Abstract
Whilst any estimation of crime costs is a challenge even at a national level and in respect to crimes not particularly problematic from the definitional viewpoint, like volume crimes, the task is much harder when one has to deal with the harm caused by organised crime, especially from a comparative perspective. First, notwithstanding many international acts and studies, the term ‘organised crime’ is still one of those most debated and blurred in criminology. To complicate matters further, any cross-country assessment encounters such a wide variety of national differences (cultural, in the definition of offences, and in crime data collection systems) that the results are hardly comparable. Though extremely difficult, discussing the topic makes a great deal of sense today, and especially within the European Union, because there is strong demand for sound knowledge on the most harmful activities perpetrated by organised criminal groups and where these are localised. Considering the increasing importance attached to the issue, this article critically discusses existing attempts to measure organised crime harm from a comparative perspective, highlighting their strengths and weaknesses. It first reviews harm assessment models developed to date at the international level, and mainly consisting of surveys. It then presents a different approach to the subject, one more centered on official statistics and which has recently resulted in the development of a methodology in the context of a EU-funded study entitled IKOC (Improving Knowledge on Organised Crime to develop a common European approach).
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For example, the 1986 Wharton Econometrics (Wharton Econometrics Forecasting Ass. Inc. 1986) estimated the income generated through the activities of criminal organisations in the US using a methodology that measured the income of criminal organised groups “as the product of the number of persons engaged in organized crime and the average annual income of persons involved with criminal organizations. The average annual income data are based on IRS tax cases and, therefore, include income from both legal and illegal activities”. The estimates were calculated by multiplying the per capita income estimates of people affiliated with organised crime by the estimates of the number of people affiliated with organised crime gangs. The per capita income figures were based on Internal Revenue Survey (IRS) cases that had already been tried and the membership information came from a survey of law enforcement agencies conducted by the President’s Commission on Organised Crime (PCOC).
Further studies have expanded the idea of costs of crime from purely economical costs to include hard measuring costs like social and societal costs. One such study is that by Maltz (1990). This represents one of the first quantitative and critical approaches to the evaluation of organised crime control efforts and contains a conceptual framework for understanding and measuring the harm caused by organised crime, defined as “the general term used to describe direct and indirect and tangible and intangible effects”. It provides a description of five typologies of harm - physical; economic; psychological; community; societal - and discusses the difficulties encountered in measuring them (Maltz 1990: 41).
In Canada, in 1998, Porteous conducted a study on the impact of some key organised crime related activities: money laundering, illicit drugs, environmental crime, selected contraband, economic crime, migrant trafficking, counterfeit products, and motor vehicle theft. The impacts were of an economic and commercial, social-political, violence-generating, health and safety-threatening, and environmental nature (Porteous 1998: 1). In the same country, Richter-White (2003) conducted a study to assess the direct and indirect impacts of organised crime on youth, both as offenders and victims. It examined many variables, including drug and alcohol use, gambling and gang affiliation (Richter-White 2003: 5).
In Italy, a 1992 study on the economic burden of illegal activities (Censis 1992) measured the economic dimensions and turnover of criminal activities by considering organised crime as a company. It estimated the turnover of organised crime starting from individual criminal activities and using a multiplier representing the proportion of total crimes not committed by organised criminals and therefore not considered in the calculation of turnover. Data came from criminal proceedings. Crimes included: theft, robbery, forgery, extortion, kidnapping, fraud, usury, money laundering, corruption, illegal drug production and trafficking, prostitution, illegal gambling, trafficking in weapons and illegal smuggling of goods.
A 1993 study by Rey (1993) estimated the impact of the illegal economy on the Italian national accounts. It focused on two specific questions: 1) how to quantify the number of workers employed in illegal activities?; 2) how to determine the effective turnover of these productive activities? With regard to the former, according to the author, the problem of doppio lavoro (second job-holding) should be considered. The answer to the second question depends on the effective working time of each individual activity carried out by the worker. The author emphasises a lack of precise indicators on the quantities produced, the difficulty of knowing prices, the important role of violence as a means of business administration, and the interactions between different and separate illegal activities. Quantification was based on several indicators: 1. number of crimes reported to the judicial authorities; 2. persons involved in each illegal activity; 3. economic value of the crime.
The questionnaire of the 2005 BEEPS survey is available at http://www.ebrd.com/country/sector/econo/surveys/beeps05q.pdf.
For further information on the BEEPS, see Fries et al. (2003).
The 2008-2009 weighted average of replies to this edition in the 133 countries is in Schwab (2009: 359).
