Description
Paying and repaying behavior are financial functions of great interest to private financial
actors and public regulators, as also to academic researchers. The purpose of the paper is to
empirically analyse paying and repaying behavior by combining theoretical insights from an
emerging field in economics known as “culture and finance” with ideas from the economic analysis of
social capital and trust in the context of different regulatory systems.
Journal of Financial Economic Policy
Are a culture of trust and morality associated with paying and repaying behavior?:
Lessons from developed countries 2004-2011
Timo Tammi
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J ournal of Financial Economic Policy, Vol. 5 Iss 3 pp. 313 - 328
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Are a culture of trust and morality
associated with paying and
repaying behavior?
Lessons from developed countries 2004-2011
Timo Tammi
Department of Business, University of Eastern Finland, Joensuu, Finland
Abstract
Purpose – Paying and repaying behavior are ?nancial functions of great interest to private ?nancial
actors and public regulators, as also to academic researchers. The purpose of the paper is to
empirically analyse paying and repaying behavior by combining theoretical insights from an
emerging ?eld in economics known as “culture and ?nance” with ideas from the economic analysis of
social capital and trust in the context of different regulatory systems.
Design/methodology/approach – The present paper investigates with the help of panel data
whether a culture of social trust and the scope of morality have an in?uence on paying and repaying
behavior in different European and OECD countries.
Findings – The analysis shows that culture has an effect on ?rms’ credit losses from the customers’
payment defaults, on the overall riskiness of paying behavior and on the level of non-performing bank
loans. Also the complexity of law-based regulation has an in?uence on paying and repaying behavior.
The analysis also shows that high trust and morality are associated with less complex regulation and
vice versa.
Practical implications – The results help private ?nancial actors, regulators and public policy
makers to design more appropriate behavioral environments for paying and repaying.
Originality/value – The paper provides the ?rst analysis of an important issue and it serves both
practical interest and further research on the topic.
Keywords Paying and repaying behavior, Culture and ?nancial behavior, Financial regulation,
Financial services, Financial institutions
Paper type Research paper
1. Introduction
Paying and repaying are ?nancial functions having a central role in economic
transactions. The behavior has evolved over millennia in conjunction with the
development of social norms and, since the birth of law in ancient Egypt, also with the
development of legal rules and systems aimed at regulating the behavior of buyers and
borrowers. Paying and repaying behavior involves a trade-off between social norms
and regulation: a transaction occurring in a strongly regulated environment does not
require a social norm and a transaction dominated by a strong social norm and a belief
that most actors will follow the norm, will likewise not require strong regulation.
Paying and repaying are also genuine economic factors determining the possibilities
for consumers to smooth consumption and for ?rms to generate pro?ts and/or obtain
?nance for their investments through both sales proceeds and loans.
From the contemporary perspective, paying and repaying behavior is important
both practically and from the point of view of academic research. On the practical side,
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
Journal of Financial Economic Policy
Vol. 5 No. 3, 2013
pp. 313-328
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-10-2012-0042
A culture of trust
and morality
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customers, ?rms, creditors and regulators participate in what can be called a “payment
architecture design”: how to improve paying and repaying behavior to contribute to
the steady ?ow of payments and repayments? This interest manifests in reports by
both private ?rms and public authorities, including tax authorities dealing with
payment behavior and its backdrop topics such as ?nancial literacy (EOS, 2012;
European Commission, 2006, 2009). In the ongoing discussions the traditional
determinants of behavior (legal institutions, culture and knowledge) are contrasted
with the increasing complexity of the credit environment:
Innovations and increasing complexity in the credit markets are transferring additional
?nancial risks to individuals, who have dif?culty in evaluating credit options available to
them and understanding the terms and conditions of their credit products (OECD, 2009, p. 4;
De Meza et al., 2008).
The need for new empirical and theoretical insights and analyses is apparent.
In scholarly research, cultural determinants of ?nancial behavior in general have
become prominent in research conducted in several behavioral disciplines (see Reuter’s
(2011) broad survey of culture and ?nance; also Breuer and Quinten, 2009). Some
profound social science conceptual innovations such as social capital, trust and culture
have been connected to the ?nancial behavior of individuals. Various data sources and
well-established survey questions are likewise available for constructing data-sets to
study the in?uence of culture on ?nancial behavior. Guiso et al. (2004) use the World
Values Survey (WVS) data to measure “culture”; they ?nd that social capital in?uences
people’s ?nancial behavior, such as reliance on informal lending, portfolio allocation
and the use of checks; and, that the in?uence is stronger when the enforcement based
on law is weaker. Breuer and Salzmann (2012) use the Schwartz Value Survey based on
45 value judgments to measure “culture”[1]. Their analysis shows that national cultures
are relatedto the use of deposits, life insurance andpensionfunds. Froma totallydifferent
approach and more closely focusing on paying and repaying behavior, Cappelen et al.
(2012) modi?ed the well-known experimental economics trust game into a
lending-borrowing game. In this case trust is measured by observing in a controlled
experiment howmuch of a certain sumof money a subject called the “sender” transfers to
the other subject in the expectation that the latter will “repay” a portion of the doubled or
tripled sumof money. Cappelen et al. (2012) found that the content of the lending contract
(be it enforceable or non-enforceable) has no role in the repaying behavior of the
borrowers in the game. Instead, the researchers suggest that moral motives related to
reciprocity, social preferences and needs considerations are more important. The
above results of the cultural determinants of ?nancial behavior give rise to an
interesting question related to paying and repaying behavior: does culture (trust and
morality) in?uence the manners in which people and ?rms pay their bills and repay
their loans?
In this paper we investigate the cultural determinants of paying and repaying
behavior in different (but, due data availability, not in all) European and OECDcountries
during the years 2004-2011. Our focus is on three different paying incidents. We use two
payment behavior indexes produced by a private credit management services company.
We use this data to ascertain how cultural differences between countries are related to:
(1) ?rms’ evaluations of the overall riskiness of paying behavior; and to
(2) credit losses reported by the ?rms.
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In addition, the International Monetary Fund (IMF) data on nonperforming loans are
used to study country differences in the repayment behavior of bank loans. The data also
enables us to compare the in?uence of cultural factors in cases where the power of the law
is fairly weak (such as payment delays in commercial transactions) and, also, where the
power of the law is rather strong (such as nonperforming bank loans). We also comment
on some ?ndings related to how ef?ciency and complexity of regulation are associated
with paying and repaying behavior and with the culture of trust and morality.
The rest of the paper is organized as follows. In Section 2 we set out our approach to
culture and then introduce the target variables of paying and repaying behavior, the
composition of the predictor variable of culture, and the controlling and instrumental
variables. Section 3 presents the analysis of the data and Section 4 consists of the
discussion of our ?ndings. Section 5 concludes the paper.
2. Culture and behavior in cases of payment and repayment
Customers are expected to take care of payment and repayment on time. We use the
term “paying behavior” to refer to hire purchase and trade credit and the term
“repaying behavior” to refer to money borrowing. Both paying and repaying situations
constitute what is theoretically called “trust situations”: actor A (a seller or banker)
trusts actor B (a customer) if A is reasonably con?dent that B will keep her promise
(to pay/repay). Many factors in?uence B’s behavior and A’s expectations of how B will
behave. The interaction between A and B may be regulated by a contract having the
force of law. On the other hand, the contract may be more or less incomplete and the
law more or less enforceable leaving room for social conventions and norms to guide
the behavior of the customer. As noted by Farrell and Knight (2003), Denzau and North
(1994), and Dasgupta (2009) for example, the functioning of both law-based and social
institutions depends on trust and culture. Aghion et al. (2010) go a step further and
explain that people expect a low level of regulation if they believe that they live in a
civic community (that is, in a community where trust and morality are at a high level);
conversely a high level of regulation is demanded if they believe they live in a non-civic
community (where trust and morality are at a low level).
Target variables: paying and repaying behavior
In our empirical analysis we measure “paying behavior” according to European
Payment Index survey conducted by a private credit management services company
IntrumJustitia (hereafter IJ). The company has conducted payment behavior surveys for
14 years (the ?rst one witha workgroupof the EuropeanCommission) inorder to provide
information of the payment practices of consumers and ?rms, credit losses, and on the
overall riskiness of paying behavior in different countries. We use the European
Payment Index 2008 edition and 2012 edition which report the annual scores on the key
variables from 2004 to 2012 (for country means and deviations, see Figure 1)
(Intrum Justitia, 2008, 2012).
Measuring the “credit losses” of ?rms is based on the following question (Intrum
Justitia, 2008): “If any, what was your baddebt loss in2007 as %of total revenue?” Inother
words the variable measures the percentage value of declared payment losses of a ?rm’s
total revenue. The overall “riskiness of paying behavior” is an index (constructed by IJ)
composed of companies’ assessments of eight dimensions of paying behavior:
contractual payment term (in days); effective payment duration (in days); age structure
A culture of trust
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of receivables; payment loss (as above); estimate risk trends; characteristics of the
consequences of late payment; causes of late payment. These eight dimensions, in turn,
are based on 21 individual variables in that survey. The computational minimum of the
index is 100 (no payment risk) and the maximumis 200 (high payment risk). The data on
risk behavior also cover the years from 2004 to 2011 (Figure 1).
