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Social Times of Network Spaces David Stark and Balazs Vedres.

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Presentation on theme: "Social Times of Network Spaces David Stark and Balazs Vedres."— Presentation transcript:

1 Social Times of Network Spaces David Stark and Balazs Vedres

2 to model, from its inception, network formation across an entire epoch of economic transformation Processes of network evolution

3 Embeddedness of foreign capital? analytic move from how a national economy is integrated into the world economy to whether and how FDI is integrated into local networks

4 Methodological innovation We modify analytic tools from DNA sequencing to reconcile the structural focus of social network analysis with the temporal orientation of historical sociology Structure as topology and temporality

5 Emergence of domestic networks Massive decline of state ownership Extraordinary institutional uncertainty Ambiguity about the rules of the game

6 Foreign investment Did massive FDI eradicate networks? Which forms were open or closed to FDI? Do foreigners build domestic networks?

7 Our question Can networks of global reach coexist and entwine with those of local embeddedness? Restated, can FDI be integrated into national networks? And, if so, how?

8 Data Largest 1,800 firms of the period by revenue between Ownership data from registry courts names of top 25 owners and their shares all changes recorded for the whole life of the firm A tie is a direct ownership stake by one of our 1,800 firms in one of the other firms in that same population (i.e., not an affiliation network)

9 The network movie Animation of network emergence

10 Month 1December, 1987

11 Month 2January, 1988

12 Month 3February, 1988

13 Month 4March, 1988

14 Month 5April, 1988

15 Month 6May, 1988

16 Month 7June, 1988

17 Month 8July, 1988

18 Month 9August, 1988

19 Month 10September, 1988

20 Month 11October, 1988

21 Month 12November, 1988

22 Month 13December, 1988

23 Month 14January, 1989

24 Month 15February, 1989

25 Month 16March, 1989

26 Month 17April, 1989

27 Month 18May, 1989

28 Month 19June, 1989

29 Month 20July, 1989

30 Month 21August, 1989

31 Month 22September, 1989

32 Month 23October, 1989

33 Month 24November, 1989

34 Month 25December, 1989

35 Month 26January, 1990

36 [Continues to 169th month ]

37 For historical network analysis from a kind of aerial sociology to the network histories of 1,800 firms.

38 To move from system-level properties to historical processes at the level of firms...

39 For historical network analysis Network analysis: topology Historical analysis: temporality Synthesis: find structures in social space and social times Methodological innovation: Sequence analysis of network positions to identify pathways through local network topologies From time as a variable to time as variable

40 Probe for differences in types of embeddedness Different local network topographic properties reflect different organizing practices Firms can use network properties, for example, to hide assets, to restructure assets, to gain access to knowledge, to increase legitimation, to secure access to supplies and markets, and so on

41 Structure as topology and temporality Studying variation in the sequences of local structures is a way to identify distinctive pathways of network evolution

42 1990



45 7. Member of a strongly cohesive group 6. Member of a cohesive group 5. Star center 4. Large star periphery 3. Small star periphery 2. Dyad component member 1. Isolate GraphColorName



48 From 1,696 firm histories we need to find similar sequences. We use optimal matching analysis to find the distance of each sequence from all others. Finding sequential equivalence

49 To the resulting matrix we then apply hierarchical clustering that groups sequences so that within-cluster distances are as low as possible and between-cluster distances are high.

50 The combination of these two algorithms, yields – not unlike the concept of structural equivalence in network analysis – sequential equivalence.





55 Sizeable foreign ownership in 2001 (Yes = 1) model 1 model 2 Star-periphery recombinants 1 (I-S) **-5.781** 2 (P) -.422**-.785** Cohesive recombinants 3 (I-P-C-P) -.065**.622** 4 (C-G-C).485**1.112** 5 (C-G-I) 1.327**2.047** 6 (I-L-C-G) **-1.341** Startups 7 (P-I) 1.565**2.087** 8 (D-I).342**1.076** 9 (P-D) 1.419**2.756** Second wave networks 10 (I-D-P) 1.218**1.752** 11 (D-P) 1.184**1.717** Foreign ownership in 2001: Logistic regression estimates Independent variables Pathways

56 Industry Agriculture ** Food 2.779** Energy and mining.996** Chemical 4.756** Heavy industry 1.768** Light industry and textile.378** Construction -.517** Wholesale.391** Retail 3.695** Finance.359** Local network position in 2001 D (dyad) -.720** P (small star periphery) -.097** L (large star periphery) 1.892** S (star center).140** C (cohesive cluster) -.039** G (strongly cohesive group) ** Early foreign ownership (1990) 4.326** Constant.205**-.935** N … LL … …. R-squared Percentage correctly classified 66.7… …... χ 2 (df) (11) (28) p-value.000…

57 Hungary is not a segregated dual economy Globalization is compatible with local embeddings Forms of recombinant property are robust Cohesive forms are adaptive

58 An internationalized market economy emerged in Hungary not despite but, instead, because of inter- organizational ownership networks.

59 Developing economies do not necessarily face a forced choice between networks of global reach and those of local embeddedness. High levels of foreign investment can be integrated into processes of inter- organizational ownership network formation in a developing economy.

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