Fixed-Mobile Substitution and the Promotion of Universal Service Glenn A. Woroch University of California at Berkeley

Universal Service Policy Meets Mobile Ubiquity Should mobile access be counted toward the goal of universal service? Should subsidies for fixed service be reduced or eliminated? Should mobile access be subsidized in certain markets, e.g., rural areas? ►Answers depend on fixed-mobile substitution

Lifeline Program Targeted subsidy to promote telephone access  Federal program implemented by 50 states.  Discount off monthly phone bill set by FCC, states. Eligibility  Income test: HH income below threshold based on Federal Poverty Guidelines.  Program test: HH participates in a federal assistance program (Medicaid, SSI, etc.).  HH must “self certify” each year.

Lifeline Program Implementation  Eligible Telecommunications Carries (ETCs) implement through billing.  Discounts range $6.75-$14.78 with an average of $11.00  Composed of federal SLC ($6.75), plus state match, plus federal 50% match (up to $1.75).  Only one fixed/mobile line covered per household. Participation  ~19 million eligible but only ~6 million participate.  Overwhelmingly on fixed not mobile.

Lifeline Programs Across States State State + federal discount Avg annual discount Income eligibility as % of FPG Estimated participation rate % of households participating % of sample households with Lifeline California$12.00$ %119.2%27.79%32.88% Florida$12.00$ %13.5%2.12%5.4% Illinois$10.85$ %9.0%1.26%3.8% Maine$12.00$ %99.2% Massachusetts$14.50$ %28.4%6.77%15.11% Michigan$9.75$ %20.1%3.74%3.62% New Jersey$6.75$ %5.9%0.95%4.26% New York$10.74$ %34.6%8.31%14.48% Ohio$6.75$ %19.9%3.76%5.15% Pennsylvania$10.50$ %5.5%1.03%2.48% Texas$12.00$ %18.6%3.5%1.68% U.S. Total/Average$ %

Household Panel Dataset TNST’s ReQuest ® Market Monitor household panel  Nationwide, 30K+ per quarter, 10 quarters (3Q99-4Q01).  Survey responses and demographics. TNST’s Bill Harvesting ® database  Fixed and mobile “bill harvesting” (~ 25% response rate).  Not a panel but some re-sampling (~ 10% of bill submitters).

Lifeline Participation Identified by service designation on household fixed line bill. Lowest income group participation rate ~30%, gradually falls with income to 2% for highest group. Lifeline also related to:  Marital Status  Size of household  Composition of household  Ages of children

Lifeline Participation by Income

Lifeline-Mobile Decision Household income, size, state Eligibility Awareness LL participation Household education, mobility, carrier switching LL subsidyAccess Choice

Fixed-Mobile Cross-Price Effects Lower fixed charges of Lifeline could elicit cross price effect on mobile subscription But the Lifeline “experiment” is not random  Might mobile response to lower fixed price be an “income effect”? Controlling for income & demos, wish to see fixed price effect of Lifeline on mobile  Check if Lifeline has an incremental effect on HH purchase of cable, personal, Internet to see if it is a “pure price effect”

Empirical Problems Errors arise in measuring fixed and mobile prices. Self selection into Lifeline program. Unobserved household characteristics. ► All likely correlated with determinants of mobile subscription decision.

Solutions Re-sampling of households  Controls for time-invariant characteristics Instrumental variables  Purges prices of common unobservable household factors Two stage estimation  Regress fixed and mobile bills on instruments  Probit choice model of cellular subscription on fitted prices

Cross Elasticity Estimates Base Model Low Income (<$20k) High Income (>$20k) Complete Sample Without Mobile Phone Initially With Lifeline Currently Never Lifeline

Cross Elasticities (cont’d) Complete Participation States* Partial Participation States No Mobile Lifeline States** Mobile Lifeline Allowed Complete Sample Without Mobile Phone Initially With Lifeline Currently Never Lifeline * - California and Maine; ** - California, Illinois, New York

Two Policy Experiments 1. Withdraw the Lifeline discount from all participating households 2. Impose the average Lifeline discount on all non-participating, eligible households

Policy Simulation Experiment Target Population Base Case Mobile Change Withdraw Lifeline Current Participant 20.4%+9.8% Expand Lifeline No Lifeline, Income < 20k 14.3%-4.5%

Conclusions Mobile ubiquity presents challenges to the design of universal service programs. Modification, or elimination, of existing subsidies turns on accurate measure of fixed-mobile substitution. A key universal service program, Lifeline, provides a natural experiment to estimate FMS. After correcting for endogeneity problems, cross elasticities of access demand are quite large. Simulation of expansion/contraction of Lifeline confirms that mobile subscription significantly impacted by fixed subsidies.