DATA INTEGRATION TO STUDY THE
ENTERPRISE GROUP STRATEGY
IN UKRAINE
Olga A. Vasechko
Scientific and Technical Complex for Statistical Research,
3, Shota Rustaveli Str., Kyiv, Ukraine
E-Mail : O.Vasechko@ukrstat.gov.ua
Michel Grun-Réhomme, Université Paris 2, ERMESUMR7181-CNRS,
12 Place du Panthéon, 75005 Paris, France
E-Mail : grun@u-paris2.fr
Plan
1.
2.
3.
4.
5.
The issue
Suggestions
Basic information and the retrospective
The models
The results for total population and nonoffshore
6. The findings
7. Conclusion
The issue (1)
1. Large businesses and enterprise groups (EG)
have a big impact on GDP formation and the
level of employment.
2. Recent theoretical and empirical researches
tend to look at multinational enterprises as the
main type of EG from the point of view of
economically advanced countries.
3. The
dynamically
developed
emerging
economies also face similar questions of EG.
The issue (2)
Basic issues of EG statistics in Ukraine are regarding to:
•
•
•
•
Definition of observation unit
Legislative base to provide the common framework of statistics of EG
Statistical register of EG
Sources of actual information
Statistics of Enterprise Groups in Ukraine, International Conference of
Globalization and Challenges for Official Statistics/ UNO-Eurostat-EFTA,
Kiev, Ukraine, 3-6 July 2007.
Administrative and Statistical Registers in Business Statistics of Ukraine, BalticNordic Conference on Survey, 2-7 June 2007, Kuusamo, Finland .
Quality Measurement of a Register for Structural Business Survey: Application
to Ukrainian Data / European Conference on Quality and Methodology in
Official Statistics. – Mainz, Germany, 2004.
•
Data integration and modeling
Suggestion (1)
In the case of data absent to study EG indirectly we can suggest
that:
•
The dominant pattern of EG is the Transnational Group, which is
multinational.
•
The reasons and consequences of multinationals' activities closely
connected to FDI.
•
FDI is defined as investment made to acquire lasting interest in
enterprises operating outside of the economy of the investor.
•
FDI relationship consists of a parent enterprise and a Ukraine
affiliate which together form a multinational.
•
To study multinational strategies we can use all types of statistics
regarding the main foreign investors that could explain the
motivation and tendencies of their policy.
Suggestion (2)
• Using available data we study actual
strategies of EG in Ukraine.
• We revise the question:
Why do the multinationals invest in
Ukraine and what are their strategies and
FDI patterns?
Basic information and the
retrospective (1)
Basic information and the retrospective (2)
The main vectors of impact on large business in Ukraine
Basic information and
the retrospective
FDI percentage by the
types of activities, 2006
35
30
32,6
26,5
25
20
14,2
15
9,7
10
8,5 7,6
7,0 7,4
5,4
7,0
8,2
6,7
4,9 4,6
5
3,9
4,4 3,8
France
Virgin
Islands,
Britannic
0,5
0
Germany
Cy prus
Austria
United
Kingdom
Netherlands
in the begin 2006
Education, health
Agriculture, forestry
care, public and
2.2%
individual services Rest
15.2%
1.4%
Real estate
9.3%
Financial activity
10.8%
Non-financial
services
21.7%
Industry
36.1%
Construction activity
3.3%
United
States
Russia
in the begin 2007
Basic information and the
retrospective (4)
FDI elasticity in Ukraine
with respect to import
3.5
3
2.5
2
1.5
1
0.5
0
with respect to FDI in industry
with respect to GDP per capita
with respect to FDI in trade activity
with respect to FDI in financial activity
3,5
3
2,5
2004
2005
2006
2
1,5
1
0,5
0
2002
2003
2004
2005
2006
with respect to import
with respect to GDP per capita
with respect to FDI in financial activity
with respect to credits and loans from direct investors
with respect to FDI in trade activity
with respect to FDI in industry
The models
Variables
A database contains 87 countries, which significantly invested to
Ukraine during the year 2006.
