Dataset
As outlined in Chapter Two, analysis of the life course requires an empirical research strategy that comprises social structure, institutions and personal biographies over time through a longitudinal design. However, at the same time, scholars of both Human Capital Theory and the life course recognise the challenges associated with this approach given the lack of appropriate data. Given these challenges, this thesis undertakes a quantitative analysis of secondary data, specifically from the ABS 2011 Census of Housing and Population.
Secondary data analysis is the analysis of previously collected data. While some sociologists and theorists view quantitative and secondary data analysis as an inappropriate model with which to study social life (Phillips 2011), the Census allows examination of relationships between many variables using population-level data. Inferential statistics are not relevant, as the Census
enumerates the entire population. As such, all findings and inferences made are based on the whole Australian population (Jackson 2013).7
Given the requirement of multiple variables to both operationalise an indicator of skill and its utilisation, as well as undertake empirical analysis over the life course, a complex data set is required. The data used to develop the indicator for skill utilisation is drawn from an unpublished, customised dataset purchased from the ABS 2011 Census of Housing and Population. The dataset is limited to the population aged 25 to 64 years with post-school qualifications, and to occupation,
7 Sources of error in the Census are addressed in the ‘Assumptions and limitations of the data’ section of this
chapter. Skill Specialisation •Field of Study Skill level •Educational Attainment Tacit knowledge •Age Occupation •ANZSCO
labour force attachment, educational attainment and field of study, and includes partnered and un- partnered males and females, with and without children8.
Data for the indicator of skill utilisation
The variables used to develop the indicator of skill utilisation are ‘Non-School Qualification: Field of Study’ and ‘Non-School Qualification: Level of Education, Age and Occupation’. Further explanation of the key variables is provided below.
Occupation
In the 2011 Census, the occupation variable is coded using the Australian and New Zealand Standard Classification of Occupations (ANZSCO), First Edition, Revision 1. The Occupation code assigned to a response is based on the occupation title and tasks of the main job held during the week prior to Census Night.
In this research, Occupation at Minor Group (three-digit) level is used. There are 97 different minor groups (see Appendix A for the full list). While minor groups are distinguished from each other based on a finer application of skill specialisation rather than skill level, they do have a predominant skill level. Data at the minor group level will therefore provide a satisfactory indication of skill level when aggregated for analytical purposes (ABS 2013). In addition, international studies generally use ISCO- 08 three-digit level data, which will enable cross-national comparison to be undertaken (see, for example, Elias and McKnight (2001); Montt (2015)).
The ‘not stated’ or ‘inadequately described’ occupation categories are removed from the data set to ensure that the assessment of skill utilisation in Australia is based on the assumption of full
knowledge of the variables contributing to the indicator.
For analytical purposes, occupation variables are aggregated up to one-digit level.
8 The customised dataset is as follows; a Customised Table from the 2011 ABS Census of Population and
Housing of Persons Aged 25 - 64 Years by Narrow Field of Study of Highest Non-School Qualification (4 Digit) by Broad Level of Highest Non-School Qualification (1 Digit) by Occupation Minor Group (3 Digit) by Sex by Five
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Non-school qualification: Field of study
In the 2011 Census, the field of study variable is coded using the Australian Standard Classification of Education (ASCED) Field of Education Classification. This describes the field of study of a person's highest completed non-school qualification.
In this research, the Narrow Field of Study at the Highest Non-School Qualification variable is used at the four-digit level (see Appendix B for a list).
The ‘not stated’ or ‘inadequately described’ field of study categories are removed from the data set to ensure the assessment of skill utilisation in Australia is based on the assumption of full knowledge of the variables contributing to the indicator.
Non-school qualification: Level of education
In the 2011 Census, the level of education variable is coded using the Australian Standard Classification of Education (ASCED) Level of Education Classification. This describes the level of a person's highest completed non-school qualification.
