Measuring the “Unmeasurable”

Abstract

Due to its considerable negative consequences, product counterfeiting is a global problem that is a growing concern for consumers, government entities, law enforcement, and businesses. Unfortunately, current assessments of the nature and extent of the problem are largely unreliable and based on methodologies with significant limitations. This article examines the current approaches to measuring product counterfeiting, complementing those with a review of methods used to examine other crimes. It concludes by discussing the applicability of both commonly used and novel research methodologies, as they might apply to the study of product counterfeiting.

Keywords

product counterfeiting measurement estimation methodology brand protection

Introduction

Product counterfeiting is a growing, global problem that is of great concern to consumers, government entities, law enforcement, and businesses. This crime involves a range of illicit activities relating to the infringement of intellectual-property rights linked to consumer goods (Organisation for Economic Cooperation and Development [OECD], 2008). A wide variety of products are targeted for counterfeiting, and research and practice indicate that all forms of products are at risk (Heinonen, Spink, & Wilson, 2014; Nasheri, 2005), including batteries, pharmaceuticals, food and beverages, medicine and medical devices, children’s toys, electronics, weapons, tobacco, gas and chemicals (as well as their storage tanks), luxury goods, and even nuclear power plant components (see, e.g., Albanese, 2011; Fenoff & Wilson, 2011; Harris, Stevens, & Morris, 2009; Heinonen et al., 2014; Nasheri, 2005; OECD, 2008; Spink, 2012; Spink & Moyer, 2011; U.S. Consumer Product Safety Commission, 2007). Product counterfeiting can have devastating impacts on public health and safety, the economy, innovation, private sector stability, and national security. Businesses face both losses and threats to their reputation and innovation as a result of counterfeiting (Heinonen et al., 2014; Speier, Whipple, Closs, & Voss, 2011). Profits from counterfeit goods also fund a variety of criminal and terrorist activities (Albanese, 2011; Heinonen & Wilson, 2012; International Anti-Counterfeiting Coalition [IACC], 2005; Sullivan, Chermak, Wilson, & Freilich, 2014; U.S. Department of Justice, 2008). These ongoing concerns make the counterfeiting of consumer goods an important emerging research area.

In this article, we discuss how to better assess the nature and extent of product counterfeiting. Given there is no “best” way to measure product counterfeiting, we highlight a number of potential approaches and their strengths and limitations. This overview of the various levels of measurement, units of observation, and types of methodologies is meant to provide guidance for those conducting the measurement to choose an approach most appropriate to their specific goals, context, and circumstances. We begin by reviewing definitions and estimates of the problem. We then review methods that have been used to estimate the extent of the problem. We also review methods used to assess other difficult-to-measure crimes and how they might be applied to product counterfeiting. We conclude by drawing lessons from this review and identifying promising approaches to estimating the nature and extent of product counterfeiting.

Defining and Measuring Product Counterfeiting

Product counterfeiting has a variety of loosely related definitions. Cordell, Wongtada, and Kieshnick (1996, p. 41) define it as “any unauthorized manufacturing of goods whose special characteristics are protected as intellectual property rights (trademarks, patents, and copyrights).” This definition incorporates unauthorized manufacturing but does not include other counterfeiting activities, such as repackaging defective or excess materials, which should have been destroyed by the manufacturer but were instead sold outside legitimate supply chains. This definition also differs from legal definitions such as that of the Trademark Counterfeiting Act (TCA) of 1984, which defined counterfeiting only as a trademark infringement (with copyright, patent, and trade secret infringement reflecting other types of intellectual property violations). The World Trade Organization (2015, p. 1) states that product counterfeiting involves “unauthorized representation of a registered trademark carried on goods identical or similar to goods for which the trademark is registered, with a view to deceiving the purchaser into believing that he/she is buying the original goods.” The U.S. Food and Drug Administration (2007) defines product counterfeiting to include parallel trade and product diversion (the sale of goods in markets unintended by the brand owner). In our discussions, we keep the TCA’s definition, focusing on the improper use of a trademark, foremost in mind, but stress only that how one wishes to define counterfeiting will help determine the best method to measure it. Several other issues compound these definitional challenges. One is the interchangeable use of similar terms (e.g., piracy, fraud) with product counterfeiting. For the purposes of this article, we focus only on trademark counterfeiting and exclude other forms of intellectual property theft, such as piracy, copyrights, and trade secrets. Legal definitions may also vary by state, country, and other jurisdictions.

Academic discourse on the definition of product counterfeiting has been limited as has been quality research on measuring its extent. Most studies cite common sources or publications as a reference point. Table 1 presents some widely cited estimates and their sources. These vary greatly in what, exactly, they measure (e.g., global value of counterfeit products, lost tax revenues in a single jurisdiction).

