IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/43395.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/43395/1/MPRA_paper_43395.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    9. 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.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    2. Álvarez, Inmaculada C. & Barbero, Javier & Zofío, José L., 2017. "A Panel Data Toolbox for MATLAB," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i06).
    3. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
    4. Debarsy, Nicolas & Ertur, Cem, 2010. "Testing for spatial autocorrelation in a fixed effects panel data model," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 453-470, November.
    5. Wu, Jianhong & Li, Guodong, 2014. "Moment-based tests for individual and time effects in panel data models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 569-581.
    6. Álvarez, Inmaculada C. & Barbero, Javier & Zofío, José L., 2016. "A spatial autoregressive panel model to analyze road network spillovers on production," Transportation Research Part A: Policy and Practice, Elsevier, vol. 93(C), pages 83-92.
    7. Sarafidis, Vasilis & Yamagata, Takashi & Robertson, Donald, 2009. "A test of cross section dependence for a linear dynamic panel model with regressors," Journal of Econometrics, Elsevier, vol. 148(2), pages 149-161, February.
    8. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    9. Li, Kunpeng, 2017. "Fixed-effects dynamic spatial panel data models and impulse response analysis," Journal of Econometrics, Elsevier, vol. 198(1), pages 102-121.
    10. Amiri, Hossein & Samadian, Farzaneh & Yahoo, Masoud & Jamali, Seyed Jafar, 2019. "Natural resource abundance, institutional quality and manufacturing development: Evidence from resource-rich countries," Resources Policy, Elsevier, vol. 62(C), pages 550-560.
    11. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    12. Berit Gerritzen, 2016. "Women's Empowerment and HIV Prevention in Rural Malawi," Feminist Economics, Taylor & Francis Journals, vol. 22(3), pages 1-25, July.
    13. Jacobs, J.P.A.M. & Ligthart, J.E. & Vrijburg, H., 2009. "Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors," Other publications TiSEM d473cc67-03f6-4389-9a9f-3, Tilburg University, School of Economics and Management.
    14. Fingleton, Bernard & Szumilo, Nikodem, 2019. "Simulating the impact of transport infrastructure investment on wages: A dynamic spatial panel model approach," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 148-164.
    15. Xuan Liang & Jiti Gao & Xiaodong Gong, 2022. "Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1784-1802, October.
    16. Su, Liangjun & Yang, Zhenlin, 2015. "QML estimation of dynamic panel data models with spatial errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 230-258.
    17. Fukasawa, Eiji & Fukasawa, Takeshi & Ogawa, Hikaru, 2020. "Intergovernmental competition for donations: The case of the Furusato Nozei program in Japan," Journal of Asian Economics, Elsevier, vol. 67(C).
    18. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    19. Harald Badinger & Peter Egger, 2009. "Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models," CESifo Working Paper Series 2556, CESifo.
    20. Bai, Jushan & Li, Kunpeng, 2013. "Spatial panel data models with common shocks," MPRA Paper 52786, University Library of Munich, Germany, revised 09 Mar 2014.

    More about this item

    Keywords

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

    JEL classification:

    • F1 - International Economics - - Trade

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:43395. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.