Session Information
22 SES 08 A, From higher education to the labour market
Paper Session
Contribution
This research explores the impact of various types of work experience while studying higher education courses and their impact on transitions from education to work in Australia. The findings are relevant to the similarly varied models of work-based learning found across the European Union and the common need to deliver quality tertiary education programs that lead to meaningful career outcomes.
We sought to explore the following research questions:
- How has the use and labour market outcomes of higher education as a pathway changed over time?
- Do transitions to skilled employment differ by prevalence of work-integrated learning?
- Does work experience while studying a higher education course make a difference to employment outcomes?
To explore these questions, we used data from the Longitudinal Survey of Australian Youth (LSAY), which is a rich data source that surveys young people annually, from the age of 15 to 25. This longitudinal data covers about 100,000 individual records and represents over 1.2 million young Australians and the pathways they have followed over 25 years from 1995 to 2019.
As the trajectories young people take from school can be complex, we grouped these trajectories using a sequence and cluster analysis. This approach follows similar work undertaken by researchers in Europe and North America (Aassve, Billari, & Piccarreta, 2007; Brzinsky-Fay, 2006; Lorentzen, Bäckman, Ilmakunnas, & Kauppinen, 2019; Martin, Schoon, & Ross, 2008; McVicar & Anyadike-Danes, 2002; Studer & Ritschard, 2016)
Our approach resulted in five major pathways young people take from education to work, which we described as 1) higher education, 2) apprenticeship/traineeship, 3) VET, 4) direct to work, and 5) mixed/not in employment, education and training.
The labour market outcomes of these five groups were then compared against a range of labour market indicators including employment rates, type of employment (full-time, part-time, casual), skilled employment rates, earnings and job satisfaction.
An innovation of this research was that we also explored the impact of work-integrated learning (WIL) and paid work experience on labour market outcomes of young people who undertook a high education pathway.
We used data from Universities Australia to identify higher education courses by prevalence of WIL, matched these to the courses undertaken by young people, and explored any potential impact on post-study labour market outcomes.
We also explored the impact of paid work experience while studying. We identified the type and level of paid employment held while studying and then explored any potential impact on post-study labour market outcomes.
Method
The methodology and research instruments used in this research builds upon previous analysis of life-course trajectories of young people known as a sequence and cluster analysis. We used statistical software packages available in R, with the WeightedCluster and TraMineR software the main packages used in the analysis (Studer, 2013; Studer & Ritschard, 2016). The sequence and cluster analysis involves, first, constructing a chronology of life events for each individual, organised into a sequence. To do this, we coded every record according to the main activity each year. The main activities we used appear below. 1. Attending school 2. Attending higher education 3. Undertaking an apprenticeship/traineeship 4. Attending vocational education and training 5. Employed 6. Unemployed 7. Not in the labour force. As there could only be one main activity each year, the above list is also a hierarchy. This means, if individuals undertaking multiple activities, we coded their main activity according to the activity that first appears in the list above. We then used Optimal Matching Analysis to cluster young people’s pathways according to major typologies (Studer, 2013). Our analysis of pathway, WIL and work experience used estimates based on ordinary least squares regressions plus a set of control variables measured when the survey respondent was roughly aged 15. These control variables include indicators for gender, Indigenous status, immigrant status, state and area (metro, regional, remote) of residence, highest parental education, parental immigrant status, and school sector (government, Catholic or independent), plus all available PISA test score measures, age in months and school year level (grade) at the time of testing. We used survey weights provided in the LSAY survey during estimation.
Expected Outcomes
The findings of this research show that work experience does matter to overall outcomes but the reasons remain unclear. Key findings include: • Higher education has grown considerably as a pathway for school leavers. Between 2005 and 2019, the data used in this research suggests the proportion of young people using higher education as their primary pathway from school has grown by about 14 percentage points. • Individuals whose highest degree has a higher prevalence of WIL generally have better labour market outcomes at age 25. These individuals are 3% to 8% more likely to be employed at age 25, and 15% to 32% more likely to be in a high-skill job at age 25, compared to individuals whose highest degree has a low prevalence of WIL. • Work experience while studying is associated with better labour outcomes at age 25. In 2005, young people who had some employment while studying a degree were between 8% and 10% more likely to be employed at age 25 compared to those who had no work experience while studying. By 2019, young people were between 21% and 25% more likely to be employed at age 25 compared to those who had no work experience while studying. • The impact of skilled work experience (work experience at a professional or managerial level) is most noticeable for individuals who study courses with a low prevalence of WIL. These individuals are at least 14% to 20% more likely to be in skilled employment at age 25 when compared to individuals who completed similar degrees but did not have work experience at a high skill level. • This data shows increasing levels of part-time work among young people at age 25 and falling levels of wage growth over time.
References
Aassve, A., Billari, F. C., & Piccarreta, R. (2007). Strings of Adulthood: A Sequence Analysis of Young British Women’s Work-Family Trajectories: Parcours de la vie adulte : Une analyse par séquence des trajectoires travail-famille des jeunes femmes britanniques. European Journal of Population / Revue européenne de Démographie, 23(3-4), 369. doi:10.1007/s10680-007-9134-6 Brzinsky-Fay, C. (2006). Lost in transition: labour market entry sequences of school leavers in Europe. In: WZB Berlin Social Science Center. Lorentzen, T., Bäckman, O., Ilmakunnas, I., & Kauppinen, T. (2019). Pathways to Adulthood: Sequences in the School-to-Work Transition in Finland, Norway and Sweden. Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 141(3), 1285. doi:10.1007/s11205-018-1877-4 Martin, P., Schoon, I., & Ross, A. (2008). Beyond Transitions: Applying Optimal Matching Analysis to Life Course Research. International Journal of Social Research Methodology, 11(3), 179-199. doi:10.1080/13645570701622025 Matthias, S., & Gilbert, R. (2016). What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures. Journal of the Royal Statistical Society. Series A (Statistics in Society), 179(2), 481-511. Retrieved from https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=edsjsr&AN=edsjsr.43965553&site=eds-live&custid=s1145751 McVicar, D., & Anyadike-Danes, M. (2002). Predicting successful and unsuccessful transitions from school to work by using sequence methods. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(2), 317. doi:10.1111/1467-985X.00641 Ranasinghe, R., Chew, E., Knight, G., & Siekmann, G. (2019). School-to-work pathways. Adelaide: NCVER Retrieved from https://www.ncver.edu.au/__data/assets/pdf_file/0029/6547412/School_to_work_pathways.pdf Studer, M. (2013). WeightedCluster Library Manual: A practical guide to creating typologies of trajectories in the social sciences with R. LIVES Working Papers, 24. doi:http://dx.doi.org/10.12682/lives.2296-1658
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