Combating Corruption: On the Interplay between Institutional Quality and
Social Trust
*
Christian Bjørnskov
May 19, 2008
Abstract: The aim of this paper is to explore under which conditions institutional quality
leads to lower corruption. A model of a simple economy where firms both chose
between bribing or not bribing bureaucrats to avoid costs and between entering the
official or unofficial economy shows that the effects of increasing institutional quality
may be ambiguous due to perverse effects of institutions in the unofficial economy.
Employing a recent index of corruption based on objective data, the paper shows that
formal institutions are more effective in combating corruption in countries with high
levels of social trust. The paper concludes by discussing the welfare and political
implications of the findings.
JEL Codes: D72, K42, Z13
Keywords: Corruption, Rent-seeking, Social trust
*
Aarhus School of Business, University of Aarhus, Department of Economics, Prismet, Silkeborgvej 2,
DK-8000 Aarhus C, Denmark; Phone: +45 89 48 61 81; e-mail: ChBj@asb.dk. I am grateful to Axel
Dreher for providing me with the corruption data, and to Partha Dasgupta and Maria Ziegler for valuable
comments on an earlier version. Christina Bjerg provided excellent research assistance. The usual
disclaimer naturally applies.
1
1. Introduction
The economic and social consequences of corruption are difficult to neglect and,
defined as the misuse of public office for private gains, one would think that it could fit
into most conceptions of political economy or public choice without complications
(Svensson, 2005). However, the economics of corruption only grew to be an important
area with the advent of internationally comparable data since the mid-1990s. The
voluminous amount of research published since then indicates that corruption, among
other things, hampers economic growth (Mauro, 1995; Sekkat and Méon, 2005), distorts
government spending (Mauro, 1998), affects productivity (Méon and Weill, 2004) and
in the long run leads to very considerable losses of income and human welfare
(Kaufmann et al., 2005).
A number of studies have therefore explored why some countries seem to
experience substantially more corrupt behaviour than others. Treisman (2000, 2007)
summarizes the determinants of corruption as income, an established democracy, press
freedom, rule of law, openness to trade, and not relying on resource exports such as oil
or minerals. De Haan and Seldadyo (2006) instead employ confirmatory factor analysis
to pool a large number of explanatory variables into a set of factors, of which only one –
regulatory capacity – is robustly associated with corruption. This factor includes such
variables as ´the rule of law, judicial independence, government effectiveness, political
stability, regulatory quality, freedom from labour market regulations, international
trade, and secondary school enrolment. A cluster of other variables including a number
of Treisman’s main findings also survives a somewhat weaker test of robustness.
2
However, cultural and social factors are also likely to be associated with the
incidence of corruption, not least because corruption at the end of the day is evidence of
unambiguously dishonest behaviour. Even if many studies do not discuss this factor,
social trust – often taken to be a measure of the strength of honesty norms in society –
has been identified as a statistically strong and quantitatively important determinant of
corruption in recent empirical studies (Uslaner, 2002, 2004; Bjørnskov, 2003). It is
nevertheless unclear whether social trust in itself is associated with corruption, or if
other factors are stronger in its presence. As such, one of the many remaining questions
in this strand of the corruption literature is whether the rule of law and social trust are
substitutes or complements. It is easy to make the case for legal protection being more
necessary in fighting corruption when the population is, in general, not trusting. Yet
whether the legal system is also as effective in that situation remains an open question,
and the argument could be made that social trust instead reinforces the effect of legal
protection.
The purpose of this paper is to explore whether social trust reinforces the
effectiveness of the quality of formal institutions or more likely works as a substitute.
The theoretical considerations that allow for an unofficial economy suggest that the
legal system may only be effective when at least part of the population acts honestly. A
set of empirical results based on unique panel data on corruption and legal quality
supports that the quality of the legal system is substantially stronger associated with
corruption when social trust scores are relatively high.
The rest of the paper is organized as follows. Section two outlines a simple theory
of how legal quality, moral behaviour and their interaction affect the likelihood of
3
corrupt transactions. Section three describes the data to be used in section four, which
estimates a set of determinants of corruption. Section five concludes.
2. Theory
Corruption is often defined as “the misuse of public office for private gains” (Treisman,
2000). Most models consequently assume that corruption occurs in the necessary
interplay between government agencies or institutions and the business sector. As in
many contributions to this literature, I therefore assume that firms need a permit in order
to produce. In this setting, corruption can arise because bureaucrats at a government
agency hold a monopoly of issuing these permits. The resulting game is depicted in
Figure 1.
