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Spatial data analysis, multiregional modeling and macroeconomic growth by Attila Varga Center for Research in Economic Policy (GKK) and Department of Economics University of Pécs, Hungary. Introduction. A -spatial mainstream growth theory

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Spatial data analysis multiregional modeling and macroeconomic growth by attila varga

Spatial data analysis, multiregional modeling and macroeconomic growth

by

Attila Varga

Center for Research in Economic Policy (GKK)

and

Department of Economics

University of Pécs, Hungary


Introduction

Introduction

  • A-spatial mainstream growth theory

  • K, L and A only? How about their spatial arrangements?

  • Why should we care about space?

    - Transport cost (evident, but can be integrated)

    - Spatial externalities (requires a different approach)

  • Policy relevance (EU)


Spatial data analysis multiregional modeling and macroeconomic growth by attila varga

Outline

  • Introduction

  • Technological progress, spatial structure and macroeconomic growth – An empirical modeling framework

  • Geographical growth studies - methodological issues:

    • Dependence in space: Spatial data analysis in knowledge spillover research

    • Spatial macro modeling: Integrating macro and regional levels

    • Endogenizing spatial structure

  • Summary


T echnological progress spatial structure and macroeconomic growth

Technological progress, spatial structure and macroeconomic growth

Complex issue treated in three separate fields of economics:

A. EG: “Endogenous economic growth” models: endogenized technological change in growth theory (Romer 1986, 1990, Lucas 1986, Aghion and Howitt 1998)

in Romer (1990):

  • for-profit private R&D

  • knowledge spillovers and growth

  • rate of technical change equals rate of per-capita growth on the steady state

  • Simplistic explanation of technological progress, no geography


Spatial data analysis multiregional modeling and macroeconomic growth by attila varga

Technological progress, spatial structure and macroeconomic growth

B. IS: „Systems of innovation”literature: innovation is an interactive process among actors of the system (Lundval 1992, Nelson 1993)

actors of the IS:

- innovating firms

- suppliers, buyers

- industrial research laboratories

- public (university) research institutes

- business services

- “institutions”

level of innovation depends on:

- the knowledge accumulated in the system

- the interactions (knowledge flows) among the actors

- codified, non-codified (tacit) knowledge and the potential significance of spatial proximity

- does not say anything about geography and growth


Spatial data analysis multiregional modeling and macroeconomic growth by attila varga

Technological progress, spatial structure and macroeconomic growth

C. NEG: “New economic geography”models: endogenized spatial economic structure in a general equilibrium model (Krugman 1991, Fujita, Krugman andVenables 1999, Fujita andThisse 2002)

- spatially extended Dixit-Stiglitz framework

- increasing returns, monopolistic competition

- spatial structure depends on some parameter conditions that determine the equilibrium level of centrifugal and centripetal forces

- „cumulative causation”

- C-P model by Krugman: still the point of departure

- models quickly become complex: simulations if analytical solutions are not accessible

- Technological change not explained (not even included until very recently), the study of its relation to growth is a recent phenomenon


T echnological progress spatial structure and macroeconomic growth1

Technological progress, spatial structure and macroeconomic growth

  • Theoretical integration: endogenous growth and new economic geography (Baldwin and Forslid 2000, Fujita and Thisse 2002, Baldwin et al. 2003)

  • EG, IS, NEG: methodological problems in THEORETICAL integration (dramatically diverging initial assumptions, different theoretical structures, research methodologies)

  • EMPIRICALintegration: very few work (Ciccone and Hall 1996, Varga and Schalk 2004, Acs and Varga 2004)


T echnological progress spatial structure and macroeconomic growth an empirical modeling framework

Technological progress, spatial structure and macroeconomic growth: an empirical modeling framework

  • Starting points:

  • Technological change is a collective process that depends on accumulated knowledge and interactions (IS)

  • Technological change is the simple most important determinant of economic growth (EG)

  • Codified and tacit knowledge: different channels of spillovers (the „geography of innovation” literature)

  • Centripetal and centrifugal forces shape geographical structure via cumulative processes (NEG)

  • The resulting geographic structure is a determinant of the rate of growth (NEG)


T echnological progress spatial structure and macroeconomic growth an empirical modeling framework1

Technological progress, spatial structure and macroeconomic growth: an empirical modeling framework

  • Y = AKαLβ(EG)