Table 1.

Commonly Cited Estimates of the Impact of Product Counterfeiting and Their Sources.

Estimate of Counterfeiting Impact	Source
US$200 billion to US$600 billion to U.S. businesses/in world trade annually	Chaudhry and Zimmerman (2009); IACC (2005); U.S. GAO (2010)
US$1 billion in annual tax revenue lost due to counterfeit products in New York City	Thompson (2004)
US$400 million in annual tax revenue lost due to counterfeit products in Los Angeles	Freeman, Sidhu, and Montoya (2007)
Average 13 months in investigation time for counterfeit incidents in Michigan	Heinonen and Wilson (2012)
US$39 billion in annual revenue for American prescription-drug companies	Bate (2008)
US$3 billion in annual revenue for U.S. automotive parts manufacturers	Motor and Equipment Manufacturers Association (2009)
US$60 billion in annual losses	U.S. International Trade Commission (1984, 1988)
5–7% of world trade	Counterfeiting Intelligence Bureau (1997)
US$5 billion to US$30 billion in annual losses	Abbott and Sporn (2002); Stern (1985)

Note. IACC = International Anti-Counterfeiting Coalition; GAO = Government Accountability Office.

Much of what is known about the effects of product counterfeiting is derived from anecdotal accounts, case studies with limited generalizability, and unreliable methodologies (National White Collar Crime Center, 2004; Piquero, 2005; U.S. Government Accountability Office [GAO], 2010). This presents concerns with the accuracy and quality of current estimates. Chaudhry and Zimmerman (2009) contend that while the first step in understanding the counterfeiting problem is to establish the size of the counterfeit market, there is no way to directly measure the trade in counterfeit goods due to the illegal nature of the activity. The U.S. Government Accountability Office (2010, pp. 16–17) summarized this challenge as follows:

Quantifying the economic impact of counterfeit and pirated goods on the US economy is challenging primarily because of the lack of available data on the extent and value of counterfeit strategies … Because of the lack of data on illicit trade, methods for calculating estimates of economic losses must involve certain assumptions, and the resulting economic losses must involve certain assumptions, and the resulting economic loss estimates are highly sensitive to the assumptions used.

Methodologies used in previous assessments, including those most frequently cited (Counterfeiting Intelligence Bureau, 1997; OECD, 2008; U.S. Federal Bureau of Investigation (FBI), 2005), are generally not provided or are unreliable or invalid. Even where methods are provided, other limitations prevent a comprehensive estimate. For instance, Freeman, Sidhu, and Montoya (2007) used enforcement seizure data to estimate the extent of counterfeiting, but, as Chaudhry and Zimmerman (2009, p. 7) caution, seizures likely reflect only a small proportion of counterfeit trade. While such an approach can be useful for providing a base level of counterfeiting (U.S. GAO, 2010), it does not approximate the true extent of the product counterfeiting problem. Additional methods have included surveys of supply and demand (e.g., Rob & Waldfogel, 2006), economic multipliers to estimate effects on the U.S. economy (e.g., Siwek, 2007), statistical modeling (e.g., Hui & Png, 2003; Oberholzer-Gee & Strumpf, 2007), and triangulation methods incorporating the extrapolation of seizure and international trade data as well as the merging of both types of data in econometric models (e.g., OECD, 2009).

A review of methods seeking to foster best practices in data collection regarding product counterfeiting in specific industries suggested that such measures focus on consumption, using volume in potential units of measurement and clarifying the geographic area of coverage for the estimation (Centre for Economics and Business Research, 2002). Those seeking to measure counterfeits should clarify their unit of analysis and measurement a priori. In other words, before estimating or measuring counterfeiting, researchers should consider two questions:

What is the level of estimation?

What is the unit of observation?

Answering these questions will point to specific methodologies and types of measurements for the aspect of product counterfeiting to be estimated. Researchers could examine product counterfeiting by time period, geographic location, industry, brand, or product. Potential units of observation include offenders; schemes; general consumers; and consumers as victims, brands, or products. We discuss these questions in more depth subsequently.

Levels of Estimation

Identifying the level of estimation is the general focus for determining what is to be estimated, why it is important to make the estimation, and how the estimation is established. While the unit of analysis is the main entity being analyzed, the level of estimation narrows the scope for which the main entity is examined. For example, if the unit of observation is the product being counterfeited, a researcher could examine the number and type of products by time period, geographic location, industry, brand, or product. Numerous approaches to each level of estimation are possible depending on the needs of those making the measurements. We review each of these characteristics below.

