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Social Times of Network Spaces

This study examines the processes of network evolution and the integration of foreign direct investment (FDI) into local networks during a period of economic transformation. Using methodological innovation and analyzing data on 1,800 firms from 1987-2001, the study explores the coexistence and entwinement of global and local networks. Sequential equivalence analysis is used to identify distinctive pathways of network evolution and FDI integration.

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Social Times of Network Spaces

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  1. Social Times of Network Spaces David Stark and Balazs Vedres

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

  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 1987-2001 • 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 1 December, 1987

  11. Month 2 January, 1988

  12. Month 3 February, 1988

  13. Month 4 March, 1988

  14. Month 5 April, 1988

  15. Month 6 May, 1988

  16. Month 7 June, 1988

  17. Month 8 July, 1988

  18. Month 9 August, 1988

  19. Month 10 September, 1988

  20. Month 11 October, 1988

  21. Month 12 November, 1988

  22. Month 13 December, 1988

  23. Month 14 January, 1989

  24. Month 15 February, 1989

  25. Month 16 March, 1989

  26. Month 17 April, 1989

  27. Month 18 May, 1989

  28. Month 19 June, 1989

  29. Month 20 July, 1989

  30. Month 21 August, 1989

  31. Month 22 September, 1989

  32. Month 23 October, 1989

  33. Month 24 November, 1989

  34. Month 25 December, 1989

  35. Month 26 January, 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 avariable 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

  43. 1989 1990 1991

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

  45. Finding sequential equivalence 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.

  46. 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.

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

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