Friday, January 22, 2021

Preferences for pink & blue were tested in children aged 4–11 years in three small‐scale societies; pairing of female & ping seems a cultural phenomenon & is not driven by an essential preference for pink in girls

Cultural Components of Sex Differences in Color Preference. Jac T. M. Davis  Ellen Robertson  Sheina Lew‐Levy  Karri Neldner  Rohan Kapitany  Mark Nielsen  Melissa Hines. Child Development, January 21 2021.

Abstract: Preferences for pink and blue were tested in children aged 4–11 years in three small‐scale societies: Shipibo villages in the Peruvian Amazon, kastom villages in the highlands of Tanna Island, Vanuatu, and BaYaka foragers in the northern Republic of Congo; and compared to children from an Australian global city (total N = 232). No sex differences were found in preference for pink in any of the three societies not influenced by global culture (ds − 0.31–0.23), in contrast to a female preference for pink in the global city (d = 1.24). Results suggest that the pairing of female and pink is a cultural phenomenon and is not driven by an essential preference for pink in girls.

3 Discussion

We found no significant differences between boys’ and girls’ preference for pink in three small‐scale societies in Peru, Vanuatu, and the northern Congo. We found that girls liked pink more than boys did in a global city, confirming earlier research (Jonauskaite et al., 2019; Mohebbi, 2014; Weisgram et al., 2014; Yeung & Wong, 2018). These results support theories that link color preferences to individual experience (Palmer & Schloss, 2010) and gender cognitions (Bem, 1981; Carter & Levy, 1988; Liben & Bigler, 2002; Martin & Halverson, 1981). That is, culture, not inherent biological dispositions, influences the gender difference in children’s preference for pink.

Our findings contradict essentialist positions that pink is linked to female gender through neural color processing or through evolved preferences linked to foraging or mate choices (Alexander, 2003; Ellis & Ficek, 2001; Hurlbert & Owen, 2015). Supporting our findings, other research indicates that children are not born with sex differences in their color preferences, and that infants show no sex differences in preference for pink until they reach at least 2.5 years of age (Franklin, Gibbons, Chittenden, Alvarez, & Taylor, 2012; Jadva et al., 2010; LoBue & DeLoache, 2011; Wong & Hines, 2015; Zemach et al., 2007). Additionally, some studies of adults in societies with limited access to global culture have found no female preference for pink (Groyecka et al., 2019; Sorokowski et al., 2014), although, as noted before, the female preference for pink over blue may be characteristic of children, rather than adults. Thus, our findings provide additional evidence that the pairing of female and pink is a cultural phenomenon and is not innate.

Results suggest that color preferences are the behavioral expression of a complex interaction between underlying biology and cultural context. Genetic, hormonal, and neural indications may predispose children to display gendered behaviors and preferences, such as color preferences (Arnold, 2009; De Vries & Simerly, 2002; Hines, 2010), but the specific expression of these preferences, such as a female preference for pink, may be learned from cultural setting and individual experience (Bandura, 2002; Carter & Levy, 1988; Martin & Ruble, 2004; Palmer & Schloss, 2010). Children in all cultures are exposed to gender role information that influences their preferences and behavior, but not all cultures include information about the color pink. In our study, male and female roles were well defined and separate in the Vanuatu kastom culture (Douglas, 2002; Lindstrom, 2008), while BaYaka (Lewis, 2017) and Shipibo (Hern, 1992) villages were traditionally egalitarian for men and women, although still with typical male and female activities (Ember & Ember, 2003). However, pink was not used in these societies as a marker for female gender. In contrast, in many industrialized settings, boys and girls grow up surrounded by gender color‐coding in marketing, toys, clothing, room decorations, and online (Auster & Mansbach, 2012; Black, Tomlinson, & Korobkova, 2016; Cunningham & Macrae, 2011; Koller, 2008; LoBue & DeLoache, 2011; Pomerleau et al., 1990; Weisgram et al., 2014). Social and cognitive theories would predict that children absorb and integrate this gender color‐coding with a wealth of other gender role information that influences them to show gender differences in color preferences. Indeed, our results suggest that it is cultural norms that influence children’s adoption of gendered preferences and behaviors, such as a female preference for pink.