Time period

Researchers may first be interested in determining the prevalence of product counterfeiting across time. Longitudinal studies are important for estimating trends of product counterfeiting to determine how the problem is changing and evolving. These studies may also focus on identifying differences in the prevalence of product counterfeiting following major events or interventions. There are likely different trends across products, locations, brands, or industries. Numbers of victims or offenders are also likely to change over time.

Geographic location

In other cases, researchers may want to know about the prevalence of product counterfeiting by location, such as by local communities or cities, states, countries, or even globally. They may be interested in whether counterfeiting by industry, brand, or product is more prevalent in one location than another or whether the characteristics of victims or offenders vary by location or just in how prevalent counterfeiting is in a given location.

Industry

Industry estimates are common and may involve identifying the number of products, brands, offenders, or victims for an entire industry. Such estimates may also attempt to document whether some industries are more susceptible to counterfeiting than others.

Brand

A firm may try to determine the extent of counterfeiting of its brands to assess where its counterfeiting problem lies. For example, brand estimates could include losses of legitimate sales due to the purchase of counterfeits in the marketplace or brand damage (in terms of money or reputation). Researchers could focus on establishing the extent of counterfeiting of individual brands to assess brand susceptibility to counterfeiting. Estimates could also be made across brands from different companies.

Product

A final level of product counterfeiting estimation examined here is the product itself. This could include, for instance, the number or wholesale or retail value of products. One possible focus is the level of counterfeiting for a specific product type. Another focus is to compare across products so as to determine which products are more susceptible to counterfeiting. Estimations focusing on the product level can help researchers to identify the characteristics of products most likely to be counterfeited.

Units of Observation

Once the level of estimation has been established, the unit of observation must be determined. The unit of observation is what is being studied. It is the population being observed and the exact measurements taken to determine prevalence levels of product counterfeiting. The specific research questions will determine the various characteristics of the main unit of observation to document. Below we examine six potential units of observation for determining the prevalence of product counterfeiting: offenders, schemes, general consumers, consumers as victims, brands, and products.

Offenders

Crime occurs when individuals decide to engage in illegal behavior. Offenders in product counterfeiting include anyone involved in the production, trafficking, distribution, accounting, or any other role related to the counterfeiting of material goods. Whether this includes only those criminally charged or others known to be involved in counterfeiting operations depends on the specific operationalization measure. The value of choosing individuals as the unit of observation is that individuals, and their behaviors and characteristics, may be relatively easy to identify and measure. Using individuals as the unit of observation allows for an in-depth examination of those involved in product-counterfeiting schemes and can help determine the overall size of the problem. However, only focusing on individuals ignores the larger context in which offenders carry out their activities. Understanding the larger context is the advantage of the scheme as a unit of observation.

Schemes

The scheme expands beyond the individual offender to consider situational and organizational elements of crime. The scheme encompasses the same concept as the criminal event, where all the distinct elements of product counterfeiting operations are considered. The scheme is a holistic consideration of numerous aspects—including individual offenders, criminal activities, victims, and temporal and geographic variables—of product counterfeiting as a crime. A scheme represents a discrete product-counterfeiting operation, where every individual involved in the scheme is considered a member of the scheme. Schemes may involve multiple criminal offenses, geographic areas, and time periods due to the measurement of all the relevant activities necessary for executing it. By identifying the schemes within specific temporal and geographic limits, researchers can uncover the size of the overall product-counterfeiting problem. By collecting all available information on product-counterfeiting schemes and the actions taken against them, researchers can ask new and innovative questions about how interventions can be formulated and targeted.

General consumers

Estimates using consumers as the unit of observation are based on levels of consumption and the decision-making processes involved in purchasing counterfeit products. Such estimates would involve determining the number of consumers willfully purchasing counterfeits so as to determine the extent of the consumer market base. Such estimates could also involve determining the extent of counterfeit purchasing, both willful and unknowing, so as to assess the scope and scale of the sale and purchase of counterfeit goods. While many consumers knowingly purchase counterfeits, many others may not and instead can be counterfeit victims, representing another potential unit of observation.

Consumers as victims

Focusing on individual victims as the unit of observation focuses on the subset of consumers who have unwillingly purchased counterfeit goods and have been harmed by them in some way. The harm may simply be monetary loss due to obtaining useless counterfeits instead of the intended product but could also extend to physical injury or even death when the products do not perform as advertised or contain harmful or ineffective components (e.g., counterfeit pharmaceuticals). Because businesses, governments, and educational and other institutions also purchase goods, they, too, can become counterfeit victims. For example, a recent U.S. GAO (2012) report examined the dangers of potential counterfeits to the Department of Defense supply chain, missions, and weapon systems and ultimately on the lives of troops. We further review the strengths and weaknesses of victimization studies below.

