Economic analysis of corruption at the company level

Bąk, Paulina

doi:10.2478/ceej-2020-0015

Figures & Tables

CPI index between 2013 and 2016. Source: Transparency International.www.transparency.org (access 04/05/2019).

Correlation between corruption and economic growth. Source: The Economist based on Transparency International, UN.https://www.economist.com/graphic-detail/2011/12/02/corrosive-corruption (accessed 07/16/2020).

Classification of types of corruption. Source: Chrustowski T, Prawne, kryminologiczne i kryminalistyczne aspekty łapówkarstwa [Legal, criminological and forensic aspects of bribery], Legal Publishing House, Warsaw 1985.

Perception of corruption by companies. Source: BEEPS database.

ROC curve (only significant variables). Source: BEEPS database.

ROC curve – only macroeconomic variables. Source: Bąk 2020.

Regression outcomes for microeconomic variables

Corruption	Coeff. (Std. Err.)	Z	P>\|z\|
Years on the market	0.0089*** (0.0027)	3.29	0.001
Employment	−0.0268 (0.0219)	−1.23	0.22
Sta_capital	−1.1087** (0.4776)	−2.32	0.02
For_capital	−0.1921 (0.1430)	−1.34	0.179
State_prod	0.0211 (0.1075)	0.2	0.845
Time_officials	0.3673*** (0.1249)	2.94	0.003
Audits	0.0089 (0.0047)	1.88	0.06
Efficiency	0.0216*** (0.0075)	2.86	0.004
Thefts	3.4493*** (1.1172)	3.09	0.002
small_loc	−0.1215 (0.0819)	−1.48	0.138
medium_loc	−0.3331*** (0.0848)	−3.93	0
large_loc	−0.3411*** (0.1159)	−2.94	0.003
small_company	−0.0388 (0.0968)	−0.4	0.689
medium_company	−0.0402 (0.0920)	−0.44	0.662
_cons	0.6286** (0.2513)	2.5	0.012
Country Effect	TAK
Industry effect	TAK
Number of observations	7486
F-Statistic	948.15
Pseudo R²	0.0940
p-value	0.0000

Descriptive statistics for the Business sector variables

Variable	Number of observations	Std. Dev.	Max.
Other	16.566	0.2715	1
Food products	16.566	0.2587	1
Textiles	16.566	0.1567	1
Clothing	16.566	0.1876	1
Chemicals	16.566	0.1516	1
Plastics and rubber	16.566	0.1428	1
Mineral products	16.566	0.1966	1
Metals and raw materials	16.566	0.0739	1
Metal products	16.566	0.2001	1
Machines and devices	16.566	0.2360	1
Electronics	16.566	0.1165	1
Construction	16.566	0.2810	1
Wholesale	16.566	0.3493	1
Retail	16.566	0.4215	1
Hotels and restaurants	16.566	0.2001	1
IT	16.566	0.1351	1

Statistics of microeconomic variables

Variable	Number of observations	SD	Min.	Max.
Corruption	15.904	0.4832	0	1
Years on the market	16.566	12.0624	0	178
Employment	15.039	1.3708	0	9.21034
Sta_capital	16.566	0.0751	0	0.99
For_capital	16.566	0.1959	0	1
State_prod	16.566	0.2607	0	1
Time_officials	16.566	0.2016	0	1
Audits	8.496	5.3148	1	150
Efficiency	15.039	3.5798	0	8.269244
Thefts	16.566	0.0253	0	1
small_loc	16.566	0.4401	0	1
medium_loc	16.566	0.4942	0	1
large_loc	16.566	0.3153	0	1
small_company	16.566	0.4990	0	1
medium_company	16.566	0.4772	0	1
large_company	16.566	0.3213	0	1

Explanatory and explained continuous variables

Name	Variable
Corruption	Corresponds to the percentage of the answer to the question ‘Is corruption an obstacle to business?’
Years on the market	The company’s existence on the market in years
Employment	number of employees
Sta_capital	State ownership in capital (%)
For_capital	Foreign ownership in capital (%)
state production	Percentage of production sold domestically
Time_officials	Time spent on contacting government officials (% of work time)
Audits	Number of state audits
Efficiency	The ratio of sales to employees
Thefts	Losses caused by theft (% of revenues)
Investments	The level of investments in the country as% of GDP
GDP per capita	GDP per capita calculated in %

