Bribe Incidence in Transition Economies:
Does Foreign Ownership matter?*
Simin Seury†‡
This version, February 2009
Abstract:
This paper attempts to trace out empirically the causal relationship between bribing, foreign
ownership structure and firm’s ability in corrupt practices for the small and medium sized
enterprises in the transition economies. Using a wide range of explanatory variables along
with host-specific control variables, this paper finds the following results for the firms in 28
transition economies. A foreign-domestic partnership reduces the probability of being forced to
pay bribe. However, given that bribing can not be avoided, the foreign firm’s share fails to
create any significant impact on magnitude of bribe payment. Also, higher the amount of time
spent in managing the authorities, higher is the realized amount of bribe payment for the firm
as a percentage share of earning. The causality is reversed when the firm is confident that
administration related to tax management is not a problem and in that case spending more
time in dealings with the officials in fact reduces the percentage of bribe payment.
Preliminary and incomplete
PhD candidate, Department of Economics, York University, CANADA; email: seury@econ.yorku.ca
‡ I am thankful to my supervisors: Prof. Sam Bucovetsky, Prof. Mario Jametti, and Prof. Adam
Wilczynski.
*
†
i
I. The Issues
Surveys related to cost of doing business often record responses that reveals firms are
facing corruption as a way of achieving certain advantages in business, either by
default or by choice. While much of the literature captures corruption as THE evil and
at times lists it among the most important obstacles for business [Schleifer (2002),
Kaufmann (2005)], this research starts from the premise that be it good or bad,
corruption has an undeniable role in firm’s strategic choice. It is therefore worth
noting what determines which firm might have to accept this role of corruption by
default and what determines who might gain from it. Does a domestic firm in a FIE
receive any differential treatment from the corrupt officials compared to another
domestic firm which is not associated with a FIE?
Firms often report in business surveys that they “had to pay unofficial gifts” to
“secure their contracts”, to get clearance with “health and safety inspection” and so on
[World Bank and IRBD (2005)]. Thus bribing appears to be an inevitable part of cost
for a group of firm. Surveys also reveal responses that a firm has been engaged in
political lobbying or influencing the government with unofficial payments and these
practices resulted in value addition to the particular firm in terms of higher sales
revenue or lower operating cost. Thus for a group of firm, bribing may appear to be
beneficial. With a micro-founded approach to these bribing phenomena [Crocker, K;
Slemrod, J (2004), Svenson (2003)], that is modeling firm’s bribe payment as an
explained variable of firm’s beneficial motive, bargaining power and cost of managing
bribe payment, this paper offers few empirical evidences from the transition
economies. The research objective is to address the following set of questions. What
determines that a firm would be forced to pay bribes and whether foreign ownership
and firm’s ability to influence governmental rules and regulation affects this
probability? Among the firms that are reporting participation in bribe payment,
whether it is true that the firms with higher ability to influence the government end
up in paying lower percentage share of profit as bribe payment. Are their differences
1
in bribing phenomena among the firms who are forced to bribe as opposed to the
firms who are not forced to bribe, but are engaged in unofficial dealings?
II. Concepts and Place in the literature
Bribing phenomena may take place in the form of petty bribing to get access to certain
service or transaction as well as it may take place in the form of state capture, which is,
to bribe the authorities to influence formulation and implementation of policies. If
bribing the officials appears as an obvious way to get things done, and cost of bribing
thus becomes an essential concern, “to tax or to pay bribe” often becomes
synonymous to choosing among substitute techniques to the investors as highlighted
by Kaufman (2000). The observation of possible trade-off among tax and bribe lead
the researchers to a number of empirical analyses, mainly to capture the effect of
bribing on the amount of management’s time wasted in handling red-tape issues (Wei
99, Kaufman), to identify the factors affecting frequency and magnitude of bribe
payment due to tax-purpose (Anderson 2008), whether to tax or to bribe is a better
way in doing business (Kaufman 2000) and so on. Some important facts noted in the
recent literature regarding the bribing practice are that the incidence of corruption can
be explained by the variation in policies or regulations across industries and
individual firm’s attribute. These attributes, which are firm’s “ability to pay” bribe
and firm’s “refusal power” against participating in bribing activities are shown to
explain a large part of the variation in bribes across graft-reporting firms [Svenson,
2003]. In analyzing the relation between time cost of corruption and magnitude of
bribe payment, Wei and Kaufman notes that corrupt officials ask a firm to "grease the
wheel" (getting access to facilities by paying bribes) according to the firm’s ability to
pay, and at times regardless of the other important features such as profitability. Thus
firms that pay higher bribes not necessarily end up with lower time cost (red-tape
cost) as a whole; they simply pay more because they are forced to do so and they can
2
survive the burden [Wei 99, Kaufmann 2000]4. However, there appears to be no
significant difference among foreign firms regarding this practice.
The present research considers a wider set of factors in explaining the bribing
phenomena. In addition to the standard measures of “ability to bribe” and “refusal
power” I wish to include firm-specific “cost of bribing” with “ability to influence”
public authority’s decisions, as well as country-specific subjective measures of
corruption and other obstacles in doing business, as perceived by individual firms. It is
presumed that only the local firms posses the ability to influence5. Moreover the local
firm in a FIE is likely to face differential treatment with bribe and extortion. While it
might be true that the corrupt officials allow certain benefits to the local firms in
exchange of unofficial payment, it might also be true that these officials do not
demand bribe from the wholly owned subsidiaries (a hundred percent foreign owned
firm). The officers allow those subsidiaries to have the benefits without any bribe,
knowing that asking for such payments would not be fruitful at all. In other words,
power and ability to refuse to pay bribe are important factors in explaining bribing
phenomena. It might be possible that a firm with local-foreign partnership emerges as
a stronger entity against a corrupt official’s illegal practices simply because the official
opts for squeezing the local firms. On the other hand, starting from the premise that
local firm’s ability (to bribe corrupt officials in host country and to create influence) is
beneficial to the foreign investor, it might be true that firms with local-foreign
partnership get to earn higher post-bribe profit (with a lower percentage of bribe
payment from profit-share) with more efficient corrupt practices.
If this research reveals graft payment is deep-rooted only because firms are forced to
take part in it, the respective policy implication to curb the supply of corruption
4
These findings are also coherent with Svenson’s portrayal of corrupt government officials as price
discriminators
5
In a previous reasearch, my finding was that, in a foreign investment relationship, given other things equal, when
a local firm have certain ability in bribing the domestic authorities and can manage to have influence from
bribing, the local firm obtains a greater share of ownership compared to the situation when the local does not
have such ability or influence. Thus the objective of this paper is finding if there is any reverse causality,
namely, does having foreign shares make a firm a) a better briber in terms of lower percentage payment and/or
b) a better defender against bribing practices.
3
would be “how to increase firm’s refusal power against unofficial dealings”6. On the
other hand, if there is evidence that firms who are more influential are appearing to be
better gainer in terms of lower percentage of bribe payment, then the policy
implication would be “how to curb the incentives that are nurturing supply of these
unofficial payments?”
III. Framework and Propositions
Based on the issues explained above, I wish to trace out whether bribing practice is
affected by ownership status and ability to be corrupt (or not to be corrupt) along with
other explanatory variables. This is carried on by focusing on the following
propositions:
a) The probability that a firm would be asked for bribe to initiate or to continue a
particular business activity is different among the foreign and the local
investors.
b) Given that bribing can not be avoided, a firm with joint local and foreign
ownership performs as a better briber (lower share of bribe payment) than a
firm with hundred-percent foreign ownership.
c) Given other things equal, a local firm would have better ability to deal with
corruption (stronger influence in state capture and/or lower share of bribe
payment) than another local firm which is not engaged in any foreign
investment relationship. That is, a local firm appears to be a better briber due to
its share in FIEs.
