Vehicle crime
in the Netherlands
A research into freight exchange fraud
E.V.A. Eijkelenboom
Erasmus University Rotterdam
Erasmus School of Economics
Date:
Supervisor:
Student number:
Master program:
August 2012
Mr. Dr. P.A. van Reeven
304873
Urban, Port and Transport Economics
Abstract
Road transport is a vital part of the Dutch economy. Unfortunately road transport crime occurs
frequently in various forms, disturbing the market and creating losses. This thesis aims to start
academic research into freight exchange fraud, an upcoming form of crime, and to enhance
understanding of vehicle crime in the Netherlands. Vehicle crime originally consists of three
components, namely cargo theft, vehicle theft and combination theft. Is freight exchange fraud
a new component of vehicle crime or is it an innovation and did vehicle crime increase in total?
Or did vehicle crime undergo a change resulting in innovation of vehicle crime and do the same
actors behave differently?
Our research investigates if freight exchange fraud is an addition to vehicle crime in the
Netherlands. We perform a correlation analysis and a vector autoregression to retrieve
information about relationships and dependencies between the different components of vehicle
crime. Results of our research are mixed. Our research indicates that criminals stealing cargo or
vehicles can be seen as experts, who are operating in a homogeneous group of actors. If
circumstances remain unchanged, criminals are expected to remain stealing. Cargo theft
criminals are found to be different than vehicle theft criminals. We did not find statistical
significant evidence for an additional component of vehicle crime. Based on a descriptive
analysis freight exchange fraud does seem to be a more advanced way of stealing cargo, vehicles
or combinations and can therefore not be seen as an individual component of vehicle crime.
Keywords: Vehicle crime, freight exchange, fraud, vector autoregression
© E.V.A. Eijkelenboom 2012
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form, or by any
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Table of Contents
LIST OF FIGURES................................................................................................................... III
LIST OF TABLES .................................................................................................................... III
ACKNOWLEDGEMENTS ......................................................................................................... V
CHAPTER 1: INTRODUCTION ...................................................................................................1
1.1 VEHICLE CRIME .......................................................................................................................... 1
1.2 AIM OF THE RESEARCH ................................................................................................................ 2
1.3 STRUCTURE ............................................................................................................................... 3
CHAPTER 2: FREIGHT EXCHANGE FRAUD .................................................................................4
2.1 LIBERALISATION OF EU FREIGHT TRANSPORT .................................................................................. 4
2.2 FREIGHT EXCHANGE .................................................................................................................... 7
2.2.1 Definition ....................................................................................................................... 8
2.2.2 Membership procedure ................................................................................................. 9
2.3 FREIGHT EXCHANGE FRAUD ........................................................................................................ 11
2.3.1 False document companies ......................................................................................... 12
2.3.2 Company take-over ..................................................................................................... 13
2.3.3 The mole ...................................................................................................................... 13
2.3.4 Consequences .............................................................................................................. 14
CHAPTER 3: DATA AND METHODS ........................................................................................ 15
3.1 DATA DESCRIPTION .................................................................................................................. 15
3.1.1 Data transformation .................................................................................................... 16
3.2 CORRELATION ......................................................................................................................... 17
3.2.1 Model I ......................................................................................................................... 17
3.2.2 Model II ........................................................................................................................ 22
3.3.3 Summary and concluding remarks .............................................................................. 27
3.3 MODEL SET UP ........................................................................................................................ 29
3.3.1 Model I ......................................................................................................................... 31
3.3.2 Model II ........................................................................................................................ 34
CHAPTER 4: RESULTS AND ANALYSIS .................................................................................... 37
4.1 MODEL I................................................................................................................................. 37
4.2 MODEL II ............................................................................................................................... 41
4.3 FREIGHT EXCHANGE FRAUD – DESCRIPTIVE ................................................................................... 42
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS ........................................................... 46
5.1 CONCLUSIONS ......................................................................................................................... 46
5.2 POLICY RECOMMENDATIONS ...................................................................................................... 48
5.3 RECOMMENDATIONS FOR FURTHER RESEARCH .............................................................................. 51
LIST OF REFERENCES............................................................................................................. 53
APPENDIX ............................................................................................................................ 55
A. LEGAL FRAMEWORK ........................................................................................................ 55
B. DATA DESCRIPTION .......................................................................................................... 59
i
B.1 DATA CHARACTERISTICS ............................................................................................................ 59
B.2 CHOW TEST ............................................................................................................................ 60
C. VECTOR ERROR CORRECTION MODEL ............................................................................... 60
D. MODEL I .......................................................................................................................... 61
D.1 STATIONARY TESTS FOR MODEL I ................................................................................................ 61
D.1.1 Cargo theft................................................................................................................... 61
D.1.2 Combination theft ....................................................................................................... 63
D.1.3 Vehicle theft ................................................................................................................ 66
D.2 LAG SELECTION FOR MODEL I ..................................................................................................... 68
D.3 IMPULSE RESPONSE FUNCTION MODEL I ...................................................................................... 69
D.4 VAR ANALYSIS AND IMPULSE RESPONSE FUNCTION FOR MODEL I INCLUDING FIRST DIFFERENCE OF CARGO
THEFT .......................................................................................................................................... 70
D.4.1 Lag selection ................................................................................................................ 70
D.4.2 VAR analysis................................................................................................................. 71
D.4.3 Impulse response function .......................................................................................... 75
E. MODEL II .......................................................................................................................... 75
E.1 STATIONARY TESTS FOR MODEL II................................................................................................ 75
E.1.1 Members ...................................................................................................................... 75
E.1.2 Posted advertisements ................................................................................................ 78
E.1.3 Viewed advertisements ............................................................................................... 80
E.2 LAG SELECTION FOR MODEL II .................................................................................................... 82
E.3 JOHANSEN COINTEGRATION TEST ................................................................................................ 83
E.4 IMPULSE RESPONSE FUNCTION MODEL II ..................................................................................... 85
F. FREIGHT EXCHANGE FRAUD .............................................................................................. 86
F.1 2008 ..................................................................................................................................... 86
F.2 2009 ..................................................................................................................................... 86
F.3 2010 ..................................................................................................................................... 87
F.4 2011 ..................................................................................................................................... 88
ii
List of figures
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Orbis 2008
EVO, 2003
Three components of vehicle crime graphed from January 1996 –
December 2012
Impulse response function VAR(2) model I vehicle to vehicle
Impulse response function VAR(2) model I cargo to cargo
Impulse response function VAR(2) model I combination to
combination
Impulse response function VAR(2) model I combination to vehicle
Impulse response function VAR(1) model II d.cargo to d.cargo
Freight exchange fraud monthly percentage allocation
Freight exchange fraud daily percentage allocation
Freight exchange fraud percentage allocation per region
European Union within CMR
Liability
Impulse response function VAR(4) model I vehicle to vehicle
Impulse response function VAR(4) model I, combination to
combination
Impulse response function VAR(4) d.cargo to d.cargo
Impulse response function VAR(4) d.cargo to combination
Impulse response function VAR(4) combination to vehicle
6
6
32
38
38
38
39
42
44
44
44
55
58
73
73
73
74
74
List of tables
Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7
Correlation table model I, 1
Correlation table model I, 2
Correlation table model II, 1
Correlation table model II,2
VAR(2) analysis model I
VAR(1) analysis model II
VAR(4) analysis model I (cargo theft in first difference)
18
20
22
25
37
41
72
iii
iv
Acknowledgements
This thesis and underlying research would not have been possible without the help and support
of the following people.
First of all I would like to thank Chris Selhorst, my supervisor at the Korps landelijke
politiediensten (KLPD). It is because of his help that I was able to conduct an internship at the
KLPD, where his enthusiasm, interest in the subject matter, work experience and his large
amount of contacts helped me to get a grip on the subject. I appreciate the great amount of
time he made available for me and my research, which has not only been a great stimulus for
my thesis but made my internship a very valuable and unique experience.
Secondly I would like to thank my colleagues at the KLPD, especially Enny Magendans, Frans
Dekker and Hans van ‘t Hart of the Landelijk Team Transportcriminaliteit (LTT), who gave me
such a warm welcome and made a very pleasant work environment.
Furthermore I am grateful to thepersons who were willing to answer my questions and provide
me with in depth information about the subject so that I was able to form my opinion. These
people were Remco Segerink (KLPD), Roger Busch (Bovenregionale Recherche Zuid-Nederland),
Alexander Oebel and Smahan Dahmani (Timocom), Henk Schenk (Teleroute), Wim DeKeyser,
Frederik DeKeyser and Dimitri DeKeyser (B.V.B.A. Wim DeKeyser International Loss Adjusters)
and last but not least Artur Romanowski, Hilde Sabbe, Cristina Checchinato and Rowan
Timmermans (Europol).
Fourthly I would like to thank my supervisor Peran van Reeven for supervising me during this
process of thesis writing. I am grateful for the fact that he allowed me to write about a subject
which is not common in the field of Urban, Port and Transport Economics and supported me
from the first research proposal onwards. His reachability, approachability and critical remarks
have been of significant positive influence on this final product.
Last but not least I would like to thank my friends and family, especially for helping me: by
drinking endless amounts of coffee whenever that felt needed (Job Jan, Suzanne); by serving
late night drinks (Hilleke, Kimberley); with the statistics (Yvonne, Gert-Jan); sharing happiness or
cheering me up (David, Irene, Linda); by constantly supporting me and showing an unceasing
amount of interest (mum, dad).
v
vi
Chapter 1: Introduction
1.1 Vehicle crime
In March 2008 a new criminal phenomenon in the transport world grabs media attention1
causing a great fuss in the Dutch transport world. An intelligent, well thought through criminal
act made it possible to let whole truckloads disappear. No one had seen something like this
before. Everyone was aware of dangers like curtain slashing, holdups and corrupted drivers,
which caused damages which were costly, but bearable. No one was prepared though for a new
type of criminal activity, which made it possible to let a company go into insolvency with only
one single criminal act. This new type of criminality is called ‘freight exchange fraud’.
Freight exchange fraud is added by the National Police Services Agency (KLPD) to the other
already familiar forms of vehicle crime, namely the theft of cargo, the theft of vehicles and the
combination of both. This new type of vehicle crime makes one wonder if transport related
criminal activities in the Netherlands increases or if the amount of criminal activity stays the
same but changes towards a new equilibrium. Since road transport is a vital part of the Dutch
economy, being responsible for almost 3% of the GDP2, it is important to investigate activities
that threaten this sector. A secure and safe transport environment is beneficial for many parties.
Road transport is not only of main importance in the Netherlands, in the European Union it
takes 46.6% of the total goods transported for its account.3 Every day more than 1.5 million
tonnage of raw materials, food and other cargo is transported by road. Criminals are aware of
this continuous cargo flow and they try to interfere in order to find a source of income or use
the haul to fund other, more complex criminal activities, such as trafficking in human beings.
Criminals perceive cargo theft as a low risk/high reward crime and therefore it is seen as a
lucrative business.4 This perception is not only caused by the size of road transport but also by
the characteristics of the sector. The goods that are transported are often not secured. This is
partially due to the high costs of protection, which are not bearable for the transport
1
See for example: http://www.ttm.nl/nieuws/id23143-Ladingdief_infiltreert_diep_in_Teleroute.html.
http://www.rijksoverheid.nl/onderwerpen/goederenvervoer-over-de-weg/beleid-goederenvervoer-overde-weg.
3
European Commission (2011), p. 19.
4
Europol Cargo Theft Report (2009), p. 20.
2
1
companies, who are suffering from low margins. The fact that the goods are not secured
properly makes it an easy target for criminals.
Pressure exerted by (inter)national policymakers in combination with the low margins force
transport companies to drive as efficient as possible.5 Inefficiency in the market creates
opportunities for businesses to implement ideas that enhance efficiency. The freight exchange is
one of these plans meant to increase efficiency which was successful. The internet platform
made it possible for supply and demand to meet in the digital world, exchanging orders and
decreasing the empty drives and cargo surplus. The previous years have made it clear that
freight exchanges do not only bring positive but also come along with negative externalities.
Criminals are currently infiltrating in the freight exchange, using the provided information to
make it possible to let whole truckloads disappear, which causes enormous losses for the
transport company.
Every day, efforts are made to decrease vehicle crime. On a daily base the Landelijk Team
Transport6 (LTT) is trying to solve road transport crime including freight exchange fraud, which is
reported in the Netherlands. Current agreements7 of interest parties such as insurance
companies, the Dutch Transport Operators Association (TLN) and the KLPD deal with a wide
variety of criminal aspects in road transport. The attention for road transport crime is necessary
since the direct and indirect losses caused by road transport crime are estimated at € 8.2 billion
per year for the European Union and € 330 million per year for the Netherlands in specific.8
Diminishing transport criminality, especially freight exchange fraud, will enhance the life of
many trucking companies.
1.2 Aim of the research
This thesis aims to start academic research into freight exchange fraud and enhance
understanding of vehicle crime in the Netherlands. Specifically we test for relationships and
5
In 2009 one in four European trucks drove empty. http://www.logistiek.nl/Distributie/transportmanagement/2010/8/Lege-vrachtwagens-probleem-of-uitdaging-LOGDOS113114W/.
6
The LTT is a part of the KLPD.
7
Tweede Convenant Aanpak Criminaliteit Transportsector, Convenant Aanpak Criminaliteit
Wegtransportsector and Convenant Informatie en Registratie Ladingdiefstal.
8
Organised Theft of Commercial Vehicles and Their Loads in the European Union, p.18. It must be
emphasized that these values are estimates since statistical data on crime in the road transport sector is
relatively poor.
2
dependencies between different components of vehicle crime to be able to answer the
following research question:
Is freight exchange fraud an addition to vehicle crime?
Although the problem of freight exchange fraud only covers a small part of vehicle crime, it
causes relatively high losses. The European police is aware of this problem, as is shown by the
Europol Report of 20099, but is unable to react due to a lack of information. This thesis tries to
provide both information and analysis on freight exchange fraud, in order to provide the
transport sector with policy recommendations that are aimed at recognizing criminal behaviour
in advance and preventing freight exchange fraud from causing enormous economic damage.
For this research we make use of Dutch vehicle crime reports gathered between January 1996
and December 2011 and freight exchange data from between January 2007 and December
2011. We will perform a correlation analysis and vector autoregression to come to our
conclusion.
1.3 Structure
This thesis is structured as follows. Chapter 2 starts with the history of the European Union to
provide the reader with an inside in how the freight exchange could. Furthermore the rise of the
freight exchange, working of the freight exchange and freight exchange fraud with its different
working methods will be addressed. Chapter 3 will give a description of the available data, a
correlation analysis and the model set up to investigate the relations between the different
variables. In chapter 4 the results of the vector autoregression analysis complemented with an
impulse response analysis are presented. These analyses will verify and complement the
correlation analyses. Furthermore freight exchange fraud is described based on the available
data. Chapter 5 will conclude this thesis with the main conclusions of this research, it provides
policy recommendations and recommendations for further research.
9
Europol Cargo Theft Report.
3
Chapter 2: Freight exchange fraud
This chapter will elaborate on the problem of freight exchange fraud. In the first paragraph the
emergence of the freight exchange is discussed following the evolution of the European Union,
the second paragraph gives a detailed explanation of the working of the freight exchange which
will lead towards the third paragraph in which the problem of freight exchange fraud is
examined.
2.1 Liberalisation of EU freight transport10
After World War II the idea of a European Union started to form, with the main idea to aim at
economic integration of the member states. Collaboration between the member states was
seen as a necessity to prevent future European unrest. On the 18th of April 1951 six states –
Belgium, Germany, France, Italy, Luxembourg and the Netherlands - signed a treaty based on
the Schuman Declaration11, named the Coal and Steel Treaty. This treaty aimed at collective
managing of the heavy industries, in order to control the production of weapons.
The Treaty of Coal and Steal was successful and the states agreed to expand the collaboration
towards other sectors than the coal and steel sectors. On the 25th of March 1957 the Treaty of
Rome12 was signed which created the European Economic Community (EEC) or the ‘common
market’. The main idea of this treaty was and still is that people, goods and services are allowed
to move freely across the borders of the states who signed the treaty. This so called ‘internal
market’ is seen as the greatest achievement of the EEC – people, goods, services and money can
travel around the different states as easily as they travel around their own country. (Amtenbrink
and Vedder, 2010)
The allowance of free movement of goods and services within the EEC enlarged the scope of
many transport companies and made it a lot easier to cross the borders and trade with foreign
partners. The open border policy lead to an increase of market size and was thought to
stimulate efficiency, openness and equality, rather for the European consumers than for the
10
This chapter is based on Amtenbrink and Vedder (2010) and on http://europa.eu/about-eu/euhistory/index_nl.htm.
11
On the 9th of May 1950 the French minister Schuman composes a plan which should lead to closer
collaboration (The Council of Europe was already established in 1949). Later this plan – the Schuman
Declaration - is recognized as the first official step towards a European Union.
12
The official name of the Treaty of Rome is Treaty establishing European Economic Community.
4
transport companies. The scope and business opportunities of transport companies did
increase, however at the same time transparency was expected to decrease. This diminished
transparency occured for example with contracting. Different languages, diverse build-up of
agreements, unknown drivers and transport companies were able to enter the road transport
market which made and still makes supervision and control more difficult.
European collaboration expanded even further, resulting for example in a common agricultural
policy (July 30, 1962) where farmers are paid the same price for their products in every member
state. Another example is the removal of custom duties (July 1, 1968) on goods imported by
member states and applying the same duties on imported goods from countries other than the
member states. This created the biggest trade group and the years after founding, trade
between the member states and the rest of the world grew rapidly.
Economic integration intensified in 1972 when member states agreed on a common exchange
rate mechanism for their currencies (so currencies can only fluctuate within limits). Although
trade is supposed to move freely across the European Community since the abolishing of
custom duties in 1968, national regulations still prevented that from happening. The transport
sector did make use of the international dimension but less than expected. The old network of
transport companies stayed intact and although the European aspect is added, the transport
sector stayed national oriented.
On the 17th of February 1986 the Single European Act13 is launched in order to sort out this
malfunctioning, furthermore it gives the European Parliament more power with regard to
environmental protection. Environmental policy does influence the transport sector because it
pushes the need to drive as efficient as possible (with full drives). The Single European Act
therefore has a great impact on the transport sector and pushes it more and more into a
European minded direction. This European mind-set is strengthened by the signing of the Treaty
on the European Union14 which changed the name of the European Community into the
European Union. The treaty states clear rules for future cooperation, for example with respect
to a single European currency, foreign and security policy, and justice and home affairs.
13
14
The Single European Act is an amending treaty revising the Treaty of Rome.
Maastricht, 7th of March 1992.
