Destructive entrepreneurship in the small business sector: bankruptcy fraud in Sweden, 1830–2010
Destructive entrepreneurship in the small business sector: bankruptcy fraud in Sweden, 1830-2010
Entrepreneurship will not always productive: Baumol (1990, 1993) distinguishes between productive, unproductive, and destructive entrepreneurial activities, and in the last two cases, new values are not created. Setting of from the notion of destructive entrepreneurship and the bankruptcy institute as framework for the empirical analysis, we use long aggregate series on bankruptcies and bankruptcy frauds in Sweden, 18302010. We operationalize destructive entrepreneurship with bankruptcy frauds. The bankruptcy institute is not a pure cleansing mechanism; assets can be redistributed by criminal procedure. Thus, a form of destructive entrepreneurship can be conducted within this system. We link bankruptcy frauds to the selection mechanism-the aggregate bankruptcy volume-over time. We cannot establish any direct linkages between the bankruptcy volume and institutional changes. However, and in line with research on bankruptcy diffusion and diffusion of economic crimes, we find that bankruptcy frauds have significant, positive impacts on the bankruptcy volume. This study has been financed by Riksbankens Jubileumsfond, P12-1122:1.
Bankruptcy; Bankruptcy fraud; Destructive entrepreneurship; Sweden
Therefore, our results indicate that increases in
bankruptcy frauds, destructive entrepreneurship, would
affect the economic system.
JEL classification E3 . C22 . G33 . K4 . L26
Entrepreneurship is commonly associated with
innovation and the increase of welfare
. However, in his seminal works, Baumol
(1990, 1993) distinguishes between three types of
entrepreneurship: productive, unproductive, and
destructive. In the last two cases, entrepreneurial activity results
in redistribution of wealth, and new values are not
created; entrepreneurship will therefore have a negative
effect on society. This fact has been more neglected;
furthermore, the distinction between unproductive and
destructive entrepreneurship has been tenuous and
therefore often ignored
(Desai et al. 2013)
fundamental problem has been to operationalize and to
distinguish the three concepts of entrepreneurship; since
productive, unproductive, and destructive
entrepreneurship come in many forms, no universal approach or
definition exists. Another problem has been to study
the effects of the different forms of entrepreneurship
(e.g., Bjørnskov and Foss 2008)
In the literature, destructive entrepreneurship has
been defined as illegal entrepreneurial activities such
as organized crime and economic crime
et al. 2016)
. In the present article, we explicitly attend to
the notion of destructive entrepreneurship and
operationalize it by using comprehensive, aggregate
series on bankruptcy fraud in Sweden. The framework
of the bankruptcy institute presents an opportunity to
elaborate on the Baumolian framework: while the
bankruptcy institute can be regarded as a cleansing or
selection mechanism that sorts out inefficient firms from the
(e.g., Miller 1991)
, it can also be regarded as a
financial institution that can be used by entrepreneurs
for distributing assets in unintended and sometimes
unwanted directions. First, it can be employed as a tool
for rent-seeking and thus for unproductive
entrepreneurship: firms themselves can, strategically, file for
bankruptcy and evade payment of debts. The bankruptcy
costs are thereby externalized to creditors, suppliers,
(Akerlof et al. 1993; Delaney 1992;
. Second, economic crimes—destructive
entrepreneurship—are committed within the bankruptcy
institute. Bankruptcy frauds are typically committed
before bankruptcy proceedings are initiated; here, an
insolvent firm has illicitly withdrawn or concealed
assets from creditors and the state
. In the case of bankruptcy fraud,
offenders have committed either dishonesty or
carelessness towards creditors, favoritism towards creditors, or
accounting fraud. Thus, a firm has illegally exploited an
economic opportunity in order to gain some form of
financial or business advantage. Just as in Bstrategic^
bankruptcies, all or some of the bankruptcy costs are
The dynamics of bankruptcies and bankruptcy frauds
should be viewed through the lens of the small business
sector: the lion’s share of all bankruptcies and business
exits each year relates to small businesses
Lundberg 1999; van Praag and Versloot 2007)
example, around 80,000 firms went bankrupt in Sweden
between 1994 and 2001; of these, nearly 90% had 0–1
employees (Statistics Sweden database), and this is also
illustrative for historical times
(Gratzer 1998; Box 2005,
2008; Statistics Sweden 1923)
. In a similar way, most
economic crimes and frauds are committed by
(Croall 1989; Korsell 2015; Sutton
and Wild 1985)
, and the Bvictims^ of these types of
crimes are typically other business organizations
Alvesalo and Virta 2010; Wheeler and Rothman 1982)
Setting out from Baumol’s (1990, 1993) notion of
destructive entrepreneurship and its potential effects, we
empirically operationalize the concept and ask whether
there is a relationship between bankruptcy frauds and
the selection mechanism—in the present case, the
bankruptcy volume. Indeed, changes and variations in the
bankruptcy volume could be conceived to reflect a
Schumpeterian process of productive entrepreneurship,
disbanding outdated structures and contributing to
renewal and new-firm formation
a complementary view is that bankruptcies also may be
disruptive and costly to a wide array of stakeholders
such as investors, creditors, suppliers, and society
Carter and Auken 2006)
. Alongside the Baumolian
framework on how the allocation of entrepreneurship
affects the economic system, this observation has
several similarities with recently developed models on
interlinkages between firms and bankruptcy diffusion:
bankruptcies would have potential Bdomino effects,^
and a bankruptcy may spillover to other firms, creating
a vicious bankruptcy cycle
(Gatti et al. 2006, 2009)
Furthermore, as noted, bankruptcy frauds are
characteristically integrated in the bankruptcy institute;
interlinkages and networks in the economy therefore
represent comparable sources of bankruptcy fraud
diffusion. Analogous to the effects of bankruptcies and
Bstrategic bankruptcies^ and as regularly observed in
the criminology literature, crimes and frauds committed
by business organizations have potential to proliferate
and to lead to the failure of creditors and suppliers—
commonly other firms
(Alvesalo and Virta 2010; Croall
Following these strands of literature, we take on an
explorative approach in the present article and strive to
assess the extent to which bankruptcy frauds could be
linked to changes in the bankruptcy volume. This
research problem also has relevance for policy-makers:
governments identify economic crime as a threat against
general welfare and, in extension, against democracy.
Furthermore, the incentive structure on the market
becomes distorted: businesses that use the bankruptcy
institute for strategic and even criminal purposes may
drive serious firms out of business
Swedish Economic Crime Authority 2017)
We employ comprehensive series on all bankruptcies
and bankruptcy frauds in Sweden during nearly
200 years (1830–2010). Clearly, several factors will
affect the probability for businesses to exit. Past
empirical research has linked variations in the bankruptcy rate,
as well as variations in the rate of (economic) crimes, to
the business cycle
(e.g., Levy and Bar-Niv 1987; Krüger
2011; Detotto and Otranto 2012)
. In line with these
findings, we take these potential linkages into
consideration. Our methodological contribution is that we make
use of and combine several contemporary and historical
sources, including archival materials, that are not readily
available in any public statistical databases; this
approach thus shows how longer economic analyses of
changes in entrepreneurship are possible to conduct.
Furthermore, the autoregressive distributed lag
(ARDL) model, developed by
Pesaran et al. (2001)
employed in the analysis. Recently, the ARDL model
has been applied to the relationship between crimes and
(Narayan and Smyth 2004; Mauro
and Carmeci 2007; Habibullah and Baharom 2009;
Detotto and Pulina 2013)
. The article’s contribution to
the entrepreneurship literature is that we present new
empirical results and approaches for the study of
bankruptcy and on the relationship between bankruptcies and
bankruptcy frauds. Furthermore, we contribute by
showing how the concept of destructive
entrepreneurship can be operationalized and used for an empirical
assessment at the macro-level across time
(Desai et al.
