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Challenges in Measuring Human Capital for the Knowledge Economy

Explore the challenges in adapting statistical systems to a life-long learning model in the knowledge economy. Discover the impact on national governance, economic policies, and insurance policies for managing risks.

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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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