5
The 1st of January 1993 is an important moment because the four fundamental freedoms are
now officially established for the common market. The policy of the European Union still mainly
aims at economic integration of member states.15 The integration of markets of member states
followed from policy which is mainly based on the four fundamental freedoms as stated in the
treaty on the functioning of the European Union. These fundamental freedoms are the free
movement of goods, services, persons and capital. The basic rules following from the
fundamental freedoms are applicable on the whole economy only excluding areas which have
their own specific policy. Sectors with such a specific policy are the agricultural, nuclear energy
and transport sector. (Amtenbrink and Vedder, 2010)
Figure 1 Orbis, 2008
Figure 2 EVO, 2003
In the transport sector economic integration is further stimulated by the introduction of
cabotage16 – the possibility to transport goods in other countries than where your own vehicle is
registered – which should increase the amount of trade and efficiency in the transport sector.
The opening of the borders resulted in a large inflow of transport firms since legislation lowered
trade barriers in the EU even further. The entry barriers in road haulage are low and the sector
is characterized by high internal competition. Consequently firms in the road transport sector
operate in a tough business climate characterized by low margins (figure 1). The high labour
costs (figure 2) suggest that that is the main cost that should be watched and improved. (Karis
and Dinwoodie, 2005)
15
Main point of the Treaty of Rome but is kept as a main ideal as follows from the preambles and the first
articles of the Treaty on European Union (TEU) and the Treaty on the functioning of the European Union
(TFEU).
16
May 14, 2010.
6
The high internal competition enforces transport companies to control their operating costs
closely. Since labour costs are a substantial part of the vehicle operating costs, it might be
profitable to use cheap labour, for example by contracting drivers from Eastern Europe which is
made possible by the four fundamental freedoms. Using drivers from abroad, or settle a
transport company abroad might lower costs but isn’t beneficial for a transparent market.
Economic integration of member states increased further when on the 1st of January 1999 the
new European currency, the Euro, is introduced and from now on used in business
transactions17. Since 1951 the group of six member states has expanded to a total of 27 member
states which all signed the Treaty of Lisbon18. This treaty builds on earlier European treaties and
still aims at economic integration but also has objectives like a higher rate of democracy,
efficiency and transparency within the European Union. This way global problems, for example
problems concerning the environment or national safety can be tackled.
One currency made trading easier, because it eliminated rate differences within the European
Union. This might be a stimulus for road hauliers which operated mainly on a national base to
explore the other member states. The high amount of road hauliers currently operating in the
European Union in the same competitive business climate enforces competitive behaviour.
Efficiency can be improved when trucks always drive fully loaded, which is also stimulated by
the EU with its environmental focus.
The constantly increasing need to become a European oriented road haulier created the perfect
environment for the freight exchange to emerge. The increase of European member states,
environmental policies and the high internal competition in the transport sector caused a
demand for information and transparency. In order to be competitive the best deals must be
made and the drives must always be full, but the amount of countries and companies hinder
transparency and lead to asymmetric information. The freight exchange might be helpful in
solving this problem of asymmetric information.
2.2 Freight exchange
The freight exchange is nowadays an essential part in the transport sector, widely used and
known by its stakeholders. In this paragraph we will take a close look at the freight exchange as
17
18
Since 2002 the Euro is not only used in business transactions but became the main European currency.
Treaty of Lisbon, December 13, 2007; Ratified by all member states before 1 st of December 2009.
7
an important part of the transport chain.19 This paragraph is subdivided into two parts. The first
part will define the freight exchange; the second part will elaborate on the membership
procedure.
2.2.1 Definition
A freight exchange is a digital platform where shippers and carriers can meet. In Europe the
most important, largest and most used freight exchanges are, TimoCom, which is situated in
Germany and Teleroute, part of multinational Wolters-Kluwer, located in the Netherlands. Next
to these large players, local players are present in the market as well and can be found in France
and in different countries in central and Eastern Europe. Our focus will be on the two largest
players because of their size which allows them to serve a substantial part of carriers and
shippers around Europe. Furthermore the smaller freight exchanges, for example those located
in Eastern Europe, only serve a local (national/regional) market which does not correspond with
the international character of road transport and the global aspect of the crime following it.
A freight exchange offers a service to the transport sector, namely the service to be the platform
which gives supply and demand the opportunity to meet. When a shipper has a load but no
transport he can enter the freight exchange. On the freight exchange he can post an
advertisement for his load. The advertisement will contain information about when the load
needs to be shipped, the origin and destination of the load and if special requirements for
transport are needed. Examples of these specials requirements are the need for a cooling-trailer
or a tank-trailer. It is not allowed to give any other details on the load than the details needed
for transport. So the amount of pallets which needs to be transported can be stated in the
advertisement but one is not allowed to define the goods on the pallet. After the shipper has
posted the advertisement on the freight exchange it is available for all other customers of the
freight exchange to see.
Such an advertisement targets a carrier who has a transport towards a destination but does not
have a load back to his country of origin. To endeavour increased efficiency the carrier will enter
the freight exchange and start searching for advertisements which offer loads located on or near
his way home. When a carrier sees a load of interest he can contact the shipper via the freight
19
This paragraph is mainly based on conversations with experts of the freight exchange (Alexander Oebel,
Shaman Dahmani, and Henk Schenk) and on the websites of the freight exchanges Timocom
(www.timocom.de), and Teleroute (www.teleroute.nl).
8
exchange. Closing of the deal takes place without interaction of the freight exchange. Just as on
an ordinary exchange, one is able to resell the products one bought. Often not only contractors
but also subcontractors are searched via the freight exchange because of possible arbitrage
opportunities. This leads to long transport chains with a lot of subsidiaries which causes
intransparancy and thereby opportunities for criminal behaviour. The freight exchange does
only offer the possibility for shipper and carrier to come in contact with each other and is
therefore not liable under current law.20
In order to be successful as a freight exchange a large group of customers is needed because
only then you are able to serve the needs of shippers and carriers and give an accurate overview
of European supply and demand. The founding of the European Union which came along with a
demand for information created the opportunity for freight exchanges like TimoCom and
Teleroute to gain such a critical mass21 and therefore they are able to serve a large part of
Europe.
2.2.2 Membership procedure
Market information is necessary in order to be able to be efficient and competitive in the
transport sector. The freight exchange offers such market information and participants of the
road transport sector acknowledge its utility and the necessity of becoming a member. This
paragraph will give a comprehensive description of the membership procedure of the freight
exchange to give an insight in safety procedures taken by the freight exchange
Once an application for a membership is received by the freight exchange, a security check
process is started. The interested transport company is asked to complete a form containing
questions regarding basic company information. This basic information includes amongst others
the company address, type of company22, if it possesses trucks, and when this is the case what
type of trucks. After completion of the form this information is send to an account manager who
tries to gain additional information23 by contacting the interested company.
20
See Appendix A for more information on liability and law in the transport sector.
For example, TimoCom has 30.000 members and has daily 300.000 offers posted on its system.
22
E.g: is the company a carrier, shipper or shipping agent.
23
This additional information can be for example a chamber of commerce registration number, insurance,
driver license, normal letterhead of the company.
21
9
The large freight exchanges are active in most European countries, but not every country
registers and archives all company information, therefore the freight exchange does not require
the same additional information from every company. The requested information can differ per
company per country per day. Furthermore a financial check is performed to check the
creditability of the company. For a freight exchange it is disastrous when companies active on
your freight exchange do not pay their business partners, this will be associated with the freight
exchange and this might have negative impact. Therefore the financial check is perceived by the
freight exchange as an important part of the application procedure.24
The financial check also involves a check of subsistence, a company has to exist and be active for
at least six months to make sure the company has a chance of survival in this highly competitive
sector. When the additional information is received and the account manager reckons the
applicant is legal and creditworthy, the application form and the additional information are sent
to the security department. This department double checks the company again and compares
the information provided by the company to public sources. By checking for example the yellow
papers and verifying the company website, the security department checks if the company truly
exists and if this company is not part of obscure activities. When the security department is
convinced of the sincere intentions of the company the application is sent back to the account
manager. The account manager seeks additional contact to negotiate about the contracts and
opens the account. The transport company is now able to take part in the freight exchange and
his company information is visible for all other users.
It might be possible that a company is granted permission to be active on the freight exchange
before the security check is finished. The personal account of the company does however state
in which verification level the company is. This information is visible for all members of the
freight exchange. Additional rights will be assigned to a company when a greater part of the
company is checked. Customers of Teleroute can also choose to sign a ‘Code of Conduct’ which
is complementary to all legal rules and lays down the expected behaviour of Teleroutecustomers. By committing to this gentlemen’s agreement you oblige yourself to be honest and
sincere. After a company signed the Code of Conduct, this will be made visible for other users in
the company’s personal account information. Checks and balances are also found in the peer
24
The freight exchange does also value creditability of their members as an assurance that they are
expected to receive the membership fee.
10
review, an elementary part of the freight exchange where users of your company can rate the
service offered by the freight exchange. The registration number of the user can be a basic and
first check on creditability. The lower the number, the longer a company is part of the freight
exchange and a long membership probably refers to a respectable company. The idea that one
is dealing with a respectable company is then used by organizations with criminal intentions.
2.3 Freight exchange fraud
The emergence of the freight exchange in combination with the characteristics of the road
freight transport sector (low margins, intense competition) lead to opportunities for criminal
behaviour. Different conversations25 with experts, and police reports led to the following
definition of freight exchange fraud:
Freight exchange fraud is the deliberate misuse of the freight exchange with the aim to steal
cargo to fund other activities of the criminal organization.
The first case of freight exchange fraud is reported in the Netherlands in 2008. Since then the
phenomenon is observed in many more European countries. Misuse of the freight exchange, as
meant in the definition of freight exchange fraud, can occur in a variety of ways. The police tries
to retrieve the working methods26 of (a group of) criminals. When formulating the different
methods of work it is most important to focus on possible repeating elements and the
evolvement of the criminal approach over time. When experts, for example the investigators
working at the KLPD, construct the modus operandi they are able to construct a policy to
prevent the criminal activity occurring in the future. Also, when the working method is clear the
possible targets of the criminals can be warned. It is therefore of great importance to be aware
of the modus operandi. This section will construct the working methods for fraudulent transport
activities with use of the freight exchange. Although this type of criminal activity is a fairly recent
phenomenon, the statements of the deceived, police investigations and conversations with
experts from the field show already a transition in working method from the moment freight
exchange fraud was noticed for the first time, up till today.
25
This paragraph is based on conversations with the following experts Chris Selhorst, Roger Busch, Remco
Segerink, Wim DeKeyser, Frederik DeKeyser, and Dimitri DeKeyser.
26
By the police referred to as ‘modus operandi’.
11
The general procedure of freight exchange fraud is that a variety of transport deals is made with
the use of the information presented on the freight exchange. After the deal is made the
criminal organization picks up the cargo at the place of origin but does not deliver at the place of
destination. The fraudulent company is active for about a week and tries to get as much
contracts as it can handle and preferably long drives at the beginning of the week in order to
postpone the date of discovery. At first it seemed that electronics and other expensive goods
were the main target of these criminals and were therefore seen as high risk-products but lately
every type of cargo seems to be at risk. Recently stolen cargo includes frozen meat, chocolate,
electronics and used truck tires.27 The observed methods of work can be divided in three main
groupings, i.e. false document companies, company take-overs and a method we named ‘the
mole’. The different methods of work will be explained in the next three paragraphs.
2.3.1 False document companies
The first method is named: false document company. Criminals make use of an existing
company name, but fabricate their own documents for that company using existing company
details. These companies are totally falsified and imaginary except for their names. This means
the insurance papers, e-mail addresses, registration numbers etcetera are all falsified but often
based on the original documents. When a falsified company tries to register at the freight
exchange it has a low chance of passing through security checks, the company is ought to fail all
basis checks. However, most of the time a falsified company will not register at a freight
exchange since another company has registered already. An insight in the advertisements is
enough to make a deal.
The false document company can often be recognized by its contact details since it makes use of
free mail providers (e.g. hotmail.com or gmail.com) and mobile phone numbers. Potential
business partners should be alerted when observing basic company information including only
free mail addresses and mobile phone numbers, which should make them reluctant from doing
business with that company. Only a minor investigation will be able to expose the falsity of this
business. An example of such a practice is a criminal organization which changed the business
location from ‘Randweg’ into ‘Rondweg’. It is only a slight difference and therefore hard to
notice when not paying very close attention. The criminals make use of an existing company
27
More information about stolen cargo due to freight exchange fraud can be found in paragraph 4.3.
12
name, therefore a company website and fixed phone connection do exist. Thus, a simple check
in advance is possible and easy.
The first time a criminal company conducted freight exchange fraud this method was used and
caused an enormous damage to a lot of Dutch companies. The security check of the freight
exchange should notice this type of falsified company because of their check with real data. In
the first halve of 2011, thirteen falsified transport licences were used in Poland. The freight
exchange reported this fraud. Unfortunately authorities did not reply or act upon the proposal
made by the freight exchange to exchange information on this specific matter.
2.3.2 Company take-over
The high probability of detection when using a false document company led to evolvement of
this working method. The chance of being caught is smaller when taking over a small business.
When taking over a company criminals make use of the good name and reputation the credible
company has built. Furthermore, they can use the existing log-in data for the freight exchange,
the current insurance papers, the fixed phone connections and business e-mail addresses. In
practice this means that such a company is not detectable. When a potential business partner
checks the company information, only trustworthy data will be found and nothing will indicate
potential criminal activities. The freight exchanges try to guard themselves against criminal
activities following from take-overs, by obliging companies to notify the freight exchange when
a take-over has taken place. At that time a new security check can take place or a new number
can be assigned to the company notifying other users of the freight exchange that the company
has changed substantially. In practice however these measures often come tardily28. For freight
exchange fraud only a short amount of time is needed to cause enormous damage to others.
2.3.3 The mole
‘The mole’ is a different working method than the methods previously described; the name
refers to the placement of a mole into a credible transport company. This method is often used
since the existence of freight exchange fraud and can occur in combination with another
working method. Transport companies have large planning departments where all people have
access to the freight exchange. When a criminal organization makes use of an infiltrator in such
28
One of the police reports stated a transport company which was taken over just before Christmas. The
former owner reported the take-over, but because of the holidays it was processed at the freight
exchange after new year’s eve. This turned out to be too late because between Christmas and New Year’s
eve several loads were already stolen and the new (criminal) owners vanished into thin air.
13
a company they will be notified when valuable cargo is offered and they can contact the
supplying company. The criminals then do not need own log-in data but will directly contact the
company, they can identify themselves as a false document company or make use of the
company name of the infiltrator. An inattentive transport company will than close a contract
and loses its freight to the criminals.
For the freight exchange the mole strategy is hard to detect. Although there might be a
noticeable change in internet behaviour, the new persons in a company could be a logical
explanation for the change. This criminal strategy is at the moment mainly observed in eastern
European countries like Slovakia.
2.3.4 Consequences
The consequences of freight exchange fraud are numerous. When goods are lost insurance
companies try to recover the losses by approaching the carrier. (Appendix A) Because of the low
margins in road haulage a small carrier will not be able to pay the compensation claims which
might lead to bankruptcy. Economically, loss of goods can lead to delayed production.
Furthermore trust within the sector will diminish creating high barriers for new entrants.
Current trends show that insurance companies consider excluding insurance of losses caused by
usage of the freight exchange.
For a criminal organization the existence of the freight exchange makes high value/ low risk
criminal activities possible. Governments do not prioritize this type of criminal behaviour and
therefore police attention stays limited as consequence which is a stimulus for criminals to
continue this type of crime. Criminals are able to improve their working methods and keep up
with the latest trends, which is enhanced by the low priority given to this type of crime.29
Modern techniques such as access via mobile apps to the freight exchange hinder the, slightly
lagging behind, police in identifying the perpetrator.
29
This is confirmed in the report Zware jongens op de weg (2006) p.71.
14
Chapter 3: Data and methods
This chapter will give a description of the data concerning vehicle crime of the Netherlands. In
the second paragraph a correlation analysis will be performed to create a first insight in the
relations that exist between the variables. The third paragraph will construe the models which
will be the base for further analysis.
3.1 Data description
For this research four parts of vehicle crime are taken into account. The KLPD used to identify
three different forms of vehicle crime related to cargo theft, which are vehicle theft,
combination theft and cargo theft, but added freight exchange fraud as a fourth category in
2008. The data used in this research originates from Dutch police reports, beginning January
1996 up to December 2011 and is collected on a monthly basis. One report can enclose several
incidents but since not all reports are available for examination the number of reports will be
the most accurate variable when making comparisons.
The different categories of vehicle crime all relate to road haulage. Vehicle theft refers to the
theft of heavy vehicles, these vehicles must weigh at least 3500 kg. Combination theft indicates
that a heavy vehicle together with its load is stolen. Cargo theft refers to theft of cargo from a
heavy vehicle which can include partial theft as well as theft of a whole load. Descriptive
statistics and the graphed evolvement over time of these variables can be found in the appendix
(Appendix B.1).
The analysis will also make use of data provided by the freight exchange. This data covers the
amount of posts and the amount of viewed advertisements on the freight exchange.
Furthermore the amount of members of the freight exchange is tracked. The dataset provided
by the freight exchange contains monthly data starting from January 2007 up to December
2011.30
All variables used in this analysis are time series variables, meaning that we use observations
which are obtained over a period of time on regular time intervals, in our case monthly
intervals. The observations do not necessarily cover the same objects. Each month is accurately
30
A confidentiality agreement prohibits us to publish descriptive statistics or a graphed evolvement over
time of these variables in this thesis.
15
kept track of vehicle crime and freight exchange details leading to a dataset without missing
values. We must keep in mind though that not all incidents are reported which leads to an
under-registration of the true amount of crime incidents.31 Time series analysis is often used to
construct forecasts which can be helpful for prevention of future crime.
Data of freight exchange fraud is available from January 2008 up to December 2011. This
variable takes in account all reported vehicle crime performed with use of the freight exchange,
the different working methods (sub 2.3.1 – 2.3.3) are not taken in account because those
narrow down the already relatively small sample, which then might influence the results.
Furthermore the small sample size of freight exchange fraud has as a consequence that it cannot
be incorporated in the statistical analysis because the number of observations per month is not
sufficient. In the next chapter a paragraph will be dedicated to a comprehensive description of
freight exchange fraud based on the Dutch police reports to be able to give the reader an insight
in the problem of freight exchange fraud so far.
3.1.1 Data transformation
In 2010 the KLPD changed their computer system in such a way that crime reports could be
processed more accurately. The new computer system allowed linkages between the different
Dutch police data storage systems which enhanced national transparency. The increased
accuracy of the system made it easier to process the different crime reports and to distinguish
the different components of vehicle related crime. Consequence of this new system is that the
measuring is more accurate which could lead to an increased crime number with respect to the
year before, while that increase may just be caused by the new way of measuring and
processing.
