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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix The Determinants of Growth rate Volatility in European Regions Davide Fiaschi, Lisa Gianmoena and Angela Parenti University of Pisa - IMT Lucca Societa

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SLIDE 1

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

The Determinants of Growth rate Volatility in European Regions

Davide Fiaschi, Lisa Gianmoena and Angela Parenti

University of Pisa - IMT Lucca

Societa’ Italiana degli Economisti Bologna 24-25-26 October

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Aim

To identify the main determinants of the volatility of the growth rate

  • f per capita GDP (GRV), in a spatial econometric framework.

Motivations

1 Countries, while growing, experience structural transformation and

shocks which may increase volatility in the output growth rate.

2 It is object of debate the idea that volatility, by creating uncertainty

in the economy, may negatively influence growth and negative impact on social welfare (agents are generally risk adverse).

3 If volatility plays a role for growth, then understanding the main

determinants of volatility may help defining more effective stabilization policies.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Main Questions

1 What is the relationship between volatility and sectoral

composition of the economy?

2 What is the impact of the European Monetary Union (EMU)

  • n volatility of participating countries?

3 Does a rise in private consumption or in government

expenditure a stabilizing effect on GRV?

4 What is the effect of external shocks? 5 Are there spatial spillovers across regions?

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Volatility measures

Various measures of volatility have been used in the literature:

  • in cross sectional analysis ⇒ standard deviation of growth

rates (Ramey and Ramey (1995); Kormendi and Meguire (1985); Martin and Ann Rogers (2000).)

  • in time series studies ⇒ ”unexpected volatility” as measured

by the variance of residual of a forecast regression (Ramey and Ramey (1995); Lensink et al (1999).) In this analysis we employ the methodology developed McConnell and Perez-Quiros (2000), and Fiaschi and Lavezzi (2011) which exploits both the longitudinal and cross-sectional variation of the data.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Volatility measures

Various measures of volatility have been used in the literature:

  • in cross sectional analysis ⇒ standard deviation of growth

rates (Ramey and Ramey (1995); Kormendi and Meguire (1985); Martin and Ann Rogers (2000).)

  • in time series studies ⇒ ”unexpected volatility” as measured

by the variance of residual of a forecast regression (Ramey and Ramey (1995); Lensink et al (1999).) In this analysis we employ the methodology developed McConnell and Perez-Quiros (2000), and Fiaschi and Lavezzi (2011) which exploits both the longitudinal and cross-sectional variation of the data.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Estimation of GRV

  • Calculate the yearly annual growth rate of per capita GDP (γit), for 257 EU

Regions in the period 1991-2008.

  • The dynamics of per capita GDP growth of region i is represented as an

AR(p) process where ǫit is assumed to be normally distributed but with time-varying variance: γit = µi + φ1γi,t−1 + ... + φpγi,t−1 + ǫit, ⇒ ˆ σǫ

it = π 2 |ˆ

ǫit| is un unbiased estimator of σǫ

it.

  • From ˆ

σǫ

it we calculate ˆ

σγ

it (see McConnell and Perez-Quiros, 2000, and

Fiaschi and Lavezzi, 2011).

Important Remarks

  • The best order of the AR process is found using the EIC criterion for small

sample (denoted by EICc, see Ishiguro, Sakamoto and Kitagawa, 1997).

  • This methodology allows to build a panel of GRVs.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 7

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Estimation of GRV

  • Calculate the yearly annual growth rate of per capita GDP (γit), for 257 EU

Regions in the period 1991-2008.

  • The dynamics of per capita GDP growth of region i is represented as an

AR(p) process where ǫit is assumed to be normally distributed but with time-varying variance: γit = µi + φ1γi,t−1 + ... + φpγi,t−1 + ǫit, ⇒ ˆ σǫ

it = π 2 |ˆ

ǫit| is un unbiased estimator of σǫ

it.

  • From ˆ

σǫ

it we calculate ˆ

σγ

it (see McConnell and Perez-Quiros, 2000, and

Fiaschi and Lavezzi, 2011).

