data guide to the 2017 diary of consumer payment …...data guide to the 2017 diary of consumer...

Data Guide to the 2017 Diary of Consumer Payment Choice

Kevin Foster∗, Federal Reserve Bank of Atlanta

December 5, 2018

Introduction

The Diary of Consumer Payment Choice (DCPC) is a survey of consumer payment behavior run in con-junction with the University of Southern California’s Understanding America Study (UAS). Respondentswere randomly assigned a three-day period between September 29th, 2017 and November 2nd, 2017 andasked to track all of their payments using an online questionnaire. Respondents were also asked to answera 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 ofUS consumers was actively taking the diary, and any given day can be made statistically representative byusing appropriate sample weights. In addition to in-person purchases, respondents were also asked to recordtheir online and mobile purchases, cash holdings, cash deposits, checking transfers, income payments, andother exchanges of liquid assets. The result is three datasets containing 14,743 unique transactions by 2,793individuals across four days each, including 11,380 expenditures, 1,618 account transfers, and 1,745 incomereceipts. 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 aretypically asked to list the number of purchases/cash withdrawals/checking transfers/etc they had on a givenday. For each transaction, the online diary asks follow-up questions to collect additional details. The variablemodule can be used to identify which module an observation was originally pulled from. Note that whilethe modules can have rather suggestive names, one should not rely on the name of the module to identifythe type of transaction an observation represents—not all transactions reported in the Purchases module arenecessarily “purchases”, as some transactions may be recategorized after-the-fact if the respondent makesa mistake. Respondents were asked many followups which are a much more reliable means of identifying atransaction’s purpose. See Structure and use of the data below for more information. In some casesa respondent would report the same transaction in multiple modules. For instance, a respondent mightreport a utility bill payment in both the Purchases and Bills module. These duplicates are culled from thedataset, and the module variable is modified to reflect that a transaction came from multiple parts of thesurvey. Transactions are considered to be duplicates if they have a matching prim key (primary respondentidentifier), date, amnt (transaction amount), and pi (payment instrument) in cases where pi is available,and prim key, date, and amnt in cases where pi is not available.

∗email: [email protected]; Special thanks to former Boston Fed Research Assistant Jason Premo.

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, somediarists were assigned diary periods beginning on September 29th and September 30th and some diaristswere assigned diary periods ending on November 1st and November 2nd. This was to ensure that everyindividual day in October has an approximately equal mix of diarists completing their 1st, 2nd, and 3rddiary days. The “burn-in” days of September 29–31 and the “burn-out” days of November 1–2 can bedropped from any analysis which attempts to describe the month of October. Because these observations donot have daily weights, they are automatically excluded if the daily weights are used, but must be excludedmanually when using the individual weights—see the weighting section below. For more information onthe sampling methodology, see the 2017 DCPC Technical Appendix. In order to conserve valuable surveytime and prevent the diary from becoming onerous, the diary employs skip patterns to determine whetheror not a respondent is asked a given question. In most cases, this is intuitive; a respondent who does notreport a credit card payment is not asked about the logo on their credit card. In other cases, however, it canbe potentially misleading. For instance, respondents are only asked if they had cash stolen if their reportedend of day cash balance fails to match their reported cash transactions (within a margin of error). Thus, insome cases it may be necessary for the researcher to trace variables back to their original diary questions inorder to obtain a full understanding of the universe of respondents for a given question.

Structure and use of the data

The 2017 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 analysisfor each kind of data.

Individual-level dataset

The individual-level dataset is structured so that each row in the dataset represents observations for onerespondent. There are 2,793 rows in this dataset—one for each respondent. Examples of variables in thisdataset include payment preferences and demographic variables. The unique identifier is prim key.

Day-level dataset

In the day-level dataset, each observation represents one diary-day per respondent. In other words, we see2,793 observations for each diary-day, for a total of 11,172 observations in this dataset. Examples of variablesthat are in this dataset include cash balances by bill denomination and the participation dates. Here, theunique identifiers are prim key and diary day.

Transaction-level dataset

Finally, the transaction-level dataset contains one transaction per row. There are 14,743 observations in thisdataset, consisting on expenditures, account transfers, and income receipts. The variable type allows thedata user to distinguish between these types of transactions. The main kind of variable in this dataset arethe variables that describe a payment. In this dataset, each observation is uniquely identified by prim key,diary day, and tran.

The type variable

Every transaction is assigned a value in the variable type, which identifies what sort of transaction theobservation represents. Observations can either represent an expenditure, a transfer, or an income receipt.

1https://www.frbatlanta.org/banking-and-payments/consumer-payments/diary-of-consumer-payment-choice/

2017-diary

2

https://www.frbatlanta.org/banking-and-payments/consumer-payments/diary-of-consumer-payment-choice/2017-diary

https://www.frbatlanta.org/banking-and-payments/consumer-payments/diary-of-consumer-payment-choice/2017-diary

Understanding the type variable, and its associated from account and to account is integral to properlyusing 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, purchasingan item at a store. Expenditures generally come from the Purchases or Bills modules, though they maycome from other modules as well. A substantial number of merchant categorization followups were asked foreach 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, forinstance, identify consumption.

Transfers

Transfers are when money is moved from one account to another, each owned by the same diarist. In orderto 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 froma checking account to cash and would come from the Cash Withdrawals module, while a credit card billpayment could be a transfer from a checking account to a credit account and might come from the Purchasesmodule.

Income

Income is defined as money coming into the respondent’s possession. Most income is reported in the Incomemodule, 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 canbe reported on diary day 0.

Dollar amounts

All transactions which represent a movement of money will have a dollar amount associated with them. Thisdollar amount is stored in the variable amnt, in the transaction-level dataset. Some outlier cleaning has beenapplied to these dollar amounts, and the original dollar amounts, as originally reported by the respondents,are stored in amnt orig. 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 someof the questions in the Survey of Consumer Payment Choice (SCPC). In many cases, unrealistically largedollar amounts are the result of an omitted decimal point.

Other key variables

Each transaction also includes, when applicable, an amount (variable amnt), a time (variable time), apayment 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., amobile phone—as well as several other variables related to the payment. Under this organization, it is avery simple matter to estimate, say, the average value of a cash transaction at a restaurant, or the averagenumber of credit payments in a given month. It is also possible, under some reasonable assumptions, togenerate running balances of the various liquidity accounts in a respondent’s possession.

3

Structure of this document

The variables in this code book are presented alphabetically. Each variable has a description that gives thedefinition, as well as the coding of the original survey question. This coding can be used to look the questionup in the survey questionnaire. When necessary, additional details are provided about how the variable wasaltered or constructed from the original survey response. Additional histograms and unweighted transaction-level summary statistics are provided for continuous-valued variables, while simple tabulations and codingsare provided for categorical variables.

Weighting

To allow for estimations that are representative of the United States, three sets of sample weights areprovided in these datasets. The first set of base weights, ind weight, are individual-level post-stratificationweights, and are available in the individual-level dataset. The second and third sets of weights are foundin the day-level dataset. The weights in the variable daily weight, are day-level weights. The third set ofweights, dow weight, are day-level day-of-week weights that attempt to account for day-of-week affects in thenumber and value of payments. We recommend that this latter set of weights be used whenever attemptingcross-year comparisons involving payments. All weights are relative weights—they have a mean of 1 andsum to the number of observations in the dataset. When subsetting the data—especially by date—it may benecessary to generate your own weights, and strictly speaking the day weights provided are not appropriateto use when including diary day 0.

For more information about how the weights are constructed, see 2017 Survey and Diary of Con-sumer Payment Choice—Sampling and Weighting by Marco Angrisani.2

2 https://www.frbatlanta.org/-/media/documents/banking/consumer-payments/diary-of-consumer-payment-choice/

2017/scpc-dcpc-2017-sampling-weights.pdf

4

https://www.frbatlanta.org/-/media/documents/banking/consumer-payments/diary-of-consumer-payment-choice/2017/scpc-dcpc-2017-sampling-weights.pdf

https://www.frbatlanta.org/-/media/documents/banking/consumer-payments/diary-of-consumer-payment-choice/2017/scpc-dcpc-2017-sampling-weights.pdf

accept card

Dataset: Transaction-level

Variable type: Numeric

N = 3341

Description: Whether a credit or debit card would have been accepted for this transaction.

Survey question: q101j

Values Number Percent0 563 16.91 2488 74.52 290 8.7

Table 1: Frequency table for accept card

Value labels:0 - No1 - Yes2 - I don’t know

5

accept cash



N = 5198

Description: Whether cash would have been accepted for this transaction.

Survey question: q103g

Values Number Percent0 200 3.81 4897 94.22 57 1.13 23 0.44 21 0.4

Table 2: Frequency table for accept cash

Value labels:0 - No1 - Yes2 - I’m not sure, but I think so3 - I’m not sure, but I do not think so4 - I don’t know

6

age

Dataset: Individual-level


N = 2792

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 havebirthdays during the diary period, the age is set to be the greater of the two ages.

min med mean max sd18.0 51.0 50.5 93.0 15.1

Table 3: Summary statistics for age

age

Fre

quen

cy

20 30 40 50 60 70

050

100

150

7

amnt



N = 14707

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 tocontrol for extremely large outliers which are, generally, the result of misplaced decimal points. Originaldollar amounts are maintained in the variable amnt orig. Data users may notice that some large transactionshave been maintained. This is usually because we were able to confirm that they are genuine.


Table 4: Summary statistics for amnt

8

amnt

Fre

quen

cy

0 200 400 600 800

020

0040

0060

0080

00

9

amnt new



N = 14693

Description: New dollar amount, after cleaning.


Details: Individual dollar-value cleaning is performed according to a subjective ”smell-test”. This is tocontrol for extremely large outliers which are, generally, the result of misplaced decimal points. Originaldollar amounts are maintained in the variable amnt orig. Data users may notice that some large transactionshave been maintained. This is usually because we were able to confirm that they are genuine.


