INTERPOL's Surveillance Network in Curbing Transnational Terrorism
2015; Wiley; Volume: 34; Issue: 4 Linguagem: Inglês
10.1002/pam.21845
ISSN1520-6688
AutoresJavier Gardeazábal, Todd Sandler,
Tópico(s)Crime, Illicit Activities, and Governance
ResumoJournal of Policy Analysis and ManagementVolume 34, Issue 4 p. 761-780 Research ArticleOpen Access INTERPOL's Surveillance Network in Curbing Transnational Terrorism Javier Gardeazabal, Javier GardeazabalSearch for more papers by this authorTodd Sandler, Todd SandlerSearch for more papers by this author Javier Gardeazabal, Javier GardeazabalSearch for more papers by this authorTodd Sandler, Todd SandlerSearch for more papers by this author First published: 12 May 2015 https://doi.org/10.1002/pam.21845Citations: 6AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract This paper investigates the role that International Criminal Police Organization (INTERPOL) surveillance—the Mobile INTERPOL Network Database (MIND) and the Fixed INTERPOL Network Database (FIND)—played in the War on Terror since its inception in 2005. MIND/FIND surveillance allows countries to screen people and documents systematically at border crossings against INTERPOL databases on terrorists, fugitives, and stolen and lost travel documents. Such documents have been used in the past by terrorists to transit borders. By applying methods developed in the treatment-effects literature, this paper establishes that countries adopting MIND/FIND experienced fewer transnational terrorist attacks than they would have had they not adopted MIND/FIND. Our estimates indicate that, on average, from 2008 to 2011, adopting and using MIND/FIND results in 0.5 fewer transnational terrorist incidents each year per 100 million people. Thus, a country like France with a population just above 64 million people in 2008 would have 0.32 fewer transnational terrorist incidents per year owing to its use of INTERPOL surveillance. This amounts to a sizeable average proportional reduction of about 30 percent. INTRODUCTION The International Criminal Police Organization (INTERPOL) benefits member countries by coordinating their police efforts. Sandler, Arce, and Enders (2011) estimated that for every dollar invested in INTERPOL's counterterrorism activities, member countries receive $200 in average returns. This is a huge rate of return for public money. This large return can be contrasted with disappointing returns on U.S. homeland security spending, which was calculated as four to ten cents on a dollar by Sandler, Arce, and Enders (2009) in their Copenhagen Consensus study. More recently, Mueller and Stewart (2014) showed that U.S. homeland security did not come near to justifying the many tens of billions of dollars spent each year. Thus, INTERPOL surveillance methods, which cost millions of euros annually, appear to be excellent value as our analysis will show, given their effectiveness in curbing transnational terrorism. INTERPOL provides multiple services—for example, police training, communication links, and coordinating the hunt for fugitives—to member countries. This paper focuses on one of these services: the control of transnational terrorism. In 2005, INTERPOL introduced two surveillance networks, the Mobile INTERPOL Network Database (MIND) and the Fixed INTERPOL Network Database (FIND), which facilitate searches of people, motor vehicles, and documents at international transit or other points. The main difference between these networks is that FIND allows access to an online database, which is continuously updated, whereas MIND provides access to an offline database, which is periodically downloaded in an updated form every 24 to 48 hours. These technologies may be effective at curbing international crime and transnational terrorism; however, as of December 2008 only 47 of the then 188 INTERPOL member countries had adopted these technologies. The associated crime-fighting transnational externalities derived from MIND/FIND were not fully internalized by member countries. In order to understand the reasons for these unexploited benefits, Enders and Sandler (2011) studied why some countries chose to join the MIND/FIND networks and others did not. They found that income per capita, population, and democratic freedoms were the main determinants of whether INTERPOL member countries installed MIND/FIND technologies. As of August 2012, more than a hundred members were connected to either the MIND or FIND networks or both. This increased membership came as INTERPOL pushed to educate its member countries about the benefits of MIND/FIND in fighting international crime and transnational terrorism. However, not all connected countries used the network. The current paper differs from Enders and Sandler (2011), which investigated not only the determinants of MIND/FIND