21 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

Study Objectives and Methodology 

Study Objectives 

The purpose of this study was to conduct a nationwide survey of FDIC-insured depository 

institutions to assess their efforts to serve unbanked and underbanked individuals and families. 

Section 7 of the Federal Deposit Insurance Reform Conforming Amendments Act of 2005 

(“Reform Act”) (Pub. L. 109-173) requires the FDIC to conduct ongoing surveys “on efforts by 

insured depository institutions to bring those individuals and families who have rarely, if ever, held a 

checking account, a savings account or other type of transaction or check cashing account at an 

insured depository institution into the conventional finance system.” 

The Reform Act further requires that the FDIC consider the following factors in conducting the 

survey: (1) the extent to which insured depository institutions promote financial education and 

financial literacy outreach; (2) the financial education efforts that appear to be the most effective in 

bringing unbanked individuals and families into the conventional finance system; (3) the efforts of 

insured institutions to convert unbanked money order, wire transfer, and international remittance 

customers into conventional account holders; (4) the cultural, language and identification issues as 

well as transactions costs that appear to most prevent unbanked individuals from establishing 

accounts; and (5) an estimate of the size and worth of the unbanked market in the United States.5

 

The FDIC retained Dove Consulting in 2007 to design and administer its initial survey effort related 

to the ongoing statutory requirement. The FDIC identified three broad objectives to address the 

specific requirements of the statutory mandate: 

 Identify and quantify the extent to which insured depositories outreach, serve, and meet the 

banking needs of the unbanked and underbanked. 

 Identify challenges affecting the ability of insured depository institutions to serve the unbanked 

and underbanked, including but not limited to cultural, language, identification issues, and 

spatial/location issues. 

 Identify innovative efforts depositories use to serve the unbanked and underbanked, including 

community storefronts, small dollar loans, basic banking accounts, remittances, and other lowcost checking accounts, products and services used by the unbanked and underbanked. 

 

5 To address this last question, the FDIC is exploring the feasibility of conducting, along with the U.S. Bureau of the Census, the first national 

household survey to collect data on the numbers and demographic characteristics of unbanked and underbanked households, as well as the barriers 

they perceive when deciding how and where to conduct financial transactions. 

Chapter 

2
 22 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

Survey Design 

The survey effort designed by Dove Consulting consisted of a mail-in survey questionnaire 

administered to a sample of FDIC-insured institutions and a limited number of detailed case studies 

of surveyed banks identified as employing innovative ways of serving unbanked and/or 

underbanked consumers. The survey instrument included questions that focused on banks' financial 

education and outreach strategies; deposit, payment, and credit products offered to entry-level 

consumers; and other related topics (the survey instrument is included in Appendix A). The survey 

instrument was also designed to identify challenges insured institutions may face serving unbanked 

and underbanked customers. 

A draft survey instrument was developed by Dove Consulting with input from the FDIC. Dove 

Consulting formally pilot-tested the survey instrument with nine FDIC-insured institutions in late 

2007 to gather feedback about the questions and the process for gathering the data. The 

questionnaire was revised as necessary based on feedback from the pilot test. It was submitted by 

the FDIC to the Office of Management and Budget (OMB) on January 4, 2008, for approval under 

the Paperwork Reduction Act. The survey instrument was subsequently revised to address 

comments received during the OMB public comment period, and approval to administer the survey 

was granted to the FDIC by OMB on March 28, 2008 (OMB Form # 3064-0158). 

Prior to survey administration, extensive efforts were made by the FDIC to build awareness of the 

study effort among the industry, to encourage banks to participate in the study, and to explain the 

value of participating in the research effort to their organization’s staff. These efforts included 

communications with industry groups and publication of the study objectives and design on the 

FDIC website. 

Guaranteeing the confidentiality of individual bank survey responses from the public and bank 

regulators was important to the banks as well as the FDIC. Under the terms of the FDIC’s contract 

with Dove Consulting, no individual survey responses were returned to the FDIC or other bank 

agencies. The FDIC and other bank regulators therefore do not have access to individual survey 

responses and are not able to link any responses to individual banks. Dove Consulting collected all 

survey responses, certified that it had destroyed all individual bank identifying data or records, and 

delivered only aggregated survey results to the FDIC. 

Case Study Design 

A limited number of case studies were developed to provide information about specific strategies 

that some financial institutions have implemented to expand their customer base and serve 

underbanked consumers. Case study banks were selected on the basis of a variety of different types 

of information, including industry research and bank survey questionnaire responses. The final 

selection of banks reflects a variety of strategies to serve the unbanked as well as types of FDICinsured institutions. 

