Introduction and Research QuestionThe Model

Results

Determinats of Regionals Convergence(Divergence)

Insights from Intradistribution Dynamics

Fabrizi E.1 Guastella G.2 Timpano F.1

1Dep. of Economics and Social SciencesFaculty of Economics - Catholic University, Piacenza

2Doctoral School in Economic PolicyCatholic University, Piacenza

AISRe Annual Conference, Aosta, 2010

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Motivation

Transition dynamics approach has been introduced as analternative test for convergenceConvergence (in the long run) is considered to be theresult of movements within the distributionThe determinants of regional development are however notconsideredThis work is a first attempt to use information fromintradistribution dynamics to discuss determinants ofregional growth

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Motivation

Transition dynamics approach has been introduced as analternative test for convergenceConvergence (in the long run) is considered to be theresult of movements within the distributionThe determinants of regional development are however notconsideredThis work is a first attempt to use information fromintradistribution dynamics to discuss determinants ofregional growth

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Motivation

Transition dynamics approach has been introduced as analternative test for convergenceConvergence (in the long run) is considered to be theresult of movements within the distributionThe determinants of regional development are however notconsideredThis work is a first attempt to use information fromintradistribution dynamics to discuss determinants ofregional growth

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Motivation

Transition dynamics approach has been introduced as analternative test for convergenceConvergence (in the long run) is considered to be theresult of movements within the distributionThe determinants of regional development are however notconsideredThis work is a first attempt to use information fromintradistribution dynamics to discuss determinants ofregional growth

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The standard approach

Regional development is analyzed by mean of growthregression

Conditional convergence (Institutions and structuralcharacteristics)Externalities and spilloversDeterminants of development (HC, R&D, Agglomerationeconomies,...)

β-convergence is however generally not sufficientσ-convergence only focuses on the SD of the incomedistribution

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The standard approach

Regional development is analyzed by mean of growthregression

Conditional convergence (Institutions and structuralcharacteristics)Externalities and spilloversDeterminants of development (HC, R&D, Agglomerationeconomies,...)

β-convergence is however generally not sufficientσ-convergence only focuses on the SD of the incomedistribution

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The standard approach

Regional development is analyzed by mean of growthregression

Conditional convergence (Institutions and structuralcharacteristics)Externalities and spilloversDeterminants of development (HC, R&D, Agglomerationeconomies,...)

β-convergence is however generally not sufficientσ-convergence only focuses on the SD of the incomedistribution

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The standard approach

Regional development is analyzed by mean of growthregression

Conditional convergence (Institutions and structuralcharacteristics)Externalities and spilloversDeterminants of development (HC, R&D, Agglomerationeconomies,...)

β-convergence is however generally not sufficientσ-convergence only focuses on the SD of the incomedistribution

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The standard approach

Regional development is analyzed by mean of growthregression

Conditional convergence (Institutions and structuralcharacteristics)Externalities and spilloversDeterminants of development (HC, R&D, Agglomerationeconomies,...)

β-convergence is however generally not sufficientσ-convergence only focuses on the SD of the incomedistribution

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The standard approach

Regional development is analyzed by mean of growthregression

Conditional convergence (Institutions and structuralcharacteristics)Externalities and spilloversDeterminants of development (HC, R&D, Agglomerationeconomies,...)

β-convergence is however generally not sufficientσ-convergence only focuses on the SD of the incomedistribution

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The alternative approach

Markov chains and long-run distributionmovements within different parts of the distributiontransition probabilitiesergodic distribution and equilibrium analysis

Markov or not Markov?classes boundaries and sensitivity of resultstime homogeneity (to make inference about equilibriumdistribution)

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The alternative approach

Markov chains and long-run distributionmovements within different parts of the distributiontransition probabilitiesergodic distribution and equilibrium analysis

Markov or not Markov?classes boundaries and sensitivity of resultstime homogeneity (to make inference about equilibriumdistribution)

