The Determinants of Broadband Availability: E conomics, Demographics, & State Policy

1. The Determinants of Broadband Availability:Economics, Demographics, & State Policy Kenneth Flamm University of Texas at Austin kflamm@mail.utexas.edu

2. Motivation • Very preliminary work presented today • FCC data on broadband entry now offers opportunity for longitudinal analysis relevant to major telecomm policy issues • Linking to multiple other data sets, have constructed rich data set, sophisticated models with greater range of explanatory variables now possible • Extends and improves on early work of others, some new approaches to be outlined below • New results, relevant to policy

3. Overview of FCC Data • FCC identifies zip codes where at least 1 high speed line installed • Estimates zip codes where no high speed lines, to track penetration • FCC maps “point” zip codes to “geographic” zip codes • Result: remote areas with no regular mail service absorbed into zips with mail delivery • Census maps remote areas with no regular mail service to post office of boxes/general delivery for remote residents • Maps geographic areas to “point” zips (actually ZCTAs) • 3245 areas with P.O. Box-only delivery zip codes, no conventional mail delivery in 2000 • Census the only organization mapping zip codes to people

4. Implications for FCC-Census Match-up • Implications: • FCC BB numbers probably overestimate providers/zip in zips to which “point” zips are mapped • FCC BB numbers for zips with ANY service probably about right • Probably very few remote areas without mail service but with broadband, that adjoin more populated areas with mail service but without BB • “Point” Census zips not showing up on FCC list do NOT necessarily not have broadband service • Confining analysis to “geographic” zips only probably best fix • But understand that remote, sparsely populated rural zips underrepresented in resulting sample • Issue important for geographic BB coverage, but no longer important for population BB coverage

5. Rapid Change in US Broadband Penetration, Competition over 4 Years Note: Census zips not showing up on FCC list credited with 0 BB providers– overestimates true zeros to unknown extent

6. >99% Population now has at least 1 provider in their zip code Note: # providers may be overestimated in geographic zips to which “point” zips have been assigned by FCC

7. Economic models of broadband penetration • L-R Approach– Firms enter markets to make profits • Market characteristics: • Demand side: consumer socioeconomics, demographics • Supply/cost sides: technology, geography, regional cost factors • Approach: estimate “reduced form” • “solve” for number of firms that “fit” into market as function of characteristics • Price and quantity “solved for” as functions of exogenous variables, given N players in market and all above characteristics • Simplest decision– for anyone to enter market—requires few assumptions—just ask whether a hypothetical monopolist would make a profit • Much more complex decision if we ask how many enter • Need to assume oligopoly model • Need to deal with asymmetries among players

8. Ordered Choice Models • The “natural” way to think about this decision • Hypothetical monopoly profit > 0, enter, otherwise don’t • An unobserved “latent variable” a function of market characteristics • Logit or probit a “natural” solution • For number of entrants: • Profit of least profitable potential entrant > 0, enter • Next least profitable entrant ends up with profit <0, they don’t enter, defines equilibrium • Construct function N* giving number of entrants that just makes marginal entrant profit =0 • Since N is integer, largest integer N <= N* defines number of entrants in equilibrium • N* is a latent variable that gives number of firms, falls below integer N “cut point” • Ordered logit or probit marginal the “natural” choice

9. Data Issues • Have constructed zip code level longitudinal (2000-2003) panel from 7 sources: • FCC high-speed survey • FCC CLEC competiton survey • 1997 Economic Census • 2000 Population & Housing Census • Universal Service Fund School and Library (“eRate”) and Rural Health Care funding Commitments • Hydrographic, topological, land cover geophysical databases • Plus, various zip code data bases • The bad news—A lot of tedious work • Still not done! • Still fixing small issues in data • The good news—A very rich data set

10. Current Research Road Map Simple logit/probit (single years) Correlated Data model (panel data) Any Entrant at all (fewest assumptions) Bivariate logit/probit (Use info on CLEC competition) Today’s Talk X Ordered logit/probit Fails proportional odds/|| lines test Number of entrants (more assumptions) Non-proportional ordered models: Partial proportional odds Continuation ratio Generalized ordered logit

