“More & better jobs” Patterns of job growth and changing quality of work in the EU: A business function approach

1. “More & better jobs”Patterns of job growth and changing quality of work in the EU: A business function approach

2. Introduction The [European] Union has today set itself a new strategic goal for the next decade: to become the most competitive and dynamic knowledge-based economy in the world capable of sustainable economic growth with more and better jobs and greater social cohesion Lisbon European Council 23 and 24 March 2000 Presidency Conclusion

3. Introduction • Policy: more and better jobs • European Employment Strategy, 1997 • Lisbon summit, 2000 (“more”-targets) • Laeken Summit, 2001 (“better”-indicators) • Europe 2020 strategy • Research questions • How to identify more jobs rather than growing sectors? • Are these jobs better? What is the quality of work? • Issues • Finding reliable data covering the EU • Including quality of work-indicators • Measuring a trend effect • And many more!

4. Contents • Data • EU-Labour Force Survey • Quality of work indicators • More Jobs • Bart score • Sector employment growth in the EU • More Jobs • Business functions • Sector composition • The business function approach • Growth of business functions • Patterns of growth • Better jobs • Critical spots in growing business functions • Patterns of growth and changing quality of work in the EU

5. EU-Labour force survey Quality of work indicators DATA

6. European Union Labour Force Survey • Large sample: approx. 1,7 mio of individuals, covering the population • In private households in the EU 27 • Of people aged 15 and over • Of employed as well as unemployed citizens • Starting in 1983, continuing harmonisation process • Now quarterly survey • Questions on labour participation using the same concepts, definitions, classifications in all countries • Unemployment • Sectors: NACE • Occupations: ISCO-88 (COM) • Qualifications: ISCED • Regions: NUTS

7. Variables • Trade off between content and sample size • Main variables • Sex • Age groups • nationality • Education • Economic activity • Occupation • Main topics • Professional status • Fulltime – parttime (+involuntary) • Fixed-term contract (+duration) • Working time (usually and actually) • Unemployment (duration) • Homework • Atypical working time (WE, shift, evening, night) • + ad hoc modules

8. Labour force

9. Accessibility • Microdata • Limited detail (1 digit NACE / ISCO) • Expensive • Downloadable tables • Eurostat web page • http://epp.eurostat.ec.europa.eu/portal/ • Custom tables • Ad hoc requests • Tables (aggregated data) • Extrapolated figures • Detailed NACE and ISCO breakdowns • Limited number of variables crossed • Hidden figures for small cell sizes

10. Quality of work • QOW dimensions • Economist approach: A. Sen, E. Schokkaert et al. (KUL) • Policy elaboration: Muñoz de Bustillo et al. • Psychological approach: D. Holman (WALQING)

11. A bit of a spider web

12. EU-LFS indicators • Insufficient for composite indexes at lower levels • Small amount of indicators/dimensions • Binary answers • Limited / no crossing • Variables • Permanency of the job • Full time/part time • Actual and usual hours • Sunday work • Working at night • Shift work • Evening work • Saturday work • Working at home • …

13. Indicators • Indicator of choice: permanency of the contract • Objective indicator • High response rate • Strong correlation with other QOW-items (Gallie, 2007) • Interaction with other QOW-items: weak contract means no escape from bad QOW • Complex interpretation • Variations can be explained • by “country” characteristics, incl. institutional context and national regulation, economic and labour market specificities, etc. etc. • By sector characteristics, incl. socio-technical conditions, regulation,… • By business function/ occupational group characteristics • EU-LFS data allow for a simple multilevel model incl. country, sector and occupation • Most variance (~75%) is explained at the occupational level

14. Assessment • Pro • Large sample • Largest data source in many countries • Eurostat co-ordinated: internationally comparable • Used as a reference for weighting other survey data • Qualitative information (often not available in administrative databases) • Long time range: started in 1983 and since 2000 harmonised for most of EU • Not perfect • Methodology may vary by country • Retrospective questions • Recall problems increase as time goes by which can increase non-response and reduce quality of results • Proxy interviews • Retrospective questioning especially problematic for proxy interviews • Coding practices vary and can change over time • Not in depth for specific topics • Stringent privacy policy

15. BART score Sector employment growth MORE jobs

16. Issues Trends Which period to study ? Scope What is an EU average ? Meaning What is growth ?

17. Period • Crises, revolutions • Change from planned to market economies NMS in 1990s • Financial crisis 2008: we do not want to test shock-resistance • Solution: select period in-between • Business cycle changes ‘sector mix’ • Construction benefits from upswing • Public sector steady at downturn • Solution: compare peak periods, assuming maximum labour input • From policy • Lisbon summit • European Union enlargement (NMS) • EMU enlargement • From theory • Technological progress • 41% internet users in 2002, 62% in 2007 • Globalization: competition, outsourcing, convergence (LME-CME)

18. Period

19. EU average • Countries are vastly different in sizes • Big five: Germany, France, UK, Italy, Spain • Medium size: Ireland, Norway, Slovakia, Denmark, Bulgaria, Belgium, … • Small ones: Malta, Cyprus, Luxemburg, … • Principle? • Some countries have a higher impact • Every country = a case • Solution • Sector size relative to national employment (shares) • Cardinalizing employment growth within sector • Averaging for the EU over all member states

