Add Banking-the-Unbanked-through-Cryptocurrency

Banking-the-Unbanked-through-Cryptocurrency/README.md

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+
+## Hard Data on Banking the Unbanked through Cryptocurrency
+
+### Instructions
+
+Install the necessary R packages:
+
+```R
+install.packages(c("data.table", "readstata13", "ggplot2", "stargazer",
+  "huxtable", "rvest", "ggcorrplot", "grid", "remotes"))
+
+remotes::install_github("dracula/ggplot2")
+```
+
+Download the survey data files and unpack them into the appropriate directories in the `raw-data` directory:
+
+- [SHED 2021-2023 STATA files](https://doi.org/10.17016/datasets.002)
+- [SPACE 2022](https://web.archive.org/web/20230922180752/https://www.ecb.europa.eu/stats/ecb_surveys/space/html/index.en.html#dd)
+- [SDCPC 2015 R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice/2015-survey)
+- [SDCPC 2016 R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice/2016-survey)
+- [SDCPC 2017 R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice/2017-survey)
+- [SDCPC 2018 R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice/2018-survey#Tab3)
+- [SDCPC 2019 R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice/2019-survey#Tab3)
+- [SDCPC 2020 R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-of-consumer-payment-choice/2020-survey#Tab3)
+- [SDCPC 2021 R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-and-diary-of-consumer-payment-choice/2021-survey-and-diary#Tab3)
+- [SDCPC 2022 individual-level R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-and-diary-of-consumer-payment-choice/2022-survey-and-diary#Tab3)
+- [SDCPC 2023 individual-level R data file](https://www.atlantafed.org/banking-and-payments/consumer-payments/survey-and-diary-of-consumer-payment-choice/2023-survey-and-diary#Tab3)
+
+Run `SHED-data-prep.R`, `SPACE-data-prep.R`, and `SDCPC-data-prep.R` to produce the processed data files and save them in the `processed-data` directory.
+
+Then run `Banking-the-Unbanked-through-Cryptocurrency.Rmd` in RStudio to produce the PDF. You may have to install some LaTeX packages.
+
+

Banking-the-Unbanked-through-Cryptocurrency/SDCPC-data-prep.R

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+
+
+library(data.table)
+
+S_DCPC.2023 <- readRDS("raw-data/S-DCPC_2023/dcpc_2023_indlevel_public_rds.rds")
+S_DCPC.2022 <- readRDS("raw-data/S-DCPC_2022/dcpc_2022_indlevel_public_rds.rds")
+S_DCPC.2021 <- readRDS("raw-data/S-DCPC_2021/dcpc_2021_indlevel_public_rds.rds")
+S_DCPC.2020 <- readRDS("raw-data/S-DCPC_2020/scpc_2020_public_rds.rds")
+S_DCPC.2019 <- readRDS("raw-data/S-DCPC_2019/scpc_2019_public_rds.rds")
+S_DCPC.2018 <- readRDS("raw-data/S-DCPC_2018/scpc_2018_rds.rds")
+S_DCPC.2017 <- readRDS("raw-data/S-DCPC_2017/scpc_2017_rds.rds")
+S_DCPC.2016 <- readRDS("raw-data/S-DCPC_2016/scpc_2016_rds.rds")
+S_DCPC.2015 <- readRDS("raw-data/S-DCPC_2015/scpc_2015_rds.rds")
+
+
+for (i in 2015:2023) {
+  print(nrow(get(paste0("S_DCPC.", i))))
+}
+
+
+
+reason <- do.call(rbind, list(
+  data.frame(year = 2023, reason = S_DCPC.2023$pa126_a, weight = S_DCPC.2023$ind_weight_all),
+  data.frame(year = 2022, reason = S_DCPC.2022$pa126_a, weight = S_DCPC.2022$ind_weight_all),
+  data.frame(year = 2021, reason = S_DCPC.2021$pa126_a, weight = S_DCPC.2021$ind_weight_all),
+  data.frame(year = 2020, reason = S_DCPC.2020$pa126_a, weight = S_DCPC.2020$r_weight),
