metadata: authors: 0: author: Mitikj,Sonja
1: author: Kaushik,Siddhesh Vishwanath
count: World
docna: 0: docna: Bridging the Gap in Trade Reporting :
            Insights from the Discrepancy Index
docty: Policy Research Working Paper
abstracts: cdata!: Accurate trade data remain central for
            empirical investigations of international trade and informed
            formulation of trade policies. However, discrepancies in
            trade reporting, stemming from reasons such as logistics to
            deliberate misclassification, pose challenges to obtaining
            an accurate representation of trade activities. This paper
            provides a systematic examination of these discrepancies by
            using the Discrepancy Index, a measure of bilateral
            asymmetry in trade reporting. First, the paper proposes a
            rich set of country- and product-level indicators that
            capture both the frequency of misreporting and its impact on
            the overall recorded trade value. Second, it demonstrates
            how the Discrepancy Index database can aid analysis and
            resolve data reliability issues in international trade. This
            comprehensive data set is used to analyze the general trends
            in trade data reporting and its reliability, providing
            empirical insights into the nature and extent of reporting
            discrepancies. Finally, the paper demonstrates the practical
            application of the developed discrepancy database and
            aggregate indicators through case studies of Senegal and the
            trade relationship between Madagascar and France, shedding
            light on reporter-specific instances. The paper seeks to
            equip trade analysts and researchers with tools and
            resources to make informed decisions on the use of reported
            trade data and their mirror. In doing so, the study
            contributes to the broader endeavor of enhancing the
            reliability of international trade data, thereby
            contributing to a more accurate empirical investigation of
            global trade patterns and their policy ramifications.
display_title: Bridging the Gap in Trade Reporting
            : Insights from the Discrepancy Index
listing_relative_url: /research/2024/06/34333251/bridging-gap-trade-reporting-insights-discrepancy-index
topicv3: Trade Policy and Integration m1327407 775
publishtoextweb_dt: 2024-06-04T00:00:00Z
dois: 10.1596/1813-9450-10792
no_of_pages: 78
author_keys: 0: author_key: 1789777
1: author_key: 961527
model_extractions: Policy Research Working Paper 10792 Bridging the Gap in Trade Reporting Insights from the Discrepancy Index _Sonja Mitikj Siddhesh Vishwanath Kaushik_ Development Economics Development Data Group June 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10792

# **Abstract**

Accurate trade data remain central for empirical investigations of international trade and informed formulation of trade policies. However, discrepancies in trade reporting, stemming from reasons such as logistics to deliberate misclassification, pose challenges to obtaining an accurate representation of trade activities. This paper provides a systematic examination of these discrepancies by using the Discrepancy Index, a measure of bilateral asymmetry in trade reporting. First, the paper proposes a rich set of country- and product-level indicators that capture both the frequency of misreporting and its impact on the overall recorded trade value. Second, it demonstrates how the Discrepancy Index database can aid analysis and resolve data reliability issues in international trade. This comprehensive data set is used to analyze the general trends in trade data reporting and its reliability, providing empirical insights into the nature and extent of reporting discrepancies. Finally, the paper demonstrates the practical application of the developed discrepancy database and aggregate indicators through case studies of Senegal and the trade relationship between Madagascar and France, shedding light on reporter-specific instances. The paper seeks to equip trade analysts and researchers with tools and resources to make informed decisions on the use of reported trade data and their mirror. In doing so, the study contributes to the broader endeavor of enhancing the reliability of international trade data, thereby contributing to a more accurate empirical investigation of global trade patterns and their policy ramifications.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at skaushik@worldbank.org and smitikj@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Bridging the Gap in Trade Reporting: Insights from the Discrepancy Index

Sonja Mitikj Siddhesh Vishwanath Kaushik JEL classification: F14, C55, C82 Keywords: International Trade data, Discrepancy Index, Data Quality, Mirror Statistics.

Acknowledgment: We are grateful to Ana Margarida Fernandes, Bob Rijkers, Hiau Loo Ki, Markie Muryawan, and Michael Ferrantino for their insightful comments and feedback that significantly improved this work. We also extend our appreciation to Umar Serajuddin and Daria Taglioni for their leadership and support throughout the project.
# Contents

|1.<br>Introduction ..................................................................................................................... 3|
|---|
|2.<br>Sources of discrepancies in international trade reporting ................................................. 5|
|3.<br>Methods for measuring discrepancies in trade reporting .................................................. 7|
|3.1 The Discrepancy Index ................................................................................................. 9|
|3.2 Aggregate indicators based on the discrepancy index ..................................................14|
|4.<br>Data description .............................................................................................................20|
|5.<br>Results and discussion ...................................................................................................21|
|5.1<br>Stylized facts about international merchandise trade reporting ................................21|
|5.2<br>Estimating the reliability of reporters ........................................................................29|
|5.3. The case of Senegal ...................................................................................................38|
|6.<br>Conclusion .....................................................................................................................40|
|Appendix ...............................................................................................................................42|
|A1) List of additional variables derived from the Discrepancy Index ...................................42|
|A2) Import reporting in all HS nomenclatures (1988–2019) ................................................43|
|A3) Export reporting in all HS nomenclatures (1988–2019) ................................................46|
|A4) Detailed look at orphan imports and lost exports .........................................................49|
|A5) Tracking re-exports and their impact on discrepancies ................................................53|
|A6) Resolving discrepancies ..............................................................................................54|
|A7) Supporting materials for the findings ...........................................................................61|
|A8) Database table details .................................................................................................68|
|A9) Country Codes and Names .........................................................................................71|
|References ............................................................................................................................75|

## DISCLAIMER

The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/the World Bank. The findings, interpretations, and conclusions expressed in this study do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.

2
# 1. Introduction

Cross-border trade plays a pivotal role in academic research on international development and the complexities of policy design. This importance stems from its ability to provide insight into economic interactions, dependencies, and the dynamics of global markets. The estimation of such cross-border activities primarily hinges on trade data, which provides a quantitative assessment of goods exchanged between countries. Accurate data informs import tariff setting, guides negotiations for trade agreements, and steers monetary decisions that impact interest rates and currency valuation. Furthermore, it influences industrial subsidies, infrastructure investments, environmental regulations, and even national security considerations. Given its importance, the increasing number of studies highlighting discrepancies in reported trade flows is concerning (Gaulie & Zignago, 2010; Carrère & Grigoriou, 2015; Fortanier, 2016; Shaar, 2019; Chen et al., 2022). Reporting discrepancies are often overlooked in the design of empirical studies of international trade, compromising the validity or robustness of the obtained results (Linsi & Mügge, 2019; Linsi, Burgoon, & Mügge, 2023). This is especially important because statistical results guiding policy interventions have been shown to be sensitive to changes in the underlying data (Bahmani-Oskooee, Hegerty & Harvey, 2013; Farhad et al., 2023). Consequently, any inaccuracy or inconsistency in trade data can distort or compromise the validity of research outcomes and the formulation of policy recommendations.

<mark>Evaluating the reliability and accuracy of trade data typically relies on the principle of dual recording of bilateral transactions. Ideally, trade transactions between countries should be recorded by both the reporting country and its trading partner, resulting in dual records.</mark> However, empirical trade data often lacks this dual recording, with many transactions having no corresponding mirror (Carrère & Grigoriou, 2015; Shaar, 2019; Chen et al., 2022). Furthermore, mirror records often report conflicting information regarding the value or quantity of the transaction, resulting in a reporting discrepancy. The absence of mirror records and the prevalence of reporting discrepancies complicates the evaluation of data reliability. This has propelled numerous studies to explore the potential drivers of discrepancies (Ferrantino & Wang 2008; Carrère & Grigoriou, 2015; Demir & Javorcik, 2020; Kee & Nicita, 2022), construct measures for reporting reliability (Shaar, 2019, Farhad et al., 2023), and develop reconciliation procedures for estimating trade flows (Gehlhar, 1996; Gaulie & Zignago, 2010, Fortanier, 2016). Yet, resolving these trade data quality challenges remains difficult due to the multitude of factors contributing to the observed discrepancies.

