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Income convergence prospects in Europe: Assessing the role of human capital dynamics
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Income convergence prospects in Europe:
Assessing the role of human capital dynamicsJesus Crespo Cuaresma
Miroslava Luchava HavettováMartin Lábaj
BRATISLAVA ECONOMIC MEETINGJune 2012
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Structure of the presentation
• Motivationo Income convergence in Europe: The recent experience
• An income projection model for Europeo The theoretical setting
o Estimation results and the projection model
• Income convergence prospects in Europeo Setting and assumptions
• Projection results
• Conclusions
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1995-2000 2000-2005 2005-2009 1995-
2009WEST 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
CEEC/WEST 0,35 0,36 0,43 0,49
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.
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8.5 9.0 9.5 10.0 10.5 11.0
-2.6%
-0.6%
1.4%
3.4%
5.4%
7.4%
WESTLinear (WEST)WEST excl. LUXLinear (WEST excl. LUX)CEECLinear (CEEC)
ln GDP per capita, 1995
Avg
. gro
wth
199
5 -
2009
1. Correlation between the logarithm of real GDP per capita in 1995 and the average growth of GDP per capita (1995 - 2009), EU-28.
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8.5 9.0 9.5 10.0 10.5 11.0
-2.6%
-0.6%
1.4%
3.4%
5.4%
7.4%
WESTLinear (WEST)WEST excl. LUXLinear (WEST excl. LUX)CEECLinear (CEEC)CEEC-BGR,ROMLinear (CEEC-BGR,ROM)
ln GDP per capita, 1995
Avg
. gro
wth
199
5 -
2000
2. Correlation between the logarithm of real GDP per capita in 1995 and the average growth of GDP per capita (1995 - 2000), EU-28.
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8.5 9.0 9.5 10.0 10.5 11.0
-2.6%
-0.6%
1.4%
3.4%
5.4%
7.4%
WESTLinear (WEST)CEECLinear (CEEC)WEST excl. LUXLinear (WEST excl. LUX)
ln GDP per capita, 2000
Avg
. gro
wth
200
0 -
2005
3. Correlation between the logarithm of real GDP per capita in 2000 and the average growth of GDP per capita (2000 - 2005), EU-28.
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8.5 9.0 9.5 10.0 10.5 11.0
-2.6%
-0.6%
1.4%
3.4%
5.4%
7.4%
WESTLinear (WEST)CEECLinear (CEEC)WEST excl. LUXLinear (WEST excl. LUX)
ln GDP per capita, 2005
Avg
. gro
wth
200
5- 2
009
4. Correlation between the logarithm of real GDP per capita in 2005 and the average growth of GDP per capita (2005 - 2009), EU-28.
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An income projection model for Europe
The theoretical setting: Standard aggregate production function with heterogeneous labour input:
Rewriting the equation in growth rates implies:
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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 frontierb) the technology innovation potential of the economyc) the technology adoption potential
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Database• Panel data for 32 European countries for the
period 1970 – 2010, with growth rates defined over 5-year non-overlapping 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
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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 1970-2000Using Demographic Back-Projection Methods
• IIASA World Population Program andVienna 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.
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Structure by age, sex, and level of education for South Africa for the year 2000
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Projected structure for South Africa for the year 2050
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Panel estimatesEstimation results and the projection model
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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 accumulationo Medium, Low and High scenario
• Human capital accumulationo Constant Attainment scenario and Global Education Trend scenario
• Global shocks in income growtho 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
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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
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Share of simulations where the country attains at least 75% of average income per capita in EU-17, by projection year
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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
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Thank you for your attention