+  data.frame(year = 2019, reason = S_DCPC.2019$pa126_a, weight = S_DCPC.2019$r_weight),
+  data.frame(year = 2018, reason = S_DCPC.2018$pa126_a, weight = S_DCPC.2018$r_weight),
+  data.frame(year = 2017, reason = S_DCPC.2017$pa126_a, weight = S_DCPC.2017$r_weight),
+  data.frame(year = 2016, reason = S_DCPC.2016$pa126_a, weight = S_DCPC.2016$r_weight),
+  data.frame(year = 2015, reason = S_DCPC.2015$pa126_a, weight = S_DCPC.2015$r_weight)
+))
+
+setDT(reason)
+
+owns.crypto <- reason
+owns.crypto[, owns.crypto := ! is.na(reason)]
+
+reason <- reason[ ! is.na(reason), ]
+reason[, reason := as.character(reason)]
+
+reason[reason %in% c(1, 2, 4), reason := "Payments"]
+reason[reason %in% c(3), reason := "Investment"]
+reason[reason %in% c(5, 9), reason := "Other"]
+reason[reason %in% c(6), reason := "Interest in new tech"]
+reason[reason %in% c(7, 8), reason := "Don't trust banks/government/USD"]
+
+reason[, reason := factor(reason, levels =
+    c("Investment", "Payments", "Interest in new tech", "Don't trust banks/government/USD", "Other"))]
+
+reason <- aggregate(weight ~ reason + year, data = reason, FUN = sum, drop = FALSE)
+
+setDT(reason)
+
+reason[is.na(weight), weight := 0]
+
+reason <- reason[, .(reason = reason, share = 100 * weight / sum(weight)), by = "year"]
+
+
+owns.crypto <- aggregate(weight ~ owns.crypto + year, data = owns.crypto, FUN = sum, drop = FALSE)
+setDT(owns.crypto)
+owns.crypto[is.na(weight), weight := 0]
+owns.crypto <- owns.crypto[, .(owns.crypto = owns.crypto, share = 100 * weight / sum(weight)), by = "year"]
+
+owns.crypto <- owns.crypto[ (owns.crypto), ]
+setnames(owns.crypto, "owns.crypto", "reason")
+owns.crypto$reason <- NULL
+owns.crypto$reason <- "Owns crypto"
+
+owns.crypto[, panel := "Owns crypto"]
+
+reason[, panel := "Primary reason"]
+
+reason.owns.crypto <- rbind(reason, owns.crypto)
+
+reason.owns.crypto[, panel := factor(panel, levels = c("Owns crypto", "Primary reason"))]
+
+
+reason.owns.crypto[, year := factor(year)]
+
+
+saveRDS(reason.owns.crypto, file = "processed-data/reason-owns-crypto.rds")
+
+
+

Banking-the-Unbanked-through-Cryptocurrency/SHED-data-prep.R

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+
+
+library(data.table)
+library(readstata13)
+library(survey)
+
+
+SHED <- list()
+
+SHED[["2021"]] <- readstata13::read.dta13("raw-data/SHED_2021/public2021.dta", generate.factors = TRUE, nonint.factors = TRUE)
+SHED[["2022"]] <- readstata13::read.dta13("raw-data/SHED_2022/public2022.dta", generate.factors = TRUE, nonint.factors = TRUE)
+SHED[["2023"]] <- readstata13::read.dta13("raw-data/SHED_2023/public2023.dta", generate.factors = TRUE, nonint.factors = TRUE)
+
+crypto.used.as.payment_annual.trend <- c()
+
+for (i in names(SHED)) {
+  SHED[[i]]$crypto.used.as.payment <- SHED[[i]]$S16_b  == "Yes"
+  SHED[[i]] <- svydesign(ids = ~0, data = SHED[[i]], weights = SHED[[i]]$weight)
+  crypto.used.as.payment_annual.trend[[i]] <-
+    round(100 * as.data.frame(svymean(~ crypto.used.as.payment, design = SHED[[i]], na.rm = TRUE))[2, 1], 1)
+}
+
+saveRDS(crypto.used.as.payment_annual.trend, file = "processed-data/SHED-annual-trend.rds")
+
+
+# Then re-do it for the pooled dataset
+
+
+SHED <- list()
+
+SHED[["2021"]] <- readstata13::read.dta13("raw-data/SHED_2021/public2021.dta", generate.factors = TRUE, nonint.factors = TRUE)