Insert Figure 1 about here
In all cases I use backwards induction to solve the game, starting with the choice
of a bureaucrat working in the government agency responsible for issuing permits and
monitoring their use. The choice the bureaucrat faces in both the official economy and
his function vis-à-vis the unofficial economy is to accept a bribe or not. With a wage w,
a bribe b, an outside option worth wout (which may include a fine or prison sentence), a
‘moral cost’ M (including both external and internal costs such as shame, the risk of
being socially ostracized etc.) of accepting a bribe, where M~iid(MA,σM) , and a risk of
4
being caught accepting the bribe given by the quality of the legal system that determines
this risk, λ, the bureaucrat will chose to accept if the following inequality holds.1
(1 − λ )(w + b ) + λwout − M > w
(1)
Solving this inequality gives the minimum bribe, bmin, acceptable to a risk-neutral
bureaucrat, which will differ between bureaucrats since M is a stochastic term.
bmin =
λ
1− λ
(w − wout ) +
M
1− λ
(2)
Here, it is worth noting that the first term exhibits increasing returns to legal
quality while the second term implies that social trust, which in the following is used as
empirical proxy for M since it reflects individuals’ moral costs (Uslaner, 2002), is
associated with a higher minimum bribe and is complemented by legal quality. Hence,
the minimum bribe in any situation is determined by the interplay between the quality
of the legal system and the idiosyncratic moral costs of the bureaucrats whom firms
face.2
Turning to firms’ profits, the economy consists of many even-sized firms that face
the choice of either 1) bearing a cost F, which can be thought of as either a handling fee
for receiving the permit or a cost of conforming to regulations, or 2) attempting to bribe
1
The relation between the theoretical moral costs and the measure of social trust employed in the
empirical part of the paper is based on the assumption that the moral costs are increasing in social trust.
Both the internal motivation to avoid dishonest behaviour, such as shame, and external motivation, such
as the risk of being socially ostracized, are stronger when an individual believes that most other people
are not likely to engage in such behaviour.
2
If it furthermore is assumed, as in Yavas (2007), that the moral cost decreases with the corruption of the
surrounding society the possibility arises that a self-enforcing equilibrium without any corruption exists,
even if the same distribution of moral costs in another society would result in some corrupt behaviour.
For simplicity, I do not consider this option in the following.
5
the bureaucrat to avoid paying the fee. Firms’ profit function is given by π(φ) - c, where
φ is a stochastic productivity parameter, and c is a fixed investment cost.
2.1. Firm behaviour in the official economy
Focussing first on what might be considered the standard part of the model,
illustrated in the lower half of the game tree in Figure 1, the question here is under
which conditions bribes arise in the official economy. In other words, under which
conditions are the expected profits from corrupt behaviour larger than those of honest
behaviour as in the right-hand side of (3)? In (3), p is the probability that the average
moral costs are sufficiently low to make it rational to attempt to bribe an average
bureaucrat, i.e. a bureaucrat with the expected average moral cost. This long expression
reduces to the short expression in (4), which simple states that firms will attempt to
bribe an official as long as the necessary bribe is smaller than the expected fine.
(1 − λ )[ p(π (ϕ ) − c − bo ) + (1 − p )(π (ϕ ) − c − F )] +
λ [ p(π (ϕ ) − c − F − bo ) + (1 − p )(π (ϕ ) − c − F )] > (π (ϕ ) − c − F )
(3)
bo < (1-λ)F
(4)
This obviously means that all bureaucrats will require the maximum bribe since
they collectively hold monopoly power and firms are either randomly assigned to a
bureaucrat or are otherwise restricted from searching for a corruptible agent. Hence, the
probability of a bureaucrat taking a bribe of the size in (4) is given by (5).
{
}
p = prob M < (1 − λ ) F − λ (w − wout )
2
(5)
Focussing at first on an entirely official economy rather clearly indicates that the
quality of the legal system, λ, is always effective, but may be more so in countries with
a high average level of moral costs since the size of bribes in (2) increases
6
proportionally with λ but the probability of corrupt behaviour decreases
disproportionally, depending on M. The latter conclusion thus also holds for the
deadweight losses of corruption that in this simple economy can be approximated as the
product of (2) and (5).
2.2. Allowing for an unofficial economy
However, introducing the additional possibility that firms can choose to produce
without a permit – i.e. in an unofficial, grey, shadow or underground economy –
complicates the analysis. Allowing for this possibility is nevertheless closer to reality in
most developing countries where the unofficial economy can affect up to three quarters
of GDP (Schneider and Enste, 2000). I therefore effectively allow for an exit option
from the lower half of the game tree as many firms in most countries exist in an
unofficial economy, which in the context of this model will consist of firms producing
without a permit. In most countries the agency employing the bureaucrat not only
handles the approval – rubber stamp or not – but also controls whether firms conform to
the regulations stipulated by the approval. Hence, I make the simple assumption that
bureaucrats from the government agency issuing permits also make control visits to
ensure that firms are in possession of a valid permit. Firms are nevertheless not
controlled from the beginning of a period, but face an expected waiting time d,
depending on the capacity of the government institutions, before receiving a control
visit.
First, for considering paying a bribe in the unofficial economy, the expected
profits have to outweigh simply ending production after having produced for the
fraction d of a period. As such, bu, the bribe paid in the unofficial economy, becomes
7
smaller over time, as the willingness of firms to pay a bribe to stay in production
decreases. Investing in the capacity of the regulatory authorities can thus actually make
bribery in this part of the economy more likely, given that firms choose to produce
unofficially as in (6).
π (ϕ ) − c − bo > dπ (ϕ ) − c ⇔
bo < (1 − d )π (ϕ )
(6)
If the expected bribe is lower than this limit and given that q is the likelihood that
the bureaucrat performing the control visit at the firm is corruptible in the sense that his
minimum bribe is smaller than the benefit of paying it, going underground yields an
expected profit πu of operating illegally.
