Number 14 n March 11, 2009

Wireless Substitution: State-level Estimates From the
 

National Health Interview Survey,
 

January–December 2007
 by Stephen J. Blumberg, Ph.D., and Julian V. Luke, Division of Health Interview Statistics, National Center for Health
 

Statistics, and Gestur Davidson, Ph.D.; Michael E. Davern, Ph.D.; Tzy-Chyi Yu, M.H.A., Ph.D.; and
 

Karen Soderberg, M.S., the State Health Access Data Assistance Center, University of Minnesota
 

Abstract

Objectives—This report presents state-level estimates of the percentage of

households that do not have a landline telephone, but do have at least one

wireless telephone. These wireless-only households made up 14.7% of U.S.

households in 2007. The report also presents state-level estimates of the

percentage of adults living in wireless-only households. These wireless-only

adults made up 13.6% of U.S. adults in 2007.

Methods—A two-sample modeling strategy was used to estimate the

prevalence of wireless-only households and adults by state. This modeling was

based on data from the 2007 National Health Interview Survey and the 2008

Current Population Survey’s Annual and Social Economic Supplement.

Results—The results show that the prevalence of wireless-only households

and adults in 2007 varied substantially across states. State-level estimates ranged

from 5.1% (Vermont) to 26.2% (Oklahoma) of households and from 4.0%

(Delaware) to 25.1% (Oklahoma) of adults. In addition, approximately one out of

four adults (25.4%) living in the District of Columbia were wireless-only.

Keywords: wireless substitution c cell phones c telephone surveys c noncoverage

Introduction Program, presents national and regional

estimates. For example, the latest results

The National Health Interview show that more than one out of every Survey (NHIS) is the most widely cited six American homes (17.5%) had only source for data on the number of

wireless telephones during the first half American homes that only have wireless

of 2008 (1). telephones. Every 6 months, the

National Center for Health Statistics Most major survey research

(NCHS) releases a report with the most organizations in the United States,

up-to-date estimates available from the including NCHS, have not traditionally

federal government concerning the size included wireless telephone numbers

and characteristics of the wireless-only when conducting random-digit-dial

population (1). That report, published as telephone surveys. The exclusion of

part of the NHIS Early Release households with only wireless

telephones has potential implications for

results from health surveys, political

polls, and other research conducted

using random-digit-dial methods.

Indeed, the potential for bias due to

incomplete coverage of the U.S.

household population (that is, due to

noncoverage of wireless-only and

phoneless households) remains a real

and growing threat to health surveys

conducted only on landline telephones

(2–4).

For this reason, survey systems that

have relied on random-digit-dial surveys

for years have been testing methods for

including samples of wireless-only

households. These systems include

several conducted by the Centers for

Disease Control and Prevention (CDC),

including the Behavioral Risk Factor

Surveillance System, the National

Immunization Survey, and the State and

Local Area Integrated Telephone Survey.

These three systems collect data and

produce results at the state level. For

them to effectively combine samples of

wireless-only households with samples

of landline households from randomdigit-dial surveys, state-level estimates

of the prevalence of wireless-only

households are needed. Yet, direct

state-level estimates of this prevalence

have not been available from NHIS data

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES 

Centers for Disease Control and Prevention 

National Center for Health Statistics 
10% to less than 15% 

Prevalence less than 10% 

15% to less than 20% 

Prevalence greater than or equal to 20% 

Page 2 National Health Statistics Reports n Number 14 n March 11, 2009

because the sample size of NHIS is

insufficient for direct reliable annual

estimates for most states.

This report presents results of

modeled estimates of the prevalence of

wireless-only households and wirelessonly adults at the state level, using data

from the 2007 NHIS and the 2008

Current Population Survey’s (CPS)

Annual and Social Economic

Supplement (ASEC). In contrast to the

NHIS, the CPS has sufficient sample

size for direct reliable annual

demographic estimates for all states, but

does not include questions necessary to

identify wireless-only households. By

incorporating data from both surveys,

the modeled estimates presented here

take advantage of the unique strengths

of both surveys. To our knowledge,

these estimates are the first state-level

estimates of the size of this population

available from the federal government.

Methods

A two-sample modeling strategy

was used to estimate the prevalence of

wireless-only households and of adults

living in such households, by state. This

strategy used an optimal blend of direct

estimates and synthetic estimates (5,6).

First, NHIS data were used to fit

multivariate regression models

predicting wireless-only status using

covariates from NHIS that were also

available from the CPS. The model was

then used with CPS data to obtain

average state-level synthetic estimates

(or predictions). Standard errors (SEs)

for these mean state-level synthetic

estimates were also obtained using Stata

version 10 with the Delta method (7).

Next, NHIS data were used to directly

obtain state-level prevalence estimates

and their corresponding (and often

large) SEs. Finally, a blended overall

estimate was calculated as the weighted

sum of the synthetic and direct estimates

for each state, where the weights

reflected the relative precision of each

estimate.

More detail regarding this

estimation methodology is available in

the ‘‘Technical Notes.’’

Results

Results from the two-sample

modeling strategy show great variation

Figure 1. State-level comparisons of the percentage of wireless-only households, modeled

estimates: United States, 2007

in the prevalence of wireless-only

households across states (see Figures 1

and 2). Household-level estimates

ranged from a low of 5.1% in Vermont

to a high of 26.2% in Oklahoma (see

Table).

Other states with a high prevalence

of wireless-only households include

Utah (25.5%), Nebraska (23.2%),

Arkansas (22.6%), Idaho (22.1%), and

Iowa (22.2%). Other states with a low

prevalence of wireless-only households

include Connecticut (5.6%), Delaware

(5.7%), South Dakota (6.4%), Rhode

Island (7.9%), New Jersey (8.0%), and

Hawaii (8.0%).

Similarly, results show great

variation in the prevalence of wirelessonly adults across states, ranging from a

low of 4.0% in Delaware to a high of

25.1% in Oklahoma (see Table). An

ostensibly but not significantly higher

prevalence rate was observed for adults

living in the District of Columbia

(25.4%). Other states with a high

prevalence of wireless-only adults

include Utah (23.9%), Nebraska

(22.4%), Kentucky (21.6%), Idaho

(21.3%), and Arkansas (21.2%). Other

states with a low prevalence of wirelessonly adults include Vermont (4.6%),

Connecticut (4.8%), Rhode Island

(5.3%), Montana (5.4%), and New

Jersey (6.1%).

