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Codebook and Data Guide to the 2018 Survey of Consumer

Payment Choice

Kevin Foster∗, Federal Reserve Bank of Atlanta

March 20, 2019

1 Introduction

One of the major goals of the Survey of Consumer Payment Choice (SCPC) is to provide publicly available,consumer-level longitudinal data to support research on consumer payments and to provide aggregate dataon trends in U.S. consumer payments. This survey is the companion to the Diary of Consumer PaymentChoice (DCPC).

The questionnaires and public datasets for the 2018 SCPC are available for download on the Atlanta Fedwebsite1. The data are provided in Stata, SAS, CSV, and Rdata formats. The Atlanta Fed assumes thatdata users are familiar with a statistical analysis software package such as Stata, SAS, or R. The AtlantaFed does not provide any software assistance.

This document is a data user’s guide for the SCPC survey data. Anyone interested in conducting researchbased on SCPC data will find it helpful to become familiar with this document. See the 2018 Surveyof Consumer Payment Choice: Technical Appendix for details on questionnaire changes, data collection,sampling methodology, and hypothesis test results.

A broad overview of the 2018 SCPC, including a summary of the survey and tables of survey results, canbe found in the The 2018 Survey of Consumer Payment Choice results paper on the Atlanta Fed website.

2 SCPC variable overview

There are three broad categories of SCPC variables. Below we provide general information about each.UAS My Household Questionnaire variables represent a small fraction of variables that come

from the UAS My Household survey (MH). The MH survey is used to gather demographic data about eachrespondent. UAS members take the MH survey quarterly, and their most recent responses to the MH surveyare included in these SCPC datasets. Visit the UAS website for more information about the UAS MHsurvey2.

Survey variables are the actual results from the SCPC survey questions. Survey variables have variablenames such as pa001 a or pu004 b. To see the exact question text, respondent instructions, response optionwording, and structure of the questions on the screen, it is recommended that data users refer to the surveyquestionnaire. The questionnaire is available on the Atlanta Fed website3. Two important considerations ofthe survey variables are:

∗email: [email protected]://www.frbatlanta.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice2Click here to see the standard variables included in the UAS MH questionnaire.3https://www.frbatlanta.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice

1

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

https://uasdata.usc.edu/index.php?r=eNpLtDK0qi62MrFSKkhMT1WyLrYyArFLk3Iyk_WKSxLzUhKLUsoSizITk3JSi5WsawGIMRC5

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

• Randomization of response option orders: To avoid potential biases arising from the order of responseoptions presented to respondents, the survey instrument randomizes response options for some ques-tions. The questionnaire clearly indicates if response options were randomized. The unrandomizedvariables have the same variable names as the original survey variables. The raw data from the unran-domized variables and the SAS macros that unrandomize the responses will be made available uponrequest.

• Responses for different time frequencies: Respondents are given the option of reporting payment usein terms of a typical week, month, or year. This dataset includes variables where responses have beenstandardized to a monthly frequency. These created, “frequency converted” variables have the samename as the original responses, but without a numeric suffix. For instance, the variable pu006a a refersto the number of cash payments for retail goods in a typical month, after frequency conversion. Theset of three original variables that produce pu006a a are pu006a a1 (respondent used the weekly boxto report these transactions), pu006a a2 (monthly) and pu006a a3 (yearly). All the variables in thePayment Use section (pu002, pu003, pu004, pu005, pu006a, pu006c, pu021 families of variables) havecorresponding created variables which are described in Section 2.2.4, Payment use. The SAS macrosfor the frequency conversions can be made available upon request. The Atlanta Fed recommends thatdata analysts use the created frequency converted variables in their analysis and research.

Created variables are created by Atlanta Fed staff to populate the SCPC results tables and to aid indata analysis. Most of these variables have descriptive names based on a combination of mnemonics. Forexample, the variable cc typ consists of two mnemonics: cc stands for “credit card”, and typ stands for“number of transactions in a typical month”. More insight into variable name mnemonics is provided inSection 2.2.

2.1 Survey variables

2.1.1 Respondent identifier

The variable uasid is a 9 digit character variable of the form yymmxxxxx, where yy is the two-digit year (17for 2017, 18 for 2018, etc.) in which the respondent joined the UAS panel, mm is the month the respondentjoined the UAS panel (01 for January, 02 for February, etc.), and xxxxx is a random five digit number. Theuasid for a UAS member is the same across all UAS surveys. This allows data users to merge other UASsurvey datasets onto the SCPC dataset.

This variable is the equivalent of the variable uasid in other UAS surveys. In prior years of the SCPCand the DCPC, the variable uasid was renamed as prim key in order to maintain backwards compatibilitywith the code used to process the SCPC data. As of the 2018 SCPC and DCPC, the unique identifier willbe named uasid. For more information about uasid, visit this UAS data website. The variable uasid andother variables described on the UAS website allow the data user to determine if other members of the samehousehold are UAS members.

2.1.2 Survey weight

The weight variable in this survey dataset is r weight. For information about how the survey weights arecalculated, please see the 2018 SCPC Technical Appendix and 2018 Survey and Diary of ConsumerPayment Choice—Sampling and Weighting by Marco Angrisani.4 The Atlanta Fed survey staff rec-ommends that data analysts use the survey weight when generating population estimates.

2.2 Created variables

Most created variable names are a combination of two or more mnemonics, combined using underscores.Typically, the first mnemonic refers to payment instrument, type of account, or a method of payment. The

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

2018/scpc-dcpc-2018-sampling-weights.pdf

2

https://uasdata.usc.edu/index.php?r=eNpLtDK0qi62MrFSKkhMT1WyLrYyArFLk3Iyk_WKSxLzUhKLUsoSizITk3JSi5WsawGIMRC5

https://www.frbatlanta.org/-/media/documents/banking/consumer-payments/diary-of-consumer-payment-choice/2018/scpc-dcpc-2018-sampling-weights.pdf

https://www.frbatlanta.org/-/media/documents/banking/consumer-payments/diary-of-consumer-payment-choice/2018/scpc-dcpc-2018-sampling-weights.pdf

second or last mnemonic often indicates the concept being communicated, such as adoption ( adopt), ortypical use ( typ). This section describes the most common mnemonics.

2.2.1 Payment instruments

csh Cashchk Checkmon Money orderdc Debit cardcc Credit cardsvc Prepaid card/Stored-value cardbanp Bank account number paymentobbp Online banking bill paymentincome Direct deduction from income (used in automatic bill

payments only)nonbanka2a Account to account payment, using a nonbank ser-

vice such as PayPal or Venmo

Payment instruments are grouped as follows:

paper Cash, check, money ordercard Credit cards, debit cards, prepaid cardselect Bank account number payments, online banking bill payments, nonbank services such as PayPal or Venmo

2.2.2 Transaction types

The payment use section of the survey asks respondents to tell us the number of payments they make in atypical time period (week, month, or year). Those numbers then get converted to monthly numbers. Eachscreen of the payment use section corresponds to a transaction type. The transaction types are listed in thistable.

abp Automatic bill paymentobp Online bill paymentipbp In-person bill payment (or via mail)op Online (non-bill) paymentsrp Retail payments (made in-person)serv Services and other payments (in-person)p2p Person-to-person payment

Transaction types are grouped as follows:

bp Bill payment i.e. sum of abp, obp, ipbpnonbp Non-bill payments, sum of op, rp, serv, and p2p

2.2.3 Payment adoption

The suffix adopt is used in variables that indicate adoption of accounts or payment instruments. It isrecommended that the data user use these variables, as the related concepts in the survey do not match one-to-one with these variables. For instance, the variable cash adopt is created from several survey variables.SAS code for creating these variables is available upon request.

3

2.2.4 Payment use

For each payment instrument and seven transaction types, respondents are asked to report their paymentuse behavior—how frequently they use a payment instrument for a specific transaction type. Therefore, atthe most disaggregated level, a payment use variable name consists of three mnemonic components: thepayment instrument (Section 2.2.1), followed by the transaction type (Section 2.2.2), and ending with thesuffix typ which indicates the variable represents the number of payments of that transaction type andpayment instrument made in a typical month. The variable tot pay typ is defined for each respondent asthe sum of all payments made in a typical month.

It is important to note that not all combinations of payment instruments and transaction types exist.This is because they were assumed not to be possible at the time of the survey. The following table illustratescombinations that do exist in the data and the corresponding combinations of mnemonic prefixes:

bp nonbp

abp obp ipbp op rp serv p2p

csh csh ipbp csh rp csh serv csh p2p

chk chk ipbp chk op chk rp chk serv chk p2p

mon mon ipbp mon op mon rp mon serv mon p2p

tc tc (not asked by transaction type)dc dc abp dc obp dc ipbp dc op dc rp dc serv dc p2p

cc cc abp cc obp cc ipbp cc op cc rp cc serv cc p2p

svc svc ipbp svc op svc rp svc serv

obbp obbp abp obbp obp obbp p2p

banp banp abp banp obp banp op banp p2p

income income abp

2.2.5 Flags for variables that were cleaned for outliers

The SCPC has many continuous variables. These variables come from survey questions where the respondentis allowed to enter a number into a box. For instance, we ask the respondent to tell us how many credit cardpayments they make for retail goods in a typical week, month, or year. Continuous variables in the SCPCare cleaned for outliers and edited based on algorithms described in the 2018 SCPC Technical Appendix.To indicate an edited variable, the prefix “f ” is added to the front of a variable name. A flag value of 0indicates that the particular observation was not edited. A flag value greater than 0 means the observationwas edited. If a value was edited, a variable of the same name but with the suffix unedited contains theoriginal value.

3 A note on histograms in this codebook

In order to display the histograms correctly, the data was trimmed at the 95th percentile. The right-mostbar displayed contains the cumulative frequency of observations from the 95th percentile (inclusive) to themaximum value.

In certain cases, the histogram may show a negative value. This occurs rarely, when the 95th percentileis equal to zero.

4

Contents

1 Introduction 1

2 SCPC variable overview 12.1 Survey variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.1.1 Respondent identifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1.2 Survey weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.2 Created variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2.1 Payment instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.2 Transaction types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.3 Payment adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.4 Payment use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2.5 Flags for variables that were cleaned for outliers . . . . . . . . . . . . . . . . . . . . . 4

