Metal theft data
Source: data.gov.uk (2015-02-09)

This data relates to a report which evaluates the government and law enforcement interventions aimed at reducing metal theft. Incidents of metal theft rose sharply between 2009 and 2011 in line with a spike in global metal prices.

In response, a government-funded taskforce launched interventions aimed at the prevention of illegal cash sales of stolen metal at scrap-yards. Previous reports demonstrated that metal thefts decreased in line with the interventions but not that the interventions caused these decreases. Metal prices also fell, which could have been the driving factor.

This report's analysis controls both for the effect of prices and other drivers of theft. It finds that the interventions reduced metal theft to a level far lower than would be expected by the drop in prices alone.

An evaluation of government/law 
enforcement interventions aimed at 
reducing metal theft 
Research Report 80 
Nick Morgan, Jacqueline Hoare, Christos Byron 
January 2015 
 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
2 
 
Contents 
Contents ............................................................................................................................................... 2 
Summary .............................................................................................................................................. 3 
Background ........................................................................................................................................... 4 
Data and methodology ......................................................................................................................... 7 
Results ................................................................................................................................................ 12 
Further evidence from other nations .................................................................................................. 16 
Conclusion .......................................................................................................................................... 18 
References .......................................................................................................................................... 19 
Technical appendix ............................................................................................................................. 20 
 
 
 
 
 
 
 
 
 
Acknowledgements: The authors would like to thank Andy Feist, Amanda White, Giles Lindon, 
Dan Sellman, Andrew Kent, Matthew Ashby, Rob Edwards and Simon Wilding for their helpful 
comments, fact-checking and general all-round contribution. 
 
The views expressed in this report are those of the authors, not necessarily those of the Home 
Office (nor do they represent Government policy).

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
3 
 
Summary 
Executive summary 
 While most acquisitive crimes have fallen consistently over the past five years, metal 
theft increased between 2009 and 2011 in line with a sharp rise in global metal prices. It 
then fell during 2012 and 2013.  
 This paper summarises results of analyses which aimed to test whether the decline from 
2012 was caused primarily by the government/law enforcement interventions launched to 
address metal theft, or was simply due to metal prices falling back from their peak. 
 The analysis is based mainly on data for metal thefts held by the Energy Networks 
Association (ENA), though data from British Transport Police (BTP) are also used to 
verify the main results. The phased roll-out of Operation Tornado across England and 
Wales helps to identify the specific impact of the interventions, as distinct from other 
factors that might have contributed to the fall in metal thefts.   
 The analysis found that metal thefts recorded by the Energy Networks Association and 
by British Transport Police fell to levels far lower during 2012 and 2013 than would be 
expected from the drop in metal prices alone.  
 This implies that the interventions launched during that period, Operation Tornado and 
cashless trading at scrap metal dealers (described below), did contribute to a substantial 
reduction in the number of offences.  
 Analysis showed a large, statistically significant effect for the interventions even when 
controlling for metal prices and other factors driving acquisitive crime.  
 Scotland, which did not receive the interventions, had a rising trend in metal theft during 
the post-intervention period, according to the Energy Networks Association data. This 
adds further weight to the main finding, and suggests that some metal theft may have 
been displaced north of the border.  
 As with most retrospective evaluations, there are necessary limitations with both the data 
and the methodology employed, but these findings are in line with the limited existing 
evidence from other nations.   

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
4 
 
Background 
Against a backdrop of falling crime in England and Wales generally, metal theft rose sharply 
between 2009 and 2011. According to one dataset, the increase in metal thefts across this 
period was more than 300 per cent.1 At the same time, global metal prices increased by a 
similar percentage (Figure 1). Indeed, studies analysing the relationship between levels of metal 
theft and the price of copper from 2006 to 2011 found a strong positive correlation, which was 
independent from changes in unemployment and other types of railway theft (Sidebottom et al., 
2011; Sidebottom et al., 2014).  
Figure 1: Comparison of trends in Energy Networks Association reported metal thefts with 
trends in copper market price, England and Wales, Jan 2009–Dec 2013. 
 
Sources: Metal prices from letsrecycle.com; metal thefts from the Energy Networks Association. 
The rise in metal theft, and the link to price, seems to have been a global trend, though robust 
data on metal theft are sparse. A report by the National Insurance Crime Bureau Strategic and 
Tactical Information Department (Kudla, 2009), using US national data on metal theft insurance 
claims as a proxy for metal theft, found that a rise in metal thefts between 2006 and 2008 was 
strongly correlated with global metal prices (aluminium and copper). Subsequent evidence has 
shown that US metal thefts dipped in line with metal prices through 2009 but then rose sharply 
with prices in 2010, such that thefts approximately tripled during that period, exactly as they did 
 
1  This rise is calculated from the ENA data (from 2009/10 to 2010/11), which is not the only source, see data section. But as 
we show in that section, trends across sources are very similar, so the 300 per cent rise probably does reflect the likely 
magnitude of the increase.  

