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Predicting European Enlargement Impacts: A Framework of Interregional General Equilibrium

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  • Artis Kancs

Abstract

Although the Computable General Equilibrium (CGE) model is not a new tool in analyzing policy impact, it has not yet gained wide popularity in regional applications such as rural economies. This study demonstrates, however,that a regional CGE model can be a quite useful regional development planning tool for analyzing the impacts of changes in global economic conditions as well as for assessing the interregional and intersectoral implications of potential policy changes even with limited computational resources and lacking a full range of regional economic data required by a formal CGE analysis. In our empirical analysis we have found that the rural economies of the Central and Eastern European (CEE) accession countries have to expect the largest welfare gains from integration into the European Union (EU) in the case of gradual market opening in comparison with the continuation of current policy and the complete liberalization of markets. Because agricultural markets are highly protected in the EU, above all, the rural regions of the CEE countries will gain from integration into the EU.

Suggested Citation

  • Artis Kancs, 2001. "Predicting European Enlargement Impacts: A Framework of Interregional General Equilibrium," Eastern European Economics, Taylor & Francis Journals, vol. 39(5), pages 31-63, September.
    • Handle: RePEc:mes:eaeuec:v:39:y:2001:i:5:p:31-63
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    Cited by:

    1. Chen, Xi & Zhou, Bin & Zhong, Funing, 2010. "Do Consumers Really Care about Genetically Modified (GM) Food Label? What Do We Know? What Else Should We Know?," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 53(2), pages 32-56.
    2. Niamir, Leila & Filatova, Tatiana & Voinov, Alexey & Bressers, Hans, 2018. "Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes," Energy Policy, Elsevier, vol. 118(C), pages 325-345.
    3. Zhang, Yan & Xia, Guoping, 2010. "Short-run cost-based pricing model for a supply chain network," International Journal of Production Economics, Elsevier, vol. 128(1), pages 167-174, November.
    4. Leila Niamir & Gregor Kiesewetter & Fabian Wagner & Wolfgang Schöpp & Tatiana Filatova & Alexey Voinov & Hans Bressers, 2020. "Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions," Climatic Change, Springer, vol. 158(2), pages 141-160, January.
    5. James Anderson, 2001. "Migration, FDI, and the Margins of Trade," EERI Research Paper Series EERI_RP_2001_05, Economics and Econometrics Research Institute (EERI), Brussels.
    6. Johannes Brocker & d’Artis Kancs, 2001. "Methodology for the Assessment of Spatial Economic Impacts of Transport Projects and Policies," EERI Research Paper Series EERI_RP_2001_03, Economics and Econometrics Research Institute (EERI), Brussels.
    7. Boppana Nagarjuna & Varadi Vijay Kumar, 2010. "Heat waves or Meteor showers: Empirical evidence from the stock markets," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 53(2), pages 57-74.

    More about this item

    JEL classification:

    • D51 - Microeconomics - - General Equilibrium and Disequilibrium - - - Exchange and Production Economies
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R13 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General Equilibrium and Welfare Economic Analysis of Regional Economies
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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