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Challenges in Measuring Human Capital for the Knowledge Economy

Challenges in Measuring Human Capital for the Knowledge Economy. Albert Tuijnman Human Capital Division European Investment Bank. 1. Purpose and Structure. Purpose:

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Challenges in Measuring Human Capital for the Knowledge Economy

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  1. Challenges in MeasuringHuman Capital for theKnowledge Economy Albert Tuijnman Human Capital Division European Investment Bank 1

  2. Purpose and Structure • Purpose: • To review some of the challenges posed to the European statistics system by the move to a life-long and life-wide learning model. • Structure: • 1. Theory: Risks, insurance and capital conversion • 2. Developments in education and life-long learning • 3. Information needs and challenges for measurement • 4. Examples of new statistical tools

  3. Background and Context • Knowledge Economy: • - Globalisation, ICTs, interpenetration of financial markets • - Increased speed and scope of change • - Increased competition (US, China), uncertainty, risks • Consequences: • - Altered economic policy landscape • - Public sector reform • - Constraints on national governance • - Reduced scope for national policy interventions • - Increased relative importance of structural policies •  NEED FOR INSURANCE POLICIES

  4. Capital Insurance Policyfor Nations and Individuals • Nations and individuals are by definition risk averse. • Risk is moderated by three capital stocks: • (a) Financial capital (money, physical capital) • (b) Human capital (knowledge, competence, skills, attitudes) • (c) Social capital (trust, networks, cohesion, shared values) • i.e. Nations and persons can better manage risks and overcome adverse shocks if they are: (a) wealthy; (b) knowledgeable; and (c) networked. • For this reason nations pursue similar goals: (a) productivity and growth; (b) knowledge and skills; and (c) social cohesion.

  5. Capital Conversion Process

  6. Human Capital Production Function • Human capital = Knowledge, competence, skills and other attributes embodied in individuals that are relevant for productive activity (OECD). • Human capital  Education • Human capital production function: the process of allocation, conversion and substitution of scarce financial, human and social capital resources over time to produce economically useful competencies. • Efficient human capital model = Life-long learning and Life-wide learning

  7. Life-long and Life-wide Learning Model • A. Life-long dimension: • - Early childhood experiences • - Schooling, tertiary education, adult education and training • - Learning in retirement, old age • B. Life-wide dimension: • - Formal: systematically organised, structured learning (school) • - Non-formal: systematically organised (continuing training work) • - Informal: experiential learning in every day life • Primary focus: - Institutional  Individual • - Education  Learning

  8. Educational Reform and Change • Educational reforms and changes with major ramifications and challenges for the statistical system: • - Measurement units: individual learners (pupils, parents, teachers, workers, senior citizens) rather than educational establishments; • - Fluid orientation: From vertically programmed curricula, grades and hierarchical educational levels to horizontally articulated competency-based orientations; • - More heterogeneous participation patterns and mixed learning modes (classroom based - virtual learning) • - Different age mixes (i.e. high median age of university students in Sweden) • - ???

  9. Statistical Information Needs in the Knowledge Economy • Move from front-end education model to a life-long learning model appropriate for the knowledge economy changes the nature of the data the statistical system is called upon to provide: • - Current information system still entirely front-loaded • - Biased towards “institutional input counts - Ns” • - Biased towards “institutional output levels - ISCED” • - Little data on processes (infamous “black box”) • - Better data on outcomes, but heavily biased towards children (IEA - 10 & 14 year-olds) and youth (OECD PISA – 15 year-olds) and towards only three subjects (reading literacy, mathematics, scientific literacy)

  10. Market Failures due to Information Gaps • Educational attainment is not synonymous with either human capital or with labour force qualifications • Labour force qualifications are not synonymous with actual job requirements or with the skills workers have acquired • Skill requirements of jobs in the knowledge economy are much more difficult to define and standardise than jobs in the “old” industrial economy • Lack of direct observations on individual skills & competencies is one source of labour market rigidities, skill mismatches and market failure in labour allocation

  11. Inadequacy of Proxy Measures • Educational attainment levels and skill levels only moderately strongly correlated because: • Quality variation in education • Education levels too indiscriminate • Continuing education and training not factored in • Experiential learning beyond front-end education • Predictive capacity of education diminishes with increasing age (i.e. mid-30s for occupation and mid-40s for earnings) • Inadequate understanding of how curricula are related to skill taxonomies • Lack of instruments to observe skills directly

  12. Conceptual and Measurement Problems • Life-long learning not tied to institutional contexts • Requires statisticians to take a holistic perspective • Consider the whole range of educational provision across the life-span • Ensure better statistics on non-formal on-the-job training • Development of data sources on informal learning • Self-directed, experiential learning: “Even the bees do it …”

  13. Five Challenges for the Statistical System • 1. Developing competency-based measures • 2. Improving statistics on formal and non-formal adult education and continuing vocational training provision • 3. Extending measurement along the life-wide axis, particularly informal, self-directed and experiential learning across the life-span • 4. Capturing the cumulative nature of learning processes • 5. Measuring occupational change in knowledge economies

  14. 1. Competency-based Measurement

  15. 2. Adult Education Measurement

  16. 3. Measurement of Life-wide Learning

  17. 4. Measuring Cumulative Learning

  18. 5. Measurement of Occupational Change

  19. Conclusions • Knowledge economy  Life-long learning system • Information gaps  market failures  new statistical tools • - Next generation (longitudinal) adult education survey • - Next generation LFS module on lifelong learning • - Next generation CVTS • - Next generation PISA (2010+) • - Next generation IALS / ALL / PIAAC • - Competency-based conceptual work - ISCO and ISCED

  20. Thank You! • Albert Tuijnman • tuijnman@eib.org

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