3
In this study, we bridge the gap by proposing a comprehensive country- and product-level Discrepancy Index database that captures both the frequency of misreporting and its impact on overall recorded trade value. The database was created by leveraging the UN Comtrade database, capturing all available Harmonized System (HS) data from 1988 to 2022. Central to this approach is use of a bilateral Discrepancy Index (DI), which calculates the level of divergence between reported data and its corresponding mirror (data reported by trading partner).

This study makes several contributions towards measuring discrepancies and assessing reporter reliability in the UNSD Comtrade database. First, we provide a comprehensive coverage of discrepancy measurements across all periods of observation, reporting countries, products, and product detail for the HS nomenclature. Hence, resources provided here can assist researchers aiming to trace discrepancies to specific causes, whether logistic or otherwise. Second, we show that the functional form of DI presented here avoids some of the pitfalls of commonly used trade asymmetry measures and is therefore more suited for penalizing discrepancies that are least likely to be explained by some logistic factor. Third, we demonstrate how the international merchandise trade reporting discrepancy database can aid discrepancy analysis and resolve data reliability issues in the UN Comtrade database. By measuring discrepancy in reported values across time, reporting nomenclatures, trading partners and products, we identify an encouraging trend of an increasing quality of reported trade over time. We identify persistent serious quality problems linked to some reporters and note potential effects of reporting discrepancies on important trade indicators such as trade balances. Thus, this study also contributes to ongoing discourse regarding quality of data in international merchandise trade by providing measures and tools for its assessment and outlining important considerations in practical applications of trade data. More importantly, our results can equip trade analysts with the necessary tools to make informed decisions regarding the use of a country’s reported data or its mirror.

The rest of this paper is organized into five sections. Section 2 discusses the numerous factors contributing to discrepancies in international merchandise trade reporting. Section 3 discusses methods for measuring reporting discrepancies in international merchandise trade and provides details on main and aggregate indicators. Section 4 describes the data used in this analysis and provides details on accessing the discrepancy analysis tools developed in this study. Section 5 outlines the study’s key findings and results along with examples and country case studies.

4
Finally, section 6 offers concluding remarks, suggests directions for future research, and acknowledges the limitations of our approach.

# 2. Sources of discrepancies in international trade reporting

A growing body of literature has studied the relative contributions of several factors to the observed discrepancies in international merchandise trade reporting (Ferrantino & Zhi Wang 2008; Carrère & Grigoriou, 2015; Demir & Javorcik, 2020; Kee & Nicita, 2022). These studies have identified both structural and non-logistic reasons for the lack of dual recording and observed inconsistencies in successfully mirrored records. In this section, we outline the causes of discrepancies and explore their effect on the quality of the obtained data.

Discrepancies in international trade reporting are structural, i.e., they are driven by logistic reasons related to the valuation methods and reporting guidelines for international merchandise trade. These guidelines are detailed in the International Merchandise Trade Statistics: Concepts and Definitions 2010 (IMTS2010), published by the United Nations Statistical Division (UNSD). The UN Comtrade databases, the primary source of official merchandise trade data collected and disseminated by UNSD https://comtradeplus.un.org/, strive to align with the IMTS2010 guidelines in the empirical data that they provide. As a result, data obtained through UN Comtrade (or any source that adheres to these recommendations) inherently possess several structural discrepancies. An example of this structural discrepancy occurs in valuation methods that typically consider freight and insurance costs for imports but not for exports. Therefore, structural discrepancies arise because the reported import value exceeds that of the corresponding mirror. Many have noted that extreme (and even negative) empirical measurements of difference between reported imports and mirror records suggest discrepancies that cannot entirely be explained by differences in valuation methods (Carrère & Grigoriou, 2015).

Moreover, IMTS2010 recommends listing the country of origin for imports, but the last known destination for exports (United Nations, International Merchandise Trade Statistics). This will structurally create transactions without a corresponding mirror in cases where exports pass through intermediate countries before reaching their final destination. In such cases, reported exports from the intermediate countries will not have a mirror as the partner that they list in their export transaction lists the original source country (and not the intermediate country) in their 5
corresponding import record. Conversely, the reported imports of both the intermediate countries (excluding the first one) and the destination will also lack a corresponding mirror. Carrère and Grigoriou (2015) estimated that as much as 23% of recorded trade flows on the aggregate level do not have a corresponding mirror. However, the relative effect of re-export reporting on partner misattribution in mirror data remains difficult to assess. Although the rising role of re-exporting in merchandise trade and its effect on reported discrepancies has been documented for the largest re-exporting hubs - Hong Kong SAR, China; Singapore; and the Netherlands (Ferrantino & Zhi Wang, 2008; Fortanier, 2016; Jones et al., 2020), there is no comprehensive methodology that considers re-exporting on a global scale. The challenge of tracking re-exports and goods undergoing processing is further complicated by the non-uniform application of re-export reporting guidelines across countries, with 118 reporters having never reported re-exports to the UN Comtrade system (Jones et al., 2020).

Therefore, structural constraints, such as differences in valuation methods, result in many inconsistencies in reported trade. However, it simultaneously suggests that the magnitudes of many observed discrepancies cannot be attributed to logistic constraints alone. Therefore, a growing number of studies have turned to country and bilateral characteristics to explain the recorded gap in reporting.

Besides logistic reasons, discrepancies in international trade reporting can also be non-logistic, i.e., they are often a deliberate misevaluation or misclassification for evading import tariffs (Cantens, 2015 and Demir and Javorcik, 2020) and export taxes (Chalendard et al., 2020), minimizing corporate profit and/or turnover tax, and other non-tariff measures (Kee and Nicita, 2022). The misclassification and misinvoicing of products have been shown to be substantial (Carrère & Grigoriou, 2015), with 77% of all bilateral trade flows reported by both partners at the aggregate level and only 47% of bilateral trade flows reported by both partners at the six-digit level. Larger discrepancies in disaggregated products have long been observed (Rozanski & Yeats, 1994) and are often attributed to human error (misclassification) or deliberate misreporting.

An important question then is how to infer whether the observed discrepancy is “expected,” i.e., implied by structural reasons, as opposed to deliberate, i.e., caused by factors that are not purely logistic in nature. If some discrepancies are structurally expected, a dependable reporter is not one without any discrepancies, but one that exhibits discrepancies that are most likely to 6
be explained by logistic constraints. This necessitates the development of measures of data reliability that prioritize identifying discrepancies that go beyond structural factors.

Numerous studies have attempted to measure and reconcile the discrepancies observed in UNSD’s Comtrade database. Often, they rely on measures of bilateral asymmetry to capture discrepancy between recorded value and its mirror (Ferrantino & Wang 2008; Chen et al., 2022; Farhad et al., 2023). However, some suggested measures are often linear to the observed discrepancy (Carrère & Grigoriou, 2015), i.e., they change linearly in relation to the unaccounted trade value irrespective of whether the unaccounted trade value is due to the addition of insurance and freight costs for imports or an underreporting of imports in the equivalent amount.

This is inconvenient because the first type of discrepancy is expected and structurally guaranteed by IMTS2010 guidelines, while the latter indicates missing imports, which is not easily explained by logistic factors alone. In addition, some measures are constructed using absolute differences in recorded values (Chen et al., 2022), resulting in indicators that cannot explicitly account for the direction of misreporting, i.e., whether an import was over- or underreported. This is similarly inconvenient because import overreporting is measured as equally severe as import underreporting. While indices of bilateral asymmetry that are non-linear to unaccounted trade value and can account for the direction of discrepancy have been proposed (Ferrantino & Wang 2008), their application in the literature is usually limited to specific countries.

# 3. Methods for measuring discrepancies in trade reporting

The quality of reported trade is usually investigated in relation to its mirror, i.e., the data reported by the trading partner. A comparison between the recorded value and its mirror is usually achieved through a measure of bilateral asymmetry that quantifies the magnitude (and direction) of the observed discrepancy. Many such measures have been proposed to assess the quality of the UN Comtrade database (Carrère & Grigoriou, 2015, Chen et al., 2022; Farhad et al., 2023). These measures have proven especially useful in understanding general patterns in trade reporting, pinpointing uncertain and problematic data, and relating country-specific and bilateral characteristics to misreporting. However, special attention must be paid to both structural and non-structural causes of discrepancies in the measure’s construction and interpretation.