A sudden increase in the reports might cause a break in the data sample when it is not the
reflection of an actual crime increase. When a break exists it is not possible to approach the
dataset as one sample. A break needs to be corrected for in a model. The knowledge about the
change in the computer systems gives us a strong indication that a break exists. A Chow test is
used to verify our expectation.32 The outcome of the test is that the null hypothesis has to be
rejected in favour of the alternative that a break exists (Appendix B.2). January 2010 does cause
31
32
This under registration is in practice referred to as the ‘black number’ of crime.
The significance level of this test is 5%.
16
a break in our data and therefore a dummy will be incorporated in the models to correct for the
break.
3.2 Correlation33
This paragraph introduces an exploratory view on the relationships that might exist between the
different variables. Correlation tables are used to show the potential relationships. It might be
possible to draw some preliminary conclusions from the observations visualized in the tables.
The suggestions in this paragraph are however made with great reluctance since the data used
to construct this correlation tables is time series data. Time series data has as a consequence
that while the same event is tracked each month, the actors can vary therefore shifts in the data
might just be a coincidence instead of having specific relevance. The observations of this
paragraph will for that reason be used as basis for further research.
This paragraph is divided in two parts. The first part takes the three vehicle crime components in
account from January 1996 up till December 2007 and is the base for the construction of model
I. The second part of this paragraph will incorporate the three vehicle crime components and
the freight exchange variables in the correlation analysis. These variables will cover the period
January 2007 up till December 2011 and they will be the basis for model II. Each paragraph will
consist of two correlation analyses, starting with a correlation table which describes the
correlation between the variables. Thereafter the relation between the variable and its lags - its
past observations - will be described since such a within-variable relationship might influence
the correlation between the variables. Underneath each correlation table the most important
conclusions will be summarized and, where necessary, compared to previous results, after
which a more comprehensive analysis will follow.
3.2.1 Model I
The correlations of the different components of vehicle crime - vehicle theft, cargo theft, and
combination theft - are shown in table 1.
33
A correlation analysis provides information about possible relationships between variables. A
correlation analysis does not clarify any causality between variables. Examples given in this chapter
regarding the sign and significance of correlation coefficients are meant to clarify the effect of the
movement, they do not imply any causal relations, it should therefore be kept in mind that the names of
the variables in an example are interchangeable.
17
Table 1 Correlation table model I,1
Correlation table
Variable
Vehicle theft
Vehicle theft
Cargo theft
Combination theft
1
Cargo theft
0.164 *
p-value
0.023
Combination theft
0.440 **
p-value
0.000
1
- 0.264 **
0.000
1
* significant on a 99% level (0.01), ** signficant on a 95% level (0.05)
The table reveals that all correlation between the vehicle crime variables is significant at the 5%
significance level.
Cargo theft and vehicle theft are positively correlated with a correlation coefficient of 0.164.
This coefficient indicates that cargo theft and vehicle theft follow the same pattern, for example
when cargo thefts increases, vehicle theft will increase as well.
Combination theft and vehicle theft show a significant positive correlation with a coefficient of
0.440 indicating that combination theft and vehicle theft are expected to follow a similar
pattern.
Combination theft and cargo theft are negatively correlated with a significant correlation
coefficient of – 0.264. This correlation coefficient indicates that combination theft and cargo
theft are expected to follow an opposite pattern, for example when combination theft
increases, cargo theft decreases and vice versa.
Vehicle, cargo and combination thefts are clustered in the group ‘vehicle crime’ which indicates
a certain connection and equality between those different types of theft. The connection can for
example be found in the nature of the thefts since all thefts relate to road haulage, furthermore
criminals active in this cluster might be the same. The assumption of the same actors being
active in vehicle crime can be supported by the negative correlation coefficient. A negative
correlation indicates that variables follow an opposite pattern, thus for example when one type
18
of theft increases, the other type of theft decreases and vice versa. The negative correlation
suggests a change in the behaviour of criminals who seem to switch from one to another type of
vehicle crime. Such a behavioural change of criminals might be stimulated by a change in
external circumstances. It might be that, when external circumstances make it easier to steal
vehicles, the ‘cargo-criminals’ shift to combination thefts. Only the negative correlation
coefficient between cargo thefts and combination thefts seems to support the assumption of
the same actors being active in vehicle crime.
On the contrary the other two relationships - vehicle theft related to cargo theft, and vehicle
theft related to combination theft - display a positive significant correlation. Following the
reasoning of the previous paragraph, does this positive correlation than indicate that different
actors are active in those criminal activities? The positive relation might suggest that the actors
are different in each crime component assuming that each actor is an expert in a certain type of
vehicle crime. When external circumstances improve, all criminal actors will profit from it and
the crime rate in each component is expected to increase. This assumption is however not
exhaustive since more convenient external circumstances will also cause an increase in crime
when the actors in the different kinds of vehicle crime are the same. In the latter situation more
‘profit’ is generated by the same actors in the same period of time. From correlation table 1 we
can only make assumptions about the actors, it is however not possible to draw conclusions
about the actors’ active in vehicle crime.
Correlation table 1 gave us insight in the correlation between the variables. The next table will
expand the insight in vehicle crime by adding lagged variables to the analysis to be able to
research possible within-variable correlation. To get a general first impression of possible
correlation with the past values of the vehicle crime variables, three lags are taken indicating
that we look back three months in time. The lag periods are denoted by L1 for the first lag,
which looks back one month; L2, for two months back; and L3, three months back.
19
Table 2 Correlation table model I, 2
Correlation table
Variable
Vehicle theft
Vehicle
Cargo theft
Combination theft
1
p-value
Vehicle L1
0.420 **
p-value
0.000
Vehicle L2
0.405 **
p-value
0.000
Vehicle L3
0.435 **
p-value
0.000
Cargo
0.164 *
p-value
0.023
1
Cargo L1
0.100
0.820 **
p-value
0.169
0.000
Cargo L2
0.110
0.818 **
p-value
0.130
0.000
Cargo L3
0.048
0.772 **
p-value
0.511
Combi
0.440 **
p-value
0.000
Combi L1
0.311 **
p-value
0.000
Combi L2
0.294 **
p-value
0.000
Combi L3
0.261 **
p-value
0.000
0.000
- 0.264 **
0.000
- 0.252 **
0.000
- 0.253**
0.000
- 0.275 **
0.000
1
0.499 **
0.000
0.419 **
0.000
0.315 **
0.000
* significant on a 99% level (0.01), ** signficant on a 95% level (0.05)
The information presented in table 2 shows us that the three vehicle crime components do all
positively and significantly correlate at the 1% significance level with their own three lagged
variables. The relations between the variables are comparable to the results of table 1. The
results of table 2 - significant between-variable correlation and significant within-variable
correlation - might be an indicator for noise in the correlation analyses.
Vehicle theft does not only show a significant within-variable correlation it does also show a
significant positive correlation with cargo theft, which is in accordance with the result from table
20
1. Vehicle theft does not show a significant correlation with the lags of cargo theft indicating
that former cargo theft activities do not seem to influence current vehicle crime.
Vehicle theft and all three lags of combination theft show a positive significant correlation at the
1% significance level. The positive relation between vehicle and combination theft already
became clear in table 1, and table 2 adds to that earlier observation that vehicle theft is
significantly positively correlated with the lags of combination theft.
Cargo theft is also not only dependent on its own lags but it also shows a negative significant
relationship at the 1% significance level with combination theft and the lags of combination
theft. This negative significant relation indicates that both variables follow an opposite pattern
hence a decrease in combination theft, increases cargo theft even three months later.
Each variable shows a positive significant relation at the 1% significant level with its own three
lags. These significant within-variable relations might indicate that the same actors are active
within each vehicle crime component. When the environment is suitable for criminal behaviour,
criminals will make use of the situation. The criminals will keep on stealing until an
environmental change makes continuing of ordinary business impossible without adapting to
the new situation. The significant correlations of the lagged variables are therefore not
unexpected since theft is the ‘profession’ of vehicle criminals which must result in a sufficient
‘salary’.
21
3.2.2 Model II
The correlations of the different components of vehicle crime – vehicle theft, cargo theft and
combination theft – and the variables presented to us by the freight exchange – posted
advertisements, viewed advertisements and the amount of members - are shown in table 3.
Table 3 Correlation table model II, 1
Correlation table
Variable
Vehicle
theft
Cargo theft
Combination
theft
Posted
advertisements
Viewed
advertisements
Members
Vehicle theft
p-value
Cargo theft
p-value
Combination
theft
p-value
Posted
advertisements
p-value
Viewed
advertisements
p-value
Members
p-value
1
0.569 **
0.000
1
0.031
- 0.115
0.817
0.382
0.288 *
0.589 **
0.102
0.026
0.000
0.440
- 0.400 **
0.002
- 0.342 **
0.008
- 0.717 **
1
0.118
0.000
- 0.609 **
0.368
- 0.015
0.000
0.910
1
- 0.695 **
1
0.000
- 0.698 **
0.000
0.810 **
0.000
1
* significant on a 99% level (0.01), ** signficant on a 95% level (0.05)
Correlation table 3 reveals that cargo theft and vehicle theft are significantly correlated with
each other and with all variables of the freight exchange. Combination theft does not show any
significant relations, contrary to table 1 and 2.
Vehicle theft is positively and significantly correlated with cargo theft. Different than table 1,
vehicle theft does not show a significant correlation with combination theft. The shorter time
frame used in this correlation analysis could be a reason for disappearance of the earlier found
significant relationship.
Vehicle theft correlates significantly with all variables from the freight exchange. A positive
relationship is found between vehicle theft and posted advertisements. A negative correlation is
22
found between the viewed advertisements and vehicle theft and the amount of members and
vehicle theft.
Cargo theft does not show a significant relationship with combination theft unlike the result of
table 1. Cargo theft significantly correlates with all variables from the freight exchange;
positively with posted advertisements and negatively with viewed advertisements and
members.
Combination theft does not show any significant correlation with the variables from the freight
exchange. The variable ‘posted advertisements’ shows a negative and significant correlation
with the amount of members at 1% significance level. The variable ‘viewed advertisements’
does show a positive and significant relation with members.
Combination theft does neither show a significant correlation with cargo theft or vehicle theft
notwithstanding the results of table 1 and 2. Although the correlation coefficients of table 3 are
not significant for combination theft we do notice that the signs of the correlation coefficients
between combination theft and the other vehicle crime variables did not change.
The vehicle crime components34 show a positive significant relation with posted advertisements
and a negative significant relation with viewed advertisements and members. The vehicle crime
variables follow a similar pattern as the posted advertisements do, for example an increase in
posted advertisements, increases vehicle crime. While an opposite pattern is observed between
vehicle crime and viewed advertisements, and vehicle crime and members; for example when
the amount of members and the amount of viewed advertisements on the freight increases,
vehicle crime decreases.
The variable combination theft shows no significant relation with any of the variables used in
this correlation analysis, in contrast to table 1. It might be that the detection of the new vehicle
crime component in 2008, freight exchange fraud, and the new measurement method from
2010 is of higher influence in this smaller sample, resulting in the non-significance of
combination theft. Reporting combination theft might experience difficulties because the
elements of combination theft (thus cargo theft and vehicle theft) could be reported separately,
34
With the phrase ‘vehicle crime components’ is in this case referred to vehicle theft and cargo theft
which are the components that significantly correlate with the variables of the freight exchange.
23
because of possible different owners or concerned parties. When ‘combination theft’ is
reported in two separate reports it has as consequence that these reports are not combined
into one combination theft report leading to measurement errors.
When freight exchange fraud was detected it became possible to report this new type of vehicle
crime which probably led to measurement errors as well. It might be that the linkage with the
freight exchange is not made when freight exchange fraud as component of vehicle crime is
reported. The category freight exchange fraud might be difficult to work with for the police
because a vehicle crime report concerns a missing load, missing vehicle or missing combination
and only by asking specific questions the relation with the freight exchange will be become
clear. This might be the reason that the correlations between vehicle crime and the freight
exchange variables give mixed results about the impact of the freight exchange on vehicle crime.
The variables of the freight exchange show on the one hand positive significant correlations
(posted advertisements) with vehicle crime and on the other hand negative significant
correlations (viewed advertisements and members) with the vehicle crime variables.
Table 4 tries to expand the information about the relations between variables of the freight
exchange, and vehicle crime variables by adding the lagged variables of vehicle crime. We
perform a three lag correlation analysis denoted by L1, for 1 lag, indicating we look back 1
month; L2 for two months; and L3 for three months. The analysis of correlation table 4 will
concentrate on the effects of the freight exchange variables on the components of vehicle crime
and will shortly describe the relation of the vehicle crime variables with their lagged variables.35
The output of table 4 is in accordance with table three. All significant relations found in table 3
continue in the lagged variables of vehicle crime. Different than table 2, combination theft does
not show significant within-variable correlation.
Cargo theft shows a significant positive correlation with its three lags at the 1% significance
level. Vehicle theft does also show a positive significant relation with its second and third lag.
Combination theft does not show significant correlations with its lags.
35
For a more detailed description see sub 3.2.1, table 2 and following.
24
Table 4 Correlation table model II, 2
Correlation table
Variable
Vehicle theft
Vehicle theft
p-value
Vehicle theft L1
p-value
Vehicle theft L2
p-value
Vehicle theft L3
p-value
1
Cargo theft
Combination
theft
Posted
advertisements
Viewed
advertisements
Members
0.569 **
0.031
0.288 *
- 0.400 **
- 0.342 **
0.000
0.817
0.026
- 0.039
0.454 **
- 0.528 **
- 0.429 **
0.769
0.000
0.000
0.001
- 0.011
0.415 **
- 0.536 **
- 0.498 **
0.937
0.001
- 0.250
0.313 *
0.061
0.018
- 0.115
0.589 **
- 0.717 **
- 0.609 **
0.382
0.000
0.000
0.000
- 0.200
0.646 **
- 0.824 **
- 0.710 **
0.130
0.000
0.000
0.000
- 0.095
0.626 **
- 0.802 **
- 0.721 **
0.479
0.000
0.000
0.000
- 0.079
0.549 **
- 0.820 **
- 0.784 **
0.000
0.000
0.000
0.102
0.118
- 0.015
0.440
0.368
0.910
0.219
0.062
0.133
0.047
0.096
0.643
0.315
0.724
0.130
- 0.089
0.124
0.058
0.329
0.508
0.353
0.666
- 0.011
- 0.124
0.100
0.053
0.936
0.357
0.459
0.693
- 0.695 **
- 0.698 **
0.000
0.000
0.091
0.403 **
0.494
0.002
0.342 **
0.009
0.316 *
0.017
0.431 **
0.000
0.267 *
0.045
Cargo theft
p-value
Cargo theft L1
p-value
Cargo theft L2
p-value
Cargo theft L3
p-value
Combination
theft
1
0.692 **
0.000
0.699 **
0.000
0.622 **
0.000
0.557
1
p-value
Combination
theft L1
p-value
Combination
theft L2
p-value
Combination
theft L3
p-value
Posted
advertisements
1
0.002
0.000
- 0.522 **
0.000
p-value
Viewed
advertisements
1
p-value
0.008
0.000
- 0.482 **
0.000
0.807 **
0.000
Members
1
p-value
* significant on a 99% level (0.01), ** signficant on a 95% level (0.05)
25
The variables posted advertisements, viewed advertisements and members show a significant
relation at the 1% significance level with vehicle theft, cargo theft and their lagged variables. In
accordance with table 3, combination theft does not show any significant correlations.
The significant correlations at the 1% level of vehicle theft and cargo theft with their lagged
variables seem to suggest that the same actors are active within a vehicle crime component or
that circumstances did not change. For combination theft we do not find significant results with
its lags in this analysis. It might be that combination theft is also committed by vehicle thieves
and cargo thieves when circumstances allow them to expand their, or switch between, criminal
activities. When combination theft is performed by a variety of actors the within-variable
correlation could be lower due to less consistency. Over the long run consistency might be
higher due to a certain pattern in advantageous criminal circumstances which might explain the
significant correlations of combination theft in table 2.
The significant negative correlation between the amount of members and vehicle crime, and the
amount of viewed advertisements and vehicle crime indicates that those variables follow an
opposite pattern, for example when the amount of members or viewed advertisements
increase, vehicle theft and cargo theft decrease. The amount of posted advertisements is
significantly and positively correlated with vehicle theft and cargo theft.
The significant correlations between the freight exchange variables and the vehicle crime
variables are rather difficult to interpret and do not seem to lead to an unambiguous
explanation. The following hypotheses might explain the results but the suggestions are made
with great reluctance.
1. Transporters mostly contract familiar parties when these contracts do not lead to full
drives it might be the case that demand for road transport is limited. When current
contracts do not offer enough rides, the transporters might start using the freight
exchange to find cargo. This indicates the amount of viewed advertisements and
members increases. Less road transport means fewer chances for a criminal to steal the
load (cargo theft) or to steal the vehicle (vehicle theft). The fact that the correlation is
also significant in all the lagged variables of vehicle theft and cargo theft can be
explained by the cyclical behaviour of the market.
26
2. When the amount of viewed advertisements on the freight exchange increases the
share of criminals watching the advertisements is expected to decline. This means more
correct contracts are closed and less freight exchange fraud is committed. Assuming
that measurement errors are common regarding reporting of freight exchange fraud,
this increase of viewed advertisements is also seen in a decrease of vehicle theft and
cargo theft.
3. Following the same reasoning as we saw at hypothesis 1 we might be able to explain the
positive correlation between posted advertisements and vehicle crime. When demand
for transport is high, subcontractors for loads are sought because they are seen by
transporters as a profit opportunity. By subcontracting a transporter is able to earn on a
load that he does not drive himself. These subcontractors can be found via the freight
exchange which leads to an increase in posted advertisements. When demand for
transport is high we might observe an increase of full drives on the road, which is an
enlarged chance for criminals to succeed. An increase in vehicle theft and cargo theft
might be the result.
3.3.3 Summary and concluding remarks
Summarizing the results of table 1 to 4 we see that:
1. Table 1 shows significant correlations between vehicle theft, cargo theft and
combination theft;
2. Table 2 adds significant within-variable correlations for all vehicle crime variables for all
three lags;
3. Table 3 reveals a different result than table 1 and 2 because combination theft does not
show any significant relations anymore. The relation between vehicle theft and cargo
theft did not change. The freight exchange variables are significantly correlated with
cargo theft and vehicle theft;
4. Table 4 shows that cargo theft and vehicle theft still (see point 2) reveal within- and
between-variable correlation, combination theft does not. The freight exchange
variables are significantly correlated with all lags of cargo theft and vehicle theft.
The within-variable correlations indicate that changes occur in a graduate way. Relating these
results to practice strengthens this preliminary conclusion. Stealing can be seen as the
27
profession of a criminal, and changing behaviour and (criminal) habits will take time. Altering
criminal behaviour can be done by complicating criminal behaviour as is done for example with
the secured parking next to the highway.36 These policy measures take time to implement
resulting in graduate changes and explaining partially the significant correlations with the lagged
variables.