+SHED[["2022"]] <- readstata13::read.dta13("raw-data/SHED_2022/public2022.dta", generate.factors = TRUE, nonint.factors = TRUE)
+SHED[["2023"]] <- readstata13::read.dta13("raw-data/SHED_2023/public2023.dta", generate.factors = TRUE, nonint.factors = TRUE)
+
+
+colnames(SHED[["2021"]]) <- paste0(colnames(SHED[["2021"]]), ".2021")
+colnames(SHED[["2022"]]) <- paste0(colnames(SHED[["2022"]]), ".2022")
+colnames(SHED[["2023"]]) <- paste0(colnames(SHED[["2023"]]), ".2023")
+
+SHED.panel <- merge(SHED[["2021"]], SHED[["2022"]], by.x = "CaseID.2021", by.y = "caseid2021.2022",
+  all = TRUE)
+
+SHED.panel <- merge(SHED.panel, SHED[["2023"]], by.x = "CaseID.2022", by.y = "caseid2022.2023",
+  all = TRUE, incomparables = NA)
+# NOTE: merge.data.frame() and merge.data.table() have different behaviors with incomparables = NA
+
+
+
+
+pooled.vars <- c(
+  "S16_a",
+  "S16_b",
+  "S16_c",
+  "S18", "S19_a", "S19_b", "S19_c", "S20_a", "S20_b", "S20_c", "S20_d",
+  "BK1",
+  "C2A",
+  "ppgender",
+  "race_5cat",
+  "ED0",
+  "ED1",
+  "pppa_lgb",
+  "ppp20197",
+  "I40",
+  "B2",
+  "I41_b",
+  "FL0",
+  "B10",
+  "ppage",
+  "ppcmdate",
+  "ind1",
+  "ppcm0160",
+  "ppcm1301",
+  "ppmsacat",
+  "ppfs0596",
+  "A1_a",
+  "BK2_a",
+  "BK2_b",
+  "BK2_c",
+  "BK2_d",
+  "BK2_e",
+  "BNPL1",
+  "ppfs1482",
+  "GE2A",
+  "GE1A",
+  "E7",
+  "E8_b",
+  "xlaptop",
+  "devicetype2",
+  "S21"
+)
+
+for (pooled.var in pooled.vars) {
+
+  # if (pooled.var == "S19_b") {stop()}
+
+  SHED.panel[, paste0(pooled.var, ".pooled")] <- NA
+
+  valid.var.col.name <- intersect(colnames(SHED.panel), paste0(pooled.var, ".", 2021:2023))[1]
+
+  var.transform <- ifelse(is.factor(SHED.panel[, valid.var.col.name]), as.character, identity)
+
+  for (i in as.character(2021:2023)) {
+
+    if ( ! (paste0(pooled.var, ".", i) %in% colnames(SHED.panel) ) ) {
+      next
+    }
+
+    SHED.panel[is.na(SHED.panel[, paste0(pooled.var, ".pooled")]), paste0(pooled.var, ".pooled")] <-
+      var.transform(SHED.panel[is.na(SHED.panel[, paste0(pooled.var, ".pooled")]), paste0(pooled.var, ".", i)])
+  }
+
+  if (is.factor(SHED.panel[, valid.var.col.name])) {
+    SHED.panel[, paste0(pooled.var, ".pooled")] <- factor(SHED.panel[, paste0(pooled.var, ".pooled")])
+  }
+
+}
+
+
+
+
+
+SHED.panel$panel_weight.common <- apply(
+  SHED.panel[, c("panel_weight.2021", "panel_weight.2022", "panel_weight.2023")], MARGIN = 1,
+  FUN = function(x) {
+    y <- unique(na.omit(x))
+    if (length(y) == 0) { return(0) }
+    return(mean(y))
+  })
+
+SHED.panel$panel_weight.common[SHED.panel$panel_weight.common == 0 & !is.na(SHED.panel$weight.2021)] <-
+  SHED.panel$weight.2021[SHED.panel$panel_weight.common == 0 & !is.na(SHED.panel$weight.2021)]
+
+SHED.panel$panel_weight.common[SHED.panel$panel_weight.common == 0 & !is.na(SHED.panel$weight.2022)] <-
+  SHED.panel$weight.2022[SHED.panel$panel_weight.common == 0 & !is.na(SHED.panel$weight.2022)]
+
+SHED.panel$panel_weight.common[SHED.panel$panel_weight.common == 0 & !is.na(SHED.panel$weight.2023)] <-
+  SHED.panel$weight.2023[SHED.panel$panel_weight.common == 0 & !is.na(SHED.panel$weight.2023)]
+# Weighting is a little improvised
+
+
+
+
+
+# S16_a
+# Bought cryptocurrency or held as an
+# investment - In the past year, have you done
+# the following with cryptocurrency, such as
+# Bitcoin or Ethereum?
