1

 Citation: Constance R. Dunham, Fritz J. Scheuren, and Douglas J. Willson (1998), “Methodological Issues

in Surveying the Nonbanked Population in Urban Areas,” in Proceedings of the Survey Research Methods Section,

American Statistical Association (1998), pp. 611-16.

METHODOLOGICAL ISSUES IN SURVEYING 

THE NONBANKED POPULATION IN URBAN AREAS1

by Constance R. Dunham, Office of the Comptroller of the Currency, 

Fritz J. Scheuren, Ernst & Young, LLP, and Douglas H. Willson, Macro International Inc.

Constance R. Dunham, OCC, Economics Dept. 2-1, 250 E Street, S.W., Washington D.C., 20219

Reprinted with permission.

Key Words: Banking; Financial access; Low-income

populations; Sensitive survey topics.

I. Introduction

The Office of the Comptroller oftheCurrency (OCC)

is responsible for regulating and supervising the national

banking system, which comprises all banks with a federal

charter and about 55 percent of the total assets of the U.S.

banking system. The OCC’s major objectives include

ensuring bank safety and soundness and seeking to assure

fair accessto financialservicesfor all Americans. As part

of the latter effort, the OCC has sponsored a Survey of

Financial Activities and Attitudes (Survey) to better

understand why millions of adults in the United States

rarely, if ever, conduct their regular financial activities,

particularly savings and transaction activities, through

banks. 

We define “nonbanked” individuals as those who

conduct their financial activities primarily outside the

banking system, including those who may have a small,

inactive bank account but who primarily patronize

nonbanks when conducting their regular financial

activities. Thus, our “nonbanked” target group includes

more individuals than those conventionally termed the

“unbanked,” the 13 percent of American households

(containing over 30 million adults) who have no

transactions accounts at any depository institution [1]. 

Early on in the development of the Survey, the OCC

convened a Forum on approaches to servicing the

nonbanked. Participants included knowledgeable

representatives of banks, check cashing and bill-payment

firms, technology providers, consumer organizations,

financialsector consultants, academic experts, and policy

makers. The discussions revealed that survey information

on the nonbanked would be useful to many; that

surprisingly few financial organizations conduct surveys;

and that few of those studies are publicly available [2].

Accordingly, the OCC sought to supplement the

sparse information available on the nonbanked, and to

provide several public benefits. The Survey will generate

new data on financial activities of the nonbanked, the

costs they thereby incur, their attitudes toward banks, and

any prior experience with banks. This information may

help reduce the uncertainty that banksface in considering

how to serve this potential market, and may help inform

bank efforts to design more appropriate products and

more effective outreach methods to increase financial

access. Public availability of the OCC’s survey results,

the survey instrument, and the survey methodology may

also help reduce costs of private-sector market research,

product development, and outreach efforts. 

Moreover, the findingsfrom the Survey may help the

OCC carry out its supervisory responsibilities more

effectively. By better understanding the factors limiting

financial access to, and use of, bank-provided financial

services, the agency can better propose waysfor banks to

considerremoving barriers; identify banking practices that

may inadvertently limit financial access; and assess the

extent to which banking practices meet local financial

service needs. 

Several factors posed special challenges to the

development of a survey design that would achieve good

coverage and high response rates. These included

characteristics of the Survey’s target population, the

sensitive topics addressed by the Survey (personal

financial matters), and the paucity of prior research on the

topic. This paper discusses the ways in which we met

those challenges in developing the survey design. 

II. Development of the Survey

A review of the literature revealed that little research

has been carried out on the nonbanked or on overlapping

populations (the unbanked, financial transactions of lowand moderate-income households, customers of alternative financial service providers, etc.) [3].

