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Balázs Vedres:
The Dissolution of Ownership Networks and the Formation of a Strict Corporate Governance System

Preliminary summary with figures

 

The research project covered the mapping of the ownership structures and networks of the top 500 Hungarian firms (according to 97 revenue) in spring/summer of 1999. These figures are from the paper accepted for publication in the Hungarian Review of Economics. The complete translation is to be finished in a month.

Hypotheses of a post socialist property system:

1. State property is still dominant
2. The property structure is fragmented
3. Besides state property the second most important is the cross ownership between domestic firms
4. Cross ownership is organized in dense knit networks
5. Cross ownership represents indirect state ownership
6. Belonging to the cross ownership network increases efficiency and growth
7. Most companies have diversified portfolios (more types of owners)
8. Having more types of owners increases efficiency and growth

 

 

Figure 1: The distribution of ownership shares in the Hungarian top 500, 1999.

 

 

Figure 2: Concentration of ownership

 

 

 

Population

Average of the largest share

Median of the largest share

Austria

50 firms on stock exchange

54,1

52,0

Belgium

150 firms on stock exchange

55,8

55,5

France

40 firms on stock exchange

29,4

20,0

Germany

347 firms on stock exchange

n.a.

52,1

Italy

216 firms on stock exchange

51,9

54,5

Holland

137 firms on stock exchange

42,8

43,5

Spain

193 firms on stock exchange

40,1

34,2

UK

250 firms on stock exchange

15,2

10,9

USA

1309 firms (NYSE)

< 5

< 5

Czech Republic

706 privatized firms

68,4*

67,2*

Hungary

411 firms (top 500)

74,2

85,0

Hungary

27 firms on stock exchange

53,4

50,9

* The sum of the five largest owners

  • The source of western data: Crama, Y. – Leruth, L. – Renneboog, L. – Urbain, J. P. (2000): Corporate Governance Structures, Control and Performance in European Markets: A Tale of Two Systems. Manuscript

    The source of Czech data: Claessens, Stijn – Djankov, Simeon (1999): Ownership concentration and Corporate Performance in the Czech Republic. Journal of comparative Economics, 1999/27.

  • Figure 3: Concentration in international comparison

     

     

     

    Number of appearance

    Capital in hand (M Ft.) (%)

    Revenues in hand (M Ft) (%)

    Average share (standard dev)

    Number of majority positions (%)

    Local Government

    318

    288 939 (20,28)

    283 788 (4,88)

    3,93 (14,28)

    9 (2% of 318)

    State

    74

    134 200 (9,42)

    780 788 (13,41)

    45,71 (41,37)

    34 (46% of 74)

    Hungarian nonfinancial firm

    578

    372 627 (26,15)

    1 088 673 (18,70)

    18,53 (29,90)

    73 (13% of 578)

    Hungarian financial

    120

    29 207 (2,05)

    139 501 (2,40)

    11,35 (21,38)

    6 (5% of 120)

    Hungarian private person

    971

    30 648 (2,15)

    295 242 (5,07)

    5,46 (13,41)

    22 (2% of 971)

    Foreign nonfinancial firm

    260

    495 467 (34,77)

    2 775 520 (47,68)

    59,94 (39,24)

    147 (57% of 260)

    Foreign financial

    90

    71 841 (5,04)

    440 846 (7,57)

    14,09 (23,56)

    6 (7% of 90)

    Foreign private person

    27

    2 067 (0,14)

    16 778 (0,29)

    8,35 (14,45)

    0 (0% of 27)

    sum

    2438

    1 424 993 (=100%)

    5 821 137 (=100%)

    16,13 (29,56)

    297 (12% of 2438)

     

     

    Number of 90%+ positions (%)

    Number of 100% positions (%)

    Average number of appearances at the same firm, if any

    Number of firms with at least one such owner

    Number of firms with such owner(s) in majority position

    Local Government

    4 (1%)

    4 (1%)

    4,18 (4,82)

    70 (17%)

    14 (3%)

    State

    19 (26%)

    12 (16%)

    1,17 (0,64)

    61 (15%)

    34 (8%)

    Hungarian nonfinancial firm

    45 (8%)

    25 (4%)

    2,98 (4,47)

    185 (45%)

    98 (24%)

    Hungarian financial

    4 (3%)

    3 (2%)

    2,20 (3,62)

    51 (12%)

    10 (2%)

