This project is funded by National Science Centre of Poland on the basis of the decision Nr DEC-2013/11/B/HS4/02126

US counties and European NUTS 3 regions in 21 st century – wealth of citizens, convergence

processes and spatial dependencies

Paweł FolfasWarsaw School of Economics

AIM OF RESEARCH

• answer question whether absolute income (GDP per capita) beta-convergence exists in the case of regions of the United States (US counties) and of the EU-28 (NUTS 3) during period 2000-2011

• Samples consist of 3130 US regions (counties) and 1352 regions of the EU (NUTS 3)

• Forthcoming Transatlantic Trade and Investment Partnership may become a crucial element in the post-crisis world economics and politics. Consequently, it is worth to scrutinize economic performance of the United States and the European Union.

METHODOLOGY (1)MODEL OF ABSOLUTE CONVERGENCE

• where y i,0 and y i,1 correspond to the GDP per capita of region i at the initial and final year respectively and n is the number of years in analysed period

average annual change of GDP per capita (static approach)

METHODOLOGY (2)SPATIAL ECONOMETRICS

• Ordinary or not-linear last squares (OLS or NLS) methods do not include the possible spatial dependencies between regions

• Spatial estimation techniques:– Spatial lagged model (SLM) and spatial error model (SEM)– Spatial Durbin model

METHODOLOGY (3)SPATIAL LAGGED MODEL

• where yi and yj correspond to dependent variable in region i and in neighbouring regions j respectively and X is the set of independent variable

• matrix W reflects spatial relations between analyzed regions (it shows how pairs of regions relate to each other – a binary matrix with values equaling 1 when the regions are neighbours and 0 otherwise)

regions from different EU Member States are neighbours – international spatial dependencies

• rho-parameter (ρ) is the spatial coefficient, which is used to assess the existence and strength of spatial relations

METHODOLOGY (4)SPATIAL ERROR MODEL

• where Wui is the spatial lagged error term, εi is the random error term of the model, and λ is a coefficient that is introduced to the model to satisfy the assumption about random error terms

• matrix W reflects spatial relations between analyzed regions (it shows how pairs of regions relate to each other – a binary matrix with values equaling 1 when the regions are neighbours and 0 otherwise)

regions from different EU Member States are neighbours – international spatial dependencies

• lambda-parameter (λ) shows to what extent shocks in neighbouring regions are transferred to the analysed region

DATA

• Data concerning GDP per capita of NUTS 3 regions are extracted from Eurostat statistical database

• Data for US counties from BEA

• Average number of neighbouring regions:– 5,85 for US counties– 5,13 for EU NUTS 3 regions

ESTIMATIONS RESULTS INTRODUCTION

• In the case of US regions error model is slightly better than lagged model

• In the case of EU regions spatial model is slightly better than error model

• The most adequate, both for US and UE regions, is Durbin spatial model including lagged and error model

ESTIMATION RESULTS (1)SPATIAL LAGGED MODELS

US EU

Intercept 0.19232499*** 0.1484877***

ln y0 -0.01659583*** -0.0140344***

ρ 0.27912*** 0.50173***

*** denotes statistical significance at level 0.001 Source: Own study based on estimation in R CRAN

beta-convergence at the level of 1.54%

(annual speed)

much stronger spatial

dependencies among EU than US

regions

ESTIMATION RESULTS (2)SPATIAL ERROR MODELS

US EU

Intercept 0. 20579953*** 0. 20227444***

ln y0 -0. 01696100*** -0. 01818721***

ρ 0. 29173*** 0. 63809***

*** denotes statistical significance at level 0.001 Source: Own study based on estimation in R CRAN

beta-convergence at the level of 1.90%

(annual speed)

ESTIMATION RESULTS (3)DURBIN MODELS

US EU

Intercept 0.14921261 *** 0. 11717139***

ln y0 -0.01695825 *** -0. 01599379***

lag ln y00.00460232*** 0.00498002***

ρ 0.29175 *** 0. 58479 ***

*** denotes statistical significance at level 0.001 Source: Own study based on estimation in R CRAN

beta-convergence at the level of 1.90%

(annual speed)

beta-convergence at the level of 1.78%

(annual speed)

much stronger spatial

dependencies among EU than US

regions

CONCLUSIONS

• During period 2000-2011 average annual speed of convergence among regions in US was faster than between regions of EU-28.

• US GDP pc/ EU-28 GDP pc

• EU-28 regions are characterized by stronger spatial dependencies that regions of the United States.

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2.06 2.09 1.97 1.69 1.55 1.57 1.55 1.39 1.31 1.43 1.48 1.41