The third target variable is related to the repayment of bank loans. This is
measured by using the IMF data on the “nonperforming loans ratio” (Figure 1). A loan
is deemed nonperforming when:
[. . .] payments of interest and/or principal are past due by90 days or more, or interest payments
equal to 90 days or more have been capitalized, re?nanced, or delayed by agreement, or
payments are less than 90 days overdue, but there are other good reasons – such as a debtor
?ling for bankruptcy – to doubt that payments will be made in full (IMF, 2005).
The variable “nonperforming loans ratio” is the value of nonperforming loans divided by
the total value of a bank’s loanportfolio (before the deductionof anyloan-loss provisions of
the nonperforming loan). The greater the ratio, the greater is the nonperforming loanstock
in relation to the total loan stock and the greater is the frequency of non-compliant
repayment behavior.
The “interpretation of nonperforming loans” needs some quali?cation. We hypothesize
that two causes of nonperformingloans are culture-sensitive (regardingtrust andmorality).
First, the level of nonperforming loans is in?uenced by decisions made by borrowing
individuals and enterprises (Bloem and Cornelis, 2001). These decision-makers may have
culturally biased tendencies to behave in a non-compliant way. Second, the level is also
in?uenced by “banking culture” – namely, by “de?cient bank management, poor
supervision, overoptimistic assessments of creditworthiness [. . .] and moral hazard that
results from(too) generous government guarantees or the expectation of assured bailouts”
(Bloem and Cornelis, 2001). This suggests that culture affects both the customer’s and the
banker’s behavior. Onthe other hand, however, the lendingbehavior of banks has longbeen
Figure 1.
Risk behavior, credit
losses and nonperforming
loans by country:
maximum, minimum
and mean
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NONPERFORMING LOANS (%)
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of interest of regulators to such an extent that it is reasonable to expect that the qualities
of law-based institutions probably also have an in?uence on the level of nonperforming
loans.
Predictor variable: culture
Culture is here de?ned as customary beliefs and values (Guiso et al., 2006) which in?uence
individual behavior together with social norms and law-based institutions. The in?uence
of cultural beliefs andvalues is basedonthe fact that theyproduce a sharedunderstanding
of both norms and institutions and that they serve as decision-makers’ devices to frame
their decision situations: what are the alternative courses of action, who are the co-players,
what are the likely consequences of each action, and what probabilities are attached to
each consequence? If the culture is “good” a person assumes that many people outside the
circle of family and community comply with social norms and law-based institutions;
therefore the personrealizes that it is pro?table for her also to comply. Inconsequence, less
complex regulatory systems work well. Alternatively, if the culture is “bad” a person
assumes that only few people outside the circle of family and community comply with
social norms and law-based institutions; therefore, it is unpro?table for her to comply
either and, consequently, more a complex regulatory system is required. Thus, if the
culture is good, there are fewer disturbances and risks in paying behavior because people
share the relevant social norms and law-based institutions and behave as expected.
Likewise, if culture is good, the banking sector functions ef?ciently.
We operationalize the notion of culture to refer to two culturally transmitted aspects
of interactive behavior of paying and repaying. The ?rst aspect is trust: a higher level of
trust also indicates more trustworthiness and, conversely, fewer risks and disturbances in
paying and repaying behavior. Trust also “reduces the need for external enforcement of
contractual agreements” while lack of trust is conversely associated with suspicion and
fear of being betrayed (Tabellini, 2008; Fehr, 2009). The second aspect of our notion of
culture is the scope of morality – that is, the extent of the circle of other people who comply
with common norms and rules. If the circle is concise, we can expect that opportunistic,
myopic and non-compliant behavior is more permissible with partners outside the circle
than otherwise (Williamson and Mathers, 2011; Tabellini, 2008).
We measure trust and the scope of morality with data provided by the fourth wave of
the WVS carried out 2005-2007. The data allows us to measure a country’s level of “trust
and morality” by combining the well-known “trust variable” with the so-called “respect
variable”. We suggest that these two aspects correspond to the trust situation between a
trustor (a seller/a banker) anda trustee (a customer) better thando the measures of culture
based on Schwartz’s or Hefstede’s value-statement approaches.
Trust in the WVS data is measured with the following question: “Generally speaking,
would you say that most people can be trusted or that you can’t be too careful in dealing
with people?” The respondents choose between the following alternatives: “Most people
can be trusted” and “Can’t be too careful”. The general practice is then to use the
country-level percentage of af?rmative answers as anindicator of the level of general trust
in the country. It is generally accepted that the percentage varies considerably between
countries, is typically high in the Nordic countries and low in the Mediterranean and
post-communist countries, and that such trust is positively correlated with many
economic indicators suchas GDPper capitaandGDPper worker. The validityof the WVS
question in measuring trust has been much debated (Sapienza et al., 2013; Uslaner, 2002;
A culture of trust
and morality
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Beugelsdijk, 2006; Nannestad, 2008; Dasgupta, 2009). However, extensive researchamong
experimental economists has recently shown that the WVS question is indeed a valid
measure of general trust ( JohnsonandMislin, 2011), althoughthere is still needto improve
it (Fehr, 2009; Naef and Schupp, 2009).
The scope of morality, on the other hand, is measured with the so-called “respect
variable”. This is constructed on one of 11 items in WVS question bank related to
values the respondents consider to be especially important in children’s upbringing at
home. One of the values[2] is “tolerance and respect for other people” which is taken to
re?ect the scope of morality discussed above (Tabellini, 2008; Williamson and Mathers,
2011). Like the trust variable, the respect variable also ranges between 0 and 1.
In our empirical analysis we use the variables of trust and respect to construct a sum
variable as a weighted average of the two variables. The weights are adjusted so as to
equalize the means of both variables. We interpret that the combined variable proxies
the following two aspects of cultural codes in situations where people interact which each
other: howmuch andhowgenerallymost people inthis country trust others and behave in
a trustworthy manner. The higher the value of this variable the more likely it is that an
individual will comply both with the general norms of “good conduct” and the more
speci?c details in the implicit or explicit contracts of the economic transaction she has
engaged in (see the Appendix, Figure A1 for each country’s unweighted levels of trust and
the scope of morality).
Control and instrumental variables
In our empirical analysis we include several variables in our models to control for
various determinants of paying and repaying behavior such as the qualities of legal
institutions, economic growth and standard of living. In addition, we also check the
pooled OLSregression results byusing variables relatedto the country’s legal originand
the fraction of Protestant adherence as instrumental variables in TSLS-regressions.
We use two kinds of data to measure the properties of legal institutions. The ?rst two
variables used as controls come fromthe World Bank Doing Business (DB) data. The ten
separate variables in the DB dataset are combined into two variables according to the
guidelines given in a World Bank report (World Bank, 2012). One variable measures the
“inef?ciency of legal institutions” and refers to the average of the country rankings
according to the following variables: getting credit, protecting investors, enforcing
contracts and resolving insolvency. The other variable measures the “complexity and
cost of regulatory processes” and is an average of the similar country rankings of the
following variables: starting a business, dealing with construction permits, getting
electricity, registering property, payingtaxes and trading across borders. Both variables
re?ect the quality of the law-based regulatory system constituting an integral part of
the business environment in different countries.
In order to control for possible simultaneity betweenculture and paying behavior, and
between regulation and paying behavior, we use an instrumental variables approach.
Hence, we chose two instrumental variables which affect the key predictors (culture and
institutions) and which affect paying behavior only through these predictors. With
relation to the culture variable we use Protestant adherence (fromBarro’s (2003) religious
adherence data) for this purpose. As shown by Guiso et al. (2003, 2006), Protestant
af?liation is positively correlated with social trust. In addition, this correlation re?ects
historical processes originating in the Protestant Reformation several hundred years ago
in which various elements of Protestantism fostered the development of social trust and
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other values and beliefs (such as our notion of the scope of morality) related to the
horizontal relations between individuals (Guiso et al., 2003, 2006; Putnam, 1993).
Regarding the regulation variable we adopted the notion presented by LaPorta et al.
(1997, 1998) andusedanddevelopedbyDjankovet al. (2002, 2008), Levine et al. (2000) and
LaPorta et al. (2008). Accordingly, the legal origins of law and regulation in different
countries are largerlya result of a longhistorical process involving wars andoccupations;
legal origins, as this research shows, have a major in?uence on the legal and regulatory
environment which, in turn, in?uences the behavior of ?rms and individuals.
Finally, we also check the in?uence of the “overall economic development” and the
general rate of “indebtedness of the private” sector in each country. In measuring overall
economic development we use the IMF/World Bank GDP per capita indicator (converted
into international dollars using purchasing power parity rates) and annual growth rate
(as percent of GDP). Regarding the rate of indebtedness we use the World Bank data on
“domestic credit to private sector” measured as a percentage of GDP. Domestic credit here
consists of loans, nonequity securities, trade credits and other accounts of receivables.