Making the simplifying assumption that the expected waiting time for a control
visit is given by d = a - μλ,3 i.e. that overall institutional quality is accompanied by
capacity, bribes will occur in the underground economy when (7) holds. Consequently,
bribes are more likely in the underground economy than in the official economy if (8)
holds:
(a − μλ )π (ϕ ) < λ (w − wout ) +
1− λ
π (ϕ ) <
M
1− λ
(7)
1− λ
F
a − μλ
(8)
As such, the structure of the model allows for a perverse effect of increasing the
costs of getting a permit, as it is likely to drive some firms into the underground
economy and at the same time is likely to increase the probability of corruption in this
3
This type of assumption allows for a variable association between institutional quality and institutional
capacity, given by μ. While this association is likely to be positive, it may not be perfect; hence the likely
parameter space is 0 < μ < 1.
8
part of the economy. Based on this assumption, the probability of a bureaucrat accepting
a bribe in the unofficial economy, i.e. on a control visit, is given by q in (9).
q = prob{M < (a − μλ )(1 − λ )π (ϕ ) − λ (w − wout )}
If the expected bribe in the unofficial economy is larger, the expected profit is
given by (10). Hence, firms will chose to operate in the unofficial economy if the total
expected profit of an underground firm E{πu} exceeds that of a corrupt firm in the
official economy E{πo} in (11).
E [π u ] = q(π (ϕ ) − bu ) + (1 − q )π (ϕ ) − c
q(π (ϕ ) − bu ) + (1 − p )q(1 − d )π (ϕ ) + (1 − q )π (ϕ ) − c >
p(π (ϕ ) − bo ) + (1 − p )(π (ϕ ) − bo − F ) − c
(10)
(11)
This inequality holds whenever:
π (ϕ ) <
1 − pq
1
b+
F
(1 − p )qd qd
(12)
Hence, the model generates the intuitive result in (12) that less productive firms
are more likely to enter the underground economy.4 However, the relation between the
likelihood of going underground and institutional quality, λ, will be ambiguous as
shown formally in the appendix. For relatively low λ’s, the relation may be positive, i.e.
increased monitoring due to better institutions drives firms underground and thus into a
corrupt environment instead of staying in the official economy where they remain
corrupt, but willing to pay higher bribes the faster they are monitored.
In other words, moving from very bad to relatively better institutional quality,
thereby coming from a likely situation of a relatively large underground economy, an
4
To obtain this result formally, it must be kept in mind that q also depends on π(φ). However, it can be
shown by simple differentiation that the likelihood of staying in the official economy is increasing in φ.
9
improvement of institutional quality has ambiguous effects on the level of corruption.
Theoretically, allowing for the existence of an underground economy affects the
association between institutional quality and corruption due to the perverse incentives
arising from firms’ ability to go underground. In summary, the theoretical model above
thus comes to imply three testable propositions to be explored in section 4.
1.
Institutional quality is more effective in combating corruption in
conjunction with high moral costs of accepting corrupt deals.
2.
Corruption is less likely when average moral costs are high regardless of
institutional quality.
3.
In countries with particularly poor institutional quality, i.e. relatively large
unofficial economies, investing in overall institutional quality can in
principle lead to more corruption.
3. Data
To test these broad implications, I draw data from a number of different sources. First,
the corruption index employed in this paper comes from Dreher et al. (2007) and is
derived by a MIMIC estimator, a method that instead of providing a direct indicator
triangulates the potential corruption of a country, based on a dataset of variables that the
literature either associates with corruption as causes or consequences. This set consists
of, among other things, legal quality, secondary education rates, cement consumption,
GDP per capita, the severity of capital restrictions, financial development, latitude, the
age of democracy and a dummy for German legal origins. Dreher et al. (2007) thus
follow the literature on robust determinants of corruption summarized in the
introduction to estimate countries’ levels of corruption from objective data. The
10
corruption data are, as all other data in the following, arranged in four periods: the
period up to 1980, the early half of the 1980s, the period around 1990 and the late 1990s
up to 1997.
Second, social trust, my proxy indicator of the M in the model, is measured as is
standard in the literature by the percentage of a population answering yes to the question
“In general, do you think most people can be trusted?” Although the question is fairly
broad, it has been used since Rosenberg (1956) introduced it and is usually considered a
reasonable proxy for actual trust and trustworthiness. Uslaner (2002) shows that the
answers measure the extent to which respondents think that strangers can be trusted to
abide by a common set of norms of morality. Knack (2001), using data from a walletdrop experiment conducted by Reader’s Digest in 16 cities around the world and 12
major American cities, furthermore show that the percentage of a population answering
yes to the social trust question above is a strong predictor of the share of the dropped
wallets that were returned to their owners with the contents intact. Most authors
therefore treat the trust scores as proxies for the average trust and honesty of citizens of
a country or region (cf. Knack, 2002; Uslaner, 2002; Bjørnskov, 2007a).5
These data are from the four waves of the World Values Survey conducted
between 1981 and 2001, the 1995 and 2003 LatinoBarometro surveys, the 2001-2004
Asian and East Asian Barometers, and the 2002-2004 Danish Social Capital Project. As
most countries are only observed in one period, I use the average of all available
observations and thus impose the restriction upon the data that social trust does not vary
5
However, a small literature is sceptical about the use of the aggregate scores of the trust question. See
for example Moore (1999), who argues that it is unclear what is standard reputation effects and what is
outcomes of social trust, or Beugelsdijk (2006) who claims that trust simple proxies for the standard of
formal institutions.