+SHED.panel$crypto.used.as.investment <- SHED.panel$S16_a.pooled == "Yes"
+
+
+# S16_b
+# In the past year, have you done the following
+# with cryptocurrency, such as Bitcoin or Ethereum?
+# - Used to buy something or make a payment
+SHED.panel$crypto.used.as.payment <- SHED.panel$S16_b.pooled == "Yes"
+
+# S16_c
+# Used cryptocurrency to send money to friends
+# or family - In the past year, have you done
+# the following with cryptocurrency, such as
+# Bitcoin or Ethereum?
+SHED.panel$crypto.used.to.send.money <- SHED.panel$S16_c.pooled == "Yes"
+
+# S18
+# Were any of the family or friends you sent
+# cryptocurrency to living outside of the
+# United States?
+SHED.panel$crypto.used.to.send.money.outside.us <- SHED.panel$S18.pooled == "Yes"
+
+# S19_a
+# Buy something in-person - Have you used
+# cryptocurrency to do each of the following?
+SHED.panel$crypto.used.as.payment.in.person <- SHED.panel$S19_a.pooled == "Yes"
+
+
+# S19_b
+# Buy something online - Have you used
+# cryptocurrency to do each of the following?
+SHED.panel$crypto.used.as.payment.online <- SHED.panel$S19_b.pooled == "Yes"
+
+# S19_c
+# Pay a bill online - Have you used
+# cryptocurrency to do each of the following?
+SHED.panel$crypto.used.as.payment.online.bill.pay <- SHED.panel$S19_c.pooled == "Yes"
+
+
+# S20_a
+# Convert cash into cryptocurrency - Have you
+# used a cryptocurrency ATM/kiosk to do each of
+# the following?
+SHED.panel$crypto.atm.convert.cash.to.crypto <- SHED.panel$S20_a.pooled == "Yes"
+
+# S20_b
+# Withdraw cash - Have you used a cryptocurrency
+# ATM/kiosk to do each of the following?
+SHED.panel$crypto.atm.withdraw.cash <- SHED.panel$S20_b.pooled == "Yes"
+
+# S20_c
+# Make a payment - Have you used a
+# cryptocurrency ATM/kiosk to do each of the
+# following?
+SHED.panel$crypto.atm.make.payment <- SHED.panel$S20_c.pooled == "Yes"
+
+# S20_d
+# Send money to family or friends - Have you
+# used a cryptocurrency ATM/kiosk to do each of
+# the following?
+SHED.panel$crypto.atm.send.money <- SHED.panel$S20_d.pooled == "Yes"
+
+
+
+
+
+# BK1
+# Do you and/or your spouse or partner
+# currently have a checking, savings or money
+# market account?
+SHED.panel$lacks.bank.account <- SHED.panel$BK1.pooled == "No"
+
+# C2A
+# Do you currently have at least one credit card?
+SHED.panel$lacks.credit.card <- SHED.panel$C2A.pooled == "No"
+
+# ppgender
+# Gender [Ipsos source]
+SHED.panel$is.male <- SHED.panel$ppgender.pooled == "Male"
+
+# race_5cat Race 5 categories
+SHED.panel$race <- SHED.panel$race_5cat.pooled
+
+# ED0
+# What is the highest level of school you have
+# completed or the highest degree you have
+# received?
