Human Capital and Inclusive Growth

Jesús Crespo CuaresmaDepartment of Economics

University of Innsbruckjesus.crespo-cuaresma@uibk.ac.at

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.

A theoretical framework Lucas‘ (1988) growth model:

Production function:

Human capital definition:

Accumulation rule:

Euler equation:

1HAKY

uhLH

)1(/ uhh

),(/ 1 cc

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)

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

Education attainment by gender and age group: Zambia, 2010-2020

http://www.iiasa.ac.at/Research/POP/edu07/index.html?sb=11

The demographic dividend and educational attainment

The demographic dividend and educational attainment

The demographic dividend and educational attainment

School enrollment

School enrollment by gender and residence: Zambia 1992-2002

School enrollment by gender and residence: Zambia 1992-2002

School enrollment by gender and residence: Zambia 1992-2002

School enrollment by gender and residence: Zambia 1992-2002

School enrollment by gender and residence: Zambia 1992-2002

School attendance by income and residence: Zambia 1992-2002

Human capital data: The macroeconomic policy view

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

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

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).

Estimating returns to education

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

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

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

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.

Migration rates by skill level

Migration rates by skill level and gender: Zambia, 2000

Migration rates within Zambia

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.

The labour demand side

The labour demand side

The labour demand side

The labour demand side

The labour demand side

The labour demand side

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