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Discrepancies Between Mirror Data on Intra-Community Trade: The Case of Poland

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  • Markowicz Iwona

    (University of Szczecin, Szczecin, Poland)

  • Baran Paweł

    (University of Szczecin, Szczecin, Poland)

Abstract

The content presented in the article is a continuation of the research on the quality of data concerning intra-Community trade. Several measures used in literature to assess differences between mirror data are presented. The research was inspired by the works by Morgenstern (1963), Federico and Tena (1991), and Ferrantino and Wang (2008). These previous works contain some directions on how Intrastat data should be analysed. Based on this the analysed data on intra-Community trade in goods for 2017. The dynamics of intra-Community trade were also examined. The obtained results can be utilised by practitioners from both the domain of official statistics and the revenue authorities. In the article discrepancies in data on Polish foreign trade are studied in the context of Poland-EU partner country (bilateral relations) and Poland-EU partners (one-to-many relations). The aim of the article is to compare the results of selected literature studies with those obtained on the basis of the analysis of the latest data on intra-Community trade in Poland (mainly in 2017) and EU member states.

Suggested Citation

  • Markowicz Iwona & Baran Paweł, 2020. "Discrepancies Between Mirror Data on Intra-Community Trade: The Case of Poland," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 24(1), pages 1-11, March.
    • Handle: RePEc:vrs:eaiada:v:24:y:2020:i:1:p:1-11:n:1
    DOI: 10.15611/eada.2020.1.01
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    References listed on IDEAS

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    1. Federico, Giovanni & Tena, Antonio, 1991. "On the accuracy of foreign trade statistics (1909-1935): Morgenstern revisited," Explorations in Economic History, Elsevier, vol. 28(3), pages 259-273, July.
    2. Javorcik, Beata S. & Narciso, Gaia, 2008. "Differentiated products and evasion of import tariffs," Journal of International Economics, Elsevier, vol. 76(2), pages 208-222, December.
    3. Shintaro Hamanaka, 2012. "Whose trade statistics are correct? Multiple mirror comparison techniques: a test case of Cambodia," Journal of Economic Policy Reform, Taylor and Francis Journals, vol. 15(1), pages 33-56.
    4. Paweł Baran & Iwona Markowicz, 2018. "Behavioral Economics and Rationality of Certain Economic Activities: The Case of Intra-Community Supplies," Springer Proceedings in Business and Economics, in: Kesra Nermend & Małgorzata Łatuszyńska (ed.), Problems, Methods and Tools in Experimental and Behavioral Economics, chapter 0, pages 285-299, Springer.
    5. Shintaro Hamanaka, 2012. "Whose trade statistics are correct? Multiple mirror comparison techniques: a test case of Cambodia," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 15(1), pages 33-56, March.
    6. Céline Carrère & Christopher Grigoriou, 2014. "Can Mirror Data Help To Capture Informal International Trade?," UNCTAD Blue Series Papers 65, United Nations Conference on Trade and Development.
    7. Raymond Fisman & Shang-Jin Wei, 2004. "Tax Rates and Tax Evasion: Evidence from "Missing Imports" in China," Journal of Political Economy, University of Chicago Press, vol. 112(2), pages 471-500, April.
    8. Ferrantino, Michael J. & Wang, Zhi, 2008. "Accounting for discrepancies in bilateral trade: The case of China, Hong Kong, and the United States," China Economic Review, Elsevier, vol. 19(3), pages 502-520, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    intra-Community trade; official statistics; mirror data;
    All these keywords.

    JEL classification:

    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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