Table 5: Summary statistics for amnt new

10

amnt_new

Fre

quen

cy

0 200 400 600 800

020

0040

0060

0080

00

11

amnt orig



N = 12080

Description: Dollar amount of the transaction, uncleaned.


Details: Uncleaned values. See amnt for cleaned values.

min med mean max sd-25.0 24.0 1620.9 18381838.0 167245.8

Table 6: Summary statistics for amnt orig

amnt_orig

Fre

quen

cy

0 100 200 300

050

015

0025

0035

00

12

authorization method



N = 3864

Description: Question text: How was this debit card purchase authorized?

Survey question: q201g


Table 7: Frequency table for authorization method

Value labels:1 - Swiping the card2 - Inserting the card’s chip3 - Tapping, waving, or other contactless method4 - Handing the card to an employee such as a waiter or waitress5 - Other (specify)

13

automatic



N = 2568

Description: Whether the bill was paid manually or automatically.

Survey question: pay002 autom, or a radio button in the bills module

Values Number Percent0 1864 72.61 704 27.4

Table 8: Frequency table for automatic

Value labels:0 - No1 - Yes

14

bill



N = 12078

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 inthe bills reminder module are bills by definition. Observations for which ”other” was chosen are manuallyrecategorized. Note that, due to the wording of the question, a very large proportion of respondents (about25-30 percent) chose ”other” and described their payment in words. We attempted to come up with rulesfor recategorizing these responses, as there were too many to do each one individually.


Table 9: Frequency table for bill


15

bill orig



N = 12078

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 inthe bills reminder module are bills by definition. Observations for which ”other” was chosen are manuallyrecategorized. Note that, due to the wording of the question, a very large proportion of respondents (about25-30 percent) chose ”other” and described their payment in words. We attempted to come up with rulesfor recategorizing these responses, as there were too many to do each one individually.


Table 10: Frequency table for bill orig


16

borrowed for purchase



N = 16

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, theresponse to pay608 is not NONE OF THE ABOVE, and the payment method is not CREDIT CARD.


Table 11: Frequency table for borrowed for purchase


17

can postpone



N = 3725

Description: Whether this transaction could have been postponed without penalty.

Survey question: q151 b


Table 12: Frequency table for can postpone


18

carry acnt2acnt

Dataset: Day-level


N = 4656

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.


Table 13: Frequency table for carry acnt2acnt


19

carry banp

Dataset: Day-level


N = 4656

Description: Whether respondent had the ability to make a bank account number payment that day.




Table 14: Frequency table for carry banp


20

carry cc

Dataset: Day-level


N = 4656

Description: Whether respondent carried cash on that diary day.




Table 15: Frequency table for carry cc


21

carry chk

Dataset: Day-level


N = 4656

Description: Whether respondent carried checks on that diary day.




Table 16: Frequency table for carry chk


22

carry csh

Dataset: Day-level


N = 4656

Description: Whether respondent carried credit cards on that diary day.




Table 17: Frequency table for carry csh


23

carry dc

Dataset: Day-level


N = 4656

Description: Whether respondent carried debit cards on that diary day.




Table 18: Frequency table for carry dc


24

carry mobile

Dataset: Day-level


N = 4656

Description: Whether respondent carried mobile device capable of making text message payments on thatdiary day.




Table 19: Frequency table for carry mobile


25

carry monord

Dataset: Day-level


N = 4656

Description: Whether respondent carried money orders on that diary day.




Table 20: Frequency table for carry monord


26

carry obbp

Dataset: Day-level


N = 4656

Description: Whether respondent had the ability to make an online banking bill payment that day.




Table 21: Frequency table for carry obbp


27

carry oth

Dataset: Day-level


N = 4656

Description: Whether respondent carried other payment methods on that diary day.




Table 22: Frequency table for carry oth


28

carry paypal

Dataset: Day-level


N = 4656

Description: Whether the repsondent had the ability to make a Paypal payment that day.




Table 23: Frequency table for carry paypal


29

carry svc

Dataset: Day-level


N = 4656

Description: Whether respondent carried a prepaid card (stored value card) on that diary day.




Table 24: Frequency table for carry svc


30

carry tc

Dataset: Day-level


N = 4656

Description: Whether respondent carried traveler’s checks on that diary day.




Table 25: Frequency table for carry tc


31

cash move



N = 255

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 questionthe transaction amount came from.


Table 26: Frequency table for cash move

Value labels:1 - Pocket to storage2 - Storage to pocket3 - Cash stolen or lost4 - Unexpected receipt of cash5 - Cash to foreign currency6 - Foreign currency to cash

32

cashwith amnt oth



N = 2787

Description: When you get cash from all other sources besides FILL FROM ANSWER PA016, whatamount do you get most often?

Survey question: pa017 b


Table 27: Summary statistics for cashwith amnt oth

cashwith_amnt_oth

Fre

quen

cy

0 50 100 150 200

050

010

0015

00

33

cashwith amnt prim



N = 2785

Description: When you get cash from FILL WITH ANSWER FROM PA016, what amount do you getmost often?

Survey question: pa017 a


Table 28: Summary statistics for cashwith amnt prim

cashwith_amnt_prim

Fre

quen

cy

0 100 200 300 400 500

010

020

030

040

050

060

0

34

cashwith location



N = 2770

Description: Question text: When you get cash, where do you get it most often?

Survey question: pa016

Values Number Percent1 1410 50.92 587 21.23 12 0.44 404 14.65 144 5.26 209 7.57 1 0.08 3 0.1

Table 29: Frequency table for cashwith location

Value labels:1 - ATM2 - Bank teller3 - Check cashing store4 - Cash back at a retail store5 - I am paid in cash6 - Family or friend7 - Payday lender8 - Other (specify)

35

cashwith num oth



N = 1327

Description: Question text: In a typical period (week, month, or year), how often do you get cash fromall other sources besides FILL FROM ANSWER PA016?

Survey question: pa018 a2, b2, c2

Details: The value of the monthly box, pa018 b2, is used if not missing. If pa018 a2 is not missing, thenmultiply by 4.348214 to convert weekly number to monthly. If pa018 c2 is not missing, divide by 12 toconvert yearly number to monthly.


Table 30: Summary statistics for cashwith num oth

36

cashwith_num_oth

Fre

quen

cy

0 1 2 3 4 5

010

020

030

040

0

37

cashwith num prim



N = 2792

Description: Question text: In a typical period (week, month, or year), how often do you get cash fromFILL WITH ANSWER FROM PA016?

Survey question: pa018 a1, b1, c1

Details: The value of the monthly box, pa018 b1, is used if not missing. If pa018 a1 is not missing, thenmultiply by 4.348214 to convert weekly number to monthly. If pa018 c1 is not missing, divide by 12 toconvert yearly number to monthly.


Table 31: Summary statistics for cashwith num prim

38

cashwith_num_prim

Fre

quen

cy

0 2 4 6 8

020

040

060

0

39

cc chip 1



N = 2041

Description: Whether the respondent’s first credit card has a chip.

Survey question: ccq 005


Table 32: Frequency table for cc chip 1


40

cc chip 2



N = 835

Description: Whether the respondent’s second credit card has a chip.





41

cc chip 3



N = 302

Description: Whether the respondent’s third credit card has a chip.





42

cc chip 4



N = 105

Description: Whether the respondent’s fourth credit card has a chip.





43

cc chip 5



N = 44

Description: Whether the respondent’s fifth credit card has a chip.





44

cc chip 6



N = 16

Description: Whether the respondent’s sixth credit card has a chip.





45

cc debt amnt



N = 415

Description: Question text: How much was the full amount due (statement balance) of the credit card bill?


Details: This question is only displayed if the diarist did not pay back the full amount due on the creditcard bill.


Table 38: Summary statistics for cc debt amnt

cc_debt_amnt

Fre

quen

cy

0 1000 2000 3000 4000 5000 6000

020

4060

8010

0

46

cc debt canpay



N = 228

Description: Question text: Did you have enough money in your checking or savings account to pay thefull 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 creditcard bill.


Table 39: Frequency table for cc debt canpay


47

cc debt whynotpay


Variable type: Character

N = 14743

Description: Question text: Why did you choose not to pay the full amount due (statement balance) forthis credit card bill?

Survey question: pay019b

Details: Open-ended text response box. This question is only displayed if the diarist did not pay back thefull amount due on the credit card bill.

48

cc hasbal 1



N = 2100

Description: Whether the respondent’s first credit card has a rolled over balance.



Table 40: Frequency table for cc hasbal 1


49

cc hasbal 2



N = 858

Description: Whether the respondent’s second credit card has a rolled over balance.





50

cc hasbal 3



N = 308

Description: Whether the respondent’s third credit card has a rolled over balance.





51

cc hasbal 4



N = 105

Description: Whether the respondent’s fourth credit card has a rolled over balance.





52

cc hasbal 5



N = 44

Description: Whether the respondent’s fifth credit card has a rolled over balance.





53

cc hasbal 6



N = 16

Description: Whether the respondent’s sixth credit card has a rolled over balance.





54

cc num



N = 2161

Description: The number of credit cards the respondent has.


Values Number Percent1 1278 59.12 567 26.23 206 9.54 63 2.95 26 1.26 21 1.0

Table 46: Frequency table for cc num

Value labels:1 - One2 - Two3 - Three4 - Four5 - Five6 - More than five

55

cc num used



N = 2452

Description: Question text: Which of your credit cards did you use to make this payment?

Survey question: q201c


Table 47: Frequency table for cc num used

Value labels:1 - First credit card (CC) listed2 - Second CC listed3 - Third CC listed4 - Fourth CC listed5 - Fifth CC listed6 - Another credit card not listed

56

cc repay plan



N = 705

Description: How the respondent intends to repay the credit card.

Survey question: pay609aa


Table 48: Frequency table for cc repay plan

Value labels:1 - Pay in full when the bill arrives2 - Pay over time in several bill payments

57

cc rewards 1



N = 2124

Description: Whether the respondent’s first credit card offers rewards.



