Marta Kopytko

PhD, associate professor, assistant professor of Department of Economics and Economic security of Lviv State University of Internal Affairs, Ukraine

Volodymyr Babenko

senior lecturer, Department of Mathematics,

Ivan Franko National University of Lviv, Ukraine

 

IMPACT OF BRIBERY ON THE PERFORMANCE OF INDUSTRIAL ENTERPRISES IN REGIONS OF UKRAINE

 

The efficiency of enterprises indicator displays gross domestic product, by region – GRP. Economic crimes have a significant impact on industrial activities, the number of which in Ukraine is quite high. Economic crimes should be considered in the division of such species as crimes against property (CAP), crimes in the sphere of economic activity (CSEA), crimes in the area of computers and computer networks (CCOM), crimes related to service activities (CSA), including bribery (bribe).

To examine the gross regional product of these factors a linear model was built with panel data with fixed effects [1].

Such a model can be written as

                                                                      (1)

Here  – value of the dependent variable for the i-th object in period t (i = 1..n, t = 1..T),  – value of k explanatory variables for the i-th object in period t, b – vector characterizing the marginal effects of independent variables on the dependent,  – constants that reflect the effect of factors specific to the i-th object,   – the remains of the model.

The evaluation of the coefficients b is performed from regression using data in the form of deviations from medium objects, and evaluation of constants – the formulas:

                                                           (2)

Regression analysis of the data as deviations from the average for the objects held in the package Statistica 8.0 [2]. The results of analysis are presented in Table 1.

Table 1

The results of calculating the coefficients b

 

Since statistically significant coefficients were only around CAP variables and CSA, the improved model kept only these variables. The results of calculations are presented in Table 2.

Table 2

The results of calculating the coefficients b

 

As can be seen from the Table 2, the characteristics of the model (coefficient of determination and Fisher statistics) have not changed. In addition, the coefficient of the variable CSA better reflects the impact of this type of crime including bribery.

Estimates of the constants are given in Table. 3.

 

 

 

Table 3

Estimates for the constants of the objects

Region

Estimated coefficient ai

Region

Estimated coefficient ai

Crimea

19844,9

Mykolaiv

19996,97

Vinnytsia

19981,54

Odesa

41197,07

Volyn

11356,46

Poltava

41443,46

Dnipropetrovsk

85022,01

Rivne

14207,94

Donetsk

123054,6

Sumy

10210,53

Zhytomyr

10557,92

Ternopil

3309,785

Zakarpattia

6349,074

Kharkiv

69024,99

Zaporizhzhia

36746,96

Kherson

12700,87

Ivano-Frankivsk

12891,28

Khmelnytsky

13077,51

Kyiv

37709,06

Cherkasy

17181,81

Kirovohrad

9827,656

Chernivtsi

-2445,92

Lugansk

38455,6

Chernihiv

14038,2

Lviv

32550,86

Kyiv

195251

 

Thus, the model can be represented as

                        GRP it = ai  – 55,95CAPit + 54,41CSAit                         (3)

The coefficient determination of model , Fisher statistics , and its level , which indicates a good adequacy of the resulting model.

Analysis of the model shows that crimes against property have a negative effect on the GRP and cause more than 25% of the variance (), and crimes in the area of performance contribute to the growth and determine GRP over 50% of the variance ().

 

References

1. Factor, discriminant and cluster analysis [Translation from English J. O. Kim, C. W. Muller, W. R. Klekk etc.; Edited by S. Enyukova] (1989), Finansy I Statistika, Moscow, 215 p.

2. Halafyan A. A. (2007) STATISTICA 6. Statistical analysis of the data. Third Edition, Binom-Press, Moscow, 512 p.