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4
3
2
graft (log)
1
0
-4
-3
-2
-1
-1
0
1
2
3
4
-2
-3
-4
Connection costs public grid (log)
Figure 2: Correlation (graft & excess cost of telephone connection)
21
19
graft (log)
17
15
13
11
9
9
10
11
12
Excess cost of telephone connection
13
14
Table 1. Partial correlation : Connection costs and bribery(i),(ii)
Equation
(1)(iii),(iv)
(2)(v)
Connection costs
public grid (log)
Excess cost of
telephone
connection (log)
Constant
9.162
(.000)
10.75
(.000)
Bribe payments (log)
.508
(.000)
.068
(.001)
.44
25
.15
62
Dependent variable
Adjusted R2
Observations
Note: (i) standard errors adjusted for heteroskedasticity (White, 1980); (ii) p-values in
parenthesis; (iii) regression 1 includes a proxy of informality (infrastructure service); (iv)
connection costs (public grid) has mean 6,330,400 USh and median 2,500,000 USh; (v)
excess cost of telephone connection has mean 155,600 USh and median 90,000 USh.
Table 2: Sample characteristics
Firms that reported Firms that reported
p-value
zero bribe payments positive bribe payments
Infrastructure service
3.24
3.70
0.048
Export
0.15
0.33
0.016
Pay tax
2.58
3.04
0.184
Pay tax
(not tax exempted only)
2.50
3.28
0.031
Time spent dealing with
taxes and regulations (log)
1.93
2.49
0.016
Cost of accountant etc.
(log)
3.30
4.74
0.016
Cost of security (log)
7.17
7.48
0.569
Incidence of robbery and
theft
0.52
0.58
0.497
Employment size (log)
3.61
3.88
0.342
Note: (i) average values in columns two and three; (ii) numbers in the fourth column are p-values for a test
of the null hypothesis that the two means are equal.
Figure 3a: Distribution of firms according to bribe payments (log)
30
25
20
15
10
5
11.1
10.1
9.2
8.2
7.3
6.4
5.4
4.5
3.6
2.6
0
Figure 3b: Distribution of firms according to bribe payments
70
60
50
40
30
20
10
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
Table 3: Probit regressions on the incidence of corruption
Equation
(1)
(2)
(3)
(4)
(5)
(6)
Constant
.203
(.554)
.747
(.000)
.647
(.000)
.613
(.004)
.309
(.255)
.179
(.588)
Employment
Infrastructure
service
8.36E-5 -7.58E-5 -7.94E-5 -7.66E-5 -1.28E-4
(.848)
(.859)
(.857)
(.865)
(.780)
.192
(.041)
Export
.596
(.031)
Trade
.430
(.070)
Pay taxes
.096
(.173)
.187
(.037)
Tax exemptions
1.14
(.018)
Pay taxes∗exemption
-.240
(.071)
Formal sector index
Observations
-.000
(.784)
176
176
167
173
172
.151
(.038)
167
Note: (i) dependent variable ei takes the value 1 if the firm reported positive bribe payments and 0
otherwise; (ii) p-values in parenthesis.
Table 4: Sample characteristics of firms that reported positive bribes(i),(ii)
All firms
All firms(iii)
Firms reporting
low bribe
payments(iv)
Firms reporting
high bribe
payments(iii)
Profit
- mean
- median
- Std. dev.
211,060
27,270
1,134,460
284,390
27,270
1,048,116
57,540
11,230
119,660
540,110
95,690
1,489,290
Bribes
- mean
- median
- Std. dev.
7,850
910
19,840
6,270
910
13,480
280
180
280
13,020
9,090
17,390
Resale value
- mean
- median
- Std. dev.
365,760
90,910
667,190
346,760
90,910
648,260
174,550
38,640
394,500
540,890
227,270
809,010
Employment
- mean
- median
- Std. dev.
119
34
262
109
33
251
36
20
53
192
81
346
Reversibility
- mean
- median
- Std. dev.
.001
.011
.034
.001
.011
.034
.002
.012
.033
-.000
.009
.035
Obs.