Conclusion

Because of the absence of statelevel prevalence estimates for the

wireless-only population, survey

researchers interested in combining

state-level samples of wireless-only

households with samples of landline

households have relied on national or

regional estimates of the relative sizes

of these two populations (8). Similarly,

telecommunications companies seeking

greater understanding of conditions in

state and local markets have relied on

regional estimates of the prevalence of

wireless-only households (9). The results

in this report clearly show that, for

many states, national and regional

estimates are not sufficiently accurate

for these purposes.
Results from the two-sample

modeling strategy show great state-level

variation in the prevalence of wirelessonly households, even within regions.

The range of prevalence exceeded

8 percentage points in the Northeast

region and 20 percentage points in the

South region. In fact, in the Midwest

region, the state with the lowest

prevalence (South Dakota, 6.4%)

borders the state with the highest

prevalence (Nebraska, 23.2%). Similar

ranges within regions were observed for

estimates of the prevalence of wirelessonly adults.

Of course, for survey researchers

and telecommunications companies

interested in local areas, these state-level

prevalence estimates still may not be

sufficiently specific. For example,

national estimates suggest that adults

living in metropolitan areas are more

likely to live in wireless-only

households than are adults living in

nonmetropolitan areas. Variation across

local areas within a state should be

expected, just as there was variation

across states within a region. NCHS

intends to continue working with the

University of Minnesota to use twosample modeling strategies like this one

to produce estimates of telephone status

for large metropolitan areas. Meanwhile,

researchers may find the state-level

model specifications (in the ‘‘Technical

Notes’’) useful for creating completely

synthetic predictions for local areas or

other subpopulations of interest.

Survey researchers and

telecommunication companies using the

estimates presented in this report should

be aware that these estimates are based

on 2007 data. The number of American

homes with only wireless telephones

continues to grow (1). The estimate

from the first half of 2008, that 17.5%

of households were wireless-only, is

nearly 3 percentage points higher than

the estimate for the 2007 calendar year

(14.7%). Similarly, the estimated

prevalence of wireless-only adults has

grown from 13.6% in 2007 to 16.1% in

the first half of 2008. We do not know

if the rates of growth in each state are

comparable to the national rates, or

whether they vary substantially (as did

the overall prevalence rates by state).

Regardless, it is very likely that the

current state-level prevalence rates of

wireless-only households and adults are

greater than the estimates presented

here.

For More Information

For more information about the

implications of wireless-only households

for health surveys based on landline

telephone interviews, see other reports

(1–4). For more information about the

design, content, and use of the NHIS,

please visit the NCHS website (http://

www.cdc.gov/nchs/nhis.htm).

State

Percent

0

5

10

15

20

25

30

OK

UT

NEAR

IA

ID

KY

NM

TX

SC

TN

DC

MS

AZ

OR

MN

ND

FL

KS

CO

GA

IL

MI

NC

WA

WI

LA

OH

AL

IN

ME

AK

NH

WV

NY

WY

MD

NV

PAVA

MO

MA

MT

CA

HI

NJ

RI

SDDE

CT

VT

Figure 2. Modeled state-level estimates of the percentage of wireless-only households: United States, 2007

National Health Statistics Reports n Number 14 n March 11, 2009 Page 3
Page 4	 National Health Statistics Reports n Number 14 n March 11, 2009

References

1.	 Blumberg SJ, Luke JV. Wireless

substitution: Early release of

estimates based on data from the

National Health Interview Survey,

January – June 2008. National Center

for Health Statistics. 2008. Available

from: http://www.cdc.gov/nchs/about/

major/nhis/releases.htm.

2.	 Blumberg SJ, Luke JV. Coverage

bias in traditional telephone surveys

of low-income and young adults.

Public Opinion Quarterly 71:734–49.

2007.

3.	 Blumberg SJ, Luke JV, Cynamon

ML. Telephone coverage and health

survey estimates: Evaluating the need

for concern about wireless

substitution. Am J Public Health

96:926–31. 2006.

4.	 Blumberg SJ, Luke JV, Cynamon

ML, Frankel MR. Recent trends in

household telephone coverage in the

United States. In JM Lepkowski et

al. (eds.), Advances in telephone

survey methodology 56–86. New

York: John Wiley and Sons, Inc.

2008.

5.	 Schirm AL, DiCarlo JR. Using

Bayesian shrinkage methods to

derive state estimates of poverty,

food stamp program eligibility, and

food stamp program participation.

Washington, DC: Mathematica Policy

Research, Inc. 1998. Available from:

http://www.mathematica-mpr.com/

publications/PDFs/bayesian.pdf.

6.	 National Research Council. Small

area income and poverty estimates:

Priorities for 2000 and beyond. Panel

on Estimates of Poverty for Small

Geographical Areas, CF Citro and G

Kalton, editors. Committee on

National Statistics. Washington, DC:

National Academy Press. 2000.

7.	 Greene WH. Econometric Analysis,

5th Ed. Upper Saddle River, NJ:

Prentice Hall. 2003.

8.	 AAPOR Cell Phone Task Force.

Guidelines and considerations for

survey researchers when planning

and conducting RDD and other

telephone surveys in the U.S. with

respondents reached via cell phone

numbers. American Association for

Public Opinion Research. 2008.

Available from: http://www.aapor.org/

uploads/Final_AAPOR_Cell_Phone_

TF_report_041208.pdf.

9.	 Petitions of Qwest Corporation for

Forbearance Pursuant to 47 U.S.C. §

160(c) in the Denver, MinneapolisSt. Paul, Phoenix, and Seattle

Metropolitan Statistical Areas, WC

Docket No. 07–97, Memorandum

Opinion and Order, FCC 08–174.

Federal Communications

Commission. July 25, 2008.

Available from: http://

hraunfoss.fcc.gov/edocs_public/

attachmatch/FCC-08–174A1.pdf.

10. Davern M, Jones A, Lepkowski J,

Davidson G, Blewitt LA. Estimating

regression standard errors with data

from the Current Population Survey’s

public use file. Inquiry 44:211–24.

