Modeling Regional Endogenous Growth
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1 Modeling Regional Endogenous Growth A Structural Equation Model Approach Patricio Aroca 1, Robert Stimson 2 and Roger Stough 3 1 Universidad Católica del Norte 2 University of Queensland 3 George Mason University NEREUS International Workshop on Regional Modeling, Brazil, Sao Paulo, November 17th, 2011 Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
2 Outline 1 Evolution of modeling approaches 2 Framework: The Stimson-Stough-Salazar Model 3 Methodology 4 Framework: The Stimson-Stough-Salazar Model 5 Application to Australian FER 6 Conclusions Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
3 Evolution of modeling approaches Evolution of modeling approaches Traditional regional economic development approaches were embedded in neo-classical economic growth theory. Based on the Solow (1956, 2000) model. In the new growth theory, the focus is directed towards endogenous factors and processes. Romer (1986, 1990), Lucas 1988; Barro (1990), Rebelo (1991), Grossman & Helpman (1991), and Arthur (1994). Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
4 Evolution of modeling approaches Evolution of modeling approaches Traditional regional economic development approaches were embedded in neo-classical economic growth theory. Based on the Solow (1956, 2000) model. In the new growth theory, the focus is directed towards endogenous factors and processes. Romer (1986, 1990), Lucas 1988; Barro (1990), Rebelo (1991), Grossman & Helpman (1991), and Arthur (1994). Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
5 Evolution of modeling approaches Endogeneous Factors Rees (1979) and Malecki (1991) Technical progress as it generates economic development. Henderson, Kuncoro & Turner (1995) Gordon & McCann (2000) Regional industrial diversity and a trend to diversification enhance opportunities for regional growth and development. Hanushek & Kimko (2000) and Goetz & Rapasingla (2001) Human capital skills and income as important factors in explaining differential levels of regional economic performance. Taylor, Catalano & Gane (2002) and Duranton & Puga (2000) The power of urban scale and agglomeration on regional performance. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
6 Evolution of modeling approaches Endogeneous Factors Rees (1979) and Malecki (1991) Technical progress as it generates economic development. Henderson, Kuncoro & Turner (1995) Gordon & McCann (2000) Regional industrial diversity and a trend to diversification enhance opportunities for regional growth and development. Hanushek & Kimko (2000) and Goetz & Rapasingla (2001) Human capital skills and income as important factors in explaining differential levels of regional economic performance. Taylor, Catalano & Gane (2002) and Duranton & Puga (2000) The power of urban scale and agglomeration on regional performance. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
7 Evolution of modeling approaches Endogeneous Factors Rees (1979) and Malecki (1991) Technical progress as it generates economic development. Henderson, Kuncoro & Turner (1995) Gordon & McCann (2000) Regional industrial diversity and a trend to diversification enhance opportunities for regional growth and development. Hanushek & Kimko (2000) and Goetz & Rapasingla (2001) Human capital skills and income as important factors in explaining differential levels of regional economic performance. Taylor, Catalano & Gane (2002) and Duranton & Puga (2000) The power of urban scale and agglomeration on regional performance. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
8 Evolution of modeling approaches Endogeneous Factors Rees (1979) and Malecki (1991) Technical progress as it generates economic development. Henderson, Kuncoro & Turner (1995) Gordon & McCann (2000) Regional industrial diversity and a trend to diversification enhance opportunities for regional growth and development. Hanushek & Kimko (2000) and Goetz & Rapasingla (2001) Human capital skills and income as important factors in explaining differential levels of regional economic performance. Taylor, Catalano & Gane (2002) and Duranton & Puga (2000) The power of urban scale and agglomeration on regional performance. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
9 Evolution of modeling approaches Additional Endogenous Factors Leadership Judd & Parkinson (1990); Bryson & Crosby (1992); Fosler (1992); Hansen (1992); Fairholm (1994); de Santis & Stough (1999) Institutions Doig & Hargrove (1987); Gray (1989); North (1990); Fukuyama (1996); Putman (1993); Mouritzen & Svara (2000) Entrepreneurship Schumpeter (1934); Kirzner (1973); Acs (1999); Jessop (1998); Acs, Audretdsch, Braunerhjelm & Carlsson (2004); Audretsch & Kreilbach (2004); High (2004); Stough, Kulkarni & Paelinck (2004). Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
10 Evolution of modeling approaches Additional Endogenous Factors Leadership Judd & Parkinson (1990); Bryson & Crosby (1992); Fosler (1992); Hansen (1992); Fairholm (1994); de Santis & Stough (1999) Institutions Doig & Hargrove (1987); Gray (1989); North (1990); Fukuyama (1996); Putman (1993); Mouritzen & Svara (2000) Entrepreneurship Schumpeter (1934); Kirzner (1973); Acs (1999); Jessop (1998); Acs, Audretdsch, Braunerhjelm & Carlsson (2004); Audretsch & Kreilbach (2004); High (2004); Stough, Kulkarni & Paelinck (2004). Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
