Conference Agenda

Future of Jobs Report 2023 projected the possible job creation and displacements for the next 4 years, revealing a great increase in all the domains in which AI knowledge and skills will be most wanted and used (ex AI and Machine Learning Specialists, Sustainability Specialists, Business Intelligence Analysts, Information Security Analysts) whereas other domains will decrease in their demand for employees (Administrative and Executive Secretaries, Data Entry Clerks, Bank Tellers and Related Clerks) (World Economic Forum, 2023).

In these times of uncertainty and challenge, the selection of an academic path with the potential to lead to a successful career brings a complex decision-making process for adolescents. The achievement in career choices and job performance is significantly shaped by vocational interests (Rounds & Su, 2014). In this context, obtaining a clear understanding of the vocational interests of high school students belonging to Generation Z (born between 1997 and 2008) would prove particularly valuable. A substantial number of these students make decisions about their college majors during the 10th and 11th grade. If their chosen path aligns with their vocational interests, it is likely to enhance their motivation to complete college (Nye, Prasad, & Rounds, 2021), in a period when Higher education is confronted with serious drop-out rates (Eurostat Statistics, 2022). This latter source reveals that in 2022, the proportion of early leavers from education and training (ages between 18 and 24) in the EU ranged from 2.3% in Croatia to 15.6% in Romania.

According to a recent national survey in Romania focusing on Generation Z, it was found that 76% of respondents identified a passion for their work as the primary motivating factor in their job search (Romanian Business Leaders, 2022). This indicates that, for this demographic, vocational interests take precedence over financial compensation when considering employment opportunities.

As all the previous generations, Gen Z has its distinct futures, being described as more pragmatical and future-oriented compared with the more idealistic Millennials (Twenge, 2020, p. 231). Being born in a digitalized and tech world, vocational interests have also changed, as the current generation is interested in more fields of activity than the previous with an increased interest in information technologies (Roganova & Lanovenko, 2020).

Interests are defined as a cognitive and motivational factor encompassing both engagement and participation in specific content areas. The effectiveness of interest lies in its capacity to generate a rewarding experience through the information search process (Renninger & Hidi,, 2020). Interests have a significant influence on career choices and academic achievement (Hoff, Song, Wee, Phan, & Rounds, 2020), (Stoll, et al., 2020). This is why the present research endeavors to explore the patterns or clusters of interests within the Generation Z adolescent demographic.

A key objective of the study is to ascertain whether distinct patterns of interests emerge among the cohort based on factors such as the year of the examination, age, or gender. This multifaceted approach seeks to provide a nuanced understanding of the intricate interplay between vocational interests and demographic variables, contributing valuable insights to the broader discourse on college domain decisions among adolescents.

Therefore, this research aims to address the following questions:

1. Are there variations in vocational interest preferences among high school students who took the test during the periods 2012-2014, 2015-2019, and 2020-2023?
2. Are there distinctions in vocational interest preferences between females and males across and between the established subcohorts?
3. What are the most prominently scored preferences in the Work Roles scales among the participants across and between subcohorts?
4. What types of Work Styles do Generation Z individuals predominantly favor across and between subcohorts?

Recent empirical research in the social sciences has emphasized the importance of causal inference. However, causal inference is challenging when using observational data, primarily cross-sectional, even if it includes relevant variable information, as in international and large-scale educational surveys. The difficulty is more pronounced when the variable of interest, such as a national-level policy, is systemic. This paper demonstrates that by using pseudo-panel data derived from repeated cross-sectional data, we can obtain findings relevant to policy-making, thereby mitigating some of the challenges in causal inference, particularly biases from unobserved confounding factors.

The specific topic addressed in this paper is the assessment of policy factors related to the youth's choice to teach. In general, improving the availability and quality of teacher personnel is a universal and important issue for public education policy (OECD 2018). These research areas concerning the choice of teaching career and teacher supply have been interdisciplinary in education (educational policy studies, sociology of education, educational psychology, etc.) and economics (economics of education, labor economics). In particular, empirical research on the basic issues of "who chooses to teach" and "what factors increase the number of people who want to teach" has been conducted in many countries. While educational and psychological research have pointed out the importance of psychological factors, work environment factors have not been recognized as the main factors influencing career choice (Watt et al. 2017). On the other hand, empirical studies in the economics of education and labor economics have focused exclusively on the impact of salary levels as a policy variable on entry and exit from the workforce and have partially argued for its contribution (Corcoran et al. 2004; Dolton 1990; Manski 1987).

Moreover, Japan, where the presenter is from, has historically excelled in maintaining high-quality teachers, as evidenced by their high competency (Hanushek et al. 2019) and low turnover rates, compared to other countries. However, recent years have seen a growing trend among young people to avoid teaching careers. Japan now faces challenges similar to many countries experiencing a structural teacher shortage. Public debates often cite the relatively inferior work environment of teaching compared to other white-collar jobs as a factor in this avoidance. Yet, substantial evidence is lacking to inform policy priorities in this area.

In this study, we position and extend the groundbreaking recent studies that have used PISA student-level data to analyze the youth’s choice of teaching profession (Park & Byun 2015; Han 2018) as important prior work. We differ from that study in terms of methodology, using pseudo-panel data composed of subpopulations of countries as units; we apply a cross-classified hierarchical model to ask "Which policy factors" promote "whose" entry into the teaching profession among young people? We specifically focus on policy factors related to the working environment, namely, the relative salary level of teachers compared to other professions and the workload of teachers (working hours, number of students per teacher, and time spent on non-teaching tasks).

Applying a cross-classified hierarchical model to the pseudo-panel data, we respond to the question of "which policy factors" encourage "whom" of young people to enter the teaching profession, addressing both causal inference (controlling for time-invariant confounders) and policy relevance (heterogeneity of policy effects). The cross-classified model, which sets up the random effects/coefficients in two types of units, country, and subpopulation, has a major advantage in that it allows for different policy implications for each country. To further increase the robustness of our model, we are expanding it into a semiparametric model (infinite mixture model) that does not rely on a multivariate normal distribution for random effects and coefficients.