To incorporate the idea of bribing as a cost of doing business, it is assume that
participating in bribing causes the firm to pay a percentage of profit B. All other
relevant costs are summarized as a percentage payment t, which can also be thought
6
A recent guide from World Bank (2008) [provided by a joint effort of The World Bank Institute (WBI), UN
Global Compact, The Center for International Private Enterprise (CIPE), Transparency International (TI),
Global Advice Network, Grant Thornton International Ltd and Siemens AG] suggests such strategies useful for
firms to form coalition against corruption.
4
as the tax payment. Thus the realized sales revenue of each firm is analyzed in
following parts: i) from the total sales revenue, a certain percentage B is paid by the
firm as bribe to corrupt officials ii) (1- v) is the percentage of the sales revenue
reported for tax purpose and tax t is paid on this portion of sales revenue iii) for rest
of the sales revenue, v percent, the firm saves tax-payment. This approach of
explaining gains from tax-evasion by paying bribe can be found in the literature as the
analysis of costly state falsification framework [Crocker, K; Slemrod, J (2004)]. The
current framework, however, does not incorporate any agency cost, compared to
Crocker’s research.
Let k denote the effort determined from firm-specific character which determines
ability to bribe and cost of bribing. It is assumed that every firm has two probable
ways to follow. If the firm does not take part in tax-evasion activities, there probability
is sg(k) that the firm can pay only t that summarizes all probable resource costs
(including cost of unavoidable petty bribing and time costs), and thereby earns profit
(1- t)sg(k)Π. The firm may also engage in bribing and tax-evasion with probability of
success sm(k), and earns post-tax and post-bribe profit (1- t + Bvt-B)sm(k)Π where
Bvt is the part of profit saved due to bribing activities. It is assumed that failure to
earn any positive payoff occurs with probability (1 – sj(k)), j = g, m. As an example
how failure may occur, one can think about a firm facing an expropriatory tax rate (1
– sg(k)) times, i.e. probability of not being able to avoid such tax rate (parameterized
by k). Or one can think about being caught red handed due to bribing (1 – sm(k))
times, again parameterized as a function of k. Thus the firm can be thought of facing
the following optimization problem:
Max (1 − t )s g (k ) + (1 − t + Bvt − B )s m (k )
k
(
)
= (1 − t ) s g + s m + B (v.t − 1)s m
(1.1)
Solving this yields optimal B as a function of optimal k as:
5
B* =
(1 − t ) (s kg* + s km* )
(1 − vt ) s km*
s km*
γ = g
s k * + s km*
where,
(
=
(1 − t )
γ (1 − vt )
)
(1.2)
Thus the magnitude of B essentially depends on sign and magnitude of γ, where
γ is
the change in marginal success in bribing and tax-evasion sm(k)) as a share of
summation of marginal successes through both activities (sg(k))+ sm(k))7.
The formulation of probabilities for empirical estimation is followed from the control
rights hypothesis [Shleifer and Vishny (1994), Svenson (2003)] that the threat point in
the negotiation between a public official and a firm is determined by the control rights
of the firm. Thus the following framework is presented for empirical estimation:
ξij
=
x ′Wi j (k ) + ei j
where,
j = g ,m
and
i = 1, 2 , ... , N firms
(1.4)
Where
ξij
is the probability of being engaged in activity sj(k), W(k) is a vector
reflecting firm characteristics, x is a coefficient vector, and ei is an unobserved error
term.
7
Equivalently with log transformation:
ln B * = ln(1 − t ) − ln (1 − vt ) − ln γ
(1.2.1)
However, this format is not used in the empirical part.
6
The firms that are probable candidate for being engaged in tax-evasion either have a
low cost of bribing or high cost of non-participation in bribing activities or both. These
costs are determined by firm’s ability to deal with bribe and firm’s refusal power of
not participating in bribing. Unlike Svenson (2003) firm’s ability is defined as “ability
to deal with bribe” and not as “ability to pay bribe”. “cost of non-participation” in
bribing mostly in the same fashion as “refusal power” as depicted by Svenson. That
is, low refusal power is synonymous to high cost of non-participation.
First, it is plausible to assume that the W(k) vector includes firm’s current and past
profitability, based on age, size, and other firm-specific factors, capital mobility and
resources spent for unofficial activities. Second it is affected by domestic business
environment factors such as corruption index of the country and property right index,
which may as well have varying degrees of impact among different sectors. For
instance sectors that have low level of capital mobility due to the inherent nature of
business activity and asset specificity, may suffer from high cost of non-participation
as refusing to pay bribe incurs higher opportunity cost. In a likewise fashion ability to
deal with bribe is determined by firm-level ability to influence the policy regulation
and implementation as well as country-level index of property rights. Given all other
country-level settings on the firms in given country are the same for each firm, the
unobserved probability of success can be represented as the following binary
dependent variable (Probit) specification:
~
ξ t ,i = β 0 + β 1π t ,i + β 2 .
where
Π t − Π t −1
+
Πt
L
J
∑ β l ,i A +
∑β
l =4
j = L +1
j ,i
z + ei ;
ei ~ N (0,1).
(1.5)
Here z summarizes all other firm-characteristics; βs are a set of coefficients to be
estimated; the first term with 0 denotes percentage of current profit-margin, and the
second term denotes past profitability performance. A proxy of percentage change in
7
earnings is used to reflect the performance of past profitability. A(k) summarizes
index for degree of direct influences through lobbying or bribing activities that
resulted in considerable changes in increasing the value of business. z represents
firm’s size, age, management’s time spent with officials,
and foreign ownership
status.
IV. Data Description and Variable Specification
The firm-level data set for this paper is obtained from The Business Environment and
Enterprise Performance Survey (BEEPS) - performed by the IBRD and World Bank for
the year 2005 for 9665 firms in 28 transition economies. The survey was conducted on
the basis of face-to-face interviews. Information from these datasets that are relevant
for the present research are: information about individual firm’s perception towards
different aspects of petty corruption and bureaucratic corruption, degree to which
they are exposed to bribing, degree to which tax administration is a problem,
percentage of earning officially reported for tax payment considering the difficulties
in reporting and managing tax-related issues, other business or firm specific details
such as past increase or decrease in earnings, ownership patterns, capital replacement
value as a share of earnings, size of the enterprises, geographic location, and sector
specific details. The sector specific information allows to group firms into sub-sectors
(for example identifying whether majority of the firms sales come from mining sector
or construction sector or retail business etc), as sector specific asset specificity is an
important factor in explaining firm’s cost of bribing according to the aforesaid
hypothesis.
Dependent Variable
The questionnaire includes the option for the firms to report what is the usual
percentage of earning that is spent for unofficial activities in a typical firm with
8
similar setting as the respondent.8 The response from this question is used as a proxy
for percentage of bribe payment, B. Almost 5.7 percent firms (549 out of 9665) do not
report this information [Table I].
When a corrupt official is dealing with a firm who is forced to bribe, the official would
wish to extort a bribe as high as possible. To screen out the firms who are likely to
face such a situation, responses from the following question is used:
“How often is the following statement true?:
“If a government agent acts against the rules I can usually go to another official or to his
superior and get the correct treatment without recourse to unofficial payments/gifts.”
From the category of responses [Never = 1; Seldom = 2; Sometimes = 3; Frequently =
4; Usually = 5; Always = 6; Don’t know = 7], the following indicator variable is
generated:
Indicator D1, i
= 1 if never/ seldom can get rid of bribe-extortion (cannot
defy illegal-treatment of an official without paying bribe)
= 0 otherwise
There is also the following question that addresses the issue of approximating the tax
evasion:
“Recognizing the difficulties that many firms face in fully complying with taxes and
regulations”, what is the percentage of total annual sales that a typical firm would reports
for tax purposes?
8
The indirect approach of the questionnaire to address what usually happens in a “firm like yours”
rather than directly asking “what you do in your firm” is to ensure that the respondent is not shying
away from the truth due to the direct confrontation.