 OCC researchers recognized the need to supplement

their knowledge of conventional bankingmarketsin order

to improve the quality of the Survey instrument and its

relevance to the nonbanked. This effort entailed: (1)

analyzing national survey data on overlapping populations

to betteridentify our target population;(2)identifying key

issues raised by participants in the OCC Forum; (3)

conducting ethnographic work (in English and Spanish)

to identify the questions, common financial terms, and

answer options most relevant for the target population;

(4) conducting several pre-tests and a set of cognitive
612

interviews, all with interviewer de-briefings, to test core

Survey questions; (5) conducting a nationwide pilot

survey to further test core Survey questions; and (6) 

providing drafts of the Survey instrument to bankers,

community representatives, researchers, policy makers,

and survey experts for review [4]. 

One national survey data set we analyzed was the

Federal Reserve’s Survey of Consumer Finances (SCF),

the source of the estimate of 13 million unbanked U.S.

households, which comprise a substantial subset of the

nonbanked. We used 1995 SCF data to develop a rough

proxy measure of nonbanked status and socio-economic

characteristics of the nonbanked [5]. 

Based on thisrough proxy measure, about 90 percent

of the nonbanked are low- and moderate-income

households. The proportions of nonbanked individuals

were particularly high for Hispanics (55 percent) and

blacks(49 percent). Even controlling for income, wealth,

and other characteristics, the percent nonbanked was

relatively high for those two groups, suggesting that they

may face higher barriers. A relatively low proportion of

non-Hispanic whites were nonbanked (15 percent), but

non-Hispanic whites are so numerous (75 percent of all

U.S. households) that their nonbanked portion comprises

about half of our target population. Together, these

considerationssupported our choice ofthe sampling strata

discussed below [6]. Since Spanish is the primary

language for a large portion of Hispanics in the United

States, the Survey is being conducted in English and

Spanish, at the respondent’s discretion [7]. 

The OCC’s nationwide pilotsurvey was administered

to 927 randomly-selected adults in 1997 to test core

questions of the Survey, as part of an ongoing national

omnibus survey conducted by University of Maryland’s

Survey Research Center (SRC). SRC’s survey

methodology entails random digit dialing (RDD)

telephone interviews in English. This methodology

limited pilot survey respondents to English-speaking

individuals with telephones, so we expected that the pilot

survey would obtain lower coverage of our target

population than surveys (such as the SCF) that are

administered in person and in English or Spanish. For

example, we expected under-representation of low-income

individuals (less likely to have telephones) and Hispanic

households (less likely to speak English). 

Indeed, while the estimated proportion of unbanked

households in the 1995 SCF was 12.9 percent, the

estimated proportion of unbanked individuals (an upper

bound of the percentage of unbanked households) in the

1997 OCC pilot survey was only 10.1 percent. Thus,

while the primary purpose of the pilot survey was to test

core Survey questions, this outcome furthersupported the

OCC’s aim of developing a survey design appropriate to

the target population, one likely to achieve good coverage

and high response rates. 

III. The Survey Design 

The survey design was developed around several key

issues: the financial access policy issues studied, the

geographical areas surveyed, the target population, and

the survey mode.

Policy issues studied. The Survey is intended to

generate new data on how nonbanked individuals conduct

financial activities (i.e., receiving and making payments,

saving, making remittances, using credit cards, and

borrowing); the institutions they use (supermarkets, post

offices, check cashing outlets, depository institutions, and

bill-paying and wire transfer services); their attitudes

toward banks and other financial service providers; any

prior experience with banks; and costs they incur in

carrying out financial activities. 

The Survey will also obtain this information for

individuals who are similarly situated (in terms of socioeconomic characteristics and geographic location) but

who primarily obtain services from banks or other

depository institutions (and whom we term “bankserviced”). Learning why these low-income households

have become and have remained bank customers may

revealmuch about why their nonbanked counterparts have

not. Patterns of terminations and item refusals in our pilot

survey showed that questions about dollar values of

assets and liabilities are particularly sensitive.

Accordingly, the Survey instrument contains no questions

on dollar asset values. 

Geographical areas surveyed. The target

population for the Survey are low- and moderate-income

residents of Los Angeles County and New York City.