The correlation tables presented in the paragraphs 3.2.1 and 3.2.2 gave us a first glimpse of the
relations that do exist between and within the variables, furthermore the results enabled us to
draw some preliminary conclusions. In the next paragraph a model will be presented which
makes us able to substantiate the preliminary conclusions made in this chapter.
36
Increasing the amount of secured parkings is one of the agreements made in the Tweede Convenant
Aanpak Criminaliteit Transportsector by the different parties concerned in the transport sector.
28
3.3 Model set up
In this paragraph a vector auto regression model is used to obtain knowledge about the
relationships that do exist between the used variables and between the variables and their lags.
The outcome of this model will be able to substantiate the correlation analyses. Two models are
presented, following the structure of the previous paragraphs. The first model uses the three
different components of vehicle crime over the full length of time. The second model covers a
shorter time period but includes the variables of the freight exchange.
The vector auto regression (VAR) model differs from a normal regression in the way that it
portrays the evolution of a set of dependent variables. This set consists of k variables which
represent the variables implemented in the model.37 The development of all the variables is
followed over the same period of time (t = 1, ..., T), which is in our sample the monthly data
starting January 1996 and ending December 201138, and is portrayed as a linear function of only
the variables’ own historic development. The historic development is defined by a certain
amount of lags implemented in the model, referred to as p. The common used formula for a
VAR model of the p-th39 order is:
𝒀𝑡 = 𝒄 + 𝑨1 𝒀𝑡−1 + 𝑨2 𝒀𝑡−2 + … + 𝑨𝑝 𝒀𝑡−𝑝 + 𝒆𝑡
In this formula c is the intercept, which is a k x 1 vector of constants indicating that c should be
seen as a one-column matrix wherein each implemented variable (k) is represented. The vector
Ai must be interpreted as a k x k matrix for every lag included in the model. The vector 𝑒𝑡 refers
to the error term, which is, just like the intercept, a k x 1 vector of error terms. The VAR model
requires the error terms to satisfy the following three conditions in order to be white noise
process40:
1. E(𝑒𝑡 ) = 0;
2. E(𝑒𝑡 𝑒𝑡′ ) = Ω;
′
3. E(𝑒𝑡 𝑒𝑡−𝑘
) = 0.
37
For example in our first model k = 3 because we implement the three components of vehicle crime.
The second model makes use of a shorter time frame namely January 2007 up and until December
2011.
39
In short this can be written as VAR(p).
40 A white noise process is a process without any observable structure.
38
29
These conditions indicate that (1) the error term must be unbiased, so have a mean of zero
furthermore (2) the expected value of the error term matrix must for every 𝑡 be semi positive
definite, so have a value of at least zero; and (3) serial correlation in the individual error terms is
not allowed. The conditions must be obeyed to obtain reliable results. (Franses, 1998)
The VAR model does not only have requirements for the error term but also for the
implemented variables which have to be stationary otherwise results might be spurious.
Stationarity indicates that the implemented variables are independent of time.41 Stationarity can
be tested for, for example by the Dickey-Fuller test. If a variable is not stationary the variable
has to be transformed in order to be able to be implemented in the model.42 (Adkins and Carter
Hill, 2011)
An important aspect of the VAR model is that it uses past observations (lags) to show
relationships between the variables, and to show relationships within a specific variable thus the
relation of the variable and its own past. To get a clear view of these relationships and to be able
to draw the right conclusions, it is very important to obtain the right amount of lags. The
optimum amount of lags is established when no more autocorrelation is found in the error
terms. The Breusch-Godfrey test is used to test for autocorrelation in the error terms and can
therefore be used to retrieve the amount of lags needed for our model. (Adkins and Carter Hill,
2011)
The results of the VAR model will give a clear and non-spurious indication of the relations
between the variables and the variables’ past, unlike the earlier presented correlation tables.
The output of the VAR analysis will indicate which of the implemented variables have significant
impact on the future values of each of the variables implemented in the model. When
interpreting a VAR model it is however not possible to explain the sign and the size of the
coefficient. Furthermore we cannot explain the duration of the effects from a VAR analysis.
(Brooks, 2008) To obtain this knowledge we will perform an impulse response function after the
VAR analysis. The impulse response function will be helpful to retrieve the responsiveness of the
dependent variables in the model to shocks to each of the variables. (Brooks, 2008) We will
41
So differently stated stationarity implies that the unconditional mean, unconditional variance and
autocorrelations of 𝒚𝑡 are constant over time.
42
The variable is than transformed to its first difference which means that a new variable is created which
is the difference of two serial observations, this procedure should make the variable stationary.
30
execute an impulse response analysis after the VAR analysis and present the results after the
VAR output (sub. 4.1 and 4.2). It should however be kept in mind that it is hard to interpret the
results of the impulse response accurately. (Runkle, 1987)
A difficulty with time series variables is that cointegration can be of influence on the output of
the VAR analysis making the results spurious. Time series variables can often be non-stationary
as individual variable but when variables move together over time, the variables are
cointegrating. Cointegrating variables indicate that the time series variables are related in the
long run caused by a certain influence on the time series. A cointegrating relationship may be
seen as a long term equilibrium phenomenon, because in the short run the variables can differ
from each other but in the long run their association becomes clear. (Brooks, 2008) When the
variables are cointegrated, the VAR model is not sufficient and a vector error correction model
(VEC model), based on the VAR model, is needed correct for the cointegration. (Adkins and
Carter Hill, 2011) The VEC model differs from the VAR in the sense that is adds error correction
features. More details on the VEC model will be provided in appendix C. The significance level
(α) throughout the whole analysis is set at 5%.
3.3.1 Model I
The first model makes use of the three components of vehicle crime namely vehicle,
combination and cargo theft over the period January 1996 – December 2011. We base our
hypotheses on the results generated by the correlation tables presented in paragraph 3.2.1
although those might be spurious.
Based on the significantly positive correlations of the variables with their lags, each component
of vehicle crime is expected to show a positive and significant relation with itself in the VAR and
impulse response analysis.
Vehicle theft and combination theft have shown a significant and positive correlation indicating
they influence each other in a positive way. We expect to see this impact in the VAR analysis as
well, so a positive and significant result for the relation between vehicle theft and combination
theft, and the relation between combination theft and vehicle theft.
31
In the correlation analysis cargo theft has shown a significant negative correlation with
combination theft. We expect to see this relation appear in the VAR and impulse response
analysis as well.
The impulse response function learns us more about the duration of the influence of an applied
shock. We expect each significant relation to have an influence of at least six months because of
the time it takes to change behaviour and adapt to newly implemented policy measures.
Before constructing the model we have to deal with the requirement of stationary data. We can
obtain an idea of the stationarity of the variables by looking at the visual representation of the
different components of vehicle crime (figure 3). Recalling that stationarity implies that the
means, variances and covariances of the different variables of the time series data cannot
depend on the period of time in which they are observed. The variables seem stationary over
time because we do not observe a specific pattern in the data.
Vehicle crime
80
Number of reports
70
60
50
40
30
20
10
0
Vehicle theft
Vehicle
and Cargo
theft
Combination
theft
Cargo theft
Figure 3 Three components of vehicle crime graphed from January 1996 – December 2011.
Although we do not observe a specific pattern in figure 3 it does seem plausible that the vehicle
crime variables are stationary because they have shown significant correlations – even at the 1%
32
significance level - with their lags and we furthermore we expect crime to be dependent on
external circumstances occurring in time. The information visualized in figure 3 might therefore
not be enough to be definite about the variables’ stationarity, therefore we test for stationarity
by using a selection of tests including the augmented Dickey-Fuller test and Phillips-Perron test.
We perform more than one test because of the possible erroneous output of the tests. A variety
of different tests should diminish the chance of a spurious outcome. The results of the four tests
on stationarity can be found in appendix D.1.
The test results of the four performed tests reveal that the variables combination theft and
vehicle theft are stationary in their level form. The outcomes of the stationarity tests for cargo
theft are ambiguous, revealing stationary as well as non-stationary outputs. Cargo theft is
stationary in all tests, when transformed to its first difference. The mixed results of the
stationary tests for cargo theft leads us to the decision to perform the analysis twice, the first
analysis will include the variables in their level form; the second analysis, found in appendix D.4,
includes the variable cargo theft transformed to its first difference.
The amount of lags43 we have to implement in our model is two, which results in the following
formula for the first model:
𝑦𝑣𝑒,𝑡
[𝑦𝑐𝑜,𝑡 ] =
𝑦𝑐𝑎,𝑡
𝑎1𝑣𝑒,𝑣𝑒
𝑐𝑣𝑒
[𝑐𝑐𝑜 ] + [𝑎1𝑐𝑜,𝑣𝑒
𝑐𝑐𝑎
𝑎1𝑐𝑎,𝑣𝑒
𝑎1𝑣𝑒,𝑐𝑜
𝑎1𝑐𝑜,𝑐𝑜
𝑎1𝑐𝑎,𝑐𝑜
2
𝑎1𝑣𝑒,𝑐𝑜 𝑦𝑣𝑒,𝑡−1
𝑎𝑣𝑒,𝑣𝑒
2
𝑎1𝑐𝑜,𝑐𝑎 ] [𝑦𝑐𝑜,𝑡−1 ] + [𝑎𝑐𝑜,𝑣𝑒
𝑦
2
𝑐𝑎,𝑡−1
𝑎1𝑐𝑎,𝑐𝑎
𝑎𝑐𝑎,𝑣𝑒
2
𝑎𝑣𝑒,𝑐𝑜
2
𝑎𝑐𝑜,𝑐𝑜
2
𝑎𝑐𝑎,𝑐𝑜
2
𝑎𝑣𝑒,𝑐𝑜
𝑦𝑣𝑒,𝑡−2
2
𝑎𝑐𝑜,𝑐𝑎 ] [𝑦𝑐𝑜,𝑡−2 ]
𝑦𝑐𝑎,𝑡−2
2
𝑎𝑐𝑎,𝑐𝑎
𝑒𝑣𝑒,𝑡
+ 𝒟𝐵𝑟𝑒𝑎𝑘 + [𝑒𝑐𝑜,𝑡 ]
𝑒𝑐𝑎,𝑡
This can be rewritten as:
̂ 1 𝒀𝑡−1 + 𝚽
̂ 2 𝒀𝑡−2 + 𝒟𝐵𝑟𝑒𝑎𝑘 + 𝒆̂𝑡 ;
𝒀𝑡 = 𝒄̂ + 𝚽
where 𝑦𝑡 = (𝑣𝑒𝑡 , 𝑐𝑜𝑡, 𝑐𝑎𝑡 )’ in which 𝑣𝑒 stands for vehicle theft, 𝑐𝑜 for combination theft and 𝑐𝑎
for cargo theft. In the model a dummy (𝒟𝐵𝑟𝑒𝑎𝑘 ) is implemented to correct for the new
measurement method which is implemented at the KLPD in 2010.
43
The results of the Breusch-Godfrey test can be found in Appendix D.2.
33
3.3.2 Model II
The second model makes use of six variables gathered over a regular monthly time interval
starting January 2007 and ending December 2011. The variables which will be implemented in
the model are cargo theft, vehicle theft and combination theft and, as exogenous variables, the
three variables offered to us by the freight exchange namely the amount of members, the
amount of viewed advertisements, and the amount of posted advertisements. By adding the
variables of the freight exchange as exogenous variables we want to learn more about the
influence of the freight exchange on vehicle crime and hope to be able to tell something about
freight exchange fraud. We have generated hypotheses based upon the results of our
correlation analysis (sub 3.3.2) although we are aware of the fact that those might be spurious.
Based on the significantly positive correlations between of the variables cargo theft and vehicle
theft with their lags variables (table 4) we expect that vehicle theft and cargo theft show a
positive and significant relation with itself in the VAR and thereto belonging impulse response
analysis.
We expect a positive significant relation between posted advertisements and cargo theft and
between posted advertisements and vehicle theft because of the significant correlations (tables
3 and 4).
We expect a negative significant relation between viewed advertisements and cargo theft and
between viewed advertisements and vehicle theft. Furthermore we expect a negative significant
relation between members and vehicle theft and between members and cargo theft.
The impulse response function informs us about the duration of the influence of an applied
shock. We expect each significant relation to have an influence of at least six months because of
the time it takes to change behaviour and adapt to newly implemented policy measures.
Before constructing the model we have to cope with the stationarity requirement which is also
applicable for exogenous variables. The vehicle crime variables are already tested on stationarity
(sub 3.3.1, appendix D.1). The stationarity tests of the freight exchange variables will be
performed in the same way as we did with the vehicle crime variables.44 The four tests for
stationarity can be found in appendix E.1. The tests reveal that the variables of the freight
44
Due to a confidentiality agreement we are not able to show the variables graphed in a figure.
34
exchange are non-stationary in their level form but are stationary in their first differences. This
might be an indication for cointegrating relationships amongst those variables. Because there
are more than two variables, more than one linear relationship between the variables could
exist. The Johansen test for cointegration is able to reveal more than one cointegrating
relationship, if present. (Brooks, 2008) A requirement for the cointegration test is that variables
can only be implemented in the test if they are non-stationary in their level form but are in first
difference. The variables meet this requirement and we are able to conduct a cointegration test.
The H0 of the Johansen test of at most one cointegrating vector is rejected.45 Therefore an error
correction for two cointegrating vectors should be implemented in the VEC model. When
implementing one lag, the VEC model will look like this in formula form:
𝚫𝟏 𝒀𝑡 = 𝒄 + 𝚪1 𝚫𝟏 𝒀𝑡−1 + 𝒟𝐵𝑟𝑒𝑎𝑘 + 𝑝𝑎𝑡 + 𝑣𝑎𝑡 + 𝑚𝑡 + 𝚷𝒀𝑡−𝑝 + 𝒆𝑡
where 𝑦𝑡 = (𝑣𝑒𝑡 , 𝑐𝑎𝑡 , 𝑐𝑜, )’ in which 𝑣𝑒 stands for vehicle theft, 𝑐𝑎 for cargo theft, 𝑐𝑜 for
combination theft. The variables of the freight exchange are added as exogenous variables in
which posted advertisements is denoted as 𝑝𝑎𝑡 , viewed advertisements as 𝑣𝑎𝑡 and members is
represented by 𝑚𝑡 . Just like in model I a dummy (𝒟𝐵𝑟𝑒𝑎𝑘 ) is implemented to correct for the data
break in 2010.
Unfortunately we are not able to perform this VEC model since the statistical program Eviews
(version 7.0) cannot correct for cointegrating relations in implemented exogenous variables.46
Therefore we have decided to perform a VAR model with the three freight exchange variables
added as exogenous variables in their first difference.47 We are aware that usage of the VAR
model instead of the VEC model might influence our results, therefore we will be reluctant
when interpreting the results and we might be able to draw some preliminary conclusions.
We will execute the VAR analysis twice, following the working method of paragraph 3.3.1, the
first VAR model will implement cargo theft in its level form, the second VAR model with cargo
theft in its first difference. The Breusch Godfrey test indicates for our first model that we have
45
Results of the Johansen cointegration test can be found in appendix E.3.
For more information see the technical support helpdesk of the statistical computerprogram Eviews:
http://forums.eviews.com/viewtopic.php?f=4&t=5340.
47
The VAR model requires implemented variables to be stationary. The variables of the freight exchange
are implemented in their first difference because they are only stationary in their first difference.
46
35
to implement zero lags.48 Implementing zero lags is inconsistent with the main idea behind the
VAR analysis. Recall that VAR stands for vector autoregression, which is a statistical method that
makes use of multiple time series variables with the aim to explain the development of the
variables by looking at the lags of the variable itself and the lags of the other variables
implemented in the model.49 Lagged variables are an essential part of the VAR analysis and
therefore we cannot perform a VAR without lagged variables.
For the model which implements cargo theft in its first difference the Breusch Godfrey test
indicates that we need to implement one lag in the VAR model which leads to the following
formula:
̂ 1 𝒀𝑡−1 + 𝒟𝐵𝑟𝑒𝑎𝑘 + 𝑑𝑝𝑎𝑡 + 𝑑𝑣𝑎𝑡 + 𝑑𝑚𝑡 + 𝒆̂𝑡 ;
𝒀𝑡 = 𝒄̂ + 𝚽
In this formula a dummy (𝒟𝐵𝑟𝑒𝑎𝑘 ) is implemented to correct for the new measurement method
which is implemented at the KLPD in 2010. The three freight exchange variables are denoted as
𝑑𝑝𝑎𝑡 for the first difference of posted advertisements; 𝑑𝑣𝑎𝑡 for the first difference of viewed
advertisements, and 𝑑𝑚𝑡 for the first difference of members. In the VAR(1) 𝑦𝑡 = (𝑣𝑒𝑡 , 𝑐𝑜𝑡, 𝑑𝑐𝑎𝑡 )
in which 𝑣𝑒 stands for vehicle theft, 𝑐𝑜 for combination theft and 𝑑𝑐𝑎 for the first difference of
cargo theft.
48
The VAR model which is tested for by the Breusch Godfrey test does include the vehicle crime variables
in their level form. The results for the lag selection test can be found in appendix E.2.
49
Autoregression can be divided in the word ‘auto’ which means self, and ‘regression’ which means
return to an earlier stage. This translation indicates the importance of lagged variables in the VAR
analysis.
36
Chapter 4: Results and analysis
In this chapter we will discuss the results of the VAR analysis of model I and II, as presented in
the previous chapter (sub 3.3.1 and 3.3.1). This chapter is divided into three paragraphs; the first
two paragraphs will present the results of the VAR analysis of model I and II respectively. The
results of the VAR are followed by the outcome of the associated impulse response functions to
show the effect of structural shocks on the dependent variables in our model. The last
paragraph will present a descriptive analysis of the freight exchange fraud data.
4.1 Model I50
The results of the VAR(2) analysis of model 1 can be found in table 5.
Table 5 VAR(2) analysis model I
Vector Auto Regression (2 lags)
Observations: 190
Variable
Vehicle L1
Vehicle theft
Cargo theft
Combination theft
0.233 *
- 0.116
0.104 *
0.078
2.998
0.108
- 1.073
0.048
2.169
0.183 *
- 0.205
0.138 *
0.082
2.240
0.113
- 1.816
0.050
2.753
Cargo L1
- 0.018
0.295 *
- 0.038
Standard error
t-statistic
0.052
- 0.349
0.072
4.074
0.032
- 1.166
Cargo L2
0.006
0.336 *
- 0.049
Standard error
t-statistic
0.053
0.109
0.074
4.564
0.033
- 1.486
Combination L1
0.176
0.171
0.233 *
Standard error
t-statistic
0.126
1.399
0.174
0.985
0.077
3.010
Combination L2
0.162
- 0.003
0.090
Standard error
t-statistic
0.122
1.327
0.169
- 0.016
0.075
1.200
Standard error
t-statistic
Vehicle L2
Standard error
t-statistic
Constant
8.861 *
10.509 *
3.937 *
Standard error
t-statistic
2.054
4.315
2.840
3.700
1.265
3.112
Dumbreak
4.207
21.145 *
0.826
Standard error
t-statistic
2.934
1.434
4.058
5.211
1.807
0.457
* signficant on a 95% level
50
Results of the VAR model including cargo theft in its first difference are comparable to the results
presented in this paragraph. Results of model I including cargo theft in its first difference can be found in
appendix D.4.
