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Data Guide to the 2018 Diary of Consumer Payment Choice Kevin Foster∗, Federal Reserve Bank of Atlanta March 1, 2019 Introduction The Diary of Consumer Payment Choice (DCPC) is a survey of consumer payment behavior run in conjunction with the University of Southern California’s Understanding America Study (UAS). Respondents were randomly assigned a three-day period between September 29, 2018 and November 2, 2018 and asked to track all of their payments using an online questionnaire. Respondents were also asked to answer a short survey and report some account balances on the night before the beginning of their diary period. To the extent possible, attempts were made to ensure that on any given day a representative sample of US consumers was actively taking the diary, and any given day can be made statistically representative by using appropriate sample weights. In addition to in-person purchases, respondents were also asked to record their online and mobile purchases, cash holdings, cash deposits, checking transfers, income payments, and other exchanges of liquid assets. The result is three datasets containing 15,155 unique transactions by 2,873 individuals across four days each, including 11,629 expenditures, 1,712 account transfers, and 1,813 income receipts. The DCPC provides researchers a unique window into the household finances of the U.S. consumer. Structure of the survey instrument Modules and duplicates The instrument is organized in several modules which deal with certain kinds of transactions—for instance, Purchases, Cash Withdrawals, and Checking Transfers. Within each of these modules, respondents are typically asked to list the number of purchases/cash withdrawals/checking transfers/etc they had on a given day. For each transaction, the online diary asks follow-up questions to collect additional details. The variable module can be used to identify which module an observation was originally pulled from. Note that while the modules can have rather suggestive names, one should not rely on the name of the module to identify the type of transaction an observation represents—not all transactions reported in the Purchases module are necessarily “purchases”, as some transactions may be recategorized after-the-fact if the respondent makes a mistake. Respondents were asked many followups which are a much more reliable means of identifying a transaction’s purpose. See Structure and use of the data below for more information. In some cases a respondent would report the same transaction in multiple modules. For instance, a respondent might report a utility bill payment in both the Purchases and Bills module. These duplicates are culled from the dataset, and the module variable is modified to reflect that a transaction came from multiple parts of the survey. Transactions are considered to be duplicates if they have a matching uasid (primary respondent identifier), date, amnt (transaction amount), and pi (payment instrument) in cases where pi is available, and uasid, date, and amnt in cases where pi is not available. ∗ email: kevin.foster@atl.frb.org 1 Some notes on the sampling methodology and skip patterns In order to balance unwanted heterogeneity in response quality across days due to diary fatigue, some diarists were assigned diary periods beginning on September 29 or 30 and some diarists were assigned diary periods ending on November 1 or 2. This was to ensure that every individual day in October has an approximately equal mix of diarists completing their 1st, 2nd, and 3rd diary days. The “burn-in” days of September 29–30 and the “burn-out” days of November 1–2 can be dropped from any analysis which attempts to describe the month of October. Because these observations do not have daily weights, they are automatically excluded if the daily weights are used, but must be excluded manually when using the individual weights—see the Weighting section below. For more information on the sampling methodology, see the 2018 DCPC Technical Appendix. In order to reduce respondent burden, the diary employs skip patterns to determine whether or not a respondent is asked a given question. In most cases, this is intuitive; a respondent who does not report a credit card payment is not asked about the logo on their credit card. In other cases, however, it can be potentially misleading. For instance, respondents are only asked if they had cash stolen if their reported end of day cash balance fails to match their reported cash transactions (within a margin of error). Thus, in some cases it may be necessary for the researcher to trace variables back to their original diary questions in order to obtain a full understanding of the universe of respondents for a given question. Structure and use of the data The 2018 DCPC data is posted as three separate datasets on the Atlanta Fed website1 : individual-level, day-level, and transaction-level. These datasets are designed to facilitate appropriate methods of analysis for each kind of data. There are 2,873 unique diarists, and as such there are 2,873 unique observations in the individual-level dataset. There are also 2,873 unique diarists in the day-level dataset—each diarist has four observations associated with their unique indentifier uasid. Finally, there are 2,617 unique diarists in the transaction-level dataset. This is due to the fact that some diarists do not report any transactions during the three day diary period. Unique identifier uasid In prior years of the Survey and Diary of Consumer Payment Choice, the unique identifier for each respondent was a variable called prim key. In 2014, the survey switched vendors to the UAS, and that vendor uses a unique respondent identifier called uasid. The survey and diary datasets from 2014 to 2017 continued to use the name prim key, but this was just a renaming of the uasid. Survey and diary data from the UAS vendor for years 2014–2018 can be merged together to create longitudinal datasets. In addition, uasid can be used to merge survey and diary data with any other survey that UAS publishes. Individual-level dataset The individual-level dataset is structured so that each row in the dataset represents observations for one respondent. There are 2,873 rows in this dataset—one for each respondent. Examples of variables in this dataset include payment preferences and demographic variables. The unique identifier is uasid. Day-level dataset In the day-level dataset, each observation represents one diary-day per respondent. In other words, we see 2,873 observations for each diary-day, for a total of 11,492 observations in this dataset. Examples of variables that are in this dataset include cash balances by bill denomination and the participation dates. Here, the unique identifiers are uasid and diary day. 1 https://www.frbatlanta.org/banking-and-payments/consumer-payments/diary-of-consumer-payment-choice/ 2018-diary 2 Transaction-level dataset Finally, the transaction-level dataset contains one transaction per row. There are 15,155 observations in this dataset, consisting on expenditures, account transfers, and income receipts. The variable type allows the data user to distinguish between these types of transactions. The main kind of variable in this dataset are the variables that describe a payment. In this dataset, each observation is uniquely identified by uasid, diary day, and tran. The type variable Every transaction is assigned a value in the variable type, which identifies what sort of transaction the observation represents. Observations can either represent an expenditure, a transfer, or an income receipt. Understanding the type variable, and its associated from account and to account is integral to properly using the data, so a short guide is included here. Expenditures Expenditures are defined to be money moving out of a respondent’s possession—for instance, purchasing an item at a store. Expenditures generally come from the Purchases or Bills modules, though they may come from other modules as well. A substantial number of merchant categorization followups were asked for each transaction reported in the Purchases and Bills modules to determine what the expenditure was for; these followups have been merged into the variables merch and purpose. Using these variables one can, for instance, identify consumption. Transfers Transfers are when money is moved from one account to another, each owned by the same diarist. In order to identify the actual movement of money, one should use the from account and to account variables. Transfers can be reported in almost any module. For instance, a cash withdrawal would be a transfer from a checking account to cash and would come from the Cash Withdrawals module, while a credit card bill payment could be a transfer from a checking account to a credit account and might come from the Purchases module. Income Income is defined as money coming into the respondent’s possession. Most income is reported in the Income module, though some types of Cash Withdrawal transactions are also considered income—for instance, receiving money from a family member. Note that, unlike other types of transactions, income receipts can be reported on diary day 0. Dollar amounts All transactions which represent a movement of money will have a dollar amount associated with them. This dollar amount is stored in the variable amnt, in the transaction-level dataset. Some outlier cleaning has been applied to these dollar amounts, and the original dollar amounts, as originally reported by the respondents, are stored in amnt orig. In addition, if the reported dollar amount was 0, then amnt was set to missing and amnt orig was set to 0 for that observation. Dollar amounts were cleaned based on their likelihood given the type of transaction, the respondent’s answer to the various merchant followups, the respondent’s written answers in some of the “other” boxes in the survey (which are not included in this dataset), and the respondent’s answers to some of the questions in the Survey of Consumer Payment Choice (SCPC). In some cases, unrealistically large dollar amounts are the result of an omitted decimal point. 3 Other key variables Each transaction also includes, when applicable, an amount (variable amnt), a time (variable time), a payment instrument (variable pi)—e.g., cash, credit, check—a merchant category (variable merch)—e.g., financial services, restaurants, transportation—and the device with which the payment was made—e.g., a mobile phone—as well as several other variables related to the payment. Under this organization, it is a very simple matter to estimate, say, the average value of a cash transaction at a restaurant, or the average number of credit payments in a month. It is also possible, under some reasonable assumptions, to generate running balances of the various liquidity accounts in a respondent’s possession. Structure of this document The variables in this code book are presented alphabetically. Each variable has a description that gives the definition, as well as the coding of the original survey question. This coding can be used to look up the question in the survey questionnaire. When necessary, additional details are provided about how the variable was altered or constructed from the original survey response. Additional histograms and unweighted summary statistics are provided for continuous-valued variables, while simple tabulations and codings are provided for categorical variables. Weighting To allow for estimations that are representative of the United States, three sets of sample weights are provided in these datasets. The first set of base weights, ind weight, are individual-level post-stratification weights, and are available in the individual-level dataset. The second and third sets of weights are found in the day-level dataset. The weights in the variable daily weight, are day-level weights. The third set of weights, dow weight, are day-level day-of-week weights that attempt to account for day-of-week affects in the number and value of payments. We recommend that this latter set of weights be used whenever attempting cross-year comparisons involving payments. All weights are relative weights—they have a mean of 1 and sum to the number of observations in the dataset. When subsetting the data—especially by date—it may be necessary to generate your own weights, and strictly speaking the day weights provided are not appropriate to use when including diary day 0. For more information about how the weights are constructed, see 2018 Survey and Diary of Consumer Payment Choice—Sampling and Weighting by Marco Angrisani.2 2 https://www.frbatlanta.org/-/media/documents/banking/consumer-payments/diary-of-consumer-payment-choice/ 2018/scpc-dcpc-2018-sampling-weights.pdf 4 Contents accept card 6 accept cash 7 age 8 amnt 9 amnt orig 11 authorization method 12 automatic 13 bill 14 bill orig 15 borrowed for purchase 16 can postpone 17 carry acnt2acnt 18 carry banp 19 carry cc 20 carry chk 21 carry coins 22 carry csh 23 carry dc 24 carry mobile 25 carry monord 26 carry obbp 27 carry oth 28 carry paypal 29 carry prepaid 30 cash move 31 cc chip 1 32 cc chip 2 33 5 cc chip 3 34 cc chip 4 35 cc chip 5 36 cc debt amnt 37 cc debt canpay 38 cc debt whynotpay 39 cc hasbal 1 40 cc hasbal 2 41 cc hasbal 3 42 cc hasbal 4 43 cc hasbal 5 44 cc num 45 cc num used 46 cc rewards 1 47 cc rewards 2 48 cc rewards 3 49 cc rewards 4 50 cc rewards 5 51 cc type 1 52 cc type 2 53 cc type 3 54 cc type 4 55 cc type 5 56 cd account 57 cd location 58 census division 59 check dep src 60 checker 61 6 chk bal 62 chk bal time 63 citizen 64 coin2cash coin amnt 65 coin2cash loc 66 coin2cash reimburse 67 cw location 68 cw source 69 daily weight 70 date 71 date authorized 72 dc acct 1 73 dc acct 2 74 dc acct 3 75 dc acct 4 76 dc acct 5 77 dc logo 1 78 dc logo 2 79 dc logo 3 80 dc logo 4 81 dc logo 5 82 dc num 83 dc num used 84 dc rewards 1 85 dc rewards 2 86 dc rewards 3 87 dc rewards 4 88 dc rewards 5 89 7 debit auth 90 denom 1 end 91 denom 1 stored 92 denom 10 end 93 denom 10 stored 94 denom 100 end 95 denom 100 stored 96 denom 2 end 97 denom 2 stored 98 denom 20 end 99 denom 20 stored 100 denom 5 end 101 denom 5 stored 102 denom 50 end 103 denom 50 stored 104 device 105 device orig 106 diary day 107 discount 108 dow weight 109 draft date 110 due date 111 durable type 112 e exp cc 113 e exp chk 114 e exp chk saved 115 e exp cover 116 e exp csh 117 8 e exp csh saved 118 e exp fam 119 e exp heloc 120 e exp od 121 e exp pawn 122 e exp payday 123 e exp prepaid 124 e exp prepaid saved 125 e exp sav 126 e exp sav saved 127 e exp tot saved 128 end cash bal 129 enough cash 130 fee amnt 131 fee flag 132 fixed amount 133 frequency 134 from account 135 from bill section 136 gender 137 gpr bal 138 gpr bal date 139 gpr bal time 140 hh size 141 highest education 142 hispaniclatino 143 hispaniclatino group 144 home debt 145 9 home value 146 homeowner 147 in person 148 in person orig 149 inc alimony 150 inc alimony freq 151 inc child 152 inc child freq 153 inc gov 154 inc gov freq 155 inc intdiv 156 inc intdiv freq 157 inc rent 158 inc rent freq 159 inc retempl 160 inc retempl freq 161 inc retsav 162 inc retsav freq 163 inc self 164 inc self freq 165 inc ss 166 inc ss freq 167 inc wage 168 inc wage freq 169 income 170 income hh 171 income howpaid 172 income type 173 10 inconsistency explain 174 ind payee 175 ind weight 176 interest level 177 last income date 178 late fee 179 loan amnt canpay 180 loan amnt due 181 loan amnt whynotpay 182 login date 183 marital status 184 memory checkbook 185 memory finrec 186 memory lpd 187 memory memory 188 memory oth 189 memory receipts 190 merch 191 merch orig 193 mobile funding 195 mobile howfunded 196 mobile method 197 mobile type 198 module 199 monord date 200 monord source 201 multipi breakdown 202 next income date 203 11 nopayments 204 num times used coins 205 other assets 206 other debts 207 other device desc 208 otherpi funding 209 otherpi type 210 ow type 211 past service 212 pay amnt coins 213 pay timing 214 pay010 215 pay011 216 pay016 217 pay020 218 pay030 219 pay040 220 pay041 221 pay042 222 pay050 223 pay082 224 payee 225 payee orig 226 payment 227 paypal bal 228 paypal bal date 229 paypal bal time 230 paypal funding 231 12 paypref 100plus 232 paypref 10to25 233 paypref 25to50 234 paypref 50to100 235 paypref b1 236 paypref b1 why 237 paypref b2 238 paypref b2 why 239 paypref lt10 240 paypref nb1 241 paypref nb1 why 242 paypref nb2 243 paypref nb2 why 244 paypref tran 245 paypref web 246 paypref web why 247 pi 248 pi orig 249 pmnt desc 250 ppload gpr 251 ppload loc 252 prepaid logo 253 prior goods 254 prior goods time 255 race asian 256 race black 257 race other 258 race white 259 13 receipt timing 260 regularity 261 report date 262 scpc date 263 shops online 264 split income deposit 265 time 266 to account 267 tran 268 tran account 269 tran days 270 tran inst 271 tran min 272 tran report 273 traveled 274 uasid 275 unexpected 276 used coins 277 used heloc 278 why nocash 279 why not billpref 280 why not pref 281 