2007.
National Health Statistics Reports n Number 14 n March 11, 2009 Page 5

Table. Modeled state-level estimates of the percentage of wireless-only households and the percentage of adults living in wireless-only

households: United States, 2007

Households Adults

Widest Widest

State Percent plausible interval Percent plausible interval

Alabama............................... 13.9 9.7–18.1 12.2 8.1–16.4

Alaska ................................ 11.7 8.9–14.8 13.3 7.3–19.5

Arizona ............................... 18.9 14.5–23.1 17.1 13.6–20.4

Arkansas .............................. 22.6 18.7–26.4 21.2 16.8–25.6

California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.0 8.1–9.8 8.4 7.7–9.1

Colorado .............................. 16.7 13.2–20.3 15.2 12.5–18.2

Connecticut ............................. 5.6 3.4–7.8 4.8 2.7–6.9

Delaware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 4.8–6.8 4.0 2.8–5.4

District of Columbia . . . . . . . . . . . . . . . . . . . . . . . . 20.0 15.5–24.5 25.4 15.2–34.1

Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.8 13.9–19.4 15.5 12.8–17.8

Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5 12.9–19.9 15.0 11.6–18.1

Hawaii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.0 6.5–9.6 8.2 7.4–8.8

Idaho................................. 22.1 18.9–25.3 21.3 19.0–23.9

Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5 14.1–18.7 15.2 12.8–17.1

Indiana. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.8 10.3–16.9 13.0 8.9–16.8

Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2 9.8–34.1 18.9 7.8–29.3

Kansas ............................... 16.8 12.8–20.6 15.2 11.9–18.1

Kentucky .............................. 21.4 11.7–30.4 21.6 11.5–30.8

Louisiana .............................. 15.0 10.2–19.6 13.8 9.6–17.9

Maine ................................ 13.4 10.5–16.5 12.0 10.6–13.9

Maryland .............................. 10.8 9.1–12.6 9.8 8.3–11.5

Massachusetts ........................... 9.3 7.9–10.7 8.4 7.1–9.8

Michigan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 12.7–19.7 15.3 11.6–18.7

Minnesota.............................. 17.4 14.4–20.3 16.5 14.7–18.2

Mississippi ............................. 19.1 11.4–26.3 20.3 12.6–27.0

Missouri ............................... 9.9 6.8–12.9 8.4 6.2–10.6

Montana. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 8.0–10.6 5.4 4.5–6.4

Nebraska .............................. 23.2 13.2–32.7 22.4 12.7–31.2

Nevada ............................... 10.8 8.8–13.0 10.1 9.0–11.3

New Hampshire .......................... 11.6 9.2–14.3 8.9 7.2–11.0

New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.0 6.0–10.0 6.1 4.8–7.5

New Mexico ............................ 21.1 11.3–29.6 20.5 10.4–28.8

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 10.0–13.0 10.6 9.4–12.2

North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 13.6–19.0 14.8 12.3–17.3

North Dakota ............................ 16.9 6.7–27.2 18.1 4.4–32.2

Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.0 11.3–16.6 13.1 11.0–15.3

Oklahoma .............................. 26.2 12.9–38.8 25.1 14.6–34.6

Oregon ............................... 17.7 14.5–20.8 18.1 15.0–20.8

Pennsylvania ............................ 10.8 8.6–13.0 9.2 7.3–11.2

Rhode Island ............................ 7.9 0.1–15.6 5.3 0.3–11.0

South Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.6 14.5–26.0 19.2 13.8–24.0

South Dakota............................ 6.4 5.7–7.1 6.8 6.1–7.6

Tennessee ............................. 20.3 16.1–23.4 20.8 14.9–25.2

Texas ................................ 20.9 18.3–23.0 19.5 17.0–21.2

Utah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.5 16.9–32.8 23.9 15.2–30.9

Vermont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 4.9–5.4 4.6 4.5–4.9

Virginia ............................... 10.8 8.8–12.9 10.0 7.9–12.2

Washington ............................. 16.3 12.4–20.2 15.6 12.2–19.0

West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 8.3–14.5 10.6 4.6–16.1

Wisconsin .............................. 15.2 11.9–18.4 13.6 10.8–16.3

Wyoming .............................. 11.4 10.8–12.2 13.0 12.3–14.2

DATA SOURCES: CDC/NCHS, National Health Interview Survey, 2007, and U.S. Census Bureau, Current Population Survey, Annual and Social Economic Supplement, 2008. Estimates were

calculated by the State Health Access Data Assistance Center, University of Minnesota.

NOTE: Please refer to the ‘‘Technical Notes’’ for a description of the calculation of the ‘‘widest plausible interval.’’
Page 6 National Health Statistics Reports n Number 14 n March 11, 2009

Technical Notes

Data sources

The state-level estimates presented

in this report are based on data from the

2007 National Health Interview Survey

(NHIS) and the 2008 CPS Annual and

Social Economic Supplement. NHIS is a

multipurpose health survey conducted

by CDC’s NCHS. The CPS is an annual

demographic survey conducted by the

U.S. Census Bureau for the Bureau of

Labor Statistics.

NHIS is an annual multistage

probability household survey of a large

sample of households drawn from the

civilian noninstitutionalized household

population of the United States. This

face-to-face survey interview is

administered by trained field

representatives from the U.S. Census

Bureau. NHIS interviews are conducted

continuously throughout the year to

collect information on health status,

health-related behaviors, and health care

utilization. The survey also includes

information about household telephones

and whether anyone in the household

has a wireless telephone (also known as

a cellular telephone, cell phone, or

mobile phone).

The sample for NHIS is stratified

by state, which allows use of NHIS for

producing state-level estimates.

However, the current NHIS sample size

is not sufficient to provide reliable

annual state-level estimates for most

states. In 2007, household telephone

status information was obtained for

29,079 households; of these, 28,492 had

sufficient nonmissing data for the

covariates to be included in the

multivariate regression analyses

presented here.

The CPS is a multistage probability

household survey that provides data on

labor force participation and

unemployment. Data are collected

through a combination of face-to-face

and telephone interviews. The ASEC is

added one time per year to the monthly

CPS (from February through April) and

is used to produce household income,

family poverty, and health insurance

coverage estimates. The reference period

for these ASEC items is the prior

calendar year; the 2008 CPS ASEC uses

2007 as its reference period. The CPS

ASEC is both nationally and state

representative and has included

approximately 78,000 households per

year since 2000.

NHIS and CPS sampling weights

adjust for the probability of selection of

each household, and they are adjusted

for nonresponse. The results reported in

this report are based on weighted

estimates. StataSE v.10 software was

used to account for the complex survey

designs.

