2011 FDIC

Survey of Banks’ 

Efforts to Serve 

the Unbanked 

and Underbanked

December 2012

Federal Deposit 

Insurance Corporation

Appendix B
2011 FDIC Survey of Banks’ Efforts to Serve the Unbanked an Uderbanked  

Members of the FDIC Unbanked/Underbanked Survey Study Group

Division of Depositor and Consumer Protection: Susan Burhouse, Sarah Campbell, 

 Timothy Critchfield, Ross Dierdorff, Keith Ernst, Ryan Goodstein, Yazmin Osaki, 

 Luke Reynolds, and Sherrie Rhine.

Division of Insurance and Research: David Chapman, Eric Robbins, and Katherine Samolyk.

Legal Division: Leneta Gregorie.

Lead Authors

Sherrie L.W. Rhine and Eric Robbins.

Lead Statistical Analysts and Advisors

David Chapman, Ryan Goodstein, and Chris MacColl.

Contributors: 

Karyen Chu, Anirudh Sarna, Masseh Tahiry, Shannon Williams, Kathy Zeidler.
2011 FDIC Survey of Banks’ Efforts to Serve the Unbanked an Uderbanked  •  

Appendix B — Survey Methodology
2011 FDIC Survey of Banks’ Efforts to Serve the Unbanked an Uderbanked  •  

1. Sample Design

The goal of this study is to obtain information from the 

headquarters (HQs) of all FDIC-insured financial institutions regarding strategies for serving the unbanked and 

underbanked and products and services offered to these 

populations. The target universe (or population) consists 

of main offices associated with all FDIC-insured bank 

charters, excluding institutions that do not have standard 

retail branching operations.1 The basic sample design was 

a stratified random sample, with three size strata defined 

by asset value. These three strata, also referred to as tiers, 

were defined as follows: 

Tier 1: Top 25 largest banks by assets

Tier 2: Banks with $1 billion or more in assets 

outside the top 25 

Tier 3: Banks with less than $1 billion in assets

For sample design development purposes, we used the 

FDIC’s Summary of Deposits (SOD) database for June, 

2010. From this database, we defined a preliminary 

universe of 7,691 FDIC-insured institutions (bank charters) having standard retail branching operations. There 

were 94,313 branch offices associated with these 7,691 

FDIC-insured institutions. Table 1 presents the number of 

bank HQs and branches by Tier size categories.2

The HQs sample was designed so that survey-based estimates of universe proportions for Tier 2 and Tier 3, such 

as the proportion of HQs in Tier 2 (or in Tier 3) that 

would say yes to a yes-no question, could be computed 

with acceptable precision (+ 5 percentage points at the 90 

percent level of confidence). For Tier 1 (the 25 largest 

banks), a 100 percent sample was selected because of the 

importance of obtaining survey information from these 

large banks. 

1 The current data universe includes banks with a) brick and mortar 

branches (code 11) and b) banks in retail locations (code 12). Examples of 

institutions excluded from the analysis are: Banks with branches with 

deposits above $900 million, Industrial Loan Companies, and Trust 

Companies.

2 In addition to the HQs sample, the sampling plan also consisted of a 

second-stage sample of 2,000 branches, selected randomly from those 

branches associated with HQs that participated in the HQs survey and 

also gave us permission to survey their branches. However, since the 

focus of this report is the HQs survey, this appendix will describe the 

statistical methodology of the HQs survey, and will not refer further to the 

branch survey, aside from the presentation of branch counts given in 

Table 1.

To achieve the precision targets for the Tier 2 and Tier 3 

strata, the target number of completed questionnaires for 

each of these two strata (shown in the third column of 

Table 2) was calculated based on the following formula: 3

n = {((t2*p*q)/d2)} / {1 + (1/N)*((t2*p*q/d2) -1) }, (1)

where 

n = the target number of completed 

questionnaires being derived,

p = the approximate value of the universe

proportion (with 0.5 used for planning

purposes),

q = 1 – p,

N = the number of banks in the HQs universe 

for the stratum,

d = the target level of precision (0.05), and

t = the appropriate constant (1.645) 

for computing a 90 percent confidence 

interval based on the normal probability

distribution. 

