This section focuses on the factors that impact the value of fake imports. The value of imports of fake goods was calculated using data from customs seizures of small parcels to which the OECD methodology described in (OECD/EUIPO, 2016[3]) and (OECD/EUIPO, 2019[1]) was applied.
Why Do Countries Import Fakes?
Linkages and Correlations with Main Socio-Economic Indicators
Illicit Trade
2. Factors that shape imports of fakes
1. Imports of fakes in small packages: what does this trade look like?
Which countries import counterfeit goods?
Figure 2.1 shows that when considering the estimated value of fake imports, the United States remains by far the first importer of counterfeit goods shipped in small packages. However, the picture differs somehow from the one based on raw customs seizures data. One can note that in addition to European countries, importers of small parcels of counterfeit goods are in different regions such as Asia, Gulf region, South America, and Oceania. Overall, fake imports were mostly destined to countries quite well integrated in international trade in absolute terms.
Figure 2.1. Distribution of the value of fake imports, by destination economies, 2019
Source: OECD/EUIPO calculations
Figure 2.2 shows the distribution of the value of fake imports into the EU by destination economies. Germany was the country with the highest value of fake imports in 2019. It was followed by United Kingdom, France, and Italy.
Figure 2.2. Distribution of the value of fake imports in the EU, by destination economies, 2019
* Note by Türkiye: the information in this document with reference to "Cyprus" relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Türkiye recognizes the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Türkiye shall preserve its position concerning the "Cyprus" issue. Note by all the European Union Member States of the OECD and the European Union: the Republic of Cyprus is recognised by all members of the United Nations with the exception of Türkiye. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus. ** United Kingdom is included in EU countries as the analysis refers to the period prior to the Brexit.
Source: OECD/EUIPO calculations.
Table 2.1 shows that United Kingdom and France appear as main importers of fakes among EU member states in both absolute and relative terms. It also indicates that Spain, Poland, and the Czech Republic are among the largest importers of counterfeit goods in the EU in relative terms.
Table 2.1. Top 15 destination economies of counterfeit imports destined to the EU countries, 2017-19
Note: * United Kingdom is included in EU countries as the analysis refers to the period prior to the Brexit.
Source: OECD/EUIPO calculations.
What counterfeit products are imported?
Figure 2.3 shows the distribution of the value of fake imports by product categories. It indicates that the range of fake goods traded is very wide including common goods (footwear, ready-to-wear items), luxury goods but also potentially dangerous fakes such as toys and games, spare parts, cosmetics, and pharmaceuticals. Jewellery and electronics were the two product categories associated with the highest value of imported fake products in 2019. They were followed by clothing and footwear.
Figure 2.3. Distribution of the value of fake imports, by product categories, 2019
Source: OECD/EUIPO calculations.
Fake clothing, leather goods and footwear are the product categories in which fakes are most often imported in relative terms (see Figure 2.4). This figure also indicates that the share of fakes in total imports is significant for musical instruments while its associated value is limited. It is also the case – but in a lesser extent – for fake watches and toys and games.
Figure 2.4. Top product categories of counterfeit goods shipped in small parcels, in relative terms, 2019
Source: OECD/EUIPO calculations.
Tables 2.2 – 2.7 focus on imports of counterfeit products by sector. Special attention was paid to sectors where fakes can pose direct health and safety risks such as food, pharmaceuticals, cosmetics and toys and games. The case of common products frequently seized like clothing and electronics was also analysed.
Table 2.2 indicates that the value of fake imports, in relative terms (e.g., in terms of fake imports of food in total imports of food), Asian and African countries were the main importers of counterfeit food products. In absolute terms, China and OECD countries dominate, however.
Table 2.2. Top 15 importers of counterfeit food products
As for imports of counterfeit medicines, six African countries are among the 15 top importers in relative terms.