9
Indicator D2, i
= 1 if the firm reports a percentage below average (88 percent)
= 0 otherwise
Finally the following interaction dummies are created to approximate whether the
firm is participating in tax-evasion while being forced to bribe:
Indicator D3, i
= 1 if the firm reported tax evasion and was forced to bribe
= 0 otherwise
Indicator D4, i
= 1 if the firm reported tax evasion and could “always” or
“almost always” avoid bribe extortion
= 0 otherwise
Explanatory Variables
Age, and Size represents to some extent a firm’s initial position in determining the cost
of bribing. While age is likely to reduce cost due to better experience and maturity,
affect of size is less obvious because a large firm may be more targeted by the corrupt
officials and a growing firm may have lower bargaining power. On the other hand,
large size and steady growth may strengthen a firm’s position in bargaining.
We include the log of firm-age to capture the affect of age. Firm size in the data is
categorized from the number of permanent, full-time employees the firm have. Firms
with less than 50 employees belongs to the category of small firm, firms with 50 to less
than 250 employees belongs to medium-sized category and firms with employees
more than that belongs to the large-size category. A dummy variable is included to
indicate small and large size with medium size being the baseline category.
10
Profitability at the level form may affect the scenario in the following way. The
likelihood that a firm would not be able to get away with injustice without paying
bribes is likely to increase with profitability. Because when any illegal action is taken
by a certain official against a firm, if the firm does not participate in bribing to manage
the illegal action, the higher the profitability of the firm, the greater would be the
opportunity cost of not paying bribe given that there is no other way than bribing.
However as I have used the percentage of profit margin to cost of the respective firm
(percentage by which sales price of a firms exceeds the operating costs, i.e., the cost of
material inputs plus wage costs but not overheads and depreciations) to have a
measure of profitability that is not biased with the firm-size9, the reasoning is now
partly reversed (contrary to Svenson (2003)). A firm that is already having a high
profit margin relative to costs, may as well be strong candidate that can forgo bribe
payment and thereby incur the cost of not having the benefit that was ready to be
sanctioned by the corrupt official upon paying unofficial gifts.
A good number of firms (around 240) did not report this information of profit margin
but they reported of having no profit during the previous year. Hence we replace the
missing values of these particular firms with zero value for the profit margin (albeit
the shortcoming is that profit margin of these firms might as well be negative).
Influence is represented as two indices about firm’s ability to influence policy
implementation and regulation, created in the following fashion from the categorical
responses from the answer to the BEEPS survey responses:
- “degree to which the act of lobbying or having information about related policy
affected the firm’s business” is used to create type-II influence
- “degree to which the act of influencing public policy or decree affected the firm’s
business” is used to create type-II influence
9
Instead of considering profit margin, if we included gross profit level or total sales without adjusting
for the number of employees, profitability would be highly correlated with the firm size
11
Ability Type-I
To reflect the ability about the first quality (which is considered as a legal activity), a
category variable is created ranging from 1 to 13 that reflects whether the firm can
have access to ministry or executives or legal authorities and to what degree the firm
has been benefited due to these activities. Here benefit refers to critical value addition
to the business and degrees of effectiveness from information gathering and lobbying
are categorized in the survey in chronological order from lowest benefit to highest
benefit as: minor impact, moderate impact, major impact and decisive impact. The
ability index is rescaled based on firm’s score among the maximum possible points
and represented in percentage format.
Ability Type-II
To reflect the ability about the degree of influencing through bribing (which is rather
associated with unofficial payments and activities), a category variable is created in the
same fashion as described above. This category variable has a scale range from 1 to
11. The ability index is rescaled based on firm’s score among the maximum possible
points and represented in percentage format.
Time cost of corruption is approximated in two parts: time cost I represents percentage of
management’s time spent in dealing with the officials (equivalent to the idea of time
spent for red-tape issues) without dealing with tax-related administrations. Time cost
II represents percentage of management’s time spent including the time for managing
tax-administration.
Corruption is represented by two alternative measures, both of which are country
specific. The first one is a subjective index, based on the responses from individual firm.
These are the percentage of firms in each country that are “always” or “almost
always” engaged in bribing activities for accessing essential goods and services from
the government for the business such as i) getting connected to electricity, telephone
and other public services ii) accessing government contracts iii) getting certified from
12
Occupational Health & Safety Inspection, Fire and Building Inspection and
Environmental Inspection iv) dealing with tax payment and so on. These are termed
as administrative corruption (or petty corruption, as mentioned at the beginning of
section 4.2) and an index is calculated from firms in each country that reports about
paying unofficial payments for the most activities. A higher number indicates more
firms are forced to be engaged in corruption and therefore a higher number is an
indicator of higher level of corruption.
The other index is an objective index and is used from the survey of Heritage
Foundation. This index shows the “Freedom from Corruption”, starting from positive
number up to hundred. A higher number indicates more firms are free from
corruption and therefore a lower number is an indicator of higher level of corruption.
Property Rights is represented using two alternate measures. The first one is an
indicator variable taking the value one if the firm is confident that his property right
would be protected. This is created from the survey responses. The second one is an
objective Property Index used from the survey of Heritage Foundation.
Table I: Expected Sign of the Coefficients
Coefficient for the
variable:
Sign
Age (log)
-ve
+ve
-ve
-ve
?
?
?
-ve
+ve
?
-ve
+ve
?
size small
size large
profit margin
Ability I
Ability II
Time cost I
GNIP (growth rate)
Corruption Index
Foreign share
Change in Sale
Tax evasion ratio
Time cost II
+ve with Higher asset specificity/
Sector Dummies
lower capital mobility
13
To conclude the variable specification, the expected signs for the coefficients are
summarize in Table I above.
V. Issues in Selection Bias and non-response
While estimating the bribe equation (1.2), if we include only the positive values of
bribe payment in our analysis, we are ignoring the fact that the existence of the firms
in the sample that are not required to pay bribe is not a random phenomenon. The
number of firms that are facing the corrupt officials and are being asked to pay bribe
is likely to be determined by a good number of factors such as how well the investors’
property rights are secured, how corrupt is the business environment as a whole, how
much is the firm’s bribing ability, how weak or strong is the bargaining position of the
firm and so on. The issue of selection bias thus becoming non-trivial, we need to
include measures to take care of the bias and we proceed with Heckman-Selection Model
in this regard. We refer to the equation with the binary dependent variable D1, i , D2, i,
D3, i , and D4, i as the selection equation and the equations in which the percentage of
bribe payment appears as the dependent variables as the bribe-incidence equation. To
satisfy the exclusion restriction, percentage change in earning over last three years is
included in the selection equation which does not appear in the bribe-incidence
equation.
Another important issue that must be mentioned is that while for some of the firms
with zero or missing bribe payment it is true that they do not need to pay bribe, some
others may choose to hide the information with certain reasons. To deal with this
problem, the distribution of the other firm-specific attributes such as profitability,
stability in increase or decrease in earning, magnitude of tax evasion and so on are
reported for the reporter group and the non-reporter group in Table IX. This
comparison may help to reveal where there is any difference between firms for which
the report about bribing practice is available as opposed to the firms for which this
report is not available.
14
Although the non-reporters have a slightly higher average for the profit margin
(higher by 1.75 percent) than that of the reporters, in terms of increase or decreasing in
earning, their average is lower by almost 3 percent. However, the maximum
percentage difference with costs in way high for the reporters, larger by 300 percent
which triggers the suspicion that these non-reporters are a group of firms that were
not doing as well financially as the others. The ability index is by and large similar
among these two groups. But average percentage of reported earning for tax-payment
is again much lower for the non-reporters, 62.35 percent as opposed 90 percent, which
is the average of for the rest of the firms. It the estimation for the rest of the firms
show that a tax-bribe trade-off is present, the high tax-evasion rate for the nonreporters would hint that the missing bribe-payment was likely to be a significant
amount, hidden purposely, rather than being a trivial amount that was not worth
mentioning.