119
117
62
55
Note: (i) sample of firms for which data on corruption and other variables are available; (ii) profits, bribes,
resale values in USD; (iii) excluding two extreme outliers; (iv) low bribe payment is graft smaller than USD
1000.
Table 5: Corruption regressions(i),(ii),(iii)
(2)(iv)
(3)
Equation
(1)
Constant
10,333
(8,337)
9,490
(6062)
10,663
(8,382)
Employment
18.06**
(8.06)
12.32**
(5.98)
18.02**
(8.12)
Profit
.0033**
(.0014)
.0037***
(.0010)
.0033**
(.0014)
Reversibility∗
Resale value
-.309**
(.118)
-.259***
(.088)
-.348***
(.126)
Resale value
.0071**
(.0032)
.0059**
(.0023)
.0071**
(.0032)
Reversibility
λ(v)
48,571
(54,844)
-18,001
(21,392)
-17,458
(15,496)
-18,578
(21,487)
Wald(vi)
17.30
(0.00)
28.23
(0.00)
17.81
(0.00)
S.E. regression
18,729
12,169
18,750
.11
119
.18
117
.11
119
Adjusted R2
Observations
Notes: (i) dependent variable is bribe payments in USD; (ii) standard errors in
parenthesis are adjusted for heteroskedasticity (Heckman, 1979); (iii) *** [**] (*)
denote significant at the 1 [5] (10) percent levels; (iv) two extreme outliers
dropped; (v) λ is the inverse Mills ratio to adjust for selectivity with selection
equation given by P(ei=1)=Φ(c,employment,formal), i.e., Regression 6, Table 3;
(vi) Wald is the Wald-test statistic for testing if the coefficients on the π, k, l, αk
are zero with p-values in parenthesis.
Table 6: Corruption regressions(i),(ii),(iii)
Equation
(1)
(2)
(3)
Constant
204,917
(148,341)
7,381
(9,122)
11,832
(8,485)
Employment
47.78**
(23.85)
19.06**
(8.42)
17.39**
(7.84)
Profit
.0030**
(.0014)
.0031**
(.0014)
.0032**
(.0013)
Reversibility∗
Resale value
-.288**
(.119)
-.307**
(.118)
-.321***
(.114)
Resale value
.0066**
(.0032)
.0078**
(.0033)
.0073**
(.0031)
Formal sector index
-17,972
(13,693)
Competitors
284.7
(260.4)
Sell to government
λ(iv)
-4,344
(3,318)
-326,054
(235,230)
-21,178
(22,307)
-17,700
(20,986)
Wald(v)
15.39
(0.00)
17.80##
(0.00)
18.52##
(0.00)
S.E. regression
18,676
18,800
18,177
.11
119
.11
118
.12
116
Adjusted R2
Observations
Notes: (i) dependent variable is bribe payments in USD; (ii) standard errors in
parenthesis are adjusted for heteroskedasticity (Heckman, 1979); (iii) *** [**] (*)
denote significant at the 1 [5] (10) percent levels; (iv) λ is the inverse Mills ratio
to
adjust
for
selectivity
with
selection
equation
given
by
P(ei=1)=Φ(c,employment,formal), i.e., Regression 6, Table 3; (v) Wald is the
Wald-test statistic for testing if the coefficients on the π, k, l, αk are zero with pvalues in parenthesis.
