The policy correlates of drug-related harm.

Alex Stevens University of Kent

Preliminary paper for presentation at 5th Annual conference of the International Society for the Study of Drug Policy, Utrecht, May 2011.

Please do not cite or quote without first contacting [email protected]

Introduction

Much commentary on drug policy limits itself to the analysis of single countries and a single field of policy, i.e. drug policy. Proponents of both sides of the drug policy debate have used such commentaries to support their entrenched positions on prohibition or liberalisation (e.g. Greenwald, 2009; McKeganey, 2010; UNODC, 2007). In this paper I illustrate two problems with this approach. The first is theoretical. Such commentaries display a form of social blindness to the wider influences on drug use and related harms (Stevens 2011). They assume that drug policies are the only ones that affect people’s drug taking decisions and habits, and thereby ignore the social contexts within which such decisions are taken. Their analytic gaze tends to be especially focused on the effects of law enforcement policies. This paper will start with a brief theoretical introduction on why this is a mistake.

The second problem with such commentaries is methodological. They assume that one can extrapolate general laws about the effects of drug policy in any country from the experience of one country. The two current favourite bases for such over-extrapolation are Portugal and Sweden (superseding the Netherlands and the USA in this role). These commentaries fail to recognise that hypothetical general effects of policies need to be tested against a range of broadly comparable countries, not just by what happens in one country in one time period. This paper provides an exploratory examination of the general relationship between law enforcement policies on the one hand – and social policies on the other – and indicators of drug related harm. It tests these policy correlates of drug-related harm to explore which perspectives offer better prospects for deepening our understanding of how these harms can be limited.

Given the huge limitations in validity and comparability of the available data on both policy effects and drug-related harms, the findings of this paper are certainly not meant to be taken to prove causal relationships. They are presented here in order to focus discussion of where we should be concentrating our efforts to increase knowledge and reduce harm.

A sociological approach

By assuming away the multiply different social contexts within which people choose to use drugs (and how they use these drugs), many analyses of drug policy tend to limit the range of policies that are brought into the discussion of how to explain these uses. Many papers and debates focus on the effects of arrests, seizures and imprisonments, as these are seen as directly within the scope of the imagined drug policy model.

1 Sociology provides a broader, but often less well specified approach to these issues. It recognises that people choose their behaviours, not just in response to policy provided stimuli, but in ways that enable them to create their own reality in ways that are meaningful for them (Giddens, 1979). It draws attention to the range of resources in people’s lives that they draw in creating these realities. It shows how people can share common ways of making choices, but that the outcomes of these choice processes can be fundamentally different according to the variables of class, gender, race and the various forms of capital to which they have access.

In my own work (Stevens, 2011), I have presented a sociological model of how people get involved and then stuck in the most harmful patterns of drug use. I have shown how illicit drug use is widespread throughout society, being especially more prevalent amongst the wealthy, but how the damages of drug use, including death, ill-health and street crime (both perpetration and victimisation) are concentrated amongst the poor. I have sought to explain this disparity using the concept of ‘subterranean structuration’. This concept is theoretically informed by Giddens’ (1979) theory of social action, Matza and Sykes’ (1961) concept of the subterranean and by a wide range of ethnographic work in the field (e.g. Bergeron, 2009; Bourgois, 1996; Collison, 1996; Lalander, 2003; Pearson, 1987; Radcliffe & Stevens, 2008; Sandberg, 2008; Taylor, 1993; Wacquant, 2007). It suggests that the spatial concentration of unemployment in post-industrial capitalism has affected the distribution and scale of drug harms. In many of these areas, the illicit drug market provides a social space in which the most vulnerable people can find purpose and company, as well as powerful, passing sensations of euphoria, comfort, social distinction and excitement.

Drugs are used in these areas, as elsewhere, to enhance the pleasure of a night out with friends. But they are also used - especially by those people who have been most damaged by abuse, neglect, bereavement and the additional pains of racism and patriarchy - as consolation; to dull the pains of existence and exclusion. The absence of other economic opportunities leaves acquisitive crime and the drug market as principle sources of cash. This market becomes an arena in which people can display their worth, their prowess and their identity. Out of nothing, they can become somebody. In a lifestyle of obtaining and spending money, of using and selling drugs, they can combine the mainstream values of work, success and consumption with the subterranean values of adventure, excitement and hedonism.

