This document discusses using transition dynamics and Markov chains to analyze regional economic convergence and divergence. It aims to use information on movements within the income distribution to examine determinants of regional growth. Specifically, it seeks to link the probability of transitions between different income classes to determinants of development, going beyond standard growth regressions. The authors introduce a multinomial logistic regression model to estimate transition probabilities and make them destination-specific while controlling for different factors in different development stages. The results could provide insights into why some regions transition between classes while others do not.

1. Introduction and Research Question
The Model
Results
Determinats of Regionals Convergence
(Divergence)
Insights from Intradistribution Dynamics
Fabrizi E.1 Guastella G.2 Timpano F.1
1 Dep. of Economics and Social Sciences
Faculty of Economics - Catholic University, Piacenza
2 DoctoralSchool in Economic Policy
Catholic University, Piacenza
AISRe Annual Conference, Aosta, 2010
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

2. Introduction and Research Question
The Model
Results
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

3. Introduction and Research Question Motivation
The Model Background
Results Research Question
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

4. Introduction and Research Question Motivation
The Model Background
Results Research Question
Motivation
Transition dynamics approach has been introduced as an
alternative test for convergence
Convergence (in the long run) is considered to be the
result of movements within the distribution
The determinants of regional development are however not
considered
This work is a first attempt to use information from
intradistribution dynamics to discuss determinants of
regional growth
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

5. Introduction and Research Question Motivation
The Model Background
Results Research Question
Motivation
Transition dynamics approach has been introduced as an
alternative test for convergence
Convergence (in the long run) is considered to be the
result of movements within the distribution
The determinants of regional development are however not
considered
This work is a first attempt to use information from
intradistribution dynamics to discuss determinants of
regional growth
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

6. Introduction and Research Question Motivation
The Model Background
Results Research Question
Motivation
Transition dynamics approach has been introduced as an
alternative test for convergence
Convergence (in the long run) is considered to be the
result of movements within the distribution
The determinants of regional development are however not
considered
This work is a first attempt to use information from
intradistribution dynamics to discuss determinants of
regional growth
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

7. Introduction and Research Question Motivation
The Model Background
Results Research Question
Motivation
Transition dynamics approach has been introduced as an
alternative test for convergence
Convergence (in the long run) is considered to be the
result of movements within the distribution
The determinants of regional development are however not
considered
This work is a first attempt to use information from
intradistribution dynamics to discuss determinants of
regional growth
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

8. Introduction and Research Question Motivation
The Model Background
Results Research Question
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

9. Introduction and Research Question Motivation
The Model Background
Results Research Question
The standard approach
Regional development is analyzed by mean of growth
regression
Conditional convergence (Institutions and structural
characteristics)
Externalities and spillovers
Determinants of development (HC, R&D, Agglomeration
economies,...)
β-convergence is however generally not sufficient
σ-convergence only focuses on the SD of the income
distribution
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

10. Introduction and Research Question Motivation
The Model Background
Results Research Question
The standard approach
Regional development is analyzed by mean of growth
regression
Conditional convergence (Institutions and structural
characteristics)
Externalities and spillovers
Determinants of development (HC, R&D, Agglomeration
economies,...)
β-convergence is however generally not sufficient
σ-convergence only focuses on the SD of the income
distribution
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

11. Introduction and Research Question Motivation
The Model Background
Results Research Question
The standard approach
Regional development is analyzed by mean of growth
regression
Conditional convergence (Institutions and structural
characteristics)
Externalities and spillovers
Determinants of development (HC, R&D, Agglomeration
economies,...)
β-convergence is however generally not sufficient
σ-convergence only focuses on the SD of the income
distribution
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

12. Introduction and Research Question Motivation
The Model Background
Results Research Question
The standard approach
Regional development is analyzed by mean of growth
regression
Conditional convergence (Institutions and structural
characteristics)
Externalities and spillovers
Determinants of development (HC, R&D, Agglomeration
economies,...)
β-convergence is however generally not sufficient
σ-convergence only focuses on the SD of the income
distribution
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

13. Introduction and Research Question Motivation
The Model Background
Results Research Question
The standard approach
Regional development is analyzed by mean of growth
regression
Conditional convergence (Institutions and structural
characteristics)
Externalities and spillovers
Determinants of development (HC, R&D, Agglomeration
economies,...)
β-convergence is however generally not sufficient
σ-convergence only focuses on the SD of the income
distribution
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