work disabled 282 work employed 283 work looking 284 work occupation 285 work onleave 286 work other 287 14 work retired 288 work self 289 work temp unemployed 290 15 accept card Dataset: Transaction-level Variable type: Numeric N = 3089 Description: Whether a credit or debit card would have been accepted for this transaction. In the case of this variable, the range of responses has been changed from the survey question q101j. In the survey question, the responses range from 1 to 3, but in this created variable, the responses range from 0 to 2, to better match up with the convention in these datasets that NO equals 0 and YES equals 1. Survey question: q101j Values 0 1 2 Number 513 2319 257 Percent 16.6 75.1 8.3 Table 1: Frequency table for accept card Value labels: 0 - No 1 - Yes 2 - I don’t know 16 accept cash Dataset: Transaction-level Variable type: Numeric N = 5337 Description: Whether cash would have been accepted for this transaction. In the case of this variable, the range of responses has been changed from the survey question q103j. In the survey question, the responses range from 1 to 5, but in this created variable, the responses range from 0 to 4, to better match up with the convention in these datasets that NO equals 0 and YES equals 1. Survey question: q103g Values 0 1 2 3 4 Number 198 5051 47 23 18 Percent 3.7 94.6 0.9 0.4 0.3 Table 2: Frequency table for accept cash Value labels: 0 - No 1 - Yes 2 - I’m not sure, but I think so 3 - I’m not sure, but I do not think so 4 - I don’t know 17 age Dataset: Individual-level Variable type: Numeric N = 2873 Description: Respondent’s age, in years. Survey question: Calculated from date of birth. Details: Date of birth is used as reported in My Household Questionnaire. For respondents who have birthdays during the diary period, the age is set to be the greater of the two ages. min 18.0 med 52.0 mean 51.5 max 100.0 sd 14.9 100 50 0 Frequency 150 Table 3: Summary statistics for age 20 30 40 50 60 age 18 70 amnt Dataset: Transaction-level Variable type: Numeric N = 15012 Description: Dollar amount of the transaction, cleaned. Survey question: Filled in by respondent in nearly every module. Details: Individual dollar-value cleaning is performed according to a subjective ”smell-test”. This is to control for extremely large outliers which are, generally, the result of misplaced decimal points. Original dollar amounts are maintained in the variable amnt orig. Data users may notice that some large transactions have been maintained. This is usually because we were able to confirm that they are genuine. min 0.0 med 30.9 mean 211.3 max 88000.0 sd 1118.0 Table 4: Summary statistics for amnt 19 0 200 400 600 amnt 20 800 1000 0 2000 4000 6000 Frequency 8000 amnt orig Dataset: Transaction-level Variable type: Numeric N = 12440 Description: Dollar amount of the transaction, uncleaned. Survey question: Filled in by respondent in nearly every module. Details: Uncleaned values. See amnt for cleaned values. min -49.8 med 25.0 mean 126.8 max 76890.0 sd 1113.3 1000 2000 3000 4000 5000 0 Frequency Table 5: Summary statistics for amnt orig 0 100 200 amnt_orig 21 300 authorization method Dataset: Transaction-level Variable type: Numeric N = 3859 Description: Question text: How was this debit card purchase authorized? Survey question: q201g Values 1 2 3 4 5 Number 884 2335 17 585 38 Percent 22.9 60.5 0.4 15.2 1.0 Table 6: Frequency table for authorization method Value labels: 1 - Swiping the card 2 - Inserting the card’s chip 3 - Tapping, waving, or other contactless method 4 - Handing the card to an employee such as a waiter or waitress 5 - Other (specify) 22 automatic Dataset: Transaction-level Variable type: Numeric N = 2778 Description: Whether the bill was paid manually or automatically. Survey question: pay002 autom, or a radio button in the bills module Values 0 1 Number 1977 801 Percent 71.2 28.8 Table 7: Frequency table for automatic Value labels: 0 - No 1 - Yes 23 bill Dataset: Transaction-level Variable type: Numeric N = 12439 Description: Whether this transaction was a bill. Survey question: pay002, ”other” responses. Details: Question pay002 is used to identify bills reported in the purchases module. All bills reported in the bills reminder module are bills by definition. Observations for which ”other” was chosen are manually recategorized. Note that, due to the wording of the question, a very large proportion of respondents (about 25-30 percent) chose ”other” and described their payment in words. We attempted to come up with rules for recategorizing these responses, as there were too many to do each one individually. Values 0 1 Number 9660 2779 Percent 77.7 22.3 Table 8: Frequency table for bill Value labels: 0 - No 1 - Yes 24 bill orig Dataset: Transaction-level Variable type: Numeric N = 12439 Description: Whether this transaction was a bill. Survey question: pay002, ”other” responses. Details: Question pay002 is used to identify bills reported in the purchases module. All bills reported in the bills reminder module are bills by definition. Observations for which ”other” was chosen are manually recategorized. Note that, due to the wording of the question, a very large proportion of respondents (about 25-30 percent) chose ”other” and described their payment in words. We attempted to come up with rules for recategorizing these responses, as there were too many to do each one individually. Values 0 1 Number 9660 2779 Percent 77.7 22.3 Table 9: Frequency table for bill orig Value labels: 0 - No 1 - Yes 25 borrowed for purchase Dataset: Transaction-level Variable type: Numeric N = 25 Description: Question text: Did you borrow money to make this purchase? Survey question: pay612 Details: This question is only displayed if the payment amount is greater than or equal to 200 dollars, the response to pay608 is not NONE OF THE ABOVE, and the payment method is not CREDIT CARD. Values 0 1 Number 24 1 Percent 96.0 4.0 Table 10: Frequency table for borrowed for purchase Value labels: 0 - No 1 - Yes 26 can postpone Dataset: Transaction-level Variable type: Numeric N = 4006 Description: Whether this transaction could have been postponed without penalty. Survey question: q151 b Values 0 1 Number 2392 1614 Percent 59.7 40.3 Table 11: Frequency table for can postpone Value labels: 0 - No 1 - Yes 27 carry acnt2acnt Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether the repsondent had the ability to make an account to account transfer that day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 11. Values 0 1 Number 4059 656 Percent 86.1 13.9 Table 12: Frequency table for carry acnt2acnt Value labels: 0 - No 1 - Yes 28 carry banp Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent had the ability to make a bank account number payment that day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 6. Values 0 1 Number 3551 1164 Percent 75.3 24.7 Table 13: Frequency table for carry banp Value labels: 0 - No 1 - Yes 29 carry cc Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent carried credit cards on that diary day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 3. Values 0 1 Number 1289 3426 Percent 27.3 72.7 Table 14: Frequency table for carry cc Value labels: 0 - No 1 - Yes 30 carry chk Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent carried checks on that diary day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 2. Values 0 1 Number 2442 2273 Percent 51.8 48.2 Table 15: Frequency table for carry chk Value labels: 0 - No 1 - Yes 31 carry coins Dataset: Day-level Variable type: Numeric N = 8618 Description: Question text: Did you start today carrying any coins in your pocket, wallet, or purse? Survey question: q5 1 Values 0 1 Number 5171 3447 Percent 60.0 40.0 Table 16: Frequency table for carry coins Value labels: 0 - No 1 - Yes 32 carry csh Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent carried cash on that diary day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 1. Values 0 1 Number 853 3862 Percent 18.1 81.9 Table 17: Frequency table for carry csh Value labels: 0 - No 1 - Yes 33 carry dc Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent carried debit cards on that diary day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 4. Values 0 1 Number 1232 3483 Percent 26.1 73.9 Table 18: Frequency table for carry dc Value labels: 0 - No 1 - Yes 34 carry mobile Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent carried mobile device capable of making text message payments on that diary day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 12. Values 0 1 Number 4076 639 Percent 86.4 13.6 Table 19: Frequency table for carry mobile Value labels: 0 - No 1 - Yes 35 carry monord Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent carried money orders on that diary day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 8. Values 0 1 Number 4592 123 Percent 97.4 2.6 Table 20: Frequency table for carry monord Value labels: 0 - No 1 - Yes 36 carry obbp Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent had the ability to make an online banking bill payment that day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 7. Values 0 1 Number 3459 1256 Percent 73.4 26.6 Table 21: Frequency table for carry obbp Value labels: 0 - No 1 - Yes 37 carry oth Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent carried other payment methods on that diary day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 13. Values 0 1 Number 4690 25 Percent 99.5 0.5 Table 22: Frequency table for carry oth Value labels: 0 - No 1 - Yes 38 carry paypal Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether the repsondent had the ability to make a Paypal payment that day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 10. Values 0 1 Number 3721 994 Percent 78.9 21.1 Table 23: Frequency table for carry paypal Value labels: 0 - No 1 - Yes 39 carry prepaid Dataset: Day-level Variable type: Numeric N = 4715 Description: Whether respondent carried a prepaid card (stored value card) on that diary day. Survey question: q97 Details: Indicator variable set to 1 if respondent checked option 5. Values 0 1 Number 4016 699 Percent 85.2 14.8 Table 24: Frequency table for carry prepaid Value labels: 0 - No 1 - Yes 40 cash move Dataset: Transaction-level Variable type: Numeric N = 283 Description: Cash movements from one form or location to another. Survey question: q106a-d, q120, q122 Details: Amounts are reported in q106a-d, q120, q122, and cash move is used to identify which question the transaction amount came from. Values 1 2 3 4 5 6 Number 73 80 15 112 2 1 Percent 25.8 28.3 5.3 39.6 0.7 0.4 Table 25: Frequency table for cash move Value labels: 1 - Pocket to storage 2 - Storage to pocket 3 - Cash stolen or lost 4 - Unexpected receipt of cash 5 - Cash to foreign currency 6 - Foreign currency to cash 41 cc chip 1 Dataset: Individual-level Variable type: Numeric N = 2224 Description: Whether the respondent’s first credit card has a chip. Survey question: ccq 005 Values 0 1 Number 205 2019 Percent 9.2 90.8 Table 26: Frequency table for cc chip 1 Value labels: 0 - No 1 - Yes 42 cc chip 2 Dataset: Individual-level Variable type: Numeric N = 443 Description: Whether the respondent’s second credit card has a chip. Survey question: ccq 005 Values 0 1 Number 52 391 Percent 11.7 88.3 Table 27: Frequency table for cc chip 2 Value labels: 0 - No 1 - Yes 43 cc chip 3 Dataset: Individual-level Variable type: Numeric N = 85 Description: Whether the respondent’s third credit card has a chip. Survey question: ccq 005 Values 0 1 Number 17 68 Percent 20.0 80.0 Table 28: Frequency table for cc chip 3 Value labels: 0 - No 1 - Yes 44 cc chip 4 Dataset: Individual-level Variable type: Numeric N = 13 Description: Whether the respondent’s fourth credit card has a chip. Survey question: ccq 005 Values 0 1 Number 3 10 Percent 23.1 76.9 Table 29: Frequency table for cc chip 4 Value labels: 0 - No 1 - Yes 45 cc chip 5 Dataset: Individual-level Variable type: Numeric N =6 Description: Whether the respondent’s fifth credit card has a chip. Survey question: ccq 005 Values 0 1 Number 2 4 Percent 33.3 66.7 Table 30: Frequency table for cc chip 5 Value labels: 0 - No 1 - Yes 46 cc debt amnt Dataset: Transaction-level Variable type: Numeric N = 435 Description: Question text: How much was the full amount due (statement balance) of the credit card bill? Survey question: pay019 Details: This question is only displayed if the diarist did not pay back the full amount due on the credit card bill. min 0.0 med 521.0 mean 2259.4 max 168300.0 sd 8692.3 80 60 40 20 0 Frequency 100 120 Table 31: Summary statistics for cc debt amnt 0 2000 4000 6000 cc_debt_amnt 47 8000 cc debt canpay Dataset: Transaction-level Variable type: Numeric N = 219 Description: Question text: Did you have enough money in your checking or savings account to pay the full amount due (statement balance) of this credit card bill? Survey question: pay019a Details: This question is only displayed if the diarist did not pay back the full amount due on the credit card bill. Values 0 1 Number 119 100 Percent 54.3 45.7 Table 32: Frequency table for cc debt canpay Value labels: 0 - No 1 - Yes 48 cc debt whynotpay Dataset: Transaction-level Variable type: Character N = 15114 Description: Question text: Why did you choose not to pay the full amount due (statement balance) for this credit card bill? Survey question: pay019b Details: Open-ended text response box. This question is only displayed if the diarist did not pay back the full amount due on the credit card bill. 49 cc hasbal 1 Dataset: Individual-level Variable type: Numeric N = 2223 Description: Whether the respondent’s first credit card has a rolled over balance. Survey question: ccq 004 Values 0 1 Number 1356 867 Percent 61.0 39.0 Table 33: Frequency table for cc hasbal 1 Value labels: 0 - No 1 - Yes 50 cc hasbal 2 Dataset: Individual-level Variable type: Numeric N = 441 Description: Whether the respondent’s second credit card has a rolled over balance. Survey question: ccq 004 Values 0 1 Number 314 127 Percent 71.2 28.8 Table 34: Frequency table for cc hasbal 2 Value labels: 0 - No 1 - Yes 51 cc hasbal 3 Dataset: Individual-level Variable type: Numeric N = 87 Description: Whether the respondent’s third credit card has a rolled over balance. Survey question: ccq 004 Values 0 1 Number 63 24 Percent 72.4 27.6 Table 35: Frequency table for cc hasbal 3 Value labels: 0 - No 1 - Yes 52 cc hasbal 4 Dataset: Individual-level Variable type: Numeric N = 13 Description: Whether the respondent’s fourth credit card has a rolled over balance. Survey question: ccq 004 Values 0 1 Number 8 5 Percent 61.5 38.5 Table 36: Frequency table for cc hasbal 4 Value labels: 0 - No 1 - Yes 53 cc hasbal 5 Dataset: Individual-level Variable type: Numeric N =6 Description: Whether the respondent’s fifth credit card has a rolled over balance. Survey question: ccq 004 Values 0 1 Number 4 2 Percent 66.7 33.3 Table 37: Frequency table for cc hasbal 5 Value labels: 0 - No 1 - Yes 54 cc num Dataset: Individual-level Variable type: Numeric N = 2231 Description: The number of credit cards the respondent has, conditional on the respondent having reported owning at least one credit card in the SCPC. The SCPC variable cc adopt indicates whether or not the respondent has adopted credit cards. Survey question: ccq 001 Values 1 2 3 4 5 6 Number 1787 357 73 8 4 2 Percent 80.1 16.0 3.3 0.4 0.2 0.1 Table 38: Frequency table for cc num Value labels: 1 - One 2 - Two 3 - Three 4 - Four 5 - Five 6 - More than five 55 cc num used Dataset: Transaction-level Variable type: Numeric N = 2701 Description: Question text: Which of your credit cards did you use to make this payment? Survey question: q201c Values 1 2 3 4 5 6 Number 2162 288 38 4 4 205 Percent 80.0 10.7 1.4 0.1 0.1 7.6 Table 39: Frequency table for cc num used Value labels: 1 - First credit card (CC) listed 2 - Second CC listed 3 - Third CC listed 4 - Fourth CC listed 5 - Fifth CC listed 6 - Another credit card not listed 56 cc rewards 1 Dataset: Individual-level Variable type: Numeric N = 2226 Description: Whether the respondent’s first credit card offers rewards. Survey question: ccq 003 Values 0 1 Number 691 1535 Percent 31.0 69.0 Table 40: Frequency table for cc rewards 1 Value labels: 0 - No 1 - Yes 57 cc rewards 2 Dataset: Individual-level Variable type: Numeric N = 443 Description: Whether the respondent’s second credit card offers rewards. Survey question: ccq 003 Values 0 1 Number 83 360 Percent 18.7 81.3 Table 41: Frequency table for cc rewards 2 Value labels: 0 - No 1 - Yes 58 cc rewards 3 Dataset: Individual-level Variable type: Numeric N = 87 Description: Whether the respondent’s third credit card offers rewards. Survey question: ccq 003 Values 0 1 Number 25 62 Percent 28.7 71.3 Table 42: Frequency table for cc rewards 3 Value labels: 0 - No 1 - Yes 59 cc rewards 4 Dataset: Individual-level Variable type: Numeric N = 13 Description: Whether the respondent’s fourth credit card offers rewards. Survey question: ccq 003 Values 0 1 Number 2 11 Percent 15.4 84.6 Table 43: Frequency table for cc rewards 4 