss research explored the ways that labour demand,
labour supply and regulatory institutions interacted to shape individual outcomes
(Rubery & Wilkinson, 1994). In a framework that emphasised the labour market’s
socially constructed segmentations, numerous studies demonstrated that
redundancy led to de‐skilling, occupational downgrading, and, for some, permanent
exclusion from the labour market (for example, Harris et al., 1987). Redundancy’s
‘losers’ were identified as those with low technical skills, skills made obsolete by
technological change, or skills that were specific to a particular firm or process
(Bosch, 1990). Particular groups of workers—such as women, migrants with poor
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English language skills and older workers—were recognised as being especially
vulnerable. Since high rates of unemployment among job losers were attributed to
skill mismatches and viewed as the product of structural economic change,
governments invested in remedial interventions such as retraining and job
placement services that would assist displaced workers to make the transition to
new industries and occupations (Standing, 1991; Evans‐Klock et al., 1999). The
adverse psychological impacts of job loss, if considered, were attributed to enforced
idleness during periods of post‐redundancy unemployment (for example, Gallie &
Vogler, 1994; Jahoda, 1982; see also Gershuny, 1994). Similarly, financial impacts
were viewed straightforwardly as consequences of inadequate income during spells
of post‐redundancy unemployment (Morris, 1995). These effects would be resolved
by addressing their cause; that is, by helping workers to find new jobs.
In the context of the ascendancy of neo‐liberal economic thought in the early
1990s, the apparent failings of interventionist labour market policies encouraged
new ‘active’ policies oriented to swift job placement (Martin, 2000). For Peck (2001),
the resulting “neo‐liberal workfare” policies combined high labour force
participation with wage flexibility and the demonisation of welfare dependence. This
framework perceives workers as autonomous and self‐motivated economic actors
who are responsible for their own fates and who compete rationally for a finite
number of quality jobs. It is assumed that since jobs lost in the maelstrom of
capitalism’s competitive dynamic are replaced by new jobs in new sectors, effective
labour market policies should focus on improving the labour market’s efficiency,
typically by removing regulatory and institutional barriers (such as trade unions) that
hamper market processes, encouraging geographical mobility to places where jobs
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are more plentiful, and encouraging wages to adjust more freely to changes in labour
supply and demand.
The less punitive ‘risk society’ approach (Beck, 1992) also holds workers
responsible for their own outcomes. In this orientation, the market’s efficiency is
maximised by increasing labour flexibility – understood as maximising workers’
capacities to respond to the changing structure of labour demand in constantly
innovating economies. Since this implies multiple job changes, improving labour
market flexibility demands that individual workers accept the risks associated with
job changes. From this perspective, workers’ mobility across occupations, industries
and sectors can be improved by institutionalising “opportunity structures” that
“overcome various asymmetries of risk perception” (Schmid, 2006, p. 3). The risk
approach posits that individual workers will accept the risks associated with job loss
if regulatory frameworks provide them with the tools to manage risks effectively.
Wilthagen’s (1998) “flexicurity” approach, for example, promotes flexibility while at
the same time offering workers the safety net security of supportive welfare, training
and job placement services.
As these individualised understandings of the labour market became
dominant, a new generation of research into job loss focused on understanding
individuals’ uneven capacities to manage risk and overcome adversity. Adopting a
medical metaphor, these studies focus on how redundancy’s ‘victims’ recover from
the ‘death’ of a firm (for example, Bennett et al., 1995; Blau, 2006). The predictors of
adverse outcomes are no longer local labour market structures or labour demand
conditions but emotional triggers such as the shock of an unexpected redundancy
event (Dooley & Catalano, 1988), perceptions of injustice in its management (Bies &
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Moag, 1986), or attachments to a former workplace (Leana & Feldman, 1990). In
contrast to labour market approaches, which in essence attributed declining post‐
redundancy wellbeing to adverse employment outcomes, the recovery script tends
to view career outcomes as the material expressions of workers’ psychological or
emotional functioning.
