Post on 29-Mar-2015
Human Capital and Inclusive Growth: Returns to
Education and Firm Constraints
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.– The overall picture.– Identifying binding constraints:
• Returns to education and return heterogeneity.• Human capital and migration patterns.• Firm perceptions.
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)
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
School enrollment
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 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 bq is the parameter vector associated with the q-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
Ethiopia
Kenya
Madag
ascar
Malawi
Mozambique
Rwanda
Somali
a
Tanzan
ia
Uganda
Zambia
Zimbab
we0.0%0.5%1.0%1.5%2.0%2.5%3.0%3.5%4.0%
Total
Ethiopia
Kenya
Madag
ascar
Malawi
Mozambique
Rwanda
Somali
a
Tanzan
ia
Uganda
Zambia
Zimbab
we0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%1.6%1.8%
Low
Ethiopia
Kenya
Madag
ascar
Malawi
Mozambique
Rwanda
Somali
a
Tanzan
ia
Uganda
Zambia
Zimbab
we0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%8.0%9.0%
10.0%
Medium
Ethiopia
Kenya
Madag
ascar
Malawi
Mozambique
Rwanda
Somali
a
Tanzan
ia
Uganda
Zambia
Zimbab
we0.0%5.0%
10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%
High
Migration rates by skill level and gender: Zambia, 2000
Male Female0.78%
0.80%
0.82%
0.84%
Total
Male Female0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
Low
Male Female0.00%0.20%0.40%0.60%0.80%1.00%1.20%1.40%1.60%
Medium
Male Female0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
High
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.
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