3. Analysis
In this section we analyze the in?uence of the culture of trust and morality on behavior
related to paying and repaying. As indicated above, we use data constructed from data
items from several sources. Table I shows the variable list and the sources of the
variables. The empirical strategy is based on estimating the following model using
both pooled OLS regression and two stage least squares regression (TSLS):
y
it
¼ b
0
b
1
x
m;i
þb
k
x
k;it
; þg
2
D2
t
þg
3
D3
t
þg
n
Dt
t
þu
it
where i is country; t is year (t ¼ 1, . . . , n); x
m,i
is the value of the mth time-invariant
regressor (culture, regulatory inef?ciency and regulatory complexity); x
k,it
is the value
of the kth time-varying regressor; and D’s are time dummy variables so that if t ¼ 2
then D2
t
¼ 1 but otherwise 0. In addition, the interactions of the year dummies and the
culture variable are added into the model. The TSLS estimations are done to correct
possible inaccuracies of the OLS regressions by using culture and regulation one after
another as instrumented variables and, respectively, fraction of Protestant adherence
and legal origin as instrumental variables. We report both OLS regressions and TSLS
regressions – the latter with F-test and Wu-Hausman endogenous test statistics to
check whether the instruments are weak and whether there is endogeneity[3].
Figure 2 shows the progression of the target variables in each country over time
2004-2011. Some interesting observations can be made both for the sake of general
informationandprovidingbackgroundinformationto our further analyses. First, it appears
that risk behavior shows small within-country variation but large between-country
variation. Then, if culture is found to have an impact on risk behavior, it is related mainly to
quite stable risk-behavior levels in different countries. Second, the between-country
variation is smaller in the cases of credit losses and nonperforming loans than in the case
of risk behavior. Correspondingly, within-country variation is somewhat larger in the case
of credit losses and much larger in the case of nonperforming loans than in the case of
risk-behavior. Although in both of these cases the loess curve shows that the overall trend
takes a turn into an increasing course in 2008, it seems that credit losses exhibit a narrowing
and nonperforming loans a widening progression. Regarding some interesting information
on speci?c countries, we see that the risk behavior index as well as credit losses have
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increased dramatically in Greece during the period 2004-2011. Regarding nonperforming
loans, in the years 2004-2006 Macedonia and Poland had remarkably high rates while
in the years 2009-2011, the nonperforming loans rate was remarkably high in Lithuania,
Montenegro, Latvia and Albania. In the analysis below we take this group of “high
within-country variation” countries into account by adding a dummy variable
(npsens_dummy, see Table I) into the regressions of nonperforming loans.
In our analysis the main interest is in the in?uence of culture on paying and repaying
behavior. To achieve a better understanding of the causal relations between culture and
paying and repaying behavior we run pooled OLS regressions and TSLS regressions.
Table II summarizes the regressions of risk behavior on various sets of regressors.
Columns (1) and(2) are basedon pooled OLS regressions. Columns (3) and(4), respectively,
show the estimates of TSLS regressions with instrument variables. In the ?rst case, the
fraction of Protestant adherence serves as the instrument for culture; the F-statistics
suggest that the fraction of Protestant adherence is not a weak instrument whereas the
endogenous test (Wu-Hausman test) means that culture is not endogenous. The same
diagnostics applies to legal origin which is the instrument for regulation complexity in
Variable Description Min. Max. Mean SD
Risk behavior Overall riskiness of paying behavior index
2004-2011 (Intrum Justitia European Payment
Index, editions 2012 and 2008)
123.00 191.00 153.47 15.51
Nonperforming
loans
Nonperforming bank loans of gross loans (%)
2004-2011 (World Bank data)
0.20 21.00 4.12 4.11
Credit losses Firms’ credit losses (percentage of total revenue)
2004-2011 (Intrum Justitia European Payment
Index, editions 2012 and 2008)
0.60 6.50 2.39 0.87
Culture Culture of trust and morality. Based on WVS-
survey questions of “trust” and “respect”
(explained in Section 2)
0.42 0.96 0.62 0.14
Regulation
complexity
Average of rankings in the World Bank Doing
Business Data 2011 (explained in Section x)
13.00 134.50 62.73 31.75
Regulation
inef?ciency
Average of rankings in the World Bank Doing
Business Data 2011 (explained in Section x)
5.33 104.33 48.41 23.98
GDP per capita Gross domestic product per capita in $1,000; GDP
based on PPP (in current international $) 2004-2011
(World Bank data)
5.63 61.34 27.05 11.91
GDP
annual growth
Annual percentage growth rate of GDP
2004-2011 (World Bank data)
217.55 12.85 1.96 4.14
Indebtedness Domestic credit provided by banking sector
(percentage of GDP) 2004-2011
(World Bank data)
9.36 234.54 102.90 56.60
Protestant
adherence
Fraction of those who adhere to Protestant religion
(Robert Barro’ religion adherence data)
0.00 0.97 0.21 0.29
Legal origin Dummy variable which has the value of unit if the
country’s legal system is classi?ed as having either
common law origin or Nordic origin
npsens_dummy Dummy for countries (Albania, Latvia, Lithuania,
Montenegro, Macedonia and Poland) which have
been highly sensitive to variations in the level of
the nonperforming loans ratio during 2004-2011
Table I.
List of variables
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the model estimation reported in column (4). Consequently, we interpret that a culture of
trust and morality has an in?uence on risk behavior: the stronger the culture the lower
is the risk behavior index. In addition, we note that the complexity of regulation also
exerts in?uence: when the complexity increases the risk behavior index increases.
Table III shows the results of the regressions of credit losses. The OLS regression
shows that culture is clearly associated with credit losses (higher score on culture is
associated with a lower level of credit losses). However, the in?uence diminishes but
remains signi?cant when controlling it for regulation variables and for GDP per capita
levels and annual growth rate. Tests of TSLS regressions with regulation complexity as
the instrumented variable indicate that legal origin may here be a weak instrument and
that regulation complexity would be endogenous. The TSLS regression with culture
as the instrumented variable, on the other hand, shows that culture is not endogenous
and that Protestant adherence is not a weak instrumental variable. We interpret that the
higher the level of trust and morality in a country the lower the level of credit losses in
that country. Regulation does not appear to be associated with credit losses, whereas
growth of the economy and GDP per capita are associated. An increase in the former
raises but an increase of the latter decreases credit losses.
Finally, Table IV shows the estimation results of nonperforming loans. Compared to
the earlier models we have added indebtedness and a country dummy in the models.
Figure 2.
Line plots of the variation
of risk behavior, credit
losses and nonperforming
loans over the time
Notes: The dotted lines and thin lines represent the course of variables in each country over
time; the bold lines are loess curves showing the moving central tendency of the data
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Indebtedness measures the share of domestic private credit (as a percentage of GDP)
provided by the country’s banking sector. The country dummy (npsens_dummy)
distinguishes the countries having extremely high within-country variation from other
countries. Here again the TSLS regressions suggest that although the fraction of
Protestant adherence and legal origin are not weak instruments neither culture nor
regulation complexity are endogenous. We therefore interpret that good culture
in?uences by decreasing the level of nonperforming loans. On the other hand, higher
annual growth also decreases the nonperforming loans level whereas complexity of
regulation increases it. Clearly, the countries with high within-country variation have a
more than four times higher level of nonperforming loans than do the other countries.
4. Results and discussion
The major result of the above empirical analysis is that a culture of trust and morality
are associated with and has in?uences on “paying and repaying behavior”. In addition,
the complexity of regulation also in?uences paying and repaying behavior (the case of
credit losses, however, is excluded). In all cases the signs of the coef?cients are as
expected. In other words, culture decreases the scores of risk behavior, credit losses
and nonperforming loans while complexity of regulation increases the risk behavior
and nonperforming loans scores. Interestingly, culture and regulation in?uence in
opposite directions and are negatively correlated.
Our interpretation of the in?uence of culture on paying and repaying is that:
.
the higher level of trust (as one element of our de?nition of culture); and
Risk behavior
OLS
TSLS TSLS
Instrumented: reg_comp
a
Instrumented: cult
b
Regressor (1) (2) (3) (4)
cult 283.09
* * *
(6.65) 277.84
* * *
(8.57) 281.74
* * *
(8.00) 2 83.97
* * *
(13.07)
gdpcap 20.20
*
(0.10) 0.22
* *
(0.11) 0.25
*
(0.12)
gdpgrowth 20.30 (0.22) 20.33
*
(0.20) 20.29 (0.22)
reg_inef 0.03 (0.03) 20.02 (0.04)
reg_comp 0.15
* * *
(0.03) 0.14 (0.12) 0.16
* * *
(0.03)
constant 207.86
* * *
(6.77) 189.77
* * *
(5.70) 194.31
* * *
(10.95) 192.96
* * *
(7.65)
Year
dummies Yes Yes Yes Yes
cult
*
year
interaction Yes Yes Yes Yes
n 197 167 158 171
Adj. R
2
0.51 0.75 0.76 0.72
F ¼ 26.58; p , 0.001 F ¼ 24.65; p , 0.001
Wu-Hausman 0.042; p ¼ 0.84 0.37; p ¼ 0.54
Notes: Coef?cient is signi?cant at:
*
10,
* *
5 and
* * *
1 percent levels;
a
legal origin as instrumenting
variable;
b
fraction of Protestant adherence as instrumenting variable; pooled OLS regression estimates
are in columns (1) and (2), TSLS regression estimates in columns (3) and (4); of the TSLS estimations
the coef?cients of instrumented variables are underlined; ?rst stage F-test and Wu-Hausman
endogenous test reported for TSLS; one instrumented and one instrumenting variable in each model
implies exact identi?cation of coef?cients and justi?es the use of F-statistic greater than 10 as a sign of
a non-weak instrument
Table II.