7
The first major point of consideration in the bilateral asymmetry measure is that it involves a comparison between trade values recorded under different valuation methods. That is, bilateral asymmetry measures compare the Free on Board (FOB) valuation of exports and the Cost Insurance and Freight (CIF) valuation of the equivalent import value. While it is preferable to simply take the FOB valuation for import, they are rarely available. This implies that bilateral asymmetry measures will indicate import overreporting in cases where import records simply include CIF adjustments. Several approaches to account for differences in the valuation of recorded values have been proposed. In the BACI database (Gaulie & Zignago, 2010), the CIF rate is estimated (and removed) via a gravity-type model before discrepancies are further resolved.

Alternatively, the influence of the CIF rate can be factored in by accounting for product composition (Farhad et al., 2023), which will inevitably contain the magnitude of this ratio. Finally, the inclusion of the CIF rate in the interpretation of bilateral asymmetry measures has been implemented using simple thresholding, with many flagging transactions whose CIF rate exceeds the 10% threshold.

However, the variability of the CIF rate across partners and products suggests that this threshold is a useful, yet rough estimate. This facilitates the need for bilateral asymmetry measures that are more penalizing toward discrepancies that are least likely to be explained by logistic factors, such as differences in valuation. In addition, many proposed bilateral asymmetry measures can indicate the magnitude of the discrepancy, but not its direction. In other words, without additional information, these measures alone cannot indicate whether the discrepancy was due to an over or under reported import value. Considering the differences in valuation outlined earlier, the direction of the discrepancy can help isolate discrepancies that go beyond CIF adjustments.

Measures of bilateral asymmetry, however, can only quantify the level of divergence of reported trade values on a single bilateral transaction. To capture reporters’ reliability, there is a clear need to summarize the distribution of bilateral asymmetries across partners and commodities. Therefore, measures of bilateral asymmetry are usually accompanied by an aggregation function that captures important distributional properties of the observed discrepancies across trade activities and partners.

8
Aggregate indicators have been driven not only by the need to assess data quality but also by their significant role in reconciliation procedures of trade data. Reconciliation procedures (Gehlhar, 1996; Gaulie & Zignago, 2010, Fortanier, 2016) attempt to provide a reconciled value for a bilateral transaction, usually as a weighted mean of differing accounts. Here, the weights reflect a measure of reliability from a reporter in each transaction. In constructing aggregatelevel indicators, weighted indicators are usually preferred because major discrepancies often arise from trades with smaller values. Adjusting measures of reliability for partner and product composition is also preferred, as often faring poorly on measures of bilateral asymmetry could be attributed to trading with poor reporters.

# 3.1 The Discrepancy Index

Here, we introduce the Discrepancy Index (DI) and investigate the main differences between the DI and other commonly used measures, demonstrating that the DI is both nonlinear to observed discrepancy and able to capture the direction of misreporting. Lastly, we introduce the aggregate-level indicators that are used to summarize the distribution of the DI to obtain important insights into trade reporting practices.

## Definition and empirical distribution

For any given year, given a pair of countries **A** and **B** and a product **C** , the Discrepancy Index for the reported import of country A from country B in product C can be defined as follows: where MABC is value in US dollars (USD) of A ’s reported import from B in product C and XBAC is value in USD of B ’s reported export to A in product C . Here, we rely on the definition of Ferrantino & Wang, 2008. For simplicity, time dimension is omitted from the notation. For any ordered pair of countries (A, B) and product C , DI ranges from -1 to 1. A value of -1 indicates an **orphan import** . DIABc = −1 , when A reports an import from B but B never reported the corresponding export to A (in product C ). A value of 1 indicates **lost export** . DIABc = 1 , when B reports an export to A but A never reported the corresponding import from B (in product C ).

9
<!-- Start of picture text --> M>X M<X<br>Overreporing import Overreporting export<br><t al<br>-1 0 1<br>Orphan import No discrepancy Lost export<br>M>0, X=0 M=X X>0,M=0<br><!-- End of picture text -->
<!-- Start of picture text --> 18<br>30<br>o 16<br>25 3<br>£14<br>c& 20 3 i?<br>ia F 10<br>a 15 Soo 8<br>- =<br>10 <e 6<br>g 4<br>5 roa<br>2<br>0 0<br>-1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1<br>Discrepancy Index Discrepancy Index<br><!-- End of picture text -->
The remaining 46% of transactions were reported by both the reporter and the corresponding partner, indicating a match. However, almost half of the matched transactions (or equivalently 22% of all transactions) have a Discrepancy Index in the interval between 0 and 1. Transactions falling within this interval suggest discrepancies that are least likely to be explained by logistic factors and may indicate data issues, poor reporting, or deliberate misreporting. However, a Discrepancy Index value outside the interval is necessary but not sufficient to conclude reliable reporting. Further analysis is required to determine the extent and reason behind the discrepancies. For example, if the Discrepancy Index of a bilateral transaction is -0.6, the reported import is four times larger than the mirror export. The Discrepancy Index should be below 0, but an important question is how much? Is there a negative value for the DI that is also unreasonable? This paper attempts to answer these questions by using the bilateral index outlined in (1) to derive indicators that can help capture the reporting patterns of specific countries and products. Before delving into the aggregate indicators that use the Discrepancy Index, it is important to understand its differentiation from other commonly employed measures of trade discrepancies. Outlining these differences shows how the Discrepancy Index can be used to enhance our understanding of discrepancies in international trade.

# Differences and similarities with other measures

Measures such as DI can be found in indexes such as the bilateral asymmetric rate introduced by Chen et al. (2022). Here, the bilateral asymmetric rate is defined similarly to the DI, with the difference that the authors take the absolute value of the numerator. Taking the absolute value of the numerator gives a measure that ranges from 0 to 1 and indicates the degree of asymmetry but not its direction. In other words, in the absence of the actual recorded trade value, this index cannot directly indicate whether exports or imports were overreported (or underreported). On the other hand, the DI ranges from -1 and 1 and its sign allows us to directly infer the direction of the discrepancy.

In addition, the DI is more suited for penalizing discrepancies that are least likely to be explained by logistic factors. This becomes evident when analyzing DI as a function of some comprehensive measure of discrepancy.

12
First, consider the **Cost, Insurance, Freight/Free on Board (CIF/FOB)** ratio, a simple ratio between the CIF valuation of the product (denoted as M ) made by the importing country and its equivalent FOB valuation (denoted as X ) made by the exporting country: The CIF valuation can be expressed relative to the FOB valuation in the following manner: In other words, the CIF valuation is equal to FOB valuation adjusted by rX , which can under optimal reporting conditions be considered as relative cost of insurance or freight. We acknowledge that in some cases, transport costs may not be a linear function of the size of shipment. However, because discrepancies occur broadly and for a variety of reasons beyond cost of insurance, r could be better described as a comprehensive measure of a range of factors contributing to discrepancy between reported imports ( M ) and exports ( X ). These factors could include not only CIF but differences in valuations due to exchange rates, misreporting, timing differences (goods leaving a country in one year might arrive in destination country next year), and even fraud or smuggling. Ideally, r would never be negative, as this would imply that CIF evaluation is less than its FOB equivalent. Yet, in empirical data reported exports sometimes appear larger than their import reporting of their partners. However, because we know that M and X are guaranteed to be positive numbers, we can at least say that r will be in the interval [-1, + ꝏ) and therefore the CIF/FOB ratio **(1.1)** will be greater than or equal to 0. In addition, this measure is equal to zero for lost export transactions and undefined for orphan import transactions. We note here that the CIF/FOB ratio is a linear function of r and can be expressed as The CIF/FOB ratio increases linearly with r. Now consider the Discrepancy Index defined as in (1.0): 13
Similarly, for CIF/FOB ratio, using (1.2), we can express the Discrepancy Index in terms of r as: In comparison to the CIF/FOB ratio, the Discrepancy Index is a **nonlinear function** of measure r . Notice that slope of DI is steeper when r is in interval [-1,0] compared with when r is positive. This signifies that DI is more sensitive to changes in r when r is in [-1,0] than when r is positive. In the context of empirical trade data, this implies DI more heavily penalizes situations when imports are underreported as opposed to when imports are overreported. We find these functional properties important because structural reasons that result in import overreporting are guaranteed as opposed to structural reasons for import underreporting (which are not).