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
5 
 
in England and Wales (Posick 2008; Whiteacre et al., 2009; Posick et al., 2012). 
Similarly, a European report (Quercia, 2013) concluded that metal theft was: “a crime with one 
of the fastest growing rates in the world and ... the most relevant emerging crime in Europe.” 
The evidence in the report is strongest for Italy. Italian Railway Network data show an increase 
in metal theft, particularly live and un-live copper cables, through the early 2000s with a 
preliminary peak in 2006–2008, a slump in line with prices in 2008/09, and then a sharp rise in 
2010, mimicking the UK and US situation. More anecdotal evidence from the report also 
suggested that metal theft was an issue in Bulgaria, Greece and Spain. The German state rail 
company reported metal theft growing 50 per cent from 2010 to 2011, and a South African 
electricity company reported 446 incidents in 2005, 1,059 in 2007 and 1,914 in 2008 (Quercia, 
2013).  
In response to the rise in offences in England and Wales, several interventions were introduced 
and the National Metal Theft Taskforce, a multi-agency taskforce, funded by government and 
led by British Transport Police (BTP), was created. The first intervention was Operation 
Tornado. It required participating scrap metal dealers in England and Wales to request 
identification documentation for every cash sale and retain copies for 12 months. The 
intervention, which was backed with police enforcement, was piloted in the police forces in the 
North East in January 2012 before being rolled out on a phased basis across England and 
Wales by September 2012. This was followed by the introduction of cashless trading in 
December 2012, which barred scrap metal yards from accepting cash payments, and then the 
Scrap Metal Dealers Act 20132, which introduced a robust licensing scheme to ensure more 
effective compliance administered by local authorities, and stronger police enforcement. 
The rationale for targeting scrap metal dealers came from previous research analysing the 
motivation for theft offences (Sutton, 1995; Clarke, 1999). These researchers found that certain 
goods were more desirable to thieves and that the relevant attributes could be summarised by 
the acronym CRAVED, which stands for: concealable, removable, available, valuable, enjoyable 
and disposable. For Clarke (1999), the last of these, disposable, is particularly important.  
“... while each element of CRAVED may be of equal importance in describing which 
products are stolen, how much they are stolen seems to depend mainly on the final 
element — how easy it is to dispose of the goods by selling them. This is because high-
volume thieves, who derive a substantial income from theft, must be able to dispose of the 
goods they steal. As habitual thieves are thought to commit a large proportion of all thefts, 
it becomes clear why disrupting markets for stolen goods is of such importance for policy.” 
 (Clarke, 1999) 
If Clarke’s thinking is correct then targeting the disposal of metal – the point at which stolen 
metal is sold on – i.e. scrap metal dealers – makes good sense if the aim is to reduce the 
volume of thefts. Subsequent analysis has supported a relationship between metal theft and the 
proximity of scrap metal dealers. Ashby et al. (2014) investigated metal theft from the UK 
railway network and found a high degree of spatial concentration: half of all metal thefts 
occurred at just three per cent of railway stations, whereas there were no metal thefts at 63 per 
cent of stations (ibid.) The same researchers also found that the presence of a scrap metal 
dealer in the vicinity was a significant predictor of these hotspots (Ashby and Bowers, 
forthcoming). Their modelling suggested that each additional scrap metal dealer in an area was 
associated with a 12 per cent increase in metal thefts (ibid.). In the US, Whiteacre et al. (2009) 
 
2 http://www.legislation.gov.uk/ukpga/2013/10/enacted  

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
6 
 
also analysed the relationship between metal theft and scrap metal yards in 51 cities. The 
number of scrap yards per 100,000 residents showed a positive correlation with the rate of 
metal theft insurance claims. In fact, the rate of scrap metal dealers was the strongest predictor 
of metal thefts in their model.  
However, none of these results proves that targeting the disposal of stolen metal at scrap yards 
will reduce thefts. Just because metal dealers are found near metal theft hotspots does not 
show that they caused the problem. Nevertheless, data show clearly that in the latter half of 
2012, in the wake of the interventions, metal thefts fell in England and Wales. But as Figure 1 
shows, metal prices also fell from their 2011 peak, so it could have been prices, rather than the 
interventions which drove the decline in thefts. This lack of clarity provides the rationale for the 
current analysis. Using robust statistical methods, the aim is to disentangle the effect of the 
interventions from the effect of metal prices and from the other factors that might have helped 
drive down acquisitive crime in England and Wales, and hence to answer the question – did the 
metal theft interventions work? 
 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
7 
 
Data and methodology 
‘Metal theft’ is not a specific offence code within police recorded crime (PRC), which groups 
offences by crime type rather than by item stolen. So metal could be stolen in a burglary or in 
another type of theft but these would be recorded in the PRC statistics as ‘burglary’ or ‘other 
theft’ rather than as metal theft. The Home Office (HO) did start to collect data on police 
recorded offences involving the theft of metal during 2012/13 and two years of data have now 
been collected and published.  The published figures show a clear reduction in theft, see Figure 
2 below, but they do not cover the period before the interventions, for the most part, meaning 
that they are not particularly useful to evaluate the interventions. Fortunately, two other metal-
theft datasets are available that have longer time series. These are metal thefts recorded by 
British Transport Police (BTP) and those recorded by the Energy Network Association (ENA), 
which collates metal-theft data relating to electricity and telecommunications networks. Data for 
the last five years were provided by BTP and ENA. The available data are summarised in Table 
1. 
Table 1: Available datasets on metal theft in England and Wales.3 
Data series 
Description 
Time period covered  Percentage of total 
PRC metal theft 
Home Office data 
Monthly counts of all 
offences recorded as 
metal theft by all 44 
police forces in 
England and Wales 
April 2012 to March 
2014 
100% 
Energy Networks 
Association (ENA) 
data 
Monthly counts of all 
offences by police 
force area in England 
and Wales + Scotland
January 2010 to 
December 2013 
6% approx 
British Transport 
Police (BTP) data 
Monthly counts of all 
offences recorded by 
BTP sub-divided into 
7 regions including 
Scotland 
September 2009 to 
December 2013 
2% approx 
 
 
 
 
3  Note that the coverage of HO metal theft is actually fractionally below 100 per cent as Norfolk and West Midlands forces did 
not submit data for some of the early months in the series. Their data for these months have been estimated for the purpose 
of the correlations and charts in which HO data are used. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
8 
 