Regressions of risk
behavior
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the broader scope of morality (as the other element of our de?nition of culture)
reduce disturbances and risks in paying and repaying behavior and also the
tolerance to opportunism and non-compliance in the behavior.
On the other hand, increasing complexity of regulation decreases trust and morality
and is associated with higher levels of risk behavior and nonperforming loans. We
suggests, along the lines of Aghion et al. (2010) that high trust and high morality
countries have been able to adjust the complexity of regulation to a low level, whereas
low trust and low morality countries have not.
At a general level the results concur with?ndings inthe research of tax compliance and
tax morale (McGee, 2012) and, especially with Torgler’s (2003) ?nding that trust in public
of?cials and legal systems has a positive in?uence on tax compliance. Our results are
also compatible with the ?nding of Cappelen et al. (2012) that moral considerations may
explain paying behavior independently of the quality of the contract (being either
enforcing nor non-enforcing). The results are also in accordance with research ?ndings
which emphasize the role of empirical and normative expectations (Bicchieri and Xiao,
2009; Sugden, 2000; Ga¨chter, 2007) when an individual decides to comply or not to comply
with a norm: there is evidence that people comply if they expect that others will also
comply, and that they do not comply if they think that others will not comply either
(Sacconi and Faillo, 2010; Tammi, 2011). It can be hypothesized that when people do not
knowhowothers will behave or of what others deemappropriate ina given situation, they
consult their culturally formed beliefs and values of social trust and scope of morality.
Credit losses
OLS
TSLS TSLS
Instrumented: reg_comp
a
Instrumented: cult
b
Regressor (1) (2) (3) (4)
cult 23.29
* * *
(0.46) 21.52
* * *
(0.58) 20.85 (0.65) 2 1.98
* *
(0.89)
gdpcap
($1,000) 20.02
* * *
(0.01) 20.02
* *
(0.01) 20.02
* *
(0.01)
gdpgrowth 0.02
*
(0.01) 0.03
*
(0.02) 0.03
*
(0.02)
reg_inef 20.02
(0.02) 20.00 (0.00)
reg_comp 0.00 (0.00) 0.02
*
(0.01) 0.00 (0.00)
constant 4.77
* * *
(0.32) 3.74
* * *
(0.39) 2.22
* *
(0.89) 3.98
* * *
(0.52)
Year dummies Yes Yes Yes Yes
cult
*
year
interaction Yes Yes Yes Yes
n 199 175 176 175
Adj. R
2
0.42 0.55 0.43 0.55
F ¼ 9.37; p , 0.001 F ¼ 12.76; p , 0.001
Wu-Hausman 3.90; p ¼ 0.05 0.47; p ¼ 0.49
Notes: Coef?cient is signi?cant at:
*
10,
* *
5 and
* * *
1 percent levels;
a
legal origin as instrumenting
variable;
b
fraction of Protestant adherence as instrumenting variable; pooled OLS regression estimates
are in columns (1) and (2), TSLS regression estimates in columns (3) and (4); of the TSLS estimations
the coef?cients of instrumented variables are underlined; ?rst stage F-test and Wu-Hausman
endogenous test reported for TSLS; one instrumented and one instrumenting variable in each model
implies exact identi?cation of coef?cients and justi?es the use of F-statistic greater than 10 as a sign of
a non-weak instrument
Table III.
Regressions of
credit losses
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The qualities of law-based regulatory institutions also have an obvious impact on
payment and repayment. That is, the percentage share of nonperforming loans of a bank’s
gross loans is smaller the less complex are the law-based regulatory institutions. This
?nding is in line with the literature on the role and meaning of nonperforming loans: most
of the analyses emphasize that effective, less complex and transparent institutions rather
thanstrongregulation cansolve or exacerbate the excess problemof nonperformingloans
(Boudriga et al., 2009). On the other hand, the percentage share of bank nonperforming
loans can also be seen as an indicator of the performance capability of the banking system
in a country (Maggi and Guida, 2011). Our results then suggest that in those countries
where law-based institutions are complex the performance capability of a bank system is
weak, leading to de?ciencies in banking and lending behavior.
Taken together, our results support the idea that culture matters: a culture of
trust and morality has a clear in?uence on payment and repayment behavior. Further
studies with larger data-sets are the obvious next step regarding the empirical
analysis of paying and repaying behavior. Regarding theoretical work, the analysis of
paying and repaying behavior should try to better understand the process whereby
social trust and morality in?uences the decision of an individual to comply promptly,
with a delay, or not to comply with the more or less enforcing contracts they
have made.
Nonperforming loans
OLS
TSLS TSLS
Instrumented: reg_comp
a
Instrumented: cult
b
Regressor (1) (2) (4) (3)
cult 210.80
* * *
(2.22) 24.55
* *
(2.38) 23.86 (2.64) 2 3.26 (3.12)
gdpcap 20.04 (0.03) 20.07 (0.06) 20.05 (0.04)
gdpgrowth 20.44
* * *
(0.06) 20.45
* * *
(0.06) 20.44
* * *
(0.06)
reg_inef 20.01 (0.01) 0.01 (0.01)
reg_comp 0.02
* * *
(0.01) 0.01 (0.03) 0.02
* * *
(0.01)
indebtedness 20.01 (0.01) 20.01
*
(0.01) 0.01 (0.01)
npsens_
dummy 4.47
* * *
(0.54) 4.01
* * *
(0.62) 4.45
* * *
(0.52)
constant 10.31
* * *
(1.48) 11.45
* * *
(1.54) 9.24
* * *
(2.58) 6.37
* * *
(1.82)
Year
dummies Yes Yes Yes Yes
cult
*
year
interaction Yes Yes Yes Yes
n 278 270 271 271
Adj. R
2
0.36 0.64 0.64 0.64
F ¼ 24.02; p , 0.001 F ¼ 23.42; p , 0.001
Wu-Hausman 0.64; p ¼ 0.43 0.31; p ¼ 0.58
Notes: Coef?cient is signi?cant at:
*
10,
* *
5 and
* * *
1 percent levels;
a
legal origin as instrumenting
variable;
b
fraction of Protestant adherence as instrumenting variable; pooled OLS regression estimates
are in columns (1) and (2), TSLS regression estimates in columns (3) and (4); of the TSLS estimations
the coef?cients of instrumented variables are underlined; ?rst stage F-test and Wu-Hausman
endogenous test reported for TSLS; one instrumented and one instrumenting variable in each model
implies exact identi?cation of coef?cients and justi?es the use of F-statistic greater than 10 as a sign of
a non-weak instrument
Table IV.
Regressions of
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5. Conclusions
Acountry speci?c culture of trust and morality is associated with country differences in
payment and repayment practices. The results have implications for both private
?nancial actors and institutions and public policy authorities and regulators. Both
practitioners of policies and designers of ?nancial systems (in private ?nancial
institutions, ?nancial monitoring regimes or consumer policy institutions) could bene?t
from including cultural factors and the quality of regulation in their models for
predicting changes in paying and repaying behavior. A major component in the
development of these practically oriented models is the relationship between cultural
factors and the strength of regulation: a “better culture” (high levels of trust and
morality) makes it possible to reduce regulation and to get the community to stay at
“high level”. Conversely, “worse culture” tends to increase the strength and complexity
of regulation. In some cases (in some countries) rapid economic growth may provide
opportunities to raise the levels of trust and morality and to improve the quality of
regulation. Conversely, negative economic growth may threaten high levels of trust and
morality and increase expectations of stronger regulation.
Notes
1. Yet another measure of “culture” is Hofstede’s dimensionalist approach, which is also based
on value-statements (Reuter, 2011).
2. The other values are good manners, independence, hard work, responsibility, imagination, thrift
& saving money, determination, perseverance, religious faith, unsel?shness, obedience.
3. The ?rst-stage F-test is applicable to cases when there is a single endogenous predictor.
We conclude that there is no sign of a weak instrument if the F-statistics exceeds 10. Regarding
the Wu-Hausman test, it is used to see if the OLS and TSLS estimates are different; lowp-value
indicates the existence of endogeneity with the implication that TSLS is preferred to OLS.
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Corresponding author
Timo Tammi can be contacted at: timo.tammi@uef.?
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Appendix
Figure A1.