14. Introduction and Research Question Motivation
The Model Background
Results Research Question
The standard approach
Regional development is analyzed by mean of growth
regression
Conditional convergence (Institutions and structural
characteristics)
Externalities and spillovers
Determinants of development (HC, R&D, Agglomeration
economies,...)
β-convergence is however generally not sufficient
σ-convergence only focuses on the SD of the income
distribution
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

15. Introduction and Research Question Motivation
The Model Background
Results Research Question
The alternative approach
Markov chains and long-run distribution
movements within different parts of the distribution
transition probabilities
ergodic distribution and equilibrium analysis
Markov or not Markov?
classes boundaries and sensitivity of results
time homogeneity (to make inference about equilibrium
distribution)
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

16. Introduction and Research Question Motivation
The Model Background
Results Research Question
The alternative approach
Markov chains and long-run distribution
movements within different parts of the distribution
transition probabilities
ergodic distribution and equilibrium analysis
Markov or not Markov?
classes boundaries and sensitivity of results
time homogeneity (to make inference about equilibrium
distribution)
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

17. Introduction and Research Question Motivation
The Model Background
Results Research Question
The alternative approach
Markov chains and long-run distribution
movements within different parts of the distribution
transition probabilities
ergodic distribution and equilibrium analysis
Markov or not Markov?
classes boundaries and sensitivity of results
time homogeneity (to make inference about equilibrium
distribution)
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

18. Introduction and Research Question Motivation
The Model Background
Results Research Question
The alternative approach
Markov chains and long-run distribution
movements within different parts of the distribution
transition probabilities
ergodic distribution and equilibrium analysis
Markov or not Markov?
classes boundaries and sensitivity of results
time homogeneity (to make inference about equilibrium
distribution)
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

19. Introduction and Research Question Motivation
The Model Background
Results Research Question
The alternative approach
Markov chains and long-run distribution
movements within different parts of the distribution
transition probabilities
ergodic distribution and equilibrium analysis
Markov or not Markov?
classes boundaries and sensitivity of results
time homogeneity (to make inference about equilibrium
distribution)
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

20. Introduction and Research Question Motivation
The Model Background
Results Research Question
The alternative approach
Markov chains and long-run distribution
movements within different parts of the distribution
transition probabilities
ergodic distribution and equilibrium analysis
Markov or not Markov?
classes boundaries and sensitivity of results
time homogeneity (to make inference about equilibrium
distribution)
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

21. Introduction and Research Question Motivation
The Model Background
Results Research Question
The alternative approach
Markov chains and long-run distribution
movements within different parts of the distribution
transition probabilities
ergodic distribution and equilibrium analysis
Markov or not Markov?
classes boundaries and sensitivity of results
time homogeneity (to make inference about equilibrium
distribution)
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

22. Introduction and Research Question Motivation
The Model Background
Results Research Question
Markov chain and the determinants of development
Probabilities give a clearer idea of the development
process
Even sustained growth may in fact be not sufficient to
transitate
However we know which regions transitate but not why
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

23. Introduction and Research Question Motivation
The Model Background
Results Research Question
Markov chain and the determinants of development
Probabilities give a clearer idea of the development
process
Even sustained growth may in fact be not sufficient to
transitate
However we know which regions transitate but not why
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

24. Introduction and Research Question Motivation
The Model Background
Results Research Question
Markov chain and the determinants of development
Probabilities give a clearer idea of the development
process
Even sustained growth may in fact be not sufficient to
transitate
However we know which regions transitate but not why
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

25. Introduction and Research Question Motivation
The Model Background
Results Research Question
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

26. Introduction and Research Question Motivation
The Model Background
Results Research Question
A first attempt to explain transition
Transition is the result of very sustained growth
We aim to find a link between
the probability of transition and
the determinants of development
very sustained growth
It is necessary to ensure that transition is not the result of a
simple statistical effect!
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

27. Introduction and Research Question Motivation
The Model Background
Results Research Question
A first attempt to explain transition
Transition is the result of very sustained growth
We aim to find a link between
the probability of transition and
the determinants of development
very sustained growth
It is necessary to ensure that transition is not the result of a
simple statistical effect!
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

28. Introduction and Research Question Motivation
The Model Background
Results Research Question
A first attempt to explain transition
Transition is the result of very sustained growth
We aim to find a link between
the probability of transition and
the determinants of development
very sustained growth
It is necessary to ensure that transition is not the result of a
simple statistical effect!
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