For the most part, the analysis in this paper uses the ENA dataset. This is because: 
i) the data are available over a long enough time period to make evaluation possible; 
ii) data are available by police force area, which can be aggregated to match the regions 
corresponding to the Operation Tornado roll-out (Table 2); this allows for more robust 
analysis because Operation Tornado was launched in different regions at different 
times so it is possible to examine whether regions experiencing the intervention first 
also saw earlier falls in crime; and  
iii) the data are independent of organisations involved with the interventions. 
The main limitation of the ENA dataset is that it only captures a small proportion of all metal 
thefts. A comparison between the volume of metal thefts recorded by the ENA data and those 
recorded in the Home Office collection would suggest that the ENA data capture around six per 
cent of the total number of thefts. However, the ENA data are likely to account for a much higher 
proportion of the total cost to the economy of metal theft because many of the incidents they 
record result in loss of energy supply to homes. The ENA calculated that metal thefts from 
January 2011 to October 2011 cost the public £48.3m, which equates to around £60m annually. 
Two separate estimates (by the Home Office and by consultancy firm Deloitte) put the total 
annual cost of metal theft at around £220m at that time suggesting that energy-related metal 
thefts account for around a quarter of the cost of metal theft.4 In other words, whilst the ENA 
data cannot strictly tell us whether the interventions reduced metal theft in totality, they can give 
us an indication of whether the interventions affected some of the most costly types of theft. 
Being slightly less strict, the ENA data can also be used as a proxy for total metal theft if we can 
be confident that they accurately capture the trend in overall offences. Comparison with the 
other data series suggests this is broadly the case, see Figure 2. 
 
4  See: http://www.energynetworks.org/news/public-information/metal-theft/overview.html and 
https://www.justice.gov.uk/downloads/legislation/bills-acts/legal-aid-sentencing/laspo-metal-theft-ia.pdf  

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
9 
 
Figure 2: Trends in incidents of reported metal thefts from the Home Office data collection, 
the Energy Network Association (ENA), and British Transport Police (BTP), England and 
Wales, Jan 2009–Dec 2013. 
 
As the chart shows, the trends are similar across the datasets. The correlation coefficients with 
the monthly Home Office data for the period April 2012 to December 2013 are 0.89 for the BTP 
data and 0.87 for the ENA data, and the correlation between the monthly BTP and ENA data for 
the whole period in the chart is 0.73.5 
Though the ENA dataset was used for the main analysis in this paper, we also explored the 
BTP and recorded crime datasets in an attempt to triangulate the main findings, and these 
results are reported below. 
To try and identify the effect of the interventions, the analysis exploited the fact that Operation 
Tornado was launched at different times in different parts of England and Wales, see Table 2. 
 
 
 
5  One other limitation of the ENA data is that there may be slight variation in its coverage through the period, due to the fact 
that the ENA series relies on data received from energy companies. Despite this, as Figure 2 shows, the ENA data produces 
a similar trend to both the BTP and Home Office metal theft data collections. This suggests that any variation is not biasing 
the series markedly. See the technical appendix for additional robustness checks that were carried out in relation to this.   

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
10 
 
 
Table 2: Timetable of Operation Tornado roll-out by region and police force. 
Date 
Regions (forces) 
Jan 2012 
North East (Cleveland, Durham, Northumbria) 
Feb 2012 
Mar 2012 
Apr 2012 
Yorkshire and the Humber (Humberside, North Yorkshire, South Yorkshire, 
West Yorkshire) East Midlands (Derbyshire, Leicestershire, Lincolnshire, 
Northamptonshire, Nottinghamshire) 
May 2012 
North West (Cheshire, Cumbria, Greater Manchester, Lancashire, Merseyside) 
Jun 2012 
South West (Avon and Somerset, Devon and Cornwall, Dorset, 
Gloucestershire, Wiltshire) Eastern (Bedfordshire, Cambridgeshire, Essex, 
Hertfordshire, Norfolk, Suffolk) South East (Hampshire, Kent, Surrey, Sussex, 
Thames Valley) 
Jul 2012 
Aug 2012 
Sep 2012 
West Midlands (Staffordshire, Warwickshire, West Mercia, West Midlands) 
Wales (Dyfed-Powys, Gwent, North Wales, South Wales) London (Metropolitan 
Police Service, City of London) 
 
A regression model was used to test the effectiveness of the interventions. This is described in 
full in the technical appendix but the main aspects of the model are set out below.  
The dependent variable for the model was the monthly total of metal theft incidents as 
measured by the ENA data, by region, but excluding Scotland, which did not receive the 
intervention. Two control variables were also included: average monthly copper prices and 
regional totals of all other police recorded acquisitive crimes. The copper price data were 
sourced from letsrecycle.com and contained monthly figures of the scrap price of copper per 
metric tonne. This was the most appropriate series given that the scrap price measures the re-
sale value to the potential thief, and given the research linking metal thefts in England and 
Wales to copper prices (Sidebottom et al., 2014.) The police-recorded crime data were sourced 
from the Office for National Statistics and were simply the monthly total of all other theft type 
crimes, by region.6 This variable is included in an attempt to account for all other factors driving 
down acquisitive crime. Both the metal prices variable and the acquisitive crime variable were 
scaled to aid interpretation. Hence the figures for prices are given in £1,000s per metric tonne 
 