The scope of morality
(?rst stack) and trust
(second stack) as
components of the culture
of trust and morality
Note: Based on non-weighted values of the variables “respect” and “trust” in the WVS
dataset
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doc_406402237.pdf
Paying and repaying behavior are financial functions of great interest to private financial
actors and public regulators, as also to academic researchers. The purpose of the paper is to
empirically analyse paying and repaying behavior by combining theoretical insights from an
emerging field in economics known as “culture and finance” with ideas from the economic analysis of
social capital and trust in the context of different regulatory systems.
Journal of Financial Economic Policy
Are a culture of trust and morality associated with paying and repaying behavior?:
Lessons from developed countries 2004-2011
Timo Tammi
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To cite this document:
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Are a culture of trust and morality
associated with paying and
repaying behavior?
Lessons from developed countries 2004-2011
Timo Tammi
Department of Business, University of Eastern Finland, Joensuu, Finland
Abstract
Purpose – Paying and repaying behavior are ?nancial functions of great interest to private ?nancial
actors and public regulators, as also to academic researchers. The purpose of the paper is to
empirically analyse paying and repaying behavior by combining theoretical insights from an
emerging ?eld in economics known as “culture and ?nance” with ideas from the economic analysis of
social capital and trust in the context of different regulatory systems.
Design/methodology/approach – The present paper investigates with the help of panel data
whether a culture of social trust and the scope of morality have an in?uence on paying and repaying
behavior in different European and OECD countries.
Findings – The analysis shows that culture has an effect on ?rms’ credit losses from the customers’
payment defaults, on the overall riskiness of paying behavior and on the level of non-performing bank
loans. Also the complexity of law-based regulation has an in?uence on paying and repaying behavior.
The analysis also shows that high trust and morality are associated with less complex regulation and
vice versa.
Practical implications – The results help private ?nancial actors, regulators and public policy
makers to design more appropriate behavioral environments for paying and repaying.
Originality/value – The paper provides the ?rst analysis of an important issue and it serves both
practical interest and further research on the topic.
Keywords Paying and repaying behavior, Culture and ?nancial behavior, Financial regulation,
Financial services, Financial institutions
Paper type Research paper
1. Introduction
Paying and repaying are ?nancial functions having a central role in economic
transactions. The behavior has evolved over millennia in conjunction with the
development of social norms and, since the birth of law in ancient Egypt, also with the
development of legal rules and systems aimed at regulating the behavior of buyers and
borrowers. Paying and repaying behavior involves a trade-off between social norms
and regulation: a transaction occurring in a strongly regulated environment does not
require a social norm and a transaction dominated by a strong social norm and a belief
that most actors will follow the norm, will likewise not require strong regulation.
Paying and repaying are also genuine economic factors determining the possibilities
for consumers to smooth consumption and for ?rms to generate pro?ts and/or obtain
?nance for their investments through both sales proceeds and loans.
From the contemporary perspective, paying and repaying behavior is important
both practically and from the point of view of academic research. On the practical side,
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
Journal of Financial Economic Policy
Vol. 5 No. 3, 2013
pp. 313-328
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-10-2012-0042
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customers, ?rms, creditors and regulators participate in what can be called a “payment
architecture design”: how to improve paying and repaying behavior to contribute to
the steady ?ow of payments and repayments? This interest manifests in reports by
both private ?rms and public authorities, including tax authorities dealing with
payment behavior and its backdrop topics such as ?nancial literacy (EOS, 2012;
European Commission, 2006, 2009). In the ongoing discussions the traditional
determinants of behavior (legal institutions, culture and knowledge) are contrasted
with the increasing complexity of the credit environment:
Innovations and increasing complexity in the credit markets are transferring additional
?nancial risks to individuals, who have dif?culty in evaluating credit options available to
them and understanding the terms and conditions of their credit products (OECD, 2009, p. 4;
De Meza et al., 2008).
The need for new empirical and theoretical insights and analyses is apparent.
In scholarly research, cultural determinants of ?nancial behavior in general have
become prominent in research conducted in several behavioral disciplines (see Reuter’s
(2011) broad survey of culture and ?nance; also Breuer and Quinten, 2009). Some
profound social science conceptual innovations such as social capital, trust and culture
have been connected to the ?nancial behavior of individuals. Various data sources and
well-established survey questions are likewise available for constructing data-sets to
study the in?uence of culture on ?nancial behavior. Guiso et al. (2004) use the World
Values Survey (WVS) data to measure “culture”; they ?nd that social capital in?uences
people’s ?nancial behavior, such as reliance on informal lending, portfolio allocation
and the use of checks; and, that the in?uence is stronger when the enforcement based
on law is weaker. Breuer and Salzmann (2012) use the Schwartz Value Survey based on
45 value judgments to measure “culture”[1]. Their analysis shows that national cultures
are relatedto the use of deposits, life insurance andpensionfunds. Froma totallydifferent
approach and more closely focusing on paying and repaying behavior, Cappelen et al.
(2012) modi?ed the well-known experimental economics trust game into a
lending-borrowing game. In this case trust is measured by observing in a controlled
experiment howmuch of a certain sumof money a subject called the “sender” transfers to
the other subject in the expectation that the latter will “repay” a portion of the doubled or
tripled sumof money. Cappelen et al. (2012) found that the content of the lending contract
(be it enforceable or non-enforceable) has no role in the repaying behavior of the
borrowers in the game. Instead, the researchers suggest that moral motives related to
reciprocity, social preferences and needs considerations are more important. The
above results of the cultural determinants of ?nancial behavior give rise to an
interesting question related to paying and repaying behavior: does culture (trust and
morality) in?uence the manners in which people and ?rms pay their bills and repay
their loans?
In this paper we investigate the cultural determinants of paying and repaying
behavior in different (but, due data availability, not in all) European and OECDcountries
during the years 2004-2011. Our focus is on three different paying incidents. We use two
payment behavior indexes produced by a private credit management services company.
We use this data to ascertain how cultural differences between countries are related to:
(1) ?rms’ evaluations of the overall riskiness of paying behavior; and to
(2) credit losses reported by the ?rms.
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In addition, the International Monetary Fund (IMF) data on nonperforming loans are
used to study country differences in the repayment behavior of bank loans. The data also
enables us to compare the in?uence of cultural factors in cases where the power of the law
is fairly weak (such as payment delays in commercial transactions) and, also, where the
power of the law is rather strong (such as nonperforming bank loans). We also comment
on some ?ndings related to how ef?ciency and complexity of regulation are associated
with paying and repaying behavior and with the culture of trust and morality.
The rest of the paper is organized as follows. In Section 2 we set out our approach to
culture and then introduce the target variables of paying and repaying behavior, the
composition of the predictor variable of culture, and the controlling and instrumental
variables. Section 3 presents the analysis of the data and Section 4 consists of the
discussion of our ?ndings. Section 5 concludes the paper.
2. Culture and behavior in cases of payment and repayment
Customers are expected to take care of payment and repayment on time. We use the
term “paying behavior” to refer to hire purchase and trade credit and the term
“repaying behavior” to refer to money borrowing. Both paying and repaying situations
constitute what is theoretically called “trust situations”: actor A (a seller or banker)
trusts actor B (a customer) if A is reasonably con?dent that B will keep her promise
(to pay/repay). Many factors in?uence B’s behavior and A’s expectations of how B will
behave. The interaction between A and B may be regulated by a contract having the
force of law. On the other hand, the contract may be more or less incomplete and the
law more or less enforceable leaving room for social conventions and norms to guide
the behavior of the customer. As noted by Farrell and Knight (2003), Denzau and North
(1994), and Dasgupta (2009) for example, the functioning of both law-based and social
institutions depends on trust and culture. Aghion et al. (2010) go a step further and
explain that people expect a low level of regulation if they believe that they live in a
civic community (that is, in a community where trust and morality are at a high level);
conversely a high level of regulation is demanded if they believe they live in a non-civic
community (where trust and morality are at a low level).
Target variables: paying and repaying behavior
In our empirical analysis we measure “paying behavior” according to European
Payment Index survey conducted by a private credit management services company
IntrumJustitia (hereafter IJ). The company has conducted payment behavior surveys for
14 years (the ?rst one witha workgroupof the EuropeanCommission) inorder to provide
information of the payment practices of consumers and ?rms, credit losses, and on the
overall riskiness of paying behavior in different countries. We use the European
Payment Index 2008 edition and 2012 edition which report the annual scores on the key
variables from 2004 to 2012 (for country means and deviations, see Figure 1)
(Intrum Justitia, 2008, 2012).
Measuring the “credit losses” of ?rms is based on the following question (Intrum
Justitia, 2008): “If any, what was your baddebt loss in2007 as %of total revenue?” Inother
words the variable measures the percentage value of declared payment losses of a ?rm’s
total revenue. The overall “riskiness of paying behavior” is an index (constructed by IJ)
composed of companies’ assessments of eight dimensions of paying behavior:
contractual payment term (in days); effective payment duration (in days); age structure
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of receivables; payment loss (as above); estimate risk trends; characteristics of the
consequences of late payment; causes of late payment. These eight dimensions, in turn,
are based on 21 individual variables in that survey. The computational minimum of the
index is 100 (no payment risk) and the maximumis 200 (high payment risk). The data on
risk behavior also cover the years from 2004 to 2011 (Figure 1).