  • The Romer (1990) equation as in Jones (1995)

    dA =  HAAφ,

    - HA: the number of researchers(“person-embodied”, codifiable/tacit knowledge component of knowledge production)

    - A: the total stock of technological knowledge (codified knowledge component of knowledge production)

    - dA: the change in technological knowledge

    - : the “research productivity parameter”(0<<1)

    φ: “codified knowledge spillovers parameter”

    - reflects spillovers with unlimited spatial accessibility

    : the “research spillovers parameter”

    - reflects localized knowledge spillover effects

    - regional and urban economics and the new economic geography suggest:  increases with geographic concentration of economic activities


T echnological progress spatial structure and macroeconomic growth an empirical modeling framework2

Technological progress, spatial structure and macroeconomic growth: an empirical modeling framework

Eq.1 Regional knowledge production

Kr = K (RDr, URDr, Zr)

Eq.2 Agglomeration effect – RD spillovers

∂Kr/∂RDr = f (RDr, URDr, Zr)

Eq.3 R&D location

dRDr = R(∂Kr/∂RDr)

Eq.4 Geography and 

 =  (GSTR(HA))

Eq.5 dA =  HAγ Aφ

Eq.6 dy/y = H(dA, ZN)


Empirical research on geography technology and growth 1986 2004

Empirical research on geography, technology and growth: 1986-2004

1986-2004: 253 papers on the geography of knowledge spillovers

journal articles: 175

books, book chapters, working papers: 78


Geographical growth studies methodological issues

Geographical growth studies - methodological issues


Geographical growth studies methodological issues1

Geographical growth studies - methodological issues

I. Dependence in space: Spatial data analysis in knowledge spillover research

II. Spatial macro modeling: Integrating macro and regional levels

III. Endogenizing spatial structure


I d ependence in space spatial data analysis in knowledge spillover research

I. Dependence in space: Spatial data analysis in knowledge spillover research

The spatial distribution of US innovations, 1982


I d ependence in space spatial data analysis in knowledge spillover research1

I. Dependence in space: Spatial data analysis in knowledge spillover research

  • Tendency of innovation to cluster in space

  • Clustering is a consequence of dependence among spatial units

  • Spatial dependence makes traditional econometric techniques no longer appropriate (Anselin 1988, 2001)

  • Spatial data analysis:

    • Exploratory spatial data analysis (ESDA)

    • Spatial econometrics


I d ependence in space spatial data analysis in knowledge spillover research2

I. Dependence in space: Spatial data analysis in knowledge spillover research

  • ESDA: global and local measures of spatial dependence

  • Global measures – general form:

    G = Si,j wij cij

  • Local measures:

    • Moran Scatterplot

    • Local Moran


Moran scatterplot

Moran Scatterplot


Local moran statistics

Local Moran statistics


I d ependence in space spatial data analysis in knowledge spillover research3

I. Dependence in space: Spatial data analysis in knowledge spillover research

  • Spatial econometrics: models with high intuitive value to study spatial knowledge spillovers

  • Basis: innovation equation in a form of a classical linear regression:

    y = Xb + e

    where: y: innovation output; x inputs to innovation

  • Modeling geographical spillovers – two main issues (Anselin 2003):

    A. their spatial extent (local or global)

    B. direct or indirect modeling


I d ependence in space spatial data analysis in knowledge spillover research4

I. Dependence in space: Spatial data analysis in knowledge spillover research

  • Modeling the spatial extent of spillovers:

    A.1. global autocorrelation modelling

    e = lWe + u = [I - lW]-1 u

    A.2. local autocorrelation modelling

    e = [I + gW] u


I d ependence in space spatial data analysis in knowledge spillover research5

I. Dependence in space: Spatial data analysis in knowledge spillover research

  • Direct or indirect modelling – the most commonly used solutions:

    B.1. Direct modelling (the „spatial lag model”):

    y= (I - rW)-1 Xb + (I-lW)-1 u = rWy + Xb + u

    B.2. Indirect modelling (the „spatial error model”)

    y= Xb + (I-lW)-1 u


The facts spatial econometrics in empirical innovation research

The facts: spatial econometrics in empirical innovation research


Spatial econometrics facts needs and opportunities

Spatial econometrics: Facts, needs and opportunities

  • Urgent need for extending the toolbox:

    spatial logit, probit, Tobit, Poisson, panel

  • User-friendly softwares with support

  • New intermediate level textbook with applications


Ii spatial macro modeling integrating macro and regional levels

II. Spatial macro modeling: Integrating macro and regional levels

  • Q: how to integrate eqs (1) to (3) (regional level) with eqs (5) and (6) by eq (7) empirically?

