Modelling Regional Growth: An Advanced MASST Model

Abstract

This paper presents a new version of a forecasting regional growth model called MASST (MAcroeconomic, Sectoral, Social, Territorial model). This new version is specifically designed to take account of sectoral and social (intangible) effects on the growth of regions, and it is therefore able to produce scenarios for all European regions according to differentiated industrial specializations and future industrial patterns of growth. The value added of this version of the model is threefold: (i) its capacity to show scenario results based on industrial resource reallocation; (ii) to measure the impacts of scenario assumptions on manufacturing and service employment separately; and (iii) its capacity to take intangible elements (like social capital) into account in regional macro-econometric growth models.

RÉSUMÉ Cette communication présente une nouvelle version d'un modèle de prévision de l'expansion régionale appelé MASST ( MAcroeconomic, Sectoral, Social, Territorial Model). Cette nouvelle version a été conçue spécifiquement pour tenir compte des effets sectoriels et sociaux (incorporels) sur l'expansion régionale, et est, par conséquent, en mesure de produire des scénarios pour toutes les régions d'Europe, d'après des spécialisations industrielles différentiées et des configurations industrielles futures de l'expansion. Cette version du modèle ajoute de la valeur à trois niveaux : (i) elle montre des résultats de scénarios sur la base d'une réaffectation des ressources industrielles ; (ii) elle a la mesure séparée des impacts des hypothèses sur l'emploi dans le secteur industriels et tertiaire ; (iii) elle a la capacité de tenir compte d’éléments de l'incorporel (capital social, par exemple) dans les modèles d'expansion macro-économique régionaux.

EXTRACTO Este estudio presenta una nueva versión de un modelo de previsión de crecimiento regional denominado MASST (Modelo MAcroeconómico, Sectorial, Social, Territorial). Esta nueva versión se ha diseñado específicamente para tener en cuenta los efectos sectoriales y sociales (intangibles) sobre el crecimiento de regiones y, por lo tanto, es capaz de producir panoramas para todas las regiones europeas según especializaciones industriales diferenciadas y modelos de crecimiento industrial futuro. El valor añadido de esta versión del modelo tiene tres aspectos: (i) su capacidad para mostrar resultados de panoramas basados en la reasignación de recursos industriales; (ii) medir por separado los impactos de las suposiciones de panoramas sobre el empleo en fabricación y servicios; (iii) su capacidad para tener en cuenta elementos intangibles (como el capital social) en modelos de crecimiento macroeconómico regional.

Keywords:

• Regional growth
• territorial capital
• forecasting
• scenario simulation

JEL CLASSIFICATION:

• R11
• R12

Acknowledgments

The authors are grateful to participants in the International Workshop ‘Modelling Innovation, Entrepreneurship and Regional Development’, Tinbergen Institute & VU University, Amsterdam, 17 and 18 May 2010, and to two anonymous referees, for their comments and suggestions.

Notes

1. Very recently, Ertur & Koch (2011) have also proposed an extension of the multi-country endogenous (Schumpeterian) growth model that includes technological interdependence between economies in order to take account of the neighborhood effects on growth and convergence processes.

2. There is much debate in the literature on the FDI and trade substitution/complementarity relationship (see Forte (2004) for a review of the issue). We assume in the model specification that complementarity exists, and see in the empirical results whether this assumption is verified.

3. The growth effects induced by the region's geographical position, whether it is close to fast-growing regions or close to regions unable to grow fast (spill), can be positive or negative depending on the role of neighbouring regions, which may induce growth through demand effects or steal it away through competition. The indicator used was a spatial growth spillover indicator for a generic region r, capturing economic potential (Clark et al., 1969) as the sum of the annual absolute difference between income growth rates of all other regions j divided by the distance between each region r and region j, defined as:

where ΔY _jt =income growth rate of region j at time t; j=all regions except region r; d _rj =physical distance between region r and j; n=all regions of the sample.

4. For the European Commission, vulnerable sectors are those with a negative trade balance, import penetration and growing import ratio, namely DB (Manufacture of textiles and textile products) + DC (Manufacture of leather and leather products); DJ (Manufacture of basic metals and fabricated metal products); DL (Manufacture of electrical and optical equipment); DN (Manufacturing NEC) (see for this definition also Affuso et al. (2011)). In addition to vulnerable sectors, two other sectors (DD, Manufacture of wood and wood products, and DE, Manufacture of pulp, paper and paper products, publishing and printing) were kept because their coefficients, which were significant with robust OLS estimation, remained very similar, though not significant, in the robust spatial lag model.

5. Once spatial dependence is corrected for, most coefficients lose statistical significance (Table 3). However, if direct and indirect effects for each regressor and the joint significance of all parameters associated with the regressors are calculated, they turn out to be non-significant. For all these reasons, coefficients of robust OLS method were inserted into the simulation exercise.

6. Although for some migration equations spatial lags appear to be stronger in the tests with respect to spatial errors, it was decided to use spatial errors models because spatial lag models are hard to justify theoretically in population growth equations.