The specific patterns of color preference seen in our study further suggest that global culture, as well as influencing girls to prefer pink, may influence boys to avoid it. We found that in three small‐scale societies, boys and girls were equally likely to choose a pink option over a blue one. But we found that, like boys in other large industrialized cities (Chiu et al., 2006; Jonauskaite et al., 2019; Mohebbi, 2014; Weisgram et al., 2014; Zentner, 2001), in a large Australian city, boys avoided pink options. This finding supports previous reports that children avoid culturally defined opposite‐sex behaviors (Golombok et al., 2008; Ruble, Martin, & Berenbaum, 2007). Previous research additionally finds that boys increasingly avoid pink choices with age (LoBue & DeLoache, 2011; Wong & Hines, 2015), and this pattern appeared in the boys from our City sample but not in any small‐scale samples, supporting the view that culture may influence boys to avoid girl‐type activities in general and pink specifically. Thus, our findings, in combination with previous research, suggest that the pairing of pink with female gender in global culture might influence boys to avoid options that are colored pink.

It is important to address the cultural bias of color‐coding items for boys and girls. Multiple researchers have suggested that gender‐coding toys by color may affect child development (Martin & Halverson, 1981; Weisgram et al., 2014; Wong & Hines, 2015; Yeung & Wong, 2018). For example, differences in boys’ and girls’ play with toys, that are usually color coded, have been hypothesized to cause sex differences in adult social and spatial skills (Auster & Mansbach, 2012; Martin & Halverson, 1981; Pomerleau et al., 1990). Additionally, cross‐cultural research suggests that sex differences in adult social and spatial skills may also relate to culture (Henrich, Heine, & Norenzayan, 2010; Henrich et al., 2012; Trumble, Gaulin, Dunbar, Kaplan, & Gurven, 2016; Vashro & Cashdan, 2015). Together, this evidence suggests that color‐coding items for boys and girls are not only unnecessary, but may be constraining, as children use these cues to signal what they may be interested in, and what they may want to avoid.

Our study combined children’s responses to red and pink. This choice followed essentialist research that tends to group red with pink as “reddish hues” when explaining sex differences in color preference (Hurlbert & Owen, 2015). Yet, as described in non‐essentialist research (Javda et al., 2010), toys marketed to boys tend to be blue and red, and those marketed to girls tend to be pink, so there may be a cultural reason to consider pink separately from more general “reddish hues.” Our study’s results indicated that sex differences are likely related to the specific color pink, and not to reddish hues in general. Although essentialist viewpoints tend to group pink with red according to hue, our results suggest instead that pink is a separate color that functions as a cultural marker for female gender.

This research investigated children’s preference for pink in small‐scale societies with limited access to global culture via mass media, mass communication, and mass‐produced children’s toys. Results suggested that the pairing of female and pink is a cultural phenomenon and is not driven by an essential preference for pink in girls. Instead, children showed a diversity of preferences with culture. This diversity points to the complex flexibility of underlying biology to drive the development of sex‐typed color preferences in non‐essential, context‐appropriate ways.

If we recover around $3 billion/y from criminals, whilst imposing compliance costs of $300 billion, it is reasonable to ask if the real target of anti-money laundering laws is legitimate enterprises rather than criminal enterprises

Anti-money laundering: The world's least effective policy experiment? Together, we can fix it. Ronald F. Pol. Policy Design and Practice, Volume 3, 2020 - Issue 1, Pages 73-94, Feb 25 2020.