Brands

Brands whose products are illegally copied and reproduced are another victim that can be observed to determine the prevalence of counterfeiting. Such observation could involve examining the number of brands that have been counterfeited or the frequency by which specific brands have been counterfeited through various methods. Specific means of observation can include official records, identifying and collecting counterfeits in various markets, or collecting data from firms. Similar methods can also be used to observe counterfeiting of individual products.

Products

A final unit of observation reviewed here is the product, which could also be conceived as another form of corporate victimization. Examining specific products identified as counterfeit can provide an estimation of the extent of counterfeiting not only for the products themselves but also for entire brands and even industries. As discussed above, some products may be more susceptible to counterfeiting than others. The extent of counterfeiting of specific products can be determined through counts of the products in industry or government data or by various other measurement approaches. We next turn to methods that may be applied to the above units of observations, including those that have been used to assess other difficult-to-measure crimes.

Measurement in Similar Research Areas

While researchers face unique challenges in estimating product counterfeiting due to the paucity of research in this area and the clandestine nature of the crime, other criminological and criminal justice research has faced similar difficulties and may offer applicable lessons. In fact, most crimes are difficult to measure accurately. Reviewing research on measuring other types of crime can help us better understand methods that may be applied to assessing product counterfeiting.

Commonly Used Research Methodologies

Among commonly used research methods to assess the extent of crime are (1) officially reported data and statistics, (2) victimization surveys, and (3) self-report surveys. While each of these approaches has benefits and shortcomings, they provide important perspectives on the prevalence of specific types of crime. We review the uses of each and how they might be used to measure product counterfeiting.

Officially reported data and statistics

One of the most common methods for measuring crime is the use of officially reported and recorded data and statistics. Such data are used to analyze many types of crime. Sources of official data on product counterfeiting may include state and federal crime reporting systems, seizure data compiled by customs and border patrol agencies, and other reporting mechanisms such as the Internet Crime Complaint Center, where consumers can report online victimization. Other potential data sources include those collected by various international agencies and organizations, such as INTERPOL, the IACC, and the National Intellectual Property Rights Coordination Center.

A drawback to official data is they likely represent only a small portion of the actual volume of crime. They reflect police activity and organizational/individual decision-making rather than actual rates of criminal activities in the society (Skogan, 1974). Such data have been criticized for being inaccurate or (in some cases) unusable (Kitsuse & Cicourel, 1963; Loftin & McDowall, 2010; Lynch & Jarvis, 2008; Seidman & Couzens, 1974).

Regarding the Uniform Crime Report statistics compiled by the U.S. FBI, for example, Seidman and Couzens (1974) found that police administrative procedures can greatly affect what is actually reported and that changes in administrative procedure can produce concerning variations across agencies and over time. MacDonald (2002) found that attempts to estimate the total cost of crime to society vary by the underlying assumptions used to develop the estimation equations and that even the highest estimates may be conservative. Such estimates may also be flawed by the number and categories of crimes that are included as well as the lack of common definitions and common understandings of crime across jurisdictions. Official data on product counterfeiting would have similar problems, as organizational methods and procedures greatly affect the development of estimations and measurements.

Official statistics do not include the “dark figure of crime” or crime victimization experiences not reported to the police and therefore not included in official counts. This problem may be even greater for product counterfeiting, given the limited focus on the problem by most law-enforcement agencies, as well as incomplete, inaccurate, or nonexistent records even when cases of counterfeiting are uncovered. Officially reported crime rates often vary widely from rates calculated from victimization surveys (MacDonald, 2002; Sims & Myhill, 2001). Many victims do not report their victimization because they do not know they have been victimized, they do not consider it a big enough issue to report, or they do not believe reporting the crime will do any good. Disparities between victimization and official statistics may be even greater for product counterfeiting, given the unique nature of the crime and the failure of victimization surveys to ask individuals about counterfeiting victimization or to ask firms about victimization. Many counterfeits are identified by firms or through seizures and do not reach the legitimate consumer market, meaning many identified cases of counterfeiting will not have associated consumer victims.

In addition to law-enforcement data, information is available through industry record keeping of products and brands subject to counterfeiting, including industry and trade associations and quality-assurance organizations (e.g., Underwriters Laboratories and the International Organization for Standardization). Industry records of investigations and information regarding potential counterfeit operations provide an alternative perspective from those publicly available or investigated by government officials. Yet much like official statistics from government sources, these data are highly unpredictable, as company brand protection, investigation, and record-keeping practices vary tremendously. Companies may also be reluctant to share the information for a variety of reasons, including protection of trade secrets, distrust of external researchers, and fear of the publication of the risk to specific products or brands or brand protection efforts.