6  To be clear, this variable was equal to total police-recorded acquisitive crime (the ‘Theft’ category’) minus the number of 
thefts within ‘all other theft’ which is the category containing metal theft. This is the closest proxy available for capturing the 
total count of thefts that do not involve metal. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
11 
 
and the figures for acquisitive offences are given in thousands of offences. Area fixed effects 
were also included to account for differences between areas that are constant over time.  
To model time, a standardised time variable, relative to the introduction of Operation Tornado, 
was used. So the month Tornado was launched (which varied by region) was coded zero in 
each case, with -1 being the month before and +1 the month after, etc. Viewing the data in chart 
form suggested that metal theft trends had already turned downwards prior to the interventions, 
possibly due to the fact that prices had already come off their peak at this point. This meant that 
it also made statistical sense to include a squared time term. This was simply equal to the 
standardised time variable squared. An intervention dummy variable was also included, which 
was set to zero for all time periods before the intervention began in each region, and set to one 
for all periods afterwards. The final version of the model also included the interaction between 
the standardised time variable and the intervention variable. This aimed to test whether the 
trend in metal thefts changed (on average) at the point when the interventions were launched 
within each region. It provides perhaps the most robust test of the interventions’ effectiveness, 
as explained in the next section.   
All the data series in the model run from January 2010 to December 2013, so effectively what is 
being tested is the package of interventions in each area. The analysis cannot tease out the 
individual effects of Operation Tornado, the introduction of cashless trading and the Scrap Metal 
Dealers Act 2013. Instead, the analysis treats these as one continuous intervention which is 
“switched on” at the point when Operation Tornado starts. The fact that this starting point varies 
by area makes the result more robust because it reduces the likelihood of obtaining a spurious 
effect driven by something else occurring at the same time as the interventions. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
12 
 
Results 
Analysis of the raw data showed that time periods in which the interventions were in place in a 
region had, on average, about 50 per cent fewer ENA-recorded metal thefts compared to time 
periods prior to the intervention. That is, the average number of thefts dropped from 47.04 per 
region per month to 23.70, a reduction of just over 23 thefts.7 
However, as demonstrated, metal prices also dropped from 2010 onwards and other factors 
have continually driven down acquisitive crime generally, so regression analysis is required to 
try and separate out the amount of that 50 per cent decline that can be attributed to the 
government/enforcement interventions.  
Results of the first model designed to do this are shown below.8  
Table 3: Initial model results. 
  
Coefficients 
Standard 
Error 
t Stat 
P-value 
Sig 
Lower 95% 
Upper 95% 
Intercept 
-78.69 
20.71
-3.80
p<0.001
*** 
-119.38 
-38.00
Intervention 
-18.21 
3.89
-4.68
p<0.001
*** 
-25.85 
-10.56
Metal prices (£000s per 
metric tonne) 
9.15 
2.97
3.08
0.002
*** 
3.32 
14.98
Other acquisitive crime 
(000s) 
0.77 
0.14
5.57
p<0.001
*** 
0.50 
1.04
Time   
-0.22 
0.19
-1.15
0.251
 
-0.58 
0.15
Time squared 
-0.04 
0.01
-6.31
p<0.001
*** 
-0.05 
-0.03
*** indicates significance to the 1% level; ** indicates significance at the 5% level; and * indicates significance at the 10% level. 
The table shows that all the main variables in the model were statistically significant at the one 
per cent level. The coefficient on the ‘intervention’ variable means that, even taking into account 
the fact that metal prices and other types of crime were lower post-intervention, the effect of the 
intervention is still significant with an average reduction of just over 18 thefts per region per 
month. This equates to an average fall of about 38 per cent (given by 18.21/47.04).  
 
 
 
7  A 95 per cent confidence interval around this difference between the two means (for numbers of metal thefts before and 
after the intervention) was calculated. It showed that the uncontrolled fall in metal thefts was 45–56 per cent. 
8  Complete tables of all the coefficients, including those on the area fixed effects and for other model specifications, can be 
found in the technical appendix. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
13 
 
However, to be absolutely sure that these lower totals would not have been observed 
irrespective of the intervention, in the final model we also included an interaction term (between 
the standardised time variable and the intervention variable). A statistically significant coefficient 
on this term would indicate that, in addition to the level of thefts being lower, the trend in thefts 
also changed at the point the intervention started in each area. This would provide strong 
evidence of effectiveness. The results of this model are shown in Table 4. 
Table 4: Final model results. 
  
Coefficients 
Standard 
Error 
t Stat 
P-value 
Sig  
Lower 95% 
Upper 95% 
Intercept 
-65.36 
21.21
-3.08
0.002
*** 
-107.05 
-23.68
Intervention 
-14.48 
4.12
-3.51
p<0.001
*** 
-22.58 
-6.37
Metal prices (£000s 
per metric tonne) 
6.26 
3.15
1.98
0.048
** 
0.06 
12.45
Other acquisitive 
crime (000s) 
0.68 
0.14
4.75
p<0.001
*** 
0.40 
0.96
Time 
-1.62 
0.57
-2.83
0.005
*** 
-2.74 
-0.49
Time squared 
-0.08 
0.02
-4.60
p<0.001
*** 
-0.11 
-0.04
Interaction 
2.30 
0.89
2.59
0.010
*** 
0.56 
4.05
*** indicates significance to the 1% level; ** indicates significance at the 5% level; and * indicates significance at the 10% level. 
Again, all the main variables in the model are statistically significant. The coefficient on the 
‘intervention’ variable remains significant at the one per cent level despite the inclusion of the 
interaction terms, but the magnitude has dropped to 14 fewer (ENA-measured) metal thefts per 
region per month, implying a reduction of about 30 per cent on average. Crucially though, the 
interaction coefficient is also significant (and of the opposite direction to the coefficients of the 
time variables), which demonstrates that the metal theft trend changed once the interventions 
were introduced. i.e. there was a “faster” reduction in offences post-intervention. We can 
therefore conclude that the intervention was successful in reducing metal thefts. 
Both control variables were also significant, suggesting that metal theft was affected both by 
metal prices and by other factors driving acquisitive crime generally. The metal prices result 
implies that a £1,000 increase in scrap metal price per metric tonne results, on average, in just 
over six extra metal thefts, per region per month. To put this into context, prices increased about 
£2,650 per metric tonne between 2009 and 2011. The coefficient on the ‘other acquisitive crime’ 
variable implies that for every 1,000 increase/decrease in other acquisitive crimes, per region, 
per month, we might expect metal theft to increase/decrease by 0.68 offences, per region, per 
month. That is, the factors that drive other acquisitive crime also seem to have a minor effect on 
metal thefts. 
 
