TABLE 9
FROM:
Comparing small world statistics over time and across countries: an introduction to the special issue comparative and transnational corporate networks
Bruce Kogut and Mariano Belinky
BACK TO ARTICLETable 9. Interlocking, 1990
| Standardized APL | Standardized CC | |||||
|---|---|---|---|---|---|---|
| Random | Robins | NSW | Random | Robins | NSW | |
| Germany | 1.23 | 0.98 | 1.25 | 8.0 | 3.62 | 3.9 |
| Sweden | 1.28 | 0.99 | 1.15 | 9.7 | 2.11 | 1.8 |
| Denmark | 1.70 | 0.62 | 1.01 | 4.8 | 3.90 | 1.2 |
| Norway | 1.10 | 1.07 | 1.39 | 8.9 | 2.38 | 2.1 |
| Switzerland | 1.19 | 0.97 | 1.14 | 4.8 | 2.10 | 2.8 |
| The Netherlands | 1.16 | 0.65 | 1.21 | 83.8 | 2.08 | 1.5 |
This table provides the calculation of the small world statistics using three different randomizations for the interlocking network using the first panel for each country. The most notable pattern is the consistent lower values found by the Robins method for average path length (APL). The pattern is less clear for clustering values. Overall, the Small World Statistic (defined for Figures 1 and 2) will tend to be greater using the Robins approach, as the average path length is the denominator and the clustering coefficient is the numerator. The Erdos–Renyi estimates show considerable variance for the clustering coefficients.