Drugs, in these areas, become objects of exchange and identification. Some people use them in creating their own life story. The story may have several phases, starting with experimentation, moving through an enjoyable period of affordable, euphoric drug use before the money runs out, life structure crumbles and they take on the identity of the ‘junkie’ (Faupel, Horowitz, & Weaver, 2004). As they move further into stigmatized and dangerous patterns of drug use, they make it more likely that their exclusion will be confirmed and their life shortened. The only industry that offers them a way out of social oblivion turns into a trap. It becomes an enclosed, criminalized, victimized and unhealthy circuit of people who suffer disproportionately from the harms of drug use. They also form a group on whom to project fearful fantasies and blame. It is these people who are most often targeted for police interventions and imprisonment, as they are seen as ‘suitable enemies’ (Christie, 1986) in the war against drugs.

The effects of deindustrialisation are experienced very differently in different countries. Some nations have pursued policies which have maximised the growth in inequality, while others have maintained systems of social support that protect the connection between the

2 unemployed and the rest of society and provide them with other sources of cash and distinction than the drug market. Esping Anderson (1990) provided a now famous classification of welfare capitalist regimes that classifies societies along axes of stratification (the level of inequality between social groups) and decommodification (the extent to which people have access to social welfare without access to the market). These different regimes show the different ways in which governments have protected or exposed their citizens to the harsh winds of economic change.

Hypothetical policy effects

This has been a necessarily brief and therefore over-simplistic account of a sociological approach to the explanation of the creation of drug harms. It suggests that drug policy analysis which prioritises direct drug policy interventions - especially those that alter the structure of incentives for engagement in drug markets through law enforcement (including seizures, arrests and imprisonments) - will be limited in its power to explain patterns of drug- related harm. In contrast, the sociological approach that is presented here prioritises wider factors, such as the level of exposure to unemployment, inequality and the protection afforded to the most vulnerable citizens by social expenditure and the welfare state (decommodification).

On the basis of these approaches, it is possible to formulate provisional hypotheses about the correlations we would expect to see between indicators of drug and other policies and indicators of drug-related harm.

At a broad level, the hypotheses generated by the more limited approach would suggest that greater levels of detection and punishment of drug offences, and higher proportions of drugs seized, would tend to be associated with lower levels of drug use and harms(as higher levels of these variables would create disincentives to engagement in drug trafficking and use).

The hypotheses generated by the broader, sociological approach, again in general, would suggest that greater levels of social support and lower levels of social exclusion and inequality would tend to be associated with lower levels of drug related harm (as people in these circumstances – rather than unprotected unemployment and relative poverty - would have greater access to alternative means for creating social realities that they value).

The more difficult task is to specify these hypotheses in ways that can be operationalised, with two particular problems standing out. The first is that it is very difficult to define and measure these variables in ways that disentangle policy and other factors that might influence the drug market from the variables on the size and nature of the drug market. One example of this is seizures. As recent work by Boivin (2010) tells us, the level of seizures can be influenced both by law enforcement activity and by the nature of the market in that country (e.g. scale of consumption, distance from drug production and role in transit of drugs). Indeed, in his analysis, market indicators provide somewhat better predictors of seizures than law enforcement indicators. The second is that these indicators are highly imperfectly measured. There are few direct measures of law enforcement intensity or drug-related harms. We can only use indirect indicators of many of these factors, and of the level of protection that people experience from unemployment and inequality. The indicators that we do have are often collected in different ways in different countries (or even regions of countries).

3 These and other difficulties mean that were are certainly not yet in a position to test these hypotheses definitively. However, advances have been made in recent years in collecting and collating indicators of these variables (and here we should acknowledge the contribution of statisticians and researchers at the UNODC, the WHO, the EMCDDA, the OECD and the national researchers who contribute to their databases). So we can start to examine what these indicators tell us about the hypotheses that can be generated.