Table 49: Frequency table for cc rewards 1


58

cc rewards 2



N = 870

Description: Whether the respondent’s second credit card offers rewards.





59

cc rewards 3



N = 312

Description: Whether the respondent’s third credit card offers rewards.





60

cc rewards 4



N = 107

Description: Whether the respondent’s fourth credit card offers rewards.





61

cc rewards 5



N = 45

Description: Whether the respondent’s fifth credit card offers rewards.





62

cc rewards 6



N = 17

Description: Whether the respondent’s sixth credit card offers rewards.





63

cc type 1



N = 2132

Description: Type (e.g. logo) of the respondent’s first credit card.



Table 55: Frequency table for cc type 1

Value labels:1 - Visa2 - MasterCard3 - Discover4 - Company or store branded credit cards5 - American Express charge card6 - American Express credit card7 - Diners Club or other charge cards8 - Other

64

cc type 2



N = 873

Description: Type (e.g. logo) of the respondent’s second credit card.


Values Number Percent1 411 47.12 235 26.93 67 7.74 73 8.45 16 1.86 45 5.28 26 3.0



65

cc type 3



N = 313

Description: Type (e.g. logo) of the respondent’s third credit card.





66

cc type 4



N = 108

Description: Type (e.g. logo) of the respondent’s fourth credit card.





67

cc type 5



N = 45

Description: Type (e.g. logo) of the respondent’s fifth credit card.





68

cc type 6



N = 17

Description: Type (e.g. logo) of the respondent’s sixth credit card.





69

cd account



N = 130

Description: Account where cash was desposited.

Survey question: cashdep account


Table 61: Frequency table for cd account

Value labels:1 - Primary checking account2 - Other checking or savings account3 - Primary general purpose reloadable prepaid card4 - Other prepaid card5 - Primary PayPal account6 - Other (specify)

70

cd location



N = 130

Description: Cash deposit location.

Survey question: Drop-down box in the cash deposits module. Called ”Deposit Method” in the question-naire.


Table 62: Frequency table for cd location

Value labels:1 - ATM2 - Bank teller3 - Other (specify)

71

check dep src



N = 391

Description: The source of the checking deposit.

Survey question: Drop-down box in the checking deposits module.


Table 63: Frequency table for check dep src

Value labels:1 - Check (personal or business)2 - Money order3 - Travelers check4 - Cashiers check5 - Certified check6 - Transfer from another account7 - Direct deposit of income8 - Venmo cash out9 - Other

72

checker



N = 12079

Description: A flag used internally for data processing.

Survey question: N/A

73

chk bal

Dataset: Day-level


N = 5168

Description: Balance of checking account.



Table 64: Summary statistics for chk bal

chk_bal

Fre

quen

cy

0 5000 10000 15000 20000

050

010

0015

00

74

chk bal date

Dataset: Day-level


N = 2831

Description: Date that diarist checked checking account balance.

Survey question: pa072 a date

75

chk bal orig

Dataset: Day-level


N = 5168

Description: Checking account balance, uncleaned



Table 65: Summary statistics for chk bal orig

chk_bal_orig

Fre

quen

cy

0 5000 10000 15000 20000

050

010

0015

00

76

chk bal time

Dataset: Day-level


N = 5303

Description: Time that diarist checked checking account balance.

Survey question: pa072 a time

77

citizen



N = 2793

Description: Whether respondent is a US citizen. Note: This variable is not provided in the public dataset.

Survey question: From UAS My Household Questionnaire.


Table 66: Frequency table for citizen


78

coin2cash coin amnt



N = 20

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 respondentswhose daily cash balance implied by their cash transactions does not match their reported end-of-day cashholdings.


Table 67: Summary statistics for coin2cash coin amnt

coin2cash_coin_amnt

Fre

quen

cy

0 5 10 15 20 25

01

23

45

6

79

coin2cash loc



N = 55

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 respondentswhose daily cash balance implied by their cash transactions does not match their reported end-of-day cashholdings.


Table 68: Frequency table for coin2cash loc

Value labels:1 - Coin machine or kiosk2 - Bank teller3 - Cash register or checkout in a store4 - Family or friend5 - Other (specify)

80

coin2cash reimburse



N = 20

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. Alsonote that the cash-to-coin/coin-to-cash module is an error-checking module, and only shown to respondentswhose daily cash balance implied by their cash transactions does not match their reported end-of-day cashholdings.


Table 69: Frequency table for coin2cash reimburse

Value labels:0 - No1 - Prepaid or gift card2 - Deposit into bank account3 - Points or value to use on a website4 - Store credit5 - Other (specify)

81

cw location



N = 661

Description: Cash withdrawal location.

Survey question: Drop-down box in the cash withdrawals module.


Table 70: Frequency table for cw location

Value labels:1 - ATM2 - Cash back at a retail store3 - Bank teller4 - Family or friend5 - Check cashing store6 - Employer7 - Cash refund from returning goods8 - Payday lender9 - Other location

82

cw source



N = 662

Description: Source of funds for cash withdrawal.

Survey question: Drop-down box in the cash withdrawals module.

Values Number Percent1 222 33.52 31 4.73 77 11.64 24 3.65 2 0.36 4 0.67 11 1.78 205 31.09 86 13.0

Table 71: Frequency table for cw source

Value labels:1 - Primary checking account2 - Other checking or savings account3 - Salary wages or tips4 - Cashing a check5 - Credit card cash advance6 - Primary GPR prepaid card cash withdrawal7 - Other prepaid card cash withdrawal8 - Another person9 - Other source

83

daily weight

Dataset: Day-level


N = 7857

Description: Whether respondent carried travelers checks on that diary day.


Details: Raked post-stratification weights. Daily weights are best used for producing single-day esti-mates. Unlike individual weights, daily weights are not trimmed. These particular daily weights corre-spond to rps w day a uasgfk in the full weights dataset. See Angrisani, M, 2015 Survey and Diary of

Consumer Payment Choice Weighting Procedure (2016) for more information about the construction ofthe weights.

84

date



N = 14728

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 fromSeptember 28th, 2017 to November 2nd, 2017. In order to ensure the representativeness of the sample andto eliminate any biases from diary fatigue, it is recommended that only dates in October be considered.


Details: In most cases, this variable is determined by the date on which the transaction was reported. Forsome bills, the date is reported by the respondent on diary day 3 and reassigned ex-post.

85

date authorized



N = 13

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 Bank-ing Bill Payment.

86

dc acct 1



N = 691

Description: Whether the respondent’s first debit card is linked to their primary checking account or an-other checking account.

Survey question: dcq 005


Table 72: Frequency table for dc acct 1

Value labels:1 - Primary account2 - Another account

87

dc acct 2



N = 183

Description: Whether the respondent’s second debit card is linked to their primary checking account oranother checking account.





88

dc acct 3



N = 14

Description: Whether the respondent’s third debit card is linked to their primary checking account oranother checking account.





89

dc acct 4



N = 4

Description: Whether the respondent’s fourth debit card is linked to their primary checking account oranother checking account.





90

dc acct 5



N = 3

Description: Whether the respondent’s fifth debit card is linked to their primary checking account or an-other checking account.





91

dc logo 1



N = 2212

Description: Logo of the respondent’s first credit card.



Table 77: Frequency table for dc logo 1

Value labels:1 - Visa2 - MasterCard3 - No logo

92

dc logo 2



N = 287

Description: Logo of the respondent’s second credit card.





93

dc logo 3



N = 34

Description: Logo of the respondent’s third credit card.





94

dc logo 4



N = 9

Description: Logo of the respondent’s fourth credit card.





95

dc logo 5



N = 5

Description: Logo of the respondent’s fifth credit card.





96

dc num



N = 2237

Description: The number of debit cards the respondent has.



Table 82: Frequency table for dc num

Value labels:1 - One2 - Two3 - Three4 - Four5 - Five6 - More than five

97

dc num used



N = 3010

Description: Question text: Which of your debit cards did you use to make this payment?

Survey question: q201d

Values Number Percent1 2683 89.12 125 4.23 3 0.15 199 6.6

Table 83: Frequency table for dc num used

Value labels:1 - First debit card (DC) listed2 - Second DC listed3 - Third DC listed4 - Fourth DC listed5 - Fifth DC listed6 - Another debit card not listed

98

dc rewards 1



N = 2201

Description: Whether the respondent’s first debit card offers rewards.



Table 84: Frequency table for dc rewards 1


99

dc rewards 2



N = 285

Description: Whether the respondent’s second debit card offers rewards.





100

dc rewards 3



N = 33

Description: Whether the respondent’s third debit card offers rewards.





101

dc rewards 4



N = 9

Description: Whether the respondent’s fourth debit card offers rewards.





102

dc rewards 5



N = 5

Description: Whether the respondent’s fifth debit card offers rewards.





103

debit auth



N = 3019

Description: Method of debit authorization (signature or PIN).

Survey question: q101c


Table 89: Frequency table for debit auth

Value labels:1 - PIN2 - Signature3 - CVC or CVV code4 - None of these5 - Some combination of two of these6 - Other (specify)

104

denom 1 end

Dataset: Day-level


N = 11169

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 dollarvalue rather than the count of bills.


Table 90: Summary statistics for denom 1 end

denom_1_end

Fre

quen

cy

0 2 4 6 8 10

010

0020

0030

0040

00

105

denom 1 stored

Dataset: Day-level


N = 11172

Description: The number of 1 dollar bills stored.

Survey question: Reported in the ”Count your paper cash stored elsewhere” screen on day 0.