The banks chosen for case studies were selected in a two-stage process. The first set of potential 

case study banks were identified by the FDIC based on industry research prior to survey 

administration. These banks were reviewed against certain “good standing” criteria which included 

regulatory ratings of 1 or 2 for compliance, Community Reinvestment Act, and composite safety and 

soundness. 
 23 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

The second set of potential case study banks was identified by Dove Consulting based on Dove 

Consulting’s confidential review of banks’ survey responses. Banks that revealed innovative and 

successful strategies to converting unbanked and underbanked individuals into the conventional 

banking mainstream in their survey responses and that met the FDIC good standing criteria were 

added to the potential case study bank list. 

Potential case study banks were invited to voluntarily participate as case study banks by Dove 

Consulting. Dove Consulting contacted each potential bank by mail, seeking to obtain agreement to 

participate from a senior manager in each targeted organization. Banks that consented to be case 

study candidates were asked to complete the survey questionnaire and were interviewed at least once 

by Dove Consulting. Draft case studies were submitted to participating banks for their approval and 

consent. 

Population, Sample Frame, and Sample Design 

Population of Interest 

For the purposes of this study, the population was all federally insured banks and thrifts operating in 

the United States during the first quarter of the year 2007. Wholesale banks and special purpose 

banks that do not operate retail branches are not relevant for this study, and were excluded from the 

survey sample based on specialty charter and branch characteristics (i.e., assets per branch indicative 

of a money-center bank.) 

Sample Frame 

The sample frame for the study was carefully considered to allow for collection of a statistically 

useful number of responses so that differences among potential geographic and financial institution 

size stratification schemes could be examined. 

For this study, the sample frame was bank holding companies (or “unique institutions” in the FDIC 

database if they do not have a bank holding company). The sample frame included FDIC-insured 

financial institutions that operate retail bank offices. Screening criteria were used to determine the 

sample frame of U.S. banks with retail operations. (In subsequent discussions of the sampling frame, 

the sampling units are referred to as banks). Additionally, some banks were excluded from the study 

due to FDIC concerns of overburdening the institutions due to other concurrent FDIC studies. 
 24 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

OM Figure 1. Sample Frame by Number of Banks 

FDIC Bank Charters

Unique Institutions

Eligible Banks

Consolidate charters at the bank 

holding company (if applicable)

Remove wholesale/special 

purpose banking institutions

Population to be segmented 

based on asset size and 

geographic region Sample Frame 

The sample frame included 7,487 banks as shown in the table below by asset-size groupings. 

OM Figure 2. Sample Frame by Number of Banks 

# of Bank 

Charters # of Banks 

Tier 1

Top 25 banks by assets 91 25 

Tier 2

Banks with $1 Billion or more in 

assets outside the Top 25 

1,115 564 

Tier 3

Banks with less than $1 Billion in 

assets 

7,435 6,898 

Sample Frame 8,641 7,487 

Ineligible* 9 5 

Total 8,650 7,492 

*Tier 1 excluded wholesale and special purpose banks. 

Sample Size and Sample Allocation to Size Groups 

The target number of completed questionnaires was 865, based on Dove Consulting’s goal of 

achieving a balance between cost and the anticipated need for computing valid survey estimates for 

banks by tiers (asset-size groups) and geographic areas (U.S. Census Bureau Divisions). This target 

sample size was based on full participation from the top 25 banks (Tier 1), along with approximately 

20 Tier 2 banks per Census Division and more than 60 Tier 3 banks per Census Division. The 

allocation of the target number of completed questionnaires to the three asset-size strata, and the 

corresponding sampling rates (percentage of banks covered), are shown in Figure 3. 
 25 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

OM Figure 3. Sample Frame – Planned Stratum Goals and Coverage

Strata 

# of Unique 

Banks 

Survey Goal 

(Completes)

Percentage of 

Banks Covered

Tier 1 (Certainty)

Top 25 banks by assets 25 25 100.0% 

Tier 2 (Mid-Sized)

Banks with more than $1 

billion in assets outside the 

Top 25 

564 180 31.9% 

Tier 3 (Under $1 Billon)

Banks with less than $1 

billion in assets 

6,898 660 9.6% 

Total 7,487 865 11.6% 

Note that the sample allocation to size groups included an oversampling of larger banks. This 

improved sampling efficiency because of the disproportionate influence of the largest banks on 

availability of retail banking services. As shown in the above table, the target sampling rate was 100 

percent for Tier 1 banks, 32 percent for Tier 2 banks, and 10 percent for Tier 3 banks. 

