Global Trade in Fakes

Annex A. Methodological notes

A.1. Constructing the General Trade-Related Index of Counterfeiting for products (GTRIC-p)

GTRIC-p is constructed through four steps:

1. For each reporting economy, the seizure percentages for sensitive goods are calculated.

2. For each product category, aggregate seizure percentages are calculated, taking the reporting economies’ share of total sensitive imports as weights.

3. From these, a counterfeit source factor is established for each industry, based on the industries’ weight in terms of total trade.

4. Based on these factors, the GTRIC-p is calculated.

Step 1: Measuring reporter-specific product seizure intensities

${\tilde{v}}_i^k$ and ${\tilde{m}}_i^k$ are, respectively, the seizure and import values of product type k (as registered according to the HS on the two-digit level) in economy i from any provenance economy in a given year. Economy i’s relative seizure intensity (seizure percentages) of good k, denoted below as ${\gamma }_i^k$ is then defined as:

${\gamma }_i^k=\frac{{\tilde{v}}_i^k}{\sum _{k=1}^{\stackrel{-}{K}}{\tilde{v}}_i^k}$, such that $\sum _{k=1}^{\stackrel{-}{K}}{\gamma }_i^k=1$ $\forall i \in \{<!--\; -->1,\dots ,\stackrel{-}{N}$}

$k=\{<!--\; -->1,\dots ,\stackrel{-}{K}\}$ is the range of sensitive goods (the total number of goods is given by K) and $i=\{<!--\; -->1,\dots ,\stackrel{-}{N}\}$ is the range of reporting economies (the total number of economies is given by N).

Step 2: Measuring general product seizure intensities

The general seizure intensity for product k, denoted ${\mathbf{\Gamma }}^{\mathit{k}}$, is then determined by averaging seizure intensities, ${\gamma }_i^k$, weighted by the reporting economies’ share of total sensitive imports in a given product category, k. Hence:

${\mathrm{\Gamma }}^k=\sum _{i=1}^{\stackrel{-}{N}}{\omega }_i{\gamma }_i^k$ , $\forall k \in \{<!--\; -->1,\dots , \stackrel{-}{K}\}$

The weight of reporting economy i is given by:

${\omega }_i=\frac{{\tilde{m}}_i^k}{\sum _{i=1}^{\stackrel{-}{N}}{\tilde{m}}_i^k}$

where ${\tilde{m}}_i$ is i’s total registered import value of sensitive goods ($\sum _{i=1}^{\stackrel{-}{n}}{\omega }_i=1$)

Step 3: Measuring product-specific counterfeiting factors

${\tilde{M}}_i^k=\sum _{i=1}^N{\tilde{m}}_i^k$ is defined as the total registered imports of sensitive good k for all economies and $\tilde{M}= \sum _{k=1}^{\stackrel{-}{K}}{\tilde{M}}^k$ is defined as the total registered world imports of all sensitive goods.

The world import share of good k, denoted $s^k$, is therefore given by:

$s^k=\frac{{\tilde{M}}^k}{\tilde{M}}$, such that $\sum _{k=1}^{\stackrel{-}{K}}{s}^k=1$

The general counterfeiting factor of product category k, denoted ${CP}^k$, is then determined as the following:

${CP}^k=\mathrm{ }\frac{{\mathrm{\Gamma }}^k}{s^k}$

The counterfeiting factor reflects the sensitivity of product infringements occurring in a particular product category, relative to its share in international trade. These are based on the seizure percentages calculated for each reporting economy and constitute the foundation of the formation of GTRIC-p.

Step 4: Establishing GTRIC-p

GTRIC-p is constructed from a transformation of the general counterfeiting factor and measures the relative likelihood that different product categories will be subject to counterfeiting and piracy in international trade. The transformation of the counterfeiting factor is based on two main assumptions:

• Assumption (A1): The counterfeiting factor of a particular product category is positively correlated with the actual intensity of international trade in counterfeit and pirated goods covered by that chapter. The counterfeiting factors must thus reflect the real intensity of actual counterfeit trade in the given product categories.