# 3.2 Aggregate indicators based on the discrepancy index

The Discrepancy Index is a bilateral asymmetry measure that quantifies the level of divergence between reported data and its mirror on a specified product and year. The product itself may be on any level of product aggregation if the level of product aggregation is consistent in both records. That is, we can quantify the discrepancy of reporting between a pair of countries on a disaggregated and sectoral level. However, countries trade in various products (or sectors) with a large set of trading partners. Therefore, the value of the Discrepancy Index on a single transaction may not be representative of the reporting patterns of the involved countries or products. This necessitates creation of aggregate level indicators that can summarize important properties of the distribution of Discrepancy Indexes. In this section, we discuss the aggregate level indicators used throughout this report.

The definition of the Discrepancy Index in (1) outlines a variable whose value is determined by selecting an ordered pair of countries ( A , B ) and product C ( DIABC = DI((A, B), C) ). Thus, calculating the DIABC for all country pairs and product combinations (for any given year) will allow us to obtain a distribution of Discrepancy Indexes that summarizes general trends in reporting across countries and products.

14
For example, the distribution presented in Figure 2 was obtained by allowing A and B to vary over all countries and C to vary over all products on a 6-digit level of product aggregation in the H0 nomenclature. As such, it motivates important discussions regarding the prevalence of orphan imports in international trade. That is, it informs us about certain aspects of trade reporting across countries and products. However, the same distribution cannot be used to make meaningful observations about the quality of Senegal’s reported exports or the quality of reporting on insulated electric circuits. Therefore, we use the general form of the Discrepancy Index in (1) to derive variables that describe the reporting practices of countries and products.

If we keep country A fixed and allow country B and product C to vary, we can obtain a new variable DIa = DIaBC = DI((a, B), C) = DI((A, B), C|A= a) . This new variable (denoted as DIa ) will describe the import reporting practices of a specific country across all trading partners and products.

Conversely, if we keep country B fixed and allow for country A and product C to vary, we obtain a new variable DIb = DIAbC = DI((A, b), C) = DI((A, B), C|B= b) that describes the discrepancy in the transactions where country B is an exporter, i.e., transactions where country B reported exports or is the partner in an import transaction.

Finally, if we keep commodity C fixed and allow countries A and B to vary, we obtain a variable DIc = DIABc = DI((A, B), C|C= c) that describes reporting practices on a specific commodity C .

Therefore, DIa can help capture the **import** reporting practices of a specific country A , while DIb can capture the **export** reporting practices of a specific country B (across all products and partners). In addition, DIc helps capture reporting practices on a commodity basis. In fact, in the appendix, we present a total of six distinct variables that can be obtained from the general form of the DI , depending on what we keep fixed and what we allow to vary. Sometimes trade analysts may be interested in obtaining a variable that describes the export reporting practices of Senegal on insulated electric circuits. This is achieved by keeping country B and product C fixed while allowing country A to vary over trading partners.

15
The reporting practices of a country (or product) are therefore represented in the distribution of these variables. The shape of this distribution can therefore easily reveal several types of reporting problems.

An important aim of this paper is to develop measures that summarize these distributions and (in conjunction with other discrepancy measurements like the CIF/FOB ratio) indicate the type of reporting problem present. To this end, we define, evaluate, and assess a rich set of statistical measures derived from the DI .

The presented list of indicators below are statistical measures that summarize the properties of the Discrepancy Index distribution. They can be applied to any of the six variables outlined above, depending on the specific goal of the analysis. For simplicity, they are presented in the context of DIa , a variable that describes the import reporting practices of a fixed country across its imported products and trading partners. For each country a, we calculate the following:

**Average DI** - The average DIa of all transactions where country A is the importer. As previously discussed, because of differences in valuation methods the average DIa should ideally be slightly below 0, indicating that, on average, the reported imports were higher than the corresponding reported exports.

**Median DI** - The median DIa of all transactions. Ideally, the median DI should be (slightly) below 0, indicating that for most transactions where country A appears as an importer, the reported trade value for the import was higher than that for the export.

**First Quintile DI:** the value of the 25th percentile of the DIa distribution. It should be less than 0 but tends toward -1 for countries with a lot of orphan imports.

**Third Quintile   DI** - the value of the 75th percentile of the DIa distribution. Ideally, should be slightly below 0, but is above 0 for most countries due to the influence of lost exports.

**Standard deviation of DI** - The standard deviation of the DIa distribution. Ideally, it should not be high, indicating more consistent reporting across transactions.

16
**Weighted average of DI** - Weighted average of the DIa distribution, where weights correspond to trade value of transaction. Here, maximum recorded trade value from both accounts is taken as weight for the DI. While average and median give an estimate of how most transactions are reported, the weighted average gives an estimate of how most of the trade value is reported.

Note that minimum DIa and maximum DIa are not defined as indicators because they do not add any additional information, as they are -1 and 1 for all countries. This is because all countries have transactions that can be classified as orphan imports or lost exports. This does not mean that orphan imports and lost exports are ignored in the calculation of other indicators, i.e., they are still included in calculation of the mean, median, Q1, Q3, and standard deviation.

However, the inclusion of orphan imports and lost exports will affect some presented indicators more than others. To obtain a more accurate representation of a country’s reporting patterns, we contrast these indicators with indicators describing discrepancies in matched transactions, i.e., successfully mirrored records, and indicators describing the prevalence and weight of transactions reported by only one partner. The below indicators are used to evaluate the quality of successfully mirrored records, i.e., transactions reported by both countries.

**Average DI Matched** - The average DIa of all transactions where country A is the importer. As previously discussed, the average DIa should ideally be slightly below 0, indicating that, on average, the reported imports were higher than the corresponding reported exports.

**Median DI Matched** - The median DIa of all transactions. Ideally, the median DI should be (slightly) below 0, indicating that for most transactions where country A appears as an importer, the reported trade value for the import was higher than the reported trade value for the export.

**First Quintile DI Matched:** the value of the 25th percentile of the DIa distribution. It should be less than 0 but tends toward -1 for countries with a lot of orphan imports.

**Third Quintile   DI Matched** - the value of the 75th percentile of the DIa distribution. Ideally, should be slightly below 0, but is above 0 for most countries due to the influence of lost exports.

**Standard deviation of DI Matched:** The standard deviation of the DIa distribution. Ideally, it should not be high, indicating more consistent reporting across transactions.

17
**Weighted average of DI Matched:** The weighted average of the DIa distribution where weights correspond to the trade value of the transaction. Here, maximum recorded trade value from both accounts is taken as weight for the DI. While average and median give an estimate of how most transactions are reported, weighted average gives an estimate of how most of trade value is reported. Figure 1 shows that a non-negligible part of transactions (around 50%) will be either orphan imports or lost exports. To account for this and capture the extent to which orphan imports (and lost exports) appear and influence the quality of reported trade, we define the following indicators:

**Percentage of orphan imports:** the percentage of all transactions for which only the importer reported the transaction. Equivalently, it is the percentage of all transactions of country A in which DIa is equal to -1.

**Percentage of lost exports:** the percentage of all transactions for which the importing country did not report any trade value, yet the transaction was reported by the exporting country. Equivalently, it is the percentage of all transactions of country A in which the DIa is equal to 1.

**Trade value of orphan imports to total reported imports (ratio)** . A ratio that indicates percentage total reported import value contained in orphan imports. It indicates the influence of orphan imports on the overall reported import trade value of a country (or product).

**Trade value of lost exports to total reported imports (ratio)** . A ratio that informs us about the severity of lost exports and provides a comparison between the sum that was not reported at all by the importer and the sum that was reported by the importer.

While the first two indicators describe the presence of orphan imports, the last two indicators measure their importance (in terms of trade value). As we will see, for many reliable reporters, the percentage of lost exports may be high; however, the actual trade value contained in those transactions is relatively minor compared to the total reported trade value.

Finally, to capture the influence of under- and over-reporting on the overall trade value, we define the following indicators: 18
**Total reported imports to mirror ratio (CIF/FOB ratio)** . This is the ratio between the total reported import value by a country and the value in the mirror data for the same transactions. Using the notations above, we can formally define this as

For any given country A , we expect this ratio to be (close to or) above 1, showing that total reported trade value of imports is slightly above total reported trade value of exports. As previously discussed, our expectation is based on structural reasons related to how trade is evaluated. This ratio is referred to as CIF/FOB ratio and is sometimes used as a proxy for transportation costs (Carrère and Grigoriou, 2015). However, as we will see, there are countries whose ratio is less than one, implying negative transportation costs. Also note that unreasonably high values for this ratio could be indicative of other reporting problems. For example, Lebanon in 2017 had a ratio of six, a clear outlier compared with other countries.