Abstract: This paper uses anti-money laundering as a case study to illustrate the benefits of cross-disciplinary engagement when major policymaking functions develop separately from public policy design principles. It finds that the anti-money laundering policy intervention has less than 0.1 percent impact on criminal finances, compliance costs exceed recovered criminal funds more than a hundred times over, and banks, taxpayers and ordinary citizens are penalized more than criminal enterprises. The data are poorly validated and methodological inconsistencies rife, so findings cannot be definitive, but there is a huge gap between policy intent and results. The scale of the problem not addressed by “solutions” repeatedly “fixing” the same perceived issues suggest that blaming banks for not “properly” implementing anti-money laundering laws is a convenient fiction. Fundamental problems may lie instead with the design of the core policy prescription itself. With an important policymaking function operating largely as an independent silo of specialist knowledge, this paper suggests that active engagement with critical, diverse perspectives, and deeper connections between the anti-money laundering movement and other disciplines (notably, policy effectiveness, outcomes and evaluation principles of public policy) should contribute to better results.

Keywords: Public policyevaluationpolicy success/failureglobal governanceanti-money launderingAML/CFT

6. How big is the problem?

This section extends a line of research showing that authorities intercept a tiny proportion of criminal funds, and introduces a wider perspective with available evidence about compliance costs and penalties.

6.1. Europe’s anti-money laundering effort “almost completely ineffective”

In response to multiple banking scandals, European policymakers asserted the need to “better address money-laundering…threats” and “contribute to promoting the integrity of the EU’s financial system” (European Commission 2018).

Like FATF’s “high-level objective”, such descriptions lack a specific, measurable policy objective. Nor did subsequent policy proposals reassess the fundamental policy objective or meaningfully connect with public policy principles. With the capacity to identify failure seemingly locked in a bubble of industry-specific knowledge, proclaimed “loopholes” and “shortcomings” would be “fixed”, apparently, by extending and more rigorously applying the current policy model. For instance, the European Commission’s explanation for a series of bank scandals asserted that financial institutions didn’t fully comply with anti-money laundering obligations, and claimed that national authorities failed adequately to cooperate or apply rules consistently (European Commission 2019a, 2019b, 2019c). The proposed “solution” therefore seeks to improve interagency co-operation, even though such explanations appear grounded on unverified, untested, and possibly false assumptions.

Even irrespective an apparent paucity of independent verification, the perceived lack of international coordination does not accord with the industry’s own evidence base. According to FATF ratings, international cooperation is the most highly rated of 11 “effectiveness” measures (Pol 2019a, 2020). The proposed “solution” also fails to countenance the possibility that, if banks complied fully with anti-money laundering obligations, the current policy intervention might still have almost zero impact on crime (Pol 2019c). Nor is that prospect untenable, with evidence suggesting astonishingly poor results (detailed later in this section).

Blaming banks and (typically, “other”) regulatory agencies may, therefore, be a convenient fiction. With complex regulations and billions of transactions, and the benefit of hindsight, fault can always be found (and may reinforce repeatedly looking for culpability in the same, easy to find, places), but the real issue may not be the extent of bank compliance or agency cooperation repeated in the echo-chamber of anti-money laundering orthodoxy. More fundamental problems may lie instead with the policy design itself, particularly in light of available data illustrating the scale of the problem persistently unaddressed by responses continually “fixing” the same perceived issues.

An extensive European study, for example, estimated “criminal revenues from [a] selected number of illicit markets (heroin, cocaine, cannabis, ecstasy, amphetamines, ITTP [illicit tobacco trade], counterfeiting, MTIC [VAT] fraud and cargo theft)” of “at least” €110 billion annually (Europol 2016, 4; Savona and Riccardi 2015, 35). Described as “very conservative”, the study excluded “important illicit markets, such as [human] trafficking…[and] extortion, illegal gambling and other types of fraud” (Savona and Riccardi 2015, 35).

In terms of the impact on profit-motivated crime revealed by such studies, Europol says that authorities only confiscate about €1.2 billion of illicit funds annually (2016, 4). This suggests that the proportion of criminal funds recovered, termed the “success rate” of anti-money laundering efforts by the UN (UNODC 2011, 14, 119, 131), is just 1.1 percent (Europol 2016, 4, 11).