This reorientation reconceptualises job loss in four principal ways. First, the
triggering event of redundancy assumes greater importance, becoming a pivotal
moment in the realignment of job losers’ life trajectories. Its repercussions persist
over time: ‘it’ is what workers are recovering ‘from’. Second, the metaphor’s
normative expectation of ‘recovery’ reconstitutes the repercussions of post‐
redundancy unemployment. Its cumulatively reinforcing (hysteretic) effects,
including the loss of confidence, disillusionment, loss of motivation, skill attrition,
and employer stigmatisation, which have been documented in numerous labour
market studies, are recast as individual failings: as a pathological inability to
‘recover’. Third, the new research tends to ignore the wider labour market contexts
in which job losses occur, such as weak labour demand, employers’ discriminatory
preferences, inadequate training or deficient job placement institutions. This
decontextualisation reinforces the individualisation of responsibility for the quality of
job loss outcomes. Fourth, with maladjustment attributed to individual
shortcomings, characteristics like age, ethnicity and gender—characteristics that
labour market research has long viewed as key axes of labour market segmentation
and as the factors that structure labour demand and shape the likelihood of
unemployed workers’ re‐employment—are stripped of any theoretical significance
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and relegated to the status of covariates governing the speed of the recovery
process.
Despite these limitations, however, some recovery studies have usefully
expanded the field of interest of redundancy research to include the effects of
financial stress. Vinokur et al. (1996) view financial hardships as ‘mediating’ the
emotional effects of redundancy, while for Price et al. (2002) financial stress plays a
key role in producing the multifaceted ‘chain of adversity’, or downward spiral of
misfortune, that some workers experience after redundancy.
Placing financial impacts at the core of a recovery process that spans
employment, psychological wellbeing and financial dimensions augments
understandings of contemporary job losses. The impacts of job losses in today’s
Western economies are not the same as they were in the 1980s. First, in the 1980s,
job losses were concentrated in less skilled occupations as jobs were automated,
eliminated, or exported to low wage countries. Second, in today’s more competitive
forms of capitalism, job losses are more likely to be unexpected, and arise from firm
failure rather than firm restructuring or ‘downsizing’. Contemporary job losses are
therefore more likely to affect skilled workers and middle‐income households (OECD,
2009). Third, with higher proportions of workers employed on a casual and contract
basis, fewer job losers can rely on generous termination payments to cushion the
impact of job loss and fund job search activities. Fourth, in comparison to the 1980s,
the reining in of welfare outlays has reduced unemployed workers’ access to
government income support.
Finally, but most importantly for this paper’s interests, there have been
significant changes in households’ financial arrangements. In the 1980s, when
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workers’ finances were relatively uncomplicated and low income households were
often free of debt, spells of unemployment meant reducing consumption, adjusting
outlays and general ‘belt‐tightening’, but in general did not produce major changes
in overall financial arrangements (McCrone, 1994; Morris, 1995; Webber and Weller,
2001). In recent years, in contrast, the escalation of household borrowings,
increasing household gearing ratios and expanding credit card debt have increased
the financial risks faced by job‐losing households. As Parkinson et al. (2009) show, in
the early 2000s an increasing proportion of households funded their consumption by
borrowing against the equity held in their homes. Moreover, contemporary
households’ savings are likely to be held in relatively inaccessible forms, such as
superannuation or pension funds. As a result, today’s households have minimal cash
reserves and are vulnerable when unexpected events interrupt income flows. This is
especially true in Australia, where a recent survey found that in the event of job loss,
one in four households did not have sufficient liquid assets to cover their expenses
for even one month (Logue, 2009). Parkinson et al. (2009) show that redundancy was
one event likely to trigger additional borrowing, especially borrowing against the
equity held in homes. In this new context, job loss triggers new and hitherto
unexplored interactions among housing, financial and labour markets.
The Cost of Job Loss
The example of the 16,000 skilled, well‐paid and secure workers who lost their jobs
when Australia’s Ansett Airlines failed unexpectedly on 13 September 2001
illuminates these effects. The unfortunate timing of Ansett’s demise meant that
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former Ansett employees’ search for new jobs took place in a depressed global
aviation labour market.2 Their prospects in local labour markets were not much
better: in 2001 Australia had the highest rate of involuntary job loss in the OECD.
