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Article

How Layers of Context and Material Deprivation Impact Reemployment in Stable or Casual Work

by
John Rodwell
1,* and
Rebecca L. Flower
2
1
Department of Management & Marketing, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
2
Department of Psychology, Counselling, & Therapy, La Trobe University, Bendigo, VIC 3550, Australia
*
Author to whom correspondence should be addressed.
Soc. Sci. 2023, 12(11), 625; https://doi.org/10.3390/socsci12110625
Submission received: 16 September 2023 / Revised: 31 October 2023 / Accepted: 6 November 2023 / Published: 9 November 2023
(This article belongs to the Section Social Policy and Welfare)
Versions Notes

Abstract

:
Reemployment for those who are unemployed is both economically and socially important but may be constrained because of the person’s context. The current study investigates key socioeconomic, structural, and individual factors that may impact the likelihood of reemployment for unemployed working age adults over the period of a year. Reemployment is further separated in terms of stability and security by delineating casual versus non-casual reemployment. A multinomial regression analysis of an Australian dataset (n = 375 adults who reported no limitation to their ability to gain employment) found that economic constraints played a substantial role and that the context issues act differently by employment type if reemployed. The results highlight the importance of socio-structural issues, reflecting resource asymmetry. Supportive neighborhoods and material deprivation set the scene, while education enables the pursuit of more stable and secure employment opportunities.

1. Introduction

Although employment plays an important part in one’s life, access to employment is not equitable (Thompson and Dahling 2019). Paid employment impacts one’s sense of purpose, financial security, mental health, and wellbeing (Felaco and Parola 2022; van der Noordt et al. 2014; Zechmann and Paul 2019). In turn, the negative impact of unemployment is well established, impacting mental health (Paul and Moser 2009), becoming increasingly worse as the period of unemployment increases (Strandh 2000), and extending to the individual’s family (Maitoza 2019; Thompson et al. 2013). Access to work for those who are looking to re-enter the job market is affected by individual and environmental factors, systemic and societal biases, and the evolving nature of the market, including an increase in contingent and casual work and decrease in stable roles (Thompson and Dahling 2019). The exit from unemployment to reemployment can protect against long term impacts on wellbeing (Rauf 2021), and permanent employment should lead to greater predictability of the future life course and improved mental health (Strandh 2000). Yet despite a considerable body of research on the outcomes of unemployment and reemployment, the key factors leading to job recovery remain understudied (Thompson et al. 2017), especially contextual factors.
Some barriers to reemployment may be due to the social stigma of unemployment and potential discrimination, especially for working age adults (Beck 2018). Consequently, there have been calls for more research on the impact of likely barriers such as the socioeconomic context, which brings opportunities and constraints (Duffy et al. 2016), ranging from socioeconomic status (Thompson et al. 2017) to specific barriers such as a lack of transport or monetary resources, care duties (van Hooft et al. 2021), and housing security (Desmond and Gershenson 2016). However, many of the major theories of career development do not integrate social class and structures apart from the psychology of working perspective (Diemer and Rasheed Ali 2009), which was developed to highlight the role that social class, privilege, and freedom of choice play (Blustein 2006).
This article will broadly examine these demographic and sociological factors through the lens of the Psychology of Working Theory (PWT; Blustein 2006), using data from the Household, Income and Labour Dynamics in Australia (HILDA) survey (Summerfield et al. 2020). Levels of context, constraints, and social issues may distinguish reemployment by work type, from ongoing unemployment. These variables are investigated in the broad context of a relatively welfare-supportive country, Australia, under relatively normal employment conditions. Consequently, this article reviews the key socioeconomic, structural, and individual-level factors that may impact the likelihood of reemployment for working age adults following a period of unemployment.

2. Literature Review: Increasing the Profile of Context

The PWT (Blustein 2001, 2006) gives primacy to sociocultural issues in seeking to understand the career decisions and work experiences of all people but particularly people near or in poverty, people who face marginalization, and people facing challenging work-oriented transitions for which contextual factors are often the primary drivers (Duffy et al. 2016). Sociological perspectives have often emphasized socio-structural constructs over internal traits, where social structures and social class resources, especially resource asymmetry, can explain career development processes in the experience of work (Diemer and Rasheed Ali 2009). Although the PWT also includes mediator variables, here we explore core predictor variables in the regaining of employment, these relating to economic constraints and marginalization, particularly given the need to consider the various levels of context that may apply.
According to the PWT, the availability of economic resources affects access to education, the quality of that education, and the ability to invest time and money in areas that may facilitate career development and achievement—all areas that impact one’s chances at securing employment (Duffy et al. 2016). Contextual influences demonstrated to be particularly important for job recovery include access to familial and social support, levels of material or financial support, experiences of discrimination or stigma, and macroeconomic factors such as regional unemployment rates (Thompson et al. 2017). The resulting socioeconomic combinations reflect how classism operates to advantage or disadvantage individuals and/or groups at different levels within the system (Diemer and Rasheed Ali 2009). Consequently, this section will work from the broader macroeconomic factors of the regional unemployment rate to a neighborhood index of resources, to household material deprivation, and then to a remaining key individual element of social class, the individual’s level of education (following the levels from Krieger et al. 1997).
These contextual and individual factors may impact a person’s chances of reemployment and may influence the type of employment gained, given the limitations faced by these groups (e.g., role/industry limitations as noted by Wildman 2020). In particular, employment may vary in terms of being employed on a casual or more permanent basis. The increase in casual work and decrease in stable roles in the labor market reflect different outcomes for those who are looking to re-enter the job market (Thompson and Dahling 2019). Casual work is associated with characteristics of precarious work, such as low pay, insufficient and variable hours, short-term contracts, and limited social protection rights (Rubery et al. 2018). Casual workers receive no paid leave entitlements, rarely have any legal guarantee of future employment, and often feel like second class workers (McGann et al. 2016).
Although reemployment leads to positive health outcomes (van der Noordt et al. 2014; Zechmann and Paul 2019), job insecurity (a likely product of casual employment) has a well-established adverse effect on mental health (Rönnblad et al. 2019). The effects of non-standard workers’ experience of job insecurity on health are both direct and indirect, such as coming to work sick, concealing occupational injuries, and putting health care on hold (McGann et al. 2016). Conversely, moving from unemployment to relatively stable and secure, and perhaps even permanent, employment should lead to a greater predictability of one’s future life course and improved mental health (Strandh 2000). Thus, these lessons about the outcomes of unemployment and reemployment are being investigated as possible key issues in job recovery. In applying PWT to understand people’s reemployment, this study will delineate casual employment from non-casual employment to gain a better understanding of job recovery and whether economic constraints or social identities distinguish the forms of reemployment from continuing unemployment.