While a national probability sample would have been

desirable, cost and our limited knowledge of the

demographics of the nonbanked precluded usfrom taking

that approach. A very large initial screening sample

would have been required to reach a sufficient number of

nonbanked respondents, as we learned from the pilot

RDD survey. In addition, we lacked sufficient data to

identify and oversample primary sampling units (PSUs)

with large expected proportions of the nonbanked. 

Limiting the geographical scope of the Survey has

several key advantages. It permits us to better interpret

the results in light of local effects, such as local

regulations or institutional practices. It permits us to

collect a large enough sample size per urban area (1,000)

to generate statistically significant results in each. It also

resembles the geographic scope of market research

conducted by many banks, which may make it easier for

some of them to adapt the Survey to other urban areas. 
613

We selected New York and Los Angeles as survey

sites based on information developed in the literature

review and obtained from OCC compliance examiners.

Thisshowed that New York City and Los AngelesCounty

are among the top few areasin the U.S. with large clusters

of innovative bank practices intended to reach the

traditionally nonbanked [8]. In those two areas the

Survey may provide useful baseline data, to which the

results of future surveys may be compared. This

comparison could reveal which bank efforts have been

most effective in reaching and servicing the nonbanked. 

Target population group. The Survey focuses on

respondents’ financial activities and their attitudestoward

financial service providers. The unit of analysis is the

individual, and no responses are permitted by proxy.

Interviewers randomly select one adult per dwelling unit.

This approach provides information on a wider range of

nonbanked individuals than would selection of only the

financiallymost knowledgeable individual within the unit,

by including those who are least well-connected to the

banking sector. 

For each of the two urban areas, we developed a

multistage stratified design. The first stage sample is of

1990 Censustractsstratified by income (low or moderate)

and race and ethnicity (predominantly white,

predominantly black, and predominantly Hispanic), plus

an integrated low- and moderate-income stratum. Tracts

were ineligible if a large portion of the residents spoke a

primary language other than English or Spanish, or if

residents were predominantly full-time students [9]. A

total of 21 Censustracts were sampled per urban area (i.e.,

three tracts per stratum, except in Los Angeles, where the

only two eligible non-Hispanic white, low-income tracts

were selected with certainty and four moderate-income

Hispanic tracts were sampled). 

Based on our analysis of SCF data, we expect to

interview approximately 50 percent nonbanked and 50

percent bank-serviced individuals. Because of the

uncertainty, we initially released half the sample in each

urban area, mainly in low-income strata where we expect

to find high rates of the nonbanked. If our expectations

are met, we will release the second half of the sample so

as to achieve an equal allocation of sample per stratum.

Otherwise, we will over-sample areas with higher interim

percentages of nonbanked in order to achieve the 50

percent target. 

Survey mode. The second stage sample is of

dwelling unit addresses in the sampled Census tracts.

Sampled addresses are designated as in-person or

telephone interviews, based on whether or not the address

can be linked to a telephone. If a telephone link exists,

the address is part of the telephone sample. If not (due to

unlisted telephone numbers, gaps in the coverage of

telephone lists, or dwelling units without a telephone), it

is part of the in-person sample [10]. About 50 percent of

the sample was drawn for each mode. 

Other modal designs were considered and rejected. 

A single-mode in-person survey would have been too

costly. A single-mode telephone survey would have had

inadequate coverage, especially of low-income

individuals, who are less likely to have telephones [11].

The low average level of literacy in our target population

argued against a mail survey [12]. The dual-mode

(telephone/in-person) approach helps us achieve coverage

and response rate goals at reasonable cost. 

A dual-mode approach helps us achieve our 75

percent response-rate target in other ways as well.

Respondents can be reached through alternative modes if

it will increase the likelihood of obtaining the interview

[13]. Some respondents call a toll-free number to be

interviewed. Respondents who do not answer their listed

telephones are visited in person. All respondents may reschedule an interview to a more convenient time. By

adjusting the mode to the respondent, we expect to be

able to achieve our response rate and coverage goals more

efficiently, and to reduce perceived respondent burden.

