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International competitiveness and investment: simulations with a bilateral trade model

Author

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  • Rossella Bardazzi

    (Dipartimento di Scienze per l'Economia e l'Impresa)

  • Leonardo Ghezzi

Abstract

The Eurozone crisis has exposed several weaknesses of the European Monetary Union economies. Slow productivity growth and competitiveness losses on international markets have been growing since the beginning of the 2000s and became evident during the recent downturn. A policy action to increase capital stock accumulation through investment could generate a double dividend: increasing domestic demand and stimulating the competitive position of European economies on international markets. This paper aims to assess the impact of an expansionary capital stock policy on the external competitiveness of EU. The analysis employs a Bilateral Trade Model built at INFORUM with several distinguishing characteristics: a comprehensive bilateral dataset, econometric estimation of key parameters, and emphasis on sectoral details. Our findings show that a capital stock increase is effective in enhancing EU trade shares although differences between sectors and markets are significant in two key destinations of European commodity exports: China and the US.

Suggested Citation

  • Rossella Bardazzi & Leonardo Ghezzi, 2017. "International competitiveness and investment: simulations with a bilateral trade model," Working Papers - Economics wp2017_01.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    • Handle: RePEc:frz:wpaper:wp2017_01.rdf
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    More about this item

    Keywords

    Bilateral trade; multisectoral modelling; EU competitiveness; policy simulation;
    All these keywords.

    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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