On its face, this is higher than the United Nations’ global success rate (0.2 percent) (UNODC 2011, 14, 119, 131). Those figures are not, however, directly comparable. The UN calculation involves an estimated $3.1 billion of criminal assets seized (2011, 119, 131), whereas Europol’s 1.1 percent is the proportion ultimately confiscated. The UN calculations also use amounts laundered as the denominator ($1.6 trillion) rather than total estimated criminal proceeds ($2.1 trillion in 2009) (UNODC 2011, 5, 7, 119, 131). Adjusting for consistency, illicit funds seized globally as a proportion of criminal proceeds ($3.1 billion/$2.1 trillion) is 0.15 percent. If as Europol reports (2016, 4, 11) about half the amount seized is ultimately confiscated, the equivalent UN “success rate” as the proportion of total proceeds of crime confiscated ($1.55 billion/$2.1 trillion) is 0.07 percent. In any event, the European confiscation rate appears higher, at 1.1 percent.

But, if “important” criminal activities excluded from Europe’s “very conservative” €110 billion estimate generate “only” another €10 billion, Europe’s success rate falls below one percent. Moreover, some of those uncounted markets are very profitable, which means that criminal revenues may be considerably higher, and the “real” success rate lower. For example, noting that “investment fraud schemes generate huge profits”, Europol (2017, 42) reported an investigation revealing fraud profits for one organized crime group up to €3 billion. In another illicit market outside the study, the International Labor Office estimated annual returns from forced labor and sex exploitation at $150.2 billion globally (€114.2 billion), with $46.9 billion (€35.6 billion) from Europe and other developed countries (ILO 2014, 13; Savona and Riccardi 2015, 57).

These reports suggest that European criminal revenues may be substantially higher than €110 billion, and the 1.1 percent success rate correspondingly lower. Nonetheless, at some undetermined fraction of one percent (Pol 2018b, 296):

…the proportion of criminal earnings seized by authorities does not even remotely approach tax rates commonly applied to legitimate businesses. At less than one percent, the disruption of criminal funds hardly constitutes a rounding error in the accounts of profit-motivated criminal enterprises. In terms of the capacity materially and substantially to disrupt criminal finances and the manifold harms caused by serious profit-motivated crime, current money laundering controls appear almost completely ineffective.

The “success rate” of Europe’s anti-money laundering effort is puny. Likewise, globally.

6.2. Global efforts no better

Based on 2009 data, the UN, with US State Department assistance, calculated the global success rate of money laundering controls at just 0.2 percent (UNODC 2011, 14, 119, 131), but, as noted above, the confiscation rate might be 0.07 percent. In other words, despite ubiquitous money laundering controls, criminals retain up to 99.93 percent of criminal proceeds.

With “mythical” numbers (Reuter 1984; Singer 1971) unsupported by “any empirical…proof” (Savona and Riccardi 2015, 34) often used as institutional “problem amplifiers” by agencies seeking power and resources (Levi 2016, 392), “official” estimates of criminal revenues vary widely in scale and reliability. But, according to the UN, an estimated $2.1 trillion in criminal proceeds was generated in 2009 (3.6 percent of global GDP) (UNODC 2011, 5, 127). At the same rate, global GDP of US$85.8 trillion suggests US$3.09 trillion illicit funds in 2018, illustrated in Figure 2.

Figure 2. UN-estimated global proceeds of crime, 3.6% GDP.

The UN estimated that authorities intercepted $3.1 billion of illicit funds in 2009, with more than 80 percent seized in North America (UNODC 2011, 119, 131). (The reference to North America seems to relate mostly to the United States. In 2009/2010, Canadian authorities successfully confiscated just C$59 million (FATF & APG 2016, 56), less than two percent of the total).

More recently, in 2017, total net deposits of $2.15 billion were paid into the US Treasury and Justice Department asset forfeiture funds (Department of Justice 2017; US Treasury 2017). If US asset forfeitures (sometimes called confiscations) represent 80 percent of the total, this suggests global forfeitures of $2.7 billion in 2017. At first glance, this appears lower than the UN’s $3.1 billion estimate for 2009. But the 2017 figure represents amounts confiscated, while the UN’s 2009 number represents sums seized, so a comparable 2009 estimation of global confiscations is $1.55 billion, using Europol’s empirical findings of a 50 percent difference between amounts initially seized and ultimately confiscated (Europol 2016, 4, 11). The $2.7 billion estimate therefore suggests a 74 percent increase in criminal asset confiscations between 2009 and 2017.

