Employment Outcomes for Men and Women Following an Economic Downturn: Labour Underutilisation in Australia

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While aggregate patterns provide a useful overview, it is insightful to consider employment outcomes across segmented markets. One such segmented market is between men and women, where it is widely understood that labour market engagement opportunities will differ. This paper provides an investigation of these uneven labour market outcomes. It presents an analysis of labour underutilisation for men and women using panel data, taking account of both individual-level supply-side factors together with the strength of the local labour market (demand-side) and the performance of the broader macro-economic environment. The result is an analysis that accounts for the impact of changing macroeconomy, local labour market conditions, and men and women's employability assets. Figures Figure 1 1. Introduction Like all large economies, the Australian labour market is characterised by uneven and volatile performance across different segments. While some groups appear resilient as the economy changes, others are more likely to face disadvantages as opportunities shift, and precarious employment situations become increasingly problematic. In recent periods attention has been given to poor labour market outcomes, including unemployment and other forms of underutilisation. Statistics are now routinely provided on labour underemployment and general underutilisation levels, and commensurately, greater academic and policy research has gone towards understanding these broader states of labour market disadvantage. Research questions have focused on exploring the drivers of different aspects of underutilisation, understanding how long-term patterns have shifted and considering the individual and aggregate consequences of underutilisation (Baum, Bill, & Mitchell, 2008 ; Campbell, 2008 ; Mitchell & Carlson, 2000 ; Rodríguez Hernández, 2018 ). As with the narrow concept of unemployment, it is generally understood that an effective evidence-based suite of policy interventions should be possible by understanding the nature of underutilisation. Comparing the patterns of labour underutilisation for women and men is the focus of this paper. Its primary purpose is to consider the factors associated with underutilisation, including the characteristics of at-risk individuals and the features of the local labour markets, and the macro-economy that individuals operate in. The analysis in the paper provides a unique opportunity to consider how these cross-cutting factors have impacted labour market outcomes by utilising panel data regarding labour market outcomes for women and men and linking individual data to other broader factors at the macro level. The data covers the years 2008 to 2015 and is taken from the Household, Income and Labour Dynamics Australia (HILDA) survey and is combined with regional labour market data obtained from the Australian Bureau of Statistics. This dataset allows the following questions to be addressed: 1. What was the effect of supply-side characteristics on the risk of an individual’s labour being underutilised? 2. What was the effect of aggregate/ spatial demand-side characteristics on the risk of an individual’s labour being underutilised? and 3. What was the effect of macro-economic forces in the post-GFC period on an individual’s underutilised labour? 1.1 The Extent of Labour Underutilisation in Australia The Australian Bureau of Statistics regularly provides data regarding the extent and scope of labour underutilisation within Australia. Taking the period of 2007/8, when the Global Financial Crisis began to bite until the most recent period, a general worsening of labour market outcomes can be noted. While there was a sharp increase in all measures in the immediate period of the GFC, the consequences for both men and women have continued to deteriorate until 2019 and have yet to return to pre-GFC levels. This is despite an improvement in aggregate labour market figures over the same time. Moreover, the post GFC period has seen underemployment taking a larger share of the total underutilisation rate than had previously been the case. In terms of magnitudes, for women, the headline unemployment rate moved from 4.9% before the GFC to peak at 6.2% in June 2015, while underemployment moved from 8.2–10.7% over a similar period. The combined underutilisation rate moved from 13.0 per cent before the GFC to a high of 16.9 per cent at the end of 2014 before declining to the current (September 2019) level of 15.4 per cent (Fig. 1 ). For men, the headline unemployment rate moved from 4.0% before the GFC to peak at 6.3% in June 2015, while underemployment moved from 4.7–6.9% over a similar period. The combined underutilisation rate moved from 8.9 per cent before the GFC to a high of 12.4 per cent at the end of 2014 before declining to the current (September 2019) level of 12.2 per cent (Fig. 1 ). If hidden unemployment (those who are unemployed, not actively looking for work, but who would take a job) is added to these figures, conservative estimates might add an extra 5 per cent to the total underemployment rate suggesting a much more severe issue. Reflecting the official statistics, empirical research based on survey data has often shown that women are more likely to find themselves underemployed or marginally attached to the labour force than men when a range of socio-demographic characteristics is controlled for. In contrast, men were more likely to face unemployment. Such differences clearly illustrate the gendered nature of segmented labour markets (Baum et al., 2008 ) and suggest that in contrast to some arguments (Flinn & Heckman, 1983 ) for men and women, the different states of underutilisation are quite distinct. 1.2 Determinants of Underutilisation Given the distinct states of underutilisation experienced by males and females, it is useful, as this paper does, to consider how the determinants in labour market engagement might differ within gender segmented outcomes. While conceptual frameworks differ, researchers have increasingly utilised a broad employability framework (Baum et al., 2008 ; Baum & Mitchell, 2010b ; Doran & Fingleton, 2016 , 2018 ; Wilkins, 2006 ) that employs aspects of both labour supply and labour demand. This broad framework provides an understanding of a person’s ability to transition into and within labour markets and to realise their potential via accessible employment opportunities. For particular individuals, employability might depend on their skills level or the way that their personal characteristics are presented. Employability may also be associated with the broader social context within which employment is sought as well as the broader economic context (Department of Higher and Further Education, 2002 ). These supply and demand (employability) characteristics include a person’s formal education, their health status, proficiency in English, the employment history of their family, their social networks and external factors such as the strength of the labour market they operate in and the condition of the broader macroeconomy (McQuaid & Lindsay, 2005 ). In many cases, the existing research has identified that particular labour supply and demand characteristics such as education or age tend to have similar impacts regardless of gender. The Australian work by Baum and Mitchell ( 2010a ) using a single wave of the Household Income and Labour Dynamics Australia (HILDA) survey identified that factors including age, ethnic background, English proficiency, family employment history and the presence of critical social networks were significant explanatory variables for both males and females when comparing the risk of unemployment and marginal attachment relative to being fully employed. Similar findings for the Australian context are also reported in the research by Wilkins ( 2006 ). In a similar vein, the more recent work by Rodríguez Hernández ( 2021 ) on underutilisation in Spain during the Global Financial Crisis identified that at the end of the economic crisis, factors such as age, education level and living with elderly parents or parents-in-law were significant in explaining the employment outcomes for both men and women. The authors also found that the strength of the local labour market was also important for both males and females, with those living in poorer performing regions being more likely to be disadvantaged in terms of employment. Focusing only on the rise of involuntary part-time work (underutilisation), the early research by Leppel and Clain ( 1988 ) found very little by way of differences in the drivers of involuntary part-time employment in their sample of males and females in the United States. In particular, they found that regardless of gender, economic slowdowns and service sector employment growth were significant factors in increasing the level of involuntary part-time work. In contrast, declining proportions of young children and a rising skill level of the employed had the opposite effect. The research by (Van Ham, Mulder, & Hooimeijer, 2001 ) also suggested that there may be only minor differences in factors contributing to the likelihood of labour market outcomes for males and females. Many of the labour supply and demand factors identified by researchers suggest little or no bias along gender lines. However, moving beyond these findings, there has also been analysis that has identified significant gender-specific effects, although often with no clear direction of the association. The circumstances where differences in gender outcomes are often considered associated with the types of occupation or industry an individual is employed in or the gendered stereotypes associated with the need to provide child care or similar caring functions. It is also argued that in line with potential differences in industry or occupational characteristics, males and females are impacted differently during economic downturns. This may also be an essential labour demand aspect accounting for differential outcomes. The Australian work by Baum and Mitchell ( 2010a ) referred to above found that males tended to be impacted by the strength of local employment opportunities, which may have a particular industry or occupation characteristic, especially in terms of them being unemployed, while for females labour underutilisation (marginal labour market attachment) tended to be associated with a need or desire to provide childcare. This finding that the presence of children was associated with a reduced likelihood of women being adequately employed was also highlighted by Kjeldstad and Nymoen ( 2012 ) in their study of the Norwegian labour market. In their analysis, the authors found that the likelihood that a female would be underemployed rose as the age of children increased. The finding that the presence of children was associated with an increased probability of marginal attachment to the labour force by women is contested by the conclusions of others who argue that the presence of children does not necessarily result in increases in the likelihood of underemployment (Cam, 2014 ; Rodríguez Hernández, 2021 ; Wilkins, 2006 ). For the Spanish sample utilised by Rodríguez Hernández ( 2021 ), the authors find that the presence of younger aged children reduces the likelihood that women would find themselves underemployed or a male would be unemployed. The Australian research by Wilkins ( 2006 ) also shows contested outcomes regarding the association between child care responsibilities and labour underutilisation. The difference between this analysis and other Australian work is explained by differences in samples and measurement of variables. Over and above the issue of childcare, research has also pointed to the impact that industry or occupation characteristics can have on gendered labour market outcomes. Here the problem is associated with the likelihood that some industry sectors or occupations are more likely to be related to part-time work and therefore more likely to be associated with underutilisation. The recent Spanish work by Acosta-Ballesteros, Osorno-del Rosal, and Rodríguez-Rodríguez (2021) illustrates this point suggesting that being employed in female-dominated occupations and industries results in a higher likelihood of underemployment than in male-dominated ones. Similar arguments are made about the impact of economic downturns on the labour market outcomes of men and women. Researchers note that in some circumstances, male-dominated occupations have tended to be hardest hit by downturns resulting in males being more likely to find themselves unemployed or marginally attached to the labour market. For example, Hoynes, Miller, and Schaller ( 2012 ), considering employment outcomes across several recessionary periods, found that men experienced more cyclical labour market outcomes when compared to women, an outcome they argue is the result of men being employed in more highly cyclical industries including construction, and manufacturing. Similar findings have been discussed in Elsby, Hobijn, and Sahin ( 2010 ) and (Albanesi & Şahin, 2018 ). 