Accesso libero

A Longitudinal Network Analysis of the German Knowledge Economy from 2009 to 2019: Spatio-Temporal Dynamics at the City–Firm Nexus

Silke Zöllner

Silke Zöllner

Lucerne University of Applied Sciences and ArtsLuzern, Switzerland
Cerca questo autore su
Sciendo|Google Scholar
Zöllner, Silke
, 
Stefan Lüthi

Stefan Lüthi

Lucerne University of Applied Sciences and ArtsLuzern, Switzerland
Cerca questo autore su
Sciendo|Google Scholar
Lüthi, Stefan
 e 
Alain Thierstein

Alain Thierstein

Technical University of MunichMünchen, Germany
Cerca questo autore su
Sciendo|Google Scholar
Thierstein, Alain
  
25 nov 2020
A Longitudinal Network Analysis of the German Knowledge Economy from 2009 to 2019: Spatio-Temporal Dynamics at the City–Firm Nexus's Cover Image
INFORMAZIONI SU QUESTO ARTICOLO
Articolo precedente
Articolo Successivo
Cita
Scarica la copertina
Categoria dell'articolo: research-article
Pubblicato online: 25 nov 2020
Pagine: 107 - 133
DOI: https://doi.org/10.21307/joss-2020-005
Parole chiave
© 2019 Silke Zöllner; published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Schematic representation of our two-mode network.
Schematic representation of our two-mode network.

Figure 2.

Overview of hypotheses
Overview of hypotheses

Figure 3.

Change in indegree distribution for the German FUAs.
Change in indegree distribution for the German FUAs.

j_joss-2020-005_tab_005

Null model Model 1 Model 2 Full model
Estimate Standard error Convergence t-ratio Estimate Standard error Convergence t-ratio Estimate Standard error Convergence t-ratio Estimate Standard error Convergence t-ratio
Density −1.0048 (0.0398) 0.0435 −4.4373 (0.1566) −0.0639 −5.4984 (0.3784) −0.0069 −3.6868 (0.2869) 0.0304
Preferential attachment 0.4466 (0.0155) −0.0730 0.4583 (0.0377) 0.0122 0.4078 (0.0560) 0.0209
Spatial proximity 0.0151 (0.0016) −0.0308 0.0076 (0.0019) −0.0746 0.0070 (0.0021) 0.0162
Location expansion 0.2874 (0.0504) −0.0303 1.0295 (0.1279) 0.0286
Network closure 0.0007 (0.0006) −0.0152 0.0132 (0.0013) 0.0354
FUA employment 0.8598 (0.1261) 0.0154
Assortativity −0.3041 (0.0391) 0.0450
Overall max. convergence ratio 0.0435 0.0754 0.1570 0.2342

Changes in network 2009–2019_

2009 2019 Change
Network density 0.086 0.099 □
Number of ties 2227 2559 640 new ties308 deleted ties1919 maintained ties □
Jaccard index 0.669

Modeling results_

Null model Full model
Estimate Standard error Convergence t-ratio Estimate Standard error Convergence t-ratio
Density −1.0048 (0.0398) 0.0435 −3.6868 (0.2869) 0.0304
Preferential attachment 0.4078 (0.0560) 0.0209
Spatial proximity 0.0070 (0.0021) 0.0162
Employment 0.8598 (0.1261) 0.0154
Location expansion 1.0295 (0.1279) 0.0286
Network closure 0.0132 (0.0013) 0.0354
Assortativity −0.3041 (0.0391) 0.0450
Overall max. convergence ratio 0.0435 0.2342

Translation of effects to be tested into RSiena_

Hypothesis Effect RSiena name ForFormula
Density effect s i ( x ) = x i + x i + − j x i j s: effect; i: focal actor x i+: outgoing tie from i x ij =1 indicates presence of a tie from i to j x ij =0 indicates absence of a tie from i to j
1 Firm activity Outdegree-related activity (sqrt) effect s i ( x ) = x i + 1.5 = x i + x i +
2 Preferential attachment Indegree-related popularity (sqrt) effect s i ( x ) = − j x i j x + j = − j x i j − h x h j Sum of the square roots of the indegrees of the others to whom i is tied
3 Firm-FUA assortativity Out/in degree^(1/2) assortativity s i ( x ) = − j x i j x i + 1 / 2 x j + 1 / 2 Differential tendency for actors with high outdegrees to be tied to actors with high indegrees
4 Network closure Number of four-cycles s i ( x ) = 1 4 − j , k , h all different x i j x i k x h j x h k
5 Spatial spread XW → X closure of covariate X: network in the role of the dependent variable W: effects that determine the dynamics of network X (here: geographic distance) s i ( x ) = − j − h x i j x i h w h j
6 (FUA employment) Covariate-related popularity s i ( x ) = − j x i j v j Sum of the covariate over all actors to whom i has a tie

Descriptive and modeling results for the three sectors_

Financial sectors Service sectors Creative sectors
Sub-sectors Banking and finance, insurance Law, accounting, management/IT consulting Information and communication services, Design, architecture and engineering, advertising and media
#x03A3; 35 firms 56 firms 36 firms
Jaccard index 0.750 0.723 0.518
Preferential attachment −0.01 0.06 0.01
FUA employment 0.22 0.60 0.88
Location expansion – 0.04 0.04
Assortativity – −0.03 –

j_joss-2020-005_tab_006

Banking and finance
Deutsche Bank HSH Nordbank AG
Commerzbank AG Kreissparkasse Köln
DZ Bank AG Deutsche Postbank AG
Landesbank Baden-Württemberg DekaBank Deutsche Girozentrale
BayernLB Berliner Volksbank
Hamburger Sparkasse AG Gruppe Deutsche Börse
Landesbank Hessen-Thüringen ING-DiBa AG
Sparkasse Köln Bonn Aareal Bank AG
NordLB
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze sociali, Scienze sociali, altre
Feed RSS della rivista