Table 7: Corruption regressions(i),(ii),(iii)
(2)(iv)
(3)(iv)
9.23***
(1.82)
9.21***
(1.81)
9.33***
(1.83)
.0029*
(.0017)
.0027
(.0017)
.0027
(.0017)
Profit
5.2E-7***
(1.1E-7)
5.5E-7***
(9.0E-8)
5.5E-7***
(1.0E-7)
Reversibility∗
Resale value
-3.8E-5***
(1.1E-5)
-3.5E-5***
(1.2E-5)
-3.7E-5**
(1.2E-5)
Resale value
1.0E-6***
(3.5E-7)
9.8E-7**
(3.7E-7)
9.9E-7**
(3.9E-7)
Equation
(1)
Constant
Employment
Competitors
-.0087
(.0250)
Sell to government
-.255
(.319)
λ(v)
-8.00*
(4.60)
-7.97*
(4.58)
-7.75*
(4.43)
Wald(vi)
40.24
(0.00)
49.30
(0.00)
47.25
(0.00)
S.E. regression
1.77
1.74
1.73
Adjusted R2
Observations
.36
119
.35
117
.35
113
Notes: (i) dependent variable is log of bribe payments in USD; (ii) standard errors
in parenthesis are adjusted for heteroskedasticity (Heckman, 1979); (iii) *** [**]
(*) denote significant at the 1 [5] (10) percent levels; (iv) two extreme outliers
dropped; (v) λ is the inverse Mills ratio to adjust for selectivity with selection
equation given by P(ei=1)=Φ(c,employment,formal), i.e., Regression 6, Table 3;
(vi) Wald is the Wald-test statistic for testing if the coefficients on the π, k, l, αk
are zero with p-values in parenthesis.
Table 8: Corruption regressions(i),(ii),(iii),(iv)
(2)(v)
(3)(v)
-14.4
(13.9)
-85.2***
(28.3)
-85.4***
(28.9)
-85.3***
(28.3)
Employment (log)
.742***
(.195)
.639***
(.214)
.602***
(.207)
.638***
(.215)
Profit (log)
1.03
(.858)
5.48***
(1.76)
5.52***
(1.81)
5.49***
(1.76)
Reversibility (log)∗
Resale value (log)
-1.86*
(.964)
-1.87**
(.941)
-2.10**
(.933)
Equation
(1)
Constant
-.950**
(.476)
Reversibility∗
Resale value (log)
Resale value (log)
(4)(v)
1.56**
(.713)
1.52**
(.693)
1.70**
(.689)
Competitors (log)
-.144
(.211)
Sell to government
-.330
(.300)
.228**
(.101)
λ(vi)
-2.71
(2.61)
-3.59
(2.92)
-3.32
(2.80)
-3.60
(2.93)
Wald(vii)
41.06
(0.00)
40.17
(0.00)
42.44
(0.00)
40.14
(0.00)
S.E. regression
1.67
1.61
1.60
1.61
Adjusted R2
Observations
.43
119
.44
117
.44
113
.44
117
Notes: (i) dependent variable is log of bribe payments in USD; (ii) standard errors in parenthesis are
adjusted for heteroskedasticity (Heckman, 1979); (iii) *** [**] (*) denote significant at the 1 [5] (10)
percent levels; (iv) a constant 1E+7 is added to log of profit and a constant 2 is added to reversibility
to avoid negative values; (v) two extreme outliers dropped; (vi) λ is the inverse Mills ratio to adjust
for selectivity with selection equation P(ei=1)=Φ(c,log(employment),formal), i.e., Regression 6,
Table 3; (vii) Wald is the Wald-test statistic for testing if the coefficients on the π, k, l, αk are zero
with p-values in parenthesis.
Table 9: Corruption rate regressions(i),(ii),(iii)
(2)(iv)
(3)(v)
140.1**
(66.4)
135.2**
(65.0)
-20.4***
(7.43)
-21.1***
(7.46)
.0040***
(7.6E-4)
.0040***
(7.4E-4)
2.17***
(.706)
2.22***
(.708)
.217**
(.104)
.217**
(.104)
Equation
(1)
Constant
Profit/employment
Profit/employment (log)
Reversibility∗
resale value
-.233**
(.096)
-.238**
(.093)
Resale value/employment
.0039*
(.0024)
.0041*
(.0023)
Resale value/employment
(log)
(4)(iv),(v)
λ(vi)
-206.2
(169.6)
-204.2
(165.7)
-2.96
(2.41)
-2.87
(2.38)
Wald(vii)
32.94
(0.00)
36.31
(0.00)
17.98
(0.00)
18.79
(0.00)
S.E. regression
127.4
122.9
1.61
1.60
.19
119
.21
117
.44
117
.44
113
Adjusted R2
Observations
Notes: (i) dependent variable is (bribe payments in USD/employment) in (1)-(2), and log of (bribe
payments in USD/employment) in (3)-(4); (ii) standard errors in parenthesis are adjusted for
heteroskedasticity (Heckman, 1979); (iii) *** [**] (*) denote significant at the 1 [5] (10) percent levels;
(iv) two extreme outliers dropped; (v) a constant 40,000 is added to log of profit/employment to avoid
negative values; (vi) λ is the inverse Mills ratio to adjust for selectivity with selection equation
P(ei=1)=Φ(c,log(employment),formal), i.e., Regression 6, Table 3; (vii) Wald is the Wald-test statistic
for testing if the coefficients on the π, k, l, αk are zero with p-values in parenthesis.
