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Survey of Consumer Payment Choice Data User’s Guide 1 Introduction One of the major goals of the Survey of Consumer Payment Choice (SCPC) is to provide a publicly available, consumer-level longitudinal dataset to support research on consumer payments and to provide aggregate data on trends in U.S. consumer payments. The questionnaires and public datasets for the 2014 SCPC are available for download on the Consumer Payments Research Center (CPRC) website at http://www.bostonfed.org/ economic/cprc/scpc/index.htm. The data are provided in SAS, Stata, and CSV formats. The CPRC assumes that data users are familiar with a statistical analysis software package such as SAS, Stata, or R. The CPRC does not provide any software assistance. This document is a data user’s guide for the SCPC survey (see the The 2014 Survey of Consumer Payment Choice: Technical Appendix for details). Anyone interested in conducting research based on SCPC data will find it helpful to become familiar with this document. A broad overview of the 2014 SCPC, including a summary of the survey and tables of survey results, can be found in the The 2014 Survey of Consumer Payment Choice. Details about data collection and data processing are found in The 2014 Survey of Consumer Payment Choice: Technical Appendix. All questions regarding the use of the data can be directed to: Kevin Foster Survey Methodologist Consumer Payment Research Center Federal Reserve Bank of Boston (617) 973-3955 kevin.foster@bos.frb.org 2 SCPC variable overview There are three broad categories of SCPC variables. Below we provide general information about each. 1 My Household Questionnaire variables represent a small fraction of variables that come from the RAND American Life Panel (ALP) My Household Questionnaire (MHQ). The MHQ is used to gather demographic data about each respondent. ALP members take the MHQ quarterly, and their most recent responses to the MHQ are included in these SCPC datasets. Survey variables are the actual results from the SCPC survey questions. Survey variables have variable names such as pa001 a or pu004 b. To see the exact question text, respondent instructions, response option wording, and structure of the questions on the screen, it is recommended to search the survey questionnaires themselves (available on the SCPC website). Two important considerations of the survey variables are: • Randomization of question orders: To avoid potential biases arising from the order of response options presented to respondents, the survey instrument randomizes response options for some questions. The questionnaire clearly indicates if response options were randomized. The unrandomized variables have the same variable names as the original survey variables. The raw data from the unrandomized variables and the SAS macros that unrandomize the responses will be made available upon request. • Responses for different time frequencies: Respondents are given the option of reporting payment use and cash management in terms of a typical week, month, or year. This dataset includes variables where responses have been standardized to a monthly frequency, in addition to the original responses for the weekly, monthly or yearly rates. The frequency converted variables have the same name as the original responses, but without a numeric suffix. For instance, the variable pu006a a refers to the number of cash payments for retail goods in a typical month, after frequency conversion. The set of three original variables that produce pu006a a are pu006a a1 (respondent used the weekly box to report these transactions), pu006a a2 (monthly) and pu006a a3 (yearly). The SAS macros for the frequency conversions can be made available upon request. Created variables are created by the CPRC to populate the SCPC results tables and to aid in data analysis. Most of these variables have descriptive names based on a combination of mnemonics. For example, the variable cc typ consists of two mnemonics: cc stands for “credit card”, and typ stands for “number of transactions in a typical month”. More insight into variable name mnemonics is provided in Section 2.2. 2 2.1 2.1.1 Survey variables Respondent identifier prim key Unique respondent identifier The variable prim key is of the form xyyzzzz:n or xxyyzzzz:n (for 2010 onward), where x or xx is year (9 for 2009, 10 for 2010, e.g.), yy is month (08 for August, e.g.), and zzzz is a household identifier within that year/month. xyyzzzz and xxyyzzzz are the unique household identifier. The number to the right of the colon is the member id (1, 2, ..., n) for a panel member inside a household. It is assigned in the order that the respondent entered the survey; panel members with member id equal to 1 are the panelist that was contacted and recruited to join the ALP. Those with member id numbers of 2 or greater are household members of the original recruits. The prim key for an ALP member is the same across all RAND ALP surveys. This allows data users to merge other RAND ALP survey datasets onto the SCPC dataset. 2.1.2 Survey weight r weight Individual-level post-stratification weights - from a raking procedure For information about how the survey weights are calculated, please see the 2014 SCPC Technical Appendix. 