Based on these target numbers of completed interviews and the response rate goals (100 percent for 

Tier 1 and a two-thirds response rate goal for Tiers 2 and 3), a stratified random sample of 1,264 

banks was selected from Tiers 2 and 3, less two that were excluded due to the burden of another 

FDIC study. Including the 25 banks in the Tier 1 group, the total sample size was 1,287 banks (25 + 

1,264 – 2). 

Stratified Sample Design 

The sample of 1,287 banks was selected as a stratified random sample from the sample frame of 

7,487 banks. The primary strata were defined by the three bank size groups: top 25 (certainties, Tier 

1), banks with total assets of $1 billion or more (Tier 2), and banks with less than $1 billion in assets 

(Tier 3). Based on the target numbers of completed questionnaires by size groups and the response 

rate goals, the 1,287 sample banks were allocated to the three size groups as follows: 25 to Tier 1, 

268 to Tier 2, and 994 to Tier 3. The corresponding sampling rates were 100 percent for Tier 1, 47.5 

percent for Tier 2, and 14.4 percent for Tier 3. 

The next level of stratification was the nine Census Divisions. Within Tiers 2 and 3, the sample was 

allocated proportionally to the nine Census Divisions. Therefore, within each tier, the selection 

probability of banks across Census Divisions was constant (except for minor variations due to the 

need to round off sample sizes to integers). The sample allocation to strata is shown in Figure 4. 
 26 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

OM Figure 4. Sample Stratification by Tier and Census Division 

Tier 1 Tier 2 Tier 3 

New England 1 14 49 

Middle Atlantic 6 39 66 

South Atlantic 4 42 129 

East South Central 2 16 91 

West South Central 0 29 157 

East North Central 8 44 202 

West North Central 1 26 187 

Mountain 1 17 58 

Pacific 2 41 55 

Total 25 268 994 

The states in each of the nine Census Divisions are: 

OM Figure 5. Listing of States by Census Division 

New England: Maine, Vermont, New Hampshire, Massachusetts, 

Connecticut, Rhode Island

Middle Atlantic: New Jersey, New York, Pennsylvania

South Atlantic: Maryland; Washington, D.C.; Delaware; West Virginia; 

Virginia; North Carolina; South Carolina; Georgia; Florida

East South Central: Kentucky, Tennessee, Alabama, Mississippi 

West South Central: Oklahoma, Arkansas, Texas, Louisiana 

East North Central: Wisconsin, Michigan, Ohio, Indiana, Illinois 

West North Central: North Dakota, South Dakota, Nebraska, Kansas, 

Missouri, Iowa, Minnesota 

Mountain: Idaho, Montana, Wyoming, Colorado, Utah, Nevada, 

Arizona, New Mexico 

Pacific: Washington, Oregon, California, Alaska, Hawaii, Guam 
 27 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

Data Collection 

A survey package was mailed to each sample bank on April 12, 2008. The packages included a 

personalized cover letter, instructions, the survey form, and a Business Reply Envelope for returning 

the survey (see Appendix A for the survey form and sample invitation letter). The survey package 

also included instructions about how to submit the completed questionnaires electronically. These 

instructions included information on how to download the MS-Word version of the survey from the 

FDIC Web site or via email. 

Data collection was performed at the highest organizational level of the bank within the United 

States. Specifically, the sampling unit was at the bank holding company level, as reflective of legacy 

arrangements. The respondent sought was the bank officer responsible for retail bank operations. 

For U.S. owned holding companies, Dove Consulting treated each of the top 25 holding companies 

as a single unit and asked the bank leadership to specify contacts for the study. 

It was necessary to collect multiple responses from some banks if they had different or legacy 

procedures at retail banks within their bank holding company. Specifically, Dove Consulting 

requested the largest banks to complete separate surveys if their policies differed by charter. For the 

one bank that submitted multiple survey forms, the mean of its item responses were incorporated in 

the dataset. 

Extensive follow-up by telephone, email, and mail was conducted by Dove Consulting and the 

FDIC to encourage strong bank participation and resolve questions. In this regard, Dove Consulting 

placed over 2,000 follow-up telephone calls. 

Once the survey data were collected and entered into a database, Dove Consulting examined the 

data for consistency. 

