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Reconciling International Trade Data

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  • Shaar, Karam

Abstract

International trade data are in substantial measurement error. Data reported by some countries mean next to nothing. This study develops an index of trade data quality based on the consistency between a country’s claims on bilateral trade and the corresponding claims of the rest of the world from 1962 to 2014. The index takes the relative significance of each partner and data availability into account. We produce a more reliable set of bilateral and total international trade data using the index. Findings include (a) the actual exports of most countries with low data quality are considerably higher than self-reported. (b) Corruption and poor data quality are strongly correlated. (c) Global trade data quality has been deteriorating in the past three decades even though more countries have improved their data quality over time. This is because low-quality reporters have recently increased their share in global trade. (d) China tends to under-report exports and over-report imports. (e) There is only a trivial difference between US self-reported and reconciled data. The same applies to all high-quality reporters. We recommend future studies on trade use our reconciled data.

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  • Shaar, Karam, 2017. "Reconciling International Trade Data," MPRA Paper 81572, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:81572
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    1. Ferrantino, Michael J. & Liu, Xuepeng & Wang, Zhi, 2012. "Evasion behaviors of exporters and importers: Evidence from the U.S.–China trade data discrepancy," Journal of International Economics, Elsevier, vol. 86(1), pages 141-157.
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    8. Wang, Zhi & Gehlhar, Mark & Yao, Shunli, 2010. "A globally consistent framework for reliability-based trade statistics reconciliation in the presence of an entrepôt," China Economic Review, Elsevier, vol. 21(1), pages 161-189, March.
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    11. Mohsen Bahmani-Oskooee & Scott Hegerty & Hanafiah Harvey, 2013. "Exchange-rate sensitivity of commodity trade flows: Does the choice of reporting country affect the empirical estimates?," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 22(8), pages 1183-1213, December.
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    Cited by:

    1. Peter B. Dixon & Maureen Rimmer & Nhi Tran, 2020. "Creating a Disaggregated CGE Model for Trade Policy Analysis: GTAP-MVH," Foreign Trade Review, , vol. 55(1), pages 42-79, February.
    2. Peter B. Dixon & Maureen Rimmer & Nhi Tran, 2019. "GTAP-MVH, A Model for Analysing the Worldwide Effects of Trade Policies in the Motor Vehicle Sector: Theory and Data," Centre of Policy Studies/IMPACT Centre Working Papers g-290, Victoria University, Centre of Policy Studies/IMPACT Centre.
    3. Caravella, Serenella & Crespi, Francesco & Cucignatto, Giacomo & Guarascio, Dario, 2023. "Technological Sovereignty and Strategic Dependencies: The case of the Photovoltaic Supply Chain," GLO Discussion Paper Series 1330, Global Labor Organization (GLO).
    4. Elina Bryngemark & Patrik Söderholm, 2022. "Green industrial policies and domestic production of biofuels: an econometric analysis of OECD countries," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 24(2), pages 225-261, April.

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    More about this item

    Keywords

    Trade data quality; data discrepancy; trade data reconciliation;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • F1 - International Economics - - Trade

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