Moreover, involuntary job losers faced longer periods of unemployment and had
poorer re‐employment outcomes than other types of job losers (ABS, 2001; Weller
and Webber, 2003). In Australia’s then newly flexible labour market, most job
growth had been in contract, casual and temporary work (Watson et al., 2003); jobs
that did not match the benefits that were routinely available in unionised workplaces
such as Ansett Airlines. Many former Ansett employees faced relegation to inferior
jobs in expanding secondary labour markets. On losing their jobs, many former
Ansett employees faced an immediate financial crisis. Australia’s welfare eligibility
rules had been tightened after 1996, following the election of the conservative
Howard Government. Although in theory income support remained universally
available, relatively few former Ansett employees could meet its stringent income,
asset and ‘activity’ tests.3 In any case, its modest level of ‘safety net’ benefits did not
provide sufficient income to maintain Ansett employees’ middle‐income households.
To make matters worse, workers’ combined savings of AUD $730 million
were entangled in the financial structures of the failed airline (KordaMetha, 2003).
The amounts owed to individual workers varied with skill, tenure and seniority but
included: (1) money invested in superannuation funds; (2) redundancy payments (as
specified in recognised industrial agreements); (3) outstanding long‐service and
recreation leave; (4) outstanding wages; (5) pay in lieu of notice; and (6) some sick
leave entitlements and workers’ compensation payments (KordaMentha, 2003). For
individual workers, the (trimmed) mean value of these entitlements was AUD
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$80,000 and the median AUD $65,000 – at the time about one year’s income for an
average Australian family. These funds were not accessible in the months after the
collapse, when workers were without income. For some time, workers thought that
these savings would be lost.
Under Australian corporate law, a failed business is put in the hands of an
Administrator firm that either restores it to viability or winds up its affairs. In
Ansett’s case, the business was wound up (see Easdown & Wilms, 2002). This
process took some time. Ansett Airlines had been part of a complexly interlinked
group of companies. The group structure had quarantined the firms that owned
workers’ unpaid entitlements from the firms that owned the airline’s saleable assets.
This meant that although the Ansett Group was asset‐rich, there were insufficient
funds in the employer firms to pay out workers’ entitlements.4 To alleviate this crisis,
the Australian Government placed a temporary levy on airline passengers to
generate funds that were then loaned to the Administrators to pay former
employees a fraction of their entitlements.
A series of court cases eventually established that all Ansett companies
should be ‘deemed’ one firm and that funds held in different firms should be pooled
to enable the proceeds of asset sales to be distributed to creditors. By 2006, most of
Ansett’s assets had been sold, and more than 90% of the money owed to workers
had been paid to them, albeit in a series of small payments spread over five years
(KordaMentha, 2006). Nonetheless, in the short term, many workers did not have
sufficient liquid assets to maintain their household commitments while searching for
new jobs. For them, timely access to entitlements would have avoided some of the
adverse financial impacts described in the next section.
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Assessing Overall Outcomes
The financial hardship faced by workers in the years after Ansett’s collapse provides
an opportunity to explore further the role of financial stress in job loss outcomes.
This section reports on data drawn from a longitudinal study that followed Ansett
employees’ outcomes over the years 2001 to 2006. In August 2002, a stratified
random sample of former employees was drawn from the Ansett Airlines employee
data base, which was then managed by the Administrators, KordaMentha Pty. Ltd. It
included information about all direct Ansett Airlines employees at the day of the
collapse, but excluded contractors and their employees (who, it is estimated,
numbered about 5,000 workers). The sample was stratified by age, gender and
occupation. Respondents were contacted initially in a letter from KordaMentha Pty.
Ltd. introducing the study. A short survey accompanied the letters. This process
achieved 715 responses from 2000 approaches, a response rate of 35.8%. All those
who provided follow‐up contact details (70.1% of the mail respondents) were then
telephoned to obtain further information about their post‐Ansett employment
histories. Follow‐up telephone interviews were then conducted at eighteen‐month
intervals.5 The April 2004 survey re‐interviewed 397 respondents, or 74.7% of the
first round interviewees who had provided contact information. In September 2006,
the 304 completed interviews reached 61.3% of contactable 2002 respondents. All
three rounds of interviews were conducted by the Australian Council of Trade
Unions’ (ACTU) call centre. If required, interviewees were transferred to qualified
counsellors and provided with follow‐up services. Interviews focused on
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employment outcomes and job search strategies and included a mix of factual, rating
and open‐ended questions.6 Survey data were supplemented by semi‐structured
interviews with selected workers, managers and government observers. These data
were also verified against documentary evidence.