Value labels: 0 - No 1 - Yes 60 cc rewards 5 Dataset: Individual-level Variable type: Numeric N =6 Description: Whether the respondent’s fifth credit card offers rewards. Survey question: ccq 003 Values 0 1 Number 3 3 Percent 50.0 50.0 Table 44: Frequency table for cc rewards 5 Value labels: 0 - No 1 - Yes 61 cc type 1 Dataset: Individual-level Variable type: Numeric N = 2228 Description: Type (e.g. logo) of the respondent’s first credit card. Survey question: ccq 002 Values 1 2 3 4 5 6 7 8 Number 1273 574 177 50 20 92 1 41 Percent 57.1 25.8 7.9 2.2 0.9 4.1 0.0 1.8 Table 45: Frequency table for cc type 1 Value labels: 1 - Visa 2 - MasterCard 3 - Discover 4 - Company or store branded credit cards 5 - American Express charge card 6 - American Express credit card 7 - Diners Club or other charge cards 8 - Other 62 cc type 2 Dataset: Individual-level Variable type: Numeric N = 444 Description: Type (e.g. logo) of the respondent’s second credit card. Survey question: ccq 002 Values 1 2 3 4 5 6 8 Number 212 117 34 26 9 38 8 Percent 47.7 26.4 7.7 5.9 2.0 8.6 1.8 Table 46: Frequency table for cc type 2 Value labels: 1 - Visa 2 - MasterCard 3 - Discover 4 - Company or store branded credit cards 5 - American Express charge card 6 - American Express credit card 7 - Diners Club or other charge cards 8 - Other 63 cc type 3 Dataset: Individual-level Variable type: Numeric N = 87 Description: Type (e.g. logo) of the respondent’s third credit card. Survey question: ccq 002 Values 1 2 3 4 5 6 7 8 Number 31 26 7 10 2 5 1 5 Percent 35.6 29.9 8.0 11.5 2.3 5.7 1.1 5.7 Table 47: Frequency table for cc type 3 Value labels: 1 - Visa 2 - MasterCard 3 - Discover 4 - Company or store branded credit cards 5 - American Express charge card 6 - American Express credit card 7 - Diners Club or other charge cards 8 - Other 64 cc type 4 Dataset: Individual-level Variable type: Numeric N = 13 Description: Type (e.g. logo) of the respondent’s fourth credit card. Survey question: ccq 002 Values 1 2 3 4 6 8 Number 5 2 1 3 1 1 Percent 38.5 15.4 7.7 23.1 7.7 7.7 Table 48: Frequency table for cc type 4 Value labels: 1 - Visa 2 - MasterCard 3 - Discover 4 - Company or store branded credit cards 5 - American Express charge card 6 - American Express credit card 7 - Diners Club or other charge cards 8 - Other 65 cc type 5 Dataset: Individual-level Variable type: Numeric N =6 Description: Type (e.g. logo) of the respondent’s fifth credit card. Survey question: ccq 002 Values 1 2 6 8 Number 1 1 2 2 Percent 16.7 16.7 33.3 33.3 Table 49: Frequency table for cc type 5 Value labels: 1 - Visa 2 - MasterCard 3 - Discover 4 - Company or store branded credit cards 5 - American Express charge card 6 - American Express credit card 7 - Diners Club or other charge cards 8 - Other 66 cd account Dataset: Transaction-level Variable type: Numeric N = 131 Description: Account where cash was desposited. Survey question: cashdep account Values 1 2 4 6 Number 96 17 1 17 Percent 73.3 13.0 0.8 13.0 Table 50: Frequency table for cd account Value labels: 1 - Primary checking account 2 - Other checking or savings account 3 - Primary general purpose reloadable prepaid card 4 - Other prepaid card 5 - Primary PayPal account 6 - Other (specify) 67 cd location Dataset: Transaction-level Variable type: Numeric N = 128 Description: Cash deposit location. Survey question: Drop-down box in the cash deposits module. Called ”Deposit Method” in the questionnaire. Values 1 2 3 Number 36 47 45 Percent 28.1 36.7 35.2 Table 51: Frequency table for cd location Value labels: 1 - ATM 2 - Bank teller 3 - Other (specify) 68 census division Dataset: Individual-level Variable type: Numeric N = 2872 Description: The Census division where the respondent lives. Survey question: statereside Details: Constructed from UAS Household Survey variable statereside Values 1 2 3 4 5 6 7 8 9 Number 95 363 610 333 566 197 253 178 277 Percent 3.3 12.6 21.2 11.6 19.7 6.9 8.8 6.2 9.6 Table 52: Frequency table for census division Value labels: 1 - New England 2 - Middle Atlantic 3 - East North Central 4 - West North Central 5 - South Atlantic 6 - East South Centra 7 - West South Central 8 - Mountain 9 - Pacific 69 check dep src Dataset: Transaction-level Variable type: Numeric N = 466 Description: The source of the checking deposit. Survey question: Drop-down box in the checking deposits module. Values 1 6 7 8 9 Number 129 101 187 4 45 Percent 27.7 21.7 40.1 0.9 9.7 Table 53: Frequency table for check dep src Value labels: 1 - Check (personal or business) 2 - Money order 3 - Travelers check 4 - Cashiers check 5 - Certified check 6 - Transfer from another account 7 - Direct deposit of income 8 - Venmo cash out 9 - Other 70 checker Dataset: Transaction-level Variable type: Numeric N = 12440 Description: A flag used internally for data processing. Survey question: N/A 71 chk bal Dataset: Day-level Variable type: Numeric N = 10594 Description: Balance of checking account. Survey question: pa072 a min -3443.0 med 1086.5 mean 4494.0 max 282132.0 sd 14041.8 3000 2000 1000 0 Frequency 4000 5000 Table 54: Summary statistics for chk bal 0 5000 10000 chk_bal 72 15000 chk bal time Dataset: Day-level Variable type: Numeric N = 10584 Description: Time that diarist checked checking account balance. Survey question: pa072 a time 73 citizen Dataset: Individual-level Variable type: Numeric N = 2873 Description: Whether respondent is a US citizen. Note: This variable is not provided in the public dataset. Survey question: From UAS My Household Questionnaire. Values 0 1 Number 25 2848 Percent 0.9 99.1 Table 55: Frequency table for citizen Value labels: 0 - No 1 - Yes 74 coin2cash coin amnt Dataset: Transaction-level Variable type: Numeric N = 24 Description: Dollar value of coins to converted to cash. Survey question: Filled in during the coin-to-cash/cash-to-coin module. Details: The cash-to-coin/coin-to-cash module is an error-checking module, and only shown to respondents whose daily cash balance implied by their cash transactions does not match their reported end-of-day cash holdings. min 0.0 med 9.0 mean 113.9 max 2506.0 sd 509.6 3 2 1 0 Frequency 4 5 6 Table 56: Summary statistics for coin2cash coin amnt 0 10 20 30 coin2cash_coin_amnt 75 40 coin2cash loc Dataset: Transaction-level Variable type: Numeric N = 45 Description: Coin to cash conversion location. Survey question: Drop-down box in the coin-to-cash/cash-to-coin module. Details: The cash-to-coin/coin-to-cash module is an error-checking module, and only shown to respondents whose daily cash balance implied by their cash transactions does not match their reported end-of-day cash holdings. Values 1 2 3 4 5 Number 9 3 16 10 7 Percent 20.0 6.7 35.6 22.2 15.6 Table 57: Frequency table for coin2cash loc Value labels: 1 - Coin machine or kiosk 2 - Bank teller 3 - Cash register or checkout in a store 4 - Family or friend 5 - Other (specify) 76 coin2cash reimburse Dataset: Transaction-level Variable type: Numeric N = 24 Description: Form in which cash was received. Survey question: Drop-down box in the coin-to-cash/cash-to-coin module. Details: The response ”no” has been set to 0, and the other responses have been adjusted accordingly. Also note that the cash-to-coin/coin-to-cash module is an error-checking module, and only shown to respondents whose daily cash balance implied by their cash transactions does not match their reported end-of-day cash holdings. Values 0 1 3 5 Number 20 1 1 2 Percent 83.3 4.2 4.2 8.3 Table 58: Frequency table for coin2cash reimburse Value labels: 0 - No 1 - Prepaid or gift card 2 - Deposit into bank account 3 - Points or value to use on a website 4 - Store credit 5 - Other (specify) 77 cw location Dataset: Transaction-level Variable type: Numeric N = 555 Description: Cash withdrawal location. Survey question: Drop-down box in the cash withdrawals module. Values 1 2 3 4 6 7 9 Number 131 49 52 200 57 13 53 Percent 23.6 8.8 9.4 36.0 10.3 2.3 9.5 Table 59: Frequency table for cw location Value labels: 1 - ATM 2 - Cash back at a retail store 3 - Bank teller 4 - Family or friend 5 - Check cashing store 6 - Employer 7 - Cash refund from returning goods 8 - Payday lender 9 - Other location 78 cw source Dataset: Transaction-level Variable type: Numeric N = 554 Description: Source of funds for cash withdrawal. Survey question: Drop-down box in the cash withdrawals module. Values 1 2 3 4 5 7 8 9 Number 180 23 63 29 1 2 193 63 Percent 32.5 4.2 11.4 5.2 0.2 0.4 34.8 11.4 Table 60: Frequency table for cw source Value labels: 1 - Primary checking account 2 - Other checking or savings account 3 - Salary wages or tips 4 - Cashing a check 5 - Credit card cash advance 6 - Primary GPR prepaid card cash withdrawal 7 - Other prepaid card cash withdrawal 8 - Another person 9 - Other source 79 daily weight Dataset: Day-level Variable type: Numeric N = 8097 Description: Day-level weights Survey question: N/A Details: Raked post-stratification weights. Daily weights are best used for producing single-day estimates. Unlike individual weights, daily weights are not trimmed. These particular daily weights correspond to rps w day a uasgfk in the full weights dataset. See Angrisani, M, 2018 Survey and Diary of Consumer Payment Choice Weighting Procedure (2018) for more information about the construction of the weights. 80 date Dataset: Transaction-level Variable type: Numeric N = 15096 Description: The date of the diary day. Each diarist participated in the diary for four consecutive days, with efforts made to ensure a representative sample of Americans on any given day. The dates range from September 28th, 2017 to November 2nd, 2017. In order to ensure the representativeness of the sample and to eliminate any biases from diary fatigue, it is recommended that only dates in October be considered. Survey question: N/A Details: In most cases, this variable is determined by the date on which the transaction was reported. For some bills, the date is reported by the respondent on diary day 3 and reassigned ex-post. 81 date authorized Dataset: Transaction-level Variable type: Numeric N = 19 Description: Question text: What is the date that you authorized this payment to pay? Survey question: q103n2 Details: Only asked for payments which use the methods Bank Account Number Payment or Online Banking Bill Payment. 82 dc acct 1 Dataset: Individual-level Variable type: Numeric N = 726 Description: Whether the respondent’s first debit card is linked to their primary checking account or another checking account. Survey question: dcq 005 Values 1 2 Number 641 85 Percent 88.3 11.7 Table 61: Frequency table for dc acct 1 Value labels: 1 - Primary account 2 - Another account 83 dc acct 2 Dataset: Individual-level Variable type: Numeric N = 115 Description: Whether the respondent’s second debit card is linked to their primary checking account or another checking account. Survey question: dcq 005 Values 1 2 Number 20 95 Percent 17.4 82.6 Table 62: Frequency table for dc acct 2 Value labels: 1 - Primary account 2 - Another account 84 dc acct 3 Dataset: Individual-level Variable type: Numeric N = 11 Description: Whether the respondent’s third debit card is linked to their primary checking account or another checking account. Survey question: dcq 005 Values 1 2 Number 2 9 Percent 18.2 81.8 Table 63: Frequency table for dc acct 3 Value labels: 1 - Primary account 2 - Another account 85 dc acct 4 Dataset: Individual-level Variable type: Numeric N =1 Description: Whether the respondent’s fourth debit card is linked to their primary checking account or another checking account. Survey question: dcq 005 Values 2 Number 1 Percent 100.0 Table 64: Frequency table for dc acct 4 Value labels: 1 - Primary account 2 - Another account 86 dc acct 5 Dataset: Individual-level Variable type: Numeric N =1 Description: Whether the respondent’s fifth debit card is linked to their primary checking account or another checking account. Survey question: dcq 005 Values 2 Number 1 Percent 100.0 Table 65: Frequency table for dc acct 5 Value labels: 1 - Primary account 2 - Another account 87 dc logo 1 Dataset: Individual-level Variable type: Numeric N = 2342 Description: Logo of the respondent’s first debit card. Survey question: dcq 002 Values 1 2 3 Number 1594 674 74 Percent 68.1 28.8 3.2 Table 66: Frequency table for dc logo 1 Value labels: 1 - Visa 2 - MasterCard 3 - No logo 88 dc logo 2 Dataset: Individual-level Variable type: Numeric N = 185 Description: Logo of the respondent’s second debit card. Survey question: dcq 002 Values 1 2 3 Number 104 65 16 Percent 56.2 35.1 8.6 Table 67: Frequency table for dc logo 2 Value labels: 1 - Visa 2 - MasterCard 3 - No logo 89 dc logo 3 Dataset: Individual-level Variable type: Numeric N = 29 Description: Logo of the respondent’s third debit card. Survey question: dcq 002 Values 1 2 3 Number 12 7 10 Percent 41.4 24.1 34.5 Table 68: Frequency table for dc logo 3 Value labels: 1 - Visa 2 - MasterCard 3 - No logo 90 dc logo 4 Dataset: Individual-level Variable type: Numeric N =9 Description: Logo of the respondent’s fourth debit card. Survey question: dcq 002 Values 1 2 3 Number 1 1 7 Percent 11.1 11.1 77.8 Table 69: Frequency table for dc logo 4 Value labels: 1 - Visa 2 - MasterCard 3 - No logo 91 dc logo 5 Dataset: Individual-level Variable type: Numeric N =7 Description: Logo of the respondent’s fifth debit card. Survey question: dcq 002 Values 2 3 Number 1 6 Percent 14.3 85.7 Table 70: Frequency table for dc logo 5 Value labels: 1 - Visa 2 - MasterCard 3 - No logo 92 dc num Dataset: Individual-level Variable type: Numeric N = 2343 Description: The number of debit cards the respondent has, conditional on the respondent having reported owning at least one debit card in the SCPC. The SCPC variable dc adopt indicates whether or not the respondent has adopted debit cards. Survey question: dcq 001 Values 1 2 3 4 5 6 Number 2155 156 20 4 6 2 Percent 92.0 6.7 0.9 0.2 0.3 0.1 Table 71: Frequency table for dc num Value labels: 1 - One 2 - Two 3 - Three 4 - Four 5 - Five 6 - More than five 93 dc num used Dataset: Transaction-level Variable type: Numeric N = 3276 Description: Question text: Which of your debit cards did you use to make this payment? Survey question: q201d Values 1 2 3 6 Number 3079 104 6 87 Percent 94.0 3.2 0.2 2.7 Table 72: Frequency table for dc num used Value labels: 1 - First debit card (DC) listed 2 - Second DC listed 3 - Third DC listed 4 - Fourth DC listed 5 - Fifth DC listed 6 - Another debit card not listed 94 dc rewards 1 Dataset: Individual-level Variable type: Numeric N = 2342 Description: Whether the respondent’s first debit card offers rewards. Survey question: dcq 003 Values 0 1 Number 1923 419 Percent 82.1 17.9 Table 73: Frequency table for dc rewards 1 Value labels: 0 - No 1 - Yes 95 dc rewards 2 Dataset: Individual-level Variable type: Numeric N = 185 Description: Whether the respondent’s second debit card offers rewards. Survey question: dcq 003 Values 0 1 Number 145 40 Percent 78.4 21.6 Table 74: Frequency table for dc rewards 2 Value labels: 0 - No 1 - Yes 96 dc rewards 3 Dataset: Individual-level Variable type: Numeric N = 29 Description: Whether the respondent’s third debit card offers rewards. Survey question: dcq 003 Values 0 1 Number 23 6 Percent 79.3 20.7 Table 75: Frequency table for dc rewards 3 Value labels: 0 - No 1 - Yes 97 dc rewards 4 Dataset: Individual-level Variable type: Numeric N =9 Description: Whether the respondent’s fourth debit card offers rewards. Survey question: dcq 003 Values 0 1 Number 7 2 Percent 77.8 22.2 Table 76: Frequency table for dc rewards 4 Value labels: 0 - No 1 - Yes 98 dc rewards 5 Dataset: Individual-level Variable type: Numeric N =6 Description: Whether the respondent’s fifth debit card offers rewards. Survey question: dcq 003 Values 0 1 Number 5 1 Percent 83.3 16.7 Table 77: Frequency table for dc rewards 5 Value labels: 0 - No 1 - Yes 99 debit auth Dataset: Transaction-level Variable type: Numeric N = 3276 Description: Method of debit authorization (signature or PIN). Survey question: q101c Values 1 2 3 4 5 6 Number 1505 511 424 673 32 131 Percent 45.9 15.6 12.9 20.5 1.0 4.0 Table 78: Frequency table for debit auth Value labels: 1 - PIN 2 - Signature 3 - CVC or CVV code 4 - None of these 5 - Some combination of two of these 6 - Other (specify) 100 denom 1 end Dataset: Day-level Variable type: Numeric N = 11491 Description: The number of 1 dollar bills carried at the end of the diary day. Survey question: From the ”Count your Paper Cash” screen at the end of each diary day. Details: Some amounts are cleaned when it is clear that the individual accidentally reported the dollar value rather than the count of bills. min 0.0 med 2.0 mean 2.9 max 81.0 sd 3.9 3000 2000 1000 0 Frequency 4000 Table 79: Summary statistics for denom 1 end 0 2 4 6 denom_1_end 101 8 10 denom 1 stored Dataset: Day-level Variable type: Numeric N = 5746 Description: The number of 1 dollar bills stored. Survey question: Reported in the ”Count your paper cash stored elsewhere” screen on day 0. min 0.0 med 0.0 mean 2.0 max 1500.0 sd 31.2 3000 2000 1000 0 Frequency 4000 5000 Table 80: Summary statistics