Table 2.3. Top 15 importers of counterfeit pharmaceuticals
In absolute terms, the United States, China, and Hong Kong (China) were the largest importers of counterfeit cosmetics. In relative terms, Afghanistan, Yemen, and India were the top destination countries for fake cosmetics, associated with the highest shares of fake imports in this sector.
Table 2.4. Top 15 importers of counterfeit cosmetics
The main importers of counterfeit toys and games are developed countries with the United States, Japan, Germany, and United Kingdom being those with the highest value of fake imports in this field. In relative terms, the United States and Japan also appear as large importers, but the largest ones were Yemen, Pakistan, and Uzbekistan.
Table 2.5. Top 15 importers of counterfeit toys and games
The United States, Germany and Japan were the three first destination economies for fake clothing in terms of value of fake clothing imported. When considering the share of fake imports of clothing among genuine imports, Yemen, Rwanda, and Afghanistan were the three economies most affected by imports of fake ready to wear articles.
Table 2.6. Top 15 importers of counterfeit clothing
Table 2.7 shows that counterfeit electronics were mostly destined to countries well integrated in the international trade with Hong Kong (China), the United States and China being the three main destinations of counterfeit electronics, in terms of value of fakes. When considering the share of fake imports of electronics, one third of the main importers are African countries, while Asian countries also figure prominently. This means that Africa and Asia can be seen as specific targets for counterfeit electronics.
Table 2.7. Top 15 importers of counterfeit electronics
2. What are the main drivers of imports of fakes?
The purpose of this section is to highlight the relations that exist between some factors and the value of imports of fakes. The analysis is carried out at a country level for a one-year period (2019). The selected factors are in principle country-level reflections of specific microeconomic drivers of intentional and unintentional demand discussed in the sections above. These factors include indices of economic development, demographics, education, use of Internet, and logistical and trade performance.
Modelling the value of fake imports
While some links between these indices and the imports of fakes are obvious a priori, some results can be more difficult to determine and require further analysis. For example, economic development can determine the ease of Internet access, but on the other hand it can be also linked to the overall level of quality of governance framework, including IP enforcement.
To better understand the role of each of the factors impacting the demand for fakes, a multiple regression model was used. This statistical method is designed to model relationships between an independent variable and various predictors. It also provides the magnitude of the effects of each predictor on the independent variable.
The factors impacting the value of fake imports are described and the results of the multiple linear regression model are discussed below. Table 2.8 presents the results of the best multiple linear regression in terms of adjusted R2, significance of coefficients, normality of errors and absence of multicollinearity between independent variables.
Table 2.8. Multiple regression of the value of fakes (in log)
Note: Standard errors in parentheses, * p<0.05, ** p<0.01, *** p<0.001.
Source: OECD/EUIPO calculations.
As can be seen in Table 2.8, the variable to be explained in our model is the log of the value of imports of fakes. The use of logarithmic specification is linked to improving model fit. The model comprises six explanatory variables and a constant term, including:
The value of imports by country (in USD). For model performance reasons, this variable is expressed in log;
The quality of trade and transport-related infrastructure. This index reflects professionals’ perceptions of quality of trade and transport-related infrastructure of each country and ranges from 1 to 5: the higher the score the better the performance;
The population aged 65 and above as a share of total population;
The share of individuals using Internet of each country;
The gross graduation ratio of tertiary education;
The GDP per capita in USD, which is expressed in log for a better model fit.
The detailed data descriptions can be found in Appendix B of this report.
Imports
Figure 2.5 indicates a very strong and positive correlation between the value of fake imports and the value of genuine imports. The higher the imports of a country, the higher the value of fake imports.
Figure 2.5. Link between the value of fake imports and total value of imports
Note: This graph should be interpreted carefully. To avoid any confusion, it is important to point out that for each country, the value of imports of fake goods is lower than the total value of imports (official trade data) but both values are high in absolute terms. On this graph, these two values may appear close due to the log entry, but they differ in reality. Indeed, the y-values of large x-values are closer together in the case of a logarithmic function, whereas the y-values of small x-values are further apart. Each point corresponds to one country for 2019.