 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
14 
 
A series of robustness checks were also conducted which included: modelling the time element 
using a full set of time dummies rather than a standardised continuous variable; using 
alternative times series of metal prices; and using a subset of the ENA data to ensure 
consistency of reporting. More details of these results can be found in the technical appendix 
but the main conclusion was that the effect of the intervention remained strongly significant 
throughout all these variations. 
These results were also triangulated using data from the other available metal-theft datasets. 
The Home Office dataset is the most comprehensive, in that it records all metal thefts from all 
forces in England and Wales. But monthly data are only available from April 2012, which as 
Table 2 shows, is already after the point where some forces had started Operation Tornado. For 
that reason it is difficult to draw too many conclusions from the HO recorded crime data in 
relation to the success of the interventions, beyond the fact that they show a similar declining 
trend, from April 2012 onwards, to the other datasets. Hence whilst the HO data cannot be used 
to formally test the validity of the main result, the data do not obviously contradict that 
conclusion.  
The British Transport Police (BTP) dataset of metal theft does contain data both before and 
after the intervention, but it uses a different set of geographical regions, which do not map 
precisely onto the regions for the Operation Tornado roll-out. For that reason, and because the 
monthly totals in the BTP data are relatively small, the data were aggregated into two groups for 
analysis: those regions which began Operation Tornado between January and April 2012 (which 
includes 12 police forces in total, labelled “early adopters”) and those which began between 
May 2012 and September 2012 (31 forces, labelled “late adopters”).9 The difference in metal 
theft trends in these two areas is shown in Figure 3 below. It is clear that all areas experienced 
a drop in thefts from the middle of 2011 to the start of 2012 in line with the fall in metal prices. 
However, when prices increased again in early 2012, those areas that introduced Operation 
Tornado early continued to see a much lower level of metal theft, while the late adopters saw 
another spike in thefts until they too began Operation Tornado, at which point the trends in all 
areas re-converged. 
 
9  See technical appendix for more on this process. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
15 
 
Figure 3: Metal theft trends for the early and late adopters of Operation Tornado using 
British Transport Police data. 
 
These data were also tested using a linear regression model similar to that used with the ENA 
data. This showed similar results with a large, strongly significant effect for the intervention and 
with the time/intervention interaction term significant at the ten per cent level.10  
Taken together, the findings from the BTP data support the main result: that the package of 
metal theft interventions had a marked downward effect on metal thefts over and above the 
effect caused by a drop in prices. 
 
 
10  The full set of results for this model is in the technical appendix. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
16 
 
Further evidence from other nations 
The rise in metal theft was very much a global problem, and many other nations have 
introduced measures to try and combat it. Virtually all US states have passed some form of 
legislation to try and combat the rise in metal theft. However, there is no systematic or reliable 
form of recording metal theft in the USA, and so accurately measuring the success of these 
interventions is problematic.  
Only one evaluation of these interventions could be located. It examined the success of a 
similar measure to that employed in England and Wales. In St. Louis, Missouri legislation was 
introduced to limit anonymous cash sales of certain scrap metals. Mares et al. (2014) used 
interrupted time-series analysis to examine weekly pre- and post-intervention levels of metal 
theft. They found that thefts fell by 50 per cent, which was a far greater decline than for other 
types of acquisitive crime (burglary fell by 15 per cent over the same period), and that the effect 
remained even after controlling for copper prices, local foreclosure rates, unemployment rates, 
and local average temperatures. This is a similar result to the one identified in this paper. 
Scotland provides something of a counter-example as it also saw a rise in metal theft, according 
to available data, but was not covered by Operation Tornado or any of the ensuing legislation. 
Using the ENA data, we compared Scotland’s trend in metal theft with that in England and 
Wales, see Figure 4.  
Figure 4: Indexed trends in incidents of reported metal thefts (three-month rolling average, 
Jan 2010 = 100) from the ENA, England and Wales, and Scotland, Jan 2010 – Oct 2013. 
 
The chart shows a clear divergence in trends starting in the second half of 2012 as the 
intervention was rolled out nationally in England and Wales, but not in Scotland. This adds 
further evidence to the finding that the interventions reduced metal theft. But it also suggests 
there may have been some displacement, as thefts in Scotland actually increased in 201311. 
 
11  And this increase is not correlated with metal prices. Indeed the correlation coefficient between metal prices and ENA metal 
theft in Scotland was actually negative for the period from January 2012 to December 2013. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
17 
 
Interestingly, the divergence in trends from late 2012 onwards is not seen in the BTP data, 
which also has figures for Scotland. In that dataset, the trend for Scotland is similar to that for 
England and Wales. Hence, any conclusions around displacement must remain tentative. It 
could be the case that the type of thief who would travel into Scotland in order to avoid the metal 
theft interventions in England and Wales would be a more organised offender and one who 
would be more likely to target the high-value metal thefts captured by ENA data. But this is 
merely a hypothesis and requires further research to test.  
 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
18 
 
