Quantitative methods

Adolescent Career Aspirations and Early Adulthood Occupational Outcomes in England

tarekmo

In the UK more than one million young people are not in education, employment, or training (NEET). They represent 1 in 8 16-24 year olds. The Milburn Review interim report highlights that we are facing a generational fault line that is no longer about short-term youth unemployment but about a generational shift characterised by youth detachment from the labour market.

Here we use longitudinal data to investigate adolescents’ career aspirations, early adulthood occupational outcomes and the mismatch between the two. We use a latent profile analysis approach (LPA) to identify profiles that best align with each young person’s outcomes and characteristics, with profile membership being explained by predictors reflecting the dimensions – including age, gender, ethnicity, socioeconomic disadvantage, education, health, disability and place of residence – identified in the Milburn Review.

Methods

We use data from the England sample of the Millennium Cohort Study (MCS): a nationally representative longitudinal survey following children born in 2000 from infancy to early adulthood. Data were drawn primarily from age 14 (Wave 6) and age 23 (Wave 8). Although more than 12,000 cohort members were included initially, attrition reduced the productive sample at age 23 to 6,439 cases. After excluding cases without occupational information or aspiration data, the analytical sample consisted of 5,231 participants.

The analysis examines the relationship between occupational aspirations in adolescence and realised occupational outcomes in early adulthood and the socio-demographic factors that explain mismatch. Three key indicators were used: occupational aspirations at age 14, occupational status at age 23, and the mismatch between aspirations and realised occupations. Aspirations and occupations were coded using the SOC2010 classification and converted into continuous occupational-status measures using CAMSIS (Cambridge Social Interaction and Stratification) scores. This allowed direct comparison between adolescent career goals and later employment outcomes.

To identify distinct patterns of aspiration-attainment relationships, we employed Latent Profile Analysis (LPA). Rather than examining aspirations and achievements separately, LPA identifies unobserved groups of young people sharing similar combinations of aspirations, occupational outcomes, and mismatch. Models were estimated using Generalised Structural Equation Modelling.

A wide range of explanatory variables was included to predict profile membership. Childhood variables captured gender, ethnicity, parental education, parental occupational class, parental unemployment, deprivation, family structure, and parental mental health. Early-adulthood variables included educational attainment, region of residence, physical and mental health, disability status, and attitudes towards work.

The main assumption of LPA is that profile membership is unobserved but can be inferred from the patterns of observed variables. Unlike traditional variable-focused econometric models that try to identify the link between aspirations at a younger age and later achievements, LPA assumes that both aspirations and achievements are shaped by experiences, living circumstances, and individual agency that ultimately define goal-outcome congruence (i.e. the latent construct), as shown in Figure 1.

Figure 1: Latent Profile Analysis

Findings

The findings indicate that occupational aspirations at age 14 were generally high regardless of subsequent occupational destination. Mean aspiration CAMSIS scores clustered between approximately 57 and 65 across all occupational groups. By contrast, realised occupational outcomes at age 23 were substantially lower and more dispersed. For most participants, occupational mismatch was negative, indicating that they had not yet reached the occupational status they aspired to during adolescence.

The mismatch was smallest among those employed in professional occupations and largest among those working in elementary, sales and customer service, and operative occupations. These findings challenge narratives suggesting a lack of ambition among young people. Instead, they indicate widespread ambition accompanied by difficulties translating aspirations into early labour-market success. They could also indicate unrealistic aspirations at age 14.

Table 1: Mean CAMSIS scores for aspirations at age 14 and occupations at age 23 by the nine SOC categories for occupations at age 23

Unweighted Freq. Weighted percent Average CAMSIS score
SOC 2010 nine category classification Age 14 aspirations Age 23 occupations Mismatch
Not in employment at age 23 1,368 24.6 63.67
Managers and senior officials 198 3.84 63.08 54.43 -8.63
Professional occupations 644 11.82 65.54 67.34 1.96
Associate professional and technical occupations 786 15.77 64.06 57.95 -6.17
Administrative and secretarial occupations 416 7.63 64.47 51.00 -13.42
Skilled trades occupations 336 7.84 58.83 41.17 -17.66
Personal service occupations 461 8.4 61.57 44.62 -16.88
Sales and customer service occupations 413 7.31 63.04 40.65 -22.25
Process, plant and machine operatives 143 2.74 57.77 34.77 -23.47
Elementary occupations 466 10.06 60.25 31.47 -28.82
Overall standard deviation 62.89 49.46 -13.15
Total and overall mean 5,231 100 10.00 12.94 14.71

From the LPA analysis four distinct groups were identified:

  1. Disillusioned (30%): reported very high aspirations but relatively low occupational outcomes, producing the largest aspiration-achievement gap.
  2. Low Trajectory (10%): combined low aspirations with low occupational outcomes, but showed relatively little mismatch because outcomes broadly matched expectations.
  3. Highflyers (36%), combined high aspirations with high-status occupations and displayed minimal mismatch.
  4. Misaligned (24%), reported moderate aspirations but low occupational outcomes, creating moderate levels of mismatch.Table 2: profile sizes, marginal means and probabilities
Profiles Variables Unweighted Frequencies Profile size % Latent profile marginal means Marginal probabilities
Profile 1: Disillusioned Age 14 aspirations 1665 30.16 70.32 0.30
Age 23 job status 41.45
Mismatch -28.72
Profile 2: Low Trajectory Age 14 aspirations 486 10.08 42.76 0.10
Age 23 job status 48.00
Mismatch 5.17
Profile 3: Highflyers Age 14 aspirations 1898 35.68 66.19 0.35
Age 23 job status 62.79
Mismatch -3.20
Profile 4: Misaligned Age 14 aspirations 1182 24.08 57.37 0.25
Age 23 job status 41.36
Mismatch -16.09

 

Occupational distributions differed markedly across profiles. Highflyers were strongly concentrated in professional and associate professional occupations and were largely absent from lower-status occupations. Disillusioned participants were heavily represented in personal service, sales, customer-service, and elementary occupations. Misaligned participants were particularly concentrated in skilled trades and elementary occupations, while Low Trajectory participants were distributed across associate professional, personal service, and elementary occupations.

The quality of jobs also differed across groups. Highflyers were least likely to experience zero-hours contracts, shift work, or NEET status. Disillusioned, Low Trajectory, and Misaligned participants were more likely to experience insecure working conditions. Highflyers were also employed in occupations with higher exposure to generative artificial intelligence, reflecting concentration in knowledge-intensive occupations.

Multinomial regression analyses showed that childhood socio-economic disadvantage was a major predictor of profile membership. Compared with Highflyers, membership of the Disillusioned, Low Trajectory, and Misaligned profiles was associated with lower parental occupational status, parental unemployment, and lower educational attainment by age 23. All ethnic groups were less likely to belong to the low trajectory profile in comparison with the White majority group.

Higher qualifications substantially increased the likelihood of belonging to the Highflyer group. Mental health also emerged as an important factor. Young adults experiencing psychological distress were significantly more likely to belong to the Disillusioned, Low Trajectory, and Misaligned profiles. The relationship was particularly strong for the Disillusioned and Misaligned groups, suggesting that poor mental health may contribute to difficulties in converting aspirations into realised occupational outcomes.

Region of residence was strongly associated with membership of the Misaligned profile. Young people living outside London were significantly more likely to experience moderate aspiration-outcome mismatch than those living in London. In an ‘escalator effect’, the concentration of opportunities in the capital may facilitate faster career progression and improve the likelihood of realising aspirations.

Overall, the analysis demonstrates that most young people hold ambitious occupational goals, but their ability to realise those ambitions is shaped by education, mental health, family socio-economic resources, and regional opportunity structures. The findings highlight that career outcomes are not solely a consequence of individual ambition but are also strongly influenced by structural conditions across childhood and the transition into adulthood.

Note: This blog is based on research conducted as part of the ‘Youth transitions to good employment: East Birmingham & North Solihull’ project. This project is undertaken by a research team at City-REDI and the School of Education at the University of Birmingham, working with local partners including Birmingham City Council, Solihull Council and the West Midlands Combined Authority. This project has been funded by the Nuffield Foundation, grant number FR-25308, but the views expressed are those of the authors and not necessarily the Foundation. Visit nuffieldfoundation.org

This blog was written by Dr Tarek Mostafa.

The Centre for Longitudinal Studies Missing Data Strategy

tarekmo

To impute or not to impute? A practical guide for handling missing data in longitudinal surveys. George Ploubidis.

Maximising the plausibility of the Missing At Random Assumption: Results from the 1958 British birth cohort. Tarek Mostafa.

How well do methods that assume data are Missing At Random perform when the missing data generating mechanism is not ignorable?  Evidence from the 1958 British birth cohort. Brian Dodgeon.

How consistent is respondent behaviour to allow linkage to health administrative data over time?

How consistent is respondent behaviour to allow linkage to health administrative data over time?

tarekmo

Tarek Mostafa and Richard D Wiggins, University College London.

This article was first published in the NCRM newsletter (Summer 2015).