11
significantly in the medium run. While this assumption is clearly not borne out in the
case of the United States in which the overall trust level has been declining from the
early 1970s to the early 1990s, social trust seems remarkably stable in most other
countries (cf. Uslaner, 2002; Bjørnskov, 2007a).6
The use of MIMIC-based corruption data gauged from objective or quasiobjective primary indices is a point of considerable importance in the present context.7
First of all, since these estimates on corruption across countries and time are primarily
based on objective data, they are probably as free of ‘pure’ perceptions as such data can
be. The bulk of the data used in previous research on the association between social
trust and corruption has been perceptional and derived from surveys, making the use of
corruption data based on ‘hard facts’ of great importance in this specific context: when
exploring non-economic determinants of corruption, an additional problem arises if
underlying, but possibly erroneous, perceptions of the status of a country confound both
trust and corruption. In that case, the use of a perceptions-based corruption index, as in
most of the empirical corruption literature, might give rise to a spurious association
6
That social trust has been declining in the US is clear in all studies, yet it is worth noting that the decline
has not been consistent across the states. While some states, for example California, have seen large
declines, trust levels in other states as Massachusetts have been stable (Bjørnskov, 2007b). Likewise,
social trust has varied in only few countries besides the US, declining slightly in the United Kingdom and
increasing in Denmark and Uruguay. Furthermore, the known trust changes do not seem to have affected
changes in corruption as the data for Denmark, the US and the UK exhibit strong declines since the late
1970s while increasing in recent years in Uruguay.
7
In addition, it is worth mentioning that while the Dreher et al. (2007) data are based on more or less
objective data, the coefficient of variation in period-to-period changes is the same (approximately 4) as in
the perceptions-based ICRG measure. Compared to previous research, the findings in the following are
there unlikely to be attributable to artificial or a priori improbable variability of the new measure.
12
between social trust and corruption. By using the data derived by Dreher et al. (2007),
this worry is substantially reduced, if not entirely eliminated.
Second, using as objective data as are available avoids the pitfalls of the many
alternative cross-country indicators of corruption, all based on perceptions, which
Knack (2007) outlines. One of Knack’s main results is that most broad corruption
indices such as Transparency International and the Kaufmann et al. ‘control of
corruption’ index seem to measure administrative corruption instead of state capture.
Knack is also seconded by, among others, Abramo’s (2007) Brazilian survey results and
Olken’s (2006) unique study from Indonesia, in assessing that surveys of the experience
with corruption among ordinary citizens reveal that corruption perceptions may be
rather weak proxies for actual corruption, at least as measured by respondents’ actual
experience, and that perceptions-based indices may underestimate actual corruption.
Instead, such perceptions may contain no more “information than is obtained by asking
about poverty, human rights etc.” (Abramo, 2007, 35). Andvig (2004) also criticizes
perceptions-based measures on the basis of their risk of being influenced by
informational cascades, which for example would make them artificially stable over
time. For example, many decision makers and experts contributing to perceptions-based
measures are likely to both be aware of alternative measures and to employ the same
information, creating potentially spurious correlations between the array of indicators
available at any point in time (Knack, 2007).
On the other hand, Kaufmann et al. (2006) defend perceptions-based measures
(see also Svensson, 2005). First, an inherent problem of the critical studies advocating
experience-based indicators is that the ‘actual’ experience with corruption of most
people can also be a misleading indicator. In countries relatively free of corruption,
13
most people may not experience bribery or similar actions. Instead, the less actual
corruption there is in a country, the more is it likely that ordinary people stretch their
concept of corruption to include nepotism, lobbyism, and unpopular but legal fees, and
therefore infer ‘corrupt’ behaviour from outcomes of professional networking or merely
expensive public regulations. Moreover, even in countries with relatively prevalent
corruption, the majority of the respondents of international surveys may not have
experienced corruption themselves if types of illegal behaviour are focused around
specific business sectors or bureaucratic agencies. As such, Kaufmann et al. (2006, 2)
note that survey questions about corruption in, for example, the police with which
ordinary people are likely to have first or second-hand experience with, “need not be
informative about corruption in public procurement”. Such effects could explain the
discrepancy between, for example, the non-negligible World Values Survey measure of
experienced corruption and most other indicators in extremely clean countries such as
Denmark or Switzerland. Knack (2007) also notes that most broad perceptions-based
indicators tend to measure administrative corruption instead of state capture and more
specific incidences of corruption. This point is of some importance in the following as
the objective indicator employed here, which is highly correlated with the popular broad
corruption measures, may suffer from some of the same problems while solving others.8
As the MIMIC data of Dreher et al. (2007) solves some of the potential problems
associated with alternative indicators, I use these data in the following to estimate the
relation between trust, institutional quality and corruption using a baseline consisting of
8
The similarities are rather obvious as the correlations between the indicator employed in this paper and
alternatives are -.69 with the ICRG measure and -.80 with the Kaufmann et al. ‘control of corruption’
indicator. These correlations change only marginally when controlling for legal quality and remain
significant when controlling for GDP per capita.