2.1. Consider Economic Constraints

Macroeconomic factors such as regional unemployment rates and labor market conditions are important and frequently overlooked conditions of the work environment in vocational scholarship (Thompson et al. 2017). The regional unemployment rate is an objective, macroeconomic factor that may facilitate or inhibit job search efforts (Dahling et al. 2013). The ability to obtain work is a direct function of having access to opportunity, although for those with limited access to opportunity, the regional unemployment rate may be considered an indicator of economic constraints (Duffy et al. 2016).
Moving to the next, more specific level in the socioeconomic structure emphasizes the importance of the neighborhood context. The neighborhood socioeconomic status (SES) may reflect both the proportion of the adults in the neighborhood who have comple ted high school and general income levels, which in turn drives the availability of local vocational role models and per-pupil funding of schools (Diemer and Rasheed Ali 2009), access to educational opportunities (Yassine and Bakass 2023) and educational resources (Kvieskiene et al. 2021), as well as indirect neighborhood effects such as differences in localized pollution (Krieger et al. 1997). Similarly, the lack of work in a community can lead to a deep, inhibiting despair in that neighborhood (Blustein 2008). Conversely, social embeddedness in the surrounding neighborhood (e.g., having social networks in the neighborhood) has been related to successful reemployment (Vandecasteele and Fasang 2021). The differential effect of the neighborhood level of the structural system is further reflected in how children of low-income households in high-income neighborhoods do better in terms of development and health than low-income children in low-income neighborhoods (Krieger et al. 1997).
The next level in the system examined here is the level of the household. The traditional approach to household SES focuses on income, educational/occupational attainment, and/or occupational prestige, where income is fraught with many problems such as the extent of non-taxed income, the potential lack of validity in using income and occupation together, and other concerns (Diemer and Rasheed Ali 2009). A narrow focus on income has also been criticized for not necessarily reflecting the experienced living standards of the household (Saunders and Naidoo 2009). Furthermore, in this paper, with its emphasis on the reemployment of the unemployed, there are concerns regarding the use of income or occupation variables due to the possibility of collinearity with other variables.
Consequently, rather than applying a traditional approach to household SES, we focus on a measure that provides clearer differentiation between those who can and cannot afford key necessities: material deprivation. Material deprivation is a measurement of poverty, whereby individuals or groups lack the minimum essential materials and resources expected to function satisfactorily within society (Townsend 1979). The approach to material deprivation used in this study focuses on the influence of a lack of resources relative to needs (applying prevailing community standards about basic necessities), which is the defining characteristic of poverty (Saunders and Naidoo 2009). Material deprivation not only impacts health (Tøge and Bell 2016) and mental health (Bjarnason and Sigurdardottir 2003), but material deprivation can also directly curtail job search activities, such as limiting the locations to which a potential job applicant could travel, thereby also impacting reemployment. That is, poverty reflects a lack of access to economic resources and has been found to impact reemployment (Duffy et al. 2016). Material deprivation is therefore an applied, relatively concrete assessment of household poverty that can impact reemployment.
At the level of the individual, a distinct indicator of socioeconomic structure that is commonly used is the highest level of the individual’s education. To some degree, the individual’s highest education level overcomes one of the problems of measuring SES, as noted by Diemer and Rasheed Ali (2009), where a focus on the current generation does not necessarily account for the contributions of previous generations. Individual characteristics such as education can reflect benefits from the individual’s past that have shaped the job seeker (Thompson et al. 2017), or a lack of education may act as a driver of a poverty trap (Korn et al. 2015). The chances of re-employment among those unemployed are significantly improved for those with higher levels of education (Riddell and Song 2011), sometimes with non-linear relationships between categories of education and reemployment (Alam and Mamun 2016). Consequently, a non-linear, categorical approach to the level of education may need to be used.