+SHED.panel$education.level <- relevel(SHED.panel$ED0.pooled, "High school degree or GED")
+
+# ED1
+# Which one of the following broad categories
+# best describes your (current/most recent)
+# educational program?
+SHED.panel$education.subject <- relevel(SHED.panel$ED1.pooled, "Business/management")
+
+# pppa_lgb [Ipsos source]
+# Q230: Which of the following best describes how you think of yourself?
+SHED.panel$lgbtq <- relevel(SHED.panel$pppa_lgb.pooled, "Straight, that is, not gay")
+
+# ppp20197 [Ipsos source]
+# QEG22: Are you a citizen of the United States?
+SHED.panel$is.noncitizen <- SHED.panel$ppp20197.pooled == "No"
+
+# I40
+# Which of the following categories best
+# describes the total income that you and/or
+# your spouse or partner received from all
+# sources, before taxes and deductions, in the
+# past 12 months?
+SHED.panel$income.category <- SHED.panel$I40.pooled
+
+# B2
+# Overall, which one of the following best
+# describes how well you are managing
+# financially these days?
+SHED.panel$overall.financial.wellbeing <- SHED.panel$B2.pooled
+
+# I41_b
+# Supplemental Nutrition Assistance Program
+# (SNAP or food stamps) - In the past 12
+# months, have you received any of the
+# following?
+SHED.panel$received.food.stamps <- SHED.panel$I41_b.pooled
+
+# FL0
+# On a scale from zero to ten, where zero is
+# not at all willing to take risks and ten is
+# very willing to take risks, what number would
+# you be on the scale?
+
+SHED.panel$risk.tolerance <- as.numeric(SHED.panel$FL0.pooled) - 1
+
+# B10
+# Overall, on a scale from zero to ten, where
+# zero is not at all satisfied and ten is
+# completely satisfied, how satisfied are you
+# with life as a whole these days?
+SHED.panel$life.satisfaction <- as.numeric(SHED.panel$B10.pooled) - 1
+
+# ppage
+# Age [Ipsos source]
+# ppcmdate
+# Date member completed Core survey
+# Must correct age variable for time of initial Ipsos survey
+SHED.panel$age <- SHED.panel$ppage.pooled + (2021 - as.numeric(substr(SHED.panel$ppcmdate.pooled, 1, 4)))
+
+# ind1
+# IND1: Industry (tight scale) in current or main job
+SHED.panel$job.industry <- relevel(SHED.panel$ind1.pooled, "Retail/Stores/Shopping (including Online Retail)")
+
+# ppcm0160 [Ipsos source]
+# Q26: Occupation (detailed) in current or main job
+SHED.panel$job.occupation <- relevel(SHED.panel$ppcm0160.pooled, "Food Preparation and Serving")
+
+# ppcm1301 [Ipsos source]
+# GOVEMP1: Employer type
+SHED.panel$employer.type <- relevel(SHED.panel$ppcm1301.pooled, "Private-for-profit company")
+
+# ppmsacat
+# MSA Status [Ipsos source]
+SHED.panel$resides.in.metro.statistical.area <- SHED.panel$ppmsacat.pooled == "Metro"
+
+# ppfs0596 [Ipsos source]
+# Q22: What is the approximate total amount of
+# your household's savings and investments?