2.1 Destructive entrepreneurship
Since the early 1980s, policy and research have mainly
focused on the Bgood^ side of entrepreneurship.
However, since the early 1990s, more research has attempted
trying to shed light on entrepreneurship’s Bdark sides.^
This discussion has drawn attention to the negative
aspects, seldom mentioned in the literature. Baumol
(1990, 1993) claims that the current understanding of
various forms of entrepreneurship remains incomplete,
almost always focusing on Schumpetarian productive
entrepreneurship. Baumol emphasizes that the
difference between productive, unproductive and destructive
entrepreneurship, and unproductive and destructive
entrepreneurship plays important roles in many, if not
most, economies. According to
entrepreneurship fulfills a central function in the
development of an economy: economic development, and thus
productive entrepreneurship, takes place through the
introduction of innovations.
Baumol (1990, 1993) argues that his own expansion
of the Schumpetarian model, which focuses on the
allocation of entrepreneurship, can enhance our
understanding significantly. Baumol shows that not only the
supply but also the very type of entrepreneurship are
determined by formal and informal institutions. In his
theory, the supply of entrepreneurial talent varies less
than the allocation entrepreneurship: individuals will put
their talent to use in activities that are either productive,
unproductive, or destructive. At least one of the prime
determinants of entrepreneurial behavior at any
particular time and place is the prevailing rules of the game that
govern the payoff of one entrepreneurial activity relative
to another: the relative returns, and thus the allocation of
these activities, are determined by the rules of the game
(see also Douhan and Henrekson 2010; Henrekson and
. Weak and unstable formal institutions,
including informal institutions such as norms and
societal values, might foster unproductive entrepreneurship.
According to Baumol (1990, 2010), unproductive
entrepreneurship may take many forms; rent-seeking via
litigation, corporate takeovers, or tax evasion constitutes
the main contemporary threat to productive
entrepreneurship. Other examples include Bsmart^ speculative
financial transactions (Lindbeck 1988) or so-called
convenience bankruptcies and strategic bankruptcies. These
are not a criminal act per se, but may often work against
the interest of creditors and the public
(Akerlof et al.
does not go into detail on destructive
entrepreneurship, or on how the concept can be
operationalized. Since there is no consensus on what activities
that exactly can be classified as unproductive and
destructive entrepreneurship, the boundary between these
two categories is unclear
(see for example Antony et al.
. However, according to scholars, examples of
destructive entrepreneurship are found in the field of
economic crime and organized crime
(Collins et al.
2016; Douhan and Henrekson 2010)
. The phenomenon
of organized crime is mostly associated with activities
such as the production and distribution of illegal drugs,
1 For instance in Sweden, a Bconvenience bankruptcy^ is carried out in
order to favor the actual debtor. The bankruptcy trustee can hire the
company’s deputy to lead the business operations during the
reconstruction period. The company’s employees may be dismissed;
according to Swedish law, their redundancy payments during the continued
operation will be paid by the State (in Swedish: Lönegaranti).
Unsecured claims rarely receive any dividends at all, which makes debt
restructuring effective (Lag om företagsrekonstruktion, Proposition
racketeering, and blackmail. We often refer to this as
Mafia activities; a Mafia can achieve significant market
positions by selling protection, illegal gambling, or by
engaging in human trafficking or in drug- and
weaponsrelated activities. Other examples of destructive
entrepreneurship in the field of organized crime are the piracy
in the Malacca Sea outside the coast of Somalia; the
emergence of system of state corruption in Russia
, or the smuggling of migrants to Europe
(European Union 2006)
, or the use of the bankruptcy
system for criminal purposes
(Croall 2001, 2004)
Baumol’s (1990) theory has received considerable
attention; nonetheless, past work elaborating on this
theory, though significantly contributing to research
progress, has generally been more theoretical and
(see Acs et al. 2013; Desai et al. 2013)
models generally assume, alike
, that the
supply of entrepreneurs is less varying than the
allocation of entrepreneurship: destructive entrepreneurship
has a negative effect on society and is therefore
rentdestroying; unproductive entrepreneurship is
rent-seeking, and productive entrepreneurship is rent-creating.
Antony et al. (2017)
have presented a model
of endogenous growth, driven by productive
entrepreneurs with an endogenous degree of rent-seeking
(unproductive entrepreneurship). Another recent study is
Elert and Henrekson (2016)
, who introduce the concept
of Bevasive entrepreneurship^—a profit-driven business
activity aimed at circumventing the existing institutional
framework by using innovations to exploit
contradictions in that very framework (inconsistencies in
regulation, lack of judicial precedence, lack of judicial
The difficulties in operationalizing Baumol’s (1990)
theory, as well the problems of measuring the effects
from productive, unproductive, and destructive
entrepreneurship, are generally acknowledged; as stated by
e.g., Bjørnskov and Foss (2008), it is theoretically and
empirically important to distinguish both the supply and
the allocation of entrepreneurship—however, available
data seldom allow for a complete empirical distinction.
In their own attempt, Bjørnskov and Foss (2008) use
business start-ups as an indicator for both the supply and
the allocation of entrepreneurship. They admit that this
captures only fragments of the allocation concept;
furthermore, it does not inform of the degree to which
entrepreneurship is productive, unproductive, or
destructive. Earlier efforts to operationalize the Baumolian
framework include both micro-oriented studies that
utilize either experimental, interview, or survey data
(Collins et al. 2016; Sauka 2008; Sauka and Welter
2007; Urbig et al. 2012)
and macro-oriented research
using aggregate and/or cross-country data
Bjørnskov and Foss 2008; Sobel 2008)
. Albeit using
different data, methodologies, approaches, and different
spatial and temporal dimensions, past empirical work
therefore commonly empirically operationalizes
fragments of the Baumolian framework. For instance,
Urbig et al. (2012)
experimentally examine the
association between individuals’ entrepreneurial intent and
their benevolent/selfish behavior. Individuals who
perceive themselves as being good in doing business invest
in more destructive entrepreneurship. As another
focuses on entrepreneurial
productivity (e.g., venture capital investments, patents,
self-employment) relative to Bunproductive^ political and legal
entrepreneurship (lobbying, lawsuits) at the state level in
the USA. Sobel finds a relationship between having
good institutions and net entrepreneurial productivity
In sum, earlier efforts show that universal
measurements are difficult, if not impossible, to generate
Bjørnskov and Foss 2008)
: some studies focus on how
the institutional framework affects the allocation of
entrepreneurship, focusing on unproductive
entrepreneurship and its effects
(but leaves out destructive
entrepreneurship; e.g., Sobel 2008)
; others focus on the
allocation of entrepreneurship, but not on the effects
(e.g., Collins et al. 2016)
. Empirical operationalizations
of the different concepts of entrepreneurship are
therefore moderately few, and
Desai et al. (2013)
number of key areas for further research on destructive
entrepreneurship: while the literature on the allocation
and determinants of entrepreneurship is expanding, the
specific dynamics, causes, and effects of destructive
entrepreneurship remain ignored. Furthermore, it is
necessary to gain more knowledge on how destructive
entrepreneurship can be a process as well as an outcome.
Finally, further elaborations on temporal dimensions are
necessary. Along the lines of these suggestions, utilizing
the framework of the bankruptcy institute, we strive to
c o n t r i b u t e t o t h e l i t e r a t u r e b y e m p i r i c a l l y
operationalizing destructive entrepreneurship and
attempt to measure the potential effects of destructive
entrepreneurship—bankruptcy frauds—over a very long
observation period. We focus on the relation between
bankruptcy frauds and bankruptcies and we make use of
several different historical and contemporary sources in
order to construct these long series. Alike earlier
attempts, we are confined to employ specific indicators
that partially are able to capture the phenomenon of
destructive entrepreneurship and its potential effects.