Table 91: Summary statistics for denom 1 stored

denom_1_stored

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

020

0040

0060

0080

00

106

denom 10 end

Dataset: Day-level


N = 11169






denom_10_end

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

020

0040

0060

00

107

denom 10 stored

Dataset: Day-level


N = 11172





denom_10_stored

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

0040

0060

0080

00

108

denom 100 end

Dataset: Day-level


N = 11169






denom_100_end

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

020

0040

0060

0080

0010

000

109

denom 100 stored

Dataset: Day-level


N = 11172





denom_100_stored

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

020

0040

0060

0080

00

110

denom 2 end

Dataset: Day-level


N = 11169






denom_2_end

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

0040

0060

0080

00

111

denom 2 stored

Dataset: Day-level


N = 11172





denom_2_stored

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

0040

0060

0080

00

112

denom 20 end

Dataset: Day-level


N = 11169






denom_20_end

Fre

quen

cy

0 1 2 3 4 5 6 7

010

0030

0050

00

113

denom 20 stored

Dataset: Day-level


N = 11172





denom_20_stored

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

020

0040

0060

0080

0010

000

114

denom 5 end

Dataset: Day-level


N = 11170






denom_5_end

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

010

0030

0050

00

115

denom 5 stored

Dataset: Day-level


N = 11172





denom_5_stored

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

0040

0060

0080

00

116

denom 50 end

Dataset: Day-level


N = 11169






denom_50_end

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

020

0040

0060

0080

0010

000

117

denom 50 stored

Dataset: Day-level


N = 11172





denom_50_stored

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

0040

0060

0080

00

118

device



N = 12023

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 valuesof this variable do not ”make sense”. Nonetheless, we have chosen not to leave them alone and allow theresearcher to interpret them as they see fit.


Table 104: Frequency table for device

Value labels:1 - Computer2 - Tablet3 - Mobile phone4 - Landline phone5 - Mail or delivery service6 - Some other device not listed7 - No device

119

device orig



N = 12025

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 valuesof this variable do not ”make sense”. Nonetheless, we have chosen not to leave them alone and allow theresearcher to interpret them as they see fit.

Values Number Percent-1 2 0.01 1565 13.02 173 1.43 604 5.04 63 0.55 366 3.06 972 8.17 8280 68.9

Table 105: Frequency table for device orig

Value labels:1 - Computer2 - Tablet3 - Mobile phone4 - Landline phone5 - Mail or delivery service6 - Some other device not listed7 - No device

120

diary day



N = 14728

Description: Diary days are numbered between 0 and 3. Note that certain account balances and incomepayments are reported on diary day 0, but no transactions.



Table 106: Frequency table for diary day

Value labels:0 - Day 01 - Day 12 - Day 23 - Day 3

121

discount



N = 9213

Description: Whether a discount was received for using the chosen payment instrument.

Survey question: q101aaa, q101d, q101f


Table 107: Frequency table for discount


122

dow weight

Dataset: Day-level


N = 7869

Description: Day-of-week weight, built to account for day-of-week effects in the number and value of pay-ments. Researchers attempting to do cross-year comparisons should employ these weights.

Survey question: Created internally.

123

draft date



N = 1450

Description: Question text: Some bills are paid on the same day they are scheduled; others are paid in thefuture. Please tell us the date you selected for the bill to be paid.


124

due date



N = 2342

Description: Date on which this bill was due.

Survey question: q67 a

Details: Converted to Stata date format.

125

durable type



N = 103

Description: If the payment is greater than or equal to 200 dollars, then the diarist is asked to describethe type of payment. The response options are several categories of durable goods.



Table 108: Frequency table for durable type

Value labels:1 - Cars trucks motorcycles other motor vehicles and parts2 - Furniture and furnishings3 - Household appliances4 - Computers cameras TVs other electronics5 - Sports equipment, sports and recreactional vehicles, boats6 - Jewelry and watches7 - Therapeutic appliances and equipment8 - None of the above

126

e exp cc



N = 1596

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover using credit cards.

Survey question: scf006 e


Table 109: Summary statistics for e exp cc

e_exp_cc

Fre

quen

cy

0 500 1000 1500 2000

020

040

060

080

010

00

127

e exp chk



N = 1909

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover using money in their checkingaccounts.

Survey question: scf006 b


Table 110: Summary statistics for e exp chk

e_exp_chk

Fre

quen

cy

0 500 1000 1500 2000

020

040

060

080

0

128

e exp chk saved



N = 2751

Description: As of today, how much money do you have saved for emergency expenses? Checking account

Survey question: scf004 b


Table 111: Summary statistics for e exp chk saved

e_exp_chk_saved

Fre

quen

cy

0 2000 4000 6000 8000 12000

050

010

0015

00

129

e exp cover



N = 2783

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover in total.

Survey question: scf006 total


Table 112: Summary statistics for e exp cover

e_exp_cover

Fre

quen

cy

0 500 1000 1500 2000

050

010

0015

00

130

e exp csh



N = 1620

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover using cash.

Survey question: scf006 a


Table 113: Summary statistics for e exp csh

e_exp_csh

Fre

quen

cy

0 500 1000 1500

020

040

060

080

010

00

131

e exp csh saved



N = 2750

Description: As of today, how much money do you have saved for emergency expenses? Cash

Survey question: scf004 a


Table 114: Summary statistics for e exp csh saved

e_exp_csh_saved

Fre

quen

cy

0 200 400 600 800 1000 1200

050

010

0015

00

132

e exp fam



N = 1504

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover by getting money from family.

Survey question: scf006 i


Table 115: Summary statistics for e exp fam

e_exp_fam

Fre

quen

cy

0 500 1000 1500 2000

020

040

060

080

010

00

133

e exp heloc



N = 1337

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover using a HELOC.

Survey question: scf006 f


Table 116: Summary statistics for e exp heloc

e_exp_heloc

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

040

060

080

012

00

134

e exp od



N = 1347

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover using overdraft protection.

Survey question: scf006 d


Table 117: Summary statistics for e exp od

e_exp_od

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

040

060

080

012

00

135

e exp pawn



N = 1321

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover using a pawn shop.

Survey question: scf006 h


Table 118: Summary statistics for e exp pawn

e_exp_pawn

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

040

060

080

012

00

136

e exp payday



N = 1331

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover using a payday loan.

Survey question: scf006 g


Table 119: Summary statistics for e exp payday

e_exp_payday

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

040

060

080

012

00

137

e exp sav



N = 1875

Description: Diary Day 1, respondents were asked if they could cover an emergency expense. This is theamount of the emergency expenditure that respondents said they could cover using money in their savingsaccounts.

Survey question: scf006 c


Table 120: Summary statistics for e exp sav

e_exp_sav

Fre

quen

cy

0 500 1000 1500 2000

020

040

060

080

0

138

e exp sav saved



N = 2754

Description: As of today, how much money do you have saved for emergency expenses? Savings account

Survey question: scf004 c


Table 121: Summary statistics for e exp sav saved

e_exp_sav_saved

Fre

quen

cy

0 10000 20000 30000 40000

050

010

0015

00

139

e exp svc saved



N = 2731

Description: As of today, how much money do you have saved for emergency expenses? Prepaid card

Survey question: scf004 d


Table 122: Summary statistics for e exp svc saved

e_exp_svc_saved

Fre

quen

cy

0 20 40 60 80

050

010

0015

0020

0025

00

140

e exp tot saved



N = 2775

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 theother dollar amounts.


Table 123: Summary statistics for e exp tot saved

e_exp_tot_saved

Fre

quen

cy

0 10000 20000 30000 40000 50000

050

010

0015

00

141

end cash bal

Dataset: Day-level


N = 11169

Description: The end-of-day balance of the cash carried by the respondent.


Details: Implied by the number of each bill that the respondent reports carrying.


Table 124: Summary statistics for end cash bal

end_cash_bal

Fre

quen

cy

0 50 100 150 200 250

010

0020

0030

0040

00

142

enough cash



N = 5198

Description: Whether respondent had enough cash available to pay for this transaction.

Survey question: q103f


Table 125: Frequency table for enough cash

Value labels:0 - No1 - Yes2 - I’m not sure, but I think so3 - I’m not sure, but I do not think so4 - I don’t know

143

fee amnt



N = 98

Description: The amount of fee paid for this transaction.

Survey question: Entered in the Remittances and Checking Transfers modules.


Table 126: Summary statistics for fee amnt

fee_amnt

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

020

4060

8010

0

144

fee amount



N = 2

Description: Amount of fee paid for remittance

Survey question: remit fee


Table 127: Summary statistics for fee amount

fee_amount

Fre

quen

cy

4.990 4.992 4.994 4.996 4.998

0.0

0.2

0.4

0.6

0.8

1.0

145

fee flag



N = 2542

Description: Whether a fee was charged.

Survey question: q101g, and as reported in several modules.


Table 128: Frequency table for fee flag


146

fixed amount



N = 2287

Description: Whether this recurring bill is a fixed amount each cycle, or whether it varies.

Survey question: pay002e


Table 129: Frequency table for fixed amount

Value labels:1 - Same amount each bill2 - Amount changes from bill to bill

147

frequency



N = 255

Description: The frequency (time per year) of the bill.

Survey question: q67 c, q67 g, pay002b

Details: Annualized according to response values.


Table 130: Summary statistics for frequency

frequency

Fre

quen

cy

0 10 20 30 40 50

010

2030

4050

6070

148

from account



N = 1633

Description: The account from which the funds for this transaction were sourced.


Details: from account and to account are purely constructed variables which tracks the movement ofmoney between accounts, as well as tracking which accounts expenditures came from and which accountsincome went to. They should generally be used in conjunction with type to truly understand the movementof money.


Table 131: Frequency table for from account

Value labels:1 - Currency2 - Primary checking3 - Other demand deposit account4 - Nonfinancial deposit account (e.g. PayPal, prepaid card)5 - Investment account6 - Credit card account7 - Other credit account8 - Other (check, money order, returned goods, etc.)

149

from bill section



N = 12079

Description: Was this bill payment reported in the bills section on diary Day 3, or was it reported in theregular payment module on Days 1, 2, or 3, and designated as a bill based on item pay002?



Table 132: Frequency table for from bill section

Value labels:1 - Yes2 - No

150

gender



N = 2793

Description: Male or female.



Table 133: Frequency table for gender

Value labels:0 - Female1 - Male

151

gpr bal

Dataset: Day-level


N = 755

Description: Balance of general purpose reloadable prepaid card.