First, to explain FDI patterns into Ukraine we dispose of 10 exogenous
variables,
7 quantitative: export, import, volume of investment from Ukraine,
distance between the examined country and Ukraine, GDP of the
per capita proper country, ratio of VAT rate of country-investor to
Ukraine, ratio of corporate income tax rate.
3 binary variables: English-speaking countries, Russian-speaking
countries and countries that are offshore.
Second, to explain the patterns both of production-oriented and
distribution-oriented FDI
3 additional variables: consumer input export and import for subcontract processing
and to precise new tendency 1 binary variable: FDI in financing.
Finally, to precise directly the FDI strategies of developed economy
investors and to display indirectly the offshore strategy we have
examined non-offshore only.
The models
Data sources
• The data on investment and import into Ukraine, export
from Ukraine, offshore are collected by the State Statistics
Committee of Ukraine.
• GDP per capita we calculated using Ukrainian and World
Bank statistics.
• A tax ratio we took from KPMG's Corporate and Indirect Tax
Rate Survey 2007.
• A distance from Ukraine was made with Internet calculator
as the distance between the capitals.
The models
Types of models
•
The generalized log linear model
•
Qualitative model with logistic regression
•
Bivariate ordinal probit model
The models
The generalized log linear model
Log ( IDE )   1 Log (1  Exp)   2 Log (1  Import )   3 Log (1  Inv )   4 Log ( PIB)
  5 I ( Russe)   6 I ( Anglais)   7 I ( offshore)   8TVA   9Taxe   10 Log (Distance )
Results (1):
The generalized log linear model
Parameter
Estimate
t Value
Log(Distance)
-0.852
-2.97*
Log(1+ Investment)
0.22
3.64**
Log(GDP)
0.929
4.22**
Log(VAT)
-0.85
-1.79°
°significant at the level 10% , * at the level significant for 5%,
** significant at the level 1%.
• The transformation of VAT and tax variables in logarithm gives very
similar result.
• Model is robust.
• The FDI diminishes with the distance and the size of VAT, and grows
with the GDP and the investment from Ukraine.
The models
Logistic regression
Y
Volume of FDI, million
USD
Number of
countries
0
Less then 10
33
1
10-55
24
2
56-300
19
3
+ 300
12
Results (2):
Logistic regression
Parameter
Estimate
Wald
Intercept 1
1.5656
9.51**(1%)
Intercept 2
3.4494
29.90***(0.1%)
Importation
-0.000015
8.14**
Investment
-0.00017
8.21**
Distance
0.000185
8.89**
GDP
-1.3777
17.86***
Results (3):
Logistic regression
• Distance, investment and GDP: identical
to the linear model.
• Import growths with FDI.
• The tax variable is not already significant.
• 83.3% of concordant pairs.
• The tests (Wald, score and coefficient of
verisimilitude) are meaningful in the
threshold size p <0,0001.
The models
Bivariate ordinal probit model
• To define what characteristics of countries and import
are the most considerable in probability of more or less
meaningful FDI.
• Bivariate model, which estimates the import and FDI
simultaneously, allows checking endogeneity of import
to FDI.
• It gives robust results, with respect to estimation of
probabilities of FDI level.
• The FDI level can reflect the impact of unobserved
characteristics too.
The models
Bivariate ordinal probit model
Z *  1 X 1  
Y *  2 X 2   Z  e
Y corresponds to the variable of FDI with 4 modalities
Z designates the variable of import
 0 if import for the country is  50 000

Z i  1 if import is between 50000 and 500000

2 if import is  500000

Results (4):
Bivariate ordinal probit model
(equation of FDI)
Parameter
Estimate
t Value
Y. Z (import)
2.1001
10.63***
Y. Log (GDP)
0.4671
2.42*
Y. Tax
-0.5530
-1.99*
Y. Russian
-0.9024
-2.43*
Y. Offshore
1.4324
4.08***
Limit2.Y
0.8438
5.58***
Limit3.Y
1.6001
7.20***
Rho
-0.8557
-3.59***
Results (5):
• The variable of import is endogenous.