2.2 Diffusion of economic crime and bankruptcy fraud
Will variations in bankruptcy frauds—destructive
entrepreneurship—affect the selection mechanism, here
defined as the aggregate bankruptcy volume? It is highly
plausible; empirical studies have provided evidence of
diffusion of personal bankruptcies to stakeholders
(Miller 2015; Mikhed and Scholnick 2014; Scholnick
, and a theoretical explanation for bankruptcy
diffusion in the corporate sector is provided by Gatti
et al. (2006, 2009), building a model on the credit
interlinkages. In their model, macro-economic business
cycles can be outcomes of a complicated interaction
between firms and banks with heterogeneous
conditions. The corporate sector consists of Bdownstream
firms^ and Bupstream firms^; upstream firms supply
intermediate inputs to the downstream firms, which are
pure borrowers. Upstream firms are lenders, supplying
trade credit to downstream firms, but they are also
borrowers from banks. Banks are pure lenders to both
down- and upstream firms. The activities of upstream
firms are principally determined by the production of
downstream firms, and a shock would affect their credit
relationship. If the shock is substantial, borrowers may
not be able to fulfill debt commitments: the default of
one firm will cause the default of another, and the
number of links between firms implies a likelihood of
(Gatti et al. 2009; for a related
approach, see Allen and Gale 2000)
In a similar spirit, a growing field of research on the
aggregate relationship between crimes, economic crimes,
and economic variables shows that crimes have a
crowding-out effect on the economy
Detotto 2016; Detotto and Pulina 2013)
criminologists assert that networks and ties between firms
can be a major source for fraud diffusion. A crime or fraud
committed by a business may ruin other businesses such
as suppliers and creditors; changes in the rate of fraudulent
bankruptcies would therefore diffuse to other business
(Alvesalo and Virta 2010; Baker and
Faulkner 2003; Croall 2004; Wheeler and Rothman 1982)
One conclusion from past research on credit
interlinkages in the corporate sector and research on crime
and the economy is that it is plausible that (economic)
crimes would have substantial effects on the economic
system. By assuming that bankruptcy fraud could be used
as an empirical indicator for destructive
entrepreneurship—a crime committed within the framework of a
Blegal^ business—and, furthermore, that destructive
entrepreneurship may vary over time
overall hypothesis is that there is a positive relationship
between bankruptcy frauds and the bankruptcy volume.
3 Bankruptcies and bankruptcy frauds, 1830–2010
It is reasonable to assume that the rates of bankruptcies
and bankruptcy frauds are dependent on the institutional
framework. Thus, one main determining factor of
entrepreneurial behavior is the prevailing rules of the game
(Baumol 1993; Douhan and Henrekson 2010;
Henrekson and Stenkula 2016)
. A macro-policy factor
which would influence the number of bankruptcies is
changes in bankruptcy legislation. In this article, it is
above all the laws regulating bankruptcy and
bankruptcy fraud that are of interest—for instance, in an
Liu and Wilson (2002)
, e.g., study corporate
failure rates, macro-economic determinants, and
changes in the UK insolvency legislation, 1961–1998. They
find an effect from the 1987 Insolvency Act,
diminishing the failure rates. However, the impact from
the act did not persist in the long term and they conclude
that the apparent effect of that one-time change leveled
off over time. Institutional change is thus a complicated
process that is difficult to measure quantitatively:
changes in the volume of bankruptcies and crime rates can be
outcomes of changes in formal rules, informal
restrictions, and the effectiveness of third-party enforcement.
In addition, institutions commonly change gradually
(e.g., North 1990)
3.1 The institutional framework
When an individual or a business applies for credit or
borrows money, some kind of written or oral agreement
is fulfilled. If there is no repayment, the debtor breaks a
contract—something considered fundamental in every
economy. The bankruptcy institute has several
functions: forcing the closing of non-viable firms; avoiding
fraud and unfair distribution of assets; coordinating
creditors; and resolving disputes between debtors and
creditors. Thus, it forces the payment of debts and may
help to restore debtors. This institute—the Binsolvency
regime^—thus tries to balance several objectives,
among them the weight given to the debtors, creditors,
the management, and to other stakeholders. Different
judicial systems across the world further complicates
balancing different incentives; furthermore, the
bankruptcy procedure is dependent on the efficiency of the
judicial system to enforce rights and objectives, or at
least to serve as a credible threat
(Claessens and Klapper
2005; Gratzer 2008; LoPucki 1982)
Can the institutional framework explain the changes
in the volumes of bankruptcies and bankruptcy frauds?
One complicating factor is that punishable bankruptcy
frauds periodically have been regulated in both the
Bankruptcy code, in the Penal code, and in the
Commercial code. In Sweden, these three codes have
overlapped in a complicated manner and cannot be described
separately. From a principal focus on the distribution of
assets, insolvency law is today viewed as an important
part of national growth through the rules of
reconstruction (equivalent to Ch.11 in the USA), rules for saving
capital, and for securing job opportunities. Insolvency
law in Sweden has developed from a creditor-debtor
focus to a stakeholder perspective
In Sweden, a first separation of punishable and
nonpunishable bankruptcies was introduced in the
mid1600s (for a comprehensive overview, see Tuula
2001). During the long period of observation, the
penalty for bankruptcy fraud in Sweden has varied, but the
definition of this particular offense has remained
essentially intact, pertaining to carelessness and/or dishonesty
towards creditors. In Sweden, penalty for carelessness
towards creditors was introduced in the Bankruptcy
code of 1818. Over time, several factors served to
gradually depersonalize the belief of the causes of
bankruptcy: the emergence and proliferation of joint-stock
companies as economic organizational form from 1848;
changes in the credit market; and the recognition of
business cycles. A more varied picture of the reasons
for economic failure slowly emerged and many
countries, among them Sweden, established modern
bankruptcy laws in the mid-nineteenth century.
Consequently, bankruptcy was increasingly perceived as an
economic failure rather than a moral one
In the new Bankruptcy code of 1862—replacing the
code from 1818—carelessness towards creditors (fraud)
was introduced, and in the Penal code of 1864, the
penalty regulations in Chapter 23 were entered under
the heading Bon debtors in bankruptcy that are
fraudulent or careless.^ From 1921, both these crimes were put
under public prosecution. After a reform of the Penal
code in 1942, five special debtor’s crimes—bankruptcy
frauds—were included: dishonesty to creditors; grave
such dishonesty; carelessness towards creditors;
favoritism to creditors; and bookkeeping crimes. All
regulations on debtor’s crimes were transferred to the Penal
Code, without any factual changes. The Bankruptcy
code from 1862 was succeeded by a new code in
1921, effective from 1922. Neither of these two codes
was radical in the sense that it could be expected that the
volume of bankruptcy filings would be affected by these
legal changes: in several respects, parts of former laws
remained or were modified in the new laws, and the
reforms mainly concerned procedural matters (Tuula
2001). Thus, the code from 1862 did not result in any
distinct break with the previous legal development
(pre1860s), and the reasons for the new code in 1921 were
that it had become obsolete Again, focus was mainly on
procedure and on lowering the administrative costs. A
reform in the early 1970s, furthermore, had the goal to
simplify voluntary agreements among parties,
something that theoretically would depress the bankruptcy
rate. In 1987, the legal framework that is effective today
was introduced. In this bankruptcy code, effective from
1988, the Bankruptcy code of 1921 has remained
practically unchanged; several rules and regulations from the
code of 1921 were transferred to the new code
(Mellqvist and Welamson 2017; Swedish Government
. One major change in the law on corporate
reconstruction (corresponding to the US Ch.11) became
effective from 1996, with the purpose of lowering the
bankruptcy rate and saving insolvent firms. One decade
after this most recent major reform, it was established
that it did not have the intended effects: in relation to all
bankruptcy filings, very few businesses applied for
(Karlsson-Tuula 2006, 2011)
3.2 The empirical picture
Figure 1 reports the volume of all registered
bankruptcies and registered bankruptcy frauds in Sweden, 1830–
2010. As can be observed in Fig. 1, Sweden has had a
very varied development in the bankruptcy volume
(registered bankruptcies). A periodization of the
bankruptcy development during the entire observation period
shows a low and quite stable bankruptcy level between
1830 and the late 1850s. A phase with relatively higher
levels commences approximately in the 1860s and ends
in the early 1920s. During this phase, the trend increases
Registered bankruptcy frauds
positively and has higher variation than before. The
lowest observed value (1873) can probably be explained
by an economic boom, and the highest observed value,
in 1868, corresponds to a severe recession that resulted
in Sweden’s last famine. The bankruptcy peaks in 1921,
1933, and in the early 1990s are most likely caused by
the three most severe economic crises during the
From 1933, there is a fall in the bankruptcy trend
up to end of World War II (1939–1945). Overall, the
entire period after the war (1946–2010) witnesses a
significant increase in the bankruptcy volume—from
778 bankruptcies in 1946, to a nearly ten times higher
level in 2010. In historical perspective, the relatively
low and stable bankruptcy volume between the 1940s
and the mid-1960s is remarkable. Reasonable
explanations are the heavily administrated and regulated
economy during the war, the economically
prosperous post-war period, and a delayed and pooled
demand for Swedish exports. From the mid-1960s, the
volume increases considerably and the period c.1985
to 2010 records the highest bankruptcy frequencies.