Table 134: Summary statistics for gpr bal

gpr_bal

Fre

quen

cy

0 100 200 300 400 500

050

100

150

152

gpr bal date

Dataset: Day-level


N = 749

Description: Date that diarist checked balance of general purpose reloadable prepaid card.

Survey question: pa074 date

153

gpr bal orig

Dataset: Day-level


N = 755

Description: General purpose reloadable prepaid card balance, uncleaned



Table 135: Summary statistics for gpr bal orig

gpr_bal_orig

Fre

quen

cy

0 100 200 300 400 500 600

050

100

150

154

gpr bal time

Dataset: Day-level


N = 752

Description: Time that diarist checked balance of general purpose reloadable prepaid card

Survey question: pa074 time

155

hand checked



N = 474

Description: A flag used internally for data processing.


156

hh income



N = 12030

Description: Household income.

Survey question: de010

Details: Based on the variable de010 from the SCPC.

Values Number Percent1 192 1.62 73 0.63 120 1.04 203 1.75 164 1.46 337 2.87 374 3.18 552 4.69 615 5.110 604 5.011 861 7.212 1001 8.313 1498 12.514 1882 15.615 1344 11.216 1507 12.517 647 5.418 56 0.5

Table 136: Frequency table for hh income

Value labels:1 - Less than 50002 - 5000 - 74993 - 7500 - 99994 - 10000 - 124995 - 12500 - 149996 - 15000 - 199997 - 20000 - 249998 - 25000 - 299999 - 30000 - 3499910 - 35000 - 3999911 - 40000 - 4999912 - 50000 - 5999913 - 60000 - 7499914 - 75000 - 99999

157

15 - 100000 - 12499916 - 125000 - 19999917 - 200000 - 49999918 - 500000 or more

158

hh size



N = 2731

Description: Size of the household in which the respondent lives.



Table 137: Summary statistics for hh size

hh_size

Fre

quen

cy

1 2 3 4 5

020

040

060

080

010

00

159

highest education



N = 2793

Description: Respondent’s highest level of education, if the respondent is from the UAS sample.


Values Number Percent1 1 0.02 1 0.03 4 0.14 11 0.45 21 0.86 22 0.87 33 1.28 41 1.59 545 19.510 636 22.811 243 8.712 184 6.613 588 21.114 354 12.715 49 1.816 60 2.1

Table 138: Frequency table for highest education

Value labels:1 - Less than 1st grade2 - 1st, 2nd, 3rd, or 4th grade3 - 5th or 6th grade4 - 7th or 8th grade5 - 9th grade6 - 10th grade7 - 11th grade8 - 12 grade - no diploma9 - High school graduate or GED10 - Some college but no degree11 - Associate degree in college - occupational or vocational program12 - Associate degree in college - academic program13 - Bachelors degree14 - Masters degree15 - Professional school degree16 - Doctorate degree

160

hispaniclatino



N = 2793

Description: Whether respondent identifies has Hispanic/Latino



Table 139: Frequency table for hispaniclatino


161

hispaniclatino group



N = 178

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



Table 140: Frequency table for hispaniclatino group

Value labels:1 - Mexican2 - Puerto Rican3 - Cuban4 - Central or South American5 - Other

162

home debt



N = 1968

Description: Approximate value of debt on primary home, including HELs and HELOCs.


Details: This is an SCPC variable merged into this dataset for convenience.


Table 141: Summary statistics for home debt

home_debt

Fre

quen

cy

0 50000 150000 250000

020

040

060

080

0

163

home value



N = 1970

Description: Approximate market value of primary home.




Table 142: Summary statistics for home value

home_value

Fre

quen

cy

0e+00 2e+05 4e+05 6e+05

050

100

150

164

homeowner



N = 2793

Description: Whether respondent owns primary home.




Table 143: Frequency table for homeowner


165

in person



N = 12069

Description: Whether the transaction occurred in person.

Survey question: Drop-down box in several modules.


Table 144: Frequency table for in person


166

in person orig



N = 12051

Description: Whether the transaction occurred in person, uncleaned

Survey question: Drop-down box in several modules.

Values Number Percent-1 2 0.01 9192 76.32 2857 23.7

Table 145: Frequency table for in person orig


167

inc alimony



N = 2767

Description: Whether the respondent receives alimony income.

Survey question: q140 h


Table 146: Frequency table for inc alimony


168

inc alimony freq



N = 9

Description: The frequency with which alimony income is received.

Survey question: q141 h


Table 147: Frequency table for inc alimony freq

Value labels:1 - Weekly2 - Every two weeks3 - Twice per month4 - Monthly5 - Quarterly6 - Yearly7 - Other, on a one-time basis8 - Other, on a regular basis9 - Other, on an irregular basis

169

inc child



N = 2764

Description: Whether the respondent receives child support income.

Survey question: q140 i


Table 148: Frequency table for inc child


170

inc child freq



N = 93

Description: The frequency with which child support income is received.

Survey question: q141 i


Table 149: Frequency table for inc child freq


171

inc gov



N = 2769

Description: Whether the respondent receives government assistance income.

Survey question: q140 g


Table 150: Frequency table for inc gov


172

inc gov freq



N = 270

Description: The frequency with which government assistance income is received.

Survey question: q141 g


Table 151: Frequency table for inc gov freq


173

inc intdiv



N = 2771

Description: Whether the respondent receives interest or dividend income.

Survey question: q140 e


Table 152: Frequency table for inc intdiv


174

inc intdiv freq



N = 406

Description: The frequency with which interest or dividend income is received.



Table 153: Frequency table for inc intdiv freq


175

inc rent



N = 2767

Description: Whether the respondent receives rental income.

Survey question: q140 f


Table 154: Frequency table for inc rent


176

inc rent freq



N = 165

Description: The frequency with which rental income is received.

Survey question: q141 f


Table 155: Frequency table for inc rent freq


177

inc retempl



N = 2775

Description: Whether the respondent receives employer-paid retirement income.



Table 156: Frequency table for inc retempl


178

inc retempl freq



N = 368

Description: The frequency with which employer-paid retirement income is received.



Table 157: Frequency table for inc retempl freq


179

inc retsav



N = 2770

Description: Whether the respondent receives IRA, 401(k), or other savings-based retirement income.

Survey question: q140 j


Table 158: Frequency table for inc retsav


180

inc retsav freq



N = 229

Description: The frequency with which IRA, 401(k), or other savings-based retirement income is received.

Survey question: q141 j


Table 159: Frequency table for inc retsav freq


181

inc self



N = 2770

Description: Whether the respondent receives self-employment income.

Survey question: q140 c


Table 160: Frequency table for inc self


182

inc self freq



N = 314

Description: The frequency with which self-employment income is received.



Table 161: Frequency table for inc self freq


183

inc ss



N = 2785

Description: Whether the respondent receives social security income.

Survey question: q140 d


Table 162: Frequency table for inc ss


184

inc ss freq



N = 725

Description: The frequency with which social security income is received.

Survey question: q141 d


Table 163: Frequency table for inc ss freq


185

inc wage



N = 2773

Description: Whether the respondent receives wage income.



Table 164: Frequency table for inc wage


186

inc wage freq



N = 1538

Description: The frequency with which wage income is received.



Table 165: Frequency table for inc wage freq


187

income hh



N = 2776

Description: Household income.



Values Number Percent1 98 3.52 42 1.53 52 1.94 70 2.55 68 2.46 107 3.97 105 3.88 157 5.79 149 5.410 143 5.211 208 7.512 224 8.113 339 12.214 384 13.815 258 9.316 279 10.117 86 3.118 7 0.3

Table 166: Frequency table for income hh

Value labels:1 - Less than 50002 - 5000 - 74993 - 7500 - 99994 - 10000 - 124995 - 12500 - 149996 - 15000 - 199997 - 20000 - 249998 - 25000 - 299999 - 30000 - 3499910 - 35000 - 3999911 - 40000 - 4999912 - 50000 - 5999913 - 60000 - 7499914 - 75000 - 99999

188

15 - 100000 - 12499916 - 125000 - 19999917 - 200000 - 49999918 - 500000 or more

189

income hh gte500k



N = 7

Description: If the household income is greater than 500,000 dollars, then ask the diarist to tell us howmuch.



Table 167: Summary statistics for income hh gte500k

income_hh_gte500k

Fre

quen

cy

500000 700000 900000 1100000

0.0

0.5

1.0

1.5

2.0

2.5

3.0

190

income howpaid



N = 962

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 providesslightly better granularity.


Table 168: Frequency table for income howpaid

Value labels:1 - Direct deposit ONLY to primary checking account2 - Direct deposit ONLY to some other checking or savings account3 - Direct deposit to more than one account4 - Paper check5 - Cash6 - Payroll card7 - Primary general purpose reloadable prepaid card8 - Other general purpose reloadable prepaid card9 - Other

191

income type



N = 986

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 receivingthat day. When the respondent reported receiving multiple types of income, multiple transactions are createdto match, each with a different value for income type.

Values Number Percent1 521 52.82 63 6.43 113 11.54 111 11.35 29 2.96 36 3.77 61 6.28 1 0.19 27 2.710 24 2.4

Table 169: Frequency table for income type

Value labels:1 - Employment income2 - Self-employment income3 - Social Security4 - Employer paid retirement5 - IRA, Roth IRA, 401k, or other retirement fund6 - Interest and dividends7 - Rental income8 - Government assistance9 - Alimony10 - Child support

192

inconsistency explain



N = 14743

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 themerchant?

Survey question: q201f

193

ind payee



N = 470

Description: Type of person to which payment was made.

Survey question: pay080, pay081

Details: These two followups are combined, for convenience.


Table 170: Frequency table for ind payee

Value labels:1 - People who provide goods and services, operating as a business2 - People who provide goods and services, not operating as a business3 - Friends or family4 - Co-worker, classmate, or fellow military5 - Other (specify)

194

ind weight



N = 2793

Description: Raked individual sample weights.