2.2. Social Identities

The PWT includes consideration of the social identities of the individuals in the context of the socio-structural issues above. The markers of social identities can include social class such as the characteristics covered above but are often considered in terms of marital status, age, gender, and gender roles at the individual level. These social identities can indicate mechanisms by which individuals or groups are differentially privileged or marginalized in the attainment of work (Duffy et al. 2016).
Marginalization occurs when an individual or group of people experience social disadvantages as a result of being considered less powerful/important than others. In the context of employment, marginalization can occur on several bases of social identity, including but not limited to age (Finkelstein et al. 1995; Wanberg et al. 2016) and sex (Bishu and Alkadry 2017; Davison and Burke 2000). These social identities may experience higher rates of unemployment and underemployment (e.g., Kaye 2009). In this paper, we will investigate these four factors: age, marital status, sex, and the care of children for their impact on reemployment.
From a life course perspective, age affects career patterns and the prospects of re-employment (Ponomarenko 2016). In a meta-analysis, older job seekers found reemployment more difficult (Wanberg et al. 2016). Some of the variation in reemployment success between younger and older individuals can be explained (e.g., older individuals may ask for a higher wage or may lack in years of formal education compared to their younger counterparts; Vansteenkiste et al. 2015). However, a significant proportion of unexplained variation in reemployment success is thought to indicate that prejudice may be at play in creating a barrier to reemployment (Vansteenkiste et al. 2015). At the same time, an individual’s age may also represent their position regarding wider political, financial, and societal views of social norms (Wildman 2020), where individuals may be increasingly expected to continue working in their older years.
Spouses can help the unemployed with guidance and socioemotional resources to motivate and maintain job search behaviors, particularly if the spouse is able to share resources and support (Thompson et al. 2017). Spousal support has been highlighted as a key form of support in navigating employment-related issues (Pluut et al. 2018). The impacts of marital status can be distinct from the individual’s sex and whether or not they have children.
Gender and gender-based roles may represent several elements of identity that can impact job recovery and reemployment. Exploring working experiences across the life course, Wildman (2020) reported that, while women noted that paid employment was a necessity to support their families, they faced barriers such as a limited choice in role/industry and difficulty maintaining or regaining employment following marriage and/or the birth of a child. Indeed, the workplace was noted as being hostile to those with children (Wildman 2020), supporting the seminal work by Correll et al. (2007), who found that employers penalize mothers on several measures, suggestive of a “motherhood penalty”. Differences by gender are typically explained in terms of traditional gender role norms (e.g., Thompson and Dahling 2010), where women can conform to traditional feminine norms despite experiencing job loss, whereas job loss poses a more direct gender role threat to men who conform to traditional masculine norms (Paul and Moser 2009; Sherman 2009). Gender role norms may also influence job seekers’ assessments of which jobs to apply for, potentially limiting opportunities under consideration (Thompson et al. 2017). With regard to the parents of young children, an assumption is often made that women only seek roles with flexibility that are “family friendly”. However, Fuller and Qian (2022) report that, while mothers of young children were certainly less likely to be re-employed after job loss than fathers, this was not explained by a preference for role flexibility. Rather, they found that re-employed mothers with young children were as often engaged in inflexible roles like other groups. Similarly, Jung and O’Brien (2019) reported that women with children have less choice in roles and number of hours worked following becoming a mother, due to the expectation of caring the responsibilities that largely fall on women.
Therefore, this study investigates whether age, sex, marital status, or whether one is a parent may affect chances of reemployment. The macroeconomic and individual factors reviewed above were investigated to determine the characteristics that distinguish job recovery by work type (i.e., reemployment in a casual or fixed/permanent role), from ongoing unemployment. From these variables, the potentially distinguishing drivers of reemployment were investigated in the broad context of a relatively welfare-supportive country, Australia, and under relatively normal employment conditions.

3. Materials and Methods

3.1. Participants

The data for this analysis come from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, which is a large-scale, nationally representative household panel survey in Australia. HILDA is based on an initial sample intended to be representative of households in Australia (excluding the military and institutions such as prisons), and that sample’s representativeness is maintained using a variety of checks and top-up processes per (Summerfield et al. 2020). The sample used in this study comprised 375 respondents in the 2014 wave who had indicated that they were unemployed when last interviewed (approximately one year prior, in 2013).
This time period was chosen so as to minimize the effects of the Global Financial Crisis (GFC) or COVID-19 on the situation of the respondents, especially in terms of avoiding GFC- and COVID-19-related unemployment, which may have had different drivers to typical unemployment and material deprivation. The results from this study can then act as a baseline for comparisons of similar issues under GFC or COVID-19 conditions, or for an emerging “new normal”.
That is, the focus of this study is on the drivers of reemployment under relatively “normal” conditions. Health and economic externalities such as the COVID-19 pandemic change the contexts within which unemployed people can seek reemployment. For example, the impacts of the COVID-19 pandemic on unemployed people include a lack of social interaction, family tensions, problems arising from isolation, as well as anxiety and mental health issues (Kvieskiene et al. 2021).
The COVID-19 pandemic changed the nature of employment to such an extent that the phenomena of “The Great Resignation” occurred in the United States, with relatively high levels of employees quitting their work (Baranes and Brown 2023). Many reasons have been offered to explain this “Great Resignation”, including pragmatic issues such as high childcare costs for working parents that may in turn be causing a trend of lower female labor force participation and stagnant, low wages, as well as the empowering of employees to address issues such as not wanting to continue working for bad bosses who create bad work environments (Lambert 2023). More broadly, the changes arising from the COVID-19 pandemic may also be considered the result of pre-pandemic instabilities that were uncovered and brought into light during pandemic lockdowns (Baranes and Brown 2023).
A new normal may arise as new work patterns stabilize and the institutions of work and our economies try to adjust in the next period after the peak of COVID-19. In terms of the nature of work, emerging themes of a new normal include an increased use of video, an increase in written communication, a lack of physical interaction, and an increase in professional and personal autonomy (DiFabio and Cooper 2023). In turn, these changes may lead to a more segmented workforce, particularly for many professional employees (Lambert 2023). More broadly, the use of remote technologies may represent a more instrumental, problem-solving approach to work, forcing changes to the institutional fabric of employment and fundamentally changing the relationship between employees and employers (Baranes and Brown 2023).
However, remote working may be new, but is far from being normal, where the concept of normal may vary by work unit, organization and country (DiFabio and Cooper 2023). During the period of institutional adjustment occurring until a “new” normal may solidify, the results of this study could provide a baseline of the drivers of reemployment under what used to be considered normal conditions in Australia, enabling future comparisons with any arising new normal conditions or if employment conditions return to something close to the old normal.
Participants were those of working age being from 25 to 54 years of age inclusive, thereby excluding respondents facing youth or new to workforce or retirement issues (e.g., studies of older people, in their ‘encore’ period; Moen and Flood 2013). Respondents indicating that they had a health condition that could limit the work that they do were excluded from the sample. Although we acknowledge the importance of understanding factors impacting individuals with a disability in accessing reemployment (e.g., O’Brien 2013) and how disability may influence older workers’ decision to retire (Rodwell et al. 2022), the small number of relevant cases would not allow for the unique experiences of these individuals to be adequately addressed, thus resulting in the decision to only include participants in HILDA who reported no limitations to their ability to access employment. Further HILDA information is available in Summerfield et al. (2020), and the HILDA Survey has the University of Melbourne Ethics approval number 1647030.