Survey Response 

Survey collection ended on July 11, 2008. Survey response rates, the mix between paper and 

electronic submission, and timing appear in Figures 6 and 7. The overall survey response rate was 

53.4 percent, reflecting 685 completed surveys. After adjusting for disqualifications (frame errors), 

replacements and bank consolidations/mergers, the adjusted response rate is 53.7 percent. Figure 8 

breaks down the 685 responses by Tier and Division. 
 28 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

OM Figure 6. Survey Response Rates 

 

Sample 

Frame 

Size

(A) 

Surveys 

Sent 

(April 12th) 

(B) 

Replacement 

Banks 

(C) 

Sample 

Size 

(D) 

Banks 

Disqualified

(E) 

Surveys 

Received 

(F) 

Response 

Rate (E/A) 

(G) 

Adjusted 

Response 

Rate 

(E/(C-D)) 

Tier 1 (Top 25 

banks) 

25 25 0 25 0 24 96.0% 96.0% 

Tier 2 (Assets 

over $1 billion, 

not Top 25) 

564 268 0 268 6 159 59.3% 60.7% 

Tier 3 (Assets 

under $1 

billion) 

6,898 990 4 994 5 502 50.6% 50.8% 

Total 7,487 1,283 4 1,287 11 685 53.4% 53.7% 

For the 685 bank responses, 422 came in by paper (including faxes) and 263 were received by email. 

The mix and number of daily incoming surveys of responses are shown below: 

OM Figure 7. Survey Response Mix and Timing 

Incoming Surveys

0

10

20

30

40

50

60

4/22 4/28 5/2 5/8 5/14 5/20 5/27 6/2 6/6 6/12 6/18 6/24 6/28 7/7 7/11

Date

Number of Surveys per Day

Paper Surveys

Electronic Surveys

Total
 29 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

OM Figure 8. Responding Banks by Tier and Region 

Region Total 

 Tier 1 Tier 2 Tier 3 

New England Count 1 6 31 38

 % within Region 2.6% 15.8% 81.6% 100.0%

 % within New Tier 4.2% 3.8% 6.2% 5.5%

 % of Total 0.1% 0.9% 4.5% 5.5%

 Mid-Atlantic Count 5 24 36 65

 % within Region 7.7% 36.9% 55.4% 100.0%

 % within New Tier 20.8% 15.1% 7.2% 9.5%

 % of Total 0.7% 3.5% 5.3% 9.5%

 South Atlantic Count 4 18 58 80

 % within Region 5.0% 22.5% 72.5% 100.0%

 % within New Tier 16.7% 11.3% 11.6% 11.7%

 % of Total 0.6% 2.6% 8.5% 11.7%

 East South 

Central 

Count 2 11 47 60

 % within Region 3.3% 18.3% 78.3% 100.0%

 % within New Tier 8.3% 6.9% 9.4% 8.8%

 % of Total 0.3% 1.6% 6.9% 8.8%

 West South 

Central 

Count 0 24 80 104

 % within Region 0.0% 23.1% 76.9% 100.0%

 % within New Tier 0.0% 15.1% 15.9% 15.2%

 % of Total 0.0% 3.5% 11.7% 15.2%

 East North 

Central 

Count 8 30 88 126

 % within Region 6.3% 23.8% 69.8% 100.0%

 % within New Tier 33.3% 18.9% 17.5% 18.4%

 % of Total 1.2% 4.4% 12.8% 18.4%

 West North 

Central 

Count 1 12 100 113

 % within Region .9% 10.6% 88.5% 100.0%

 % within New Tier 4.2% 7.5% 19.9% 16.5%

 % of Total 0.1% 1.8% 14.6% 16.5%

 Mountain Count 1 8 35 44

 % within Region 2.3% 18.2% 79.5% 100.0%

 % within New Tier 4.2% 5.0% 7.0% 6.4%

 % of Total .1% 1.2% 5.1% 6.4%

 Pacific Count 2 26 27 55

 % within Region 3.6% 47.3% 49.1% 100.0%

 % within New Tier 8.3% 16.4% 5.4% 8.0%

 % of Total 0.3% 3.8% 3.9% 8.0%

Total Count 24 159 502 685

% within Region 3.5% 23.2% 73.3% 100.0%
 30 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

Derivation of Respondent Weights and Survey Estimates 

For the purpose of making industry-wide estimates from the 685 completed survey questionnaires 

received from banks, appropriate respondent weights were derived that were based on bank 

selection probabilities and survey response rates. These weights were computed as the product of a 

base weight (inverse of the selection probability) and a nonresponse adjustment factor based on the 

assumption that nonrespondents were “missing at random” within asset-size groups. Survey 

estimates were computed using these respondent weights. To the extent that the “missing at 

random” assumption is valid, the survey estimates of population statistics are unbiased. 

Following are the details of the derivation of respondent weights and survey estimates. 

Base Weights 

The base weight for a respondent is the reciprocal of the respondent’s selection probability. The 

sample design for the survey of banks was a stratified random sample where the selection 

probabilities varied across the three size groups (major strata) but were constant within a size group. 