The third target variable is related to the repayment of bank loans. This is
measured by using the IMF data on the “nonperforming loans ratio” (Figure 1). A loan
is deemed nonperforming when:
[. . .] payments of interest and/or principal are past due by90 days or more, or interest payments
equal to 90 days or more have been capitalized, re?nanced, or delayed by agreement, or
payments are less than 90 days overdue, but there are other good reasons – such as a debtor
?ling for bankruptcy – to doubt that payments will be made in full (IMF, 2005).
The variable “nonperforming loans ratio” is the value of nonperforming loans divided by
the total value of a bank’s loanportfolio (before the deductionof anyloan-loss provisions of
the nonperforming loan). The greater the ratio, the greater is the nonperforming loanstock
in relation to the total loan stock and the greater is the frequency of non-compliant
repayment behavior.
The “interpretation of nonperforming loans” needs some quali?cation. We hypothesize
that two causes of nonperformingloans are culture-sensitive (regardingtrust andmorality).
First, the level of nonperforming loans is in?uenced by decisions made by borrowing
individuals and enterprises (Bloem and Cornelis, 2001). These decision-makers may have
culturally biased tendencies to behave in a non-compliant way. Second, the level is also
in?uenced by “banking culture” – namely, by “de?cient bank management, poor
supervision, overoptimistic assessments of creditworthiness [. . .] and moral hazard that
results from(too) generous government guarantees or the expectation of assured bailouts”
(Bloem and Cornelis, 2001). This suggests that culture affects both the customer’s and the
banker’s behavior. Onthe other hand, however, the lendingbehavior of banks has longbeen
Figure 1.
Risk behavior, credit
losses and nonperforming
loans by country:
maximum, minimum
and mean
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of interest of regulators to such an extent that it is reasonable to expect that the qualities
of law-based institutions probably also have an in?uence on the level of nonperforming
loans.
Predictor variable: culture
Culture is here de?ned as customary beliefs and values (Guiso et al., 2006) which in?uence
individual behavior together with social norms and law-based institutions. The in?uence
of cultural beliefs andvalues is basedonthe fact that theyproduce a sharedunderstanding
of both norms and institutions and that they serve as decision-makers’ devices to frame
their decision situations: what are the alternative courses of action, who are the co-players,
what are the likely consequences of each action, and what probabilities are attached to
each consequence? If the culture is “good” a person assumes that many people outside the
circle of family and community comply with social norms and law-based institutions;
therefore the personrealizes that it is pro?table for her also to comply. Inconsequence, less
complex regulatory systems work well. Alternatively, if the culture is “bad” a person
assumes that only few people outside the circle of family and community comply with
social norms and law-based institutions; therefore, it is unpro?table for her to comply
either and, consequently, more a complex regulatory system is required. Thus, if the
culture is good, there are fewer disturbances and risks in paying behavior because people
share the relevant social norms and law-based institutions and behave as expected.
Likewise, if culture is good, the banking sector functions ef?ciently.
We operationalize the notion of culture to refer to two culturally transmitted aspects
of interactive behavior of paying and repaying. The ?rst aspect is trust: a higher level of
trust also indicates more trustworthiness and, conversely, fewer risks and disturbances in
paying and repaying behavior. Trust also “reduces the need for external enforcement of
contractual agreements” while lack of trust is conversely associated with suspicion and
fear of being betrayed (Tabellini, 2008; Fehr, 2009). The second aspect of our notion of
culture is the scope of morality – that is, the extent of the circle of other people who comply
with common norms and rules. If the circle is concise, we can expect that opportunistic,
myopic and non-compliant behavior is more permissible with partners outside the circle
than otherwise (Williamson and Mathers, 2011; Tabellini, 2008).
We measure trust and the scope of morality with data provided by the fourth wave of
the WVS carried out 2005-2007. The data allows us to measure a country’s level of “trust
and morality” by combining the well-known “trust variable” with the so-called “respect
variable”. We suggest that these two aspects correspond to the trust situation between a
trustor (a seller/a banker) anda trustee (a customer) better thando the measures of culture
based on Schwartz’s or Hefstede’s value-statement approaches.
Trust in the WVS data is measured with the following question: “Generally speaking,
would you say that most people can be trusted or that you can’t be too careful in dealing
with people?” The respondents choose between the following alternatives: “Most people
can be trusted” and “Can’t be too careful”. The general practice is then to use the
country-level percentage of af?rmative answers as anindicator of the level of general trust
in the country. It is generally accepted that the percentage varies considerably between
countries, is typically high in the Nordic countries and low in the Mediterranean and
post-communist countries, and that such trust is positively correlated with many
economic indicators suchas GDPper capitaandGDPper worker. The validityof the WVS
question in measuring trust has been much debated (Sapienza et al., 2013; Uslaner, 2002;
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Beugelsdijk, 2006; Nannestad, 2008; Dasgupta, 2009). However, extensive researchamong
experimental economists has recently shown that the WVS question is indeed a valid
measure of general trust ( JohnsonandMislin, 2011), althoughthere is still needto improve
it (Fehr, 2009; Naef and Schupp, 2009).
The scope of morality, on the other hand, is measured with the so-called “respect
variable”. This is constructed on one of 11 items in WVS question bank related to
values the respondents consider to be especially important in children’s upbringing at
home. One of the values[2] is “tolerance and respect for other people” which is taken to
re?ect the scope of morality discussed above (Tabellini, 2008; Williamson and Mathers,
2011). Like the trust variable, the respect variable also ranges between 0 and 1.
In our empirical analysis we use the variables of trust and respect to construct a sum
variable as a weighted average of the two variables. The weights are adjusted so as to
equalize the means of both variables. We interpret that the combined variable proxies
the following two aspects of cultural codes in situations where people interact which each
other: howmuch andhowgenerallymost people inthis country trust others and behave in
a trustworthy manner. The higher the value of this variable the more likely it is that an
individual will comply both with the general norms of “good conduct” and the more
speci?c details in the implicit or explicit contracts of the economic transaction she has
engaged in (see the Appendix, Figure A1 for each country’s unweighted levels of trust and
the scope of morality).
Control and instrumental variables
In our empirical analysis we include several variables in our models to control for
various determinants of paying and repaying behavior such as the qualities of legal
institutions, economic growth and standard of living. In addition, we also check the
pooled OLSregression results byusing variables relatedto the country’s legal originand
the fraction of Protestant adherence as instrumental variables in TSLS-regressions.
We use two kinds of data to measure the properties of legal institutions. The ?rst two
variables used as controls come fromthe World Bank Doing Business (DB) data. The ten
separate variables in the DB dataset are combined into two variables according to the
guidelines given in a World Bank report (World Bank, 2012). One variable measures the
“inef?ciency of legal institutions” and refers to the average of the country rankings
according to the following variables: getting credit, protecting investors, enforcing
contracts and resolving insolvency. The other variable measures the “complexity and
cost of regulatory processes” and is an average of the similar country rankings of the
following variables: starting a business, dealing with construction permits, getting
electricity, registering property, payingtaxes and trading across borders. Both variables
re?ect the quality of the law-based regulatory system constituting an integral part of
the business environment in different countries.
In order to control for possible simultaneity betweenculture and paying behavior, and
between regulation and paying behavior, we use an instrumental variables approach.
Hence, we chose two instrumental variables which affect the key predictors (culture and
institutions) and which affect paying behavior only through these predictors. With
relation to the culture variable we use Protestant adherence (fromBarro’s (2003) religious
adherence data) for this purpose. As shown by Guiso et al. (2003, 2006), Protestant
af?liation is positively correlated with social trust. In addition, this correlation re?ects
historical processes originating in the Protestant Reformation several hundred years ago
in which various elements of Protestantism fostered the development of social trust and
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other values and beliefs (such as our notion of the scope of morality) related to the
horizontal relations between individuals (Guiso et al., 2003, 2006; Putnam, 1993).
Regarding the regulation variable we adopted the notion presented by LaPorta et al.
(1997, 1998) andusedanddevelopedbyDjankovet al. (2002, 2008), Levine et al. (2000) and
LaPorta et al. (2008). Accordingly, the legal origins of law and regulation in different
countries are largerlya result of a longhistorical process involving wars andoccupations;
legal origins, as this research shows, have a major in?uence on the legal and regulatory
environment which, in turn, in?uences the behavior of ?rms and individuals.
Finally, we also check the in?uence of the “overall economic development” and the
general rate of “indebtedness of the private” sector in each country. In measuring overall
economic development we use the IMF/World Bank GDP per capita indicator (converted
into international dollars using purchasing power parity rates) and annual growth rate
(as percent of GDP). Regarding the rate of indebtedness we use the World Bank data on
“domestic credit to private sector” measured as a percentage of GDP. Domestic credit here
consists of loans, nonequity securities, trade credits and other accounts of receivables.
