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1
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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

100%
Markowicz I., Baran P.
Oeconomia Copernicana
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2019
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vol. 10
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issue 1
55-68
EN
Research background: As a system of official EU statistics, Intrastat contains data collected by Member States aggregated by Eurostat on the Union's level in the form of COMEXT database. Country-level data are based on declarations made by businesses dispatching or acquiring goods from other EU Member States. Since the same transaction is declared twice - as an ICS in one country and at the same time as an ICA in another country by the partner - the database contains mirror data. Analysis of mirror data lets us assess the quality of public statistics data on international trade. Purpose of the article: The aim of the article is to rank EU Member States according to quality of data on intra-Community trade in goods collected by Intrastat. Foreign trade stimulates economic development on one hand and is the development's reflection on the other. Thus it is very important that official statistics in this area be of good quality. Analysis of mirror data from partner states in intra-Community trade in goods allows us to claim that not every Member State pro-vides data of satisfactory quality level. Methods: We used the authors' methodology of assessing quality of mirror data. These include data asymmetry indices, both proposed by Eurostat and the authors' own proposals. We have also examined the changes in the above mentioned rankings over time. Findings & Value added: The result of the survey is ordering of EU Member States according to the quality of data on intra-Community trade in goods. The rankings are presented for the period of 2014-2017, during which there were 28 Member States of the EU. Changes in distinct countries' positions were shown as a result of changes in overall quality of statistical data collected in these countries. The research methodology can be used in the process of monitoring data quality of the Intrastat system.
2
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A new method for calculating mirror data asymmetry in international trade

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Markowicz I., Baran P.
Oeconomia Copernicana
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2020
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vol. 11
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issue 4
637-656
EN
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.
3
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Mirror data asymmetry in international trade by commodity group: the case of intra-Community trade

100%
Markowicz I., Baran P.
Oeconomia Copernicana
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2021
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vol. 12
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issue 4
889-905
EN
Research background: Transactions in international trade of goods are recorded in two sources, on the side of the seller's country and on the side of the buyer's country. The confrontation of such data makes it possible to measure their quality. An inconsistency between the data is called mirror data asymmetry. Purpose of the article: The aim of the paper is to adapt the methods developed by the Authors to study mirror data asymmetry to commodity group markets examination. The quality of data on trade within specific commodity groups (CN chapters) in intra-Community trade was compared. The data were aggregated by country. The indicators used allow for the indication of commodity groups with high mirror data compatibility and those with data asymmetry between intra-Community supplies (ICS) and acquisitions (ICA). Moreover, the commodity groups for which the value-based and quantity-based approaches give different results have been identified. Methods: Based on the literature on the subject and their own research, the Authors have developed a group of methods for studying the asymmetry of mirror data. The proposed indicator formulas are based on various data aggregation approaches. The research used data on intra-Community supplies and acquisitions of goods broken down into 97 chapters of the Combined Nomenclature (CN). Differences between the ICS and ICA in particular commodity groups were aggregated for all pairs of EU countries. The data comes from the Comext database, provided by Eurostat. Findings & value added: The results of the analysis are rankings of the Combined Nomenclature (CN) chapters by the quality of data on ICS and ICA. Lists of CN chapters have been created for discrepancies both in value and weight of goods. Thus, areas of necessary intensification of the work of public statistics services to improve data reliability were identified.
4
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Discrepancies between mirror data on intra-Community trade: the case of Poland

75%
Markowicz I., Baran P.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2020
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issue vol. 24, nr 1
1-11
EN
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.
PL
Prezentowane w artykule treści są kontynuacją badań prowadzonych nad jakością danych dotyczących handlu wewnątrzwspólnotowego. Studia literaturowe pozwoliły na wskazanie propozycji oceny poziomu jakości danych. Inspiracją do przeprowadzonych badań były publikacje autorów takich jak: Morgenstern (1963), Federico i Tena (1991) oraz Ferrantino i Wang (2008). To w nich znaleziono pewne wskazówki. Na tej podstawie analizie poddano dane dotyczące obrotu towarowego między krajami unijnymi w 2017 roku. Zbadano także zmiany zachodzące w czasie. Wyniki takich badań mogą być wykorzystane w zakresie zarówno statystyki publicznej, jak i podatkowym. W artykule podjęto temat analizy rozbieżności danych w handlu Polski w relacjach Polska–kraj UE (relacje dwustronne) i Polska–kraje UE (relacja kraj–kraje, nazwana zagregowaną). Celem badania jest porównanie wyników wybranych badań literaturowych z wynikami uzyskanymi na podstawie analizy najnowszych danych dotyczących obrotu wewnątrzwspólnotowego Polski (głównie 2017 rok) oraz krajów unijnych.
5
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Jakość danych dotyczących wewnątrzunijnej wymiany towarowej