2. Data And Methods 2.1 Data This paper aims to model individual labour market outcomes as a function of a range of individual-level socioeconomic and demographic variables, a temporal dimension, and a local labour market performance measure. To carry out such an analysis, data from two primary sources are utilised (1) Individual-level longitudinal panel data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, managed by the Melbourne Institute of Applied Economic and Social Research (Wilkins, Vera-Toscano, Botha, & Dahmann, 2021 ); and (b) aggregate level local labour market data from Small Area Labour Markets data, published by the Australian Commonwealth Department of Employment (Australian Government, 2021 ) and aggregated into Functional Economic Regions. The individual-level data from the HILDA Survey follows a large cohort of Australians across consecutive years, gathering responses on various economic, social and labour/employment questions. The HILDA Survey began in 2000-01 (Wave 1) and has produced 16 consecutive output waves, with a high participant retention rate. This paper uses Waves 8 to 15 (2008–2015) data. The Small Area Labour Markets data consists of regional labour force estimates of unemployment for approximately 2,200 Australian Bureau of Statistics Statistical Area 2 (SA2s) every quarter (Australian Government, 2021 ). As the purpose of using the labour force estimates is to account for the impact of local labour markets on individuals’ employment outcomes, the SA2 level data was aggregated into a form representing local labour market regions. We define an individual’s local labour market as the functional region they live in using the Centre of Full employment and Equity’s Functional Economic Regions (CFERs) (Stimson, Mitchell, Flanagan, Baum, & Shyy, 2016 ). These regions, which cover the whole of Australia, are designed explicitly as labour markets, informed by the commuting patterns of workers throughout the country. The areas are unencumbered by administrative or political requirements and have been shown to produce better measures of labour market statistics. This is important as we use the region's unemployment rate to measure a region's influence on an individual's labour force outcomes. As the CFERs are made up of aggregations of Statistical Area 2s (SA2s), a region's unemployment rate is determined by the unemployment and labour force numbers of its constituent SA2s, as provided in the Small Area Labour Markets publication and aggregated up to the constituent CFER. Local labour market data was calculated to match each wave (year) of the HILDA data. An essential aspect of the dataset development was linking an individual respondent from the HILDA data to their specific functional economic region. This was achieved using the SA2 geography code provided for each respondent in the survey with the corresponding SA2 codes associated with each aggregate functional economic area. In this way, the dataset comprised a set of individual-level variables and an associated functional economic region indicator. 2.3 Variables As stated, the model is set up to determine the influence a range of individual and aggregate level explanatory variables have on the response variable, employment status. Employment status for a respondent is divided into one of four categories: Fully employed (FE) – employed full-time, or employed part-time without wanting more work; Underemployed (UDE) – employed part-time and wanting more work; Unemployed (UNE) – not employed and actively looking for work; and Marginally attached to the labour force (MALF) – not employed and not actively looking for work but would work if a job became available. Given the nature of the dependent variable, the appropriate model to use is a multinomial logit model with a categorical dependent variable. The logit compares a base response variable state to the other response variable states. To account for the panel nature of the data, the multinomial logit model is altered to introduce individual-specific random effects. This then becomes a mixed logit model, where the parameters are assumed to vary between individuals, thus taking into account the population's heterogeneity (Croissant, 2012 ). Separate models are run for both men and women. The explanatory variables are listed in Table 1 below. Most of the explanatory variables are categorical variables where, like the response variable, a baseline reference category is chosen to which all the other categories are compared. Table 1 Independent Variables Used in Analysis Variable Description Reference Variable Age 15 to 24 1 if person i is aged between 15–24 years at time t ; 0 otherwise Person aged between 25–54 years at time t Age 55 to 64 1 if person i is aged between 55–64 years at time t ; 0 otherwise Person aged between 25–54 years at time t Age 65 plus 1 if person i is aged 65 years or greater at time t ; 0 otherwise Person aged between 25–54 years at time t Poor health 1 if person i reports a long-term health condition at time t ; 0 otherwise No long-term health condition reported at time t Born in a non-English speaking country 1 if person i was born in a non-English speaking country; 0 otherwise Person born in English speaking country Highest education-post secondary 1 if person i ’s highest level of education at time t is post-secondary (inc certificate and diploma); 0 otherwise Person has no post-school qualification at time t Highest education- tertiary 1 if person i has completed tertiary level education at time t (bachelor degree and above); 0 otherwise Person has no post-school qualification at time t Person in couple household with children 1 if person i is part of a couple relationship with dependent children at time t ; 0 otherwise Person is single at time t Person in single parent household 1 if person i is a single parent at time t ; 0 otherwise Person is single at time t Person in couple household without children 1 if person i is part of a couple with dependent children at time t ; 0 otherwise Person is single at time t Parent unemployment 1 if both parents of person i were not in paid employment when person i was 14; 0 otherwise At least one of person’s parents were in paid employment when 14 Previous unemployment 1 if person i did not have a job anytime in the previous 12 months at time t ; 0 otherwise Person had a job some time in last 12 months Social capital Social capital/networks value for person i at time t-1 . This was calculated through a Principal Components Analysis of responses to 9 questions from HILDA survey N/A Regional unemployment rate Log of the unemployment rate of the region (CFER) person i is resident in at time t N/A 2009 1 if period is time 2 (2009), 0 otherwise Year 1 (2008) 2010 1 if period is time 3 (2010), 0 otherwise Year 1 (2008) 2011 1 if period is time 4 (2011), 0 otherwise Year 1 (2008) 2012 1 if period is time 5 (2012), 0 otherwise Year 1 (2008) 2013 1 if period is time 6 (2013), 0 otherwise Year 1 (2008) 2014 1 if period is time 7 (2014), 0 otherwise Year 1 (2008) 2015 1 if period is time 8 (2015), 0 otherwise Year 1 (2008) 3. Results Tables 2 to 4 present the regressions performed for the cohorts of men and women, comparing the likelihood of being fully employed to each of the underutilised states. A person’s age has a significant impact on their labour force status. Regardless of gender, a person aged 15 to 24 years old is more likely to have their available labour underutilised than being fully employed, compared to persons aged 25 to 54, with men having more substantial odds than women for each of the underutilisation states. Further, older workers, from 55 to 64 years and over 65 years of age, are more likely to be marginally attached to the labour force than those in the reference age group, regardless of gender, again with men having more substantial odds. However, the other significant impacts favour full employment to underutilisation. Women between 55 and 64 years are more likely to be fully employed than underemployed or unemployed than those in the reference age group. Meanwhile, men and women over 65 years are more likely to be fully employed than unemployed. A person with a long-term health condition is significantly more likely to have their available labour underutilised rather than be fully employed, regardless of gender. Command of the English language affects a woman’s ability to be fully employed more than a man. A woman born in a non-English speaking country is more likely to be underemployed, unemployed or marginally attached to the labour force than fully employed, compared to women born in English speaking countries. However, for men, being born in a non-English speaking country only significantly increases their likelihood of being underemployed rather than fully employed; it does not significantly affect the possibility of being unemployed or marginally attached instead of fully employed. Education level also has a gender bias on a person’s ability to be fully employed. Men with a post-school qualification are more likely to be fully employed than in a state of labour underutilisation compared to men without a post-school qualification. However, women with a post-school qualification only increase their chances of being fully employed rather than marginally attached to the labour force, but it has no impact on their ability to be fully employed instead of underemployed or unemployed. On the other hand, a tertiary education significantly improves both cohorts’ likelihood to be fully employed rather than underutilised, with this increasing a woman’s odds much more than a man’s across the board. A person’s gender significantly impacts their ability to be fully employed if they are in a couple relationship with children. It seems the traditional roles of men and women remain strong. Men in these relationships are more likely to be fully employed than underutilised, compared to single men, but women in these relationships are more likely to be underemployed or marginally attached to the labour force than fully employed, compared to single women. Interestingly, women in these relationships are more likely to be fully employed than unemployed compared to single women. This reflects those traditional relationships where men are the primary income earners, and women work part-time or not at all. The fact that women in these relationships are more likely to be underemployed than fully employed reflects their willingness to take part-time jobs, perhaps to fulfil their home and caring duties outside this paid employment. Similarly, the fact that women in these relationships are more likely to be marginally attached to the labour force than fully employed, yet fully employed rather than unemployed, also reflects these women’s situation. Women in these relationships who do not have paid employment are generally supported financially and have things to fill in their time, so they do not need to look for work actively yet would take on work if available. There is no gender bias for single parents, with both men and women more likely to be underemployed or marginally attached to the labour force than fully employed, compared to single people. There is no significant relationship between being fully employed and unemployed for single parents. Similarly, there is no gender bias for people in couple relationships without children. In these relationships, both men and women are more likely to be fully employed than underutilised, with men having a more substantial likelihood across the board. Women are generally more influenced by their parents’ employment situation when they were children than men. Women whose parents were not in paid employment when they were 14 are more likely to have their available labour underutilised than those with at least one parent in paid work. However, there is only a significant relationship for men in the same situation in being unemployed rather than fully employed. The strength of a person’s social capital and networks positively influences them being fully employed rather than underemployed, unemployed or marginally attached to the labour force. This is the case for both men and women, with women having slightly stronger odds of full employment given their social capital. The local labour market a man lives in has a more substantial influence on their chances of being fully employed than a woman. The higher a region’s unemployment rate, the more opportunity a man has of being underemployed or unemployed, while there is a much slighter chance of women being underemployed only. A region’s unemployment rate has no impact on whether a man or woman is marginally attached to the labour force rather than fully employed. The macroeconomic effects of the Global Financial Crisis hit men harder relative to women in terms of their ability to find and maintain full employment. As the first effects were felt nationally in late 2008, men had lower unemployment and underemployment rates than women. Still, these advantages were eroded mainly in terms of unemployment, and the gap narrowed in terms of underemployment. The same was true of HILDA respondents, with fully employed men as a proportion of those in or wanting work (including marginally attached men) falling from 87 per cent in 2008 to 81 per cent in 2015, while for women, it fell from 78 per cent to 74 per cent. Further, the groups of men and women alternated in which cohort had a higher unemployment rate in the ensuing years. The underemployment rate among respondents rose by three percentage points for men, over the eight years, compared to two for women. This reflection of the national situation is also evident in the impact the different years had on the ability of men and women to be fully employed relative to underemployed. With only one exception, through