  • An example: the EcoRET model (Schalk and Varga 2004, Varga and Schalk 2004)


Ecoret the main characteristics

EcoRET: The main characteristics

  • macroEconometric model with Regionally Endogenized Technological change

  • General features (cost minimization; vintage capital production function; technology and labor/capital demand, output; goods markets; final demand)

  • Geography and technology development: the conceptual basis

    -New economic geography

    -Endogenizing technological change in “endogenous economic growth” models (Romer 1986, 1990, Lucas 1986, Aghion and Howitt 1998)

    -The geography of knowledge spillovers (Jaffe, Trajtenberg and Henderson 1993, Audretsch and Feldman 1996, Anselin, Varga and Acs 1997)


Ecoret the modeling framework

EcoRET: The modeling framework

  • Structure of EcoRET – four blocks:

  • The supply side block (labor market, production, productivity, investment, employment and unemployment, production costs, inflation)

  • The demand side block (behavioral relationship of private households, consumption, and other components of final demand (government consumption, foreign trade etc.) in real and nominal terms and their deflators)

  • The income distribution block (determining private and government income - labor and property income, profits - and the transfers of income between private households and the government - taxes, social security and other transfers)

  • The Total Factor Productivity (TFP) block (modeling changes in regional level TFP as a function of certain knowledge-related variables as well as CSF measures such as promotion of physical infrastructure and human capital)

    EcoRET consists of: 106 variables, 32 of them are explained by behavioral or technical relationships, 16 variables are exogenous while the remainder of the endogenous variables is explained by definitional identities


Ecoret data and estimation

EcoRET: Data and estimation

  • Various Hungarian (Hungarian Central Statistical Office, Hungarian Patent Office) and international (OECD, IMF) data sources

  • For the period of 1990 - 2000

  • Units of observation:

    - country (macromodel)

    - counties (technology model)

  • Parameters

    - estimation/calibration (macromodel)

    - pooled estimation (technology model)


Ecoret the regional tfp block

EcoRET: The regional TFP block

The estimated regional model of technological change

TFPGR = α0 + α1KNAT + α2RD+ α3KIMP + α4INFRAINV + α5HUMCAPINV + ε,

TFPGR: the annual rate of growth of Total Factor Productivity (TFP),

KNAT: domestically available technological knowledge accessible with no geographical restrictions (measured by stock of patents),

RD: private and public regional R&D,

KIMP: imported technologies (measured by FDI),

INFRAINV: investment in physical infrastructure,

HUMCAPINV: investment in human capital,

region i and time t

α1 estimates domestic knowledge spillover effects

α2 estimates localized (regional) knowledge spillover effects

α3 estimates international knowledge spillover effects


Ecoret linking the tfp block to the rest of ecoret in policy simulations

EcoRET: Linking the TFP block to the rest of EcoRET in policy simulations

  • Problem:

    -Macro blocks: time series estimation

    -TFP block: time-space data

  • Literature: agglomeration and technological change (Feldman 1994, Fujita and Thisse 2002, Varga 2000)

  • Solution: weighted averaged county TFP growth rates (Excellent historical forecast of national level TFP!)

  • The linkage:

    TFP = TFP-1eeDNTFPGR


Ecoret simulated effect of the geography of csf support on the national growth rate

EcoRET: Simulated effect of the geography of CSF support on the national growth rate

  • The ratios of the growth effects of concentrating CSF resources in:

  • leading areas (LEAD/LAG)

  • lagging areas (LAG/EQUAL)

  • equal distribution (LEAD/EQUAL)


Iii endogenizing spatial structure

III. Endogenizing spatial structure

  • Q: How to endogenize and integrate: equation (3), the R&D location equation, i.e., the long run spatial effects?

  • A promising solution is to integrate Spatial Computable Equilibrium (SCGE) models (to endogenize R&D distribution) with macroeconometric models to simulate the macroeconomic growth effects.


Summary

Summary

  • An empirical modeling framework is presented

  • Methodological reasons for a relative negligence of the spatial aspects of macroeconomic growth are reviewed:

    • Challenges in spatial data analysis

    • Difficulties in integrating regional and macro levels

    • Complications in endogenizing spatial structure in empirical macroeconomic growth models


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