However, amounts seized and forfeited are highly variable, illustrated in Figure 3 (Department of Justice 2019b; US Treasury 2019). An alternative measure might use the average or median confiscated over an extended period, for example, $3.6 billion or $2.8 billion, respectively, over the period shown, but neither is necessarily more accurate than 2017 data alone, because earlier years include “unusually large” cases (Department of Justice 2019a, 2).

Figure 3. US asset forfeitures.

For some purposes, some cases, like settlements involving JP/Madoff ($3.9 billion), Poker Stars ($1.4 billion), Toyota ($1.2 billion), General Motors ($900 million) and Google ($500 million), might be excluded as dissimilar from “normal” crime. The impact of such spikes is significant, with “regular deposits” from criminal forfeitures “remarkably consistent”, at around $1 billion annually (Department of Justice 2019a, 2).

Nonetheless, if US forfeitures represent 80 percent of the total, average confiscations of $3.6 billion suggest global estimates around $4.5 billion. Or, using 2017 data for consistency (which, coincidentally, more closely accords with “regular” confiscations), suggests global forfeitures around $2.7 billion.

But whether global authorities successfully confiscated $4.5 billion or $2.7 billion of perhaps $2.9 trillion illicit funds generated in 2017, the success rate is trivial, at 0.16 or 0.09 percent, respectively.

6.3. Imperfect data, but stark clarity of policy effectiveness gap

These figures are far from definitive. Most estimates lack methodological clarity, few are validated, and there are obvious gaps. For example, simple extrapolation for global estimates ignores nuanced reality in more than 190 countries. Even in the few with available data, criminal asset forfeitures often use net amounts paid to the relevant government fund, excluding allocations to administrative costs. Confiscations from agencies not recorded in centralized databases may be missing. Authorities in many countries also frequently claim increasing forfeitures, but such claims are highly date-range specific. For example, Canadian forfeitures rose in each of the four years since 2009, then fell in two subsequent years. The total amount confiscated in 2014/2015 (C$77 million) was barely C$18 million more than 2009/2010 (FATF & APG 2016, 56). Much the same appears to have occurred in the United States between 2009 and 2017, illustrated in Figure 3. It is also difficult to reconcile European and global data. “Eighty percent” of forfeitures originating from North America do not match €1.2 billion from Europe.

Nonetheless, although detailed research is needed to validate such claims, it seems a reasonable hypothesis that forfeitures increased since 2009, at least on a rolling average basis. This paper generally uses a broad estimate of $3 billion confiscated globally.

Overall, data are poorly substantiated, so the apparent precision of subtle distinctions is illusory. Likewise, the seemingly cavalier rounding from $2.7 billion to $3 billion in the preceding paragraph. The real issue, however, is not the apparent precision of inherently imprecise estimates, but the “huge gap between the profits criminals [generate] and the amounts eventually seized and confiscated” (Europol 2016, 11).

Moreover, that gap is so large that imperfect illicit funds estimates have little or no effect on the proportion of criminal funds confiscated. Whether the “real” success rate is 0.1 percent, or ten times as much, it would be challenging to claim success in the detection and prevention of serious crime if up to 99.9 percent or “only” 99 percent of illicit funds remain in criminal hands; enabling, facilitating and rewarding the continued expansion of serious crime.

Anti-money laundering’s policy impact may be inconsequential, but policies also impose costs.

6.4. Burgeoning compliance cost

In the same year as the latest available asset forfeiture data noted above, the estimated annual cost of anti-money laundering compliance in four EU countries1 was $81.4 billion, according to LexisNexis (2017). Those countries represent 52.2 percent of European Union gross domestic product (GDP), according to the World Bank (2017). Simple GDP-based extrapolation suggests EU compliance costs of $156 billion (€144 billion).2

LexisNexis (2017, 2018a, 2018b) also examined compliance costs elsewhere. The estimated annual cost was $83.5 billion in five European countries,3 $25.3 billion in the United States, and $2.05 billion in South Africa, or $110.85 billion in the surveyed countries. According to World Bank data, those countries represent 36.5 percent of world GDP (2017). Again, simple extrapolation suggests global compliance costs in the order of $304 billion, or 0.38 percent GDP. [Some estimates are higher still. Thomson Reuters (2018, 4, 26) says that companies on average spend 3.1 percent of turnover combating financial crime, or $1.28 trillion globally].

