1. Heterogeneity of the labour input: methodological issues in constructing labour quality measures By Mary OMahony, Catherine Robinson, Michela Vecchi Helsinki June 2005

2. Importance of labour quality In productivity studies: different types of labour have different productivities Estimates of the Solow residual Importance of skills Increasing importance of education in the IT economy

3. Computation of a quality adjusted labour input Where h are labour force characteristics Education/qualification Age Gender

4. Data used in this study UK LFS, 1996-2002, 47 industries Five skill groups have been identified

5. Data Gender distinction 3 age bands: under 30, between 30 and 45, over 45 Estimated returns to skills: Use LFS microdata and Mincer wage equation (OMahony and Stevens 2005)

6. Inconsistent or missing data

7. Outliers in the data

9. Employment data

10. Summary of the problem Desirable data Ws1a1g1 Ws2a1g1 Ws1a2g1 Ws2a2g1 Available data Wa1g1 Wa2g1 Example: 2 skill groups, 2 agebands, 2 genders

11. Estimated quality adjusted labour Mincer equation (Mincer 1974):

12. Derivation of the correction term Generalised to N skill groups:

13. Corrected data Available data Wa1g1 Wa2g1 Desirable data Ws1a1g1 Ws2a1g1 Ws1a2g1 Ws2a2g1 Corrected data Wa1g1* Wa2g1*

14. Estimated quality adjusted labour wage bill share Average wage by skill, age and gender

15. Figure 2

16. Figure 3

17. Figure 4

18. Aggregate quality adjusted labour Data for the distributive sector (d) to calculate the returns to education For each skill, age and gender group

19. Figure 5

20. Figure 6

21. Figure 7

22. Figure A.1

23. Figure A.2

24. Figure A.3

25. Conclusions The two methodologies give a good approximation of the actual data Estimated QAL: allows to account for other factors affecting the returns to education but assumes equal returns across all industries Aggregate QAL: less refined but allows for different returns in more broadly defined industry groups