Indicators

Table 1: Indicators and sources Year Sources References Law enforcement Recorded drug offences per 2002 European Sourcebook and ACC 2003; FBI 2003; Aebi et 100000 cannabis users national governments al 2006; Statistics Canada 2011 Cannabis seizures per 2000- UNODC UNODC 2005 1,000 consumer (kg) 2002 Heroin seizures per 1,000 2000- UNODC UNODC 2005 consumer (kg) 2002 Cocaine seizures per 1,000 2000- UNODC UNODC 2005 consumer (kg) 2002 Imprisonment rate European sourcebook and Aebi et al 2006; Walmsley (prisoners per 100,000 World Prison Brief 2003 population) Social context Unemployment rate 2002 OECD OECD Statistics Portal Income inequality (Gini 2002 OECD OECD Statistics Portal coefficient) Social expenditure (as a 2002 OECD OECD Statistics Portal percentage of GDP) Welfare support 2002 Scruggs 2005 (decommodification index) Drug harms Problem drug users (per 2000- EMCDDA and national SAMHSA 2004; EMCDDA 1,000 population) 2003 governments 2005 Prevalence of injecting drug 1996- Reference Group to the Mathers et al 2008 use 2005 UN on HIV and Injecting Drug Use Prevalence of cannabis 2001/2 Health Behaviours in Currie et al 2004 among 15 year olds School-aged Children survey HIV prevalence in injecting 1996- Reference Group to the Mathers et al 2008 drug users 2005 UN on HIV and Injecting Drug Use Drug related deaths (ICD 10 2002 WHO WHO data repository. classification)

In order to carry out this exploratory examination, I have used – as far as possible – indicators that are: available for at least 12 highly developed countries (e.g. members of the OECD); collected according to the same or similar protocols in each country; and are able to be adjusted for the size of the market where necessary (e.g. using rates per 1,000 drug consumers or 100,000 population, rather than absolute numbers which will be affected by the sizes of markets and countries). I trawled through the websites of various international

4 organisations (including those acknowledged above) and supplemented it with organisation from national organisations where the international records were unavailable or incomplete. This enabled me to create the following list of independent variables (indicators of policies that might be expected to affect drug related harms) and dependent variables (indicators of drug related harm). I used the year 2002 as the reference year, as this was the most recent year for which the greatest number of indicators was available. Some indicators were not available for this year, so I have included data from the nearest available years as indicated above.

For law enforcement indicators, there is no direct indicator of the intensity of policing of drug users. I judged the best proxy for this to be the rate of recorded drug offences per 100,000 cannabis users. This is because the vast majority of drug offences are recorded after an arrest (or other form of police intervention) is made. Recorded drug offences therefore give some indication of the scale of policing of drug markets. However, this would not give a measure of the intensity of this policing unless it was adjusted for the size of the market. As cannabis is the most commonly used drug, I adjusted the number of offences by the estimated number of cannabis users (as calculated from UNODC estimates).

Similarly, there is little data available on imprisonment of drug related offenders (I could find data on this for only 10 countries). More data is available for imprisonment for all offences. It correlates well with the available data on the imprisonment rate for drug offences (r =.77, p<0.001). So I used the general imprisonment rate as measure of the penal severity of a country, in line with other analysts (Cavadino & Dignan, 2006).

Seizures are another complex area, as noted in the discussion of Boivin’s work above. I used the amounts of cannabis, cocaine and heroin that were recorded as seized in the UNODC’s World Drug Report. As these vary widely between years, I used the average of the years 2000, 2001 and 2002 to smooth out these fluctuations. The UNODC figures separate out herbal and resin cannabis. I added the two together, on the basis that contemporary analysis showed that the THC content of these forms was broadly comparable, at around 6% on average across the averages provided by European countries (EMCDDA, 2004). In order again to indicate the intensity of drug seizures relative to the size of the market, I adjusted these seizures by the estimated numbers of consumers of each drug. The resultant indicator, unhelpfully, takes no account of the distance from production or the country’s role in transit, but it does give some indication of the amount of seized drugs, relative to the size of the domestic market in each country.

