Parallel Session IARIW 2014

1. Composition of Human Capital, Distance to the
Frontier and Productivity
Marianna Papakonstantinou
(University of Groningen, Netherlands)
Paper for the 33rd IARIW General Conference
Rotterdam, Netherlands
August 24-30, 2014, Session 2D

2. I. Background
II. Objectives
III. Model
IV. Data
V. Results
VI. Contributions/Conclusions

3. I. Background:
• Studies examining role of human capital on productivity
growth produced mixed results
• Could the effect of human capital vary with stages of
economic development?

4. II. Objectives:
• To examine effect of dissimilar types of human capital on
productivity growth for countries at different distances from
world technology frontier
• Focus: Externality Effects of Productivity Growth
• Hypothesis: Whether skilled human capital contributes more
to productivity in countries closer to frontier and better suited
to innovation?
• Empirical Support:
○ Vandenbussche et al [2006] for OECD countries
○ Ang et al [2011] for high & middle-income countries
• What is Important? : Types of Human Capital need to be
taken into account.

5. • Argument:
○ Average & upper-tail human capital had different impacts on
growth before & after industrialization
(Squicciarini & Voigtländer, 2014).
○ Interaction between education & distance to frontier has been
significant determinant of growth (Madsen, 2013)
○ Effect of high-skilled human capital turns negative for countries
very far from the technology frontier
(Vandenbussche et al, 2006)
• Motivation of the Study: Examine the mechanism on the
relationship between high-skilled human capital and productivity
growth in countries far (close) from (to) the frontier

6. III. Model:
Vandenbussche et al [2006] Model:
TFP Growth = f (Proximity to TFP frontier, High-skilled Human
Capital, Interaction Variable)
TFP Growth = Productivity growth in 5-year interval
Proximity to TFP frontier = Ratio of country TFP level to that of
USA at current PPP
High-skilled Human Capital = Population aged 25 and over with
higher education

7. IV. Data:
• Barro and Lee [2013] dataset on educational attainment in 5-year
intervals
• Revised Penn World Tables (Feenstra et al., 2013) on productivity
measures
○ Why Revised PWT better:
* Accounts for differences in asset composition across countries
and over time
* Do not use same labor share across countries
* Human capital of workers approximated by years of
schooling & rate of return

8. V. Results:
• Restricted Sample of 19 OECD countries: 1960-2000
• Extended Sample of 109 countries: 1950-2010
• OLS Results
IV Results (using past regressors as instruments to control
endogeneity)
V.1 Summary Statistics:
• Productivity growth higher in OECD economies (0.056)
compared to extended sample (0.025) → OECD countries lie
closer to the world technology frontier (US)
• Tertiary & Secondary human capital higher in OECD sample
than in extended sample

9. V.2 Findings of Baseline Regressions: 19 OECD
Countries:
• Proximity to TFP frontier has negative & significant effect on
productivity growth
• Tertiary human capital almost has a positive & significant impact
on productivity growth
• Interaction term enters positively & significantly when country
effects are omitted but with insignificant coefficient when they are
included
• Country fixed effects are not jointly significant
• Overall OECD (high-income) results are consistent with
Vandenbussche et al [2006] & Ang et al [2011] that effect of tertiary
human capital on productivity growth increases as countries get
closer to frontier.
• Use of sophisticated productivity measure has not altered
Vandenbussche conclusions

10. V. 3 Findings of Extended Sample Regressions:
• Could effects of high-skilled human capital turn negative for
countries with low productivity levels?
• Results from extended sample of countries (109 countries) and years
(1950-2010):
• Country fixed effects are now highly significant
• Proximity to TFP frontier has negative & significant effect on
productivity growth
• Tertiary human capital yields positive & insignificant coefficient
• Interaction term enters with negative & significant coefficient
Tertiary human capital bears an overall positive effect on
productivity growth that decreases as countries move closer to the
frontier ↔ Support to the paper’s hypothesis

11. • Could there be non-linear forces that work at distances from the frontier
↔ Proximity to TFP frontier squared & its interaction with tertiary
human capital are added in regression
○ Effect of proximity variable is negative and significant
○ Level effect of human capital is positive and significant
• What is the marginal effect of tertiary education at the frontier?
○ Plot of marginal effect of tertiary human capital on productivity growth
shows larger effects for countries far from the frontier compared to those
closer to it ↔ Large externalities in low-productivity countries due to
higher incidence of learning from others but the effect diminishes as
higher education leads to unemployment/rent seeking
=> Greater impact of human capital on productivity growth in ‘imitating’
countries. The impact diminishes as country benefits from technology
adoption & the turning point indicates existence of U-shaped relationship

12. V.4 Introduction of Secondary Education:
• Could externalities originate from secondary-educated workforce?
↔ medium-skilled/secondary human capital, its interaction with
proximity & proximity-squared are added in regression:
○ Effect of tertiary human capita is positive and significant
○ Effect of secondary human capita is positive and insignificant
○ Interactions are jointly significant (not individually always)
• Plot of marginal effect reveals larger effects of tertiary human
capital than secondary & there is a U-shaped relationship between
tertiary human capital and productivity growth, which does not hold
for secondary education
• Marginal effect of secondary human capital is universally
decreasing, whereas that of tertiary reaches a minimum then starts
increasing
Overall, high-skilled human capital has increasing impacts on
productivity growth for countries close to frontier.

13. VI. Contributions/Conclusions of the Paper:
VI.1 Studies the relationship between high-skilled (tertiary) human
capital & productivity growth for both developed & developing
world
Main Conclusions:
• Tertiary human capital positively affects productivity growth in all
countries
• Tertiary education involve U-shaped impact on productivity growth:
Large impact for countries far from frontier, decreases as countries
move closer & then increases again.
• Confirms Vandenbussche [2006] hypothesis on complementarities
of high-skilled human capital & innovation in countries close to
world technology frontier

14. •Tertiary human capital in “innovating” country (close to
frontier) contributes to productivity growth, because high-
skilled people are aware & have better understanding of the
processes
•Tertiary human capital in an “imitating” country (far from
frontier) prompts new technologies being adopted & operated
efficiently through diffusion of knowledge

15. VI. 2 Allows for the possibility that externalities also originate
from ‘medium-skilled’ (secondary-educated workforce)
Main Conclusions:
• Medium skilled’ human capital has positive (smaller) effect
on productivity growth
• Secondary education involves decreasing impact on
productivity growth ↔ lacks the complementarities to
innovation

16. VI.3 Uses sophisticated productivity measure incorporating
inter-country differences in work-hours & educational
composition of workforce
○ Crude Productivity Measure: Vandenbussche et al [2006]
○ Sophisticated Productivity Measure: Inklaar et al [2008]
○ Inklaar et al [2008] found differing result: No evidence of
high-skilled human capital externalities with close proximity
to frontier.

17. Thank You - - -