for denom 1 stored 0 1 2 3 denom_1_stored 102 4 5 denom 10 end Dataset: Day-level Variable type: Numeric N = 11491 Description: The number of 10 dollar bills carried at the end of the diary day. Survey question: From the ”Count your Paper Cash” screen at the end of each diary day. Details: Some amounts are cleaned when it is clear that the individual accidentally reported the dollar value rather than the count of bills. min 0.0 med 0.0 mean 0.6 max 68.0 sd 1.5 4000 2000 0 Frequency 6000 Table 81: Summary statistics for denom 10 end 0.0 0.5 1.0 1.5 2.0 denom_10_end 103 2.5 3.0 denom 10 stored Dataset: Day-level Variable type: Numeric N = 5746 Description: The number of 10 dollar bills stored. Survey question: Reported in the ”Count your paper cash stored elsewhere” screen on day 0. min 0.0 med 0.0 mean 0.3 max 35.0 sd 1.7 3000 2000 1000 0 Frequency 4000 5000 Table 82: Summary statistics for denom 10 stored 0.0 0.2 0.4 0.6 denom_10_stored 104 0.8 1.0 denom 100 end Dataset: Day-level Variable type: Numeric N = 11491 Description: The number of 100 dollar bills carried at the end of the diary day. Survey question: From the ”Count your Paper Cash” screen at the end of each diary day. Details: Some amounts are cleaned when it is clear that the individual accidentally reported the dollar value rather than the count of bills. min 0.0 med 0.0 mean 0.2 max 100.0 sd 1.4 6000 4000 2000 0 Frequency 8000 Table 83: Summary statistics for denom 100 end 0.0 0.2 0.4 0.6 denom_100_end 105 0.8 1.0 denom 100 stored Dataset: Day-level Variable type: Numeric N = 5746 Description: The number of 100 dollar bills stored. Survey question: Reported in the ”Count your paper cash stored elsewhere” screen on day 0. min 0.0 med 0.0 mean 1.6 max 1000.0 sd 18.1 3000 2000 1000 0 Frequency 4000 5000 Table 84: Summary statistics for denom 100 stored 0 1 2 3 denom_100_stored 106 4 5 denom 2 end Dataset: Day-level Variable type: Numeric N = 11491 Description: The number of 2 dollar bills carried at the end of the diary day. Survey question: From the ”Count your Paper Cash” screen at the end of each diary day. Details: Some amounts are cleaned when it is clear that the individual accidentally reported the dollar value rather than the count of bills. min 0.0 med 0.0 mean 0.1 max 40.0 sd 1.0 6000 2000 0 Frequency 10000 Table 85: Summary statistics for denom 2 end −1.0 −0.8 −0.6 −0.4 denom_2_end 107 −0.2 0.0 denom 2 stored Dataset: Day-level Variable type: Numeric N = 5746 Description: The number of 2 dollar bills stored. Survey question: Reported in the ”Count your paper cash stored elsewhere” screen on day 0. min 0.0 med 0.0 mean 0.4 max 700.0 sd 13.3 3000 1000 0 Frequency 5000 Table 86: Summary statistics for denom 2 stored −1.0 −0.8 −0.6 −0.4 denom_2_stored 108 −0.2 0.0 denom 20 end Dataset: Day-level Variable type: Numeric N = 11491 Description: The number of 20 dollar bills carried at the end of the diary day. Survey question: From the ”Count your Paper Cash” screen at the end of each diary day. Details: Some amounts are cleaned when it is clear that the individual accidentally reported the dollar value rather than the count of bills. min 0.0 med 0.0 mean 1.5 max 57.0 sd 3.0 3000 1000 0 Frequency 5000 Table 87: Summary statistics for denom 20 end 0 1 2 3 4 denom_20_end 109 5 6 denom 20 stored Dataset: Day-level Variable type: Numeric N = 5746 Description: The number of 20 dollar bills stored. Survey question: Reported in the ”Count your paper cash stored elsewhere” screen on day 0. min 0.0 med 0.0 mean 1.2 max 115.0 sd 5.0 3000 2000 1000 0 Frequency 4000 5000 Table 88: Summary statistics for denom 20 stored 0 2 4 6 denom_20_stored 110 8 denom 5 end Dataset: Day-level Variable type: Numeric N = 11491 Description: The number of 5 dollar bills carried at the end of the diary day. Survey question: From the ”Count your Paper Cash” screen at the end of each diary day. Details: Some amounts are cleaned when it is clear that the individual accidentally reported the dollar value rather than the count of bills. min 0.0 med 0.0 mean 0.9 max 26.0 sd 1.5 3000 1000 0 Frequency 5000 Table 89: Summary statistics for denom 5 end 0.0 0.5 1.0 1.5 2.0 denom_5_end 111 2.5 3.0 denom 5 stored Dataset: Day-level Variable type: Numeric N = 5746 Description: The number of 5 dollar bills stored. Survey question: Reported in the ”Count your paper cash stored elsewhere” screen on day 0. min 0.0 med 0.0 mean 0.4 max 142.0 sd 3.3 3000 2000 1000 0 Frequency 4000 5000 Table 90: Summary statistics for denom 5 stored 0.0 0.5 1.0 1.5 denom_5_stored 112 2.0 denom 50 end Dataset: Day-level Variable type: Numeric N = 11491 Description: The number of 50 dollar bills carried at the end of the diary day. Survey question: From the ”Count your Paper Cash” screen at the end of each diary day. Details: Some amounts are cleaned when it is clear that the individual accidentally reported the dollar value rather than the count of bills. min 0.0 med 0.0 mean 0.1 max 18.0 sd 0.6 6000 4000 2000 0 Frequency 8000 Table 91: Summary statistics for denom 50 end 0.0 0.2 0.4 0.6 denom_50_end 113 0.8 1.0 denom 50 stored Dataset: Day-level Variable type: Numeric N = 5746 Description: The number of 50 dollar bills stored. Survey question: Reported in the ”Count your paper cash stored elsewhere” screen on day 0. min 0.0 med 0.0 mean 0.3 max 37.0 sd 1.7 3000 2000 1000 0 Frequency 4000 5000 Table 92: Summary statistics for denom 50 stored 0.0 0.2 0.4 0.6 denom_50_stored 114 0.8 1.0 device Dataset: Transaction-level Variable type: Numeric N = 12426 Description: Device used to complete transaction. Survey question: Drop-down box in the purchases and bills modules. Details: Responses are presented as they were reported by the respondent. Note that some of the values of this variable do not ”make sense”. Nonetheless, we have chosen not to leave them alone and allow the researcher to interpret them as they see fit. Values 1 2 3 4 5 6 7 8 Number 1765 206 846 60 378 1036 8012 123 Percent 14.2 1.7 6.8 0.5 3.0 8.3 64.5 1.0 Table 93: Frequency table for device Value labels: 1 - Computer 2 - Tablet 3 - Mobile phone 4 - Landline phone 5 - Mail or delivery service 6 - Some other device not listed 7 - No device 115 device orig Dataset: Transaction-level Variable type: Numeric N = 12427 Description: Device used to complete transaction, uncleaned. Survey question: Drop-down box in the purchases and bills modules. Details: Responses are presented as they were reported by the respondent. Note that some of the values of this variable do not ”make sense”. Nonetheless, we have chosen not to leave them alone and allow the researcher to interpret them as they see fit. Values -1 1 2 3 4 5 6 7 8 Number 1 1765 206 846 60 378 1036 8012 123 Percent 0.0 14.2 1.7 6.8 0.5 3.0 8.3 64.5 1.0 Table 94: Frequency table for device orig Value labels: 1 - Computer 2 - Tablet 3 - Mobile phone 4 - Landline phone 5 - Mail or delivery service 6 - Some other device not listed 7 - No device 116 diary day Dataset: Transaction-level Variable type: Numeric N = 15097 Description: Diary days are numbered between 0 and 3. Note that certain account balances and income payments are reported on diary day 0, but no transactions. Survey question: N/A Values 0 1 2 3 Number 324 4922 4954 4897 Percent 2.1 32.6 32.8 32.4 Table 95: Frequency table for diary day Value labels: 0 - Day 0 1 - Day 1 2 - Day 2 3 - Day 3 117 discount Dataset: Transaction-level Variable type: Numeric N = 9469 Description: Whether a discount was received for using the chosen payment instrument. Survey question: q101aaa, q101d, q101f Values 0 1 Number 9137 332 Percent 96.5 3.5 Table 96: Frequency table for discount Value labels: 0 - No 1 - Yes 118 dow weight Dataset: Day-level Variable type: Numeric N = 8097 Description: Day-of-week weight, built to account for day-of-week effects in the number and value of payments. Researchers attempting to do cross-year comparisons should employ these weights. Survey question: Created internally. 119 draft date Dataset: Transaction-level Variable type: Numeric N = 1669 Description: Question text: Some bills are paid on the same day they are scheduled; others are paid in the future. Please tell us the date you selected for the bill to be paid. Survey question: pay205 120 due date Dataset: Transaction-level Variable type: Numeric N = 2504 Description: Date on which this bill was due. Survey question: q67 a Details: Converted to Stata date format. 121 durable type Dataset: Transaction-level Variable type: Numeric N = 91 Description: If the payment is greater than or equal to 200 dollars, then the diarist is asked to describe the type of payment. The response options are several categories of durable goods. Survey question: pay608 Values 1 2 3 4 5 6 7 8 Number 12 10 7 6 6 2 1 47 Percent 13.2 11.0 7.7 6.6 6.6 2.2 1.1 51.6 Table 97: Frequency table for durable type Value labels: 1 - Cars trucks motorcycles other motor vehicles and parts 2 - Furniture and furnishings 3 - Household appliances 4 - Computers cameras TVs other electronics 5 - Sports equipment, sports and recreactional vehicles, boats 6 - Jewelry and watches 7 - Therapeutic appliances and equipment 8 - None of the above 122 e exp cc Dataset: Individual-level Variable type: Numeric N = 1616 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using credit cards. Survey question: scf006 e min 0.0 med 0.0 mean 426.4 max 2000.0 sd 745.4 600 400 200 0 Frequency 800 1000 Table 98: Summary statistics for e exp cc 0 500 1000 1500 e_exp_cc 123 2000 e exp chk Dataset: Individual-level Variable type: Numeric N = 1941 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using money in their checking accounts. Survey question: scf006 b min 0.0 med 200.0 mean 607.9 max 2000.0 sd 787.9 400 200 0 Frequency 600 800 Table 99: Summary statistics for e exp chk 0 500 1000 1500 e_exp_chk 124 2000 e exp chk saved Dataset: Individual-level Variable type: Numeric N = 2811 Description: As of today, how much money do you have saved for emergency expenses? Checking account Survey question: scf004 b min 0.0 med 5.0 mean 2781.5 max 218345.0 sd 10521.3 1000 500 0 Frequency 1500 Table 100: Summary statistics for e exp chk saved 0 5000 10000 e_exp_chk_saved 125 15000 e exp cover Dataset: Individual-level Variable type: Numeric N = 2865 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover in total. Survey question: scf006 total min 0.0 med 2000.0 mean 1429.4 max 2000.0 sd 779.0 1000 500 0 Frequency 1500 Table 101: Summary statistics for e exp cover 0 500 1000 1500 e_exp_cover 126 2000 e exp csh Dataset: Individual-level Variable type: Numeric N = 1633 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using cash. Survey question: scf006 a min 0.0 med 0.0 mean 230.4 max 2000.0 sd 523.1 600 400 200 0 Frequency 800 1000 Table 102: Summary statistics for e exp csh 0 500 1000 1500 e_exp_csh 127 2000 e exp csh saved Dataset: Individual-level Variable type: Numeric N = 2812 Description: As of today, how much money do you have saved for emergency expenses? Cash Survey question: scf004 a min 0.0 med 0.0 mean 607.0 max 300000.0 sd 6909.6 1000 500 0 Frequency 1500 2000 Table 103: Summary statistics for e exp csh saved 0 200 400 600 800 1000 e_exp_csh_saved 128 1400 e exp fam Dataset: Individual-level Variable type: Numeric N = 1483 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover by getting money from family. Survey question: scf006 i min 0.0 med 0.0 mean 188.0 max 2000.0 sd 478.9 600 400 200 0 Frequency 800 1000 Table 104: Summary statistics for e exp fam 0 500 1000 e_exp_fam 129 1500 e exp heloc Dataset: Individual-level Variable type: Numeric N = 1350 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using a HELOC, or Home Equity Line Of Credit. Survey question: scf006 f min 0.0 med 0.0 mean 48.4 max 2000.0 sd 292.2 200 400 600 800 0 Frequency 1200 Table 105: Summary statistics for e exp heloc −1.0 −0.8 −0.6 −0.4 e_exp_heloc 130 −0.2 0.0 e exp od Dataset: Individual-level Variable type: Numeric N = 1357 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using overdraft protection. Survey question: scf006 d min 0.0 med 0.0 mean 15.5 max 2000.0 sd 120.1 200 400 600 800 0 Frequency 1200 Table 106: Summary statistics for e exp od −1.0 −0.8 −0.6 −0.4 e_exp_od 131 −0.2 0.0 e exp pawn Dataset: Individual-level Variable type: Numeric N = 1326 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using a pawn shop. Survey question: scf006 h min 0.0 med 0.0 mean 10.1 max 2000.0 sd 91.5 200 400 600 800 0 Frequency 1200 Table 107: Summary statistics for e exp pawn −1.0 −0.8 −0.6 −0.4 e_exp_pawn 132 −0.2 0.0 e exp payday Dataset: Individual-level Variable type: Numeric N = 1344 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using a payday loan. Survey question: scf006 g min 0.0 med 0.0 mean 16.8 max 2000.0 sd 133.3 200 400 600 800 0 Frequency 1200 Table 108: Summary statistics for e exp payday −1.0 −0.8 −0.6 −0.4 e_exp_payday 133 −0.2 0.0 e exp prepaid Dataset: Individual-level Variable type: Numeric N = 1360 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using prepaid cards. Survey question: scf006 j min 0.0 med 0.0 mean 7.8 max 2000.0 sd 78.3 200 400 600 800 0 Frequency 1200 Table 109: Summary statistics for e exp prepaid −1.0 −0.8 −0.6 −0.4 e_exp_prepaid 134 −0.2 0.0 e exp prepaid saved Dataset: Individual-level Variable type: Numeric N = 2775 Description: As of today, how much money do you have saved for emergency expenses? Prepaid card Survey question: scf004 d min 0.0 med 0.0 mean 14.2 max 6000.0 sd 142.5 1500 1000 500 0 Frequency 2000 2500 Table 110: Summary statistics for e exp prepaid saved 0 10 20 30 e_exp_prepaid_saved 135 40 50 e exp sav Dataset: Individual-level Variable type: Numeric N = 1896 Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is the amount of the emergency expenditure that respondents said they could cover using money in their savings accounts. Survey question: scf006 c min 0.0 med 200.0 mean 766.4 max 2000.0 sd 881.7 600 400 200 0 Frequency 800 Table 111: Summary statistics for e exp sav 0 500 1000 1500 e_exp_sav 136 2000 e exp sav saved Dataset: Individual-level Variable type: Numeric N = 2816 Description: As of today, how much money do you have saved for emergency expenses? Savings account Survey question: scf004 c min 0.0 med 150.0 mean 9282.9 max 1074057.0 sd 42439.0 1000 500 0 Frequency 1500 Table 112: Summary statistics for e exp sav saved 0 10000 20000 30000 e_exp_sav_saved 137 40000 e exp tot saved Dataset: Individual-level Variable type: Numeric N = 2844 Description: As of today, how much money do you have saved for emergency expenses? Total Survey question: scf004 total Details: Value is automatically calculated in real time on the screen while the respondent is entering the other dollar amounts. min 0.0 med 1153.0 mean 40351.3 max 79056873.0 sd 1482894.4 1000 500 0 Frequency 1500 Table 113: Summary statistics for e exp tot saved 0 10000 20000 30000 40000 50000 e_exp_tot_saved 138 end cash bal Dataset: Day-level Variable type: Numeric N = 11491 Description: The end-of-day balance of the cash carried by the respondent. Survey question: From the ”Count your Paper Cash” screen at the end of each diary day. Details: Implied by the number of each bill that the respondent reports carrying. min 0.0 med 24.0 mean 63.9 max 10580.0 sd 165.9 2000 1000 0 Frequency 3000 4000 Table 114: Summary statistics for end cash bal 0 50 100 150 end_cash_bal 139 200 enough cash Dataset: Transaction-level Variable type: Numeric N = 5336 Description: Whether respondent had enough cash available to pay for this transaction. Survey question: q103f Values 0 1 2 3 4 Number 2776 2485 37 22 16 Percent 52.0 46.6 0.7 0.4 0.3 Table 115: Frequency table for enough cash Value labels: 0 - No 1 - Yes 2 - I’m not sure, but I think so 3 - I’m not sure, but I do not think so 4 - I don’t know 140 fee amnt Dataset: Transaction-level Variable type: Numeric N = 140 Description: The amount of fee paid for this transaction. Survey question: Entered in the Remittances and Checking Transfers modules. min 0.0 med 0.0 mean 0.0 max 1.2 sd 0.1 80 60 40 20 0 Frequency 100 120 140 Table 116: Summary statistics for fee amnt −1.0 −0.8 −0.6 −0.4 fee_amnt 141 −0.2 0.0 fee flag Dataset: Transaction-level Variable type: Numeric N = 2788 Description: Whether a fee was charged. Survey question: q101g, and as reported in several modules. Values 0 1 Number 2745 43 Percent 98.5 1.5 Table 117: Frequency table for fee flag Value labels: 0 - No 1 - Yes 142 fixed amount Dataset: Transaction-level Variable type: Numeric N = 2452 Description: Whether this recurring bill is a fixed amount each cycle, or whether it varies. Survey question: pay002e Values 1 2 Number 1393 1059 Percent 56.8 43.2 Table 118: Frequency table for fixed amount Value labels: 1 - Same amount each bill 2 - Amount changes from bill to bill 143 frequency Dataset: Transaction-level Variable type: Numeric N = 248 Description: The frequency (time per year) of the bill. Survey question: q67 c, q67 g, pay002b Details: Annualized according to response values. min 0.0 med 12.0 mean 25.0 max 300.0 sd 38.0 30 20 10 0 Frequency 40 Table 