Source: UN Comtrade database and OECD/EUIPO calculations.
The statistical model confirms that genuine imports are an important driver of fake imports, as Table 2.8 shows an increase in value of genuine imports is associated to a significantly increase of the value of fakes imports.
This result is somehow straightforward, particularly when considering that the regression uses the value of imports of fakes as the dependant variable. In this context the value of imports to an economy, in addition to its straightforward explanatory power, captures the scale effect, when a large economy is a more significant importer of fakes only because of its large volumes of trade.
Infrastructure
As highlighted in previous OECD-EUIPO studies, good quality trade infrastructure tends to facilitate counterfeit imports to the same extent it facilitates licit trade. This is particularly the case for countries with relatively low governance standards related to IP respect and protection.
To verify if economies with efficient logistics are more likely to import counterfeit and pirated products, the study uses the Logistics Performance Index (LPI) provided by the World Bank and Turku School of Economics. The LPI is an interactive benchmarking tool updated every two years; it ranks 160 countries on the efficiency of their international supply chains. It is based on a worldwide survey of logistics professionals on the ground who provide feedback on the logistics friendliness of the countries in which they operate and those with which they trade. LPI is the combination of country scores on six dimensions : the ability to track and trace consignments, the level of competence and quality of logistics services (e.g. transport operators, customs brokers), the ease of arranging competitively priced shipments, the efficiency of customs clearance processes (i.e. speed, simplicity and predictability of formalities), how often the shipments to the assessed country reach the consignee within the scheduled or expected delivery time and the quality of trade and transport-related infrastructure (e.g. ports, railroads, roads, information technology). Scores are averaged across all respondents and all indices range from 1 to 5: the higher the score the better the performance.
In this study, the focus is the infrastructure quality dimension as it seems to be a determinant of imports of fakes. Indeed, Figure 2.6 shows a strong and positive correlation between the quality of trade and transport infrastructure and the value of fake imports.
Figure 2.6. Link between value of fake imports and quality of trade and transport infrastructure
Note: Each point corresponds to one country for 2019.
Source: World Bank (Logistics Performance Index) and OECD/EUIPO calculations.
Table 2.8 indicates that infrastructure is an important driver of counterfeit and pirated imports as the two columns show that an increase in quality of trade and transport related infrastructure significantly increase the value of fake imports. The quality of logistics and transport related infrastructure tends to facilitate counterfeit imports and in the framework of small parcels, infrastructure such as big airports are clearly considered as facilitating both genuine and illicit trade.
This result corroborates the trends already highlighted by the OECD and the EUIPO on counterfeiters’ strategies. Their strategies are multifaceted and consist in misusing all facilitation of international trade. For example, the misuse of containerized maritime, small parcels, free trade zones and online environment was already described in several publications1.
GDP per capita
A priori, the economic wealth of consumers can influence the purchase of counterfeit goods in several ways. It is important to recall that demand of fakes is specific as we distinguish two types of consumers. There are consumers who deliberately buy fake goods and those who buy fakes thinking the goods are genuine. On the one hand, people with low wealth may be motivated by lower prices in the illicit market. On the other hand, for people who are deceived, the purchase of counterfeit goods is not motivated by the price and consequently the role of economic wealth is less marked in this case.
The results of the multiple regression model (see Table 2.8) indicate that higher wealth per capita tend to reduce the value of fake imports. Ceteris paribus, an increase of the GDP per capita leads to a decrease of the value of counterfeit goods imported.
This result indicates that countries where wealth is lower are more likely to consume counterfeit and pirated goods which are often more attractively priced. Moreover, it can also be linked to the fact that countries with lower GDP per capita tend to pay less attention to IPR protection. In fact, as shown by several empirical studies, GDP capita is usually positively correlated with the overall quality of respect for IP in a country2 Consequently, this finding indirectly shows that countries that enjoy a higher respect for IP and stronger IP protection levels, also report lower volumes of import of fake goods.