11 Evolution of modeling approaches Additional Endogenous Factors Leadership Judd & Parkinson (1990); Bryson & Crosby (1992); Fosler (1992); Hansen (1992); Fairholm (1994); de Santis & Stough (1999) Institutions Doig & Hargrove (1987); Gray (1989); North (1990); Fukuyama (1996); Putman (1993); Mouritzen & Svara (2000) Entrepreneurship Schumpeter (1934); Kirzner (1973); Acs (1999); Jessop (1998); Acs, Audretdsch, Braunerhjelm & Carlsson (2004); Audretsch & Kreilbach (2004); High (2004); Stough, Kulkarni & Paelinck (2004). Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
12 Framework: The Stimson-Stough-Salazar Model The Stimson-Stough-Salazar Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
13 Framework: The Stimson-Stough-Salazar Model Current Advances in Growth Theory Rodrick, D. (2008). Second Best Institutions, AER. May Entry regulations and entrepreneurship. Entrepreneurship can be suppressed for a variety of reasons. Entry costs may be high, property rights may not be well protected, the contracting environment may be poor (either because relational contracting does not work well or the courts are ineffective), or the perceived returns may be low. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
14 Framework: The Stimson-Stough-Salazar Model The Stimson-Stough-Salazar Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
15 Methodology Methodological Challenges Measurement There are good measures for traditional variables: output, labor, investment - capital. However, it is difficult to find good measures for variables o factor like Leadership, Institutions, Entrepreneurship among others. Endogeneity The new set of variable is highly correlated. In addition, it is influenced by the outcome which makes arise the endogeneity problem. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
16 Methodology Methodological Challenges Measurement There are good measures for traditional variables: output, labor, investment - capital. However, it is difficult to find good measures for variables o factor like Leadership, Institutions, Entrepreneurship among others. Endogeneity The new set of variable is highly correlated. In addition, it is influenced by the outcome which makes arise the endogeneity problem. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
17 Methodology Traditional Econometric Approach Regressions OLS corrected by autocorrelation, heterocedasticity, specification errors, etc. Instrumental Variables to tackle endogeneity GMM Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
18 Map Methodology Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
19 Framework: The Stimson-Stough-Salazar Model The Stimson-Stough-Salazar Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
20 Variables Framework: The Stimson-Stough-Salazar Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
21 Framework: The Stimson-Stough-Salazar Model OLS Results. Dependent Variable: Regional-Shift Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
22 Framework: The Stimson-Stough-Salazar Model Traditional Econometric Approach Simultaneous Equation System 2SLS, 3SLS FIML, LIML Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
23 Framework: The Stimson-Stough-Salazar Model Simultaneous Equation System Structural Form B yt + Γ xt = u t t = 1, 2,..., T. Reduced Form y t = Π xt + v t t = 1, 2..., T. W here Π = B 1 Γ and v t = B 1 u t Problems Endogeneity in the model Endogeneity by measurement problems Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
24 Framework: The Stimson-Stough-Salazar Model Simultaneous Equation System Structural Form B yt + Γ xt = u t t = 1, 2,..., T. Reduced Form y t = Π xt + v t t = 1, 2..., T. W here Π = B 1 Γ and v t = B 1 u t Problems Endogeneity in the model Endogeneity by measurement problems Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
25 Framework: The Stimson-Stough-Salazar Model Structural Equation Approach The Structural Model (Kolenikov et al, 2010 and Kaplan, 2009) η i = α η + βη i + τξ i + ζ i where η i is a vector of the latent endogenous variables, α η a vector of the intercept terms for the equation, β the matrix of coefficients giving the impact of the latent endogenous variable on each other, ξ i the vector of latent exogenous variables, τ the coefficient matrix giving the effects of the latent exogenous variables on the latent endogenous variables, and ζ i the vector of disturbances. [ η1 η 2 ] = [ αη1 α η2 ] + [ 0 0 β 21 0 ] [ ] η1 + η 2 [ γ11 γ 21 ] ξ 1 + [ ξ1 ξ 2 ] Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
26 Framework: The Stimson-Stough-Salazar Model Structural Equation Approach Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
27 Framework: The Stimson-Stough-Salazar Model The Measurement Model, (Kolenikov et al, 2010 and Kaplan, 2009) y i = α y + Λ y η i + ɛ i x i = α x + Λ x ξ i + δ i where y i and x i are vectors of the observed indicators of η i and ξ i,respectively; α y and α x are intercept vectors;λ y and Λ x are matrices of factor loadings or regression coefficients giving the impact of the latent η i and ξ i on y i and x i, respectively; and ɛ i and δ i are the unique factors of y i and x i. x = α x + Λ x ξ + δ x x 2 = α x2 + λ x21 ξ 1 + x 3 α x3 λ x31 δ 1 δ 2 δ 3 Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
28 Framework: The Stimson-Stough-Salazar Model Structural Equation Approach Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
29 Application to Australian FER Application to Australia Functional Economic Regions: Model Specification Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
