A new method for calculating mirror data asymmetry in international trade
DOI:
https://doi.org/10.24136/oc.2020.026Keywords:
international trade, intra-community trade, mirror data, COMEXTAbstract
Research background: Some statistics are of a bilateral nature. This is how foreign trade data is organized. They are recorded both in the supplier and recipient countries, hence they are called mirror data. The data recorded at both trading partner countries are not the same for different reasons. Such differences between data on the same groups of transactions are often referred to as the asymmetry of mirror data. The information about the value of the flows of goods are of great importance in economic analyses and therefore their quality is particularly important.
Purpose of the article: The aim of this paper is to present a new measure of data asymmetry ? the aggregated quantity index with value-based weights.
Methods: The proposed measure combines the quantity and the value of turn-over in individual trade relations. Such a measure makes it possible to eliminate basic deficiencies in value-based measures, while considering the specificity of trade in individual countries. The proposed measure of data asymmetry was confronted with several measures present in the literature and previously used by the Authors. The numerical example uses Comext data on intra-Community trade in 2017 provided by Eurostat.
Findings & Value added: The proposed measure performs better than all the previously used data asymmetry indices. It is to some extent immune to exchange rate differences and inconsistencies resulting from the inclusion of transport and insurance costs in the value of goods. In addition, it gives lower weights to unimportant trade directions than other data asymmetry indices. Since the new index has proved to be better than the measures previously used, it is worth applying to those trade relations where the data are not de-rived from customs documents, but from declarations made by businesses, as in the case of intra-Community trade.
Downloads
References
Bahmani-Oskooee, M., Usman, A., & Ullah, S. (2020). Asymmetric J-curve in the commodity trade between Pakistan and United States: evidence from 41 industries. Eurasian Economic Review, 10, 163-188. doi: 10.1007/s40822-019-00137-x. DOI: https://doi.org/10.1007/s40822-019-00137-x
View in Google Scholar
Baran, P., & Markowicz, I. (2018). Analysis of intra-community supply of goods shipped from Poland. Socio-Economic Modelling and Forecasting, 1, 12-21.
View in Google Scholar
Carr?re, C., & Grigoriou, Ch. (2014). Can mirror data help to capture informal international trade? Policy issues in international trade and commodities research study series No. 65. New York: UNCTAD.
View in Google Scholar
Cate ten, A. (2007). Modelling the reporting discrepancies in bilateral data. CPB Netherlands Bureau for Economic Policy Analysis Memorandum, 179.
View in Google Scholar
Cate ten, A. (2014). The identification of reporting accuracies from mirror data. Jahrbücher für Nationalökonomie und Statistik, 234(1), 70-84. doi: 10.1515/ jbnst-2014-0106. DOI: https://doi.org/10.1515/jbnst-2014-0106
View in Google Scholar
Eurostat (2017). National requirements for the Intrastat system. Luxembourg: Publications Office of the European Union.
View in Google Scholar
Federico, G., & Tena, A. (1991). On the accuracy of foreign trade statistics (1909?1935). Morgenstern revisited. Explorations in Economic History, 28(3), 259?273. doi: 10.1016/0014-4983(91)90007-6. DOI: https://doi.org/10.1016/0014-4983(91)90007-6
View in Google Scholar
Ferrantino, M. J., Liu, X., & Wang, Z. (2012). Evasion behaviors of exporters and importers: evidence from the U.S.?China trade data discrepancy. Journal of International Economics, 86(1), 147-157. doi: 10.1016/j.jinteco.2011.08.006. DOI: https://doi.org/10.1016/j.jinteco.2011.08.006
View in Google Scholar
Ferrantino, M. J., & Wang, Z. (2008). Accounting for discrepancies in bilateral trade: the case of China, Hong Kong, and the United States. China Economic Review, 19(3), 502-520. doi: 10.1016/j.chieco.2008.02.002. DOI: https://doi.org/10.1016/j.chieco.2008.02.002
View in Google Scholar
Fisman, R., & Wei, S-J. (2004). Tax rates and tax evasion: evidence from ?missing imports? in China. Journal of Political Economy, 112(2), 471-496. doi: 10.1086 /381476. DOI: https://doi.org/10.1086/381476
View in Google Scholar
Guo, D. (2010). Mirror statistics of international trade in manufacturing goods: the case of China. UNIDO, Research and Statistics Branch Working Paper, 19/2009.