3. Analysis
In this section we analyze the in?uence of the culture of trust and morality on behavior
related to paying and repaying. As indicated above, we use data constructed from data
items from several sources. Table I shows the variable list and the sources of the
variables. The empirical strategy is based on estimating the following model using
both pooled OLS regression and two stage least squares regression (TSLS):
y
it
¼ b
0
b
1
x
m;i
þb
k
x
k;it
; þg
2
D2
t
þg
3
D3
t
þg
n
Dt
t
þu
it
where i is country; t is year (t ¼ 1, . . . , n); x
m,i
is the value of the mth time-invariant
regressor (culture, regulatory inef?ciency and regulatory complexity); x
k,it
is the value
of the kth time-varying regressor; and D’s are time dummy variables so that if t ¼ 2
then D2
t
¼ 1 but otherwise 0. In addition, the interactions of the year dummies and the
culture variable are added into the model. The TSLS estimations are done to correct
possible inaccuracies of the OLS regressions by using culture and regulation one after
another as instrumented variables and, respectively, fraction of Protestant adherence
and legal origin as instrumental variables. We report both OLS regressions and TSLS
regressions – the latter with F-test and Wu-Hausman endogenous test statistics to
check whether the instruments are weak and whether there is endogeneity[3].
Figure 2 shows the progression of the target variables in each country over time
2004-2011. Some interesting observations can be made both for the sake of general
informationandprovidingbackgroundinformationto our further analyses. First, it appears
that risk behavior shows small within-country variation but large between-country
variation. Then, if culture is found to have an impact on risk behavior, it is related mainly to
quite stable risk-behavior levels in different countries. Second, the between-country
variation is smaller in the cases of credit losses and nonperforming loans than in the case
of risk behavior. Correspondingly, within-country variation is somewhat larger in the case
of credit losses and much larger in the case of nonperforming loans than in the case of
risk-behavior. Although in both of these cases the loess curve shows that the overall trend
takes a turn into an increasing course in 2008, it seems that credit losses exhibit a narrowing
and nonperforming loans a widening progression. Regarding some interesting information
on speci?c countries, we see that the risk behavior index as well as credit losses have
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increased dramatically in Greece during the period 2004-2011. Regarding nonperforming
loans, in the years 2004-2006 Macedonia and Poland had remarkably high rates while
in the years 2009-2011, the nonperforming loans rate was remarkably high in Lithuania,
Montenegro, Latvia and Albania. In the analysis below we take this group of “high
within-country variation” countries into account by adding a dummy variable
(npsens_dummy, see Table I) into the regressions of nonperforming loans.
In our analysis the main interest is in the in?uence of culture on paying and repaying
behavior. To achieve a better understanding of the causal relations between culture and
paying and repaying behavior we run pooled OLS regressions and TSLS regressions.
Table II summarizes the regressions of risk behavior on various sets of regressors.
Columns (1) and(2) are basedon pooled OLS regressions. Columns (3) and(4), respectively,
show the estimates of TSLS regressions with instrument variables. In the ?rst case, the
fraction of Protestant adherence serves as the instrument for culture; the F-statistics
suggest that the fraction of Protestant adherence is not a weak instrument whereas the
endogenous test (Wu-Hausman test) means that culture is not endogenous. The same
diagnostics applies to legal origin which is the instrument for regulation complexity in
Variable Description Min. Max. Mean SD
Risk behavior Overall riskiness of paying behavior index
2004-2011 (Intrum Justitia European Payment
Index, editions 2012 and 2008)
123.00 191.00 153.47 15.51
Nonperforming
loans
Nonperforming bank loans of gross loans (%)
2004-2011 (World Bank data)
0.20 21.00 4.12 4.11
Credit losses Firms’ credit losses (percentage of total revenue)
2004-2011 (Intrum Justitia European Payment
Index, editions 2012 and 2008)
0.60 6.50 2.39 0.87
Culture Culture of trust and morality. Based on WVS-
survey questions of “trust” and “respect”
(explained in Section 2)
0.42 0.96 0.62 0.14
Regulation
complexity
Average of rankings in the World Bank Doing
Business Data 2011 (explained in Section x)
13.00 134.50 62.73 31.75
Regulation
inef?ciency
Average of rankings in the World Bank Doing
Business Data 2011 (explained in Section x)
5.33 104.33 48.41 23.98
GDP per capita Gross domestic product per capita in $1,000; GDP
based on PPP (in current international $) 2004-2011
(World Bank data)
5.63 61.34 27.05 11.91
GDP
annual growth
Annual percentage growth rate of GDP
2004-2011 (World Bank data)
217.55 12.85 1.96 4.14
Indebtedness Domestic credit provided by banking sector
(percentage of GDP) 2004-2011
(World Bank data)
9.36 234.54 102.90 56.60
Protestant
adherence
Fraction of those who adhere to Protestant religion
(Robert Barro’ religion adherence data)
0.00 0.97 0.21 0.29
Legal origin Dummy variable which has the value of unit if the
country’s legal system is classi?ed as having either
common law origin or Nordic origin
npsens_dummy Dummy for countries (Albania, Latvia, Lithuania,
Montenegro, Macedonia and Poland) which have
been highly sensitive to variations in the level of
the nonperforming loans ratio during 2004-2011
Table I.
List of variables
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the model estimation reported in column (4). Consequently, we interpret that a culture of
trust and morality has an in?uence on risk behavior: the stronger the culture the lower
is the risk behavior index. In addition, we note that the complexity of regulation also
exerts in?uence: when the complexity increases the risk behavior index increases.
Table III shows the results of the regressions of credit losses. The OLS regression
shows that culture is clearly associated with credit losses (higher score on culture is
associated with a lower level of credit losses). However, the in?uence diminishes but
remains signi?cant when controlling it for regulation variables and for GDP per capita
levels and annual growth rate. Tests of TSLS regressions with regulation complexity as
the instrumented variable indicate that legal origin may here be a weak instrument and
that regulation complexity would be endogenous. The TSLS regression with culture
as the instrumented variable, on the other hand, shows that culture is not endogenous
and that Protestant adherence is not a weak instrumental variable. We interpret that the
higher the level of trust and morality in a country the lower the level of credit losses in
that country. Regulation does not appear to be associated with credit losses, whereas
growth of the economy and GDP per capita are associated. An increase in the former
raises but an increase of the latter decreases credit losses.
Finally, Table IV shows the estimation results of nonperforming loans. Compared to
the earlier models we have added indebtedness and a country dummy in the models.
Figure 2.
Line plots of the variation
of risk behavior, credit
losses and nonperforming
loans over the time
Notes: The dotted lines and thin lines represent the course of variables in each country over
time; the bold lines are loess curves showing the moving central tendency of the data
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Indebtedness measures the share of domestic private credit (as a percentage of GDP)
provided by the country’s banking sector. The country dummy (npsens_dummy)
distinguishes the countries having extremely high within-country variation from other
countries. Here again the TSLS regressions suggest that although the fraction of
Protestant adherence and legal origin are not weak instruments neither culture nor
regulation complexity are endogenous. We therefore interpret that good culture
in?uences by decreasing the level of nonperforming loans. On the other hand, higher
annual growth also decreases the nonperforming loans level whereas complexity of
regulation increases it. Clearly, the countries with high within-country variation have a
more than four times higher level of nonperforming loans than do the other countries.
4. Results and discussion
The major result of the above empirical analysis is that a culture of trust and morality
are associated with and has in?uences on “paying and repaying behavior”. In addition,
the complexity of regulation also in?uences paying and repaying behavior (the case of
credit losses, however, is excluded). In all cases the signs of the coef?cients are as
expected. In other words, culture decreases the scores of risk behavior, credit losses
and nonperforming loans while complexity of regulation increases the risk behavior
and nonperforming loans scores. Interestingly, culture and regulation in?uence in
opposite directions and are negatively correlated.
Our interpretation of the in?uence of culture on paying and repaying is that:
.
the higher level of trust (as one element of our de?nition of culture); and
Risk behavior
OLS
TSLS TSLS
Instrumented: reg_comp
a
Instrumented: cult
b
Regressor (1) (2) (3) (4)
cult 283.09
* * *
(6.65) 277.84
* * *
(8.57) 281.74
* * *
(8.00) 2 83.97
* * *
(13.07)
gdpcap 20.20
*
(0.10) 0.22
* *
(0.11) 0.25
*
(0.12)
gdpgrowth 20.30 (0.22) 20.33
*
(0.20) 20.29 (0.22)
reg_inef 0.03 (0.03) 20.02 (0.04)
reg_comp 0.15
* * *
(0.03) 0.14 (0.12) 0.16
* * *
(0.03)
constant 207.86
* * *
(6.77) 189.77
* * *
(5.70) 194.31
* * *
(10.95) 192.96
* * *
(7.65)
Year
dummies Yes Yes Yes Yes
cult
*
year
interaction Yes Yes Yes Yes
n 197 167 158 171
Adj. R
2
0.51 0.75 0.76 0.72
F ¼ 26.58; p , 0.001 F ¼ 24.65; p , 0.001
Wu-Hausman 0.042; p ¼ 0.84 0.37; p ¼ 0.54
Notes: Coef?cient is signi?cant at:
*
10,
* *
5 and
* * *
1 percent levels;
a
legal origin as instrumenting
variable;
b
fraction of Protestant adherence as instrumenting variable; pooled OLS regression estimates
are in columns (1) and (2), TSLS regression estimates in columns (3) and (4); of the TSLS estimations
the coef?cients of instrumented variables are underlined; ?rst stage F-test and Wu-Hausman
endogenous test reported for TSLS; one instrumented and one instrumenting variable in each model
implies exact identi?cation of coef?cients and justi?es the use of F-statistic greater than 10 as a sign of
a non-weak instrument
Table II.