Definition of a wireless-only

household

For each family contacted by NHIS,

one adult family member was asked

whether ‘‘you or anyone in your family

has a working cellular telephone.’’ A

family can be an individual or a group

of two or more related persons living

together in the same housing unit. Thus,

a family can consist of only one person,

and more than one family can live in a

household (including, for example, a

household where there are multiple

single-person families, as when

unrelated roommates are living

together).

To produce the statistics for this

report, families were identified as

wireless families if anyone in the family

had a working cellular telephone.

Households were identified as wirelessonly if they included at least one

wireless family and if there were no

working landline telephones inside the

household. To determine if there was a

working landline telephone inside the

household, survey respondents were

asked if there was ‘‘at least one phone

inside your home that is currently

working and is not a cell phone.’’

Household telephone status (rather

than family telephone status) is used in

this report because most telephone

surveys draw samples of households

rather than families. Adults are

identified as wireless-only if they live in

a wireless-only household. Individual

ownership or use of cellular telephones

is not determined.

Two-sample state-level

estimation

The goal was to develop a robust

set of blended direct and synthetic

estimates of the wireless-only

population. We used both the NHIS and

the CPS Annual and Social Economic

Supplement in a two-sample modeling

approach (5,6). Specifically, we first

fitted a multivariate regression model

using NHIS data. NHIS is the only

survey that provides information on

wireless-only households. We then used

data from the CPS to derive state-level

predictions through the NHIS-fitted

model. The CPS has a larger sample

size and a survey design that produces

reliable, state-representative estimates.

We then blended the direct estimates

from the NHIS and the modeled

estimates from the CPS data and NHIS

model to yield a set of improved

state-level estimates. We undertook this

modeling exercise separately for two

units of analysis: for the household and

for adults aged 18 years and older. For

ease of exposition, we describe the

process undertaken using the household

as the unit of analysis. The process was

the same when adults were the unit of

analysis.

Formally, this two-sample strategy

has five steps. First, using the

multinomial logistic (MNL) regression

command for survey data in Stata 10.1

(‘‘svy: mlogit’’) and confidential internal

NHIS data files, we fitted a fixed-effects

model (at the level of state) on this

national sample of 28,492 households.

The three categories for our multinomial

dependent variable were wireless-only,

landline (with or without the presence of

a wireless telephone), and phoneless.

With access to the detailed confidential

NHIS sample design and using Stata’s

survey suite commands, we were able to

take full account of the complex survey

design to obtain robust standard errors

for this multivariate model.

Second, using publicly available

CPS data files, we recycled the CPS

data on all our model covariates (i.e.,

recycled predictions) through our

NHIS-fitted model to obtain the average

state model-based estimates. These

average state model-based estimates
cps)

2
 

blended

1

/(SEp,j
 cps

P

ˆ wo, j = Pˆ wo, j +

cps)

2 de) [1/(SE 2] p, j +1/(SEp, j

de) 1 2 /(SEp, j de DE . wo, j cps)

2 de) [1/(SE 2] p +1/(SEp, j

National Health Statistics Reports n Number 14 n March 11, 2009 Page 7

were obtained as the sum of the

model-estimated probabilities of each

household in a state being wireless-only

divided by the number of sample

observations at the level of the state.

Symbolically denoting the j th state’s

estimated rate of wireless-only (wo) ˆ cps using the MNL model as Pwo,j , we refer ˆ cps to these Pwo,j , as our synthetic

estimates.

Third, we used the Stata command

‘‘adjust’’ to obtain the standard errors of

these mean state-level model predictions

(SE cps

p ). With access to the publicly

available CPS data files only, we could

not take full account of the complex

survey design when calculating these

standard errors. Instead, we used the

Delta method (7) with the Stata

command ‘‘adjust,’’ and we identified

the lowest level of identifiable

geography in the publicly available CPS

data files as the strata variable. This

alternative method has been shown to

yield standard errors for multivariate

regression models of dichotomous

dependent variables that are very close

to those obtained when the full

confidential survey design is available

(10). As such, these standard errors are

likely to fully reflect both sampling and

model-based imputation errors.

Fourth, we used the NHIS survey

data and Stata (‘‘svy: mean’’) to obtain

the direct prevalence estimate for the

wireless-only variable for each state,

denoted by DEwo,j, along with the

standard error of these means, SE de

p,j .

We call these values of DEwo,j our direct

estimates (Table I). As noted earlier, we

were able to take full account of the

complex NHIS design to obtain these

standard errors.

Finally, we formed the blended

wireless-only estimate as the weighted

sum of the synthetic estimate and direct

estimate for each state, where the

weights reflect the relative precision of

each state’s pair of synthetic and direct

estimates, as:

We used a MNL regression model

with the three categories of wirelessonly, landline, and phoneless rather than

a binomial logistic regression model

with the two categories of wireless-only

and ‘‘all others.’’ We used three

categories because, though the

prevalence of phoneless households is

quite small, our MNL regression

analyses revealed that the phoneless

equation’s coefficients were almost as

large and significant as those for the

wireless-only equation. Thus, combining

the landline and phoneless categories

into a single ‘‘all others’’ category and

estimating a binomial logistic regression

for wireless-only would have resulted in

heterogeneity within this combined ‘‘all

others’’ category. As a consequence, the

wireless-only equation’s coefficients

would likely have been biased.

We tested the appropriateness of our

use of a MNL regression model rather

than the use of much more

computationally demanding multinomial

probit regression models (MNP). As part

of their estimation approach, MNL

models make the assumption—referred

to as Independence from Irrelevant

Alternatives (IIA)—that the coefficients

in a MNL model will not depend on

whether an outcome-level within the

multinomial response variable is

included in the estimation or excluded

(and its data removed). MNP does not

make this assumption and consequently

it is often recommended as the

appropriate estimator. MNP is very

computationally demanding, however.

The IIA assumption underlying MNL

can be tested empirically, and there are

two variants of it formulated as

Hausman tests, plus a third IIA test

known as the Small-Hsiao test. In our

particular modeling example, the IIA

tests can be simply described as

assessing whether the coefficients of the

wireless-only equation in a MNL model

are significantly different from the

coefficients estimated from a wirelessonly binomial logistic regression model

that excludes the phoneless data. Two of

the three tests favored the IIA

assumption. Therefore, we believe our

use of a MNL model is appropriate.

Selection of a fixed-effects

model

We note that the NHIS has a

relatively large sample size—although

not designed for representative annual

state-level estimates—and that the CPS

has an even greater sample size and is

designed to produce representative

annual state-level estimates.