The study design focused on workers’ labour market outcomes. Its analyses
found that outcomes varied with gender and skill, and that significantly poorer
employment outcomes were experienced by aviation‐specialised workers (Weller,
2008), former flight attendants (Weller, 2007), and mature aged aviation‐specialised
managers (Weller, 2008). The 2006 survey also collected some information about
workers’ households and the financial impacts of job loss. Financial costs included
the cost of maintaining households during periods of post‐redundancy
unemployment, the difference between workers’ wages at Ansett Airlines and their
wages in new jobs, the cost of job search, and the opportunity cost of lost career
seniority. In addition, 19% of 2006 survey respondents reported being forced to sell
their homes or other assets and 15% said that they had been forced to borrow
additional money to cover debt repayments. A further 7% had incurred costs
associated with stress‐related medical conditions. Responses to open‐ended
questions suggested a strong link between financial and personal stress.
In the final 2006 survey, which was conducted five years after Ansett’s
failure, respondents were also asked to sum up the overall career, personal
wellbeing and financial effects of job loss. To maximise the comparability of
responses, the questions took a similar form for each of the three outcome
dimensions. The response options framed outcomes as a recovery process: (1) I am
probably better off now than I would have been; (2) The collapse had no effect on
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my career/personal wellbeing/financial situation; (3) I suffered for a short time, but
my career/personal wellbeing/financial situation has recovered; (4) I suffered for
some time, but my career/personal wellbeing/financial situation has recovered; (5) I
have not recovered my career/personal wellbeing/financial situation. Since survey
interviewers offered no definitions of these concepts, their meanings depend on the
way that individual respondents interpreted each question. Given that this
workforce was culturally and socially homogenous, a shared understanding has been
assumed. This stance is supported by the accord between these responses,
responses to open‐ended survey questions and the elaborations provided in face‐to‐
face interviews. Without further testing of the reliability or validity of these
questions, however, the results of this exercise should be interpreted cautiously.
TABLE 1 here
The responses are shown in Table 1. As expected, the intensities of reported
personal, career and financial impacts of job loss were closely aligned. With regard
to careers, 23.6% of men and 31.0% of women experienced no adverse impacts,
while 18.1% of men and 14.0% of women quickly recovered their career positions.
The careers of 28.3% of men and 30.2% of women “suffered for some time” but then
recovered, while almost a third of the men (29.9%) and a quarter of the women
(24.8%) said they had “not recovered” their careers. With regard to wellbeing, again
most respondents reported either no ill‐effects (26.9% of men and 22.5% of women)
or a quick recovery (25.7% and 34.9% respectively). More than a quarter of the men
(25.3%) and almost a third of the women (31.0%) reported they had “suffered for
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some time” but had recovered. However, 22.1% of men and 11.6% of women
reported that they had “not recovered” their wellbeing. As for financial outcomes,
27.0% of men and 17.6% of women reported that their financial situation was either
better than before losing their Ansett jobs or that the collapse had not affected them
financially. A further 22.4% of men and 24.8% of women had recovered their
financial position fairly quickly. However 29.1% of men and 28.7% of women
reported suffering sustained financial impacts. Almost a quarter of the respondents
(24.8% of men and 25.6% of women) reported that they had “not recovered”
financially.
Overall, and considering all three outcome dimensions together, the data in
Table 1 suggest that perhaps a quarter of the respondents had “not recovered” five
years after Ansett’s collapse. This group appears to have fallen into what Price et al.
(2002) call a “chain of adversity” of poor outcomes experienced in multiple
dimensions. This conclusion is borne out by statistically significant inter‐dimension
correlations. Career and financial outcomes were most strongly correlated and have
the largest gender difference (Kendall’s tau‐b of 0.54, p. < .01 for men and 0.47, p. <
.01 for women). The correlation between personal and financial impacts was lower
and similar for men and women (Kendall’s tau‐b 0.43, p. < .01 for men and 0.45, p. <
.01 for women). Career and personal outcomes were the least strongly correlated
(Kendall’s tau‐b of 0.34 for men and 0.38 for women, both p. < .01).7 In open‐ended
comments about these outcomes, men tended to emphasise the financial impacts
and women the emotional impacts of job loss, but men were more likely than
women to report severe personal impacts such as family breakdown, loss of housing
and forced relocation.