3.2. Explanatory and Outcome Variables

The first two variables listed below (regional employment rate and neighborhood economic resources) were obtained from the Australian Bureau of Statistics and matched to the participants’ address details. Before the standard dataset was released, address details were removed to maintain the confidentiality of the respondents. The remaining variables were sourced from the participants’ responses on the HILDA survey.

3.2.1. Regional Unemployment Rate

The unemployment rate as of October 2014 for persons in same major statistical region from the Australian Bureau of Statistics was matched to the most detailed area code provided by the respondent (Summerfield et al. 2020). The unemployment rates were grouped into three approximately tertile bands: 0—relatively low (<5.65%), 1—middle (5.65% to 6.60%), and 2—relatively high (>6.60%).

3.2.2. Neighborhood Economic Resources

The Australian Bureau of Statistics, the statistical bureau for the federal government in Australia, has Socio-Economic Indexes for Areas (SIEFA), such as the Index of Economic Resources (IER), that were matched to the most detailed area level available for each of the respondents by the HILDA data process. The indexes are based on information from the five yearly Census.
The index summarizes variables relating to aspects of relative socioeconomic advantages and disadvantages. The index includes 14 variables, of which only two are measures of income. These include indicators of high and low income, as well as variables that correlate with high or low wealth. Areas with higher scores have relatively greater access to economic resources than areas with lower scores (for details see: Australian Bureau of Statistics 2013). To preserve the anonymity of the respondents, the confidential HILDA data reported the SIEFA–IER in terms of deciles. The deciles were banded together in order to lessen the occurrence of small or empty cells that could impact the efficacy of multinomial regression. The deciles were grouped to form the Neighborhood Economic Resources variable such that the richest four deciles = 0, the next four deciles = 1, and the bottom (poorest) two deciles = 2.

3.2.3. Household Poverty—Material Deprivation

Material deprivation was measured using an approach building based on that of Townsend 1979, where people were asked if they each had of a list of items then regarded as necessities, and the number of items that each household lacked was then calculated. Later studies improved on that approach by asking whether each of the items from a set list of items that a simple majority of people would consider to be a necessity were present or used and then differentiating between those who lacked an item because they could not afford it from those who chose not to have it (Saunders and Naidoo 2009). The list used here excluded child-related items because they could “induce a bias when comparing deprivation between households with and without children” (Saunders and Naidoo 2009, p. 424). The resulting survey first asked whether the household had accessed an item or service and if not, then asked whether or not the lack was by choice. Survey questions concerned each of these 18 items: medical treatment when needed, warm clothes and bedding if it is cold, a substantial meal at least once a day, medicines when prescribed by a doctor, dental treatment when needed, a decent secure home, meeting with friends or relatives for a drink or meal at least once a month, a home with doors and windows that are secure, a roof and gutters that do not leak, furniture in reasonable conditions, being able to keep at least one room of the house adequately warm when it is cold, a telephone (landline or mobile), at least 500 AUD in savings for an emergency, a washing machine, home contents insurance, buying presents for immediate family or close friends at least once a year, access to the internet at home, a week’s holiday away from home each year, and a motor vehicle. The original nineteenth item regarding comprehensive motor vehicle insurance was removed because it had a large number of missing values, given the prerequisite of having a motor vehicle.
The enforced absence of each item was then summed across the items to create a material deprivation scale. The scores on the scale were grouped such that a score of zero was coded as “0 low material deprivation”, respondents indicating that they had experienced the enforced absence of one to two items on the list were coded as 1—moderate, and respondents indicating three or more enforced absences were coded as “2 high material deprivation”.

3.2.4. Education—Highest Level of Education Achieved

The education variable was created by coding respondents’ answers across two questions. The first question was “What was the highest year of school you completed/are currently attending?” and the second was “Since leaving school what qualifications have you completed?” The respondents were asked to “not include hobby or recreation courses”. Across the responses, the highest level of education was coded and then grouped into bands of similar standards such that 0 = Year 11 of school or less, or Certificate 3 or 4 in vocational training; 1 = Year 12; 2 = bachelor’s degree, diploma, graduate diploma or postgraduate degree—holding a master’s or doctorate.

3.2.5. Age

The age of the respondents was obtained when the respondents indicated, “Age last birthday at the 30th June 2014”.

3.2.6. Sex

The variable representing the sex of the respondent was generated by asking the person their sex. The responses were coded 1—male and 2—female.

