FR3066_20250220_omb_B

Supporting Statement Part B for the
Federal Reserve Payments Study
(FR 3066; OMB No. 7100-0351)
Summary
For all information collections that involve surveys or require a statistical methodology,
the Board of Governors of the Federal Reserve System (Board) is required to provide a complete
justification and explanation of the use of such a methodology. For collections that employ
surveys without such a methodology, the Board should be prepared to justify its decision not to
use statistical methods in any case where such methods might reduce burden or improve
accuracy of results.
Background
The FR 3066a and FR 3066b are part of the latest iteration of the Federal Reserve
Payments Study (FRPS), which has been a collaborative effort of the Federal Reserve Bank of
Atlanta (FRB Atlanta) and the Board since 2000. The FRPS originated from a Federal Reserve
System-wide effort to improve the measurement and public availability of information on
volumes and trends in checks and other noncash payments. The FRPS filled a significant gap in
quantitative information on U.S. noncash payments by providing a reliable and transparent nonmandatory approach to surveying payment institutions, constructing U.S. domestic total
estimates from the survey data, and publishing them. The focus of the surveys has adapted to the
substantial evolution and growth in emerging and innovative payment types over time, as well as
the refreshed strategic direction of Federal Reserve Financial Services. The strategic direction
includes a focus on meeting the evolving needs of payment system users for end-to-end payment
speed, efficiency, and security, while remaining true to a longstanding financial services mission
to foster the integrity, efficiency, and accessibility of the U.S. payment system. Staff members in
the Payments Forum and the Economic Survey Research Center at FRB Atlanta along with staff
members of the Payment System Studies section at the Board jointly conduct the study.
Surveys in previous years received robust industry support and participation, and the
aggregate estimates produced from the survey data are widely cited in academic working papers,
journal articles, and industry publications, reported in the media, and used by the public,
industry, and policy makers as a quantitative aggregate benchmark of noncash payments and
cash withdrawal and deposit activity in the United States. As the noncash payments system
grows larger and more complex, the Board expects the data collected under the FRPS to play a
crucial role in objectively maintaining and updating quantitative information on the U.S. noncash
payments system. The information collected through the FRPS is not available from other
sources.

Universe and Respondent Selection
FR 3066a
The FR 3066a collects the number and value of noncash payments, cash withdrawals and
deposits, third-party payments fraud, and related information from a nationally representative
sample of commercial banks, savings institutions, and credit unions. A stratified population or
universe is defined using administrative data on the types and sizes of insured depository
institutions from reports filed with the Federal Reserve.1 After consolidating affiliates, the
calendar year 2021 population, used for the 2022 triennial survey, consisted of 9,252
independently operated institutions at the highest holding company level with non-zero
transaction deposits. The 2025 survey will collect retrospective data for calendar year 2024, and
the population will be based on administrative data for that year.
As in 2022, the planned 2025 triennial version of the survey will have a sample size of
approximately 3,800 institutions. Sample stratification and selection methods follow classical
and innovative techniques based on the state of the art of the literature on business survey
methods. As before, the survey will be administered using a complex planned-missing-data
design with 11 questionnaire versions allowing shorter questionnaires for smaller institutions.2
For 2021 data, the certainty group of the largest institutions was 1,665. The remaining 2,135
institutions were selected at random with probabilities declining with size. The allocation to size
groups and sampling rates will be adjusted to adapt to lessons learned from the 2022 data
collection and the 2024 distribution of depository institutions once the population frame has been
determined. The unit response rate in previous surveys has been roughly one-third overall, with
higher response rates among the largest institutions and is expected to be similar in 2025.
FR 3066b
The FR 3066b is a set of surveys that collect the number and value of electronic
payments, payments fraud, and related information from a census of major card networks,
payment processors, and card issuers. There were 17 different surveys in the most recent
triennial surveys (conducted in 2019 and 2022), to which participants provided information only
in the survey forms applicable to their organizations. The Federal Reserve will identify the final
list of networks, processors, and issuers from which to collect data once the suite of surveys to be
administered is determined and prior to commencing the data collection process.
The population or universe is based on developing a sample of all relevant organizations
(up to 390) and requesting data from each.3 For cases where a response is not returned, the
Size in recent triennial surveys has been defined as the sum of “checkable” transaction deposits plus funds that
reside in money market deposit accounts which may be used for payment.
2
This method has been used since 2016. Analysis of the outcome of the 2016 planned missing data survey design
compared with the 2013 full survey design is discussed in Geoffrey Gerdes and Xuemei (May) Liu, “Improving
Response Quality with Planned Missing Data: An Application to a Survey of Banks” in The Econometrics of
Complex Survey Data: Theory and Applications, Advances in Econometrics, Volume 39, 2019, pp 237-58.
3
In the case of transit operators, the population is defined using “unlinked rides” data reported by the US
Department of Transportation. For comparison with the 2015 and 2019 surveys, a certainty sample of the largest
operators may be supplemented by a stratified, representative, random sample of smaller operators.
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missing items would need to be imputed using publicly available information and analysis of
data from similar organizations that did provide data. In such cases, expertise and heuristic
methods will be employed to estimate the missing data. Totals are constructed by summing the
reported and estimated data. The 2019 triennial survey had a response rate of approximately 80
percent. Similar response rates are expected in future surveys.
Procedures for Collecting Information
FR 3066a
Using size measures obtained from regulatory reports, the population of depository
institutions is stratified into sub-populations by type and size, and separate samples are drawn
from each, with the sampling rate declining with size. To draw the sample, we use classical
methods for determining sub-population size boundaries and total sample allocations within
types, based on a general goal of minimizing the standard error of the aggregate estimates. The
use of these allocation methods leads to the treatment of sub-populations with the largest
institutions as a census, i.e. each member is sampled with certainty.
The size distribution of U.S. depository institutions is highly skewed, although less than
in many other industries, and far less than is typically the case in other developed countries,
many of which have fewer than a dozen significant deposit taking institutions. Aggregate
estimates are constructed for the sub-populations using a ratio estimator technique, which, taking
advantage of the high covariance between the size measures available from the population data
and the volumes being collected, is substantially more efficient than alternative estimators that
ignore this covariance. The approach has been designed to achieve high precision across all
variables using the size covariate as a proxy. Past surveys have been able to achieve estimated
confidence intervals that range as low as +/- 3 percent for some variables at the 95 percent level.
This kind of precision is primarily only achieved for the most completely reported “top-line”
variables, however. Nonetheless, given the unique data collected the estimates should be
considered the best available national estimates for many items.
Annual supplements are conducted that collect data from the largest 120 institutions.
These typically achieve approximately a 50 percent response rate. Such data are used to
construct rates of change amongst the responding institutions from year-to-year.
FR 3066b
The uniqueness of each participant does not generally lend these surveys to use of formal
statistical techniques, although approaches similar.
Methods to Maximize Response
FR 3066a
A large-scale effort is made to recruit the participation of sampled institutions, the survey
is designed to use language and organizing principles familiar to the institutions, and review and