Additionally, we note that, with our

model specification, we were able to

account for significant variation in

wireless-only households. Given all

these considerations, we believe it was

appropriate that differences in each

state’s direct and synthetic estimates

should reflect only the differences across

NHIS and CPS surveys in their

covariate means, °X, where these

covariates have been shown empirically

to be significant predictors of whether

households are wireless-only. For this

reason, we believe that a fixed effect

modeling strategy is a better

specification than a nonfixed effect

strategy would have been.

Assume that after estimating our

fixed effects model with NHIS data we

had generated a mean predicted NHIS

value for each state by recycling NHIS

data through the model. Let these values

be denoted as βrecycled

NHIS . We in fact

generated a mean predicted value for

each state by recycling the CPS data

through the model. Let these values be

denoted as βrecycled

CPS . Finally, we also used

NHIS data to produce a direct

prevalence estimate for each state. Let

these values be denoted as DENHIS.

Given these three estimates, for any

state, we can form the ratio:

recycled]+[ β NHIS recycled] recycled [DENHIS – β NHIS – β CPS , recycled DENHIS –β CPS

which is identically equal to 1.0. By the

first order, conditions of the logistic
Page 8 National Health Statistics Reports n Number 14 n March 11, 2009

Table I. Direct state-level estimates of the percentage of wireless-only households and the percentage of adults living in wireless-only

households: United States, 2007

Households Adults

State Percent

95% confidence

interval Percent

95% confidence

interval

Alabama............................... 12.5 5.8–19.2 11.7 5.0–18.4

Alaska ................................ 9.8 1.1–18.6 11.2 1.8–20.6

Arizona ............................... 18.8 13.4–24.2 17.1 13.0–21.1

Arkansas .............................. 22.3 17.5–27.0 20.7 14.8–26.5

California . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 7.3–9.4 7.7 6.8–8.6

Colorado .............................. 18.1 12.2–24.1 17.0 10.9–23.2

Connecticut ............................. 6.1 2.9–9.2 5.0 1.9–8.1

Delaware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 0.3–15.0 5.0 0.0–10.4

District of Columbia . . . . . . . . . . . . . . . . . . . . . . . . 23.0 15.3–30.8 25.7 17.1–34.3

Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.8 13.1–18.5 14.5 12.0–17.0

Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5 11.6–19.4 13.7 10.0–17.3

Hawaii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 5.2–10.0 8.2 7.4–9.0

Idaho................................. 23.2 18.5–28.0 21.5 17.6–25.4

Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 13.7–18.9 15.0 12.9–17.2

Indiana. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.8 10.5–17.2 13.5 9.2–17.8

Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.6 10.5–36.7 21.4 9.9–33.0

Kansas ............................... 15.8 11.7–20.0 13.9 10.6–17.2

Kentucky .............................. 22.0 13.1–30.9 22.8 13.2–32.3

Louisiana .............................. 13.8 8.3–19.2 13.2 8.4–17.9

Maine ................................ 17.3 4.7–29.8 15.7 4.0–27.3

Maryland .............................. 10.1 7.3–12.9 8.9 6.4–11.5

Massachusetts ........................... 8.9 6.9–10.9 8.0 6.0–10.0

Michigan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4 13.0–21.8 16.5 11.9–21.2

Minnesota.............................. 16.2 12.3–20.0 15.7 13.5–17.8

Mississippi ............................. 18.2 10.9–25.5 19.1 12.3–25.8

Missouri ............................... 9.9 6.2–13.7 8.3 5.4–11.3

Montana. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.0 0.5–9.4 3.1 1.8–4.3

Nebraska .............................. 22.3 12.0–32.6 20.8 12.2–29.5

Nevada ............................... 10.3 5.8–14.7 9.0 7.4–10.5

New Hampshire .......................... 12.1 0.0–25.3 9.1 0.0–19.8

New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9 5.3–10.5 5.9 4.1–7.6

New Mexico ............................ 20.3 12.6–27.9 19.4 11.7–27.1

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.8 9.2–16.5 12.1 8.5–15.8

North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.9 12.2–19.6 14.6 11.2–18.1

North Dakota ............................ 14.3 0.0–30.4 15.3 0.0–32.0

Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5 10.8–18.2 13.7 10.3–17.1

Oklahoma .............................. 27.4 14.2–40.6 26.5 16.3–36.7

Oregon ............................... 17.3 13.5–21.1 17.4 14.3–20.5

Pennsylvania ............................ 11.0 8.0–14.0 9.6 6.7–12.5

Rhode Island ............................ 6.8 0.0–14.4 4.4 0.0–10.5

South Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.6 14.2–24.9 18.3 13.2–23.4

South Dakota............................ 4.8 3.0–6.6 5.5 4.3–6.6

Tennessee ............................. 19.9 16.9–23.0 20.2 15.9–24.5

Texas ................................ 20.6 18.3–22.8 18.7 16.8–20.7

Utah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.0 17.3–30.7 22.2 15.6–28.8

Vermont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 0.0–8.8 2.8 0.0–6.3

Virginia ............................... 11.9 8.3–15.6 10.9 7.0–14.7

Washington ............................. 16.6 11.2–22.1 16.1 11.4–20.7

West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.0 7.2–12.9 9.6 4.6–14.7

Wisconsin .............................. 16.3 12.1–20.4 15.2 11.3–19.2

Wyoming .............................. 14.2 4.6–23.7 16.8 10.0–23.5

0.0 Quantity more than zero but less than 0.05.

DATA SOURCE: CDC/NCHS, National Health Interview Survey, 2007.
recycled recycled � NHIS – β CPS ≡ 1.0. recycled DENHIS –β CPS

National Health Statistics Reports n Number 14 n March 11, 2009 Page 9

model (for binomial or MNL

regressions), when a model is a fixed

effects model at the state level, then for

each state, DENHIS = β recycled

NHIS , in which

case, the above equation becomes:

In other words, in our state-level fixed

effects models, the difference of any

state’s direct and synthetic estimates in

the estimated proportion of households

that are wireless-only reflects—solely—

the weighted differences across the

NHIS and CPS surveys in their

covariate means, °X. That is, differences

in direct and synthetic estimates are a

weighted average across NHIS and CPS

surveys of their covariate means, X°,

with these weights being the size of the

coefficients in our MNL regression

model.

Selection of variables used

in the models

The wireless-only classification is a

collective attribute or characteristic of a

household. It is either present in a

household or not present, and its

significance is that no member of that

household can be contacted with a

landline associated with that household.