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Did the money owed to workers play a role in producing these adverse longer
term outcomes? To answer this question, Tables 2 and 3 show the results of
multivariate logistic regression analyses assessing the importance of various
predictors for those workers who experienced adverse long‐term outcomes, here
defined as those reporting that they had either “not recovered” or “took some time
to recover” on each of the three outcome dimensions. Most of the predictor
variables were factors that have been identified with adverse labour market
outcomes (see Bosch, 1990; Webber & Campbell, 1997).8 They included, first, a set
of employment‐related variables: the duration of workers’ employment with Ansett
Airlines (a measure of firm‐specific skill specialisation), whether workers’ skills were
specific to aviation occupations and workers’ highest level of formal education. The
intensity of the job loss event was represented by each person’s actual termination
date (employees who had continued working during the wind‐up of Ansett firms
effectively had more notice of their impending job loss and therefore more time to
plan their response). Labour market conditions were represented by a variable
indicating whether or not workers lived in the collapse’s epicentre of Victoria, where
a large number of former Ansett employees had flooded the local labour market. A
dichotomous age variable (over 45 for men and over 35 for women) was included to
acknowledge the additional difficulty that mature age workers faced in finding new
jobs. Dichotomous variables were included to take into account household
circumstances (whether or not respondents had dependent children living at home),
job search strategies (whether or not job search had utilised Ansett‐based social
networks), short‐term outcomes (whether or not respondents had found new jobs
(of any quality) in the first year after the collapse) and financial impact (the amount
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of money owed to each respondent at redundancy). Given the large number of
predictors relative to the number of cases, the analysis used forward and backward
stepwise (maximum likelihood) methods to eliminate less important factors. Since
they operate in different labour markets and have different experiences of job loss
(Gonas & Westin, 1993), separate analyses were conducted for men and women.
Table 2 shows the results for men. In the table, the column labelled ‘B’ is the
parameter effect, ‘S.E.’ gives its standard error, ‘Wald’ is the Wald statistic, ‘d.f.’ the
number of degrees of freedom and ‘sig.’ the significance of the effect based on the
Wald statistic. The final column, labelled ‘Exp (B)’, is the odds ratio for each
independent variable. It is the factor by which each predictor increases or decreases
the log odds of an adverse overall outcome.
TABLE 2 about here
Of the four variables included in Table 2’s model for men, three—the use of
Ansett social networks in job search, age, and employment status in the first year
after Ansett’s collapse—echo the findings of earlier analyses of labour market
outcomes. The first variable, ‘Ansett networks’ represents the use of Ansett’s social
networks in job search. It has a positive sign and Exp(B) value of 2.27, indicating that
those with a stronger reliance on Ansett networks had more than twice the log odds
of experiencing poorer overall outcomes. As reported in Weller (2008), some men
who relied on their ‘Ansett family’ for both employment and social support struggled
to find their way after Ansett’s failure. The inclusion of the second variable ‘Over 45
in 2001’, and its relatively high Exp(B) value of 2.03 indicates that older men were
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also more likely to experience adverse outcomes. The model’s inclusion of career
outcomes in the first year after redundancy (‘LM status in Sept. 2002’), and
especially the high Exp(B) values for the categories ‘Unemployment’ and ‘Insecure
Aviation’ employment, confirms that being unemployed in the months immediately
following job loss is associated with adverse overall effects in the longer term. Often
men who had found insecure employment in the aviation sector in the first year
after Ansett had lost their jobs a second time by the third or fourth year. These
results suggest that, for men, adverse overall impacts are aligned to their career
outcomes.
However, the final variable included in the model is the amount of money
owed to workers at redundancy. Men who were owed between AUD $50,000 and
$99,000 experienced the poorest outcomes, as evidenced by the high Exp (B) value
of 13.69. These men were mostly long‐serving workers from less well‐paid aviation‐
specialised occupations, such as aircraft maintenance technicians and baggage
handlers. They were also predominantly family breadwinners in traditional husband‐
wife households; in most instances they also had mortgages, high levels of
household debt, minimal cash reserves, and no personal risk insurance. In many
cases, their wives’ part‐time jobs had excluded them from welfare assistance.