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The alternative approach

Markov chains and long-run distributionmovements within different parts of the distributiontransition probabilitiesergodic distribution and equilibrium analysis

Markov or not Markov?classes boundaries and sensitivity of resultstime homogeneity (to make inference about equilibriumdistribution)

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The alternative approach

Markov chains and long-run distributionmovements within different parts of the distributiontransition probabilitiesergodic distribution and equilibrium analysis

Markov or not Markov?classes boundaries and sensitivity of resultstime homogeneity (to make inference about equilibriumdistribution)

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The alternative approach

Markov chains and long-run distributionmovements within different parts of the distributiontransition probabilitiesergodic distribution and equilibrium analysis

Markov or not Markov?classes boundaries and sensitivity of resultstime homogeneity (to make inference about equilibriumdistribution)

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The alternative approach

Markov chains and long-run distributionmovements within different parts of the distributiontransition probabilitiesergodic distribution and equilibrium analysis

Markov or not Markov?classes boundaries and sensitivity of resultstime homogeneity (to make inference about equilibriumdistribution)

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

The alternative approach

Markov chains and long-run distributionmovements within different parts of the distributiontransition probabilitiesergodic distribution and equilibrium analysis

Markov or not Markov?classes boundaries and sensitivity of resultstime homogeneity (to make inference about equilibriumdistribution)

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Markov chain and the determinants of development

Probabilities give a clearer idea of the developmentprocessEven sustained growth may in fact be not sufficient totransitateHowever we know which regions transitate but not why

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Markov chain and the determinants of development

Probabilities give a clearer idea of the developmentprocessEven sustained growth may in fact be not sufficient totransitateHowever we know which regions transitate but not why

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Markov chain and the determinants of development

Probabilities give a clearer idea of the developmentprocessEven sustained growth may in fact be not sufficient totransitateHowever we know which regions transitate but not why

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

A first attempt to explain transition

Transition is the result of very sustained growthWe aim to find a link between

the probability of transition andthe determinants of development

very sustained growth

It is necessary to ensure that transition is not the result of asimple statistical effect!

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

A first attempt to explain transition

Transition is the result of very sustained growthWe aim to find a link between

the probability of transition andthe determinants of development

very sustained growth

It is necessary to ensure that transition is not the result of asimple statistical effect!

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

A first attempt to explain transition

Transition is the result of very sustained growthWe aim to find a link between

the probability of transition andthe determinants of development

very sustained growth

It is necessary to ensure that transition is not the result of asimple statistical effect!

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

A first attempt to explain transition

Transition is the result of very sustained growthWe aim to find a link between

the probability of transition andthe determinants of development

very sustained growth

It is necessary to ensure that transition is not the result of asimple statistical effect!

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

MotivationBackgroundResearch Question

A first attempt to explain transition

Transition is the result of very sustained growthWe aim to find a link between

the probability of transition andthe determinants of development

very sustained growth

It is necessary to ensure that transition is not the result of asimple statistical effect!

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Multinomial Logistic Regression

With Multinomial model it is possibleto model the transition from different origins

different factors are important in different stages ofdevelopment

to get coefficient estimates which are destination specificsome factors determine larger transitions

to normalize coefficientcoefficients represent the change in probabilities to move toanother classwrt the probability to stay in the origin class

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Multinomial Logistic Regression

With Multinomial model it is possibleto model the transition from different origins

different factors are important in different stages ofdevelopment

to get coefficient estimates which are destination specificsome factors determine larger transitions

to normalize coefficientcoefficients represent the change in probabilities to move toanother classwrt the probability to stay in the origin class

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Multinomial Logistic Regression

With Multinomial model it is possibleto model the transition from different origins

different factors are important in different stages ofdevelopment

to get coefficient estimates which are destination specificsome factors determine larger transitions

to normalize coefficientcoefficients represent the change in probabilities to move toanother classwrt the probability to stay in the origin class