The Tier 2 and Tier 3 target numbers of completes were 

computed from Equation 1, using the initial universe 

counts (number of HQs) shown in the first column of 

Table 2. These sample sizes were also computed based on 

the final universe counts (second column of Table 2), 

which were obtained from FDIC’s Call Report data as of 

June 2011, with additional screening criteria used to filter 

out non-standard retail institutions. As it turned out, the 

target numbers of completes for Tier 2 and Tier 3, based 

on the final universe counts, were the same as those based 

3 See William Cochran, Sampling Techniques, Third Edition, 1977, formula 

4.1, page 75.

Table B1: Universe Counts (HQs and Branches) 

by Asset Size

Initial 

Number of 

HQs 

Number of 

Branches

Tier 1

Top 25 banks by assets 25 39,707

Tier 2

Banks with $1 Billion or more in assets 

outside the Top 25

562 25,401

Tier 3

Banks with less than $1 Billion in assets 7,104 29,205

Universe/Sample Frame 7,691 94,313
2011 FDIC Survey of Banks’ Efforts to Serve the Unbanked an Uderbanked  •  

The designated initial samples, as specified in Table 2, 

were selected randomly from the updated Tier 2 and 

Tier 3 universe lists. In addition, there were plans in place 

for the possibility of having to supplement the Tier 2 or 

Tier 3 samples with additional randomly selected banks 

from a “reserve sample,” in the event that the response 

rates were below 65 percent. However, since the response 

rates for Tier 2 and Tier 3 were substantially higher than 

65 percent, there was no need to supplement the sample. 

2. Data Collection Procedures

Gallup, Inc. received from the FDIC updated lists of HQs 

containing the name, asset size, and mailing address of all 

FDIC-insured HQs. These lists were used as the source of 

the universe (sampling frame) for the selection of the HQs 

sample. The sample was selected as a stratified random 

sample of 709 HQs from the list provided. The sampling 

unit for the survey of HQs, as mentioned above, was main 

offices associated with bank charters, excluding charters 

that do not have standard retail operations. The respondent sought was the bank officer expected to be most 

knowledgeable about strategies and practices for serving 

the unbanked and underbanked households. 

Data collection was conducted entirely online, although a 

mixed-mode approach was used for inviting and prompting participation by HQs management. The initial survey 

request was sent by mail, by means of a letter from the 

FDIC introducing the study, accompanied by a letter from 

Gallup outlining the procedures for logging onto the 

website to respond to the survey. The website required a 

unique user name and password to respond in order to 

monitor response behavior and ensure privacy. 

A series of prompts by mail, email (when available), and 

telephone were carefully spaced out in order to boost 

response rates. All prompts encouraged the representative 

of the financial institution to go online to Gallup’s secure 

website to respond to the survey. If an institution did not 

have access to the Internet, a paper version of the questionnaire would have been made available. However, 

providing a paper version was not necessary for any of the 

HQs respondents. 

3. Survey Estimation Procedures

Survey estimates for HQs were based on weighted survey 

responses, where the weights were derived from HQs probabilities of selection, and adjusted for response levels. 

Weighting and estimation formulas are given in the 

following subsections. A summary description of how 

significant differences in sample estimates of proportions 

and means were computed is also provided. 

on the preliminary universe counts, except that the Tier 3 

target number of completes was 260, rather than 261. Due 

to the closeness of these numbers, the target number of 

completed questionnaires for Tier 3 was left as 261. 

Table 2 also provides, in the last two columns, the survey 

sample sizes and the number of completed HQs Internetbased questionnaires. As indicated previously, all 25 Tier 

1 banks were included in the sample. Also, a strong effort 

was made to obtain a response rate of 100 percent among 

Tier 1 banks, which was achieved. (As such, there is no 

sampling error or nonresponse bias associated with any of 

the Tier 1 results.) 

For the Tier 2 and Tier 3 strata, the sample sizes (shown 

in column 4, Table 2) were based on the target number of 

completed questionnaires and an assumed response rate of 

65 percent. Therefore, the sample sizes for Tier 2 and Tier 

3 were computed by dividing the target number of 

completes for each stratum by 0.65. For example, for Tier 

2, the sample size of 282 was computed by dividing 183 by 

0.65. 

As it turned out, the response rates for Tiers 2 and 3 far 

exceeded the minimum target of 65 percent. The response 

rate for Tier 2 was 80 percent, and the response rate for 

Tier 3 was 79 percent, for an overall response rate across 

the three tiers of 80 percent (567/709). The number of 

completed online questionnaires is shown for each tier in 

the last column in Table 2. 