TABLE 3 here
The results for women are completely different. Although the form of
analysis was identical, none of the factors that influence men’s outcomes appear to
impact on women. Table 3 shows the results of the best among a set of weak
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models. Only two variables, tenure at Ansett Airlines and tertiary‐level formal
education, approach significance.9 The Exp(B) value of 3.93 indicates that women
who had worked for Ansett for more than 10 years had the poorest outcomes.
Adverse outcomes were also associated with aviation ‘Cabin crew’ occupations
(Exp(B) = 1.59), such as former flight attendants, and women who had lived and
worked in Ansett’s home state of Victoria (Exp(B) = 1.80) (for elaboration, see
Weller, 2007). The variable identifying the amount of money owed to women by
Ansett Airlines did not load into the model, even though some women were owed
quite large amounts. This suggests that the money owed was not as important a
determinant of overall outcomes for women as it was for men. From qualitative data
we know that this reflects the fact that few of the women in the sample were their
households’ primary wage‐earners.
Without data on household debt levels or spouses’ incomes, it is not
possible to draw strong conclusions about the role of debt in shaping the outcomes
of job loss. However, this analysis does provide a starting point for thinking through
these relationships and developing more robust assessments of their interactions.
The analysis provides a strong indication that the relationships among career,
financial and personal impacts were different for men and women, reflecting their
different positions in their households and in the labour market. Still, there is no
doubt that for men, restricted access to savings intensified the adverse effects of job
loss.
Policy Implications
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The central problem faced by Ansett Airlines’ workers in the months and years after
the loss of their jobs was maintaining their household consumption without
sufficient income. Workers’ savings were locked into the Ansett corporate structure,
creating financial problems and anxieties that threw some households into crisis.
Most workers had tried to maintain their established lifestyles, as expressed in
desires to ‘keep the kids in school’ and ‘hold onto the house’. In the absence of other
means of financial support, many held on by reorganising or refinancing their
mortgages to fund their unemployment.10 In effect, drawing on home equity became
a de‐facto welfare system. At the time, housing values in Australia were rising
rapidly, so this strategy did not appear to involve excessive risk. Had Ansett collapsed
at a time of deflating property values and stifled labour demand, as has occurred
during the recent global financial crisis, these borrowers would have been in a more
serious predicament. Nonetheless, for about 15% of survey respondents, changes in
labour market status reverberated into financial markets and housing markets
through loan restructuring or loan defaults.
The Ansett experience shows that Australia’s social security system, which is
designed to provide a basic ‘safety net’ for low income families, does little to support
middle‐income job losers. Given the cost of social security outlays, this is perfectly
reasonable. However, it is also true that, for a large proportion of Ansett employees,
there was no ‘security’ to accompany their experience of the labour market’s
‘flexibility’. Rather, being forced to borrow money to maintain their households and
lifestyles was perceived as abandonment, and as an injustice that exacerbated the
emotional devastation that accompanied job loss.
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The experience of Ansett’s employees suggests that the relationships among
the employment, emotional and financial repercussions of job loss are changing. At
the risk of oversimplifying, the labour market studies of the 1980s tended to view
career outcomes (unemployment) as producing adverse financial effects (restricted
consumption) and personal effects (disillusionment) (Figure 1(a)). Recovery studies
reversed this sequence by focusing on the personal qualities required to turn
adversity into opportunity (the capacity to ‘recover’). Financial worries intensify
personal stresses and therefore might inhibit the recovery process (Figure 1(b)). The
third option, suggested by the Ansett experience, is a causal sequence in which
financial stress exacerbates both adverse career and adverse personal outcomes
(Figure 1(c)). Conversely, financial security mollifies adverse impacts in the other
dimensions.