adoption, but also what could be done to encourage greater adoption. In the current paper, we ask whether the implementation and use of MIND/FIND technology reduced the amount of transnational terrorism in the implementing countries. A favorable answer to this question can provide strong positive inducements for other INTERPOL countries to adopt and use MIND/FIND. Countries must, however, remember that keeping transnational terrorists from moving about freely from country to country is a weakest-link public good problem because terrorists will seek to transit the least-vigilant borders (Enders & Sandler, 2012). If, in addition, MIND/FIND limits transnational terrorist attacks in adopting countries, then planned attacks are likely displaced to other countries, where similar technologies are not deployed (see Enders & Sandler, 1993).1 Displacement effects are ameliorated when main airport hub countries are utilizing INTERPOL surveillance, so that terrorists must take circuitous routes and cannot enter their prime-target countries. We apply causal inference methods developed in the treatment-effects literature (Angrist & Pischke, 2009; Wooldridge, 2010) in order to establish a causal relationship between the treatment, MIND/FIND adoption, and transnational terrorist incidents. Applying causal inference methods to assess treatment effects at the country level is a challenging task as some of the assumptions maintained in the treatment-effects literature might not hold at the aggregate level. However, we are not the first to investigate causal effects at an aggregate level: for instance, Lin and Ye (2007) assessed the effectiveness of the inflation-targeting policy; Gilligan and Sergenti (2008) looked at the effect of United Nations peacekeeping missions on building a sustainable peace after civil war; Nielsen et al. (2011) examined the effect of foreign aid on armed conflict; and Chang and Lee (2011) analyzed the trade-promoting effect of the World Trade Organization. Using the treatment-effects approach for causal inference, we find that MIND/FIND adopters, who used the technology, experienced fewer transnational terrorist attacks than nonusers. Even though the reduction in incidents per adopter is small, the proportional reduction is large for the period 2008 to 2011, at about 30 percent on average for countries using the network. Although transnational terrorism is a concern for many countries, the MIND/FIND network is not primarily intended to curb transnational terrorism; rather, it is meant to reduce international crime (e.g., human trafficking). MIND/FIND also dissuades criminals other than terrorists from crossing international borders, thereby providing another benefit, not captured in our study. The remainder of the paper contains six sections. The next section presents necessary preliminaries on INTERPOL and MIND/FIND, while the ensuing section describes our data set. The following sections indicate our methodology and report estimates of the treatment effects. The last section concludes with a discussion of our findings. PRELIMINARIES ON INTERPOL AND MIND/FIND Terrorism is the premeditated use or threat to use violence by individuals or subnational groups to obtain a political or social objective through the intimidation of a large audience beyond that of the immediate victims (Enders & Sandler, 2012). A relevant distinction for this study is between domestic and transnational terrorist attacks. Domestic terrorism is homegrown and home-directed with no international externalities for other countries. For domestic terrorism, the perpetrators, the victims, and the targets (e.g., the institution receiving terrorist demands) are all from the venue country where the attack takes place. The Oklahoma City bombing by Timothy McVeigh on April 19, 1995 is a domestic terrorist incident since the perpetrator and the victims were Americans from the venue country. In contrast, transnational terrorism involves perpetrators, victims, or targets from two or more countries. If the terrorists stage an attack in another country, then the incident is transnational terrorism. When a terrorist attack in, say, England, kills or injures an American, the attack is a transnational terrorist event. The Boston Marathon bombing by the Tsarnaev brothers on April 15, 2013 is a transnational terrorist incident because one of the perpetrators was a non-U.S. citizen and one of the murdered victims was a student from China. Our distinction between domestic and transnational terrorist incidents is the one used by the three terrorist event data sets, including the one used in this study, described in the next section.1 Transnational terrorism generates transnational externalities for countries other than where the attack takes place. INTERPOL's international mission has, in some terrorist