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feedback sessions are designed to ensure the surveyed information addresses payment issues of
interest and relevance to participating institutions. An incentive of a peer report is provided.
Efforts are made to recruit, assist, and accommodate the needs of institutions in two main
ways. First, an elevated level of effort to recruit and garner participation from a select set of very
large institutions and organizations with large payment volumes is made due to (1) the inability
to increase the sampling probability of a census which is already 100 percent and (2) the fact
that, all else equal, it is always preferred to expend resources at the margin on obtaining a
response from the largest non-responding institution. Second, the length of surveys declines with
institution size, reducing the burden on smaller institutions, and making participation more likely
for a given sample size.
The ratio estimator technique—which computes the within-sample ratio of each item of
interest to the corresponding institution size variable and then expands that ratio to the
population size of each stratum individually—implicitly accounts for unit-level non-response as
part of the estimation technique. In addition, an imputation method is used to account for missing
item-level data (which included planned missing as well as unplanned) using correlations
between reported items from peer respondents. Logical constraints between items are also used
to enforce adding-up constraints throughout the survey. Past studies of the data have revealed no
evidence of self-selection.
FR 3066b
Information from past responses and public data are used to estimate and validate the
missing items of nonparticipants. Estimation is based on expert judgement in most cases as
formal statistical methods are not robust enough for extremely small samples with highly
heterogeneous subjects.
Testing of Procedures
Each survey builds on lessons learned from previous surveys, and changes from year-toyear are examined for plausibility. In addition, aggregate estimates that come from FR 3066a and
FR 3066b that should match, such as total debit card transactions reported by depository
institutions and card networks, are compared for consistency. Anomalies are investigated,
described, and accounted for before finalizing estimates and are explained in reports.
FR 3066a
Estimation methods have been stable for two decades and improved incrementally when
the opportunity arises. For the national aggregate estimates conducted on a triennial basis, the
joint estimates based on imputed data are compared with independent estimates using only the
reported data. Aggregates are built up from the stratum-level estimates, and any unusual patterns
in the data or implausibly high standard errors of estimates are examined for invalid or outlying
response data and adjusted accordingly.

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FR 3066b
Information from past responses and public data are used to estimate and validate the
missing items of nonparticipants. Testing is based on expert judgement in most cases as formal
statistical methods are not robust enough for extremely small samples with highly heterogeneous
subjects.

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