3 A note on histograms in this codebook 4

abp adopt 6

abp typ 7

as003 a1 8

as003 a2 9

as003 a3 10

as003 a4 11

as003 a5 12

as003 a6 13

as003 b1 14

as003 b2 15

as003 b3 16

as003 b4 17

as003 b5 18

as003 b6 19

as003 c1 20

as003 c2 21

as003 c3 22

as003 c4 23

as003 c5 24

5

as003 c6 25

as003 d1 26

as003 d2 27

as003 d3 28

as003 d4 29

as003 d5 30

as003 d6 31

as003 e1 32

as003 e2 33

as003 e3 34

as003 e4 35

as003 e5 36

as003 e6 37

as003 f1 38

as003 f2 39

as003 f3 40

as003 f4 41

as003 f5 42

as003 f6 43

as003 g1 44

as003 g2 45

as003 g3 46

as003 g4 47

as003 g5 48

as003 g6 49

as003 h1 50

as003 h2 51

as003 h3 52

6

as003 h4 53

as003 h5 54

as003 h6 55

asian 56

banp abp typ 57

banp adopt 58

banp bp typ 59

banp ipbp typ 60

banp obp typ 61

banp op typ 62

banp p2p typ 63

banp typ 64

black 65

bnk acnt adopt 66

bp typ 67

calcage 68

card abp typ 69

card bp typ 70

card ipbp typ 71

card obp typ 72

card op typ 73

card p2p typ 74

card rp typ 75

card serv typ 76

card typ 77

cc abp typ 78

cc adopt 79

cc bp typ 80

7

cc ipbp typ 81

cc obp typ 82

cc op typ 83

cc p2p typ 84

cc rp typ 85

cc serv typ 86

cc typ 87

cellphone 88

census div text 89

census division 90

chk acnt adopt 91

chk adopt 92

chk bp typ 93

chk ipbp typ 94

chk op typ 95

chk p2p typ 96

chk rp typ 97

chk serv typ 98

chk typ 99

cs 001 100

csh adopt 101

csh bp typ 102

csh ipbp typ 103

csh p2p typ 104

csh rp typ 105

csh serv typ 106

csh typ 107

currentlivingsituation 108

8

dateofbirth year 109

dc abp typ 110

dc adopt 111

dc bp typ 112

dc ipbp typ 113

dc obp typ 114

dc op typ 115

dc p2p typ 116

dc rp typ 117

dc serv typ 118

dc typ 119

de010 120

de011 121

de012 122

de013 123

de014 124

de015 125

de016 126

de018 127

de019 128

disabled 129

elect abp typ 130

elect bp typ 131

elect ipbp typ 132

elect obp typ 133

elect op typ 134

elect p2p typ 135

elect typ 136

9

employmenttype 137

end date 138

evercell 139

f pu002 a 140

f pu002 b 141

f pu002 c 142

f pu002 d 143

f pu002 e 144

f pu002 f 145

f pu003 a 146

f pu003 b 147

f pu003 c 148

f pu003 d 149

f pu003 e 150

f pu004 a 151

f pu004 b 152

f pu004 bmo 153

f pu004 c 154

f pu004 d 155

f pu004 e 156

f pu004 f 157

f pu005 a 158

f pu005 amo 159

f pu005 b 160

f pu005 c 161

f pu005 d 162

f pu005 e 163

f pu006a a 164

10

f pu006a b 165

f pu006a bmo 166

f pu006a c 167

f pu006a d 168

f pu006a e 169

f pu006c a 170

f pu006c b 171

f pu006c bmo 172

f pu006c c 173

f pu006c d 174

f pu006c e 175

f pu021 a 176

f pu021 b 177

f pu021 bmo 178

f pu021 c 179

f pu021 d 180

f pu021 e 181

f pu021 f 182

f pu021 g 183

fr001 a 184

fr001 b 185

fr001 d 186

fr001 e 187

gender 188

hardware 189

hhincome 190

highesteducation 192

hispaniclatino 194

11

hisplatino group 195

hourswork 196

income abp typ 197

income adopt 198

income bp typ 199

income typ 200

ipbp typ 201

laborstatus 202

lf other 203

livewithpartner 204

maritalstatus 205

mon adopt 206

mon bp typ 207

mon ipbp typ 208

mon op typ 209

mon p2p typ 210

mon rp typ 211

mon serv typ 212

mon typ 213

nativeamer 214

newob 215

newobbp 216

newsav 217

newtb 218

nonbanka2a p2p typ 219

nonbp typ 220

obbp abp typ 221

obbp adopt 222

12

obbp bp typ 223

obbp obp typ 224

obbp p2p typ 225

obbp typ 226

obp typ 227

op typ 228

p2p typ 229

pa001 a 230

pa001 b 231

pa001 d1 232

pa001 d2 233

pa001 e 234

pa001 f 235

pa001 fs10 236

pa001 fs11 237

pa001 fs12 238

pa001 fs13 239

pa001 fs14 240

pa001 fs15 241

pa001 fs16 242

pa001 fs2 243

pa001 fs3 244

pa001 fs4 245

pa001 fs5 246

pa001 fs6 247

pa001 fs8 248

pa001g1 249

pa001g2 250

13

pa001g3 251

pa002 252

pa002 other 253

pa003 254

pa004 a 255

pa004 b 256

pa007 a 257

pa008 a 258

pa008 a1 259

pa008 a2 260

pa008 a3 261

pa008 b 262

pa008 b1 263

pa008 b2 264

pa008 b3 265

pa009 266

pa010 267

pa011 a 268

pa011 b 269

pa011 c 270

pa012 271

pa013 272

pa014 273

pa016 274

pa016 other 275

pa019 a 276

pa019 b 277

pa019 c 278

14

pa019 d 279

pa019 e 280

pa019 f 281

pa019 g 282

pa020 283

pa024 284

pa025 285

pa026 a 286

pa031 287

pa031 b 288

pa035 289

pa040 a 290

pa040 b 291

pa040 c 292

pa040 d 293

pa040 e 294

pa041 295

pa041 b 296

pa041 c 297

pa041 e 298

pa042 a 299

pa042 e 300

pa044 a 301

pa045 302

pa045 a 303

pa045 b 304

pa045 c 305

pa048 a1 306

15

pa048 b1 307

pa048 c1 308

pa048 d1 309

pa048 e1 310

pa050 311

pa051 a 312

pa051 b 313

pa051 c 314

pa051 d 315

pa051 e 316

pa052 317

pa053 318

pa054 a 319

pa054 a1 320

pa054 a2 321

pa054 b 322

pa054 b1 323

pa054 b2 324

pa054 c 325

pa054 c1 326

pa054 c2 327

pa054 cards 328

pa054 d 329

pa054 d1 330

pa054 d2 331

pa054 e 332

pa054 e1 333

pa054 e2 334

16

pa054 f 335

pa054 f1 336

pa054 f2 337

pa054 g 338

pa054 g1 339

pa054 g2 340

pa054 z 341

pa055 a2 342

pa055 b1 343

pa055 b2 344

pa055 b3 345

pa055 b4 346

pa055 b5 347

pa075 a 348

pa075 b 349

pa076 a 350

pa076 b 351

pa079 a 352

pa079 b 353

pa103 354

pa108 a 355

pa108 a other 356

pa108 b 357

pa108 b other 358

pa120 a 359

pa120 b1 360

pa120 b2 361

pa120 b6 362

17

pa120 b7 363

pa120 b8 364

pa120 b9 365

pa120 c 366

pa120 c other 367

pa121 a 368

pa121 b 369

pa121 c 370

pa121 g 371

pa121 h 372

pa121 i 373

pa121 j 374

pa122 a 375

pa122 b 376

pa122 c 377

pa122 g 378

pa122 h 379

pa122 i 380

pa122 j 381

pa123 a number 382

pa123 a us 383

pa123 b number 384

pa123 b us 385

pa123 c number 386

pa123 c us 387

pa123 g number 388

pa123 g us 389

pa123 h number 390

18

pa123 h us 391

pa123 i number 392

pa123 i us 393

pa123 j number 394

pa123 j us 395

pa123 other 396

pa126 a 397

pa126 a other 398

pa126 b 399

pa126 b other 400

pa128 a 401

pa128 b 402

pa128 c 403

pa128 g 404

pa128 h 405

pa128 i 406

pa128 j 407

pa129 d 408

pa129 d combos 409

pa129 d2 other 410

pa129 d2s1 411

pa129 d2s2 412

pa129 d2s3 413

pa129 d2s4 414

pa129 d2s5 415

pa129 d2s6 416

pa130 a 417

pa130 b 418

19

pa130 c 419

pa130 d 420

pa130 e 421

pa130 f 422

pa131a1 423

pa131a2 424

pa133 a 425

pa133 b 426

pa133 c 427

pa133 g 428

pa133 h 429

pa133 i 430

pa133 j 431

pa135 a 432

pa135 b 433

pa135 c 434

pa135 g 435

pa135 h 436

pa135 i 437

pa135 j 438

pa137 439

pa138 440

pa139 441

pa140 a 442

pa140 b 443

pa140 c 444

pa140 g 445

pa140 h 446

20

pa140 i 447

pa140 j 448

pa188 449

pa188 other 450

pa189 a 451

pa189 b 452

pa189 c 453

pa193 454

pa193 other 455

pa194 456

pa198 a 457

pa198 b 458

pa198 c 459

pa198 d 460

pa198 e 461

pa198 f 462

pa198 g 463

pa198 h 464

pa198 i 465

pa198 j 466

pa198 k 467

pa198 l 468

pa198 m 469

pa201 a 470

pa201 b 471

pa201 c 472

pa201 d 473

pa201 e 474

21

pa201 f 475

pa201 h 476

pa201 i 477

pa202 a 478

pa202 c 479

pa202 d 480

pa202 e 481

pa202 f 482

pa202 g 483

pa202 h 484

pa202 i 485

pa202 j 486

pa202 k 487

pa202 l 488

pa202 m 489

pa203 a 490

pa203 c 491

pa203 d 492

pa203 e 493

pa203 f 494

pa203 g 495

pa203 h 496

pa203 i 497

pa203 j 498

pa203 k 499

pa203 l 500

pa203 m 501

pacific 502

22

paper bp typ 503

paper ipbp typ 504

paper op typ 505

paper p2p typ 506

paper rp typ 507

paper serv typ 508

paper typ 509

ph004 510

ph006 511

ph007 512

ph009 a 513

ph009 b 514

ph009 c 515

ph009 d 516

ph020 a 517

ph020 b 518

ph022 a 519

ph022 b 520

ph022 c 521

ph022 d 522

ph023 a 523

ph023 b 524

ph023 c 525

ph023 d 526

ph024 b 527

ph024 c 528

ph024 d 529

ph025 b 530

23

ph025 c 531

ph025 d 532

primary respondent 533

pu002 a 534

pu002 a unedited 536

pu002 b 537

pu002 b unedited 539

pu002 c 540

pu002 c unedited 542

pu002 d 543

pu002 d unedited 545

pu002 e 546

pu002 e unedited 548

pu002 f 549

pu002 f unedited 551

pu003 a 552


pu003 b 555


pu003 c 558


pu003 d 561


pu003 e 564


pu004 a 567


pu004 b 570

24


pu004 bmo 573

pu004 bmo unedited 575

pu004 c 576


pu004 d 579


pu004 e 582


pu004 f 585


pu005 a 588


pu005 amo 591

pu005 amo unedited 593

pu005 b 594


pu005 c 597


pu005 d 600


pu005 e 603


pu006a a 606

pu006a a unedited 608

pu006a b 609

pu006a b unedited 611

pu006a bmo 612

25

pu006a bmo unedited 614

pu006a c 615

pu006a c unedited 617

pu006a d 618

pu006a d unedited 620

pu006a e 621

pu006a e unedited 623

pu006c a 624

pu006c a unedited 626

pu006c b 627

pu006c b unedited 629

pu006c bmo 630

pu006c bmo unedited 632

pu006c c 633

pu006c c unedited 635

pu006c d 636

pu006c d unedited 638

pu006c e 639

pu006c e unedited 641

pu009 642

pu010 643

pu010 followup 644

pu011 645

pu013 646

pu013 a 647

pu013 a other 648

pu013 b 649

pu013 followup 650

26

pu020 651

pu021 a 652


pu021 b 655


pu021 bmo 658

pu021 bmo unedited 660

pu021 c 661


pu021 d 664


pu021 e 667


pu021 f 670


pu021 g 673

pu021 g unedited 675

r weight 676

race 677

retired 678

rp typ 679

sav acnt adopt 680

serv typ 681

sick leave 682

smartphone 683

start date 684

surveyyear 685

svc abp typ 686

27

svc adopt 687

svc bp typ 688

svc ipbp typ 689

svc obp typ 690

svc op typ 691

svc rp typ 692

svc serv typ 693

svc typ 694

tot pay typ 695

tsend 696

tsstart 697

uasid 698

uasmembers 699

unemp layoff 700

unemp look 701

white 702

workfullpart 703

working 704

28

abp adopt

Dataset: SCPC

Variable type: Numeric

N = 3142

Description: Is the respondent an AUTOMATIC BILL PAYMENT adopter?

Survey question: N/A

Details: Created variable

Values Number Percent0 1000 31.81 2142 68.2

Table 1: Frequency table for abp adopt

Value labels:0 - Not an adopter1 - Adopter

29

abp typ

Dataset: SCPC


N = 3153

Description: The number of AUTOMATIC BILL PAYMENTS made in a typical month.

Survey question: sum(pu002 a, b, c, d, e, f)

Details: This created variable has been edited for outliers.

min med mean max sd0.0 3.0 6.8 122.0 11.4

Table 2: Summary statistics for abp typ

abp_typ

Fre

quen

cy

0 5 10 15 20 25 30

020

040

060

080

010

00

30

as003 a1

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. ACCEPTANCE FOR PAYMENT of cash

Survey question: as003 a1

Details: Survey variable. See questionnaire for exact wording, question layout, and design.

Values Number Percent1 51 1.62 83 2.63 212 6.74 487 15.55 2315 73.5

Table 3: Frequency table for as003 a1

Value labels:1 - Rarely accepted2 - Occasionally accepted3 - Often accepted4 - Usually accepted5 - Almost always accepted

31

as003 a2

Dataset: SCPC


N = 3145

Description: Assessment of payment instrument characteristics. COST of cash





Value labels:1 - Very high cost2 - High cost3 - Neither high nor low cost4 - Low cost5 - Very low cost

32

as003 a3

Dataset: SCPC


N = 3150

Description: Assessment of payment instrument characteristics. CONVENIENCE of cash





Value labels:1 - Very inconvenient2 - Inconvenient3 - Neither inconvenient nor convenient4 - Convenient5 - Very convenient

33

as003 a4

Dataset: SCPC


N = 3150

Description: Assessment of payment instrument characteristics. SECURITY of cash





Value labels:1 - Very risky2 - Risky3 - Neither risky nor secure4 - Secure5 - Very secure

34

as003 a5

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. GETTING and SETTING UP of cash





Value labels:1 - Very hard to get or set up2 - Hard to get or set up3 - Neither hard nor easy4 - Easy to get or set up5 - Very easy to get or set up

35

as003 a6

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. PAYMENT RECORDS of cash





Value labels:1 - Very poor records2 - Poor records3 - Neither good nor poor4 - Good records5 - Very good records

36

as003 b1

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. ACCEPTANCE FOR PAYMENT ofchecks

Survey question: as003 b1



Table 9: Frequency table for as003 b1


37

as003 b2

Dataset: SCPC


N = 3146

Description: Assessment of payment instrument characteristics. COST of checks






38

as003 b3

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. CONVENIENCE of checks






39

as003 b4

Dataset: SCPC


N = 3149

Description: Assessment of payment instrument characteristics. SECURITY of checks






40

as003 b5

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. GETTING and SETTING UP of checks






41

as003 b6

Dataset: SCPC


N = 3146

Description: Assessment of payment instrument characteristics. PAYMENT RECORDS of checks






42

as003 c1

Dataset: SCPC


N = 3144

Description: Assessment of payment instrument characteristics. ACCEPTANCE FOR PAYMENT of debitcards

Survey question: as003 c1



Table 15: Frequency table for as003 c1


43

as003 c2

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. COST of debit cards






44

as003 c3

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. CONVENIENCE of debit cards






45

as003 c4

Dataset: SCPC


N = 3149

Description: Assessment of payment instrument characteristics. SECURITY of debit cards






46

as003 c5

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. GETTING and SETTING UP of debitcards






47

as003 c6

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. PAYMENT RECORDS of debit cards






48

as003 d1

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. ACCEPTANCE FOR PAYMENT ofcredit cards

Survey question: as003 d1



Table 21: Frequency table for as003 d1


49

as003 d2

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. COST of credit cards






50

as003 d3

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. CONVENIENCE of credit cards






51

as003 d4

Dataset: SCPC


N = 3150

Description: Assessment of payment instrument characteristics. SECURITY of credit cards






52

as003 d5

Dataset: SCPC


N = 3145

Description: Assessment of payment instrument characteristics. GETTING and SETTING UP of creditcards






53

as003 d6

Dataset: SCPC


N = 3149

Description: Assessment of payment instrument characteristics. PAYMENT RECORDS of credit cards






54

as003 e1

Dataset: SCPC


N = 3146

Description: Assessment of payment instrument characteristics. ACCEPTANCE FOR PAYMENT of pre-paid cards

Survey question: as003 e1



Table 27: Frequency table for as003 e1


55

as003 e2

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. COST of prepaid cards






56

as003 e3

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. CONVENIENCE of prepaid cards






57

as003 e4

Dataset: SCPC


N = 3149

Description: Assessment of payment instrument characteristics. SECURITY of prepaid cards






58

as003 e5

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. GETTING and SETTING UP of prepaidcards






59

as003 e6

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. PAYMENT RECORDS of prepaid cards






60

as003 f1

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. ACCEPTANCE FOR PAYMENT of bankaccount number payments

Survey question: as003 f1



Table 33: Frequency table for as003 f1


61

as003 f2

Dataset: SCPC


N = 3145

Description: Assessment of payment instrument characteristics. COST of bank account number payments






62

as003 f3

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. CONVENIENCE of bank account numberpayments






63

as003 f4

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. SECURITY of bank account numberpayments






64

as003 f5

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. GETTING and SETTING UP of bankaccount number payments






65

as003 f6

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. PAYMENT RECORDS of bank accountnumber payments






66

as003 g1

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. ACCEPTANCE FOR PAYMENT of on-line banking bill payments

Survey question: as003 g1



Table 39: Frequency table for as003 g1


67

as003 g2

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. COST of online banking bill payments






68

as003 g3

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. CONVENIENCE of online banking billpayments






69

as003 g4

Dataset: SCPC


N = 3149

Description: Assessment of payment instrument characteristics. SECURITY of online banking bill pay-ments






70

as003 g5

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. GETTING and SETTING UP of onlinebanking bill payments






71

as003 g6

Dataset: SCPC


N = 3149

Description: Assessment of payment instrument characteristics. PAYMENT RECORDS of online bankingbill payments






72

as003 h1

Dataset: SCPC


N = 3146

Description: Assessment of payment instrument characteristics. ACCEPTANCE FOR PAYMENT ofmoney orders

Survey question: as003 h1



Table 45: Frequency table for as003 h1


73

as003 h2

Dataset: SCPC


N = 3145

Description: Assessment of payment instrument characteristics. COST of money orders






74

as003 h3

Dataset: SCPC


N = 3147

Description: Assessment of payment instrument characteristics. CONVENIENCE of money orders






75

as003 h4

Dataset: SCPC


N = 3150

Description: Assessment of payment instrument characteristics. SECURITY of money orders






76

as003 h5

Dataset: SCPC


N = 3149

Description: Assessment of payment instrument characteristics. GETTING and SETTING UP of moneyorders






77

as003 h6

Dataset: SCPC


N = 3148

Description: Assessment of payment instrument characteristics. PAYMENT RECORDS of money orders






78

asian

Dataset: SCPC


N = 3145

Description: Did the respondent select ASIAN in the race question


Details: Provided by the survey vendor. This is a check-all-that-apply question, and the respondent isself-describing their race.


Table 51: Frequency table for asian

Value labels:0 - No1 - Yes

79

banp abp typ

Dataset: SCPC


N = 3153

Description: The number of automatic bill payments made by BANK ACCOUNT NUMBER PAYMENTSin a typical month.

Survey question: pu002 c



Table 52: Summary statistics for banp abp typ

banp_abp_typ

Fre

quen

cy

0 1 2 3 4 5 6 7

050

010

0015

00

80

banp adopt

Dataset: SCPC


N = 3153

Description: Is the respondent a BANK ACCOUNT NUMBER PAYMENT adopter?




Table 53: Frequency table for banp adopt


81

banp bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by BANK ACCOUNT NUMBER PAYMENTS ina typical month.

Survey question: NA



Table 54: Summary statistics for banp bp typ

banp_bp_typ

Fre

quen

cy

0 5 10 15

020

040

060

080

010

00

82

banp ipbp typ

Dataset: SCPC


N = 3153

Description: The number of in person or by mail bill payments made by BANK ACCOUNT NUMBERPAYMENT in a typical month.

Survey question: pu004 f



Table 55: Summary statistics for banp ipbp typ

banp_ipbp_typ

Fre

quen

cy

0 1 2 3 4 5 6

050

010

0015

0020

00

83

banp obp typ

Dataset: SCPC


N = 3153

Description: The number of online bill payments made by BANK ACCOUNT NUMBER PAYMENTS ina typical month.




Table 56: Summary statistics for banp obp typ

banp_obp_typ

Fre

quen

cy

0 2 4 6 8

050

010

0015

00

84

banp op typ

Dataset: SCPC


N = 3153

Description: The number of non-bill online payments paid by BANK ACCOUNT NUMBER PAYMENTSin a typical month.




Table 57: Summary statistics for banp op typ

banp_op_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

0020

0025

00

85

banp p2p typ

Dataset: SCPC


N = 3153

Description: The number of person-to-person payments paid by BANK ACCOUNT NUMBER PAYMENTin a typical month.

Survey question: pu021 e



Table 58: Summary statistics for banp p2p typ

banp_p2p_typ

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

86

banp typ

Dataset: SCPC


N = 3153

Description: The total number of BANK ACCOUNT NUMBER PAYMENTS in a typical month.

Survey question: NA



Table 59: Summary statistics for banp typ

banp_typ

Fre

quen

cy

0 5 10 15 20

020

040

060

080

010

00

87

black

Dataset: SCPC


N = 3145

Description: Did the respondent select BLACK OR AFRICAN AMERICAN in the race question




Table 60: Frequency table for black


88

bnk acnt adopt

Dataset: SCPC


N = 3147

Description: Is the respondent a BANK ACCOUNT adopter?




Table 61: Frequency table for bnk acnt adopt


89

bp typ

Dataset: SCPC


N = 3153

Description: The number of BILL PAYMENTS in a typical month.

Survey question: sum(all pu002, pu003, pu004 variables)



Table 62: Summary statistics for bp typ

bp_typ

Fre

quen

cy

0 20 40 60 80

050

100

150

200

250

90

calcage

Dataset: SCPC


N = 3153

Description: Age


Details: Calculated age, based on the date of birth and the date the respondent took the survey.


Table 63: Summary statistics for calcage

calcage

Fre

quen

cy

20 30 40 50 60 70

050

100

150

91

card abp typ

Dataset: SCPC


N = 3153

Description: The total number of automatic bill payments made by CARDS in a typical month.

Survey question: NA



Table 64: Summary statistics for card abp typ

card_abp_typ

Fre

quen

cy

0 5 10 15 20

050

010

0015

00

92

card bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by CARD methods in a typical month.

Survey question: NA



Table 65: Summary statistics for card bp typ

card_bp_typ

Fre

quen

cy

0 10 20 30 40

020

040

060

080

010

00

93

card ipbp typ

Dataset: SCPC


N = 3153

Description: The total number of in-person or by mail bill payments made by CARD methods in a typicalmonth.

Survey question: NA



Table 66: Summary statistics for card ipbp typ

card_ipbp_typ

Fre

quen

cy

0 5 10 15 20

050

010

0015

0020

00

94

card obp typ

Dataset: SCPC


N = 3153

Description: The total number of online bill payments made by CARDS in a typical month.

Survey question: NA



Table 67: Summary statistics for card obp typ

card_obp_typ

Fre

quen

cy

0 5 10 15 20

020

060

010

0014

00

95

card op typ

Dataset: SCPC


N = 3153

Description: The total number of non-bill online payments made by CARD methods in a typical month.

Survey question: NA



Table 68: Summary statistics for card op typ

card_op_typ

Fre

quen

cy

0 5 10 15 20

050

010

0015

00

96

card p2p typ

Dataset: SCPC


N = 3153

Description: The total number of person-to-person payments made by CARD methods in a typical month.

Survey question: NA



Table 69: Summary statistics for card p2p typ

card_p2p_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

0020

0025

00

97

card rp typ

Dataset: SCPC


N = 3153

Description: The total number of retail goods payments made by CARD methods in a typical month.

Survey question: NA



Table 70: Summary statistics for card rp typ

card_rp_typ

Fre

quen

cy

0 10 20 30 40 50

010

030

050

070

0

98

card serv typ

Dataset: SCPC


N = 3153

Description: The total number of retail services payments made by CARD methods in a typical month.

Survey question: NA



Table 71: Summary statistics for card serv typ

card_serv_typ

Fre

quen

cy

0 10 20 30 40

020

040

060

080

010

00

99

card typ

Dataset: SCPC


N = 3153

Description: The total number of CARD payments in a typical month.

Survey question: NA



Table 72: Summary statistics for card typ

card_typ

Fre

quen

cy

0 20 40 60 80 100 120

010

020

030

040

0

100

cc abp typ

Dataset: SCPC


N = 3153

Description: The number of automatic bill payments made by CREDIT CARDS in a typical month.

Survey question: pu002 b



Table 73: Summary statistics for cc abp typ

cc_abp_typ

Fre

quen

cy

0 2 4 6 8

050

010

0015

0020

00

101

cc adopt

Dataset: SCPC


N = 3137

Description: Is the respondent a CREDIT CARD adopter?




Table 74: Frequency table for cc adopt


102

cc bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by CREDIT CARD in a typical month.

Survey question: NA



Table 75: Summary statistics for cc bp typ

cc_bp_typ

Fre

quen

cy

0 5 10 15 20 25

050

010

0015

00

103

cc ipbp typ

Dataset: SCPC


N = 3153

Description: The number of in person or by mail bill payments made by CREDIT CARD in a typical month.

Survey question: pu004 d



Table 76: Summary statistics for cc ipbp typ

cc_ipbp_typ

Fre

quen

cy

0 2 4 6 8 10

050

010

0015

0020

0025

00

104

cc obp typ

Dataset: SCPC


N = 3153

Description: The number of online bill payments made by CREDIT CARD in a typical month.




Table 77: Summary statistics for cc obp typ

cc_obp_typ

Fre

quen

cy

0 1 2 3 4 5 6

050

010

0015

0020

00

105

cc op typ

Dataset: SCPC


N = 3153

Description: The number of non-bill online payments paid by CREDIT CARD in a typical month.




Table 78: Summary statistics for cc op typ

cc_op_typ

Fre

quen

cy

0 2 4 6 8 10

050

010

0015

0020

00

106

cc p2p typ

Dataset: SCPC


N = 3153

Description: The number of person-to-person payments paid by CREDIT CARD in a typical month.




Table 79: Summary statistics for cc p2p typ

cc_p2p_typ

Fre

quen

cy

0.0 0.1 0.2 0.3 0.4

050

010

0020

0030

00

107

cc rp typ

Dataset: SCPC


N = 3153

Description: The number of retail goods payments paid by CREDIT CARD in a typical month.

Survey question: pu006a d



Table 80: Summary statistics for cc rp typ

cc_rp_typ

Fre

quen

cy

0 5 10 15 20 25 30

050

010

0015

00

108

cc serv typ

Dataset: SCPC


N = 3153

Description: The number of retail services payments paid by CREDIT CARD in a typical month.

Survey question: pu006c d



Table 81: Summary statistics for cc serv typ

cc_serv_typ

Fre

quen

cy

0 5 10 15 20

050

010

0015

00

109

cc typ

Dataset: SCPC


N = 3153

Description: The total number of CREDIT CARD payments in a typical month.

Survey question: NA



Table 82: Summary statistics for cc typ

cc_typ

Fre

quen

cy

0 20 40 60 80

020

040

060

080

012

00

110

cellphone

Dataset: SCPC


N = 3153

Description: Do you have a cell phone?

Survey question: cellphone



Table 83: Frequency table for cellphone

Value labels:1 - Yes2 - No

111

census div text

Dataset: SCPC

Variable type: Character

N = 3153

Description: Text label for respondent’s Census division


Details: Based on UAS demographic variable statereside

112

census division

Dataset: SCPC


N = 3151

Description: Census division where respondent lives


Details: Based on UAS demographic variable statereside

Values Number Percent1 105 3.32 393 12.53 662 21.04 364 11.65 627 19.96 220 7.07 280 8.98 201 6.49 299 9.5

Table 84: Frequency table for census division

Value labels:NA

113

chk acnt adopt

Dataset: SCPC


N = 3146

Description: Is the respondent a CHECKING ACCOUNT adopter?




