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

2. Outline Human capital and inclusive growth. – A tentative decision tree. Tools for country analysis: the example of Zambia. – Human capital and demographic trends – The labour supply side: Identifying binding constraints: – Returns to education and return heterogeneity. – Human capital and migration patterns. – The labour demand side: Identifying binding constraints: Firm perceptions.

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

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

5. Education attainment by gender and age group: Zambia, 1970-2000

6. Education attainment by gender and age group: Zambia, 2010-2020 http://www.iiasa.ac.at/Research/POP/edu07/index.html?sb=11

7. The demographic dividend and educational attainment

10. School enrollment

11. School enrollment by gender and residence: Zambia 1992-2002

16. School attendance by income and residence: Zambia 1992-2002

17. Human capital data: The macroeconomic policy view

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: – „Years of education“: Average return to education. No distinction between different attainments. Potential nonlinearities. – Educational attainment levels. Comparability issues. Probably more helpful to identify bottlenecks and constraints. – Interaction terms to assess differences across social groups. Differences male/female. Quantile regressions to assess differences across parts of the 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. Enterprise fixed effects Female0.0019-0.383*0.00364 Age0.0005150.000262-0.00572 Age sq.0.0001480.0001410.000155 Experience0.0398*** 0.0421*** Experience sq.-0.00107***-0.00104***-0.00102*** Trade union-0.076-0.0682-0.0181 Fulltime0.05520.0455-0.00766 Education years0.0793***0.0743*** Ed. Years × female0.0326* Primary Ed.0.33 General Sec. Ed.0.512** Technical Sec. Ed.0.723*** Vocational Ed.0.896*** Tertiary Ed. 1st dg.1.581*** Tertiary Ed. 2nd dg.1.630*** Constant3.923***6.470***6.690*** Observations923 R-squared0.8950.8960.903

23. Estimating returns to education Parameters differ across quantiles, where   is the parameter vector associated with the  -th quantile of the conditional distribution of the wage variable.

24. Estimating returns to education q=0.1q=0.25q=0.5q=0.75q=0.9 Female-0.0222-0.00610.01450.04980.0359 Age-0.0007280.008880.00443-0.00919-0.0323 Age sq.4.07E-05-8.52E-051.22E-050.0002840.000618 Experience0.002270.008510.0187**0.0296**0.0461*** Experience sq.-4.33E-05-7.77E-05-0.000369-0.00063-0.00141*** Trade union0.03030.0317-0.06-0.0627-0.0974 Fulltime0.0315-0.0467-0.0365-0.09830.035 Education years0.0199***0.0244***0.0267***0.0507***0.0793*** Constant6.856***6.720***6.713***6.731***6.758*** Observations923

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 rates by skill level and gender: Zambia, 2000

28. Migration rates within Zambia

29. Migration patterns by education and gender 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

36. 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