37
8
8
4
4
Response to Cholesky One S.D. Innovations ± 2 S.E.
The VAR(2) output is complemented by the thereto0belonging impulse response analysis.51 We
0
Response of CARGO to VEHICLE
Response of CARGO to COMBINATION
are able to explain significant relations, signs and effects of the different variables when
12
22
12
-4
combining
2
4
6 both
8 10outputs.
12 14
24
16
18
20
22
2
8
4
6
8
10
12
14
16
18
20
22
24
8
O
Response of VEHICLE to VEHICLE
4
-4
24
Response of VEHICLE to COMBINATION
The VAR analysis shows a significant result of
8
4
8
vehicle theft and its two lags. The impulse
0
6
6
0
response
function shows us that, considering the
4
sign of4 the response to the applied shock, vehicle
-4
24
-4
2 2
4
6
8
10
12
14
16
18
20
22
0
Response of VEHICLE to VEHICLE
24
4
8 10 12 14 16 18 20 22 24
always22 has
a 6positive
impact on itself since the
impulse
response
is positive.
The effect of a
0 Response
of VEHICLE
to COMBINATION
8
shock
dies down after about seven months.
8
-2
22
24
RGO
-2
2
6
10
12
14
16
18
20
22
24
8
10
12
14
16
18
20
22
24
6
4
2
4dies down which indicates that our model is
0
about seven months. (figure 5) After that, the shock
stable. The impact of the variable cargo theft on -2itself is positive as we can observe in the graph.
2
4
6
8
10
12
14
16
18
20
22
2
2
24
4
6
8
10
12
14
16
18
20
22
24
Response to Cholesky One S.D. Innovations ± 2 S.E.
0
Response
of COMBINATION
to COMBINATION
Response of CARGO
to VEHICLE
12
Re
12
6
-2
12
-2
24
4
2
8
2
0
4
6
8
10
12
14
16
18
20
22
24
2
4
6
8
10
12
14
16
18
20
22
24
4
8
8
4
2
4
4
0
0
0
0
-4
-2
24
6
function adds to this observation that a shock 2applied to cargo theft will be of influence for
6
22
4
Response of COMBINATION to COMBINATION
4
0 Response of Response
COMBINATION
to VEHICLE
of CARGO
to CARGO
O
2
6
Response of COMBINATION to VEHICLE
6
-2
8
The VAR analysis gives significant results for cargo theft and its two lags. The impulse response
2
24
6
Figure 4 Impulse response function VAR(2) model I
4
0
4
2
4
-4
-2
62
84
6
10
8
12
10 16
12 18
14 20
16 22
18
14
20
24 22
24
Response
of VEHICLE
to CARGO
Figure 5 Impulse
response
function VAR(2)
model I
2
-4
4
26
48
610 812 10
14 12
16 1418 1620 1822 2024 22
24
2
Response
of VEHICLE
to VEHICLE
Figure 6 Impulse response
function
VAR(2) model
I
8
Re
8
8
The VAR analysis revealed a significant result for combination theft and its first lag. A shock
6
6
6
4
4
2
2
applied to the variable combination theft results in a positive effect which has a duration of
4
about
3 months after which the shock dies down.
2
51
The full impulse response function of model I can be found in Appendix D.3, in the main text only the
0
0
impulse
responses of the significant relations are shown.
38
-2
0
-2
2
4
6
8
10
12
14
16
18
20
22
24
Response of COMBINATION to CARGO
-2
2
4
6
8
10
12
14
16
18
20
22
24
Response of COMBINATION to VEHICLE
2
Respo
-4
6
8
10
12
14
16
18
20
22
24
-4
2
esponse of VEHICLE to CARGO
4
6
8
10
12
14
16
18
20
22
24
2
Response of VEHICLE to VEHICLE
4
6
8
10
12
14
16
18
2
Response of VEHICLE to COMBIN
8
8
6
6
The relation between combination theft and vehicle theft is the only relation between the
4
vehicle crime variables that
is significant in the VAR analysis. When looking at4the VAR output
2 between combination theft and the first and second
2 lag of vehicle
we see a significant result
theft. The impulse response
analysis adds that the response of combination 0theft to vehicle
0
theft is positive for the first
seven months and does than die out. This indicates -2that the result of
-2
6
8
10
12
14
16
18
20
22
24
2
4
6
8
10
12
14
16
18
20
22
24
ponse of COMBINATION to CARGO
Response of COMBINATION to VEHICLE
8
10
12
14
16
18
20
22
6
4
4
2
2
0
0
24
4
6
8
10
12
14
16
18
2
Response of COMBINATION to COM
6
-2
6
2
combination theft is partially dependent on the development of vehicle theft.
-2
2
4
6
8
10
12
14
16
18
20
22
24
2
4
6
8
Figure 7 Impulse response function VAR(2) model I
Relating the output of the VAR analysis and impulse response analysis to our hypotheses (sub
3.3.1) we can conclude that most of our hypotheses are confirmed. Each component of vehicle
crime does show a significant and positive relation with its own lagged variables. This
strengthens our idea of the same actors being active in a certain component or that
circumstances do not change. Combination theft is only significantly related to its first lag,
indicating a lesser dependence on past results than vehicle theft and cargo theft show. This
might be because of the fact that combination theft is a difficult category to report. More than
one party may be concerned with the theft. Furthermore could this result be an indication that a
variety of parties is active in the combination theft component, making the result less
dependent on each other.
The correlation analysis showed a significant positive correlation between vehicle theft and
combination theft which resulted in a similar hypothesis. This hypothesis is confirmed; the
output of the VAR analysis shows that combination theft is significantly related to the two lags
of vehicle theft. The impulse response function shows that the impact of vehicle crime on
combination theft is positive and that a shock applied to vehicle theft will be visible for around
39
10
12
14
16
18
2
seven months in combination theft. Furthermore this result adds to the correlation analysis that
combination theft is dependent on vehicle crime but vehicle crime is not dependent on
combination theft. This result might indicate that the criminals active in vehicle theft are able to
switch to combination theft. An incident in vehicle theft will than result in an effect in
combination theft. This idea is strengthens when we look at the impulse response functions.
We expected the duration of shocks to be 6 months based on the time it takes to adapt to new
policy measures. This hypothesis is confirmed for vehicle theft, cargo theft and combination to
vehicle theft. Each of these impulse response functions show us a timeframe of the shock to
work out of the system of about seven months. Combination theft shows a timeframe of about
two months indicating that combination thefts adapt quicker than the other vehicle crime
components. This result might be an indication that combination theft might be performed by
different parties for example by vehicle theft criminals. When the environment is advantageous
a vehicle criminal might take the cargo as well. Severing the circumstances might than lead to a
shift making it plausible that combination theft adapts quicker to shocks.
We expected to see a significant relation between cargo theft and combination theft but the
VAR output does not show any significant output between cargo theft and combination theft.
Relating these results to the correlation analysis (3.2.1, tables 1 and 2) we see that:
1. All within-variable correlation is supported by the VAR analysis;
2. Between-variable correlation was significant for all vehicle crime components. The VAR
only reveals a significant relation between vehicle theft and combination theft but adds
a causality to it.
40
4.2 Model II
The results of the VAR(1) of model 2 can be found in table 6.
Table 6 VAR(1) analysis model II
Vector Auto Regression (1 lags)
Observations:
Variable
Vehicle theft
D.Cargo theft
Combination theft
Vehicle L1
- 0.181
- 0.253
- 0.040
Standard error
t-statistic
0.147
- 1.231
0.346
- 0.732
0.085
- 0.471
Cargo L1
- 0.008
- 0.522 *
- 0.043
Standard error
t-statistic
0.056
- 0.148
0.133
- 3.923
0.033
-1.323
Combination L1
0.085
0.459
0.260
Standard error
t-statistic
0.241
0.353
0.569
0.806
0.140
1.858
22.048 *
0.946
7.261 *
3.557
6.199
8.391
0.113
2.062
3.521
7.949 *
3.614
- 0.003
2.079
3.823
4.905
0.737
1.206
- 0.002
2.38E-05
1.21E-04
1.17E-05
3.9E-05
0.607
9.2E-05
1.310
2.3E-05
0.517
Constant
Standard error
t-statistic
Dumbreak
Standard error
t-statistic
D.Posted advertisements
Standard error
t-statistic
D.Viewed advertisements
6.16E-06
2.01E-05
-1.35E-06
Standard error
t-statistic
1.0E-05
0.601
2.4E-05
0.830
5.9E-06
-0.228
D.Members
-0.045
0.087
- 0.024
Standard error
t-statistic
0.075
- 0.597
0.177
0.488
0.044
- 0.540
* signficant on a 95% level
Most of the hypotheses formulated in sub 3.3.2 cannot be confirmed because the output of the
VAR analysis of model II contains only one signficant result. The first difference of cargo theft is
significantly related to its first lag. The transformation of the variable has effect on the impulse
response analysis which shows us a positive as well as negative influence. Such a graph is hard
to interpret correctly (Runkle, 1987). We can derive from the graph however that the impulse
response function is stable since the shock works it way out of the system and dies down after
five months. Another conclusion we can draw is that the variable cargo theft does have a
significant impact on itself.
41
Response to Cholesky One S.D. Innova
Response of DIF_CARGO to DIF_CARGO
Response of DIF_CARGO to VEH
20
20
10
10
0
0
-10
-10
-20
-20
2
4
6
8
10
12
14
16
18
20
2
Response
VEHICLE
to DIF_CARGO
Figure
8 Impulseof
response
function
VAR(1) model II
8
4
6
8
10
12
14
16
Response of VEHICLE to VEHI
8
We build this model because we wanted to know more about the relation between the freight
6
6
exchange and vehicle crime
but we cannot confirm or reject our hypotheses because
the VAR
4
4
output does not show any
significant results between freight exchange variables
and vehicle
2
2
crime variables.
0
0
-2
-2
-4
-4
It might be that because
of the
small
sample
(60 12
observations)
significant
results
are limited.
2
4
6
8
10
14
16
18
20
2
4
6
8
10
12
14
16
Also the fact that we were not able to implement the cointegrating relationship in the
Response of COMBINATION to DIF_CARGO
exogenous variables might
be of influence on the results.
6
Response of COMBINATION to VE
6
4
4
Relating the result of this VAR analysis to our correlation analyses and VAR model I we can see
2
2
that only the within-variable
correlation of cargo theft is able to endure all tests
while all other
significant effects dissapeared
0
4.3 Freight exchange-2 fraud
–4 descriptive
2
6
8
10
0
-2
12
14
16
18
20
2
4
6
The phenomenon of freight exchange fraud is known since 2008 but because of the small
amount of yearly reports a thorough statistical analysis is not possible, this paragraph will
therefore give a description of the available data of freight exchange fraud. Tables containing all
information can be found in appendix F. All reports are from the Netherlands and made
available by the KLPD.
In 2008 freight exchange fraud is reported eight times. In the majority of the reports, seven
cases, the missing cargo concerned electronics. All frauds were committed in the spring and the
winter in the midst week and halve of the crimes were reported in Limburg. The preliminary
conclusion might be drawn with great reluctance that the south of Netherlands is at greater risk.
42
8
10
12
14
16
This conclusion seems plausible while the borders of the Netherlands are found there so that
thefts can be moved out of the country very easily making retrieving more difficult due
inadequate jurisdiction.
In 2009 only three reports of freight exchange fraud are made in the Netherlands. Noticeable is
that two of the three reports concern a transport towards the Netherlands while in 2008 all
reports concerned a trip from the Netherlands. The type of cargo is also different than in 2008,
now alcoholic beverages and food supplies are missing. All reports are from the spring and
summer months. Although the goods stolen are less valuable they are still easily transferable
and therefore probably of great value for the criminals. Furthermore high value loads are often
better secured than food supplies making the latter easier to steal.
In 2010 the amount of reports came up to the level of 2008 with a total of thirteen reports. With
reluctance we conclude that we observe a spread in region, month and day of committing of the
crime and type of targeted cargo; still we might observe a slide preference for the end of week
(Wednesday – Friday) and March as a popular month. The missing cargo varies from electronics,
alcoholic beverages, food supplies and car components amongst others. The idea rises that the
type of good seems to be not the main consideration when preparing a crime.
In 2011 the increase in reports continues and reaches the amount of seventeen. In 2011 it is
difficult to point out the most popular crime committing month as the spread seems quit even.
The days of the week do not show a clear trend, but the end of the week (Wednesday –
Thursday) still seems most attractive for committing a crime. Noticeable is though that in 2011
crimes are also committed on weekend days, while that was not the case in the months before.
Most of the missing cargo concerns food supplies.
A graphed representation, allocated in percentages, of the committed crimes subdivided in
months, days and region respectively can be found in figures 9 - 11.
43
Percentage
70%
60%
50%
40%
30%
20%
10%
0%
2008
2009
2010
2011
Month
Percentage
Figure 9 Freight exchange fraud montly percentage allocation
70%
60%
50%
40%
30%
20%
10%
0%
2008
2009
2010
2011
Day
Figure 10 Freight exchange fraud daily percentage allocation
60%
40%
30%
Unknown
Noord-Holland
Zuid-Holland
Zeeland
Limburg
Brabant
Utrecht
Flevoland
2010
Gelderland
2009
0%
Overijsel
10%
Drenthe
2008
Friesland
20%
Groningen
Amount
50%
Region
Figure 11 Freight exchange fraud percentage allocation per region
44
2011
Although it is difficult to say something about trends in such a small dataset our preliminary
conclusion regarding the region (figure 14) is that the southern provinces of the Netherlands are
most often hit by freight exchange fraud. A possible explanation for this might be that those
provinces are close to the border (Belgium, Germany). Although the European Union aims at
European unity and collaboration police forces of different countries do not cooperate very
easily. So when stolen goods cross the border it is far more difficult to instantly act upon and
trace the goods. The highways in the southern regions (A2, A67, and A73) are important trade
routes indicating that a lot of cargo passes those regions increasing the chance of vehicle crime
incidents. Furthermore do the graphs seem to show a slight preference for March as a popular
month and Wednesday as a day of interest for freight exchange fraud.
It is interesting to see (Appendix F) that there seems no explicit interest in a typical type of
cargo. The stolen goods vary from expensive goods like electronics and copper to alcoholic
beverages and food supplies. The stolen goods do have in common that they are all easily
transferable. The spread of goods stolen did appear later on. In 2008 only electronics were
stolen en in 2009 just food and alcoholic beverages (Appendix F.1 and F.2). Reason to change
the targeted goods might be due to the fact that national transport companies do know each
other quite well and that a new type of crime which seems to focus on electronics is spread fast
by the word of mouth. Maybe the reaction of transport companies was by adding additional
security on electronics drives which made the electronics market more difficult to target which
lead to a switch.
The rise of freight exchange fraud did change vehicle crime by adding a new dimension to it, the
internet. Although the observations made in this chapter cannot add statistical significant
conclusions to our research they do add a first insight in the fourth component of vehicle crime.
45
Chapter 5: Conclusion and recommendations
This chapter will present in the first paragraph the conclusions of the research, followed by
policy recommendations and will conclude with limitations and areas for further research.
5.1 Conclusions
This thesis began with the aim to start academic research into freight exchange fraud and
enhance understanding of vehicle crime in the Netherlands, in order to answer the main
research question:
Is freight exchange fraud an addition to vehicle crime?
We will come to our conclusion by looking back on the obtained knowledge in each of the
previous chapters.
Liberalisation of EU freight transport lead to the opportunity for the freight exchange to
enhance efficiency but also came along with a new form of crime, freight exchange fraud. The
freight exchange and freight exchange fraud were examined based on conversations with
experts and inspection of police reports. This research resulted in the following definition of
freight exchange fraud:
Freight exchange fraud is the deliberate misuse of the freight exchange with the aim to steal
cargo to fund other activities of the criminal organization.
According to our definition, the target of freight exchange fraud is cargo. Based on the
definition, freight exchange fraud does not seem an addition to vehicle crime but an innovation
in vehicle crime. Cargo criminals seem to have enlarged their scope by involving the freight
exchange as addition source of knowledge. The different working methods of freight exchange
fraud – false document companies, company take-overs and infiltrators – seem to be complex,
but lucrative, ways to prepare cargo thefts.
We have performed a variety of correlation analyses. The first two analyses revealed that the
components of vehicle crime – cargo theft, vehicle theft and combination theft – significantly
depend on each other. In addition to significant between-variable correlation, also significant, at
46
the 1% significance level, within-variable correlation with all three lags is found for each of the
vehicle crime variables.
The significant within-variable correlation seems to suggest that actors are the same within a
specific component of vehicle crime or that the circumstances did not change and that the
criminals keep on performing a certain crime. The significant between-variable correlation might
on the one hand plea for the same actors and on the other hand for different actors. From this
result we might be able to conclude, although with great reluctance, that when actors are
different, freight exchange fraud is expected to be an addition to vehicle crime. When the actors
are the same, this would indicate that freight exchange fraud is perceived as an innovation to
vehicle crime.
We also included some variables from the freight exchange in our correlation analysis (sub. 3.2.2
tables 3 and 4) and cargo theft and vehicle theft showed significant correlations with all three
variables of the freight exchange. The freight exchange and vehicle crime are related, a result
which could indicate that freight exchange fraud is an innovation in vehicle crime rather than an
addition to it.
Interpreting correlation results of time series variables has to be done with great reluctance.
Therefore we performed a vector autoregression with an impulse response analysis for model I
and model II in chapter four. The vector autoregression and impulse response analysis showed
that all three vehicle components generated positive significant results with their lagged
variables, in accordance with the results of the correlation analysis. Between the variables only
combination theft and vehicle theft showed a significant relationship. The second model only
revealed a significant within-variable relationship of cargo theft.
Relating the results of the VAR analyses to our main research question we know that withinvariable relations are significant which is an indication for the same group of criminals being
active in a certain crime component and for circumstances to be unchanged. The relation
between combination theft and vehicle theft seems to suggest that combinations are stolen
when the circumstances make it possible. Combination theft does therefore seem to consist of
47
experts in vehicle theft and perhaps also out of cargo theft experts.52 Based on these results we
cannot conclude that there is, or is not, an additional party active in vehicle crime
In the last paragraph of chapter four freight exchange fraud is described based on the Dutch
police reports. It is interesting to see that a wide variety of cargo is stolen from food supplies to
used tires but that vehicle theft or combination theft is not mentioned. The descriptions of
paragraph 4.3 could indicate that freight exchange fraud is an innovation of cargo theft. The
cargo criminals make use of modern techniques to trace down valuable cargo and by using
different fraud-techniques they are able to steal numerous goods in a short period of time.