Put Figure 1 about here
The Ansett experience shows that social protection policies have not kept pace
with the changing structures of household finances. Ordinary workers’ households
are now embedded in complex long‐term financial arrangements that commit them
to onerous repayment schedules, reduce their cash reserves and simultaneously
limit their access to savings. The outcome is a new generation of vulnerable
households that are too rich to benefit from ‘safety net’ social protection provisions
but too poor to self‐provision given the financial structures in which they are
embedded. A social protection framework that delivered risk‐reducing social security
for these households would have to incorporate mechanisms to either provide
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access to savings (for example, by allowing easier access to superannuation funds),
access to credit, or access to government assistance, perhaps by extending social
protection from its contemporary focus at the lower end of the labour market
(OECD, 2007).
This example also shows that, given contemporary relationships between
household income and household borrowings, the impacts of job loss can no longer
be contained in the labour market. In this context, new protective mechanisms might
examine how to insulate employment crises from housing crises and vice‐versa ‐ so
that large scale job losses are prevented from reverberating in housing markets and
housing market crises are prevented from producing negative effects in the labour
market. This requires new and different ways of thinking about employment
security. For example, the Ansett case draws attention to the increasing amounts of
employees’ savings that are held by firms and which are vulnerable in the event of
corporate failure. In the wake of the Ansett experience, trade unions and labour law
advocates in Australia have proposed that corporate laws be amended to outlaw the
practice of quarantining workers’ entitlements in ‘shell’ companies that have no
saleable assets. They have also advocated placing workers’ entitlements at the front
of the queue of creditors in the process of winding up insolvent firms, so that
workers are paid before institutional (bank) creditors.11 Identifying means by which
exiting overseas firms might be forced to honour their obligations in Australia have
also been discussed (see Whelan & Zwier, 2005).
Conclusion
22
The experiences of former Ansett Airlines employees demonstrate that labour
market policies based on the ideas of secure flexibility have not paid sufficient
attention to the financial repercussions of job loss for middle‐income households or
to the increasing interdependence of labour, housing, and financial markets. For
contemporary middle‐income households, the impacts of job loss reverberate across
realms, triggering loan defaults and, for a minority of workers, producing a
debilitating chain of adversity. The development of institutional frameworks that
protect workers’ interests requires greater recognition of these interdependencies
and a fresh approach to worker security.
Acknowledgements
(removed for refereeing)
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End Notes
1 Pusey (2003) defined middle Australians as those with incomes below the 90th percentile and above
the 20th percentile of the income distribution. Earlier, Gregory (1995) had described the economic
reforms of the 1990s as producing a ‘disappearing middle’ in Australia’s income distribution.
2 Some commentators believe that the terrorist attacks of September 2001 influenced the decision to
close Ansett Airlines, principally because it was clear after 9/11 that Ansett would not be able to trade
out of difficulty (see Easdown & Wilms, 2002).
3 Most frequently, they were excluded from benefits because their spouses were employed. In
Australia, unemployment benefits are paid by the Federal Government and are funded entirely from
general revenue; there is no direct employer or employee contribution. The modest level of benefits
is unrelated to workers’ previous wages and there is no limit on the duration of assistance. The 6% of
Australians with private income insurance are predominantly self‐employed small business owners. 4 Space limitations prohibit discussion of the role of Ansett’s owner, Air New Zealand (but see
Easdown & Wilms, 2002).
5 The study began as a consultancy for the state government in Ansett’s home state of Victoria,
Australia, which also facilitated access to the Ansett database.
6 The average duration of interviews was twenty‐five minutes in the first year and ten minutes in
subsequent years (see Weller and Webber, 2004).
7 With this small sample size, elaboration using conditional cross‐tabulations failed to further identify
these relationships. 8 The study’s design focused on labour market outcomes in the context of the Ansett structure and
did not collect information about spouses’ incomes or household debt levels. 9 This would be expected given the small sample size.
28
10 Almost overnight, therefore, these typical middle‐income borrowers became vulnerable ‘sub‐prime’
borrowers. Despite widespread criticism of sub‐prime mortgages, closing off the option of borrowing
against home equity closes off one of the only avenues through which temporarily jobless middle‐
income households can sustain their livelihoods.
11 However, unions have opposed government intervention to pay out redundant workers’ lost
entitlements because such actions might have the effect of shifting responsibility for these payments
from firms to the state (see Harris, 2006).