instances, assisted member countries’ efforts to capture transnational terrorists using INTERPOL's resources. INTERPOL was established in 1923 as an independent international organization with the mission to promote international cooperation in fighting international crime. Currently, INTERPOL has 190 member countries, whose assigned membership fees mostly fund the organization's staff, infrastructure, and operations. The remainder of INTERPOL's funding comes from voluntary donations. INTERPOL links law enforcement agencies, the members’ National Central Bureaus (NCBs), and INTERPOL General Secretariat (IPGS) in fighting transnational crime and terrorism. In particular, INTERPOL addresses six primary criminal concerns: corruption, drugs and organized crime, financial and high-technology crime, fugitives, trafficking in humans, and transnational terrorism (INTERPOL, 2011). After the four skyjackings on September 11, 2001 (henceforth, 9/11), INTERPOL channeled up to 20 to 25 percent of its annual crime-fighting resources into coordinating international law enforcement efforts to address transnational terrorism (Sandler, Arce, & Enders, 2011). INTERPOL provides its communication networks, its training facilities, its best practices, its data banks, and other assets to member countries to assist in their arrest of suspected terrorists. Many of these arrests occur as terrorists are identified when they attempt to transit countries’ borders. INTERPOL members and their law enforcement agents can communicate over INTERPOL's secure communication linkage, I-24/7, which is a restricted-access internet portal. When connected to I-24/7, members’ law enforcement agents can share information and access INTERPOL databases and online resources. I-24/7 is also used by INTERPOL to issue arrest (red) notices and to broadcast country-initiated diffusions to alert member countries to detain suspected criminals or terrorists. Among many other things, INTERPOL databases contain information on suspected terrorists and stolen and lost travel documents (SLTD). Such documents have been used by terrorists and criminals to transit international borders—this was true of some 9/11 hijackers (Sandler, Arce, & Enders, 2011). The ability of member countries to apprehend criminals and terrorists at their borders was greatly enhanced at the end of 2005 when INTERPOL offered MIND or FIND (or both) to interested members. MIND/FIND provides an efficient systematic means for checking people, motor vehicles, and travel documents against INTERPOL's global databases. With MIND/FIND, countries can check all passports and motor vehicles at border crossings and other points. In a matter of seconds, scanned passports or vehicle documents are checked by MIND/FIND against national and INTERPOL data banks. In the absence of MIND/FIND, these searches would be prompted by suspicious behavior, which has a strong random component. Moreover, the border official would have to leave his or her duty post and key in the passport or other document numbers at the I-24/7 portal. Such action is subject to error. Countries may rely on MIND, FIND, or both, depending on their infrastructure. One key difference between MIND and FIND involves the freshness of accessed information. FIND allows real-time online access to INTERPOL databases, while MIND contains a copy of these databases. This offline copy is updated periodically, within 48 hours or less (Enders & Sandler, 2011). Thus, FIND provides somewhat more up-to-date data; however, this advantage is likely to dissipate over time as MIND is updated more regularly. In practice, this short lag should not make any effective difference between MIND and FIND. MIND/FIND allows access to huge databases containing millions of records contributed by all INTERPOL member countries (whether connected to the MIND/FIND network or not). These databases are continuously updated; however, the magnitude of the flow of records is minuscule compared with the size of the stock of records already in the databases. Therefore, searching INTERPOL databases through the MIND network misses the flow of records since the last update. This could make a difference between MIND and FIND if, for instance, a new terrorist suspect surfaces since the last update, but then a red alert can be issued in real time to INTERPOL member countries. Terrorists are anticipated to use SLTD already in the database unless acquired within a day or so of travel, which is not a likely event. Countries can still make arrests without having MIND/FIND when a person's behavior raises suspicions, an alert has been issued, or a person turns him- or herself in. Not all countries linked to MIND/FIND utilize the technology for searches. This is particularly true of countries whose MIND/FIND linkage was externally funded—for