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Human Capital and Inclusive Growth

Human Capital and Inclusive Growth. Jesús Crespo Cuaresma Department of Economics University of Innsbruck j esus.crespo-cuaresma@uibk.ac.at. Outline. Human capital and inclusive growth . A tentative decision tree . Tools for country analysis : the example of Zambia .

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Human Capital and Inclusive Growth

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  1. Human Capital and Inclusive Growth Jesús CrespoCuaresma Department of Economics University of Innsbruck jesus.crespo-cuaresma@uibk.ac.at

  2. Outline • Human capitalandinclusivegrowth. • A tentative decisiontree. • Tools forcountryanalysis: theexampleofZambia. • Human capitalanddemographictrends • The laboursupplyside: • Identifyingbindingconstraints: • Returns toeducationandreturnheterogeneity. • Human capitalandmigrationpatterns. • The labourdemandside: • Identifyingbindingconstraints: Firm perceptions.

  3. A theoretical framework Lucas‘ (1988) growth model: Production function: Human capital definition: Accumulation rule: Euler equation:

  4. A tentative decisiontreefor human capital Problem: Low levels of human capital investment High cost of finance Low returns to education Skill mismatch Low demand for skilled labor (brain drain) Lack of access to (public) finance for education Problems in school access and/or infrastructure Supply-side factors Demand-side factors

  5. Education attainmentbygenderandagegroup: Zambia, 1970-2000

  6. Education attainmentbygenderandagegroup: Zambia, 2010-2020 http://www.iiasa.ac.at/Research/POP/edu07/index.html?sb=11

  7. The demographicdividendandeducationalattainment

  8. The demographicdividendandeducationalattainment

  9. The demographicdividendandeducationalattainment

  10. School enrollment

  11. School enrollmentbygenderandresidence: Zambia 1992-2002

  12. School enrollmentbygenderandresidence: Zambia 1992-2002

  13. School enrollmentbygenderandresidence: Zambia 1992-2002

  14. School enrollmentbygenderandresidence: Zambia 1992-2002

  15. School enrollmentbygenderandresidence: Zambia 1992-2002

  16. School attendancebyincomeandresidence: Zambia 1992-2002

  17. Human capitaldata: The macroeconomicpolicyview

  18. Estimating returns to education • Mincerian wage regressions, where X contains variables summarizing characteristics of the individual (age, experience, gender, education) and the firm (sector).

  19. Estimating returns to education • Mincerian wage regressions, • Education in wage regressions: • „Yearsofeducation“: Averagereturntoeducation. • Nodistinctionbetween different attainments. • Potential nonlinearities. • Educational attainmentlevels. • Comparabilityissues. • Probablymorehelpfultoidentifybottlenecksandconstraints. • Interaction termstoassessdifferencesacrosssocialgroups. • Differences male/female. • Quantile regressionstoassessdifferencesacrosspartsofthe wage distribution.

  20. Estimating returns to education • Zambia: Productivity and Investment Climate Survey 2007 (Employee questionaire) • Data on over 900 employees for 153 enterprises. • Personal characteristics: age, gender, previous experience, job experience, … • Education information: • Years of education. • Educational attainment: Primary, secondary general, secondary technical, vocational training, university first degree (domestic/foreign), university second degree (domestic/foreign).

  21. Estimating returns to education

  22. Estimating returns to education

  23. Estimating returns to education • Parameters differacrossquantiles, wherebq is the parameter vector associated with the q-thquantileoftheconditionaldistributionofthe wage variable.

  24. Estimating returns to education

  25. Estimating returns to education • Differences in returns to education: • Across educational attainment levels. • For women/men. • Across quantiles of the conditional distribution of wages. • Constraints on the supply side? • Vocational training and tertiary education receive relatively high returns. • Technical versus general secondary schooling. • Much higher returns in higher quantiles of the conditional distribution of wage levels.

  26. Migration rates by skill level

  27. Migration ratesbyskilllevelandgender: Zambia, 2000

  28. Migration rates within Zambia

  29. Migration patternsbyeducationandgender • Brain drain versus labour migration. • „Feminization“ of the brain drain. • Relatively low levels for African standards. • Lack of statistics and monitoring. • Particularly important for the health sector.

  30. The labour demand side

  31. The labour demand side

  32. The labour demand side

  33. The labour demand side

  34. The labour demand side

  35. The labour demand side

  36. The labour demand side • Skill of labor force is not reported as an important constraint by firms, although • Domestic firms report it to be more of a problem than foreign firms • Self selection? • Wage competition? • Exporting firms report it to be more of a problem than non-exporting firms • Medium-sized firms report it to be more of a problem than small and large firms

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