The authors calculated percentage changes in rates of offending in robbery and various categories of theft were calculated for the period 2000 to 2012. Changes in the extent to which rates of crime across areas have become more similar were quantified by comparing the standard deviation in crime rates across areas in 2000 to the standard deviation in crime rates in 2012. Product moment calculations were used to measure (a) the extent to which areas with high crime rates in 2000 also had high crime rates in 2012 and (b) the extent to which areas with the highest crime rates in 2000 had the largest falls in crime in 2012.
They comment that
Between 2000 and 2012, New South Wales (NSW), along with most other Australian States and Territories, experienced a remarkable fall in theft and robbery offences. Figure 1 shows the annual rate of these two types of offence for 2000 and 2012. Over this period the robbery rate fell 66.5 per cent while the theft rate fell 54.8 per cent. Rates of these two categories of recorded crime in NSW are now the lowest they have been since 1995. The fall in theft and robbery is not specific to any particular kind of theft or robbery offence. As can be seen from Table 1, there have been substantial reductions across the State in all the major categories of robbery and theft. While the overall decline in theft and robbery over the long term is welcome news, not all communities throughout NSW have benefited equally from the fall in these crimes. The variation in crime trends across the State is quite substantial. In some areas, rates of theft have actually increased.They conclude that
the fall in property crime and robbery across NSW between 2000 and 2012 has been very uneven; being much larger in Sydney and other urban areas than in rural areas. The fall in theft offence rates ranges from 62 per cent in the Sydney Statistical Division (SD) to 5.9 per cent in the Northern SD. Similarly, the fall in robbery rates ranges from 70.8 per cent in the Sydney SD to 21.9 per cent in the Northern SD. In some areas some offences actually increased. The Murray, Northern, Murrumbidgee, North Western, Hunter and Central West SDs, for example, all experienced an increase in steal from a retail store. ... State Plan performance measures for improvements in public safety should take into account regional changes in rates of offending as well as changes in the overall volume of offending. ... The fall in theft and robbery in NSW (and other Australian States and Territories) over the last 13 years has been remarkable. The NSW theft rate in 2012 was less than half what it was in 2000. The robbery rate in 2012 was less than a third of what it was in 2000. Sydney and other urban areas, however, have benefited much more from this fall in crime than rural NSW. In some rural areas, rates of theft have actually increased. These findings raise two questions: 1) What caused the fall in property crime and robbery? and 2) why has the fall been more pronounced in urban NSW areas than in regional ones?
In the two decades prior to the heroin shortage, theft and robbery rates in Australia were rising rapidly (Mukherjee & Dagger 1990; Australian Bureau of Statistics 2001). The dramatic fall in theft and robbery offences from 2000 onwards was both unprecedented and unexpected. It is true that the United States and Britain experienced falls in crime around this time but the crime drop in these countries began some years earlier than in Australia and affected a much wider range of offences (US Department of Justice 2013; UK Office for National Statistics 2013). If the fall in theft and robbery offences in Australia was caused by factors within Australia, it is important to know what they were. If they can be manipulated or controlled in any way, they may provide valuable insights into the effectiveness of existing or future policies in controlling crime.
As it happens, very little research has been conducted into why theft and robbery rates have fallen in Australia. Only two studies have been conducted to date. The first, by Moffatt et al. (2005), focussed on the influence of the Australian heroin shortage on burglary and robbery in NSW. The second, by Wan et al. (2012), focussed on the effect of the NSW criminal justice system on property and violent crime, but included a measure of the influence of the heroin shortage. Some background information is necessary in order to understand the significance of the heroin shortage.
Past research has shown that dependent drug users, especially dependent heroin users, frequently commit theft and robbery offences in order to fund their drug purchases (Dobinson & Ward 1985; Hogg 1987; Stevenson & Forsythe 1998; Chilvers & Weatherburn 2003). The rise in theft and robbery rates in Australia during the 1980s and 90s coincided with falling heroin prices, increasing heroin purity and a rapid growth in heroin use (Degenhardt & Day 2004). Around Christmas 2000, the price of heroin rose by 75 per cent and the purity fell from around 70 per cent to around 30 per cent. From this point on, both heroin use and crimes known to be commonly committed by heroin users (viz. burglary and robbery) began to fall (Degenhardt & Day 2004).