The selection probabilities for all units within a stratum were equal to the sampling rate for the 

stratum. (As noted previously, although we also stratified by Census Division, the selection 

probability of banks in a given stratum/tier was constant across Division, aside from minor 

variations due to rounding.) 

Let h denote the stratum (size group). If we select nh (the sample size for stratum h) out of the Nh

(the population size for stratum h) banks in stratum h, then the selection probability for banks in 

stratum h is the following: 

 h .

h

h

N

n 

Then, the base weight (or design weight) assigned to each respondent bank in stratum h was 

computed as follows: 

wh  1  h . 

The base weights for the three major strata (tiers) are shown in Figure 9. 

OM Figure 9. Base Weights 

Let h denote the size group (tier) Tier 1 Tier 2 Tier 3 

Selection probability  h 1.000 0.475 0.144 

Base weight (or design weight), wh, assigned 

to the respondent banks in each tier 

1.00 2.104 6.940 
 31 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

Nonresponse Weight Adjustment 

The base weights were adjusted to account for the nonresponding banks. This weight adjustment 

was applied within each stratum, based on the assumption that sample banks will be “missing at 

random” within strata, or at least that the survey variables for a nonresponding bank will be more 

like those of responding banks in the same stratum than those in other strata. 

As before, nh represents the number of banks that were sampled from stratum h. The number of 

those sampled banks that responded to the survey is denoted as hr . Subsequently some of the nh

sample cases were determined to be ineligible (or out of scope) for the survey. The nonresponse 

weight adjustment was calculated as the ratio of the eligible number of sampled banks (both 

respondents and nonrespondents), denoted nhe , in the stratum to the number of banks in the 

stratum that responded to the survey. Specifically, the weight adjustment due to the nonresponding 

banks within stratum h, (nr)

hA , was computed as follows: 

. ( )

h

he nr

h r

n A 

To obtain the final weights, the base weight for stratum h, wh, was multiplied by the nonresponse 

adjustment factor, (nr)

hA . The stratum level design weight adjusted for nonresponse (the final 

weight) was computed as follows: 

h

nr wh Ah w * ( )  . 

The above final weight was assigned to all banks belonging to stratum h for which survey data were 

obtained. 

OM Figure 10. Response Adjusted Base Weights 

Let h denote the stratum (tier) Tier 1 Tier 2 Tier 3 

Base weight (or design weight) 1.00 2.104 6.940 

Sample Size ( nh Banks) 25 268 994 

Eligible Sample Size ( nhe Banks) 25 262 

(268 - 6 

ineligible) 

989 

(994 - 5 

ineligible) 

Respondents ( hr banks) 24 159 502 

Nonresponse weight adjustment, (nr)

hA 1.042 1.648 1.970 

Final weight (Base weight x Nonresponse weight 

adjustment) 

1.042 3.468 13.672 
 32 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

Survey Estimates 

Survey estimates of population totals or means/percentages were computed using the weights 

described in the previous section. In particular, if yhi represents the response to a survey question 

provided by respondent i in stratum (tier) h, the estimated population total, Yˆ , for that question 

would be computed as follows: 

. ˆ 3

1 1

 

 h

r

i

h hi

h

Y w y 

An example of an estimated total for the survey would be the estimated number of automated teller 

machines (ATMs) that banks have that are located in low- and moderate-income (LMI) Census 

tracts. 

However, many of the estimated totals from the survey are estimated bank counts, like the estimated 

number of banks that perceive that there are unbanked or underbanked populations in their market 

area. In that case, the yhi variable is a 0-1 variable, which takes on the value of 1 if the respondent 

answered yes to this question, and zero otherwise. The estimate would then be the sum of the 

weights of all respondents that answered yes to the question. 

A survey mean per bank, Y , in the universe would be estimated as follows: 

. ˆ ˆ 3

1 1

 



h

r

i

h

h

w

Y Y 

The estimated mean is the estimated total (weighted responses for the survey question) divided by 

the sum of the weights of all of the respondents who answered the question. 

Many of the survey items are yes/no questions, such as whether the bank participates in education 

or outreach efforts that could bring unbanked or underbanked individuals into the conventional 

banking system. In those cases, the above formula for estimating a population mean was used to 

estimate a population proportion (percent) where the y variable is a 0-1 variable. For such an 

application, the estimated total was computed as the sum of the weights of all respondents that 

answered yes to the question (as discussed above as an estimated count). The denominator was 

computed as the sum of the weights for all respondents who answered the question. Therefore, an 

estimate of the population percent who would answer yes to a specific question was calculated as the 

sum of the weights of all respondents that answered yes to the question divided by the sum of the 

weights of all respondents who answered the question. 