Use of internet
Figure 2.7 shows a positive correlation between the percentage of people using the Internet and the value of imports of counterfeit goods. Such a relationship is logical given the growing importance of online shopping for both genuine and counterfeit purchases. As highlighted in (OECD/EUIPO, 2021[36]), 56% of seizures made by European customs between 2017 and 2019 were related to online sales.
Figure 2.7. Link between the share of individuals using Internet and the value of fake imports
Note: Each point corresponds to one country for 2019.
Source: World Economic Forum and OECD calculations.
The regression model confirms (see Table 2.8) the positive relationship between the use of Internet and the value of fake imports. This result is not surprising since the online business is a complex and fast-moving environment where the consumer can be easily deceived. It is also a common way to serve the intentional demand for counterfeit goods through social media where the promotion and sale of replicas is made.
Population aged 65 and more
The relation between age and imports of fakes is a priori difficult to determine. On the one hand the relationship could be negative as studies show that young people are more likely to use counterfeit products than older people. On the other hand, the ageing countries are often developed countries that are well integrated in world trade and have a high import value.
The econometric model presented in Table 2.8 indicates that the age of the population is also a factor that impacts the value of imports of counterfeit goods. The results of the multiple regression indicate that an increase in the share of the elderly population (aged 65 and more) significantly decreases the value of imports of fakes. This result confirms the hypothesis that young people can be considered as the suitable target for bad actors supplying counterfeit goods.
The exact mechanism that reduces the overall propensity to import fakes with the share of elderly populations cannot be determined at this stage. In fact, several mechanisms could act independently. A greater awareness on the threat represented by counterfeiting as well as lower economic constraints for elderly (compared to younger people) may be elements of explanation. Last, less intense use of the Internet - which is a major source of counterfeit goods - by older people for their purchases may also explain such a relationship between the age of population and the value of imported fake goods.
Education
The data show a positive relationship between the gross graduation ratio of tertiary education and the value of imports of counterfeit goods. The multiple regression model confirms that an increase of the share of graduates from tertiary education leads to an increase in the value of fake imports (see Table 2.8).
More in-depth analysis is needed to explain this pattern, as several underlying facts could potentially explain such a relationship. Possible explanatory factors, include for example lack of awareness about this risk (including the presence of counterfeit goods in all sectors and not only in the fashion or luxury goods sectors), or higher digital skills that might result in a higher ability to look for bargains online
In addition, the evidence highlighted by the OECD in several previous reports indicate that criminals are very creative and use all possible means to deceive consumers. The specific case of fake medicines is illustrative. A priori, the purchase of fake medicines is less demand-driven than other sectors like clothing or leather items (see Table 1.1). However, a survey conducted by the FDA (Food and Drug Administration) in 2020 reveals that nearly half of Americans (42%) are currently purchasing medications online and 45% of Americans erroneously believe all websites offering healthcare services/prescription medications to Americans via the Internet have been approved by the FDA or state regulators. This means that for some sectors the risk of counterfeiting is not so well-known, and the purchase of counterfeit goods can potentially be made by all people, those with high digital skills to look for good offers online.
Figure 2.8. Link between tertiary education and the value of fake imports
Note: Number of graduates from first degree programmes (at ISCED 6 and 7) expressed as a percentage of the population of the theoretical graduation age of the most common first-degree programme. Each point corresponds to one country for 2019.
Source: World Bank and OECD/EUIPO.
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Notes
← 2. (Gould, 1996[40]); (Besley T, 2010[42]) Besley T, Ghatak M (2010) Property rights and economic development. In: Rodrick D, Rosenzweig M (eds) Handbook of development economics, 5th edn. Elsevier, North Holland, pp 4525–4595; (Haydaroğlu, 2015[41]).