14
legal quality, social trust, the logarithm to GDP per capita, openness, government size,
political competition (the Herfindahl index), and a set of dummies for postcommunist
countries, Latin America, the MENA region, Asia, Sub-Saharan Africa, and the four
time periods. Employing this baseline yields an unbalanced panel of 194 observations
from 74 countries with full data. All sources for the additional data are given in the
appendix.
All estimates are obtained using a feasible least squares estimator with random
effects as Hausmann tests in the majority of cases strongly support this choice. In one
case, I also provide an instrumental variables estimate of the influence of social trust so
as to address the possibility that corruption or factors entering the MIMIC procedure
generating the corruption data may affect individuals’ perceptions of the trustworthiness
of their fellow citizens.
4. Results
At a first glance at the data, there is a quite strong association between social trust and
corruption as the simple correlation of -.64 is clearly visible in Figure 2 although the
assumption of stability of the trust scores might well serve to bias this correlation. In
particular, if trust depends on corruption one would expect that the present trust scores
are most strongly correlated with corruption in the latest period. However, the
correlations with corruption in the four periods differ only between -.64 and -.68. The
partial correlations when controlling for the quality of the legal system similarly exhibit
no change over time.
Such simple correlations can nonetheless obviously be misleading and Table 2
consequently applies the full baseline specification. The table first of all shows that
15
economic development, political competition and legal quality are negatively associated
with corruption while countries with larger government sectors tend to have more
corruption. The results also exhibit strong differences across regions of the world with
postcommunist countries having most corruption, all other things being equal, followed
by the well-known Latin American problems and a weakly significant tendency to more
corruption in the Middle East and North Africa region. While the association with legal
quality is warranted by most other studies but is here entirely an effect of the MIMIC
method used to construct the corruption data, it is worth noting that the other findings
do not appear by construction but are still broadly in line with previous studies using
data on perceptions of corruption (Aidt, 2003; de Haan and Seldadyo, 2006; Treisman,
2000, 2007).
Insert Figure 2 about here
Turning to the association between social trust and corruption, the association is
strongly significant in both statistical and economic terms. The coefficient indicates that
a one standard deviation shock to social trust (14 percentage points) is associated with a
corruption decline of 26 percent of a standard deviation (.06 points), all other things
being equal. The IV estimate reported in a lower panel in Table 2 provides support for
the notion of a causal effect from trust to corruption even though the estimate is
somewhat larger. The results in column 2, in which observations with residuals larger
than ±2 standard deviations have been removed, in general gives much clearer results
16
consistent with the existing literature, as well as support for a strongly significant
estimate of social trust, which is only slightly smaller than that in column 1.9
In the following columns of Table 2, I explore the question whether social trust
complements or substitutes that of legal quality, as captured by simply adding an
interaction term. The results rather clearly support the former, as the interaction term is
strongly and significantly negative when evaluated at the sample mean. The interaction
has a cut-off point at a score of about three on the legal quality index, indicating that
neither legal quality nor social trust are associated with corruption at very low levels of
legal quality. Hence, this is broadly consistent with the implications of the simple
theoretical model in section 2.
Another way to evaluate the shape of this relation is to split the sample in two
equal halves, one with relatively poor legal quality, and the other with relatively good
quality (not shown in table). The result of this exercise is that in the half-sample with
poor legal quality, the coefficient on trust is -.174 (standard error .058) while in the top
half, it is -.673 (standard error .183). Yet, it is equally worth noting that legal quality is
not significant in the bottom half. While this may of course be an artefact of the legal
quality index, it is noteworthy that legal quality fails significance even though it forms
one of the constituting parts of the MIMIC method used to generate the corruption
9
Only two countries exhibit consistently large residuals across periods in the regressions. The notable
outliers are Japan, which appears substantially more corrupt in the raw data than predicted by the model,
and Australia that is substantially less corrupt than predicted. The Japanese case is particularly interesting
as certain studies questioning the standard trust measure are based on comparisons between the US and
Japan (e.g. Miller and Mitamura, 2003). This and other studies therefore may suggest that the problem is
not with the trust measure per se, but its implementation in Japan where it may fail to accurately estimate
actual trust.
17
scores. Given that legal quality is also associated with the size of the unofficial
economy (cf. Schneider and Enste, 2000), this particular finding is consistent with the
implications of the theoretical model in which institutional quality has ambiguous
effects in countries with relatively large unofficial economies.
On a broad level, the results indicate that the effectiveness of the legal system in
combating corruption is increasing in the level of social trust. Employing the
instrumental variables technique to the two subsamples provides further support, as the
IV estimate of social trust is -.943 (standard error .238) in the upper half with relatively
good legal systems, whereas it is -.235 (standard error .108) in the comparatively bad
half. Even while both estimates are somewhat larger than the random effects
coefficients, the ratio between the effects in the two halves is 4.0 and thus not
substantially different from the ratio between the corresponding random effects
estimates at 3.7.
Insert table 2 about here
These results could of course be spurious for multiple reasons.10 A number of
different effects could in principle drive the heterogeneous trust effect. First of all, both
corruption and social trust are arguably more precisely measured in relatively richer
countries, which could give rise to a spurious interaction between trust and legal quality
10
Reviewing the more recent literature on social trust published since 2000, Nannestad (2008) in
particular stresses that many studies exhibit two problems. First of all, the literature suffers from a lack of
robustness analysis, and second, many well-known and often-quoted studies do only little in terms of
causality analysis. Both points are emphasized in Bjørnskov (2007a) that show how few results from the
previous literature on the determinants of social trust survive addressing these problems.