The issue of non-response is apparent for the influence variables as well. The
distribution of the ability parameters is summarized for the entire sample in graph I
and graph II. For each country, graph-I presents the distribution of ability categories
where the number of categories appears in the horizontal axis and frequency for each
category appears in the vertical axis. Further the kernel density for ability type-I and
type-II for the FIEs is presented in graph-II. From graphical representation, it is
evident that firms in certain countries, such as Slovenia and Estonia were rather
reluctant about reporting type-I and type-II ability respectively. Survey interpretation
attached to the BEEPS 2005 survey questionnaire (not reported) explains that
questions regarding corruption, bribery, “unofficial payments”, gains from unofficial
activity and employment levels did not receive enthusiastic response from certain
participants as they were afraid that the information they provide could fall into the
hands of criminal elements or the “intelligence service”. It was also noted that a good
number of managers report verbally the discomfort due to the discretionary power of
tax authorities to impose taxes under patronage of the government and the resulting
corruption due to party-politics, but they do not report how their business has been
affected by this.
15
Finally few points are noted from comparing the data regarding tax and bribe
payment for the firms. Calculating percentage difference between the bribe payment,
B and share of revenue not reported to avoid tax payment, v reveals that in a scale
range from -100 to +100, the minimum difference for a local is -40 while for a foreign
firm this is -20. The mean value is higher for the locals (9.2) than the foreign firms
(6.2). For almost 20 percent foreign firms and for almost 18 percent local firms this
entry is positive. That is, these firms pay a higher percentage share of revenue as bribe
payment than the percentage share they save by not reporting for tax payment.
Almost 7 percent firms do not report this information at all. The percentage of nonresponse in the same between the foreign and the local firms (614 local firm among
total of 8503 firms and 81 foreign firms among total of 1152 firms). For almost 4
percent firms v=B and again this percentage is the same among the foreign and the
local firms.
Graph-III summarizes the distribution of firms evading and being forced to bribe
using Venn diagram. For each country B represents set of firms that are forced to bribe
and H represents set of firms that are not engaged in tax-evasion, with complement
set defined accordingly for each group. In each diagram the oval area shows the set of
firms that are engaged in tax-evasion while the area of rectangle (excluding the oval
area) shows set of firms that are not evading. The left trapezoid shows set of firms that
are forced to bribe, while the right one shows if the situation is otherwise. It is
apparent from the relative ratio of numbers that countries show notable range of
variation in evading and bribing practice. Estonia has only 5 percent of firms engaged
in tax-evasion even though almost 25 percent firms are forced to pay bribe, while for
Albania the evading firms are more than 50 percent of the sample (almost 42 percent
of firms are forced to bribe in Albania). Belarus, Slovak Republic and Slovenia also
shows low percentage of evading firms, from 9 to 15 percent, compared to high
percentage of firms that are being forced to pay bribe (almost 25 to 40 percent).
Yugoslavia, Bulgaria, Czech Republic and Turkey shows from 30 to 50 percent of
firms engaged in tax-evasion although the percentage of forced-bribe payers are
almost in the same range with some other low-evading countries such as Belarus.
16
VI. Estimation Results
Estimation results from initial OLS specification is summarized in Table II. on average
age may reduce from 10 percent to 11 percent of the bribe payment and while large
firms are likely to avoid 20 to 22 percent of bribe payment, small firms are more likely
to pay a lot more, starting from 20 percent (Eqn B5reg2) to as much as 30 percent (Eqn
B5reg8). Ability type-II is strongly significant with an average 2 percent increase in the
bribe payment for every percentage increase in the firm’s index. Ability type- I
however does not show any effect. Time cost I, which is devoid of tax management
issues appear to reduce the bribe payment. While in general, firms who face
problematic tax-management issues, higher the time cost II, higher is the percentage
share of bribe.
The probability of bribe incidence is reported in Table III, IV, V with various groups
of firms in terms of which firm is engaged in tax-evasion, who needs to pay bribe as a
compulsory procedure and who gets to take part in both activities. As Table V shows,
there is weak evidence that foreign firms are unlikely to take part in tax-evasion
activities. Given the fact that Table III shows strong enough evidence that being a
foreign firm reduces the probability of being asked for bribe for 4 to 6 percentage
points, one might suspect that the weak effect of non-participation in tax-evasion may
result from the fact that they were not forced to bribe in the first place and hence they
did not have the incentive for tax-bribe trade-off. However, table IV (Eqn B5prob7 and
B5prob8) shows that the probability of being engaged in tax-evasion from a foreign
firm is indeed negative even when these firms are being forced to bribe.
Table VI, VII and VIII reports results with correction for the sample selection bias and
portrays the picture of bribe payment among several groups of firms. Among the
firms who are forced to bribe, a percentage increase in ability type II increases the
percentage of bribe payment more than 2.5 percent. Time spent in handling red tape
issues without having the problem in tax management may reduce up to 41
17
percentage of bribe payment. This rather surprisingly large effect is also strongly
significant.
Table VII shows firms that are engaged in tax-evasion, are paying almost 3 percent
higher bribe payment with an increase in time cost II, than the ones who are not
engaged in tax-evasion. In both cases time cost II increases bribe payment with strong
significance. The former also pays almost 20 percentage point higher bribe with every
percentage increase in ability type-II.
Age effects and size effects that were evident from baseline OLS estimations are
largely wiped away after controlling for other characteristics among the particular
group of firms. The subjective corruption index however continues to have a strong
positive effect, explaining around 6 percent of the increase in bribe payment on
average. The objective corruption index and property rights indices returns high
degree of correlation with country income and country dummies (not reported) and
therefore were excluded from estimation report. Finally current profit margin, foreign
ownership status and ability type-I fails to leave any effect on percentage of bribe
payment at all.
VII. Conclusion and Suggestive Implications
Using a wide range of explanatory variable along with host-specific control variables,
this paper finds the following: assuming all other country-specific characteristics have
been taken care of , for a firm, (i) having a foreign-domestic partnership reduces the
probability of being asked to bribe. However, (ii) given that bribing can not be
avoided, the local partner’s share fail to create any significant impact on magnitude of
bribe payment among the FIEs. Also, (iii) higher the amount of time spent in
managing the authorities, higher is the realized amount of bribe payment as a
percentage of earning. Interestingly, (iv) the causality is reversed when the firm is
confident that administration related to tax management is not a problem and in that
case spending more time in dealings with the officials in fact reduces the percentage
18
of bribe payment. There is strong evidence that in the last case, a firm might reduce
almost 40 percent of the bribe payment by increasing percentage share of
management’s time to deal with the government authorities.
Finally, although there is weak evidence that firm’s age may reduce the magnitude of
bribe-payment, firm-size and profit margin hardly leave any effect on that payment.
There is weak evidence that foreign firms show a lower probability of being engaged
in tax-evasion activities compared to a domestic firm, even when they are forced to
bribe. These findings shed light on the probable factors that could explain a firm’s
refusal power against corruption. Findings from this paper can be utilized in
formatting safety-net for business against corruption. Also the strong influence of
management’s time related to tax issues and evidence on tax-evasion issues suggest
that fiscal reform and reducing red-tape delays in tax-management may largely
contribute towards the effectiveness of anti-corruption measures [to be completed].