The general increase in the early 1990s and the
bankruptcy peak in 1992 coincide with government
failures: a crisis in the financial and real estate markets
raged in full due to credit market deregulations and
unemployment soared (
Lönnborg et al. 2003
Figure 1 also reports registered bankruptcy frauds.
The development of bankruptcy frauds is somewhat
different from the bankruptcy volume. With variation,
the level of bankruptcy frauds is historically low up to
the 1870s/1880s. From here to the mid-1940s, the level
increases, and the fraud volume takes off from the late
1940s/early 1950s—particularly from the 1950s (as
noted, the bankruptcy volume takes off some 20 years
later). One factor that complicates the picture in the
post-war period is the strong fluctuations in the 1960s
and 1970s; however, the fraud trend is positively rising
in a linear manner from the 1960s. From visual
inspection of Fig. 1, it can be noted that some periods of peaks
and troughs in bankruptcy frauds appear to correspond
to the general macro-economic development: for
example, the level is reduced during the two World Wars
(1914–1918; 1939–1945); similarly, frauds increase
distinctively in the speculative years of the 1980s. They fall
in the early 1990s but increase again in the following
economic recovery. In 2001, there is a fall in the fraud
volume, coinciding with the BDot.com^ crisis.
Our overall assessment of the changes in the volume
of bankruptcies and bankruptcy frauds, between 1830
and 2010, is that it could be questioned if major changes
in the formal institutional framework have had
observable effects. Scholars have noted that recent reforms
have not resulted in any noticeable changes in the
(Karlsson-Tuula 2006, 2011)
observation is partially reflected in the data of the present
article; from visual assessment of the development of
the bankruptcy volume—and the bankruptcy fraud
volume—it is generally difficult to discern any direct or
immediate relationship with major institutional reforms
across time. The volume of bankruptcy frauds takes off
distinctly from the early 1950s, i.e., around a decade
after the reform of the Penal code in 1942 (and appears
to occasionally vary with the macro-economic
development). Furthermore, legal changes and reforms on
insolvency—the early 1860s, early 1920s, early 1970s,
late 1980s, and mid-1990s—do not directly appear to
correspond to changes in the aggregate bankruptcy
volume. For instance, neither the reform in the early 1970s
nor the reform in the mid-1990s did not distinctly appear
to affect the bankruptcy trend. More importantly and as
shown in the formal analysis (Sect. 5), we are not able to
identify structural breaks in the bankruptcy series that
would correspond to these institutional reforms.
4 Research framework
With economic crime and in both national and
international contexts, we here mean crime occurring in
economic activities in or in relation to an essentially legal
(Swedish Government 2008; definitional
problems and conflicts are well-documented in academic
literature, see Larsson 2001)
. In our study, bankruptcy
frauds relates to all individuals sentenced for this
offense; regardless of data source, bankruptcy fraud is
consistently measured throughout the period of
observation in the sense that the view of crime and dishonesty
towards creditors has not changed substantially over
time.2 This offense is detected after a firm has filed for
2 The following sources and databases have been used for constructing
the series on bankruptcies and bankruptcy frauds. Statistics Sweden:
Bidrag till Sveriges officiella statistik (BISOS), Rättsväsendet:
Sammandrag af justitie-statsministerns underdåniga embetsberättelser
för åren 1830 till och med 1856, and Utdrag ur Hans Exc. Herr
justitiestatsministerns underd. Brottmålsberättelse för åren 1857 och 1858 (af
O. Carlheim-Gyllensköld) I: 141–150; III:223–235; Statistisk tidskrift,
1860–1913 (including supplements); Statistisk tidskrift, 1952–1984
(Stockholm: Norstedt); Sveriges officiella statistik (1870–1913);
Statistisk årsbok för Sverige (1914–2010); Rättsstatistisk årsbok,
1975–1984 (Stockholm/Örebro: SCB förlag). In addition, statistical
databases from both Statistics Sweden and BRÅ (The Swedish
National Council for Crime Prevention) have been used; BRÅ,
Kriminalstatistik: elektroniska databaser över anmälda-,
handlagdaoch lagförda brott.
bankruptcy, and it has thus occurred prior, or in direct
relation, to the bankruptcy event.
Generally, economic crimes have not been
systematically studied by historians; organized analyses of the
extent to which economic crimes were noticed by
authorities during the nineteenth century in Sweden are
still lacking. Furthermore, Swedish printed historical
statistics are largely restricted to traditional
criminality—that is, violence and theft crimes—and it is
generally difficult to identify economic or corporate crimes
von Hofer 2011
). Crimes against
creditors, bankruptcy frauds, are relatively few,
equivalent to 2–3% of the total of all economic crime. Like all
types of statistics, it is generally problematic to
apprehend Btrue^ rate of the variable of interest. This is also
the case for bankruptcy frauds; like all crime statistics,
only detected, reported, and/or convicted frauds appear
in the statistics
(Ahlberg 1999; Korsell 2015)
general, empirical case studies and cross-sectional
investigations on bankruptcy frauds in both Sweden and
internationally show that the bankruptcy fraud rate may vary
between 30 and 90% of all registered bankruptcies
(Kedner 1975; Langli 2001; Liebl 1988; Magnusson
1999; Weyand 1997)
. According to a recent
investigation by the Swedish Enforcement Authority ( 2010),
notifications for or suspicion of crime were made in
35% of all registered bankruptcies in 2010. In sum, this
means that the exact numbers remain unknown. The
main reason for why we, in spite of this, choose
bankruptcy frauds as a proxy for destructive entrepreneurship
is that other longitudinal, unbroken series on other types
of business-related crimes in Swedish statistics are
Another challenge for all longitudinal studies is that
the object of study transforms over time. Between 1830
and 2010, the composition of the total stock of
bankruptcies has changed: in the beginning of the period of
investigation, and different from today, bankruptcies
consisted predominantly of individual persons’
bankruptcies (as well as of a smaller part of bankruptcies
related to inheritance of estate).3 At that time, the
organization of business was mainly conducted within the
framework of the family or the household. Modern
forms of organization of business commenced in 1848
with the introduction of the joint-stock company, but
only very large businesses, such as mines or railroad
3 In this type of bankruptcies, the heirs to an estate refrained from
inheritance in order to avoid debts to creditors.
firms, initially transformed to joint-stock companies. It
would take several decades until business forms such as
the joint-stock company, trading companies, or sole
proprietorships would spread to trades dominated by
small firms. However, the absolute majority of the
bankruptcies in our data concern small business failures.