18
as the latter is correlated with GDP per capita (r=.47 in the present sample). Second,
legal quality could in principle also proxy for democracy (r=.44), regulatory freedom,
(r=.28) political competition (r=-.34), government size (r=-.19) or income inequality
(r=-.36), and the results could also capture a non-linear effect of social trust due to its
correlation with legal quality (r=.39). These possibilities are tested for in Table 3 that
includes the additional variables and their interaction with social trust.
Insert Table 3 about here
The table shows that these worries are likely to be unfounded. While the size of
the coefficient on social trust varies by 50 percent from the minimum to the maximum
estimate in Table 3, the interaction term only changes about seven percent, indicating
that the overall findings are very robust to this exercise. The cut-off point of legal
quality at which the estimate of social trust becomes positive is also stable, shifting
between index values of 3 to 3.5. As for the further results of the exercise (not shown),
the interaction with legal quality are unchanged but two results may deserve mention.
First, trust interacted with Gini coefficients (income inequality) is significant (at five
percent), suggesting that trust may be more effective in unequal countries.11 Second, the
Hausmann test becomes strongly significant in column 10, indicating that the random
effects estimator may not be consistent. However, the fixed effects results pertaining to
the interaction term remain significant at conventional levels (not shown).
11
Another possible explanation is that social trust is measured with systematic error in relatively unequal
societies. This would nevertheless represent a potentially severe problem for the trust literature, as one of
its most robust results is that high levels of inequality are detrimental to social trust.
19
Relying on these simple tests, the findings above therefore seem to be robustly
indicating that social trust is significantly associated with corruption as well as making
the legal system more efficient in combating corruption. As an alternative way to
exemplify these findings, Table 4 provides some very simple dynamic statistics by
splitting the set of data with more than one observation on corruption in two, according
to their changes in corruption between 1980 and 1997. The result of this simple
alternative test is that the ‘good’ half in terms of success in combating corruption has
seen a decrease in corruption of roughly -.075 points (34 percent of a standard
deviation) compared to the global average while the ‘bad’ half has seen a similar
increase in the corruption index. Among the top performers are Denmark, Norway,
Japan and the Netherlands, all high-trust countries in which only the latter has seen a
marked improvement of the legal system in the same period. Among the top are also
Belgium, Austria, the United Kingdom and the United States that have higher trust
scores than the global average. In the bottom are such countries as Kenya and Nigeria
that both score well below the global average.
Insert Table 4 about here
The table demonstrates that the general improvement of legal quality of 1.3 points
does not differ between the two halves. Instead, while most countries in the world
exhibited judicial improvements in this period, the simple average dynamics suggest
that these improvements have, on average, been accompanied by a real decrease in the
instance of corruption in countries with above-average levels of social trust, but
generally not in countries with below-average trust. As such, even though such averages
20
should be interpreted with extreme care, they indicate that the static estimates in Tables
2 and 3 are sufficiently strong to be clearly visible in corruption dynamics over a longer
period of time. Combined with the panel data estimates above, the findings thus provide
clear support for the notion that social trust and institutional quality are complementary
when combating corruption, a finding that is discussed and connected to the theory in
the final section.
5. Conclusions
This paper has revisited the question of whether moral norms of honesty, measured here
by social trust, are negatively associated with the corruption problems many countries
face. A simple model of potentially corrupt bureaucratic behaviour first illustrated that
while moral costs relating to corrupt behaviour are unambiguously associated with less
corruption, the association between corruption and the quality of formal institutions is
ambiguous, at least at relatively low levels of quality and fragile moral norms. The
reason for the theoretical ambiguity is the potential decision of entering the unofficial
economy in which stronger institutional quality implies a smaller likelihood of
bureaucrats being offered and accepting bribes but also implies more willingness to pay
large bribes whenever they are offered. The source of this perverse effect is that as
unofficial firms are on average audited earlier within a given period, the value of their
potentially remaining time in the market increases, thus raising the opportunity costs of
turning down a corrupt offer.
While the specific theoretical intricacies cannot be explored with any data
available today, the broader implications are tested in a panel dataset consisting of 74
countries observed in up to four periods, which yields 194 observations with full data.
21
The empirical analysis in the paper differs from previous research in using corruption
data from a MIMIC exercise, i.e. the estimates on corruption across countries and time
are ‘triangulated’ from objective data. The data used in previous research on the
association between social trust and corruption are perceptional indicators deriving from
surveys, a difference that makes the use of corruption data based on ‘hard facts’ of great
importance in this specific context. If underlying, erroneous perceptions of the status of
a country confound both social trust and perceptions of corruption, the use of a
perceptions-based corruption index, as in most of the empirical corruption literature, can
easily give rise to a spurious association between social trust and corruption. By using
the data derived by Dreher et al. (2007), the paper avoids this pitfall of the existing
literature.
The results in the paper nonetheless closely follow the overall findings of the
corruption literature that social trust is negatively associated with the incidence of
corruption, and are consistent with recent work on links between social trust, corruption,
and the shadow economy (cf. Ahmed et al., 2005; d’Hernoncourt and Méon, 2008).