For indicators of social contexts, there are rather more established indicators available, largely from the Organisation for Economic Cooperation and Development. The OECD provides annual, national data for unemployment rates, the proportion of GDP that is taken by social expenditure and the level of income inequality (as measured by the Gini coefficient). It does not provide a direct indicator of the level of welfare support in cash benefits (which is a different - although closely correlated - indicator to social expenditure, which also includes the provision of services). Such an indicator has been developed (by Scruggs & Allan, 2006) to measure Esping Anderson’s concept of decommodification. Their index is based on the generosity of unemployment benefit, sickness pay and pensions.

For drug harms, I decided not to use include indicators of the prevalence of adult drug use as dependent variables. This is because the basis for intervention in drugs markets is the reduction of drug related harms (which are not necessarily a direct result of drug use – see the

5 higher rates of drug use but lower rates of harms amongst the wealthy mentioned above). I focused on indicators of the most harmful patterns of drug use. Use by 15 year olds is more likely to be harmful than use by adults, so I included data on this from the WHO survey of Health Behaviour in School-aged Children (Currier et al 2004). This has the advantage over other indicators of adolescent use of using the same methods across a wide variety of countries (even wider then the ESPAD survey, and at a slightly younger age). The number of problem drug users has a standard definition in Europe (thanks to the EMCDDA), but is still measured in different ways there, and in even more different ways in the USA and Australia. Nevertheless, it does give some indication of the numbers of people who are not merely occasional users of illicit substances, but who have developed more damaging patterns of drug use. The prevalence of injecting drug use and of HIV in injecting drug users has been collected for the United Nations by Mathers et al (2008), who state their own reservations about the comparability of the data.

Method

The method I adopted to test the associations between these variables was simply to calculate the bivariate correlations between them, using SPSS. In order to do so, I explored the data and checked for outliers. Where I found them, I transformed them to within the box plot, or (where the distribution of the data was strikingly positively skewed) transformed the entire sample by log transformation of that variable.

I then tested the correlation using both Pearson’s r and Spearman’s rho using the untransformed values of the variables. In general, the results of the two tests concurred on the statistical significance (where they did not, I report this below). I also report whether any significant correlations were not stable to the transformation and exclusion of monovariate outliers.

I used the conventional level of 5% as the arbitrary cut-off point for statistical significance, although I do report on those tests that approached significance at under 10%. There is the danger when running 45 correlation tests – as reported here - that a number of them will appear to be statistically significant merely by the operation of chance.

I chose not to undertake more sophisticated analysis. This is just an early exploratory analysis of the correlations between a relatively small sample of these imperfectly measured variables. I fear that increasingly sophisticated analyses tend to encourage the reader to forget the limitations of the underlying data.

Results

The majority of the tests performed showed no significant correlation between the independent variables on law enforcement and social contexts and the dependent variables of drug related harms. But, as table 2 below shows, there were some significant correlations.

Law enforcement indicators

As far as they go, these correlations suggest very little support for the hypothesis that higher levels of law enforcement intensity are associated with lower levels of drug related harms. Only one significant correlation was found that supported this association; a negative correlation (r = -.539. p<0.05, see figure 1) between the rate of recorded drug offences per

6 100,000 cannabis users and the prevalence of HIV among injecting drug users. The causal mechanism between increased arrests and lower HIV is not clear, and has been contradicted by other studies which have found a relationship in the opposite direction (Friedman et al., 2006; Sarang, Rhodes, Sheon, & Page, 2010).

Figure 1: Prevalence of HIV in injecting drug users by recorded drug offences per 100,000 cannabis users

The only other significant relationships between indicators of law enforcement intensity and drug-related harms were in the opposite direction to that hypothesised in the usual models. The rate of HIV in injecting drug users tends to be higher in countries with a higher average weight of cannabis seized per consumer (r = .805, p<0.01) and those with a higher rate of cocaine seizures (r = .552, p<0.05, correlation stable to transformation but not to exclusion of outliers, see figure 2). It is unclear why seizures of these drugs should affect HIV among injecting drug users (seizures of heroin were not significantly correlated with this indicator), although it is possible that countries with higher rates of seizures may also be hubs of international transport and migration where the HIV epidemic was most likely to start early and to affect injecting drug users before harm reduction measures could be adopted. Or this may be correlation by chance.