the seven years following 2008, men were more likely to be underemployed than fully employed, while this was only true in two years for women. Further, men were significantly more likely to be marginally attached to the Labour force than fully employed in all but two of the ensuing years after 2008. Whereas in no years was this significant for women. With regard to unemployment, women showed a slight tendency to be unemployed rather than fully employed in 3 of the years after 2008, while men only showed this once. Table 2 Regression Analysis Results. Likelihood of Being Underemployed Compared to Being Fully Employed Men Women Intercept -4.917 *** -3.235 *** Age 15 to 24 2.043 *** 1.073 *** Age 55 to 64 0.175 -0.181 * Age 65 plus -0.096 -0.350 Poor health 0.702 *** 0.301 *** Born in a non-English speaking country 0.738 *** 0.410 *** Highest education-post secondary -0.555 *** -0.084 Highest education- tertiary -0.507 *** -0.671 *** Person in couple household with children -0.255 ** 0.470 *** Person in single parent household 0.625 *** 0.727 *** Person in couple household without children -0.384 *** -0.208 ** Parent unemployment 0.334 0.689 *** Previous unemployment 2.140 1.594 Social capital -0.050 *** -0.071 *** Regional unemployment rate 10.17 *** 4.207 * 2009 0.294 * 0.067 2010 0.131 0.077 2011 0.336 ** 0.241 ** 2012 0.248 * 0.065 2013 0.494 *** 0.067 2014 0.492 *** 0.329 *** 2015 0.471 *** 0.147 (Source: HILDA Survey, DoE Small Area Labour Markets, authors’ calculations) Note ***p < 0.001; **p < 0.01; *p < 0.05 Table 3 Regression Analysis Results. Likelihood of Being Unemployed Compared to Being Fully Employed Men Women Intercept -5.019 *** -4.638 *** Age 15 to 24 1.458 *** 1.346 *** Age 55 to 64 -0.160 -0.467 *** Age 65 plus -1.671 *** -1.457 *** Poor health 0.459 *** 0.430 *** Born in a non-English speaking country 0.163 0.348 ** Highest education-post secondary -0.410 *** 0.079 Highest education- tertiary -0.297 ** -0.679 *** Person in couple household with children -0.462 *** -0.382 *** Person in single parent household 0.144 0.212 Person in couple household without children -0.470 *** -0.282 * Parent unemployment 0.853 *** 0.746 *** Previous unemployment 20.076 19.231 Social capital -0.081 *** -0.091 *** Regional unemployment rate 12.96 *** 1.534 2009 0.105 0.088 2010 0.140 0.326 * 2011 0.156 0.166 2012 0.408 ** 0.177 2013 0.252 0.308 * 2014 0.153 0.249 2015 0.220 0.380 * (Source: HILDA Survey, DoE Small Area Labour Markets, authors’ calculations) Note: ***p < 0.001; **p < 0.01; *p < 0.05 Table 4 Regression Analysis Results. Likelihood of Being Marginally Attached Compared to Being Fully Employed Men Women Intercept -5.000 *** -4.248 *** Age 15 to 24 1.594 *** 0.850 *** Age 55 to 64 0.763 *** 0.484 *** Age 65 plus 1.799 *** 1.274 *** Poor health 0.980 *** 0.716 *** Born in a non-English speaking country 0.014 0.333 ** Highest education-post secondary -0.482 *** -0.367 *** Highest education- tertiary -0.599 *** -0.730 *** Person in couple household with children -0.230 * 0.421 *** Person in single parent household 0.519 ** 0.611 *** Person in couple household without children -0.502 *** -0.218 * Parent unemployment 0.350 0.611 ** Previous unemployment 21.124 20.353 Social capital -0.069 *** -0.074 *** Regional unemployment rate 1.195 -1.460 2009 0.281 0.063 2010 0.420 * 0.178 2011 0.449 ** 0.227 2012 0.583 *** 0.163 2013 0.732 *** 0.200 2014 0.387 * 0.167 2015 0.191 0.005 (Source: HILDA Survey, DoE Small Area Labour Markets, authors’ calculations) Note: ***p < 0.001; **p < 0.01; *p < 0.05 4. Discussion This paper has sought to develop an analysis of the circumstances associated with labour underutilisation between men and women in Australia in the immediate aftermath of the Global Financial Crisis. Specifically, the paper uses available data from waves 8 to 15 of the Household, Income and Labour Dynamics in Australia (HILDA) survey and small area labour statistics. It undertakes an analysis of the differences between men and women in the likelihood of being underutilised in one of three forms of labour underutilisation (underemployment, unemployment and marginal labour force attachment) relative to being adequately employed. Considering the analysis undertaken in this paper, it is important to recognise that the identified outcomes and patterns have several potential limitations. Firstly, it is recognised that the paper does not seek to identify causal relationships. Instead, the analysis has identified associations between a range of independent variables net of other factors in the model and the dependent variable of interest, namely labour underutilisation. Moreover, while the independent variables covered a wide range of possible factors that might be hypothesised to impact underutilisation, in some cases, the indicators only provided broad proxies. Additionally, it may also be the case that several possible associations have not been accounted for due to the inability to identify appropriate data. These caveats aside, the research presented here provides insights into the issues surrounding labour underutilisation for men and women. Given the established literature dealing with labour underutilisation, it is not surprising that several individual characteristics such as formal higher education and health status are implicated in the employment outcomes for both men and women. That is, for both men and women, factors such as personal capabilities measured by higher levels of education or the absence of severe health issues are associated with a person’s ability to successfully engage in the labour market—not be underutilised—albeit with differing levels of influence. Other factors measuring the broader social context within which a person finds themselves are also influential across men and women. One such example is the employment history of parents. Regardless of gender, individuals who grew up in job-poor families were more likely to be unemployed, reflecting the potential impacts of intergenerational transfers of disadvantage discussed by authors such as Berloffa et al. ( 2016 ) and identified in the earlier Australian research by Baum and Mitchell ( 2010a ). The impact of social capital was also equally important across men and women in the sample. Using a proxy measurement of an individual’s level of social capital, the analysis found that regardless of gender, higher levels of social capital were associated with a lower likelihood of underutilisation. Over and above the factors that have similar impacts across men and women, several note-worthy differences were also identified in the analysis. Several studies have recognised the importance of regional labour market performance on employment outcomes for individuals. There is clear evidence presented here that men are more likely to be impacted by weakness in the local labour market than women, regardless of other factors. Men were more likely to be underemployed or unemployed as the level of unemployment in the local region increased. One explanation for this outcome may be that during the economic downturn following the Global Financial Crisis, men were more likely to find themselves unemployed or underemployed due to the types of jobs impacted by the downturn (Borland, 2009 ). As these jobs are likely to have a particular spatial pattern, the impact of the regional labour market performance indicator may be accounting for these patterns. Throughout much of the literature, it is often considered that gendered employment outcomes are in part a reflection of life cycle choices and constraints, particularly in the context of child care and through broader gender ideologies (Besamusca, Tijdens, Keune, & Steinmetz, 2015 ; Crompton & Harris, 1998 ; Steiber & Haas, 2012 ). In line with the previous Australian research by Baum and Mitchell ( 2010a ), the analysis undertaken in this paper found that family responsibilities may be influencing women’s engagement in different labour market outcomes with the presence of children within a couple only household is associated with a higher likelihood that women find themselves either be underemployed or being marginally attached to the labour force. The opposite is found concerning couple only households, where both men and women are significantly less likely to be in a state of underutilisation. The presence of children in a single-parent household raises the likelihood that both men and women would be underemployed or marginally attached to the labour force, suggesting that the need to provide childcare is impacting labour market outcomes. The finding that the need to provide child care is impacting employment outcomes is at odds with several other existing studies that report no such association (Cam, 2014 ; Rodríguez Hernández, 2021 ) and, especially concerning the impact of women in couple households, maybe a function of how policy in different countries enable or hinder women’s engagement in the labour market (Signorelli, Choudhry, & Marelli, 2012 ). The final noteworthy finding from the analysis relates to the last research question posed for this paper—the impact of the broader macroeconomy over time. Again, men were more likely to be negatively impacted by more general macro-economic conditions (measured by year) than women, especially in terms of being underemployed or marginal to the labour market. It also appeared that men were more likely to be negatively impacted across more and more extended periods. For the years 2009 and 2011 to 2015, men were more likely to be underemployed than fully employed than the beginning of the GFC (2008) and were more likely to be marginally attached to the labour market during the years 2010 to 2014. One possible explanation for these patterns is that overall economic conditions following the GFC were less in favour of men than women. Hence, men were more likely to face underemployment as hours were cut or where they reduced their participation in the labour market (become marginal to the labour force) (Plumb, Baker, & Spence, 2010 ). In addition, these patterns were also likely to be explained by the uneven impacts of government fiscal policy, which, when wound back following an initial increase at the start of the GFC, had differing effects on employment outcomes. If one of the reasons for analysing the drivers of individual labour underutilisation is to contribute to debates around policy, the findings of this paper provide a valuable addition to the labour market evidence base. The impact of individual-level characteristics on underutilisation are evidence of the need to progress the employment capacity of an individual through place neutral approaches including skills training. This has been a focus of a substantial amount of Australian labour market policy in the past. However, as pointed out elsewhere (Baum et al., 2008 ), a focus on these people based or place neutral policies can only be seen as a necessary but not sufficient condition towards improving labour market outcomes. An emphasis on the strength and performance of local labour markets through place-based policies will provide demand-side approaches that complement policies targeting factors such as skill improvement. The final take-home message from this research relates to the responsibility of governments to act as an enabler for the inclusion of individuals into all aspects of society, including the paid labour market (Signorelli et al., 2012 ). While there is significant policy discussion about individuals taking more responsibility for their own employment, as has been illustrated here and elsewhere (Baum et al., 2008 ), governments must actively pursue policy approaches and programs that ensure positive labour market outcomes for all. References Acosta-Ballesteros, J., Osorno-del Rosal, M. d. P., & Rodríguez-Rodríguez, O. M. (2021). Measuring the effect of gender segregation on the gender gap in time-related underemployment. Journal for Labour Market Research, 55 (1), 1–16. Albanesi, S., & Şahin, A. (2018). The gender unemployment gap. Review of Economic Dynamics, 30 , 47–67. Australian Government. (2021). Labour Market Information Portal. Retrieved from https://lmip.gov.au/ Baum, S., Bill, A., & Mitchell, W. (2008). Labour underutilisation in metropolitan labour markets in Australia: individual characteristics, personal circumstances and local labour markets. Urban Studies, 45 (5–6), 1193–1216. Baum, S., & Mitchell, W. F. (2010a). Labour underutilisation and gender: Unemployment versus hidden-unemployment. Population Research and Policy Review, 29 (2), 233–248. Baum, S., & Mitchell, W. F. (2010b). People, space and place: A multidimensional analysis of unemployment in metropolitan labour markets. Geographical Research, 48 (1), 13–23. Berloffa, G., Filandri, M., Matteazzi, E., Nazio, T., Negri, N., O’Reilly, J.,.. . Zuccotti, C. (2016). Work-poor and work-rich families: Influence on youth labour market outcomes. Besamusca, J., Tijdens, K., Keune, M., & Steinmetz, S. (2015). Working women worldwide. Age effects in female labor force participation in 117 countries. World Development, 74 , 123–141. Borland, J. (2009). What happens to the Australian labour market in recessions? Australian Economic Review, 42 (2), 232–242. Cam, S. (2014). The underemployed: evidence from the UK labour force survey for a conditionally gendered top-down model? Journal of Social Science Studies, 1 (2), 47–65. Campbell, I. (2008). Pressing towards Full Employment?: The Persistence of Underemployment in Australia. Journal of Australian