1. Human capital spillovers: The importance of training Mary O’Mahony * and Rebecca Riley ** *Birmingham Business School, University of Birmingham **National Institute of Economic and Social Research and LLAKES 18-19 October 2012 LLAKES Conference, University of London Disclaimer: This work contains statistical data provided by the European Commission, Eurostat (European Community Household Panel Longitudinal User's Database, 1994-2001, Waves 1-8). Eurostat bears no responsibility for the analysis or interpretation of the data reported here. Acknowledgements: The financial support of the Economic and Social Research Council (ESRC) and the European Commission is gratefully acknowledged. The work was part of the programme of the Centre for Learning and Life Chances in Knowledge Economies and Societies (LLAKES), an ESRC- funded Research Centre – grant reference RES-594-28-0001, and the INDICSER project financed by the EU 7th Framework Programme – grant no. 244709.

2. Background & Motivation Knowledge transfer between workers is thought to be an important driver of economic growth – (Romer, 1986; Lucas, 1988; Jovanovic & Rob, 1989) Evidence of human capital externalities associated with formal learning – Geographical concentration of skilled workers (Moretti, 2004; Rosenthal & Strange, 2008; Heuermann, 2011) – Industry concentration of skilled workers (Winter-Ebmer, 1994; Sakellariou & Maysami, 2004; Kirby & Riley, 2008) – Establishment use of skilled workers (Battu, Belfield & Sloane, 2004; Martins & Jin, 2010; Bauer & Vorell, 2010)

3. What drives these knowledge spillovers? – Evidence on level at which these occur – Alternative theoretical explanations A role for intangibles in enhancing knowledge transfer and spillovers to wages? – Employer provided training may increase the relevance of knowledge exchange to the production process – IT should facilitate the sharing of ideas, technologies, experience – Separate from the notion of production complementarities between IT and skilled labour or between different aspects of skill Background & Motivation

4. This study Analyses the extent of knowledge spillovers from tertiary education within broad sectors – Mincerian approach to identification (Rauch, 1993; Moretti, 2004) – Using cross-country longitudinal data on individuals’ wages Explores the importance of intangibles such as IT and training in determining the extent of these knowledge spillovers

5. Identifying spillovers from education using wage equations

6. Estimating equation Based on Moretti (2004) Journal of Econometrics.

7. Identification issues Selection on unobserved ability into high-skilled industries so that cov(θ i,H jct )≠0 Solution: Include Individual*Country/Industry fixed effects Time-varying country/industry shocks correlated with skill levels so that cov(v jct,H jct )≠0 Solution: Control for productivity and 5-year employment growth

8. The importance of intangibles for these spillovers First term captures a complementarity between highly educated labour and training Second term captures an association between training and spillovers from education Include in estimating equation:

9. Data sources European Community Household Survey – 8 waves 1994 – 2001 (can track individuals over time) – Contains information on earnings from employment and highest educational qualification (as well as training; demographics) – NACE recorded at a relatively aggregate level EUKLEMS and INDICSER data items (from 1995) – Training capital stocks (O’Mahony, 2012) distinguished by qualification – IT capital services, tangible capital services, labour productivity, employment growth, workforce qualifications, output price deflators (O’Mahony & Timmer, 2009)

10. Estimation sample Countries for which we have qualification specific training stocks: – France, Spain, Germany, UK – Denmark, Sweden, Netherlands excluded due to data issues Restrict sample to male full-time employees age 26-55 with one job – Who have tertiary education (ISCED 5-7) upon entering the sample and throughout the sample Public and financial sector excluded

11. Sample sizes

12. Country/industry characteristics

13. Spillovers from tertiary education

14. Education spillovers and training

15. Education spillovers, training & IT

16. Conclusions Evidence from wage equations using cross-country longitudinal data is consistent with the presence of significant spillovers from tertiary education at sector level – A 1pp increase in the sector share of tertiary educated workers/hours raises individuals’ wages by approx 0.8%. – Individuals do not internalise the full benefits of their human capital investments We have highlighted some of the mechanisms through which intangibles may contribute to the growth process – Employers’ investments in training are positively associated with the extent of spillovers from tertiary education – In some models IT is positively associated with knowledge spillovers – Agnostic about the direction of causality