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Human Capital and Inclusive Growth

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Jes s Crespo Cuaresma Department of Economics University of Innsbruck jesus.crespo-cuaresma_at_uibk.ac.at – PowerPoint PPT presentation

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Title: Human Capital and Inclusive Growth


1
Human Capital and Inclusive Growth
Jesús Crespo Cuaresma Department of
Economics University of Innsbruck jesus.crespo-cua
resma_at_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
High cost of finance
Low returns to education
Skill mismatch
Low demand for skilled labor (brain drain)
Problems in school access and/or infrastructure
Lack of access to (public) finance for education
Demand-side factors
Supply-side factors
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.ht
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7
The demographic dividend and educational
attainment
8
The demographic dividend and educational
attainment
9
The demographic dividend and educational
attainment
10
School enrollment
11
School enrollment by gender and residence Zambia
1992-2002
12
School enrollment by gender and residence Zambia
1992-2002
13
School enrollment by gender and residence Zambia
1992-2002
14
School enrollment by gender and residence Zambia
1992-2002
15
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
Estimating returns to education
23
Estimating returns to education
  • Parameters differ across quantiles,
  • where bq is the parameter vector associated with
    the q-th quantile of the conditional distribution
    of the 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 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

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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