Political Economy, The(61), 156–180. Croissant, Y. (2012). Estimation of multinomial logit models in R: The mlogit Packages. R package version 0.2-2. URL : http://cran . r-project . org/web/packages/mlogit/vignettes/mlogit . pdf . Crompton, R., & Harris, F. (1998). Explaining women's employment patterns:'orientations to work'revisited. British journal of Sociology, 118–136. Department of Higher and Further Education. (2002). Report of the Taskforce on Employability and Long-Term Unemployment . Retrieved from Doran, J., & Fingleton, B. (2016). Employment resilience in Europe and the 2008 economic crisis: Insights from micro-level data. Regional Studies, 50 (4), 644–656. Doran, J., & Fingleton, B. (2018). US metropolitan area resilience: insights from dynamic spatial panel estimation. Environment and Planning A: Economy and Space, 50 (1), 111–132. Elsby, M. W., Hobijn, B., & Sahin, A. (2010). The labor market in the Great Recession . Retrieved from Flinn, C. J., & Heckman, J. J. (1983). Are unemployment and out of the labor force behaviorally distinct labor force states? Journal of labor economics, 1 (1), 28–42. Hoynes, H., Miller, D. L., & Schaller, J. (2012). Who suffers during recessions? Journal of Economic Perspectives, 26 (3), 27–48. Kjeldstad, R., & Nymoen, E. H. (2012). Underemployment in a gender-segregated labour market. Economic and Industrial Democracy, 33 (2), 207–224. Leppel, K., & Clain, S. H. (1988). The growth in involuntary part-time employment of men and women. Applied Economics, 20 (9), 1155–1166. McQuaid, R. W., & Lindsay, C. (2005). The concept of employability. Urban Studies, 42 (2), 197–219. Mitchell, W. F., & Carlson, E. (2000). Beyond the unemployment rate–labour underutilisation and underemployment in Australia and the USA. Centre of Full Employment and Equity Working Paper , 00–06. Plumb, M., Baker, M., & Spence, G. (2010). The labour market during the 2008–2009 downturn. Reserve Bank of Australia Bulletin, 1–6. Rodríguez Hernández, J. E. (2018). Factors determining labor underutilization in Spain by gender before and after the economic crisis. Economic and Industrial Democracy, 0143831X17752266. Rodríguez Hernández, J. E. (2021). Factors determining labor underutilization in Spain by gender before and after the economic crisis. Economic and Industrial Democracy, 42 (1), 92–115. Signorelli, M., Choudhry, M., & Marelli, E. (2012). The impact of financial crises on female labour. The European Journal of Development Research, 24 (3), 413–433. Steiber, N., & Haas, B. (2012). Advances in explaining women's employment patterns. Socio-Economic Review, 10 (2), 343–367. Stimson, R., Mitchell, W., Flanagan, M., Baum, S., & Shyy, T.-K. (2016). Demarcating functional economic regions across Australia differentiated by work participation categories. Australasian Journal of Regional Studies, 22 (1), 27. Van Ham, M., Mulder, C. H., & Hooimeijer, P. (2001). Local underemployment and the discouraged worker effect. Urban Studies, 38 (10), 1733–1751. Wilkins, R. (2006). Personal and Job Characteritics Associated with Underemployment . Retrieved from https://melbourneinstitute.unimelb.edu.au/assets/documents/hilda-bibliography/working-discussion-research-papers/2006/Wilkins_Personal_and_Job_Characteristics.pdf Wilkins, R., Vera-Toscano, E., Botha, F., & Dahmann, S. (2021). The Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 19. Retrieved from https://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_file/0009/3963249/HILDA-Statistical-Report-2021.pdf Declarations Competing interests: The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2022 Read the published version in The Indian Journal of Labour Economics → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1495530","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":93992143,"identity":"8c3b7f1f-79ca-4ab8-84ff-019f433637fc","order_by":0,"name":"Scott Baum","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFACHhBhw8DAzPjgQwLDASAngSgtaUAtzIYzEhKI13IYiIFaGIjRIt/Ae/Azb855u+3tzIwND3/cYeBnzzFg+NmGW4vBAb5kad5tt5PnHAZqSUh4xiDZ88aAsRefFgYeA7AWCWb+4w8SEg4zGNwA2sKLR4t8A4/xb95t54BawLYcZrAHamH8i0cLwwEeM6AtB+zgWgwkcgyY8dlicJgvzXLutuQEiJa0wzwSZ54VHJY5h8dh7b2Hb7zdZmcvwX+YsfGHzWE5/vbkjQ/flOFxGDOESmyA8sHRdACPBjiwJ0bRKBgFo2AUjFAAAFmXThE1zcDHAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1711-2087","institution":"Griffith University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Scott","middleName":"","lastName":"Baum","suffix":""},{"id":93992350,"identity":"4cdb9625-9fe5-4877-be62-34ffd5842b0d","order_by":1,"name":"William Mitchell","email":"","orcid":"","institution":"University of Newcastle","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Mitchell","suffix":""}],"badges":[],"createdAt":"2022-03-28 02:54:29","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-1495530/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1495530/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41027-022-00363-z","type":"published","date":"2022-04-01T18:50:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":19798216,"identity":"62d672f6-067d-4312-b9c1-b06e02809e0d","added_by":"auto","created_at":"2022-03-30 20:43:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15808,"visible":true,"origin":"","legend":"\u003cp\u003eUnemployment, Underemployment and Underutilization Men and Women, Australia, 2007-2019 (trend data) \u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003e(Source: Australian Bureau of Statistics, Labour Force Australia, cat no. 6202.0)\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1495530/v1/2bf7d6aca551c9ca6b9d49cd.png"},{"id":19871284,"identity":"170dd186-8aae-4515-89e5-38b9842b7ed1","added_by":"auto","created_at":"2022-04-01 18:50:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":272625,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1495530/v1/fb64b5c4-84a4-4f6f-8eba-8d495ba34ba9.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eEmployment Outcomes for Men and Women Following an Economic Downturn: Labour Underutilisation in Australia\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLike all large economies, the Australian labour market is characterised by uneven and volatile performance across different segments. While some groups appear resilient as the economy changes, others are more likely to face disadvantages as opportunities shift, and precarious employment situations become increasingly problematic. In recent periods attention has been given to poor labour market outcomes, including unemployment and other forms of underutilisation. Statistics are now routinely provided on labour underemployment and general underutilisation levels, and commensurately, greater academic and policy research has gone towards understanding these broader states of labour market disadvantage. Research questions have focused on exploring the drivers of different aspects of underutilisation, understanding how long-term patterns have shifted and considering the individual and aggregate consequences of underutilisation (Baum, Bill, \u0026amp; Mitchell, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Campbell, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mitchell \u0026amp; Carlson, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Rodr\u0026iacute;guez Hern\u0026aacute;ndez, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). As with the narrow concept of unemployment, it is generally understood that an effective evidence-based suite of policy interventions should be possible by understanding the nature of underutilisation.\u003c/p\u003e\n\u003cp\u003eComparing the patterns of labour underutilisation for women and men is the focus of this paper. Its primary purpose is to consider the factors associated with underutilisation, including the characteristics of at-risk individuals and the features of the local labour markets, and the macro-economy that individuals operate in. The analysis in the paper provides a unique opportunity to consider how these cross-cutting factors have impacted labour market outcomes by utilising panel data regarding labour market outcomes for women and men and linking individual data to other broader factors at the macro level. The data covers the years 2008 to 2015 and is taken from the Household, Income and Labour Dynamics Australia (HILDA) survey and is combined with regional labour market data obtained from the Australian Bureau of Statistics. This dataset allows the following questions to be addressed:\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e1. What was the effect of supply-side characteristics on the risk of an individual\u0026rsquo;s labour being underutilised?\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e2. What was the effect of aggregate/ spatial demand-side characteristics on the risk of an individual\u0026rsquo;s labour being underutilised? and\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e3. What was the effect of macro-economic forces in the post-GFC period on an individual\u0026rsquo;s underutilised labour?\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec2\"\u003e\n \u003ch2\u003e1.1 The Extent of Labour Underutilisation in Australia\u003c/h2\u003e\n \u003cp\u003eThe Australian Bureau of Statistics regularly provides data regarding the extent and scope of labour underutilisation within Australia. Taking the period of 2007/8, when the Global Financial Crisis began to bite until the most recent period, a general worsening of labour market outcomes can be noted. While there was a sharp increase in all measures in the immediate period of the GFC, the consequences for both men and women have continued to deteriorate until 2019 and have yet to return to pre-GFC levels. This is despite an improvement in aggregate labour market figures over the same time. Moreover, the post GFC period has seen underemployment taking a larger share of the total underutilisation rate than had previously been the case. In terms of magnitudes, for women, the headline unemployment rate moved from 4.9% before the GFC to peak at 6.2% in June 2015, while underemployment moved from 8.2\u0026ndash;10.7% over a similar period. The combined underutilisation rate moved from 13.0 per cent before the GFC to a high of 16.9 per cent at the end of 2014 before declining to the current (September 2019) level of 15.4 per cent (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For men, the headline unemployment rate moved from 4.0% before the GFC to peak at 6.3% in June 2015, while underemployment moved from 4.7\u0026ndash;6.9% over a similar period. The combined underutilisation rate moved from 8.9 per cent before the GFC to a high of 12.4 per cent at the end of 2014 before declining to the current (September 2019) level of 12.2 per cent (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). If hidden unemployment (those who are unemployed, not actively looking for work, but who would take a job) is added to these figures, conservative estimates might add an extra 5 per cent to the total underemployment rate suggesting a much more severe issue.\u003c/p\u003e\n \u003cp\u003eReflecting the official statistics, empirical research based on survey data has often shown that women are more likely to find themselves underemployed or marginally attached to the labour force than men when a range of socio-demographic characteristics is controlled for. In contrast, men were more likely to face unemployment. Such differences clearly illustrate the gendered nature of segmented labour markets (Baum et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) and suggest that in contrast to some arguments (Flinn \u0026amp; Heckman, \u003cspan class=\"CitationRef\"\u003e1983\u003c/span\u003e) for men and women, the different states of underutilisation are quite distinct.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e1.2 Determinants of Underutilisation\u003c/h2\u003e\n \u003cp\u003eGiven the distinct states of underutilisation experienced by males and females, it is useful, as this paper does, to consider how the determinants in labour market engagement might differ within gender segmented outcomes. While conceptual frameworks differ, researchers have increasingly utilised a broad employability framework (Baum et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Baum \u0026amp; Mitchell, \u003cspan class=\"CitationRef\"\u003e2010b\u003c/span\u003e; Doran \u0026amp; Fingleton, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wilkins, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) that employs aspects of both labour supply and labour demand. This broad framework provides an understanding of a person\u0026rsquo;s ability to transition into and within labour markets and to realise their potential via accessible employment opportunities. For particular individuals, employability might depend on their skills level or the way that their personal characteristics are presented. Employability may also be associated with the broader social context within which employment is sought as well as the broader economic context (Department of Higher and Further Education, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). These supply and demand (employability) characteristics include a person\u0026rsquo;s formal education, their health status, proficiency in English, the employment history of their family, their social networks and external factors such as the strength of the labour market they operate in and the condition of the broader macroeconomy (McQuaid \u0026amp; Lindsay, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn many cases, the existing research has identified that particular labour supply and demand characteristics such as education or age tend to have similar impacts regardless of gender. The Australian work by Baum and Mitchell (\u003cspan class=\"CitationRef\"\u003e2010a\u003c/span\u003e) using a single wave of the Household Income and Labour Dynamics Australia (HILDA) survey identified that factors including age, ethnic background, English proficiency, family employment history and the presence of critical social networks were significant explanatory variables for both males and females when comparing the risk of unemployment and marginal attachment relative to being fully employed. Similar findings for the Australian context are also reported in the research by Wilkins (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn a similar vein, the more recent work by Rodr\u0026iacute;guez Hern\u0026aacute;ndez (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) on underutilisation in Spain during the Global Financial Crisis identified that at the end of the economic crisis, factors such as age, education level and living with elderly parents or parents-in-law were significant in explaining the employment outcomes for both men and women. The authors also found that the strength of the local labour market was also important for both males and females, with those living in poorer performing regions being more likely to be disadvantaged in terms of employment.