30 Application to Australian FER SEM Stages Model Identification Model Estimation Model Fit Model Respecification Repeat until getting the best Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
31 Application to Australian FER SEM Stages Model Identification Model Estimation Model Fit Model Respecification Repeat until getting the best Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
32 Application to Australian FER SEM Stages Model Identification Model Estimation Model Fit Model Respecification Repeat until getting the best Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
33 Application to Australian FER SEM Stages Model Identification Model Estimation Model Fit Model Respecification Repeat until getting the best Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
34 Application to Australian FER SEM Stages Model Identification Model Estimation Model Fit Model Respecification Repeat until getting the best Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
35 Application to Australian FER Results for the Structural Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
36 Application to Australian FER Results for the Measurement Model Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
37 Application to Australian FER Correlations Among Factor R5 - Human Capital Measures R6 - Human Capital Changes Measures Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
38 Conclusions Conclusions Human Capital Factor in a region at the beginning of the period is the most important factor explaining variation in endogenous regional growth across FERs in Australia. Location attributes factor suggest that endogenous growth will be higher for metropolitan and other urban regions, and while still positive it will be lower for regions located on the coast. While this factor will be negative for regions in remote locations. The Population Change Variable tends to be dominated in some regions by the impact of internal migration flows, is well known to often be a driver of regional endogenous growth. If we compare this results with the ones obtained using traditional econometric methods for the same data, we get more information and a better understanding of the process that is generating the data. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
39 Conclusions Conclusions Human Capital Factor in a region at the beginning of the period is the most important factor explaining variation in endogenous regional growth across FERs in Australia. Location attributes factor suggest that endogenous growth will be higher for metropolitan and other urban regions, and while still positive it will be lower for regions located on the coast. While this factor will be negative for regions in remote locations. The Population Change Variable tends to be dominated in some regions by the impact of internal migration flows, is well known to often be a driver of regional endogenous growth. If we compare this results with the ones obtained using traditional econometric methods for the same data, we get more information and a better understanding of the process that is generating the data. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
40 Conclusions Conclusions Human Capital Factor in a region at the beginning of the period is the most important factor explaining variation in endogenous regional growth across FERs in Australia. Location attributes factor suggest that endogenous growth will be higher for metropolitan and other urban regions, and while still positive it will be lower for regions located on the coast. While this factor will be negative for regions in remote locations. The Population Change Variable tends to be dominated in some regions by the impact of internal migration flows, is well known to often be a driver of regional endogenous growth. If we compare this results with the ones obtained using traditional econometric methods for the same data, we get more information and a better understanding of the process that is generating the data. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
41 Conclusions Conclusions Human Capital Factor in a region at the beginning of the period is the most important factor explaining variation in endogenous regional growth across FERs in Australia. Location attributes factor suggest that endogenous growth will be higher for metropolitan and other urban regions, and while still positive it will be lower for regions located on the coast. While this factor will be negative for regions in remote locations. The Population Change Variable tends to be dominated in some regions by the impact of internal migration flows, is well known to often be a driver of regional endogenous growth. If we compare this results with the ones obtained using traditional econometric methods for the same data, we get more information and a better understanding of the process that is generating the data. Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
42 Conclusions Modeling Regional Endogenous Growth A Structural Equation Model Approach Patricio Aroca 1, Robert Stimson 2 and Roger Stough 3 1 Universidad Católica del Norte 2 University of Queensland 3 George Mason University NEREUS International Workshop on Regional Modeling, Brazil, Sao Paulo, November 17th, 2011 Aroca, Stimson and Stough (NEREUS) Modeling Regional Endogenous Growth Sao Paulo, Nov. 17th, / 27
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