This is also true in a historical setting: in early twentieth
century Sweden, more than 80% of all the registered
bankruptcies were personal bankruptcies, consisting of
individuals in small-scale businesses and trades: master
craftsmen, journeymen, tradesmen, and traveling
(Statistics Sweden 1923)
. Over the course of time,
the composition of bankruptcies transformed,
dominated by formal business firms—in particular by small
joint-stock companies. In a relative sense, joint-stock
companies are today over-represented in the Swedish
bankruptcy statistics, while personal bankruptcies
amount to around 6–7% per year of the total bankruptcy
4.1 Data and variables
A critical evaluation of the sources and data used in this
article gives the following: historical and contemporary
statistics on bankruptcies are generally reliable across
time. Records and databases on economic property,
transactions, and financial behavior are
well-documented, since it lies in the interests of both the government,
the public, and the market (however, one problem with
collecting historical statistics on bankruptcies has to do
with the organization of the Swedish judiciary).4 Data
on criminal behavior and economic crimes, and thus
registered bankruptcy frauds, are relatively more
uncertain (a fact well-known in the criminology literature):
only detected, reported, and/or convicted bankruptcy
frauds are made visible. Overall, there is no
straightforward way to estimate the Bactual^ rate
. Social scientists are generally guided by and are
4 Until 1849, there were six different district and municipal courts on
the countryside and in the towns where bankruptcies could be filed.
During the 1800s, bankruptcies could be filed in several courts
(including academic courts; universities had their own jurisdictions).
Earlier, court statistics on bankruptcies were not published annually;
yearly figures must therefore be collected in different court archives—a
time-consuming task. We have no precise knowledge of the accuracy
of authorities when registering cases. From 1822, the provincial
governor’s reports are printed every 5 years. From 1856 to 1860 through
1901 to 1905, the reports were sent to Statistics Sweden. Even today,
we can encounter different information on the number of bankruptcies
in public statistics
(Gratzer and Tuula 2007)
forced to employ official statistics. Activities that fall
outside the purview of government accounting—such as
Bshadow economy^ activities—when using indicators
on GDP, trade, and investment (which we normally
accept as Bobjective^) are not included and often
problematic to approximate
(Fleming et al. 2000)
From the various sources, we have constructed three
longitudinal series of non-interrupted data: (i) annual
series on all registered bankruptcies; (ii) series on
court convicted offenses on bankruptcy, i.e.,
bankruptcy frauds, and (iii) series on the difference
between total bankruptcies and fraudulent bankruptcies.
The latter variable is the dependent variable. The
reason for this approach is the following. For any
calendar year, the total number of bankruptcies will
always entail a certain number of bankruptcy
frauds—that is, each bankruptcy fraud is also a
bankruptcy event, thus counted twice in the statistics. The
average share of bankruptcy frauds in relation to all
registered bankruptcies for the whole observation
period is 5.2%, but is varying between 0.27 (in the
year of 1853) and 26.2% (in 2008). This implies that
some bankruptcy also is bankruptcy fraud events; this
is the motivation for using the net bankruptcy volume
as dependent variable.
Furthermore, we employ an indicator for the business
(growth rate of the gross domestic product; Schön
and Krantz 2015)
. Since long, economists have assumed
that firm behavior such as the frequency of establishing
and closing down firms systematically covaries with the
(Birch 1987; Koellinger
and Thurik 2012)
. In an upturn, business exits and
bankruptcies are claimed to decrease and vice versa.
Overall, the empirical literature provides evidence of
the relation between exit rates and bankruptcy volume
and the business cycle at both the micro-level
Everett and Watson 1998; Bhattacharjee et al. 2009)
and the macro-level
(Levy and Bar-Niv 1987; Balcaen
and Ooghe 2006; Hol 2007)
. It would be surprising if
crime rates were immune to the business cycle (Cook
and Zarkin 1985). Empirical studies have found that the
effect of real economic activity is different between
different types of crimes. Property crimes, violence
crimes, and sex crimes are countercyclical, while the
reverse is true for economic crimes due to increasing
opportunity during economic expansions
et al. 2012; Krüger 2011; Povel et al. 2007)
example, Detotto and Otranto (2012) have recently found that
economic crimes, including bankruptcy frauds, display
a sensitivity to macro-economic conditions. Overall, we
could expect a negative relationship between changes in
bankruptcies and changes in economic growth:
bankruptcies increase during economic downturns and
recessions and are reduced during economic upturns.
Therefore, and following past research results,
bankruptcy crimes should reveal a positive relationship. As
noted, we use GDP growth between 1830 and 2010
(Schön and Krantz 2015)
as an indicator for the business
cycle. Indeed, indicators such as the unemployment rate
could be more suitable for this task; however, such long
series are not available. According to Okun’s law
1962; Baily and Okun 1965)
and the Bdifference
, there is a stable, negative
relation between GDP growth and changes in the
(Ball et al. 2014)
We employ data on GDP growth, GGDP, along with
the three variants of our bankruptcy data: the total
registered number of bankruptcies, RB; the total
number of registered bankruptcy frauds, BF; and finally
the net series obtained by subtracting the bankruptcy
fraud series from the total registered bankruptcy
series. For simplicity’s sake, we call this third series
Bbankruptcies,^ B. Our main exploration aims at
identifying the long-run relationship, and we regress
GDP growth and bankruptcy frauds on bankruptcies.
Following the discussion in Sect. 4.1, we also carry
out two additional regressions: the long-run relation
between GDP growth and bankruptcies (dependent)
and the relation between GDP growth and
bankruptcy frauds (dependent), respectively. These results,
which essentially build on the same methodology as
the main analysis (see Sect. 5.1), are discussed in the
end of the present section (see also Appendix). In
short, these results show that we fail to establish
long-run relationships between the variables.
Our analysis focuses on the logarithm of Bankruptcies,
lnB. Thus, we analyze the relationship between lnB and
GGDP. More importantly, following the main
hypothesis in the article, we explore whether there is any
spillover or diffusion effect from bankruptcy frauds, lnBF,
on lnB in the long run. Hence, we introduce lnBF as
α2 ¼ ∑qip1¼1θθi−1
α1 ¼ ∑
α3 ¼ ∑i¼q02 iθBF
βi ¼ −∑m¼iþ1θm
β0GGDP ¼ θ0GGDP and βiGGDP ¼ −∑q1 θGGDP; for i > 0
0 ¼ θ0BF and βiBF ¼ −∑qm2¼imþ¼1iþBm1F ;mfor i > 0:
5 We disregard the time trend in Eq. (3), since BF is trended.
additional independent variable in our regression model.
The long-run relation is as follows5
lnB ¼ μGGDPGGDP þ μBFlnBF þ v
where μGGDP and μBF are the long-run coefficients.
μGGDP refers to impacts from 1% age point change in
GGDP on B (μGGDP ∗ 100%). At the same time, 1%
change in BF would lead μBF % changes in bankruptcies
B in the long run. The ARDL model has been widely
used in economic studies, including crime and
(e.g., Narayan and Smyth 2004; Detotto and
, and the regression is based on this model:
lnBt ¼ α0 þ ∑ip¼1θilnBt−i þ ∑iq¼10θiGGDPGGDPt−i
þ ∑iq¼20θiBF lnBFt−i þ ut
The least squares estimation can be applied here, and
the determination of p, q1, and q2 can rely on Akaike
and/or Schwarz information criteria. At the same time,
the determination should also take autocorrelation of u
into consideration. In general, we need to increase p, q1,
and q2 if autocorrelation appears. Note that if any of the
involved variable(s) is (are) non-stationary, the
distribution of estimates from Eq. (2) would not be Gaussian.