Important Remarks

  • The best order of the AR process is found using the EIC criterion for small

sample (denoted by EICc, see Ishiguro, Sakamoto and Kitagawa, 1997).

  • This methodology allows to build a panel of GRVs.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 8

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

1992 1994 1996 1998 2000 2002 2004 2006 2008 1.0 1.5 2.0 2.5 3.0 Year GRV EMS crisis EURO financial crisis

Figure: Cross-section estimate of average growth rate volatility

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Estimation of the Determinants of GRV

  • Panel of 257 EU regions (EU 27 less Bulgaria, Latvia and

Lithuania) in the period 1991-2008

dataset

  • Econometric model:

GRVit = αi + β1SizeEcoit + β2BusCycleit + β3OutputCompit + β4RegVariabit + β5CountryControlsit + β6AggShockst + β7Dummyt + uit

  • Main issues:

1 Asymmetric Effects of the GRV determinants. 2 Spatial Spillovers. 3 Potential Endogeneity of the GRV determinants.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix 1) Asymmetric effects 1992 1995 1998 2001 2004 2007 1 2 3 Year GR.GDPpc

volatility in response to − shocks ε ^ < 0 volatility in response to + shocks ε ^ > 0

Two interaction variables capture asymmetric effects: dp = 1 if I(ˆ ǫit > 0)

  • therwise

dn =

  • 1

if I(ˆ ǫit < 0)

  • therwise

GRVit = αi + β1p(dpSizeEcoit) + β1n(dnSizeEcoit) + β2p(dpBusCycleit) + β2n(dnBusCycle it) + β3p(dpOutputCompit) + β3n(dnOutputCompit) + β4p(dpRegVariabit) + β4n(dnRegVariabit) + β5p(dpCountryControlsit) + β5n(dnCountryControlsit) + β6p(dpAggShockst) + β6n(dnAggShockst) + β7p(dpEMUDummyt) + β7n(dnEMUDummyt) + uit Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix 2) Spatial Spillovers

  • Idea: the GRV in one region could depends on the values of its
  • neighbours. We expect to find a sort of clusterization of region

characterized by similar values of GRV.

  • The presence of Spatial Spillovers in GRV imply a geographical

dependence of GRV across neighbours.

  • The Moran’s I test on GRV is positive and significant, suggesting the

presence of spatial correlation.

map

⇒ Spatial SARAR(1) Model: GRVit = αi + λW GRVit + β1Size Ecoit + β2Cycleit + β3Output Compit + β4Controlsit + β5Agg. shockst + β7EMUDummyt + uit uit = ρW uit + ǫit

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix 2) Spatial Spillovers

  • Idea: the GRV in one region could depends on the values of its
  • neighbours. We expect to find a sort of clusterization of region

characterized by similar values of GRV.

  • The presence of Spatial Spillovers in GRV imply a geographical

dependence of GRV across neighbours.

  • The Moran’s I test on GRV is positive and significant, suggesting the

presence of spatial correlation.

map

⇒ Spatial SARAR(1) Model: GRVit = αi + λW GRVit + β1Size Ecoit + β2Cycleit + β3Output Compit + β4Controlsit + β5Agg. shockst + β7EMUDummyt + uit uit = ρW uit + ǫit

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix 3) Endogeneity

  • a. Endogeneity of the spatial lag WGRV

Endogeneity due to simultaneous spatial interaction induced by the spatial lag W GRV : GRV in a region i may be influenced by the value of GRV of its neighbour j and vice-versa. ⇒ Kelejian and Prucha (1998), generalized spatial two-stage least squares (GS2SLS) to estimate models with spatial dependence.

GS2SLS

  • b. Endogeneity of regressors X

Endogeneity due to an unknown or imprecisely known set of structural equations ⇒ modified version of GS2SLS (see Fingleton and Le Gallo, 2008; Millo and Piras, 2012) for additional endogenous regressors in unbalanced panel.