63%
Markowicz I., Baran P.
Wiadomości Statystyczne. The Polish Statistician
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2019
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vol. 64
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issue 6
5-15
EN
Official statistics on trade in goods between EU member states are collected on country-level and then aggregated by Eurostat. Methodology of data collecting differs slightly between member states (e.g. various statistical thresholds and coverage), including differences in exchange rates as well as undeclared or late-declared transactions, errors in classification of goods and other mistakes. It often involves incomparability of mirror data (nominally concerning the same transactions recorded in statistics of both dispatcher and receiver countries). A huge part of these differences can be explained with the variable quality of data resources in the Eurostat database. In the study data quality on intra-EU trade in goods for 2017 was compared between Poland and neighbouring EU countries, i.e.: Germany, Czech Republic, Slovakia, Lithuania, and other Baltic states – Latvia and Estonia. The additional aim was to indicate the directions having the greatest influence on the observed differences in mirror data. The results of the study indicate that the declarations made in Estonia affect the poor quality of data on trade in goods between the countries mentioned above to the greatest extent.
PL
Dane statystyki publicznej dotyczące wymiany towarowej między krajami Unii Europejskiej (UE) gromadzone są na poziomie krajowym, a następnie agregowane przez Eurostat. Metodyka zbierania danych różni się w pewnym zakresie w poszczególnych krajach członkowskich (np. różne są progi statystyczne i poziom pokrycia), a do tego występują różnice kursowe, transakcje niezgłoszone, opóźnione zgłoszenia, błędy klasyfikacji towarowej i inne. Powoduje to, że dane lustrzane, dotyczące w założeniu tych samych transakcji odnotowanych w statystyce kraju wywozu i kraju przywozu, są często nieporównywalne. Znaczną część rozbieżności można tłumaczyć zróżnicowaną jakością zbiorów danych zasilających bazę Eurostatu. Celem przedstawionego w artykule badania jest porównanie jakości danych za 2017 r. o wewnątrzunijnej wymianie towarowej Polski i sąsiednich krajów UE: Niemiec, Czech, Słowacji i Litwy oraz pozostałych krajów bałtyckich – Łotwy i Estonii. Dodatkowym celem jest wskazanie, które kierunki w największym stopniu wpłynęły na występowanie różnic w danych lustrzanych. Wyniki przeprowadzonej analizy wskazują, że najczęściej wpływ na niską jakość danych dotyczących wymiany towarowej między badanymi krajami mają deklaracje składane w Estonii.
6
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Analiza rozbieżności danych lustrzanych dotyczących masy towarów w statystykach handlu wewnątrzunijnego

51%
Markowicz I., Baran P.
Wiadomości Statystyczne. The Polish Statistician
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2020
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vol. 65
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issue 8
27-38
EN
In the research carried out to date by the authors of the article, the assessment of the quality of mirror data in the exchange of goods between European Union (EU) countries was based on the value of goods. A similar approach is applied by many researchers. The aim of the research discussed in the article is to assess the quality of data relating to intra-EU trade based on not only the value, but also on the quantity of goods. The analysis of discrepancies in data relating to trade between EU countries, with a particular emphasis on Poland, was based on selected research methods known from literature. Both the value-based and the quantitative approach constitute the authors' contribution to the development of research methodology. Data quality indicators were proposed and data pertaining to the weight of goods were used. Information on trade in goods between EU countries in 2017 obtained from Eurostat's Comext database was analysed. The research relating to the dynamics also covered the years 2005, 2008, 2011, and 2014. The results of the analysis demonstrated that the total share of export of goods from Poland to a given country within the EU is different for data expressed in value (value of goods) and in quantity (weight of goods). Therefore, both approaches should be applied in the study of the quality of mirror data.
PL
W badaniach prowadzonych dotychczas przez autorów artykułu ocena jakości danych lustrzanych w wymianie towarowej między krajami Unii Europejskiej (UE) opierała się na wartości towarów. Analogiczne podejście stosuje wielu badaczy. Celem badania omawianego w artykule jest ocena jakości danych dotyczących obrotu wewnątrzunijnego na podstawie nie tylko wartości, lecz także ilości towarów. W analizie rozbieżności danych w handlu między krajami UE, ze szczególnym uwzględnieniem Polski, wzorowano się na wybranych metodach badania znanych z literatury przedmiotu. Podejście zarówno wartościowe, jak i ilościowe stanowi wkład własny autorów w metodykę badawczą. Zaproponowano wskaźniki jakości danych oraz wykorzystano dane dotyczące masy towarów. Analizie poddano informacje na temat obrotu towarowego między krajami unijnymi w 2017 r. pochodzące z bazy Eurostatu Comext. Badanie dynamiki obejmowało również lata: 2005, 2008, 2011 i 2014. Wyniki analizy pokazały, że ogółem udział wywozu z Polski towarów do danego kraju na obszarze UE jest różny dla danych wyrażonych wartościowo (wartość towarów) i ilościowo (masa towarów). W badaniu jakości danych lustrzanych należy zatem stosować oba podejścia.
7
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Quality of INTRASTAT DATA. Comparison Between the ‘Old’ and the ‘New’ EU Member States

51%
Markowicz I., Baran P.
Acta Universitatis Lodziensis. Folia Oeconomica
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2019
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vol. 2
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issue 341
69-80
PL
W systemie INTRASTAT zbierane są dane statystyczne dotyczące wymiany towarowej między krajami UE. Eurostat agreguje dane pochodzące z poszczególnych państw członkowskich. Specyfika procesu pozyskiwania danych różni się w poszczególnych krajach, w związku z czym dane lustrzane (dotyczące w założeniu tych samych transakcji, odnotowanych w statystyce kraju wywozu i kraju przywozu) często się nie pokrywają. Celem przeprowadzonych analiz była ocena jakości danych o wewnątrzunijnej wymianie towarowej krajów „starej” piętnastki i „nowych” członków UE ze wskazaniem, które kierunki w największym stopniu wpłynęły na występowanie obserwowanych różnic w danych lustrzanych. Artykuł jest kontynuacją badań jakości danych dotyczących handlu zagranicznego.
EN
The Intrastat system is used for gathering statistical data on trade in goods between the EU Member States. Data from all the Member States are aggregated by Eurostat. Specifics of the data collection process are different in different countries and that is why mirror data (regarding by default the same transactions revealed in statistics of both the acquirer and supplier country) often do not match. The goal of the analysis conducted was to assess the quality of data on intra‑Community trade in goods between the ‘old’ fifteen and the ‘new’ EU Member States as well as to point out these directions that influenced the observed differences in mirror data the most. The paper is a follow‑up of previous research on the quality of foreign trade data.
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