Thus, conventional t and F tests may not be valid when
I(1) process is involved. In such a situation, it is
important that the relation of Eq. (1) is a cointegration relation
(Pesaran et al. 2001)
. The existence of cointegration can
be identified by the bounds test. According to
and Wolters (2006)
, we may reparameterize Eq. (2) into
ΔlnBt ¼ α0 þ α1lnBt−1 þ α2GGDPt−1
þ α3lnBFt−1 þ ∑ip¼−11βiΔlnBt−i
þ ∑iq¼2−01βiBF ΔlnBFt−i þ ut
The null hypothesis of bounds test is
corresponding to no cointegration. Since
distributions of estimated coefficients are non-Gaussian, the
standard F test is not valid. The bounds test affirms
rejecting the null when the F statistic exceeds the
upper bound provided in
Pesaran et al. (2001)
Besides autocorrelation, we also carry out other
diagnostic tests: heteroscedasticity, autoregressive
conditional heteroscedasticity (ARCH), and RESET. For
coefficients stability, we carry out the Ramsey’s
RESET, cumulative sum of the recursive residual
(CUSUM), and cumulative sum of the recursive
residual squares (CUSUMSQ). These diagnostic tests
are designed for making the regression results
reliable. Note that dummy variable(s) would help to
overcome specification errors due to possible
structural breaks. However, as clarified by
Pesaran et al.
, this procedure would not have any sensible
and meaningful interpretation for the equilibrium
condition. In addition, the ARDL model with the
bounds test approach is robust to the degrees of
integrations of involved variables. Specifically, the
regression result would be valid no matter if GGDP
and lnBF are pure I(1) or I(0) process. Furthermore,
the regression is also valid when GGDP and lnBF are
cointegrated. With optimally selected p, q1, and q2,
the ARDL model can also take care of possible
endogeneities of GGDP and lnBF.
Equation (3) can be further renormalized in the form
ΔlnBt ¼ α0 þ α1ðBt−1−μGGDPGGDPt−1−μBF BFt−1Þ
þ∑ip¼−11βiΔlnBt−i þ ∑iq¼1−01βiGGDPΔGGDPt−i
þ∑iq¼2−01βiBF ΔlnBFt−i þ ut
where the long-run coefficients μGGDP≡−αα21 and μBF≡−αα31 .
α1 measure the short-run adjustment speed of
bankruptcies according to disequilibrium.
Table 1 provides descriptive statistics, and Table 2
reports the statistics of unit-root tests. We consider the
augmented Dickey-Fuller (ADF) test
(Dickey and Fuller
based on the asymptotic critical values from
Phillips-Perron (PP) test
(Phillips and Perron 1988)
Elliot, Rothenberg, and Stock (ERS) test
(Elliott et al.
, and Kwiatkowski, Phillips, Schmidt, and Shin
(Kwiatkowski et al. 1992)
The ADF, PP, and ERS tests have the null of
nonstationarity, but the KPSS has the null of stationarity.
We also carry out a unit-root test with structural
breaks in the intercept and time trend along the line
Vogelsang and Perron (1998)
Zivot and Andrews
Banerjee et al. (1992)
endogenously determine break dates from data by using the
F statistic for the break coefficients. Furthermore,
, we consider two types
of break dynamics: the innovational outlier and the
additive outlier models, respectively. The
innovational outlier model assumes that the break takes place
gradually, while the additive outlier model considers
the break to take place immediately.
We start with the growth series: ADF, PP, and
ERS clearly reject the null of unit root for GDP
growth, GGDP, growth of registered bankruptcies
(ΔlnRB), and the growth of bankruptcies, ΔlnB. At
the same time, KPSS cannot reject the null of
stationarity for all growth rates at 5%. We can conclude
that all growth rates are I(0). For lnRB and lnB,
ADF, PP, and ERS cannot reject the null of unit root
at 5%. At the same time, KPSS rejects the null of
stationarity at 5% for both variables. We also check
the unit root with break, for lnRB, we find that with
the assumption of innovational outlier, the unit-root
null can be rejected at 5% and the estimated break
year is 1933 (coinciding with the deep recession of
the early 1930s). The unit-root test in sub-samples
divided with the break year, 1933, shows that lnRB
is not stationary in the second sub-sample period.
On the other hand, with the additive outlier
assumption, the unit root can only be rejected at 10%.
Overall, lnRB is not stationary and thus I(1)
process. Concerning lnB, the unit-root null cannot
be rejected at 5% for both outliers. Consequently,
† Unit-root test with the intercept and time trend
†† Non-stationarity cannot be rejected the unit-root tests in the second subsample period
††† Stationarity cannot be reject by KPSS in both subsample periods
lnB is not stationary and is thus I(1) process. The
unit-root tests for bankruptcy frauds, lnBF, show
some inconsistencies: ADF, PP, and ERS reject
the null of unit root at 5%. However, KPSS also
rejects the null of stationarity at 1%; therefore, we
further carry out the unit-root tests with a break.
According to both approaches, the null of unit root
with break can be rejected at 5%. With an innovation
outlier, the break year is 1933 but with the additive
outlier the break year is 1941. The unit-root tests are
carried out in all corresponding sub-sample periods.
lnBF is stationary in all sub-samples; thus,
bankruptcy frauds is trend stationary.
The results of the bounds test and the estimation
of the ARDL-ECM model, Eq. (4), are reported in
the first column of Table 3 (full sample, without
outlier dummies). The result shows that the optimal
lags are all 1 based on AIC. We fail to pass the
heteroscedasticity test. In solving this, the
standardized residuals and identify outliers which have
standard deviations more extreme than 3 (or − 3) is
predicted. Four outliers are discovered: the years
1919, 1921, 1945, and 1991; alike the break year
of 1933, these years all coincide with substantial
economic, political, and social changes, but no
break year is directly associated with changes in
the legal frameworks related to insolvency in
general or to bankruptcy fraud. Dummy variables are set
up to capture the identified outliers; the dummies are
treated as exogenous variables when the ARDL
model is re-estimated. The result is reported in the
second column of Table 3 (full sample with outliers
dummies); note that the null of homoscedasticity is
The Pesaran, Shin, and Smith bounds test shows
that we may reject the null of no level relationship
at 1%; we conclude there is a long-run linear
relationship between bankruptcies, per capita GDP
growth, and bankruptcy frauds. Both long-run
coefficients are significant. The coefficient for GDP
growth is significant at the 5% level, and the one
for bankruptcy frauds is significant at 1%. The
GDP growth coefficient indicates a negative
impact from GDP growth; essentially, accelerated
GDP growth leads to lower bankruptcies. The
coefficient is about − 0.12, implying that one
percentage point increased output growth would lead to
about a 12% decrease in the bankruptcy volume (note
that growth is already in the unit of percentage). At
the same time, the coefficient for bankruptcy frauds
is positive at a value of 0.31. This suggests that a 1%
increase in bankruptcy frauds—destructive
entrepreneurship—would lead to a 0.31% increase in
bankruptcies. The result in Table 3 (second column) also
shows that the adjustment coefficient α1 is
approximately − 0.11, suggesting that bankruptcies would
adjust to remove the disequilibrium. The speed is
about one tenth of the gap per year—in other words,
it would take about 10 years to eliminate the
disequilibrium. The main implication from this is that
FPSS reports the PSS’s F statistics for the bounds test. aaa indicates the rejection of the null of no cointegration hypothesis at 1%. Dummy
variables = d(year). χ2heter, χa2uto (4), RESET(2) and ARCH(2), CUSUM and CUSUMSQ provide the diagnostic statistics for Breusch-Pagan’s
heteroscedasticity, the Breusch-Godfrey’s LM serial correlation with 4 lags, the Ramsey’s RESET function form with 2 lags, ARCH with 2
lags, CUSUM and CUSUMSQ stability of parameters
*p < 0.1
**p < 0.05
***p < 0.01
bankruptcies actively respond to any disequilibrium
caused by either GDP growth and/or bankruptcy frauds.
