Dynamic network signatures of Labor Flows

Dynamic network signatures of Labor Flows

NetSci 2019

Dynamic network signatures of Labor Flows

DSOC R&Dグループ 西田貴紀

▼Sansan Builders Box



May 31, 2019


  1. 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)
  2. Strength of Weak Ties Hypothesis Weak Ties Strong Ties Results

    are mixed!
  3. Bird’s-Eye View Static Dynamic Online Offline (Face to Face) Previous

    Studies Facebook LinkedIn Our Research
  4. Data ▶ The longitudinal dataset includes anonymous user’s profile and

    daily trajectories among over 1.5 million individuals over 3 years (from 2015 to 2017) Data that is anonymized within the permission scope of the "Eight Service Terms of Use" is analysed only statistically. The result of this research will be used for improving the service.
  5. Data on job change Current Job Previous Jobs

  6. Empirical Definition of Weak ties and Strong Ties Job Change

    Strong Ties T-1 T T-2 = Number of New Bridging Ties =Δ Clustering coefficient Weak Ties Bridging Ties Ties in same community
  7. Empirical Model ▶ We estimate the following Fixed effects model

    ̈ "#$ = &' ̈ (#$)* /$), + ̈ .#$ /ℎ121 ̈ "#$ = "#$ − 4 "# (within transformation),5 "# = 1/7 ∑#9* : "#$ Fixed effects model ▶ Using Variables ▶ Dependent Variable: Dummy variable for job change (T) ▶ Explanatory Variables: Weak ties and Strong Ties (T-1) ▶ Control Variables: Dummy variable for job change (T-1); Number of newly formed inner community ties (T-1); degree (T-2); clustering coefficients (T-1); and time fixed effects
  8. Results Weak Ties Job Change Strong Ties T-1 T T-2

    -0.006*** (0.001) 0.003*** (0.001) R-squared:0.067 Num. obs.: 4,357,566 - Standard errors in parentheses - ***p < 0.001, **p < 0.01, *p < 0.05
  9. Discussion (1) An increase in strong ties is associated with

    an increase in the probability of job change. ▶One explanation could be that individuals in tightly-knit communities tend to form new ties with those in the same community who deliver time-critical information on desirable jobs. 
  10. Discussion (2) Contrary to the strength of weak tie hypothesis,

    we found a negative impact of new weak ties on job change ▶Allocating limited resources to connect with weak tie may reduce spending time with strongly connected people, which are more likely to deliver time critical information (e.g., with job opportunity) ▶Due to social capacity limits (Aral, 2011), new weak and strong ties are complementary. 
  11. Future work  Our motivation: ▶We live in the world

    where knowledge drives innovations, which lead to economic growth. ▶We are interested in job change as one driver for knowledge transfer across social network. Dynamic change in network structure Job change ???