Details: Raked post-stratification weights. Individual weights are best used for producing full-sample full-period estimates. These particular daily weights correspond to rps w uasgfk in the full weights dataset.See Angrisani, M, 2015 Survey and Diary of Consumer Payment Choice Weighting Procedure (2016)for more information about the construction of the weights.

195

interest level



N = 2785

Description: The self-reported level of interest the respondent had in the survey.

Survey question: cs 001


Table 171: Frequency table for interest level

Value labels:1 - Very interesting2 - Interesting3 - Neither interesting nor uninteresting4 - Uninteresting5 - Very uninteresting

196

last income date



N = 2604

Description: The date on which the most recent income payment was received, as of diary day 0.



197

late fee



N = 1367

Description: Whether a late fee was charged for this payment.



Table 172: Frequency table for late fee


198

loan amnt canpay



N = 2

Description: Question text: Did you have enough money in your checking or savings account to pay theamount due this period?



Table 173: Frequency table for loan amnt canpay


199

loan amnt due



N = 205

Description: Question text: How much was the amount due this period?



Table 174: Summary statistics for loan amnt due

loan_amnt_due

Fre

quen

cy

0 500 1000 1500

05

1015

20

200

loan amnt whynotpay



N = 14743

Description: Question text: Why did you choose not to pay the amount due this period for this loanpayment?


Details: Open-ended text response box.

201

marital status



N = 2792

Description: Respondent’s marital status.



Table 175: Frequency table for marital status

Value labels:1 - Married (spouse lives with me)2 - Married (spouse lives elsewhere)3 - Separated4 - Divorced5 - Widowed6 - Never married

202

memory checkbook



N = 2781

Description: Whether the respondent used the small checkbook memory aid.



Table 176: Frequency table for memory checkbook


203

memory finrec



N = 2781

Description: Whether the respondent referenced financial records as a memory aid.



Table 177: Frequency table for memory finrec


204

memory lpd



N = 2781

Description: Whether the respondent used the large paper diary as a memory aid.



Table 178: Frequency table for memory lpd


205

memory oth



N = 2781

Description: Whether the respondent used some other memory aid.



Table 179: Frequency table for memory oth


206

memory receipts



N = 2781

Description: Whether the respondent kept receipts to use as a memory aid.



Table 180: Frequency table for memory receipts


207

merch



N = 12092

Description: Merchant – 8 categories.

Survey question: Drop-down box in the purchases module and pay090 for 9-coded merchants. Questionsq66 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 Number Percent1 2048 16.92 1333 11.03 770 6.44 1645 13.65 1873 15.56 396 3.37 325 2.78 454 3.89 62 0.510 518 4.311 50 0.412 77 0.613 43 0.414 103 0.915 955 7.916 470 3.917 339 2.818 228 1.919 96 0.820 123 1.021 184 1.5

Table 181: Frequency table for merch

Value labels:1 - Grocery stores, convenience stores without gas stations, pharmacies2 - Gas stations3 - Sit-down restaurants and bars4 - Fast food restaurants, coffee shops, cafeterias, food trucks5 - General merchandise stores, department stores, other stores, online shopping6 - General services: hair dressers, auto repair, parking lots, laundry or dry cleaning, etc.7 - Arts, entertainment, recreation8 - Utilities not paid to the government: electricity, natural gas, water, sewer, trash, heating oil9 - Taxis, airplanes, delivery

208

10 - Telephone, internet, cable or satellite tv, video or music streaming services, movie theaters11 - Building contractors, plumbers, electricians, HVAC, etc.12 - Professional services: legal, accounting, architectural services; veterinarians; photographers or photo

processers13 - Hotels, motels, RV parks, campsites14 - 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 remittances16 - 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 donations18 - Hospital, doctor, dentist, nursing homes, etc.19 - Government taxes or fees20 - Schools, colleges, childcare centers21 - Public transportation and tolls

209

merch orig



N = 12071

Description: The original merchant category that the respondent used to report the payment, withoutany recategorization of other responses, or backwards-imputation of bill reminder module payments intomerchant categories, etc.

Survey question: Drop-down box in the purchases module.

Values Number Percent1 2051 17.02 1333 11.03 770 6.44 1644 13.65 1868 15.56 375 3.17 325 2.78 456 3.89 62 0.510 519 4.311 50 0.412 81 0.713 43 0.414 102 0.815 951 7.916 471 3.917 339 2.818 228 1.919 96 0.820 123 1.021 184 1.5

Table 182: Frequency table for merch orig

Value labels:1 - Grocery stores, convenience stores without gas stations, pharmacies2 - Gas stations3 - Sit-down restaurants and bars4 - Fast food restaurants, coffee shops, cafeterias, food trucks5 - General merchandise stores, department stores, other stores, online shopping6 - General services: hair dressers, auto repair, parking lots, laundry or dry cleaning, etc.7 - Arts, entertainment, recreation8 - Utilities not paid to the government: electricity, natural gas, water, sewer, trash, heating oil9 - Taxis, airplanes, delivery10 - Telephone, internet, cable or satellite tv, video or music streaming services, movie theaters11 - Building contractors, plumbers, electricians, HVAC, etc.

210

12 - Professional services: legal, accounting, architectural services; veterinarians; photographers or photoprocessers

13 - Hotels, motels, RV parks, campsites14 - 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 remittances16 - 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 donations18 - Hospital, doctor, dentist, nursing homes, etc.19 - Government taxes or fees20 - Schools, colleges, childcare centers21 - Public transportation and tolls

211

mobile funding



N = 93

Description: How this mobile payment was funded.

Survey question: q101 mobile b


Table 183: Frequency table for mobile funding

Value labels:1 - Credit card2 - Debit card3 - Prepaid card4 - Linked bank account5 - Money stored with a payment service such as PayPal6 - Other (specify)

212

mobile howfunded



N = 31

Description: How the mobile app used for the payment is funded.



Table 184: Frequency table for mobile howfunded


213

mobile method



N = 120

Description: How this mobile payment was completed.



Table 185: Frequency table for mobile method

Value labels:1 - Tapped to pay2 - Scanned a QR code or showed screen to cashier or ticket-taker3 - Paid in advance or remotely4 - Used a web browser

214

mobile type



N = 99

Description: Type of mobile payment.

Survey question: q101 mobile a


Table 186: Frequency table for mobile type

Value labels:1 - App payment2 - Text message payment3 - Payment made in browser4 - Other (specify)

215

module



N = 14743

Description: Module from which this observation was drawn. This can be helpful in mapping observationsback 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 ratherfrom questions q106a-d, q120, and q122.

216

monord date



N = 21

Description: Date on which the money order was purchased.

Survey question: q103s


Table 187: Frequency table for monord date

Value labels:1 - I bought it today2 - Between today and less than 7 days ago3 - Between 7 and less than 14 days ago4 - Between 14 and less than 30 days ago5 - 30 or more days ago

217

monord source



N = 21

Description: Where the money order was purchased from.

Survey question: q103r


Table 188: Frequency table for monord source

Value labels:1 - Bank2 - Post office3 - Western Union or someplace similar4 - Other (specify)

218

multipi breakdown



N = 14743

Description: Which payment instruments did the diarist use if the payment was reported as MULTIPLEPAYMENT INSTRUMENTS?

Survey question: q125 a through q125 n

219

next income date



N = 2464

Description: The next date on which income is expected to be received, as of the third diary day.



220

nopayments

Dataset: Day-level


N = 3499

Description: Why the respondent made no payments on a given day.

Survey question: q98a


Table 189: Frequency table for nopayments

Value labels:1 - I did not need to make any payments today2 - I was too busy to make payments today3 - I am trying to spend less4 - Other (specify)

221

other assets



N = 2757

Description: Approximate value of other assets, not including primary home.




Table 190: Summary statistics for other assets

other_assets

Fre

quen

cy

0e+00 2e+05 4e+05 6e+05

020

040

060

080

012

00

222

other debts



N = 2768

Description: Approximate value of other debts, not including debt on primary home.




Table 191: Summary statistics for other debts

other_debts

Fre

quen

cy

0e+00 2e+04 4e+04 6e+04 8e+04 1e+05

020

040

060

080

012

00

223

other device desc



N = 14743

Description: Question text: You told us that you used some other device to make this payment. Pleasetell us more about the device.

Survey question: q201e

Details: This question is only displayed if OTHER is selected for the payment device.

224

otherpi funding



N = 59

Description: The method by which the ’other’ payment instrument is funded.

Survey question: q101i followup


Table 192: Frequency table for otherpi funding


225

otherpi type



N = 140

Description: The type of ’other’ payment instrument used by the respondent.

Survey question: q101i


Table 193: Frequency table for otherpi type

Value labels:1 - EZPass or other electronic toll device2 - Apple Pay, Samsung Pay, or Android Pay3 - Bitcoin or other virtual currency4 - Remittance5 - Other (specify)

226

ow type



N = 35

Description: The type of ”Other Withdrawal” reported in the other withdrawals module. This is a place forrespondents to report if they purchased any money orders, traveler’s checks, or certified checks on a diary day.



Table 194: Frequency table for ow type

Value labels:1 - Money order2 - Travelers check3 - Certified check

227

past service



N = 191

Description: Whether payment was for medical goods or services received in the past or future.

Survey question: pay031, pay032

Details: Variable is set to 0 based on the response to pay031. Otherwise, the codings to pay032 are used.


Table 195: Frequency table for past service

Value labels:1 - Within the last month2 - Between 3 months and 1 month ago3 - Between 1 year and 3 months ago4 - Longer than 1 year ago

228

pay timing



N = 110

Description: When OBBP/BANP payment is scheduled to pay.

Survey question: q103n


Table 196: Frequency table for pay timing

Value labels:1 - Today2 - At a later date

229

payee



N = 12072

Description: Payee designation.


Details: Based on the value of variable merch.