Table 85: Frequency table for chk acnt adopt


114

chk adopt

Dataset: SCPC


N = 3145

Description: Is the respondent a CHECK adopter?




Table 86: Frequency table for chk adopt


115

chk bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by CHECK in a typical month.

Survey question: NA



Table 87: Summary statistics for chk bp typ

chk_bp_typ

Fre

quen

cy

0 2 4 6 8

050

010

0015

00

116

chk ipbp typ

Dataset: SCPC


N = 3153

Description: The number of in person or by mail bill payments made by CHECK in a typical month.




Table 88: Summary statistics for chk ipbp typ

chk_ipbp_typ

Fre

quen

cy

0 2 4 6 8

050

010

0015

00

117

chk op typ

Dataset: SCPC


N = 3153

Description: The number of non-bill online payments paid by CHECK in a typical month.

Survey question: pu005 a



Table 89: Summary statistics for chk op typ

chk_op_typ

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

0025

00

118

chk p2p typ

Dataset: SCPC


N = 3153

Description: The number of person-to-person payments paid by CHECK in a typical month.




Table 90: Summary statistics for chk p2p typ

chk_p2p_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

0020

00

119

chk rp typ

Dataset: SCPC


N = 3153

Description: The number of retail goods payments paid by CHECK in a typical month.

Survey question: pu006a b



Table 91: Summary statistics for chk rp typ

chk_rp_typ

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

0025

00

120

chk serv typ

Dataset: SCPC


N = 3153

Description: The number of retail services payments paid by CHECK in a typical month.

Survey question: pu006c b



Table 92: Summary statistics for chk serv typ

chk_serv_typ

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

00

121

chk typ

Dataset: SCPC


N = 3153

Description: The total number of CHECK payments in a typical month.

Survey question: NA



Table 93: Summary statistics for chk typ

chk_typ

Fre

quen

cy

0 5 10 15 20

020

040

060

080

010

00

122

cs 001

Dataset: SCPC


N = 3136

Description: Could you tell us how interesting or uninteresting you found the questions in this interview?

Survey question: cs 001



Table 94: Frequency table for cs 001

Value labels:1 - Very interesting2 - Interesting3 - Neither interesting or uninteresting4 - Uninteresting5 - Very uninteresting

123

csh adopt

Dataset: SCPC


N = 3147

Description: Is the respondent a CASH adopter?




Table 95: Frequency table for csh adopt


124

csh bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by CASH in a typical month.

Survey question: NA



Table 96: Summary statistics for csh bp typ

csh_bp_typ

Fre

quen

cy

0 2 4 6 8 10 12 14

050

010

0015

0020

00

125

csh ipbp typ

Dataset: SCPC


N = 3153

Description: The number of in person or by mail bill payments made by CASH in a typical month.




Table 97: Summary statistics for csh ipbp typ

csh_ipbp_typ

Fre

quen

cy

0 2 4 6 8 10 12 14

050

010

0015

0020

00

126

csh p2p typ

Dataset: SCPC


N = 3153

Description: The number of person-to-person payments paid by CASH in a typical month.




Table 98: Summary statistics for csh p2p typ

csh_p2p_typ

Fre

quen

cy

0 2 4 6 8

050

010

0015

0020

00

127

csh rp typ

Dataset: SCPC


N = 3153

Description: The number of retail goods payments paid by CASH in a typical month.

Survey question: pu006a a



Table 99: Summary statistics for csh rp typ

csh_rp_typ

Fre

quen

cy

0 5 10 15 20 25 30

020

040

060

080

010

00

128

csh serv typ

Dataset: SCPC


N = 3153

Description: The number of retail services payments paid by CASH in a typical month.

Survey question: pu006c a



Table 100: Summary statistics for csh serv typ

csh_serv_typ

Fre

quen

cy

0 5 10 15 20

020

040

060

080

012

00

129

csh typ

Dataset: SCPC


N = 3153

Description: The total number of CASH payments in a typical month.

Survey question: NA



Table 101: Summary statistics for csh typ

csh_typ

Fre

quen

cy

0 10 20 30 40 50 60

010

020

030

040

050

060

0

130

currentlivingsituation

Dataset: SCPC


N = 3152

Description: A created variable based on UAS demographic variable maritalstatus


Details: A created variable based on UAS demographic variables maritalstatus and livewithpartner


Table 102: Frequency table for currentlivingsituation

Value labels:1 - Married or lives with partner2 - Separated3 - Divorced4 - Widowed5 - Never married

131

dateofbirth year

Dataset: SCPC


N = 3153

Description: Year of birth


Details: Provided by the survey vendor.


Table 103: Summary statistics for dateofbirth year

dateofbirth_year

Fre

quen

cy

1920 1940 1960 1980

050

100

150

200

132

dc abp typ

Dataset: SCPC


N = 3153

Description: The number of automatic bill payments made by DEBIT CARDS in a typical month.




Table 104: Summary statistics for dc abp typ

dc_abp_typ

Fre

quen

cy

0 2 4 6 8 10

050

010

0015

0020

00

133

dc adopt

Dataset: SCPC


N = 3097

Description: Is the respondent a DEBIT CARD adopter?




Table 105: Frequency table for dc adopt


134

dc bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by DEBIT CARD in a typical month.

Survey question: NA



Table 106: Summary statistics for dc bp typ

dc_bp_typ

Fre

quen

cy

0 5 10 15 20 25 30

050

010

0015

00

135

dc ipbp typ

Dataset: SCPC


N = 3153

Description: The number of in person or by mail bill payments made by DEBIT CARD in a typical month.




Table 107: Summary statistics for dc ipbp typ

dc_ipbp_typ

Fre

quen

cy

0 2 4 6 8 10 12

050

010

0015

0020

00

136

dc obp typ

Dataset: SCPC


N = 3153

Description: The number of online bill payments made by DEBIT CARD in a typical month.




Table 108: Summary statistics for dc obp typ

dc_obp_typ

Fre

quen

cy

0 2 4 6 8 10

050

010

0015

0020

00

137

dc op typ

Dataset: SCPC


N = 3153

Description: The number of non-bill online payments paid by DEBIT CARD in a typical month.




Table 109: Summary statistics for dc op typ

dc_op_typ

Fre

quen

cy

0 5 10 15

050

010

0015

0020

00

138

dc p2p typ

Dataset: SCPC


N = 3153

Description: The number of person-to-person payments paid by DEBIT CARD in a typical month.




Table 110: Summary statistics for dc p2p typ

dc_p2p_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

0020

0025

00

139

dc rp typ

Dataset: SCPC


N = 3153

Description: The number of retail goods payments paid by DEBIT CARD in a typical month.

Survey question: pu006a c



Table 111: Summary statistics for dc rp typ

dc_rp_typ

Fre

quen

cy

0 10 20 30 40

050

010

0015

00

140

dc serv typ

Dataset: SCPC


N = 3153

Description: The number of retail services payments paid by DEBIT CARD in a typical month.

Survey question: pu006c c



Table 112: Summary statistics for dc serv typ

dc_serv_typ

Fre

quen

cy

0 5 10 15 20 25

050

010

0015

00

141

dc typ

Dataset: SCPC


N = 3153

Description: The total number of DEBIT CARD payments in a typical month.

Survey question: NA



Table 113: Summary statistics for dc typ

dc_typ

Fre

quen

cy

0 20 40 60 80 100

020

040

060

080

012

00

142

de010

Dataset: SCPC


N = 3094

Description: Please tell us the total combined income of all members of your family living here during thepast 12 months.

Survey question: de010



Table 114: Summary statistics for de010

de010

Fre

quen

cy

0 50000 100000 150000

050

100

150

200

250

300

143

de011

Dataset: SCPC


N = 3131

Description: Where does your own personal income rank within your household?



Values Number Percent1 1653 52.82 405 12.93 833 26.64 240 7.7

Table 115: Frequency table for de011

Value labels:1 - Highest in my household2 - About equal to the highest3 - Second highest4 - Third highest or lower

144

de012

Dataset: SCPC


N = 109

Description: Is the amount you reported correct?






145

de013

Dataset: SCPC


N = 3135

Description: Do you and-or your spouse-partner own your primary home?






146

de014

Dataset: SCPC


N = 2193

Description: What is the approximate market value of your primary home?





de014

Fre

quen

cy

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

050

100

150

147

de015

Dataset: SCPC


N = 2197

Description: About how much do you owe on loans for your primary home, including mortgages, homeequity loans, and home equity lines of credit?





de015

Fre

quen

cy

0 50000 150000 250000

020

040

060

080

0

148

de016

Dataset: SCPC


N = 3082

Description: Excluding the market value of your primary home, what is the approximate value of yourhousehold’s other assets?





de016

Fre

quen

cy

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

050

010

0015

00

149

de018

Dataset: SCPC


N = 3134

Description: While answering this survey, did you look at your financial statements, receipts, old checks,websites, or other records at any time? (It is ok if you did).






150

de019

Dataset: SCPC


N = 3100

Description: Excluding the debt on the mortgage of your primary home, what is the approximate dollaramount of your remaining debts?





de019

Fre

quen

cy

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

020

060

010

0014

00

151

disabled

Dataset: SCPC


N = 3149

Description: Disabled


Details: Provided by the survey vendor. This is a check-all-that-apply question.


Table 123: Frequency table for disabled


152

elect abp typ

Dataset: SCPC


N = 3153

Description: The total number of automatic bill payments made by ELECTRONIC methods in a typicalmonth.

Survey question: NA



Table 124: Summary statistics for elect abp typ

elect_abp_typ

Fre

quen

cy

0 2 4 6 8 10

020

060

010

0014

00

153

elect bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by ELECTRONIC methods in a typical month.

Survey question: NA



Table 125: Summary statistics for elect bp typ

elect_bp_typ

Fre

quen

cy

0 5 10 15 20

020

040

060

080

0

154

elect ipbp typ

Dataset: SCPC


N = 3153

Description: The total number of in-person or by mail bill payments made by ELECTRONIC methods ina typical month.

Survey question: NA



Table 126: Summary statistics for elect ipbp typ

elect_ipbp_typ

Fre

quen

cy

0 1 2 3 4 5 6

050

010

0015

0020

00

155

elect obp typ

Dataset: SCPC


N = 3153

Description: The total number of online bill payments made by ELECTRONIC methods in a typical month.

Survey question: NA



Table 127: Summary statistics for elect obp typ

elect_obp_typ

Fre

quen

cy

0 2 4 6 8 10 12

020

040

060

080

012

00

156

elect op typ

Dataset: SCPC


N = 3153

Description: The total number of non-bill online payments made by ELECTRONIC methods in a typicalmonth.

Survey question: NA



Table 128: Summary statistics for elect op typ

elect_op_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

0020

0025

00

157

elect p2p typ

Dataset: SCPC


N = 3153

Description: The total number of person-to-person payments made by ELECTRONIC methods in a typicalmonth.

Survey question: NA



Table 129: Summary statistics for elect p2p typ

elect_p2p_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

050

010

0015

0020

0025

00

158

elect typ

Dataset: SCPC


N = 3153

Description: The total number of ELECTRONIC payments in a typical month.

Survey question: NA



Table 130: Summary statistics for elect typ

elect_typ

Fre

quen

cy

0 5 10 15 20 25

020

040

060

080

0

159

employmenttype

Dataset: SCPC


N = 1891

Description: Are you employed by government, by a private company, a nonprofit organization, or are youself-employed?




Table 131: Frequency table for employmenttype

Value labels:1 - Government2 - Private-for-profit company3 - Non-profit organization including tax exempt and charitable organizations4 - Self-employed

160

end date

Dataset: SCPC


N = 3138

Description: The date the respondent completed the survey


Details: Provided by the survey vendor. Missing if the respondent did not complete the survey

161

evercell

Dataset: SCPC


N = 113

Description: Have you ever had a cell phone?

Survey question: evercell



Table 132: Frequency table for evercell


162

f pu002 a

Dataset: SCPC


N = 1788

Description: A flag indicating if the value of variable pu002 a was edited or not.



Values Number Percent0 1759 98.41 20 1.12 9 0.5

Table 133: Frequency table for f pu002 a

Value labels:0 - No cleaning1 - This observation was cleaned for outliers by replacing the old value with the value of the 98th per-

centile of the non-missing observations for this variable2 - This observation was cleaned for outliers by proportionally shrinking the value (code for cleaning will

be made available upon request)

163

f pu002 b

Dataset: SCPC


N = 1808

Description: A flag indicating if the value of variable pu002 b was edited or not.




Table 134: Frequency table for f pu002 b




164

f pu002 c

Dataset: SCPC


N = 2065

Description: A flag indicating if the value of variable pu002 c was edited or not.




Table 135: Frequency table for f pu002 c




165

f pu002 d

Dataset: SCPC


N = 2050

Description: A flag indicating if the value of variable pu002 d was edited or not.




Table 136: Frequency table for f pu002 d




166

f pu002 e

Dataset: SCPC


N = 1251

Description: A flag indicating if the value of variable pu002 e was edited or not.




Table 137: Frequency table for f pu002 e




167

f pu002 f

Dataset: SCPC


N = 1202

Description: A flag indicating if the value of variable pu002 f was edited or not.




Table 138: Frequency table for f pu002 f




168

f pu003 a

Dataset: SCPC


N = 2476









169

f pu003 b

Dataset: SCPC


N = 2427









170

f pu003 c

Dataset: SCPC


N = 2871









171

f pu003 d

Dataset: SCPC


N = 1532









172

f pu003 e

Dataset: SCPC


N = 1756









173

f pu004 a

Dataset: SCPC


N = 3071









174

f pu004 b

Dataset: SCPC


N = 2842









175

f pu004 bmo

Dataset: SCPC


N = 524

Description: A flag indicating if the value of variable pu004 bmo was edited or not.




Table 146: Frequency table for f pu004 bmo




176

f pu004 c

Dataset: SCPC


N = 2470









177

f pu004 d

Dataset: SCPC


N = 2427









178

f pu004 e

Dataset: SCPC


N = 1768









179

f pu004 f

Dataset: SCPC


N = 2881









180

f pu005 a

Dataset: SCPC


N = 2838









181

f pu005 amo

Dataset: SCPC


N = 532

Description: A flag indicating if the value of variable pu005 amo was edited or not.




Table 152: Frequency table for f pu005 amo




182

f pu005 b

Dataset: SCPC


N = 2478









183

f pu005 c

Dataset: SCPC


N = 2866









184

f pu005 d

Dataset: SCPC


N = 2432









185

f pu005 e

Dataset: SCPC


N = 1764









186

f pu006a a

Dataset: SCPC


N = 3071

Description: A flag indicating if the value of variable pu006a a was edited or not.




Table 157: Frequency table for f pu006a a




187

f pu006a b

Dataset: SCPC


N = 2833

Description: A flag indicating if the value of variable pu006a b was edited or not.




Table 158: Frequency table for f pu006a b




188

f pu006a bmo

Dataset: SCPC


N = 528

Description: A flag indicating if the value of variable pu006a bmo was edited or not.




Table 159: Frequency table for f pu006a bmo




189

f pu006a c

Dataset: SCPC


N = 2464

Description: A flag indicating if the value of variable pu006a c was edited or not.




Table 160: Frequency table for f pu006a c




190

f pu006a d

Dataset: SCPC


N = 2428

Description: A flag indicating if the value of variable pu006a d was edited or not.




Table 161: Frequency table for f pu006a d




191

f pu006a e

Dataset: SCPC


N = 1770

Description: A flag indicating if the value of variable pu006a e was edited or not.




Table 162: Frequency table for f pu006a e




192

f pu006c a

Dataset: SCPC


N = 3072

Description: A flag indicating if the value of variable pu006c a was edited or not.




Table 163: Frequency table for f pu006c a




193

f pu006c b

Dataset: SCPC


N = 2828

Description: A flag indicating if the value of variable pu006c b was edited or not.




Table 164: Frequency table for f pu006c b




194

f pu006c bmo

Dataset: SCPC


N = 528

Description: A flag indicating if the value of variable pu006c bmo was edited or not.




Table 165: Frequency table for f pu006c bmo




195

f pu006c c

Dataset: SCPC


N = 2467

Description: A flag indicating if the value of variable pu006c c was edited or not.




Table 166: Frequency table for f pu006c c




196

f pu006c d

Dataset: SCPC


N = 2433

Description: A flag indicating if the value of variable pu006c d was edited or not.




Table 167: Frequency table for f pu006c d




197

f pu006c e

Dataset: SCPC


N = 1763

Description: A flag indicating if the value of variable pu006c e was edited or not.




Table 168: Frequency table for f pu006c e




198

f pu021 a

Dataset: SCPC


N = 3077









199

f pu021 b

Dataset: SCPC


N = 2840









200

f pu021 bmo

Dataset: SCPC


N = 533

Description: A flag indicating if the value of variable pu021 bmo was edited or not.




Table 171: Frequency table for f pu021 bmo




201

f pu021 c

Dataset: SCPC


N = 2471









202

f pu021 d

Dataset: SCPC


N = 2440









203

f pu021 e

Dataset: SCPC


N = 2884









204

f pu021 f

Dataset: SCPC


N = 1540









205

f pu021 g

Dataset: SCPC


N = 2883

Description: A flag indicating if the value of variable pu021 g was edited or not.




Table 176: Frequency table for f pu021 g




206

fr001 a

Dataset: SCPC


N = 3147

Description: In your household, how much responsibility do you have for these tasks? Paying monthlybills (rent or mortgage, utilities, cell phone, etc.)

Survey question: fr001 a



Table 177: Frequency table for fr001 a

Value labels:1 - None or almost none2 - Some3 - Shared equally with other household members4 - Most5 - All or almost all

207

fr001 b

Dataset: SCPC


N = 3140

Description: In your household, how much responsibility do you have for these tasks? Doing regular shop-ping for the household (groceries, household supplies, pharmacy, etc.)

Survey question: fr001 b



Table 178: Frequency table for fr001 b


208

fr001 d

Dataset: SCPC


N = 3150

Description: In your household, how much responsibility do you have for these tasks? Making decisionsabout saving and investments (whether to save, how much to save, where to invest, how much to borrow)

Survey question: fr001 d



Table 179: Frequency table for fr001 d


209

fr001 e

Dataset: SCPC


N = 3152

Description: In your household, how much responsibility do you have for these tasks? Making decisionsabout other household financial matters (where to bank, what payment methods to use, setting up onlinebill payments, filing taxes)

Survey question: fr001 e



Table 180: Frequency table for fr001 e


210

gender

Dataset: SCPC


N = 3153

Description: Male or female?




Table 181: Frequency table for gender

Value labels:0 - Female1 - Male

211

hardware

Dataset: SCPC


N = 3153

Description: Did the respondent require hardware from the survey vendor to access the internet




Table 182: Frequency table for hardware


212

hhincome

Dataset: SCPC


N = 3149

Description: Which category represents the total combined income of all members of your family (livingin your house) during the past 12 months?



Values Number Percent1 110 3.52 37 1.23 56 1.84 90 2.95 79 2.56 121 3.87 146 4.68 170 5.49 161 5.110 152 4.811 255 8.112 280 8.913 346 11.014 435 13.815 444 14.116 267 8.5

Table 183: Frequency table for hhincome

Value labels:1 - Less than 5,0002 - 5,000 to 7,4993 - 7,500 to 9,9994 - 10,000 to 12,4995 - 12,500 to 14,9996 - 15,000 to 19,9997 - 20,000 to 24,9998 - 25,000 to 29,9999 - 30,000 to 34,99910 - 35,000 to 39,99911 - 40,000 to 49,99912 - 50,000 to 59,99913 - 60,000 to 74,99914 - 75,000 to 99,99915 - 100,000 to 149,999

213

16 - 150,000 or more

214

highesteducation

Dataset: SCPC


N = 3153

Description: What is the highest level of school that you have completed or the highest degree you havereceived?