\u003c/p\u003e\n \u003cp\u003eFocusing only on the rise of involuntary part-time work (underutilisation), the early research by Leppel and Clain (\u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e) found very little by way of differences in the drivers of involuntary part-time employment in their sample of males and females in the United States. In particular, they found that regardless of gender, economic slowdowns and service sector employment growth were significant factors in increasing the level of involuntary part-time work. In contrast, declining proportions of young children and a rising skill level of the employed had the opposite effect. The research by (Van Ham, Mulder, \u0026amp; Hooimeijer, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e) also suggested that there may be only minor differences in factors contributing to the likelihood of labour market outcomes for males and females.\u003c/p\u003e\n \u003cp\u003eMany of the labour supply and demand factors identified by researchers suggest little or no bias along gender lines. However, moving beyond these findings, there has also been analysis that has identified significant gender-specific effects, although often with no clear direction of the association. The circumstances where differences in gender outcomes are often considered associated with the types of occupation or industry an individual is employed in or the gendered stereotypes associated with the need to provide child care or similar caring functions. It is also argued that in line with potential differences in industry or occupational characteristics, males and females are impacted differently during economic downturns. This may also be an essential labour demand aspect accounting for differential outcomes.\u003c/p\u003e\n \u003cp\u003eThe Australian work by Baum and Mitchell (\u003cspan class=\"CitationRef\"\u003e2010a\u003c/span\u003e) referred to above found that males tended to be impacted by the strength of local employment opportunities, which may have a particular industry or occupation characteristic, especially in terms of them being unemployed, while for females labour underutilisation (marginal labour market attachment) tended to be associated with a need or desire to provide childcare. This finding that the presence of children was associated with a reduced likelihood of women being adequately employed was also highlighted by Kjeldstad and Nymoen (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) in their study of the Norwegian labour market. In their analysis, the authors found that the likelihood that a female would be underemployed rose as the age of children increased. The finding that the presence of children was associated with an increased probability of marginal attachment to the labour force by women is contested by the conclusions of others who argue that the presence of children does not necessarily result in increases in the likelihood of underemployment (Cam, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rodr\u0026iacute;guez Hern\u0026aacute;ndez, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wilkins, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). For the Spanish sample utilised by Rodr\u0026iacute;guez Hern\u0026aacute;ndez (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), the authors find that the presence of younger aged children reduces the likelihood that women would find themselves underemployed or a male would be unemployed. The Australian research by Wilkins (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) also shows contested outcomes regarding the association between child care responsibilities and labour underutilisation. The difference between this analysis and other Australian work is explained by differences in samples and measurement of variables.\u003c/p\u003e\n \u003cp\u003eOver and above the issue of childcare, research has also pointed to the impact that industry or occupation characteristics can have on gendered labour market outcomes. Here the problem is associated with the likelihood that some industry sectors or occupations are more likely to be related to part-time work and therefore more likely to be associated with underutilisation. The recent Spanish work by Acosta-Ballesteros, Osorno-del Rosal, and Rodr\u0026iacute;guez-Rodr\u0026iacute;guez (2021) illustrates this point suggesting that being employed in female-dominated occupations and industries results in a higher likelihood of underemployment than in male-dominated ones. Similar arguments are made about the impact of economic downturns on the labour market outcomes of men and women. Researchers note that in some circumstances, male-dominated occupations have tended to be hardest hit by downturns resulting in males being more likely to find themselves unemployed or marginally attached to the labour market. For example, Hoynes, Miller, and Schaller (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), considering employment outcomes across several recessionary periods, found that men experienced more cyclical labour market outcomes when compared to women, an outcome they argue is the result of men being employed in more highly cyclical industries including construction, and manufacturing. Similar findings have been discussed in Elsby, Hobijn, and Sahin (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) and (Albanesi \u0026amp; Şahin, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"2. Data And Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eThis paper aims to model individual labour market outcomes as a function of a range of individual-level socioeconomic and demographic variables, a temporal dimension, and a local labour market performance measure. To carry out such an analysis, data from two primary sources are utilised (1) Individual-level longitudinal panel data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, managed by the Melbourne Institute of Applied Economic and Social Research (Wilkins, Vera-Toscano, Botha, \u0026amp; Dahmann, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); and (b) aggregate level local labour market data from Small Area Labour Markets data, published by the Australian Commonwealth Department of Employment (Australian Government, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and aggregated into Functional Economic Regions.\u003c/p\u003e \u003cp\u003eThe individual-level data from the HILDA Survey follows a large cohort of Australians across consecutive years, gathering responses on various economic, social and labour/employment questions. The HILDA Survey began in 2000-01 (Wave 1) and has produced 16 consecutive output waves, with a high participant retention rate. This paper uses Waves 8 to 15 (2008\u0026ndash;2015) data.\u003c/p\u003e \u003cp\u003eThe Small Area Labour Markets data consists of regional labour force estimates of unemployment for approximately 2,200 Australian Bureau of Statistics Statistical Area 2 (SA2s) every quarter (Australian Government, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As the purpose of using the labour force estimates is to account for the impact of local labour markets on individuals\u0026rsquo; employment outcomes, the SA2 level data was aggregated into a form representing local labour market regions. We define an individual\u0026rsquo;s local labour market as the functional region they live in using the Centre of Full employment and Equity\u0026rsquo;s Functional Economic Regions (CFERs) (Stimson, Mitchell, Flanagan, Baum, \u0026amp; Shyy, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These regions, which cover the whole of Australia, are designed explicitly as labour markets, informed by the commuting patterns of workers throughout the country. The areas are unencumbered by administrative or political requirements and have been shown to produce better measures of labour market statistics. This is important as we use the region's unemployment rate to measure a region's influence on an individual's labour force outcomes. As the CFERs are made up of aggregations of Statistical Area 2s (SA2s), a region's unemployment rate is determined by the unemployment and labour force numbers of its constituent SA2s, as provided in the Small Area Labour Markets publication and aggregated up to the constituent CFER. Local labour market data was calculated to match each wave (year) of the HILDA data.\u003c/p\u003e \u003cp\u003eAn essential aspect of the dataset development was linking an individual respondent from the HILDA data to their specific functional economic region. This was achieved using the SA2 geography code provided for each respondent in the survey with the corresponding SA2 codes associated with each aggregate functional economic area. In this way, the dataset comprised a set of individual-level variables and an associated functional economic region indicator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Variables\u003c/h2\u003e \u003cp\u003eAs stated, the model is set up to determine the influence a range of individual and aggregate level explanatory variables have on the response variable, employment status. Employment status for a respondent is divided into one of four categories:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFully employed (FE) \u0026ndash; employed full-time, or employed part-time without wanting more work;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnderemployed (UDE) \u0026ndash; employed part-time and wanting more work;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnemployed (UNE) \u0026ndash; not employed and actively looking for work; and\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMarginally attached to the labour force (MALF) \u0026ndash; not employed and not actively looking for work but would work if a job became available.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eGiven the nature of the dependent variable, the appropriate model to use is a multinomial logit model with a categorical dependent variable. The logit compares a base response variable state to the other response variable states. To account for the panel nature of the data, the multinomial logit model is altered to introduce individual-specific random effects. This then becomes a mixed logit model, where the parameters are assumed to vary between individuals, thus taking into account the population's heterogeneity (Croissant, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Separate models are run for both men and women.\u003c/p\u003e \u003cp\u003eThe explanatory variables are listed in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below. Most of the explanatory variables are categorical variables where, like the response variable, a baseline reference category is chosen to which all the other categories are compared.