• Assumption (A2): This acknowledges that the assumption A1 may not be entirely correct. For instance, the fact that infringing goods are detected more frequently in certain categories could imply that differences in counterfeiting factors across products merely reflect that some goods are easier to detect than others or that some goods, for one reason or another, have been specially targeted for inspection. The counterfeiting factors of product categories with lower counterfeiting factors could, therefore, underestimate actual counterfeiting and piracy intensities in these cases.

In accordance with assumption A1 (positive correlation between counterfeiting factors and actual infringement activities) and assumption A2 (lower counterfeiting factors may underestimate actual activities), GTRIC-p is established by applying a positive monotonic transformation of the counterfeiting factor index using natural logarithms. This standard technique of linearisation of a non-linear relationship (in the case of this study between counterfeiting factors and actual infringement activities) allows the index to be flattened and gives a higher relative weight to lower counterfeiting factors (Verbeek, 2000[14]).

In order to address the possibility of outliers at both ends of the counterfeiting factor index (i.e. some categories may be measured as particularly susceptible to infringement even though they are not, whereas others may be measured as insusceptible although they are), it is assumed that GTRIC-p follows a left-truncated normal distribution, with GTRIC-p only taking values of zero or above.

The transformed counterfeiting factor is defined as:

${cp}^k=\mathrm{l}\mathrm{n}({CP}^k+1)$

Assuming that the transformed counterfeiting factor can be described by a left-truncated normal distribution with ${cp}^k\ge 0$, then, following Hald (Hald, 1952[15]), the density function of GTRIC-p is given by:

$f_{LTN }\left({cp}^k\right)=\{<!--\; -->\begin{array}{c} 0 if {cp}^k \le 0\\ \frac{f \left({cp}^k\right)}{{\int }_0^{\infty }f \left({cp}^k\right)d{cp}^k} if {cp}^k\ge 0\end{array}\}$

where $f\left({cp}^k\right)$ is the non-truncated normal distribution for ${cp}^k$ specified as:

$f\left({cp}^k\right)=\frac{1}{\sqrt{2\pi {\sigma }_{cp}^2}} exp\left(-\frac{1}{2} \left(\frac{\left({cp}^k\right)- {\mu }_{cp}}{{\sigma }_{cp}}\right)²\right)$

The mean and variance of the normal distribution, here denoted ${\mu }_{cp}$ and ${\sigma }_{cp}^2$, are estimated over the transformed counterfeiting factor index, ${cp}^k$, and given by ${\widehat{\mu }}_{cp}^2$ and ${\sigma }_{cp}^2$. This enables the calculation of the counterfeit import propensity index (GTRIC-p) across HS codes, corresponding to the cumulative distribution function of ${cp}^k$.

A.2. Constructing the general trade-related index of counterfeiting economies (GTRIC-e)

GTRIC-e is also constructed through four steps:

1. For each reporting economy, the seizure percentages for provenance economies are calculated.

2. For each provenance economy, aggregate seizure percentages are calculated, taking the reporting economies’ share of total sensitive imports as weights.

3. From these, each economy’s counterfeit source factor is established, based on the provenance economies’ weight in terms of total trade.

4. Based on these factors, the GTRIC-e is calculated.

Step 1: Measuring reporter-specific seizure intensities from each provenance economy

${\tilde{v}}_i^j$ is economy i’s registered seizures of all types of infringing goods (i.e. all k) originating from economy j in a given year in terms of their value. ${\gamma }_i^j$ is economy i’s relative seizure intensity (seizure percentage) of all infringing items that originate from economy j, in a given year:

${\gamma }_i^j=\frac{{\tilde{v}}_i^j}{\sum _{j=1}^{\stackrel{-}{J}}{\tilde{v}}_i^j}$ such that $\sum _{j=1}^{\stackrel{-}{J}}{\gamma }_i^j=1$ $\forall i \in \{<!--\; -->1,\dots ,\stackrel{-}{N}$}

Where $j=\{<!--\; -->1,\dots ,\stackrel{-}{J}\}$ is the range of identified provenance economies (the total number of exporters is given by J) and $i=\{<!--\; -->1,\dots ,\stackrel{-}{N}\}$ is the range of reporting economies (the total number of economies is given by N).