Values Number Percent1 1 0.02 1 0.03 5 0.24 13 0.45 25 0.86 31 1.07 41 1.38 49 1.69 607 19.310 721 22.911 282 8.912 217 6.913 669 21.214 372 11.815 52 1.616 67 2.1

Table 184: Frequency table for highesteducation

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 - 12th grade, no diploma9 - High school graduate - high school diploma or the equivalent (for example GED)10 - Some college but no degree11 - Associate degree in college - Occupational or vocational program12 - Associate degree in college - Academic program13 - Bachelors degree (for example: BA, AB, BS)14 - Masters degree (for example: MA, MS, M Eng, M Ed, MSW, MBA)15 - Professional school degree (For example: MD, DDS, DVM, LLB, JD)

215

16 - Doctorate degree (For example: PhD, EdD)

216

hispaniclatino

Dataset: SCPC


N = 3153

Description: Are you Spanish, Hispanic or Latino?




Table 185: Frequency table for hispaniclatino


217

hisplatino group

Dataset: SCPC


N = 203

Description: What is your Spanish, Hispanic or Latino group?




Table 186: Frequency table for hisplatino group

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

218

hourswork

Dataset: SCPC


N = 1891

Description: How many hours per week do you work?




Table 187: Summary statistics for hourswork

hourswork

Fre

quen

cy

0 10 20 30 40 50 60

020

040

060

080

0

219

income abp typ

Dataset: SCPC


N = 3153

Description: The number of automatic bill payments made by DIRECT FROM INCOME PAYMENTS ina typical month.




Table 188: Summary statistics for income abp typ

income_abp_typ

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

0025

00

220

income adopt

Dataset: SCPC


N = 3142

Description: Is the respondent a DIRECT FROM INCOME PAYMENT adopter?




Table 189: Frequency table for income adopt


221

income bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by DIRECT PAYMENT FROM INCOME in atypical month.

Survey question: NA



Table 190: Summary statistics for income bp typ

income_bp_typ

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

0025

00

222

income typ

Dataset: SCPC


N = 3153

Description: The number of DIRECT FROM INCOME payments made in a typical month.




Table 191: Summary statistics for income typ

income_typ

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

0025

00

223

ipbp typ

Dataset: SCPC


N = 3153

Description: The number of IN PERSON OR BY MAIL BILL PAYMENTS made in a typical month.

Survey question: sum(pu004 a, b, bmo, c, d, e, f)



Table 192: Summary statistics for ipbp typ

ipbp_typ

Fre

quen

cy

0 10 20 30 40

020

040

060

080

0

224

laborstatus

Dataset: SCPC


N = 3149

Description: What is your labor force status? Please choose all that apply.



Values Number Percent1 1771 56.22 7 0.23 17 0.54 142 4.55 581 18.56 251 8.07 165 5.28 215 6.8

Table 193: Frequency table for laborstatus

Value labels:1 - Currently working2 - On sick or other leave3 - Unemployed - on layoff4 - Unemployed - looking5 - Retired6 - Disabled7 - Other8 - Selected some combination of the above

225

lf other

Dataset: SCPC


N = 3149

Description: Other




Table 194: Frequency table for lf other


226

livewithpartner

Dataset: SCPC


N = 1254

Description: Are you currently living with a boyfriend, girlfriend or partner?




Table 195: Frequency table for livewithpartner


227

maritalstatus

Dataset: SCPC


N = 3152

Description: Are you now married, widowed, divorced, separated or never married?



Values Number Percent1 1858 58.92 40 1.33 47 1.54 510 16.25 165 5.26 532 16.9

Table 196: Frequency table for maritalstatus

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

228

mon adopt

Dataset: SCPC


N = 3147

Description: Is the respondent a MONEY ORDER adopter?




Table 197: Frequency table for mon adopt


229

mon bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by MONEY ORDER in a typical month.

Survey question: NA



Table 198: Summary statistics for mon bp typ

mon_bp_typ

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0020

00

230

mon ipbp typ

Dataset: SCPC


N = 3153

Description: The number of in person or by mail bill payments made by MONEY ORDER in a typicalmonth.

Survey question: pu004 bmo



Table 199: Summary statistics for mon ipbp typ

mon_ipbp_typ

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0020

00

231

mon op typ

Dataset: SCPC


N = 3153

Description: The number of non-bill online payments paid by MONEY ORDER in a typical month.

Survey question: pu005 amo



Table 200: Summary statistics for mon op typ

mon_op_typ

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

232

mon p2p typ

Dataset: SCPC


N = 3153

Description: The number of person-to-person payments paid by MONEY ORDER in a typical month.




Table 201: Summary statistics for mon p2p typ

mon_p2p_typ

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

233

mon rp typ

Dataset: SCPC


N = 3153

Description: The number of retail goods payments paid by MONEY ORDER in a typical month.

Survey question: pu006a bmo



Table 202: Summary statistics for mon rp typ

mon_rp_typ

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

234

mon serv typ

Dataset: SCPC


N = 3153

Description: The number of retail services payments paid by MONEY ORDER in a typical month.

Survey question: pu006c bmo



Table 203: Summary statistics for mon serv typ

mon_serv_typ

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

235

mon typ

Dataset: SCPC


N = 3153

Description: The total number of MONEY ORDER payments in a typical month.

Survey question: NA



Table 204: Summary statistics for mon typ

mon_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

0020

0025

00

236

nativeamer

Dataset: SCPC


N = 3145

Description: Did the respondent select AMERICAN INDIAN OR ALASKA NATIVE in the race question




Table 205: Frequency table for nativeamer


237

newob

Dataset: SCPC


N = 685

Description: Have you ever set up access to online banking?

Survey question: newob



Table 206: Frequency table for newob


238

newobbp

Dataset: SCPC


N = 982

Description: Have you ever set up online bill pay at your bank’s website?

Survey question: newobbp



Table 207: Frequency table for newobbp


239

newsav

Dataset: SCPC


N = 866

Description: Have you ever had a savings account?

Survey question: newsav



Table 208: Frequency table for newsav


240

newtb

Dataset: SCPC


N = 1808

Description: Have you ever set up access to telephone banking?

Survey question: newtb



Table 209: Frequency table for newtb


241

nonbanka2a p2p typ

Dataset: SCPC


N = 3153

Description: The number of person-to-person payments paid by NONBANK SERVICES LIKE VENMOOR PAYPAL in a typical month.

Survey question: pu021 g



Table 210: Summary statistics for nonbanka2a p2p typ

nonbanka2a_p2p_typ

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

242

nonbp typ

Dataset: SCPC


N = 3153

Description: The number of NON-BILL PAYMENTS made in a typical month.

Survey question: NA



Table 211: Summary statistics for nonbp typ

nonbp_typ

Fre

quen

cy

0 20 40 60 80 100 120 140

050

100

150

200

243

obbp abp typ

Dataset: SCPC


N = 3153

Description: The number of automatic bill payments made by ONLINE BANKING BILL PAYMENTS ina typical month.




Table 212: Summary statistics for obbp abp typ

obbp_abp_typ

Fre

quen

cy

0 1 2 3 4 5 6

050

010

0015

0020

00

244

obbp adopt

Dataset: SCPC


N = 3152

Description: Is the respondent an ONLINE BANKING BILL PAY adopter?




Table 213: Frequency table for obbp adopt


245

obbp bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by ONLINE BANKING BILL PAYMENT in atypical month.

Survey question: NA



Table 214: Summary statistics for obbp bp typ

obbp_bp_typ

Fre

quen

cy

0 2 4 6 8 10 12

050

010

0015

0020

00

246

obbp obp typ

Dataset: SCPC


N = 3153

Description: The number of online bill payments made by ONLINE BANKING BILL PAYMENTS in atypical month.




Table 215: Summary statistics for obbp obp typ

obbp_obp_typ

Fre

quen

cy

0 2 4 6 8

050

010

0015

0020

00

247

obbp p2p typ

Dataset: SCPC


N = 3153

Description: The number of person-to-person payments paid by ONLINE BANKING BILL PAYMENTin a typical month.




Table 216: Summary statistics for obbp p2p typ

obbp_p2p_typ

Fre

quen

cy

0.00 0.05 0.10 0.15

050

010

0020

0030

00

248

obbp typ

Dataset: SCPC


N = 3153

Description: The total number of ONLINE BANKING BILL PAY payments in a typical month.

Survey question: NA



Table 217: Summary statistics for obbp typ

obbp_typ

Fre

quen

cy

0 2 4 6 8 10 12 14

050

010

0015

0020

00

249

obp typ

Dataset: SCPC


N = 3153

Description: The number of ONLINE BILL PAYMENTS made in a typical month.

Survey question: sum(pu003 a, b, c, d, e)



Table 218: Summary statistics for obp typ

obp_typ

Fre

quen

cy

0 5 10 15 20 25

020

040

060

080

010

00

250

op typ

Dataset: SCPC


N = 3153

Description: The number of non-bill ONLINE PAYMENTS paid in a typical month.

Survey question: sum(pu005 a, amo, b, c, d, e)



Table 219: Summary statistics for op typ

op_typ

Fre

quen

cy

0 5 10 15 20 25

020

040

060

080

012

00

251

p2p typ

Dataset: SCPC


N = 3153

Description: The number of PERSON-TO-PERSON payments paid in a typical month.

Survey question: sum(pu021 a, b, bmo, c, d, e, f, g)



Table 220: Summary statistics for p2p typ

p2p_typ

Fre

quen

cy

0 5 10 15

050

010

0015

00

252

pa001 a

Dataset: SCPC


N = 3143

Description: How many accounts do you have at banks, credit unions, brokerages, or investment firms?Number of checking accounts

Survey question: pa001 a



Table 221: Summary statistics for pa001 a

pa001_a

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

0020

00

253

pa001 b

Dataset: SCPC


N = 3143

Description: How many accounts do you have at banks, credit unions, brokerages, or investment firms?Number of savings accounts

Survey question: pa001 b



Table 222: Summary statistics for pa001 b

pa001_b

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

020

040

060

080

012

00

254

pa001 d1

Dataset: SCPC


N = 3144

Description: Do you have an account with any of the following payment services? PayPal

Survey question: pa001 d1



Table 223: Frequency table for pa001 d1


255

pa001 d2

Dataset: SCPC


N = 3143

Description: Do you have an account with any of the following payment services? Other non-bank paymentservices






256

pa001 e

Dataset: SCPC


N = 3143

Description: Do you have any of the following mobile apps or online accounts?

Survey question: pa001 e



Table 225: Frequency table for pa001 e


257

pa001 f

Dataset: SCPC


N = 3153

Description: Which one of these mobile apps or online accounts do you have?

Survey question: pa001 f

Details: Value of variable is a text string of all the response options selected in the check-all-that-applyquestion

258

pa001 fs10

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Venmo

Survey question: pa001 fs10



Table 226: Frequency table for pa001 fs10


259

pa001 fs11

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Samsung Pay






260

pa001 fs12

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Square Cash App






261

pa001 fs13

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Your bank’s mobile bankingapp






262

pa001 fs14

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Google Pay






263

pa001 fs15

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Amazon Payments






264

pa001 fs16

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Zelle






265

pa001 fs2

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Apple Pay






266

pa001 fs3

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Dash






267

pa001 fs4

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? Facebook Messenger






268

pa001 fs5

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? iTunes






269

pa001 fs6

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? LevelUp






270

pa001 fs8

Dataset: SCPC


N = 1822

Description: Which one of these mobile apps or online accounts do you have? PopMoney






271

pa001g1

Dataset: SCPC


N = 1000

Description: In the past 12 months, have you used any of the following features of your bank’s mobilebanking app? Online banking bill payment

Survey question: pa001g1



Table 239: Frequency table for pa001g1


272

pa001g2

Dataset: SCPC


N = 999

Description: In the past 12 months, have you used any of the following features of your bank’s mobilebanking app? Pay using a person’s email address or mobile phone number






273

pa001g3

Dataset: SCPC


N = 1000

Description: In the past 12 months, have you used any of the following features of your bank’s mobilebanking app? Other account-to-account payment






274

pa002

Dataset: SCPC


N = 252

Description: Please choose the most important reason why you don’t have a checking account.

Survey question: pa002


Values Number Percent1 63 25.02 9 3.63 80 31.74 31 12.35 3 1.26 26 10.37 40 15.9

Table 242: Frequency table for pa002

Value labels:1 - I don’t write enough checks to make it worthwhile2 - The minimum balance is too high3 - I don’t like dealing with banks4 - The fees and service charges are too high5 - No bank has convenient hours or location6 - No bank will give me a checking account7 - Other (explain)

275

pa002 other

Dataset: SCPC


N = 3153

Description: Please choose the most important reason why you don’t have a checking account. Other(specify)

Survey question: pa002 other


276

pa003

Dataset: SCPC


N = 254

Description: Have you ever had a checking account?






277

pa004 a

Dataset: SCPC


N = 1051

Description: What interest rate do you earn on the balance in your checking account?



Values Number Percent2 574 54.63 107 10.24 39 3.75 23 2.26 41 3.97 31 2.98 18 1.79 25 2.410 35 3.311 15 1.412 16 1.513 14 1.314 4 0.415 109 10.4

Table 244: Frequency table for pa004 a

Value labels:2 - 0.01-0.053 - 0.06-0.104 - 0.11-0.155 - 0.16-0.206 - 0.21-0.257 - 0.26-0.508 - 0.51-0.759 - 0.76-1.0010 - 1.01-1.5011 - 1.51-2.0012 - 2.01-2.5013 - 2.51-3.0014 - More than 3 percent15 - Don’t know

278

pa004 b

Dataset: SCPC


N = 341

Description: What interest rate do you earn on the balance in your checking account(s)? Secondary account



Values Number Percent2 173 50.73 32 9.44 13 3.85 14 4.16 13 3.87 12 3.58 6 1.89 8 2.310 8 2.311 6 1.812 5 1.513 5 1.514 6 1.815 40 11.7

Table 245: Frequency table for pa004 b

Value labels:2 - 0.01-0.053 - 0.06-0.104 - 0.11-0.155 - 0.16-0.206 - 0.21-0.257 - 0.26-0.508 - 0.51-0.759 - 0.76-1.0010 - 1.01-1.5011 - 1.51-2.0012 - 2.01-2.5013 - 2.51-3.0014 - More than 3 percent15 - Don’t know

279

pa007 a

Dataset: SCPC


N = 2276

Description: What interest rate do you earn on the balance in your primary savings account?



Values Number Percent1 271 11.92 944 41.53 242 10.64 90 4.05 43 1.96 75 3.37 61 2.78 64 2.89 77 3.410 96 4.211 72 3.212 19 0.813 15 0.714 12 0.515 195 8.6


Value labels:1 - 0 percent2 - 0.01-0.053 - 0.06-0.104 - 0.11-0.155 - 0.16-0.206 - 0.21-0.257 - 0.26-0.508 - 0.51-0.759 - 0.76-1.0010 - 1.01-1.5011 - 1.51-2.0012 - 2.01-2.5013 - 2.51-3.0014 - More than 3 percent15 - Don’t know

280

pa008 a

Dataset: SCPC


N = 2886

Description: Total number of debit cards


Details: Created variable, sum of pa008 a1, pa008 a2, and pa008 a3



pa008_a

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

00

281

pa008 a1

Dataset: SCPC


N = 2881

Description: How many ATM cards and-or debit cards do you have? Number of debit cards. (One accountor primary account.)

Survey question: pa008 a1



Table 248: Summary statistics for pa008 a1

pa008_a1

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

0020

00

282

pa008 a2

Dataset: SCPC


N = 951

Description: How many ATM cards and-or debit cards do you have for these checking accounts? Numberof debit cards, Secondary account





pa008_a2

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

010

020

030

040

050

0

283

pa008 a3

Dataset: SCPC


N = 233

Description: How many ATM cards and-or debit cards do you have for these checking accounts? Numberof debit cards, all other accounts





pa008_a3

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

020

4060

8010

012

014

0

284

pa008 b

Dataset: SCPC


N = 2878

Description: Total number of ATM cards


Details: Created variable, sum of pa008 b1, pa008 b2, and pa008 b3



pa008_b

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

0020

00

285

pa008 b1

Dataset: SCPC


N = 2875

Description: How many ATM cards and-or debit cards do you have? Number of ATM cards. (One accountor primary account.)

Survey question: pa008 b1



Table 252: Summary statistics for pa008 b1

pa008_b1

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

00

286

pa008 b2

Dataset: SCPC


N = 943

Description: How many ATM cards and-or debit cards do you have for these checking accounts? Numberof ATM cards, Secondary account





pa008_b2

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

020

040

060

0

287

pa008 b3

Dataset: SCPC


N = 232

Description: How many ATM cards and-or debit cards do you have for these checking accounts? Numberof ATM cards, all other accounts





pa008_b3

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

100

150

200

288

pa009

Dataset: SCPC


N = 2145

Description: Have you ever had an ATM card?






289

pa010

Dataset: SCPC


N = 528

Description: Have you ever had a debit card?






290

pa011 a

Dataset: SCPC


N = 2498

Description: Do any of your debit cards give rewards? Primary checking account





Value labels:1 - Yes2 - No3 - I don’t know

291

pa011 b

Dataset: SCPC


N = 662

Description: Do any of your debit cards give rewards? Secondary checking account






292

pa011 c

Dataset: SCPC


N = 94

Description: Do any of your debit cards give rewards? Other checking accounts

Survey question: pa011 c



Table 259: Frequency table for pa011 c


293

pa012

Dataset: SCPC


N = 2894

Description: Have you set up any of the following methods of accessing your checking account(s)? Tele-phone banking






294

pa013

Dataset: SCPC


N = 2892

Description: Have you set up any of the following methods of accessing your checking account(s)? Onlinebanking






295

pa014

Dataset: SCPC


N = 2362

Description: Have you set up online bill pay at your bank’s website?






296

pa016

Dataset: SCPC


N = 3149

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



Values Number Percent1 1618 51.42 701 22.33 19 0.64 489 15.55 131 4.26 140 4.47 5 0.28 46 1.5


Value labels:1 - ATM2 - Bank teller3 - Check cashing store4 - Cash back at a retail store5 - I get some or all of my wage or salary in cash6 - Family or friend7 - Payday lender8 - Other (specify)

297

pa016 other

Dataset: SCPC


N = 3153

Description: When you get cash, where do you get it most often? Other (specify)



298

pa019 a

Dataset: SCPC


N = 756

Description: Do you have any of the following types of credit cards or charge cards? Visa credit cards






299

pa019 b

Dataset: SCPC


N = 756

Description: Do you have any of the following types of credit cards or charge cards? Company or storebranded credit cards






300

pa019 c

Dataset: SCPC


N = 758

Description: Do you have any of the following types of credit cards or charge cards? American Expresscharge cards






301

pa019 d

Dataset: SCPC


N = 759

Description: Do you have any of the following types of credit cards or charge cards? American Expresscredit cards

Survey question: pa019 d



Table 267: Frequency table for pa019 d


302

pa019 e

Dataset: SCPC


N = 759

Description: Do you have any of the following types of credit cards or charge cards? Diners Club or othercharge cards






303

pa019 f

Dataset: SCPC


N = 755

Description: Do you have any of the following types of credit cards or charge cards? MasterCard creditcards




Table 269: Frequency table for pa019 f


304

pa019 g

Dataset: SCPC


N = 757

Description: Do you have any of the following types of credit cards or charge cards? Discover credit cards

Survey question: pa019 g



Table 270: Frequency table for pa019 g


305

pa020

Dataset: SCPC


N = 669

Description: Have you ever had a credit card or charge card?






306

pa024

Dataset: SCPC


N = 3142

Description: Do you have any automatic bill payments set up to occur this month?






307

pa025

Dataset: SCPC


N = 1001

Description: Have you ever had an automatic bill payment set up in the past?