    Hungarian private person

    5 (0,5%)

    3 (0,3%)

    5,77 (6,96)

    158 (38%)

    49 (12%)

    Foreign nonfinancial firm

    103 (40%)

    78 (30%)

    1,33 (0,78)

    196 (48%)

    156 (38%)

    Foreign financial

    3 (3%)

    2 (2%)

    1,98 (2,06)

    44 (11%)

    9 (2%)

    Foreign private person

    0 (0%)

    0 (0%)

    1,60 (0,58)

    19 (5%)

    1 (0,2%)

    sum

    183 (8%, 2438=100%)

    127 (5%, 2438=100%)

    5,92 (6,98)

    411 (100%)

    371 (90%, 411=100%)

    Figure 4: The features of the ownership position of major types of owners

     

     

     

    Local Government

    State

    Hungarian nonfinancial firm Hungarian financial Hungarian private person Foreign nonfinancial firm Foreign financial Foreign private person
    Local Government

    1,000

    -,033

    -,051

    -,031

    -,086

    -,145**

    -,028

    -,025

    State

    -,033

    1,000

    -,182**

    -,033

    -,102*

    -,263**

    -,052

    -,040

    Hungarian nonfinancial firm

    -,051

    -,182**

    1,000

    -,080

    -,195**

    -,461**

    -,084

    ,022

    Hungarian financial

    -,031

    -,033

    -,080

    1,000

    -,048

    -,172**

    ,050

    -,028

    Hungarian private person

    -,086

    -,102*

    -,195**

    -,048

    1,000

    -,317**

    -,076

    ,008

    Foreign nonfinancial firm

    -,145**

    -,263**

    -,461**

    -,172**

    -,317**

    1,000

    -,152**

    -,065

    Foreign financial

    -,028

    -,052

    -,084

    ,050

    -,076

    -,152**

    1,000

    -,015

    Foreign private person

    -,025

    -,040

    ,022

    -,028

    ,008

    -,065

    -,015

    1,000

    ** p< 0.01

    * p< 0.05

    Figure 5: Correlation between the ownership positions of the major types of owners at the same firm

     

     

     

    Cluster 1: Foreign dominance

    Cluster 2: Domestic dominance

    Cluster 3: Domestic private persons

    Cluster 4: State and financials

    Cluster 6:Local gov. and foreign financials

    Local Government

    0,22

    0,53

    0,38

    1,59

    43,64

    State

    0,47

    1,72

    0,72

    66,59

    1,26

    Hungarian nonfinancial firm

    7,05

    87,42

    8,13

    1,90

    12,34

    Hungarian financial

    0,53

    2,35

    1,26

    20,76

    0,42

    Hungarian private person

    0,65

    2,29

    78,78

    2,90

    1,17

    Foreign nonfinancial firm

    88,21

    0,36

    5,12

    0,00

    5,74

    Foreign financial

    0,57

    1,59

    0,00

    2,83

    30,38

    Number of firms (%)

    168 (44%)

    90 (23%)

    57 (15%)

    45 (12%)

    24 (6%)

    Figure 6: Clusters of ownership structures according to type of owner

     

     

    Group

    Number of firms

    %

    No network connection

    278

    67,6

    1. Bábolna-groupt

    9

    2,2

    2. Financial group

    19

    4,6

    3. Dunaferr-group

    8

    1,9

    4. MOL-group

    9

    2,2

    5. MFB-group

    5

    1,2

    6. Wheat group

    13

    3,2

    Groups total

    63

    15,3

    In dyads or triads

    70

    17,0

    Total

    411

    100,0

    Figure 7: The importance of ownership networks: firms according to ownership network connections (owning or beeing owned by another top 500 firm)

     

     

     

    Only owner 68 cég

    Both 10 cég

    Only owned 74 cég

    Only owner 68 cég

    -

    11 (14,6%)

    108 (29,7%)

    Both 10 cég

    -

    5 (33,4%)

    14 (25,3%)

    Only owned 74 cég

    -

    -

    -

    Figure 8: The network roles: the roles in the network are clear: a firm is eighter an owner or an affiliate, an owned unit. The level of ambiguity is low.

     

     

    1. Bábolna-group of agrarian firms

    2. Financial group of banks with joint projects as firms

    3. Metal-group (the Heavy Metal group in Stark's recombinant property paper)

    4. MOL-group around the giant oil company

    5. MFB-group around the state development bank

    6. Wheat group with mills and wheat trading houses

    Figure 9: Groups beyond the triad in the ownership network: mostly out of the recombinet shape.