Household surveys are increasingly being linked to administrative records with the potential of greatly enriching survey content on subjects such as health, education and income. One major challenge to data linkage is nonconsent. Non-consent occurs when respondents refuse permission to link their administrative records to their survey data. This problem inevitably leads to information loss and possibly bias if consent is correlated with key characteristics of the respondents.

Despite the recent developments in the analysis of consent [2, 3, 4, 5] the evidence is still scarce. The existing literature focused on the patterns of consent arising in cross-sectional surveys and very little is known about consent behaviour over time. Our study is one of the first longitudinal explorations of consent in the context of attempts to link survey and administrative data. It relies on a theoretical framework which distinguishes between passive, active, consistent and inconsistent consent behaviour and uses a series of models including multivariate probit analyses in order to identify the nature of consent as a latent trait and linear probability models which include interviewers as fixed effects.

The study exploits three consent questions to link health records held by the National Health Service (NHS) to the Millennium Cohort Study (MCS). The questions correspond to waves 1 (age 9 months), 2 (age 3 years) and 4 (age 7 years) of MCS. The questions are: To what extent is consent behaviour consistent over time? and Can consent behaviour be described as active or passive?

In order to answer the questions Cialdini’s [1] framework on compliance in survey research is developed by testing four scenarios which sub-divide consistency/ inconsistency in consent behaviour along the lines of activeness/passiveness. Active consistency is the case where respondents are aware of their previous choices and are committed to make the same choices on future occasions because of stable beliefs or personality traits (e.g. belief in the importance of scientific research, being a private person, etc). Passive consistency is the case where respondents make consistent choices over time even though the decision making process is passive. This means that consent decisions do not reflect an active adherence to well-defined beliefs but rather external influences such as the respondents’ circumstances at the time of the interview and the impact of the interviewers. Active inconsistency is the case where respondents are aware of their previous choices and intentionally behave in inconsistent ways. This change in behaviour could be the result of a change in convictions. For instance, a past consenter might actively decide to withhold consent after a breach to data confidentiality. Passive inconsistency is the case where respondents switch from consenters to non-consenters or vice versa. This switch is not the result of changes in convictions but rather the result of changes to the circumstances of the respondent (e.g. divorce, acute health problems), changes to the interviewers over time (e.g. persistence in pursuing consent), and the fact that respondents could have forgotten what they did in the past. In all cases, the respondent has a passive role and the changes in consent behaviour are caused by extrinsic factors.

In summary the analyses provide evidence in support of passive consistent behaviour. Firstly, 77% of respondents behaved consistently over time by either consenting or refusing to consent in all waves. Secondly, the cross-equation correlations from the multivariate probit models showed that the unobserved parts of the consent outcomes are weakly associated over time, and therefore, cannot really be held to indicate the existence of strongly held latent convictions about consent. Thirdly, the likelihood of consent and the likelihood of switching behaviour over time are related to the respondents’ circumstances, and to the variation the impact interviewers have on the respondents’ willingness to consent. These three findings indicate that, for the majority of respondents, consent is not driven by personal convictions but rather depends on the circumstances of the respondent at the time of the interview and on the potential influence of the interviewers.

In sum when it comes to using linked survey and administrative data, users need to take into account the potential sample bias resulting from the correlations between the respondents’ characteristics and the agreement to consent. On a practical level the longitudinal evidence based on our project suggest that it is important to brief interviewers about what to expect and encourage them to adapt to the respondent’s circumstances when attempting to gain their cooperation to consent to record linkage.

References

1 Cialdini, R. Wosinska, W. Barrett, D. Butner, J. and Gornik-Durose, M. 1999. Compliance with a Request in Two Cultures: The Differential Influence of Social Proof and Commitment/ Consistency on Collectivists and Individualists. Personality and Social Psychology Bulletin 25, 1242-1253.

2 Korbmacher, J. and Schroeder, M. 2013. Consent When Linking Survey Data with Administrative Records: The Role of the Interviewer. Survey Research Methods 7, 115–131.

3 Mostafa, T. 2015. Variation within Households in Consent to Link Survey Data to Administrative Records: Evidence from the UK Millennium Cohort Study. International Journal of Social Research Methodology, published online on: 23 March 2015.

4 Sakshaug, J. Couper, M. Ofstedal, M. and Weir, D. 2012. Linking survey and administrative records mechanisms of consent. Sociological Methods and Research 41, 535-569.

5 Sala, E. Burton, J. and Knies, G. 2012. Correlates of obtaining informed consent to data linkage: respondent, interview and interviewer characteristics. Sociological Methods and Research 41, 414–439.