The same formulas were used for estimating totals, means, and percents for population subgroups, 

where the summations were taken over the subgroup respondents. For example, the proportion of 

banks in the Middle Atlantic Division that use credit report scores as part of the screening process 

for new checking accounts was estimated as the ratio of the sum of the weights of responding banks 

in the Middle Atlantic Division that answered yes to that survey question divided by the sum of the 

weights of all respondents in the Middle Atlantic Division that answered that question. 
 33 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 

Reliability of the Estimates 

Because estimates are based on sample data, they differ from figures that would have been obtained 

from complete enumeration of the population using the same instruments. Results are subject to 

both sampling and nonsampling errors. Nonsampling errors include biases due to inaccurate 

reporting, processing, and measurement, as well as errors due to nonresponse. These types of errors 

cannot be measured readily. However, to the extent possible, each error of this type has been 

minimized through the procedures used for data collection, editing, quality control, and nonresponse 

adjustment. 

The sampling error of an estimate is the error that occurs solely because population estimates are 

generated from only a portion (sample) of the population. These types of errors can be estimated 

from the sample results. The most common measure of sampling error is the standard error of an 

estimate. Generally, the size of the standard error is inversely proportional to the square root of the 

number of observations in the sample. Thus, as the sample size increases, the standard error 

decreases. Standard error estimates for this survey could be derived for estimates computed for the 

full population or for population subgroups, assuming that respondents are random samples by 

strata (i.e., that the “missing at random” assumption mentioned earlier is reasonable). These 

standard error estimates could be used to test significant differences between various pairs of 

subgroups. However, due to the sample size limitations, Census Division comparisons may not be 

very meaningful. 

Research Limitations 

Due to the classification and reporting process used, readers should be careful in the inferences 

drawn from segments other than bank size (tier). This is important as the region (Census Division) 

and rural/urban segmentations are based on the location of the responding bank’s headquarters. 

Therefore, some results might not be meaningful. For example, in analyzing the distribution of 

ATMs per region, a large bank with operations that span multiple regions would have all the ATMs 

mapped to the headquarters region. 
 34 Study Objectives and Methodology FDICBankSurvey_Report_204pjg.doc 
 35 Participating Bank Characteristics and Retail Bank Information FDICBankSurvey_Report_204pjg.doc 

Participating Bank Characteristics 

and Retail Bank Information 

This chapter provides information about the characteristics of the banks that responded to the 

survey. Key characteristics include: 

 Bank Assets 

 Deposit Branches 

 Consumer Accounts and Cards 

 ATMs Deployed 

Summary 

The data collected for this survey on banks’ efforts to serve the unbanked and underbanked are 

representative of the U.S. banking industry in the year 2008. The survey’s stratified design and the 

excellent participation by banks, specifically the largest banks from the Tier 1 (top 25) and Tier 2 

(over $1 billion in assets), have permitted this study to gather an unparalleled and highly 

representative dataset for analysis. Collectively, the banks that responded to the survey had over $8.3 

trillion in assets (which represents approximately 70 percent of the total assets for commercial banks 

in the United States) at the time that the sample was drawn in 2007. Responding banks operated 

43,761 deposit branches, which represent about half of the bank offices at the time the sample was 

drawn. 

Figure 1. Bank Size (Assets) 

Assets (in 000s) as of June 30, 2007 

Frequency

N Valid 683

 Missing 2

Mean $11,961,677.89

Median $209,990.00

Chapter 

3
 36 Participating Bank Characteristics and Retail Bank Information FDICBankSurvey_Report_204pjg.doc 

Consumer Accounts and Cards 

Question I A. Please provide the following information related to consumer 

accounts/cards as of December 31, 2007: 

 ___ Number of conventional transaction accounts 

(e.g., checking, DDA, NOW, MMDA) 

 ___ Number of non-transaction savings accounts 

 ___ Number of entry deposit accounts* designed for 

individuals not qualified for conventional accounts 

 ___ Number of debit cards issued and active 

 ___ Number of prepaid cards issued and active 

 ___ Number of credit cards issued and outstanding 

An average bank issued 8,922 credit cards, 3,790 prepaid cards, and 16,245 debit cards at the time of 

the survey. Additionally, it had 28,313 conventional transaction accounts, 17,745 savings accounts, 

and 318 entry deposit accounts for individuals not qualified for conventional accounts. 

