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Income convergence prospects in Europe:
Assessing the role of human capital dynamics
Jesus Crespo Cuaresma
Miroslava Luchava Havettová
Martin Lábaj
BRATISLAVA ECONOMIC MEETING
June 2012
Structure of the presentation
• Motivation
o Income convergence in Europe: The recent experience
• An income projection model for Europe
o The theoretical setting
o Estimation results and the projection model
• Income convergence prospects in Europe
o Setting and assumptions
• Projection results
• Conclusions
Average GDP per capita growth in EU-17 and EU-11 in %
and the share of an average GDP per capita in CEEC countries
on average GDP per capita in EU-17.
1995-2000
2000-2005
2005-2009
1995-2009
WEST
EU-17
2,53
1,26
-0,07
1,33
CEEC
EU-11
3,51
4,64
3,43
3,89
1995
2000
2005
2009
0,35
0,36
0,43
0,49
CEEC/WEST
1. Correlation between the logarithm of real GDP per capita in 1995
and the average growth of GDP per capita (1995 - 2009), EU-28.
Avg. growth 1995 - 2009
7.4%
5.4%
WEST
3.4%
WEST excl. LUX
CEEC
1.4%
-0.6% 8.5
-2.6%
9.0
9.5
10.0
ln GDP per capita, 1995
10.5
11.0
2. Correlation between the logarithm of real GDP per capita in 1995
and the average growth of GDP per capita (1995 - 2000), EU-28.
Avg. growth 1995 - 2000
7.4%
5.4%
WEST
3.4%
WEST excl. LUX
CEEC
1.4%
-0.6% 8.5
-2.6%
CEEC-BGR,ROM
9.0
9.5
10.0
ln GDP per capita, 1995
10.5
11.0
3. Correlation between the logarithm of real GDP per capita in 2000
and the average growth of GDP per capita (2000 - 2005), EU-28.
Avg. growth 2000 - 2005
7.4%
5.4%
WEST
3.4%
CEEC
WEST excl. LUX
1.4%
-0.6%
8.5
9.0
9.5
10.0
-2.6%
ln GDP per capita, 2000
10.5
11.0
4. Correlation between the logarithm of real GDP per capita in 2005
and the average growth of GDP per capita (2005 - 2009), EU-28.
Avg. growth 2005- 2009
7.4%
5.4%
WEST
3.4%
CEEC
WEST excl. LUX
1.4%
-0.6% 8.5
-2.6%
9.0
9.5
10.0
ln GDP per capita, 2005
10.5
11.0
An income projection model for Europe
The theoretical setting:
Standard aggregate production function with heterogeneous labour input:
Rewriting the equation in growth rates implies:
In the spirit of the Nelson-Phelps paradigm (Nelson and Phelps, 1966;
Benhabib and Spiegel, 1994; Lutz et al., 2008)
we assume that the role of education also plays the role through its effect on
innovation and technology adoption
Total factor productivity depends on:
a) the distance to the technology frontier
b) the technology innovation potential of the economy
c) the technology adoption potential
Database
• Panel data for 32 European countries for the period 1970
– 2010, with growth rates defined over 5-year nonoverlapping intervals
• The data on income per capita and total GDP – from the
Penn World Table 7.0
• Physical capital stocks – estimated using PIM
(investment rates from PWT 7.0) with 6 % depreciation rate
• The data on population by age and educational
attainment level – IIASA-VID dataset
IIASA - International Institute for Applied Systems
Analysis – World population program
• Education, Reconstruction and Projections
• Reconstruction of Populations by Age, Sex and Level of
Educational Attainment for 120 Countries for 1970-2000
Using Demographic Back-Projection Methods
• IIASA World Population Program and
Vienna Institute of Demography (VID)
Anne Goujon ([email protected]),
Samir K.C. ([email protected]),
Wolfgang Lutz ([email protected]),
Warren Sanderson ([email protected])
• Correspondence and requests should be addressed to
Anne Goujon or Samir K.C.
Structure by age, sex, and level of education for
South Africa for the year 2000
Projected structure for South Africa for the year 2050
Estimation results and the projection model
Panel estimates
An income projections
• EU-11 (CEEC)and EU-17 (WEST) regions
• over the period 2010-2070
• We design different simple scenarios for each one of the main
drivers of economic growth in the model
• Physical capital accumulation
o Medium, Low and High scenario
• Human capital accumulation
o Constant Attainment scenario and Global Education Trend scenario
• Global shocks in income growth
o Constant scenario and Trend scenario
• Since there are 12 possible scenarios for each region, this
results in 144 possible income per capita scenarios for the 28
European countries in our sample
Kernel density of ratio of average income per capita in EM-11 to average
income per capita in EU-17 based on projections for all scenarios
Share of simulations where the country attains at least 75% of average income
per capita in EU-17, by projection year
Concluding remarks
• We have computed several income convergence
scenarios in Europe with a concentration on human
capital dynamics
• Our results indicate that improvements in human capital
contribute significantly to the income convergence
potential of European emerging economies
• Convergence over the longer-period, bi-modal structure
and still great variation among the EU-11 countries
Thank you for your attention