308

pa026 a

Dataset: SCPC


N = 2894

Description: Have you set up any of the following methods of accessing your checking account(s)? Mobilebanking






309

pa031

Dataset: SCPC


N = 2894

Description: Do you have any blank, unused checks?






310

pa031 b

Dataset: SCPC


N = 430

Description: Have you ever had blank paper checks for your checking account?






311

pa035

Dataset: SCPC


N = 2894

Description: Have you written a paper check to make a payment in the past 12 months?






312

pa040 a

Dataset: SCPC


N = 3147

Description: In the past 12 months, have you used any of the following payment methods, even once?Money order






313

pa040 b

Dataset: SCPC


N = 3148

Description: In the past 12 months, have you used any of the following payment methods, even once?Travelers check






314

pa040 c

Dataset: SCPC


N = 3149

Description: In the past 12 months, have you used any of the following payment methods, even once?Cashiers check






315

pa040 d

Dataset: SCPC


N = 3089

Description: In the past 12 months, have you used a certified check, even once?






316

pa040 e

Dataset: SCPC


N = 3148

Description: In the past 12 months, have you used any of the following payment methods, even once?Remittance






317

pa041

Dataset: SCPC


N = 2601

Description: Have you ever used a money order, even once?






318

pa041 b

Dataset: SCPC


N = 3129

Description: Have you ever used a travelers check, even once?






319

pa041 c

Dataset: SCPC


N = 2813

Description: Have you ever used a cashier’s check, even once?






320

pa041 e

Dataset: SCPC


N = 3076

Description: Have you ever sent a remittance, even once?






321

pa042 a

Dataset: SCPC


N = 548

Description: Did you purchase any of the money orders you used in the past 12 months from a non-banksource?






322

pa042 e

Dataset: SCPC


N = 72

Description: Did you send any of the remittances you used in the past 12 months from a non-bank source?






323

pa044 a

Dataset: SCPC


N = 1429

Description: In the past 12 months, have you used PayPal to make a purchase or pay another person?






324

pa045

Dataset: SCPC


N = 3030

Description: Have you made a text message payment in the past 12 months?






325

pa045 a

Dataset: SCPC


N = 146

Description: In the past 12 months, have you authorized a text message payment using one of the followingmethods? Via your bank






326

pa045 b

Dataset: SCPC


N = 146

Description: In the past 12 months, have you authorized a text message payment using one of the followingmethods? Using a non-bank payment service such as PayPal






327

pa045 c

Dataset: SCPC


N = 146

Description: In the past 12 months, have you authorized a text message payment using one of the followingmethods? Authorize your mobile phone company to pay for you






328

pa048 a1

Dataset: SCPC


N = 1098

Description: In the past 12 months, have you used any of the following methods to make payments withyour PayPal account? Credit card




Table 294: Frequency table for pa048 a1


329

pa048 b1

Dataset: SCPC


N = 1099

Description: In the past 12 months, have you used any of the following methods to make payments withyour PayPal account? Debit card




Table 295: Frequency table for pa048 b1


330

pa048 c1

Dataset: SCPC


N = 1100

Description: In the past 12 months, have you used any of the following methods to make payments withyour PayPal account? Bank account

Survey question: pa048 c1



Table 296: Frequency table for pa048 c1


331

pa048 d1

Dataset: SCPC


N = 1100

Description: In the past 12 months, have you used any of the following methods to make payments withyour PayPal account? Money stored with PayPal






332

pa048 e1

Dataset: SCPC


N = 1100

Description: In the past 12 months, have you used any of the following methods to make payments withyour PayPal account? Some other method

Survey question: pa048 e1



Table 298: Frequency table for pa048 e1


333

pa050

Dataset: SCPC


N = 3137

Description: In the past 12 months, have you used any of the following payment methods, even once? Cash






334

pa051 a

Dataset: SCPC


N = 935

Description: You told us you have FILL:pa054 a Visa cards. How many of these are branded with a logoof a company, store, or gas station?





pa051_a

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

010

020

030

040

050

0

335

pa051 b

Dataset: SCPC


N = 722

Description: You told us you have FILL:pa054 f MasterCard cards. How many of these are branded witha logo of a company, store, or gas station?





pa051_b

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

010

020

030

040

0

336

pa051 c

Dataset: SCPC


N = 287

Description: You told us you have FILL:pa054 g Discover cards. How many of these are branded with alogo of a company, store, or gas station?




Table 302: Summary statistics for pa051 c

pa051_c

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

100

150

200

250

337

pa051 d

Dataset: SCPC


N = 55

Description: You told us you have FILL:pa054 c American Express charge cards. How many of these arebranded with a logo of a company, store, or gas station?




Table 303: Summary statistics for pa051 d

pa051_d

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0 1.2

05

1015

2025

3035

338

pa051 e

Dataset: SCPC


N = 232

Description: You told us you have FILL:pa054 d American Express credit cards. How many of these arebranded with a logo of a company, store, or gas station?




Table 304: Summary statistics for pa051 e

pa051_e

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

020

4060

8010

012

014

0

339

pa052

Dataset: SCPC


N = 2418

Description: Do you own any kinds of credit cards that also are branded with a company logo?






340

pa053

Dataset: SCPC


N = 3148

Description: Do you have any credit cards or charge cards?






341

pa054 a

Dataset: SCPC


N = 1986

Description: Number of Visa credit cards


Details: sum(pa054 a1, pa054 a2)



pa054_a

Fre

quen

cy

1.0 1.5 2.0 2.5 3.0 3.5 4.0

020

040

060

080

010

00

342

pa054 a1

Dataset: SCPC


N = 2357

Description: Please tell us how many credit or charge cards you have of each type. Number of Visa creditcards with rewards





pa054_a1

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

020

040

060

080

010

00

343

pa054 a2

Dataset: SCPC


N = 2278

Description: Please tell us how many credit or charge cards you have of each type. Number of Visa creditcards without rewards





pa054_a2

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

00

344

pa054 b

Dataset: SCPC


N = 1086

Description: Number of company or store branded credit cards


Details: sum(pa054 b1, pa054 b2)



pa054_b

Fre

quen

cy

1 2 3 4 5

010

020

030

040

0

345

pa054 b1

Dataset: SCPC


N = 2329

Description: Please tell us how many credit or charge cards you have of each type. Number of companyor store branded credit cards with rewards





pa054_b1

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

00

346

pa054 b2

Dataset: SCPC


N = 2285

Description: Please tell us how many credit or charge cards you have of each type. Number of companyor store branded credit cards without rewards





pa054_b2

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

00

347

pa054 c

Dataset: SCPC


N = 107

Description: Number of American Express charge cards


Details: sum(pa054 c1, pa054 c2)



pa054_c

Fre

quen

cy

1.0 1.2 1.4 1.6 1.8 2.0

020

4060

80

348

pa054 c1

Dataset: SCPC


N = 2301

Description: Please tell us how many credit or charge cards you have of each type. Number of AmericanExpress charge cards with rewards




Table 314: Summary statistics for pa054 c1

pa054_c1

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0015

0020

00

349

pa054 c2

Dataset: SCPC


N = 2261

Description: Please tell us how many credit or charge cards you have of each type. Number of AmericanExpress charge cards without rewards




Table 315: Summary statistics for pa054 c2

pa054_c2

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0015

0020

00

350

pa054 cards

Dataset: SCPC


N = 2458

Description: Number of credit cards


Details: Created variable, created by the survey instrument


Table 316: Summary statistics for pa054 cards

pa054_cards

Fre

quen

cy

2 4 6 8 10

010

020

030

040

050

0

351

pa054 d

Dataset: SCPC


N = 408

Description: Number of American Express credit cards


Details: sum(pa054 d1, pa054 d2)



pa054_d

Fre

quen

cy

1.0 1.2 1.4 1.6 1.8 2.0

050

100

150

200

250

300

350

352

pa054 d1

Dataset: SCPC


N = 2314

Description: Please tell us how many credit or charge cards you have of each type. Number of AmericanExpress credit cards with rewards




Table 318: Summary statistics for pa054 d1

pa054_d1

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

00

353

pa054 d2

Dataset: SCPC


N = 2258

Description: Please tell us how many credit or charge cards you have of each type. Number of AmericanExpress credit cards without rewards




Table 319: Summary statistics for pa054 d2

pa054_d2

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0015

0020

00

354

pa054 e

Dataset: SCPC


N = 30

Description: Number of Diners Club or other charge cards


Details: sum(pa054 e1, pa054 e2)



pa054_e

Fre

quen

cy

1 2 3 4 5

05

1015

355

pa054 e1

Dataset: SCPC


N = 2305

Description: Please tell us how many credit or charge cards you have of each type. Number of Diners Clubor other charge cards with rewards




Table 321: Summary statistics for pa054 e1

pa054_e1

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0015

0020

00

356

pa054 e2

Dataset: SCPC


N = 2262

Description: Please tell us how many credit or charge cards you have of each type. Number of Diners Clubor other charge cards without rewards




Table 322: Summary statistics for pa054 e2

pa054_e2

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0015

0020

00

357

pa054 f

Dataset: SCPC


N = 1465

Description: Number of MasterCard credit cards


Details: sum(pa054 f1, pa054 f2)


Table 323: Summary statistics for pa054 f

pa054_f

Fre

quen

cy

1.0 1.5 2.0 2.5 3.0

020

040

060

080

010

00

358

pa054 f1

Dataset: SCPC


N = 2343

Description: Please tell us how many credit or charge cards you have of each type. Number of MasterCardcredit cards with rewards

Survey question: pa054 f1



Table 324: Summary statistics for pa054 f1

pa054_f1

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

020

040

060

080

012

00

359

pa054 f2

Dataset: SCPC


N = 2282

Description: Please tell us how many credit or charge cards you have of each type. Number of MasterCardcredit cards without rewards

Survey question: pa054 f2



Table 325: Summary statistics for pa054 f2

pa054_f2

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

00

360

pa054 g

Dataset: SCPC


N = 605

Description: Number of Discover credit cards


Details: sum(pa054 g1, pa054 g2)


Table 326: Summary statistics for pa054 g

pa054_g

Fre

quen

cy

1.0 1.2 1.4 1.6 1.8 2.0

010

020

030

040

050

0

361

pa054 g1

Dataset: SCPC


N = 2327

Description: Please tell us how many credit or charge cards you have of each type. Number of Discovercredit cards with rewards

Survey question: pa054 g1



Table 327: Summary statistics for pa054 g1

pa054_g1

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

00

362

pa054 g2

Dataset: SCPC


N = 2247

Description: Please tell us how many credit or charge cards you have of each type. Number of Discovercredit cards without rewards

Survey question: pa054 g2



Table 328: Summary statistics for pa054 g2

pa054_g2

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0015

0020

00

363

pa054 z

Dataset: SCPC


N = 3153

Description: Please help us understand by telling us why you did not enter the number of credit cards.

Survey question: pa054 z


364

pa055 a2

Dataset: SCPC


N = 3149

Description: In the past 12 months, have you used a check cashing store to get cash?




Table 329: Frequency table for pa055 a2


365

pa055 b1

Dataset: SCPC


N = 3134

Description: In the past 12 months, did you use any of the following financial services? Payday loan






366

pa055 b2

Dataset: SCPC


N = 3135

Description: In the past 12 months, did you use any of the following financial services? Selling an item ata pawn shop






367

pa055 b3

Dataset: SCPC


N = 3135

Description: In the past 12 months, did you use any of the following financial services? Rent to own services






368

pa055 b4

Dataset: SCPC


N = 3134

Description: In the past 12 months, did you use any of the following financial services? Tax refund antic-ipation loan






369

pa055 b5

Dataset: SCPC


N = 3135

Description: In the past 12 months, did you use any of the following financial services? Auto title loan






370

pa075 a

Dataset: SCPC


N = 2884

Description: Is this checking account jointly owned with someone else?





Value labels:1 - Yes, spouse or partner2 - Yes, other family member3 - Yes, someone else who is not a family member4 - No

371

pa075 b

Dataset: SCPC


N = 950

Description: Are your checking accounts jointly owned with someone else? Secondary checking account





Value labels:1 - Yes, spouse or partner2 - Yes, other family member3 - Yes, someone else who is not a family member4 - No

372

pa076 a

Dataset: SCPC


N = 2886

Description: Do your checking accounts pay interest? Primary account






373

pa076 b

Dataset: SCPC


N = 957

Description: Do your checking accounts pay interest? Secondary account






374

pa079 a

Dataset: SCPC


N = 2893

Description: Do your checking accounts have overdraft protection? Primary account






375

pa079 b

Dataset: SCPC


N = 960

Description: Do your checking accounts have overdraft protection? Secondary account






376

pa103

Dataset: SCPC


N = 1344

Description: Have you ever had a prepaid card?






377

pa108 a

Dataset: SCPC


N = 970

Description: Please help us understand why you reported that you have more than one debit or ATM cardfor your checking account.





Value labels:1 - I counted a card twice, as a debit card and an ATM card2 - I counted cards held by another joint account owner on this account3 - My bank issued me more than one card for this account4 - Other (specify)

378

pa108 a other

Dataset: SCPC


N = 3153

Description: Please help us understand why you reported that you have more than one debit or ATM cardfor your checking account. Other (specify)

Survey question: pa108 a other


379

pa108 b

Dataset: SCPC


N = 257

Description: Please help us understand why you reported that you have more than one ATM and-or debitcard for your secondary checking account.





Value labels:1 - I counted a card twice, as a debit card and an ATM card2 - I counted cards held by another joint account owner on this account3 - My bank issued me more than one card for this account4 - Other (specify)

380

pa108 b other

Dataset: SCPC


N = 3153

Description: Please help us understand why you reported that you have more than one ATM and-or debitcard for your secondary checking account. Other (specify)

Survey question: pa108 b other


381

pa120 a

Dataset: SCPC


N = 3149

Description: Have you heard of Bitcoin (BTC)?






382

pa120 b1

Dataset: SCPC


N = 3145

Description: Have you heard of any of these other virtual currencies? XRP






383

pa120 b2

Dataset: SCPC


N = 3144

Description: Have you heard of any of these other virtual currencies? Litecoin






384

pa120 b6

Dataset: SCPC


N = 3147

Description: Have you heard of any of these other virtual currencies? Ethereum






385

pa120 b7

Dataset: SCPC


N = 2163

Description: Have you heard of Bitcoin Cash (BCH)?






386

pa120 b8

Dataset: SCPC


N = 3147

Description: Have you heard of any of these other virtual currencies? Stellar






387

pa120 b9

Dataset: SCPC


N = 3147

Description: Have you heard of any of these other virtual currencies? EOS






388

pa120 c

Dataset: SCPC


N = 3146

Description: Have you heard of any other virtual currencies besides Bitcoin (BTC), Bitcoin Cash (BCH),and those listed above?





Value labels:1 - Yes, please specify2 - No

389

pa120 c other

Dataset: SCPC


N = 3153

Description: Have you heard of any other virtual currencies besides Bitcoin (BTC), Bitcoin Cash (BCH),and those listed above?

Survey question: pa120 c other


390

pa121 a

Dataset: SCPC


N = 2163

Description: Do you have or own any of these virtual currencies? Bitcoin






391

pa121 b

Dataset: SCPC


N = 71

Description: Do you have or own any of these virtual currencies? XRP






392

pa121 c

Dataset: SCPC


N = 246

Description: Do you have or own any of these virtual currencies? Litecoin






393

pa121 g

Dataset: SCPC


N = 275

Description: Do you have or own any of these virtual currencies? Ethereum






394

pa121 h

Dataset: SCPC


N = 923

Description: Do you have or own any of these virtual currencies? Bitcoin Cash

Survey question: pa121 h



Table 356: Frequency table for pa121 h


395

pa121 i

Dataset: SCPC


N = 72

Description: Do you have or own any of these virtual currencies? Stellar

Survey question: pa121 i



Table 357: Frequency table for pa121 i


396

pa121 j

Dataset: SCPC


N = 111

Description: Do you have or own any of these virtual currencies? EOS

Survey question: pa121 j



Table 358: Frequency table for pa121 j


397

pa122 a

Dataset: SCPC


N = 2126

Description: Have you ever had or owned any of these virtual currencies? Bitcoin






398

pa122 b

Dataset: SCPC


N = 63

Description: Have you ever had or owned any of these virtual currencies? XRP






399

pa122 c

Dataset: SCPC


N = 226

Description: Have you ever had or owned any of these virtual currencies? Litecoin






400

pa122 g

Dataset: SCPC


N = 255

Description: Have you ever had or owned any of these virtual currencies? Ethereum






401

pa122 h

Dataset: SCPC


N = 909

Description: Have you ever had or owned any of these virtual currencies? Bitcoin Cash






402

pa122 i

Dataset: SCPC


N = 69

Description: Have you ever had or owned any of these virtual currencies? Stellar






403

pa122 j

Dataset: SCPC


N = 107

Description: Have you ever had or owned any of these virtual currencies? EOS






404

pa123 a number

Dataset: SCPC


N = 34

Description: How much virtual currency do you have or own? Number of coins. Bitcoin

Survey question: pa123 a number



Table 366: Summary statistics for pa123 a number

pa123_a_number

Fre

quen

cy

0 5 10 15 20

05

1015

2025

405

pa123 a us

Dataset: SCPC


N = 35

Description: How much virtual currency do you have or own? Equivalent value in US dollars. Bitcoin

Survey question: pa123 a us



Table 367: Summary statistics for pa123 a us

pa123_a_us

Fre

quen

cy

0 10000 20000 30000 40000

05

1015

2025

406

pa123 b number

Dataset: SCPC


N = 8

Description: How much virtual currency do you have or own? Number of coins. XRP

Survey question: pa123 b number



Table 368: Summary statistics for pa123 b number

pa123_b_number

Fre

quen

cy

0 200 400 600

0.0

0.5

1.0

1.5

2.0

407

pa123 b us

Dataset: SCPC


N = 8

Description: How much virtual currency do you have or own? Equivalent value in US dollars. XRP

Survey question: pa123 b us



Table 369: Summary statistics for pa123 b us

pa123_b_us

Fre

quen

cy

0 50 100 150 200 250 300

01

23

4

408

pa123 c number

Dataset: SCPC


N = 20

Description: How much virtual currency do you have or own? Number of coins, Litecoin

Survey question: pa123 c number



Table 370: Summary statistics for pa123 c number

pa123_c_number

Fre

quen

cy

0 10 20 30 40

02

46

810

12

409

pa123 c us

Dataset: SCPC


N = 20

Description: How much virtual currency do you have or own? Equivalent value in US dollars, Litecoin

Survey question: pa123 c us



Table 371: Summary statistics for pa123 c us

pa123_c_us

Fre

quen

cy

0 500 1000 1500 2000 2500

02

46

810

410

pa123 g number

Dataset: SCPC


N = 19

Description: How much virtual currency do you have or own? Number of coins, Ethereum

Survey question: pa123 g number



Table 372: Summary statistics for pa123 g number

pa123_g_number

Fre

quen

cy

0 2 4 6 8 10

02

46

8

411

pa123 g us

Dataset: SCPC


N = 20

Description: How much virtual currency do you have or own? Equivalent value in US dollars, Ethereum

Survey question: pa123 g us



Table 373: Summary statistics for pa123 g us

pa123_g_us

Fre

quen

cy

0 500 1000 1500

02

46

8

412

pa123 h number

Dataset: SCPC


N = 14

Description: How much virtual currency do you have or own? Number of coins, Bitcoin Cash

Survey question: pa123 h number



Table 374: Summary statistics for pa123 h number

pa123_h_number

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

02

46

810

12

413

pa123 h us

Dataset: SCPC


N = 13

Description: How much virtual currency do you have or own? Equivalent value in US dollars, Bitcoin Cash

Survey question: pa123 h us



Table 375: Summary statistics for pa123 h us

pa123_h_us

Fre

quen

cy

0 50 100 150

02

46

8

414

pa123 i number

Dataset: SCPC


N = 3

Description: How much virtual currency do you have or own? Number of coins, Stellar

Survey question: pa123 i number



Table 376: Summary statistics for pa123 i number

pa123_i_number

Fre

quen

cy

0 1000 2000 3000 4000 5000 6000

0.0

0.5

1.0

1.5

2.0

415

pa123 i us

Dataset: SCPC


N = 3

Description: How much virtual currency do you have or own? Equivalent value in US dollars, Stellar

Survey question: pa123 i us



Table 377: Summary statistics for pa123 i us

pa123_i_us

Fre

quen

cy

0 200 400 600 800 1000 1200

0.0

0.2

0.4

0.6

0.8

1.0

416

pa123 j number

Dataset: SCPC


N = 4

Description: How much virtual currency do you have or own? Number of coins, EOS

Survey question: pa123 j number



Table 378: Summary statistics for pa123 j number

pa123_j_number

Fre

quen

cy

0 1 2 3 4 5 6

0.0

0.5

1.0

1.5

2.0

417

pa123 j us

Dataset: SCPC


N = 4

Description: How much virtual currency do you have or own? Equivalent value in US dollars, EOS

Survey question: pa123 j us



Table 379: Summary statistics for pa123 j us

pa123_j_us

Fre

quen

cy

0 5 10 15 20 25 30 35

0.0

0.2

0.4

0.6

0.8

1.0

418

pa123 other

Dataset: SCPC


N = 3153

Description: Please tell us why you did not answer both the ¡U+0093¿Number of coins¡U+0094¿ and¡U+0093¿Equivalent value in U.S. dollars¡U+0094¿ for the following rows: (open ended text response box)



419

pa126 a

Dataset: SCPC


N = 50

Description: Please tell us your primary reason for owning virtual currency.





