1. 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, November 2016, Thessaloniki

2. 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

3. Current approach Currently the focus is on occupation within sectors
2-digit level (43 ISCO08 categories, (27 on ISCO88 basis))
Aim: around 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)

4. 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?

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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?

11. 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

12. 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?

13. 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

14. 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

15. Contact details
Professor Rob Wilson Institute for Employment Research University of Warwick COVENTRY, CV4 7AL Tel: +(44)