This research did not reveal an unambiguous answer to our main research question. Although
some results seem to indicate that freight exchange fraud is no addition to vehicle crime but an
extension of the vehicle crime component cargo theft. By trying to answer this research
question we did succeed in enhancing the knowledge of vehicle crime. This thesis pioneered in
defining and describing freight exchange fraud. By analyzing how the freight exchange could
evolve and how it works we were able to retrieve the origin of freight exchange fraud which
helps the understanding of vehicle crime. The obtained knowledge is not only applicable on
national but also on international road transport due to the international character of crime.
5.2 Policy recommendations
Vehicle crime and freight exchange fraud unfortunately became a familiar aspect of the current
transport market, which is not only recognized by the (inter)national police, but also by trade
organizations and policy makers. Based on the results of the analyses and conversations we
composed some policy recommendations.
The significant results generated by VAR analysis and impulse response function enhanced our
knowledge about vehicle crime. The analysis shows that it is very important to monitor the
different types of crime and collect data. The data regarding vehicle crime should be as
complete as possible and constantly kept up to date. It is therefore recommended that the
different (inter)national police forces work together and share information. Difficulties for the
police are however found in legislation which hardens the cooperation opportunities between
different (inter)national parties. Rules for privacy protection prevent the ability to share
52
See appendix D.4 for the VAR analysis of model I including cargo theft in its first difference.
48
knowledge and also international cooperation requires formal procedures which take a long
time and complicated police actions. We therefore recommend policymakers to reconsider
current legislation.
At the moment different police forces in the Netherlands are actively dealing with crime, since it
is possible to report crime at every police station. The reports and thereto belonging
information concerning vehicle crime are collected and processed at one central point, at the
Landelijk Team Transport (LTT) of the KLPD. In other European countries the police force is not
as strictly organized as in the Netherlands, leading to splintered parts of information across
different parties. The international police force, Europol, has the ability to generate a European
coordinated action against criminal organizations but will only start an investigation if there is
evidence for criminal organizations which are active in different European member states. It
therefore seems recommendable for national governments to revise their police systems and
add expertise clusters in order to decline information asymmetry across different parties.
Not only keeping track of the knowledge on criminal behaviour is important but also accurate
sharing and acting upon the collected knowledge is of great importance. Transport companies
can be protected when all related parties are transparent and communicate on a regular basis.
The professional trade organizations look after the concerns of transporters, they can be
influential and are able to address a substantial part of road transport companies. When such an
influential party receives up to date information about a vehicle crime threat, it can quickly act
upon it by immediately warning its members. We recommend policymakers to look at
opportunities to enable such an instant warning system. We advise the different parties
involved in such a warning system to invest in relations and transparency.
There are difficult procedures of the legal transfer of risks and ownership and a wide amount of
(inter)national legislation applicable on the transport sector. These factors, together with the
real risks a transporter experiences on the road, create the need for insurance. Insurers fulfil an
important role in the transport sector since they take over parts of the risk of the transporter
against the payment of a premium. However, the complex laws regarding road transport make it
difficult for transporters to retrieve loss compensation when their load is stolen. This is
reinforced by the contrary interests of the insurer and the insured. Jaap Stalenburg, employee at
TVM Insurance Company argues that transport companies are too easily captured by criminals.
49
According to Stalenburg transport companies need to think before they act.53 He praises the
freight exchange for their safety measures. The private Belgium detective Wim DeKeyser does
not completely agree. He mentions that freight exchanges promote themselves with their
excellent security checks and safety measures which lead to the wrongful thought among
members that the customers of the freight exchange are all screened and watched, and
therefore trustworthy.54 The expression of both opinions makes clear that transparency is
needed.
Based upon the previous paragraph we recommend freight exchanges to be clear towards their
members; not only about what they screen but also what they screen for. An investigation in
how current information is perceived at the members of the freight exchange might help in
enhancing transparency, because it can reveal where the members’ assumptions do not stroke
with the freight exchanges’ meaning. Members of the freight exchange might have too much
confidence in the safety checks.
Although freight exchange fraud is only a very small percentage of all deals closed via the freight
exchange the freight exchange might be able to diminish this number any further. In paragraph
2.2 the procedure of closing deals on the freight exchange is explained, after placing the
advertisement, the contact is made outside the freight exchange. By obliging members to make
the first contact via the website of the freight exchange, an extra first safety check is
implemented and the transporter knows that he is doing business with a real freight exchange
member. This could be done by only releasing the contact details after the first contact. The
freight exchange retrieves more information about their members and to what extent they use
the freight exchange and website. Transporters gain as well, because by following this working
method they are at least sure the company is a member of the freight exchange.
Transporters are advised to stay critical and alert when closing a contract, with or without
interference of the freight exchange. When transporters notice that a potential business partner
only works with a free mail address or is only reachable by mobile phone a transporter should
be alerted and do some additional checks. The warnings provided by experts55 such as police,
53
Met gezond verstand de digitale vrachtbeurs op, p. 10.
Work cited (note 42), p. 11.
55
Warnings can for example be found in industry-specific magazines such as the Belgian magazine Private
Veiligheid •Sécurité Privée (nr 50, 2011) in which the Belgium private investigator Wim DeKeyser raises
54
50
freight exchange and professional trade unions to transport companies should alert transporters
and make them more aware of the dangers, on order for them to be better prepared.
5.3 Recommendations for further research
The results of our analysis enhanced our knowledge about vehicle crime. We researched freight
exchange fraud, but based our conclusions on information about cargo theft, vehicle theft and
combination theft. We would recommend repeating this research in the future when there is
more data available on freight exchange fraud to verify the conclusions made in this research.
One of the mayor limitations of this research is that the data used for the statistical analysis
solely makes use of Dutch police reports concerning vehicle crime. Liberalisation of the EU and
the rise of the internet enhanced the possibility for transport to cross national borders. As a
consequence vehicle crime became more international as well. Although we are aware of the
international aspect of crime it is left unaccounted in current research. We therefore
recommend including European vehicle crime reports when repeating this research.
Model II (sub 4.2) resulted in only one significant result. This could be due to the fact that the
analysis could not be performed as it should be. When statistical programs allow us to perform
the test we aimed at the results might be different. Furthermore it could be that the lack of
results is due to the amount of available data. The model included data from the freight
exchange starting January 2007 up till December 2011 on a monthly base, which is only 60
observations per variable. Making use of a larger timeframe might enhance the results.
This thesis tried to investigate the upcoming phenomenon of freight exchange fraud. Different
parties56 active in the field of vehicle crime agreed on the fact that freight exchange fraud is a
rising problem but we were not able to support or reject the idea of growth of freight exchange
fraud with a statistical significant analysis. The limited availability of data on the freight
exchange fraud complicated a thorough analysis of freight exchange fraud. We recommend
investigating freight exchange fraud in a few years when more data is available. Cooperation
with other countries in data collection is expected to speed up the possibility to investigate
freight exchange fraud.
awareness for freight exchange fraud; or in the Transport en Logistiek, the magazine of industry
association Transport Logistiek Nederland, warns Hélène Minderman thransporters for vehicle crime.
56
Including the KLPD, Bovenregionale recherche Zuid-Nederland and DeKeyser B.V.B.A. amongst others.
51
This thesis followed an economic approach; nevertheless other disciplines did gain some
attention as well. For a more complete view on vehicle crime and in particular freight exchange
fraud, we advise to study this subject again from a different point of view. Behavioural studies
might fathom the criminal mind and could help identifying the method of work of criminals. A
legal approach might help comprehend the complex liability questions which often rise in
criminal incidents. Furthermore questions concerning contracts can be addressed when
performing a legal study. Policy recommendations can supply recommendations for legislative
change. Forecasts might be helpful for crime prevention, thus also constructing forecasts might
be of interest. We believe a multidisciplinary analysis will contribute to a better understanding
of freight exchange fraud.
52
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Ministerie van Economische Zaken (2009), Tweede Convenant Aanpak Criminaliteit
Transportsector, December, nr. 09OI56, The Hague.
Engel, A.W. van den and Prummel, E. (2007), Organised Theft of Commercial Vehicles and Their
Loads in the European Union, May, European Parliament, Brussels.
Runkle, D.E. (1987), Vector Autoregressions and Reality, Journal of Business and Economic
Statistics, 5(4), 437-442.
53
Theft Report (2009), Applying the Brakes to Road Cargo Crime in Europe, The Hague.
Unknown (2012), Met gezond verstand de digitale vrachtbeurs op, Nieuwsblad Transport, nr. 3,
January 20.
Legislation
Treaty establishing the European Coal and Steel Community (1951)
Convention on the Contract for the International Carriage of Goods by Road (1956)
Treaty establishing the European Economic Community (1957)
Treaty on European Union (1992)
Treaty of Lisbon (2007)
Websites
www.europa.eu
www.eviews.com
www.logistiek.nl
www.rijksoverheid.nl
www.teleroute.nl
www.timocom.de
www.tln.nl
www.ttm.nl
54
Appendix
A. Legal Framework57
Road transport is most often an international business as goods are carried from one country to
another. During international road transport national laws can be valid but usually the (CMR) is
preferred. When a truck or load is damaged or stolen the CMR lays down rules for liability. The
legal framework presented in this chapter has a direct influence on the willingness of carriers to
report theft and thereby influences the available statistics on the economic damage caused by
cargo theft. This chapter will not take specific national law in consideration because of
differences between the EU countries.
International carriage of goods by road is regulated by the Treaty of Geneva from 1956, called
the Convention on the Contract for the International Carriage of Goods by Road or in short the
CMR. The Netherlands ratified the CMR as one of the first countries, and in 1961 the CMR came
into force. The CMR became a huge success and even crossed the European borders. The red
countries in figure A1 show the countries that have ratified the CMR, the yellow countries show
the countries of the European Union.
Figure 12 European Union within CMR
57
Based upon Van Haven en handel, hoofdzaken van het handelsverkeersrecht (Haak and Zwitser, 2010).
55
One may conclude from the above figure, that the CMR is a widely spread and very important
convention which may not be neglected in international road transport. The CMR is the starting
point when looking at the legal aspects of transport law, national law may be applied when it
deviates and in practice in the Netherlands the Algemene Vervoerscondities 2011 (AVC)
complement national law and CMR. Specific national law including the AVC won’t be described
while that will be too detailed for an economic paper.
The CMR applies on the carriage of goods when sender or receiver of the goods resides in a
country subject to the CMR (art. 1 CMR). The CMR is obligatory and parties can only in
exceptional cases diverge from it. The importance of the CMR for the concept of fraud on the
freight exchange follows from the rules of responsibility. Article 17 of the CMR states:
“1. The carrier shall be liable for the total or partial loss of the goods and for damage thereto
occurring between the time when he takes over the goods and the time of delivery, as well as for
any delay in delivery”
In the case of fraud the goods do not reach their final destination and the contractor of the
carrier can legally hold the carrier responsible for the loss. Here the first difficulty arises; the
carrier had the intention to steal the fraud and therefore already made use of a false identity so
isn’t traceable that easy.
It becomes even more complex when the transport chain consists of more than one carrier. Our
research shows that these complex constructions are often found in a case of fraud via the
freight exchange. The CMR states in article 3:
“[..] the carrier shall be responsible for the acts of omissions of his agents and servants and of
any other persons of whose services he makes use for the performance of carriage, when such
agents, servants or other persons are acting within the scope of their employment, as if such acts
or omissions were his own”.
This indicates that a carrier which is subject to the CMR is responsible for all the parties he
contracts to effectuate the job. This responsibility is extensive and not excludable. The rule from
article 17 applies, so the contractor of the main carrier can hold him responsible for the lost
56
cargo but the carrier himself probably can’t find the fraudulent substitute carrier tohold
responsible for the loss he experiences.
The responsibility of the carrier is however limited. Article 23 of the CMR states that the
compensation for the lost goods has its base in the value of the goods at the moment of
sending, the compensation is limited to 8.33 SDR58 per kg. Compensation can also be given for
costs following from the transport but not being consequential damage.
“4. In addition, the carriage charges, Customs duties and other charges incurred in respect of the
carriage of goods shall be refunded in full in case of total loss […], but no further damage shall be
payable.”
For example: When a carrier is supposed to transport a printer weighing 1000 kg and worth
€50.000 from Belgium to France but the printer does not arrive at the printing house. The
compensation is limited to a maximum of 1000 kg * 8,33 SDR (8,33 SDR/kg * 1,34 Euro/SDR *
1000 kg = € 11.162) and eventual toll charges. The loss of production for the printing house
which is generated by the missing printer cannot be refunded. The CMR does provide the
opportunity to bypass the limited responsibility in the contract but in practice these
opportunities are hardly ever used. Reason for this observation might be that in practice
everyone is used to the concept of limited liability, it is the most common way of contracting in
the carriage of goods but even more important is that the insurance practice is adjusted to the
concept of limited liability. It is possible however to bypass the limited liability later on by
applying article 29.
“1.The carrier shall not be entitled to avail himself of the provisions of this chapter which exclude
or limit his liability or which shift the burden of proof if the damage was caused by his wilful
misconduct or by such default on his part as, in accordance with the law of the court or tribunal
seised of the case, is considered as equivalent to wilful misconduct.”
The exact definition of wilful misconduct differs per court so forumshopping is enhanced. But
generally speaking criminal intentions are a wilful misconduct. The combination of article 3 and
58
The SDR (Special Drawing Right) is a fictive unit made up out of the value of the four prominent
currencies (Euro, Dollar, Yen and Pound). The SDR should make sure the compensation subject to the
responsibility is as stable as possible. The rate of the SDR can be found on www.imf.org, February 2012
the rate was 1 SDR is approximately €1.34.
57
29 of the CMR leads therefore to the conclusion that the first contractor is fully liable for the
loss.
The main rule when you want to hold someone
responsible for missing cargo is that you can
only approach your contracting party (figure
A2). The CMR prevents alternative claims based
on different laws (article 28) so the carrier and
substitute carriers stay responsible for the
obliged limited amount. Noticeable is that the
freight exchange in current law systems cannot
be hold responsible for possible fraudulent
activities.
Figure 13– liability, only parties connected by an
arrow can sue each other.
58
B. Data description
B.1 Data characteristics
Vehicle crime
80
Number of reports
70
60
50
40
30
20
10
0
Vehicle theft
Variable
Vehicle
and Cargo
theft
Combination
theft
Cargo theft
Observations
Mean
Standard Deviation
Minimum
Maximum
Vehicle theft
192
22.099
8.226
1
45
Combination theft
192
11.203
5.437
0
33
Cargo theft
192
21.245
20.245
0
81
59
B.2 Chow test
. reg vl 11.vl 11.v 11.l break break_v break_vl break_l
note: break_v omitted because of collinearity
note: break_l omitted because of collinearity
Source
df
MS
Model
Residual
239.198598
5274.17509
5
184
47.8397197
28.663995
Total
5513.37368
189
29.1712893
vl
Coef.
11.vl
11.v
11.l
break
break_v
break_vl
break_l
_cons
-.633475
-1.497506
.8856457
-3.336637
(omitted)
3.285649
(omitted)
11.68446
. test break
(
(
(
(
SS
1)
2)
3)
4)
Std. Err.
t
Number of obs
F( 5,
184)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
=
=
=
=
=
=
190
1.67
0.1441
0.0434
0.0174
5.3539
[95% Conf. Interval]
1.614404
2.750053
1.852669
1.201313
-0.39
-0.54
0.48
-2.78
0.695
0.587
0.633
0.006
-3.818597
-6.923197
-2.76956
-5.706757
2.551647
3.928185
4.540851
-.9665179
5.702329
0.58
0.565
-7.964707
14.536
.4437258
26.33
0.000
10.80902
12.55991
break_v break_vl break_l
break = 0
o.break_v = 0
break_vl = 0
o.break_l = 0
Constraint 2 dropped
Constraint 4 dropped
F(
2,
184) =
Prob > F =
3.86
0.0229
C. Vector error correction model
The formula for the Vector error correction (VEC) model can be derived from the VAR model.
Recall the formula for the VAR model.
𝒀𝑡 = 𝒄 + 𝑨1 𝒀𝑡−1 + 𝑨2 𝒀𝑡−2 + … + 𝑨𝑝 𝒀𝑡−𝑝 + 𝒆𝑡
In this model the matrices (𝑨) can be denoted as follows:
𝒀𝑡 = 𝒄 + 𝚽1 𝒀𝑡−1 + 𝚽2 𝒀𝑡−2 + … + 𝚽𝑝 𝒀𝑡−𝑝 + 𝒆𝑡
For our transformation to the VEC model it is convenient to write the VAR formula with an (m x
1) time series 𝒀𝑡 as follows:
𝚫𝟏 𝒀𝑡 = 𝒄 + 𝚪1 𝚫𝟏 𝒀𝑡−1 + 𝚪𝟐 𝚫1 𝒀𝑡−2 + … + 𝚪𝑝−1 𝚫𝟏 𝒀𝑡−𝑝+1 + 𝚷𝒀𝑡−𝑝 + 𝒆𝑡
where
60
𝚪𝑖 = (𝚽1 + 𝚽2 + … + 𝚽𝑖 ) − 𝐈𝑚 , for 𝑖 = 1,2, …, p – 1
and
𝚷 = 𝚽1 + 𝚽2 + … + 𝚽𝑝 − 𝐈𝑚
The symbol Π stands for information about potential cointegrating relationships between the
different elements of 𝐘t and is therefore implemented in the model. (Franses, 1998)
The VEC model can be used when the variables are (1) not stationary in their levels, but are in
their differences; and (2) when the variables are cointegrated. After the VEC model an impulse
response option can be applied. When applying an impulse response function after a VEC model
the standard error bands in the impulse response function will be absent because the VEC
model already corrects the error and can therefore not provide additional standard error
correction bands for the impulse response function.
D. Model I
D.1 Stationary tests for model I
D.1.1 Cargo theft
CARGO
Augmented DF test
Phillips-Perron test
KPSS test
ERS test
Stationary in level form
No
Yes
No
No
Stationary in first difference
Yes
Yes
Yes
Yes
Augmented Dickey-Fuller test
Null Hypothesis: CARGO has a unit root
Exogenous: Constant
Lag Length: 4 (Automatic - based on SIC, maxlag=14)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-1.006461
-3.465392
-2.876843
-2.575006
0.7508
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(CARGO) has a unit root
Exogenous: Constant
61
Lag Length: 3 (Automatic - based on SIC, maxlag=14)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-11.11186
-3.465392
-2.876843
-2.575006
0.0000
Adj. t-Stat
Prob.*
-3.695682
-3.464643
-2.876515
-2.574831
0.0049
Adj. t-Stat
Prob.*
-55.38305
-3.464827
-2.876595
-2.574874
0.0001
*MacKinnon (1996) one-sided p-values.