Value labels:1 - I use it to buy goods and services in the United States2 - I use it to make remittances or other international payments3 - It is an investment4 - It allows me to make payments anonymously5 - It uses secure blockchain technology to prevent loss and fraud6 - I am interested in new technologies7 - I do not trust banks8 - I do not trust the government or the US dollar9 - Other (specify)

420

pa126 a other

Dataset: SCPC


N = 3153

Description: Please tell us your primary reason for owning virtual currency. Other (specify)

Survey question: pa126 a other


421

pa126 b

Dataset: SCPC


N = 50

Description: Please tell us your secondary reason for owning virtual currency.





Value labels:1 - I use it to buy goods and services in the United States2 - I use it to make remittances or other international payments3 - It is an investment4 - It allows me to make payments anonymously5 - It uses secure blockchain technology to prevent loss and fraud6 - I am interested in new technologies7 - I do not trust banks8 - I do not trust the government or the US dollar9 - Other (specify)

422

pa126 b other

Dataset: SCPC


N = 3153

Description: Please tell us your secondary reason for owning virtual currency. Other (specify)

Survey question: pa126 b other


423

pa128 a

Dataset: SCPC


N = 54

Description: In the past 12 months, have you used virtual currency to make a payment for goods or servicesor to another person? Bitcoin






424

pa128 b

Dataset: SCPC


N = 12

Description: In the past 12 months, have you used virtual currency to make a payment for goods or servicesor to another person? XRP



Values Number Percent2 12 100.0



425

pa128 c

Dataset: SCPC


N = 26

Description: In the past 12 months, have you used virtual currency to make a payment for goods or servicesor to another person? Litecoin






426

pa128 g

Dataset: SCPC


N = 25

Description: In the past 12 months, have you used virtual currency to make a payment for goods or servicesor to another person? Ethereum






427

pa128 h

Dataset: SCPC


N = 20

Description: In the past 12 months, have you used virtual currency to make a payment for goods or servicesor to another person? Bitcoin Cash






428

pa128 i

Dataset: SCPC


N = 7

Description: In the past 12 months, have you used virtual currency to make a payment for goods or servicesor to another person? Stellar






429

pa128 j

Dataset: SCPC


N = 6

Description: In the past 12 months, have you used virtual currency to make a payment for goods or servicesor to another person? EOS






430

pa129 d

Dataset: SCPC


Description: Who did you pay using virtual currency?



431

pa129 d combos

Dataset: SCPC


N = 3153

Description: Combinations that were selected in the check-all-that-apply question


Details: Variable is created by the survey instrument.

432

pa129 d2 other

Dataset: SCPC


N = 3153

Description: Who did you pay using virtual currency? Other (specify) Text responses

Survey question: pa129 d2 other


433

pa129 d2s1

Dataset: SCPC


N = 8

Description: Who did you pay using virtual currency? A person

Survey question: pa129 d2s1



Table 389: Frequency table for pa129 d2s1


434

pa129 d2s2

Dataset: SCPC


N = 8

Description: Who did you pay using virtual currency? Store, company, or other business, ONLINE






435

pa129 d2s3

Dataset: SCPC


N = 8

Description: Who did you pay using virtual currency? Store, company, or other business, IN PERSON






436

pa129 d2s4

Dataset: SCPC


N = 8

Description: Who did you pay using virtual currency? Currency exchange






437

pa129 d2s5

Dataset: SCPC


N = 8

Description: Who did you pay using virtual currency? I’d rather not say






438

pa129 d2s6

Dataset: SCPC


N = 8

Description: Who did you pay using virtual currency? Other (specify)






439

pa130 a

Dataset: SCPC


N = 2141

Description: How do you expect the value of one Bitcoin (BTC) to change over the following time periods?Next week





Value labels:1 - Decrease a lot2 - Decrease some3 - Stay about the same4 - Increase some5 - Increase a lot

440

pa130 b

Dataset: SCPC


N = 2141

Description: How do you expect the value of one Bitcoin (BTC) to change over the following time periods?Next month






441

pa130 c

Dataset: SCPC


N = 2143

Description: How do you expect the value of one Bitcoin (BTC) to change over the following time periods?Next year






442

pa130 d

Dataset: SCPC


N = 911

Description: How do you expect the value of one Bitcoin Cash coin (BCH) to change over the followingtime periods? Next week






443

pa130 e

Dataset: SCPC


N = 912

Description: How do you expect the value of one Bitcoin Cash coin (BCH) to change over the followingtime periods? Next month






444

pa130 f

Dataset: SCPC


N = 912

Description: How do you expect the value of one Bitcoin Cash coin (BCH) to change over the followingtime periods? Next year






445

pa131a1

Dataset: SCPC


N = 2163

Description: How familiar are you with how the following virtual currencies work? Bitcoin

Survey question: pa131a1



Table 401: Frequency table for pa131a1

Value labels:1 - Not at all familiar2 - Slightly familiar3 - Somewhat familiar4 - Moderately familiar5 - Extremely familiar

446

pa131a2

Dataset: SCPC


N = 922

Description: How familiar are you with how the following virtual currencies work? Bitcoin Cash

Survey question: pa131a2



Table 402: Frequency table for pa131a2

Value labels:1 - Not at all familiar2 - Slightly familiar3 - Somewhat familiar4 - Moderately familiar5 - Extremely familiar

447

pa133 a

Dataset: SCPC


N = 54

Description: In the past 12 months, have you exchanged virtual currency for U.S. dollars or exchangedU.S. dollars for virtual currency? Bitcoin






448

pa133 b

Dataset: SCPC


N = 12

Description: In the past 12 months, have you exchanged virtual currency for U.S. dollars or exchangedU.S. dollars for virtual currency? XRP






449

pa133 c

Dataset: SCPC


N = 26

Description: In the past 12 months, have you exchanged virtual currency for U.S. dollars or exchangedU.S. dollars for virtual currency? Litecoin






450

pa133 g

Dataset: SCPC


N = 25

Description: In the past 12 months, have you exchanged virtual currency for U.S. dollars or exchangedU.S. dollars for virtual currency? Ethereum






451

pa133 h

Dataset: SCPC


N = 20

Description: In the past 12 months, have you exchanged virtual currency for U.S. dollars or exchangedU.S. dollars for virtual currency? Bitcoin Cash






452

pa133 i

Dataset: SCPC


N = 7

Description: In the past 12 months, have you exchanged virtual currency for U.S. dollars or exchangedU.S. dollars for virtual currency? Stellar






453

pa133 j

Dataset: SCPC


N = 6

Description: In the past 12 months, have you exchanged virtual currency for U.S. dollars or exchangedU.S. dollars for virtual currency? EOS






454

pa135 a

Dataset: SCPC


N = 24

Description: In the past 12 months, how many times did you exchange virtual currency for U.S. dollars orexchange U.S. dollars for virtual currency? Bitcoin





pa135_a

Fre

quen

cy

0 2 4 6 8

02

46

810

455

pa135 b

Dataset: SCPC


N = 5

Description: In the past 12 months, how many times did you exchange virtual currency for U.S. dollars orexchange U.S. dollars for virtual currency? XRP





pa135_b

Fre

quen

cy

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

456

pa135 c

Dataset: SCPC


N = 16

Description: In the past 12 months, how many times did you exchange virtual currency for U.S. dollars orexchange U.S. dollars for virtual currency? Litecoin





pa135_c

Fre

quen

cy

5 10 15 20

02

46

8

457

pa135 g

Dataset: SCPC


N = 12

Description: In the past 12 months, how many times did you exchange virtual currency for U.S. dollars orexchange U.S. dollars for virtual currency? Ethereum





pa135_g

Fre

quen

cy

0 50 100 150

02

46

8

458

pa135 h

Dataset: SCPC


N = 6

Description: In the past 12 months, how many times did you exchange virtual currency for U.S. dollars orexchange U.S. dollars for virtual currency? Bitcoin Cash




Table 414: Summary statistics for pa135 h

pa135_h

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

459

pa135 i

Dataset: SCPC


N = 2

Description: In the past 12 months, how many times did you exchange virtual currency for U.S. dollars orexchange U.S. dollars for virtual currency? Stellar




Table 415: Summary statistics for pa135 i

pa135_i

Fre

quen

cy

1.0 1.2 1.4 1.6 1.8

0.0

0.2

0.4

0.6

0.8

1.0

460

pa135 j

Dataset: SCPC


N = 3

Description: In the past 12 months, how many times did you exchange virtual currency for U.S. dollars orexchange U.S. dollars for virtual currency? EOS




Table 416: Summary statistics for pa135 j

pa135_j

Fre

quen

cy

1 2 3 4

0.0

0.2

0.4

0.6

0.8

1.0

461

pa137

Dataset: SCPC


N = 3153

Description: Please describe where you deposit or store your virtual currency.



462

pa138

Dataset: SCPC


N = 38

Description: Did you own any Bitcoin (BTC) on August 1, 2017?






463

pa139

Dataset: SCPC


N = 50

Description: Did you refer to records or websites to know the number of coins or the equivalent value inU.S. dollars?





Value labels:1 - Yes, for the number of coins2 - Yes, for the value in US dollars3 - Yes, for both4 - No

464

pa140 a

Dataset: SCPC


N = 8

Description: In the past 12 months, how many payments for goods or services or to another person didyou make using a virtual currency? Bitcoin





pa140_a

Fre

quen

cy

0 1 2 3 4

01

23

45

465

pa140 b

Dataset: SCPC


Description: In the past 12 months, how many payments for goods or services or to another person didyou make using a virtual currency? XRP



466

pa140 c

Dataset: SCPC


Description: In the past 12 months, how many payments for goods or services or to another person didyou make using a virtual currency? Litecoin



467

pa140 g

Dataset: SCPC


Description: In the past 12 months, how many payments for goods or services or to another person didyou make using a virtual currency? Ethereum



468

pa140 h

Dataset: SCPC


Description: In the past 12 months, how many payments for goods or services or to another person didyou make using a virtual currency? Bitcoin Cash



469

pa140 i

Dataset: SCPC


Description: In the past 12 months, how many payments for goods or services or to another person didyou make using a virtual currency? Stellar



470

pa140 j

Dataset: SCPC


Description: In the past 12 months, how many payments for goods or services or to another person didyou make using a virtual currency? EOS



471

pa188

Dataset: SCPC


N = 930

Description: When you pay with your mobile phone, what payment method do you use most often?





Value labels:1 - Credit card2 - Debit card3 - Prepaid card4 - Bank account number5 - Other (specify)

472

pa188 other

Dataset: SCPC


N = 3153

Description: When you pay with your mobile phone, what payment method do you use most often? Other(specify)



473

pa189 a

Dataset: SCPC


N = 3097

Description: In the past 12 months, have you used a mobile phone to make any of these kinds of payments?I used tap-and-pay at the point of sale






474

pa189 b

Dataset: SCPC


N = 3097

Description: In the past 12 months, have you used a mobile phone to make any of these kinds of payments?I scanned a QR code or showed my phone to a clerk, driver, or restaurant staff at the point of sale






475

pa189 c

Dataset: SCPC


N = 3097

Description: In the past 12 months, have you used a mobile phone to make any of these kinds of payments?I used a mobile app to pay






476

pa193

Dataset: SCPC


N = 670

Description: How is the electronic toll payment device that you use most often funded?





Value labels:1 - Credit card2 - Debit card3 - Prepaid card4 - Bank account number5 - Other (specify)

477

pa193 other

Dataset: SCPC


N = 3153

Description: How is the electronic toll payment device that you use most often funded? Other (specify)



478

pa194

Dataset: SCPC


N = 3143

Description: In the past 12 months, have you used one of these electronic toll payment devices to pay a toll?






479

pa198 a

Dataset: SCPC


N = 3134

Description: Please tell us how many of each type of prepaid card that you have. Gift card from a store,merchant, or website





pa198_a

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

050

010

0015

0020

00

480

pa198 b

Dataset: SCPC


N = 3136

Description: Please tell us how many of each type of prepaid card that you have.





pa198_b

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0020

0030

00

481

pa198 c

Dataset: SCPC


N = 3135

Description: Please tell us how many of each type of prepaid card that you have. Public transportationcard or pass





pa198_c

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0020

0030

00

482

pa198 d

Dataset: SCPC


N = 3129

Description: Please tell us how many of each type of prepaid card that you have. Phone card





pa198_d

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

483

pa198 e

Dataset: SCPC


N = 3132

Description: Please tell us how many of each type of prepaid card that you have. Direct Express





pa198_e

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

484

pa198 f

Dataset: SCPC


N = 3132






pa198_f

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

485

pa198 g

Dataset: SCPC


N = 3134






pa198_g

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

486

pa198 h

Dataset: SCPC


N = 3131






pa198_h

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

487

pa198 i

Dataset: SCPC


N = 3132






pa198_i

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

488

pa198 j

Dataset: SCPC


N = 3136





Table 435: Summary statistics for pa198 j

pa198_j

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

489

pa198 k

Dataset: SCPC


N = 3134


Survey question: pa198 k



Table 436: Summary statistics for pa198 k

pa198_k

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0020

0030

00

490

pa198 l

Dataset: SCPC


N = 3136


Survey question: pa198 l



Table 437: Summary statistics for pa198 l

pa198_l

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

491

pa198 m

Dataset: SCPC


N = 3135


Survey question: pa198 m



Table 438: Summary statistics for pa198 m

pa198_m

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

492

pa201 a

Dataset: SCPC


N = 3133

Description: Please tell us how many of each type of prepaid card that you have. NetSpend





pa201_a

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

493

pa201 b

Dataset: SCPC


N = 3134

Description: Please tell us how many of each type of prepaid card that you have. Green Dot





pa201_b

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

494

pa201 c

Dataset: SCPC


N = 3134

Description: Please tell us how many of each type of prepaid card that you have. American ExpressBluebird





pa201_c

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

495

pa201 d

Dataset: SCPC


N = 3132

Description: Please tell us how many of each type of prepaid card that you have. Walmart MoneyCard





pa201_d

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

496

pa201 e

Dataset: SCPC


N = 3132

Description: Please tell us how many of each type of prepaid card that you have. Visa Prepaid Card





pa201_e

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

497

pa201 f

Dataset: SCPC


N = 3133

Description: Please tell us how many of each type of prepaid card that you have. MasterCard Prepaid Card





pa201_f

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

498

pa201 h

Dataset: SCPC


N = 3135

Description: Please tell us how many of each type of prepaid card that you have. AccountNow Gold Card





pa201_h

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

499

pa201 i

Dataset: SCPC


N = 3135

Description: Please tell us how many of each type of prepaid card that you have. Prepaid card from a bank





pa201_i

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

500

pa202 a

Dataset: SCPC


N = 827

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Gift card






501

pa202 c

Dataset: SCPC


N = 159

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Public transportation






502

pa202 d

Dataset: SCPC


N = 62

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Phone card






503

pa202 e

Dataset: SCPC


N = 52

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Direct Express






504

pa202 f

Dataset: SCPC


N = 331

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?EBT, WIC, SNAP, TANF






505

pa202 g

Dataset: SCPC


N = 72

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Payroll card






506

pa202 h

Dataset: SCPC


N = 46

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Employee incentive card






507

pa202 i

Dataset: SCPC


N = 400

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Benefit card






508

pa202 j

Dataset: SCPC


N = 4

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Remittance card






509

pa202 k

Dataset: SCPC


N = 187

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Rebate card




Table 456: Frequency table for pa202 k


510

pa202 l

Dataset: SCPC


N = 36

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Location specific card




Table 457: Frequency table for pa202 l


511

pa202 m

Dataset: SCPC


N = 57

Description: Do any of these cards have a logo from Visa, MasterCard, Discover or American Express?Other government benefit card




Table 458: Frequency table for pa202 m


512

pa203 a

Dataset: SCPC


N = 82

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Gift card






513

pa203 c

Dataset: SCPC


N = 15

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Public transportation






514

pa203 d

Dataset: SCPC


N = 3

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Phone card






515

pa203 e

Dataset: SCPC


N = 7

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Direct Express






516

pa203 f

Dataset: SCPC


N = 22

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? EBT, WIC, SNAP, TANF






517

pa203 g

Dataset: SCPC


N = 4

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Payroll card






518

pa203 h

Dataset: SCPC


N = 2

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Employee incentive card






519

pa203 i

Dataset: SCPC


N = 39

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Benefit card






520

pa203 j

Dataset: SCPC


N = 1

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Remittance card






521

pa203 k

Dataset: SCPC


N = 26

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Rebate card




Table 468: Frequency table for pa203 k


522

pa203 l

Dataset: SCPC


N = 3

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Location specific card




Table 469: Frequency table for pa203 l


523

pa203 m

Dataset: SCPC


N = 7

Description: Can any of these cards be used to make purchases anywhere credit or debit cards are ac-cepted? Other government benefit card




Table 470: Frequency table for pa203 m


524

pacific

Dataset: SCPC


N = 3145

Description: Did the respondent select NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER in therace question




Table 471: Frequency table for pacific


525

paper bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by PAPER methods in a typical month.

Survey question: NA



Table 472: Summary statistics for paper bp typ

paper_bp_typ

Fre

quen

cy

0 5 10 15

020

040

060

080

010

00

526

paper ipbp typ

Dataset: SCPC


N = 3153

Description: The total number of in-person or by mail bill payments made by PAPER methods in a typicalmonth.

Survey question: NA



Table 473: Summary statistics for paper ipbp typ

paper_ipbp_typ

Fre

quen

cy

0 5 10 15

020

040

060

080

010

00

527

paper op typ

Dataset: SCPC


N = 3153

Description: The total number of non-bill online payments made by PAPER methods in a typical month.

Survey question: NA



Table 474: Summary statistics for paper op typ

paper_op_typ

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

0025

00

528

paper p2p typ

Dataset: SCPC


N = 3153

Description: The total number of person-to-person payments made by PAPER methods in a typical month.

Survey question: NA



Table 475: Summary statistics for paper p2p typ

paper_p2p_typ

Fre

quen

cy

0 2 4 6 8

050

010

0015

00

529

paper rp typ

Dataset: SCPC


N = 3153

Description: The total number of retail goods payments made by PAPER methods in a typical month.