Regressions of risk
behavior
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the broader scope of morality (as the other element of our de?nition of culture)
reduce disturbances and risks in paying and repaying behavior and also the
tolerance to opportunism and non-compliance in the behavior.
On the other hand, increasing complexity of regulation decreases trust and morality
and is associated with higher levels of risk behavior and nonperforming loans. We
suggests, along the lines of Aghion et al. (2010) that high trust and high morality
countries have been able to adjust the complexity of regulation to a low level, whereas
low trust and low morality countries have not.
At a general level the results concur with?ndings inthe research of tax compliance and
tax morale (McGee, 2012) and, especially with Torgler’s (2003) ?nding that trust in public
of?cials and legal systems has a positive in?uence on tax compliance. Our results are
also compatible with the ?nding of Cappelen et al. (2012) that moral considerations may
explain paying behavior independently of the quality of the contract (being either
enforcing nor non-enforcing). The results are also in accordance with research ?ndings
which emphasize the role of empirical and normative expectations (Bicchieri and Xiao,
2009; Sugden, 2000; Ga¨chter, 2007) when an individual decides to comply or not to comply
with a norm: there is evidence that people comply if they expect that others will also
comply, and that they do not comply if they think that others will not comply either
(Sacconi and Faillo, 2010; Tammi, 2011). It can be hypothesized that when people do not
knowhowothers will behave or of what others deemappropriate ina given situation, they
consult their culturally formed beliefs and values of social trust and scope of morality.
Credit losses
OLS
TSLS TSLS
Instrumented: reg_comp
a
Instrumented: cult
b
Regressor (1) (2) (3) (4)
cult 23.29
* * *
(0.46) 21.52
* * *
(0.58) 20.85 (0.65) 2 1.98
* *
(0.89)
gdpcap
($1,000) 20.02
* * *
(0.01) 20.02
* *
(0.01) 20.02
* *
(0.01)
gdpgrowth 0.02
*
(0.01) 0.03
*
(0.02) 0.03
*
(0.02)
reg_inef 20.02
(0.02) 20.00 (0.00)
reg_comp 0.00 (0.00) 0.02
*
(0.01) 0.00 (0.00)
constant 4.77
* * *
(0.32) 3.74
* * *
(0.39) 2.22
* *
(0.89) 3.98
* * *
(0.52)
Year dummies Yes Yes Yes Yes
cult
*
year
interaction Yes Yes Yes Yes
n 199 175 176 175
Adj. R
2
0.42 0.55 0.43 0.55
F ¼ 9.37; p , 0.001 F ¼ 12.76; p , 0.001
Wu-Hausman 3.90; p ¼ 0.05 0.47; p ¼ 0.49
Notes: Coef?cient is signi?cant at:
*
10,
* *
5 and
* * *
1 percent levels;
a
legal origin as instrumenting
variable;
b
fraction of Protestant adherence as instrumenting variable; pooled OLS regression estimates
are in columns (1) and (2), TSLS regression estimates in columns (3) and (4); of the TSLS estimations
the coef?cients of instrumented variables are underlined; ?rst stage F-test and Wu-Hausman
endogenous test reported for TSLS; one instrumented and one instrumenting variable in each model
implies exact identi?cation of coef?cients and justi?es the use of F-statistic greater than 10 as a sign of
a non-weak instrument
Table III.
Regressions of
credit losses
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The qualities of law-based regulatory institutions also have an obvious impact on
payment and repayment. That is, the percentage share of nonperforming loans of a bank’s
gross loans is smaller the less complex are the law-based regulatory institutions. This
?nding is in line with the literature on the role and meaning of nonperforming loans: most
of the analyses emphasize that effective, less complex and transparent institutions rather
thanstrongregulation cansolve or exacerbate the excess problemof nonperformingloans
(Boudriga et al., 2009). On the other hand, the percentage share of bank nonperforming
loans can also be seen as an indicator of the performance capability of the banking system
in a country (Maggi and Guida, 2011). Our results then suggest that in those countries
where law-based institutions are complex the performance capability of a bank system is
weak, leading to de?ciencies in banking and lending behavior.
Taken together, our results support the idea that culture matters: a culture of
trust and morality has a clear in?uence on payment and repayment behavior. Further
studies with larger data-sets are the obvious next step regarding the empirical
analysis of paying and repaying behavior. Regarding theoretical work, the analysis of
paying and repaying behavior should try to better understand the process whereby
social trust and morality in?uences the decision of an individual to comply promptly,
with a delay, or not to comply with the more or less enforcing contracts they
have made.
Nonperforming loans
OLS
TSLS TSLS
Instrumented: reg_comp
a
Instrumented: cult
b
Regressor (1) (2) (4) (3)
cult 210.80
* * *
(2.22) 24.55
* *
(2.38) 23.86 (2.64) 2 3.26 (3.12)
gdpcap 20.04 (0.03) 20.07 (0.06) 20.05 (0.04)
gdpgrowth 20.44
* * *
(0.06) 20.45
* * *
(0.06) 20.44
* * *
(0.06)
reg_inef 20.01 (0.01) 0.01 (0.01)
reg_comp 0.02
* * *
(0.01) 0.01 (0.03) 0.02
* * *
(0.01)
indebtedness 20.01 (0.01) 20.01
*
(0.01) 0.01 (0.01)
npsens_
dummy 4.47
* * *
(0.54) 4.01
* * *
(0.62) 4.45
* * *
(0.52)
constant 10.31
* * *
(1.48) 11.45
* * *
(1.54) 9.24
* * *
(2.58) 6.37
* * *
(1.82)
Year
dummies Yes Yes Yes Yes
cult
*
year
interaction Yes Yes Yes Yes
n 278 270 271 271
Adj. R
2
0.36 0.64 0.64 0.64
F ¼ 24.02; p , 0.001 F ¼ 23.42; p , 0.001
Wu-Hausman 0.64; p ¼ 0.43 0.31; p ¼ 0.58
Notes: Coef?cient is signi?cant at:
*
10,
* *
5 and
* * *
1 percent levels;
a
legal origin as instrumenting
variable;
b
fraction of Protestant adherence as instrumenting variable; pooled OLS regression estimates
are in columns (1) and (2), TSLS regression estimates in columns (3) and (4); of the TSLS estimations
the coef?cients of instrumented variables are underlined; ?rst stage F-test and Wu-Hausman
endogenous test reported for TSLS; one instrumented and one instrumenting variable in each model
implies exact identi?cation of coef?cients and justi?es the use of F-statistic greater than 10 as a sign of
a non-weak instrument
Table IV.
Regressions of
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5. Conclusions
Acountry speci?c culture of trust and morality is associated with country differences in
payment and repayment practices. The results have implications for both private
?nancial actors and institutions and public policy authorities and regulators. Both
practitioners of policies and designers of ?nancial systems (in private ?nancial
institutions, ?nancial monitoring regimes or consumer policy institutions) could bene?t
from including cultural factors and the quality of regulation in their models for
predicting changes in paying and repaying behavior. A major component in the
development of these practically oriented models is the relationship between cultural
factors and the strength of regulation: a “better culture” (high levels of trust and
morality) makes it possible to reduce regulation and to get the community to stay at
“high level”. Conversely, “worse culture” tends to increase the strength and complexity
of regulation. In some cases (in some countries) rapid economic growth may provide
opportunities to raise the levels of trust and morality and to improve the quality of
regulation. Conversely, negative economic growth may threaten high levels of trust and
morality and increase expectations of stronger regulation.
Notes
1. Yet another measure of “culture” is Hofstede’s dimensionalist approach, which is also based
on value-statements (Reuter, 2011).
2. The other values are good manners, independence, hard work, responsibility, imagination, thrift
& saving money, determination, perseverance, religious faith, unsel?shness, obedience.
3. The ?rst-stage F-test is applicable to cases when there is a single endogenous predictor.
We conclude that there is no sign of a weak instrument if the F-statistics exceeds 10. Regarding
the Wu-Hausman test, it is used to see if the OLS and TSLS estimates are different; lowp-value
indicates the existence of endogeneity with the implication that TSLS is preferred to OLS.
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Corresponding author
Timo Tammi can be contacted at: timo.tammi@uef.?
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Appendix
Figure A1.
The scope of morality
(?rst stack) and trust
(second stack) as
components of the culture
of trust and morality
Note: Based on non-weighted values of the variables “respect” and “trust” in the WVS
dataset
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