Phillips-Perron test
Null Hypothesis: CARGO has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: DIF_CARGO has a unit root
Exogenous: Constant
Bandwidth: 147 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
KPSS test
Null Hypothesis: CARGO is stationary
Exogenous: Constant
Bandwidth: 10 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
Null Hypothesis: DIF_CARGO is stationary
Exogenous: Constant
62
1.141658
0.739000
0.463000
0.347000
Bandwidth: 42 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.129781
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
ERS test
Null Hypothesis: CARGO has a unit root
Exogenous: Constant
Lag length: 4 (Spectral OLS AR based on SIC, maxlag=14)
Sample: 1996M01 2011M12
Included observations: 192
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
6.368408
1.913200
3.165200
4.317200
*Elliott-Rothenberg-Stock (1996, Table 1)
Null Hypothesis: DIF_CARGO has a unit root
Exogenous: Constant
Lag length: 3 (Spectral OLS AR based on SIC, maxlag=14)
Sample (adjusted): 1996M02 2011M12
Included observations: 191 after adjustments
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
0.778095
1.913600
3.164600
4.315600
*Elliott-Rothenberg-Stock (1996, Table 1)
D.1.2 Combination theft
COMBINATION
Augmented DF test
Phillips-Perron test
KPSS test
ERS test
Stationary in level form
Yes
Yes
Yes
Yes
Stationary in first difference
Yes
Yes
Yes
No
Augmented Dickey-Fuller test
Null Hypothesis: COMBINATION has a unit root
Exogenous: Constant
Lag Length: 3 (Automatic - based on SIC, maxlag=14)
63
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-3.596150
-3.465202
-2.876759
-2.574962
0.0067
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: DIF_COMBINATION has a unit root
Exogenous: Constant
Lag Length: 5 (Automatic - based on SIC, maxlag=14)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-10.05907
-3.465780
-2.877012
-2.575097
0.0000
*MacKinnon (1996) one-sided p-values.
Phillips-Perron test
Null Hypothesis: COMBINATION has a unit root
Exogenous: Constant
Bandwidth: 7 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-8.741524
-3.464643
-2.876515
-2.574831
0.0000
Adj. t-Stat
Prob.*
-55.91330
-3.464827
-2.876595
-2.574874
0.0001
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: DIF_COMBINATION has a unit root
Exogenous: Constant
Bandwidth: 64 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
KPSS test
Null Hypothesis: COMBINATION is stationary
Exogenous: Constant
Bandwidth: 9 (Newey-West automatic) using Bartlett kernel
64
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.389400
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
Null Hypothesis: DIF_COMBINATION is stationary
Exogenous: Constant
Bandwidth: 90 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.205791
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
ERS test
Null Hypothesis: COMBINATION has a unit root
Exogenous: Constant
Lag length: 3 (Spectral OLS AR based on SIC, maxlag=14)
Sample: 1996M01 2011M12
Included observations: 192
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
1.829434
1.913200
3.165200
4.317200
*Elliott-Rothenberg-Stock (1996, Table 1)
Null Hypothesis: DIF_COMBINATION has a unit root
Exogenous: Constant
Lag length: 5 (Spectral OLS AR based on SIC, maxlag=14)
Sample (adjusted): 1996M02 2011M12
Included observations: 191 after adjustments
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
10.14157
1.913600
3.164600
4.315600
*Elliott-Rothenberg-Stock (1996, Table 1)
65
D.1.3 Vehicle theft
VEHICLE
Augmented DF test
Phillips Perron test
KPSS test
ERS test
Stationary in level form
Yes
Yes
Yes
Yes
Stationary in first difference
Yes
Yes
Yes
Yes
Augmented Dickey-Fuller test
Null Hypothesis: VEHICLE has a unit root
Exogenous: Constant
Lag Length: 3 (Automatic - based on SIC, maxlag=14)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-3.180142
-3.465202
-2.876759
-2.574962
0.0227
t-Statistic
Prob.*
-13.29381
-3.465202
-2.876759
-2.574962
0.0000
Adj. t-Stat
Prob.*
-10.38149
-3.464643
-2.876515
-2.574831
0.0000
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: DIF_VEHICLE has a unit root
Exogenous: Constant
Lag Length: 2 (Automatic - based on SIC, maxlag=14)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
Phillips-Perron test
Null Hypothesis: VEHICLE has a unit root
Exogenous: Constant
Bandwidth: 9 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: DIF_VEHICLE has a unit root
Exogenous: Constant
Bandwidth: 14 (Newey-West automatic) using Bartlett kernel
66
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-42.47017
-3.464827
-2.876595
-2.574874
0.0001
*MacKinnon (1996) one-sided p-values.
KPSS test
Null Hypothesis: VEHICLE is stationary
Exogenous: Constant
Bandwidth: 10 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.414301
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
Null Hypothesis: DIF_VEHICLE is stationary
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.025703
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
ERS test
Null Hypothesis: VEHICLE has a unit root
Exogenous: Constant
Lag length: 3 (Spectral OLS AR based on SIC, maxlag=14)
Sample: 1996M01 2011M12
Included observations: 192
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
1.818627
1.913200
3.165200
4.317200
*Elliott-Rothenberg-Stock (1996, Table 1)
Null Hypothesis: DIF_VEHICLE has a unit root
Exogenous: Constant
Lag length: 2 (Spectral OLS AR based on SIC, maxlag=14)
67
Sample (adjusted): 1996M02 2011M12
Included observations: 191 after adjustments
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
2.91E-05
1.913600
3.164600
4.315600
*Elliott-Rothenberg-Stock (1996, Table 1)
D.2 Lag selection for model I
VAR Lag Order Selection Criteria
Endogenous variables: CARGO VEHICLE COMBINATION
Exogenous variables: C DUMBREAK
Date: 08/08/12 Time: 15:50
Sample: 1996M01 2011M12
Included observations: 184
Lag
LogL
LR
FPE
AIC
SC
HQ
0
1
2
3
4
5
6
7
8
-1907.413
-1844.298
-1823.716
-1813.794
-1808.837
-1802.106
-1794.980
-1781.670
-1776.018
NA
122.7998
39.37253
18.65927
9.158820
12.21805
12.70297
23.29301*
9.706560
216281.1
120110.2
105916.7
104887.2*
109649.8
112466.7
114896.1
109786.0
114056.5
20.79796
20.20976
20.08387
20.07384*
20.11779
20.14246
20.16283
20.11598
20.15237
20.90280
20.47184*
20.50321
20.65044
20.85164
21.03355
21.21118
21.32158
21.51522
20.84045
20.31598
20.25384*
20.30754
20.41523
20.50363
20.58774
20.60462
20.70475
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
VAR Residual Serial Correlation LM Tests
Null Hypothesis: no serial correlation at lag
order h
Date: 08/08/12 Time: 15:55
Sample: 1996M01 2011M12
Included observations: 189
68
Lags
LM-Stat
Prob
1
2
3
4
5
6
7
9.981372
5.455356
10.11484
7.344354
11.39669
11.50631
25.66212
0.3520
0.7929
0.3413
0.6013
0.2495
0.2426
0.0023
8
9
10
11
12
16.16161
5.309461
14.31505
8.591891
19.71498
0.0636
0.8065
0.1116
0.4758
0.0198
Probs from chi-square with 9 df.
D.3 Impulse response function Model I
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of CARGO to CARGO
Response of CARGO to VEHICLE
Response of CARGO to COMBINATION
12
12
12
8
8
8
4
4
4
0
0
0
-4
-4
2
4
6
8
10
12
14
16
18
20
22
24
-4
2
Response of VEHICLE to CARGO
4
6
8
10
12
14
16
18
20
22
24
2
Response of VEHICLE to VEHICLE
8
8
6
6
6
4
4
4
2
2
2
0
0
0
-2
2
4
6
8
10
12
14
16
18
20
22
24
4
6
8
10
12
14
16
18
20
22
24
2
Response of COMBINATION to VEHICLE
6
6
4
4
4
2
2
2
0
0
0
-2
2
4
6
8
10
12
14
16
18
20
22
24
10
12
14
16
18
20
22
24
4
6
8
10
12
14
16
18
20
22
24
Response of COMBINATION to COMBINATION
6
-2
8
-2
2
Response of COMBINATION to CARGO
6
Response of VEHICLE to COMBINATION
8
-2
4
-2
2
4
6
8
10
12
14
16
18
20
22
24
2
4
6
8
10
12
14
16
18
20
22
24
69
D.4 VAR analysis and impulse response function for Model I including first
difference of cargo theft
D.4.1 Lag selection
VAR Lag Order Selection Criteria
Endogenous variables: DIF_CARGO COMBINATION VEHICLE
Exogenous variables: C DUMBREAK
Sample: 1996M01 2011M12
Included observations: 183
Lag
LogL
LR
FPE
AIC
SC
HQ
0
1
2
3
4
5
6
7
8
-1905.568
-1842.851
-1827.261
-1818.640
-1804.485
-1802.099
-1788.032
-1778.341
-1771.500
NA
122.0056
29.81846
16.20433
26.14451
4.328741
25.05951
16.94556*
11.73838
237476.0
132031.5
122871.2
123415.2
116708.2*
125546.2
118903.6
118172.2
121211.2
20.89145
20.30439
20.23236
20.23651
20.18016*
20.25245
20.19707
20.18952
20.21311
20.99668
20.56746*
20.65327
20.81527
20.91677
21.14690
21.24936
21.39966
21.58109
20.93411
20.41102
20.40297*
20.47111
20.47875
20.61501
20.62362
20.68005
20.76762
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
VAR Residual Serial Correlation LM Tests
Null Hypothesis: no serial correlation at lag
order h
Sample: 1996M01 2011M12
Included observations: 189
Lags
LM-Stat
Prob
1
2
3
4
5
6
7
8
9
10
11
12
21.54681
23.63641
23.83941
10.74413
5.161498
15.35836
24.66798
13.81390
4.715356
10.21039
14.29852
22.20811
0.0104
0.0049
0.0046
0.2937
0.8200
0.0816
0.0034
0.1291
0.8584
0.3337
0.1121
0.0082
Probs from chi-square with 9 df.
70
D.4.2 VAR analysis
The amount of lags (following from appendix D.4.1) we have to implement in our model is four,
which leads us to the following formula for the model:
𝑎1𝑣𝑒,𝑣𝑒
𝑦𝑣𝑒,𝑡
𝑐𝑣𝑒
[𝑦𝑐𝑜,𝑡 ] = [𝑐𝑐𝑜 ] + [𝑎1𝑐𝑜,𝑣𝑒
𝑦𝑐𝑎,𝑡
𝑐𝑐𝑎
𝑎1𝑐𝑎,𝑣𝑒
3
𝑎𝑣𝑒,𝑣𝑒
3
+ [𝑎𝑐𝑜,𝑣𝑒
3
𝑎𝑐𝑎,𝑣𝑒
𝑎1𝑣𝑒,𝑐𝑜
𝑎1𝑐𝑜,𝑐𝑜
𝑎1𝑐𝑎,𝑐𝑜
3
𝑎𝑣𝑒,𝑐𝑜
3
𝑎𝑐𝑜,𝑐𝑜
3
𝑎𝑐𝑎,𝑐𝑜
2
𝑎1𝑣𝑒,𝑐𝑜 𝑦𝑣𝑒,𝑡−1
𝑎𝑣𝑒,𝑣𝑒
2
𝑎1𝑐𝑜,𝑐𝑎 ] [𝑦𝑐𝑜,𝑡−1 ] + [𝑎𝑐𝑜,𝑣𝑒
2
𝑎1𝑐𝑎,𝑐𝑎 𝑦𝑐𝑎,𝑡−1
𝑎𝑐𝑎,𝑣𝑒
3
4
𝑎𝑣𝑒,𝑐𝑜
𝑎𝑣𝑒,𝑣𝑒
𝑦𝑣𝑒,𝑡−3
3
4
𝑎𝑐𝑜,𝑐𝑎
] [𝑦𝑐𝑜,𝑡−3 ] + [𝑎𝑐𝑜,𝑣𝑒
𝑦𝑐𝑎,𝑡−3
3
4
𝑎𝑐𝑎,𝑐𝑎
𝑎𝑐𝑎,𝑣𝑒
2
𝑎𝑣𝑒,𝑐𝑜
2
𝑎𝑐𝑜,𝑐𝑜
2
𝑎𝑐𝑎,𝑐𝑜
4
𝑎𝑣𝑒,𝑐𝑜
4
𝑎𝑐𝑜,𝑐𝑜
4
𝑎𝑐𝑎,𝑐𝑜
2
𝑎𝑣𝑒,𝑐𝑜
𝑦𝑣𝑒,𝑡−2
2
𝑎𝑐𝑜,𝑐𝑎 ] [𝑦𝑐𝑜,𝑡−2 ]
𝑦𝑐𝑎,𝑡−2
2
𝑎𝑐𝑎,𝑐𝑎
4
𝑎𝑣𝑒,𝑐𝑜
𝑦𝑣𝑒,𝑡−4
4
𝑎𝑐𝑜,𝑐𝑎 ] [𝑦𝑐𝑜,𝑡−4 ]
𝑦𝑐𝑎,𝑡−4
4
𝑎𝑐𝑎,𝑐𝑎
𝑒𝑣𝑒,𝑡
+ 𝒟𝐵𝑟𝑒𝑎𝑘 + [𝑒𝑐𝑜,𝑡 ]
𝑒𝑐𝑎,𝑡
This can be rewritten as:
̂ 1 𝒀𝑡−1 + 𝚽
̂ 2 𝒀𝑡−2 + 𝚽
̂ 3 𝒀𝑡−3 + 𝚽
̂ 4 𝒀𝑡−4 + 𝒟𝐵𝑟𝑒𝑎𝑘 + 𝒆̂𝑡 ;
𝒀𝑡 = 𝒄̂ + 𝚽
where 𝑦𝑡 = (𝑣𝑒𝑡 , 𝑐𝑜𝑡, 𝑑𝑐𝑎𝑡 )’ in which 𝑣𝑒 stands for vehicle theft, 𝑐𝑜 for combination theft and
𝑑𝑐𝑎 for the first difference of cargo theft. In the model a dummy is implemented to correct for
the new measurement method which is implemented at the KLPD in 2010.
The results of the VAR analysis can be found in table 7 and the results of the thereto belonging
impulse response analysis can be found in appendix D.4.3.
71
Table 7 VAR(4) analysis model I (cargo theft in first difference)
Vector Auto Regression (4 lags)
Observations: 187
Variable
Vehicle theft
D.Cargo theft
Combination theft
Vehicle L1
0.158 *
-0.179
0.057
Standard error
t-statistic
0.080
1.983
0.115
-1.556
0.051
1.124
0.106 *
Vehicle L2
0.119
0.180
Standard error
t-statistic
0.082
1.457
0.118
- 1.525
0.053
2.019
Vehicle L3
0.238 *
- 0.079
0.114 *
Standard error
t-statistic
0.084
2.845
0.121
- 0.651
0.054
2.129
Vehicle L4
0.118
0.008
0.016 *
Standard error
t-statistic
0.085
1.382
0.123
0.069
0.054
2.129
D.Cargo L1
0.039
- 0.657 *
0.014
Standard error
t-statistic
0.053
0.736
0.076
- 8.587
0.034
0.414
D.Cargo L2
0.102
- 0.304 *
0.015
Standard error
t-statistic
0.062
1.646
0.089
- 3.421
0.040
0.377
D.Cargo L3
- 0.031
- 0.275 *
0.002
Standard error
t-statistic
0.062
- 0.507
0.089
- 3.088
0.040
0.063
D.Cargo L4
- 0.031
- 0.317 *
- 0.009
Standard error
t-statistic
0.052
- 0.601
0.075
- 4.256
0.033
- 0.268
Combination L1
0.093
0.356 *
0.248 *
Standard error
t-statistic
0.123
0.754
0.178
2.001
0.079
3.135
Combination L2
0.035
0.142
0.071
Standard error
t-statistic
0.126
0.277
0.182
0.781
0.081
0.877
Combination L3
- 0.060
- 0.147
- 0.109
Standard error
t-statistic
0.125
- 0.483
0.181
- 0.816
0.080
- 1.355
Combination L4
0.142
0.219
0.167 *
Standard error
t-statistic
0.119
1.192
0.172
1.274
0.077
2.183
Constant
5.542 *
2.891
0.945
Standard error
t-statistic
1.941
2.855
2.798
1.033
1.244
0.759
Dumbreak
1.939
5.824 *
- 3.551 *
Standard error
t-statistic
1.941
0.999
2.798
2.082
1.244
- 2.854
* signficant on a 95% level
We see that, according to our expectations of sub 3.3.1, the variables vehicle theft, cargo theft
and combination theft show significant relations with their own lagged variables. Vehicle theft is
significantly related to its first and third lag; cargo theft is significantly related to all of its four
lags; and combination theft is related to its first and fourth lag.
72
0
30
35
1
-5
0
-10
-1
40
5
OMBINATION
5
10
10
15
15
20
20
25
25
30
30
35
0
0
-5
-5
-10
40
35
40
Response
of VEHICLE
to VEHICLE
Response of
COMBINATION
to DIF_CARGO
-10
5
10
15
20
25
30
35
40
5
Response of COMBINATION to COMBINATION
Response o
5
8
5
5
4
6
4
4
3
3
2
2
1
1
0
0
3
10
4
2
2
1
30
35
0
0
-1
-2
5
40
-1
10
5
15
10
20
15
25
20
30
25
35
30
40
35
Response
of VEHICLE
to DIF_CARGO
Figure
14 Impulse
response function
VAR(4) model I
Response of DIF_CARGO to DIF_CARGO
15
20
25
30
35
40
5
15
8
Response to Cholesky One S.D. Innovations ± 2 S.E.
8
6
Response of DIF_CARGO to COMBINATION
6
2
4
2
2
0
of vehicle theft to vehicle theft (figure 14)
0
-2
shows
us
-5 5
-2
in figure
14 – 16. The impulse response function
5
5
0
0
10
5
0
-2
10
15
20
25
30
35
40
that, considering the signs of the
10
15
20
25
30
35
40
Respons
15
4
crime
10 component with their own lags are shown
10
10
Respons
The 15impulse response functions of the vehicle
4
-5 5
10
Response
of VEHICLE
to COMBINATION
Figure 15
Impulse response
function
VAR(4) model I
8
6
-1
5
40
0
-5
5
10
responses, vehicle theft always has a positive
-10
-10
-10
40
15 the
20 impulse
25
30
35
40
impact on 5itself10 since
response
is
Figure 16 Impulse response function VAR(4) model I
positive.Response
The effect
of a shock dies down after
of COMBINATION to COMBINATION
5
10
15
20
25
30
35
Response of COMBINATION to DIF_CARGO
59
5
Response
about twelve months. The impulse response function of combination theft shows us a positive
5
4
impact
of about four months after which the shock4 appears to have worked itself out of the
4
3
3
system.