Table 197: Frequency table for payee

Value labels:1 - Financial services provider2 - Education provider3 - Hospital, doctor, dentist, etc.4 - Government5 - Nonprofit, charity, religious6 - A person7 - Retail store or online retailer8 - Business that primarily sells services

230

payee orig



N = 12071

Description: Original payee designation, prior to editing.


Details: Based on the value of variable merch.


Table 198: Frequency table for payee orig

Value labels:1 - Financial services provider2 - Education provider3 - Hospital, doctor, dentist, etc.4 - Government5 - Nonprofit, charity, religious6 - A person7 - Retail store or online retailer8 - Business that primarily sells services

231

payment



N = 14743

Description: Whether the transaction is a payment. A payment is defined as a transaction with a non-missing payment instrument. It may, in some cases, be an asset transfer – for instance, if a person uses adebit 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.


Details: For non-placeholder transactions, payment is set equal to 1 if pi is not missing, or if the transactionwas reported in the Purchases or Bills module of the questionnaire. Otherwise it is set to 0.


Table 199: Frequency table for payment


232

paypal bal

Dataset: Day-level


N = 454

Description: The balance of the respondent’s PayPal account.

Survey question: paypal balday0


Table 200: Summary statistics for paypal bal

paypal_bal

Fre

quen

cy

0 200 400 600 800

050

100

150

233

paypal bal date

Dataset: Day-level


N = 453

Description: The date on which the PayPal balance was checked.

Survey question: pa074 date


234

paypal bal time

Dataset: Day-level


N = 11172

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.

235

paypal funding



N = 116

Description: Question text: How did you fund this PayPal payment?

Survey question: q101 paypal


Table 201: Frequency table for paypal funding

Value labels:1 - Credit card2 - Debit card3 - Linked bank account4 - Money stored with PayPal

236

paypref 100plus



N = 2785

Description: The respondent’s preferred payment method for transactions greater than 100 dollars.

Survey question: q160 pm e

Values Number Percent1 203 7.32 180 6.53 1253 45.04 1012 36.35 34 1.26 16 0.67 29 1.08 32 1.110 9 0.311 4 0.112 2 0.113 11 0.4

Table 202: Frequency table for paypref 100plus

Value labels:1 - Cash2 - Check3 - Credit card4 - Debit card5 - Prepaid/gift/EBT card6 - Bank account number payment7 - Online banking bill payment8 - Money order9 - Traveler’s check10 - PayPal11 - Account-to-account transfer12 - Mobile phone payment13 - Other payment method

237

paypref 10to25



N = 2789

Description: The respondent’s preferred payment method for transactions between 10 and 25 dollars.

Survey question: q160 pm b

Values Number Percent1 956 34.32 40 1.43 643 23.14 1105 39.65 28 1.06 3 0.17 2 0.18 3 0.19 1 0.010 1 0.011 1 0.012 2 0.113 4 0.1

Table 203: Frequency table for paypref 10to25


238

paypref 25to50



N = 2788


Survey question: q160 pm c




239

paypref 50to100



N = 2788


Survey question: q160 pm d




240

paypref b1



N = 2792

Description: Preferred bill payment method.



Table 206: Frequency table for paypref b1


241

paypref b1 why



N = 2778

Description: Reason for preferred bill payment method.



Table 207: Frequency table for paypref b1 why

Value labels:1 - Accepted at lots of places2 - Budget control3 - Convenience4 - Cost5 - Getting and setting-up6 - Payment records7 - Rewards8 - Security9 - Speed10 - Other (specify)

242

paypref b2



N = 2791

Description: Fallback bill payment method.



Table 208: Frequency table for paypref b2


243

paypref b2 why



N = 2777

Description: Reason for fallback bill payment method.



Table 209: Frequency table for paypref b2 why


244

paypref lt10



N = 2787

Description: The respondent’s preferred payment method for transactions less than 10 dollars.

Survey question: p160 pm a


Table 210: Frequency table for paypref lt10


245

paypref nb1



N = 2792

Description: Preferred non-bill payment method.



Table 211: Frequency table for paypref nb1


246

paypref nb1 why



N = 2762

Description: Reason for preferred non-bill payment method.



Table 212: Frequency table for paypref nb1 why


247

paypref nb2



N = 2792

Description: Fallback non-bill payment method.



Table 213: Frequency table for paypref nb2


248

paypref nb2 why



N = 2784

Description: Reason for fallback non-bill payment method.



Table 214: Frequency table for paypref nb2 why


249

paypref tran



N = 267

Description: Question text: What is the most important characteristic for this payment?

Survey question: q201b


Table 215: Frequency table for paypref tran


250

paypref web



N = 2264

Description: Preferred online payment method.



Table 216: Frequency table for paypref web


251

paypref web why



N = 2264

Description: Reason for preferred online payment method.



Table 217: Frequency table for paypref web why

Value labels:1 - Accepted at lots of places2 - Budget control3 - Convenience4 - Cost5 - Getting and setting-up6 - Payment records7 - Rewards8 - Security9 - Speed10 - Other (specify)11 -

252

pi



N = 12056

Description: Payment instrument.

Survey question: Drop-down box in a large number of modules.

Details: Note that while ”Traveler’s Check” was an option, it was never chosen by respondents.

Values Number Percent0 44 0.41 3694 30.62 872 7.23 2465 20.44 3028 25.15 240 2.06 610 5.17 603 5.08 20 0.210 117 1.011 114 0.912 48 0.413 151 1.314 50 0.4

Table 218: Frequency table for pi

Value labels:0 - Multiple payment methods1 - Cash2 - Check3 - Credit card4 - Debit card5 - Prepaid/gift/EBT card6 - Bank account number payment7 - Online banking bill payment8 - Money order9 - Traveler’s check10 - PayPal11 - Account-to-account transfer12 - Mobile phone payment13 - Other payment method14 - Deduction from income

253

pi orig



N = 12076

Description: Payment instrument, uncleaned.

Survey question: Drop-down box in a large number of modules.

Details: Note that while ”Traveler’s Check” was an option, it was never chosen by respondents.

Values Number Percent-1 49 0.40 36 0.31 3694 30.62 870 7.23 2459 20.44 3026 25.15 237 2.06 610 5.17 604 5.08 21 0.210 116 1.011 112 0.912 48 0.413 148 1.214 46 0.4

Table 219: Frequency table for pi orig

Value labels:0 - Multiple payment methods1 - Cash2 - Check3 - Credit card4 - Debit card5 - Prepaid/gift/EBT card6 - Bank account number payment7 - Online banking bill payment8 - Money order9 - Traveler’s check10 - PayPal11 - Account-to-account transfer12 - Mobile phone payment13 - Other payment method14 - Deduction from income

254

pmnt desc



N = 14743

Description: An open-ended response box giving the diarist one last chance to tell us any informationthey’d like to tell about the payment.

Survey question: paydescribe001

255

ppload gpr



N = 32

Description: A counter used internally to order the prepaid card loading transactions.


256

ppload loc



N = 32

Description: Location of prepaid load.

Survey question: Drop-down box in the prepaid loads module.


Table 220: Frequency table for ppload loc

Value labels:1 - Retail location2 - Online3 - Mobile phone4 - ATM5 - Card machine6 - Bank teller7 - Check casher8 - Other location

257

prepaid logo



N = 232

Description: The logo on the prepaid card.

Survey question: q101hhh


Table 221: Frequency table for prepaid logo

Value labels:1 - Visa2 - MasterCard3 - Discover4 - American Express5 - No logo6 - Other logo

258

prim key



N = 14743

Description: A respondent’s unique identifier. Using a respondent’s prim key, a data user can merge theDCPC with the SCPC or any other UAS survey.


Details: Provided by the survey vendor.

259

prior goods



N = 789

Description: Question text: Was this payment made for services that you received prior to today?


Details: See questionnaire for list of conditions that make this question display.


Table 222: Frequency table for prior goods


260

prior goods time



N = 1037

Description: Approximate time when goods or services were ordered or received.



Table 223: Frequency table for prior goods time

Value labels:1 - Within the last month2 - Between 3 months and 1 month ago3 - Between 1 year and 3 months ago4 - Longer than 1 year ago

261

purpose



N = 2361

Description: A qualitative description of the payment’s purpose, as determined by the various merchantfollowup questions, as well as the categories in the bill reminder module.

Survey question: pay010, pay020, pay030, pay040, pay041, pay050, pay082, pay090, pay700, as well asthe bill reminder categories

Details: In general, the diary has two kinds of merchant followups: ”who did you pay” and ”what did youbuy”. This variable is constructed based on the response to the ”what did you buy” followup responses.A complete treatment of the mapping between the various questions and their ultimate codings cannot begiven here. However, careful study of the questionnaire and the values of this variable should make it clearhow the responses were categorized.

Value labels:1 - Credit card repayment2 - Mortgage3 - HELOC4 - Auto or car loan5 - Installment loan6 - Zero-interest or no-money-down loan7 - Payday loan8 - Student loan9 - Marketplace or peer-to-peer loan10 - Loan from another person11 - Health insurance12 - Life insurance13 - Umbrella insurance14 - Vehicle insurance15 - Homeowners or renters insurance16 - Other type of insurance17 - Parking18 - Tolls19 - Public transit20 - Utilities21 - Federal taxes22 - State taxes23 - Local taxes24 - Property taxes25 - Car or vehicle taxes26 - Charitable donation27 - Offering, tithe, collection plate28 - Purchase goods or services29 - Gift or allowance30 - Lend money31 - Split check or share expenses

262

32 - Make a remittance33 - Alimony or child support34 - Pay a fee35 - Transfer money to another owned account36 - Make an investment37 - Tuition or fees38 - Child care39 - Pharmacy40 - Doctor dentist or other health care professional41 - Hospital, residential care, or other medical institution

263

Values Number Percent1 417 17.72 108 4.63 17 0.74 86 3.65 12 0.56 3 0.17 4 0.28 28 1.29 1 0.010 26 1.111 31 1.312 64 2.713 3 0.114 82 3.515 13 0.616 19 0.817 2 0.118 2 0.119 1 0.020 464 19.721 9 0.422 2 0.123 2 0.124 16 0.725 7 0.326 133 5.627 158 6.728 217 9.229 112 4.730 29 1.231 49 2.132 1 0.033 2 0.134 5 0.235 12 0.536 6 0.337 17 0.738 24 1.039 29 1.240 130 5.541 18 0.8

Table 224: Frequency table for purpose

264

purpose orig



N = 2354

Description: The original merchant purpose that the respondent used to report the payment, withoutany recategorization. Note that some of the information used to recategorize responses is not available forprivacy reasons. As such, using purpose is recommended.