View in Google Scholar
Hamanaka, S. (2012). Whose trade statistics are correct? Multiple mirror comparison techniques: a test of Cambodia. Journal of Economic Policy Reform, 15(1), 33-56. doi: 10.1080/17487870.2012.657827. DOI: https://doi.org/10.1080/17487870.2012.657827
View in Google Scholar
HMRC Trade Statistics (2014). A reconciliation of asymmetries in trade-in-goods statistics published by the UK and other European Union member states. Southend-on-Sea.
View in Google Scholar
Hummels, D. L., & Lugovskyy, V. (2006) Are matched partner trade statistics a usable measure of transportation costs? Review of International Economics 14(1), 69-86. doi: 10.1111/j.1467-9396.2006.00561.x. DOI: https://doi.org/10.1111/j.1467-9396.2006.00561.x
View in Google Scholar
Javorcik, B. S., & Narciso, G. (2008). Differentiated products and evasion of import tariffs. Journal of International Economics, 76(2), 208-222. DOI: https://doi.org/10.1016/j.jinteco.2008.07.002
View in Google Scholar
LeSage, J. P., & Llano-Verduras, C. (2014). Forecasting spatially dependent origin and destination commodity flows. Empirical Economics, 47, 1543-1562. doi: 10.1007/s00181-013-0786-2. DOI: https://doi.org/10.1007/s00181-013-0786-2
View in Google Scholar
Markowicz, I., & Baran, P. (2019a). ICA and ICS-based rankings of EU countries according to quality of mirror data on intra-Community trade in goods in the years 2014?2017. Oeconomia Copernicana, 10(1), 55-68. doi: 10.24136/oc. 2019.003. DOI: https://doi.org/10.24136/oc.2019.003
View in Google Scholar
Markowicz, I., & Baran, P. (2019b). Quality of intrastat data. Comparison between ?old? and ?new? EU member states. Acta Universitatis Lodziensis. Folia Oeconomica, 2(341), 69?80. doi: 10.15611/eada.2020.1.01. DOI: https://doi.org/10.18778/0208-6018.341.05
View in Google Scholar
Markowicz I., & Baran P. (2020a). Discrepancies between mirror data on intra-community trade: the case of Poland. Econometrics. Ekonometria, 24(1), 1-11. doi: 10.15611/eada.2020.1.01. DOI: https://doi.org/10.15611/eada.2020.1.01
View in Google Scholar
Markowicz, I., & Baran, P. (2020b). Identification of EU countries due to the quality of data on intra-community supplies of goods shipped from Poland in the years 2005?2017. In K. Nermend & M. Łatuszyńska (Eds.). Experimental and quantitative methods in contemporary economics: computational methods in experimental economics (CMEE) 2018 Conference. Cham: Springer International Publishing. doi: 10.1007/978-3-030-30251-1_22. DOI: https://doi.org/10.1007/978-3-030-30251-1_22
View in Google Scholar
Morgenstern, O. (1963). On the accuracy of economic observations. New Jersey: Princeton University Press.
View in Google Scholar
Parniczky, G. (1980). On the inconsistency of world trade statistics. International Statistical Review, 48(1), 43-48. doi: 10.2307/1402404. DOI: https://doi.org/10.2307/1402404
View in Google Scholar
Rasoulinezhad, E. (2018). A new evidence from the effects of Russia?s WTO accession on its foreign trade. Eurasian Economic Review, 8, 73-92. doi: 10.1007/ s40822-017-0081-1. DOI: https://doi.org/10.1007/s40822-017-0081-1
View in Google Scholar
Splunk (2019). The state of dark data. Industry leaders reveal the gap between AI?s potential and today?s data reality.
View in Google Scholar
Tsigas, M. E., Hertel, T. W., & Binkley, J. K. (1992). Estimates of systematic reporting biases in trade statistics. Economic Systems Research, 4(4), 297-310. doi: 10.1080/09535319200000028. DOI: https://doi.org/10.1080/09535319200000028
View in Google Scholar
Roszko-Wójtowicz, E., Grzelak, M.M., Laskowska, I., (2019). The impact of research and development activity on the TFP level in manufacturing in Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 14(4), 711-737. doi: 10.24136/eq.2019.033. DOI: https://doi.org/10.24136/eq.2019.033
View in Google Scholar
Yurik, S., Pushkin, N., Yurik, V., Halík, J., & Smutka, L., (2020). Analysis of Czech agricultural exports to Russia using mirror statistics. Entrepreneurial Business and Economics Review, 8(2), 27-46. doi: 10.15678/EBER.2020.080 202. DOI: https://doi.org/10.15678/EBER.2020.080202
View in Google Scholar
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Oeconomia Copernicana
This work is licensed under a Creative Commons Attribution 4.0 International License.