Conclusion 
Overall, we found good evidence that the package of interventions aimed at targeting metal theft 
did drive a reduction in offences over and above the effect of a fall in metal prices and the other 
factors driving trends in acquisitive crime. Areas had about half the number of metal thefts when 
the interventions were in place, compared to the time periods when they were not. Modelling 
suggests that the interventions themselves can be credited with a fall of around 30 per cent, 
with the rest being attributable to falling prices and other downward pressures on acquisitive 
crime. 
The analysis was mostly directed at the more costly sub-set of thefts captured by the ENA data. 
But given the similarity in (post-intervention) trends between the ENA data and that for all 
recorded metal thefts, it seems likely that the interventions did have an effect on the totality of 
metal theft, though we can be less sure of its magnitude. Analysis of the BTP data also supports 
this by showing that later adopters of the metal theft intervention also had later drops in theft 
offences, on average. 
We found tentative evidence of some displacement of offences into Scotland, possibly by more 
organised offenders, as signs of this displacement are only evident in the ENA data, which 
generally captures high-value offences. 
There are several limitations to the analysis that need to be acknowledged. This evaluation was 
a retrospective one, so areas were not randomised to receive the intervention. The analysis 
therefore assumes that the timing of the intervention was exogenous to theft trends. We also 
look only at the effect on metal thefts. It is possible that the effect on overall crime may be less 
strong, if – for example – thieves deterred by the metal theft interventions switched to another 
type of acquisitive crime. Finally, data limitations mean the effectiveness of the different aspects 
of the intervention ‘package’ cannot be analysed separately, the results apply to the package as 
a whole. Trying to look at the separate effects and any possible displacement into other crimes 
may therefore be fruitful areas for further research.   

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
19 
 
References 
Ashby, M. P., Bowers, K. J., Borrion, H. and Fujiyama, T. (2014). ‘The when and where of an 
emerging crime type: The example of metal theft from the railway network of Great Britain’. 
Security Journal. Available at: http://www.palgrave-
journals.com/sj/journal/vaop/ncurrent/full/sj201443a.html  
Ashby, M. P. and Bowers, K J. (Forthcoming) ‘Concentrations of railway metal theft and the 
locations of scrap-metal dealers’. Forthcoming. 
Clarke, R.V. (1999). Hot products: Understanding anticipating and reducing demand for stolen 
goods. Police Research Series Paper 112. London, England: Home Office Policing and 
Reducing Crime Unit. 
Kudla, J. (2009). Metal theft claims from January 2006 to November 2008. Strategic Report, 
February 13. National Insurance Crime Bureau Strategic and Tactical Information Department. 
Mares, Dennis M., Blackburn, Emily A., (2014). ‘Reducing metal thefts through the use of 
local ordinances: An evaluation of an impromptu Market Reduction Approach in St. Louis, MO’. 
Security Journal, vol. 26 2014. 
Posick, C. (2008). Copper burglary and copper prices in Rochester, N.Y . Working Paper # 120. 
Rochester Institute of Technology Center for Public Safety Initiatives. 
Posick, C., Rocque, M., Whiteacre, K., & Mazeika, D. (2012). Examining Metal Theft in 
Context: An Opportunity Theory Approach. Justice Research and Policy, 14(2), 79-102. 
Quercia, Paolo, ‘Metal theft: An emerging threat to Europe’s economic security’ Pol Primett, 
2013. Available at: http://www.agenformedia.com/assets/files/pdf/Pol-
PRIMETT%20Research%20Paper%20February%202013%20Version%206.pdf  
Sidebottom, A., Belur J., Bowers K., Tompson L. and Johnson S. (2011). Theft in Price-
Volatile Markets: On the Relationship between Copper Price and Copper Theft. Journal of 
Research in Crime and Delinquency.  Available at: http://jrc.sagepub.com/content/48/3/396  
Sidebottom, A., Ashby, M. and Johnson, S. D. (2014). Copper Cable Theft Revisiting the 
Price–Theft Hypothesis. Journal of Research in Crime and Delinquency, 0022427814521216. 
Sutton, M. (1995). ‘Supply by theft’. British Journal of Criminology, 35, 3, 400-415. 
Whiteacre, Kevin W. and Howes, Raeann., (2009), ‘Scrap Yards and Metal Theft Insurance 
Claims in 51 U.S. Cities.’ University of Indianapolis, Community Research Center, Research 
Brief #2. Available at: 
http://www.uindy.edu/documents/Scrap_Yards_and_Metal_Theft_Insurance_Claims_in_51_US
_Cities.pdf

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
20 
 
Technical appendix 
1. The Linear Regression model 
The linear regression model used for the main analysis was as follows: 
Theftit = a0 + a1Iit + a2Ri + a3Tit + a4T2
it + a5(Iit x Tit) + a6MPt + a7OACt +uit 
where:  
 Theftit is the level of ENA metal theft in region i in month t. ENA data are available at 
force level from 2010 so the data were aggregated to regions to fit with the roll-out of 
Operation Tornado. 
 I is a ‘policy on’ dummy variable equal to 0 for each area, and in each time period, before 
Operation Tornado came into effect and 1 afterwards. 
 R is a vector of area fixed effects. 
 T is a standardised time variable relative to the introduction of Operation Tornado (so -1 
means one month prior to Operation Tornado, +1 is one month after Tornado, and so 
on).  
 T2 is simply equal to the standardised time variable (above) squared. 
 (Iit x Tit) is an interaction term capturing the interaction between the time and intervention 
variables. 
 MP is scrap metal prices in £000s per metric tonne. 
 OAC is the number of other acquisitive crimes in 000s.  
 U is a random error term, following a normal distribution12 with mean zero and variance 
equal to 
 
 
 
 
12  Note that the residuals were graphed to check this normality assumption. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
21 
 
The full results for the interim model are shown below: 
  
Coefficients 
Standard 
Error 
t Stat 
P-value 
Sig 
Lower 
95% 
Upper 95% 
Intercept 
-78.69 
20.71
-3.80
p<0.001
*** 
-119.38 
-38.00 
Intervention 
-18.21 
3.89
-4.68
p<0.001
*** 
-25.85 
-10.56 
Metal prices (£000s per 
metric tonne) 
9.15 
2.97
3.08
0.002
*** 
3.32 
14.98 
Other acquisitive crime 
(000s) 
0.77 
0.14
5.57
p<0.001
*** 
0.50 
1.04 
Time   
-0.22 
0.19
-1.15
0.251
  
