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Dynamic Network Signatures of Labor Flows: Evidence from a Large Business Social Network
Takanori Nishida
(Sansan Inc., Tokyo, Japan)
Lav R. Varshney
(University of Illinois at Urbana-Champaign)
Yoshiki Ishikawa
(Habitech, Inc., Tokyo, Japan)
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Motivation
( )(
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Strength of Weak Ties Hypothesis
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Bird’s-Eye View
Static
Dynamic
Online Offline (Face to Face)
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Data Visualization ~Dawn of Innovation~
0 1 2 0 2
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Data
Eight
#'*/2015,1"2017,12"
)3 /%&.
&.Eight21 0!(
+-$1
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User Profile
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Longitudinal Dataset (Unbalanced Panel Data)
1st wave
2nd wave
3rd wave
Jul 2015 Jan 2016 Jul 2016
Jan 2017
Jul 2017
6 months 6 months
T
T-1
T-2
T
T-1
T-2
6 months
T
T-1
T-2
6 months
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Empirical Definition of Weak ties and Strong Ties
T-2 T-1 T
=Δ Clustering coefficient
= # of New Bridging Ties
Ties in same community
Bridging Ties
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Empirical Model
̈
"#$
= &' ̈
(#$)* /$),
+ ̈
.#$
/ℎ121 ̈
345
= 345
− 7
34 (within transformation),8
34
= 1/: ∑4<=
> 345
W W
W - -
W W 1 2T
: ; ( ) W
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Results
) *-
10
.) *-
T-1 T
T-2
(
R-squared:0.114
Num. obs.: 417,428
- Standard errors in parentheses
- ***p < 0.001, **p < 0.01, *p < 0.05
) ( ( 0 -
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Discussion
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