19
Table II: Bribe Payment Equation (OLS estimation)
Eqn B5reg1
Eqn B5reg2
Eqn B5reg7
Eqn B5reg8
Age (log)
-0.107
(0.000)
a
-0.107
(0.000)
b
-0.111
(0.000)
a
-0.114
(0.000)
a
size small
0.245
(0.001)
a
0.297
(0.001)
a
0.248
(0.001)
a
0.308
(0.001)
a
size large
-0.221
(0.001)
b
-0.202
(0.001)
b
-0.211
(0.001)
b
-0.204
(0.001)
c
0.002
(0.000)
0.001
(0.000)
0.002
(0.000)
0.002
(0.000)
-0.001
(0.000)
-0.002
(0.000)
-0.001
(0.000)
-0.002
(0.000)
0.021
(0.000)
0.021
(0.000)
a
b
profit margin
Ability I
0.022
(0.000)
a
Time cost I
-0.418
(0.001)
a
GNIP (growth rate)
c
Ability II
a
0.021
(0.000)
a
-0.426
(0.001)
a
c
-0.024
(0.000)
-0.029
(0.000)
-0.025
(0.000)
-0.029
(0.000)
Corruption Index
0.015
(0.000)
0.020
(0.000)
0.016
(0.000)
0.022
(0.000)
Foreign share
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
0.000
(0.000)
Change in Sale
0.001
(0.000)
0.000
(0.000)
0.001
(0.000)
0.000
(0.000)
0.009
(0.000)
0.009
(0.000)
a
0.023
(0.000)
0.020
(0.000)
a
0.495
(0.004)
0.464
(0.004)
-0.103
(0.001)
-0.097
(0.001)
0.429
(0.001)
0.431
(0.001)
-0.034
-0.041
(0.001)
-0.187
-0.193
(0.001)
-0.171
-0.157
(0.001)
b
Tax evasion ratio
Time cost II
0.524
(0.004)
-0.132
(0.001)
0.414
(0.001)
Transport
-0.045
(0.001)
Retail
-0.204
(0.001)
Other
-0.155
(0.001)
Mining
Manufacturing
Construction
a
c
a
a
0.516
(0.004)
-0.144
(0.001)
0.393
(0.001)
(0.001)
-0.063
(0.001)
(0.001)
-0.223
(0.001)
(0.001)
-0.146
(0.001)
a
a
c
a
c
Note: Significance labeling: a for 1%, b for 5%, c for 10% level of significance. Standard errors are in parenthesis
20
Table III: Probability of Bribe Payment (Probit Estimation)
Independent Variable
D1,i = 1 if Firms is Subject to Bribe
Extortion; 0 otherwise
D1,i = 1 if Firms is Subject to Bribe
Extortion; 0 otherwise
Eqn B5prob1
Age (log)
-0.0024
D1,i = 1 if Firms is Subject to Bribe
Extortion; 0 otherwise
Eqn B5prob2
(0.008)
-0.0064
Eqn B5prob3
(0.008)
0.0070
(0.007)
size small
0.0134
(0.014)
0.0096
(0.015)
-0.0111
(0.013)
size large
-0.0151
(0.021)
-0.0226
(0.022)
0.0019
(0.019)
profit margin
-0.0002
(0.000)
-0.0004
(0.000)
-0.0011
(0.000)
Ability I
-0.0006
(0.000)
c
-0.0008
(0.000)
b
-0.0002
(0.000)
Ability II
0.0011
(0.000)
a
0.0012
(0.000)
a
-0.0009
(0.000)
Time cost I
-0.0344
(0.012)
b
-0.0419
(0.013)
b
0.0060
(0.011)
GNIP (growth rate)
-0.0054
(0.003)
c
-0.0058
(0.003)
c
-0.0104
(0.002)
Corruption Index
-0.0070
(0.003)
c
-0.0072
(0.003)
c
-0.0031
(0.003)
Foreign share
-0.0007
(0.000)
b
-0.0006
(0.000)
b
-0.0002
(0.000)
Change in Sale
-0.0004
(0.000)
b
-0.0004
(0.000)
b
-0.0004
(0.000)
0.0023
(0.001)
a
Time cost II
Mining
-0.0016
(0.055)
-0.0041
(0.057)
0.0311
(0.052)
Manufacturing
-0.0084
(0.017)
-0.0075
(0.017)
0.0040
(0.015)
Construction
-0.0017
(0.021)
-0.0025
(0.022)
0.0066
(0.020)
Transport
-0.0061
(0.025)
-0.0106
(0.026)
0.0142
(0.023)
Retail
0.0105
(0.017)
0.0105
(0.018)
0.0143
(0.016)
Other
-0.0663
(0.026)
-0.0755
(0.026)
-0.0341
(0.024)
c
b
b
b
a
b
Note: Significance labeling: a for 1%, b for 5%, c for 10% level of significance. Standard errors are in parenthesis
21
Table IV: Probability of Bribe Payment (Probit Estimation)
Independent Variables
D3,i = 1 if Firms is engaged in taxevasion and firm is subject to Bribe
Extortion; 0 otherwise
D3,i = 1 if Firms is engaged in taxevasion and firm is subject to Bribe
Extortion; 0 otherwise
D4,i = 1 if Firms is NOT engaged in
tax-evasion, but firm is subject to
Bribe Extortion; 0 otherwise
D4,i = 1 if Firms is NOT engaged in taxevasion, but firm is subject to Bribe
Extortion; 0 otherwise
Eqn B5prob5
Eqn B5prob6
Eqn B5prob7
Eqn B5prob8
Age (log)
-0.0096
(0.005)
c
-0.0106
(0.00)
c
-0.0027
(0.01)
-0.0059
(0.01)
size small
0.0229
(0.008)
b
0.0248
(0.01)
b
0.0139
(0.01)
0.0109
(0.02)
size large
-0.0214
(0.012)
-0.0241
(0.01)
-0.0193
(0.02)
-0.0264
(0.02)
0.0009
(0.000)
a
0.0008
(0.00)
a
-0.0001
(0.00)
-0.0003
(0.00)
-0.0004
(0.000)
c
-0.0005
(0.00)
b
-0.0007
(0.00)
b
-0.0009
(0.00)
a
0.0016
(0.00)
a
0.0013
(0.00)
a
profit margin
Ability I
Ability II
Time cost I
0.0015
(0.000)
a
-0.0371
(0.008)
a
a
0.0066
(0.002)
Corruption Index
-0.0023
(0.002)
Foreign share
-0.0004
(0.000)
Change in Sale
-0.0001
(0.000)
GNIP (growth rate)
b
Time cost II
0.0070
(0.00)
-0.0023
(0.00)
-0.0004
(0.00)
0.0000
(0.00)
0.0008
(0.00)
a
b
0.0011
(0.00)
a
-0.0347
(0.01)
b
-0.0055
(0.00)
c
-0.0055
(0.00)
c
-0.0068
(0.00)
c
-0.0069
(0.00)
c
-0.0007
(0.00)
b
-0.0006
(0.00)
b
-0.0004
(0.00)
b
-0.0004
(0.00)
c
0.0025
(0.00)
b
-0.0011
(0.00)
-0.0012
(0.00)
Mining
-0.0251
(0.028)
-0.0222
(0.03)
-0.0110
(0.06)
-0.0149
(0.06)
Manufacturing
-0.0102
(0.009)
-0.0057
(0.01)
-0.0127
(0.02)
-0.0112
(0.02)
Construction
-0.0089
(0.011)
-0.0032
(0.01)
-0.0095
(0.02)
-0.0069
(0.02)
Transport
-0.0206
(0.012)
-0.0167
(0.01)
-0.0119
(0.03)
-0.0142
(0.03)
Retail
-0.0019
(0.010)
0.0029
(0.01)
0.0081
(0.02)
0.0097
(0.02)
Other
-0.0271
(0.012)
-0.0239
(0.01)
-0.0716
(0.03)
-0.0795
(0.03)
c
Note: Significance labeling: a for 1%, b for 5%, c for 10% level of significance. Standard errors are in parenthesis
22
Table V: Probability of Engaging in Tax-Evasion (Probit Estimation)
Independent Variable
Dependent Variable:
D5,i = 1 if Firms is taking part in tax-evasion activities; 0 otherwise
Eqn B5prob9
Eqn B5prob10
Age (log)
-0.0298
(0.007)
a
-0.0335
(0.007)
a
size small
0.0703
(0.012)
a
0.0645
(0.012)
a
size large
-0.0451
(0.019)
c
-0.0487
(0.019)
c
0.0017
(0.000)
a
0.0018
(0.000)
a
Ability I
-0.0004
(0.000)
-0.0004
(0.000)
Ability II
0.0037
(0.000)
profit margin
a
Time cost I
GNIP (growth rate)
0.0177
(0.003)
Corruption Index
0.0033
(0.003)
Foreign share
-0.0005
(0.000)
Change in Sale
0.0002
(0.000)
a
c
0.0038
(0.000)
a
-0.0626
(0.011)
a
0.0174
(0.003)
a
0.0032
(0.003)
-0.0005
(0.000)
0.0002
(0.000)
0.0016
(0.000)
-0.0535
(0.049)
-0.0524
(0.049)
Manufacturing
0.0139
(0.016)
0.0035
(0.015)
Construction
0.0283
(0.020)
0.0215
(0.020)
-0.0492
(0.021)
-0.0560
(0.020)
Time cost II
Mining
Transport
c
a
c
Retail
0.0010
(0.016)
-0.0147
(0.015)
Other
-0.0096
(0.024)
-0.0165
(0.024)
Note: Significance labeling: a for 1%, b for 5%, c for 10% level of significance. Standard errors are in
parenthesis.