20. Introduction

21. Introduction

22. Growth index (BART score) • Relative growth (%) • Measuring “revolution” • More outspoken for small sectors • Absolute growth (pp) • Measuring “impact” • More outspoken for large sectors • Between revolution & impact • Change or structural growth • Measuring small sectors becoming big, big sectors becoming bigger • Solution • “Reverse logic” • Accent on RG for large sectors • Accent on AG for small sectors • BART score

23. BART score • Absolute growth vs. relativegrowth • RG: measure of revolution • AG: measure of evolution • WALQING: quality of work in new and growingjobs • RG: newoccupations stand out • AG: growingoccupations stand out • BART: balanced absolute & relative trends • Similar to BIRCH scale (product of RG & AG) • For trends basedon percentages (score, shares, …) • Weighted average of AG and (transformed) RG • BART = •RG + (1- )•AG • Weights: the percentages in t-1 • If 0: only absolute growthcounts • Towards 1: absolute growth

24. Formula Weights Trends 1/exp(-RG) ranges [1;0] Central point needs to be 0 (not 1/e = 0.67) Restoring min/max Increase Decrease

25. Distribution of components

26. Comments • “Structuralgrowth” • Important changes in relative trends show AG • Important changes in absolute trends show RG • Preventsoutliersforverysmallfigures • Hinders catch-up effects • Tested for sector data • Close to AG • … because of smallshares • Applicable to otherfigures • Growth of business functions • Growth in percentage score on QOW indicator • … • Assumptions • Perfect substitution elasticity: 1pp = 1% • Ordinal equivalent but no strict metric • Form of transformations (other possibilities: chart)

27. Other transformations

28. Results (BART)

29. Results (BART)

30. Results (BART)

31. Divergence

32. Divergence

33. Conclusions • Strong dispersion • Average BART score  0 • Structural growth in a wide range of sectors

34. Business functions Sector composition The business function approach Growth of business functions Patterns of growth MORE jobs

35. Business functions • Definition • A cluster of technologically and economically distinct activities • Which are usually performed together • As a result of processes of division of labourwithin and between companies • Distinctions between ‘core’ and ‘support’ • Scheme: Porter, 1985

36. Levels of analysis Sector employment Business function Individual job

37. Business functions in practice • Occupation groups within sectors are used as proxies • Occupation classification: 3 digit ISCO • Sector classification: 2 digit NACE • For 10 sectors, we linked 502 combinations of ISCO and NACE to a business function (more than 90% of workforce in each country) • For high qualification occupations, it was not possible to distinguish functions

38. Business Functions • Level 1: position of the job • Core • Administration (non core) • Support (non core) • Level 2: nature of the job, qualification level • Professional level • Management • Experts • Operational level • Clerks • Technical work • Service work • Sales • Transport & logistics • Level 1 & 2 can be combined (see scheme)

39. Business Functions (ISCO 3 digit) Level 1 Core Level 2 Non core Operational Professional Administration Clerks Management Support Sales Experts Serviceoperational Technics Transport & logistics

40. Example: construction Level 1 Administration Core Support Technics Transport & logistics Level 2 Management Clerks Experts Services Sales

41. Example: wholesale trade Level 1 Administration Core Support Sales Technics Level 2 Management Clerks Transport & logistics Services Experts

42. Exercises • Manufacturing of metal products: • 412 Numerical clercks • 913 domestic and related helpers, cleaners and launderers • 832 Motor vehicle drivers • Construction: • 214 Architects, engineers and related professionals • 742 wood treaters, cabinet-makers and related trade workers • 341 finance and sales associated professionals • Wholesale trade and commission trade • 110 legislators and senior officials • 832 motor vehicle drivers • 933 transport labourers and freight handlers • 722 blacksmith, toolmakers and related trade workers • Hotels and restaurants • 422 client information clercks • 741 food processing and related trade workers • 913 domestic and related helpers, cleaners and launderers

43. Construction

44. Hotels & restaurants

45. Health & social work

46. L1 Sector comparison (2007)

47. Conclusion • Sectoral diversity of business function compositions • Level 1 • The core (almost) always stands out • Large core: health and social work, construction • Large administration: e.g. wholesale, real estate • Large support: recycling, travel agencies

48. L2 Sector comparison (2007)

49. Conclusion • Also the variety in differentiation of business functions with respect to qualification levels is high • Level 2 • Big differentiation of business functions: wholesale, travel agencies, recycling, real estate, • Small differentiation: manufacturing, construction, hotels and restaurants, computer and related, R&D • Clear profiles because of a prominent business function: • Technical profile: metal products, construction, • Specialist profile: R&D, IT, • Service profile (operational): hotels & restaurants, real estate • Sales profile: wholesale, travel agencies

50. The business function approach • Business function is a lower level than the sector and a higher level than the individual job • The business function is where the decisions hit • Attention for dynamics • Administration: bureaucratization • Professionalization • Core: specialisation • Support: outsourcing, in-housing • Structuralist approach: the structure of a sector has an effect on the meaning of a job. For example: • More management  enhanced productivity of blue collar work • More core technical work  higher workload for transport & distribution • More experts, less technical work  only prototyping in-house • Study of the growth of business functions