Step 2: Measuring general seizure intensities of each provenance economy

The general seizure intensity for economy j, denoted ${\mathrm{\Gamma }}^j$, is then determined by averaging seizure intensities, ${\gamma }_i^j$, weighted by the reporting economy’s share of total imports from known counterfeit and pirate origins.1 Hence:

${\mathrm{\Gamma }}^j=\sum _{i=1}^{\stackrel{-}{N}}{\omega }_i{\gamma }_i^j$ , $\forall j \in \{<!--\; -->1,\dots , \stackrel{-}{J}\}$

The weight of reporting economy i is given by:

${\omega }_i=\frac{{\tilde{m}}_i^j}{\sum _{i=1}^{\stackrel{-}{N}}{\tilde{m}}_i^j}$, such that $\sum _{i=1}^{\stackrel{-}{N}}{\omega }_i=1$

Step 3: Measuring partner-specific counterfeiting factors

${\stackrel{-}{M}}_i^j=\sum _{i=1}^N{\stackrel{-}{m}}_i^j$ is defined as the total registered world imports of all sensitive products from j,2 and $\stackrel{-}{M}= \sum _{j=1}^{\stackrel{-}{J}}{\stackrel{-}{M}}^j$ is the total world import of sensitive goods from all provenance economies.

The share of imports from provenance economy j in total world imports of sensitive goods, denoted $s^j$, is then given by:

$s^j=\frac{{\stackrel{-}{M}}^j}{\stackrel{-}{M}}$, such that $\sum _{j=1}^{\stackrel{-}{J}}{s}^j=1$

From this, the economy-specific counterfeiting factor is established by dividing the general seizure intensity for economy j by the share of total imports of sensitive goods from j.

${CE}^j=\mathrm{ }\frac{{\mathrm{\Gamma }}^j}{s^j}$

Step 4: Establishing GTRIC-e

Gauging the magnitude of counterfeiting and piracy from a provenance economy perspective can be done in a similar fashion as for sensitive goods. Hence, a General Trade-Related Index of Counterfeiting for economies (GTRIC-e) is established along similar lines and assumptions:

• Assumption (A3): The intensity by which any counterfeit or pirated article from a particular economy is detected and seized by customs is positively correlated with the actual amount of counterfeit and pirate articles imported from that location.

• Assumption (A4): This acknowledges that assumption A3 may not be entirely correct. For instance, a high seizure intensity of counterfeit or pirated articles from a particular provenance economy could be an indication that the provenance economy is part of a customs profiling scheme or that it is specially targeted for investigation by customs. The importance that provenance economies with low seizure intensities play regarding actual counterfeiting and piracy activity could, therefore, be under-represented by the index and lead to an underestimation of the scale of counterfeiting and piracy.

As with the product-specific index, GTRIC-e is established by applying a positive monotonic transformation of the counterfeiting factor index for provenance economies using natural logarithms. This follows from assumption A3 (positive correlation between seizure intensities and actual infringement activities) and assumption A4 (lower intensities tend to underestimate actual activities). Considering the possibilities of outliers at both ends of the GTRIC e-distribution (i.e. some economies may be wrongly measured as being particularly susceptible sources of counterfeit and pirated imports, and vice versa), GTRIC‑e is approximated by a left-truncated normal distribution as it does not take values below zero.

The transformed general counterfeiting factor across provenance economies on which GTRIC-e is based is therefore given by applying logarithms onto economy-specific general counterfeit factors (see, for example, Verbeek (Verbeek, 2000[14]):

$c{e}^j=\mathit{ln}(C{E}^j+1)$

In addition, following GTRIC-p, it is assumed that GTRIC-e follows a truncated normal distribution with $c{e}^j\ge 0$ for all j. Following Hald (Hald, 1952[15]), the density function of the left-truncated normal distribution for $c{e}^j$ is given by:

$g_{LTN}\left(c{e}^j\right)=\{<!--\; -->\begin{array}{c}\begin{array}{c}0\\ \end{array}\\ \frac{g\left(c{e}^j\right)}{{\int }_0^{\infty }g\left(c{e}^j\right)dce}\end{array}\hspace{1em}\begin{array}{c}\begin{array}{c}if\hspace{0.33em}c{e}^j\le 0\\ \\ \\ if\hspace{0.33em}c{e}^j\ge 0\end{array}\\ \end{array}$

where $g\left(c{e}^j\right)$ is the non-truncated normal distribution for $c{e}^j$ specified as:

$g\left(c{e}^j\right)=\frac{1}{\sqrt{2\pi {\sigma }_{ce}^2}}\mathit{exp}\left(-\frac{1}{2}{\left(\frac{c{e}^j-{\mu }_{ce}}{{\sigma }_{ce}}\right)}^2\right)$

The mean and variance of the normal distribution, here denoted ${\mu }_{ce}$ and ${\sigma }_{ce}^2$, are estimated over the transformed counterfeiting factor index, $c{e}^j$, and given by ${\stackrel{̑}{\mu }}_{ce}$ and ${\stackrel{̑}{\sigma }}_{ce}^2$. This enables the calculation of the counterfeit import propensity index (GTRIC-e) across provenance economies, corresponding to the cumulative distribution function of $c{e}^j$.

A.3. Constructing the General Trade-Related Index of Counterfeiting (GTRIC)

In the (OECD/EUIPO, 2016[1]) and (OECD/EUIPO, 2019[3]) studies, propensities to import infringing goods from different trading partners were developed using seizure data as a basis. The use of data is maximised by applying a generalised approach in which the propensities for products to be counterfeit and for economies to be sources of counterfeit goods were analysed separately. This increased the data coverage of both products and provenance economies significantly, which increases the robustness of the overall estimation results. Unfortunately, it also reduced the detail of the analysis, meaning that counterfeit trade patterns specific to individual reporting economies, for both product types and trading partners, were not simultaneously accounted for; this introduced bias into the results. On balance, however, given the large scope of the analysis, the advantages of increasing data coverage can be viewed as outweighing the biases.

This approach combines the two indices: GTRIC-p and GTRIC-e. In this regard, it is important to emphasise that the index resulting from this combination does not account for differences in infringement intensities across different types of goods that may exist between economies. For instance, imports of certain counterfeit and pirated goods could be particularly large from some trading partners and small from others. An index taking such “infringement specialisation”, or concentration, into account is desirable and possible to construct; but it would require detailed seizure data. The combined index, denoted GTRIC, is, therefore, a generalised index that approximates the relative likelihoods that particular product types, imported from specific trading partners, are counterfeit and/or pirated.

Establishing likelihoods for product and provenance economy

In this step, for each trade flow from a given provenance economy and for a given product category the likelihoods of containing counterfeit and pirated products will be established.

The general propensity for an economy to export infringed items of HS category k is denoted $P^k$, and given by GTRIC-p, so that:

$P^k=F_{LTN}\left(c{p}^k\right)$

where $F_{LTN}\left(c{p}^k\right)$ is the cumulative probability function of $f_{LTN}\left(c{p}^k\right)$.

Furthermore, the general likelihood of importing any type of infringing goods from economy j is denoted as ${P^j}_{}$, and given by GTRIC-e, so that:

$P^j=G_{LTN}\left(c{e}^j\right)$

where $G_{LTN}\left(c{e}^j\right)$ is the cumulative probability function of $f_{LTN}\left(c{e}^j\right)$.

The general probability of importing counterfeit or pirated items of type k originating from economy j is then denoted $P^{jk}$ and approximated by:

$P^{jk}=P^k{P}^j$

Therefore, $P^{jk}\in [{\epsilon }_p{\epsilon }_e;1)$, $\forall j,k$, with ${\epsilon }_p{\epsilon }_e$ denoting the minimum average counterfeit export rate for each sensitive product category and each provenance economy,3 it is assumed that ${\epsilon }_p={\epsilon }_e=0.05$.

A.4. Calculating the absolute value

$\alpha$ is the fixed point, i.e. the maximum average counterfeit import rate of a given type of infringing good, k, originating from a given trading partner, j.