23
Table VI: Bribe Payment and Selection Equations (Heckman Selection Procedure)
Independent Variables
Bribe Equation
Dependent Variable: Percentage of Bribe Share of Earnings
Eqn: B5eck1
Eqn: B5eck2
Age (log)
-0.1662
(0.070)
-0.1681
(0.071)
c
size small
0.2167
(0.124)
0.2552
(0.126)
c
size large
-0.2019
(0.190)
-0.1911
(0.195)
profit margin
Ability I
c
0.0046
(0.003)
0.0032
(0.003)
-0.0026
(0.002)
-0.0027
(0.002)
0.0252
(0.003)
-0.0196
(0.016)
0.0266
(0.003)
a
Time cost I
-0.4117
(0.102)
a
GNIP (growth rate)
-0.0191
(0.015)
0.0612
(0.007)
-0.0008
-0.1499
Ability II
Corruption Index
Foreign share
a
0.0591
(0.007)
(0.002)
-0.0010
(0.002)
0.0218
(0.004)
(0.479)
-0.3245
(0.489)
Time cost II
_cons (structutal)
a
a
a
Selection Equation
Dependent Variable: D1,i = 1 if Firms is Subject to Bribe Extortion; 0 otherwise
Age (log)
-0.0105
(0.021)
-0.0198
size small
0.0306
(0.039)
0.0226
(0.039)
size large
-0.0313
(0.057)
-0.0601
(0.059)
profit margin
-0.0006
(0.001)
-0.0010
(0.001)
Ability I
-0.0017
(0.001)
-0.0021
(0.001)
c
(0.022)
b
0.0031
(0.001)
a
0.0031
(0.001)
a
Time cost I
-0.0748
(0.033)
c
-0.1114
(0.035)
a
GNIP (growth rate)
-0.0166
(0.007)
c
Corruption Index
-0.0165
(0.009)
Foreign share
-0.0017
(0.001)
Change in Sale
-0.0011
(0.000)
0.0053
(0.147)
-0.0054
Ability II
Mining
Manufacturing
-0.0171
(0.007)
b
-0.0171
(0.009)
c
b
-0.0016
(0.001)
b
b
-0.0010
(0.000)
b
0.0060
(0.001)
a
(0.046)
0.0122
(0.150)
(0.047)
0.0199
(0.057)
-0.0102
-0.0267
(0.067)
0.0033
(0.059)
Retail
0.0362
(0.047)
-0.0446
(0.069)
Other
Construciton
Transport
-0.1862
(0.075)
_cons (selection)
0.2156
(0.184)
lambda
0.4091
(0.289)
rho
0.1598
sigma
2.5601
c
0.0292
(0.048)
-0.2099
(0.077)
0.2898
(0.186)
(0.315)
Note: Significance labeling: a for 1%, b for 5%, c for 10% level of significance. Standard errors are in
parenthesis.
24
Table VII: Bribe Payment and Selection Equations (Heckman Selection Procedure)
Independent
Variables
Bribe Equation
Dependent Variable: Percentage of Bribe Share of Earnings
Eqn:B5eck3
Age (log)
-0.1653
(0.066)
size small
0.1823
size large
Eqn: B5eck5
Eqn: B5eck6
-0.2099
(0.210)
-0.2204
(0.121)
0.2056
(0.364)
0.3680
(0.371)
-0.0751
(0.178)
-0.9547
(0.629)
-1.0507
(0.658)
0.0031
(0.003)
0.0079
(0.009)
0.0053
(0.009)
Ability I
-0.0003
(0.002)
-0.0118
(0.006)
-0.0101
(0.007)
Ability II
0.0276
(0.003)
0.0313
(0.008)
-0.4818
(0.273)
profit margin
c
a
Time cost I
GNIP (growth rate)
Corruption Index
Foreign share
Time cost II
_cons (structutal)
-0.0261
(0.014)
-0.0114
(0.048)
0.0476
(0.007)
0.0902
(0.016)
-0.0027
(0.002)
0.0143
(0.004)
-3.0322
(1.863)
a
a
a
-0.2083
(0.441)
Selection Equation
D4,i = 1 if Firms is not engaged in
tax-evasion, but firm is subject to
Bribe Extortion; 0 otherwise
(0.217)
0.0307
(0.009)
-0.0131
(0.048)
0.0879
(0.016)
0.0071
(0.006)
a
a
0.0449
(0.012)
a
-4.0899
(2.017)
c
D3,i = 1 if Firms is engaged in tax-evasion and firm is subject to
Bribe Extortion; 0 otherwise
Age (log)
0.0100
(0.023)
-0.0666
(0.031)
c
-0.0736
(0.032)
c
size small
-0.0493
(0.041)
0.1431
(0.056)
b
0.1582
(0.057)
b
size large
-0.0115
(0.060)
-0.1314
(0.091)
-0.1670
(0.094)
profit margin
-0.0037
(0.001)
Ability I
-0.0011
(0.001)
Ability II
-0.0025
(0.001)
0.0000
(0.000)
a
b
Time cost I
GNIP (growth rate)
0.0051
(0.001)
a
0.0050
(0.001)
-0.0020
(0.001)
c
-0.0024
(0.001)
c
0.0099
(0.001)
a
0.0101
(0.001)
a
-0.1978
(0.044)
a
0.0430
(0.012)
a
0.0408
(0.012)
a
-0.0104
(0.013)
-0.0030
(0.001)
c
-0.0001
(0.001)
0.0067
(0.011)
-0.0108
(0.013)
Foreign share
-0.0005
(0.001)
-0.0028
(0.001)
Change in Sale
-0.0002
(0.001)
Corruption Index
-0.0011
(0.000)
b
Time cost II
0.0041
(0.002)
b
0.0055
(0.002)
-0.1169
(0.225)
Mining
0.0795
(0.154)
-0.1201
(0.222)
-0.0098
(0.065)
Manufacturing
0.0036
(0.049)
Construciton
0.0082
(0.062)
-0.0262
(0.063)
-0.0062
(0.080)
-0.0223
(0.079)
-0.1253
Transport
0.0021
(0.072)
(0.099)
-0.1300
(0.097)
0.0330
(0.065)
Retail
0.0221
(0.050)
0.0223
(0.063)
Other
-0.1513
(0.081)
-0.1997
(0.106)
_cons (selection)
-0.6176
(0.215)
-1.9861
(0.312)
lambda
0.1167
(0.295)
2.0766
(0.672)
rho
0.0549
0.5400
0.5903
sigma
2.1260
3.8454
3.9361
b
a
a
a
b
-0.1786
(0.108)
-2.1947
(0.311)
a
2.3234
(0.730)
b
Note: Significance labeling: a for 1%, b for 5%, c for 10% level of significance. Standard errors are in
parenthesis.