Survey question: pay010, pay020, pay030, pay040, pay041, pay050, pay082, pay090, pay700, as well asthe bill reminder categories

Details: In general, the diary has two kinds of merchant followups: ”who did you pay” and ”what did youbuy”. This variable is constructed based on the response to the ”what did you buy” followup responses.A complete treatment of the mapping between the various questions and their ultimate codings cannot begiven here. However, careful study of the questionnaire and the values of this variable should make it clearhow the responses were categorized.


Table 225: Summary statistics for purpose orig

265

purpose_orig

Fre

quen

cy

0 10 20 30 40

010

020

030

040

050

0

266

race asian



N = 2786

Description: Respondent reported their race as Asian.



Table 226: Frequency table for race asian


267

race black



N = 2786

Description: Respondent reported their race as Black.



Table 227: Frequency table for race black


268

race other



N = 2793

Description: Respondent reported their race as something other than White, Black, or Asian.



Table 228: Frequency table for race other


269

race white



N = 2786

Description: Respondent reported their race as White.



Table 229: Frequency table for race white


270

receipt timing



N = 2567

Description: Whether bill payment was for previously received goods/services or future goods/services.

Survey question: pay002d


Table 230: Frequency table for receipt timing

Value labels:1 - Previously received goods or services3 - Goods or services to be received in the future

271

regularity



N = 2566

Description: The regularity of the bill.



Table 231: Frequency table for regularity

Value labels:1 - Just once2 - Less often than once a month3 - Monthly4 - More often than once a month

272

rem howmade



N = 2

Description: How the remittance was made.

Survey question: Drop-down box is the remittances module.


Table 232: Frequency table for rem howmade

Value labels:1 - Western Union2 - Bank or credit union3 - PayPal4 - MoneyGram5 - Bitcoin6 - Other (specify)7 -

273

report dat



N = 32

Description: Helper variable, used internally.


274

report date



N = 13760

Description: The date the diarist logged in to report their payments.


Details: This is different than the assigned diary date. If the diarist logged on to report their activity onthe actual diary date, then report date should equal date, otherwise, this date will be after date.

275

scpc date



N = 2793

Description: Date on which the SCPC was begun. Variables which are pulled from the SCPC, likehomeowner, 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.

276

shops online



N = 2792

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


Table 233: Frequency table for shops online


277

state reside



N = 2792

Description: State of residence. Note that this variable is not available in the public use dataset.

Survey question: statereside

Details: As reported in My Household Questionnaire.

278

time



N = 12043

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.

279

to account



N = 3308

Description: The account to which the funds for this transaction were transfered.


Details: from account and to account are purely constructed variables which tracks the movement ofmoney between accounts, as well as tracking which accounts expenditures came from and which accountsincome went to. They should generally be used in conjunction with type to truly understand the movementof money.


Table 234: Frequency table for to account

Value labels:1 - Currency2 - Primary checking3 - Other demand deposit account4 - Nonfinancial deposit account (e.g. PayPal, prepaid card)5 - Investment account6 - Credit card account7 - Other credit account8 - Other (check, money order, returned goods, etc.)

280

tran



N = 14743

Description: Within-day transaction counter.


Details: Constructed by ordering the transactions according to time, and then creating an ascending counter.


Table 235: Summary statistics for tran

tran

Fre

quen

cy

1 2 3 4 5 6

010

0030

0050

00

281

tran account



N = 112

Description: Checking transfer-specific followup regarding the destination account.

Survey question: Drop-down box in the checking transfers (checking withdrawals) module.


Table 236: Frequency table for tran account

Value labels:1 - Another checking or savings account that I own2 - Another checking or savings account belonging to someone else3 - Investment account that I own4 - Investment account belonging to someone else5 - General purpose reloadable prepaid card that I own6 - General purpose reloadable prepaid card belonging to someone else7 - Other

282

tran days



N = 96

Description: Number of days in which the recipient of the checking transfer is supposed to receive the funds.


Details: Note that the value is the number of days, except for 8 which is coded to mean ”more than oneweek”.


Table 237: Frequency table for tran days

Value labels:0 - Today1 - Tomorrow2 - Two days3 - Three days4 - Four days5 - Five days6 - Six days7 - Seven days8 - More than seven days

283

tran inst



N = 109

Description: Whether the funds were transferred to an account at the same institution.



Table 238: Frequency table for tran inst


284

tran min



N = 8198

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.


Table 239: Frequency table for tran min

Value labels:0 - No1 - Yes2 - I’m not sure but I think so3 - I’m not sure but I do not think so4 - I don’t know

285

tran report



N = 12264

Description: A counter used internally to order the transactions.



Table 240: Summary statistics for tran report

tran_report

Fre

quen

cy

1 2 3 4 5

010

0020

0030

0040

0050

00

286

traveled

Dataset: Day-level


N = 8377

Description: Whether the respondent traveled on this diary day.



Table 241: Frequency table for traveled


287

type



N = 14743

Description: The type of transaction – expenditure, transfer, or income. Also identify placeholder obser-vations.

Survey question: In some cases, based purely on the module in which the transaction is reported. In othercases, based on the response to followup questions.

Details: Expenditures are defined as money or liquidity moving out of a respondent’s possession. Transfersare defined as money or liquidity moving from one account/form to another – for instance, a cash withdawal.Income is defined as money coming into the respondents possession. Expenditures typically come from thepurchases or bills modules, income from the income module, and transfers from the rest of the modules,though there are some exceptions. Placeholder observations are added for every respondent-day, and existto allow for easier filtering of variables without having to worry about accidentally removing respondentsfrom the dataset.


Table 242: Frequency table for type

Value labels:1 - Expenditure2 - Transfer3 - Income

288

unexpected



N = 3725

Description: Whether this expenditure was unexpected.



Table 243: Frequency table for unexpected


289

used heloc



N = 14

Description: Whether the respondent used a HELOC during the three-day diary period.


Values Number Percent0 14 100.0

Table 244: Frequency table for used heloc


290

used linked sbdc



N = 26

Description: Question text: What merchant issued the store-branded card linked to your bank accountthat you used for this payment?

Survey question: q sbldc 002


Table 245: Frequency table for used linked sbdc

Value labels:1 - Target2 - Cumberland Farms3 - Speedway4 - Nordstrom5 - Royal Farms6 - Flash Floods7 - Fastrac Markets8 - Pride Stores9 - Shell10 - Other (specify)

291

used sbdc



N = 457

Description: Question text: Did you use a store-branded card linked to your bank account for this payment?

Survey question: q sbldc 001


Table 246: Frequency table for used sbdc


292

why nocash

Dataset: Day-level


N = 518

Description: Why the respondent does not have any cash, as reported on diary day 0.

Survey question: q1a


Table 247: Frequency table for why nocash

Value labels:1 - I just ran out and I need to get more2 - I am broke3 - I usually do not carry cash4 - I gave my cash to someone else5 - My cash was stolen or lost6 - Other

293

why not billpref



N = 1321

Description: Why the respondent did not use his or her preferred bill payment method. The preferredpayment method is as reported in variable paypref b1.

Survey question: q103h


Table 248: Frequency table for why not billpref

Value labels:1 - Preferred payment method (PPM) was not accepted2 - I did not have PPM with me3 - I did not have enough money available to use PPM4 - The payment would have been late if I used PPM5 - The payment method I used (PMU) is more secure than PPM6 - I received a discount for using PMU7 - I would have paid a surcharge if I used PPM8 - For this size transaction I prefer to use PMU9 - For this type of bill I prefer to use PMU10 - Other (specify)

294

why not pref



N = 4450

Description: Why the respondent did not use his or her preferred non-bill payment method. The preferredpayment method is as reported in variable paypref nb1.

Survey question: q103b


Table 249: Frequency table for why not pref

Value labels:1 - Preferred payment method (PPM) was not accepted2 - I did not have PPM with me3 - Speed of payment was important for this transaction4 - Security of the transaction was important5 - I received a discount for using Payment Method Used (PMU)6 - I would have paid a surcharge if I used PPM7 - For this size transaction, I prefer to use PMU8 - For this type of merchant I prefer to use PMU9 - Other (specify)

295

work disabled



N = 2789

Description: Respondent is disabled.


Details: Note that, while respondents were given the option to type in some ”Other” employment response,all of those that did were easily recategorized.


Table 250: Frequency table for work disabled


296

work employed



N = 2789

Description: Respondent is employed.




Table 251: Frequency table for work employed


297

work looking



N = 2789

Description: Respondent is unemployed and looking.




Table 252: Frequency table for work looking


298

work occupation



N = 1662

Description: Whether respondent works for government, non-profit, or is self-employed.



Table 253: Frequency table for work occupation


299

work onleave



N = 2789

Description: Respondent is on sick or other leave.




Table 254: Frequency table for work onleave


300

work oth



N = 2789

Description: Respondent replied OTHER to question about employment status.




Table 255: Frequency table for work oth


301

work retired



N = 2789

Description: Respondent is retired.




Table 256: Frequency table for work retired


302

work self



N = 1662

Description: Respondent is self-employed.




Table 257: Frequency table for work self


303

work temp unemployed



N = 2789

Description: Respondent is temporarily unemployed.




Table 258: Frequency table for work temp unemployed


304

data guide to the 2017 diary of consumer payment …...data guide to the 2017 diary of consumer...

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