Instruments:

  • W GRV ⇒ W X and W 2X
  • EMG.GR ⇒ ACTIVE.POP,
  • INV.RATE, GOV.EXP.on.GDP, CREDIT.PRIV.SECTOR.on.GDP ⇒ lagged Variables.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 14

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix 3) Endogeneity

  • a. Endogeneity of the spatial lag WGRV

Endogeneity due to simultaneous spatial interaction induced by the spatial lag W GRV : GRV in a region i may be influenced by the value of GRV of its neighbour j and vice-versa. ⇒ Kelejian and Prucha (1998), generalized spatial two-stage least squares (GS2SLS) to estimate models with spatial dependence.

GS2SLS

  • b. Endogeneity of regressors X

Endogeneity due to an unknown or imprecisely known set of structural equations ⇒ modified version of GS2SLS (see Fingleton and Le Gallo, 2008; Millo and Piras, 2012) for additional endogenous regressors in unbalanced panel.

Instruments:

  • W GRV ⇒ W X and W 2X
  • EMG.GR ⇒ ACTIVE.POP,
  • INV.RATE, GOV.EXP.on.GDP, CREDIT.PRIV.SECTOR.on.GDP ⇒ lagged Variables.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 16

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 17

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 18

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 19

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 20

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 21

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

slide-22
SLIDE 22

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

slide-23
SLIDE 23

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix Model with Asymmetric Effects controlling for Endogeneity and Spatial dependence.

Spatial coeff. coeff. p-value λ 0.8481 ρ 0.099 NA shock (-) shock (+) coeff. p-value coeff. p-value log.TOTGDP

  • .0052

.3744

  • .0032

.5890 log.DENSITY.POP .0431∗∗∗ .0052 .0442∗∗∗ .0041 EMPL.GR

  • .1994∗∗∗

.0003

  • .0551

.3470 INV.RATE

  • .0023

.9004 .0532∗∗∗ .0021 SHARE.AGRI .1652∗∗∗ .0000 .1742∗∗∗ .0000 SHARE.MIN .0098 .8271 .0641 .1618 SHARE.MANU .0532∗∗∗ .0086 .0546∗∗∗ .0065 SHARE.FIN .1836∗∗∗ .0002 .0558 .2543 SHARE.NMS .0395 .1080 .0734∗∗∗ .0034 SHARE.CONST .0735∗ .0700 .1232∗∗∗ .0018 HOUSE.EXP.on.GDP .0977∗∗∗ .0000 .0835∗∗∗ .0000 GOV.EXP.on.GDP

  • .0020∗∗∗

.0000

  • .0030∗∗∗

.0000 CREDIT.PRIV.SECTOR.on.GDP .0001∗∗∗ .0011 .0000 .8170 FDI.on.GDP

  • .0001∗

.0598 .0000 .6543 INFL .0003∗∗∗ .0000

  • .0001

.5726 GRV.PETROLEUM.Price .0034∗∗ .0280

  • .0043∗∗∗

.0016 EMUDummy .0024∗ .0991

  • .0043∗∗∗

.0034

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 24

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Thank you for your attention.

Fiaschi, Gianmoena, Parenti Determinants of GRV

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SLIDE 25

Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

Varaibles Code Description and Sources References Expected sign Size of the economy TOTGDP Total GDP at constant level 2000 (mln of euro) Fiaschi and Lavezzi (2005), (2011), Alesina and Spolaore (2003) negative relationship DEN.POP Population density Collier (2007) positive relationship Cyclical components EMPL.GR Employment growth rate INV.RATE Investment rate Sectoral composition SHARE.AGRI Share of agriculture on total GDP Koren and Tenreyro (2007) positive relationship SHARE.MIN Share of mining on total GDP positive relationship SHARE.MANU Share of manufacture on total GDP positive relationship SHARE.FIN Share of finance on total GDP SHARE.NMS Share of no market services on total GDP SHARE.CONSTS Share of construction on total GDP Other regio. variables HOUSE.EXP.on.GDP Household Expenditure on total GDP % Countries variables GOVExp.on.GDP General government final consump- tion expenditure (World Bank na- tional accounts data) Van den Noord (2000) (2010), Gali(1994) negative relationship FDI.on.GDP CREDIT.PRIV.SECTOR Domestic credit to private sector (%