Private data do have the advantage of not relying on governmental resources and of providing a different picture than publicly available information or intelligence data. While this information can be obtained through surveys (described further below), researchers may also seek to collect these data on a wide scale across companies and industries in order to supplement government statistics and create a more complete picture of the scope and scale of product counterfeiting.

Victimization surveys

Victimization surveys are another commonly used research method for estimating the extent of crime. Victimization surveys (theoretically) account for the dark figure of crime through interviews of those who have been victimized regardless of whether they have reported their victimization experiences to the police. Victimization surveys allow respondents to discuss what has happened to them, improving the quality of information. Victimization surveys allow for the estimation of victimization risks, responses, and consequences (Cantor & Lynch, 2000). They also allow researchers to examine the differences between victims who do and do not report their victimizations to the police.

Unfortunately, there are discrepancies between individual perceptions of victimization experiences and what the police might record as a victimization experience. Respondents’ lack of knowledge regarding the laws governing product counterfeiting and their own victimization status hinder efforts to obtain accurate estimates of product counterfeiting (Heinonen, Holt, & Wilson, 2012). A lack of clarity on definitions of different crime categories can create confusion and ambiguity in response patterns (as caused, e.g., by individuals indicating that they were robbery victims when their house was burgled). The key difficulty in getting accurate information on areas such as human trafficking, terrorism, and product counterfeiting is that the activities are covert and the victims are difficult to find, so many survey samples may be unable to identify a sufficient number of victims for analysis.

Despite these potential limitations, some studies have begun to explore the prevalence of product counterfeiting through victimization surveys. Spink and Heinonen (2012), for example, surveyed a representative sample of nearly 1,000 Michigan residents about their experiences with counterfeit products. More than 1 in 10 respondents reported purchasing a product that they thought was authentic but was instead counterfeit. Bush, Bloch, and Dawson (1989) similarly surveyed a sample of manufacturers with a net worth greater than US$500,000 and with membership in the IACC. Of the 103 firms that responded to the survey, about half reported their product(s) had been counterfeited. Large firms were more likely to report such victimization than small ones: Only 26% of firms with annual sales less than US$10 million reported such victimization, compared to 71% of firms with annual sales exceeding US$50 million. Although the precise sampling method for this survey and hence its generalizability is not clear, it highlights how surveys may be used to measure product-counterfeit victimization and its variation. Numerous other approaches are possible, including surveys of consumers similar to surveys of the general population such as the National Crime Victimization Survey or the International Crime Victimization Survey.

Self-report surveys

Self-report surveys entail surveying or interviewing individuals on whether they have committed a certain type of crime. The value of self-report measures has long been recognized in criminology, with many important data collection efforts using this approach (Thornberry & Krohn, 2000). Offending surveys measure the frequency and the type of offending behaviors over a period of time with the goal of identifying the prevalence of offending. By gathering a representative sample of the population, such surveys can enable rough estimates of the prevalence of individual offending behavior. There are several disadvantages to self-report surveys, including the reliability of self-reporting and the cost of obtaining a sufficient sample to extrapolate to the larger population. Reliability can vary with recall error and either fabrication of offenses or reluctance to report offending.

Self-report surveys are valuable for obtaining offending estimates, especially in light of the level of consumer complicity in product counterfeiting. Indeed, consumer demand is a main driver of the counterfeit market (Wilson & Kinghorn, 2015), and, as reviewed by Staake, Thiesse, and Fleisch (2009), many studies have been conducted on demand-side investigations. Self-report surveys have been used to build an extensive literature assessing consumer attitudes toward product counterfeiting and factors influencing intentions to purchase counterfeits (e.g., see Albers-Miller, 1999; Bian & Moutinho, 2009; Bian & Veloutsou, 2007; Bloch, Bush, & Campbell, 1993; Business Action to Stop Counterfeiting and Piracy, 2009; Casola, Kemp, & Mackenzie, 2009; Chakraborty, Allred, & Bristol, 1996; Chaudhry & Stumpf, 2011; Chaudhry & Zimmerman, 2009; Cordell, Wongtada, & Kieschnick, 1996; Furnham & Valgeirsson, 2007; Penz & Stöttinger, 2008; Phau & Teah, 2009; Swami, Chamorro-Premuzic, & Furnham, 2009; Teah, Phau, & Huang, 2015; Tom, Garibaldi, Zeng, & Pilcher, 1998; Veloutsou & Bain, 2008; Vida, 2007; Wee, Tan, & Cheok, 1995; Wilcox, Kim, & Sen, 2009; Yoo & Lee, 2009). Self-report surveys have also been used to assess managerial perspectives on counterfeiting problems and potential solutions (see Bush, Bloch, & Dawson, 1989; Chaudhry & Zimmerman, 2009; Chaudhry, Zimmerman, Peters, & Cordell, 2009; IP Crime Investigators College, 2015).