$\alpha$ can be applied to propensities for importing infringing goods of type j from trading partner k ($\alpha P^{jk}$). As a result, a matrix of counterfeit import propensities C is obtained.

$\mathit{C}=\left(\begin{array}{ccccc}\mathit{\alpha }{\mathit{P}}^{11}& \mathit{\alpha }{\mathit{P}}^{21}& \mathit{ }& \mathit{ }& \mathit{\alpha }{\mathit{P}}^{1\mathit{K}}\\ \mathit{\alpha }{\mathit{P}}^{12}& \ddots & \mathit{ }& \mathit{ }& \mathit{ }\\ ⋮& \mathit{ }& \mathit{\alpha }{\mathit{P}}^{\mathit{j}\mathit{k}}& \mathit{ }& ⋮\\ \mathit{ }& \mathit{ }& \mathit{ }& \ddots & \mathit{ }\\ \mathit{\alpha }{\mathit{P}}^{\mathit{J}1}& \mathit{ }& \mathit{ }& \mathit{ }& \mathit{\alpha }{\mathit{P}}^{\mathit{J}\mathit{K}}\end{array}\right)$ with dimension J x K

The matrix of world imports is denoted by M. Applying C on M yields the absolute volume of trade in counterfeit and pirated goods.

In particular, the import matrix M is given by:

$\mathit{M}=\left(\begin{array}{c}{\mathit{M}}_1\\ ⋮\\ {\mathit{M}}_i\\ ⋮\\ {\mathit{M}}_n\end{array}\right)$ with dimension n x J x K

Each element is defined by economy i’s unique import matrix of good k from trading partner j.

${\mathit{M}}_{\mathit{i}}=\left(\begin{array}{ccccc}{\mathit{m}}_{\mathit{i}1}^1& {\mathit{m}}_{\mathit{i}1}^2& \mathit{ }& \mathit{ }& {\mathit{m}}_{\mathit{i}1}^{\mathit{K}}\\ {\mathit{m}}_{\mathit{i}2}^1& \ddots & \mathit{ }& \mathit{ }& \mathit{ }\\ ⋮& \mathit{ }& {\mathit{m}}_{\mathit{i}\mathit{j}}^{\mathit{k}}& \mathit{ }& ⋮\\ \mathit{ }& \mathit{ }& \mathit{ }& \ddots & \mathit{ }\\ {\mathit{m}}_{\mathit{i}\mathit{J}}^1& \mathit{ }& \mathit{ }& \mathit{ }& {\mathit{m}}^{\mathit{J}\mathit{K}}\end{array}\right)$ with dimension J x K

Hence, the element $m_{ij}^k$ denotes i’s imports of product category k from trading partner j, where $i=\{<!--\; -->1,...,n\}$, $j=\{<!--\; -->1,...,J\}$, and $k=\{<!--\; -->1,...,K\}$.

Denoted by $\Psi$, the product-by-economy percentage of counterfeit and pirated imports can be determined as the following:

$\Psi =\mathit{C}\mathrm{\text{'}}\mathit{M}÷\mathit{M}$

Total trade in counterfeit and pirated goods, denoted by the scalar TC, is then given by:

$\mathit{T}\mathit{C}={\mathit{i}}_1\mathrm{\text{'}}\Psi {\mathit{i}}_2$

where ${\mathit{i}}_1$ is a vector of one with dimension nJ x 1, and ${\mathit{i}}_2$ is a vector of one with dimension K x 1. Then, by denoting total world trade by the scalar $\mathit{T}\mathit{M}={\mathit{i}}_1\text{'}\mathit{M}{\mathit{i}}_2$, the value of counterfeiting and piracy in world trade, s_TC, is determined by:

$s_{\mathit{T}\mathit{C}}=\frac{\mathit{T}\mathit{C}}{\mathit{T}\mathit{M}}$

A.5. Construction of RCAP-e and RCAT-e

Relative comparative advantage for production of a given good (RCAP-e)

The first statistical filter that can be used to tell producers from transit points looks at the production capacities of a given economy in a given sector. The rationale behind this test is simple: production activity often relies on certain skills, or resources. It also exhibits certain returns to scale properties that results in specialisation of this particular economy in the production of that good. Hence, production of counterfeits in a sector is more likely to occur in a known provenance economy that specialises in the legitimate production of a given good, than in a country without production capacity in a given sector.