3.2.7. Marital Status of the Respondents

The variable representing the marital status of the respondents was obtained via asking the following question: “Looking at [the options below], which of these best describes your current marital status? And by “married” we mean in a registered marriage.” The response options were as follows: 1—Married (in a registered marriage), 2—Separated but not divorced, 3—Divorced, 4—Widowed, 5—Never married but living with someone in a relationship, and 6—Never married and not living with someone in a relationship. The responses were grouped such that 0—Married at some point (codes 1, 2, and 4) and 1—Never Married (codes 3, 5, and 6).

3.2.8. Children

Variables indicating the total number of children ever had and whether or not there was a child resident with the respondent at least 50 percent of the time were found to be largely collinear. The two variables were merged into one variable representing the number of children ever had and whether any of those children were resident with the respondent. The initial categories were 0—Had one child, still resident; 1—Had two children, at least 1 still resident; 2—Had three or more children, at least 1 still resident; 3—No resident children, whether they had any or not. Repeating the multinomial regression without those who had had children, but no children were resident effectively led to the same results as those below.

3.2.9. Re-Employment

All of the respondents indicated that they were unemployed when last interviewed (approximately a year prior, in 2013). Respondents were then asked whether they did any work in a job, business, or farm at any time during the previous 7 days. Those indicating that they did not work in a job were coded as still being unemployed (=0). Those indicating that they were working in a job were asked which category best described their current contract of employment from the options “Employed on a fixed-term contract”, “Employed on a casual basis”, or “Employed on a permanent or ongoing basis”. Permanent and fixed-term roles were combined (1) to reflect how both had leave entitlements and distinctly higher levels of stability and security of work than the contrasting casual (2) responses.

4. Results

A multinomial regression analysis was conducted for the previously unemployed respondents across three endpoint positions indicating whether they were reemployed: still unemployed, having a permanent or fixed term contract, or obtaining casual work, with education, age, sex, marital status, regional unemployment rate, neighborhood economic resources, and material deprivation as predictors. The data checking processes of Osborne (2016), specifically checking for inappropriately influential cases, found multiple influential cases, particularly in terms of leverage scores and DFBetas; those cases were removed. The sample analyzed had 375 respondents (see Table 1 for demographic information).
Missing values analyses found that the missing values were missing completely at random (MCAR) with Little’s p = 0.626. Table 1 shows a breakdown of the sample into the categorical variables that were analyzed for each of the endpoints, regarding the question of reemployment for the previously unemployed respondents. For the continuous variable of age in years, the mean (and standard deviation) for each of the re-employment categories at the later time was 36.63 (8.561) for the unemployed, 35.85 (8.282) for those reemployed on permanent or fixed contracts, and 35.96 (8.266) for those reemployed on a casual basis.
The analyses were performed using IBM SPSS version 26. The analyses were conducted following Tabachnick and Fidell (2007) and the best practice guidelines from Osborne 2016. The only continuous predictor, age, was transformed into z-scores. Box-Tidwell transforms were created to check for curvilinearity in the age variable (following Osborne 2016), but no curvilinear relationship was indicated. A summary of logit parameter estimates (their Standard Errors), Odds Ratios, and their 95% confidence limits are presented in Table 2. The specific parameters distinguish the two employed end points from the referent status where the respondents continued to be unemployed. The results were checked using pooled multinomial regression across 20 multiply imputed datasets. The results found had the same significant relationships, confirming that missing values had not affected the initial results.
The full model had −2LL = 671.865, which was a significant improvement over the intercept only base model of 813.010 (χ2(32) = 141.145, p < 0.001), indicating that the predictors, as a set, distinguished between the three employment status endpoints (i.e., obtaining a permanent or fixed contract, obtaining casual work, or unemployed). The variables that had significant overall effects were education, neighborhood economic resources, regional unemployment rate, and material deprivation (see Table A1 in Appendix A). The Nagelkerke pseudo R-square was 0.354, and the Cox and Snell pseudo R-square was 0.314. To check that the 30 respondents with fixed contracts were similar to those with permanent or ongoing contracts with whom they are combined in these analyses, the multinomial regression was repeated with the fixed, temporary contract cases excluded from the analyses, and all of the significant results (p < 0.05) were the same.
Furthermore, some of the variables, particularly the regional unemployment rate and neighborhood economic resources, may appear to be clustering variables, within which material deprivation and other variables may be nested. To check whether these variables acted as clustering variables, the procedures for multilevel multinomial modelling outlined in Sommet and Morselli (2017) and Heck et al. (2013) were followed. The Intra-class Correlation Coefficient (ICC) was calculated for each of the two possible clustering variables. The ICCs, using the underlying key variables, were all near zero, being less than 0.01 for neighborhood economic resources and <0.00001 for regional unemployment rate. the ICCs for the grouped variables were also calculated and still negligible (being < 0.026 for neighborhood economic resources and <0.001 for the regional unemployment rate), indicating that there was negligible variance associated with these variables acting as clustering variables. Consequently, the analyses were presented at a disaggregated level as a one-level multinomial regression.
The characteristics that distinguish reemployment in a fixed/permanent job or a casual job from ongoing unemployment were mostly different by type of employment, with the exception that material deprivation distinguished both forms of employment from unemployment. More specifically, education, particularly having a degree; being in a neighborhood with high economic resources rather than low economic resources; and having lower levels of material deprivation significantly distinguished those who obtained a permanent or fixed contract from those who remained unemployed. The characteristics distinguishing those who obtained a casual job from those who remained unemployed were being in a neighborhood with moderate economic resources rather than low economic resources, being in a region with high unemployment rather than moderate or low unemployment, and having lower levels of material deprivation. Overall, the broader contextual variables were all distinctive to some extent, whereas none of the social identity variables were distinctive.