It is hard to interpret the variable cargo theft
since we have transformed it to its first
3
5
5
2
2
2
1
1
1
0
0
0
difference. The impulse response function (figure 16) does show us that the shock appears to
have worked its way out of the system after five periods. The impulse response function is
stable since the effect of the shock dies down.
-1
5
10
15
20
25
30
35
-1
40
10
-1
5
10
15
20
25
30
35
40
5
10
The VAR output reveals significant relations between the variables cargo theft and the first lag
Response of VEHICLE to DIF_CARGO
Response of VEHICLE to COMBINATION
of8 combination theft and between combination theft
and the second, third and fourth lag of
8
vehicle theft.
Respo
8
6
6
6
4
4
4
2
2
2
59 0
We
0 two periods, because of the revival of the
do notice the standard error band crossing the zero at
standard error band we have interpreted that the shock truly died out after twelve periods.
-2
0
-2
5
10
15
20
25
30
35
40
5
10
15
20
25
30
35
-2
40
73
5
10
0
0
0
-5
-5
-10
35
40
15
20
25
30
35
40
Response
of DIF_CARGO
to COMBINATION
Response
of COMBINATION
to COMBINATION
5
15
4
10
4
10
3
5
2
0
20
25
30
35
40
5
0
1
-5
0
-1
15
2
1
3540
10
Response
of DIF_CARGO
to VEHICLE
Response
of COMBINATION
to VEHICLE
15
3
3035
5
5
0
-1
-10
5
40
CARGO
o DIF_CARGO
0
10
Response to Cholesky One S.D. Innovations ± 2 S.E.
DIF_CARGO
F_CARGO
0
-10
5
510
15
10
20
15
25
20
30
25
35
30
40
35
40
-5
-10
5
105
15
10
20
15
2520
3025
3530
4035
40
Response
of of
VEHICLE
to COMBINATION
Response
of VEHICLE
toVAR(4)
VEHICLE
Response
COMBINATION
to COMBINATION
Response
offunction
COMBINATION
to VEHICLE
Figure
17 Impulse
response
function
VAR(4)
model I
Figure 18 Impulse
response
model
I
8
5
8
5
6
4
6
4
4
3
Although
we found a significant relation between4 cargo3 theft and the first lag of combination
2
2
2
theft
the impulse response function shows that the
impact
of the shock is close to zero. This
2
0
1
1
indicates
that the significant relation between the two
variables seems not to be of any
0
0
0
influence. A shock of combination theft does not impact cargo theft.
-2
35
30
40
35
40
-1
-2
5
10
5
15
10
20
15
25
20
30
25
35
30
40
35
40
-1
5
10
5
15
10
20
15
25
20
30
25
35
30
40
35
The relationship between combination and vehicle theft (figure 18) is positive and the shock
40
60
VEHICLE
COMBINATION
Response
of VEHICLE
to VEHICLE
appearsResponse
to have ofworked
itstoway
out of the system after thirteen
months.
The response
of
IF_CARGO
8
8
combination to combination is positive for four months. After that the shock seems to gradually
6 its way out of the system.
work
6
4
4
The results of this analysis match the results of the analysis presented in sub 4.1. Each variable
2
2
0
0
shows within variable significance and between the variables combination and vehicle are
significantly related. The result of this analysis between cargo theft and combination theft is not
30
35
40
-2
-2
found
in sub 4.1, but the significant relation does not seem
to be of any influence (figure 17).
5
10
15
20
25
30
35
40
5
10
15
20
25
30
35
Conclusions drawn in sub 4.1 can therefore be applied on this section as well.
60
We do notice the standard error band crossing the zero in the first period of the impulse response
function but because of the revival of the standard error band in the second period we have interpreted
that the shock to truly die out after thirteen periods.
74
40
D.4.3 Impulse response function
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DIF_CARGO to DIF_CARGO
Response of DIF_CARGO to COMBINATION
Response of DIF_CARGO to VEHICLE
15
15
15
10
10
10
5
5
5
0
0
0
-5
-5
-5
-10
-10
-10
5
10
15
20
25
30
35
40
5
Response of COMBINATION to DIF_CARGO
10
15
20
25
30
35
40
5
Response of COMBINATION to COMBINATION
5
5
4
4
4
3
3
3
2
2
2
1
1
1
0
0
0
-1
-1
-1
10
15
20
25
30
35
40
5
Response of VEHICLE to DIF_CARGO
10
15
20
25
30
35
40
5
Response of VEHICLE to COMBINATION
8
8
6
6
6
4
4
4
2
2
2
0
0
0
-2
5
10
15
20
25
30
35
40
20
25
30
35
40
10
15
20
25
30
35
40
Response of VEHICLE to VEHICLE
8
-2
15
Response of COMBINATION to VEHICLE
5
5
10
-2
5
10
15
20
25
30
35
40
5
10
15
20
25
30
35
40
E. Model II
E.1 Stationary tests for model II
E.1.1 Members
MEMBERS
Augmented DF test
Phillips-Perron test
KPSS test
ERS test
Stationary in level form
No
No
No
No
Stationary in first difference
Yes
Yes
No
Yes
75
Augmented Dickey-Fuller test
Null Hypothesis: MEMBERS has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
0.503898
-3.546099
-2.911730
-2.593551
0.9855
t-Statistic
Prob.*
-7.541604
-3.548208
-2.912631
-2.594027
0.0000
Adj. t-Stat
Prob.*
0.503898
-3.546099
-2.911730
-2.593551
0.9855
Adj. t-Stat
Prob.*
-7.541057
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(MEMBERS) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
Phillips-Perron test
Null Hypothesis: MEMBERS has a unit root
Exogenous: Constant
Bandwidth: 0 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(MEMBERS) has a unit root
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
76
KPSS test
Null Hypothesis: MEMBERS is stationary
Exogenous: Constant
Bandwidth: 6 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.595542
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
Null Hypothesis: D(MEMBERS) is stationary
Exogenous: Constant
Bandwidth: 1 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.768809
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
ERS test
Null Hypothesis: MEMBERS has a unit root
Exogenous: Constant
Lag length: 0 (Spectral OLS AR based on SIC, maxlag=10)
Sample: 2007M01 2011M12
Included observations: 60
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
22.34798
1.886000
2.998000
3.962000
*Elliott-Rothenberg-Stock (1996, Table 1)
Null Hypothesis: D(MEMBERS) has a unit root
Exogenous: Constant
Lag length: 0 (Spectral OLS AR based on SIC, maxlag=10)
Sample (adjusted): 2007M02 2011M12
Included observations: 59 after adjustments
77
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
1.584276
1.884400
2.995200
3.956800
*Elliott-Rothenberg-Stock (1996, Table 1)
E.1.2 Posted advertisements
POSTED ADVERTISEMENTS
Augmented DF test
Phillips-Perron test
KPSS test
ERS test
Stationary in level form
No
No
No
Yes
Stationary in first difference
Yes
Yes
Yes
Yes
Augmented Dickey-Fuller test
Null Hypothesis: POSTED_ADDS has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-2.804993
-3.546099
-2.911730
-2.593551
0.0637
t-Statistic
Prob.*
-7.984689
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(POSTED_ADDS) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
Phillips-Perron test
Null Hypothesis: POSTED_ADDS has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
78
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
Adj. t-Stat
Prob.*
-2.651499
-3.546099
-2.911730
-2.593551
0.0887
Adj. t-Stat
Prob.*
-10.94176
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(POSTED_ADDS) has a unit root
Exogenous: Constant
Bandwidth: 22 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
KPSS test
Null Hypothesis: POSTED_ADDS is stationary
Exogenous: Constant
Bandwidth: 5 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.595158
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
Null Hypothesis: D(POSTED_ADDS) is stationary
Exogenous: Constant
Bandwidth: 26 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.175021
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
ERS test
79
Null Hypothesis: POSTED_ADDS has a unit root
Exogenous: Constant
Lag length: 0 (Spectral OLS AR based on SIC, maxlag=10)
Sample: 2007M01 2011M12
Included observations: 60
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
2.572423
1.886000
2.998000
3.962000
*Elliott-Rothenberg-Stock (1996, Table 1)
Null Hypothesis: D(POSTED_ADDS) has a unit root
Exogenous: Constant
Lag length: 0 (Spectral OLS AR based on SIC, maxlag=10)
Sample (adjusted): 2007M02 2011M12
Included observations: 59 after adjustments
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
0.863896
1.884400
2.995200
3.956800
*Elliott-Rothenberg-Stock (1996, Table 1)
E.1.3 Viewed advertisements
VIEWED ADVERTISEMENTS
Augmented DF test
Phillips-Perron test
KPSS test
ERS test
Stationary in level form
No
No
No
No
Stationary in first difference
Yes
Yes
Yes
Yes
Augmented Dickey-Fuller test
Null Hypothesis: VIEWED_ADDS has a unit root
Exogenous: Constant
Lag Length: 2 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
80
t-Statistic
Prob.*
-0.554578
-3.550396
-2.913549
-2.594521
0.8720
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(VIEWED_ADDS) has a unit root
Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=10)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-9.508594
-3.550396
-2.913549
-2.594521
0.0000
Adj. t-Stat
Prob.*
-1.176349
-3.546099
-2.911730
-2.593551
0.6791
Adj. t-Stat
Prob.*
-18.10566
-3.548208
-2.912631
-2.594027
0.0000
*MacKinnon (1996) one-sided p-values.
Phillips-Perron test
Null Hypothesis: VIEWED_ADDS has a unit root
Exogenous: Constant
Bandwidth: 3 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: D(VIEWED_ADDS) has a unit root
Exogenous: Constant
Bandwidth: 14 (Newey-West automatic) using Bartlett kernel
Phillips-Perron test statistic
Test critical values:
1% level
5% level
10% level
*MacKinnon (1996) one-sided p-values.
KPSS test
Null Hypothesis: VIEWED_ADDS is stationary
Exogenous: Constant
Bandwidth: 6 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.910652
0.739000
0.463000
0.347000
81
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
Null Hypothesis: D(VIEWED_ADDS) is stationary
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
LM-Stat.
Kwiatkowski-Phillips-Schmidt-Shin test statistic
Asymptotic critical values*:
1% level
5% level
10% level
0.066125
0.739000
0.463000
0.347000
*Kwiatkowski-Phillips-Schmidt-Shin (1992, Table 1)
ERS test
Null Hypothesis: VIEWED_ADDS has a unit root
Exogenous: Constant
Lag length: 2 (Spectral OLS AR based on SIC, maxlag=10)
Sample: 2007M01 2011M12
Included observations: 60
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
69.47715
1.886000
2.998000
3.962000
*Elliott-Rothenberg-Stock (1996, Table 1)
Null Hypothesis: D(VIEWED_ADDS) has a unit root
Exogenous: Constant
Lag length: 1 (Spectral OLS AR based on SIC, maxlag=10)
Sample (adjusted): 2007M02 2011M12
Included observations: 59 after adjustments
P-Statistic
Elliott-Rothenberg-Stock test statistic
Test critical values:
1% level
5% level
10% level
0.509050
1.884400
2.995200
3.956800
*Elliott-Rothenberg-Stock (1996, Table 1)
E.2 Lag selection for model II
The first panel shows the lag selection test for model II including cargo theft in its level
form. The test results indicate a lag order of zero which does not stroke with the idea
82
behind a vector autoregression. The second panel shows the results of the lag selection
test of model II including cargo theft in its first difference. Based on Akaike information
criterium a VAR(1) seems appropriate.
VAR Lag Order Selection Criteria
Endogenous variables: VEHICLE CARGO COMBINATION
Exogenous variables: C DUMBREAK DIF_POSTED_ADDS DIF_VIEWED_ADDS DIF_MEMBERS
Sample: 2007M01 2011M12
Included observations: 55
Lag
LogL
LR
FPE
AIC
SC
HQ
0
1
2
3
4
5
-542.3001
-535.9674
-530.7233
-521.4930
-511.2974
-508.1658
NA*
10.82315
8.390602
13.76155
14.08847
3.985675
127177.4*
140794.3
163046.9
164613.2
162144.2
209304.4
20.26546*
20.36245
20.49903
20.49065
20.44718
20.66057
20.81291*
21.23838
21.70343
22.02353
22.30852
22.85039
20.47716*
20.70118
20.96478
21.08343
21.16698
21.50739
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
VAR Lag Order Selection Criteria
Endogenous variables: DIF_CARGO VEHICLE COMBINATION
Exogenous variables: C DUMBREAK DIF_POSTED_ADDS DIF_VIEWED_ADDS DIF_MEMBERS
Sample: 2007M01 2011M12
Included observations: 54
Lag
LogL
LR
FPE
AIC
SC
HQ
0
1
2
3
4
5
-549.7849
-539.4687
-530.5045
-524.5420
-518.7588
-514.0496
NA
17.57570*
14.27636
8.833344
7.925016
5.930164
244243.9
233787.9*
236627.8
269880.2
313308.9
383984.0
20.91796
20.86921*
20.87054
20.98304
21.10218
21.26110
21.47045*
21.75320
22.08603
22.53002
22.98066
23.47108
21.13103*
21.21013
21.33930
21.57965
21.82664
22.11340
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
E.3 Johansen cointegration test
Date: 08/09/12 Time: 16:20
83
Sample (adjusted): 2007M03 2011M12
Included observations: 58 after adjustments
Trend assumption: Linear deterministic trend
Series: MEMBERS POSTED_ADDS VIEWED_ADDS
Exogenous series: DUMBREAK
Warning: Critical values assume no exogenous series
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None *
At most 1 *
At most 2
0.353786
0.273508
0.030253
45.63863
20.31438
1.781756
29.79707
15.49471
3.841466
0.0004
0.0087
0.1819
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None *
At most 1 *
At most 2
0.353786
0.273508
0.030253
25.32425
18.53262
1.781756
21.13162
14.26460
3.841466
0.0121
0.0100
0.1819
Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
The panels show the results of the Johansen cointegration test for the variables members,
posted advertisements and viewed advertisements of the freight exchange. The trace test
(panel 1) rejects the H0 that of at most one cointegrating vectors. The max test (panel 2)
confirms the rejection of the H0 of one cointegrating vector. Two integrating equations should
be implemented in our model.
84
E.4 Impulse response function Model II
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DIF_CARGO to DIF_CARGO
Response of DIF_CARGO to VEHICLE
Response of DIF_CARGO to COMBINATION
20
20
20
10
10
10
0
0
0
-10
-10
-10
-20
-20
2
4
6
8
10
12
14
16
18
20
-20
2
Response of VEHICLE to DIF_CARGO
4
6
8
10
12
14
16
18
20
2
Response of VEHICLE to VEHICLE
8
8
6
6
6
4
4
4
2
2
2
0
0
0
-2
-2
-2
-4
2
4
6
8
10
12
14
16
18
20
4
6
8
10
12
14
16
18
20
2
Response of COMBINATION to VEHICLE
6
6
4
4
4
2
2
2
0
0
0
-2
2
4
6
8
10
12
14
16
18
20
10
12
14
16
18
20
4
6
8
10
12
14
16
18
20
Response of COMBINATION to COMBINATION
6
-2
8
-4
2
Response of COMBINATION to DIF_CARGO
6
Response of VEHICLE to COMBINATION
8
-4
4
-2
2
4
6
8
10
12
14
16
18
20
2
4
6
8
10
12
14
16
85
18
20
F. Freight exchange fraud
F.1 2008
2008
Region
Month
Day
Transport section
Load
Loss
Amount of reports
8
Gelderland-Midden
Kennemerland
Limburg-Noord
Zeeland
Unknown
February
March
May
December
Tuesday
Wednesday
Thursday
Within the Netherlands
The Netherlands - Italy
The Netherlands - Germany
Unknown
Electronics
Unknown
€ 50.000 - € 100.00
> € 250.000
Unknown
1
1
4
1
1
3
3
1
1
2
3
3
1
3
3
1
7
1
2
1
5
Amount of reports
3
Haaglanden
Kennemerland
Midden- en West-Brabant
April
May
August
Tuesday
Wednesday
Spain – The Netherlands
France – The Netherlands
Spain - Belgium
Alcoholic beverages
Food supplies
€ 50.000 - € 100.00
Unknown
1
1
1
1
1
1
2
1
1
1
1
2
1
2
1
F.2 2009
2009
Region
Month
Day
Transport section
Load
Loss
86
F.3 2010
2010
Region
Month
Day
Transport section
Load
Loss
Amount of reports
Amsterdam-Amstelland
Brabant Zuid-Oost
Flevoland
Gelderland Midden
Kennemerland
Limburg-Noord
Noord- en Oost-Gelderland
Utrecht
Zuid-Holland Zuid
Unknown
January
March
June
October
December
Unknown
Monday
Tuesday
Wednesday
Thursday
Friday
Unknown
The Netherlands - France
The Netherlands - Italy
The Netherlands - Germany
Italy – The Netherlands
Spain – Belgium
Unknown
Electronics
Alcoholic beverages
Car components
Miscellaneous
Food supplies
Unknown
< € 50.000
€ 50.000 - € 100.000
€ 100.000 - € 250.000
> € 250.000
Unknown
13
2
1
1
1
1
1
1
2
1
2
1
8
1
1
1
1
1
1
3
3
4
1
2
2
6
1
1
1
7
1
1
2
1
1
3
2
5
2
1
87
F.4 2011
2011
Region
Month
Day
Transport section
Load
88
Amount of reports
Amsterdam-Amstelland
Brabant Noord
Flevoland
Groningen
Limburg-Noord
Limburg - Zuid
Midden- en West-Brabant
Noord- en Oost-Gelderland
Rotterdam-Rijnmond
Utrecht
Zeeland
Zuid-Holland Zuid
March
April
May
August
September
October
December
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
The Netherlands - France
The Netherlands - Italy
The Netherlands - Spain
The Netherlands – United
Kingdom
The Netherlands – Czech
Republic
The Netherlands - Poland
Spain – The Netherlands
Austria – The Netherlands
Belgium - France
Germany - France
Belgium - Spain
Alcoholic beverages
17
1
1
1
1
3
1
1
2
2
2
1
1
3
2
3
2
2
3
2
2
3
5
4
1
1
1
3
1
3
1
1
1
2
2
1
1
1
1
Loss
Car components
Miscellaneous
Food supplies
Copper
< € 50.000
€ 50.000 - € 100.000
€ 100.000 - € 250.000
> € 250.000
Unknown
1
1
13
1
3
5
1
1
7
89