(By contrast, all persons in a landline

household can be contacted with a

landline, in the telephone-survey-based

sense that any one household member

contacted on a landline can tell the

survey interviewer about himself or

herself and all others in the household.)

As a collective household characteristic,

the wireless-only status of households is

best modeled using variables that are

also measured at the household level.

We also used household-level variables

when we estimated our adult-level

models. That is, in the adult-level

models, we looked for characteristics of

the household that predicted whether

that adult resides within a wireless-only

household.

Given the nature of our two-sample

modeling approach, the variables that

could be used in the model had to be

available in both the NHIS and CPS

surveys. In addition, we required

identical coding of all responses in these

two sets of variables. We used previous

work on predictors of telephone status

(4) as a starting point for choosing our

variables. In addition, as suggested by

their work, we tested the importance and

significance of several interactions

between variables for home ownership,

age ranges within the household, and

number of persons in the household.

In addition, we expected that there

would be a direct relationship between

the prevalence of wireless-only

households in a state and the number of

wireless telephones per capita. From the

Federal Communications Commission’s

Automated Reporting Management

Information System database, we

obtained the number of wireless

telephone subscriptions as of December

2006 and June 2007, and we divided

these respectively by the U.S. Census

Bureau’s July 1, 2006, and July 1, 2007,

population estimates. We then entered

these two state-wide values of wireless

telephones per capita into the NHIS data

set in conjunction with the NHIS

variable that designates whether a

household participated in the survey

during the first half of the year or the

second half, to form one overall

state-specific cell subscriber variable.

This state-level variable could be used

in a fixed-effects model because there

was variation in it within a state, and

due to rapidly rising numbers of

wireless telephone subscriptions, there

was adequate variability. Indeed, the

effect of this ‘‘within-state’’

estimator—it relates variation in a

state-level variable within the individual

states to variation in wireless-only

prevalence within the individual

states—was quantitatively large,

positive, and significant. For the

household-level model, β = 5.05 and

p = .015.

MNL regression results

Table II presents the MNL

regression results for the final

household-level model for the wirelessonly equation. Table III presents the

MNL regression results for the final

adult-level model for the wireless-only

equation.

Variability of the estimates

For policy purposes, it’s natural to

seek some quantitative measure of the

level of uncertainty surrounding the

estimates, analogous to a 95%

confidence interval (CI). Of course,

these blended estimates are a weighted

average of two random variables, where

the weights are complex functions of

each of the two estimates’ SEs and thus

these weights must be considered

themselves to be random variables. This

high level of complexity holds even if

we assume, appropriately, that the NHIS

and CPS survey samples are

independent.

A full bootstrap procedure involving

the simultaneous re-sampling of both the

estimation sample (NHIS) and the

prediction sample (CPS) could be

conceptualized as a complex way of

obtaining 95% CI for our blended

estimates. Given this high level of

complexity, we instead derived a simple

quantitative measure of the level of

uncertainty about these blended

estimates, which due to its construction

we refer to as the widest plausible

interval for our blended estimates. Our

direct estimates, DEwo,j , have lower

and upper 95% CI values, denoted as

DE L

wo,j, and DE Uwo,j, and our synthetic ˆ cps estimates, Pwo,j , similarly have lower

and upper 95% CI values, denoted ˆ cps,L ˆ cps,U P wo,j ,and P wo,j . A maximal difference

in our blended estimates could be

conceived of as arising from taking the

upper bounds of both our direct

estimates and synthetic estimates and

weighting these two upper bound values

by their precision, as is done in our

overall blended estimate, and then

taking the lower bounds of both our

direct estimates and synthetic estimates

and again weighting these two lower

bound values by their precision. The

weights—although complex functions of

SEs—are considered fixed for this

purpose.
Page 10 National Health Statistics Reports n Number 14 n March 11, 2009

Table II. Multinomial logistic regression results for the fixed-effects household-level model

Standard

Predictor Coefficient error t-value p-value

Age

All adults in the household are less than or equal to 30 years of age........ 1.866711 0.089851 20.78 <.001

Household includes an adult 65 years or older...................... –1.539320 0.124413 –12.37 <.001

All adults in the household are between 31 and 44 years of age, inclusive .... 0.415542 0.073732 5.64 <.001

Home ownership status

Renting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.116378 0.068147 16.38 <.001

Interaction of ‘‘renting’’ and ‘‘all adults in the household are less than or equal to

30 years of age’’ ....................................... –0.565510 0.114927 –4.92 <.001

Sex

All adults in the household are male ............................ 0.892477 0.068968 12.94 <.001
 

All adults in the household are female ........................... 0.319934 0.072536 4.41 <.001
 

Household structure

Household includes unrelated adults ............................ 0.609820 0.078856 7.73 <.001
 

Household includes children under 18 years of age .................. –0.246420 0.087131 –2.83 0.005
 

Household includes only one or two adults ........................ –0.158120 0.097213 –1.63 0.105
 

Race/ethnicity

All persons in the household are Hispanic ........................ 0.406733 0.065491 6.21 <.001
 

All persons in the household are non-Hispanic black ................. –0.198420 0.074202 –2.67 0.008
 

Household poverty status

Near-poor household ..................................... 0.326522 0.061096 5.34 <.001
 

Poor household......................................... 0.241867 0.083452 2.90 0.004
 

Other demographics

Household includes at least one adult with a job or business............. 0.402410 0.076572 5.26 <.001

At least one person in the household has a 4-year college degree or higher

education............................................ –0.174650 0.050613 –3.45 0.001

Number of persons in the household............................ –0.104420 0.037508 –2.78 0.006

State-level estimate of the number of wireless subscribers per capita

Estimate from December 2006 or June 2007, depending on date of interview . . 5.047416 2.055141 2.46 0.015

State

Alabama ............................................. 1.272225 0.238382 5.34 <.001
 

Alaska .............................................. 1.417395 0.469514 3.02 0.003
 

Arizona .............................................. 1.607334 0.256893 6.26 <.001
 

Arkansas ............................................. 2.014621 0.200072 10.07 <.001
 

Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.179756 0.197636 5.97 <.001
 

Connecticut ........................................... –0.154310 0.356241 –0.43 0.665
 

Delaware. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.155140 0.173824 –0.89 0.373
 

District of Columbia ...................................... –2.797410 1.508089 –1.85 0.065
 

Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.165666 0.133942 8.70 <.001
 

Georgia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.232165 0.171628 7.18 <.001
 