example, some Caribbean and African countries. So, possessing MIND/FIND is no guarantee that it will be applied to border or other searches (Enders & Sandler, 2011). In March 2014, this was made abundantly clear when two passengers boarded Malaysian Airlines flight 370 with stolen passports and were not screened by Malaysia, a FIND country since June 2007. THE DATA We collected yearly data on INTERPOL member countries, the units of analysis, from 2005 to 2011. Definition of the treatment (i.e., the connection to or moderate use of the MIND/FIND network) requires a detailed explanation. IPGS provided us with the exact date of MIND/FIND connections, which ran from December 13, 2005 (when Switzerland linked to the MIND network) to July 19, 2012 (when the Ivory Coast linked to the MIND network). Some countries joined MIND, others joined FIND, and some joined both. Despite minor differences between MIND and FIND, indicated earlier, we treat them as equivalent in this study. Figure 1 plots the number of countries connected to the MIND/FIND network from 2004 to 2011. During 2005, Switzerland and Liechtenstein were the first countries to join the network. By the end of 2006, Belgium, Lithuania, Spain, St. Kitts and Nevis, and Turkey had joined the network. The number of countries connected to the MIND/FIND network rose to 24 in 2007, 47 in 2008, 71 in 2009, 94 in 2010, and 102 in 2011. Figure 1Open in figure viewerPowerPoint Number of Countries Connected to MIND/FIND Network and Number of Countries with Total Searches Above 1,000. In addition to the exact date of connection, IPGS also provided the number of searches by each member country for the years 2008 to 2011. The total number of MIND/FIND searches by member countries showed that some connected countries did not actually use the network and also that some formally unconnected countries made searches through their I-24/7 portal when prompted by suspicious behavior or international events. Figure 1 also plots the number of countries that carried out 1,000 or more annual searches using the MIND/FIND network. Such a mild threshold makes a difference in terms of the number of countries that actually used MIND/FIND and therefore can be considered as treated. Table 1 reports the number of country-year cases and average number of searches, classified according to whether countries were formally connected to MIND/FIND and whether the number of searches was above or below the 1,000 threshold. Table 1 shows that in 124 country-year cases, countries were formally connected to MIND/FIND but performed less than 1,000 searches on the network (on average 81.1 searches per year). These figures indicate that those countries, although formally connected to MIND/FIND, did not use the network systematically. In addition, our sample includes seven country-year cases where countries not formally connected to the network performed more than 1,000 searches on the network through their I-24/7 portal (on average 897,974.9 searches). Although these countries were not MIND/FIND countries, their volume of searches is sufficiently high to suggest a more than casual use of the network. In summary, Table 1 shows that the 1,000 searches threshold identifies a more systematic use of the network than the connection status. Henceforth, we consider a country as treated when it actually used the MIND/FIND or I-24/7 network to perform a minimum of a thousand searches per year. The choice of a particular threshold number of searches is certainly arbitrary. A good reason for our 1,000 searches threshold choice is that INTERPOL itself used the 1,000 searches threshold to classify countries into those which "used/did not use" their Automated Search Facility (ASF). Table 1. Number of searches and MIND/FIND connection status Connected Not connected Number of searches Average searches Country-year cases Average searches Country-year cases ≥1,000 11,005,625.0 135 897,974.9 7 <1,000 81.1 124 3.0 298 Notes: This table shows the average number of searches and the number of country-year cases classified according to MIND/FIND connection status and whether countries performed at least 1,000 searches or not. Therefore, a country in a particular year is considered as treated if the total number of searches (either MIND or FIND) exceeds 1,000. However, the time domain of our analysis runs from 2005 to 2011. It includes not only the 2008 to 2011 period for which the number of searches is available but also the 2005 to 2007 period with only information on MIND/FIND connection status but no information on the number of searches. For the 2005 to 2007 period, treatment is defined according to connection status with a few exceptions: a number of countries formally connected at some point during the 2005 through 2007 period have no searches (or searches below the 1,000 