119: Summary statistics for frequency 0 10 20 30 frequency 144 40 50 from account Dataset: Transaction-level Variable type: Numeric N = 1639 Description: The account from which the funds for this transaction were sourced. Survey question: N/A Details: from account and to account are purely constructed variables which tracks the movement of money between accounts, as well as tracking which accounts expenditures came from and which accounts income went to. They should generally be used in conjunction with type to truly understand the movement of money. Values 1 2 3 4 5 6 7 Number 357 1144 80 21 3 33 1 Percent 21.8 69.8 4.9 1.3 0.2 2.0 0.1 Table 120: Frequency table for from account Value labels: 1 - Currency 2 - Primary checking 3 - Other demand deposit account 4 - Nonfinancial deposit account (e.g. PayPal, prepaid card) 5 - Investment account 6 - Credit card account 7 - Other credit account 8 - Other (check, money order, returned goods, etc.) 145 from bill section Dataset: Transaction-level Variable type: Numeric N = 12440 Description: Was this bill payment reported in the bills section on diary Day 3, or was it reported in the regular payment module on Days 1, 2, or 3, and designated as a bill based on item pay002? Survey question: pay002 Values 1 2 Number 896 11544 Percent 7.2 92.8 Table 121: Frequency table for from bill section Value labels: 1 - Yes 2 - No 146 gender Dataset: Individual-level Variable type: Numeric N = 2873 Description: Male or female. Survey question: From UAS My Household Questionnaire. Values 0 1 Number 1632 1241 Percent 56.8 43.2 Table 122: Frequency table for gender Value labels: 0 - Female 1 - Male 147 gpr bal Dataset: Day-level Variable type: Numeric N = 1415 Description: Balance of general purpose reloadable prepaid card. Survey question: pa074 min -63.0 med 20.0 mean 93.6 max 7757.0 sd 349.4 200 100 0 Frequency 300 400 Table 123: Summary statistics for gpr bal 0 100 200 gpr_bal 148 300 400 gpr bal date Dataset: Day-level Variable type: Numeric N = 1408 Description: Date that diarist checked balance of general purpose reloadable prepaid card. Survey question: pa074 date 149 gpr bal time Dataset: Day-level Variable type: Numeric N = 1412 Description: Time that diarist checked balance of general purpose reloadable prepaid card Survey question: pa074 time 150 hh size Dataset: Individual-level Variable type: Numeric N = 2815 Description: Size of the household in which the respondent lives. Survey question: From UAS My Household Questionnaire. min 1.0 med 2.0 mean 2.7 max 11.0 sd 1.4 600 400 200 0 Frequency 800 1000 Table 124: Summary statistics for hh size 1 2 3 4 hh_size 151 5 highest education Dataset: Individual-level Variable type: Numeric N = 2873 Description: Respondent’s highest level of education, if the respondent is from the UAS sample. Survey question: From UAS My Household Questionnaire. Values 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Number 1 1 4 12 18 27 37 45 556 645 259 197 616 343 48 64 Percent 0.0 0.0 0.1 0.4 0.6 0.9 1.3 1.6 19.4 22.5 9.0 6.9 21.4 11.9 1.7 2.2 Table 125: Frequency table for highest education Value labels: 1 - Less than 1st grade 2 - 1st, 2nd, 3rd, or 4th grade 3 - 5th or 6th grade 4 - 7th or 8th grade 5 - 9th grade 6 - 10th grade 7 - 11th grade 8 - 12 grade - no diploma 9 - High school graduate or GED 10 - Some college but no degree 11 - Associate degree in college - occupational or vocational program 12 - Associate degree in college - academic program 13 - Bachelors degree 14 - Masters degree 15 - Professional school degree 16 - Doctorate degree 152 hispaniclatino Dataset: Individual-level Variable type: Numeric N = 2873 Description: Whether respondent identifies has Hispanic/Latino Survey question: From UAS My Household Questionnaire. Values 0 1 Number 2687 186 Percent 93.5 6.5 Table 126: Frequency table for hispaniclatino Value labels: 0 - No 1 - Yes 153 hispaniclatino group Dataset: Individual-level Variable type: Numeric N = 186 Description: Question text: What is your Spanish, Hispanic or Latino group? 1 Mexican, 2 Puerto Rican, 3 Cuban, 4 Central or South American, 5 Other Spanish Survey question: From UAS My Household Questionnaire. Values 1 2 3 4 5 Number 126 17 6 16 21 Percent 67.7 9.1 3.2 8.6 11.3 Table 127: Frequency table for hispaniclatino group Value labels: 1 - Mexican 2 - Puerto Rican 3 - Cuban 4 - Central or South American 5 - Other 154 home debt Dataset: Individual-level Variable type: Numeric N = 2026 Description: Approximate value of debt on primary home, including HELs and HELOCs. Survey question: de015 Details: This is an SCPC variable merged into this dataset for convenience. min 0.0 med 45000.0 mean 82714.3 max 2250000.0 sd 117734.9 400 200 0 Frequency 600 800 Table 128: Summary statistics for home debt 0 50000 150000 home_debt 155 250000 home value Dataset: Individual-level Variable type: Numeric N = 2024 Description: Approximate market value of primary home. Survey question: de014 Details: This is an SCPC variable merged into this dataset for convenience. min 0.0 med 175000.0 mean 232112.1 max 3500000.0 sd 241739.2 80 60 40 20 0 Frequency 100 140 Table 129: Summary statistics for home value 0e+00 2e+05 4e+05 home_value 156 6e+05 homeowner Dataset: Individual-level Variable type: Numeric N = 2870 Description: Whether respondent owns primary home. Survey question: de013 Details: This is an SCPC variable merged into this dataset for convenience. Values 0 1 Number 829 2041 Percent 28.9 71.1 Table 130: Frequency table for homeowner Value labels: 0 - No 1 - Yes 157 in person Dataset: Transaction-level Variable type: Numeric N = 12444 Description: Whether the transaction occurred in person. Survey question: Drop-down box in several modules. Values 0 1 Number 3326 9118 Percent 26.7 73.3 Table 131: Frequency table for in person Value labels: 0 - No 1 - Yes 158 in person orig Dataset: Transaction-level Variable type: Numeric N = 12420 Description: Whether the transaction occurred in person, uncleaned Survey question: Drop-down box in several modules. Values -1 1 2 Number 1 9118 3301 Percent 0.0 73.4 26.6 Table 132: Frequency table for in person orig Value labels: 0 - No 1 - Yes 159 inc alimony Dataset: Individual-level Variable type: Numeric N = 2849 Description: Whether the respondent receives alimony income. Survey question: q140 h Values 0 1 Number 2840 9 Percent 99.7 0.3 Table 133: Frequency table for inc alimony Value labels: 0 - No 1 - Yes 160 inc alimony freq Dataset: Individual-level Variable type: Numeric N =9 Description: The frequency with which alimony income is received. Survey question: q141 h Values 1 3 4 Number 2 2 5 Percent 22.2 22.2 55.6 Table 134: Frequency table for inc alimony freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 161 inc child Dataset: Individual-level Variable type: Numeric N = 2847 Description: Whether the respondent receives child support income. Survey question: q140 i Values 0 1 Number 2748 99 Percent 96.5 3.5 Table 135: Frequency table for inc child Value labels: 0 - No 1 - Yes 162 inc child freq Dataset: Individual-level Variable type: Numeric N = 99 Description: The frequency with which child support income is received. Survey question: q141 i Values 1 2 3 4 5 9 Number 21 17 7 43 2 9 Percent 21.2 17.2 7.1 43.4 2.0 9.1 Table 136: Frequency table for inc child freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 163 inc gov Dataset: Individual-level Variable type: Numeric N = 2853 Description: Whether the respondent receives government assistance income. Survey question: q140 g Values 0 1 Number 2576 277 Percent 90.3 9.7 Table 137: Frequency table for inc gov Value labels: 0 - No 1 - Yes 164 inc gov freq Dataset: Individual-level Variable type: Numeric N = 277 Description: The frequency with which government assistance income is received. Survey question: q141 g Values 1 2 3 4 5 6 Number 1 5 1 266 3 1 Percent 0.4 1.8 0.4 96.0 1.1 0.4 Table 138: Frequency table for inc gov freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 165 inc intdiv Dataset: Individual-level Variable type: Numeric N = 2848 Description: Whether the respondent receives interest or dividend income. Survey question: q140 e Values 0 1 Number 2434 414 Percent 85.5 14.5 Table 139: Frequency table for inc intdiv Value labels: 0 - No 1 - Yes 166 inc intdiv freq Dataset: Individual-level Variable type: Numeric N = 413 Description: The frequency with which interest or dividend income is received. Survey question: q141 e Values 1 2 3 4 5 6 7 8 9 Number 1 3 1 210 119 28 2 11 38 Percent 0.2 0.7 0.2 50.8 28.8 6.8 0.5 2.7 9.2 Table 140: Frequency table for inc intdiv freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 167 inc rent Dataset: Individual-level Variable type: Numeric N = 2849 Description: Whether the respondent receives rental income. Survey question: q140 f Values 0 1 Number 2700 149 Percent 94.8 5.2 Table 141: Frequency table for inc rent Value labels: 0 - No 1 - Yes 168 inc rent freq Dataset: Individual-level Variable type: Numeric N = 149 Description: The frequency with which rental income is received. Survey question: q141 f Values 1 3 4 5 6 7 8 9 Number 1 1 121 1 16 1 1 7 Percent 0.7 0.7 81.2 0.7 10.7 0.7 0.7 4.7 Table 142: Frequency table for inc rent freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 169 inc retempl Dataset: Individual-level Variable type: Numeric N = 2853 Description: Whether the respondent receives employer-paid retirement income. Survey question: q140 b Values 0 1 Number 2469 384 Percent 86.5 13.5 Table 143: Frequency table for inc retempl Value labels: 0 - No 1 - Yes 170 inc retempl freq Dataset: Individual-level Variable type: Numeric N = 382 Description: The frequency with which employer-paid retirement income is received. Survey question: q141 b Values 1 2 3 4 6 7 8 9 Number 2 11 4 354 3 2 2 4 Percent 0.5 2.9 1.0 92.7 0.8 0.5 0.5 1.0 Table 144: Frequency table for inc retempl freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 171 inc retsav Dataset: Individual-level Variable type: Numeric N = 2846 Description: Whether the respondent receives IRA, 401(k), or other savings-based retirement income. Survey question: q140 j Values 0 1 Number 2558 288 Percent 89.9 10.1 Table 145: Frequency table for inc retsav Value labels: 0 - No 1 - Yes 172 inc retsav freq Dataset: Individual-level Variable type: Numeric N = 287 Description: The frequency with which IRA, 401(k), or other savings-based retirement income is received. Survey question: q141 j Values 1 2 3 4 5 6 7 8 9 Number 1 18 2 129 22 63 14 5 33 Percent 0.3 6.3 0.7 44.9 7.7 22.0 4.9 1.7 11.5 Table 146: Frequency table for inc retsav freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 173 inc self Dataset: Individual-level Variable type: Numeric N = 2848 Description: Whether the respondent receives self-employment income. Survey question: q140 c Values 0 1 Number 2516 332 Percent 88.3 11.7 Table 147: Frequency table for inc self Value labels: 0 - No 1 - Yes 174 inc self freq Dataset: Individual-level Variable type: Numeric N = 332 Description: The frequency with which self-employment income is received. Survey question: q141 c Values 1 2 3 4 5 6 7 8 9 Number 58 30 10 72 8 13 17 10 114 Percent 17.5 9.0 3.0 21.7 2.4 3.9 5.1 3.0 34.3 Table 148: Frequency table for inc self freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 175 inc ss Dataset: Individual-level Variable type: Numeric N = 2864 Description: Whether the respondent receives social security income. Survey question: q140 d Values 0 1 Number 2072 792 Percent 72.3 27.7 Table 149: Frequency table for inc ss Value labels: 0 - No 1 - Yes 176 inc ss freq Dataset: Individual-level Variable type: Numeric N = 790 Description: The frequency with which social security income is received. Survey question: q141 d Values 1 2 3 4 5 7 8 9 Number 3 4 2 775 2 1 1 2 Percent 0.4 0.5 0.3 98.1 0.3 0.1 0.1 0.3 Table 150: Frequency table for inc ss freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 177 inc wage Dataset: Individual-level Variable type: Numeric N = 2856 Description: Whether the respondent receives wage income. Survey question: q140 a Values 0 1 Number 1300 1556 Percent 45.5 54.5 Table 151: Frequency table for inc wage Value labels: 0 - No 1 - Yes 178 inc wage freq Dataset: Individual-level Variable type: Numeric N = 1556 Description: The frequency with which wage income is received. Survey question: q141 a Values 1 2 3 4 5 7 8 9 Number 299 839 222 170 2 6 3 15 Percent 19.2 53.9 14.3 10.9 0.1 0.4 0.2 1.0 Table 152: Frequency table for inc wage freq Value labels: 1 - Weekly 2 - Every two weeks 3 - Twice per month 4 - Monthly 5 - Quarterly 6 - Yearly 7 - Other, on a one-time basis 8 - Other, on a regular basis 9 - Other, on an irregular basis 179 income Dataset: Transaction-level Variable type: Numeric N = 15114 Description: This transaction is an income receipt Survey question: In some cases, based purely on the module in which the transaction is reported. In other cases, based on the response to followup questions. Details: Income is defined as money coming into the respondents possession. Income is typically reported in the income module. Values 0 1 Number 13308 1806 Percent 88.1 11.9 Table 153: Frequency table for income Value labels: 0 - Not an income receipt 1 - Income receipt 180 income hh Dataset: Individual-level Variable type: Numeric N = 2838 Description: Household income. Survey question: de010 Details: This is an SCPC variable merged into this dataset for convenience. In 2017 and before, this variable was categorical. In 2018 and going forward, this variable is continuous, and it describes the respondent’s self-reported household income. min 0.0 med 60000.0 mean 72238.3 max 1250000.0 sd 73455.0 150 100 50 0 Frequency 200 250 Table 154: Summary statistics for income hh 0 50000 100000 income_hh 181 150000 income howpaid Dataset: Transaction-level Variable type: Numeric N = 990 Description: How this income was paid to the respondent. Survey question: q143 a-i Details: Note that to account is based on this variable for income receipts, though this variable provides slightly better granularity. Values 1 2 3 4 5 6 7 8 9 Number 624 59 31 131 67 11 25 23 19 Percent 63.0 6.0 3.1 13.2 6.8 1.1 2.5 2.3 1.9 Table 155: Frequency table for income howpaid Value labels: 1 - Direct deposit ONLY to primary checking account 2 - Direct deposit ONLY to some other checking or savings account 3 - Direct deposit to more than one account 4 - Paper check 5 - Cash 6 - Payroll card 7 - Primary general purpose reloadable prepaid card 8 - Other general purpose reloadable prepaid card 9 - Other 182 income type Dataset: Transaction-level Variable type: Numeric N = 996 Description: Type of income payment. Survey question: q142 a-i, q144 a-i Details: This factor variable is defined based on which type(s) of income the respondent reported receiving that day. When the respondent reported receiving multiple types of income, multiple transactions are created to match, each with a different value for income type. Values 1 2 3 4 5 6 7 8 9 10 Number 560 58 105 127 22 24 54 1 23 22 Percent 56.2 5.8 10.5 12.8 2.2 2.4 5.4 0.1 2.3 2.2 Table 156: Frequency table for income type Value labels: 1 - Employment income 2 - Employer paid retirement 3 - Self-employment income 4 - Social Security 5 - Interest and dividends 6 - Rental income 7 - Government assistance 8 - Alimony 9 - Child support 10 - IRA, Roth IRA, 401k, or other retirement fund 183 inconsistency explain Dataset: Transaction-level Variable type: Character N = 15114 Description: Question text: You told us that this payment was not in person and that you used no device. Please tell us more about how you made this payment. In particular, how was the payment paid to the merchant? Survey question: q201f 184 ind payee Dataset: Transaction-level Variable type: Numeric N = 472 Description: Type of person to which payment was made. Survey question: pay080, pay081 Details: These two followups are combined, for convenience. Values 1 2 3 4 5 Number 53 56 249 45 69 Percent 11.2 11.9 52.8 9.5 14.6 Table 157: Frequency table for ind payee Value labels: 1 - People who provide goods and services, operating as a business 2 - People who provide goods and services, not operating as a business 3 - Friends or family 4 - Co-worker, classmate, or fellow military 5 - Other (specify) 185 ind weight Dataset: Individual-level Variable type: Numeric N = 2872 Description: Raked individual sample weights. Survey question: N/A Details: Raked post-stratification weights. Individual weights are best used for producing full-sample fullperiod estimates. These particular daily correspond to rps w uasgfk in the full weights dataset. See Angrisani, M, 2018 Survey and Diary of Consumer Payment Choice Weighting Procedure (2018) for more information about the construction of the weights. 