5. Discussion

In this study, we aimed to broadly apply the PWT to examine whether economic constraints or social identities distinguish the forms of reemployment (i.e., reemployment in a casual or fixed/permanent role) from continuing unemployment. Data from a nationally representative household panel survey, HILDA, were used.
Starting with the broadest context issue examined here, the regional unemployment rate distinguished casual from unemployed but not fixed term/permanent from unemployed. That is, in areas with lower levels of regional unemployment, fewer respondents took casual jobs. Conversely, people becoming reemployed in regions with higher unemployment rates were more likely to take casual jobs. The regional unemployment rate may reflect access to opportunity (Duffy et al. 2016), but it may be that that opportunity is more for casual employment at first.
The next level of economic constraint distinguished continuing unemployment from both forms of employment, albeit in different ways. For the comparisons of both forms of employment, low neighborhood economic resources indicated that the respondents were more likely to remain unemployed. Such a link is a concern, because a lack of work in an already disadvantaged community could lead to deep despair in that neighborhood (Blustein 2008). More specifically, high levels of neighborhood economic resources were associated with a higher likelihood of the respondents being reemployed in permanent/fixed-term employment. This virtuous effect may reflect the presence of local vocational role models (Diemer and Rasheed Ali 2009) and access to educational opportunities such as universities (Yassine and Bakass 2023). Yet moderate levels of neighborhood economic resources were associated with the respondents being more likely to have casual work compared to neighborhoods with low economic resources. The stepped differences in the level of neighborhood economic resources may also reflect an initial accumulation of educational resources (Kvieskiene et al. 2021), which can lead to educational opportunities, such as high school, and indirect neighborhood effects, such as differences in localized pollution (Krieger et al. 1997).
Only one variable was common across the two employed endpoints and it distinguished both contract and casual from continuing unemployment: the variable of material deprivation. Household material deprivation is an indicator of poverty but provides a clearer differentiation between those who can and cannot afford key necessities (Saunders and Naidoo 2009). Low levels of material deprivation were associated with higher levels of employment in both forms of employment. These results support the argument that poverty reflects a lack of access to economic resources and impacts reemployment (Duffy et al. 2016), such as through a lack of transport or monetary resources or required care duties (van Hooft et al. 2021); and extends that finding to apply to both permanent/fixed and casual employment. The use of material deprivation more specifically reflects the experienced living standards of the household, rather than a narrow focus on income (Saunders and Naidoo 2009), and highlights how unemployed individuals with the minimum essential materials and resources expected to function satisfactorily within society (Townsend 1979) may be more easily able to be reemployed.
In terms of socioeconomic structure at the individual level, being more educated, particularly with a bachelor’s degree or above, characterized those employed in permanent/fixed term roles from those remaining unemployed. That is, education can reflect cumulative efforts (Thompson et al. 2017), which can be particularly beneficial for gaining permanent/fixed employment. These results support previous studies (e.g., Riddell and Song 2011) for permanent employment; although, in a similar manner to Alam and Mamun (2016), the effect of education was non-linear.
Quite surprisingly, none of the social identity variables were significant for any of the employment outcomes. Age did not significantly distinguish between the three reemployment end points, despite previous meta-analyses finding that older job seekers found reemployment more difficult (Wanberg et al. 2016). The non-result for age in this study may be because other variables associated with prior age effects (e.g., levels of formal education; Vansteenkiste et al. 2015), sex, and other variables had been analyzed as distinct variables in their own right. Further, this sample was specifically of people from the core of working age, thereby side-stepping the specific and specialist issues that younger people and retirement age people may face. Similarly, the age non-result may not indicate an absence of age prejudice in employment decisions but rather highlight that those prejudices may be more applicable to the ends of the age spectrum.
Marital status, sex, having had children, or having children resident at home did not significantly distinguish the continuing unemployed from either forms of reemployment. There was a wide range around the confidence intervals for the odds ratios for all of the social identity variables, suggesting that the individuals for any given score on a social identity variable were quite varied and/or that the variables were blunt. Furthermore, individual social identity variables were analyzed in the presence of context variables. For example, sex effects in previous studies may have included effects due to the costs of childcare (Wildman 2020), whereas such affordability may have been more specifically captured by household material deprivation in this study. Sex may often be a blunt proxy variable for multiple social issues and may not have been significant here because some of those other issues may have been parsed into other variables. Perhaps the variety and changing nature of gender role norms were not represented in sufficient detail to specify the role such norms could play, limiting the opportunities under consideration (Thompson et al. 2017), because the extent of those opportunities had already been considered here, in a statistical sense, through economic constraint variables. That is, more broadly, the results here for the social identity variables may differ from other studies that had not considered as varied a set of context variables.