IV. Sample Design

Sampling frame. We employed a single, area

sampling frame. We developed a comprehensive list of

addresses for Census tracts in which we are surveying.

We then carried out a telephone directory match on the

entire address frame for those tracts. 

Consideration had been given to using a dual frame,

with RDD used to select telephone interview cases. We

decided against this for several reasons. First, a single

frame approach enabled usto send an advance letter from

the Comptroller of the Currency to every sample

household. Advance letters from federal government

sponsoring agencies can raise response rates [14]. This

suggests that an advance letter should be sent to every

sample household, to avoid biasing the sample. With a

dual frame approach, we could not send out advance

letters to all respondents, since we would not have

addresses linked to telephone numbers for a part of the

RDD sample. This could result in a lower response rate

for that part of the sample.

The area frame closely links sample households to

specific Censustracts, while an RDD identifiestelephone

numbersthat can be only loosely linked to specificCensus

tracts. RDD generates enough out-of-sample area

telephone numbers that RDD interviews would need to

start with a question about the respondent’s location in

orderto screen out the sizeable number of geographicallyineligible individuals. That question could increase
614

respondents’ concerns about the confidentiality of their

answers. Since personal finance is a highly sensitive

topic, heightened concern over confidentiality could

impair response rates. In contrast, the area frame

generates sufficiently few out-of-sample interviews

(caused, for example, by reassignment of a telephone

number; by respondents moving outside the survey area

but keeping their telephone number; or errors in directory

listings), that we need ask about respondent location only

at the end of a telephone interview, which does not affect

the response rate. 

Finally, a single frame survey generates results that

are easier to analyze and interpret than those from a dual

frame survey. This is a clear advantage for the Survey,

through which the OCC aims to demonstrate a viable

method by which banks can collect and assessinformation

on the nonbanked. 

 Representative sample. We chose a multi-stage

stratified random sample design. A purposive selection of

several neighborhoods per city might have enabled us to

better interpret local area effects, but research on the

nonbanked is still too sparse to reliably guide purposive

selection of specific survey sites. If we chose

neighborhoods based on inaccurate prior views, we might

obtain more precise but less useful results. A multi-stage

stratified design, in contrast, generates results that are

unbiased, if somewhat less precise. 

We decided to accept the lossin precision in favor of

obtaining unbiased results. Having unbiased results will

enhance our confidence in policy recommendations that

are based on the statistically significant findings.

We employed Census tracts as PSUs and selected

them with probabilities proportional to an estimate of the

size of the nonbanked population within each tract.

Samples of equal size were selected from the telephone

(i.e., listed) and non-telephone (i.e., unlisted telephone

and non-telephone) strata. To estimate the variances for

themultistage stratified design,we selected 1,000 samples

of Census tracts, estimated the variance for a sample

proportion conditional on the selected tracts for each

sample, and then accounted for the effect of clustering on

each sample using the relationship between variances of

a single-stage clustersample and a simple random sample

of the same size. 

The variances from the multistage stratified design

depend in part on D, the intracluster correlation

coefficient, which measures the heterogeneity within a 

cluster (e.g., a Census tract) relative to the heterogeneity

within the population as a whole. In most situations, D is

positive because clusters are relatively homogeneous

when compared to the population. Such cluster samples

are less efficient than simple random samples, and their

variance of estimates will be larger than variances of

estimates from a simple random sample of the same size.

On the other hand, the more heterogeneous each cluster

the more similar is its variance to that developed from a

simple random sample.

To obtain some idea about probable values of D, we

used 1990 Census data for the relevant tractsin New York

City and Los Angeles County to construct an estimate for

the population. For the proportion of households that had

interest, dividend, and net rental income, in the

appropriate tracts and in each stratum, D was

approximately 0.02 [15].