**Total reported imports to mirror ratio (CIF/FOB ratio) for matched transactions** . While the CIF/FOB ratio looks at the total reported imports (and mirror), which includes both orphan imports and lost exports, this indicator compares the total reported sum only on transactions reported by both the importer and the exporting countries. It is a CIF/FOB ratio for transactions where the DIa is above -1 and less than 1. Ideally, this ratio should also be (close to or) above 1. There are some countries for which the overall CIF/FOB ratio will be above 1, while the CIF/FOB ratio of matched transactions is slightly below 1. These countries report (slightly less) than their partners in the matched transactions yet have a non-negligible value of trade in their orphan imports.

**Missing value ratio** - The total trade value reported as a surplus (by the partners) as a ratio of total reported imports. This indicator takes all transactions for which the DIa >0 (meaning the exporter reported more) and sums the difference in reported sums. This value is always positive and can be interpreted as the trade value that is missing in the import reporting of the country. The missing trade value is represented as a ratio of total reported imports. Large values for this ratio show reporting problems. This tells us that the value that was not reported by the importing countries is large. When the value of this indicator is above 1, it suggests that there were more unreported trade values than reported.

19
While outlined indicators are presented in context of the DIa variable, which describes discrepancies in import reporting, they can be applied to any of the six variables discussed above, including the general DI distribution outlined in Figure 1. This report uses indicators related to DIa , DIb , DIc variables. Thus, we supply country level indicators that can describe both import and export reporting practices of a country and product level indicators that describe the quality of reporting on products.

# 4. Data description

This analysis relies on UNSD’s Comtrade annual database, the largest and most widely used resource in empirical investigations of international trade. It covers all trade transactions recorded in the Harmonized System (HS) and Standard International Trade Classification (SITC) product nomenclatures. See Appendix 6 for details on the database tables. The Annual Discrepancy Index and Trade Reporting Indicators below currently provides discrepancy measurement in HS Nomenclature, while SITC was used for nomenclature comparison. The Annual Discrepancy and Reporting index data can be downloaded from the World Bank Data Catalog using the links below. See Appendix 6 for details on the database tables.

- <u>Discrepancy Index Annual for H5 nomenclature</u>

- <u>Discrepancy Index Annual for H4 nomenclature.</u>

- <u>Discrepancy Index Annual for H3 nomenclature</u>

- <u>Discrepancy Index Annual for H2 nomenclature</u>

- <u>Discrepancy Index Annual for H1 nomenclature</u>

- <u>Discrepancy Index Annual for H0 nomenclature</u>

- <u>Trade reporting Indicators all nomenclatures</u>

- <u>Trade reporting Indicators by commodity all nomenclatures</u>

Country and product level trade reporting indicators provide coverage for quality of reported trade across the various versions of the Harmonized System nomenclatures (H0 to H5). The number of countries included in our database for each year and nomenclature version is detailed in Appendix tables 3 through 14. We provide both import and export reporting indicators for each country and product. The quality of reported trade for countries and products is evaluated at the 6-digit product groups in the HS nomenclature.

20
<!-- Start of picture text --> 90%<br>3<br>5 80%<br>Vv<br>2<br>3mL 70%<br>°-<br>—<br>£ 60%<br>io<br>3s<br>50%<br>un<br>o<br>VV<br>3 40%<br>Pa<br>°<br>Do 30%‘°o<br>~<br>£<br>o<br>2 20%<br>a<br>a<br>10%<br>1970 1980 1990 2000 2010 2020<br>Year<br><!-- End of picture text -->
||S1<br>|s2<br>|s3<br>|Nomenc<br>s4<br>HO<br><br>|lature<br>H1<br>|H2<br>|H3<br>|H4<br>|H5<br>|
|---|---|---|---|---|---|---|---|---|---|
|2022|®|Co)|e|®<br>o)|e|(e)|°|e|®|
|2021<br>|e<br>|®<br>|°<br>|®<br>°<br><br>|e<br>|e<br>|fo)<br>|@<br>|te)<br>|
|2020|S)|fe)|fe)|@<br>le)|fe)|e|e|e|(a)|
|2019<br>|°<br>|®<br>|©<br>|@<br>fe)<br><br>|fe)<br>|@<br>|ie)<br>|fe)<br>|to)<br>|
|2018<br>|e<br>|(o)<br>|°<br>|e<br>°<br><br>|e<br>|e<br>|°<br>|9<br>|io)<br>|
|2017|r)|°|fe)|e<br>°|@|@|fo)|fe)|(o)|
|2016|r)|®|®|®<br>°|°|e|fe)|e||
|2015<br>|fe)<br>|[e)<br>|°<br>|@<br>°<br><br>|°<br>|°<br>|9<br>|fe)<br>||
|2014<br>|)<br>|e<br>|fe)<br>|@<br>°<br><br>|e<br>|@<br>|e<br>|°<br>||
|2013|e|C)|®|®<br>9|@|e|°|fe)||
|2012|e|e|le)|®<br>°|@|e|e|e||
|2011<br>|®<br>|@<br>|°<br>|®<br>e<br><br>|CY<br>|°<br>|fe)<br>|||
|2010|®|®|fe)|e<br>°|e|e|fe)|||
|2009<br>|fo)<br>|@<br>|°<br>|®<br>°<br><br>|®<br>|@<br>|°<br>|||
|2008|)|fo)|e|e<br>@|@|@|°|||
|2007|©|@|fe)|®<br>fo)|©|e|@|||
|2006<br>|®<br>|C)<br>|r)<br>|e<br>|@<br>|fe)<br>||||
|2005|(o}|®|e|C)|@|fo)||||
|2004|rey|@|@|C)|@|@||||
|2003|@|i)|e|e|r)|@||||
|2002<br>|@<br>|ie)<br>|e<br>|fa)<br>|fe)<br>|e||||
|2001|@|()|@|@|@|||||
|2000|@|@|@|e|@|||||
|1999|e|®|@|@|ts)|||||
|1998|@|®|@|e|e|||||
|1997|®|@|r)|@|©|||||
|1996|@|@|@|@|fe)|||||
|1995|©|@|@|e||||||
|1994|@|@|@|fo)||||||
|1.1993<br>|©<br>|®@<br>|@<br>|@<br>||||||
|<br>91992<br>|©<br>|e<br>|e<br>|e<br>||||||
|~1991<br>|@<br>|@<br>|@<br>|°<br>||||||
|1990|©|)|@|te)||||||
|1989|@|@|@|Ce)||||||
|1988|©|fe)|@|(a)||||||
|1987<br>|e<br>|@<br>||||||||
|1986|®|®||||||||
|1985<br>1984<br>|(a)<br>®@<br>|@<br>®<br>||||||||
|1983|@|©||||||||
|1982<br>|®<br>|e<br>||||||||
|1981|9)|®||||||||
|1980|@|@||||||||
|1979|@|®||||||||
|1978|CY|@||||||||
|1977|@|@||||||||
|1976|)|fe)||||||||
|1975<br>|@<br>|||||||||
|1974|@|||||||||
|1973|@|||||||||
|1972|@|||||||||
|1971<br>|@<br>|||||||||
|1970|@|||||||||
|1999<br>i968<br>|@<br>@<br>|||||||||
|1967|@|||||||||
|19660<br>|@<br>|||||||||
|1965<br>|@<br>|||||||||
|1964<br>|@<br>|||||||||
|1993|@|||||||||
|1962|@|||||||||
<!-- Start of picture text --> The Standard International Trade Classification (SITC Rev.1) The Harmonized System (HS 1988)<br>100%y I SFE 100%i ee,<br>py 8A wae7 r " - Ki‘<br>$= 22 o- rn a. ' 4;<br>so 80% 4<br>a<br>a<br>7<br>“<br>] 60% 60% f<br>a v=) ’<br>BS%cooBS6c ii<br>25 4<br>ao ao i<br>40% 40% é<br>‘<br>‘fy<br>n<br>Ui<br>20% "<br>20%,<br>0% 0%<br>1970 1980 1990 2000 2010 2020 1990 1995 2000 2005 2010 2015 2020<br>Year Year<br><!-- End of picture text --> — Dual recording - ‘Export value with dual recording ~ -Import value with dual recording
This has important implications for empirical investigations of international trade. Typically, studies use trade data from specific nomenclatures, ensuring consistent comparisons for products in the years in which that nomenclature is used. However, this approach can inadvertently include years marked by major discrepancies due to the nomenclature’s introduction. This is especially significant given that many empirical applications of trade data bypass nomenclature conversions. Some studies have moved toward replication of findings across different nomenclatures to show the robustness of the obtained results.