Survey question: NA



Table 476: Summary statistics for paper rp typ

paper_rp_typ

Fre

quen

cy

0 5 10 15 20 25 30

020

040

060

080

0

530

paper serv typ

Dataset: SCPC


N = 3153

Description: The total number of retail services payments made by PAPER methods in a typical month.

Survey question: NA



Table 477: Summary statistics for paper serv typ

paper_serv_typ

Fre

quen

cy

0 5 10 15 20

020

040

060

080

010

00

531

paper typ

Dataset: SCPC


N = 3153

Description: The total number of PAPER payments in a typical month.

Survey question: NA



Table 478: Summary statistics for paper typ

paper_typ

Fre

quen

cy

0 20 40 60

010

020

030

040

0

532

ph004

Dataset: SCPC


N = 3135

Description: In the past 12 months, have you, or anyone you know well (family, friends, neighbors, cowork-ers, etc), been a victim of identity theft?

Survey question: ph004



Table 479: Frequency table for ph004

Value labels:1 - Yes, myself and someone I know well2 - Yes, someone I know well only3 - Yes, myself only4 - No

533

ph006

Dataset: SCPC


N = 3136

Description: Please estimate your most recent credit rating, as measured by a FICO score?





Value labels:1 - Below 6002 - 600-6493 - 650-6994 - 700-7495 - 750-8006 - Above 8007 - I don’t know

534

ph007

Dataset: SCPC


N = 3074

Description: During the past 12 months, did you overdraw any of your bank accounts?





Value labels:1 - Yes and I paid an overdraft fee2 - Yes but I did not pay an overdraft fee3 - No

535

ph009 a

Dataset: SCPC


N = 3135

Description: During the past 12 months, did you experience any of these financial difficulties? You orsomeone else in your household lost their primary job

Survey question: ph009 a



Table 482: Frequency table for ph009 a


536

ph009 b

Dataset: SCPC


N = 3135

Description: During the past 12 months, did you experience any of these financial difficulties? You declaredbankruptcy

Survey question: ph009 b



Table 483: Frequency table for ph009 b


537

ph009 c

Dataset: SCPC


N = 3135

Description: During the past 12 months, did you experience any of these financial difficulties? Mortgageforeclosure on your primary home

Survey question: ph009 c



Table 484: Frequency table for ph009 c


538

ph009 d

Dataset: SCPC


N = 3135

Description: During the past 12 months, did you experience any of these financial difficulties? Credit cardaccount closed or frozen by the bank or card company

Survey question: ph009 d



Table 485: Frequency table for ph009 d


539

ph020 a

Dataset: SCPC


N = 3107

Description: During the past 7 years, have you experienced any of these financial difficulties? You declaredbankruptcy






540

ph020 b

Dataset: SCPC


N = 3119

Description: During the past 7 years, have you experienced any of these financial difficulties? Mortgageforeclosure on your primary home






541

ph022 a

Dataset: SCPC


N = 3136

Description: In the past 12 months, have you had any of the following stolen or lost? Cash






542

ph022 b

Dataset: SCPC


N = 2469

Description: In the past 12 months, have you had any of the following stolen or lost? Credit card






543

ph022 c

Dataset: SCPC


N = 2510

Description: In the past 12 months, have you had any of the following stolen or lost? Debit card






544

ph022 d

Dataset: SCPC


N = 2883

Description: In the past 12 months, have you had any of the following stolen or lost? Checks or check book






545

ph023 a

Dataset: SCPC


N = 183

Description: In the past 12 months, what was the total amount of cash was lost or stolen?




Table 492: Summary statistics for ph023 a

ph023_a

Fre

quen

cy

0 500 1000 1500 2000

020

4060

80

546

ph023 b

Dataset: SCPC


N = 305

Description: In the past 12 months, what was the total value of the fraudulent charges on your credit card?




Table 493: Summary statistics for ph023 b

ph023_b

Fre

quen

cy

0 500 1000 1500 2000 2500 3000

020

4060

8010

012

0

547

ph023 c

Dataset: SCPC


N = 191

Description: In the past 12 months, what was the total value of the fraudulent charges on your debit card?




Table 494: Summary statistics for ph023 c

ph023_c

Fre

quen

cy

0 200 400 600 800 1000 1200

010

2030

40

548

ph023 d

Dataset: SCPC


N = 7

Description: In the past 12 months, what was the total value of the fraudulent activity on your checkingaccount?




Table 495: Summary statistics for ph023 d

ph023_d

Fre

quen

cy

0 2000 4000 6000 8000 10000

01

23

4

549

ph024 b

Dataset: SCPC


N = 278

Description: Of the [ENTER NUMBER FROM PH023 B] of fraudulent charges on your credit card, howmuch of that were you personally liable for?




Table 496: Summary statistics for ph024 b

ph024_b

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

100

150

200

250

550

ph024 c

Dataset: SCPC


N = 175

Description: Of the [ENTER NUMBER FROM PH023 c] of fraudulent charges on your debit card, howmuch of that were you personally liable for?




Table 497: Summary statistics for ph024 c

ph024_c

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5

050

100

150

551

ph024 d

Dataset: SCPC


N = 6

Description: Of the [ENTER NUMBER FROM PH023 c] of fraudulent activity on your checking account,how much of that were you personally liable for?




Table 498: Summary statistics for ph024 d

ph024_d

Fre

quen

cy

0 2000 4000 6000 8000 10000

01

23

45

552

ph025 b

Dataset: SCPC


N = 2468

Description: In the past 12 months, have you had any fraud or fraudulent activity committed on any ofthese payment methods that you own? Credit card






553

ph025 c

Dataset: SCPC


N = 2510

Description: In the past 12 months, have you had any fraud or fraudulent activity committed on any ofthese payment methods that you own? Debit card






554

ph025 d

Dataset: SCPC


N = 2883

Description: In the past 12 months, have you had any fraud or fraudulent activity committed on any ofthese payment methods that you own? Checks or check book






555

primary respondent

Dataset: SCPC


N = 3153

Description: Is the person participating in the survey the primary UAS member in the household.




Table 502: Frequency table for primary respondent


556

pu002 a

Dataset: SCPC


N = 1786

Description: In a typical period (week, month, or year), how many automatic bill payments do you make?Paid with your debit cards


Details: This variable is based on pu002 a1 (weekly), pu002 a2 (monthly), and pu002 a3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 503: Summary statistics for pu002 a

557

pu002_a

Fre

quen

cy

0 5 10 15 20

020

040

060

080

0

558

pu002 a unedited

Dataset: SCPC


N = 27

Description: The original value of pu002 a if it was edited, as indicated by the flag f pu002 a.




Table 504: Summary statistics for pu002 a unedited

pu002_a_unedited

Fre

quen

cy

0 100 200 300 400 500 600

01

23

45

559

pu002 b

Dataset: SCPC


N = 1807

Description: In a typical period (week, month, or year), how many automatic bill payments do you make?Paid with your credit cards


Details: This variable is based on pu002 b1 (weekly), pu002 b2 (monthly), and pu002 b3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 505: Summary statistics for pu002 b

560

pu002_b

Fre

quen

cy

0 2 4 6 8 10 12

020

040

060

080

0

561

pu002 b unedited

Dataset: SCPC


N = 36

Description: The original value of pu002 b if it was edited, as indicated by the flag f pu002 b.




Table 506: Summary statistics for pu002 b unedited

pu002_b_unedited

Fre

quen

cy

0 500 1000 1500 2000

05

1015

2025

562

pu002 c

Dataset: SCPC


N = 2065

Description: In a typical period (week, month, or year), how many automatic bill payments do you make?Paid using your bank account and routing numbers


Details: This variable is based on pu002 c1 (weekly), pu002 c2 (monthly), and pu002 c3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 507: Summary statistics for pu002 c

563

pu002_c

Fre

quen

cy

0 2 4 6 8

010

020

030

040

050

060

0

564

pu002 c unedited

Dataset: SCPC


N = 34

Description: The original value of pu002 c if it was edited, as indicated by the flag f pu002 c.




Table 508: Summary statistics for pu002 c unedited

pu002_c_unedited

Fre

quen

cy

0 200 400 600 800

05

1015

20

565

pu002 d

Dataset: SCPC


N = 2050

Description: In a typical period (week, month, or year), how many automatic bill payments do you make?Paid directly from your income


Details: This variable is based on pu002 d1 (weekly), pu002 d2 (monthly), and pu002 d3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 509: Summary statistics for pu002 d

566

pu002_d

Fre

quen

cy

0 1 2 3 4 5

050

010

0015

00

567

pu002 d unedited

Dataset: SCPC


N = 16

Description: The original value of pu002 d if it was edited, as indicated by the flag f pu002 d.




Table 510: Summary statistics for pu002 d unedited

pu002_d_unedited

Fre

quen

cy

0 200 400 600 800 1000 1200 1400

01

23

45

67

568

pu002 e

Dataset: SCPC


N = 1251

Description: In a typical period (week, month, or year), how many automatic bill payments do you make?Paid using the online banking bill payment function on your bank’s website


Details: This variable is based on pu002 e1 (weekly), pu002 e2 (monthly), and pu002 e3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 511: Summary statistics for pu002 e

569

pu002_e

Fre

quen

cy

0 2 4 6 8 10

010

020

030

040

050

0

570

pu002 e unedited

Dataset: SCPC


N = 26

Description: The original value of pu002 e if it was edited, as indicated by the flag f pu002 e.




Table 512: Summary statistics for pu002 e unedited

pu002_e_unedited

Fre

quen

cy

0 500 1000 1500 2000

05

1015

571

pu002 f

Dataset: SCPC


N = 1202

Description: In a typical period (week, month, or year), how many automatic bill payments do you make?Paid with your prepaid cards


Details: This variable is based on pu002 f1 (weekly), pu002 f2 (monthly), and pu002 f3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 513: Summary statistics for pu002 f

572

pu002_f

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

020

040

060

080

010

00

573

pu002 f unedited

Dataset: SCPC


N = 10

Description: The original value of pu002 f if it was edited, as indicated by the flag f pu002 f.




Table 514: Summary statistics for pu002 f unedited

pu002_f_unedited

Fre

quen

cy

0 50 100 150 200 250 300

01

23

4

574

pu003 a

Dataset: SCPC


N = 2476

Description: In a typical period (week, month, or year), how many online bill payments do you make?Debit cards





575

pu003_a

Fre

quen

cy

0 2 4 6 8 10 12 14

020

060

010

0014

00

576

pu003 a unedited

Dataset: SCPC


N = 47






pu003_a_unedited

Fre

quen

cy

0 200 400 600 800

02

46

810

577

pu003 b

Dataset: SCPC


N = 2427

Description: In a typical period (week, month, or year), how many online bill payments do you make?Credit cards





578

pu003_b

Fre

quen

cy

0 2 4 6 8

050

010

0015

00

579

pu003 b unedited

Dataset: SCPC


N = 42






pu003_b_unedited

Fre

quen

cy

0 100 200 300 400 500 600

02

46

810

12

580

pu003 c

Dataset: SCPC


N = 2871

Description: In a typical period (week, month, or year), how many online bill payments do you make?Bank account and routing numbers





581

pu003_c

Fre

quen

cy

0 2 4 6 8

050

010

0015

00

582

pu003 c unedited

Dataset: SCPC


N = 37






pu003_c_unedited

Fre

quen

cy

0 200 400 600 800 1000 1200

05

1015

20

583

pu003 d

Dataset: SCPC


N = 1532

Description: In a typical period (week, month, or year), how many online bill payments do you make?Online banking bill payment





584

pu003_d

Fre

quen

cy

0 2 4 6 8 10 12

010

020

030

040

050

060

0

585

pu003 d unedited

Dataset: SCPC


N = 31






pu003_d_unedited

Fre

quen

cy

0 1000 2000 3000 4000

05

1015

2025

586

pu003 e

Dataset: SCPC


N = 1756

Description: In a typical period (week, month, or year), how many online bill payments do you make?Prepaid cards





587

pu003_e

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

00

588

pu003 e unedited

Dataset: SCPC


N = 9






pu003_e_unedited

Fre

quen

cy

0 100 200 300 400 500 600

0.0

0.5

1.0

1.5

2.0

2.5

3.0

589

pu004 a

Dataset: SCPC


N = 3071

Description: In a typical period (week, month, or year), how many bill payments by mail, in person, or byphone do you make? Cash





590

pu004_a

Fre

quen

cy

0 2 4 6 8 10 12 14

050

010

0015

0020

00

591

pu004 a unedited

Dataset: SCPC


N = 51






pu004_a_unedited

Fre

quen

cy

0 200 400 600 800

02

46

810

12

592

pu004 b

Dataset: SCPC


N = 2842

Description: In a typical period (week, month, or year), how many bill payments by mail, in person, or byphone do you make? Check





593

pu004_b

Fre

quen

cy

0 2 4 6 8

020

040

060

080

012

00

594

pu004 b unedited

Dataset: SCPC


N = 35






pu004_b_unedited

Fre

quen

cy

0 100 200 300 400

02

46

810

595

pu004 bmo

Dataset: SCPC


N = 524

Description: In a typical period (week, month, or year), how many bill payments by mail, in person, or byphone do you make? Money order


Details: This variable is based on pu004 b1mo (weekly), pu004 b2mo (monthly), and pu004 b3mo (yearly).The weekly and yearly values have been coverted to monthly values. Code for the frequency conversion willbe made available upon request.


Table 529: Summary statistics for pu004 bmo

596

pu004_bmo

Fre

quen

cy

0 1 2 3 4

050

100

150

200

250

300

597

pu004 bmo unedited

Dataset: SCPC


N = 9

Description: The original value of pu004 bmo if it was edited, as indicated by the flag f pu004 bmo.




Table 530: Summary statistics for pu004 bmo unedited

pu004_bmo_unedited

Fre

quen

cy

0 100 200 300 400 500 600

0.0

0.5

1.0

1.5

2.0

2.5

3.0

598

pu004 c

Dataset: SCPC


N = 2470

Description: In a typical period (week, month, or year), how many bill payments by mail, in person, or byphone do you make? Debit cards





599

pu004_c

Fre

quen

cy

0 5 10 15

050

010

0015

00

600

pu004 c unedited

Dataset: SCPC


N = 37






pu004_c_unedited

Fre

quen

cy

0 100 200 300 400 500

02

46

810

601

pu004 d

Dataset: SCPC


N = 2427

Description: In a typical period (week, month, or year), how many bill payments by mail, in person, or byphone do you make? Credit cards





602

pu004_d

Fre

quen

cy

0 2 4 6 8 10 12 14

050

010

0015

00

603

pu004 d unedited

Dataset: SCPC


N = 37






pu004_d_unedited

Fre

quen

cy

0 20 40 60 80

01

23

45

67

604

pu004 e

Dataset: SCPC


N = 1768

Description: In a typical period (week, month, or year), how many bill payments by mail, in person, or byphone do you make? Prepaid cards





605

pu004_e

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

00

606

pu004 e unedited

Dataset: SCPC


N = 11






pu004_e_unedited

Fre

quen

cy

20 40 60 80

0.0

0.5

1.0

1.5

2.0

2.5

3.0

607

pu004 f

Dataset: SCPC


N = 2881

Description: In a typical period (week, month, or year), how many bill payments by mail, in person, or byphone do you make? Bank account and routing numbers





608

pu004_f

Fre

quen

cy

0 1 2 3 4 5 6

050

010

0015

00

609

pu004 f unedited

Dataset: SCPC


N = 36






pu004_f_unedited

Fre

quen

cy

0 200 400 600

05

1015

20

610

pu005 a

Dataset: SCPC


N = 2838

Description: In a typical period (week, month, or year), how many non-bill internet payments for goodsor services do you make? Check





611

pu005_a

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

00

612

pu005 a unedited

Dataset: SCPC


N = 25






pu005_a_unedited

Fre

quen

cy

0 50 100 150

02

46

8

613

pu005 amo

Dataset: SCPC


N = 532

Description: In a typical period (week, month, or year), how many non-bill internet payments for goodsor services do you make? Money order

Survey question: pu005 amo

Details: This variable is based on pu005 a1mo (weekly), pu005 a2mo (monthly), and pu005 a3mo (yearly).The weekly and yearly values have been coverted to monthly values. Code for the frequency conversion willbe made available upon request.


Table 541: Summary statistics for pu005 amo

614

pu005_amo

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

010

020

030

0

615

pu005 amo unedited

Dataset: SCPC


N = 6

Description: The original value of pu005 amo if it was edited, as indicated by the flag f pu005 amo.




Table 542: Summary statistics for pu005 amo unedited

pu005_amo_unedited

Fre

quen

cy

0 100 200 300 400 500

0.0

0.5

1.0

1.5

2.0

616

pu005 b

Dataset: SCPC


N = 2478

Description: In a typical period (week, month, or year), how many non-bill internet payments for goodsor services do you make? Debit card





617

pu005_b

Fre

quen

cy

0 5 10 15 20

050

010

0015

00

618

pu005 b unedited

Dataset: SCPC


N = 50






pu005_b_unedited

Fre

quen

cy

0 50 100 150 200 250 300

02

46

810

12

619

pu005 c

Dataset: SCPC


N = 2866

Description: In a typical period (week, month, or year), how many non-bill internet payments for goodsor services do you make? Bank account and routing numbers





620

pu005_c

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

00

621

pu005 c unedited

Dataset: SCPC


N = 25






pu005_c_unedited

Fre

quen

cy

0 50 100 150

01

23

45

6

622

pu005 d

Dataset: SCPC


N = 2432

Description: In a typical period (week, month, or year), how many non-bill internet payments for goodsor services do you make? Credit card





623

pu005_d

Fre

quen

cy

0 2 4 6 8 10 12 14

020

040

060

080

012

00

624

pu005 d unedited

Dataset: SCPC


N = 41






pu005_d_unedited

Fre

quen

cy

0 20 40 60 80 100

01

23

45

6

625

pu005 e

Dataset: SCPC


N = 1764

Description: In a typical period (week, month, or year), how many non-bill internet payments for goodsor services do you make? Prepaid card





626

pu005_e

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

00

627

pu005 e unedited

Dataset: SCPC


N = 8






pu005_e_unedited

Fre

quen

cy

0 20 40 60 80 100 120 140

0.0

0.5

1.0

1.5

2.0

2.5

3.0

628

pu006a a

Dataset: SCPC


N = 3071

Description: In a typical period (week, month, or year), how many payments for retail goods do you make?Cash

Survey question: pu006a a

Details: This variable is based on pu006a a1 (weekly), pu006a a2 (monthly), and pu006a a3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 551: Summary statistics for pu006a a

629

pu006a_a

Fre

quen

cy

0 5 10 15 20 25 30

020

040

060

080

010

00

630

pu006a a unedited

Dataset: SCPC


N = 61

Description: The original value of pu006a a if it was edited, as indicated by the flag f pu006a a.




Table 552: Summary statistics for pu006a a unedited

pu006a_a_unedited

Fre

quen

cy

0 200 400 600 800 1000

05

1015

20

631

pu006a b

Dataset: SCPC


N = 2833

Description: In a typical period (week, month, or year), how many payments for retail goods do you make?Check

Survey question: pu006a b

Details: This variable is based on pu006a b1 (weekly), pu006a b2 (monthly), and pu006a b3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 553: Summary statistics for pu006a b

632

pu006a_b

Fre

quen

cy

0 1 2 3 4 5

050

010

0015

0020

00

633

pu006a b unedited

Dataset: SCPC


N = 28

Description: The original value of pu006a b if it was edited, as indicated by the flag f pu006a b.




