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الآثار الإقتصادية للتجارة الخارجية بين مصر والكوميسا بإستخدام نموذج الجاذبية للتحليل المكانى
[Economic Impact for Trade Between Egypt and COMESA By Using Gravity Model of Spatial Analysis]

Author

Listed:
  • Shehata, Emad Abd Elmessih

Abstract

Common Market for Eastern and Southern Africa (COMESA) is considered one of the most economic blocks in Africa, where membership includes nineteen countries, including Egypt since the mid-1998, and aims to increase the prospects for cooperation and increase trade between COMESA countries. Research problem concerned with that, volume of trade exchange between Egypt and COMESA is relatively small, which will reflect difficulty reducing the increasing in trade deficit, and also difficulty to obtain foreign exchange which is necessary for economic development. Objective of research was how to increase the volume of trade exchange between Egypt and COMESA, in the light of regional and spatial association among them, and to identify the most important factors affecting the foreign trade of Egypt with COMESA, moreover, standing on the countries which are responsible for increasing or decreasing Egypt's exports or imports. Gravity models via spatial analysis were estimated, via tobit random effect in the case of general spatial autocorrelation (SAC) model during (1995-2010), and using spatial weight matrix that reflects borders among neighboring countries. Results of both basic and augmented gravity models for exports, showed that Libya, Sudan, Ethiopia, and Kenya, are responsible for increasing Egypt's exports, while Djibouti, Mauritius, and Zambia, are responsible for decreasing Egypt's exports, which may be due to too long distance between Egypt, and Zambia or Mauritius, resulting in high transport costs, and low GDP in Djibouti. Results indicated also that at high level of per capita GDP for citizen of countries studied, demand on Egypt exports decrease, which may imply that Egyptian commodity is an inferior goods in the African markets in this case. Results of basic gravity model for imports, indicated to that Libya, Kenya, and Mauritius are responsible for increasing Egypt's imports, while: Sudan, Ethiopia, Djibouti and Zambia are responsible for decreasing Egypt's imports. Finally, results of augmented gravity model for imports, indicated that there is an inverse relationship between per capita GDP in Egypt and per capita GDP in COMESA and geographical distance with Egypt's imports, it turns out that increasing per capita GDP in: Sudan, Kenya, and Mauritius leads to increase Egypt's imports from them. While increasing per capita GDP in: Libya, Ethiopia, Djibouti, and Zambia leads to a decrease in Egypt's imports from them. The study recommended to develop infrastructure projects, and improve tools of transport, particularly with neighboring countries, i.e., Libya and Sudan, as well as export goods and services. Moreover, Egypt should take into account the taste of the African consumer and quality requirements, studying the internal market for COMESA countries and establishment common area of investment.

Suggested Citation

  • Shehata, Emad Abd Elmessih, 2011. "الآثار الإقتصادية للتجارة الخارجية بين مصر والكوميسا بإستخدام نموذج الجاذبية للتحليل المكانى [Economic Impact for Trade Between Egypt and COMESA By Using Gravity Model of Spatial Analysis]," MPRA Paper 43395, University Library of Munich, Germany, revised Dec 2011.
  • Handle: RePEc:pra:mprapa:43395
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    References listed on IDEAS

    as
    1. Emad Abd Elmessih Shehata, 2012. "SPMSTARXT: Stata module to Estimate (m-STAR) Spatial Panel Multiparametric Spatio Temporal AutoRegressive Regression Models," Statistical Software Components S457389, Boston College Department of Economics, revised 26 Jan 2013.
    2. Michele FRATIANNI, 2007. "The Gravity Equation in International Trade," Working Papers 307, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Emad Abd Elmessih Shehata, 2012. "SPXTTOBIT: Stata module to estimate Tobit Spatial Panel Autoregressive Generalized Least Squares Regression," Statistical Software Components S457422, Boston College Department of Economics, revised 26 Jan 2013.
    4. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    5. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
    6. Emad Abd Elmessih Shehata, 2011. "GS2SLSXT: Stata module to estimate Generalized Spatial Panel Autoregressive Two-Stage Least Squares Regression," Statistical Software Components S457386, Boston College Department of Economics, revised 21 Dec 2012.
    7. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    8. Emad Abd Elmessih Shehata, 2012. "LMCOVXT: Stata module to Compute Breusch-Pagan Lagrange Multiplier Diagonal Covariance Matrix Test for Panel Data," Statistical Software Components S457412, Boston College Department of Economics, revised 02 Feb 2014.
    9. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    10. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    11. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    COMESA; Gravity; Spatial Econometrics; Stata; SAR; SEM; SDM; SAC; mSTAR;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade

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