Moffatt et al. (2005) recognised that heroin shortage could have affected levels of burglary and robbery but pointed out that other factors correlated with the shortage, such as increased use of imprisonment, reduced levels of unemployment or growing consumer confidence, might also have played a role. They noted that these factors continued to change in a favourable direction (along with crime) long after the primary indicator of heroin use (e.g. heroin overdoses) had stabilised (at a lower level). To test the hypothesis that the heroin shortage contributed to the fall in burglary and robbery they examined the influence of heroin use on burglary and robbery between January 1998 to December 2003, while controlling for changes in long-term unemployment, consumer confidence (a proxy for average weekly earnings) and the aggregate prison time being served by offenders.
The results revealed a strong association between crime trends and heroin use (as measured by the number of heroin overdoses) even after adjusting for the effects of long-term unemployment, consumer confidence and the aggregate prison time being served by offenders. These other factors, however, also had a significant effect on crime trends (although aggregate prison time affected burglary, not robbery). That study also found that rates of entry into drug treatment were significantly correlated with falling crime rates, even after adjusting for all the factors mentioned above. The research by Moffatt et al (2005), then, suggested that the drop in property crime was attributable to falling drug use, an improving economy, a tougher criminal justice system and greater access to drug treatment.
In 2012, Wan et al. (2012) published a more comprehensive study of trends in property and violent crime across 153 NSW LGAs between 1996 and 2008. Their study, like that conducted by Moffatt et al. (2005), included measures of the economy (average weekly income) and heroin use (heroin overdoses). It also included measures of the likelihood of arrest, the likelihood of imprisonment given arrest and the average prison term if sentenced to prison. As with Moffatt et al. (2005), their measure of heroin use remained strongly associated with the fall in crime even after adjusting for the effects of changes in income, the risk of arrest, the risk of imprisonment and the length of the average prison term. All these other factors except the last, however, were also significantly associated with the fall in property crime. The research by Moffatt et al. (2005) and Wan et al. (2012) has yielded some important insights into the fall in theft and robbery in NSW but much work remains to be done before our understanding of the fall in NSW or, indeed, across Australia, is complete. No-one has yet examined the contribution of changes in the number of people in the peak offender-prone age bracket (16-24 years), changes in vehicle and household security, changes in the market for stolen goods (Fitzgerald & Poynton 2011) or changes in police tactics and resources, although any or all of these factors might have influenced crime. Nor has anyone tested the possible effect of changes in abortion laws or falling lead levels, both of which have been cited as possible causes of the long-term fall in crime in the United States and both of which have been the focus (in that country) of considerable research (Levitt 2004; Nevin 2007).
This makes it difficult to answer the question of why the fall in theft and robbery in NSW was much more pronounced in urban than in rural areas. The correlations reported earlier show that the size of the fall in crime in a given area was (for most offences) not strongly related to the rate of that crime in that area in 2000. This rules out any explanation based on regression to the mean. It would be interesting to know whether the regional pattern in the size of the crime drop observed in NSW is mirrored in other States and Territories. Unfortunately, the Australian Bureau of Statistics does not publish any regional breakdown of national crime data. It is therefore impossible to determine whether the regional pattern observed in NSW is due to a State-specific set of factors, factors impacting the country as a whole or some combination of the two.
Some of the factors identified as contributing to the general drop in theft and robbery may have had effects that were more pronounced in urban areas than in regional areas. The growth in average weekly earnings is an example. In terms of State-specific factors, it is worth noting that the major markets for heroin in NSW at the time of the heroin shortage were Kings Cross, Cabramatta and Redfern (Degenhardt & Day 2004). If the reduction in theft and robbery is partly attributable to the fall in heroin use and if heroin users commit crime in areas close to where they purchase heroin, we would expect the reduction in theft and robbery to be larger in the Sydney SD than elsewhere. This prediction is broadly supported by the data in Figures 6 to 17.