Furthermore, all short-run coefficients are significant,
indicating that there are Granger causalities from GDP
growth and bankruptcy frauds to bankruptcies.6
We also carry out a robustness test by dividing the
sample period into two sub-sample periods.7 The break
year is determined according to the following criteria:
first, the number of observations in sub-sample periods
should roughly be similar. Second, since we identify a
6 We do not carry out the full version of Granger causality test since our
long-run residuals from Eq. (1) are not estimated independently.
7 An alternative way to carry out the out-sample forecasting. Since we
are interested in possible breaks caused by possible institutional change
and economic events, we decide to adopt the sub-sampling approach.
cointegration relation, the least squares estimates now
follow standard distributions; thus, the approach
Bai and Perron (1998)
to identify structural
break(s) can be applied. However, as pointed out by
Pesaran et al. (2001)
, ARDL model is not suitable for
capturing structural changes with dummies; therefore,
we re-estimate Eq. (3) and carry out the bounds tests for
all sub-samples again. The chosen break year is
1933/1934,8 and results are reported in the two columns
farthest to the right in Table 3
and 1934–2010, respectively)
It can firstly be noted that there is a change in lag
structures: in the second sub-sample period, the optimal
lag for GDP growth, q1, is 4, according to AIC. Second,
the explanation power is, somehow, changed. While the
estimation in the first sub-sample has not improved in
comparison with that in the whole sample, the
estimation in second sub-sample has improved in terms of R
squares, adjusted R squares, AIC, and SIC. Third, by
keeping the outlier dummies, we do not identify
specification errors in both sub-sample periods (RESET test
is significant at 10% in the first sub-sample). Notably,
the impact from GDP growth becomes insignificant in
the first period, indicating that GDP growth might have
no profound effects on the bankruptcy volume prior to
the 1930s, while it becomes crucial after 1934 in our
analysis. Similar to the whole sample, it can be observed
that accelerated growth rates would lead to lower
bankruptcies. The diffusion effect from bankruptcy frauds
remains robust and is significant in both sub-samples.
The magnitudes of these effects are larger in both
subsamples, 0.45 and 0.57, respectively, in comparison with
0.31 in the whole sample estimation; furthermore, if we
compare the two sub-sample periods, the diffusion
effect seems to be enhanced. We also note that
bankruptcies are more sensitive to the disequilibria in the
subsamples (− 0.29 and − 0.17, respectively) in comparison
with − 0.11 for the whole sample estimation; however,
that sensitivity is weakened in the second period. As for
the short-run coefficients, in the first period, only
bankruptcy frauds Granger-cause bankruptcies in the first
period. At the same time, GDP growth loses prediction
power. In the second period, the opposite is true: GDP
8 When two breaks are allowed, the Bai and Perron approach identifies
1991 as an additional structural break. But the sub-sample period
1991–2010 is too short.
provides critical values of
bounds tests for small size of samples; however, the minimum size
requires 30 observations—thus, we decide to use one break only.
growth Granger-cause bankruptcies, while bankruptcy
frauds lose power to predict bankruptcies.
We also shortly summarize the two complementary
analyses (details are found in the Appendix). First, we
study the impacts of GDP growth on registered
bankruptcies, and we identify the same break years as in the
main analysis. The model suffers from specification
errors. More importantly, GDP growth—the business
cycle—reveals no impact on registered bankruptcies
(RB). At the same time, we identify the same break year
for carrying out the robustness test: GDP growth is
nonsignificant in both sub-sample periods. GDP growth
Granger-causes registered bankruptcies in both the
whole and the second sub-sampler periods but not in
the first sub-sample period. The result is robust: there
are no significant impacts of GDP growth on
bankruptcies in all sub-sample periods. Furthermore, we analyze
the relationship between GDP growth and bankruptcy
frauds. Since bankruptcy fraud is trend stationary, we
employ the OLS method and find no evidence of that
GDP growth would have any impact on bankruptcy
frauds. By applying the Bai and Perror
approach, we find structural breaks in the
years 1875 and 1915, which do not correspond to any
known major changes in insolvency legislation.
Our main analysis (Table 3) between the bankruptcy
volume, economic variation, and bankruptcy frauds
shows a similar pattern: we cannot directly link structural
breaks in the bankruptcy trend to institutional changes
over time in Sweden. Rather, as revealed, a break occurs
in the early 1930s; from this period, both macro-economic
variation and bankruptcy frauds appear to have more
profound impacts on the bankruptcy volume in
comparison to the previous period (pre-1933). In
applying a long period of investigation, we can establish
that there is a long-run relation between bankruptcies
and bankruptcy frauds, and that the impact from
bankruptcy frauds on the bankruptcy volume has
increased over time. Thus, our results support the
notion in the literature that destructive
entrepreneurship may have effects on the economic system
Desai et al. 2013; Douhan and Henrekson 2010)
6 Conclusions and discussion
The bankruptcy institute is a mechanism for both
selection and for the regulation of credit relationships
(Claessens and Klapper 2005; Miller 1991; Schumpeter
. Within this system, entrepreneurial activities may
be both productive, unproductive, and destructive
Akerlof et al. 1993; Croall 2001, 2004)
. In the present
study, we have empirically operationalized destructive
(Baumol 1990, 1993)
frauds at the aggregate level across 180 years in Sweden.
Past research has identified criminal activities as one
form of destructive entrepreneurship
(e.g., Douhan and
. Obviously, bankruptcy fraud is one of
several potential indicators that could be used. In
practice, however, and since there are no universal
definitions, comprehensive measures are often very difficult to
construct. Thus, we are often confined to utilize data that
measures fragments the supply and allocation of
(e.g., Bjørnskov and Foss 2008)
Baumol shows that not only destructive
entrepreneurship has many different faces across historical
periods, economies, and activities but also destructive
entrepreneurship may also vary over time due to
changes in the institutional environment. In this study, we
have found no direct support for the hypothesis that
the allocation across productive and destructive
entrepreneurship is determined by institutions and
institutional change. Specifically, our methodological strategy
could not identify any particular years or periods that
would coincide with major changes in the formal
institutional framework for neither bankruptcy nor
bankruptcy fraud. Yet, it seems unreasonable to conclude that this
would be the case: past studies show that cross-country
variations in bankruptcies can be explained by the legal
(Claessens and Klapper 2005; Liu and
. However, the present study, as well as
(Karlsson-Tuula 2006, 2011)
, has not been
able to link distinct institutional changes to any
profound variations the bankruptcy volume in Sweden.
Future research and other data would be able to
investigate these linkages more in detail.
The essential definition of bankruptcy fraud in
Sweden has principally remained intact: fraud or carelessness
towards creditors. The majority of bankruptcy frauds and
bankruptcies concern small business activity—in our
time as well as historically. Presently, we can only
speculate on the causes of this increase. More importantly,
and in line with past research, we have made an effort to
link bankruptcy frauds to variables that reflect economic
behavior—in our case: the aggregate bankruptcy volume.
In line with recent suggestions, we have focused on key
issues relate to the dynamics, causes, and effects of
destructive entrepreneurship, applying a distinct temporal
(see Desai et al. 2013)
. What we
have shown, at the aggregate level, is that destructive
entrepreneurship not only varies over time but also may
have effects on economic agents and thus on the selection
mechanism. We were unable to establish any apparent
linkages between the bankruptcy fraud volume and the
business cycle. Hence, opposed to past results
Krüger 2011; Detotto and Otranto 2012)
, we found no
support for the case of Sweden; similarly, we could not
directly link changes in the overall bankruptcy volume to
macro-economic variation (see Appendix). Several
studies have attempted to assess the link between
bankruptcies and the business cycle
(Levy and Bar-niv 1987; Hol
. However, efforts to link variations in bankruptcies
to the cycle might have to control for or consider the fact
that the total number of bankruptcies will always consist
of bankruptcy frauds.