5.1. Supporting the Primacy of Contextual Issues

The results of this study highlight that economic constraints played a substantial role across several levels of context, supporting the primacy given to contextual and sociocultural issues by the PWT (per Blustein 2006). Contextual issues such as poverty, operationalized here in terms of material deprivation, were found to be key issues in driving work experience (per Duffy et al. 2016), particularly in this case for recovering from unemployment to employment. The strength of the results in this study for socio-structural constructs highlight the importance of considering social structures and social class resources, especially resource asymmetry, in explaining the experience of work (Diemer and Rasheed Ali 2009) for processes associated with reemployment from unemployment.
Expanding our analysis of the PWT to apply to casual versus non-casual reemployment provided new insights. Moving from unemployment to relatively stable and secure employment should lead to greater predictability of the future life course and improved mental health (Strandh 2000). The resources required to support neighborhoods and avoid material deprivation set the scene, and those with higher levels of education were able to pursue more stable and secure employment opportunities. Together, these show the levels of social structures that can be engaged to facilitate more stable reemployment in order to obtain the structure and predictability that support mental health. For the individual, education remains a great enabler.
By contrast, a focus on any employment, where contextual issues suggest that casual work may be the main viable option, could lead to the growth of precarious work with little stability and security. In turn, casual work, with its variable hours and limited social protection rights (Rubery et al. 2018), may present ongoing challenges.
In terms of the focus of PWT, the broader contextual variables were all distinctive to some extent across both forms of reemployment outcomes examined in this study. But the inclusion of varying reemployment outcomes, particularly varying in this case in terms of stability and security, surfaced some issues that show stepped differences from remaining unemployed to casual work and to permanent/fixed work, through the level of neighborhood economic resources.

5.2. Limitations

In the spirit of giving more emphasis to context in reemployment studies, a key limitation of this current study is that it was based in Australia, a country with relatively high levels of welfare support, relatively universal healthcare, and nominally no time limit to unemployment support. Any or all of these effects may impact or modify the results found in this study to those that may be found in other countries. Perhaps this contextual issue could have led to some people remaining unemployed while they waited for a suitable job, compared to individuals in countries offering less support.
An unusual finding in this study was that none of the social identity variables were significant. The text above details how the inclusion of multiple levels of contextual variables in this study may have statistically altered the impact and meaning of some of the social identity variables. Consequently, future research could use more faceted measures for those identity variables. For example, if spousal support has been previously found to be a key form of support in navigating employment-related issues (Pluut et al. 2018), future research could use faceted measures such as work-oriented social support from the spouse instead of blunt measures of marital status. This study was unable to specify such a variable because of the constraints of using HILDA, a data source with set items.
Unlike many surveys, common method variance should be less of an issue in this study because the variables, especially the context variables, effectively came from two different sources. The regional employment rate and neighborhood economic resources were obtained from the Australian Bureau of Statistics, matched to the participants’ address details. Before the standard dataset was released, address details were then removed to maintain the confidentiality of the respondents. The remaining variables were sourced from the participants’ responses on the HILDA survey. Obtaining the three significant context variables from different data sources would also further the validity and stability of the results.
A further limitation is that the sample analyzed above had explicitly filtered out respondents with a health condition that impacted their workability. Given that there were insufficient cases to properly analyze workability effects, an investigation on the impacts of disability on reemployment was beyond the scope of this study. Future research might aim to specifically examine access to reemployment among disabled individuals. Lastly, we do not include selection bias controls in our estimation. That is, there may be a “discouraged worker” effect, where discouraged individuals had exited the work force (Buss and Redburn 1988). Therefore, demographic groups less likely to become discouraged may have self-selected into the unemployed group in our sample. Future research might aim to consider such possible selection effects, controlling for unemployment rates over longer time periods and across countries.

6. Conclusions

Many of the major theories of career development do not integrate social class and structures into their study of employment and reemployment (Diemer and Rasheed Ali 2009). Yet the results found in this study highlight the substantial role that contextual issues, across levels of context, played in distinguishing people obtaining a form of reemployment from those who were not reemployed. The inclusion of layers of contextual considerations in the PWT highlights the role that social class, privilege, and freedom of choice play (Blustein 2006) in reemployment.
The incorporation of considering the stability and security of the form of reemployment in the target variable provided new insights, despite being relatively blunt. Future research could gain even more insights by having finer-delineated reemployment outcomes, particularly if those outcomes were aligned with the forms of social identities being examined. For example, if women and/or women with children have less in their opportunity sets to choose from (e.g., role/industry limitations as noted by Wildman 2020 and by Jung and O’Brien 2019), then the outcome variables could be further delineated by role or industry to investigate those possible relationships.
To some extent, the investigation of PWT in this study with the delineation of casual reemployment aimed to continue building the foundation that highlights key issues leading to job recovery—an area that remains understudied, despite a considerable body of research on the outcomes of unemployment and reemployment (Thompson et al. 2017).
With the important role that employment plays in life, inequitable access to employment is a concern (Thompson and Dahling 2019) and is likely to remain a concern, especially as people try to recover employment after the impact of COVID-19. The results of this study should enable more fine-tuned policy interventions, depending on whether the political response indicates that any employment is good employment, or whether it focuses on facilitating less precarious, non-casual re-employment through ameliorating material deprivation and enhancing educational opportunities.

Author Contributions

Conceptualization, J.R. and R.L.F.; methodology, J.R.; formal analysis, J.R.; writing—original draft preparation, J.R. and R.L.F.; writing—review and editing, J.R. and R.L.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The University of Melbourne ethics approval number for the HILDA Survey Project is 1647030.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The HILDA data are available via application through the Commonwealth of Australia’s Department of Social Services Longitudinal Studies Dataverse (https://dataverse.ada.edu.au/dataverse/DSSLongitudinalStudies; accessed on 16 September 2023). The data are organized by Wave, not by accessed date. The data used above were from the Wave 19 Release.