186 interest level Dataset: Individual-level Variable type: Numeric N = 2866 Description: The self-reported level of interest the respondent had in the survey. Survey question: cs 001 Values 1 2 3 4 5 Number 1157 1210 451 37 11 Percent 40.4 42.2 15.7 1.3 0.4 Table 158: Frequency table for interest level Value labels: 1 - Very interesting 2 - Interesting 3 - Neither interesting nor uninteresting 4 - Uninteresting 5 - Very uninteresting 187 last income date Dataset: Individual-level Variable type: Numeric N = 2696 Description: The date on which the most recent income payment was received, as of diary day 0. Survey question: q18 Details: Converted to Stata date format. 188 late fee Dataset: Transaction-level Variable type: Numeric N = 1522 Description: Whether a late fee was charged for this payment. Survey question: q67 e Values 0 1 Number 1466 56 Percent 96.3 3.7 Table 159: Frequency table for late fee Value labels: 0 - No 1 - Yes 189 loan amnt canpay Dataset: Transaction-level Variable type: Numeric N =1 Description: Question text: Did you have enough money in your checking or savings account to pay the amount due this period? Survey question: pay014 Values 0 Number 1 Percent 100.0 Table 160: Frequency table for loan amnt canpay Value labels: 0 - No 1 - Yes 190 loan amnt due Dataset: Transaction-level Variable type: Numeric N = 310 Description: Question text: How much was the amount due this period? Survey question: pay013 min 0.0 med 435.5 mean 677.4 max 10167.0 sd 934.3 30 20 10 0 Frequency 40 Table 161: Summary statistics for loan amnt due 0 500 1000 1500 loan_amnt_due 191 2000 loan amnt whynotpay Dataset: Transaction-level Variable type: Character N = 15114 Description: Question text: Why did you choose not to pay the amount due this period for this loan payment? Survey question: pay015 Details: Open-ended text response box. 192 login date Dataset: Day-level Variable type: Numeric N = 11492 Description: The date the diarist logged in to report their payments. Survey question: N/A Details: This is different than the assigned diary date. If the diarist logged on to report their activity on the actual diary date, then report date should equal date, otherwise, this date will be after date. 193 marital status Dataset: Individual-level Variable type: Numeric N = 2872 Description: Respondent’s marital status. Survey question: From UAS My Household Questionnaire. Values 1 2 3 4 5 6 Number 1696 32 39 472 148 485 Percent 59.1 1.1 1.4 16.4 5.2 16.9 Table 162: Frequency table for marital status Value labels: 1 - Married (spouse lives with me) 2 - Married (spouse lives elsewhere) 3 - Separated 4 - Divorced 5 - Widowed 6 - Never married 194 memory checkbook Dataset: Individual-level Variable type: Numeric N = 2860 Description: Whether the respondent used the small checkbook memory aid. Survey question: q25 Values 0 1 Number 1849 1011 Percent 64.7 35.3 Table 163: Frequency table for memory checkbook Value labels: 0 - No 1 - Yes 195 memory finrec Dataset: Individual-level Variable type: Numeric N = 2860 Description: Whether the respondent referenced financial records as a memory aid. Survey question: q25 Values 0 1 Number 1521 1339 Percent 53.2 46.8 Table 164: Frequency table for memory finrec Value labels: 0 - No 1 - Yes 196 memory lpd Dataset: Individual-level Variable type: Numeric N = 2860 Description: Whether the respondent used the large paper diary as a memory aid. Survey question: q25 Values 0 1 Number 2454 406 Percent 85.8 14.2 Table 165: Frequency table for memory lpd Value labels: 0 - No 1 - Yes 197 memory memory Dataset: Individual-level Variable type: Numeric N = 2860 Description: Whether the respondent used their memory to recall transactions. Survey question: q25 Values 0 1 Number 1495 1365 Percent 52.3 47.7 Table 166: Frequency table for memory memory Value labels: 0 - No 1 - Yes 198 memory oth Dataset: Individual-level Variable type: Numeric N = 2860 Description: Whether the respondent used some other memory aid. Survey question: q25 Values 0 1 Number 2727 133 Percent 95.3 4.7 Table 167: Frequency table for memory oth Value labels: 0 - No 1 - Yes 199 memory receipts Dataset: Individual-level Variable type: Numeric N = 2860 Description: Whether the respondent kept receipts to use as a memory aid. Survey question: q25 Values 0 1 Number 1043 1817 Percent 36.5 63.5 Table 168: Frequency table for memory receipts Value labels: 0 - No 1 - Yes 200 merch Dataset: Transaction-level Variable type: Numeric N = 12455 Description: Merchant – 21 categories. Survey question: Drop-down box in the purchases module and pay090 for 9-coded merchants. Questions q66 02, q66 07, q66 08, q66 09, q66 11, q66 20, q66 21, q66 22, q66 23, q66 35 in the bills module. Details: As reported in the purchases module, based on the followup pay090. The bills module followups (q66 *) are also recategorized into the merchant codes. Values 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Number 2068 1337 805 1624 1915 392 318 502 64 554 45 109 35 129 1057 473 302 274 110 135 207 Percent 16.6 10.7 6.5 13.0 15.4 3.1 2.6 4.0 0.5 4.4 0.4 0.9 0.3 1.0 8.5 3.8 2.4 2.2 0.9 1.1 1.7 Table 169: Frequency table for merch Value labels: 1 - Grocery stores, convenience stores without gas stations, pharmacies 2 - Gas stations 3 - Sit-down restaurants and bars 4 - Fast food restaurants, coffee shops, cafeterias, food trucks 5 - General merchandise stores, department stores, other stores, online shopping 6 - General services: hair dressers, auto repair, parking lots, laundry or dry cleaning, etc. 7 - Arts, entertainment, recreation 8 - Utilities not paid to the government: electricity, natural gas, water, sewer, trash, heating oil 9 - Taxis, airplanes, delivery 201 10 - Telephone, internet, cable or satellite tv, video or music streaming services, movie theaters 11 - Building contractors, plumbers, electricians, HVAC, etc. 12 - Professional services: legal, accounting, architectural services; veterinarians; photographers or photo processers 13 - Hotels, motels, RV parks, campsites 14 - Rent for apartments, homes, or other buildings, real estate companies, property managers, etc. 15 - Mortgage companies, credit card companies, banks, insurance companies, stock brokers, IRA funds, mutual funds, credit unions, sending remittances 16 - Can be a gift or repayment to a family member, friend, or co-worker. Can be a payment to somebody who did a small job for you. 17 - Charitable or religious donations 18 - Hospital, doctor, dentist, nursing homes, etc. 19 - Government taxes or fees 20 - Schools, colleges, childcare centers 21 - Public transportation and tolls 202 merch orig Dataset: Transaction-level Variable type: Numeric N = 12430 Description: The original merchant category that the respondent used to report the payment, without any recategorization of other responses, or backwards-imputation of bill reminder module payments into merchant categories, etc. Survey question: Drop-down box in the purchases module. Values 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Number 2066 1338 804 1630 1915 367 317 501 64 553 45 109 35 129 1056 473 302 274 110 135 207 Percent 16.6 10.8 6.5 13.1 15.4 3.0 2.6 4.0 0.5 4.4 0.4 0.9 0.3 1.0 8.5 3.8 2.4 2.2 0.9 1.1 1.7 Table 170: Frequency table for merch orig Value labels: 1 - Grocery stores, convenience stores without gas stations, pharmacies 2 - Gas stations 3 - Sit-down restaurants and bars 4 - Fast food restaurants, coffee shops, cafeterias, food trucks 5 - General merchandise stores, department stores, other stores, online shopping 6 - General services: hair dressers, auto repair, parking lots, laundry or dry cleaning, etc. 7 - Arts, entertainment, recreation 8 - Utilities not paid to the government: electricity, natural gas, water, sewer, trash, heating oil 9 - Taxis, airplanes, delivery 10 - Telephone, internet, cable or satellite tv, video or music streaming services, movie theaters 11 - Building contractors, plumbers, electricians, HVAC, etc. 203 12 - Professional services: legal, accounting, architectural services; veterinarians; photographers or photo processers 13 - Hotels, motels, RV parks, campsites 14 - Rent for apartments, homes, or other buildings, real estate companies, property managers, etc. 15 - Mortgage companies, credit card companies, banks, insurance companies, stock brokers, IRA funds, mutual funds, credit unions, sending remittances 16 - Can be a gift or repayment to a family member, friend, or co-worker. Can be a payment to somebody who did a small job for you. 17 - Charitable or religious donations 18 - Hospital, doctor, dentist, nursing homes, etc. 19 - Government taxes or fees 20 - Schools, colleges, childcare centers 21 - Public transportation and tolls 204 mobile funding Dataset: Transaction-level Variable type: Numeric N = 124 Description: How this mobile payment was funded. Survey question: q101 mobile b Details: If the value of the variable mobile funding is 1, 2, 3, or 4, then the value of the variable pi is recoded to match the payment instrument which funds the mobile payment. For example, if the diarist reports payment method = mobile banking (12) for their payment, and then in item q101 mobile b, they report 1, or credit card, then Atlanta Fed staff will recode the payment method variable pi to equal 3, or credit card. Values 1 2 3 4 5 6 Number 24 34 1 50 8 7 Percent 19.4 27.4 0.8 40.3 6.5 5.6 Table 171: Frequency table for mobile funding Value labels: 1 - Credit card 2 - Debit card 3 - Prepaid card 4 - Linked bank account 5 - Money stored with a payment service such as PayPal 6 - Other (specify) 205 mobile howfunded Dataset: Individual-level Variable type: Numeric N = 50 Description: How the mobile app used for the payment is funded. Survey question: q161 Values 1 2 3 4 6 7 Number 8 22 2 10 1 7 Percent 16.0 44.0 4.0 20.0 2.0 14.0 Table 172: Frequency table for mobile howfunded Value labels: 1 - Credit card 2 - Debit card 3 - Prepaid card 4 - Linked bank account 5 - Money stored with a payment service such as PayPal 6 - Other (specify) 206 mobile method Dataset: Transaction-level Variable type: Numeric N = 808 Description: How this mobile payment was completed. Survey question: q150 Values 1 2 3 4 Number 247 59 101 401 Percent 30.6 7.3 12.5 49.6 Table 173: Frequency table for mobile method Value labels: 1 - Tapped to pay 2 - Scanned a QR code or showed screen to cashier or ticket-taker 3 - Paid in advance or remotely 4 - Used a web browser 207 mobile type Dataset: Transaction-level Variable type: Numeric N = 126 Description: Type of mobile payment. Survey question: q101 mobile a Values 1 2 3 4 Number 87 2 16 21 Percent 69.0 1.6 12.7 16.7 Table 174: Frequency table for mobile type Value labels: 1 - App payment 2 - Text message payment 3 - Payment made in browser 4 - Other (specify) 208 module Dataset: Transaction-level Variable type: Character N = 15114 Description: Module from which this observation was drawn. This can be helpful in mapping observations back to their source in the survey instrument, to understand why certain variables may have missing values. Survey question: q106a-d, q120, q122 Details: Note that ”Cash lost/stolen/found/forex/etc” does not come from a separate module, but rather from questions q106a-d, q120, and q122. 209 monord date Dataset: Transaction-level Variable type: Numeric N = 33 Description: Date on which the money order was purchased. Survey question: q103s Values 1 2 Number 22 11 Percent 66.7 33.3 Table 175: Frequency table for monord date Value labels: 1 - I bought it today 2 - Between today and less than 7 days ago 3 - 7 or more days ago 210 monord source Dataset: Transaction-level Variable type: Numeric N = 33 Description: Where the money order was purchased from. Survey question: q103r Values 1 2 3 4 Number 4 11 5 13 Percent 12.1 33.3 15.2 39.4 Table 176: Frequency table for monord source Value labels: 1 - Bank 2 - Post office 3 - Western Union or someplace similar 4 - Other (specify) 211 multipi breakdown Dataset: Transaction-level Variable type: Character N = 15114 Description: Which payment instruments did the diarist use if the payment was reported as MULTIPLE PAYMENT INSTRUMENTS? Survey question: q125 a through q125 n 212 next income date Dataset: Individual-level Variable type: Numeric N = 2537 Description: The next date on which income is expected to be received, as of the third diary day. Survey question: q19 Details: Converted to Stata date format. 213 nopayments Dataset: Day-level Variable type: Numeric N = 3599 Description: Why the respondent made no payments on a given day. Survey question: q98a Values 1 2 3 4 Number 3214 185 174 26 Percent 89.3 5.1 4.8 0.7 Table 177: Frequency table for nopayments Value labels: 1 - I did not need to make any payments today 2 - I was too busy to make payments today 3 - I am trying to spend less 4 - Other (specify) 214 num times used coins Dataset: Day-level Variable type: Numeric N = 467 Description: Question text: For how many cash payments did you use coins to pay for some or all of the payment? Survey question: q5 3 min 0.0 med 1.0 mean 1.0 max 4.0 sd 0.6 100 150 200 250 300 350 50 0 Frequency Table 178: Summary statistics for num times used coins 0.0 0.5 1.0 1.5 num_times_used_coins 215 2.0 other assets Dataset: Individual-level Variable type: Numeric N = 2829 Description: Approximate value of other assets, not including primary home. Survey question: de016 Details: This is an SCPC variable merged into this dataset for convenience. min 0.0 med 25000.0 mean 148516.5 max 12000000.0 sd 491732.9 200 400 600 800 0 Frequency 1200 Table 179: Summary statistics for other assets 0e+00 2e+05 4e+05 other_assets 216 6e+05 other debts Dataset: Individual-level Variable type: Numeric N = 2847 Description: Approximate value of other debts, not including debt on primary home. Survey question: de019 Details: This is an SCPC variable merged into this dataset for convenience. min 0.0 med 8000.0 mean 27257.3 max 1500000.0 sd 69542.3 800 600 400 200 0 Frequency 1200 Table 180: Summary statistics for other debts 0e+00 2e+04 4e+04 6e+04 other_debts 217 8e+04 1e+05 other device desc Dataset: Transaction-level Variable type: Character N = 15114 Description: Question text: You told us that you used some other device to make this payment. Please tell us more about the device. Survey question: q201e Details: This question is only displayed if OTHER is selected for the payment device. 218 otherpi funding Dataset: Transaction-level Variable type: Numeric N = 13 Description: The method by which the ’other’ payment instrument is funded. Survey question: q101i followup Values 1 2 3 4 5 Number 6 2 1 3 1 Percent 46.2 15.4 7.7 23.1 7.7 Table 181: Frequency table for otherpi funding Value labels: 1 - Credit card 2 - Debit card 3 - Prepaid card 4 - Linked bank account 5 - Money stored with a payment service such as PayPal 6 - Other (specify) 219 otherpi type Dataset: Transaction-level Variable type: Numeric N = 23 Description: The type of ’other’ payment instrument used by the respondent. Survey question: q101i Values 1 4 5 Number 11 2 10 Percent 47.8 8.7 43.5 Table 182: Frequency table for otherpi type Value labels: 1 - EZPass or other electronic toll device 2 - Apple Pay, Samsung Pay, or Android Pay 3 - Bitcoin or other virtual currency 4 - Remittance 5 - Other (specify) 220 ow type Dataset: Transaction-level Variable type: Numeric N = 34 Description: The type of ”Other Withdrawal” reported in the other withdrawals module. This is a place for respondents to report if they purchased any money orders, traveler’s checks, or certified checks on a diary day. Survey question: N/A Values 1 3 Number 31 3 Percent 91.2 8.8 Table 183: Frequency table for ow type Value labels: 1 - Money order 2 - Travelers check 3 - Certified check 221 past service Dataset: Transaction-level Variable type: Numeric N = 225 Description: Question text: When did you receive these medical goods or services? Survey question: pay031, pay032 Details: Variable is set to 0 based on the response to pay031. Otherwise, the codings to pay032 are used. Values 1 2 3 4 Number 161 35 22 7 Percent 71.6 15.6 9.8 3.1 Table 184: Frequency table for past service Value labels: 1 - Within the last month 2 - Between 3 months and 1 month ago 3 - Between 1 year and 3 months ago 4 - Longer than 1 year ago 222 pay amnt coins Dataset: Day-level Variable type: Numeric N = 467 Description: Question text: What was the total dollar amount of the coins you used for payments today? Survey question: q5 3 a min 0.0 med 0.5 mean 9.0 max 1000.0 sd 61.8 200 100 50 0 Frequency 300 Table 185: Summary statistics for pay amnt coins 0 10 20 30 pay_amnt_coins 223 40 pay timing Dataset: Transaction-level Variable type: Numeric N = 137 Description: When OBBP/BANP payment is scheduled to pay. Survey question: q103n Values 1 2 Number 117 20 Percent 85.4 14.6 Table 186: Frequency table for pay timing Value labels: 1 - Today 2 - At a later date 224 pay010 Dataset: Transaction-level Variable type: Numeric N = 1055 Description: Question text: Please tell us the purpose of your payment to a financial services provider. Survey question: pay010 Values 1 2 3 5 6 7 8 Number 435 310 222 9 16 14 49 Percent 41.2 29.4 21.0 0.9 1.5 1.3 4.6 Table 187: Frequency table for pay010 Value labels: 1 - Pay a credit card bill 2 - Make a loan payment (Examples: mortgage, student loan, auto, home equity, installment, zero interest, no-money-down) 3 - Pay for insurance (Examples: health, auto, homeowners, renters, life, umbrella) 4 - Make a remittance to a person in a foreign country 5 - Pay a fee (Examples: checking account, foreign ATM, overdraft, late payment, loan origination) 6 - Transfer money to another account that you own 7 - Make an investment (bought stocks, bonds, mutual funds) 8 - Other (specify) 225 pay011 Dataset: Transaction-level Variable type: Numeric N = 310 Description: Question text: What kind of loan payment did you make? Survey question: pay011 Values 1 2 3 4 5 6 8 9 Number 119 9 111 22 24 4 2 19 Percent 38.4 2.9 35.8 7.1 7.7 1.3 0.6 6.1 Table 188: Frequency table for pay011 Value labels: 1 - Mortgage 2 - Student loan 3 - Auto loan 4 - Home equity loan or home equity line of credit 5 - Installment loan 6 - Zero-interest or no-money-down loan 7 - Payday loan 8 - Online marketplace or peer-to-peer lender (examples: Lending Club, Prosper) 9 - Another type of loan 226 pay016 Dataset: Transaction-level Variable type: Numeric N = 222 Description: Question text: What kind of insurance payment did you make? Survey question: pay016 Values 1 2 3 4 5 6 7 Number 23 4 28 85 52 2 28 Percent 10.4 1.8 12.6 38.3 23.4 0.9 12.6 Table 189: Frequency table for pay016 Value labels: 1 - Homeowners insurance 2 - Renters insurance 3 - Health insurance 4 - Vehicle insurance 5 - Life insurance 6 - Umbrella insurance 7 - Other types of insurance 227 pay020 Dataset: Transaction-level Variable type: Numeric N = 135 Description: Question text: Please tell us the purpose of your payment to an education provider. Survey question: pay020 Values 1 2 3 4 Number 18 16 37 64 Percent 13.3 11.9 27.4 47.4 Table 190: Frequency table for pay020 Value labels: 1 - Tuition or fees 2 - Repay student loan 3 - Childcare 4 - Other (specify) 228 pay030 Dataset: Transaction-level Variable type: Numeric N = 274 Description: Question text: Please tell us the purpose of your payment to a medical care provider. Survey question: pay030 Values 1 2 3 4 5 Number 147 26 27 20 54 Percent 53.6 9.5 9.9 7.3 19.7 Table 191: Frequency table for pay030 Value labels: 1 - Doctor, dentist, other health care professional 2 - Hospital, residential care, other medical institution 3 - Pharmacy 4 - Insurance company 5 - Other (specify) 229 pay040 Dataset: Transaction-level Variable type: Numeric N = 110 Description: Question text: Please tell us the purpose of your payment to a government. Survey question: pay040 Values 1 2 4 Number 24 47 39 Percent 21.8 42.7 35.5 Table 192: Frequency table for pay040 Value labels: 1 - Purchases of goods and services (Examples: local utilities and other services (like trash collection), public transportation, entrance to National Parks, municipal parking.) 2 - Taxes (Examples: Federal, state, local taxes, including property and excise taxes.) 3 - Fines 4 - Other (specify) 230 pay041 Dataset: Transaction-level Variable type: Numeric N = 24 Description: Question text: Please tell us what you paid for. [for a payment to the government that was primarily for goods or services] Survey question: pay041 Values 1 4 6 11 Number 13 1 1 9 Percent 54.2 4.2 4.2 37.5 Table 193: Frequency table for pay041 Value labels: 1 - Electricity, water, sewer 2 - Tuition 3 - Daycare 4 - Parking 5 - Tolls 6 - Trash collection 7 - Public transportation 8 - Health insurance - out of pocket, including Medicare supplemental insurance 9 - Childcare 10 - Used goods 11 - Other (specify) 231 pay042 Dataset: Transaction-level Variable type: Numeric N = 47 Description: Question text: What kind of tax payment did you make to the government? Survey question: pay042 Values 1 2 3 4 5 Number 13 11 3 14 6 Percent 27.7 23.4 6.4 29.8 12.8 Table 194: Frequency table for pay042 Value labels: 1 - Federal taxes 2 - State taxes 3 - Local taxes 4 - Property taxes 5 - Car or vehicle taxes 6 - Other kind of payment to the government (Specify) 232 pay050 Dataset: Transaction-level Variable type: Numeric N = 302 Description: Question text: Please tell us the purpose of your payment to a nonprofit, charity, or religious organization. Survey question: pay050 Values 1 2 3 4 Number 104 128 36 34 Percent 34.4 42.4 11.9 11.3 Table 195: Frequency table for pay050 Value labels: 1 - Make a donation 2 - Make an offering, tithe, put money in the collection plate, etc. 3 - Purchase goods and services 4 - Other (specify) 233 pay082 Dataset: Transaction-level Variable type: Numeric N = 473 Description: Question text: Please tell us the purpose of your payment [to another person] Survey question: pay082 Values 1 2 3 4 5 6 Number 106 25 35 184 38 85 Percent 22.4 5.3 7.4 38.9 8.0 18.0 Table 196: Frequency table for pay082 Value labels: 1 - To give a gift or allowance 2 - To lend money 3 - To repay money I borrowed (a loan) 4 - To purchase goods or pay for services 5 - To split a check or share expenses 6 - Other (specify) 234 payee Dataset: Transaction-level Variable type: Numeric N = 12430 Description: Payee designation. Survey question: N/A Details: Based on the value of variable merch. Values 1 2 3 4 5 6 7 8 Number 1056 135 274 317 302 473 7753 2120 Percent 8.5 1.1 2.2 2.6 2.4 3.8 62.4 17.1 Table 197: Frequency table for payee Value labels: 1 - Financial services provider 2 - Education provider 3 - Hospital, doctor, dentist, etc. 4 - Government 5 - Nonprofit, charity, religious 6 - A person 7 - Retail store or online retailer 8 - Business that primarily sells services 235 payee orig Dataset: Transaction-level Variable type: Numeric N = 12430 Description: Original payee designation, prior to editing. Survey question: N/A Details: Based on the value of variable merch. Values 1 2 3 4 5 6 7 8 Number 1056 135 274 317 302 473 7753 2120 Percent 8.5 1.1 2.2 2.6 2.4 3.8 62.4 17.1 Table 198: Frequency table for payee orig Value labels: 1 - Financial services provider 2 - Education provider 3 - Hospital, doctor, dentist, etc. 4 - Government 5 - Nonprofit, charity, religious 6 - A person 7 - Retail store or online retailer 8 - Business that primarily sells services 236 payment Dataset: Transaction-level Variable type: Numeric N = 15114 Description: Whether the transaction is a payment. A payment is defined as a transaction with a nonmissing payment instrument. It may, in some cases, be an asset transfer – for instance, if a person uses a debit card to buy a bond – or it may be an expenditure – buying a cup of coffee with cash. It does not, however, include direct transfers from one owned account to another. Survey question: N/A Details: For non-placeholder transactions, payment is set equal to 1 if pi is not missing, or if the transaction was reported in the Purchases or Bills module of the questionnaire. Otherwise it is set to 0. Values 0 1 Number 2621 12493 Percent 17.3 82.7 Table 199: Frequency table for payment Value labels: 0 - No 1 - Yes 237 paypal bal Dataset: Day-level Variable type: Numeric N = 505 Description: The balance of the respondent’s PayPal account. Survey question: paypal balday0 min 0.0 med 10.0 mean 161.5 max 7309.0 sd 641.0 200 150 100 50 0 Frequency 250 300 Table 200: Summary statistics for paypal bal 0 100 200 300 400 paypal_bal 238 500 600 700 paypal bal date Dataset: Day-level Variable type: Numeric N = 503 Description: The date on which the PayPal balance was checked. Survey question: pa074 date Details: Converted to Stata date format. 239 paypal bal time Dataset: Day-level Variable type: Character N = 505 Description: The time at which the PayPal balance was checked. Survey question: pa074 time Details: Coded simply as a 24-hour clock – i.e. a value of 0 is midnight, 100 is 1 AM, 1400 is 2 PM, etc. 240 paypal funding Dataset: Transaction-level Variable type: Numeric N = 91 Description: Question text: How did you fund this PayPal payment? Survey question: q101 paypal Details: If the value of the variable paypal funding is 1, 2, or 3, then the value of the variable pi is recoded to match the payment instrument which funds the paypal payment. For example, if the diarist reports payment method = PayPal (10) for their payment, and then in item q101 paypal, they report 1, or credit card, then Atlanta Fed staff will recode the payment method variable pi to equal 3, or credit card. Values 1 2 3 4 Number 19 15 39 18 Percent 20.9 16.5 42.9 19.8 Table 201: Frequency table for paypal funding Value labels: 1 - Credit card 2 - Debit card 3 - Linked bank account 4 - Money stored with PayPal 241 paypref 100plus Dataset: Individual-level Variable type: Numeric N = 2872 Description: The respondent’s preferred payment method for transactions greater than 100 dollars. Survey question: q160 pm e Values 1 2 3 4 5 6 7 8 10 11 12 13 Number 186 182 1316 1029 49 19 31 36 10 2 6 6 Percent 6.5 6.3 45.8 35.8 1.7 0.7 1.1 1.3 0.3 0.1 0.2 0.2 Table 202: Frequency table for paypref 100plus Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 242 paypref 10to25 Dataset: Individual-level Variable type: Numeric N = 2871 Description: The respondent’s preferred payment method for transactions between 10 and 25 dollars. Survey question: q160 pm b Values 1 2 3 4 5 6 8 10 12 13 Number 967 38 698 1130 27 1 1 1 6 2 Percent 33.7 1.3 24.3 39.4 0.9 0.0 0.0 0.0 0.2 0.1 Table 203: Frequency table for paypref 10to25 Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 243 paypref 25to50 Dataset: Individual-level Variable type: Numeric N = 2870 Description: The respondent’s preferred payment method for transactions between 25 and 50 dollars. Survey question: q160 pm c Values 1 2 3 4 5 6 7 8 10 12 13 Number 466 85 885 1373 36 2 3 5 3 7 5 Percent 16.2 3.0 30.8 47.8 1.3 0.1 0.1 0.2 0.1 0.2 0.2 Table 204: Frequency table for paypref 25to50 Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 244 paypref 50to100 Dataset: Individual-level Variable type: Numeric N = 2873 Description: The respondent’s preferred payment method for transactions between 50 and 100 dollars. Survey question: q160 pm d Values 1 2 3 4 5 6 7 8 10 11 12 13 Number 294 129 1036 1311 50 6 12 17 6 1 6 5 Percent 10.2 4.5 36.1 45.6 1.7 0.2 0.4 0.6 0.2 0.0 0.2 0.2 Table 205: Frequency table for paypref 50to100 Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 245 paypref b1 Dataset: Individual-level Variable type: Numeric N = 2872 Description: Preferred bill payment method. Survey question: q115 b Values 1 2 3 4 5 6 7 8 10 11 12 13 Number 191 464 362 654 36 318 731 34 6 31 31 14 Percent 6.7 16.2 12.6 22.8 1.3 11.1 25.5 1.2 0.2 1.1 1.1 0.5 Table 206: Frequency table for paypref b1 Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 246 paypref b1 why Dataset: Individual-level Variable type: Numeric N = 2851 Description: Reason for preferred bill payment method. Survey question: q116 b Values 1 2 3 4 5 6 7 8 9 10 Number 167 163 1518 35 14 334 124 289 173 34 Percent 5.9 5.7 53.2 1.2 0.5 11.7 4.3 10.1 6.1 1.2 Table 207: Frequency table for paypref b1 why Value labels: 1 - Accepted at lots of places 2 - Budget control 3 - Convenience 4 - Cost 5 - Getting and setting-up 6 - Payment records 7 - Rewards 8 - Security 9 - Speed 10 - Other (specify) 247 paypref b2 Dataset: Individual-level Variable type: Numeric N = 2871 Description: Fallback bill payment method. Survey question: q117 b Values 1 2 3 4 5 6 7 8 10 11 12 13 Number 385 698 456 554 37 281 228 97 21 30 59 25 Percent 13.4 24.3 15.9 19.3 1.3 9.8 7.9 3.4 0.7 1.0 2.1 0.9 Table 208: Frequency table for paypref b2 Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 248 paypref b2 why Dataset: Individual-level Variable type: Numeric N = 2858 Description: Reason for fallback bill payment method. Survey question: q118 b Values 1 2 3 4 5 6 7 8 9 10 Number 328 96 1400 34 20 436 69 265 187 23 Percent 11.5 3.4 49.0 1.2 0.7 15.3 2.4 9.3 6.5 0.8 Table 209: Frequency table for paypref b2 why Value labels: 1 - Accepted at lots of places 2 - Budget control 3 - Convenience 4 - Cost 5 - Getting and setting-up 6 - Payment records 7 - Rewards 8 - Security 9 - Speed 10 - Other (specify) 249 paypref lt10 Dataset: Individual-level Variable type: Numeric N = 2871 Description: The respondent’s preferred payment method for transactions less than 10 dollars. Survey question: p160 pm a Values 1 2 3 4 5 6 7 8 10 12 13 Number 1827 8 390 620 13 2 1 1 2 4 3 Percent 63.6 0.3 13.6 21.6 0.5 0.1 0.0 0.0 0.1 0.1 0.1 Table 210: Frequency table for paypref lt10 Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 250 paypref nb1 Dataset: Individual-level Variable type: Numeric N = 2873 Description: Preferred non-bill payment method. Survey question: q115 a Values 1 2 3 4 5 6 7 8 10 11 12 13 Number 637 66 836 1215 34 12 17 15 20 3 11 7 Percent 22.2 2.3 29.1 42.3 1.2 0.4 0.6 0.5 0.7 0.1 0.4 0.2 Table 211: Frequency table for paypref nb1 Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 251 paypref nb1 why Dataset: Individual-level Variable type: Numeric N = 2837 Description: Reason for preferred non-bill payment method. Survey question: q116 a Values 1 2 3 4 5 6 7 8 9 10 Number 358 193 1420 25 10 177 273 167 199 15 Percent 12.6 6.8 50.1 0.9 0.4 6.2 9.6 5.9 7.0 0.5 Table 212: Frequency table for paypref nb1 why Value labels: 1 - Accepted at lots of places 2 - Budget control 3 - Convenience 4 - Cost 5 - Getting and setting-up 6 - Payment records 7 - Rewards 8 - Security 9 - Speed 10 - Other (specify) 252 paypref nb2 Dataset: Individual-level Variable type: Numeric N = 2870 Description: Fallback non-bill payment method. Survey question: q117 a Values 1 2 3 4 5 6 7 8 10 11 12 13 Number 1167 370 465 509 68 42 52 56 71 12 25 33 Percent 40.7 12.9 16.2 17.7 2.4 1.5 1.8 2.0 2.5 0.4 0.9 1.1 Table 213: Frequency table for paypref nb2 Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 253 paypref nb2 why Dataset: Individual-level Variable type: Numeric N = 2863 Description: Reason for fallback non-bill payment method. Survey question: q118 a Values 1 2 3 4 5 6 7 8 9 10 Number 607 146 1332 45 15 233 53 169 243 20 Percent 21.2 5.1 46.5 1.6 0.5 8.1 1.9 5.9 8.5 0.7 Table 214: Frequency table for paypref nb2 why Value labels: 1 - Accepted at lots of places 2 - Budget control 3 - Convenience 4 - Cost 5 - Getting and setting-up 6 - Payment records 7 - Rewards 8 - Security 9 - Speed 10 - Other (specify) 254 paypref tran Dataset: Transaction-level Variable type: Numeric N = 292 Description: Question text: What is the most important characteristic for this payment? Survey question: q201b Values 1 2 3 4 5 6 7 8 9 10 Number 32 25 13 35 35 28 28 22 24 50 Percent 11.0 8.6 4.5 12.0 12.0 9.6 9.6 7.5 8.2 17.1 Table 215: Frequency table for paypref tran Value labels: 1 - Accepted at lots of places 2 - Budget control 3 - Convenience 4 - Cost 5 - Getting and setting-up 6 - Payment records 7 - Rewards 8 - Security 9 - Speed 10 - Other (specify) 255 paypref web Dataset: Individual-level Variable type: Numeric N = 2302 Description: Preferred online payment method. Survey question: q115 c Values 1 2 3 4 5 6 7 10 12 13 Number 1 1 1214 751 61 18 6 239 5 6 Percent 0.0 0.0 52.7 32.6 2.6 0.8 0.3 10.4 0.2 0.3 Table 216: Frequency table for paypref web Value labels: 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 256 paypref web why Dataset: Individual-level Variable type: Numeric N = 2302 Description: Reason for preferred online payment method. Survey question: q116 c Values 1 2 3 4 5 6 7 8 9 10 Number 117 48 972 10 7 156 224 645 101 22 Percent 5.1 2.1 42.2 0.4 0.3 6.8 9.7 28.0 4.4 1.0 Table 217: Frequency table for paypref web why Value labels: 1 - Accepted at lots of places 2 - Budget control 3 - Convenience 4 - Cost 5 - Getting and setting-up 6 - Payment records 7 - Rewards 8 - Security 9 - Speed 10 - Other (specify) 11 - 257 pi Dataset: Transaction-level Variable type: Numeric N = 12438 Description: Payment instrument. Survey question: Drop-down box in a large number of modules. Details: Note that in 2018, and going forward, ”Traveler’s Check” is no longer an option. Travelers Check has never been chosen by respondents in any diary. Values 0 1 2 3 4 5 6 7 8 10 11 12 13 14 Number 22 3440 840 2752 3321 251 734 698 34 18 115 7 148 58 Percent 0.2 27.7 6.8 22.1 26.7 2.0 5.9 5.6 0.3 0.1 0.9 0.1 1.2 0.5 Table 218: Frequency table for pi Value labels: 0 - Multiple payment methods 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 14 - Deduction from income 258 pi orig Dataset: Transaction-level Variable type: Numeric N = 12436 Description: Payment instrument, uncleaned. Survey question: Drop-down box in a large number of modules. Details: Note that in 2018, and going forward, ”Traveler’s Check” is no longer an option. Travelers Check has never been chosen by respondents in any diary. Values -1 0 1 2 3 4 5 6 7 8 10 11 12 13 14 Number 49 22 3428 840 2704 3278 244 675 698 34 91 114 65 145 49 Percent 0.4 0.2 27.6 6.8 21.7 26.4 2.0 5.4 5.6 0.3 0.7 0.9 0.5 1.2 0.4 Table 219: Frequency table for pi orig Value labels: 0 - Multiple payment methods 1 - Cash 2 - Check 3 - Credit card 4 - Debit card 5 - Prepaid/gift/EBT card 6 - Bank account number payment 7 - Online banking bill payment 8 - Money order 9 - Traveler’s check 10 - PayPal 11 - Account-to-account transfer 12 - Mobile phone payment 13 - Other payment method 14 - Deduction from income 259 pmnt desc Dataset: Transaction-level Variable type: Character N = 15114 