threshold) in 2008. It seemed reasonable to consider those cases as untreated.1 If this assumption were erroneous, then we would be biasing the treatment effect in the direction of finding a smaller treatment effect (in absolute value). The outcome variable is the number of transnational terrorist incidents ending in a country, which is available from International Terrorism: Attributes of Terrorist Events (ITERATE; Mickolus et al., 2012). ITERATE uses the news media to identify transnational terrorist incidents and their country venue. Figure 2 shows a time series plot of the number of transnational terrorism incidents in our sample for the period 2000 to 2011. During the subperiod 2005 to 2011, the total number of incidents is split into the group of treated (MIND/FIND) countries and untreated (not MIND/FIND) countries. A simple comparison of the raw number of incidents across groups would lead to erroneous inference. To begin with, countries that joined MIND/FIND first had only a few incidents prior to joining the network or afterwards, for example, Switzerland experienced no incidents during the entire sample period, before or after joining MIND/FIND. Second, the number of incidents should be adjusted for various other risk factors affecting the number of transnational terrorist incidents. Third, as the number of treated countries increases, so will the number of terrorist incidents even when the treatment might be reducing incidents. Figure 2Open in figure viewerPowerPoint Total Number of Transnational Terrorism Incidents for the Period 2000 to 2011 as well as the Number of Transnational Terrorist Incidents in MIND/FIND Countries and not MIND/FIND Countries during the 2005 to 2011 Period. In addition to the treatment and outcome variables, other covariates of interest for our analysis are those that are determinants of the treatment status and the outcome variable. Enders and Sandler (2011) found that income per capita, population, and democratic freedoms were the main determinants of whether INTERPOL member countries installed MIND/FIND technologies. We gather real GDP per capita and population data from the World Bank and we obtain a measure of democratic freedoms, Polity, from POLITY IV PROJECT (Marshall, Jaggers, & Gurr, 2011).1 We dropped from the analysis countries with incomplete data, resulting in a balanced panel of 141 countries as listed in the first panel of Table 2. The temporal framework of the analysis takes the years from 2000 to 2004 as the pretreatment or presample period, and uses years 2005 to 2011 as the sample or estimation period. The effect of MIND/FIND adoption and use on transnational terrorism is evaluated for the 2008 to 2011 period. Table 2. List of countries One hundred forty-one countries sample used for REP-MLE estimation Albania, Algeria, Angola, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bolivia, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Central African Republic, Chad, Chile, China, Colombia, Comoros, Republic of the Congo, Costa Rica, Cote d'Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Kenya, Republic of Korea, Kuwait, Kyrgyz Republic, Lao PDR, Latvia, Lesotho, Liberia, Lithuania, Macedonia FYR, Madagascar, Malawi, Malaysia, Mali, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, the Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russian Federation, Rwanda, Saudi Arabia, Senegal, Sierra Leone, Singapore, Slovak Republic, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Swaziland, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Togo, Trinidad and Tobago, Turkey, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan, Venezuela RB, Vietnam, Republic of Yemen, Zambia. Fifty-seven countries sample used for FEP-MLE estimation Algeria, Angola, Austria, Azerbaijan, Bahrain, Belgium, Bolivia, Burundi, Cameroon, Chad, China, Colombia, Republic of the Congo, Cote d'Ivoire, Czech Republic, Denmark, Egypt, Equatorial Guinea, Ethiopia, France, Germany, Greece, Guatemala, Haiti, India, Indonesia, Israel, Italy, Jordan, Kenya, Kuwait, Mali, Mauritania, Mexico, Morocco, the Netherlands, Niger, Nigeria, Norway, Pakistan, Philippines, Poland, Qatar, Russian Federation, Saudi Arabia, Spain, Sri Lanka, Sudan, Sweden, Thailand, Trinidad and Tobago, Turkey, Uganda, United Arab Emirates, United Kingdom, United States, Republic of Yemen. Seventy-seven countries sample used for PSM-GMM estimation Algeria, Angola, Australia, Bahrain, Bangladesh, Belgium, Brazil, Burundi, Cambodia, Canada, Chad, Chile, China, Colombia, Republic of the Congo, Cote d'Ivoire, Cyprus, Czech Republic, Ecuador, Egypt, El Salvador, Eritrea, France, Georgia, Germany, Greece, Guinea, Haiti, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kenya, Kuwait, Kyrgyz Republic, Lao PDR, Liberia, Macedonia