• The unobserved factors can also explain
FDI and import, for example:
- Public policy like any close ties between
EG and the government.
- Family controlled EG.
- Bilateral relations, etc.
Complementary results (1)
• We input 3 new variables:
- export of semifinished goods for sub-contracts
processing,
- import of semifinished goods for sub-contracts
processing,
- investment in financial activity (in % of FDI for each
country).
• The similar results are likely for the 10 variables (for
each type of model). The tax variable is not significant in
explaining FDI.
• The variables of export of semifinished goods for
processing and investment financial are significant in
explaining FDI.
Comparison of results,
2006
10 variables
13 variables
GDP per capita
4
Investment from Ukraine
2
Distance
0
Import
-2
Vat ratio
-4
Offshore
Tax ratio
Finance activity
English speaking
Russian speaking
Export of customer input
GDP per capita
4
2
Distance
Vat ratio
0
-2
Import of customer input
Import
FDI
-4
Tax ratio
Investment from Ukraine
Russian speaking
Import
English speaking
Offshore
Import
FDI
Subpopulations: offshore (23) and non-offshore
(64)countries-investors
8
6
l
n
y
4
2
0
0
5
10
limp
15
For offshore countries,
there are not any
explicative variables
from the 10 and 13
variables.
Only a variable of
finance is explicative
with the positive
meaning in the
threshold of 10%.
Comparison of results,
non-offshore, 2006
10 variables
13 variables
Finance activity
GDP per capita
3
Distance
2
Export of customer input
Investment from Ukraine
Import
2
1
Distance
Investment from Ukraine
Tax ratio
Russian speaking
Import
0
-1
English speaking
VAT ratio
English speaking
Tax ratio
Russian speaking
Import
0
Import of customer input
GDP per capita
3
VAT ratio
1
FDI
Import
FDI
Findings (1)
• The literature's benchmark distinction between horizontal and vertical FDI
does not capture the growing diversity of strategies the multinationals use,
particular, in emerging economy.
• There is evidence of two subpopulations of the countries-investors: offshore
and non-offshore. These two subpopulations behavior differently.
• The variable of GDP per capita looks not so much significant for FDI coming
in the Ukraine.
• The variable of distance is significant at all for FDI. But it arises with
negative sign for import.
• The VAT ratio explains weak FDI of non-offshore only. But it can explain the
EG’ strategy to invest through the import.
• Production-oriented FDI consists of two patterns. One of them locates
production in Ukraine because of low material and manual-labor cost (light
industry). Another one makes outsourcing in Ukraine because of skilledlabor cost are low here (IT industry).
Findings (2)
•
Offshore factor plays essential role as well as CIS countries’ EG prefer to
realize IT outsourcing through the offshore companies.
•
The variables of Russian and English speaking explain weakly EG strategy in
Ukraine.
•
There is evidence for relation between FDI, import and explicative variable of
the investment from Ukraine. This variable does not explain FDI directly, but
through the import. Obviously, it explains, fist of all, the FDI activity not foreign
but domestic EGs, who invest in other countries to import then in Ukraine.
•
The study did not confirm our suggestion about the production-oriented EGs
importing a semi-finished input to future process. Instead of to import, EGs
prefer to invest in order to export a semi-finished input to future process in the
parent country.
•
Beside evidence of vertical FDI, there is a new pattern mostly arising through
mixes of vertical and horizontal FDI (diversification) in Russian and Ukraine
EGs, what we refer to as diagonal FDI.
•
A diagonal FDI pattern suggests an agglomeration of quite different activities
with input of different types of production factors.