-0.58 
0.15 
Time_squared 
-0.04 
0.01
-6.31
p<0.001
*** 
-0.05 
-0.03 
area1_North_East 
-2.54 
2.76
-0.92
0.359
  
-7.96 
2.89 
area2_Yorks_and_Humb
er 
23.97 
2.72
8.81
p<0.001
*** 
18.62 
29.31 
area3_East Midlands 
-5.47 
2.72
-2.01
0.045
** 
-10.82 
-0.12 
area4_North West 
21.07 
2.72
7.76
p<0.001
*** 
15.73 
26.41 
area5_South West 
-32.04 
2.72
-11.80
p<0.001
*** 
-37.38 
-26.70 
area6_East 
20.52 
2.72
7.56
p<0.001
*** 
15.19 
25.86 
area7_South_East 
23.21 
2.72
8.55
p<0.001
*** 
17.87 
28.55 
area8_West 
3.29 
2.73
1.21
0.228
  
-2.07 
8.66 
area9_Wales 
-25.44 
2.73
-9.31
p<0.001
*** 
-30.80 
-20.07 
Area10_London 
-26.58 
2.73
-9.73
p<0.001
*** 
-31.95 
-21.22 
*** indicates significance to the 1% level; ** indicates significance at the 5% level; and * indicates significance at the 10% level. 
The difference in the area coefficients, some of which are significant, indicates that some areas 
have naturally higher or lower levels (depending on the coefficient) than average. For example, 
Yorkshire and Humberside has about 24 more thefts per region per month than the average for 
all regions. The full results for the final model, which includes the interaction term, are shown 
below: 
 
 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
22 
 
 
  
Coefficients 
Standard 
Error 
t Stat 
P-value 
Sig 
Lower 95% 
Upper 95% 
Intercept 
-65.36 
21.21
-3.08
0.002
*** 
-107.05 
-23.68 
Intervention 
-14.48 
4.12
-3.51
p<0.001
*** 
-22.58 
-6.37 
Metal prices (£000s per 
metric tonne) 
6.26 
3.15
1.98
0.048
** 
0.06 
12.45 
Other acquisitive crime 
(000s) 
0.68 
0.14
4.75
p<0.001
*** 
0.40 
0.96 
Time   
-1.62 
0.57
-2.83
0.005
*** 
-2.74 
-0.49 
Time_squared 
-0.08 
0.02
-4.60
p<0.001
*** 
-0.11 
-0.04 
Interaction 
2.30 
0.89
2.59
0.010
*** 
0.56 
4.05 
area1_North_East 
-2.47 
2.74
-0.90
0.369
  
-7.86 
2.93 
area2_Yorks_and_Humber 
23.83 
2.70
8.81
p<0.001
*** 
18.51 
29.14 
area3_East Midlands 
-5.61 
2.70
-2.07
0.039
** 
-10.92 
-0.29 
area4_North West 
20.94 
2.70
7.75
p<0.001
*** 
15.63 
26.24 
area5_South West 
-32.14 
2.70
-11.91
p<0.001
*** 
-37.45 
-26.84 
area6_East 
20.42 
2.70
7.57
p<0.001
*** 
15.12 
25.73 
area7_South_East 
23.11 
2.70
8.56
p<0.001
*** 
17.80 
28.41 
area8_West 
3.51 
2.72
1.29
0.197
-1.83 
8.84 
area9_Wales 
-25.22 
2.72
-9.29
p<0.001
*** 
-30.56 
-19.89 
area10_London 
-26.367 
2.715
-9.71
p<0.001
*** 
-31.70 
-21.03 
*** indicates significance to the 1% level; ** indicates significance at the 5% level; and * indicates significance at the 10% level. 
The model was also run with a full set of time dummies in place of the standardised time 
variable. The coefficient on the intervention term was similar to the result in our preferred model 
included in the main paper. Though the coefficient of the intervention term was significant, this 
model did not allow for an interaction term, which is the key issue for evaluating the intervention. 
Another problem with this model was that it didn’t converge properly because of collinearity 
problems. This is why a standardised time variable was ultimately used instead for the main 
results. Other robustness checks were also completed including using different measures of 
metal prices, and regional-specific acquisitive crime trends, but the main results stayed robust to 
all these variations. 
The ENA series was also checked for underlying data consistency. This revealed that there was 
some variation in the contribution of different utility companies. Not all the companies 
contributed data through the entire period. There is a risk that this could bias results. So, an 
alternate version of the regression was run which only used data from companies that 
contributed at least one incident of metal theft in every month of the series. The results of this 
are shown below. Again, the main finding was that both the intervention term and the interaction 
term were statistically significant and implied a large crime-reduction effect for the metal theft 
initiatives.13 
 
 
13  This model included the natural logarithm of the time-squared term (in place for the time-squared term), to allow for the 
reduction of metal theft prior to the interventions.  