A notable finding from Figure 5 is that, although only about half of all records are successfully mirrored, a substantial part of the trade value in cross-border transactions can be compared against mirror records, applying equally to both imports and exports. The percentage of trade values verifiable against mirror data is impressively high. This has important implications for reconciliation procedures of trade data. Reconciliation procedures supply a reconciled value for records that can be successfully mirrored, and Figure 5 demonstrates that a considerable volume of global trade is contained in such records. However, caution is still necessary as the large concentration of trade volumes in few reporting countries obscures persistent reporting problems in data quality from some reporters.

The high percentage of trade value with successful mirroring does not necessarily imply accuracy in reporting and absence of discrepancies. To investigate whether discrepancies in reporting have decreased, for each year, we calculate the weighted discrepancy index across all bilateral transactions at the highest level of product aggregation. This will allow us to explore the magnitude and direction of the discrepancy in international trade flows. In addition, we calculate the weighted discrepancy index for successful mirrored records. This will allow us to explore whether the improvements were mostly due to an increasing number of reporters or accompanied by an increase in the quality of reporting on successfully mirrored records. We observe a welcoming trend in which the divergence in recorded values, as indicated by the weighted discrepancy index, is declining. Thus, we note not only an increase in dual recording but also a reduction in the discrepancy level between recorded values. Like the percentage of mirrored records and verifiable trade value, significant differences between reporters remain. Appendix Figure 6 hints at the differences in the quality of reporting from different reporters in the UNSD Comtrade database. Therefore, these differences are explored in detail in the later subsection.

24
Lastly, Figures 3 and 5 underscore that reporting has tended to be less dependable for the most recent years. While there is significant interest in the latest data, it is also more prone to discrepancies as recent data are continuously updated in the UN Comtrade systems.

# **Discrepancies increase with product disaggregation.**

Discrepancies in merchandise trade statistics tend to intensify with greater levels of product detail, suggesting misclassification or misinvoicing. We support these findings by comparing differences in successful mirroring between highest and lowest level of product granularity within HS and SITC nomenclatures. We find that an increase of product granularity significantly decreases the number of successfully mirrored records and the portion of trade value that can be verified (Figure 6). This pattern was consistently observed across the various nomenclatures considered in this study. Two of them are highlighted in Figure 6 and seem to suggest less reliable recording of higher-resolution data. Consider, for instance, percentage of dual recording for H0 nomenclature in 2017. At aggregate level, 56% of records demonstrate dual recording. However, when zooming into a more granular 6-digit level, this figure drops to 39%.

Typically, some countries will be impacted by product disaggregation to a larger extent. Figure 7 shows the average annual per country reduction in successful mirroring that occurs in disaggregated data for the selected nomenclatures. In the H0 nomenclature for 2017, the rate of successfully mirrored records at the most detailed level fell by an average of 37% compared to the aggregated level. Similarly, the share of verifiable trade value decreased by an average of 15% at the most detailed level. Consequently, countries displaying larger reductions in verifiable data at finer levels of product detail are likely to see more substantial distortions in their export and import structures.

A common belief is that dual recording discrepancies at broader categorizations often stem from errors in attributing trading partners. In contrast, discrepancies at finer levels typically indicate challenges in correct product categorization. This divergence in matched transaction percentages across product granularity levels is crucial to note since many applications use detailed data. Although high-resolution data is coveted for its detailed insights in empirical research and policy formulation, it is also the most vulnerable to issues such as misclassification and mis-invoicing.

25
<!-- Start of picture text --> The Standard International Trade Classification (SITC Rev.1) The Harmonized System (HS 1988)<br>100% 100%<br>80%, 2 /~~ ==" NN - ts -i, me tse et 80% f' aTe - ee --- poctsecn \v:<br>U<br>f<br>i,<br>LF<br>eo 60% e 60%<br>s sures =<br>€ yea Nt* . = denende--=4,<br>y or Lawl Y Sf errr<br>o yy o ¢<br>a 40%'7 a 40% L' -<br>1<br>=] 4 4<br>4<br>20% 20%: + z 4<br>ir<br>£<br>4<br>0% 1970 1980 1990 2000 2010 2020 0% 1990 1995 2000 2005 2010 2015 2020<br>Year Year<br>— Dual recording - Aggregated — Import value with dual recording - Aggregated<br>= «Disaggregated = «Disaggregated<br>Most disaggregated Most disaggregated<br>The Standard International Trade Classification (SITC Rev.1) The Harmonized System (HS 1988)<br>100% 100%<br>&&<br>aa<br>33<br>&.=+<br>Z80% Z 80%<br>s£ |Ss 4<br>oa&co<br>°°<br>J2<br>&&<br>22£5 60% 25 60%<br>=—<br>H40% H 40%<br>==**<br><=°°<br>:2<br>é& 20% 2& 20%<br>&&<br>
2<s<br>cia 1995 2000 2005 2010 2015 O% 2002 2004 2006 2008 2010 2012 2014 2016 +2018<br>Year Year<br>95% Confidence Interval — Mean<br><!-- End of picture text -->
# **Discrepancies distort trade balances between countries.**

The previous subsection highlighted how discrepancies in detailed data have the potential to introduce notable distortions in the import or export structure of reporters. However, we show that reporting discrepancies can distort findings even at the most aggregate level. To prove the extent to which the specific choice of international merchandise trade data impacts its findings, consider the case of trade balances. Trade surpluses and deficits are often important drivers of empirical research and policy design and rely on a simple calculation using exports and imports between a pair of countries. However, both export and import values can be estimated using two separate sources. As suggested by trade balances (Farhad et al., 2023), they can be severely impacted by reporting discrepancies. In calculating the trade balance, it is important to use either the CIF or FOB valuation for both the recorded import and export, so as not to distort the balance with CIF rates that inflate the import value. This means that any calculation of the trade balance using unmirrored (one-sided) data will naturally be biased due to the structural discrepancy that occurs in the valuation method. We empirically demonstrate this point by calculating trade balances for all countries using unmirrored data, i.e., using FOB valuation for exports and CIF valuation for imports.

As an example, consider the trade between Madagascar and France. France has consistently been one of Madagascar’s top export destinations, accounting for 21% of Madagascar’s exports in 2015 and 17.5% of Madagascar’s exports in 2021. In addition, France is the largest European origin of Madagascar’s imports. Even though both countries have consistently reported a trade surplus in favor of Madagascar, they have different accounts regarding its magnitude, as shown in Figure 8. For example, in 2015, Madagascar reported a trade surplus of 253 million USD with France. However, according to France's accounts, the same surplus amounted to only 84 million USD.

