human capital and inclusive growth jesús crespo cuaresma department of economics university of...
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Human Capital and Inclusive Growth
Jesús Crespo CuaresmaDepartment of Economics
University of [email protected]
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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.
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A theoretical framework Lucas‘ (1988) growth model:
Production function:
Human capital definition:
Accumulation rule:
Euler equation:
1HAKY
uhLH
)1(/ uhh
),(/ 1 cc
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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)
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Education attainment by gender and age group: Zambia, 1970-2000
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Education attainment by gender and age group: Zambia, 2010-2020
http://www.iiasa.ac.at/Research/POP/edu07/index.html?sb=11
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The demographic dividend and educational attainment
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The demographic dividend and educational attainment
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The demographic dividend and educational attainment
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School enrollment
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School enrollment by gender and residence: Zambia 1992-2002
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School enrollment by gender and residence: Zambia 1992-2002
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School enrollment by gender and residence: Zambia 1992-2002
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School enrollment by gender and residence: Zambia 1992-2002
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School enrollment by gender and residence: Zambia 1992-2002
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School attendance by income and residence: Zambia 1992-2002
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Human capital data: The macroeconomic policy view
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Estimating returns to education
• Mincerian wage regressions,
where X contains variables summarizing characteristics of the individual (age, experience, gender, education) and the firm (sector).
,)ln( iii Xwage
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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.
,)ln( iii Xwage
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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).
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Estimating returns to education
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Estimating returns to educationEnterprise fixed effects Enterprise fixed effects Enterprise fixed effects
Female 0.0019 -0.383* 0.00364Age 0.000515 0.000262 -0.00572
Age sq. 0.000148 0.000141 0.000155Experience 0.0398*** 0.0398*** 0.0421***
Experience sq. -0.00107*** -0.00104*** -0.00102***Trade union -0.076 -0.0682 -0.0181
Fulltime 0.0552 0.0455 -0.00766Education years 0.0793*** 0.0743***
Ed. Years × female 0.0326*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***
Constant 3.923*** 6.470*** 6.690***Observations 923 923 923
R-squared 0.895 0.896 0.903
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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.
,)ln( iii Xwage
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Estimating returns to education
q=0.1 q=0.25 q=0.5 q=0.75 q=0.9
Female -0.0222 -0.0061 0.0145 0.0498 0.0359
Age -0.000728 0.00888 0.00443 -0.00919 -0.0323
Age sq. 4.07E-05 -8.52E-05 1.22E-05 0.000284 0.000618
Experience 0.00227 0.00851 0.0187** 0.0296** 0.0461***
Experience sq. -4.33E-05 -7.77E-05 -0.000369 -0.00063 -0.00141***
Trade union 0.0303 0.0317 -0.06 -0.0627 -0.0974
Fulltime 0.0315 -0.0467 -0.0365 -0.0983 0.035
Education years 0.0199*** 0.0244*** 0.0267*** 0.0507*** 0.0793***
Constant 6.856*** 6.720*** 6.713*** 6.731*** 6.758***
Observations 923 923 923 923 923
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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.
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Migration rates by skill level
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Migration rates by skill level and gender: Zambia, 2000
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Migration rates within Zambia
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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.
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The labour demand side
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The labour demand side
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The labour demand side
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The labour demand side
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The labour demand side
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The labour demand side
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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