Comparison of the estimation results of the logit and probit models

Variable	logit	probit
Years on the market	0.00876502	0.00530975
Employment	−0.00008314	−0.00004948
Sta_capital	−1.133988	−0.62454681
For_capital	−0.197049	−0.1253618
State_prod	0.02541215	0.01607862
Time_officials	0.36951254	0.22890388
Audits	0.00907696	0.00543676
Efficiency	0.02150128	0.01288578
Thefts	3.4365243	1.8378906
Other	0.03748113	0.02313259
Food products	−0.14439709	−0.0845678
Textiles	0.3521586	0.21495443
Clothing	0.11952786	0.07476893
Chemicals	0.52001581	0.31532122
Plastics and rubber	−0.03725552	−0.01415368
Mineral products	0.32495071	0.19949212
Metals and raw materials	−0.0577727	−0.02944644
Metal products	0.02482283	0.01903884
Machines and devices	0.13714882	0.08354676
Electronics	0.2596035	0.15581276
Construction	0.3544781	0.22319262
Wholesale	0.20622816	0.13128126
Retail	−0.0664786	−0.03453299
Hotels and restaurants	−0.1737417	−0.09251344
IT	0.33941652	0.2147104
small_loc	−0.12165995	−0.07618389
medium_loc	−0.33236935	−0.2025015
large_loc	−0.33919617	−0.20681956
small_company	−0.0014848	0.00030351
medium_company	−0.02023952	−0.01255519
medium_company	−0.02023952	−0.01255519
Bulgaria	–1.1978914	−0.73944942
Albania	−1.1592144	−0.71822055
Croatia	−1.1413132	−0.70407362
Belarus	−2.0420131	−1.2431616
Georgia	−2.6648174	−1.5931303
Tajikistan	−1.4865773	−0.9180442
Turkey	−1.2972634	−0.80528348
Ukraine	−0.40252307	−0.24803484
Uzbekistan	−3.5135959	−2.0308543
Russia	−0.88202438	−0.54765707
Romania	−0.13433538	−0.08237083
Kazakhstan	−1.2451245	−0.76820525
Bosnia_and_Herzegovina_Ha	−1.0461743	−0.6461674
Azerbaijan	−3.0499892	−1.792391
Macedonia	−1.8585547	−1.1448237
Armenia	−1.8512206	−1.1417558
Kyrgyzstan	0.35124133	0.21087963
Estonia	−3.0235201	−1.8171122
Czech Republic	−1.1688309	−0.7194871
Italy	−2.2520163	−1.3720082
Latvia	−1.9601472	−1.2002027
Lithuania	−1.7678234	−1.0833992
Slovakia	−0.99406504	−0.6160503
Slovenia	−2.1529345	−1.304369
Serbia	−1.5845897	−0.97913306
Cyprus	−1.6468074	−1.0145575
Greece	0.46229253	0.26145001
Moldova	−1.0586318	−0.65745899
Mongolia	−1.665327	−1.025903
Montenegro	−2.827147	−1.6969201
Poland	−1.5308906	−0.939425
_cons	0.54153984	0.33342044

Model estimation for the country effect

Corruption	Coeff. (Std. Err.)	z	P>\|z\|
Bulgaria	−1.2071*** (0.2338)	−5.160	0
Albania	−1.1592*** (0.2098)	−5.520	0
Croatia	−1.1568*** (0.2430)	−4.760	0
Belarus	−2.0415*** (0.2905)	−7.030	0
Georgia	−2.6626*** (0.3789)	−7.030	0
Tajikistan	−1.4810*** (0.2225)	−6.660	0
Turkey	−1.2983*** (0.1958)	−6.630	0
Ukraine	−0.3955** (0.1922)	−2.060	0.04
Uzbekistan	−3.5079*** (0.4045)	−8.670	0
Russia	−0.8858*** (0.1792)	−4.940	0
Romania	−0.1506 (0.2055)	−0.730	0.464
Kazakhstan	−1.2396*** (0.2477)	−5.000	0
Bosnia and Herzegovina	−1.0536*** (0.2142)	−4.920	0
Azerbaijan	−3.0388*** (0.2907)	−10.450	0
Macedonia	−1.8757*** (0.2192)	−8.560	0
Armenia	−1.8567*** (0.2236)	−8.300	0
Kyrgyzstan	0.3629 (0.2298)	1.580	0.114
Estonia	−3.0317*** (0.5035)	−6.020	0
Czech Republic	−1.1714*** (0.2448)	−4.790	0
Italy	−2.2347*** (0.2640)	−8.460	0
Latvia	−1.9786*** (0.2901)	−6.820	0
Lithuania	−1.7852*** (0.2866)	−6.230	0
Slovakia	−1.0013*** (0.2618)	−3.820	0
Slovenia	−2.1769*** (0.4199)	−5.180	0
Serbia	−1.5975*** (0.2292)	−6.970	0
Cyprus	−1.6615*** (0.3262)	−5.090	0
Greece	0.4579 (0.3170)	1.440	0.149
Moldova	−1.0548*** (0.2134)	−4.940	0
Mongolia	−1.6670*** (0.2219)	−7.510	0
Montenegro	−2.8361*** (0.3518)	−8.060	0
Poland	−1.5388*** (0.2477)	−6.210	0