The ‘not stated’ or ‘inadequately described’ level of education categories are removed from the data set to ensure the assessment of skill utilisation in Australia is based on the assumption of full
knowledge of the variables contributing to the indicator.
Age
In the 2011 Census, age is a person's age at last birthday, and is collected for each person. Age is calculated from date of birth, but if this was not provided, stated age is used. If neither is provided, age is imputed by the ABS.
In this research, the age variable is used both to inform the indicator of skill utilisation and for the empirical analysis. Age data are provided in five-year age groups, from 25 years of age to 64 years of age. These ages were selected on the assumption that ages 25 to 64 are the prime working ages, and that by the age of 25 individuals will have undertaken post-school qualifications and/or obtained the necessary years of experience as required for the ANZSCO skill level education classification.
For the purposes of informing an indicator of skill utilisation for this research, all those in the dataset are aged 25 and over, and therefore meet the skill level and experience requirements.
Data for the analytical approach
The data used to undertake the empirical analysis of skill utilisation in Australia are drawn from the same unpublished, customised dataset purchased from the ABS 2011 Census of Housing and Population used to develop the indicator of skill utilisation. In addition to the variables used to develop the indicator of skill utilisation, a number of other variables are used to undertake an analysis informed by the principles of the life course. These variables include age, sex, labour force status, social marital status and presence of children.
For analytical purposes, occupation variables are aggregated up to one-digit level.
Age
Age data provides insight into skill utilisation over the lifespan, which will enable any age, cohort and/or period effects to be identified.
Sex
In the 2011 Census, each person's sex, either male or female, is recorded. If sex is not stated it is imputed by the ABS. This variable will enable differences in skill utilisation to be analysed by sex.
Labour force status
In the 2011 Census the labour force status of all people aged over 15 the week prior to Census Night is recorded.
For the purposes of this research, the variables used are 'employed full time, employed part time, employed, away from work,9 unemployed, and not in the labour force.
Social marital status
In the 2011 Census the relationship status of all persons aged over 15 years is recorded based on their current living arrangements. Where a couple relationship exists in the household, the type of relationship is identified. For the purposes of this research, social marital status identifies ‘linked lives’, and is defined as ‘partnered’ (including married and de-facto couples) or ‘not-partnered’.
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Presence of a child
To determine the impact of children on skill utilisation for both men and women, the presence of a child variable for this research is determined from the Relationships in Household question in the 2011 Census.
The question records the relationship of each person in a family to the family reference person, or, where a person is not part of a family, that person's relationship to the household reference person.
For the purposes of the Census, a child is considered a person of any age who is a natural, adopted, step, foster or nominal son or daughter of a couple or lone parent, usually resident in the same household. A child is also any individual under 15, usually resident in the household, who forms a parent-child relationship with another member of the household. A child is also a dependent student who is a natural, adopted, step, or foster child, 15 to 24 years of age, and who attends a secondary or tertiary educational institution as a full-time student, and for whom there is no identified partner or child of his/her own usually resident in the same household.
For this research, and from the life course perspective, the presence of children reveals ‘linked lives’. Three aggregated categories are used in this research: dependent children (those aged 0 to 14 and those aged 15 to 25 participating in full time study), non-dependent children only (those aged 15 to 24 not in full time study), and no children living in the household.
Assumptions and limitations of the data
Using Census data will enable cross-sectional analysis of population-level data – that is, an observational study of a population at a point in time rather than a longitudinal study (which provides the mechanism to observe changes over time). Under the principles of the life course, longitudinal data are particularly relevant for lifespan analysis. Even so, the cross-sectional approach will enable cohort and period effects to be identified, given that the age variable is included in the dataset. Importantly, the ABS Australian Census Longitudinal Dataset (ACLD) will bring together data from the 2006, 2011 and 2016 Censuses to create a research tool for exploring how Australian society is changing over time. As a result, it will be possible to apply the methodology of this indicator of skill utilisation longitudinally in the future.