29. Introduction and Research Question Motivation
The Model Background
Results Research Question
A first attempt to explain transition
Transition is the result of very sustained growth
We aim to find a link between
the probability of transition and
the determinants of development
very sustained growth
It is necessary to ensure that transition is not the result of a
simple statistical effect!
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

30. Introduction and Research Question Motivation
The Model Background
Results Research Question
A first attempt to explain transition
Transition is the result of very sustained growth
We aim to find a link between
the probability of transition and
the determinants of development
very sustained growth
It is necessary to ensure that transition is not the result of a
simple statistical effect!
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

31. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

32. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Multinomial Logistic Regression
With Multinomial model it is possible
to model the transition from different origins
different factors are important in different stages of
development
to get coefficient estimates which are destination specific
some factors determine larger transitions
to normalize coefficient
coefficients represent the change in probabilities to move to
another class
wrt the probability to stay in the origin class
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

33. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Multinomial Logistic Regression
With Multinomial model it is possible
to model the transition from different origins
different factors are important in different stages of
development
to get coefficient estimates which are destination specific
some factors determine larger transitions
to normalize coefficient
coefficients represent the change in probabilities to move to
another class
wrt the probability to stay in the origin class
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

34. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Multinomial Logistic Regression
With Multinomial model it is possible
to model the transition from different origins
different factors are important in different stages of
development
to get coefficient estimates which are destination specific
some factors determine larger transitions
to normalize coefficient
coefficients represent the change in probabilities to move to
another class
wrt the probability to stay in the origin class
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

35. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Multinomial Logistic Regression
With Multinomial model it is possible
to model the transition from different origins
different factors are important in different stages of
development
to get coefficient estimates which are destination specific
some factors determine larger transitions
to normalize coefficient
coefficients represent the change in probabilities to move to
another class
wrt the probability to stay in the origin class
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

36. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Multinomial Logistic Regression
With Multinomial model it is possible
to model the transition from different origins
different factors are important in different stages of
development
to get coefficient estimates which are destination specific
some factors determine larger transitions
to normalize coefficient
coefficients represent the change in probabilities to move to
another class
wrt the probability to stay in the origin class
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

37. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Multinomial Logistic Regression
With Multinomial model it is possible
to model the transition from different origins
different factors are important in different stages of
development
to get coefficient estimates which are destination specific
some factors determine larger transitions
to normalize coefficient
coefficients represent the change in probabilities to move to
another class
wrt the probability to stay in the origin class
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

38. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Multinomial Logistic Regression
With Multinomial model it is possible
to model the transition from different origins
different factors are important in different stages of
development
to get coefficient estimates which are destination specific
some factors determine larger transitions
to normalize coefficient
coefficients represent the change in probabilities to move to
another class
wrt the probability to stay in the origin class
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

39. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
The MLG: Problems
Trade off between
number of classes (detail of the analysis)
degree of freedom (for each regression)
Low number of transition for more than 1 class
the transition is the result of a statistical effect due to class
boundaries
the choice of boundaries should guarantee a sufficient
number of transition
with 1 class transition the model reduces to a simple logistic
regression
Conclusion
Classes boundaries are chose according to results: sensitivity
of results
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

40. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
The MLG: Problems
Trade off between
number of classes (detail of the analysis)
degree of freedom (for each regression)
Low number of transition for more than 1 class
the transition is the result of a statistical effect due to class
boundaries
the choice of boundaries should guarantee a sufficient
number of transition
with 1 class transition the model reduces to a simple logistic
regression
Conclusion
Classes boundaries are chose according to results: sensitivity
of results
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

41. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
The MLG: Problems
Trade off between
number of classes (detail of the analysis)
degree of freedom (for each regression)
Low number of transition for more than 1 class
the transition is the result of a statistical effect due to class
boundaries
the choice of boundaries should guarantee a sufficient
number of transition
with 1 class transition the model reduces to a simple logistic
regression
Conclusion
Classes boundaries are chose according to results: sensitivity
of results
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

42. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
The MLG: Problems
Trade off between
number of classes (detail of the analysis)
degree of freedom (for each regression)
Low number of transition for more than 1 class
the transition is the result of a statistical effect due to class
boundaries
the choice of boundaries should guarantee a sufficient
number of transition
with 1 class transition the model reduces to a simple logistic
regression
Conclusion
Classes boundaries are chose according to results: sensitivity
of results
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

43. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
The MLG: Problems
Trade off between
number of classes (detail of the analysis)
degree of freedom (for each regression)
Low number of transition for more than 1 class
the transition is the result of a statistical effect due to class
boundaries
the choice of boundaries should guarantee a sufficient
number of transition
with 1 class transition the model reduces to a simple logistic
regression
Conclusion
Classes boundaries are chose according to results: sensitivity
of results
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

44. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
The MLG: Problems
Trade off between
number of classes (detail of the analysis)
degree of freedom (for each regression)
Low number of transition for more than 1 class
the transition is the result of a statistical effect due to class
boundaries
the choice of boundaries should guarantee a sufficient
number of transition
with 1 class transition the model reduces to a simple logistic
regression
Conclusion
Classes boundaries are chose according to results: sensitivity
of results
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

45. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
The MLG: Problems
Trade off between
number of classes (detail of the analysis)
degree of freedom (for each regression)
Low number of transition for more than 1 class
the transition is the result of a statistical effect due to class
boundaries
the choice of boundaries should guarantee a sufficient
number of transition
with 1 class transition the model reduces to a simple logistic
regression
Conclusion
Classes boundaries are chose according to results: sensitivity
of results
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

46. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
The MLG: Problems
Trade off between
number of classes (detail of the analysis)
degree of freedom (for each regression)
Low number of transition for more than 1 class
the transition is the result of a statistical effect due to class
boundaries
the choice of boundaries should guarantee a sufficient
number of transition
with 1 class transition the model reduces to a simple logistic
regression
Conclusion
Classes boundaries are chose according to results: sensitivity
of results
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

47. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

48. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

49. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

50. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

51. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

52. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

53. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

54. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

55. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

56. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
Logistic regression
Transition is modelled according to
Move forward (1) vs stay (0)
Move backward (1) vs stay (0)
no differentiation according to origin class
differentiation based on NMS
Differentiation based on income level
high number of classes
low sensitivity to boundaries
still enought to ensure ergodic properties of TPM
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

57. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Dependent: per capita gdp in PPS (1999-2007)
Regressors
share of employment in services, industry and agricolture
long-term unemployment
population density
red
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

58. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Dependent: per capita gdp in PPS (1999-2007)
Regressors
share of employment in services, industry and agricolture
long-term unemployment
population density
red
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

59. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Dependent: per capita gdp in PPS (1999-2007)
Regressors
share of employment in services, industry and agricolture
long-term unemployment
population density
red
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

60. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Dependent: per capita gdp in PPS (1999-2007)
Regressors
share of employment in services, industry and agricolture
long-term unemployment
population density
red
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

61. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Dependent: per capita gdp in PPS (1999-2007)
Regressors
share of employment in services, industry and agricolture
long-term unemployment
population density
red
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

62. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Dependent: per capita gdp in PPS (1999-2007)
Regressors
share of employment in services, industry and agricolture
long-term unemployment
population density
red
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

63. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Regressors
roadkm and intacc
funds received up to 1999
More data?
country dummy: fixed effects capturing also some dep var
other structural characteristics: need for data reduction
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

64. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Regressors
roadkm and intacc
funds received up to 1999
More data?
country dummy: fixed effects capturing also some dep var
other structural characteristics: need for data reduction
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

65. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Regressors
roadkm and intacc
funds received up to 1999
More data?
country dummy: fixed effects capturing also some dep var
other structural characteristics: need for data reduction
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

66. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Regressors
roadkm and intacc
funds received up to 1999
More data?
country dummy: fixed effects capturing also some dep var
other structural characteristics: need for data reduction
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

67. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Regressors
roadkm and intacc
funds received up to 1999
More data?
country dummy: fixed effects capturing also some dep var
other structural characteristics: need for data reduction
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

68. Introduction and Research Question
The Multinomial Response Model
The Model
Binary response model
Results
ESPON dataset 1999-2000
Regressors
roadkm and intacc
funds received up to 1999
More data?
country dummy: fixed effects capturing also some dep var
other structural characteristics: need for data reduction
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

69. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

70. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Table of probabilities - ML estimates
final
init 0.6 0.741 0.834 0.922 1 1.07 1.13 1.22 1.38 Inf
0.6 0.828 0.172
0.741 0.050 0.750 0.150 0.050
0.834 0.200 0.440 0.200 0.120 0.040
0.922 0.381 0.381 0.095 0.095 0.048
1 0.538 0.308 0.077 0.038 0.038
1.0 0.435 0.087 0.043
1.13 0.091 0.318 0.273 0.318
1.22 0.036 0.036 0.250 0.250 0.321 0.107
1.38 0.042 0.250 0.625 0.083
Inf 0.040 0.280 0.680
ergodic 0.6 0.741 0.834 0.922 1 1.07 1.13 1.22 1.38 Inf
0.05 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)

71. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

72. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Results with 4 classes
ML estimates
Forward 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)

73. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Results with 10 classes
ML estimates
Forward 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)

74. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Results with NMS regimes - Forward
ML estimates
NMS 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)

75. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Results with NMS regimes - Backward
ML estimates
NMS 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)

76. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Convergence analysis
ML estimates
Forward 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)

77. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Outline
1 Introduction and Research Question
Motivation
Background
Research Question
2 The Model
The Multinomial Response Model
Binary response model
3 Results
Transition probabilities
Regression Output
Conclusion
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

78. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Summary of Results
Regional development in Europe is characterized by
divergence - low mobility of regions
Relative importance of infrastructure, agglomeration
economies and labor market
Short run vs Long run effect
Not only benefits
Strong relevance of Human capital and technological
infrastructures
Structural changes in NMS
several evidences supporting the role of funds
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

79. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Summary of Results
Regional development in Europe is characterized by
divergence - low mobility of regions
Relative importance of infrastructure, agglomeration
economies and labor market
Short run vs Long run effect
Not only benefits
Strong relevance of Human capital and technological
infrastructures
Structural changes in NMS
several evidences supporting the role of funds
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

80. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Summary of Results
Regional development in Europe is characterized by
divergence - low mobility of regions
Relative importance of infrastructure, agglomeration
economies and labor market
Short run vs Long run effect
Not only benefits
Strong relevance of Human capital and technological
infrastructures
Structural changes in NMS
several evidences supporting the role of funds
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

81. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Summary of Results
Regional development in Europe is characterized by
divergence - low mobility of regions
Relative importance of infrastructure, agglomeration
economies and labor market
Short run vs Long run effect
Not only benefits
Strong relevance of Human capital and technological
infrastructures
Structural changes in NMS
several evidences supporting the role of funds
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

82. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Summary of Results
Regional development in Europe is characterized by
divergence - low mobility of regions
Relative importance of infrastructure, agglomeration
economies and labor market
Short run vs Long run effect
Not only benefits
Strong relevance of Human capital and technological
infrastructures
Structural changes in NMS
several evidences supporting the role of funds
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

83. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Summary of Results
Regional development in Europe is characterized by
divergence - low mobility of regions
Relative importance of infrastructure, agglomeration
economies and labor market
Short run vs Long run effect
Not only benefits
Strong relevance of Human capital and technological
infrastructures
Structural changes in NMS
several evidences supporting the role of funds
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

84. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Summary of Results
Regional development in Europe is characterized by
divergence - low mobility of regions
Relative importance of infrastructure, agglomeration
economies and labor market
Short run vs Long run effect
Not only benefits
Strong relevance of Human capital and technological
infrastructures
Structural changes in NMS
several evidences supporting the role of funds
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

85. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Policy relevance
Human Capital is the real driver of development
Be careful with interpretation of infrastrucure
Finland and Sweden have high growth but not so many
infrastructures
In NMS infrastructures are important!
Need for a more detailed analysis of funds and cohesion
policy
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

86. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Policy relevance
Human Capital is the real driver of development
Be careful with interpretation of infrastrucure
Finland and Sweden have high growth but not so many
infrastructures
In NMS infrastructures are important!
Need for a more detailed analysis of funds and cohesion
policy
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

87. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Policy relevance
Human Capital is the real driver of development
Be careful with interpretation of infrastrucure
Finland and Sweden have high growth but not so many
infrastructures
In NMS infrastructures are important!
Need for a more detailed analysis of funds and cohesion
policy
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

88. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Policy relevance
Human Capital is the real driver of development
Be careful with interpretation of infrastrucure
Finland and Sweden have high growth but not so many
infrastructures
In NMS infrastructures are important!
Need for a more detailed analysis of funds and cohesion
policy
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)

89. Introduction and Research Question Transition probabilities
The Model Regression Output
Results Conclusion
Policy relevance
Human Capital is the real driver of development
Be careful with interpretation of infrastrucure
Finland and Sweden have high growth but not so many
infrastructures
In NMS infrastructures are important!
Need for a more detailed analysis of funds and cohesion
policy
Fabrizi-Guastella-Timpano Determinats of Regionals Convergence (Divergence)