Table 10: IV-regressions on corruption (i),(ii),(iii)
Equation
(1)(iv)
Constant
8706.9*
(5,163)
Profit
.0042***
(.0010)
-.261***
(.089)
-.245**
(.094)
Reversibility∗
Resale value/employment
Resale value
121.6*
(53.1)
.0050***
(.0007)
Profit/employment
Reversibility∗resale value
(2)(iv)
.0059**
(.0023)
.0039*
(.0023)
Resale value/employment
Employment
11.30**
(5.70)
λ(v)
-16,469
(13,714)
-186.9
(142.7)
Wald(vi)
32.10
(0.00)
51.02
(0.00)
Hausman(vii)
29.25
(0.70)
24.10
(0.90)
S.E. regression
12,184
123.8
117
117
Adjusted R2
Observations
Notes: (i) dependent variable is bribe payments in USD in Regression (1) and bribe
payments in USD/employment in Regressions (2); (ii) standard errors in parenthesis are
adjusted for heteroskedasticity (Heckman, 1979); (iii) *** [**] (*) denote significant at
the 1 [5] (10) percent levels; (iv) 2SLS estimation; (v) λ is the inverse Mills ratio to
adjust for selectivity with selection equation P(ei=1)=Φ(c, employment,formal); (vi)
Wald is the Wald-test statistic for testing if the coefficients on the π, k, l, αk are zero
with p-values in parenthesis. (vii) Hausman is the TR2-test statistic for no
overidentifying restrictions, with p-values in parenthesis.
Table 11: Effects on corruption of changes in firm characteristics
Equation
(1)(i)
(2)(ii)
Change in bribe
payment due to a one
standard deviation
increase in
[USD (st.d.)]
3,696
(0.27)
Change in bribe
payment due to a one
percent increase in
[%]
Profits(iv)
4,402
(0.33)
0.152
Reversibility
index(v)
-3,077
(-0.23)
-0.118
Employment
2,836
(0.23)
0.632
Capital stock(iii)
0.218
Note: (i) Calculations based on Regression 3; Table 10, with standard deviations in
parenthesis. Regression is evaluated at the mean values of the explanatory variables; (ii)
Calculations based on Regression 2; Table 8. The elasticity of corruption with respect
to changes in profit is γπ∗[2.8E5/(2.8E5+1E7)] where 2.8E5 is mean profit and 1E7 is
the scale factor, the elasticity of corruption with respect to capital is γα∗ln(2+.011)+γk
where .011 is the median value of the reversibility index and 2 is the scale factor, the
elasticity of corruption with respect to the reversibility index is
γα∗ln(3.5E5)∗[.011/(2+.011)] where 3.5E5 is mean capital stock.
Figure 4a: The technology effect
1.2
1
ϕα
0.8
0.6
0.4
ϕk
0.2
p
0.98
0.91
0.84
0.77
0.7
0.63
0.56
0.49
0.42
0.35
0.28
0.21
0.14
0.07
0
0
Figure 4b: The technology effect
200
180
160
140
120
100
80
60
40
20
0
p
0.9
0.84
0.78
0.72
0.66
0.6
0.54
0.48
0.42
0.36
0.3
0.24
0.18
0.12
0.06
0
ϕα/ϕk