+SHED.panel$total.household.savings <- relevel(SHED.panel$ppfs0596.pooled, "$100,000 - $249,999")
+
+# A1_a
+# In the past 12 months, has each of the following happened to you:
+# - Turned down for credit
+SHED.panel$rejected.for.credit <- SHED.panel$A1_a.pooled == "Yes"
+
+# BK2_a
+# In the past 12 months, did you and/or spouse or partner:
+# - Purchase a money order from a place other than a bank
+SHED.panel$purchase.non.bank.money.order <- SHED.panel$BK2_a.pooled == "Yes"
+
+# BK2_b
+# In the past 12 months, did you and/or spouse or partner:
+# - Cash a check at a place other than a bank
+SHED.panel$cash.check.non.bank <- SHED.panel$BK2_b.pooled == "Yes"
+
+# BK2_c
+# In the past 12 months, did you and/or spouse or partner:
+# - Take out a payday loan or payday advance
+SHED.panel$take.payday.loan <- SHED.panel$BK2_c.pooled == "Yes"
+
+# BK2_d
+# In the past 12 months, did you and/or spouse or partner:
+# - Take out a pawn shop loan or an auto title loan
+SHED.panel$take.auto.or.pawn.shop.loan <- SHED.panel$BK2_d.pooled == "Yes"
+
+# BK2_e
+# In the past 12 months, did you and/or spouse or partner:
+# - Obtain a tax refund advance to receive your refund faster
+SHED.panel$take.tax.refund.advance <- SHED.panel$BK2_e.pooled == "Yes"
+
+# BNPL1
+# In the past year, have you used a “Buy Now
+# Pay Later” service to buy something?
+SHED.panel$used.buy.now.pay.later <- SHED.panel$BNPL1.pooled == "Yes"
+
+# ppfs1482 [Ipsos source]
+# Q108: Where do you think your credit score falls
+SHED.panel$perceived.credit.score <- factor(SHED.panel$ppfs1482.pooled, levels = c("Fair", "Excellent", "Good", "Poor", "Very poor", "Don’t know"))
+# Note "Don’t know" has a curved apostrophe ’
+
+# GE2A
+# Some people earn money by selling items at
+# places like flea markets and garage sales or
+# through online marketplaces like eBay or
+# Etsy. In the past month, have you made money
+# by selling items in any of these ways?
+
+SHED.panel$informal.selling.of.goods <- SHED.panel$GE2A.pooled == "Yes"
+
+# GE1A
+# In the past month, have you done any
+# freelance or gig-work, either to supplement
+# your income or as your main job?
+
+SHED.panel$freelance.or.gig.work <- SHED.panel$GE1A.pooled == "Yes"
+
+# E7
+# During the past 12 months, have you
+# personally experienced discrimination or
+# unfair treatment because of your race,
+# ethnicity, age, religion, disability status,
+# sexual orientation, gender, or gender
+# identity?
+SHED.panel$experienced.discrimination <- SHED.panel$E7.pooled == "Yes"
+
+# E8_b
+# In the past 12 months, did you personally experience
+# discrimination or unfair treatment while
+# doing any of the following?
+# - Banking or applying for a loan
+SHED.panel$experienced.discrimination.in.banking <- SHED.panel$E8_b.pooled == "Yes"
+
+# xlaptop
+# Is R a KP laptop user?
+SHED.panel$is.kp.laptop.user <- SHED.panel$xlaptop.pooled == "Yes"
+
+# devicetype2
+# DOV: Device Type - at the end of survey
+SHED.panel$respondent.device.type <- relevel(SHED.panel$devicetype2.pooled, "WinPC")
+
+# S21
+# What was the main reason you used
+# cryptocurrency to buy something, make a
+# payment, or send money?