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
23 
 
  
Coefficients 
Standard 
Error 
t Stat 
P-value 
Sig 
Lower 
95% 
Upper 
95% 
Intercept 
-25.77 
18.14
-1.42
0.11
  
-61.4 
9.87 
Intervention 
-17.29 
3.50
-4.94
p<0.001
*** 
-24.17 
-10.41 
Metal prices (£000s per 
metric tonne) 
1.08 
2.67
0.40
0.67
 
-4.17 
6.32 
Other acquisitive crime 
(000s) 
0.52 
0.12
4.22
p<0.001
*** 
0.28 
0.76 
Time    
1.53 
0.25
6.08
p<0.001
*** 
1.04 
2.03 
Time  (ln) 
3.04 
1.30
2.34
0.004
*** 
0.49 
5.60 
Interaction  
-3.37 
0.69
-4.92
p<0.001
*** 
0.49 
5.60 
area1_Nort East 
-2.30 
2.42
-0.95
0.34
  
-7.05 
2.45 
area2_Yorks_and_Humber 
23.32 
2.38
9.80
p<0.001
*** 
18.64 
28.00 
area3_East Midlands 
-3.64 
2.38
-1.53
0.12
  
-8.32 
1.04 
area4_North West 
18.56 
2.38
7.81
p<0.001
*** 
13.89 
23.23 
area5_South West 
-29.10 
2.38
-12.25
p<0.001
*** 
-33.77 
-24.43 
area6_East 
19.74 
2.38
8.31
p<0.001
*** 
15.07 
24.40 
area7_South_East 
20.49 
2.38
8.63
p<0.001
*** 
15.82 
25.15 
area8_West 
2.38 
2.39
1.00
0.23
-2.31 
7.08 
area9_Wales 
-22.51 
2.39
-9.42
p<0.001
*** 
-27.21 
-17.82 
area10_London 
-26.93 
2.39
-11.27
p<0.001
*** 
-31.63 
-22.24 
*** indicates significance to the 1% level; ** indicates significance at the 5% level; and * indicates significance at the 10% level. 
2. Analysis of the British Transport Police (BTP) data 
The BTP data were grouped by different geographical regions from those that were used for the 
roll-out of Operation Tornado. So for the analysis the police forces were allocated to each region 
depending on the proportional coverage. For example, figures obtained from BTP showed that 
Leicestershire constabulary is 95 per cent covered by the region ‘North Eastern’ and five per 
cent covered by the region ‘London North’. So this force was coded to ‘North Eastern’ for the 
purposes of the analysis. Table 4 summarises the results of this process. 
 
 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
24 
 
 
Table 4: BTP regions split by police force. 
London North 
London South London 
Underground 
North Eastern 
North 
Western 
Wales & 
Western 
Thames Valley 
Dorset 
Metropolitan 
Police Service 
Northumbria 
Cumbria 
North Wales 
Bedfordshire 
Hampshire 
City of London 
Police 
Durham 
Lancashire 
Dyfed-Powys 
Cambridgeshire Surrey 
 
Cleveland 
Greater 
Manchester 
South Wales 
Norfolk 
Sussex 
 
North Yorkshire 
Merseyside 
Gwent 
Suffolk 
Kent 
 
West Yorkshire 
Cheshire 
West Mercia 
Essex 
 
 
South Yorkshire 
 
Staffordshire 
Hertfordshire 
 
 
Humberside 
 
West Midlands 
 
 
 
Derbyshire 
 
Warwickshire 
 
 
 
Nottinghamshire 
 
Gloucestershire
 
 
 
Lincolnshire 
 
Wiltshire 
 
 
 
Leicestershire 
 
Avon & 
Somerset 
 
 
 
Northamptonshire
 
Devon & 
Cornwall 
 
Unfortunately these regions still could not be analysed in a similar way to the ENA regions, 
which mapped directly onto the roll-out of Operation Tornado. This was because different forces 
within each of the BTP regions commenced Tornado at different times. For example, within the 
North Eastern group, Northumbria, Durham and Cleveland started Tornado in January 2012, but 
the rest of the North Eastern group did not start until April 2012. So the regions were grouped 
into just two areas for analysis: early adopters, which included all the areas in the North Eastern 
group (i.e. Northumbria, Durham, Cleveland, N. Yorkshire, W. Yorkshire, S. Yorkshire, 
Humberside, Derbyshire, Nottinghamshire, Lincolnshire, Leicestershire and Northamptonshire); 
and late adopters, which included all the other areas. Comparison with Table 2 shows that the 
early adopters started Tornado between January and April 2012 while the late adopters started 
between May and September 2012. 
The data for these two regional blocks were used in a separate regression analysis, which 
otherwise employed the same variables as the model for the ENA data.14 The results of this 
model are shown below: 
 
 
 
14  This model did not include a squared time term. 

 
An evaluation of government/law enforcement interventions aimed at reducing metal theft 
25 
 
 
Regression Statistics 
Multiple R 
0.929 
R Square 
0.863 
Adjusted R Square 
0.854 
Standard Error 
16.146 
Observations 
96 
ANOVA 
  
df 
SS 
MS 
F 
Significance 
F 
Regression 
6 
146562.24
24427.04
93.70 
0.00
Residual 
89 
23201.72
260.69
Total 
95 
xx-xxxx-xxxx
  
  
  
  
Coefficients 
Standard Errors 
t Stat 
P-value 
Lower 95% 
Upper 95% 
Intercept 
-227.29 
39.94
-5.69
P<0.001 
-306.65
-147.93
Intervention 
-28.13 
6.74
-4.18
P<0.001 
-41.52
-14.74
Metal Prices (£000s) 
28.81 
5.25
5.49
P<0.001 
18.38
39.24
Other acquisitive crime 
(000s) 
1.58 
0.26
6.11
P<0.001 
1.06
2.09
Time 
0.08 
0.31
0.26
0.79 
-0.53
0.69
Interaction 
-0.82 
0.48
-1.70
0.09 
-1.78
0.14
 
These results are similar to the results for the main model that uses the ENA metal-theft data for 
the dependent variable rather than the BTP counts of metal theft. Note though, that in this 
specification the interaction variable is only significant at the ten per cent level, though given 
there were only two regions in this model, and hence less variation, that is perhaps not 
surprising. 

 
 
 
ISBN:  978-1-78246-700-7 
ISSN:  1756-3666 
 
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