25
Table VIII: Bribe Payment and Selection Equations (Heckman Selection Procedure)
Independent Variable
Bribe Equation
Dependent Variable: Percentage of Bribe Share of Earnings
Eqn: B5eck7
Eqn: B5eck8
Age (log)
-0.1864
(0.071)
-0.1725
(0.073)
c
size small
0.2415
(0.127)
b
0.2663
(0.129)
c
size large
-0.1698
(0.196)
-0.1570
(0.200)
0.0048
(0.003)
0.0047
(0.003)
Ability I
-0.0024
(0.002)
-0.0008
(0.002)
Ability II
0.0265
(0.003)
a
0.0265
(0.003)
Time cost I
-0.4076
(0.104)
a
GNIP (growth rate)
-0.0172
(0.016)
-0.0200
(0.016)
0.0614
(0.007)
-0.0009
Tax evasion ratio
_cons (structutal)
profit margin
Corruption Index
Foreign share
a
0.0616
(0.007)
(0.002)
-0.0006
(0.002)
0.0073
(0.008)
0.0091
(0.008)
-0.2124
(0.487)
-0.2922
(0.491)
a
a
Selection Equation
Dependent Variable: D1,i = 1 if Firms is Subject to Bribe Extortion; 0 otherwise
Age (log)
-0.0100
(0.022)
-0.0188
size small
0.0309
(0.039)
0.0253
(0.040)
size large
-0.0425
(0.058)
-0.0661
(0.060)
profit margin
-0.0005
(0.001)
-0.0009
(0.001)
Ability I
-0.0018
(0.001)
c
-0.0022
(0.001)
c
Ability II
0.0030
(0.001)
b
0.0031
(0.001)
a
Time cost I
-0.0794
(0.034)
c
-0.0163
(0.007)
GNIP (growth rate)
-0.0163
(0.007)
-0.0161
(0.009)
Corruption Index
-0.0098
(0.007)
-0.0017
(0.001)
b
Foreign share
-0.0032
(0.003)
-0.0010
(0.000)
c
Change in Sale
-0.0017
(0.001)
b
-0.0048
(0.002)
c
0.0006
(0.008)
b
Mining
-0.0046
(0.002)
c
-0.0206
(0.152)
Manufacturing
-0.0029
(0.150)
-0.0153
(0.048)
Construciton
-0.0127
(0.047)
-0.0080
(0.060)
Transport
-0.0014
(0.059)
-0.0310
(0.070)
Retail
-0.0133
(0.069)
0.0304
(0.049)
Other
0.0374
(0.048)
-0.2118
(0.079)
-0.1880
(0.077)
0.1769
(0.185)
lambda
0.4515
(0.295)
0.1873
(0.311)
rho
0.1750
0.0729
sigma
2.5800
2.5703
Time cost II
Tax evasion ratio
_cons (selection)
c
(0.023)
0.0066
(0.001)
a
0.001
(0.008)
b
b
Note: Significance labeling: a for 1%, b for 5%, c for 10% level of significance. Standard errors are in
parenthesis.
26
Table IX: Summary Statistics for Responders vs. Non-responders about Bribe Report
Variable
Obs
Mean
Std. Dev.
Min
Max
Summary Statistics for selected variables for Firm that report Bribe Payment
Percentage of Profit Margin to Costs
Percentage of Increase/Decrease in Earnings
Percentage of Revenue reported for tax
A1
A2
9000
8789
8786
8752
9106
22.03
12.86
89.98
23.20
31.03
13.96
39.01
18.56
22.92
16.42
0
-98
2
7.69
9.09
401
400
100
100
100
Summary Statistics for selected variables for Firm that did not report Bribe Payment
Percentage of Profit Margin to Costs
Percentage of Increase/Decrease in Earnings
Percentage of Revenue reported for tax
A1
A2
533
521
408
515
549
23.78
9.93
62.35
31.07
29.28
15.01
37.24
24.16
24.69
17.56
0
-95
10
7.69
9.09
100
300
99
100
100
27
Graph I: Ability (Type-I and Type-II) Distribution in all Firms, by country
[Representation: Frequency according to Category]
70
140
60
Albania
50
160
250
Armenia
120
100
Belarus
200
Azerbaijan
140
120
1 00
150
40
80
30
60
80
20
60
100
40
40
50
20
10
20
0
0
0
0
1
2
3
4
5
6
7
8
9
10
1
100
2
3
4
5
6
7
8
9
10
11
12
140
BIH
90
80
2
3
4
5
6
7
8
9
10
11
100
3
4
5
6
7
8
9
200
Croatia
90
80
180
Czech Rep.
160
70
140
60
120
50
100
60
80
2
12
100
Bulgaria
120
70
1
1
13
50
40
60
40
80
40
30
60
20
20
40
30
20
10
20
10
0
0
1
2
3
4
5
6
7
8
9
0
0
10
1
2
3
4
5
6
7
8
9
10
11
12
13
1
120
3
4
5
6
7
8
9
10
1
11
FYROM
120
1 00
2
3
4
5
6
7
8
9
10
11
2 00
140
140
Estonia
1 00
2
Ge orgia
120
1 00
1 80
Hungary
1 60
1 40
80
1 20
80
80
1 00
60
60
60
40
80
40
60
40
40
20
20
20
20
0
0
1
1
2
3
4
5
6
7
8
9
2
3
4
5
6
7
8
9
10
11
10
1
140
160
Kyrgyzstan
140
120
0
0
2
3
4
5
6
7
8
9
10
Latvia
2
3
4
5
6
7
8
9
10
11
12
1 20
160
120
1
11
Lithuania
140
Moldova
1 00
120
80
1 00
100
1 00
60
80
80
80
60
40
60
60
40
40
20
20
40
20
20
0
1
0
0
2
3
4
5
6
7
8
9
10
11
0
1
2
3
4
5
6
7
8
9
10
1
11
1
400
2
3
4
5
6
7
8
9
10
11
Romania
140
120
300
3
4
5
6
7
8
9
10
11
12
12 0
450
160
Poland
350
2
12
R ussia
4 00
Slovak Rep.
100
350
80
3 00
250
1 00
60
250
200
80
150
60
100
40
1 00
20
50
2 00
40
150
20
50
0
1
2
3
4
5
6
7
8
9
10
11
0
0
0
1
1
2
3
4
5
6
7
8
9
10
11
12
1
13
14 0
2
3
4
5
6
7
8
9
10
11
3
4
5
6
7
8
9
10
11
12
13
12
180
Slovenia
12 0
2
4 00
300
Tajikistan
160
140
Turkey
250
100
120
200
Ukraine
350
3 00
250
1 00
80
2 00
150
80
150
60
60
100
1 00
40
40
50
50
20
20
0
0
0
1
0
1
1
2
3
4
5
6
7
8
9
10
11
2
3
4
5
6
7
8
2
3
4
5
6
7
8
9
10
11
12
13
9
1
12
2
3
4
5
6
7
8
9
10
11
12
13
140
1 60
1 40
Uzbekistan
1 20
Yugoslavia
120
100
100
80
80
60
60
40
40
20
Legend
Chart type
x-axis: category
y-axis: frequency
Type-I: lines
Type-II: bars
20
0
0
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
28
Graph II: Ability Distribution (Type-I & Type-II) among FIEs, by Country
[Representation: Kernel Density]
29
Graph III: Distribution of Firms by
country [Evading, Forced to Bribe]
Country: Albania
(No. of firms = 204)
Country: Belarus
(No. of firms =325)
(# No Tax-evasion = 88)
(# No Tax-evasion =274)
Bc ∩ H = 55
B ∩ H =33
Bc ∩ H = 199
B∩H
= 75
Bc ∩ Hc = 64
Bc ∩ Hc = 25
B ∩ Hc = 52
B ∩ Hc = 26
(# Evasion = 116)
Country: Armenia
(No. of firms =351)
(# Evasion =51)
Country: BIH
(No. of firms =200)
(# No Tax-evasion = 306)
(# No Tax-evasion =151)
Bc ∩ H = 192
B ∩ H =114
Bc ∩ H = 109
B ∩ H = 42
Bc ∩ Hc = 33
Bc ∩ Hc = 36
B ∩ Hc = 12
B ∩ Hc = 13
(# Evasion = 49 )
(# Evasion = 45)
Country: Azerbaijan
(No. of firms =350)
Notes:
(# No Tax-evasion =223)
Bc ∩ H = 164
B∩H=
59
Bc ∩ Hc = 96
B ∩ Hc = 31
Left Trapezoid, B = Ac = Always forced to
bribe; right Trapezoid, A = Bc = Not always
forced to bribe; area outside the oval H =
Dc = Not engaged in Tax-evasion; area
inside the oval D = Hc = Engaged in Taxevasion
(# Evasion = 127)
30
Graph-III contd..
