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A Regionalized National Input-Output Modell for Chile (COFORCE) Methodology and Applications

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A Regionalized National Input-Output Modell for Chile (COFORCE) Methodology and Applications GWS DISCUSSION PAPER 2020/3 A regionalized national Input-Output Modell for Chile (COFORCE) Methodology and Applications Anett Großmann, Svenja Schwarz Frank Hohmann, Anke Mönnig GWS DISCUSSION PAPER 2020 / 3 Imprint AUTHORS Anett Großmann Tel.: +49 (541) 40933-180, E-Mail: [email protected] Svenja Schwarz Frank Hohmann Tel.: +49 (541) 40933-130, E-Mail: [email protected] Anke Mönnig Tel.: +49 (541) 40933-210, E-Mail: [email protected] TITLE A regionalized national Input-Output Modell for Chile (COFORCE) – Methodology and Applications PUBLICATION DATE © GWS mbH Osnabrück, Mai 2020 DISCLAIMER Opinions expressed in this paper are those of the author(s) and do not necessarily reflect the views of the GWS mbH. FUNDING The results at hand have been prepared in the course of a research project for the German Federal Ministry of Research and Education (BMBF FKZ: 01DN16030). PUBLISHER OF THE GWS DISCUSSION PAPER SERIES Gesellschaft für Wirtschaftliche Strukturforschung (GWS) mbH Heinrichstr. 30 49080 Osnabrück (Germany) ISSN 1867-7290 WWW.GWS-OS.COM 2 GWS DISCUSSION PAPER 2020 / 3 TABLE OF CONTENTS 1 Motivation 5 2 Approaches to economic regional modelling 6 2.1 General overview 6 2.2 Examples of regional economic modelling approaches 8 3 Regionalizing the national model COFORCE 9 3.1 Modelling Approach 10 3.1.1 Final demand (investments, private and government consumption, exports) 11 3.1.2 Intermediate demand, gross output and value added 12 3.1.3 Employment 13 3.2 Regional baseline results 13 3.2.1 Final demand by demanding regions 13 3.2.2 Interregional, cross-border Imports and the Degreee of self-sufficiency of the regions 17 3.2.3 Regional output multiplier, Output and employment 21 4 Scenario analysis 24 4.1 Graphical User Interface “solver” 25 4.2 Export scenario 26 4.2.1 General description of the scenario 26 4.2.2 Scenario settings 27 4.2.3 Scenario results 27 4.3 Adjusting the regional demand share indicators 36 4.3.1 General description of the scenario 36 4.3.2 Scenario settings 37 4.3.3 Scenario results 38 4.4 Adjusting the regional market share indicators 41 4.4.1 General description of the scenario 41 4.4.2 Scenario settings 43 4.4.3 Scenario results 43 5 Conclusions and outlook 45 WWW.GWS-OS.COM 3 GWS DISCUSSION PAPER 2020 / 3 Annex 47 References 52 WWW.GWS-OS.COM 4 GWS DISCUSSION PAPER 2020 / 3 1 MOTIVATION The research project “Development of sustainable strategies in the Chilean mining sector through a regionalized national model” – funded by the German Federal Ministry of Educa- tion and Research – analyses the socio-economic impacts of copper on the Chilean econ- omy. For this, the model COFORCE (COpper FORecasting in ChilE, www.coforce.cl) was developed from scratch. First, a macro-econometric input-output (IO) model for Chile (COFORCE) was built in line with the INFORUM (Interindustry FORcasting at the University of Maryland) modelling ap- proach to forecast and simulate the impact of copper industry on the overall economy. Second, due to the importance of Chilean copper exports, the COFORCE model is linked to the bilateral trade model TINFORGE which captures among other world trade of copper between 153 countries. The national COFORCE model receives export demand and import prices from the world model according to its global market shares. Third, the COFORCE model was regionalized by using an Interregional Input-Output table developed by partners in Brazil (Haddad et al. 2018). The national and 15 regional models for Chile are linked via final demand components and industries by applying a top-down approach. Therefore, regional economic growth is mainly driven by the industry structure and inter- and intraregional trade. This set of three projection and simulation models considers the main aspects regarding copper: 1. It is the main exporting product, 2. It has a huge impact on the economic devel- opment and 3. The copper industry is regionally differently concentrated. The modelling tools are applied for the evaluation of alternative economic scenarios, e. g. copper export scenarios at the national and subnational level. The main focus of this paper is to introduce the methodology used to regionalize the national model COFORCE, to explain the main transmission channels and to present regional mod- elling results. The national model COFORCE and the underlying model philosophy and characteristics are explained in detail in Mönnig/Bieritz 2019. Section 4 shows examples of applications and how to implement scenarios by using the graphical user interface solver(c) (see section 4.1). which includes the underlying data set (historic data and forecasted) and supports the user in scenario design, calculation and evaluation.1 This paper ends with some conclusions and an outlook. 