There are a number of assumptions and limitations within the dataset that prevent a definitive assessment of skill utilisation in Australia. These assumptions and limitations result directly from the large, complex dataset that is required to both develop an indicator of skill utilisation and undertake empirical analysis. The extent of desirable variables and associated levels of dis/aggregation required data to be selected strategically. Key assumptions and limitations are outlined below.
Data
Because Census data are collected with a high reliance on self-enumeration, content error10 will be
apparent. However, given that the Census covers the entire population, it is also considered to have high levels of reliability and validity (Jackson 2013). In this thesis, content errors have been reduced to a minimum by using data from the customised datasheet provided by the ABS.
To ensure the dataset is complete, with full knowledge of each of the variables used to develop the indicator and the empirical analysis, all ‘not stated’ and ‘inadequately described’ categories are removed from each variable.
Age variable
The age variable is used to construct a ‘synthetic’ lifespan from which to analyse skill utilisation over the life course. This approach could be argued to be problematic, given the cross-sectional rather than longitudinal nature of the data, because it cannot capture the change in individuals’ skill utilisation over time as they transition through life events. Analysis of longitudinal data provides the ability to correct for the ‘cohort effect’ (Kupper et al. 1985), that is, it allows for analysis of the individual components – cohort, period and age –, and accounts for the impact of each separately. This is of particular concern for women given the considerable changes in their participation in education and work since the 1950s. While the increase in female labour force participation rates can be explained by cohort, period and age effects, several studies suggest that any further increases will become smaller and smaller for subsequent cohorts, and that there hasn’t been the revolution in women’s work that the participation rates may suggest (see for example Chevalier & Viitanen 2002; Daley, McGannon & Ginnivan 2012; Euwals, Knoef & Van Vuuren 2011; Macran, Joshi & Dex 1996; Parr 2012; Tapper 2010). As historical increases were primarily due to cohort effects, such as changing social norms and legislative changes (e.g. working after marriage, sex discrimination and equal pay), these are no longer as relevant to successive, younger generations. Even so, women are likely to continue to transition in and out of education and work over their lifespan as they balance work and family (Johnes 2006; Moen 2016; Treas & Widmer 2000; Yerkes 2010).
To alleviate these concerns, this research utilises a cohort, period and age effect lens in the analysis of the findings. More specifically, the thesis does not infer that skill utilisation increases/decreases over the lifespan, but rather that skill utilisation is higher/lower at certain points in the lifespan for
65 different socio-demographic profiles. Where relevant, these differences are explained by age, period or cohort effects.
Importantly, in the absence of longitudinal data to investigate skill utilisation, cross-sectional data offer the best available proxy for examining skill utilisation over the life course.
Occupation variable
Due to size restrictions with the dataset, the occupation data are limited to the minor group (three- digit) level rather than to a greater level of disaggregation, such as unit level, in which occupations have only one skill level; the unit level would provide much greater ability to match both skill level and skill specialisation. Even so, the ABS (2013) states that the minor group provides a satisfactory indication of skill level when aggregated for analytical purposes. The indicator for skill utilisation is developed using occupation categories at the three-digit level, and occupations are aggregated up to one-digit level for analytical purposes.
Level of education and field of study
In this research, Broad Level of Highest Non-School Qualification is used at the one-digit level (see Appendix C for a list). To align with the skill level classification within ANZSCO, the Highest Non- School Qualification at the one-digit level is further grouped to align with the five ANZSCO skill levels, as detailed in Table 4.2, below. As the table shows, there is no clear delineation of ANZSCO skill and education level requirements and ASCED at the one-digit level. Greater delineation would have been possible at the ASCED two-digit level, but access to data at this level was constrained in terms of size and by budget, when incorporating data requirements for the empirical analysis (see the ‘Analytical framework’ section, below, for further information on data for the empirical analysis). Given that the field of study variable will identify any skill mismatch, regardless of education level, data priority was given to occupation at the three-digit level and to field of study at the four-digit level to ensure a greater disaggregation of occupations by skill specialisation.