11. Initial observations • Functional form • Preliminary work showed logs for selected continuous variables worked marginally better • Little difference in coefficient signs, significance • Years covered  terrain variables • Results for 2000 led to investigation of geophysical/terrain variables • 2000 known to have data collection problems, FCC revised • 2000 results qualitatively different from later years • 2000 dropped • CLEC competition data • In principle, could be used to separate telephone competition from other elements of state BB policy • Simultaneity, identification issues • Stuck with “completely” reduced form

12. Econometric Approach • Start with standard binary logit models for 12/00, 12/01, 12/02, 12/03 • Any statistically significant variable (10% level) in any year goes into “interesting” pool • Relax statistical assumptions • Error term generalized to entire exponential family • Calculate robust standard errors • Exploit information in repeated observations on zips over time • Longitudinal panel data structure • Allow coefficients to vary over time • Allow for correlations in observations over time • Generalize estimating equation (GEE) estimator • A cousin of generalized method of moments (GMM) estimator

13. Effect on BBand Penetration Population density Geographic size of zip Mean CTI “Wetness” index (actually marginal at 11% level) MODIS land cover classification (=1, 4, 11, baseline urban) NAICs 31, 44, 54, 72, 81 Estabs Pct Pop on Farms Pct Pop 55-74 Afro American (2001 only), Native American, Native Hawaiian Pop English is 2nd language Higher educational attainment Share Over 16 in Armed Forces, Civ Labor Force or Not in Labor Force Share pop working in education Per capita income Occupied housing density Share of houses occupied Share of housing indoor plumbing Share Living in new building Share living in 50yr+ old building Share living in pre WWII building Average home age Average home value Sign + + + - + - + - + + + - + - + + + - + + + What’s Statistically Significant, Inclusive GEE Model (all significant variables in any standard 2001-03 logit included, 10% level), 2001-02

14. Effect on BBand Penetration Population density Geographic size of zip Mean CTI “Wetness” index (actually marginal at 11% level) MODIS land cover classification (=1, 11, baseline urban) NAICs 31, 44, 54, 72, 81 Estabs Pct Pop on Farms Pct Pop 55-74 Afro American (2001 only), Native American, Native Hawaiian Pop English is 2nd language Higher educational attainment Share Over 16 in Armed Forces, Civ Labor Force or Not in Labor Force Share pop working in education Per capita income Percent pop female Occupied housing density Share of houses occupied Share of housing indoor plumbing Share living in 50yr+ old building Share living in pre WWII building Average home age Average home value Sign + + + - + - + - + + + - + - - + + - + + + What’s Statistically Significant, Parsimonious GEE Model (only significant variables from inclusive models, 10% level), 2001-02

15. Dogs that did not bark:Small point estimates, not significant • Numbers of households • for given pop density, zip size is scale variable • Similarity of coefficientspop is scale • Pop/housing unit • Household size • Age variables (except 55-74, over 16) • eRate, rural health care grants

16. Role of State Policy/Effects • Baseline was Texas, impact on BB • May not be best baseline, but many zip codes, active state subsidy program (TIF, 1996-2004, $1.5B), relatively competitive market • Statistically significant + effects: • CA,CO,FL, MD, MA, NY, NC, OR, TN, • MD & TN increasing in 2002 relative to 2001 • Statistically significant – effects: • IL, IN, IA, KS, MN, MO, NE, NV, ND, PA, SD, UT, VA, WI • IL, IA, KS increasing in 2002 relative to 2001 • Greater in 2001, parity in 2002 • CT, ME • Less in 2001, parity in 2002 • HI, MI, WV

17. First Pass at an Ordered Model 4 levels: 0, 1-3, 4-7, 8+ Score Test for the Proportional Odds Assumption Chi-Square DF Pr > ChiSq 2001 2406 218 <.0001 2002 2665 218 <.0001 2003 2716 218 <.0001 More flexible model needed!

18. Possible endogeneity issues • Industry  broadband availability • But pre-bb industrial mix (‘97 econ census) • e-Rate $  broadband availability • But long lags for e-Rate apps-approvals-commitments-disbursements • Similar issues for car ownership, home quality?

19. Conclusions • Estimated state effects correlate with accounts • Terrain effects significant in some parts of country • Terrain effects exciting! • Instrumental variables for demand studies • Income, pop density expected effects • eRate irrelevant • Industrial activity very significant (prof & technical services largest) • Gender, education, farm location as expected • Age effects generally not supported • Digital divide ethnicity/gender show up, but small effects and decreasing • Tests for proportional odds/parallel lines hypothesis critical in ordered logit/probit models