The Determinants of
Broadband Availability:
Economics, Demographics, & State Policy
Kenneth Flamm
University of Texas at Austin
kflamm@mail.utexas.edu
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
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
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



“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


Probably very few remote areas without mail service but with
broadband, that adjoin more populated areas with mail service but
without BB
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
Rapid Change in US Broadband
Penetration, Competition over 4
Years
Distribution of Zip Codes by # BB Providers
50
45
40
35
30
25 Percent
20
15
10
5
Jun-00
Dec-00
3-Jun
3-Dec
0
3-Dec
8
3-Jun
7
6
Dec-00
5
Jun-00
4
1-3
0
9
10
11
12
13
14
15
16
17
18
19
20
Note: Census zips not showing up on FCC list
credited with 0 BB providers– overestimates
true zeros to unknown extent
>99% Population now has at
least 1 provider in their zip code
Pop-weighted Distribution of Zip Codes by # BB Providers
50
45
40
35
30
25 Percent
20
15
% Pop 6/00
% Pop 12/03
10
5
0
% Pop 12/03
% Pop 6/00
0
1-3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Note: # providers may be overestimated in
geographic zips to which “point” zips have
been assigned by FCC
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
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
Data Issues

Have constructed zip code level longitudinal (2000-2003) panel
from 7 sources:








The bad news—A lot of tedious work



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
Still not done!
Still fixing small issues in data
The good news—A very rich data set
Current Research Road Map
Simple logit/probit
(single years)
Any Entrant at all
(fewest assumptions)
Today’s Talk
Correlated Data model
(panel data)
Bivariate logit/probit
(Use info on CLEC competition)
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
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
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



Exploit information in repeated observations on zips
over time




Error term generalized to entire exponential family
Calculate robust standard errors
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
What’s Statistically Significant, Inclusive GEE
Model (all significant variables in any standard
2001-03 logit included, 10% level), 2001-02

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, Parsimonious
GEE Model (only significant variables from
inclusive models, 10% level), 2001-02

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





















+
+
+
+
+
+
+
+
+
+
+
+
+
+
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
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,


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


MD & TN increasing in 2002 relative to 2001
CT, ME
Less in 2001, parity in 2002

HI, MI, WV
First Pass at an Ordered Model
4 levels: 0, 1-3, 4-7, 8+
Score Test for the Proportional Odds Assumption
Chi-Square
2001
2002
2003
2406
2665
2716
DF
218
218
218
More flexible model needed!
Pr > ChiSq
<.0001
<.0001
<.0001
Possible endogeneity issues

Industry  broadband availability


e-Rate $  broadband availability


But pre-bb industrial mix (‘97 econ census)
But long lags for e-Rate apps-approvalscommitments-disbursements
Similar issues for car ownership, home
quality?
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