To assess counterfeit offending behavior, self-report surveys on product counterfeiting could ask consumers whether they have knowingly purchased a counterfeit product or have produced, distributed, sold, or otherwise been involved in a counterfeiting scheme. For example, James and Lemon (2013) surveyed a nationally representative sample (n = 1,073) of UK consumers about their purchasing behavior and perspectives, finding more than half reported purchasing some form of counterfeit product, with 18% reporting they had purchased counterfeit alcohol and 16% reporting they had purchased counterfeit pharmaceuticals. While such surveys do have limitations, as noted, they offer a unique way to assess this crime that may complement other approaches.

Novel and Innovative Research Methodologies

In addition to the commonly used methods outlined above, several other novel and innovative approaches may be useful for examining the nature and extent of product counterfeiting. These methods are growing in popularity and have great potential when applied to product-counterfeiting crimes. While not all directly measure prevalence, they can help advance understanding of product counterfeiting, thereby pointing to new directions for developing more complete and accurate measurements. These methods include use of (1) open-source data, (2) ethnographic and snowball sampling, (3) respondent-driven sampling (RDS) and related methods, (4) script and network analysis, and (5) simulation studies. We review each of these below.

Open-source data

An increasingly used method to examine the extent of difficult-to-measure crimes involves searches of open-source materials. These searches can include information from government databases, court records, law-enforcement reports, news articles, academic journals, private watch groups, and industry and professional associations. Triangulation of multiple data sources overcomes the limitations of single sources, reducing the chances of bias and increasing reliability and validity (Chermak, Freilich, Parkin, & Lynch, 2012). Overcoming the limitations of a single source is a very attractive feature of using multiple open sources, allowing for the collection of information on every available crime. Ideally, researchers would clearly identify the inclusion and exclusion criteria prior to the searches and then use trained searchers to find the relevant materials. Once materials are selected for inclusion, they are coded and entered into a database for analysis. This approach has been used to study human trafficking (Kutnick, Belser, & Danailova-Trainor, 2007; Wilson & Dalton, 2008), terrorism and extremism (Chermak et al., 2012; Freilich, Chermak, Belli, Gruenewald, & Parkin, 2014), and the intersection of product counterfeiting and terrorism (Sullivan et al., 2014).

One such effort at using this method to study product counterfeiting has been the Michigan State University Center for Anti-Counterfeiting and Product Protection (A-CAPP) Product Counterfeiting Database, which assembles information on crimes involving counterfeit products committed in the United States (Heinonen & Wilson, 2012; Sullivan et al., 2014; Wilson & Heinonen, 2010, 2011). In building the database, A-CAPP researchers reviewed more than 3,100 documents from industry, government, media, scholarly, and still other sources. This research identified more than 800 product counterfeiting schemes in the United States since 2000 involving a large number of different products.

While open sources can be useful for building information and data for measuring product counterfeiting, they do not address the dark figure of crime. Open-source searches are limited to incidents that have been identified in publicly available materials, subjecting them to publication and selection bias. To establish a manageable breadth of scope, open-source searches are typically limited to indicted crimes. As a result, an open-source search on product counterfeiting may not include those identified by the government or industry but who have not been made public. Despite these limitations, analysis of product-counterfeiting crimes through open-source databases can be a powerful way to make informative recommendations for policy and practice.

Ethnography and snowball sampling

Ethnography and snowball-sampling techniques have been used, where the populations of interest might otherwise be difficult to access (e.g., street criminals—Anderson, 1999; police officers—Manning, 1997, 2008; Moskos, 2008; narcotics markets—Goffman, 2014; Williams, 1989). Ethnographic work is based on methods frequently used in anthropology but has been applied across a variety of disciplines and subjects. In ethnographic research, the researcher takes on the role of a participant observer and becomes embedded in the specific group of interest. The initial stage involves obtaining access, which is often the most difficult to navigate. The researcher must find an individual affiliated with the group under study and gain his or her trust to a degree that a gatekeeper is willing to expose the researcher to his or her affiliates. This form of pseudo-snowball sampling continues until meeting additional gatekeepers who provide more access and exposure to others. Once trust is established, the researcher can record his or her experiences and interactions. This is particularly useful with difficult-to-reach populations as well as organizations not normally accessible to researchers. After prolonged exposure, observed behaviors and social interactions become more normal and representative of routine, everyday experiences.