  • f GDP) (World Bank national ac-

counts data) Esterley, Islam and Stigliz (2000), Checchetti et al(2006) negative relationship up to a point, but too much private credit can increase volatility INFLATION Inflation measured by the annual growth rate of the GDP implicit de- flater (World Bank national accounts data) Esterley, Islam and Stigliz (2000) associate with grater volatility Aggregate shocks GRV.PETROLEUM.Price Estimated volatility

  • f

petroleum prices (estimated using the same methodology for GRV) UNCTAD Fiaschi and Lavezzi (2011) positive effect on volatility Dummies EMUDummies

go back Fiaschi, Gianmoena, Parenti Determinants of GRV

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Aim of the Paper Volatility of Income Growth Methodology Empirical Analysis Appendix

0.00 0.05 0.10 0.15 0.20 0.25 0.30 −0.02 0.00 0.02 0.04 0.06

Moran Scatterplot I=0.224, p−value= 0.0009

GRV 1992 W x GRV 1992

CZ03 DE22 DE26 DE27 DE71 DE72 DEB2 DEB3 DEC DED1 DED3 NL32 NL33 PL31 PL33 PL41 High−high High−low Low−high Low−low Not Significant NA

−0.02 0.00 0.02 0.04 0.06 0.08 0.10 −0.02 −0.01 0.00 0.01 0.02

Moran Scatterplot I=0.226, p−value= 0.0009

GRV 2008 W x GRV 2008

EE ES13 ES21 ES22 ES23 ES3 ES41 ES42 ES51 ES52 ES53 ES62 ES63 ES64 ES7 GR3 LU PT11 RO32 SK01 SK02 SK03 High−high High−low Low−high Low−low Not Significant NA

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Generalized Spatial two-stage least squares (GS2SLS)

The three-steps procedure of GS2SLS

Yit = αi + λW Yit + βXit + uit uit = ρW uit + ǫit (1)

1 First-step: the model (1) is estimate by two-stage least squares (2SLS),

using as instrumental variables H(X, W X, W 2X). That is, in the first-stage regress W Y on X, W X, W 2X; then use the fitted values ˆ W Y in the second-stage.

2 Second-step: estimate the autoregressive parameter in the error term ρ

(ρW uit) by GMM using the residuals obtained in the first-step.

3 Third-step: reestimate the model (1) by 2SLS after filtering the variables

via a Cochrane-Orcutt type transformation Y ∗ = Y − ˆ ρW Y , X∗ = X − ˆ ρW X, W Y ∗.

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Spatial Autocorrelation Test

  • Moran’s I: tests for global spatial autocorrelation.

Based on cross-products of the deviations from the mean it is calculated for the GRV variable at locations i, j as: I = N

i

  • j wij(xi − ¯

x)(xj − ¯ x) (

i

  • j wij)

i(xi − ¯

x)2 where, ¯ x is the mean of the GRV variable, wij are the elements of the weight matrix, and

i

  • j wij is the sum of

the elements of the weight matrix W.

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Descriptive Statistics

GRV log.GDP log.PopDens EmpGr InvRate ShAg min 6.44

  • 5.81
  • 0.26

0.08

  • 0.02

max 0.33 13.06 2.24 0.43 0.53 0.24 mean 0.03 9.95

  • 1.87

0.06 0.22 0.04 sdt.dev 0.03 1.07 1.2 0.03 0.06 0.04 ShMin ShMan ShFin ShNms ShCon HouseholdExp.on.GDP min

  • 0.04

0.06 0.02

  • 1.59

max 0.38 0.48 0.25 0.56 0.19 0.26 mean 0.04 0.19 0.04 0.23 0.06

  • 0.47

sdt.dev 0.03 0.07 0.02 0.06 0.02 0.26 GovExp.on.GDP DomCred.on.GDP FDI.NETinflow.on.GDP Inflat GRVpetroleumPrive EMUDummy min 5.69 7.17

  • 15.03
  • 1.88

max 28.84 252.06 564.92 873.64 1.6 1 mean 20 91.72 4.89 5.95 0.41 0.33 sdt.dev 3.11 42.6 19.83 21.67 0.37 0.47

Fiaschi, Gianmoena, Parenti Determinants of GRV