Identification of EU Countries Due to the Quality of Data on Intra-Community Supplies of Goods Shipped from Poland in the Years 2005–2017

  • Iwona MarkowiczEmail author
  • Paweł Baran
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


Public statistics data regarding trade in goods between European Union Member States are collected and disseminated by Eurostat. They are aggregated data collected from national statistical offices from all Member States. In Poland, as in all other countries within EU, Intrastat system was introduced. Its main goal is collecting data from declarations submitted by businesses shipping and acquiring goods from partners in other EU Member States. Data from Intrastat (intra-Community trade) as well as Extrastat (foreign trade with third parties, i.e. partners residing outside of EU) serve as a base in research of foreign trade or economic development as well as establishing many macroeconomic measures. In the survey presented, data from Eurostat’s COMEXT database was used. We utilised individual as well as aggregated indices of mirror data divergence. The aim of the paper was to analyse intra-Community supplies from Poland to the whole of EU as well as to individual countries over the period 2005–2017. Then, a ranking of Poland’s trade-partner countries was created for every year under consideration. Those rankings had been compiled together and further conclusions about dynamics were drawn, particularly we show changes in data quality of Poland’s intra-Community supplies.


Trade in goods shipped from Poland Eurostat’s Comext database Mirror data 


  1. 1.
    Ainsworth, R.T.: The Morphing of MTIC Fraud: VAT Fraud Infects Tradable CO2 Permits. Boston University School of Law Working Paper No. 09-35 (2009)Google Scholar
  2. 2.
    Baran, P., Markowicz, I.: Analysis of intra-community supply of goods shipped from Poland. In: Papież, M., Śmiech, S. (eds.) The 12th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena. Conference Proceedings, Zakopane. Socio-Economic Modelling and Forecasting, vol. 1, pp. 12–21 (2018a)Google Scholar
  3. 3.
    Baran, P., Markowicz, I.: Behavioral economics and rationality of certain economic activities. The case of intra-community supplies. In: Nermend, K., Łatuszyńska, M. (eds.) Problems, Methods and Tools in Experimental and Behavioral Economics. Proceedings of Computational Methods in Experimental Economics (CMEE) 2017 Conference, pp 285–299. Springer, Cham (2018b)CrossRefGoogle Scholar
  4. 4.
    Borselli, F.: Pragmatic policies to tackle VAT fraud in the European union. Int. Vat Monit. 5(19), 333–342 (2008)Google Scholar
  5. 5.
    European Court of Auditors: Tackling intra-community VAT fraud: more action needed. Special Report, No. 24. Publications Office of the European Union, Luxembourg (2016)Google Scholar
  6. 6.
    Eurostat: Compilers guide on European statistics on international trade in goods, 2017 edition. Manuals and guidelines. Publications Office of the European Union, Luxembourg (2017)Google Scholar
  7. 7.
    Federico, G., Tena, A.: On the accuracy of foreign trade statistics (1909–l935). Morgenstern Revisited. Explor. Econ. Hist. 28(3), 259–273 (1991)CrossRefGoogle Scholar
  8. 8.
    Ferrantino, M.J., Wang, Z.: Accounting for discrepancies in bilateral trade: the case of China, Hong Kong, and The United States. China Econ. Rev. 19(3), 502–520 (2008)CrossRefGoogle Scholar
  9. 9.
    Gu, Z., Gu, L., Eils, R., Schlesner, M., Brors, B.: Circlize implements and enhances circular visualization in R. Bioinformatics 30(19), 2811–2812 (2014)CrossRefGoogle Scholar
  10. 10.
    Guo, D.: Mirror Statistics of International Trade in Manufacturing Goods: The Case of China. UNIDO. Research and Statistics Branch Working Paper 19/2009 (2009)Google Scholar
  11. 11.
    Hamanaka, S.: Whose trade statistics are correct? Multiple mirror comparison techniques: a test of Cambodia. J. Econ. Policy Reform 15(1), 33–56 (2012)CrossRefGoogle Scholar
  12. 12.
    HMRC Trade Statistics: A Reconciliation of Asymmetries in Trade-in-Goods Statistics. Published by the UK and Other European Union Member States. Southend-on-Sea (2014)Google Scholar
  13. 13.
    Javorsek, M.: Asymmetries in International Merchandise Trade Statistics. UN ESCAP Statistics Division Working Paper 02 (2016)Google Scholar
  14. 14.
    Keen, M., Smith, S.: VAT Fraud and Evasion: What Do We Know, and What Can Be Done? IMF Working Papers, vol. 31, issues 7, pp. 1–33 (2007)Google Scholar
  15. 15.
    Kim, S.Y.: An Analytical Approach to Detect MTIC/MTEC VAT Fraud Involving Energy Trading. IOTA Papers (2017)Google Scholar
  16. 16.
    Leeper, T.J.: Slopegraph: Edward Tufte-inspired Slopegraphs. R package version 0.1.14 (2017)Google Scholar
  17. 17.
    Parniczky, G.: On the inconsistency of world trade statistics. Int. Stat. Rev. 48(1), 43–48 (1980)CrossRefGoogle Scholar
  18. 18.
    Pope, S., Stone, R.: Civil interventions for tackling MTIC fraud: a UK perspective. Amicus Curiae 80, 8–13 (2009)Google Scholar
  19. 19.
    Regulation (EC) No 638/2004 of the European Parliament and of the Council of 31 March 2004 on Community statistics relating to the trading of goods between Member States. (5.06.2018)
  20. 20.
    Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European statistics. (5.06.2018)
  21. 21.
    Tsigas, M.E., Hertel, T.W., Binkley, J.K.: Estimates of systematic reporting biases in trade statistics. Econ. Syst. Res. 4(4), 297–310 (1992)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Statistics, Faculty of Economics and ManagementEconometrics and Statistics Institute, University of SzczecinSzczecinPoland
  2. 2.Department of Operations Research and Applied Mathematics in Economics, Faculty of Economics and ManagementEconometrics and Statistics Institute, University of SzczecinSzczecinPoland

Personalised recommendations