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Multinomial Logistic Regression

With Multinomial model it is possibleto model the transition from different origins

different factors are important in different stages ofdevelopment

to get coefficient estimates which are destination specificsome factors determine larger transitions

to normalize coefficientcoefficients represent the change in probabilities to move toanother classwrt the probability to stay in the origin class

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Multinomial Logistic Regression

With Multinomial model it is possibleto model the transition from different origins

different factors are important in different stages ofdevelopment

to get coefficient estimates which are destination specificsome factors determine larger transitions

to normalize coefficientcoefficients represent the change in probabilities to move toanother classwrt the probability to stay in the origin class

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Multinomial Logistic Regression

With Multinomial model it is possibleto model the transition from different origins

different factors are important in different stages ofdevelopment

to get coefficient estimates which are destination specificsome factors determine larger transitions

to normalize coefficientcoefficients represent the change in probabilities to move toanother classwrt the probability to stay in the origin class

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Multinomial Logistic Regression

With Multinomial model it is possibleto model the transition from different origins

different factors are important in different stages ofdevelopment

to get coefficient estimates which are destination specificsome factors determine larger transitions

to normalize coefficientcoefficients represent the change in probabilities to move toanother classwrt the probability to stay in the origin class

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

The MLG: Problems

Trade off betweennumber of classes (detail of the analysis)degree of freedom (for each regression)

Low number of transition for more than 1 classthe transition is the result of a statistical effect due to classboundariesthe choice of boundaries should guarantee a sufficientnumber of transitionwith 1 class transition the model reduces to a simple logisticregression

ConclusionClasses boundaries are chose according to results: sensitivityof results

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

The MLG: Problems

Trade off betweennumber of classes (detail of the analysis)degree of freedom (for each regression)

Low number of transition for more than 1 classthe transition is the result of a statistical effect due to classboundariesthe choice of boundaries should guarantee a sufficientnumber of transitionwith 1 class transition the model reduces to a simple logisticregression

ConclusionClasses boundaries are chose according to results: sensitivityof results

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

The MLG: Problems

Trade off betweennumber of classes (detail of the analysis)degree of freedom (for each regression)

Low number of transition for more than 1 classthe transition is the result of a statistical effect due to classboundariesthe choice of boundaries should guarantee a sufficientnumber of transitionwith 1 class transition the model reduces to a simple logisticregression

ConclusionClasses boundaries are chose according to results: sensitivityof results

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

The MLG: Problems

Trade off betweennumber of classes (detail of the analysis)degree of freedom (for each regression)

Low number of transition for more than 1 classthe transition is the result of a statistical effect due to classboundariesthe choice of boundaries should guarantee a sufficientnumber of transitionwith 1 class transition the model reduces to a simple logisticregression

ConclusionClasses boundaries are chose according to results: sensitivityof results

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

The MLG: Problems

Trade off betweennumber of classes (detail of the analysis)degree of freedom (for each regression)

Low number of transition for more than 1 classthe transition is the result of a statistical effect due to classboundariesthe choice of boundaries should guarantee a sufficientnumber of transitionwith 1 class transition the model reduces to a simple logisticregression

ConclusionClasses boundaries are chose according to results: sensitivityof results

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

The MLG: Problems

Trade off betweennumber of classes (detail of the analysis)degree of freedom (for each regression)

Low number of transition for more than 1 classthe transition is the result of a statistical effect due to classboundariesthe choice of boundaries should guarantee a sufficientnumber of transitionwith 1 class transition the model reduces to a simple logisticregression

ConclusionClasses boundaries are chose according to results: sensitivityof results

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

The MLG: Problems

Trade off betweennumber of classes (detail of the analysis)degree of freedom (for each regression)

Low number of transition for more than 1 classthe transition is the result of a statistical effect due to classboundariesthe choice of boundaries should guarantee a sufficientnumber of transitionwith 1 class transition the model reduces to a simple logisticregression