Hawaii .............................................. 0.252015 0.169125 1.49 0.137
 

Idaho ............................................... 2.273645 0.333961 6.81 <.001
 

Illinois. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.296925 0.173017 7.50 <.001
 

Indiana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.497265 0.331355 4.52 <.001
 

Iowa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.035218 0.469920 4.33 <.001
 

Kansas .............................................. 1.393183 0.218114 6.39 <.001
 

Kentucky ............................................. 1.879856 0.413259 4.55 <.001
 

Louisiana............................................. 1.079136 0.231553 4.66 <.001
 

Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.782551 0.380313 4.69 <.001
 

Maryland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.486007 0.152081 3.20 0.002
 

Massachusetts ......................................... 0.344432 0.136055 2.53 0.012
 

Michigan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.674510 0.273125 6.13 <.001
 

Minnesota ............................................ 1.563082 0.239372 6.53 <.001
 

Mississippi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.179626 0.340533 6.40 <.001
 

Missouri ............................................. 0.672953 0.304624 2.21 0.028
 

Montana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.829946 0.351831 2.36 0.019
 

Nebraska............................................. 1.866004 0.333427 5.60 <.001
 

Nevada.............................................. 0.379044 0.156361 2.42 0.016
 
cps)

2
 

blended,L

1

/(SEp,j
 cps,L

P

ˆ wo, j = Pˆ wo, j +

cps)

2 de) [1/(SE 2] p, j +1/(SEp, j

de)2
 

de,L

1

/(SEp, j
 DEwo, j
 , cps)

2 de) [1/(SE 2] p +1/(SEp, j

cps)

2
 

blended,U

1

/(SEp,j
 cps,U

P

ˆ wo, j = Pˆ wo, j +

cps)

2 de) 1 2 /(SE p, j +1/(SEp, j

de)2

de,U

1

/(SEp, j DE . wo, j 1/(SE cps)

2 de) [ 2] p +1/(SEp, j

[ ]

t

National Health Statistics Reports n Number 14 n March 11, 2009 Page 11

Table II. Multinomial logistic regression results for the fixed-effects household-level model—Con.

Standard

Predictor Coefficient error t-value p-value

New Hampshire ........................................ 1.136946 0.255660 4.45 <.001
 

New Jersey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.170389 0.202940 0.84 0.402
 

New Mexico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.004247 0.667762 3.00 0.003
 

New York. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.530698 0.122055 4.35 <.001
 

North Carolina ......................................... 1.333125 0.185659 7.18 <.001
 

North Dakota .......................................... 1.243878 0.417006 2.98 0.003
 

Ohio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.229178 0.213456 5.76 <.001
 

Oklahoma ............................................ 2.236250 0.406260 5.50 <.001
 

Oregon .............................................. 1.615444 0.228845 7.06 <.001
 

Pennsylvania .......................................... 1.048467 0.266023 3.94 <.001
 

Rhode Island .......................................... 0.564256 0.622844 0.91 0.366
 

South Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.929321 0.264777 7.29 <.001
 

South Dakota .......................................... 0.228156 0.257332 0.89 0.376
 

Tennessee ............................................ 1.119440 0.262129 4.27 <.001
 

Texas ............................................... 1.454220 0.149066 9.76 <.001
 

Utah. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.551921 0.344626 7.40 <.001
 

Vermont ............................................. 0.750223 0.479845 1.56 0.119
 

Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.987165 0.198595 4.97 <.001
 

Washington ........................................... 1.195146 0.204184 5.85 <.001
 

West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.273231 0.507029 4.48 <.001
 

Wisconsin ............................................ 1.757524 0.416058 4.22 <.001
 

Wyoming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.720274 0.127978 5.63 <.001
 

Constant

Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –7.645990 1.725050 –4.43 <.001

DATA SOURCES: CDC/NCHS, National Health Interview Survey (NHIS), 2007, and Federal Communications Commission, Automated Reporting Management Information System, 2006–2007.

NHIS sample size = 28,492 households.

In equation form, we have for our

widest plausible interval:

and

Clearly, this widest plausible interval is

a very conservative measure of

uncertainty because it uses the upper

bounds of both direct and synthetic

estimates simultaneously and the lower

bounds of both direct and synthetic

estimates simultaneously. Although they

are not CIs and almost certainly exceed

in width the CIs that might be obtained

from a full bootstrapping of the blended

estimates, we believe these estimates of

uncertainty nevertheless serve a useful

purpose. These estimates are included in

the Table.

Calibration of the estimates

The values of our blended estimates

have been adjusted to give an overall

weighted average that equals the overall

NHIS wireless-only prevalence rate.

This process is known in the small area

estimation literature as ‘‘raking’’ the

blended estimates, and it is a process

usually undertaken when the stateweighted average prevalence rate from

he blended methodology does not

perfectly match the overall directly

estimated national rate (6).

First, we calculated each state’s

total weight from the CPS data, formed

as the product of the state’s average

survey-weight times the state’s sample

size in the CPS. Next, we multiplied

each state’s total weight by its blended

estimate. Then, we summed these state

products and divided this sum by the

total of all the states’ CPS weights. This

yielded a rate of .1416 for households.

Finally, we formed the ratio of the

overall national NHIS rate to the

state-weighted blended estimate rate,

which is the ‘‘raking factor.’’ This

‘‘raking factor’’ was applied to each

state’s blended estimate to arrive at a

final raked blended estimate. The direct

estimate from the NHIS was .147 for

households, and this yielded a ‘‘raking

factor’’ of 1.0381. The raking factor for

the adult estimates was 1.0599.