With D = 0.02, random selection of tracts will

generate estimatesin Los Angeles within 5.5 percent, and

in New York within 4.5 percent, of true values with 95

percent confidence. (In addition, to account for random

selection of eligible adults within dwelling units,

estimated variances should be increased a further 20 to

30 percent for either a purposive or multi-stage stratified

random sample design.) 

We consider the decrease in precision for these

estimates that is due to clustering an acceptable cost

associated with gaining representation of the larger group

of tracts underlying the cluster sample.

V. Challenges in Developing the Sampling Frame

PSUs. We chose Census tracts as our PSU, since the

coverage of telephone lists at the block level is highly

variable. Some blocks had many households but no listed

telephone numbers. (We do not know if this is a greater

problem in low-income areas.) 

Base lists. Most area probability samples require

development of an up-to-date list of residential addresses.

This usually requires having an enumeration team count

and list dwelling units in each survey site. The efficiency

of this process is greatly improved if the team starts with

a comprehensive base list that excludes geographically

ineligible units. 

One commonly-used type of base list are the

consumer databases compiled by firmssuch asMetroMail

and Donnelly/ First Source. These lists contain data on

the great majority of U.S. households [16]. However,

non-telephone and unlisted telephone households are not

included in these base lists before augmentation. In large

urban areas, the coverage of these listsis under 50 percent

in many Census tracts, even after augmentation. If we

used lists that had such poor coverage in our survey sites,

enumerators would have had much more work to develop

up-to-date residential addresses. 

For our purposes, a better source was the U.S. Postal

Service Computerized Delivery Sequence (CDS) file,

which contains every physical mailing address that

receives mail delivery. Extracts from the CDS file can be
615

obtained from a variety of direct mail vendors. This list

distinguishes business and residential units and includes

zip+4, which can be used to closely match the addresses

to Census tracts. We developed a base list of addresses,

mapped the areas using GIS software, and produced an

enumeration manual with street maps, address lists, and

instructions. Enumerators updated the base list by

walking each street within each PSU of our sample. 

Telephone-address links. Another challenge came

in matching telephone numbersto dwelling unit addresses

in multi-unit buildings. Residents of multi-unit buildings

do not usually report their unit number to the telephone

company for the listing. Thus, while we could match a

telephone number to a building, we could not match it to

a specific dwelling unit.However,we needed the dwelling

unit number to draw the sample and send each respondent

an advance letter. To solve this problem, we gave

enumerators lists of multi-unit building addresses and

names of individuals with telephones. They verified exact

dwelling unit addressesfrom tenant listings or through the

building manager. 

VI. Conclusions

We encountered a number of challenges in

developing a survey design that would be consistent with

the topic and target population of the OCC Survey of

Financial Activities and Attitudes. Our solutions should

improve response rates, coverage, and reliability of the

data. These solutions may also facilitate subsequent

market research efforts undertaken by banks and othersto

improve financial access for traditionally nonbanked

individuals.

Notes and References:

[1] Kennickell, A.B. et al. (1997) “Family Finances in

the U.S.: Recent Evidence from the Survey of

Consumer Finances,” Federal Reserve Bulletin

(January) p. 7. 

[2] The Forum proceedings, OCC, Financial Access in

the 21st Century (1997), Washington, D.C., can be

ordered from the OCC Communications Department

at 202-874-4700 or downloaded from: 

http://www.occ.treas.gov/occfinac.pdf.

[3] See Kennickell, A.B. et al., op. cit.; Caskey, J.P.

(1997) Lower Income Americans, Higher Cost

Financial Services, Filene Research Inst.: Madison,

WI; Bond, P. and R. Townsend (1996) “Formal and

Informal Financing in a Chicago Ethnic Neighborhood, Federal Reserve Bank of Chicago

Economic Perspectives, (July/Aug.); Caskey, J.P.

(1994) Fringe Banking, Russell Sage Foundation,

New York.