Applying this technique to examine product counterfeiting would be challenging but highly beneficial. While identifying and accessing an initial gatekeeper would be most challenging, once access is established, the quality and richness of the resulting data can provide greater understanding of the intricacies of product-counterfeiting networks, production, and culture. The nonprobabilistic nature of snowball sampling, however, limits its ability to estimate the true prevalence of product-counterfeiting activity except in particularly defined circumstances. Supplementing this method with others can greatly enhance the ability of researchers to measure the nature and extent of product counterfeiting.

RDS and related methods

RDS is an intriguing research strategy that has been used to study human trafficking. RDS was developed by Heckathorn (1997) in an attempt to address limitations with snowball-sampling (and related) methodologies. RDS is similar to snowball sampling, informant sampling, and target sampling but applies a more rigorous method to allow extrapolation to the larger population or subpopulation being studied. RDS is also appropriate, where traditional snowball-sampling techniques are either not possible or successful. RDS relies on a structured referral process with a Markov property to achieve diversity and equilibrium (Zhang, 2012). This is accomplished through successive waves of participant recruitment and the use of incentives and a systematic recruitment process. Using these methods, the researcher is ultimately able to make inferences about the target population from the initial convenience sample (Zhang, 2012). The use of RDS allows researchers to use preexisting social networks to develop a sample of the subpopulation of interest. While it has been challenged as nonprobabilistic, as it is based on an initial convenience sample (Heckathorn, 2002), the method is believed to allow an unbiased estimation of the target population (Volz & Heckathorn, 2008).

A similar method was used by Maguire and colleagues to examine child sex trafficking in the Philippines (Maguire & Gantley, 2010; Maguire, Gantley, & Snipes, 2009; Maguire & Snipes, 2007). Their approach used 10 people (8 investigator/researchers, 1 data collection expert, and 1 security expert) to collect data. The investigators each spent seven to eight nights visiting bars, brothels, massage parlors, and other sex trade hubs over three waves gathering information by posing as “sex tourists” and seeking out minors who were either prostituted or commercially exploited. They would approach intermediaries such as taxi drivers and bellhops and request locations where they could find prostitutes. They then submitted data on each encounter to a central hub by voice or text message. Using this methodology, they observed 1,550 commercially exploited or prostituted young women and girls and confirmed that 103 of these were minors. Although it provided rich data, ethical and safety concerns have been raised regarding this particular research.

Similar strategies could be used with product counterfeiting by observing areas known as “hot spots” for counterfeit goods and systematically identifying and interviewing individuals linked to the buying and selling of counterfeits. The goal of such research would be to find others in the counterfeit market at different levels of the supply chain, branching from an initial starting point to create a more fully developed picture of different markets. These data collection techniques could also be used in conjunction with script and network analyses.

Script and network analysis

The “crime script” approach allows researchers to examine how a specific type or category of crime is committed in an effort to identify its key events and roles (Cornish, 1994). Different crime categories result in different crime scripts, and these scripts can be useful in identifying the routine activities involved in the commission of different types of crimes and in developing prevention and intervention approaches (Cornish & Clarke, 2002). Product counterfeiting occurs in a manner similar to drug crime and cybercrime in that it can include organized criminal activities and networks with organizational structures, numerous perpetrators, and multiple jurisdictions over extended periods of time, challenging the ability of researchers to study these criminal activities. Crime scripts can be used to further understand how product counterfeiting occurs and therefore guide researchers to specific areas to search and identify counterfeiting-related activities, thereby improving researchers’ ability to estimate the true extent of the problem.

Script analysis can be used in conjunction with network analysis to examine the different roles that are essential to committing the various elements of product counterfeiting schemes and offenders carrying out those specific roles. Cornish and Clarke (2002) demonstrate that it is possible to examine the choices and actions that occur throughout the crime script, allowing researchers to better understand the roles that different individuals in the criminal network might take on. This typically involves a series of networks (both legitimate and illegitimate) that are interconnected and where individuals vary in their levels of criminal involvement. To use a network-based approach, researchers can identify a starting point and follow the network, as they would in a snowball or respondent-driven sample, to develop a complete network of all those linked to the original “seed” individuals. Alternatively, researchers may collect data on all those involved in product counterfeiting who meet a specific set of criteria (e.g., offenders involved in the counterfeiting of pharmaceuticals that passed through pharmacies in France from 2014 to 2015). Systematic documentation of the involvement of these networks can aid researchers and practitioners in developing a more complete understanding of network breadth and depth, thereby creating more accurate estimates of the prevalence of product counterfeiting.