Table B2: Universe and Sample Counts for the HQs 

Survey, by Strata

Sampling 

Strata

Initial 

Number 

of HQs 

(1)

Final 

Number 

of HQs

(2)

Target Number 

of Completed 

Questionnaires 

(3)

Sample 

Size 

of HQs

(4)

Actual 

Number of 

Completed 

Questionnaires

(5)

Tier 1

Top 25 

banks by 

asset size 

(equated to 

total assets 

greater 

than $38 

billion)

25 25 25 25 25

Tier 2

Banks with 

at least $1 

billion in 

assets and 

less than 

$38 billion 

in assets

562 559 183 282 225

Tier 3

Banks with 

less than 

$1 billion 

in assets

7,104 6,745 261 402 317

Total 7,691 7,329 469 709 567
2011 FDIC Survey of Banks’ Efforts to Serve the Unbanked an Uderbanked  •  

Preparing Survey Estimates and Frequency Tables

Most of the estimates derived from the survey results were 

estimates of universe proportions or universe means. An 

example of a statistic for the HQs universe that was estimated was the proportion of banks that give top priority 

to reach out to unbanked and underbanked households. 

Defining yhi as “1” if the ith respondent bank in stratum h 

does assign top priority to this effort, and assigning “0” to 

yhi otherwise, the universe proportion was estimated as 

follows: 

p = Σ whi yhi / Σ whi,

 (2)

where the sum is taken over all of the sample HQs in the 

domain for which the proportion is being estimated. Note 

that the estimated proportion is simply the sum of the 

weights of all responding HQs in the domain that do 

assign top priority to reaching out to unbanked and underbanked individuals, divided by the sum of the weights of 

all responding HQs in the domain. 

For deriving frequency tables, estimates of universe cell 

frequencies for HQs were estimated using the formula in 

the numerator of Equation (2) above. 

For estimates of universe means, Equation (2) above was 

used, where the value of the y-variable was the value of 

the statistic (like a fee amount) for which the universe 

mean was being computed (rather than being the 0-1 

indicator variable used for estimating proportions). 

Comparing Sample Proportions or Means for 

Significance

For various universe domains, either defined by strata or 

by other variables, including variables based on survey 

responses, estimates of proportions or means were made to 

determine whether the differences of the sample estimates 

were “significant,” indicating that it would be unlikely 

that the corresponding universe proportions (or means) 

would be equal. The criterion used to identify significant 

differences was the 10 percent level of significance. 

Sample differences had to be significant at this level to be 

reported here as identifying universe differences. The 

significance determinations were made using the SurveyReg procedure in SAS software v9. 3. 

Weighting the Sample Data

The sample of HQs, as described above, was a single-stage 

stratified random sample. As such, the selection probabilities for all units within a stratum will be equal to the stratum sampling rate. 

Let h denote the stratum and the index i denote a bank 

(i.e., HQs) within the stratum. If we select nho (the 

initial sample size for stratum h) out of the banks in the 

sampling frame (or list) for stratum h, then the selection 

probability for all banks in stratum h is given by

	 πhi= nho/Nh

The probability or base weight (w1hi) assigned to all 

sampled banks (HQs) in stratum h is the inverse of the 

selection probability: 

w1hi =Nh/nho 

 

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

redistributing the weights of the nonresponding banks in a 

stratum across the responding banks in the stratum. (Some 

consideration was given to possibly define nonresponse 

adjustment cells by other variables in addition to the size 

tier; however, mostly because of the high response rates, it 

was decided to use only the size tiers to define nonresponse adjustment cells. ) 

Suppose that nh of the nho banks sampled from stratum 

h turned out to be eligible for the survey, and that hr of 

these banks participated in the survey. Then the adjustment for nonresponse in stratum h, w2h, was calculated as 

the ratio of the number of eligible sampled banks (both 

respondents and nonrespondents) in the stratum to the 

number of banks that participated in the survey:

w2h = nh/rh

The final weight (whi ), adjusted for nonresponse, for each 

bank in stratum h was computed as follows:

whi = (w1hi)( w2h) = (Nh/nho)(nh/rh) (1)

As it turned out, all banks selected from stratum h were 

classified as eligible for the survey. Therefore, nho = nh, and 

the final weight for each bank in stratum h was simply Nh/

r

h

. Furthermore, the sum of the weights of all of the rh

respondents in stratum h equals the number of banks in 

the sample frame for stratum h, Nh.