27
<!-- Start of picture text --> Madagascar's annual trade surplus with France<br>—— 85 reported by Madagascar<br>400 — as reported by France<br>350<br>300<br>-"<br>O 250<br>2<br>< 200<br>2<br>= 150<br>=<br>100<br>50<br>Qo<br>2000 2002 2004 2006 2008 2019 2012 2014 2016 2015 2020<br>Year<br>1.A) Exports from Madagascar to France 2.B) Exports from France to Madagascar<br>et] as reported by Madagascar —<br>soa as reported by France 500] —asmo portedreported byby Maceguscar Fresoe<br>asa 450]<br>baa sq &3 ae9]<br>5 #0 =<br>=* 450aay350) =“ 350)200]<br>13]<br>300)<br>250 i)<br>spe GREE RUM EGE ROB EAH BBLS GG 2b ants one 2000 2002 «200 2002008 | 2080 202 aod) 2OB | 2020<br>Year Year<br>1.B) 2.B)<br>120k<br>120k<br>00k<br>20ak|<br>ov ‘B0k] wo<br>= = sak]<br>==<br>|60k 7<br>aF==SeokF<br>40k 49k<br>20k| zak<br>oO! =i “0.5 a a5 Fy o! =2 -0.5 0 0.5 t<br>Diserepaney Index Diserepaney Index<br><!-- End of picture text -->
By looking at mirror data, analysis suggests that even though Madagascar’s exports to France were reported by both countries over time (Figure 8. **1.A** ), France’s exports to Madagascar are more contentious (Figure 8. **2.A** ) and contribute much more to observed differences in the reported trade balance between the two countries. This is confirmed by looking at Figure 8. **1. B** and Figure 8. **2.B** . Figure **1.B** shows that most of the trade value in export flow from Madagascar to France is reported with a DI of around zero and lies within a range that could be explained by logistic factors. However, the latter ( **2.B** ) reveals that most of the trade value in export flow from France to Madagascar is traded with a DI above 0, and therefore shows either overreporting of exports from France or underreporting of imports from Madagascar. In fact, more than a third of France’s reported exports to Madagascar were traded with a DI of over 0.95, showing little (if any) account of this value in Madagascar’s imports. Therefore, in case of estimating Madagascar’s exports to France, data are consistent in both sources (except for 2007 and 2008) and would seem to suggest that the main difference is CIF/FOB adjustments to reported trade value.

However, in the case of estimating France’s exports to Madagascar, we have different accounts of the value. Depending on the choice of underlying data, the trade deficit could increase threefold. This case study is explored in detail in section A.6) of the Appendix. In addition, users can construct similar profiles and estimates of trade balances by using the software tools accompanying this report.

Mirror data, therefore, is a necessary tool for deriving correct estimates of trade balances. Yet, even when mirror data was applied to obtain FOB and CIF merchandise trade balances, discrepancies persisted and influenced the obtained results. Appendix Figure 33 highlights that the disagreements between FOB and CIF estimates can range from very mild (Argentina) to severe (The Bahamas, Lithuania).

# 5.2 Estimating the reliability of reporters

This subsection explores how country trade reporting indicators can be used to identify and understand different reporting encountered in trade data. The indicators allow us to isolate countries that did not report any imports or exports for the given year and product nomenclature.

29
For example, in 2017, in the H0 nomenclature, there are 208 countries that appeared as importers (either appear as import reporters or partners in an export transaction). However, 36 countries did not report any imports themselves (Table 1). For these countries, mirror data will remain the only way to estimate imports. Tables 3 through 14 in the Appendix show the number of import and export reporting countries throughout the different nomenclatures considered in the study. Finding these countries is a crucial step in empirical investigations, as analysis that considers only reported imports (or exports) to estimate international trade flows will inevitably miss those countries whose trade activities are visible only in mirror data. While these countries represent a small portion of the global recorded trade value, certain regions and income levels tend to be overrepresented in the group of non-reporters (Appendix Figures 13 to 19).

**Table 1: In 2017 number of countries that reported by nomenclature as of Jan 20, 2023**

|Nomenclature|Number of export reporters|Number of import reporters|
|---|---|---|
|H0|172|172|
|H1|172|172|
|H2|172|172|
|H3|168|168|
|H4|158|158|
|H5|101|101|

Note that the most noticeable difference in the number of reporting countries appears for H5 nomenclature. As suggested in the previous subsection, this result could be partially attributed to the recency of H5 nomenclature. In addition, to a smaller pool of reporters, values of trade reporting indicators calculated based on this nomenclature also show more severe discrepancies. For example, in 2017 the average percentage of lost exports for the import reporting of countries in the H0 nomenclature was 34%, while for the H5 nomenclature it was 60%. In fact, rank correlations between indicator values in each nomenclature (excluding the H5 nomenclature) are remarkably high. This suggests that countries’ reporting styles are mostly consistent across the different nomenclatures, but caution is still necessary when analyzing data from most recent product nomenclatures.

30
Next, we explore how to apply the trade reporting indicators to decide when the use of mirror data is a more appropriate alternative than reported data. The following discussion provides guidelines; however, we encourage the user to perform more due diligence based on the reporter, partner, and product of their interest. To identify countries for which more information can be obtained by looking at the mirror rather than the reported import data, we consider the following indicators.

Using the **CIF/FOB ratio,** we investigate whether the trade value of a country’s reported imports is smaller than the total trade value contained in the mirror. A CIF/FOB ratio that is less than one implies that looking at mirror data might be a better way to estimate the volume of imports for this country. Moreover, when the **matched CIF/FOB ratio** is less than 1, the trade value of a country’s reported imports, on the transactions that were reported both by the importing and exporting country, is smaller than the total trade value that their partner reported. Although usually correlated, an interesting case to consider here is when overall CIF/FOB ratio is greater than one, but matched CIF/FOB ratio is not. Because orphan imports are also considered in calculation of overall CIF/FOB ratio but are not considered in calculation of matched CIF/FOB ratio, such cases indicate that while more information about the trade value is obtained by looking at mirror data on transactions that match, there is a considerable loss of information that will occur if we disregard the orphan imports. We outline 22 countries for which this is the case in the Appendix.

Moreover, we complement the CIF/FOB ratios, using country trade reporting indicators which are based on the DI. As discussed in the introduction of the indicators, the overall DI may be skewed by presence of orphan imports and lost exports yet remains useful for analysts seeking for a summary measure of reporting across transactions. However, reliable reporters are expected to have a **median DI on matched transactions** that is smaller than zero, indicating that on most transactions with dual recording, reported imports were larger than their corresponding reported exports. When this is not the case, i.e., when matched DI is greater than zero, it shows that on most transactions, more information is contained in the mirror.

Note that CIF/FOB ratios and DI statistics describe a slightly different dimension of a country’s reporting style. While CIF/FOB ratios account for reported sums, DI statistics inform us about the presence of discrepancies across transactions (which vary in sum and importance). This means that a country can have satisfactory CIF/FOB ratios while having suboptimal DI values.

31
This scenario occurs when the number (and trade value) of orphan imports is large when considering the overall DI and CIF/FOB ratio. This scenario can also occur when most transactions were not accurately reported by the importing country, yet those transactions that carry a larger weight (large trade values) were more accurately reported. In fact, we find 16 countries (Table 15) for which this is precisely the case. Of the 16 countries, Pakistan is the only country for which the median matched DI was substantially larger than 0, yet the CIF/FOB ratio was larger than one, indicating that Pakistan had reported more in terms of overall trade value relative to the mirror, yet it reported less than the mirror on most transactions.

Also note that the exact opposite could also occur. That is, there are 35 countries (in Table 16) for which the matched CIF/FOB ratio is less than 1, yet the matched median DI is below 0. While these countries report a larger import trade value than the mirror on most transactions, there are some important transactions, in terms of trade value, that were inaccurately reported.

The weighted variant of the DI might be a better suited indicator for those seeking to strike a balance between the CIF/FOB ratio and the average (or median) DI. The Weighted DI for the export reporters in the H0 nomenclature for 2017 is given in Figure 9. The highlighted countries, such as the export overreporting from Hong Kong SAR, China, and Singapore or the export underreporting of Saudi Arabia have been well documented in the literature (Ferrantino & Wang 2008; Farhad et al., 2023).