Horizontal and vertical integration in EG
NACE –
PRODCOM
DN
Manufacturing of furniture
chairs and
seats 36.11
NACE –
Technology
cycle
A 02
Logging and tree
felling
DD 20
Manufacturing of
wood and wood
products
DN 36
Manufacturing of
furniture
G 51, 52
Wholesale and retail
trade
office and
shop
furniture –
36.12
kitchen
furniture –
36.13
other
furniture –
36.14
Diagonal integration in EG
NACE
CB 13
Mining of
metals ores
DF 23
Coke oven
products
DJ 27 Basic
metals and
metal products
DK
Machinery and
equipment
DA 15.96
Manufacture of
beer
G 51.51
Wholesale of
solid, liquid and
gaseous fuels
H
Hotels and
restaurants
J 65, 66
Financial
intermediation
CB 13
Mining of
metals ores
DF 23
Coke oven
products
DJ 27 Basic
metals and
metal products
DK
Machinery and
equipment
DA 15.96
Manufacture of
beer
G 51.51
Wholesale of
solid, liquid
and gaseous
fuels
H
Hotels and
restaurants
J 65, 66
Financial
intermediation
System Capital Management Group Structure (SCM), the head of group is situated in
Donetsk.
SCM has a controlling interest over 90 enterprises, for which 160 000 employers work.
The key business of SCM includes the following
areas:
•
Mining and metallurgical activity
•
Power energy activity
•
Financial activity
•
Additional activities
The largest companies of SCM:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Metallurgical Company Azovstal
Еnakievо Metallurgical Works
Joint Enterprise LLC Metalen
Ferriera Valsider S.P.A. (Italy)
Khartsyzskiy Pipe Plant
OJSC Avdeevskiy Coke-Processing Works
Krasnodonugol
Northern Mining and Ore-Processing Plant
Central Mining and Ore-Processing Plant
Pavlogradugol
“Komsomolets Donbassa” Mine
Vostokenergo
Tekhrempostavka
Druzhkovskiy Machine Building Plant
Kaolin Production (3 enterprises)
•
•
•
•
Dongorbank
First Ukrainian International Bank
Ukrainian Joint Stock Insurance
Company ASKA
Ukrainian Joint Stock Insurance
Company ASKA – LIFE
•
•
•
Mobile Operator Life: LLC Astelit
Optima Telecom
Fixed-Communications operator PJSC FarlepInvest
•
•
•
Druzhkovka Ore Management - refractory and
fire-clays
Vesco - refractory and fire-clays
Gefest – chain of gas stations
•
Brewing Group “Sarmat”
•
“Donbass Palace” Hotel
•
•
Publishing Group “Segodnya”
TV and Radio Company “Ukraine”
•
Football Club “Shakhter” (Donetsk)
Findings (3)
• In order to serve the financial flows there is always the banking
structure in the centre of such group.
• The last explicative variable of finance activity is very
significant for FDI of all population and for two subpopulations
• Ukraine is very attractive for financial activity as financial
sector was not developed before.
• Financial and banking enterprises arise due either horizontal
FDI from developed economies or diagonal FDI (diversification)
of Ukrainian and especially Russian groups.
• Thanks to them, FDI is the source of different credits for
businesses and consumers. The consumer credit system is
quickly developing in trade and real estate.
The control of the kaolin production in the group System
Capital Management, 2007
Offshore
The head of
the group
JSC SCM
UMG Limited
JSC Ogneupornerud,
100%
Sub - group 1,
100%
JSC Druzhkovsk,
90.6%
JSC Vesko,
93.9%
Ukraine
SCM
Limited
Sub - group 2,
100%
Conclusion
•
Indirect sources can be useful auxiliary information to study EG as a complex
phenomenon. It needs to define the unit of observation.
•
The empirical findings acknowledge that despite Ukraine is very weak in
business and investment freedom it becomes more and more attractive for FDI.
•
Obtained results suggest a marked difference in the FDI patterns and their
evolution. Multinationals act in Ukraine as production-oriented and
distribution-oriented FDI with growing variety.
•
These differences may be a source of both positive and adverse impacts on the
Ukraine economy.
•
Lastly, we found evidence of diversified business of domestic EG and coming
from emerging countries with a financial intermediary in the group's core.
•
We found statistically significant coefficients for some variables for their
impact power on FDI strategies.
•
FDI patterns in Ukraine depend on both endogenous and exogenous factors.
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Thank you