The small size of these raking

factors (i.e., the discrepancy between the

directly estimated national rate and the

initial state-weighted average prevalence

rate from the blended methodology)

provides empirical support for the

appropriateness of our modeling

procedures. Bias introduced by the

modeling procedures, if any, was minor.
Page 12 National Health Statistics Reports n Number 14 n March 11, 2009

Table III. Multinomial logistic regression results for the fixed-effects adult-level model

Standard

Predictor Coefficient error t-value p-value

Age

All adults in the household are less than or equal to 30 years of age ...................... 1.799508 0.090836 19.81 <.001

Household includes an adult 65 years or older ................................... –1.382090 0.134494 –10.28 <.001

All adults in the household are between 31 and 44 years of age, inclusive .................. 0.301825 0.072248 4.18 <.001

Home ownership status

Renting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.143872 0.067472 16.95 <.001

Interaction of ‘‘renting’’ and all ‘‘adults in the household are less than or equal to 30 years of age’’ .... –0.565430 0.112602 –5.02 <.001

Sex

All adults in the household are male .......................................... 0.873861 0.073431 11.90 <.001

All adults in the household are female ......................................... 0.277123 0.073664 3.76 <.001

Household structure

Household includes unrelated adults .......................................... 0.616546 0.079639 7.74 <.001

Household includes children under 18 years of age ................................ –0.208900 0.091222 –2.29 0.023

Household includes only one or two adults ...................................... –0.155960 0.094759 –1.65 0.101

Race/ethnicity

All persons in the household are Hispanic ...................................... 0.356096 0.072708 4.90 <.001

All persons in the household are non-Hispanic black ................................ –0.128510 0.075034 –1.71 0.088

Household poverty status

Near-poor household ................................................... 0.362912 0.063380 5.73 <.001

Poor household ....................................................... 0.363780 0.083803 4.34 <.001

Other demographics

Household includes at least one adult with a job or business ........................... 0.473183 0.081272 5.82 <.001

At least one person in the household has a 4-year college degree or higher education ........... –0.194480 0.054364 –3.58 <.001

Number of persons in the household .......................................... –0.111140 0.037656 –2.95 0.003

State-level estimate of the number of wireless subscribers per capita

Estimate from December 2006 or June 2007, depending on date of interview ................ 4.280246 2.203306 1.94 0.053

State
 

Alabama ........................................................... 1.091400 0.268269 4.07 <.001
 

Alaska ............................................................. 1.617397 0.555650 2.91 0.004
 

Arizona ............................................................ 1.480033 0.258784 5.72 <.001
 

Arkansas ........................................................... 1.918280 0.219244 8.75 <.001
 

Colorado . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.105028 0.189786 5.82 <.001
 

Connecticut.......................................................... –0.250100 0.381567 –0.66 0.513
 

Delaware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.493910 0.309229 –1.60 0.111
 

District of Columbia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –1.915190 1.650917 –1.16 0.247
 

Florida. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.092798 0.130196 8.39 <.001
 

Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.161430 0.177353 6.55 <.001
 

Hawaii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.385860 0.088463 4.36 <.001
 

Idaho ............................................................. 2.182128 0.354031 6.16 <.001
 

Illinois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.250206 0.185081 6.75 <.001
 

Indiana ............................................................ 1.443714 0.373140 3.87 <.001
 

Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.624521 0.558052 2.91 0.004
 

Kansas ............................................................ 1.273341 0.206036 6.18 <.001
 

Kentucky ........................................................... 1.865205 0.423037 4.41 <.001
 

Louisiana ........................................................... 1.073218 0.210920 5.09 <.001
 

Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.586943 0.389421 4.08 <.001
 

Maryland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.489238 0.145614 3.36 0.001
 

Massachusetts........................................................ 0.314262 0.141998 2.21 0.028
 

Michigan ........................................................... 1.624691 0.291254 5.58 <.001
 

Minnesota .......................................................... 1.489495 0.233542 6.38 <.001
 

Mississippi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.214357 0.343132 6.45 <.001
 

Missouri ............................................................ 0.512488 0.277719 1.85 0.066
 

Montana ........................................................... 0.252518 0.388014 0.65 0.516
 

Nebraska ........................................................... 1.856366 0.359632 5.16 <.001
 

Nevada ............................................................ 0.357242 0.124442 2.87 0.004
 

New Hampshire ....................................................... 0.782579 0.273309 2.86 0.004
 

New Jersey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . –0.049300 0.182665 –0.27 0.787
 

New Mexico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.958091 0.645469 3.03 0.003
 

New York . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.528292 0.111597 4.73 <.001
 

North Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.192146 0.193867 6.15 <.001
 

North Dakota......................................................... 1.502527 0.487668 3.08 0.002
 

Ohio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.190100 0.208526 5.71 <.001
 
National Health Statistics Reports n Number 14 n March 11, 2009 Page 13

Table III. Multinomial logistic regression results for the fixed-effects adult-level model—Con.

Predictor Coefficient

Standard

error t-value p-value

Oklahoma ........................................................

Oregon ..........................................................

Pennsylvania ......................................................

Rhode Island ......................................................

South Carolina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

South Dakota ......................................................

Tennessee ........................................................

Texas ...........................................................

Utah. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Vermont .........................................................

Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Washington .......................................................

West Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Wisconsin ........................................................

Wyoming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Constant

Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 

2.133292

1.598406

0.874146

0.136707

1.794088

0.300735

1.273948

1.404436

2.377257

0.576505

0.901419

1.167482

2.015832

1.532290

0.991193

–7.029340

0.383744

0.239406

0.278287

0.670396

0.254067

0.273356

0.266752

0.149124

0.371590

0.515766

0.225554

0.200486

0.648016

0.430925

0.132367

1.848191

5.56

6.68

3.14

0.20

7.06

1.10

4.78

9.42

6.40

1.12

4.00

5.82

3.11

3.56

7.49

–3.80

<.001

<.001

0.002

0.839

<.001

0.272

<.001

<.001

<.001

0.265

<.001

<.001

0.002

<.001

<.001

<.001

DATA SOURCES: CDC/NCHS, National Health Interview Survey (NHIS), 2007, and Federal Communications Commission, Automated Reporting Management Information System, 2006–2007.

NHIS sample size = 53,770 adults.
Page 16 National Health Statistics Reports n Number 14 n March 11, 2009

Acknowledgments

This research was supported by a grant from the Robert Wood Johnson Foundation to the State Health Access Data Assistance Center at the

University of Minnesota. The authors are solely responsible for the content of this report. Gestur Davidson of the University of Minnesota was

the primary author of the ‘‘Technical Notes.’’ We thank Jane Gentleman, Graham Kalton, Chris Moriarity, and Allen Schirm for their comments

on earlier versions of the ‘‘Technical Notes.’’

Suggested citation

Blumberg SJ, Luke JV, Davidson G, Davern

ME, Yu T, Soderberg K. Wireless substitution:

State-level estimates from the National Health

Interview Survey, January–December 2007.

National health statistics reports; no 14.

Hyattsville, MD: National Center for Health

Statistics. 2009.

Copyright information

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public domain and may be reproduced or

copied without permission; citation as to

source, however, is appreciated.

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DHHS Publication No. (PHS) 2009–1250

CS200764-A

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