7 Figure 2: Prevalence of HIV in injecting drug users by average cocaine seizures per 1,000 cocaine users (untransformed variables)

The association between heroin seizures and the rate of injecting drug use came close to significance in the direction hypothesised (r = -.42, p<0.1), but this association became clearly insignificant with the removal of the outlier (the Netherlands, see figure 3).

8 Figure 3: Prevalence of injecting drug use by average heroin seizures by 1,000 heroin users

The imprisonment rate showed a strongly positive correlation with the rates of problem drug use (r = .855, p<0.001). This was highly influenced by the USA, which is an extreme (high) outlier in both its imprisonment rate and its recorded rate of problem drug use. With both outlier values transformed to within the box plots for these variables, the correlation remained significant (r = .640, p<0.05, see figure 4). With outliers excluded, the significance fell again but was still within the 10% threshold.

This correlation is interesting because it is in the opposite direction to that hypothesised by expectations of peoples’ aversion to the risk and length of imprisonment. But it is supported by the sociological hypothesis on the effect of penal exclusion in shutting off alternative avenues for the creation of meaningful identities. This would predict that higher rates of imprisonment would damage the life chances of the people who are most vulnerable to problematic use, and therefore increase the numbers of problematic users. However, we should again be careful here, given the deep limitations of the indicators in this analysis.

9 Figure 4: Rate of problem drug use by rate of imprisonment (outliers transformed).

Social contexts

Most of the associations between social context variables and the indicators of drug related harms were also non-significant. However, compared to the law enforcement indicators, a larger proportion of the social context variables showed significant (or nearly significant) associations in the hypothesised directions.

The strongest examples were the relations between welfare decommodification and the rates of both problem drug use (r = -.713, p<0.05, see figure 5 [p=.128 for Spearman’s rho]) and prevalence of cannabis use at age 15 (r = -.715, p<0.01, see figure 6). For problem use, the significance fell to within the 10% threshold with exclusion of the outlier (USA). It became non-significant with the exclusion of the USA. There were no outliers for prevalence of cannabis at age 15. The relationship between welfare decommodification and the prevalence of injecting drug use fell just outside the 5% threshold for significance using a Pearson correlation (r = -.454, p<0.01, see figure 7), but it was significant using Spearman’s rho (rho = -.541, p<0.05). This pattern was stable to the transformation of the outliers (USA and Canada), but the correlations became non-significant with their exclusion.

These findings provisionally support the hypothesis that more generous welfare support protects people from the need to seek pleasure, income, distinction and activity through the more harmful forms of drug use and more substantial involvement in drug markets.

Three of the tests showed associations that were in the hypothesised directions, but falling just short of statistical significance at the 5% level. For example, there does apparently tend to be a higher number of problem drug users in countries with higher levels of income inequality (r =.511, p<0.1). This confirms the findings reported by Wilkinson and Pickett

10 (2008) in this area, but the correlation became much less significant with the transformation or removal of the outlying value for the USA (see figure 7)

Figure 5: Rate of problem drug use by index of welfare decommodification

Figure 6: Prevalence of cannabis use at age 15 by welfare decommodification

11 Figure 7: Prevalence of injecting drug use by welfare decommodification

Figure 8: Rate of problem drug use by income inequality

There was one example of a correlation that went in the opposite direction to that hypothesised in the sociological framework presented above. Countries which had higher

12 rates of unemployment tended to have lower, not higher, rates of drug related death (r = -.573, p<0.05, see figure 9). This correlation was stably significant to the transformation and exclusion of outliers. However, it appeared when using the WHO data on drug related deaths, but not when using national data on drug related deaths collected by the EMCDDA and national authorities outside Europe (despite a strong correlation between these two indicators [r = .7, p<0.01]).