Model estimation for the industry effect

Corruption	Coeff. (Std. Err.)	z	P>\|z\|
Other	0.0385 (0.1409)	0.270	0.785
Food products	−0.1382 (0.1439)	−0.960	0.337
Textiles	0.3672* (0.1912)	1.920	0.055
Clothing	0.1311 (0.1701)	0.770	0.441
Chemicals	0.5306*** (0.1964)	2.700	0.007
Plastics and rubber	−0.0306 (0.2059)	−0.150	0.882
Mineral products	0.3376** (0.1634)	2.070	0.039
Metals and raw materials	−0.0580 (0.3297)	−0.180	0.860
Metal products	0.0288 (0.1669)	0.170	0.863
Machines and devices	0.1393 (0.1485)	0.940	0.348
Electronics	0.2530 (0.2361)	1.070	0.284
Construction	0.3605*** (0.1373)	2.630	0.009
Wholesale	0.1995 (0.1263)	1.580	0.114
Retail	−0.0695 (0.1201)	−0.580	0.563
Hotels and restaurants	−0.1644 (0.1623)	−1.010	0.311
IT	0.3336 (0.2302)	1.450	0.147

Regression outcomes only for significant variables

Corruption	Coeff. (Std. Err.)	z	P>\|z\|
Years on the market	0.0062*** (0.0019)	3.33	0.001
Sta_capital	−1.2882*** (0.3850)	−3.35	0.001
Time_officials	0.5488*** (0.0914)	6.01	0.000
Efficiency	0.0232*** (0.0053)	4.35	0.000
Thefts	2.8411*** (0.7840)	3.36	0.000
medium_loc	−0.1124** (0.0522)	−2.15	0.031
large_loc	0.5336 (0.0767)	0.70	0.487
_cons	0.5986*** (0.1652)	3.62	0.000
Country effect	TAK
Industry effect	TAK
Number of observations	14470
F-Statistic	1831.32
p-value	0.0000
pseudo R²	0.0954

Statistics of macroeconomic variables

Variable	Number of observations	SD	Min.	Max.
CPI	16.566	11.6013	21	70
Investments	15.457	4.7292	4.2	31.1
GDP per capita	16.566	3174.36	796	30829.5
OECD	16.566	0.500	0	1

Estimation outcomes for macroeconomic variables

Corruption	Coeff. (Std. Err.)	z	P>\|z\|
CPI	−0.6106*** (0.0760)	−8.03	0.000
Investments	1.0797*** (0.1118)	9.66	0.000
GDP per capita	0.0023*** (0.0003)	7.75	0.000
OECD	−8.3657*** (1.1951)	−7.00	0.000
_cons	13.6031*** (1.7468)	7.79	0.000
Country effect	TAK
Industry effect	TAK
Number of observations	14.830
F-Statistic	1664.09
Pseudo R²	0.0843
p-value	0.0000

Descriptive statistics for the country variables

Variable	Number of observations	Std. Dev.	Max.
Bulgaria	16.566	0.1318	1
Albania	16.566	0.1458	1
Croatia	16.566	0.1458	1
Belarus	16.566	0.1458	1
Georgia	16.566	0.1458	1
Tajikistan	16.566	0.1456	1
Turkey	16.566	0.2730	1
Ukraine	16.566	0.2384	1
Uzbekistan	16.566	0.1516	1
Russia	16.566	0.4357	1
Romania	16.566	0.1776	1
Kazakhstan	16.566	0.1868	1
Bosnia and Herzegovina	16.566	0.1458	1
Azerbaijan	16.566	0.1516	1
Macedonia	16.566	0.1458	1
Armenia	16.566	0.1458	1
Kyrgyzstan	16.566	0.1266	1
Estonia	16.566	0.1273	1
Czech Republic	16.566	0.1229	1
Italy	16.566	0.1355	1
Latvia	16.566	0.1410	1
Lithuania	16.566	0.1266	1
Slovakia	16.566	0.1262	1
Slovenia	16.566	0.1266	1
Serbia	16.566	0.1458	1
Cyprus	16.566	0.1458	1
Greece	16.566	0.1383	1
Moldova	16.566	0.1458	1
Mongolia	16.566	0.1458	1
Montenegro	16.566	0.0947	1
Poland	16.566	0.1779	1
Kosovo	16.566	0.1098	1

Economic analysis of corruption at the company level

Figures & Tables

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. A1

Fig. A2

Fig. A3

Regression outcomes for microeconomic variables

Descriptive statistics for the Business sector variables

Statistics of microeconomic variables

Explanatory and explained continuous variables

Comparison of the estimation results of the logit and probit models

Model estimation for the country effect

Model estimation for the industry effect

Regression outcomes only for significant variables

Statistics of macroeconomic variables

Estimation outcomes for macroeconomic variables

Descriptive statistics for the country variables

Paradigm

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