However, when using the net bankruptcy rate, a
negative relationship between bankruptcies and the
cycle could be verified. More importantly, in the main
analysis, our results show that the net bankruptcy
volume varied positively with bankruptcy frauds: periods
of 'boosts in the fraud volume were significantly related
to increases in the aggregate bankruptcy volume. Our
interpretation of this result is that increases in
bankruptcy frauds would have diffusion or spillover effects. If
fraudulent bankruptcies increase, the bankruptcy risk for
firms that have claims on or other types of relationships
with the former may increase. Several strands of
literature maintain that both business failures in general and
economic crimes and frauds have propensity to diffuse
to other agents—which often are other economic
(Baker and Faulkner 2003; Croall 2004; Gatti
et al. 2006, 2009; Miller 2015; Mikhed and Scholnick
2014; Wheeler and Rothman 1982)
. We have
established that bankruptcy frauds have varied over
longer periods—and that this particular type of
destructive entrepreneurship generally has increased. The
analysis furthermore indicates that the impact from
bankruptcy fraud has magnified over time. Again, while it is
theoretically and empirically desirable to separate and
distinguish the three concepts of entrepreneurship, it is
often difficult to accomplish this empirically in.
Admittedly, our own definition of the concept is not an ideal
one. These empirical findings are in line with the
literatures on diffusion and the Baumolian framework
et al. 2013; Baumol 1990; Desai et al. 2013)
that destructive entrepreneurship is rent-destroying and
that it would have effects on the economic system.
Our empirical results raise several questions. Our
findings point to that changes in destructive
entrepreneurial behavior—variations in bankruptcy frauds—
would affect the selection mechanism. As noted in
passing, research that uses aggregate bankruptcy data in
attempting to capture the effect from economic cycles
might have to consider that bankruptcy frauds are
integrated in the statistical data. More importantly, and
turning to the core question in our article, future
analyses of our data could utilize other analytical methods,
such as Poisson or negative binomial regression models.
Additionally, we may have measurement errors in the
present study: we only observe reported bankruptcy
frauds, and changes in frauds would lead to changes in
both reported and unreported frauds. The long-run
relation can also be studied by using the dynamic ordinal
least squares (DOLS) method which takes leads and lags
into consideration. Finally, an asymmetric impact could
provide evidence for a rejection of spuriousness;
nonlinear impacts with a non-linear ARDL (NARDL)
model could be a fruitful way—using the same dataset and
the NARDL framework,
Box et al. (2018)
results of the present study.
Future empirical research on the linkages between
bankruptcies and frauds—both analyses of our own our
data and other datasets on bankruptcies—should focus in
detail on, and attempt to measure, specific changes in
policies and actions taken by governments and authorities;
the period of analysis does not have to be as extended as in
the present article. We mean that this could make progress
for analyses of the effects of various forms of destructive
entrepreneurship. Future studies should elaborate more on
temporal dimensions and how changes in the institutional
framework would affect bankruptcies—both at the
macroand micro-levels. Furthermore, and as shown by this study,
future empirical research should also further attempt to
study the interlinkages between firms and agents in the
economic system as well as within the bankruptcy institute
(e.g., Gatti et al. 2006, 2009)
: the extent to which both
bankruptcies and bankruptcy frauds may diffuse is relevant
for both researchers, policy-makers, and practitioners.
The aim is to identify a long-run relationship between
GGDP and RB. Since lnRB is I(1) process, we employ
the ARDL bounds test approach. The estimation model
is specified as:
ΔlnRBt ¼ α0 þ δt þ α1lnRBt−1 þ α2GGDPt−1
þ ∑qj¼0θ jΔGGDPt− j þ ut
A time trend is added in the model, since lnlöRB is
trended but GDP growth is not. Similarly, (A1) can be
further rewritten in the ARDL-ECM format:
ΔlnRBt ¼ α0 þ α1 lnRBt−1−μGGDPt−1−δ*t
þ ∑qj¼0θ jΔGGDPt− j þ ut
where the long-run coefficient is given by μ≡−αα21 and
represents the extent to which changes in GGDP would
have an impact on RB. The interpretation of μ is the
following: a one percentage point change in GGDP will
lead to total change of μ ∗ 100% in RB. Results are
reported in the first two columns in Table 4 (full
sample: without outlier dummies and with outlier
dummies, respectively). When no outlier dummy is
considered, the model suffers non-stable variance of
residuals and ARCH component. We apply the same
rule in the main text to identify outliers, and identify
the same outliers. Now, the CUSUMSQ test shows
no sign of instability of residuals’ variance, but the
ARCH component remains. Non-counteraction null
can be rejected at 5%; however, the long-run
coefficient is no longer significant at 5%. Thus, GGDP
would not have any significant impact on RB.
Following the established approach in the main text, we
divide the sample into two sub-samples
(sub-samples: 1833–1933 and 1934–2010, respectively)
the first period, and despite the introduction of
dummies, the model still suffers non-stable variance
of residuals. The estimation in the second period
suffers from ARCH specification error and
nonstable coefficients. Overall, this procedure does not
improve the estimations.
The aim is to identify a long-run relationship between
GGDP and bankruptcy frauds, BF. Since lnBF is
trend-stationary, the least squares estimation and
Table 4 Registered bankruptcies and GDP growth. Full sample with and without outlier dummies, and sub-samples (1833–1933; 1934–
FPSS reports the PSS’s F statistics for the bounds test. aaa indicates the rejection of the null of no cointegration hypothesis at 1%. Dummy
variables = d(year). χ2heter, χa2uto (4), RESET(2) and ARCH(2), CUSUM and CUSUMSQ provide the diagnostic statistics for Breusch-Pagan’s
heteroscedasticity, the Breusch-Godfrey’s LM serial correlation with 4 lags, the Ramsey’s RESET function form with 2 lags, ARCH with 2
lags, CUSUM and CUSUMSQ stability of parameters
*p < 0.1
**p < 0.05
***p < 0.01
associated tests are valid if we estimate the ARDL model
lnBFt ¼ α0 þ δt þ ∑ip¼1θilnBFt−i
þ ∑iq¼0θiGGDPGGDPt−i þ ut
The ARDL specification for overcoming possible
autocorrelation is employed, and two models are
considered: with and without breaks. Breaks are
estimated according to
Bai and Perron (1998)
, and no
outlier is detected. The results are reported in Table 5.
The results show that bankruptcy frauds (BF) are
highly persistent. However, the impacts from GGDP
are insignificant. Furthermore, the models suffer many
specification errors. Introducing breaks improves
nothing in terms of these specification errors. In sum, we are
CUSUMSQ provide the diagnostic statistics for the tests of
Breusch-Pagan’s heteroscedasticity, the Breusch-Godfrey’s LM
serial correlation with 4 lags, the Ramsey’s RESET function form
with 2 lags, ARCH with 2 lags, CUSUM and CUSUMSQ stability
†Using HAC robust standard errors
*p < 0.1
**p < 0.05
***p < 0.01
not able to identify any reliable and significant
relationship between bankruptcy frauds, the indicator
for destructive entrepreneurship, and GDP growth. The
results are not consistent with, e.g.,
Detotto and Otranto
that find that economic crimes are sensitive to
macro-economic conditions. However, the trend of BF
in Sweden 1830–2010 has an increasing tendency, while
GGDP has not. Therefore, bankruptcy frauds may have
other driving factors.
Open Access This article is distributed under the terms of the
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creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided
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