1 The COFORCE model is handed over to the Chilean partners from the University Adolfo Ibañez in Viña del Mar, so that they can carry out scenario analyses independently by using solver© WWW.GWS-OS.COM 5 GWS DISCUSSION PAPER 2020 / 3 2 APPROACHES TO ECONOMIC REGIONAL MODELLING 2.1 GENERAL OVERVIEW According to Miller/Blair (2009), regional modelling is an important feature to consider as the structure of production in every region is specific and the smaller the region the more it depends on exports and imports. As regions have a strong interaction and interdependence with each other, spill over and feedback effects occur. National economic or trade policies have different impacts on different regions. Thus, regional modelling is an important tool for policymakers to determine economic effects at subnational level. An important prerequisite for quantitative models is data. At national level, there is usually plenty of data available, at subnational level often only a limited set of data. Thus, the start- ing point for regional modelling is an economic model that represents the economy as a whole but neglects the regional specifics (such as size, economic structure, technology of production) and the interactions between regions. The approach chosen for regionalization is highly dependent on data availability. Simple regionalization approaches use measures of spatial concentration and regional specializa- tion (e. g. location quotient, Krugman-and Hirschman-Herfindahl indices, shift-share analy- sis) to derive the regional distribution of industries. Common indicators are production and employment by regions. Spatial concentration means the agglomeration of companies or employees in a sector in one or a few regions, while regional specialization describes the focus of one region on one or a few industries. Reasons for the concentration and special- ization are usually not explained. Furthermore, interconnections between regions are ne- glected and spill over and feedback effects ignored. Nevertheless, the identified spatial pat- terns are used for the regionalization of national data. Other, more sophisticated, regional methods are the approach of the shift-share analysis (Dunn 1960) and regional IO analysis (Miller/Blair 2009). The former approach analyses the relationship between regions, taking into account their importance and distance. Shift-share analysis examines the reasons for regional changes which is differentiated by overall eco- nomic factors (“share” effect), structural and specific locational factors (“shift” effect). Com- prehensive regional IO approaches model the economic relations between regions and in- dustries (e. g. (multi- and inter-) regional IO models). Since 1950, different types of regional IO models were developed which differ with respect to (i) the number of regions taken into account and (ii) the consideration of as well as the degree of detail in interregional trade (Miller/Blair 2009, p. 69-118, Sargento 2009). (i) Regional models can be distinguished by the number of regions. Single-region IO models comprise only one region of a nation ignoring regional interactions and therefore spillover and feedback effects. Interregional and multi-regional IO models consider two or more regions. WWW.GWS-OS.COM 6 GWS DISCUSSION PAPER 2020 / 3 (ii) Models with more than one region capture usually2 interactions and feedback effects between the regions. Trade flows can be modelled following e. g. the method of Isard (IRIO) or Chenery-Moses (Sargento 2009, Miller/Blair 2009) while the latter approach (MRIO) is a simplified and less data-intensive version of the Isard model by applying the import proportionality assumption. A major prerequisite of a many-region IO model is a national IO table which serves as a starting point for regional modelling. The following two steps are the main issues in region- alizing national input-output tables: (a) the calculation of regional technical coefficients (no distinction between domestic production and imports) or regional input coefficients (con- sider only domestic flows) and (b) modelling of interregional trade flows. The required techniques can be distinguished as survey-based, non-survey-based and hy- brid approaches. Survey-based models demand a high requirement of data – which are partly difficult to obtain – but are most accurate compared to non-survey methods due to the use of real data. The disadvantages are – aside from being cost and time intensive – the possible errors related to data gathering and processing (Sargento 2009). Hybrid ap- proaches combine both concepts to overcome the hurdles and at the same time combine the advantages of each approach (e. g. RIMS II, BEA 2013). Non-survey approaches which deal with the regionalization of input coefficients and trade are amongst others the location quotients (LQ) approach and its extensions (e. g. pur- chases-only-LQ, cross-industry-LQ, Flegg location quotient 3 in Schröder/Zimmermann 2014, Flegg/Webber 1995) as well as the Supply-Demand-Pool (SDP) method and the cross-hauling adjusted regionalization method (CHARM, Többen 2017, pp. 7-32). While the CHARM and Fleggs’ location quotient regionalize the national IO table and interregional trade step by step and care for cross hauling, LQ and Supply-Demand-Pool methods con- sider only net trade flows.
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