This specialisation of a given trading economy in production of a given good is captured by an indicator of the relative comparative advantage for production (RCAP-e). The indicator looks at the share of industrial activity in a given sector with the total industrial activity in a given economy.

Construction of this indicator is based on industry statistics. Importantly, these statistics are based on a different taxonomy than the trade statistics, hence a matching exercise was performed (see Box B.1). A detailed description of the methodology used to calculate the RCAP-e is provided below.

Box A.1. Product classification methods

Although the datasets on trade and industrial activity in principle classify the same goods, they differ in the taxonomies used. Industry data (output) are extracted from the industrial statistics database of the United Nations Industrial Development Organization (UNIDO). These data are classified according to the categories of industrial activity (ISIC-Rev3) at a two-digit level. Trade data and seizure data are classified using the Harmonized Tariff Schedule (HTS) classification scheme. These differences are due to the fact that although they cover the same issues, they were created and are run independently.

In order to create the RCAP-e indicator, the HS code that refers to the GTRIC-p tables and to categories of international trade are matched with the relevant categories of industrial activity (ISIC). This is done following the concordance tables proposed by the United Nations Statistics Division (available at: http://unstats.un.org/unsd/cr/registry/regot.asp?Lg=1).

More formally, the revealed comparative advantage in production for an economy e in a given product category p (RCAP-e) measures whether this economy produces more of this given type of product as a share of its total production than the “average” country:

where is the output of product p by economy e in a given year.

Relative comparative advantage for being a transit point (RCAT-e).

The relative comparative advantage for being a transit point in global trade (RCAT-e) is the second filter used to determine the actual role of a provenance economy. This indicator represents the degree to which a given economy specialises in re-exporting a given product, e.g. through development of advanced logistical infrastructure, or by its convenient geographical location. Consequently, it is assumed that such factors that facilitate transiting of genuine products will also facilitate transit of fake products in the same product categories.

The RCAT-e indicator is calculated by comparing relative volumes of re-export of a given good to the shares calculated for other exporting economies. This is done based on re-export data that come from the UN Comtrade database. A detailed description of the methodology used to calculate the RCAT-e is provided in Annex B.

Formally, the revealed comparative advantage in transit for an economy e within a given product category p (RCAP-e) measures whether this economy re-exports more goods of this given type of product as a share of its total re-exports than the “average” country:

where is re-exports of product p by economy e in a given year.

Application of both filters

Once the statistical filters (RCAP-e and RCAT-e indicators) are constructed, they are applied to distinguish the producing economies from the key potential transit points. Both filters are applied for every economy on the top provenance list for counterfeit goods, i.e. economies with a high GTRIC-e score. The selection of top economies is done arbitrarily, depending on the distribution of the GTRIC within a given product category.

The rationale for using the filters is as follows: if an economy is not a significant producer of a fake good (i.e. its RCAP-e for this good is low) and/or is a large re-exporter of this good in legitimate trade (i.e its RCAT-e for this good is high), then it is likely to be a transit point.

On the other hand, if this top listed provenance economy of counterfeit goods within the product category is a significant producer (i.e. has a high RCAP-e score) or is a small re-exporter (i.e. has a low RCAT-e score), it is likely to be a producer of the fake goods.

This exercise results in a list of producers and a list of transit points. Together with the information on the place of seizure, this will allow the development of maps of trade in fake goods in given product categories, showing key producers, main transit point and main destination points.

References

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Notes

← 1.  This is different to the economy’s share of total imports of sensitive goods used to calculate GTRIC-p.

← 2.  This is different to the total imports of sensitive goods as used in calculation of GTRIC-p.

← 3.  In the OECD methodology, these factors were applied to all provenance economies and all HS modules in order to account for counterfeit and pirated exports of products and/or from provenance economies that were not identified. This assumption is relaxed in this study, given the overall good data quality.