29
Table 1: Comparing Outcomes: Career, Wellbeing and Finances
Men Women
Career n. % n. % I am probably more advanced now ... 29 11.4 16 12.4 The collapse didn't affect my career prospects 31 12.2 24 18.6 Subtotal – No adverse career impacts 60 23.6 40 31.0 My career suffered initially … 46 18.1 18 14.0 My career suffered for some time … 72 28.3 39 30.2 I was not able to recover my career 76 29.9 32 24.8
Personal Wellbeing I am probably more advanced now … 27 10.7 14 10.9 The collapse didn't affect my wellbeing 41 16.2 15 11.6 Subtotal – No adverse wellbeing impacts 88 26.9 29 22.5 My wellbeing suffered initially … 65 25.7 45 34.9 My wellbeing suffered for some time … 64 25.3 40 31.0 I was not able to recover my wellbeing 56 22.1 15 11.6
Finances I am probably more advanced now … 35 13.8 10 7.8 The collapse didn't affect my finances 25 13.2 17 9.8 Subtotal – No adverse financial impacts 60 27.0 25 17.6 My finances suffered initially … 57 22.4 32 24.8 My finances suffered for some time … 74 29.1 37 28.7 I was not able to recover my finances 63 24.8 33 25.6
Note: n = 383, 254 men and 129 women. Only in the case of ‘wellbeing’ does the Chi‐square statistic indicate a significant difference between men and women (Chi‐sq. = 9.8; d.f. = 3; sig. = 0.043).
30
Table 2: Adverse Outcomes, Men
B S.E. Wald d.f. Sig. Exp (B)
Ansett networks .821 .470 3.053 1 .081 2.272
Over 45 in 2001 .711 .397 3.202 1 .074 2.036
Owed in 2001 12.754 4 .013
Owed AUD $10K–$49K .991 1.157 .734 1 .392 2.694
Owed AUD $50K–$99K 2.617 1.159 5.100 1 .024 13.691
Owed AUD $100K–$250K 1.981 1.172 2.856 1 .091 7.251
Owed > AUD $250K 1.870 1.257 2.212 1 .137 6.489
LM status Sept. 2002 13.368 6 .038
Insecure aviation 1.703 .642 7.044 1 .008 5.491
Secure non‐aviation .513 .687 .559 1 .455 1.671
Insecure non‐aviation .832 .617 1.816 1 .178 2.297
Unemployment 2.084 .735 8.034 1 .005 8.036
Not in the labour force .610 .837 .530 1 .467 1.840
Other/unclassifiable 1.232 1.549 .633 1 .426 3.430
Constant –3.447 1.212 8.089 1 .004 .032
Note 1: Variable(s) entered: aviation (specialised), Ansett (networks), dependants, education, (Ansett) tenure, over 45, Victorian, owed (ref. cat. <AUD$10,000), LM (labour market) status 2002 (ref. cat. secure aviation). Note 2: Final model step 6. n = 160; –2 log likelihood = 179.449; Hosmer and Lemeshow test: 6.83, d.f. = 8, sig. = 555. Percentage of cases correctly classified: 71.9%.
31
Table 3: Adverse Outcomes, Women
B S.E. Wald d.f. Sig. Exp (B)
Education (ref cat. < Year 12) 4.469 3 .215
Education: Year 12 –.178 .673 .070 1 .792 .837
Education: Post‐secondary –.069 .693 .010 1 .921 .934
Education: Tertiary –1.610 .842 3.653 1 .056 .200
Tenure (ref cat. 1–5 years) 3.890 2 .143
Tenure at Ansett 6–10 years .699 .883 .626 1 .429 2.012
Tenure at Ansett > 10 years 1.370 .743 3.396 1 .065 3.935
Victorian .592 .520 1.295 1 .255 1.807
Cabin crew .464 .525 .783 1 .376 1.591
Constant –1.590 .828 3.683 1 .055 .204
Note 1: Variable(s) entered: aviation (specialised), Ansett (networks), dependants, education, (Ansett) tenure, over 45, Victorian, owed (by Ansett), LM status 2002. Note 2: Model shown is step 8 of 12. n = 83; –2 log likelihood = 97.679; no final model. Percentage of cases correctly classified in this model: 66.3%.
32
Figure 1: The career, financial and personal impacts of job loss: possible relationships