It is again hard to see a causal mechanism that would link lower unemployment to higher drug related death rates. And the discrepancy between the correlations of unemployment to the two sources of indicators of drug-related deaths also casts doubt on the usefulness of this correlation (especially as the data from national statistics suggests a much higher death rate for the USA than that collected by the WHO).

Figure 9: Drug related deaths per 100,000 by unemployment rate (%)

13 14 Discussion

Not too much should be read into these scatterplots and correlations, due to the many limitations alluded to above. Correlation is not causality. And lack of correlation cannot prove that there is no general causal relationship between the uncorrelated indicators of policies and of problematic drug use. However, lack of correlation does suggest that, for the countries included in the analysis, any effect that such policies have is too weak to overcome the myriad potential confounders. And this does bring us to one definitive remark. Despite the difficulty of drawing strong conclusions from this data (which may be limited but is the best that we have available at cross national level) we can conclude that people who claim – as do many participants in drug policy debates – that we know that certain policies are needed because they produce certain results in individual countries are not talking from the evidence but from opinion. The cross-national data does not, for example, show that countries that arrest more drug users, imprison more people or seize more drugs necessarily have lower rates of drug related problems. Indeed, countries which imprison more people tend to have higher apparent rates of problematic drug use.

This links with the finding that higher rates of welfare decommodification are associated with lower rates of problem, injecting and adolescent drug use. This makes sense when seen through the perspectives of sociological criminologists who have shown that imprisonment and welfare often operate as alternative methods for dealing with structural problems of deindustrialisation and underemployment (Cavadino & Dignan, 2006; Garside, 2009; Lappi-Seppälä, 2008). Neo-liberal welfare states (such as the USA, Australia and the UK) have chosen to opt for relatively minimal welfare provision with limited entitlements while expanding the number of unemployed people who end up in prison. Social democratic states (for example the countries of Scandinavia) have instead opted to provide more generous welfare benefits and have limited the growth of their prison populations. The subterranean structurationist approach would predict that the minimal welfare/maximal prison approach would lead to higher rates of the most problematic forms of drug use. This prediction appears to be borne out by the data assembled for this paper, but this cannot be taken as definitive proof of this theoretical suggestion.

These correlations draw our attention towards the social contexts within which drug use occurs and which policy can affect. They suggest that we, as drug policy analysts, need to broaden our gaze from the stimuli and incentives that specific drug policies (and especially law enforcement policies) provide to include the social policies that affect unemployment and welfare support. It is interesting, for example, that two countries that appear at opposite ends of the scale for recorded drug offences per 100,000 cannabis users – the Netherlands (with 1,902) and Sweden (with 38,560) – occupy similar positions when it comes to the rate of problem drug use (with the Netherlands moreover having a lower WHO reported rate of drug-related death than Sweden).

A final limitation to be noted is that this analysis does not include data on two other areas of policy that are likely to impact on drug-related harms, and which may well also co-vary with some of the independent variables in these analyses. It is likely that the coverage of problematic drug users by both treatment and harm reduction measures could be associated with indicators of drug related harm. Unfortunately, the

15 data is not yet available which would enable us to test these associations (although the WHO’s ATLAS-SU project will be releasing national data on treatment coverage in the Autumn of 2011 [personal communication]).

Conclusion

This paper started by counterposing narrow analyses of the effects of direct drug policies to broader sociological ideas on the generation and resolution of drug related harms. It has used none of the sophisticated modelling methods developed by economists. And it has used none of the rich, ethnographic analysis that has been provided by sociologists. Instead, it has sought to provide simple, exploratory tests of the correlations between various indicators of law enforcement policies, social contexts (which are affected by social policies) and drug related harms.

As far as these tests go, they suggest that there is little empirical support for the argument that countries with more intense law enforcement have lower rates of drug related problems. They provide some more support for the argument that social policy can have positive effects in reducing drug related harm. The conclusion of this paper, even more strongly than most other papers that end with a similar recommendation, is that more research is needed. This research should include the full gamut of policies which are likely to have an effect on drug related harms and should incorporate more testing of sociological perspectives on drug-related harms and policies.

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