ConclusionClasses boundaries are chose according to results: sensitivityof results

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

The MLG: Problems

Trade off betweennumber of classes (detail of the analysis)degree of freedom (for each regression)

Low number of transition for more than 1 classthe transition is the result of a statistical effect due to classboundariesthe choice of boundaries should guarantee a sufficientnumber of transitionwith 1 class transition the model reduces to a simple logisticregression

ConclusionClasses boundaries are chose according to results: sensitivityof results

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

Logistic regression

Transition is modelled according toMove forward (1) vs stay (0)Move backward (1) vs stay (0)

no differentiation according to origin classdifferentiation based on NMSDifferentiation based on income level

high number of classeslow sensitivity to boundariesstill enought to ensure ergodic properties of TPM

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Dependent: per capita gdp in PPS (1999-2007)Regressors

share of employment in services, industry and agricolturelong-term unemploymentpopulation densityred

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Dependent: per capita gdp in PPS (1999-2007)Regressors

share of employment in services, industry and agricolturelong-term unemploymentpopulation densityred

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Dependent: per capita gdp in PPS (1999-2007)Regressors

share of employment in services, industry and agricolturelong-term unemploymentpopulation densityred

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Dependent: per capita gdp in PPS (1999-2007)Regressors

share of employment in services, industry and agricolturelong-term unemploymentpopulation densityred

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Dependent: per capita gdp in PPS (1999-2007)Regressors

share of employment in services, industry and agricolturelong-term unemploymentpopulation densityred

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Dependent: per capita gdp in PPS (1999-2007)Regressors

share of employment in services, industry and agricolturelong-term unemploymentpopulation densityred

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Regressorsroadkm and intaccfunds received up to 1999

More data?country dummy: fixed effects capturing also some dep varother structural characteristics: need for data reduction

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Regressorsroadkm and intaccfunds received up to 1999

More data?country dummy: fixed effects capturing also some dep varother structural characteristics: need for data reduction

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Regressorsroadkm and intaccfunds received up to 1999

More data?country dummy: fixed effects capturing also some dep varother structural characteristics: need for data reduction

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Regressorsroadkm and intaccfunds received up to 1999

More data?country dummy: fixed effects capturing also some dep varother structural characteristics: need for data reduction

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Regressorsroadkm and intaccfunds received up to 1999

More data?country dummy: fixed effects capturing also some dep varother structural characteristics: need for data reduction

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

The Multinomial Response ModelBinary response model

ESPON dataset 1999-2000

Regressorsroadkm and intaccfunds received up to 1999

More data?country dummy: fixed effects capturing also some dep varother structural characteristics: need for data reduction

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Table of probabilities - ML estimates

finalinit 0.6 0.741 0.834 0.922 1 1.07 1.13 1.22 1.38 Inf0.6 0.828 0.1720.741 0.050 0.750 0.150 0.0500.834 0.200 0.440 0.200 0.120 0.0400.922 0.381 0.381 0.095 0.095 0.0481 0.538 0.308 0.077 0.038 0.0381.0 0.435 0.087 0.0431.13 0.091 0.318 0.273 0.3181.22 0.036 0.036 0.250 0.250 0.321 0.1071.38 0.042 0.250 0.625 0.083Inf 0.040 0.280 0.680

ergodic 0.6 0.741 0.834 0.922 1 1.07 1.13 1.22 1.38 Inf0.05 0.173 0.173 0.186 0.13 0.106 0.04 0.05 0.061 0.03

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Results with 4 classes

ML estimatesForward Backward

Estimate z value Estimate z value(Intercept) -29.7546 (-3.345)*** -3.0315 (-0.419)seragri 0.4398 (0.843) 0.9713 (1.937).ltu 1.2463 (1.089) 3.5127 (3.577)***popd -0.7294 (-1.634) -1.0499 (-2.408)*educ 1.6455 (1.893). -1.6331 (-2.545)*roadkm -0.2027 (-1.319) 0.2372 (1.434)red 0.2684 (0.564) -0.1564 (-0.450)intacc 0.4276 (0.326) -0.2256 (-0.240)funds 0.9949 (3.939)*** -0.2653 (-1.292)nms 17.0169 (3.853)*** -7.0361 (-1.801).Note: .,*,**,*** indicate significance at 90%, 95%, 99%, 99.9%.