Table 554: Summary statistics for pu006a b unedited

pu006a_b_unedited

Fre

quen

cy

20 40 60 80 100

01

23

45

634

pu006a bmo

Dataset: SCPC


N = 528

Description: In a typical period (week, month, or year), how many payments for retail goods do you make?Money order

Survey question: pu006a bmo

Details: This variable is based on pu006a b1mo (weekly), pu006a b2mo (monthly), and pu006a b3mo (yearly).The weekly and yearly values have been coverted to monthly values. Code for the frequency conversion willbe made available upon request.


Table 555: Summary statistics for pu006a bmo

635

pu006a_bmo

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

010

020

030

040

0

636

pu006a bmo unedited

Dataset: SCPC


N = 6

Description: The original value of pu006a bmo if it was edited, as indicated by the flag f pu006a bmo.




Table 556: Summary statistics for pu006a bmo unedited

pu006a_bmo_unedited

Fre

quen

cy

0 50 100 150

0.0

0.5

1.0

1.5

2.0

637

pu006a c

Dataset: SCPC


N = 2464

Description: In a typical period (week, month, or year), how many payments for retail goods do you make?Debit card

Survey question: pu006a c

Details: This variable is based on pu006a c1 (weekly), pu006a c2 (monthly), and pu006a c3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 557: Summary statistics for pu006a c

638

pu006a_c

Fre

quen

cy

0 10 20 30 40

020

040

060

080

0

639

pu006a c unedited

Dataset: SCPC


N = 61

Description: The original value of pu006a c if it was edited, as indicated by the flag f pu006a c.




Table 558: Summary statistics for pu006a c unedited

pu006a_c_unedited

Fre

quen

cy

50 100 150 200 250 300 350

05

1015

20

640

pu006a d

Dataset: SCPC


N = 2428

Description: In a typical period (week, month, or year), how many payments for retail goods do you make?Credit card

Survey question: pu006a d

Details: This variable is based on pu006a d1 (weekly), pu006a d2 (monthly), and pu006a d3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 559: Summary statistics for pu006a d

641

pu006a_d

Fre

quen

cy

0 5 10 15 20 25 30 35

020

040

060

080

010

00

642

pu006a d unedited

Dataset: SCPC


N = 38

Description: The original value of pu006a d if it was edited, as indicated by the flag f pu006a d.




Table 560: Summary statistics for pu006a d unedited

pu006a_d_unedited

Fre

quen

cy

0 200 400 600 800 1000

05

1015

643

pu006a e

Dataset: SCPC


N = 1770

Description: In a typical period (week, month, or year), how many payments for retail goods do you make?Prepaid card

Survey question: pu006a e

Details: This variable is based on pu006a e1 (weekly), pu006a e2 (monthly), and pu006a e3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 561: Summary statistics for pu006a e

644

pu006a_e

Fre

quen

cy

0 1 2 3 4

020

040

060

080

012

00

645

pu006a e unedited

Dataset: SCPC


N = 15

Description: The original value of pu006a e if it was edited, as indicated by the flag f pu006a e.




Table 562: Summary statistics for pu006a e unedited

pu006a_e_unedited

Fre

quen

cy

50 100 150

0.0

0.5

1.0

1.5

2.0

2.5

3.0

646

pu006c a

Dataset: SCPC


N = 3072

Description: In a typical period (week, month, or year), how many payments for retail services do youmake? Cash

Survey question: pu006c a

Details: This variable is based on pu006c a1 (weekly), pu006c a2 (monthly), and pu006c a3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 563: Summary statistics for pu006c a

647

pu006c_a

Fre

quen

cy

0 5 10 15 20

020

040

060

080

012

00

648

pu006c a unedited

Dataset: SCPC


N = 60

Description: The original value of pu006c a if it was edited, as indicated by the flag f pu006c a.




Table 564: Summary statistics for pu006c a unedited

pu006c_a_unedited

Fre

quen

cy

0 100 200 300 400 500

05

1015

649

pu006c b

Dataset: SCPC


N = 2828

Description: In a typical period (week, month, or year), how many payments for retail services do youmake? Check

Survey question: pu006c b

Details: This variable is based on pu006c b1 (weekly), pu006c b2 (monthly), and pu006c b3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 565: Summary statistics for pu006c b

650

pu006c_b

Fre

quen

cy

0 1 2 3 4

050

010

0015

0020

00

651

pu006c b unedited

Dataset: SCPC


N = 29

Description: The original value of pu006c b if it was edited, as indicated by the flag f pu006c b.




Table 566: Summary statistics for pu006c b unedited

pu006c_b_unedited

Fre

quen

cy

0 20 40 60 80

01

23

4

652

pu006c bmo

Dataset: SCPC


N = 528

Description: In a typical period (week, month, or year), how many payments for retail services do youmake? Money order

Survey question: pu006c bmo

Details: This variable is based on pu006c b1mo (weekly), pu006c b2mo (monthly), and pu006c b3mo (yearly).The weekly and yearly values have been coverted to monthly values. Code for the frequency conversion willbe made available upon request.


Table 567: Summary statistics for pu006c bmo

653

pu006c_bmo

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0 1.2

010

020

030

040

0

654

pu006c bmo unedited

Dataset: SCPC


N = 3

Description: The original value of pu006c bmo if it was edited, as indicated by the flag f pu006c bmo.




Table 568: Summary statistics for pu006c bmo unedited

pu006c_bmo_unedited

Fre

quen

cy

10 20 30 40 50 60 70

0.0

0.2

0.4

0.6

0.8

1.0

655

pu006c c

Dataset: SCPC


N = 2467

Description: In a typical period (week, month, or year), how many payments for services do you make?Debit card

Survey question: pu006c c

Details: This variable is based on pu006d c1 (weekly), pu006d c2 (monthly), and pu006d c3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 569: Summary statistics for pu006c c

656

pu006c_c

Fre

quen

cy

0 5 10 15 20 25 30

020

040

060

080

010

00

657

pu006c c unedited

Dataset: SCPC


N = 47

Description: The original value of pu006c c if it was edited, as indicated by the flag f pu006c c.




Table 570: Summary statistics for pu006c c unedited

pu006c_c_unedited

Fre

quen

cy

0 50 100 150 200

02

46

810

658

pu006c d

Dataset: SCPC


N = 2433

Description: In a typical period (week, month, or year), how many payments for retail services do youmake? Credit card

Survey question: pu006c d

Details: This variable is based on pu006e d1 (weekly), pu006e d2 (monthly), and pu006e d3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 571: Summary statistics for pu006c d

659

pu006c_d

Fre

quen

cy

0 5 10 15 20 25

020

040

060

080

010

00

660

pu006c d unedited

Dataset: SCPC


N = 38

Description: The original value of pu006c d if it was edited, as indicated by the flag f pu006c d.




Table 572: Summary statistics for pu006c d unedited

pu006c_d_unedited

Fre

quen

cy

0 50 100 150

01

23

45

6

661

pu006c e

Dataset: SCPC


N = 1763

Description: In a typical period (week, month, or year), how many payments for retail services do youmake? Prepaid card

Survey question: pu006c e

Details: This variable is based on pu006a e1 (weekly), pu006a e2 (monthly), and pu006a e3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 573: Summary statistics for pu006c e

662

pu006c_e

Fre

quen

cy

0 1 2 3 4

020

060

010

0014

00

663

pu006c e unedited

Dataset: SCPC


N = 9

Description: The original value of pu006c e if it was edited, as indicated by the flag f pu006c e.




Table 574: Summary statistics for pu006c e unedited

pu006c_e_unedited

Fre

quen

cy

20 40 60 80 100 120 140

0.0

0.5

1.0

1.5

2.0

2.5

3.0

664

pu009

Dataset: SCPC


N = 1325

Description: During the past 12 months, did you carry an unpaid balance on any credit card and-or chargecard from one month to the next (that is, you did not pay the balance in full at the monthly due date)?

Survey question: pu009



Table 575: Frequency table for pu009


665

pu010

Dataset: SCPC


N = 2468

Description: Last month, about how much was the unpaid balance on all of your credit cards and-or chargecards that you carried over from the previous month?




Table 576: Summary statistics for pu010

pu010

Fre

quen

cy

0 5000 10000 15000

050

010

0015

00

666

pu010 followup

Dataset: SCPC


N = 34

Description: The value of this variable is the corrected number for pu010. If the respondent answers NOto pua013 a, then they are able to correct their original response to pu010.

Survey question: pu010 followup



Table 577: Summary statistics for pu010 followup

pu010_followup

Fre

quen

cy

0 2000 4000 6000 8000

02

46

810

667

pu011

Dataset: SCPC


N = 1337

Description: How would you compare your unpaid balance last month to your unpaid balance 12 monthsago? Last month’s balance is:




Table 578: Frequency table for pu011

Value labels:1 - Much lower2 - Lower3 - About the same4 - Higher5 - Much higher6 - I did not have a balance 12 months ago

668

pu013

Dataset: SCPC


N = 2465

Description: Today, about how much is the total credit limit of all your credit cards?





pu013

Fre

quen

cy

0 20000 40000 60000

010

020

030

040

0

669

pu013 a

Dataset: SCPC


N = 49

Description: Is it correct that your balance is larger than your credit limit?




Table 580: Frequency table for pu013 a

Value labels:1 - Yes, explain2 - No

670

pu013 a other

Dataset: SCPC


N = 3153

Description: Is it correct that your balance is larger than your credit limit? Yes, explain

Survey question: pu013 a other


671

pu013 b

Dataset: SCPC


N = 56

Description: You told us that your credit limit is FILL: pu013. Is it correct?




Table 581: Frequency table for pu013 b


672

pu013 followup

Dataset: SCPC


N = 34

Description: The value of this variable is the corrected number for pu013. If the respondent answers NOto pua013 b, then they are able to correct their original response to pu013.

Survey question: pu013 followup



Table 582: Summary statistics for pu013 followup

pu013_followup

Fre

quen

cy

0 5000 10000 15000 20000

02

46

8

673

pu020

Dataset: SCPC


N = 2464

Description: On your last bill(s), about how much were the new charges made to all of your credit cardsand-or charge cards?





pu020

Fre

quen

cy

0 1000 2000 3000 4000 5000

020

040

060

080

010

00

674

pu021 a

Dataset: SCPC


N = 3077

Description: In a typical period (week, month, or year), how many person-to-person payments do youmake? Cash





675

pu021_a

Fre

quen

cy

0 2 4 6 8

050

010

0015

0020

00

676

pu021 a unedited

Dataset: SCPC


N = 38






pu021_a_unedited

Fre

quen

cy

0 50 100 150 200 250 300

02

46

810

12

677

pu021 b

Dataset: SCPC


N = 2840

Description: In a typical period (week, month, or year), how many person-to-person payments do youmake? Check


Details: This variable is based on pu021 b1 (weekly), pu021 b2 (monthly), and pu021 a3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.



678

pu021_b

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

0020

00

679

pu021 b unedited

Dataset: SCPC


N = 25






pu021_b_unedited

Fre

quen

cy

0 5 10 15 20 25 30

02

46

8

680

pu021 bmo

Dataset: SCPC


N = 533

Description: In a typical period (week, month, or year), how many person-to-person payments do youmake? Money order


Details: This variable is based on pu021 b1mo (weekly), pu021 b2mo (monthly), and pu021 b3mo (yearly).The weekly and yearly values have been coverted to monthly values. Code for the frequency conversion willbe made available upon request.


Table 588: Summary statistics for pu021 bmo

681

pu021_bmo

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

010

020

030

040

0

682

pu021 bmo unedited

Dataset: SCPC


N = 2

Description: The original value of pu021 bmo if it was edited, as indicated by the flag f pu021 bmo.




Table 589: Summary statistics for pu021 bmo unedited

pu021_bmo_unedited

Fre

quen

cy

10 12 14 16

0.0

0.2

0.4

0.6

0.8

1.0

683

pu021 c

Dataset: SCPC


N = 2470

Description: In a typical period (week, month, or year), how many person-to-person payments do youmake? Debit card





684

pu021_c

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

0020

00

685

pu021 c unedited

Dataset: SCPC


N = 25






pu021_c_unedited

Fre

quen

cy

0 20 40 60 80

02

46

8

686

pu021 d

Dataset: SCPC


N = 2440

Description: In a typical period (week, month, or year), how many person-to-person payments do youmake? Credit card





687

pu021_d

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

00

688

pu021 d unedited

Dataset: SCPC


N = 24






pu021_d_unedited

Fre

quen

cy

0 10 20 30 40

02

46

810

689

pu021 e

Dataset: SCPC


N = 2884

Description: In a typical period (week, month, or year), how many person-to-person payments do youmake? Account to account payment service provided by your bank





690

pu021_e

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

691

pu021 e unedited

Dataset: SCPC


N = 17






pu021_e_unedited

Fre

quen

cy

0 20 40 60 80 100

01

23

45

67

692

pu021 f

Dataset: SCPC


N = 1540

Description: In a typical period (week, month, or year), how many person-to-person payments do youmake? Online banking bill payment





693

pu021_f

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

020

040

060

080

012

00

694

pu021 f unedited

Dataset: SCPC


N = 22






pu021_f_unedited

Fre

quen

cy

0 5 10 15 20

02

46

810

12

695

pu021 g

Dataset: SCPC


N = 2883

Description: In a typical period (week, month, or year), how many person-to-person payments do youmake? Nonbank service like PayPal or Venmo

Survey question: pu021 g

Details: This variable is based on pu021 g1 (weekly), pu021 g2 (monthly), and pu021 g3 (yearly). Theweekly and yearly values have been coverted to monthly values. Code for the frequency conversion will bemade available upon request.


Table 598: Summary statistics for pu021 g

696

pu021_g

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

697

pu021 g unedited

Dataset: SCPC


N = 15

Description: The original value of pu021 g if it was edited, as indicated by the flag f pu021 g.




Table 599: Summary statistics for pu021 g unedited

pu021_g_unedited

Fre

quen

cy

0 50 100 150 200 250

02

46

810

698

r weight

Dataset: SCPC


N = 3152

Description: Weight variable


Details: Provided by the survey vendor. See the document 2018 SCPC-DCPC Sampling and Weighting byMarco Angrisani

699

race

Dataset: SCPC


N = 3145

Description: Here is a list of five race categories. Please choose all that apply.




Table 600: Frequency table for race

Value labels:1 - Selected WHITE only2 - Selected BLACK or AFRICAN AMERICAN only3 - Selected AMERICAN INDIAN OR ALASKA NATIVE only4 - Selected ASIAN only5 - Selected NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER only6 - Selected some combination of the above

700

retired

Dataset: SCPC


N = 3149

Description: Retired




Table 601: Frequency table for retired


701

rp typ

Dataset: SCPC


N = 3153

Description: The number of RETAIL GOODS payments paid in a typical month.

Survey question: sum(pu006a a, b, bmo, c, d, e)



Table 602: Summary statistics for rp typ

rp_typ

Fre

quen

cy

0 10 20 30 40 50 60 70

050

100

150

200

250

300

702

sav acnt adopt

Dataset: SCPC


N = 3147

Description: Is the respondent a SAVINGS ACCOUNT adopter?




Table 603: Frequency table for sav acnt adopt


703

serv typ

Dataset: SCPC


N = 3153

Description: The number of RETAIL SERVICES payments paid in a typical month.

Survey question: sum(pu006c a, b, bmo, c, d, e)



Table 604: Summary statistics for serv typ

serv_typ

Fre

quen

cy

0 10 20 30 40 50

010

020

030

040

050

0

704

sick leave

Dataset: SCPC


N = 3149

Description: On sick or other leave




Table 605: Frequency table for sick leave


705

smartphone

Dataset: SCPC


N = 3040

Description: Is your mobile phone a smart phone?

Survey question: smartphone



Table 606: Frequency table for smartphone


706

start date

Dataset: SCPC


N = 3153

Description: The date the respondent started the survey



707

surveyyear

Dataset: SCPC


N = 3153

Description: Always equal to 2018


Details: Created variable, created by the Atlanta Fed survey group

708

svc abp typ

Dataset: SCPC


N = 3153

Description: The number of automatic bill payments made by PREPAID CARDS in a typical month.




Table 607: Summary statistics for svc abp typ

svc_abp_typ

Fre

quen

cy

−1.0 −0.8 −0.6 −0.4 −0.2 0.0

050

010

0020

0030

00

709

svc adopt

Dataset: SCPC


N = 3139

Description: Is the respondent a PREPAID CARD adopter?




Table 608: Frequency table for svc adopt


710

svc bp typ

Dataset: SCPC


N = 3153

Description: The total number of bill payments made by PREPAID CARD in a typical month.

Survey question: NA



Table 609: Summary statistics for svc bp typ

svc_bp_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

0020

0025

00

711

svc ipbp typ

Dataset: SCPC


N = 3153

Description: The number of in person or by mail bill payments made by PREPAID CARD in a typicalmonth.




Table 610: Summary statistics for svc ipbp typ

svc_ipbp_typ

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

712

svc obp typ

Dataset: SCPC


N = 3153

Description: The number of online bill payments made by PREPAID CARD in a typical month.




Table 611: Summary statistics for svc obp typ

svc_obp_typ

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0020

0030

00

713

svc op typ

Dataset: SCPC


N = 3153

Description: The number of non-bill online payments paid by PREPAID CARD in a typical month.




Table 612: Summary statistics for svc op typ

svc_op_typ

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

010

0015

0020

0025

00

714

svc rp typ

Dataset: SCPC


N = 3153

Description: The number of retail goods payments paid by PREPAID CARD in a typical month.

Survey question: pu006a e



Table 613: Summary statistics for svc rp typ

svc_rp_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0 2.5 3.0

050

010

0015

0020

0025

00

715

svc serv typ

Dataset: SCPC


N = 3153

Description: The number of retail services payments paid by PREPAID CARD in a typical month.

Survey question: pu006c e



Table 614: Summary statistics for svc serv typ

svc_serv_typ

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

0020

0025

00

716

svc typ

Dataset: SCPC


N = 3153

Description: The total number of PREPAID CARD payments in a typical month.

Survey question: NA



Table 615: Summary statistics for svc typ

svc_typ

Fre

quen

cy

0 2 4 6 8 10

050

010

0015

0020

0025

00

717

tot pay typ

Dataset: SCPC


N = 3153

Description: The TOTAL number of payments made in typical month.

Survey question: NA



Table 616: Summary statistics for tot pay typ

tot_pay_typ

Fre

quen

cy

0 50 100 150

050

100

150

718

tsend

Dataset: SCPC


N = 3153

Description: Time stamp for when the respondent completed the survey



719

tsstart

Dataset: SCPC


N = 3153

Description: Time stamp for when the respondent started the survey



720

uasid

Dataset: SCPC


N = 3153

Description: A respondent’s unique identifier. Using a respondent’s uasid, a data user can merge theDCPC with the SCPC or any other UAS survey. NOTE: In prior years this variable was known as prim key.The name was changed to allow easier compatability with other UAS surveys.



721

uasmembers

Dataset: SCPC


N = 3153

Description: Number of household UAS members besides the respondent




Table 617: Summary statistics for uasmembers

uasmembers

Fre

quen

cy

0.0 0.5 1.0 1.5 2.0

050

010

0015

00

722

unemp layoff

Dataset: SCPC


N = 3149

Description: Unemployed - on layoff




Table 618: Frequency table for unemp layoff


723

unemp look

Dataset: SCPC


N = 3149

Description: Unemployed - looking




Table 619: Frequency table for unemp look


724

white

Dataset: SCPC


N = 3145

Description: Did the respondent select WHITE in the race question




Table 620: Frequency table for white


725

workfullpart

Dataset: SCPC


N = 1891

Description: Do you work full-time or part-time?




Table 621: Frequency table for workfullpart

Value labels:1 - Full-time2 - Part-time

726

working

Dataset: SCPC


N = 3149

Description: Currently working




Table 622: Frequency table for working


727

Date post:	03-Jul-2020
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