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Aisre 2010

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Aisre 2010

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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)

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