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Results with 10 classes

ML estimatesForward Backward

Estimate z value Estimate z value(Intercept) -13.35170 (-2.405)* 16.16831 (3.249)**seragri 0.13173 (0.366) 0.35112 (1.068)ltu -0.22029 (-0.353) 0.38235 (0.869)popd -0.20579 (-0.668) -0.25530 (-0.953)educ 1.07204 (1.886). -1.37568 (-2.959)**roadkm -0.24873 (-2.309)* 0.14600 (1.503)red 0.41517 (1.320) 0.24917 (0.940)intacc 0.06951 (0.080) -1.56432 (-2.124)*funds1 0.55746 (3.366)*** -0.42325 (-2.776)**nms 11.04681 (3.804)*** -25.69953 (-0.027)Note: .,*,**,*** indicate significance at 90%, 95%, 99%, 99.9%.

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Results with NMS regimes - Forward

ML estimatesNMS NON-NMS

Estimate z value Estimate z value(Intercept) -9.54762 (-0.777)*** -25.53175 (-3.463)***seragri 3.93361 (1.841). -0.91786 (-1.859).ltu -0.72519 (-0.288) 0.02364 (0.029)popd -1.23960 (-0.756) 0.30936 (0.852)educ 0.97002 (0.637)** 2.23724 (2.653)**roadkm 0.27350 (0.417)* -0.27109 (-2.302)*red 2.07724 (1.651) -0.12609 (-0.328)intacc 1.32019 (0.582). 1.74943 (1.655).funds1 0.52072 (0.976)** 0.62902 (3.143)**Note: .,*,**,*** indicate significance at 90%, 95%, 99%, 99.9%.

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Results with NMS regimes - Backward

ML estimatesNMS NON-NMS

Estimate z value Estimate z value(Intercept) -1.857e+01 (-0.001)** 1.617e+01 (3.249)**seragri -4.170e-12 (-2.02e-15) 3.511e-01 (1.068)ltu -1.815e-11 (-3.31e-15) 3.823e-01 (0.869)popd 3.625e-12 (1.47e-15) -2.553e-01 (-0.953)educ -8.648e-13 (-2.77e-16)** -1.376e+00 (-2.959)**roadkm 3.190e-13 (2.89e-16) 1.460e-01 (1.503)red -7.394e-13 (-4.05e-16) 2.492e-01 (0.940)intacc -4.382e-12 (-1.53e-15)* -1.564e+00 (-2.124)*funds1 -1.010e-12 (-1.47e-15)** -4.233e-01 (-2.776)**

Note: .,*,**,*** indicate significance at 90%, 95%, 99%, 99.9%.

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Convergence analysis

ML estimatesForward Backward

Estimate z value Estimate (z value)(Intercept) -5.8301 (-0.838) 25.09524 (2.715)**seragri 0.4779 (0.986) -0.38279 (-0.728)ltu -0.3206 (-0.326) 0.20090 (0.357)popd -0.3812 (-0.874) 0.35340 (0.841)educ 1.4155 (1.808). -1.13022 (-1.561)roadkm -0.2068 (-1.388) 0.19029 (1.170)red 0.7479 (1.822). 0.15476 (0.399)intacc -1.5327 (-1.279) -5.47048 (-3.101)**funds1 0.4583 (2.154)* -0.07864 (-0.353)nms 9.4957 (2.478)* -16.98825 (-0.017)Note: .,*,**,*** indicate significance at 90%, 95%, 99%, 99.9%.