QIA. Figure 1. Overall: Account Statistics 

 

Conventional 

transaction 

accounts 

Nontransaction 

savings 

accounts 

Entry deposit 

accounts for 

individuals not 

qualified for 

conventional 

accounts Debit Cards

Prepaid 

Cards 

Credit 

Cards 

N Valid 669 666 668 670 669 669

 Missing 16 19 17 15 16 16

Weighted Mean 28,313.31 17,745.32 317.59 16,245.19 3,789.74 8,922.06

Median 4,026.00 2,224.00 .00 2,000.00 .00 .00

 

There is a difference between tiers for all types of consumer accounts and cards. Tier 1 banks have a 

larger number of credit cards (mean of 2.58 million) than Tier 2 (mean of 4,761) and Tier 3 (mean of 

478). 

QIA. Figure 2. Tier 1: Account Statistics 

 

Conventional 

transaction 

accounts 

Non-transaction 

savings 

accounts 

Entry deposit 

accounts for 

individuals not 

qualified for 

conventional 

accounts Debit Cards Prepaid Cards Credit Cards 

N Valid 24 24 23 23 22 23

 Missing 1 1 2 2 3 2

Mean 5,139,718.87 2,997,061.52 22,163.86 2,679,187.36 1,233,532.67 2,579,975.91

Median 2,710,000.00 926,633.00 555.50 1,129,422.00 .00 137,500.00
 37 Participating Bank Characteristics and Retail Bank Information FDICBankSurvey_Report_204pjg.doc 

QIA. Figure 3. Tier 2: Account Statistics 

 

Conventional 

transaction 

accounts 

Non-transaction 

savings 

accounts 

Entry deposit 

accounts for 

individuals not 

qualified for 

conventional 

accounts Debit Cards Prepaid Cards Credit Cards 

N Valid 541 538 541 541 541 541

 Missing 10 14 10 10 10 10

Mean 77,616.02 48,159.99 2,246.53 61,806.69 859.64 4,761.20

Median 47,578.00 26,250.00 .00 31,251.00 .00 .00

Tier 3 banks issue relatively few prepaid cards and credit cards. The mean number of prepaid cards 

issued is 25, and 478 for credit cards. The median and mode are 0 for both types of cards. 

QIA. Figure 4. Tier 3: Account Statistics 

 

Conventional 

transaction 

accounts 

Non-transaction 

savings 

accounts 

Entry deposit 

accounts for 

individuals not 

qualified for 

conventional 

accounts Debit Cards Prepaid Cards Credit Cards 

N Valid 6699 6672 6699 6727 6727 6713

 Missing 164 191 164 137 137 150

Mean 6,046.27 4,593.01 87.06 3,505.61 25.00 477.52

Median 3,705.50 2,073.50 .00 1,748.50 .00 .00

There are few differences between regions for all types of consumer accounts and cards. 

There are differences between urban and rural banks for the number of conventional accounts, nontransaction savings accounts, and debit cards. Urban banks have a higher mean and median than 

rural banks across all accounts and cards. 

QIA. Figure 5. Urban: Account Statistics 

 

Conventional 

transaction 

accounts 

Nontransaction 

savings 

accounts 

Entry deposit 

accounts for 

individuals not 

qualified for 

conventional 

accounts Debit Cards 

Prepaid 

Cards Credit Cards 

N Valid 3346 3342 3345 3359 3358 3348

 Missing 134 138 136 122 123 132

Mean 53,338.95 33,529.18 521.28 30,026.64 8,200.42 18,971.05

Median 5,220.00 2,924.00 .00 2,492.00 .00 .00
 38 Participating Bank Characteristics and Retail Bank Information FDICBankSurvey_Report_204pjg.doc 

QIA. Figure 6. Rural: Account Statistics 

 

Conventional 

transaction 

accounts 

Nontransaction 

savings 

accounts 

Entry deposit 

accounts for 

individuals not 

qualified for 

conventional 

accounts Debit Cards 

Prepaid 

Cards Credit Cards 

N Valid 3918 3891 3918 3932 3932 3929

 Missing 41 68 41 27 27 31

Mean 6,943.49 4,186.57 143.72 4,473.64 23.49 357.14

Median 3,661.00 2,092.00 .00 1,604.00 .00 .00

ATMs Deployed 

Question I B. Number of ATMs operated by your bank. Please indicate 

approximate numbers of ATMs by location and functionality: 

Location Number Functionality Number 

Inside LMI tracts...... ___ Basic cash dispense 

only........................... 

___ 

Outside LMI tracts.... ___ Basic cash dispense and 

deposit acceptance... 

___ 

Advanced functionality 

with bill payment and/or 

automated money order 

and/or prepaid card.... 

___ 

Total ATMs ___ = Total ATMs ___ 

The average bank has a total of 20 ATMs, with about six located inside LMI tracts. 

