Income convergence prospects in

Europe:

Assessing the role of human capital

dynamicsJesus Crespo Cuaresma Miroslava Luchava Havettová

Martin Lábaj

BRATISLAVA ECONOMIC MEETINGJune 2012

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

1995-2000

2000-2005 2005-20091995-2009

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

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.

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

. g

row

th 1

99

5 -

20

09

1. Correlation between the logarithm of real GDP per capita in 1995 and the average growth of GDP per capita (1995 - 2009), EU-28.

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

. g

row

th 1

99

5 -

20

00

2. Correlation between the logarithm of real GDP per capita in 1995 and the average growth of GDP per capita (1995 - 2000), EU-28.

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

. g

row

th 2

00

0 -

20

05

3. Correlation between the logarithm of real GDP per capita in 2000 and the average growth of GDP per capita (2000 - 2005), EU-28.

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

. g

row

th 2

00

5-

20

09

4. Correlation between the logarithm of real GDP per capita in 2005 and the average growth of GDP per capita (2005 - 2009), EU-28.

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 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

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 (goujon@iiasa.ac.at), Samir K.C. (kc@iiasa.ac.at), Wolfgang Lutz (lutz@iiasa.ac.at), Warren Sanderson (wsanderson@notes.cc.sunysb.edu)

• 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

Panel estimates

Estimation results and the projection

model

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

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