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Market as a Multilevel System

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Multilevel Network Analysis for the Social Sciences

Part of the book series: Methodos Series ((METH,volume 12))

Abstract

Economic sociology has established the interdependencies between economic and social structures using the notion of embeddedness of the former in the latter. However research usually studies inter-organizational commercial networks and inter-individual informal networks separately. In this article we use a multilevel framework to jointly analyze economic networks between firms and informal networks between their members in order to reframe this embeddedness hypothesis. Based on a network study of a trade fair for television programs in Eastern Europe we show that while each level has its own specific processes, they are partly nested. Beyond this result, we observe that these levels of agency emerge in different contexts and that they are diachronically related. To conclude, we show that in order to understand performance in a market one needs to look at this dual positioning of individuals and organizations.

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Notes

  1. 1.

    Parallel to the local buzz of permanent clusters (Storper and Venables 2004), temporary clusters generate global buzz if the event combines the following conditions: explicit co-participation to maximize face-to-face interactions; possibilities for observation; existence of practice and epistemic communities from different parts of the world; dense and multiplex socio-economic relationships (Bathelt and Schuldt 2010).

  2. 2.

    A TV program that is written on paper but has not yet been actually produced.

  3. 3.

    A team of eight persons (four sociologists and four hostesses) collected for each individual their information exchange network and the contract network of their organization through face-to-face interviews (20 min on average) during the trade fair. In order to improve the response rate after the event, we also tried to reach attendees by fax, phone, mail, email and internet.

  4. 4.

    MIPTV is the most important TV program trade fair in the world. It will be described more precisely in the next section.

  5. 5.

    ERGM models presented here are estimated with PNet (Wang et al. 2006).

  6. 6.

    The meeting network was extracted from the trade fair organizer’s meeting platform.

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Author information

Authors and Affiliations

  1. Swinburne University of Technology, Center for Transformative Innovation, Melbourne, Australia

    Julien Brailly

  2. University of Toulouse Jean-Jaurès, LISST-Cers CNRS, Toulouse, France

    Guillaume Favre

  3. Université Paris-Dauphine, IRISSO-CNRS, PSL, Paris, France

    Josiane Chatellet

  4. CSO-CNRS, Institut d’Etudes Politiques de Paris, SPC, 19 rue Amélie, 75007, Paris, France

    Emmanuel Lazega

Authors
  1. Julien Brailly
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  2. Guillaume Favre
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  3. Josiane Chatellet
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  4. Emmanuel Lazega
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Corresponding author

Correspondence to Julien Brailly .

Editor information

Editors and Affiliations

  1. CSO-CNRS, Institut d’Etudes Politiques de Paris, SPC, Paris, France

    Emmanuel Lazega

  2. Nuffield College, University of Oxford, Oxford, United Kingdom

    Tom A.B. Snijders

Appendixes

Appendixes

Appendix 1: Configuration Visualization for the Interorganizational Network

PNet name

Configuration visualisation

Arc

Alternating k-star

Alternating two-path

Appendix 2: Goodness of Fit for the Interorganizational Level

PNet name

Observed

Mean

Standard deviation

t-ratio

Edge

347

348.91

34.89

−0.06

2-star

3307

3195.09

566.36

0.20

3-star

13,590

12,677.50

3892.20

0.23

4-star

48,435

48,923.55

29,084.34

−0.02

5-star

146,009

190,931.50

208,698.59

−0.22

triangles

118

118.08

30.11

0.00

4-clique

8

5.98

4.54

0.44

5-clique

0

0.06

0.27

−0.20

6-clique

0

0.00

0.01

−0.01

7-clique

0

0.00

0.00

NA

Isolates

10

9.11

3.34

0.27

Triangle2

291

249.56

118.68

0.35

Bow_tie

1151

1317.59

803.30

−0.21

3Path

30,582

28,220.36

7156.81

0.33

4Cycle

1202

794.43

255.31

1.60

AS(2.00)

1021.602

1028.65

126.43

−0.06

AS(2.00)

1021.602

1028.65

126.43

−0.06

AT(2.00)

252.262

259.55

53.80

−0.14

AT(2.00)

252.262

259.55

53.80

−0.14

A2P(4.00)

2808.958

2841.31

463.98

−0.07

AC(2.00)

8

5.96

4.49

0.46

AET(2.00)

688

690.82

179.33

−0.02

Std Dev degree dist

5.172

4.87

0.39

0.77

Skew degree dist

1.243

1.22

0.49

0.05

Global Clustering

0.107

0.11

0.01

−0.21

Mean Local Clustering

0.108

0.10

0.02

0.21

Variance Local Clustering

0.02

0.02

0.01

0.17

Appendix 3: Configuration Visualization for the Interindividual Network

PNet name

Configuration visualisation

Edge

Reciprocity

Alternating k-in-star

Alternating k-out-star

Alternating transitive k-triangles

Alternating transitive k-two-paths

Alternating down and up k-triangles

Alternating down and up k-two-paths

Appendix 4

Table 10.3 ERGM model for the inter-individual network with coparticipation effects specified by economic category
Full size table

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Brailly, J., Favre, G., Chatellet, J., Lazega, E. (2016). Market as a Multilevel System. In: Lazega, E., Snijders, T. (eds) Multilevel Network Analysis for the Social Sciences. Methodos Series, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-24520-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-24520-1_10

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