QIB. Figure 1. ATM Statistics 

 

ATMS located 

inside LMI 

tracts 

ATMS 

located 

outside LMI 

tracts 

Basic cash 

dispense ATMs

Basic cash 

dispense and 

deposit 

acceptance 

ATMs 

Advanced 

functionality 

ATMs 

Total 

ATMs 

N Valid 641 642 670 667 663 677

 Missing 44 43 15 18 22 8

Sum 21,325 27,780 59,681 27,780 1,672 85,164

Weighted Mean 5.95 8.11 14.48 8.11 .32 20.36

Weighted Median 1.00 2.00 2.00 2.00 .00 4.00

There are little differences between regions for the number of ATMs across all ATM types. 
 39 Participating Bank Characteristics and Retail Bank Information FDICBankSurvey_Report_204pjg.doc 

There is a difference between tiers for the number of ATMs across all types of ATMs. In every case, 

Tier 1 banks have deployed more ATMs than Tier 2 and Tier 3 banks. The mean total number of 

ATMs for Tier 1 is 3,132, compared with 65 in Tier 2 and 6 in Tier 3. 

In Tier 1, the mean number of ATMs located inside LMI tracts (777) differs from Tier 2 (16) and 

Tier 3 (2). 

QIB. Figure 2. Tier 1: ATM Statistics 

 

ATMS located 

inside LMI 

tracts 

ATMS 

located 

outside LMI 

tracts 

Basic cash 

dispense ATMs

Basic cash 

dispense and 

deposit 

acceptance 

ATMs 

Advanced 

functionality 

ATMs 

Total 

ATMs 

N Valid 24 23 24 24 20 24

 Missing 1 2 1 1 5 1

Mean 777.26 2,292.95 945.13 2,151.70 76.32 3,131.83

Median 473.00 1,373.00 559.00 1,285.00 .00 2,095.00

QIB. Figure 3. Tier 2: ATM Statistics 

 

ATMS located 

inside LMI 

tracts 

ATMS 

located 

outside LMI 

tracts 

Basic cash 

dispense 

 ATMs 

Basic cash 

dispense and 

deposit 

acceptance 

ATMs 

Advanced 

functionality 

ATMs 

Total 

ATMs 

N Valid 538 538 544 544 541 551

 Missing 14 14 7 7 10 0

Mean 15.66 49.73 28.89 36.82 1.40 64.77

Median 8.00 31.00 9.00 24.00 .00 41.00

QIB. Figure 4. Tier 3: ATM Statistics 

 

ATMS located 

inside LMI 

tracts 

ATMS 

located 

outside LMI 

tracts 

Basic cash 

dispense 

ATMs 

Basic cash 

dispense and 

deposit 

acceptance 

ATMs 

Advanced 

functionality 

ATMs 

Total 

ATMs 

N Valid 6330 6357 6699 6658 6672 6768

 Missing 533 506 164 205 191 96

Mean 2.21 3.29 3.07 2.78 .01 5.72

Median 1.00 1.00 1.00 1.00 .00 4.00

There are differences between urban and rural banks for the number of ATMs inside LMI tracts, 

basic cash dispense ATMs, ATMs outside LMI tracts, basic cash dispense and deposit acceptance 

ATMs, and total number of ATMs. The mean for urban banks is significantly higher for these types 

of ATMs than for rural banks. 
 40 Participating Bank Characteristics and Retail Bank Information FDICBankSurvey_Report_204pjg.doc 

There is little difference between urban and rural banks for advance functionality machines. 

QIB. Figure 5. Rural: ATM Statistics 

 

ATMS located 

inside LMI 

tracts 

ATMS 

located 

outside LMI 

tracts 

Basic cash 

dispense ATMs

Basic cash 

dispense and 

deposit 

acceptance 

ATMs 

Advanced 

functionality 

ATMs Total ATMs 

N Valid 3641 3641 3641 3877 3877 3932

 Missing 318 318 318 82 82 27

Mean 2.69 3.63 4.10 2.74 .07 6.75

Median 1.00 1.00 2.00 .00 .00 3.00

QIB. Figure 6. Urban: ATM Statistics 

 

ATMS located 

inside LMI 

tracts 

ATMS 

located 

outside LMI 

tracts 

Basic cash 

dispense 

ATMs 

Basic cash 

dispense and 

deposit 

acceptance 

ATMs 

Advanced 

functionality 

ATMs Total ATMs 

N Valid 3250 3277 3363 3349 3355 3411

 Missing 230 204 117 131 125 69

Mean 9.60 26.54 12.77 23.73 .61 36.05

Median 1.00 3.00 1.00 3.00 .00 5.00