Necessarily applying a broad brush, the current anti-money laundering policy prescription helps authorities intercept about $3 billion of an estimated $3 trillion in criminal funds generated annually (0.1 percent success rate), and costs banks and other businesses more than $300 billion in compliance costs, more than a hundred times the amounts recovered from criminals.

In Europe, the anti-money laundering movement apparently makes private businesses spend as much as €144 billion in compliance costs to help authorities confiscate up to €1.2 billion of more than €110 billion generated by criminals each year. This suggests a higher recovery rate, at 1.1 percent, but for reasons outlined above may be overstated, and offset by compliance costs 120 times the amount successfully recovered from criminals. (Bizarrely, by these estimates, compliance costs exceed total criminal funds).

Overall, estimated compliance costs are poorly validated, but whether they are $304 billion (based on LexisNexis research), closer to $1.28 trillion (per Thomson Reuters), or some other amount, the cost of compliance is high, and seems markedly to exceed amounts recovered from criminals.

Nevertheless, compliance cost estimates may yet be understated if they only include private sector operational costs. Public sector costs for the many policy, regulatory and enforcement agencies involved in anti-money laundering activities, and penalties for breach of anti-money laundering laws, add to the regime’s total cost.

6.5. Hidden costs of supranational and government agencies

The costs of approximately 80 international bodies and thousands of government agencies in 205 countries and jurisdictions with a role in anti-money laundering efforts are unknown. More precisely, costs information is available to each agency, but few seem to collate such data, despite being a crucial component of any rigorous cost-benefit analysis of the anti-money laundering experiment. Moreover, the value of illicit assets successfully recovered from criminals is also known by authorities in each jurisdiction. In any event, such data, notable for its perennial absence, would improve the accuracy of the inadequately substantiated estimates outlined above. Likewise, the costs of noncompliance.

6.6. Businesses and citizens penalized more than criminals

The combined value of anti-money laundering penalties in 2018 and 2019, mostly levied on banks, was $4.3 billion and $8.1 billion, respectively, according to Balani (2019; Burns 2019, 2020). Between 2002 and 2019, the combined value of 340 penalties was $34.7 billion, representing an average penalty of $102 million. Between 2002 and 2017, the average was $88 million.

By 2018 and 2019, average penalties rose considerably, to $147 million and $140 million, respectively. The researchers recorded more countries penalizing more businesses (“in 2019, penalties were handed out by 14 countries, compared to just three a decade ago in 2009”) and more penalties over a billion dollars, including two in 2019 alone. They attributed an “increased focus” on penalizing breaches of money laundering controls to “the severity with which it is viewed at a global level”, which they considered unsurprising “given [money laundering’s] negative economic and societal repercussions” (Burns 2020).

However, these findings appear consistent with other possibilities, for example, that “banks are a much easier target for regulators” (Pol 2019c) than criminals. If authorities recover around $3 billion per annum from criminals, whilst imposing compliance costs of $300 billion and penalizing businesses another $8 billion a year, it is reasonable to ask if the real target of anti-money laundering laws is legitimate enterprises rather than criminal enterprises.

It is reasonable also to ask whether ordinary citizens are harmed more than banks and criminals, at least financially, by laws ostensibly aimed at financial crime. After all, banks typically pass their costs on to shareholders and customers - in lower dividends, higher fees, lower interest rates for savers, and higher rates for borrowers. Moreover, taxpayers pay the costs of government, including scores of international agencies involved in the anti-money laundering agenda, and up to several dozen government agencies in each of 205 countries and jurisdictions. Individuals, communities, economies, and society also suffer the economic and social harms from serious crime.

