A summary of work in progress analyzing social graphs of FCC lobbying activity

1. The evolution of lobbying coalitions Work in progress summary, 9/30/2009For more detail, see http://bit.ly/4mxfm8 Pierre de Vries, Economic Policy Research CenterUniversity of Washington, Seattle

2. Conclusions Graph-theory clusters representreal-world alliances Tracking the evolution of clusters can reveal shifts in alliances Improving FCC data will improve public knowledge of lobbying activity

3. The Data Metadata from FCC Electronic Comment Filing System (ECFS) database Focused on an inter-carrier compensation proceeding, FCC docket 01-92 2,9015 filings, 756 unique filers Data from inception in April 2001 to December 2008 Considered sub-set where two or more entities file together Filing entities (companies, associations, individuals) are nodes in a network; they’re linked when they make a filing together The more often they file together, the darker the line between them The number of times an entity filed is shown by the size of its node – this is a measure of investment/activity More influential nodes are pink, less influential are blue – nodes are influential if they link to other nodes that are themselves highly linked

4. Companies typically either always file solo, or always jointly 498 entities always filed alone, e.g. BellSouth, NARUC 152 entities always filed with someone else, e.g. Broadview, Maine PUC 25 entities filed with others in 40%-60% of cases, e.g. tw telecom, Pac-West Solo filers excluded from co-filing analysis

5. All the filings 2001-2008

6. A Time Series Looking at separate time periods shows how coalitions evolved The source data set has day-by-day granularity; these snapshots are integrated over much longer periods

7. 2001-2002 CLEC reply comments to NPRM T-Mobile et al petition for declaratory ruling

8. 2003-2004 CLECs’ “Cost-Based Intercarrier Compensation Coalition” (CBICC) Intercarrier Compensation Forum, filed ICF Plan 5 Oct 2004 “Indep. Wireless Carriers”: T-Mobile, W Wireless, Dobson “CMRS Petitioners”: T-Mobile, W Wireless, Nextel

9. 2005 – summer 2006 CLECs Major CLECs – FNPRM comments & replies CLECs, some eventually merging e.g. Lightship, CTC, Conversent; and Xspedius & tw telecom Rural LECs and their associations

10. Fall 2006 – end 2007 Missoula Plan Allies Missoula Plan Opponents: Mix of CLECs, ILECs and Indep. Wireless Oregon Rural LECs, supporting Missoula Plan

11. Jan – July 2008 The calm before the storm

12. Aug/Sep 2008 ILEC/IXC coalition: Ex parte advocating federalizing VOIP, uniform comp rate for all traffic CLECs opposing Verizon’s September 12 proposal, incl. uniform rate

13. Oct 2008 Five State regulatory commissions objecting to “eleventh hour filings” Small ILECs trying to slow down process Broadening CLEC coalition opposing change towards flat rate Mid-size rural LECs opposing flat rate comp, supporting status quo OPASTCO/WTA Plan

14. Nov/Dec 2008 “Coalition for Rational Universal Service and Intercarrier Reform” – urban & rural CLECs Opposition to AT&T/IXC “self-help” from small LEC and conf-call players Rural cellular – note they’re closer to the CLECs than the RLECs

15. Summary of Coalition Patterns Opponents are connected: ILECs, CLECs, and cellular Rural LECs and their associations keep to themselves

16. Top 20 Impact Depends on Chosen Metric * Filers that appear in three or more columns are color coded

17. Value of the approach Insiders can use graphs to identify: detailed trends at a glance potential collaborators or defectors, e.g. by looking for coalition members who are bridges between groups, or peripheral Outsiders can grasp the overall structure of a proceeding without having to read the entire record Communications and advocacy players can use: cluster evolution to find and show changes in coalitions network structure to guide understanding of search results

18. Implications for the Regulator Poor quality of information input by filers impedes transparency Require more information in metadata Use standard web techniques to facilitate data input and retrieval Improve systems for correctingerrors