Country: Bulgaria
(No. of firms =300)
Country: Estonia
(No. of firms =219)
(# No Tax-evasion =204)
(# No Tax-evasion =165)
Bc ∩ H = 157
B∩H
= 42
Bc ∩ H = 107
B∩H=
58
Bc ∩ Hc = 14
B ∩ Hc = 7
Bc ∩ Hc = 26
B ∩ Hc = 14
(# Evasion = 21)
Country: Croatia
(No. of firms =236)
(# Evasion =40)
Country: FYROM
(No. of firms =200)
(# No Tax-evasion =152)
(# No Tax-evasion =116)
Bc ∩ H = 133
B ∩ H = 62
Bc ∩ H = 65
B∩H=
51
Bc ∩ Hc = 19
B ∩ Hc = 22
Bc ∩ Hc = 47
B ∩ Hc = 37
(# Evasion =41)
Country: Czech Republic
(No. of firms =343)
(# No Tax-evasion =234)
(# Evasion = 84)
Country: Georgia
(No. of firms =200)
(# No Tax-evasion =156)
Bc ∩ H = 159
B∩H
= 75
Bc ∩ H = 99
B∩H=
57
Bc ∩ Hc = 66
B ∩ Hc = 43
(# Evasion =109)
Bc ∩ Hc = 30
B ∩ Hc = 14
(# Evasion =44)
31
Graph-III contd..
Country: Hungary
(No. of firms =610)
Country: Latvia
(No. of firms =205
(# No Tax-evasion =442)
(# No Tax-evasion =165)
Bc ∩ H = 246
B∩H
= 196
Bc ∩ H = 107
B∩H=
58
Bc ∩ Hc = 89
B ∩ Hc = 79
Bc ∩ Hc = 26
B ∩ Hc = 14
(# Evasion =168)
Country: Kazakhstan
(No. of firms =585)
(# Evasion =40)
Country: Lithuania
(No. of firms =205)
(# No Tax-evasion =493)
(# No Tax-evasion =161)
Bc ∩ H = 292
B∩H
= 201
Bc ∩ H = 94
B∩H=
67
Bc ∩ Hc = 47
B ∩ Hc = 45
Bc ∩ Hc = 34
B ∩ Hc = 10
(# Evasion =82)
Country: Kyrgyzstan
(No. of firms =202)
(# Evasion = 44)
Country: Moldova
(No. of firms =350)
(# No Tax-evasion =131)
(# No Tax-evasion =253)
Bc ∩ H = 72
B∩H=
59
Bc ∩ H = 117
B∩H=
136
Bc ∩ Hc = 72
B ∩ Hc = 39
(# Evasion =71)
Bc ∩ Hc = 48
B ∩ Hc =
49
(# Evasion =97 )
32
Graph-III contd..
Country: Poland
(No. of firms =975)
Country: Slovak Republic
(No. of firms =220)
(# No Tax-evasion =670)
(# No Tax-evasion =171)
Bc ∩ H = 458
B∩H=
212
Bc ∩ H = 157
B∩H
= 42
Bc ∩ Hc = 209
B ∩ Hc = 96
Bc ∩ Hc = 14
B ∩ Hc = 42
(# Evasion =305)
Country: Romania
(No. of firms =600)
(# Evasion =21)
Country: Slovenia
(No. of firms =223)
(# No Tax-evasion =494)
(# No Tax-evasion =223)
Bc ∩ H = 309
B∩H
= 185
Bc ∩ H = 132
B∩H=
53
Bc ∩ Hc = 70
B ∩ Hc = 36
Bc ∩ Hc = 28
B ∩ Hc = 10
(# Evasion = 106)
Country: Russia
(No. of firms =601)
(# Evasion = 38)
Country: Tajikistan
(No. of firms =200)
(# No Tax-evasion =403)
(# No Tax-evasion =150)
Bc ∩ H = 228
B∩H=
175
Bc ∩ H = 100
B∩H=
50
Bc ∩ Hc = 113
B ∩ Hc = 85
(# Evasion =198)
Bc ∩ Hc = 33
B ∩ Hc = 17
(# Evasion =50)
33
Graph-III contd..
Country: Turkey
(No. of firms =)
Country: Uzbekistan
(No. of firms = 300)
(# No Tax-evasion =)
(# No Tax-evasion =276)
Bc ∩ H =
B∩H=
Bc ∩ H = 150
B∩H=
126
B c ∩ Hc =
B ∩ Hc =
B c ∩ Hc = 9
B ∩ Hc = 15
(# Evasion = )
Country: Ukrain
(No. of firms =594)
(# Evasion =24 )
Country: Yugoslavia
(No. of firms = 300)
(# No Tax-evasion =470)
(# No Tax-evasion = 222)
Bc ∩ H = 259
B∩H
= 211
Bc ∩ H = 151
B ∩ H =71
Bc ∩ Hc = 50
B ∩ Hc =
74
(# Evasion =124 )
Bc ∩ Hc = 41
B ∩ Hc = 37
(# Evasion = 78)
34
References
[1] Aidt, T. S. (2003) “Economic analysis of corruption: a survey”, Economic Journal 113: 632-652.
[2] Crocker, Keith J. and Slemrod, Joel B.,Corporate Tax Evasion with Agency Costs (August 2004). NBER
Working Paper No. W10690. Available at SSRN: http://ssrn.com/abstract=579221
[3] Djankov, S., R. La Porta, F. Lopez de Silanes and A. Schleifer (2002) “The regulation of entry”,
Quarterly Journal of Economics 117: 1-37.
[4] Hellman, J. S., Jones, G. & Kaufmann, D. (2002). “Far From Home: Do Foreign Investors Import
Higher Standards of Governance in Transition Economies? World Bank Working Paper. The
World Bank, Washington DC.
[5] Hellman, J. S., Jones, G. & Kaufmann, D. (2003). Seize the State, Seize the Day: State Capture
and Influence in Transition Economies. Journal of Comparative Economics, 31 (4), 751–773.
[6] Hellman, J. S., Jones, G. & Kaufmann, D. (2006). Far From Home: Do Foreign Investors Import
Higher Standards of Governance in Transition Economies? In Corporate Governance and
Globalization. Sage Publication Ltd. (Forthcoming).
[7] Kaufmann, Daniel; Kraay, Aart; Mastruzzi, Massimo (2005) “Governance Matters IV:
Governance Indicators for 1996-2004”, The World Bank
[8] Kaufman, D., Kraay, A. & Mastruzzi, M. (2005). Measuring Governance Using Cross-Country
Perceptions Data.” World Bank Working Paper. Washington, DC: World Bank.
[9] Kaufmann, D. and Wei,. S. (1999). Does ‘Grease Payment’ Speed up the Wheels of Commerce.
NBER Working Paper 7093.
[10] Shleifer, A., & Vishny, R. (1993). Corruption. Quarterly Journal of Economics, 108 (3), 599-617.
[11] Smarzynska, B. K. & Wei, S. (2002). Corruption and Cross-Border Investment: Firm-Level
Evidence. NBER Working Paper.
[12] Svensson, J. (2003). Who Must Pay Bribes and How Much? Evidence from a Cross-Section of
Firms. Quarterly Journal of Economics, 118 (1), 207-229.
[13] Wei, S. (2000). How Taxing is Corruption on International Investors? Review of Economics and
Statistics, 82 (1), 1-11.
[14] Wei, S. (2001). Corruption in Economic Transition and Development: Grease or Sand? Mimeo.
[15] Wei, S. (1997). “Why is Corruption so much more Taxing than Tax? Arbitrariness Kills. NBER
Working Paper 6255. Cambridge, MA.
[16] World Bank (2008) “Fighting Corruption through Collective Action: A guide for business
Copyright; International Bank for Reconstruction and Development, The World Bank Group.
35