Given that an instrumental variables approach can inform about causality, the estimates
suggest that the association reflects an influence of social trust on corruption, although
some feedback from the reverse direction cannot be rejected. The results are also of
economic significance, as a one standard deviation increase of social trust (28
percentage points) is on average associated with a reduction of corruption of roughly a
quarter of a standard deviation – the difference between estimated corruption in Greece
and Australia.
Yet, the results rather clearly also point towards an increasing role of social trust
in the course of the development of judicial institutions. At low levels of legal quality,
22
the estimates in Table 2 suggest that the association between social trust and corruption
is weak and potentially zero at very low levels, whereas the association is strong at
higher levels of legal quality. Splitting the sample nevertheless provides support for a
small but significant effect of trust in countries with weak or deficient institutions.
Following the implications of the theoretical model, this exercise also provides support
for an ambiguous effect on corruption of improving formal institutions in such
countries.
While it must be stressed that the results are derived under the assumption that
trust scores are sufficiently stable over time – an assumption borne out in recent
empirical studies (Uslaner, 2002; Bjørnskov, 2007a) – the use of panel data in this paper
therefore suggests that high levels of social trust tend to complement the development
of formal institutions. The results consequently do not support the view that social trust
can, for example, supplement the lack of formal protection of property rights as hinted
at in some studies (e.g. Fukuyama, 1995), but instead follow the implications of the
theoretical model in supporting complementarity.
The effects of legal quality and trust thus come to differ across levels of economic
and institutional development. On a broader level, the findings in this paper may
therefore also hold implications for the literature on social trust. For example, this type
of evidence seems to refute Beugelsdijk’s (2006) claim that the answers to the social
trust question simply measure the quality of formal institutions. It also refutes the notion
found in some studies that social trust is a panacea for solving a number of very
different social and economic problems. Taking the findings in this paper seriously
leaves the question open whether some of the beneficial effects of having a trusting and
trustworthy population only emerge under specific conditions.
23
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Appendix
To show that the relation between the likelihood of going underground and instutional
quality, λ, is ambiguous, take as a starting point that firms go underground when (11)
holds, that is when:
qπ (ϕ ) −
1 − pq
1
b− F <0
(1 − p )d d
(A1)
Differentiating the left hand side with respect to λ yields equation A2 where f(•) is
the density function associated with q:
− π (ϕ ) f (•)[μ (1 − λ ) + a − μλ ]π (ϕ )
1 − pq
−
f (•)[μ (1 − λ ) + a − μλ ]π (ϕ )
(1 − p )d
1 − p w − wout + M
−
(1 − p )d (1 − λ )2
(A2)
Here, the last term is unambiguously negative, but the sign of the first two terms is
ambiguous, and can be easily shown to depend on the sign of μ + a – 2μλ. For low
values of λ relative to the other parameters, this implies that the sign becomes positive
and therefore that the two first terms outweigh the influence of the last term.
Insert Table A1 about here
28
Table 1. Descriptive statistics
Corruption
Log GDP per capita
Openness
Political competition
Government size
Legal quality
Postcommunist
Latin American
Asian
MENA
SSA
Social trust
Democracy
Checks (Keefer)
Political constraints III
Monarchy
Regulatory freedom
Log cement consumption
Average
.047
8.635
.718
.248
.206
5.310
.033
.254
.132
.108
.213
.276
Standard deviation
.220
1.062
.486
.274
.085
1.916
.179
.436
.339
.311
.409
.139
Observations
334
333
334
321
334
289
334
334
334
334
334
304
5.280
2.871
.277
.186
5.290
7.437
4.184
1.683
.207
.389
1.012
1.867
326
332
329
334
300
334
29
Tabel 2. Results
RE
Log GDP per
capita
Openness
Political
competition
Government
size
Legal quality
Postcommunist
Latin
American
Asian
MENA
SSA
Social trust
1
-.163***
(.025)
.015
(.029)
-.149***
(.052)
.285*
(.152)
-.009
(.005)
.231***
(.059)
.119**
(.055)
.038
(.057)
.141*
(.072)
.086
(.077)
-.416***
(.123)
RE, no
outliers
2
-.135***
(.015)
-.020
(.018)
-.082**
(.035)
.248***
(.096)
-.011***
(.004)
.199***
(.034)
109***
(.031)
.062*
(.033)
.127***
(.041)
.097**
(.044)
-.352***
(.068)
194
.821
174
.921
194
.841
154
.958
171
.823
172
.911
.223
.349
.374
.597
.421
.446
326.45
810.12
406.94
1463.78
329.46
706.3
36.37***
4.12
9.31***
5.28
48.47***
3.84
2.90*
6.63
49.55***
9.03
13.38***
7.43
Legal * trust
Observations
Between R
square
Within R
square
Wald Chi
square
Breusch-Pagan
Hausmann test
IV estimate:
Social trust
RE
RE, no
large corr
RE, no
small corr
3
-.153***
(.023)
.001
(003)
-.120**
(.047)
.263*
(.141)
-.009*
(.005)
.212***
(.056)
.120**
(.052)
.016
(.054)
.120*
(.068)
.079
(.072)
-.346***
(.117)
-.168***
(.028)
RE, no
outliers
4
-.129***
(.012)
-.036**
(.015)
-.104***
(.027)
.112
(.075)
-.014***
(.003)
.209***
(.027)
.110***
(.025)
.054**
(.027)
.121***
(.032)
.087**
(.037)
-.339***
(.051)
-.174***
(.017)
-.162***
(.027)
.026
(.029)
-.140**
(.057)
.070
(.165)
-.005
(.006)
.199***
(.066)
.108*
(.057)
-.016
(.058)
.109
(.074)
.061
(.084)
-.389***
(.125)
-.164***
(.031)
-.115***
(.014)
-.033*
(.017)
-.082***
(.032)
.194**
(.088)
-.009***
(.003)
.197***
(.033)
.126***
(.030)
.055*
(.032)
.133***
(.039)
.106**
(.042)
-.232***
(.069)
-.113***
(.024)
-.598***
(.164)
1.958
Hansen Jstatistic
First stage F32.05
statistic
Note: all regressions include period dummies and a constant; *** (**) [*] denotes significance at p<.01
(p<.05) [p<.10]; standard errors in parentheses. Instruments in the IV regression are monarchy, checks,
political constraints III, and an interaction term between the latter variables. Outliers in columns 2 and 4
are defined as observations with residuals larger than 2± standard deviations. Column 5 excludes
observations with corruption above .253, column 6 excludes observations with corruption below -.363.