+SHED.panel$why.used.crypto.as.payment <- SHED.panel$S21.pooled
+
+
+
+SHED.panel.survey <- svydesign(ids = ~0, data = SHED.panel, weights = SHED.panel$panel_weight.common)
+SHED.survey <- SHED.panel.survey
+
+
+saveRDS(SHED.survey, file = "processed-data/SHED.rds")
+
+
+
+
+

Banking-the-Unbanked-through-Cryptocurrency/SPACE-data-prep.R

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+
+
+library(data.table)
+library(survey)
+library(stargazer)
+library(huxtable)
+library(rvest)
+
+
+SPACE <- read.csv("raw-data/SPACE_2022/SPACE_wide_2022.csv", stringsAsFactors = FALSE)
+
+country.names <- matrix(c(
+  "CY", "Cyprus",
+  "MT", "Malta",
+  "BE", "Belgium",
+  "IT", "Italy",
+  "ES", "Spain",
+  "GR", "Greece",
+  "AT", "Austria",
+  "EE", "Estonia",
+  "FI", "Finland",
+  "IE", "Ireland",
+  "LT", "Lithuania",
+  "LU", "Luxembourg",
+  "LV", "Latvia",
+  "PT", "Portugal",
+  "SI", "Slovenia",
+  "SK", "Slovakia",
+  "FR", "France"
+), ncol = 2, byrow = TRUE)
+
+colnames(country.names) <- c("COUNTRY", "COUNTRY.name")
+country.names <- as.data.frame(country.names)
+
+SPACE <- merge(SPACE, country.names)
+
+continuous.age <- matrix(c(
+  1,	median(c(18, 24)),
+  2,	median(c(25, 29)),
+  3,	median(c(30, 34)),
+  4,	median(c(35, 39)),
+  5,	median(c(40, 44)),
+  6,	median(c(45, 49)),
+  7,	median(c(50, 54)),
+  8,	median(c(55, 59)),
+  9,	median(c(60, 64)),
+  10,	median(c(64, 69)),
+  11,	median(c(70, 74)),
+  12,	85),
+  ncol = 2, byrow = TRUE)
+
+colnames(continuous.age) <- c("AGE", "Age")
+continuous.age <- as.data.frame(continuous.age)
+
+SPACE <- merge(SPACE, continuous.age)
+
+colnames(continuous.age) <- c("AGE", "Age")
+continuous.age <- as.data.frame(continuous.age)
+
+SPACE <- merge(SPACE, continuous.age)
+
+SPACE$Gender <- factor(SPACE$D1, level = 1:3, labels = c("Male", "Female", "Other, non-binary"))
+SPACE$Gender <- relevel(SPACE$Gender, "Female")
+
+
+cash.reserves.amount <- c(
+  "Less than €100",
+  "€100 - €250",
+  "€250 - €500",
+  "€500 - €1,000",
+  "€1,000 - €5,000",
+  "€5,000 - €10,000",
+  "More than €10,000"
+)
+
+SPACE$cash.reserves.amount <- factor(SPACE$QQ10, level = 1:7, labels = cash.reserves.amount)
+
+SPACE$cash.reserves.any <- factor(SPACE$QQ9, level = 0:1, labels = c("No", "Yes"))
+
+SPACE$cash.reserves <- ifelse(SPACE$cash.reserves.any == "Yes",
+  as.character(SPACE$cash.reserves.amount), "€0")
+
+SPACE$cash.reserves <- as.factor(SPACE$cash.reserves)
+
+setDT(SPACE)
+
+SPACE[, has.crypto := factor(QQ1A_3, levels = 0:1, labels = c("No", "Yes"))]
+
+SPACE[, crypto.use := factor(QQ1B, levels = 0:3, labels = c("None", "Investment", "Payment", "Both"))]
+SPACE[is.na(crypto.use), crypto.use := "None"]
+
+table(SPACE$crypto.use)
+table(SPACE$has.crypto)
+
+
+SPACE[, has.payment.account := factor(QQ1A_1, labels = c("No", "Yes"))]
+SPACE[, has.card := factor(QQ1A_2, labels = c("No", "Yes"))]
+
+SPACE[, has.financial.investments := factor(D10_6, labels = c("No", "Yes"))]
+
+SPACE[, has.savings.account := factor(D10_3, labels = c("No", "Yes"))]
+
+SPACE[, PaymentPreference := factor(QQ3, levels = c(3, 1:2), labels = c("No preference", "Cash", "Cashless"))]
+# Put no preference first so it is the contrast level
+
+SPACE[, "cash.wide.acceptance" := factor(QQ13A_1, labels = c("No", "Yes"))]
+SPACE[, "cash.faster" := factor(QQ13A_2, labels = c("No", "Yes"))]
+SPACE[, "cash.privacy" := factor(QQ13A_3, labels = c("No", "Yes"))]