2.2 Created variables Most created variable names are a combination of 2 or more mnemonics, combined using underscores. Typically, the first mnemonic refers to payment instrument, type of account, or a method of payment. The second or last mnemonic often indicates the concept being communicated, such as its characteristic, adoption, or typical use. This section describes the most common mnemonics. 3 2.2.1 Payment instruments banp Bank account number payment cc Credit card chk Check csh Cash dc Debit card income Direct deduction from income (used in automatic bill payments only) mon Money order obbp Online banking bill payment svc Stored-value card/prepaid card tc Travelers check Payment instruments are grouped as follows: 2.2.2 card Credit cards, debit cards, prepaid cards elect Bank account number payments, online banking bill payments paper Cash, check, money order, travelers checks pi All payment instruments Transaction types abp Automatic bill payment ipbp In-person bill payment (or via mail) obp Online bill payment op Online (non-bill) payments p2p Person-to-person payment rp Retail payments (made in-person) serv Services and other payments (in-person) Transaction types are grouped as follows: 4 2.2.3 2.2.4 bp Bill payment i.e. union of abp, obp, ipbp op Online (non-bill) payments posp2p All in-person (non-bill) payments, i.e. union of rp, serv and p2p Assessment of payment characteristics acceptance Acceptance for payment convenience Convenience cost Cost records Payment records security Security setup Getting and setting up Payment adoption adopt discard Respondent is currently an adopter (Y/N) Respondent was an adopter, not anymore (Y/N) ever Respondent was an adopter in the past but does not currently have or own the item in question (Y/N) num Number of payment instruments (equals 0 for non-adopters) 2.2.5 Payment use For each payment instrument and seven transaction types, respondents are asked to report their payment use behavior - how frequently they use a payment instrument for a specific transaction type. Therefore, at the most disaggregated level, a payment use variable name consists of three mnemonic components: the payment instrument (Section 2.2.1), followed by the transaction type (Section 2.2.2), and ending with a suffix that indicates the type of payment use information (incidence of use, frequency of use, and share of all transactions made): 5 sh Number of transactions in a typical month, as proportion of all payments typ Number of transactions in a typical month tm Respondent makes the corresponding type of payment at least once in a typical month (Y/N) ty Respondent makes the corresponding type of payment at least once in a typical year (Y/N) It is important to note that not all combinations of payment instruments and transaction types exist. This is because they were assumed not to be possible at the time of the survey. The following table illustrates combinations that do exist in the data and the corresponding combinations of mnemonic prefixes: bp op abp obp ipbp banp banp abp banp obp cc cc abp cc obp op posp2p rp serv banp op p2p banp p2p cc ipbp cc op cc rp cc serv cc p2p chk chk ipbp chk op chk rp chk serv chk p2p csh csh ipbp csh rp csh serv csh p2p dc dc abp income income abp dc obp mon obbp svc tc obbp abp dc ipbp dc op dc rp dc serv dc p2p mon ipbp mon op mon rp mon serv mon p2p obbp obp obbp p2p svc ipbp svc op svc rp svc serv tc (not asked by transaction type) The variable tot pay typ is defined for each respondent as the sum of all payments made in a typical month. The share variables “ sh” express the original “typ” variable as a proportion of tot pay typ for that respondent. The tables in the 2014 SCPC results paper describing payment shares are not computed using these individually defined variables. Instead, each share denotes the total number of transactions falling under that category as a proportion of all reported transactions, aggregated over all respondents. This differs slightly from taking means of the sh variables defined in this document: it weights respondents who have a large number of transactions more heavily than respondents who have a smaller number of transactions. 6 2.2.6 Variables defined conditional on adoption Some tables in the 2014 SCPC results paper include statistics that are calculated conditional on the adoption of a bank account, a certain payment instrument, or other payment technology. Separate variables were created to facilitate this calculation for the tables; these variables either end with the suffix “ adoptonly” or contain the term “oadopt”, indicating the conditional coding of the underlying variable. Such variables contain missing values (rather than zeros) for non-adopters of the respective account/instrument/technology. 2.2.7 Flags for variables that were cleaned for outliers The SCPC has many continuous variables. These variables come from survey questions where the respondent is allowed to enter a number into an open ended text box. For instance, we ask the respondent to tell us how many credit card payments they make for retail goods in a typical week, month, or year. Continuous variables in the SCPC are cleaned for outliers and edited based on algorithms described in the 2014 SCPC Technical Appendix. To indicate an edited variable, the prefix “f ” is added to the front of a variable name. A flag value of 0 indicates that the particular observation was not edited. A flag value greater than 0 means the observation was edited. 7