FYR, Madagascar, Malaysia, Mexico, Morocco, Namibia, Nepal, the Netherlands, Niger, Nigeria, Norway, Pakistan, Peru, Philippines, Qatar, Russian Federation, Saudi Arabia, Sierra Leone, Singapore, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Sweden, Tajikistan, Thailand, Turkey, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States, Uzbekistan, Venezuela RB, Republic of Yemen. THE METHODS Introducing the methods requires some definitions. Let be the number of transnational terrorist incidents that take place in country i during year t. Let be the transnational terrorism incidence rate (TTIR) defined as the number of transnational terrorist incidents divided by population, that is, where is population of country i in year t. Past studies normalized terrorism by population because more populated countries imply greater terrorist exposure (see, e.g., Gassebner & Luechinger, 2011). In our application, population is measured in hundreds of millions. The treatment status variable, equals one when country i receives treatment as defined in the Data section in year t and equals zero otherwise. To assess the treatment effects, we use a Rubin causal model (Rubin, 1974; Sekhon, 2007). Let be the potential number of transnational terrorist incidents under treatment, and let be the potential number of transnational terrorist incidents under no treatment. The observed number of transnational terrorist incidents, is related to the potential numbers of transnational terrorist incidents according to We assume that the conditional expectation of the potential number of transnational terrorist incidents is exponential (1)for , where is a vector of covariates, is a vector that includes powers and interactions of the covariates as well as unity, is a conforming vector of parameters which depends on whether treatment is in place or not, is a time-invariant country-specific unobserved effect, and is a unit-invariant period-specific unobserved effect. Further assume that conditional on covariates, unobserved heterogeneity, and common time trends, potential outcomes and treatment status are mean independent, that is, . Therefore, the conditional mean of the observed number of transnational terrorist incidents is (2)where . Notice that (log) population enters the exponential mean function with the associated coefficient restricted to be unity. Thus, because the exponential and logarithm functions cancel out, the conditional mean is proportional to population, in other words, population is being used as a measure of exposure. Furthermore, moving population to the other side of equation 2 results in (3)where the dependent variable is the TTIR. Therefore, we can use the predicted values generated using equation 3 and then average them over countries to compute average TTIRs. A desirable feature of the specification in equation 2 is that it allows for different functional forms for treated and control units by including interactions of the treatment indicator with all elements of , which includes unity. In addition, equation 2 permits a flexible parametric functional form by including powers and interactions of the covariates. These flexible functional forms are obtained after a careful specification search procedure. Details about the procedure are reported in an online appendix.1 In essence, the specification search procedure is as follows. We start with an initial specification including the baseline covariates, together with their interactions with the treatment indicator. The final specification is obtained after several rounds of regressions. At each round, a set of exponential regressions is estimated, each of these regressions includes all terms included in the previous round plus a pair of nonlinear terms: a squared covariate or interaction of two covariates and its interaction with the treatment indicator.1 Among all regressions in a round, we select the most statistically significant pair of nonlinear terms, which is then included in the next round of regressions. When no more pairs of terms turn out to be significant, we run a final round of regressions, in each of which an insignificant term is eliminated. Retaining only significant terms is important for the estimation of the treatment effects, which could otherwise be affected by large but insignificant parameter estimates. The specification search follows a procedure very similar to the so-called stepwise regression. Using stepwise regression requires adjusting the significance levels to take into account the model building process. Had we adjusted the significance levels accordingly, confidence intervals (CIs) reported below would have been wider.1 Although the MIND/FIND network was not devised as a counterterrorism tool, it can be argued that MIND/FIND adoption is not entirely
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