These findings raise serious questions about the efficiency and effectiveness of the current policy model, but scholars rued that designers tasked with updating the anti-money laundering framework were told “not to pay attention to the costs of the system, direct or indirect.” Instead, it is simply “taken for granted that actions taken against money laundering and especially the financing of terrorism will have a positive welfare impact, both gross and net of costs” (Levi et al. 2018, 309). Likewise, the oft-proclaimed benefits of anti-money laundering efforts are seldom quantified or tested robustly, despite researchers “howl[ing] into the wind their warnings of unintended consequences, of law and regulations with costs far exceeding ephemeral benefits…only to be totally ignored” (Cochrane 2014, 2).

However, recognition that costs outweigh benefits, or that core objectives are not met, remains a pre-condition to start reshaping the policy paradigm for better outcomes. Change starts with acknowledging reality. In that regard, verifiable cost and recoveries information, readily available (albeit seldom produced), remains critically important if a rigorous “official” assessment of anti-money laundering effectiveness is ever undertaken. (Benefits attributable to anti-money laundering efforts, including social and economic benefits from less crime, should also be included).

In the meantime, irrespective of costs, the success rate of money laundering controls may be even less than noted above.

7. Whither policy effectiveness?

The trivial confiscation of 0.1 percent of criminal funds potentially overstates the policy impact of money laundering controls. That’s because criminal asset forfeitures often occur independently of anti-money laundering obligations. For example, confiscations frequently result from traditional policing methods such as drug trafficking investigations uncovering assets purchased with criminal funds. Empirical research in New Zealand found that conventional methods triggered 80 percent of confiscations involving lawyers, accountants and real estate agents facilitating illicit real estate transactions. Only 20 percent started with anti-money laundering’s key mechanism, legitimate businesses reporting suspicious transactions (Pol 2018b, 302).

Different percentages likely apply in different circumstances, but the success rate of money laundering controls is unrealistically high when it implicitly attributes all criminal asset confiscations to anti-money laundering efforts. For example, if 20 percent of forfeitures are attributable to money laundering controls, the global success rate may be one-fifth of 0.1 percent i.e. 0.02 percent, or one-fiftieth of one percent, illustrated in Table 1. Empirical research is necessary to identify appropriate proportions in relevant markets. In the meantime, Table 1 suggests a mid-point for indicative purposes, indicating that the global success rate of money laundering controls may be in the order of 0.05 percent (one-twentieth of one percent).

Table 1 Anti-money laundering: effective policies?

Notwithstanding its dismal success rate, the modern anti-money laundering model also has many success stories. In policy terms, progress on both the process and political dimensions in Figure 1 supports reexamining policy design to help transform failure on the remaining program dimension toward comprehensive success. In practical terms, criminal enterprises no longer holding $3 billion of illicit assets confiscated each year, and leaders less readily able to recapitalize illegal endeavors, are profoundly affected. Likewise, criminal activities are frequently disrupted and thwarted. This can be difficult to measure but may help lift success rates noted above.

In the meantime, however, if the impact of three decades of money laundering controls barely registers as a rounding error in criminal accounts and “Criminals, Inc” keep up to 99.95 percent of the earnings from misery, and reasonable prospects for better outcomes remain persistently unexplored, the harsh reality is that the current policy prescription inadvertently protects, supports and enables much of the serious profit-motivated crime that it seeks to counter. In any event, the anti-money laundering experiment remains a viable candidate for the title of least effective policy initiative, ever, anywhere (Cassara 2017, 2).

Moreover, if the modern anti-money laundering paradigm is characterized by a self-reinforcing continuous loop of policy failure, with “solutions” repeatedly “doing more of the same” producing much the same results, and with powerful stakeholder incentives maintaining the status-quo, it will be difficult to recalibrate for better outcomes. But not impossible. Key issues enabling policy success are commonplace in policy science, and the questions simple. What’s the “right” policy objective? Is there a robust, validated evidence-base to measure success? If not, what data are needed? Are policy objectives being met? If not, what policy design changes would help recalibrate for better outcomes? This paper suggests that active engagement with critical, diverse perspectives, and deeper connections with the rigor of policy science, would help contribute to better results.