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Human Capital and Inclusive Growth: Returns to Education and Firm Constraints Jesús Crespo Cuaresma Department of Economics University of Innsbruck

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Presentation on theme: "Human Capital and Inclusive Growth: Returns to Education and Firm Constraints Jesús Crespo Cuaresma Department of Economics University of Innsbruck"— Presentation transcript:

1 Human Capital and Inclusive Growth: Returns to Education and Firm Constraints Jesús Crespo Cuaresma Department of Economics University of Innsbruck

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

3 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)

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,

6 Education attainment by gender and age group: Zambia,

7 School enrollment

8 Human capital data: The macroeconomic policy view

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

10 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.

11 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).

12 Estimating returns to education Enterprise fixed effects Female * Age Age sq Experience0.0398*** *** Experience sq *** *** *** Trade union Fulltime 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-squared

13 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.

14 Estimating returns to education q=0.1q=0.25q=0.5q=0.75q=0.9 Female Age Age sq.4.07E E E Experience **0.0296**0.0461*** Experience sq.-4.33E E *** Trade union Fulltime Education years0.0199***0.0244***0.0267***0.0507***0.0793*** Constant6.856***6.720***6.713***6.731***6.758*** Observations923

15 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.

16 Migration rates by skill level

17 Migration rates by skill level and gender: Zambia, 2000

18 Migration rates within Zambia

19 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.

20 The labour demand side

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26 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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