CEDEFOP Session 2: Closer look at the roadmap Moving towards detailed analysis of occupations – use of expert opinion Production of Skills Supply and Demand Forecasts Alphametrics Professor Rob Wilson, Institute for Employment Research Skillsnet Technical Workshop, 24-25 November 2016, Thessaloniki

Issues Demand for detail Data limitations Policy makers; education & training providers; individuals making career choices - demand more detail Present analysis goes down to the 2 digit level (c. 40 occupational categories) Data limitations Limited sample sizes in EULFS – lack of robust data at the 3 digit level (c.140 occupational categories) make it difficult to assess current occupational demands at this level Recent switch to the ISCO08 system for classifying occupations implies no reliable information on trends - so predicting the future is even more problematic

Current approach Currently the focus is on occupation within sectors 2-digit level (43 ISCO08 categories, (27 on ISCO88 basis)) Aim: around 140 3-digit level ISCO08 categories But this pushes the EU-LFS data beyond its limits in terms of identifying patterns and trends within sectors: sample size inadequate (140 occupations in 40 sectors represents some 5,600 data points, often above the total number of people surveyed in some countries) no immediate prospect of a more robust system (analogous to the US Occupational Employment Statistics Survey)

Best practice worldwide Traditionally skills projections are driven by Sectoral employment projections combined with detailed analysis of occupational structures within industries (Cedefop approach, USA BLS, UK Working Futures, and similar approaches in many other countries) In order to provide a sound foundation for any quantitative projections and to ensure a robust approach across countries sector should remain at the heart of the method But how to link more detailed occupational analysis within sectors?

Key elements – Back to basics Where are we now? Where are we going? For the first element we need a solid statistical foundation provided by the EULFS Needs to be tied in to the main National Accounts based E3ME employment database

Going forward How to project future changes in detailed occupational employment structure Tie in to macro and sectoral forecasts Detailed historical analysis of LFS (descriptive) Econometric methods Expert judgement (as in US BLS approach

Possible solutions Aggregation across other dimensions (e.g: industry or possibly countries?) - Focusing on combinations of 2 and 3 digit occupational data aggregated in order to increase sample sizes (many countries in the EU are very small and EULFS sample sizes reflect this) Focus on Disaggregation of 2 digit level occupations to 3-digit level - Link 3-digit projections to aggregate occupational projections (as in UK Working Futures) One approach is to move towards a combination of ISCO 2- and 3-digit occupations focusing on jobs which are important in term of numbers employed. For example, it may make sense to distinguish engineering professionals from the group “Science and engineering Professionals”, which includes chemists and biologists as well as engineers; It may make less sense to distinguish market sales persons from shop sales persons, or material-recording and transport clerks from general office clerks or numerical clerks. This can be done on a flexible basis according to the number of people employed in the occupations. Such an approach would overcome the small sample size problem and make maximum use of the LFS data. However it would move away from the approach based on regarding changes in industrial employment structure as the key driver of occupational /skills demand – projections become more ad hoc

Aggregation across other dimensions Aggregation across other dimensions (e.g: industry (or country?) to highlight more detailed occupations employing significant numbers For example: 3 digit & 2 digit occupations within broader sectors (i.e. aggregation across a selection of industries OR - 3 digit occupations within 2 digit categories (this could draw upon the experience of the IER in developing 4-digit level occupational projections for the UK in Working Futures (aggregation across all industries) Focus on: Identifying current occupational structures (pointing out significant differences across countries); and Possible future trends from the short time series available

Other possibilities Use of Qualitative techniques, to assess trends – tapping in to expert opinion (e.g. as in US BLS system) Multivariate methods, e.g: multilevel modelling (as in analysis of PIAAC data for OSPs in OF3); or more multinomial logit analysis Reliance on CGE approaches – theoretical considerations rather than empirical trends

Use of expert judgement: The US BLS approach The BLS approach Focus is on detailed changes in occupational structure within sectors Sectoral experts employed to assess evidence & make judgements on which occupations will grow and decline Could we do the same but focusing on countries rather than sectors? How to engage ICEs? Use of country specific information?

Qualitative techniques The US Bureau of Labor Statistics (BLS) approach relies on expert judgement about trends in occupational employment patterns within industries rather than quantitative /econometric modelling This approach has a well-established track record - the US has relied on such qualitative approaches for many years. US focus is on sectoral experts to provide the judgement - The main emphasis here would be on the use of expertise and judgement of country experts to inform the assumptions adopted about future changes (i.e. a Dephi style approach) This would draw upon the detailed quantitative analysis of the historical data from the EU LFS (current structures & possible future trends) One option is to make alternative projections based on expert judgements about trends within industries and / or countries Country specific robustness checks could be supported by other information from national sources

Practical issues How can we tap into country knowledge and expertise? Does this provide a reliable alternative? How would it be resourced? Can we draw on country specific information?

More econometric analysis? An important advantage of using regression analysis is that this enables predictions of the future outcome of the dependent variable (detailed occupational structure) given information about the explanatory variables Multinomial logistic regression techniques can be used to estimate the log-odds and hence probabilities of individuals being employed in detailed occupations This technique has considerable advantages when data and resources for expert judgement (such as employed by the US BLS) is more limited

Ways forward: extension of the shift-share analysis Current shift-share analysis (2-digit level) is used to hold ISCO08 2-digit occupational shares fixed; allow shares within sectors to change in line with previous trends in analogous ISCO88 categories Could be extended to 3-digit level (focussing on patterns within occupations and / or industries), allowing for changes in line with observed trends in the LFS data (recognising the very limited time series available) in line with expert judgements about likely changes based on some new econometric analysis

Contact details Professor Rob Wilson Institute for Employment Research University of Warwick COVENTRY, CV4 7AL r.a.wilson@warwick.ac.uk Tel: +(44) 2476-523530 www.warwick.ac.uk/ier