HUMAN CAPITAL: EXTENDING THE MEASURES MARY O’MAHONY PRESENTATION AT THE SEM CONFERENCE 2015 PARALLEL SESSION D: MEASURING CAPITAL AND WEALTH This research benefited from funding from BIS, CEDEFOP, LLAKES - an ESRC-funded Research Centre – grant reference ES/J019135/1, and the INDICSER project financed by the EU 7th Framework Programme – grant no Department of Management

Overview Conventional measures of human capital Extensions: – Employer provided training – Age cohort effects – Health and Human capital

Measures of human capital Many international exercises to measure human capital, e.g. Barro and Lee database, Inclusive Wealth, World Bank plus international testing exercises such as PISA and PIACC Fraumeni (2015) summarises methods and impacts on rankings of countries Human capital stocks measured in satellite accounts by many statistical offices Most popular measures are those based on the Jorgenson Fraumeni discounted life time income approach – Applied now in 20 (mostly OECD) countries – Some countries prefer accumulated expenditure on education inputs

Measures of human capital: JF model Applies neoclassical theory of investment to human capital Value of human capital depends on an individual’s discounted lifetime income Calculations divide population into various life stages (in education, in the labour force and retirement) Implementation requires data on: – working population and school enrolments, – educational attainments and earnings associated with those attainments – survival rates

Measures of human capital: JF model Implementation also depends on a number of assumptions including: – Returns from human capital accumulation accrue to individuals (no role for additions to human capital provided and paid for by employers) – Relative wage rates by educational attainment levels are determined by contemporaneous relative wage rates (the lifetime income of a 25 year old with a university degree measured by income earned by older age groups with the same level of attainment) – Assumptions on depreciation and survival rates (frequently assume all persons retire at age 65 and no role for impact of health)

Extending the model: Employer provided training Literature on measuring intangible assets (e.g.Corrado, Hulten and Sichel, 2005) include measures of firm specific human capital. Investments in training provided by firms which does not feature in the earnings of employees, cumulated to stocks of intangible training capital Estimates suggest that this training capital is sizeable and important for productivity – O’Mahony (2015) suggests that in the EU15 investments in intangible training account for about 2% of GDP – Comparable to expenditure on secondary education in these countries

Extending the model: Employer provided training Econometric estimates in Mason et al. (2014) show significant direct effects from training capital on productivity and significant interactions between training and certain types of skills (degree and above and upper intermediate)

Capital-labour ratio0.3962***0.2991** [0.137][0.147] Higher skills *** [0.059][0.079] Upper intermediate vocational skills [0.051][0.060] Lower intermediate vocational skills [0.078][0.091] Lower intermediate general skills [0.061][0.072] Average high-skilled training capital per hour worked *** [0.077][0.087] Average intermediate-skilled training capital per hour worked [0.095][0.180] Training capital (higher)*Higher skills *** [0.028] Training capital (intermediate)*Upper intermediate vocational [0.041] Training capital (intermediate)*Lower intermediate vocational [0.042] Training capital (intermediate)*Lower intermediate general [0.046] Observations1456 Adj. R Fixed effects estimates of average levels of labour productivity, , All Countries

High- apprentice countries Low- apprentice countries Capital-labour ratio0.4938***0.3928***0.3593**0.3338* [0.159][0.141][0.164][0.176] Higher skills **0.4415*** [0.103][0.148][0.109][0.107] Upper intermediate vocational skills0.0843*0.1660** [0.047][0.073][0.089][0.082] Lower intermediate vocational skills * ** [0.445][0.430][0.086][0.110] Lower intermediate general skills0.0924*0.1822** [0.051][0.076][0.103][0.097] Average high-skilled training capital per hour worked0.3040***0.2851*** * [0.072][0.101][0.088][0.153] Average intermediate-skilled training capital per hour worked ** [0.107][0.302][0.099][0.200] Training capital (higher)*Higher skills *** [0.051] [0.038] Training capital (intermediate)*Upper intermediate vocational ** [0.046] [0.068] Training capital (intermediate)*Lower intermediate vocational ** [0.203] [0.037] Training capital (intermediate)*Lower intermediate general ** [0.044] [0.049] Observations Adj. R Fixed effects estimates of average levels of labour productivity, , Comparing high- and low-apprenticeship countries

Extending the model: Employer provided training The impact of training varies by systems of educational provision – countries that target more general education (e.g. UK, Scandinavian countries) see greater productivity impacts from training the high skilled – Countries with more vocational orientation education systems (e.g. Germany and Austria) also derive significant productivity benefits from training at upper intermediate level Econometric estimates in O’Mahony and Riley (2012) suggest significant spillovers from high skilled labour that are facilitated by employer provided training

Education spillovers and training

FinlandFranceGermanyNetherlandsSwedenUK United States+ Output per person hour Percentage point contribution Training Labour composition(Skills) Recent work that extends growth accounting to the period after the financial suggests significant declines in the contributions of training capital to labour productivity growth Extending the model: Employer provided training

Extending the model: Relative wages Results from the recent PIACC survey questions the contemporaneous relative wage assumption Fraumeni (2015) shows country rankings according to the JF model and those from PIACC. Of the 20 countries for which JF estimates are available: – The US ranks first on JF and only 10 th on PIACC – Similarly the UK ranks 2 nd on JF and 8 th on PIACC – In contrast Australia ranks 9 th on JF and 4 th on PIACC – And the Netherlands is 1oth on JF and 2 nd on PIACC Results suggest the need to revisit whether earnings reflect skills especially for older age cohorts Probably needs a second wave of PIACC to fully understand the implications

Extending the model: Including health Simultaneously with work on measuring human capital many countries also experiment with estimating health capital stocks Need to integrate the two measures Health affects human capital through: – Affecting the retirement age (survival in the JF model) – Impacts on earnings of those who continue working in poor health – Incentives to invest in human capital – greater longevity might increase the returns to investing in education Health effects depend not only on the individuals own health but also on their dependents – Retirement can also occur due to caring activities

Extending the model: Including health Current work Estimating the impact of health (both own and relatives) on retirement decisions Estimating the impact of health on earnings Constructing human capital stocks dividing also by health status – (Lea Samek - PhD at King’s) First estimates for the UK with possible extension to Germany Also modeling the impact of health on education incentives – Martin Weale for SPINTAN project

Extending the model: Conclusions Highlighted three areas for further work Employer provided training is most complete and there is a strong case for including this in national accounts as part of intangible investments Combining health and conventional human capital in satellite accounts is likely to be both feasible and important for policy More work required to fully understand the implications of PIACC

References Corrado, C., C Hulten and D Sichel (2005), “Measuring capital and technology: an expanded framework”, In C Corrado, J Haltiwanger and D Sichel (eds), Measuring Capital in the New Economy, The University of Chicago Press, p Fraumeni, B. (2015), Choosing a human capital measure: Educational attainment gaps and rankings, NBER working paper no Mason G, M O’Mahony, A. Rincon, R Riley (2014) “Macroeconomic benefits of vocational education and training”, Cedefop Research Paper No. 40. O’Mahony M (2012) ‘Human Capital Formation and Continuous Training: Evidence for EU countries’, The Review of Income and Wealth, Vol. 58, No. 3. p O’Mahony M and Riley R (2012) “Human capital spillovers: the importance of training”, INDICSER Discussion Paper No. 23.

CONTACT DETAILS MARY O’MAHONY PROFESSOR OF APPLIED ECONOMICS DEPARTMENT OF MANAGEMENT KING’S COLLEGE LONDON