Download Growth and Volatility in EU Regions: Does Space Matter?

Growth and Volatility in
EU Regions: Does Space
Matter?
Author: Vicente Rios
Universidad Pública de Navarra
OUTLINE
• Scientific Problem
• Theoretical Framework
• Quantitative Analaysis
• Contribution
1. The Scientific Problem
What is the relationship between volatility and growth in European regions?
Empirical studies in this topic at the regional level:
i) are few
ii) do not account for unobserved spatial heterogeneity
iii) reach diverging conclusions
Sample regions (N)
and time (T)
Econometric
Method
Sign and
significance of
the coefficient
Martin and Rogers (2000)
N=90 European Regions
T=1979-1992
Cross-Section
Negative and significant at
the 5% level
Falk and Sinabell (2009)
N= 991 NUTS3 Regions
T=1995-2004
Spatial Analysis, CrossSection
Positive and significant at
the 5% level
Authors
Focus:
i) Re-Examine the relationship between volatility and growth (spatial panel)
ii) Explore the role played by spatial spillovers and interdependences
2. Theoretical Framework
There are many reasons to believe that:
i) volatility and growth are connected
Positively: Schumpeter (1939); Mirman (1971), Black (1987); Bean (1990); Hall,
(1991); Saint-Paul (1993); Helpman and Trajtenberg (1998),
Negatively: Pindyck (1982); Bernanke (1983)
Ambiguously: De Hek (1999), (2002); Blackburn and Galindev (2003); Manuelli and
Jones (2005); Galindev (2007)
ii) space might be a channel of difussion (with some frictions)
Spatially Augmented Growth Models: López-Bazo et. al (2004), Ertur and Koch
(2007), M. Fisher (2009)
iii) Work in progress  derive a SDM equation from
-
stochastic endogenous growth model with
spatial diffussion , iid productivity shocks, CES function
3. Quantitative Analysis
Sample: 198 NUTS 2 Regions (EU 13 + Norway + Switzerland)
Time:1980-2010
Model
Y: GDP pc growth rate
Key variable: Volatility (Std GDP pc growth rate)
Controls: Investment, Initial GDP pc, Industry Mix, Pop, Agglomeration
Methodology
-
Estimation and Selection of Spatial Panel Data
Simulation: Internal to region + Neighbor’s volatility effect
Robustness Analysis
3. Quantitative Analysis
Mixed Approach: recommended by Elhorst(2010)
- Estimate non spatial models
-
Use Robust Lagrande Multiplier Tests to check the spatial dependence form
(Spatial Error, Spatial Lag).
-
W Matrix: distance matrix, exogeneity
Result 1: Reject the null of no spatial dependence in all cases
Result 2: Time Effects are jointly significant (not shown)
•
Result 3: Spatial Durbin best describes the data
3. Quantitative Analysis
Check if Spatial Durbin can be simplified to Spatial Lag or Spatial Error versions
Model Selection Tests
Result 4: Selected Model is Spatial Durbin
3. Quantitative Analysis
Key Result 1: Volatility and Growth are positively related
Key Resul 2: Spatial spillovers account for half of the impact
3. Quantitative Analysis:
Robustness
Results are robust to different W’s
4. Contributions
•
•
European regions with high volatility tend to grow faster
Spatial spillovers are a key factor reinforcing the effect of internal
volatility on growth
•
The result is robust to spatial weight matrixes
•
Expected (Link Theory-Empirical Model)