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Outline

1 Introduction and Research QuestionMotivationBackgroundResearch Question

2 The ModelThe Multinomial Response ModelBinary response model

3 ResultsTransition probabilitiesRegression OutputConclusion

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Summary of Results

Regional development in Europe is characterized bydivergence - low mobility of regionsRelative importance of infrastructure, agglomerationeconomies and labor market

Short run vs Long run effectNot only benefits

Strong relevance of Human capital and technologicalinfrastructuresStructural changes in NMSseveral evidences supporting the role of funds

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Summary of Results

Regional development in Europe is characterized bydivergence - low mobility of regionsRelative importance of infrastructure, agglomerationeconomies and labor market

Short run vs Long run effectNot only benefits

Strong relevance of Human capital and technologicalinfrastructuresStructural changes in NMSseveral evidences supporting the role of funds

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Summary of Results

Regional development in Europe is characterized bydivergence - low mobility of regionsRelative importance of infrastructure, agglomerationeconomies and labor market

Short run vs Long run effectNot only benefits

Strong relevance of Human capital and technologicalinfrastructuresStructural changes in NMSseveral evidences supporting the role of funds

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Summary of Results

Regional development in Europe is characterized bydivergence - low mobility of regionsRelative importance of infrastructure, agglomerationeconomies and labor market

Short run vs Long run effectNot only benefits

Strong relevance of Human capital and technologicalinfrastructuresStructural changes in NMSseveral evidences supporting the role of funds

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Summary of Results

Regional development in Europe is characterized bydivergence - low mobility of regionsRelative importance of infrastructure, agglomerationeconomies and labor market

Short run vs Long run effectNot only benefits

Strong relevance of Human capital and technologicalinfrastructuresStructural changes in NMSseveral evidences supporting the role of funds

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Summary of Results

Regional development in Europe is characterized bydivergence - low mobility of regionsRelative importance of infrastructure, agglomerationeconomies and labor market

Short run vs Long run effectNot only benefits

Strong relevance of Human capital and technologicalinfrastructuresStructural changes in NMSseveral evidences supporting the role of funds

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Summary of Results

Regional development in Europe is characterized bydivergence - low mobility of regionsRelative importance of infrastructure, agglomerationeconomies and labor market

Short run vs Long run effectNot only benefits

Strong relevance of Human capital and technologicalinfrastructuresStructural changes in NMSseveral evidences supporting the role of funds

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Policy relevance

Human Capital is the real driver of developmentBe careful with interpretation of infrastrucure

Finland and Sweden have high growth but not so manyinfrastructuresIn NMS infrastructures are important!

Need for a more detailed analysis of funds and cohesionpolicy

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Policy relevance

Human Capital is the real driver of developmentBe careful with interpretation of infrastrucure

Finland and Sweden have high growth but not so manyinfrastructuresIn NMS infrastructures are important!

Need for a more detailed analysis of funds and cohesionpolicy

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Policy relevance

Human Capital is the real driver of developmentBe careful with interpretation of infrastrucure

Finland and Sweden have high growth but not so manyinfrastructuresIn NMS infrastructures are important!

Need for a more detailed analysis of funds and cohesionpolicy

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Policy relevance

Human Capital is the real driver of developmentBe careful with interpretation of infrastrucure

Finland and Sweden have high growth but not so manyinfrastructuresIn NMS infrastructures are important!

Need for a more detailed analysis of funds and cohesionpolicy

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

Introduction and Research QuestionThe Model

Results

Transition probabilitiesRegression OutputConclusion

Policy relevance

Human Capital is the real driver of developmentBe careful with interpretation of infrastrucure

Finland and Sweden have high growth but not so manyinfrastructuresIn NMS infrastructures are important!

Need for a more detailed analysis of funds and cohesionpolicy

Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)