\u003c/p\u003e \u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndependent Variables Used in Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference Variable\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge 15 to 24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e is aged between 15\u0026ndash;24 years at time \u003cem\u003et\u003c/em\u003e; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson aged between 25\u0026ndash;54 years at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge 55 to 64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e is aged between 55\u0026ndash;64 years at time \u003cem\u003et\u003c/em\u003e; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson aged between 25\u0026ndash;54 years at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge 65 plus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e is aged 65 years or greater at time \u003cem\u003et\u003c/em\u003e; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson aged between 25\u0026ndash;54 years at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e reports a long-term health condition at time \u003cem\u003et\u003c/em\u003e; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo long-term health condition reported at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorn in a non-English speaking country\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e was born in a non-English speaking country; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson born in English speaking country\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest education-post secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e\u0026rsquo;s highest level of education at time \u003cem\u003et\u003c/em\u003e is post-secondary (inc certificate and diploma); 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson has no post-school qualification at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest education- tertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e has completed tertiary level education at time \u003cem\u003et\u003c/em\u003e (bachelor degree and above); 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson has no post-school qualification at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerson in couple household with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e is part of a couple relationship with dependent children at time \u003cem\u003et\u003c/em\u003e; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson is single at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerson in single parent household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e is a single parent at time \u003cem\u003et\u003c/em\u003e; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson is single at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerson in couple household without children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e is part of a couple with dependent children at time \u003cem\u003et\u003c/em\u003e; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson is single at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParent unemployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if both parents of person \u003cem\u003ei\u003c/em\u003e were not in paid employment when person \u003cem\u003ei\u003c/em\u003e was 14; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAt least one of person\u0026rsquo;s parents were in paid employment when 14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious unemployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if person \u003cem\u003ei\u003c/em\u003e did not have a job anytime in the previous 12 months at time \u003cem\u003et\u003c/em\u003e; 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePerson had a job some time in last 12 months\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial capital/networks value for person \u003cem\u003ei\u003c/em\u003e at time \u003cem\u003et-1\u003c/em\u003e. This was calculated through a Principal Components Analysis of responses to 9 questions from HILDA survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional unemployment rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLog of the unemployment rate of the region (CFER) person \u003cem\u003ei\u003c/em\u003e is resident in at time \u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if period is time 2 (2009), 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear 1 (2008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if period is time 3 (2010), 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear 1 (2008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if period is time 4 (2011), 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear 1 (2008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if period is time 5 (2012), 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear 1 (2008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if period is time 6 (2013), 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear 1 (2008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if period is time 7 (2014), 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear 1 (2008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 if period is time 8 (2015), 0 otherwise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear 1 (2008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e present the regressions performed for the cohorts of men and women, comparing the likelihood of being fully employed to each of the underutilised states. A person\u0026rsquo;s age has a significant impact on their labour force status. Regardless of gender, a person aged 15 to 24 years old is more likely to have their available labour underutilised than being fully employed, compared to persons aged 25 to 54, with men having more substantial odds than women for each of the underutilisation states. Further, older workers, from 55 to 64 years and over 65 years of age, are more likely to be marginally attached to the labour force than those in the reference age group, regardless of gender, again with men having more substantial odds. However, the other significant impacts favour full employment to underutilisation. Women between 55 and 64 years are more likely to be fully employed than underemployed or unemployed than those in the reference age group. Meanwhile, men and women over 65 years are more likely to be fully employed than unemployed.\u003c/p\u003e \u003cp\u003eA person with a long-term health condition is significantly more likely to have their available labour underutilised rather than be fully employed, regardless of gender. Command of the English language affects a woman\u0026rsquo;s ability to be fully employed more than a man. A woman born in a non-English speaking country is more likely to be underemployed, unemployed or marginally attached to the labour force than fully employed, compared to women born in English speaking countries. However, for men, being born in a non-English speaking country only significantly increases their likelihood of being underemployed rather than fully employed; it does not significantly affect the possibility of being unemployed or marginally attached instead of fully employed.\u003c/p\u003e \u003cp\u003eEducation level also has a gender bias on a person\u0026rsquo;s ability to be fully employed. Men with a post-school qualification are more likely to be fully employed than in a state of labour underutilisation compared to men without a post-school qualification. However, women with a post-school qualification only increase their chances of being fully employed rather than marginally attached to the labour force, but it has no impact on their ability to be fully employed instead of underemployed or unemployed. On the other hand, a tertiary education significantly improves both cohorts\u0026rsquo; likelihood to be fully employed rather than underutilised, with this increasing a woman\u0026rsquo;s odds much more than a man\u0026rsquo;s across the board.\u003c/p\u003e \u003cp\u003eA person\u0026rsquo;s gender significantly impacts their ability to be fully employed if they are in a couple relationship with children. It seems the traditional roles of men and women remain strong. Men in these relationships are more likely to be fully employed than underutilised, compared to single men, but women in these relationships are more likely to be underemployed or marginally attached to the labour force than fully employed, compared to single women. Interestingly, women in these relationships are more likely to be fully employed than unemployed compared to single women. This reflects those traditional relationships where men are the primary income earners, and women work part-time or not at all. The fact that women in these relationships are more likely to be underemployed than fully employed reflects their willingness to take part-time jobs, perhaps to fulfil their home and caring duties outside this paid employment. Similarly, the fact that women in these relationships are more likely to be marginally attached to the labour force than fully employed, yet fully employed rather than unemployed, also reflects these women\u0026rsquo;s situation. Women in these relationships who do not have paid employment are generally supported financially and have things to fill in their time, so they do not need to look for work actively yet would take on work if available.\u003c/p\u003e \u003cp\u003eThere is no gender bias for single parents, with both men and women more likely to be underemployed or marginally attached to the labour force than fully employed, compared to single people. There is no significant relationship between being fully employed and unemployed for single parents. Similarly, there is no gender bias for people in couple relationships without children. In these relationships, both men and women are more likely to be fully employed than underutilised, with men having a more substantial likelihood across the board.\u003c/p\u003e \u003cp\u003eWomen are generally more influenced by their parents\u0026rsquo; employment situation when they were children than men. Women whose parents were not in paid employment when they were 14 are more likely to have their available labour underutilised than those with at least one parent in paid work. However, there is only a significant relationship for men in the same situation in being unemployed rather than fully employed.\u003c/p\u003e \u003cp\u003eThe strength of a person\u0026rsquo;s social capital and networks positively influences them being fully employed rather than underemployed, unemployed or marginally attached to the labour force. This is the case for both men and women, with women having slightly stronger odds of full employment given their social capital.\u003c/p\u003e \u003cp\u003eThe local labour market a man lives in has a more substantial influence on their chances of being fully employed than a woman. The higher a region\u0026rsquo;s unemployment rate, the more opportunity a man has of being underemployed or unemployed, while there is a much slighter chance of women being underemployed only. A region\u0026rsquo;s unemployment rate has no impact on whether a man or woman is marginally attached to the labour force rather than fully employed.\u003c/p\u003e \u003cp\u003eThe macroeconomic effects of the Global Financial Crisis hit men harder relative to women in terms of their ability to find and maintain full employment. As the first effects were felt nationally in late 2008, men had lower unemployment and underemployment rates than women. Still, these advantages were eroded mainly in terms of unemployment, and the gap narrowed in terms of underemployment. The same was true of HILDA respondents, with fully employed men as a proportion of those in or wanting work (including marginally attached men) falling from 87 per cent in 2008 to 81 per cent in 2015, while for women, it fell from 78 per cent to 74 per cent. Further, the groups of men and women alternated in which cohort had a higher unemployment rate in the ensuing years. The underemployment rate among respondents rose by three percentage points for men, over the eight years, compared to two for women.\u003c/p\u003e \u003cp\u003eThis reflection of the national situation is also evident in the impact the different years had on the ability of men and women to be fully employed relative to underemployed. With only one exception, through the seven years following 2008, men were more likely to be underemployed than fully employed, while this was only true in two years for women. Further, men were significantly more likely to be marginally attached to the Labour force than fully employed in all but two of the ensuing years after 2008. Whereas in no years was this significant for women. With regard to unemployment, women showed a slight tendency to be unemployed rather than fully employed in 3 of the years after 2008, while men only showed this once.