Mobility and the changing structure of occupations: a cross-cohort comparison

1. Mobility and the changing structure of occupations: a cross-cohort comparison Craig Holmes, Ken Mayhew and Felix Chow RC28 Conference, University of Essex, April 14th 2011

2. Introduction • Occupational structures change over time • This structure is important for a number of labour market outcomes. • Much focus on wage distributions and wage inequality • Changes may also impact on occupational mobility • Previous work focused on single cohort and mitigating factors • In this paper, we compare two cohorts: • Where do displaced workers end up, and does this differs between younger and older workers? • Do younger workers recognize that changes in structure and choose different entry points into the labour market than previous cohorts?

3. Introduction • What might cause a change in the occupational structure? • Skill-biased technical change • Computer capital take-up increases firm demand for skilled workers and replaces unskilled workers • Routinisation hypothesis (Autor, Levy and Murnane, 2003): • Computer capital replaces tasks, not skills • Labour employed in routine tasks can be swapped for technology • Occupations performing non-routine tasks grow • Polarisation hypothesis (Goos and Manning, 2007) • Routine occupations found in middle of income distribution • Non-routine occupations found at top and bottom of distribution

4. Theory • Model of occupations with task-biased technological progress and task • Following routinisation: • More qualified move to higher skill non routine, less qualified move to lower skill service jobs • Older workers moves to higher skill jobs, younger workers move to lower skill jobs, everything else being equal • Workers with more routine occupation specific skills are less likely to move • Routine jobs are “getting old” (Autor and Dorn, 2009)

5. Data • National Child Development Study (NCDS) • Members all born in a single week in March 1958 • Use waves 1981, 1991, 1999-2000, 2004-5 • Data covers age 23 to age 46-7 • N = 10-12,000 in each wave • British Cohort Study (BCS) • Members all born in a single week in April 1970. • Use waves 1996, 1999, 2004, 2008 • Data covers age 25 to age 38 • N = 9,000 in each wave

6. Data • Occupations coded in KOS (1981) SOC90 (1991, 1999) and SOC2000 (2004). • Manually converted to SOC2000 based on occupation descriptions • Reduced to 3 digit coding to reduce dropped observations • Occupations placed into one of six groups: • Professional, managerial, intermediate, routine, service, manual non-routine • Allocation based on description, wages and wider economy employment changes • Managerial and intermediate are both higher skill, non-routine occupations without qualification entry requirements • Manual non-routine and service are both low skill non-routine occupations.

7. Methodology • Would like to ask how routinisation has affected transitions from routine occupations • Counterfactual dataset does not exist • Alternative: • Look at 6 periods of transitions: 1981-1986, 1986-1991, 1991-1995, 1995-1999, 1999-2004 and 2004-2008 • Include a measure of routinisation using changes in employment share of routine workers across entire economy (LFS data)

8. Methodology • Logit model: • Dependent variable : end of period occupation dummy • One equation estimated for each destination occupation. • Conditional on starting in a routine occupation  N=16,853 • Baseline model: • ACADEMIC and VOCATIONAL are vectors of dummies • Reference group: white, male, level 3 qualifications

9. Results • Baseline estimation: • Higher academic qualifications increase probability of “upwards” moves • Level 2 and 3 academic qualifications not significantly different for all transitions • Vocational qualifications below level 4 not significant. Role of level 3 qualifications for upward mobility? • Level 3+ qualifications reduce probability of moving to low skill service occupations • Routine occupation experience reduces mobility • Routinisation increases probability of upward and downward moves

10. Results • Logit is a non-linear model, therefore size of effects vary across different types • e.g. Marginal effect of probability of transition to intermediate job from cohort may also depend on age, experience or qualifications • In logit models with interaction terms, significance and even direction of effects not the same as shown by coefficients (Ai and Norton, 2003).

11. Results • Illustrative example used to show size of effects • Example: white male between the ages of 28 and 33, who has worked in a routine occupation for one prior period

12. Results Autor and Dorn (2009): younger workers are more likely to avoid routine occupations early in working life Estimate a logit regression on initial occupation (aged 23-25) Dependent variables: qualifications, demongraphics, cohort, interactions Decline in routine employment, but by smaller proportion than total population

13. Results • Regression results: no evidence more qualified avoid routine occupations

14. Conclusion • This paper offers an attempt to estimate some of the effects on occupational mobility resulting from the decline in routine jobs. • Results: • Routinisation has been an important driver of occupational mobility over past thirty years • Mobility patterns vary across cohorts • Younger cohort more mobile in general, but less affected by routinisation • Comparing like for like, no evidence that younger cohort is avoiding routine occupations early in working life • May be result of non-human capital barriers to mobility

15. Conclusion • Further research: • Wage mobility • Closer examination of career paths • Policy relevance: • UK government acknowledges polarisation phenomena • Maintains a simple story: growing good (non-routine) jobs  greater opportunities for upward mobility • Has yet to consider the more complex ways it may affect patterns of mobility and limits of policies based purely on more and more upskilling

16. Contact Details Craig Holmes ESRC Centre on Skills, Knowledge and Organisational Performance (SKOPE), Department of Education, Norham Gardens, Oxford Email: craig.holmes@education.ox.ac.uk