Acknowledgments

This paper uses unit record data from the HILDA Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the author and should not be attributed to either DSS or the Melbourne Institute.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

The variables that had significant overall effects were neighborhood economic resources, household material deprivation, education, and the regional unemployment rate (see Table A1 for the details of the likelihood ratio tests).
Table A1. Likelihood Ratio Tests of Overall Variables.
Table A1. Likelihood Ratio Tests of Overall Variables.
Variables−2LL of Reduced ModelChi-Square (df)
Regional Unemployment Rate701.69929.834(4) **
Neighborhood Economic Resources690.17118.306(4) *
Household Material Deprivation691.50619.641(4) **
Education700.56928.704(4) **
Sex674.6062.741(2)
Marital Status673.9192.054(6)
Children675.2913.426(6)
Age (Z)674.3732.509(2)
Intercept671.865
Note. −2LL: −2 Log Likelihood. * p < 0.01, ** p < 0.001.
The variable neighborhood economic resources had a p value of approximately 0.001075 and all of the other significant variables had p values less than 0.0001. The chi-square statistic is the difference in −2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are 0. The reduced model for the intercept is equivalent to the final model because omitting the intercept does not increase the degrees of freedom.

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Table 1. Frequency counts for each of the categorical variables by re-employed status.
Table 1. Frequency counts for each of the categorical variables by re-employed status.
Categorical VariablesUnemployed (n)Permanent or Fixed Contract (n)Casual (n)
Regional Unemployment Rate
 <5.65%305333
 5.65–6.6%465620
 >6.6%354062
Neighborhood Economic Resources
 High266730
 Middle405261
 Low453024
Household Material Deprivation
 Low 4010662
 Moderate433746
 High2867
Education
 LTE Year 11 and Certificate 3, 4764066
 Year 1213208
 Bachelor’s, Diploma, Postgraduate degree 228941
Sex
 Male474345
 Female2910670
Marital Status
 Married296449
 Separated, Divorced, Widowed181613
 Never married but living with someone in a relationship273125
 Never married and not living with someone in a relationship373828
Children
 Had 1 child, still resident132916
 Had 2 children, at least 1 still resident213131
 Had 3+ children, at least 1 still resident272054
 No resident children, whether had any or not 506944
Note. LTE: Less than or equal to.
Table 2. Odds ratios 1 of characteristics distinguishing types of re-employment.
Table 2. Odds ratios 1 of characteristics distinguishing types of re-employment.
Ref: Remain UnemployedPermanent or Fixed ContractCasual
VariablesB(SE B)Odds Ratio95% CIB(SE B)Odds Ratio95% CI
Regional Unemployment Rate
 <5.65%−0.071 (0.381)0.9320.441–1.968−0.818 (0.372)0.441 *0.213–0.915
 5.65–6.6%−0.061 (0.359)0.9410.466–1.901−1.715 (0.383)0.180 **0.085–0.381
 >6.6% REF = 0 REF = 0
Neighborhood Economic Resources
 High0.801 (0.385)2.227 *1.047–4.7380.538 (0.412)1.7120.763–3.843
 Middle−0.209 (0.362)2.2330.606–2.5081.055 (0.370)2.871 **1.391–5.923
 Low REF = 0 REF = 0
Household Material Deprivation
 Low 1.662 (0.551)5.271 **1.789–15.5281.769 (0.545)5.866 **2.016–17.070
 Moderate0.999 (0.548)2.714 †0.927–7.9481.607 (0.531)4.988 **1.761–14.127
 High REF = 0 REF = 0
Education
 LTE Year 11 and Certificate 3, 4−1.513 (0.341)0.220 **0.113–0.429−0.313 (0.358)0.7320.363–1.475
 Year 12−0.636 (0.462)0.5290.214–1.310−0.918 (0.567)0.3990.131–1.214
 Bachelor’s, Diploma, Postgraduate degree REF = 0 REF = 0
Age (Z)0.011 (0.162)1.0110.737–1.388−0.215 (0.166)0.8060.583–1.116
Sex
 Male−0.320 (0.327)0.7260.383–1.3780.216 (0.335)1.2410.643–2.394
 Female REF = 0 REF = 0
Marital Status
 Married0.359 (0.438)0.4120.607–3.3760.458 (0.448)1.5810.657–3.805
 Separated, Divorced, Widowed0.268 (0.521)1.3080.471–3.6340.206 (0.524)1.2290.440–3.430
 Never married but living with someone in a relationship0.424 (0.407)1.5280.688–3.3940.174 (0.420)1.1900.522–2.712
 Never married and not living with someone in a relationship REF = 0 REF = 0
Children
 Had 1 child, still resident 0.324 (0.474)1.3820.546–3.5000.415 (0.507)1.5140.560–4.093
 Had 2 children, at least 1 still resident−0.148 (0.457)0.8620.352–2.1120.327 (0.468)1.3870.554–3.474
 Had 3+ children, at least 1 still resident−0.369 (0.477)0.6910.271–1.7620.307 (0.474)1.3600.537–3.443
 No resident children, whether had any or not REF = 0 REF = 0
Intercept−0.532 (0.705) −1.526 * (0.710)
1 Note: † p < 0.10, * p < 0.05, ** p < 0.005. Ref = Reference. Reference categories were set to 0. LTE: Less than or equal to.
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Rodwell, J.; Flower, R.L. How Layers of Context and Material Deprivation Impact Reemployment in Stable or Casual Work. Soc. Sci. 2023, 12, 625. https://doi.org/10.3390/socsci12110625

AMA Style

Rodwell J, Flower RL. How Layers of Context and Material Deprivation Impact Reemployment in Stable or Casual Work. Social Sciences. 2023; 12(11):625. https://doi.org/10.3390/socsci12110625

Chicago/Turabian Style

Rodwell, John, and Rebecca L. Flower. 2023. "How Layers of Context and Material Deprivation Impact Reemployment in Stable or Casual Work" Social Sciences 12, no. 11: 625. https://doi.org/10.3390/socsci12110625

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