[4] Advice from MargueriteRobinson (Harvard Institute

for International Development) regarding the

importance of preliminary ethnographic work is

gratefully acknowledged, as is her input to the

ethnographic work carried out for the OCC by

Aguirre International. Advice from Deborah Stone

(then at the Bureau of Labor Statistics, now at

SAMHSA/CSAP) on survey instrument design and

implementation, and her thoughtful review of

numerous survey instrument drafts, are also gratefully

acknowledged.

[5] We estimated the number of nonbanked households

based on an observation by J.P. Caskey that many

nonbank customers have bank accounts but lack

sufficient balances to cover their income checks

(Fringe Banking, op. cit., p. 73.). Our proxy is the

sum of all households without a transaction account

at a depository institution and households with

aggregate deposit balances of under $200.

[6] Other socio-economic factors we considered had

weaker independent effects on nonbanked status.

These factors included age, net worth, metro/nonmetro location, and education level.
616

[7] About 78 percent of Hispanic households in the U.S.

speak Spanish at home; about half of these report that

they do not speak English “very well.” U.S. Dept. of

Commerce, Bureau of the Census (1993) We the

American ...Hispanics. Washington, D.C. 

[8] In a third such area, Chicago, ongoing survey work

on similar populationsis being conducted. See Bond,

P. and R. Townsend, op. cit. 

[9] The number of nonbanked Asian-Americans may be

under-represented in surveys,such asthe SCF, which

are conducted only in English and Spanish.

Nevertheless, Asian-Americans were not chosen as a

stratum in the OCC sampling design because of their

small expected numbers among the nonbanked and

because the multiple Asian languages would greatly

increase the level of effort for this exploratory

research.

[10] Schwarz, N. (1997) "Questionnaire Design: The

Rocky Road from Concepts to Answers," in Survey

Measurement and Process Quality, Lyberg L. et al.

(eds.), John Wiley & Sons, Inc., New York.

[11] Special runs of Current Population Survey data 

(three-year average, 1994-1996) were carried out for

the OCC by the Census Bureau. The data show that

6 percent of U.S. households had no telephones. This

percentage is considerably higher for low-income

households (under $15,000 income) who are nonHispanic white (10 percent), Hispanic (25 percent) or

black (23 percent). See also Keeter, S. (1995)

“Estimating Telephone Noncoverage Bias with a

Telephone Survey,” Public Opinion Quarterly, Vol.

59: pp. 196-217; and Thornberry Jr, O.T. and J.T.

Massey (1988) “Trends in United States telephone

coverage across time and subgroups,” Ch. 3 in

Groves, M. et al. (eds.), Telephone Survey

Methodology, John Wiley & Sons.

[12] National Center for Educational Statistics (1993) 

Adult Literacy in America. Washington D.C., App.

Table 2.3, pp. 144-45.

[13] Dillman, D.A. and J. Tarnai, Ch. 32 in Telephone

Survey Methodology, op. cit., pp. 509-28.

[14] See Dillman, D.A., Gallegos, J.G., and Frey, J.H.

(1976) “Reducing refusal rates for telephone

interviews,”Public Opinion Quarterly, vol.40, No. 1,

pp. 66-78.

[15] We assumed an intracluster correlation coefficient

(D) of about 0.02 based on Census data on the

proportion of households in the appropriate tracts

(Pij) and each stratum j (Pj) in each urban area that

had interest, dividend, and net rental income in

1989. For a fixed number of M households per tract,

the intracluster correlation coefficient for the

population in each stratum can be defined as: 

We approximate D for each stratum using the average

number of households in each tract within stratum j,

Mj

, in place of M. Values of D averaged .023 for

Los Angeles County strata (ranging from .014 to

.037) and .033 for New York City strata (ranging

from .020 to 054). Values of D for the sample will

probably be lower, as households are sub-sampled

within each tract.

[16] These consumer databases are based on sourcessuch

as white pages telephone directories, department of

motor vehicle records, and real estate transactions.

The addresses are geocoded using U.S. Postal

Service products, to attach a Census tract and zip+4.