+SPACE[, "cash.easier" := factor(QQ13A_4, labels = c("No", "Yes"))]
+SPACE[, "cash.safer" := factor(QQ13A_5, labels = c("No", "Yes"))]
+SPACE[, "cash.immediately.settled" := factor(QQ13A_6, labels = c("No", "Yes"))]
+SPACE[, "cash.spending.awareness" := factor(QQ13A_7, labels = c("No", "Yes"))]
+SPACE[, "cash.other" := factor(QQ13A_8, labels = c("No", "Yes"))]
+SPACE[, "cash.no.use" := factor(QQ13A_9, labels = c("No", "Yes"))]
+SPACE[, "cash.none" := factor(QQ13A_10, labels = c("No", "Yes"))]
+
+
+
+SPACE.survey <- svydesign(ids = ~ID, data = SPACE, weights = SPACE$Gross_weight)
+
+saveRDS(SPACE.survey, file = "processed-data/SPACE.rds")
+
+
+# Load the data again to get the diary data
+
+space <- read.csv("raw-data/SPACE_2022/SPACE_wide_2022.csv", stringsAsFactors = FALSE)
+
+
+setDT(space)
+
+space.diary <- melt(space[, c("ID", "D1", "AGE", "WEEKDAY", "COUNTRY", "Gross_weight", colnames(space)[grepl("^QB", colnames(space))]), with = FALSE],
+  measure.vars = patterns(purpose = "^QB1", value = "^QB3", instrument = "^QB4"))
+
+
+
+SPACE.diary <- melt(SPACE[, c("ID", "D1", "AGE", "WEEKDAY", "COUNTRY", "Gross_weight", colnames(SPACE)[grepl("^QB", colnames(SPACE))]), with = FALSE],
+  measure.vars = patterns(purpose = "^QB1", value = "^QB3", instrument = "^QB4"))
+
+
+purpose.label <- c("Clothes/sportswear",
+  "Electronic goods/household appliances",
+  "Food/daily supplies",
+  "Medicine/cosmetics/drugstore products",
+  "Media/games/entertainment",
+  "Charitable donations",
+  "Travel/accommodation",
+  "Furniture/other household items",
+  "Tickets for events/attractions",
+  "Luxury goods",
+  "Financial: insurance, investment, crypto-assets",
+  "Household related services",
+  "Other")
+
+
+
+SPACE.diary <- SPACE.diary[complete.cases(SPACE.diary), ]
+SPACE.diary[purpose == 999999, purpose := NA]
+SPACE.diary[value == 999999, value := NA]
+SPACE.diary[instrument == 999999, instrument := NA]
+
+SPACE.diary[, D1 := factor(D1)]
+SPACE.diary[, AGE := factor(AGE)]
+SPACE.diary[, WEEKDAY := factor(WEEKDAY)]
+SPACE.diary[, COUNTRY := factor(COUNTRY)]
+SPACE.diary[, purpose := factor(purpose, levels = seq_along(purpose.label), labels = purpose.label)]
+SPACE.diary[, instrument := factor(instrument)]
+
+SPACE.diary[, `Paid with crypto` := instrument == 10]
+
+
+SPACE.diary.survey <- svydesign(ids = ~ID, data = SPACE.diary, weights = SPACE.diary$Gross_weight)
+
+
+saveRDS(SPACE.diary.survey, file = "processed-data/SPACE_diary.rds")
+
+
+# This computation takes a while
+
+aggregate.crypto.spending.clustered <- as.data.frame(svyby(~I(365*value/1e+09), ~`Paid with crypto`,
+  design = as.svrepdesign(subset(SPACE.diary.survey,
+    purpose != "Financial: insurance, investment, crypto-assets")), svytotal))
+
+aggregate.crypto.spending.clustered.CI <- aggregate.crypto.spending.clustered[2, 3] * 1.645
+aggregate.crypto.spending.clustered[2, 2]
+aggregate.crypto.spending.clustered[2, 2] + c(-1 * aggregate.crypto.spending.clustered.CI, aggregate.crypto.spending.clustered.CI)
+
+aggregate.crypto.spending.clustered <- list(
+  point.estimate = aggregate.crypto.spending.clustered[2, 2],
+  CI.90.percent = aggregate.crypto.spending.clustered[2, 2] +
+    c(-1 * aggregate.crypto.spending.clustered.CI, aggregate.crypto.spending.clustered.CI)
+)
+
+saveRDS(aggregate.crypto.spending.clustered, file = "processed-data/EU-aggregate-crypto-spending.rds")
+
+
+
+