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.
16
Embed
Human capital spillovers: The importance of training Mary O’Mahony * and Rebecca Riley ** *Birmingham Business School, University of Birmingham **National.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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 2012LLAKES 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.
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)
• 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
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
Identifying spillovers from education using wage equations
𝐻𝑖 = indicator of tertiary education 𝐻𝑎 = share of individuals with tertiary education in unit 𝑎 𝛽𝐼𝑁= private return 𝛽𝐸𝑋= external return or spillover within unit 𝑎
Risks conflating complementarities between high and low educated groups with spillovers from education �֜ Restrict sample to 𝐻𝑖 = 1.
• Selection on unobserved ability into high-skilled industries so that cov(θi,Hjct)≠0
Solution: Include Individual*Country/Industry fixed effects
• Time-varying country/industry shocks correlated with skill levels so that cov(vjct,Hjct)≠0
Solution: Control for productivity and 5-year employment growth
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:
+𝜌𝐼𝑁𝑇𝑗𝑐𝑡 + 𝜋𝐻𝑗𝑐𝑡𝐼𝑁𝑇𝑗𝑐𝑡
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,
• 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
Log tertiary TRAINING capital per tertiary hours 0.059 0.003 0.108* 0.040(0.341) (0.931) (0.088) (0.282)
Log IT capital-labour ratio 0.061 0.082*** 0.019 0.043(0.162) (0.003) (0.677) (0.132)
Person*Industry Fixed effects no yes no yesProductivity shocks no no yes yes
Fixed effects 48 2879 48 2879
Notes: Dependent variable is the log hourly wage; 8095 observations; Controls include log capital labour ratio, industry*country fixed effects, year effects, indicator for managerial and professional occupations, marriage, quadratic i age and quadratic in job tenure, workplace size, permanent contract, vocational training course; Controls for aggregate productivity shocks are log labour productivity and employment growth in the last 5 years; Standard errors corrected for clustering on country*industry*year cells.
Log tertiary TRAINING capital per tertiary hours -0.089 -0.063 -0.016 -0.005(0.222) (0.144) (0.836) (0.907)
Interaction between EDUCATION & TRAINING 1.047*** 0.424** 0.779*** 0.253(0.000) (0.013) (0.008) (0.152)
Log IT capital-labour ratio -0.001 0.032 -0.043 -0.013(0.980) (0.312) (0.426) (0.699)
Interaction between EDUCATION & IT 0.045 0.135* 0.128 0.195***(0.707) (0.066) (0.293) (0.010)
Person*Industry Fixed effects no yes no yesProductivity shocks no no yes yes
Fixed effects 48 2879 48 2879
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