30
Table 3. Robustness tests
Social trust
Legal quality
Legal * trust
Including:
Observations
Between
Within
Wald Chi Square
Breusch-Pagan
Hausmann test
Social trust
Legal quality
Legal * trust
Including:
1
-.304***
(.127)
-.009*
(.005)
-.164***
(.029)
Democracy
2
-.313***
(.118)
-.009*
(.005)
-.154***
(.029)
Log GDP
per capita
3
-.367***
(.125)
-.008
(.005)
-.153***
(.029)
Regulatory
freedom
4
-.333***
(.118)
-.009*
(.005)
-.165***
(.029)
Political
competition
5
-.365***
(.115)
-.009*
(.005)
-.169***
(.028)
Government
size
192
.845
.385
406.50
49.77***
6.39
6
-.347***
(.126)
-.009*
(.005)
-.168***
(.028)
Trust squared
194
.847
.377
414.98
50.09***
4.26
7
-.331***
(.119)
-.008
(.005)
-.158***
(.032)
Income
inequality
182
.835
.432
386.97
48.32***
8.84
8
-.242**
(.124)
-.008
(.005)
-.145***
(.029)
Secondary
schooling
194
.844
.372
410.13
49.40***
5.02
9
-.333***
(.119)
-.009*
(.005)
-.165***
(.029)
Political
constraints
194
.850
.370
426.16
51.70***
5.89
10
-.284**
(.139)
-.009**
(.005)
-.145***
(.027)
Log cement
consumption
Observations
194
175
192
194
194
Between
.841
.855
.848
.844
.813
Within
.375
.362
.393
.372
.466
Wald Chi Square
401.27
412.54
405.06
412.15
349.03
Breusch-Pagan
48.47***
34.89***
45.44***
48.68***
49.53***
Hausmann test
3.81
19.17*
4.51
6.66
98.34***
Note: all regressions include the full baseline specification; *** (**) [*] denotes significance at p<.01
(p<.05) [p<.10]; standard errors in parentheses.
31
Table 4. Top and bottom performers, simple averages
Change from 1980-1997
Trust level
Corruption change
Legal change
Top performers
.373
-.075
1.217
Bottom performers
.194
.078
1.334
Significance of difference
.000
.000
.827
Note: the differences are based on the 62 countries with corruption data from at least three periods.
32
Table A1. Data sources
Corruption
GDP per capita
Openness
Political competition
Government size
Legal quality
Postcommunist
Latin American
Asian
MENA
SSA
Social trust
Democracy
Income inequality
Secondary schooling
Political constraints
Cement consumption
Regulatory freedom
Source
Dreher et al. (2007)
Heston et al. (2002)
Heston et al. (2002)
Beck et al. (2004)
Heston et al. (2002)
Gwartney and Lawson (2006)
Inglehart et al. (2004),
Marshall and Jaggers (2004)
UNU (2006)
World Bank (2007)
Henisz (2002)
Cembureau (1998, 1999)
Gwartney and Lawson (2006)
33
Mean
.005
10,602
64.187
.447
19.899
5.597
.047
.276
.131
.065
.121
28.899
6.556
41.212
24.558
.342
10,066
5.283
Std. dev.
.249
7,996
40.181
.217
7.943
1.945
.212
.448
.338
.248
.327
14.946
3.746
9.937
16.353
.189
16,788
.953
Obs.
214
214
214
194
214
214
214
214
214
214
214
214
211
191
208
214
214
200
Figure 2. Corruption and Social Trust
0.6
0.4
Corruption
0.2
0.0
0
10
20
30
-0.2
-0.4
-0.6
-0.8
-1.0
Social trust
34
40
50
60
70
Figure 1. The Game Tree
1
No
Unofficial
2
No
Visit
No
Bribe
3
Accept
Official
Pay
4
5
Accept
Bribe
No
4
Note that the payoffs at the different nodes of the game tree are as follows: 1) π(φ) – c; 2) (1-d)π(φ) – c; 3) π(φ) – c – bU; 4) π(φ) – c – F; 5) and π(φ) – c – bO.
35