     

     

     

    Models of efficiency (revenue/employee)

    model

    1

    2

    3

    4

    5

    6

    7

    R square

    ,120**

    ,146**

    ,149**

    ,169**

    ,316**

    ,372**

    ,383**

    R square change

     

    ,026**

    ,003

    ,020**

    ,146**

    ,056**

    ,011**

    Constant

    2,233**

    2,389**

    2,312**

    ,781

    ,224

    ,633

    1,128*

    Cluster of foreign firm owner dominance

    ,853**

    ,816**

    ,803**

    ,777**

    ,830**

    ,775**

    ,719**

    Cluster of domestic firm owner dominance

    ,286*

    ,294*

    ,292*

    ,313*

    ,337**

    ,289*

    ,257*

    Cluster of domestic private person owner dom.

    ,721**

    ,715**

    ,723**

    ,815**

    ,724**

    ,643**

    ,566**

    Member of a network group beyond triad

     

    ,196

    ,225

    ,193

    ,204

    ,258*

    ,212

    Diversified owner portfolio (more than one type)

     

    -,328**

    -,326**

    -,372**

    -,293**

    -,327**

    -,275**

    Few owners, quite equal shares

       

    ,159

    ,198

    ,211

    ,215*

    ,170

    Few owners, concentrated

       

    ,079

    ,104

    ,014

    -,079

    -,126

    Revenue+

         

    ,171**

    ,197**

    ,144**

    ,143**

    Agrarian, food industry

           

    ,154

    ,217

    ,219

    Wood, textile, light industry

           

    -,069

    -,167

    -,158

    Retail, wholesale trade

           

    1,021**

    ,858**

    ,843**

    Service, transport, telecom, finance (not bank)

           

    ,428**

    ,131

    ,068

    Budapest (registered in)

             

    ,554**

    ,581**

    1997 number of employees per 1994 number

               

    -,442**

    N=297
    †Dependent: the natural logarithm of the 1997 net revenues per number of employees.
    +Natural log.
    *: p less than 0,10
    **: p less than 0,05

    Figure 10: Linear regression models of efficiency.

     

     

     

     

    Models of growth†

    model

    1

    2

    3

    4

    5

    6

    7

    R square

    ,067**

    ,089**

    ,094**

    ,107**

    ,178**

    ,182**

    ,263**

    R square change

     

    ,022*

    ,004

    ,013*

    ,072**

    ,004

    ,081**

    Constant

    1,036**

    1,094**

    1,096**

    ,633**

    ,818**

    ,767**

    ,681**

    Cluster of foreign firm owner dominance

    ,148**

    ,135**

    ,129**

    ,122*

    ,166**

    ,176**

    ,069

    Cluster of domestic firm owner dominance

    -,067

    -,060

    -,064

    -,060

    ,009

    ,015

    -,012

    Cluster of domestic private person owner dom.

    ,184**

    ,182**

    ,174**

    ,202**

    ,264**

    ,274**

    ,192**

    Member of a network group beyond triad

     

    ,069

    ,070

    ,056

    ,070

    ,067

    ,054

    Diversified owner portfolio (more than one type)

     

    -,118**

    -,137**

    -,144**

    -,165**

    -,163**

    -,122**

    Few owners, quite equal shares

       

    ,059

    ,066

    ,032

    ,034

    -,003

    Few owners, concentrated

       

    -,006

    -,003

    -,009

    ,000

    ,025

    Revenue+

         

    ,051*

    ,035

    ,041

    ,018

    Agrarian, food industry

           

    -,064

    -,070

    -,108*

    Wood, textile, light industry

           

    -,124

    -,114

    -,094

    Retail, wholesale trade

           

    -,284**

    -,261**

    -,383**

    Service, transport, telecom, finance (not bank)

           

    ,058

    ,089

    ,095

    Budapest (registered in)

             

    -,058

    -,130**

    Efficiency (revenue/employee)++

               

    ,144**

    N=242
    † Dependent: the 1997 net revenues per 1994 net revenues (both in 1990 prices): real growth.
    + Natural log.
    ++ The natural logarithm of the 1997 net revenues per number of employees.
    *: p less than 0,10
    **: p less than 0,05

    Figure 11: Linear regression