Impact
Longitudinal studies are especially useful for analysing labour market trajectories because of their repeated observation design. They show how people move (or don’t move) between different employment statuses through their working lives. Likewise, longitudinal studies are very useful for assessing the causal impact of job status on other factors – health, for example – and vice-versa.
Examples
Cappellari and Tatsiramos (2011), for example, uses the British Household Panel Survey (BHPS) data to investigate the effect of social interactions/networks on labour market outcomes such as finding a job, wages and re-employment matches (Friends’ networks and job finding rates). Green and Heywood (2010) evaluate the effect of profit sharing on employee satisfaction with the boss using BHPS data. Also based on BHPS data, Roberts (2011) investigates whether the daily commute affects the psychological health of women more than those of men (Does the daily commute affect women more than men?); and Perales (2010) research focus is on the persistence of unequal pay for men and women, also known as the gender pay-gap (Occupational feminization, specialized human capital and wages: evidence from the British labour market).
A very recent project undertaken by Taylor and Jenkins (2012) sheds light on non-employment issues (rather than unemployment issues) during the current recession compared to the recession in the early 1990s and thereafter the recovery and boom through to the mid-2000s (Non-employment, age, and the economic cycle). Taylor and Jenkins use the first wave, first year data of Understanding Society, the successor to the British Household Panel Survey, which ran from 1991 to 2008, and combine it with all BHPS waves providing a picture spanning the entire period 1991-2009. However, real longitudinal analysis is now enabled with Understanding Society data from wave 2 onwards as the BHPS sample is included in those Understanding Society wave samples. Nevertheless, the exercise by Taylor and Jenkins has indicated the comparability of BHPS and Understanding Society data when taking into account a couple of specifics. Also, socio-biomedical research will become possible in the near future by new data deposits for Understanding Society and the Millennium Cohort Study (MCS).
The English Longitudinal Study of Ageing (ELSA) data have been analysed by, for example, Steptoe (Enjoying life and living longer), who looks into what is it that allows us to live longer and healthier. Hyde and Jones (2007) investigate whether time since labour market exit has an effect on the association between socio-economic position and health in a post-working population.
The Youth Cohort Study data provide a chance to follow the transition from education into the labour market. Yates et al. (2010), for example, investigate early occupational aspirations and fractured transitions and here especially the entry into in the ‘NEET’ (not in employment, education or training) status in the UK (Early Occupational Aspirations and Fractured Transitions: A Study of Entry into ‘NEET’ Status in the UK). Gayle et al. (2003) use the data to establish which factors influence young people’s entry to degree level higher education (Econometric Analysis of the Demand for Higher Education).
Employment history files
Usefully, for several of the major longitudinal studies, variables have been derived that summarise the employment histories of the study members. For the two older cohort studies held by the UK Data Service – the 1958 and 1970 cohorts – activity histories are available under SNs 6942 and 6943 respectively. For the BHPS, a work-life history file is available under SN 3954.