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Analysis Results. Likelihood of Being Underemployed Compared to Being Fully Employed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.917 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.235 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 15 to 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.043 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.073 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 55 to 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.181 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 65 plus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.702 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.301 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn in a non-English speaking country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.738 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.410 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest education-post secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.555 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest education- tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.507 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.671 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in couple household with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.255 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.470 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in single parent household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.625 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.727 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in couple household without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.384 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.208 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.050 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.071 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional unemployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.17 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.207 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.294 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.336 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.241 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.248 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.494 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.492 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.329 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.471 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e(Source: HILDA Survey, DoE Small Area Labour Markets, authors\u0026rsquo; calculations)\u003c/sup\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e\u003csup\u003eNote\u003c/sup\u003e\u003c/strong\u003e \u003cp\u003e \u003csup\u003e***p \u0026lt; 0.001; **p \u0026lt; 0.01; *p \u0026lt; 0.05\u003c/sup\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Analysis Results. Likelihood of Being Unemployed Compared to Being Fully Employed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.019 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.638 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 15 to 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.458 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.346 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 55 to 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.467 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 65 plus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.671 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.457 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.459 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.430 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn in a non-English speaking country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.348 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest education-post secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.410 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest education- tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.297 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.679 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in couple household with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.462 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.382 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in single parent household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in couple household without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.470 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.282 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.853 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.746 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.081 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.091 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional unemployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.96 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.408 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.308 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.380 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e(Source: HILDA Survey, DoE Small Area Labour Markets, authors\u0026rsquo; calculations)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003eNote: ***p \u0026lt; 0.001; **p \u0026lt; 0.01; *p \u0026lt; 0.05\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Analysis Results. Likelihood of Being Marginally Attached Compared to Being Fully Employed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.000 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.248 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 15 to 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.594 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.850 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 55 to 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.763 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.484 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 65 plus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.799 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.274 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.980 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.716 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn in a non-English speaking country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.333 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest education-post secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.482 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.367 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest education- tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.599 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.730 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in couple household with children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.230 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.421 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in single parent household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.519 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.611 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson in couple household without children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.502 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.218 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.611 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.069 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.074 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional unemployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.420 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.449 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.583 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.732 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.387 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e(Source: HILDA Survey, DoE Small Area Labour Markets, authors\u0026rsquo; calculations)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003eNote: ***p \u0026lt; 0.001; **p \u0026lt; 0.01; *p \u0026lt; 0.05\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis paper has sought to develop an analysis of the circumstances associated with labour underutilisation between men and women in Australia in the immediate aftermath of the Global Financial Crisis. Specifically, the paper uses available data from waves 8 to 15 of the Household, Income and Labour Dynamics in Australia (HILDA) survey and small area labour statistics. It undertakes an analysis of the differences between men and women in the likelihood of being underutilised in one of three forms of labour underutilisation (underemployment, unemployment and marginal labour force attachment) relative to being adequately employed.\u003c/p\u003e \u003cp\u003eConsidering the analysis undertaken in this paper, it is important to recognise that the identified outcomes and patterns have several potential limitations. Firstly, it is recognised that the paper does not seek to identify causal relationships. Instead, the analysis has identified associations between a range of independent variables net of other factors in the model and the dependent variable of interest, namely labour underutilisation. Moreover, while the independent variables covered a wide range of possible factors that might be hypothesised to impact underutilisation, in some cases, the indicators only provided broad proxies. Additionally, it may also be the case that several possible associations have not been accounted for due to the inability to identify appropriate data.\u003c/p\u003e \u003cp\u003eThese caveats aside, the research presented here provides insights into the issues surrounding labour underutilisation for men and women. Given the established literature dealing with labour underutilisation, it is not surprising that several individual characteristics such as formal higher education and health status are implicated in the employment outcomes for both men and women. That is, for both men and women, factors such as personal capabilities measured by higher levels of education or the absence of severe health issues are associated with a person\u0026rsquo;s ability to successfully engage in the labour market\u0026mdash;not be underutilised\u0026mdash;albeit with differing levels of influence. Other factors measuring the broader social context within which a person finds themselves are also influential across men and women. One such example is the employment history of parents. Regardless of gender, individuals who grew up in job-poor families were more likely to be unemployed, reflecting the potential impacts of intergenerational transfers of disadvantage discussed by authors such as Berloffa et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and identified in the earlier Australian research by Baum and Mitchell (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e). The impact of social capital was also equally important across men and women in the sample. Using a proxy measurement of an individual\u0026rsquo;s level of social capital, the analysis found that regardless of gender, higher levels of social capital were associated with a lower likelihood of underutilisation.\u003c/p\u003e \u003cp\u003eOver and above the factors that have similar impacts across men and women, several note-worthy differences were also identified in the analysis. Several studies have recognised the importance of regional labour market performance on employment outcomes for individuals. There is clear evidence presented here that men are more likely to be impacted by weakness in the local labour market than women, regardless of other factors. Men were more likely to be underemployed or unemployed as the level of unemployment in the local region increased. One explanation for this outcome may be that during the economic downturn following the Global Financial Crisis, men were more likely to find themselves unemployed or underemployed due to the types of jobs impacted by the downturn (Borland, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). As these jobs are likely to have a particular spatial pattern, the impact of the regional labour market performance indicator may be accounting for these patterns.\u003c/p\u003e \u003cp\u003eThroughout much of the literature, it is often considered that gendered employment outcomes are in part a reflection of life cycle choices and constraints, particularly in the context of child care and through broader gender ideologies (Besamusca, Tijdens, Keune, \u0026amp; Steinmetz, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Crompton \u0026amp; Harris, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Steiber \u0026amp; Haas, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In line with the previous Australian research by Baum and Mitchell (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e), the analysis undertaken in this paper found that family responsibilities may be influencing women\u0026rsquo;s engagement in different labour market outcomes with the presence of children within a couple only household is associated with a higher likelihood that women find themselves either be underemployed or being marginally attached to the labour force. The opposite is found concerning couple only households, where both men and women are significantly less likely to be in a state of underutilisation. The presence of children in a single-parent household raises the likelihood that both men and women would be underemployed or marginally attached to the labour force, suggesting that the need to provide childcare is impacting labour market outcomes. The finding that the need to provide child care is impacting employment outcomes is at odds with several other existing studies that report no such association (Cam, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rodr\u0026iacute;guez Hern\u0026aacute;ndez, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and, especially concerning the impact of women in couple households, maybe a function of how policy in different countries enable or hinder women\u0026rsquo;s engagement in the labour market (Signorelli, Choudhry, \u0026amp; Marelli, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe final noteworthy finding from the analysis relates to the last research question posed for this paper\u0026mdash;the impact of the broader macroeconomy over time. Again, men were more likely to be negatively impacted by more general macro-economic conditions (measured by year) than women, especially in terms of being underemployed or marginal to the labour market. It also appeared that men were more likely to be negatively impacted across more and more extended periods. For the years 2009 and 2011 to 2015, men were more likely to be underemployed than fully employed than the beginning of the GFC (2008) and were more likely to be marginally attached to the labour market during the years 2010 to 2014. One possible explanation for these patterns is that overall economic conditions following the GFC were less in favour of men than women. Hence, men were more likely to face underemployment as hours were cut or where they reduced their participation in the labour market (become marginal to the labour force) (Plumb, Baker, \u0026amp; Spence, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition, these patterns were also likely to be explained by the uneven impacts of government fiscal policy, which, when wound back following an initial increase at the start of the GFC, had differing effects on employment outcomes.