The following questions on corruption were included in the survey:
“On average, what percentage of revenues do firms like yours pay in unofficial payments per annum to public officials?” (0, < 1%, 1 - 2%, 2 - 10%, 10 - 12%, 13 - 25%, > 25%, Don't know);
“How frequently is the following statement true?” • Firms in my line of business usually know in advance how much the ‘additional payment’ is • More than one official will require payment for the same service • If a firm makes an ‘additional payment’ the service is delivered as agreed (Always, Mostly, Frequently, Sometimes, Seldom, Never, Don't know);
“How frequently do firms in your line of business have to pay some irregular ‘additional payments’ for any of the following?” • To get things done (Always, Mostly, Frequently, Sometimes, Seldom, Never, Don't know);
“What percentage of the contract value is typically offered in unofficial payments when firms in your industry do business with the government?” (0, < 5%, 6 - 10%, 11 - 15%, 16 - 20%, > 20%, Don't know, Don't do business with the government).
Further information on the WBES is available at http://info.worldbank.org/governance/wbes/index2.html.
For further information see http://www.imd.ch/research/publications/wcy/index.cfm.
The 2009 survey is currently being conducted. For further information see http://www.pwc.com/gx/en/economic-crime-survey/index.jhtml.
These cities were the following: Tirana (Albania), Minsk (Belarus), Sofia (Bulgaria), Zagreb (Croatia), Budapest (Hungary), Vilnius (Lithuania), Bucharest (Romania), Moscow (Russia), Kiev (Ukraine).
For further details on the ICBS see Alvazzi del Frate (2004).
On the factors hampering cross-national comparisons of official crime data, see e.g. Aromaa et al. (2003: 3-6).
Following the Hague Programme adopted by the European Council in 2004 (Council of the European Communities 2005), Eurostat was assigned the task of establishing European instruments for collecting, analysing and comparing information on crime and victimisation and their respective trends in Member States, using national statistics and other sources of information as agreed indicators. As a result, a database on crime and criminal justice has been set up to bring together statistics on volume crimes from the Member States, though its extension to more complex crime typologies is currently under discussion. The database can be accessed at this Internet address: http://epp.eurostat.ec.europa.eu/portal/page/portal/crime/introduction.
Further information on the study can be found at the dedicated website http://ikoc.unicatt.it/index.html. The detailed research findings are in Savona (2009).
The systemic effect can be divided into three layers. The first may lead to a collapse of the system. This is clearly the case when corruption or violence reaches a certain level (unpredictable, ex ante). Close to this level, society is on an edge where any increase in the evil leads to a dramatic degradation of all social interaction. Terrorism provides an excellent example of how society’s roots can be threatened by a very small but poisonous increase in this kind of activity. The second layer of the systemic effect illustrates the case where society’s global efficiency is negatively affected by the increase in crime. This is clearly the case of racketeering and extortion which distort competition, narrow markets, and distance society from competitive efficiency. Such an effect can be reduced by strong public intervention which increases the accountable cost but can, in the short term, lead to global improvement. The third layer consists of the marginal effects of crime on the society. Society can easily cope with this kind of threat by increasing public spending.
The social cost approach has problems intrinsic to every study, and problems of comparison between results. The first difficulty of the method is that the measurement’s perimeter can vary from one team to another. The scope of the social cost approach can be more or less broad. The main difference among studies lies in the inclusion of what is generally referred to as ‘intangible costs’, which are measured using inquiry methods aimed at enabling individuals to provide a monetary evaluation of the pain that they feel. Second, comparative use is problematic because the final result of the social cost calculation depends crucially on certain parameters, such as the actualisation rate. Changing the actualisation rate by one point extensively modifies the social cost. Thirdly, the social cost measures the present cost of consequences occurring in the future of a phenomenon whose source is in the past. Finally, the measurement of indirect costs is fairly imprecise. It supposes that it is possible to value a human life by one or another of two methods (the willingness to pay or the human capital approaches). The human capital method is the simplest. Its inconvenience is the propagation of salary distortions due to the existence of less competitive markets into the core of the social cost calculation. When we use a person’s revenue to calculate the value of his/her life, we integrate market distortions into the calculation. The alternative method, the willingness to pay approach, consists of asking the individuals how much they would be ready to pay to avoid being victim of a type of crime. The individuals are supposed to reveal the real price that they allocate to their time. This method has the inconvenience of being cumbersome to implement. Moreover, the interpretation of results is complex. The respondents must answer the question without considering their wealth constraints. The majority of individuals have however a tendency to limit the amount to their income, which distorts the calculation.
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Ernesto U. Savona has authored sections 3 (A Methodology Recently Developed at the EU Level) and 4 (Conclusions) of this article; Barbara Vettori sections 1 (Introduction: The Challenge of Evaluating the Costs of Organized Crime at a Comparative Level) and 2 (Organized Crime Harm Assessment Models at Supranational Level.)
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Savona, E.U., Vettori, B. Evaluating the Cost of Organised Crime from a Comparative Perspective. Eur J Crim Policy Res 15, 379–393 (2009). https://doi.org/10.1007/s10610-009-9111-1
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DOI: https://doi.org/10.1007/s10610-009-9111-1