Spatial data analysis, multiregional modeling and macroeconomic growth
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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

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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

  • 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)


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


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


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


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


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)


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)


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


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

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

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

The spatial distribution of US innovations, 1982


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. 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


Local Moran statistics


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. 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. 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


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

  • 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

  • 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

  • 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

  • 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

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

  • 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

  • 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

  • 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

  • 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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