Description: An open-ended response box giving the diarist one last chance to tell us any information they’d like to tell about the payment. Survey question: paydescribe001 260 ppload gpr Dataset: Transaction-level Variable type: Numeric N = 44 Description: A counter used internally to order the prepaid card loading transactions. Survey question: N/A 261 ppload loc Dataset: Transaction-level Variable type: Numeric N = 43 Description: Location of prepaid load. Survey question: Drop-down box in the prepaid loads module. Values 1 2 3 5 6 7 8 Number 14 8 9 2 1 2 7 Percent 32.6 18.6 20.9 4.7 2.3 4.7 16.3 Table 220: Frequency table for ppload loc Value labels: 1 - Retail location 2 - Online 3 - Mobile phone 4 - ATM 5 - Card machine 6 - Bank teller 7 - Check casher 8 - Other location 262 prepaid logo Dataset: Transaction-level Variable type: Numeric N = 241 Description: The logo on the prepaid card. Survey question: q101hhh Values 1 2 4 5 6 Number 43 87 4 88 19 Percent 17.8 36.1 1.7 36.5 7.9 Table 221: Frequency table for prepaid logo Value labels: 1 - Visa 2 - MasterCard 3 - Discover 4 - American Express 5 - No logo 6 - Other logo 263 prior goods Dataset: Transaction-level Variable type: Numeric N = 798 Description: Question text: Was this payment made for services that you received prior to today? Survey question: pay701 Details: See questionnaire for list of conditions that make this question display. Values 0 1 Number 737 61 Percent 92.4 7.6 Table 222: Frequency table for prior goods Value labels: 0 - No 1 - Yes 264 prior goods time Dataset: Transaction-level Variable type: Numeric N = 1169 Description: Approximate time when goods or services were ordered or received. Survey question: pay702 Values 1 2 3 4 Number 989 75 42 63 Percent 84.6 6.4 3.6 5.4 Table 223: Frequency table for prior goods time Value labels: 1 - Within the last month 2 - Between 3 months and 1 month ago 3 - Between 1 year and 3 months ago 4 - Longer than 1 year ago 265 race asian Dataset: Individual-level Variable type: Numeric N = 2865 Description: Respondent reported their race as Asian. Survey question: From UAS My Household Questionnaire. Values 0 1 Number 2791 74 Percent 97.4 2.6 Table 224: Frequency table for race asian Value labels: 0 - No 1 - Yes 266 race black Dataset: Individual-level Variable type: Numeric N = 2865 Description: Respondent reported their race as Black. Survey question: From UAS My Household Questionnaire. Values 0 1 Number 2593 272 Percent 90.5 9.5 Table 225: Frequency table for race black Value labels: 0 - No 1 - Yes 267 race other Dataset: Individual-level Variable type: Numeric N = 2873 Description: Respondent reported their race as something other than White, Black, or Asian. Survey question: From UAS My Household Questionnaire. Values 0 1 Number 2834 39 Percent 98.6 1.4 Table 226: Frequency table for race other Value labels: 0 - No 1 - Yes 268 race white Dataset: Individual-level Variable type: Numeric N = 2865 Description: Respondent reported their race as White. Survey question: From UAS My Household Questionnaire. Values 0 1 Number 341 2524 Percent 11.9 88.1 Table 227: Frequency table for race white Value labels: 0 - No 1 - Yes 269 receipt timing Dataset: Transaction-level Variable type: Numeric N = 2774 Description: Whether bill payment was for previously received goods/services or future goods/services. Survey question: pay002d Values 1 3 Number 1930 844 Percent 69.6 30.4 Table 228: Frequency table for receipt timing Value labels: 1 - Previously received goods or services 3 - Goods or services to be received in the future 270 regularity Dataset: Transaction-level Variable type: Numeric N = 2778 Description: The regularity of the bill. Survey question: pay200 Values 1 2 3 4 Number 325 116 2203 134 Percent 11.7 4.2 79.3 4.8 Table 229: Frequency table for regularity Value labels: 1 - Just once 2 - Less often than once a month 3 - Monthly 4 - More often than once a month 271 report date Dataset: Transaction-level Variable type: Numeric N = 137 Description: Date the respondent is reporting for, if not the assigned date Survey question: q199 date Details: If the respondent answers NO to q199, then the survey asks them to tell us what date they are reporting for. 272 scpc date Dataset: Individual-level Variable type: Numeric N = 2873 Description: Date on which the SCPC was begun. Variables which are pulled from the SCPC, like homeowner, can be reliably dated to this date. Survey question: start date Details: This is an SCPC variable merged into this dataset for convenience. Converted to Stata date format. 273 shops online Dataset: Individual-level Variable type: Numeric N = 2873 Description: Question text: In the past 12 months, have you made any online purchases (on the internet) to buy goods and services (not to pay bills)? Survey question: q115 c filter Values 0 1 Number 571 2302 Percent 19.9 80.1 Table 230: Frequency table for shops online Value labels: 0 - No 1 - Yes 274 split income deposit Dataset: Transaction-level Variable type: Numeric N = 31 Description: The amount deposited into the primary checking account when some income was desposited into multiple accounts. Survey question: q147 a-i Details: The respondent told us that some income was deposited into more than one account. How much was deposited to their primary checking account? min 0.0 med 790.0 mean 1540.3 max 6871.2 sd 1618.8 4 3 2 1 0 Frequency 5 6 7 Table 231: Summary statistics for split income deposit 0 1000 2000 3000 split_income_deposit 275 4000 5000 time Dataset: Transaction-level Variable type: Numeric N = 12297 Description: The time of the transaction. Survey question: Clock widget in the various modules. Details: Coded simply as a 24-hour clock – i.e. a value of 0 is midnight, 100 is 1 AM, 1400 is 2 PM, etc. 276 to account Dataset: Transaction-level Variable type: Numeric N = 3472 Description: The account to which the funds for this transaction were transfered. Survey question: N/A Details: from account and to account are purely constructed variables which tracks the movement of money between accounts, as well as tracking which accounts expenditures came from and which accounts income went to. They should generally be used in conjunction with type to truly understand the movement of money. Values 1 2 3 4 5 6 7 8 Number 934 1186 215 81 22 435 409 190 Percent 26.9 34.2 6.2 2.3 0.6 12.5 11.8 5.5 Table 232: Frequency table for to account Value labels: 1 - Currency 2 - Primary checking 3 - Other demand deposit account 4 - Nonfinancial deposit account (e.g. PayPal, prepaid card) 5 - Investment account 6 - Credit card account 7 - Other credit account 8 - Other (check, money order, returned goods, etc.) 277 tran Dataset: Transaction-level Variable type: Numeric N = 15114 Description: Within-day transaction counter. Survey question: N/A Details: Constructed by ordering the transactions according to time, and then creating an ascending counter. min 1.0 med 2.0 mean 2.5 max 22.0 sd 1.9 3000 1000 0 Frequency 5000 Table 233: Summary statistics for tran 1 2 3 4 tran 278 5 6 tran account Dataset: Transaction-level Variable type: Numeric N = 141 Description: Checking transfer-specific followup regarding the destination account. Survey question: Drop-down box in the checking transfers (checking withdrawals) module. Values 1 2 3 4 7 Number 93 24 8 1 15 Percent 66.0 17.0 5.7 0.7 10.6 Table 234: Frequency table for tran account Value labels: 1 - Another checking or savings account that I own 2 - Another checking or savings account belonging to someone else 3 - Investment account that I own 4 - Investment account belonging to someone else 5 - General purpose reloadable prepaid card that I own 6 - General purpose reloadable prepaid card belonging to someone else 7 - Other 279 tran days Dataset: Transaction-level Variable type: Numeric N = 137 Description: Number of days in which the recipient of the checking transfer is supposed to receive the funds. Survey question: Drop-down box in the checking transfers (checking withdrawals) module. Details: Note that the value is the number of days, except for 8 which is coded to mean ”more than one week”. Values 0 1 2 3 5 Number 118 10 2 5 2 Percent 86.1 7.3 1.5 3.6 1.5 Table 235: Frequency table for tran days Value labels: 0 - Today 1 - Tomorrow 2 - Two days 3 - Three days 4 - Four days 5 - Five days 6 - Six days 7 - Seven days 8 - More than seven days 280 tran inst Dataset: Transaction-level Variable type: Numeric N = 133 Description: Whether the funds were transferred to an account at the same institution. Survey question: Drop-down box in the checking transfers (checking withdrawals) module. Values 0 1 Number 26 107 Percent 19.5 80.5 Table 236: Frequency table for tran inst Value labels: 0 - No 1 - Yes 281 tran min Dataset: Transaction-level Variable type: Numeric N = 8538 Description: Whether there was a transaction minimum for this purchase using this payment instrument. Survey question: q101k, q101m, q101n, q101u Details: The different survey questions listed above relate to different types of payment instruments. Values 0 1 2 3 4 Number 6016 252 289 1141 840 Percent 70.5 3.0 3.4 13.4 9.8 Table 237: Frequency table for tran min Value labels: 0 - No 1 - Yes 2 - I’m not sure but I think so 3 - I’m not sure but I do not think so 4 - I don’t know 282 tran report Dataset: Transaction-level Variable type: Numeric N = 12618 Description: A counter used internally to order the transactions. Survey question: N/A min 1.0 med 2.0 mean 2.2 max 17.0 sd 1.6 1000 2000 3000 4000 5000 0 Frequency Table 238: Summary statistics for tran report 1 2 3 4 tran_report 283 5 6 traveled Dataset: Day-level Variable type: Numeric N = 8617 Description: Whether the respondent traveled on this diary day. Survey question: q13 Values 0 1 Number 8333 284 Percent 96.7 3.3 Table 239: Frequency table for traveled Value labels: 0 - No 1 - Yes 284 uasid Dataset: Transaction-level Variable type: Character N = 15114 Description: A respondent’s unique identifier. Using a respondent’s uasid, a data user can merge the DCPC with the SCPC or any other UAS survey. NOTE: In prior years this variable was known as prim key. The name was changed to allow easier compatability with other UAS surveys. Survey question: N/A Details: Provided by the survey vendor. 285 unexpected Dataset: Transaction-level Variable type: Numeric N = 4005 Description: Whether this expenditure was unexpected. Survey question: q151 a Values 0 1 Number 3649 356 Percent 91.1 8.9 Table 240: Frequency table for unexpected Value labels: 0 - No 1 - Yes 286 used coins Dataset: Day-level Variable type: Numeric N = 2340 Description: Question text: Did you use coins to pay for all or part of a cash payment you made today? Survey question: q5 2 Values 0 1 Number 1873 467 Percent 80.0 20.0 Table 241: Frequency table for used coins Value labels: 0 - No 1 - Yes 287 used heloc Dataset: Transaction-level Variable type: Numeric N = 20 Description: Whether the respondent used a HELOC (Home Equity Line Of Credit) during the three-day diary period. Survey question: pay617 Values 0 Number 20 Percent 100.0 Table 242: Frequency table for used heloc Value labels: 0 - No 1 - Yes 288 why nocash Dataset: Day-level Variable type: Numeric N = 592 Description: Why the respondent does not have any cash, as reported on diary day 0. Survey question: q1a Values 1 2 3 4 6 Number 98 133 334 22 5 Percent 16.6 22.5 56.4 3.7 0.8 Table 243: Frequency table for why nocash Value labels: 1 - I just ran out and I need to get more 2 - I am broke 3 - I usually do not carry cash 4 - I gave my cash to someone else 5 - My cash was stolen or lost 6 - Other 289 why not billpref Dataset: Transaction-level Variable type: Numeric N = 1428 Description: Why the respondent did not use his or her preferred bill payment method. The preferred payment method is as reported in variable paypref b1. Survey question: q103h Values 1 2 3 4 5 6 7 8 9 10 Number 149 14 16 36 126 42 22 131 629 263 Percent 10.4 1.0 1.1 2.5 8.8 2.9 1.5 9.2 44.0 18.4 Table 244: Frequency table for why not billpref Value labels: 1 - Preferred payment method (PPM) was not accepted 2 - I did not have PPM with me 3 - I did not have enough money available to use PPM 4 - The payment would have been late if I used PPM 5 - The payment method I used (PMU) is more secure than PPM 6 - I received a discount for using PMU 7 - I would have paid a surcharge if I used PPM 8 - For this size transaction I prefer to use PMU 9 - For this type of bill I prefer to use PMU 10 - Other (specify) 290 why not pref Dataset: Transaction-level Variable type: Numeric N = 4272 Description: Why the respondent did not use his or her preferred non-bill payment method. The preferred payment method is as reported in variable paypref nb1. Survey question: q103b Values 1 2 3 4 5 6 7 8 9 Number 370 241 506 136 121 18 1122 824 934 Percent 8.7 5.6 11.8 3.2 2.8 0.4 26.3 19.3 21.9 Table 245: Frequency table for why not pref Value labels: 1 - Preferred payment method (PPM) was not accepted 2 - I did not have PPM with me 3 - Speed of payment was important for this transaction 4 - Security of the transaction was important 5 - I received a discount for using Payment Method Used (PMU) 6 - I would have paid a surcharge if I used PPM 7 - For this size transaction, I prefer to use PMU 8 - For this type of merchant I prefer to use PMU 9 - Other (specify) 291 work disabled Dataset: Individual-level Variable type: Numeric N = 2867 Description: Respondent is disabled. Survey question: q14 Details: Note that, while respondents were given the option to type in some ”Other” employment response, all of those that did were easily recategorized. Values 0 1 Number 2563 304 Percent 89.4 10.6 Table 246: Frequency table for work disabled Value labels: 0 - No 1 - Yes 292 work employed Dataset: Individual-level Variable type: Numeric N = 2867 Description: Respondent is employed. Survey question: q14 Details: Note that, while respondents were given the option to type in some ”Other” employment response, all of those that did were easily recategorized. Values 0 1 Number 1169 1698 Percent 40.8 59.2 Table 247: Frequency table for work employed Value labels: 0 - No 1 - Yes 293 work looking Dataset: Individual-level Variable type: Numeric N = 2867 Description: Respondent is unemployed and looking. Survey question: q14 Details: Note that, while respondents were given the option to type in some ”Other” employment response, all of those that did were easily recategorized. Values 0 1 Number 2733 134 Percent 95.3 4.7 Table 248: Frequency table for work looking Value labels: 0 - No 1 - Yes 294 work occupation Dataset: Individual-level Variable type: Numeric N = 1697 Description: Whether respondent works for government, non-profit, or is self-employed. Survey question: q15 Values 1 2 3 4 Number 338 956 229 174 Percent 19.9 56.3 13.5 10.3 Table 249: Frequency table for work occupation Value labels: 1 - Government 2 - Private-for-profit company 3 - Non-profit organization including tax exempt and charitable organizations 4 - Self-employed 295 work onleave Dataset: Individual-level Variable type: Numeric N = 2867 Description: Respondent is on sick or other leave. Survey question: q14 Details: Note that, while respondents were given the option to type in some ”Other” employment response, all of those that did were easily recategorized. Values 0 1 Number 2840 27 Percent 99.1 0.9 Table 250: Frequency table for work onleave Value labels: 0 - No 1 - Yes 296 work other Dataset: Individual-level Variable type: Numeric N = 2867 Description: Respondent replied OTHER to question about employment status. Survey question: q14 Details: Note that, while respondents were given the option to type in some ”Other” employment response, all of those that did were easily recategorized. Values 0 1 Number 2689 178 Percent 93.8 6.2 Table 251: Frequency table for work other Value labels: 0 - No 1 - Yes 297 work retired Dataset: Individual-level Variable type: Numeric N = 2867 Description: Respondent is retired. Survey question: q14 Details: Note that, while respondents were given the option to type in some ”Other” employment response, all of those that did were easily recategorized. Values 0 1 Number 2213 654 Percent 77.2 22.8 Table 252: Frequency table for work retired Value labels: 0 - No 1 - Yes 298 work self Dataset: Individual-level Variable type: Numeric N = 1697 Description: Respondent is self-employed. Survey question: q14 Details: Note that, while respondents were given the option to type in some ”Other” employment response, all of those that did were easily recategorized. Values 0 1 Number 1523 174 Percent 89.7 10.3 Table 253: Frequency table for work self Value labels: 0 - No 1 - Yes 299 work temp unemployed Dataset: Individual-level Variable type: Numeric N = 2867 Description: Respondent is temporarily unemployed. Survey question: q14 Details: Note that, while respondents were given the option to type in some ”Other” employment response, all of those that did were easily recategorized. Values 0 1 Number 2844 23 Percent 99.2 0.8 Table 254: Frequency table for work temp unemployed Value labels: 0 - No 1 - Yes 300