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Human Capital Statistics in the EU: Operationalizing the Lisbon Mandate

This paper explores the adequacy of human capital data in the EU and proposes strategies for operationalizing the Lisbon mandate, focusing on efficiency and equity. It discusses returns to education at both micro and macro levels and highlights the need for comprehensive and timely data on education costs, attainment, and distribution.

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Human Capital Statistics in the EU: Operationalizing the Lisbon Mandate

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  1. Human capital statistics in the EU: An approachGeorge Psacharopoulosgpsach@rcn.com Luxembourg, December 8-9 2005

  2. Lisbon mandate Human capital (HC) Adequacy of HC data?

  3. Operationalizing the Lisbon mandate • Efficiency (Growth, competitiveness) • Equity (Distributive incidence)

  4. Efficiency • - Micro: Returns to education • (private and social) • - Macro: Aggregate production function • (HC inputs: Quantity, Quality)

  5. Growth Accounting • The core of Lisbon’s agenda on the efficiency side is • Y = f(……………, HC) • HC = g(…., Y) • i.e., human capital (HC) plays a major role in generating national income (Y), • while the production of human capital uses national resources. • This macro framework is based on contemporary endogenous growth theory

  6. Returns to investment in education • The micro underpinning of the above, is that investment in human capital yields returns ( r ) to the individual and society at large, where W refers to the earnings/productivity of a more (subscript u) and a less (subscript s) educated person, and C is the cost of education.

  7. Micro data • Public cost of education by educational level/training/type • Private cost of education by educational level/training/type • Age-earnings profiles by level of education/training/type

  8. Macro data • National resources invested in education/training • Educational attainment of the labor force • Including lifelong learning

  9. Equity • - Who pays and who benefits from public expenditure on education/training? • - Appropriation of in-kind education/training subsidies by income decile • - On the job training share of costs between employee and employer.

  10. Two road maps • A. Start from the existing data • B. Start from the required data

  11. Contrast to the EU data state • Missing data • Quality of education (beyond narrow PISA) • University quality (beyond Shanghai or THES) • Centralization of educational systems • Externalities

  12. Contrast - 2 • Inadequate data • Private cost of formal education • Resource cost of training/lifelong learning • Income, voluntary!

  13. Contrast - 3 • Diluted data (e.g, too much on income, yet lots of missing data) • Returns to education, unclear estimation, most countries missing • Late data • Distributive incidence missing

  14. Recommendations • - Do less, but better (e.g., single survey for income variable) • - Deliver faster • - Add new variables (education quality beyond PISA, higher education) • - Simplify questionnaires for faster turnover • - Make answers compulsory (ECOFIN style!) • - Engage researchers on what kind of variables they want

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