\u003c/p\u003e \u003cp\u003eIf one of the reasons for analysing the drivers of individual labour underutilisation is to contribute to debates around policy, the findings of this paper provide a valuable addition to the labour market evidence base. The impact of individual-level characteristics on underutilisation are evidence of the need to progress the employment capacity of an individual through place neutral approaches including skills training. This has been a focus of a substantial amount of Australian labour market policy in the past. However, as pointed out elsewhere (Baum et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), a focus on these people based or place neutral policies can only be seen as a necessary but not sufficient condition towards improving labour market outcomes. An emphasis on the strength and performance of local labour markets through place-based policies will provide demand-side approaches that complement policies targeting factors such as skill improvement. The final take-home message from this research relates to the responsibility of governments to act as an enabler for the inclusion of individuals into all aspects of society, including the paid labour market (Signorelli et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). While there is significant policy discussion about individuals taking more responsibility for their own employment, as has been illustrated here and elsewhere (Baum et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), governments must actively pursue policy approaches and programs that ensure positive labour market outcomes for all.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcosta-Ballesteros, J., Osorno-del Rosal, M. d. P., \u0026amp; Rodr\u0026iacute;guez-Rodr\u0026iacute;guez, O. M. (2021). Measuring the effect of gender segregation on the gender gap in time-related underemployment. Journal for Labour Market Research, \u003cem\u003e55\u003c/em\u003e(1), 1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbanesi, S., \u0026amp; Şahin, A. (2018). The gender unemployment gap. Review of Economic Dynamics, \u003cem\u003e30\u003c/em\u003e, 47\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustralian Government. (2021). Labour Market Information Portal. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lmip.gov.au/\u003c/span\u003e\u003cspan address=\"https://lmip.gov.au/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaum, S., Bill, A., \u0026amp; Mitchell, W. (2008). Labour underutilisation in metropolitan labour markets in Australia: individual characteristics, personal circumstances and local labour markets. Urban Studies, \u003cem\u003e45\u003c/em\u003e(5\u0026ndash;6), 1193\u0026ndash;1216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaum, S., \u0026amp; Mitchell, W. F. (2010a). Labour underutilisation and gender: Unemployment versus hidden-unemployment. Population Research and Policy Review, \u003cem\u003e29\u003c/em\u003e(2), 233\u0026ndash;248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaum, S., \u0026amp; Mitchell, W. F. (2010b). People, space and place: A multidimensional analysis of unemployment in metropolitan labour markets. Geographical Research, \u003cem\u003e48\u003c/em\u003e(1), 13\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerloffa, G., Filandri, M., Matteazzi, E., Nazio, T., Negri, N., O\u0026rsquo;Reilly, J.,.. . Zuccotti, C. (2016). Work-poor and work-rich families: Influence on youth labour market outcomes.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBesamusca, J., Tijdens, K., Keune, M., \u0026amp; Steinmetz, S. (2015). Working women worldwide. Age effects in female labor force participation in 117 countries. World Development, \u003cem\u003e74\u003c/em\u003e, 123\u0026ndash;141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorland, J. (2009). What happens to the Australian labour market in recessions? Australian Economic Review, \u003cem\u003e42\u003c/em\u003e(2), 232\u0026ndash;242.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCam, S. (2014). The underemployed: evidence from the UK labour force survey for a conditionally gendered top-down model? Journal of Social Science Studies, \u003cem\u003e1\u003c/em\u003e(2), 47\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell, I. (2008). Pressing towards Full Employment?: The Persistence of Underemployment in Australia. Journal of Australian Political Economy, The(61), 156\u0026ndash;180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCroissant, Y. (2012). Estimation of multinomial logit models in R: The mlogit Packages. \u003cem\u003eR package version 0.2-2. URL\u003c/em\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cran\u003c/span\u003e\u003cspan address=\"http://cran\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003er-project\u003c/span\u003e\u003cspan address=\"http://r-project\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eorg/web/packages/mlogit/vignettes/mlogit\u003c/span\u003e\u003cspan address=\"http://org/web/packages/mlogit/vignettes/mlogit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003epdf\u003c/span\u003e\u003cspan address=\"http://pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrompton, R., \u0026amp; Harris, F. (1998). Explaining women's employment patterns:'orientations to work'revisited. British journal of Sociology, 118\u0026ndash;136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepartment of Higher and Further Education. (2002). \u003cem\u003eReport of the Taskforce on Employability and Long-Term Unemployment\u003c/em\u003e. Retrieved from\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoran, J., \u0026amp; Fingleton, B. (2016). Employment resilience in Europe and the 2008 economic crisis: Insights from micro-level data. Regional Studies, \u003cem\u003e50\u003c/em\u003e(4), 644\u0026ndash;656.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoran, J., \u0026amp; Fingleton, B. (2018). US metropolitan area resilience: insights from dynamic spatial panel estimation. Environment and Planning A: Economy and Space, \u003cem\u003e50\u003c/em\u003e(1), 111\u0026ndash;132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsby, M. W., Hobijn, B., \u0026amp; Sahin, A. (2010). \u003cem\u003eThe labor market in the Great Recession\u003c/em\u003e. Retrieved from\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlinn, C. J., \u0026amp; Heckman, J. J. (1983). Are unemployment and out of the labor force behaviorally distinct labor force states? Journal of labor economics, \u003cem\u003e1\u003c/em\u003e(1), 28\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoynes, H., Miller, D. L., \u0026amp; Schaller, J. (2012). Who suffers during recessions? Journal of Economic Perspectives, \u003cem\u003e26\u003c/em\u003e(3), 27\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKjeldstad, R., \u0026amp; Nymoen, E. H. (2012). Underemployment in a gender-segregated labour market. Economic and Industrial Democracy, \u003cem\u003e33\u003c/em\u003e(2), 207\u0026ndash;224.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeppel, K., \u0026amp; Clain, S. H. (1988). The growth in involuntary part-time employment of men and women. Applied Economics, \u003cem\u003e20\u003c/em\u003e(9), 1155\u0026ndash;1166.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcQuaid, R. W., \u0026amp; Lindsay, C. (2005). The concept of employability. Urban Studies, \u003cem\u003e42\u003c/em\u003e(2), 197\u0026ndash;219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitchell, W. F., \u0026amp; Carlson, E. (2000). Beyond the unemployment rate\u0026ndash;labour underutilisation and underemployment in Australia and the USA. \u003cem\u003eCentre of Full Employment and Equity Working Paper\u003c/em\u003e, 00\u0026ndash;06.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlumb, M., Baker, M., \u0026amp; Spence, G. (2010). The labour market during the 2008\u0026ndash;2009 downturn. Reserve Bank of Australia Bulletin, 1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez Hern\u0026aacute;ndez, J. E. (2018). Factors determining labor underutilization in Spain by gender before and after the economic crisis. Economic and Industrial Democracy, 0143831X17752266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez Hern\u0026aacute;ndez, J. E. (2021). Factors determining labor underutilization in Spain by gender before and after the economic crisis. Economic and Industrial Democracy, \u003cem\u003e42\u003c/em\u003e(1), 92\u0026ndash;115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSignorelli, M., Choudhry, M., \u0026amp; Marelli, E. (2012). The impact of financial crises on female labour. The European Journal of Development Research, \u003cem\u003e24\u003c/em\u003e(3), 413\u0026ndash;433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteiber, N., \u0026amp; Haas, B. (2012). Advances in explaining women's employment patterns. Socio-Economic Review, \u003cem\u003e10\u003c/em\u003e(2), 343\u0026ndash;367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStimson, R., Mitchell, W., Flanagan, M., Baum, S., \u0026amp; Shyy, T.-K. (2016). Demarcating functional economic regions across Australia differentiated by work participation categories. Australasian Journal of Regional Studies, \u003cem\u003e22\u003c/em\u003e(1), 27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Ham, M., Mulder, C. H., \u0026amp; Hooimeijer, P. (2001). Local underemployment and the discouraged worker effect. Urban Studies, \u003cem\u003e38\u003c/em\u003e(10), 1733\u0026ndash;1751.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkins, R. (2006). \u003cem\u003ePersonal and Job Characteritics Associated with Underemployment\u003c/em\u003e. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://melbourneinstitute.unimelb.edu.au/assets/documents/hilda-bibliography/working-discussion-research-papers/2006/Wilkins_Personal_and_Job_Characteristics.pdf\u003c/span\u003e\u003cspan address=\"https://melbourneinstitute.unimelb.edu.au/assets/documents/hilda-bibliography/working-discussion-research-papers/2006/Wilkins_Personal_and_Job_Characteristics.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkins, R., Vera-Toscano, E., Botha, F., \u0026amp; Dahmann, S. (2021). \u003cem\u003eThe Household, Income and Labour Dynamics in Australia Survey: Selected Findings from Waves 1 to 19.\u003c/em\u003e Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_file/0009/3963249/HILDA-Statistical-Report-2021.pdf\u003c/span\u003e\u003cspan address=\"https://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_file/0009/3963249/HILDA-Statistical-Report-2021.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"ccdca033-166e-4d65-95fe-4faefc772d6e","identifier":"10.13039/501100000923","name":"Australian Research Council","awardNumber":"DP160100532","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Griffith University","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1495530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1495530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Australia, as elsewhere, there has been continuing interest in understanding questions regarding unequal employment opportunities. While aggregate patterns provide a useful overview, it is insightful to consider employment outcomes across segmented markets. One such segmented market is between men and women, where it is widely understood that labour market engagement opportunities will differ. This paper provides an investigation of these uneven labour market outcomes. It presents an analysis of labour underutilisation for men and women using panel data, taking account of both individual-level supply-side factors together with the strength of the local labour market (demand-side) and the performance of the broader macro-economic environment. The result is an analysis that accounts for the impact of changing macroeconomy, local labour market conditions, and men and women's employability assets.\u003c/p\u003e","manuscriptTitle":"Employment Outcomes for Men and Women Following an Economic Downturn: Labour Underutilisation in Australia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-30 20:43:04","doi":"10.21203/rs.3.rs-1495530/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0704252-381a-4266-bb78-d152aa9c3493","owner":[],"postedDate":"March 30th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2022-04-01T18:50:53+00:00","versionOfRecord":{"articleIdentity":"rs-1495530","link":"https://doi.org/10.1007/s41027-022-00363-z","journal":{"identity":"the-indian-journal-of-labour-economics","isVorOnly":false,"title":"The Indian Journal of Labour Economics"},"publishedOn":"2022-04-01 18:50:53","publishedOnDateReadable":"April 1st, 2022"},"versionCreatedAt":"2022-03-30 20:43:04","video":"","vorDoi":"10.1007/s41027-022-00363-z","vorDoiUrl":"https://doi.org/10.1007/s41027-022-00363-z","workflowStages":[]},"version":"v1","identity":"rs-1495530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1495530","identity":"rs-1495530","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}