Simulation studies

More recent criminological and criminal justice research has used simulation modeling approaches to study crime. These approaches apply simulation models commonly used in computer science (and video gaming), weather prediction, geography, and geology to the study of crimes such as street robbery (Groff, 2007a, 2007b) as well as police responses to crime (e.g., Groff & Birks, 2008). Simulation models combine known data with decision-making models to predict how patterns (in weather, geologic activity, migration, or human behavior) will emerge and evolve. Simulations can also be used to predict how individuals in product-counterfeiting networks interact and adapt over time, either when uninhibited or facing anti-counterfeiting measures.

Simulation models address concerns related to inadequate data, data quality, and appropriate available methods (Groff, 2007a). Often simulation models are used where researchers cannot easily manipulate conditions (e.g., physical environment or social interactions) without incurring prohibitive expenses or where manipulation might be unethical (e.g., random assignment to poverty, middle-class, and upper-class groupings). Instead, a series of variables are used to extrapolate what would likely occur in different circumstances.

Key challenges in simulation-modeling techniques relate to the availability of micro-level data on individual behavior as well as methods appropriate for capturing and modeling spatial and temporal dynamics on interactions among offenders, victims, and guardians (Groff, 2007a). Groff (2007a, 2007b) merged agent-based modeling techniques with geographic information systems to test routine activities theory as it applies to street robbery. This work, combined with that of Groff and Birks (2008) examining police-response tactics, demonstrates the potential efficacy for simulation modeling techniques, where access to data is limited or incomplete and modeling human interaction may be difficult.

Lessons Learned: New Directions in Product Counterfeiting Research

Existing estimates of product counterfeiting suffer various methodological shortcomings. Too often, insufficient information exists on how commonly cited estimates were developed and on how accurate or appropriate these estimates might be. Techniques such as analysis of official data, victimization surveys, and self-reports commonly used in other areas of criminology may help establish a low-end estimate of product counterfeiting but are likely to fall short of estimating its true extent. More novel techniques for estimating difficult-to-measure crime show promise for assessing product counterfeiting but also have limitations. Each of these techniques will offer valuable perspectives on the nature and extent of product counterfeiting due to their unique characteristics and approaches.

A number of key concerns and challenges still need to be addressed when attempting to assess the nature and extent of product counterfeiting. These concerns can impact measurement attempts in several ways. For example, there are difficulties in accessing useful information. Brand owners and law enforcement organizations typically do not publicize or share the level of data (if any) necessary for developing useful measures (or studying the nature of the crime and its harms and victims). Existing sources of information are limited at best. Many companies fail altogether to recognize their product counterfeiting risk (Wilson & Kinghorn, 2014), so they may not even gather useful data on the problem. Victims of product counterfeiting also might not know that they are victims, and if they do know, they might not care or be willing to share information.

Attempts to access information through law enforcement and government entities also present significant challenges. Product counterfeiting is often not a priority for law enforcement and prosecution agencies, which decreases the likelihood of successful detection and prosecution. While enhancing awareness through training and education on these issues could aid law enforcement in detecting incidents, concerns with the accuracy of record keeping and official reports on the criminal justice system may still limit the reliability of these data.

The final challenge that needs to be addressed involves access to those who engage in product counterfeiting. Some researchers attempt to examine crime problems through interviews with those who have been incarcerated for committing particular crime. Interviewing prisoners who have been convicted of product counterfeiting and related crimes could be useful, but there are several limitations of this approach. Prisoners would only represent the (likely small) portion of product counterfeiters who have been caught, charged, and convicted of the crime. At each stage, there is a funnel effect, where the remaining group is an increasingly smaller portion of the total population of product counterfeiters. Furthermore, these individuals may have lingering loyalties to the counterfeiting networks in which they are involved. Out of loyalty and interests in avoiding further harm to these networks or themselves, they may be unwilling to share information that could implicate others. This concern also relates to the difficulty in identifying and accessing gatekeepers who might be the most useful contacts for ethnographic analysis of product-counterfeiting networks and cultures. This raises the question, and need for research, on whether accessible individuals are part of mainstream society or are isolated in a manner similar to some terrorist cells in an effort to protect the larger network.

While the approaches we have suggested to estimate the nature and extent of product counterfeiting are far from perfect, they can enhance or supplement existing methods. They may be most fruitful when the level of estimation and unit of analysis are specifically articulated and consistent with the method. To help offset the shortcomings of specific methods, future attempts may seek to use multiple methods to assess the nature and extent of product counterfeiting. Doing so may provide a more complete picture of the overall product-counterfeiting problem.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article was supported by an award from Qualcomm and the Qualcomm Foundation. The ideas expressed herein are those of the authors and do not necessarily represent the opinions of Qualcomm or the Qualcomm Foundation.

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