32
<!-- Start of picture text --> Exporters and their weighted Discrepancy Index<br>worldEurope & Central Asia East Asia & Pacific iserepaney,.<br>DEU FRA CHN<br>8% 3% 15%<br>NLD ITA GBR<br>3% 3% 2% Overreporting export<br>JPN HKG SGP<br>4% 3% 2%<br>RUS ESP POL CZE<br>2% 2% 1% 1%<br>MYS THA AUS<br>IRL SWE HUN NOR 2% 1% 1% 0<br>BEL 1% 1% 1% 1%<br>2%: KOR<br>TUR DNK —JROM PRT go |0270% |37<br>ae 1% 0.402%| 0.345%<br>—<br>CHE9 [oe 1% DN PAE<br>2% AUT 0.474% niall ° =<br>1% FIN gy eekr= “Ee inti]<br>0.408% 0.166% [i a<br>North America Latin America & Caribbean Middle East & North Africa South Asia Underreporting export<br>USA CAN MEX BRA SAU ARE IND :<br>9% 2% 2% 1% 1% 1% 2%<br>1<br>nN<br>9/ On eR Sub-Saharan=<br>CHL 0.39% 0.369% 0.341% Oke eT]<br>ARG0.399% a A)Beae||":0.388% e =- 1% BciaeEEE<br>0.352% eeeies?a se aoeDEAS | Saeee [|= S= Sess.ca lalee 4<br><!-- End of picture text -->
<!-- Start of picture text --> a eC eC<br>eseCeC<br>eC<br>EC ©<br>a Ec es es<br>=<br>Sc<br>SO<br>a<br>Se CO<br>es22<br>[ce 0326 [ose osetete<br>2S<br><!-- End of picture text --> a eC eC eseCeC eC EC © a Ec es es = Sc SO a Se CO es22 [ce 0326 [ose osetete 2S
<!-- Start of picture text --> .<br>° e e ° e °<br>g 4 e e e e e e<br>i<br>oo2<br>3 o. ° , ° i 0 nr)° hd. ° °BrOS 9 ° eo . . wee° Ht tere oe fo ow °<br>23—°e.°°<br>€as<br>2<br>2 2<br>=s e e e ° e °<br>‘iE : : : . .<br>an | oe oo wes rer a &<br>5 7 ° ° fat ae ° Bo ° °<br>=z ° 3 os ete ye<br>e eo °<br>0.8<br>Bos<br>g 0.2 og ° ootCH e ae" ee? angi ° . of — ° se °<br>28 -0.2 4% ° ° ° ° ee° o 6 eBe . ° Se. eo, ° Sala° e . °<br>5eo85 ° ° : ° °<br>8 et _! 4 0 oot a 0 : cC<br>z ‘r oeeoe°S, os seeee steewg °° 2 Sees@ coe . ome ge tt ogresec0 ©<br>2 eo @ ° fo,<br>e e ome ° e ° e eo? e<br>235<br>2 ° ° ° e ° °<br>Bs3<br>~5aSe .1 .:eCd °ooof ° ® . ceeee°od . eo @Joo° oo ° °dio° eee° . ohO°y lo<br>5 ee 3 oa 280 © mae? o 32 es 4 ee<br>3<br>Bse2 5 A 5 5 .<br>s 4<br>33> 38 ° %. ee oa) ° e 8 . 2°<br>ae 2 ° e ° °<br>i]g 0 % Ms ° e te :., a oie m— ) oo ° lig° of 6 eeeo?<br>0 2 4 6 0 1 2 3 5 0 0s 0 os 02 04 06 08 0 05 1 15 0 2 ‘<br>cif fob ratio matched cif fob ratio weighted DI matched median DI percentage lost export lost exports to reported imports unaccounted value ratio<br><!-- End of picture text -->
<!-- Start of picture text --> TGO<br>5 e<br>fe} 4<br>2<br>;oO<br>v 2<br>= LCA<br>>@<br>= &<br>c 2<br>a7) e<br>YLa &.<br>1 @<br>:<br>Po RWA SYC PSE<br>5 bet. ic) e =<br>0 1 2 3 4 5<br>CIF/FOB ratio<br><!-- End of picture text -->
networks in discrepancy modeling. Network-based centrality measures can easily inform us about the extent to which poor reporting is influenced by trading with poor reporters. Both approaches are explored in the software tools accompanying this study. The application of network-based centrality measures allows us to compare, for example, the weighted discrepancy index for import reporting of a country with the average weighted discrepancy index for export reporting of its trading partners.

To further illustrate how the trading partners of a country might influence its trade reporting indicators, we highlight the case of Senegal in the subsequent section.

Lastly, we highlight that one of the explicit goals of this study is to provide resources for researchers aiming to track discrepancies to specific causes, whether logistic or otherwise. A consideration of the logistic reasons contributing to the observed discrepancies is given in Appendix Figures 28, 29 and 30. In particular, we approximate the logistic causes using distance and contiguity, a common approach in the literature. We find statistically significant relationships, suggesting that our reporting indicators are correlated with these factors. We also show that our reporting indicators are correlated with statistical performance indicators (Dang et al., 2023). These findings offer support to the idea that the statistical capacity of a country may be an important contributor to the quality of its reported trade.

37
<!-- Start of picture text --> 14 ios As reported by partners<br>[As reported by SEN<br>12 :<br>we «210 7<br>vi 8 os ita<br>c“a<br>26<br>=<br>4<br>weapesesanmaa?”<br>o<br>1990 1995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
<!-- Start of picture text --> ~°~ CIF/FOB Ratio -*- Median DI<br>Pit tee P Saal,<br>1 RADET A ED .reseT | > Ne.~ we a7 ¢ = =<, -= a ~ hw «<br>i} -—- OS aap re ee Sr ig<br>0.5 7<br>&<br>0 ns ae pd A pikeitat cs i<br>: eoeegesstmecomee”<br>-0.5 + wa<br>+,*, amen ie "28<br>ta rl<br>1990 1995 2000 2005 2010 2015 2020<br>SM +++» As reported by partners<br>— As reported by SEN<br>4M “F<br>O 3M R # ws<br>o3.<br>=<br>-vo<br>obsctassessstees<br>1990 1995 2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
For this reason, in the case of export reporting, Senegal’s own reports may be preferred. BACI data also seems to prefer these reports over the mirror, mainly because Senegal’s export destinations are much poorer reporters than Senegal’s import partners. This may be counterintuitive to the conventional approach, where import data is always preferred because of reliability assumptions. A more detailed look at Senegal’s trade reporting is given in section A6 of the Appendix.

# 6. Conclusion

This paper offers a review of bilateral trade reporting discrepancies seen in the UN Comtrade database, the largest and most widely used database for empirical investigations of international trade. As such, this paper is intended to serve as a guide for users on when the use of mirror trade data is appropriate. It outlines that (1) discrepancies in international trade reporting can occur because of both logistic and non-logistic factors. Logistic factors relating to reporting guidelines or valuation methods are guaranteed to induce certain types of discrepancies, such as an import valuation that is higher than its corresponding export valuation. Therefore, there is a clear need to define measures of bilateral asymmetry that penalize discrepancies that are least likely to be explained by some logistic constraint.

To measure these discrepancies, we (2) introduce DI as a measure of bilateral asymmetry and derive indicators that summarize some properties of the distribution of this index. We show that compared to similar measures, the DI is neatly bounded between -1 and 1, penalizes export overreporting more and can directly indicate the direction of discrepancies. Finally, (3) we also show how our different reliability indicators could be used to infer several types of reporting problems at the country, commodity, or trade partnership level. These tools are intended to support any analysis that examines bilateral trade asymmetries on a case-by-case basis and analysis aiming to track reporting discrepancies to specific causes, whether logistic or otherwise.

However, it should be noted that many important limitations remain. The problem of tracking reexports and the non-uniform standard by which they are reported still contribute to many orphan imports and lost exports, which affect the data quality of countries. We note that some indicators proposed here will also be influenced by the number and size of these transactions. To mitigate 40
the influence of unmatched transactions, this study introduces additional indicators that operate solely on successfully mirrored transactions.

Taken together, these indicators provide a more holistic overview of whether reporting problems of countries can be attributed solely to the problem of re-export reporting and tracking. In addition, it is worth noting that indicators introduced here will not only reflect a country’s own reporting quality, but also be influenced by the reporting quality of its partners. Even if a country accurately reports its data, it may fare poorly on these indicators because of poor reporting from its trading partners. Future work should focus on more sophisticated approaches such as network structures, as a potential model for understanding how the value of country indicators could be influenced by the trading partners to which they are connected.

Moreover, certain influencing factors behind trade discrepancies remain ambiguous. While valuation guidelines inevitably introduce discrepancies, it is challenging to determine the extent to which other logistical issues contribute to these discrepancies. For example, while currency conversion issues and the time of arrival of goods may cause an FOB valuation that is higher than its corresponding mirror, the prevalence of such problems in empirical data remains difficult to assess.

This study underscores the complexities surrounding trade reporting asymmetries and offers tools to navigate them. Consequently, this study contributes to the ongoing discourse regarding the quality of reported data in international merchandise trade and its implications for empirical investigation.

41